# This file is auto generated by `tools/gen_tensor_stub.py`. # Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # The `Tensor` template `tensor.prototype.pyi` for `tools/gen_tensor_stub.py` to generate the stub file `tensor.pyi`. # Add docstring, attributes, methods and alias with type annotations for `Tensor` in `tensor.prototype.pyi` # if not conveniently coding in original place (like c++ source file). # Import common typings for generated methods # isort: off from typing import * # noqa: F403 from typing_extensions import * # type: ignore # noqa: F403 from paddle._typing import * # noqa: F403 # isort: on from builtins import ( # noqa: F401 bool as _bool, bytes as _bytes, complex as _complex, float as _float, int as _int, str as _str, ) from collections.abc import Iterator from typing import Any, Literal, overload import numpy.typing as npt import paddle from paddle import ( ParamAttr, # noqa: F401 _typing, ) from paddle.base.dygraph.tensor_patch_methods import ( TensorHookRemoveHelper, # noqa: F401 ) from paddle.tensor.linalg import _POrder # noqa: F401 from paddle.tensor.stat import _Interpolation # noqa: F401 # annotation: ${eager_param_base_begin} class AbstractEagerParamBase: # annotation: ${eager_param_base_docstring} r""" EagerParamBase is derived from Tensor( Which is the concept in Eager-Dygraph Mode). A EagerParamBase is a persistable Tensor, and will be updated by optimizers after each iteration. The training of a neural network is essentially the updating of its EagerParamBase. Relative to a general Tensor, a EagerParamBase has several its own member variables: Args: trainable(bool): True if the EagerParamBase need to be updated after iterations. optimize_attr(map): EagerParamBase attributes related with optimizing. Currently, it only contains 'learning_rate'. Default: {'learning_rate': 1.0} regularizer(WeightDecayRegularizer): The Regularizer which will be applied on the EagerParamBase. Default: None do_model_average(bool): True if the model average strategy will be applied on this EagerParamBase. need_clip (bool): Whether the parameter gradient need to be clipped in optimizer. Default is True. """ # annotation: ${eager_param_base_attributes} # annotation: ${eager_param_base_methods} def __deepcopy__(self, memo): r""" Deep copy parameter, it will always performs Tensor copy. Examples: .. code-block:: python >>> import paddle >>> import copy >>> linear = paddle.nn.Linear(1, 3) >>> linear_copy = copy.deepcopy(linear) >>> print(linear.weight) >>> # doctest: +SKIP('it will be different') Parameter containing: Tensor(shape=[1, 3], dtype=float32, place=Place(cpu), stop_gradient=False, [[-0.30929261, -0.90929240, -1.07851017]]) >>> # doctest: -SKIP >>> print(linear_copy.weight) >>> # doctest: +SKIP('it will be different') Parameter containing: Tensor(shape=[1, 3], dtype=float32, place=Place(cpu), stop_gradient=False, [[-0.30929261, -0.90929240, -1.07851017]]) """ ... def __init__(self, *args, **kwargs): r""" New Usage Example: 1. paddle.Tensor() 2. paddle.Tensor(device="cpu") 3. paddle.Tensor(1,2,3) 4. paddle.Tensor(1,2,3, device="cpu") 5. paddle.Tensor([1,2,3]) 6. paddle.Tensor([1,2,3], device="cpu") 7. paddle.Tensor(data=[1,2,3]) 8. paddle.Tensor(data=[1,2,3], device="cpu") Original Usage Example: 9. paddle.Tensor(value=data, place="cpu", persistable=False, zero_copy=False, name=None, stop_gradient=True) """ ... def __init_by_shape__(self, shape, dtype, **kwargs): ... def __init_by_tensor__(self, data: 'paddle.Tensor | None' = None, requires_grad: '_bool' = True, **kwargs): ... def __repr__(self): r""" Convert a EagerParamBase object to a readable string. Returns(str): A readable string. Examples: .. code-block:: python >>> import paddle >>> linear = paddle.nn.Linear(3, 3) >>> print(linear.weight) >>> # doctest: +SKIP('it will be different') Parameter containing: Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=False, [[ 0.48948765, 0.05829060, -0.25524026], [-0.70368278, 0.52986908, -0.68742192], [-0.54217887, 0.48439729, 0.34082305]]) """ ... def __setattr__(self, name, value): r""" Implement setattr(self, name, value). """ ... def from_tensor(self, **kwargs): ... def initialize(self): ... def set_init_func(self, obj): ... @property def trainable(self) -> _bool: ... @trainable.setter def trainable(self, trainable: _bool) -> None: ... # annotation: ${eager_param_base_alias} __str__ = __repr__ # annotation: ${eager_param_base_end} # annotation: ${tensor_begin} class AbstractTensor: # annotation: ${tensor_attributes} # If method defined below, we should make the method's signature complete, # and ignore the signature extracted from `paddle.Tensor`. # `gen_tensor.stub.py` will NOT overwrite the signature below. # If method has docstring (ignoring the spaces), `gen_tensor.stub.py` also will NOT overwrite it. # annotation: ${tensor_methods} @overload def atleast_1d(self, *inputs: 'Tensor', name: '_str | None' = ...) -> 'list[Tensor]': ... @overload def atleast_1d(self, name: '_str | None' = ...) -> 'Tensor': ... @overload def atleast_2d(self, *inputs: 'Tensor', name: '_str | None' = ...) -> 'list[Tensor]': ... @overload def atleast_2d(self, name: '_str | None' = ...) -> 'Tensor': ... @overload def atleast_3d(self, *inputs: 'Tensor', name: '_str | None' = ...) -> 'list[Tensor]': ... @overload def atleast_3d(self, name: '_str | None' = ...) -> 'Tensor': ... @overload def gather(self, dim: '_int', index: 'Tensor', out: 'Tensor | None' = None) -> 'Tensor': ... @overload def gather(self, index: 'Tensor', axis: 'Tensor | _int | None' = None, name: '_str | None' = None, out: 'Tensor | None' = None) -> 'Tensor': ... @overload def lu(self, pivot: '_bool' = ..., get_infos: 'Literal[False]' = ..., name: '_str | None' = ...) -> 'tuple[Tensor, Tensor]': ... @overload def lu(self, pivot: '_bool' = ..., get_infos: 'Literal[True]' = ..., name: '_str | None' = ...) -> 'tuple[Tensor, Tensor, Tensor]': ... @overload def lu(self, pivot: '_bool' = ..., get_infos: '_bool' = ..., name: '_str | None' = ...) -> 'tuple[Tensor, Tensor] | tuple[Tensor, Tensor, Tensor]': ... @overload def median(self, axis: '_int | None' = ..., keepdim: '_bool' = ..., mode: "Literal['avg', 'min']" = ..., name: '_str | None' = ...) -> 'Tensor': ... @overload def median(self, axis: '_int' = ..., keepdim: '_bool' = ..., mode: "Literal['min']" = ..., name: '_str | None' = ..., *, out: 'tuple[Tensor, Tensor] | None' = ...) -> 'tuple[Tensor, Tensor]': ... @overload def nanmedian(self, axis: '_int | Sequence[_int] | None' = ..., keepdim: '_bool' = ..., mode: "Literal['avg', 'min']" = ..., name: '_str | None' = ...) -> 'Tensor': ... @overload def nanmedian(self, axis: '_int', keepdim: '_bool' = ..., mode: "Literal['min']" = ..., name: '_str | None' = ...) -> 'tuple[Tensor, Tensor]': ... @overload def nonzero(self, as_tuple: 'Literal[False]' = ..., *, out: 'Tensor | None' = None) -> 'Tensor': ... @overload def nonzero(self, as_tuple: 'Literal[True]' = ..., *, out: 'Tensor | None' = None) -> 'tuple[Tensor, ...]': ... @overload def nonzero(self, as_tuple: '_bool' = ..., *, out: 'Tensor | None' = None) -> 'Tensor | tuple[Tensor, ...]': ... @overload def qr(self, mode: "Literal['r']" = ..., name: '_str | None' = ...) -> 'Tensor': ... @overload def qr(self, mode: "Literal['reduced', 'complete']" = ..., name: '_str | None' = ...) -> 'tuple[Tensor, Tensor]': ... @overload def scatter(self, dim: '_int', index: 'Tensor', src: 'Tensor | None' = None, reduce: '_str | None' = None, out: 'Tensor | None' = None, value: 'Tensor | None' = None) -> 'Tensor': ... @overload def scatter(self, index: 'Tensor', updates: 'Tensor', overwrite: '_bool' = True, name: '_str | None' = None, out: 'Tensor | None' = None) -> 'Tensor': ... @overload def scatter_(self, dim: '_int', index: 'Tensor', src: 'Tensor | None' = None, reduce: '_str | None' = None, value: 'Tensor | None' = None) -> 'Tensor': ... @overload def scatter_(self, index: 'Tensor', updates: 'Tensor', overwrite: '_bool' = True, name: '_str | None' = None) -> 'Tensor': ... @overload def to(self, device: 'PlaceLike', dtype: 'DTypeLike | None' = ..., blocking: '_bool | None' = ...) -> 'Tensor': ... @overload def to(self, dtype: 'DTypeLike', blocking: '_bool | None' = ...) -> 'Tensor': ... @overload def to(self, other: 'Tensor', blocking: '_bool | None' = ...) -> 'Tensor': ... @overload def unique(self, return_index: 'Literal[False]' = ..., return_inverse: 'Literal[False]' = ..., return_counts: 'Literal[False]' = ..., axis: '_int | None' = ..., dtype: 'DTypeLike' = ..., sorted: '_bool' = ..., name: '_str | None' = ...) -> 'Tensor': ... @overload def unique(self, return_index: 'Literal[False]' = ..., return_inverse: 'Literal[False]' = ..., return_counts: 'Literal[True]' = ..., axis: '_int | None' = ..., dtype: 'DTypeLike' = ..., sorted: '_bool' = ..., name: '_str | None' = ...) -> 'tuple[Tensor, Tensor]': ... @overload def unique(self, return_index: 'Literal[False]' = ..., return_inverse: 'Literal[True]' = ..., return_counts: 'Literal[False]' = ..., axis: '_int | None' = ..., dtype: 'DTypeLike' = ..., sorted: '_bool' = ..., name: '_str | None' = ...) -> 'tuple[Tensor, Tensor]': ... @overload def unique(self, return_index: 'Literal[False]' = ..., return_inverse: 'Literal[True]' = ..., return_counts: 'Literal[True]' = ..., axis: '_int | None' = ..., dtype: 'DTypeLike' = ..., sorted: '_bool' = ..., name: '_str | None' = ...) -> 'tuple[Tensor, Tensor, Tensor]': ... @overload def unique(self, return_index: 'Literal[True]' = ..., return_inverse: 'Literal[False]' = ..., return_counts: 'Literal[False]' = ..., axis: '_int | None' = ..., dtype: 'DTypeLike' = ..., sorted: '_bool' = ..., name: '_str | None' = ...) -> 'tuple[Tensor, Tensor]': ... @overload def unique(self, return_index: 'Literal[True]' = ..., return_inverse: 'Literal[False]' = ..., return_counts: 'Literal[True]' = ..., axis: '_int | None' = ..., dtype: 'DTypeLike' = ..., sorted: '_bool' = ..., name: '_str | None' = ...) -> 'tuple[Tensor, Tensor, Tensor]': ... @overload def unique(self, return_index: 'Literal[True]' = ..., return_inverse: 'Literal[True]' = ..., return_counts: 'Literal[False]' = ..., axis: '_int | None' = ..., dtype: 'DTypeLike' = ..., sorted: '_bool' = ..., name: '_str | None' = ...) -> 'tuple[Tensor, Tensor, Tensor]': ... @overload def unique(self, return_index: 'Literal[True]' = ..., return_inverse: 'Literal[True]' = ..., return_counts: 'Literal[True]' = ..., axis: '_int | None' = ..., dtype: 'DTypeLike' = ..., sorted: '_bool' = ..., name: '_str | None' = ...) -> 'tuple[Tensor, Tensor, Tensor, Tensor]': ... @overload def unique(self, return_index: '_bool' = False, return_inverse: '_bool' = False, return_counts: '_bool' = False, axis: '_int | None' = ..., dtype: 'DTypeLike' = ..., sorted: '_bool' = ..., name: '_str | None' = ...) -> 'Tensor | tuple[Tensor, ...]': ... @property def T(self): ... @property def __cuda_array_interface__(self): r""" Array view description for cuda tensors. See: CUDA Array Interface (Version 2) https://numba.pydata.org/numba-doc/dev/cuda/cuda_array_interface.html """ ... @property def block(self): ... @property def device(self): r""" Return the device descriptor string indicating where the tensor is located. Returns: str: A string representing the device where the tensor resides. Possible formats include: - 'cpu' for CPU tensors - 'cuda:{device_id}' for GPU tensors (e.g., 'cuda:0') - 'xpu:{device_id}' for XPU tensors (e.g., 'xpu:0') - '{device_type}:{device_id}' for custom device tensors Examples: .. code-block:: python >>> import paddle >>> # CPU tensor >>> cpu_tensor = paddle.to_tensor([1, 2, 3]).to("cpu") >>> print(cpu_tensor.device) 'cpu' """ ... @property def inplace_version(self): r""" The inplace version of current Tensor. The version number is incremented whenever the current Tensor is modified through an inplace operation. **Notes: This is a read-only property** Examples: .. code-block:: python >>> import paddle >>> var = paddle.ones(shape=[4, 2, 3], dtype="float32") >>> print(var.inplace_version) 0 >>> var[1] = 2.2 >>> print(var.inplace_version) 1 """ ... @property def is_cuda(self): ... @property def itemsize(self): r""" Returns the number of bytes allocated on the machine for a single element of the Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.randn((2,3),dtype=paddle.float64) >>> x.itemsize 8 """ ... @property def mT(self): r""" Return the last two dimensions of a Tensor transposed. Args: Returns: Tensor: A new Tensor with its last two dimensions swapped. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.randn([2, 3, 4]) >>> x_transposed = x.mT >>> x_transposed.shape paddle.Size([2, 4, 3]) """ ... @property def requires_grad(self): r""" Whether this Tensor requires gradient computation. This is a convenience property that returns the opposite of stop_gradient. Setting requires_grad=True is equivalent to setting stop_gradient=False. Examples: .. code-block:: python >>> import paddle >>> x = paddle.randn([2, 3]) >>> print(x.requires_grad) # False by default >>> >>> x.requires_grad = False >>> print(x.stop_gradient) # True """ ... def __abs__(self) -> 'Tensor': ... def __array__(self, dtype: 'npt.DTypeLike | None' = None, copy: '_bool | None' = None) -> 'npt.NDArray[Any]': r""" Returns a numpy array shows the value of current Tensor. Returns: ndarray: The numpy value of current Tensor. Returns type: ndarray: dtype is same as current Tensor Examples: .. code-block:: python >>> import paddle >>> import numpy as np >>> x = paddle.randn([2, 2]) >>> x_array = np.array(x) >>> print(type(x_array)) >>> print(x_array.shape) (2, 2) """ ... def __deepcopy__(self, memo: 'dict[_int, Tensor]') -> 'Tensor': r""" Deep copy Tensor, it will always performs Tensor copy. Examples: .. code-block:: python >>> import paddle >>> import copy >>> x = paddle.to_tensor(2.) >>> y = copy.deepcopy(x) >>> print(x) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 2.) >>> print(y) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 2.) """ ... def __delattr__(self, name, /): r""" Implement delattr(self, name). """ ... def __dir__(self, /): r""" Default dir() implementation. """ ... def __dlpack__(self, *, stream: '_int | None' = None, max_version: 'tuple[_int, _int] | None' = None, dl_device: 'tuple[IntEnum, _int] | None' = None, copy: '_bool | None' = None) -> 'CapsuleType': r""" Creates a DLPack capsule of the current tensor to be exported to other libraries. Args: max_version (tuple[int, int] | None): An optional Python tuple with 2 integers, representing the maximum version the caller supports. If None (default), we will fallback to DLPack 0.8. dl_device (tuple[IntEnum, int] | None, optional): The DLPack device type. Default is None, meaning the exported capsule should be on the same device as self is. When specified, the format must be a 2-tuple, following that of the return value of array.__dlpack_device__(). copy (bool | None, optional): Whether or not to copy the input. If True, the output tensor always copied. If False, the output tensor must never copied, and raise a BufferError in case a copy is deemed necessary. If None, the output tensor must reuse the existing memory buffer if possible and copy otherwise. Default: None. """ ... def __dlpack_device__(self): r""" Extract the DLPack device type and device ID for the current tensor. Returns: tuple: A tuple containing the DLPack device type and device ID. - device_type (DLDeviceType): The type of device (e.g., kDLCPU, kDLCUDA, etc.). - device_id (int): The device ID. """ ... def __format__(self, format_spec: '_str') -> '_str': ... def __getattribute__(self, name, /): r""" Return getattr(self, name). """ ... def __getstate__(self, /): r""" Helper for pickle. """ ... def __lshift__(self, y: 'Tensor | _int', is_arithmetic: '_bool' = True) -> 'Tensor': ... def __or__(self, y: 'Tensor', out: 'Tensor | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Apply ``bitwise_or`` on Tensor ``X`` and ``Y`` . .. math:: Out = X | Y Note: ``paddle.bitwise_or`` supports broadcasting. If you want know more about broadcasting, please refer to please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``other`` can be used as an alias for ``y``. For example, ``bitwise_or(input=tensor_x, other=tensor_y, ...)`` is equivalent to ``bitwise_or(x=tensor_x, y=tensor_y, ...)``. Args: y (Tensor): Input Tensor of ``bitwise_or`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64. alias: ``oth``. out (Tensor|None, optional): Result of ``bitwise_or`` . It is a N-D Tensor with the same data type of input Tensor. Default: None. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Result of ``bitwise_or`` . It is a N-D Tensor with the same data type of input Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-5, -1, 1]) >>> y = paddle.to_tensor([4, 2, -3]) >>> res = paddle.bitwise_or(x, y) >>> print(res) Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, [-1, -1, -3]) """ ... def __reduce__(self, /): r""" Helper for pickle. """ ... def __reduce_ex__(self, proto): ... def __repr__(self) -> '_str': r""" Convert a Tensor object to a readable string. Returns(str): A readable string. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2023) >>> x = paddle.rand([2, 5]) >>> print(x) Tensor(shape=[2, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.86583614, 0.52014720, 0.25960937, 0.90525323, 0.42400089], [0.40641287, 0.97020894, 0.74437362, 0.51785129, 0.73292869]]) """ ... def __rlshift__(self, y: 'Tensor | _int', is_arithmetic: '_bool' = True): ... def __rrshift__(self, y: 'Tensor | _int', is_arithmetic: '_bool' = True): ... def __rshift__(self, y: 'Tensor | _int', is_arithmetic: '_bool' = True) -> 'Tensor': ... def __setattr__(self, name, value, /): r""" Implement setattr(self, name, value). """ ... def __sizeof__(self, /): r""" Size of object in memory, in bytes. """ ... def __tvm_ffi_env_stream__(self) -> '_int': r""" Returns the raw stream pointer of the current tensor's device context. This is used for TVM FFI environment integration. """ ... def __xor__(self, y: 'Tensor', out: 'Tensor | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Apply ``bitwise_xor`` on Tensor ``X`` and ``Y`` . .. math:: Out = X ^\wedge Y Note: ``paddle.bitwise_xor`` supports broadcasting. If you want know more about broadcasting, please refer to please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: y (Tensor): Input Tensor of ``bitwise_xor`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64. out (Tensor|None, optional): Result of ``bitwise_xor`` . It is a N-D Tensor with the same data type of input Tensor. Default: None. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Result of ``bitwise_xor`` . It is a N-D Tensor with the same data type of input Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-5, -1, 1]) >>> y = paddle.to_tensor([4, 2, -3]) >>> res = paddle.bitwise_xor(x, y) >>> print(res) Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, [-1, -3, -4]) """ ... def abs(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Perform elementwise abs for input `x`. .. math:: out = |x| .. note:: Alias Support: 1. The parameter name ``input`` can be used as an alias for ``x``. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output tensor. Default: None. Returns: Tensor.A Tensor with the same data type and shape as :math:`x`. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.abs(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [0.40000001, 0.20000000, 0.10000000, 0.30000001]) """ ... def abs_(self, name=None): r""" Inplace version of ``abs`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_abs`. """ ... def acos(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Acos Activation Operator. .. math:: out = cos^{-1}(x) Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor. Output of Acos operator, a Tensor with shape same as input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.acos(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [1.98231316, 1.77215421, 1.47062886, 1.26610363]) """ ... def acos_(self, name=None): r""" Inplace version of ``acos`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_acos`. """ ... def acosh(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Acosh Activation Operator. .. math:: out = acosh(x) Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor. Output of Acosh operator, a Tensor with shape same as input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1., 3., 4., 5.]) >>> out = paddle.acosh(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [0. , 1.76274717, 2.06343699, 2.29243159]) """ ... def acosh_(self, name=None): r""" Inplace version of ``acosh`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_acosh`. """ ... def add(self, y: 'Tensor', name: '_str | None' = None, *, alpha: 'Number' = 1, out: 'Tensor | None' = None) -> 'Tensor': r""" Elementwise Add Operator. Add two tensors element-wise. The equation is: .. math:: Out=X+Y $X$ the tensor of any dimension. $Y$ the tensor whose dimensions must be less than or equal to the dimensions of $X$. This operator is used in the following cases: 1. The shape of $Y$ is the same with $X$. 2. The shape of $Y$ is a continuous subsequence of $X$. For example: .. code-block:: text shape(X) = (2, 3, 4, 5), shape(Y) = (,) shape(X) = (2, 3, 4, 5), shape(Y) = (5,) shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5), with axis=-1(default) or axis=2 shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0 shape(X) = (2, 3, 4, 5), shape(Y) = (2, 1), with axis=0 .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``other`` can be used as an alias for ``y``. For example, ``add(input=tensor_x, other=tensor_y)`` is equivalent to ``add(x=tensor_x, y=tensor_y)``. Args: y (Tensor): Tensor of any dimensions. Its dtype should be bool, bfloat16, float16, float32, float64, int8, int16, int32, int64, uint8, complex64, complex128. alias: ``other``. alpha (Number, optional): Scaling factor for Y. Default: 1. out (Tensor, optional): The output tensor. Default: None. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: N-D Tensor. A location into which the result is stored. It's dimension equals with x. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([2, 3, 4], 'float64') >>> y = paddle.to_tensor([1, 5, 2], 'float64') >>> z = paddle.add(x, y) >>> print(z) Tensor(shape=[3], dtype=float64, place=Place(cpu), stop_gradient=True, [3., 8., 6.]) """ ... def add_(self, y: 'Tensor', name: '_str | None' = None, *, alpha: 'Number' = 1) -> 'Tensor': r""" Inplace version of ``add`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_add`. """ ... def add_n(self, name: '_str | None' = None) -> 'Tensor': r""" Sum one or more Tensor of the input. For example: .. code-block:: text Case 1: Input: input.shape = [2, 3] input = [[1, 2, 3], [4, 5, 6]] Output: output.shape = [2, 3] output = [[1, 2, 3], [4, 5, 6]] Case 2: Input: First input: input1.shape = [2, 3] Input1 = [[1, 2, 3], [4, 5, 6]] The second input: input2.shape = [2, 3] input2 = [[7, 8, 9], [10, 11, 12]] Output: output.shape = [2, 3] output = [[8, 10, 12], [14, 16, 18]] Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, the sum of input :math:`inputs` , its shape and data types are consistent with :math:`inputs`. Examples: .. code-block:: python >>> import paddle >>> input0 = paddle.to_tensor([[1, 2, 3], [4, 5, 6]], dtype='float32') >>> input1 = paddle.to_tensor([[7, 8, 9], [10, 11, 12]], dtype='float32') >>> output = paddle.add_n([input0, input1]) >>> output Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[8. , 10., 12.], [14., 16., 18.]]) """ ... def addmm(self, x: 'Tensor', y: 'Tensor', beta: '_float' = 1.0, alpha: '_float' = 1.0, name: '_str | None' = None) -> 'Tensor': r""" **addmm** Perform matrix multiplication for input $x$ and $y$. $input$ is added to the final result. The equation is: .. math:: Out = alpha * x * y + beta * input $Input$, $x$ and $y$ can carry the LoD (Level of Details) information, or not. But the output only shares the LoD information with input $input$. Args: x (Tensor): The first input Tensor for matrix multiplication. y (Tensor): The second input Tensor for matrix multiplication. beta (float, optional): Coefficient of $input$, default is 1. alpha (float, optional): Coefficient of $x*y$, default is 1. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The output Tensor of addmm. Examples: .. code-block:: python >>> import paddle >>> x = paddle.ones([2, 2]) >>> y = paddle.ones([2, 2]) >>> input = paddle.ones([2, 2]) >>> out = paddle.addmm(input=input, x=x, y=y, beta=0.5, alpha=5.0) >>> print(out) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[10.50000000, 10.50000000], [10.50000000, 10.50000000]]) """ ... def addmm_(self, x: 'Tensor', y: 'Tensor', beta: '_float' = 1.0, alpha: '_float' = 1.0, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``addmm`` API, the output Tensor will be inplaced with input ``input``. Please refer to :ref:`api_paddle_addmm`. """ ... def all(self, axis: '_int | Sequence[_int] | None' = None, keepdim: '_bool' = False, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Computes the ``logical and`` of tensor elements over the given dimension. Args: axis (int|list|tuple|None, optional): The dimensions along which the ``logical and`` is compute. If :attr:`None`, and all elements of :attr:`x` and return a Tensor with a single element, otherwise must be in the range :math:`[-rank(x), rank(x))`. If :math:`axis[i] < 0`, the dimension to reduce is :math:`rank + axis[i]`. keepdim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result Tensor will have one fewer dimension than the :attr:`x` unless :attr:`keepdim` is true, default value is False. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Keyword Args: out (Tensor|optional): The output tensor. Returns: Tensor: Results the ``logical and`` on the specified axis of input Tensor `x`, it's data type is bool. Examples: .. code-block:: python >>> import paddle >>> # x is a bool Tensor with following elements: >>> # [[True, False] >>> # [True, True]] >>> x = paddle.to_tensor([[1, 0], [1, 1]], dtype='int32') >>> x Tensor(shape=[2, 2], dtype=int32, place=Place(cpu), stop_gradient=True, [[1, 0], [1, 1]]) >>> x = paddle.cast(x, 'bool') >>> # out1 should be False >>> out1 = paddle.all(x) >>> out1 Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True, False) >>> # out2 should be [True, False] >>> out2 = paddle.all(x, axis=0) >>> out2 Tensor(shape=[2], dtype=bool, place=Place(cpu), stop_gradient=True, [True , False]) >>> # keepdim=False, out3 should be [False, True], out.shape should be (2,) >>> out3 = paddle.all(x, axis=-1) >>> out3 Tensor(shape=[2], dtype=bool, place=Place(cpu), stop_gradient=True, [False, True ]) >>> # keepdim=True, out4 should be [[False], [True]], out.shape should be (2, 1) >>> out4 = paddle.all(x, axis=1, keepdim=True) >>> out4 Tensor(shape=[2, 1], dtype=bool, place=Place(cpu), stop_gradient=True, [[False], [True ]]) """ ... def allclose(self, y: 'Tensor', rtol: '_float' = 1e-05, atol: '_float' = 1e-08, equal_nan: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Check if all :math:`x` and :math:`y` satisfy the condition: .. math:: \left| x - y \right| \leq atol + rtol \times \left| y \right| elementwise, for all elements of :math:`x` and :math:`y`. This is analogous to :math:`numpy.allclose`, namely that it returns :math:`True` if two tensors are elementwise equal within a tolerance. Args: y (Tensor): The input tensor, it's data type should be float16, float32, float64. rtol (float, optional): The relative tolerance. Default: :math:`1e-5` . atol (float, optional): The absolute tolerance. Default: :math:`1e-8` . equal_nan (bool, optional): ${equal_nan_comment}. Default: False. name (str|None, optional): Name for the operation. For more information, please refer to :ref:`api_guide_Name`. Default: None. Returns: Tensor: The output tensor, it's data type is bool. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([10000., 1e-07]) >>> y = paddle.to_tensor([10000.1, 1e-08]) >>> result1 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name="ignore_nan") >>> print(result1) Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True, False) >>> result2 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=True, name="equal_nan") >>> print(result2) Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True, False) >>> x = paddle.to_tensor([1.0, float('nan')]) >>> y = paddle.to_tensor([1.0, float('nan')]) >>> result1 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name="ignore_nan") >>> print(result1) Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True, False) >>> result2 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=True, name="equal_nan") >>> print(result2) Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True, True) """ ... def amax(self, axis: '_int | Sequence[_int] | None' = None, keepdim: '_bool' = False, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Computes the maximum of tensor elements over the given axis. Note: The difference between max and amax is: If there are multiple maximum elements, amax evenly distributes gradient between these equal values, while max propagates gradient to all of them. Args: axis (int|list|tuple|None, optional): The axis along which the maximum is computed. If :attr:`None`, compute the maximum over all elements of `x` and return a Tensor with a single element, otherwise must be in the range :math:`[-x.ndim(x), x.ndim(x))`. If :math:`axis[i] < 0`, the axis to reduce is :math:`x.ndim + axis[i]`. keepdim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result tensor will have one fewer dimension than the `x` unless :attr:`keepdim` is true, default value is False. out (Tensor|None, optional): Output tensor. If provided in dynamic graph, the result will be written to this tensor and also returned. The returned tensor and `out` share memory and autograd meta. Default: None. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Keyword args: out(Tensor, optional): The output tensor. Returns: Tensor, results of maximum on the specified axis of input tensor, it's data type is the same as `x`. Examples: .. code-block:: python >>> import paddle >>> # data_x is a Tensor with shape [2, 4] with multiple maximum elements >>> # the axis is a int element >>> x = paddle.to_tensor([[0.1, 0.9, 0.9, 0.9], ... [0.9, 0.9, 0.6, 0.7]], ... dtype='float64', stop_gradient=False) >>> # There are 5 maximum elements: >>> # 1) amax evenly distributes gradient between these equal values, >>> # thus the corresponding gradients are 1/5=0.2; >>> # 2) while max propagates gradient to all of them, >>> # thus the corresponding gradient are 1. >>> result1 = paddle.amax(x) >>> result1.backward() >>> result1 Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=False, 0.90000000) >>> x.grad Tensor(shape=[2, 4], dtype=float64, place=Place(cpu), stop_gradient=False, [[0. , 0.20000000, 0.20000000, 0.20000000], [0.20000000, 0.20000000, 0. , 0. ]]) >>> x.clear_grad() >>> result1_max = paddle.max(x) >>> result1_max.backward() >>> result1_max Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=False, 0.90000000) >>> x.clear_grad() >>> result2 = paddle.amax(x, axis=0) >>> result2.backward() >>> result2 Tensor(shape=[4], dtype=float64, place=Place(cpu), stop_gradient=False, [0.90000000, 0.90000000, 0.90000000, 0.90000000]) >>> x.grad Tensor(shape=[2, 4], dtype=float64, place=Place(cpu), stop_gradient=False, [[0. , 0.50000000, 1. , 1. ], [1. , 0.50000000, 0. , 0. ]]) >>> x.clear_grad() >>> result3 = paddle.amax(x, axis=-1) >>> result3.backward() >>> result3 Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=False, [0.90000000, 0.90000000]) >>> x.grad Tensor(shape=[2, 4], dtype=float64, place=Place(cpu), stop_gradient=False, [[0. , 0.33333333, 0.33333333, 0.33333333], [0.50000000, 0.50000000, 0. , 0. ]]) >>> x.clear_grad() >>> result4 = paddle.amax(x, axis=1, keepdim=True) >>> result4.backward() >>> result4 Tensor(shape=[2, 1], dtype=float64, place=Place(cpu), stop_gradient=False, [[0.90000000], [0.90000000]]) >>> x.grad Tensor(shape=[2, 4], dtype=float64, place=Place(cpu), stop_gradient=False, [[0. , 0.33333333, 0.33333333, 0.33333333], [0.50000000, 0.50000000, 0. , 0. ]]) >>> # data_y is a Tensor with shape [2, 2, 2] >>> # the axis is list >>> y = paddle.to_tensor([[[0.1, 0.9], [0.9, 0.9]], ... [[0.9, 0.9], [0.6, 0.7]]], ... dtype='float64', stop_gradient=False) >>> result5 = paddle.amax(y, axis=[1, 2]) >>> result5.backward() >>> result5 Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=False, [0.90000000, 0.90000000]) >>> y.grad Tensor(shape=[2, 2, 2], dtype=float64, place=Place(cpu), stop_gradient=False, [[[0. , 0.33333333], [0.33333333, 0.33333333]], [[0.50000000, 0.50000000], [0. , 0. ]]]) >>> y.clear_grad() >>> result6 = paddle.amax(y, axis=[0, 1]) >>> result6.backward() >>> result6 Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=False, [0.90000000, 0.90000000]) >>> y.grad Tensor(shape=[2, 2, 2], dtype=float64, place=Place(cpu), stop_gradient=False, [[[0. , 0.33333333], [0.50000000, 0.33333333]], [[0.50000000, 0.33333333], [0. , 0. ]]]) """ ... def amin(self, axis: '_int | Sequence[_int] | None' = None, keepdim: '_bool' = False, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Computes the minimum of tensor elements over the given axis Note: The difference between min and amin is: If there are multiple minimum elements, amin evenly distributes gradient between these equal values, while min propagates gradient to all of them. Args: axis (int|list|tuple|None, optional): The axis along which the minimum is computed. If :attr:`None`, compute the minimum over all elements of `x` and return a Tensor with a single element, otherwise must be in the range :math:`[-x.ndim, x.ndim)`. If :math:`axis[i] < 0`, the axis to reduce is :math:`x.ndim + axis[i]`. keepdim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result tensor will have one fewer dimension than the `x` unless :attr:`keepdim` is true, default value is False. out (Tensor|None, optional): Output tensor. If provided in dynamic graph, the result will be written to this tensor and also returned. The returned tensor and `out` share memory and autograd meta. Default: None. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, results of minimum on the specified axis of input tensor, it's data type is the same as input's Tensor. Keyword args: out(Tensor, optional): The output tensor. Examples: .. code-block:: python >>> import paddle >>> # data_x is a Tensor with shape [2, 4] with multiple minimum elements >>> # the axis is a int element >>> x = paddle.to_tensor([[0.2, 0.1, 0.1, 0.1], ... [0.1, 0.1, 0.6, 0.7]], ... dtype='float64', stop_gradient=False) >>> # There are 5 minimum elements: >>> # 1) amin evenly distributes gradient between these equal values, >>> # thus the corresponding gradients are 1/5=0.2; >>> # 2) while min propagates gradient to all of them, >>> # thus the corresponding gradient are 1. >>> result1 = paddle.amin(x) >>> result1.backward() >>> result1 Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=False, 0.10000000) >>> x.grad Tensor(shape=[2, 4], dtype=float64, place=Place(cpu), stop_gradient=False, [[0. , 0.20000000, 0.20000000, 0.20000000], [0.20000000, 0.20000000, 0. , 0. ]]) >>> x.clear_grad() >>> result1_min = paddle.min(x) >>> result1_min.backward() >>> result1_min Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=False, 0.10000000) >>> x.clear_grad() >>> result2 = paddle.amin(x, axis=0) >>> result2.backward() >>> result2 Tensor(shape=[4], dtype=float64, place=Place(cpu), stop_gradient=False, [0.10000000, 0.10000000, 0.10000000, 0.10000000]) >>> x.grad Tensor(shape=[2, 4], dtype=float64, place=Place(cpu), stop_gradient=False, [[0. , 0.50000000, 1. , 1. ], [1. , 0.50000000, 0. , 0. ]]) >>> x.clear_grad() >>> result3 = paddle.amin(x, axis=-1) >>> result3.backward() >>> result3 Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=False, [0.10000000, 0.10000000]) >>> x.grad Tensor(shape=[2, 4], dtype=float64, place=Place(cpu), stop_gradient=False, [[0. , 0.33333333, 0.33333333, 0.33333333], [0.50000000, 0.50000000, 0. , 0. ]]) >>> x.clear_grad() >>> result4 = paddle.amin(x, axis=1, keepdim=True) >>> result4.backward() >>> result4 Tensor(shape=[2, 1], dtype=float64, place=Place(cpu), stop_gradient=False, [[0.10000000], [0.10000000]]) >>> x.grad Tensor(shape=[2, 4], dtype=float64, place=Place(cpu), stop_gradient=False, [[0. , 0.33333333, 0.33333333, 0.33333333], [0.50000000, 0.50000000, 0. , 0. ]]) >>> # data_y is a Tensor with shape [2, 2, 2] >>> # the axis is list >>> y = paddle.to_tensor([[[0.2, 0.1], [0.1, 0.1]], ... [[0.1, 0.1], [0.6, 0.7]]], ... dtype='float64', stop_gradient=False) >>> result5 = paddle.amin(y, axis=[1, 2]) >>> result5.backward() >>> result5 Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=False, [0.10000000, 0.10000000]) >>> y.grad Tensor(shape=[2, 2, 2], dtype=float64, place=Place(cpu), stop_gradient=False, [[[0. , 0.33333333], [0.33333333, 0.33333333]], [[0.50000000, 0.50000000], [0. , 0. ]]]) >>> y.clear_grad() >>> result6 = paddle.amin(y, axis=[0, 1]) >>> result6.backward() >>> result6 Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=False, [0.10000000, 0.10000000]) >>> y.grad Tensor(shape=[2, 2, 2], dtype=float64, place=Place(cpu), stop_gradient=False, [[[0. , 0.33333333], [0.50000000, 0.33333333]], [[0.50000000, 0.33333333], [0. , 0. ]]]) """ ... def angle(self, name: '_str | None' = None) -> 'Tensor': r""" Element-wise angle of complex numbers. For non-negative real numbers, the angle is 0 while for negative real numbers, the angle is :math:`\pi`, and NaNs are propagated.. Equation: .. math:: angle(x)=arctan2(x.imag, x.real) Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: An N-D Tensor of real data type with the same precision as that of x's data type. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.to_tensor([-2, -1, 0, 1]).unsqueeze(-1).astype('float32') >>> y = paddle.to_tensor([-2, -1, 0, 1]).astype('float32') >>> z = x + 1j * y >>> z Tensor(shape=[4, 4], dtype=complex64, place=Place(cpu), stop_gradient=True, [[(-2.00000000-2.00000000j), (-2.00000000-1.00000000j), (-2.00000000+0.00000000j), (-2.00000000+1.00000000j)], [(-1.00000000-2.00000000j), (-1.00000000-1.00000000j), (-1.00000000+0.00000000j), (-1.00000000+1.00000000j)], [(0.00000000-2.00000000j) , (0.00000000-1.00000000j) , (0.00000000+0.00000000j), (0.00000000+1.00000000j)], [ (1.00000000-2.00000000j), (1.00000000-1.00000000j), (1.00000000+0.00000000j), (1.00000000+1.00000000j)]]) >>> theta = paddle.angle(z) >>> theta Tensor(shape=[4, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[-2.35619450, -2.67794514, 3.14159274, 2.67794514], [-2.03444386, -2.35619450, 3.14159274, 2.35619450], [-1.57079637, -1.57079637, 0. , 1.57079637], [-1.10714877, -0.78539819, 0. , 0.78539819]]) """ ... def any(self, axis: '_int | Sequence[_int] | None' = None, keepdim: '_bool' = False, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Computes the ``logical or`` of tensor elements over the given dimension, and return the result. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and the parameter name ``dim`` can be used as an alias for ``axis``. For example, ``any(input=tensor_x, dim=1)`` is equivalent to ``any(x=tensor_x, axis=1)``. Args: axis (int|list|tuple|None, optional): The dimensions along which the ``logical or`` is compute. If :attr:`None`, and all elements of :attr:`x` and return a Tensor with a single element, otherwise must be in the range :math:`[-rank(x), rank(x))`. If :math:`axis[i] < 0`, the dimension to reduce is :math:`rank + axis[i]`. alias: ``dim``. keepdim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result Tensor will have one fewer dimension than the :attr:`x` unless :attr:`keepdim` is true, default value is False. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output tensor. Default: None. Returns: Tensor: Results the ``logical or`` on the specified axis of input Tensor `x`, it's data type is bool. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 0], [1, 1]], dtype='int32') >>> x = paddle.assign(x) >>> x Tensor(shape=[2, 2], dtype=int32, place=Place(cpu), stop_gradient=True, [[1, 0], [1, 1]]) >>> x = paddle.cast(x, 'bool') >>> # x is a bool Tensor with following elements: >>> # [[True, False] >>> # [True, True]] >>> # out1 should be True >>> out1 = paddle.any(x) >>> out1 Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True, True) >>> # out2 should be [True, True] >>> out2 = paddle.any(x, axis=0) >>> out2 Tensor(shape=[2], dtype=bool, place=Place(cpu), stop_gradient=True, [True, True]) >>> # keepdim=False, out3 should be [True, True], out.shape should be (2,) >>> out3 = paddle.any(x, axis=-1) >>> out3 Tensor(shape=[2], dtype=bool, place=Place(cpu), stop_gradient=True, [True, True]) >>> # keepdim=True, result should be [[True], [True]], out.shape should be (2,1) >>> out4 = paddle.any(x, axis=1, keepdim=True) >>> out4 Tensor(shape=[2, 1], dtype=bool, place=Place(cpu), stop_gradient=True, [[True], [True]]) """ ... def apply(self, func: 'Callable[[Tensor], Tensor]') -> 'Tensor': r""" Apply the python function to the tensor. Returns: None Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[0.3, 0.5, 0.1], >>> [0.9, 0.9, 0.7], >>> [0.4, 0.8, 0.2]]).to("cpu", "float64") >>> f = lambda x: 3*x+2 >>> y = x.apply(f) >>> print(y) Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True, [[2.90000004, 3.50000000, 2.30000000], [4.69999993, 4.69999993, 4.09999996], [3.20000002, 4.40000004, 2.60000001]]) >>> x = paddle.to_tensor([[0.3, 0.5, 0.1], >>> [0.9, 0.9, 0.7], >>> [0.4, 0.8, 0.2]]).to("cpu", "float16") >>> y = x.apply(f) >>> x = paddle.to_tensor([[0.3, 0.5, 0.1], >>> [0.9, 0.9, 0.7], >>> [0.4, 0.8, 0.2]]).to("cpu", "bfloat16") >>> y = x.apply(f) >>> if paddle.is_compiled_with_cuda(): >>> x = paddle.to_tensor([[0.3, 0.5, 0.1], >>> [0.9, 0.9, 0.7], >>> [0.4, 0.8, 0.2]]).to("gpu", "float32") >>> y = x.apply(f) """ ... def apply_(self, func: 'Callable[[Tensor], Tensor]') -> 'Tensor': r""" Inplace apply the python function to the tensor. Returns: None Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[0.3, 0.5, 0.1], >>> [0.9, 0.9, 0.7], >>> [0.4, 0.8, 0.2]]).to("cpu", "float64") >>> f = lambda x: 3*x+2 >>> x.apply_(f) >>> print(x) Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True, [[2.90000004, 3.50000000, 2.30000000], [4.69999993, 4.69999993, 4.09999996], [3.20000002, 4.40000004, 2.60000001]]) >>> x = paddle.to_tensor([[0.3, 0.5, 0.1], >>> [0.9, 0.9, 0.7], >>> [0.4, 0.8, 0.2]]).to("cpu", "float16") >>> x.apply_(f) >>> x = paddle.to_tensor([[0.3, 0.5, 0.1], >>> [0.9, 0.9, 0.7], >>> [0.4, 0.8, 0.2]]).to("cpu", "bfloat16") >>> x.apply_(f) >>> if paddle.is_compiled_with_cuda(): >>> x = paddle.to_tensor([[0.3, 0.5, 0.1], >>> [0.9, 0.9, 0.7], >>> [0.4, 0.8, 0.2]]).to("gpu", "float32") >>> x.apply_(f) """ ... def argmax(self, axis: '_int | None' = None, keepdim: '_bool' = False, dtype: 'DTypeLike' = 'int64', name: '_str | None' = None) -> 'Tensor': r""" Computes the indices of the max elements of the input tensor's element along the provided axis. Args: axis (int|None, optional): Axis to compute indices along. The effective range is [-R, R), where R is x.ndim. when axis < 0, it works the same way as axis + R. Default is None, the input `x` will be into the flatten tensor, and selecting the min value index. keepdim (bool, optional): Whether to keep the given axis in output. If it is True, the dimensions will be same as input x and with size one in the axis. Otherwise the output dimensions is one fewer than x since the axis is squeezed. Default is False. dtype (str|np.dtype, optional): Data type of the output tensor which can be int32, int64. The default value is ``int64`` , and it will return the int64 indices. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor, return the tensor of int32 if set :attr:`dtype` is int32, otherwise return the tensor of int64. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[5,8,9,5], ... [0,0,1,7], ... [6,9,2,4]]) >>> out1 = paddle.argmax(x) >>> print(out1.numpy()) 2 >>> out2 = paddle.argmax(x, axis=0) >>> print(out2.numpy()) [2 2 0 1] >>> out3 = paddle.argmax(x, axis=-1) >>> print(out3.numpy()) [2 3 1] >>> out4 = paddle.argmax(x, axis=0, keepdim=True) >>> print(out4.numpy()) [[2 2 0 1]] """ ... def argmin(self, axis: '_int | None' = None, keepdim: '_bool' = False, dtype: 'DTypeLike' = 'int64', name: '_str | None' = None) -> 'Tensor': r""" Computes the indices of the min elements of the input tensor's element along the provided axis. Args: axis (int|None, optional): Axis to compute indices along. The effective range is [-R, R), where R is x.ndim. when axis < 0, it works the same way as axis + R. Default is None, the input `x` will be into the flatten tensor, and selecting the min value index. keepdim (bool, optional): Whether to keep the given axis in output. If it is True, the dimensions will be same as input x and with size one in the axis. Otherwise the output dimensions is one fewer than x since the axis is squeezed. Default is False. dtype (str|np.dtype, optional): Data type of the output tensor which can be int32, int64. The default value is 'int64', and it will return the int64 indices. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor, return the tensor of `int32` if set :attr:`dtype` is `int32`, otherwise return the tensor of `int64`. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[5,8,9,5], ... [0,0,1,7], ... [6,9,2,4]]) >>> out1 = paddle.argmin(x) >>> print(out1.numpy()) 4 >>> out2 = paddle.argmin(x, axis=0) >>> print(out2.numpy()) [1 1 1 2] >>> out3 = paddle.argmin(x, axis=-1) >>> print(out3.numpy()) [0 0 2] >>> out4 = paddle.argmin(x, axis=0, keepdim=True) >>> print(out4.numpy()) [[1 1 1 2]] """ ... def argsort(self, axis: '_int' = -1, descending: '_bool' = False, stable: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Sorts the input along the given axis, and returns the corresponding index tensor for the sorted output values. The default sort algorithm is ascending, if you want the sort algorithm to be descending, you must set the :attr:`descending` as True. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and the parameter name ``dim`` can be used as an alias for ``axis``. For example, ``argsort(input=tensor_x, dim=1)`` is equivalent to ``(x=tensor_x, axis=1)``. Args: axis (int, optional): Axis to compute indices along. The effective range is [-R, R), where R is Rank(x). when axis<0, it works the same way as axis+R. Default is -1. alias: ``dim``. descending (bool, optional) : Descending is a flag, if set to true, algorithm will sort by descending order, else sort by ascending order. Default is false. stable (bool, optional): Whether to use stable sorting algorithm or not. When using stable sorting algorithm, the order of equivalent elements will be preserved. Default is False. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor, sorted indices(with the same shape as ``x`` and with data type int64). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[[5,8,9,5], ... [0,0,1,7], ... [6,9,2,4]], ... [[5,2,4,2], ... [4,7,7,9], ... [1,7,0,6]]], ... dtype='float32') >>> out1 = paddle.argsort(x, axis=-1) >>> out2 = paddle.argsort(x, axis=0) >>> out3 = paddle.argsort(x, axis=1) >>> print(out1) Tensor(shape=[2, 3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[[0, 3, 1, 2], [0, 1, 2, 3], [2, 3, 0, 1]], [[1, 3, 2, 0], [0, 1, 2, 3], [2, 0, 3, 1]]]) >>> print(out2) Tensor(shape=[2, 3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[[0, 1, 1, 1], [0, 0, 0, 0], [1, 1, 1, 0]], [[1, 0, 0, 0], [1, 1, 1, 1], [0, 0, 0, 1]]]) >>> print(out3) Tensor(shape=[2, 3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[[1, 1, 1, 2], [0, 0, 2, 0], [2, 2, 0, 1]], [[2, 0, 2, 0], [1, 1, 0, 2], [0, 2, 1, 1]]]) >>> x = paddle.to_tensor([1, 0]*40, dtype='float32') >>> out1 = paddle.argsort(x, stable=False) >>> out2 = paddle.argsort(x, stable=True) >>> print(out1) Tensor(shape=[80], dtype=int64, place=Place(cpu), stop_gradient=True, [55, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 1 , 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 17, 11, 13, 25, 7 , 3 , 27, 23, 19, 15, 5 , 21, 9 , 10, 64, 62, 68, 60, 58, 8 , 66, 14, 6 , 70, 72, 4 , 74, 76, 2 , 78, 0 , 20, 28, 26, 30, 32, 24, 34, 36, 22, 38, 40, 12, 42, 44, 18, 46, 48, 16, 50, 52, 54, 56]) >>> print(out2) Tensor(shape=[80], dtype=int64, place=Place(cpu), stop_gradient=True, [1 , 3 , 5 , 7 , 9 , 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 0 , 2 , 4 , 6 , 8 , 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78]) """ ... def argwhere(self) -> 'Tensor': r""" Return a tensor containing the indices of all non-zero elements of the `input` tensor. The returned tensor has shape [z, n], where `z` is the number of all non-zero elements in the `input` tensor, and `n` is the number of dimensions in the `input` tensor. Args: Returns: Tensor, The data type is int64. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1.0, 0.0, 0.0], ... [0.0, 2.0, 0.0], ... [0.0, 0.0, 3.0]]) >>> out = paddle.tensor.search.argwhere(x) >>> print(out) Tensor(shape=[3, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 0], [1, 1], [2, 2]]) """ ... def as_complex(self, name: '_str | None' = None) -> 'Tensor': r""" Transform a real tensor to a complex tensor. The data type of the input tensor is 'float32' or 'float64', and the data type of the returned tensor is 'complex64' or 'complex128', respectively. The shape of the input tensor is ``(* ,2)``, (``*`` means arbitrary shape), i.e. the size of the last axis should be 2, which represent the real and imag part of a complex number. The shape of the returned tensor is ``(*,)``. The image below demonstrates the case that a real 3D-tensor with shape [2, 3, 2] is transformed into a complex 2D-tensor with shape [2, 3]. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/as_complex.png :width: 500 :alt: Illustration of as_complex :align: center Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, The output. Data type is 'complex64' or 'complex128', with the same precision as the input. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.arange(12, dtype=paddle.float32).reshape([2, 3, 2]) >>> y = paddle.as_complex(x) >>> print(y) Tensor(shape=[2, 3], dtype=complex64, place=Place(cpu), stop_gradient=True, [[(0.00000000+1.00000000j) , (2.00000000+3.00000000j) , (4.00000000+5.00000000j) ], [(6.00000000+7.00000000j) , (8.00000000+9.00000000j) , (10.00000000+11.00000000j)]]) """ ... def as_real(self, name: '_str | None' = None) -> 'Tensor': r""" Transform a complex tensor to a real tensor. The data type of the input tensor is 'complex64' or 'complex128', and the data type of the returned tensor is 'float32' or 'float64', respectively. When the shape of the input tensor is ``(*, )``, (``*`` means arbitrary shape), the shape of the output tensor is ``(*, 2)``, i.e. the shape of the output is the shape of the input appended by an extra ``2``. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, The output. Data type is 'float32' or 'float64', with the same precision as the input. Examples: .. code-block:: python >>> import paddle >>> x = paddle.arange(12, dtype=paddle.float32).reshape([2, 3, 2]) >>> y = paddle.as_complex(x) >>> z = paddle.as_real(y) >>> print(z) Tensor(shape=[2, 3, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[[0. , 1. ], [2. , 3. ], [4. , 5. ]], [[6. , 7. ], [8. , 9. ], [10., 11.]]]) """ ... def as_strided(self, shape: 'Sequence[_int]', stride: 'Sequence[_int]', offset: '_int' = 0, name: '_str | None' = None) -> 'Tensor': r""" View x with specified shape, stride and offset. Note that the output Tensor will share data with origin Tensor and doesn't have a Tensor copy in ``dygraph`` mode. The following image illustrates an example: transforming an input Tensor with shape [2,4,6] into a Tensor with ``shape [8,6]`` and ``stride [6,1]``. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/as_strided.png :alt: Legend Args: shape (list|tuple): Define the target shape. Each element of it should be integer. stride (list|tuple): Define the target stride. Each element of it should be integer. offset (int, optional): Define the target Tensor's offset from x's holder. Default: 0. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, A as_strided Tensor with the same data type as ``x``. Examples: .. code-block:: pycon >>> import paddle >>> paddle.base.set_flags({"FLAGS_use_stride_kernel": True}) >>> x = paddle.rand([2, 4, 6], dtype="float32") >>> out = paddle.as_strided(x, [8, 6], [6, 1]) >>> print(out.shape) paddle.Size([8, 6]) >>> # the stride is [6, 1]. """ ... def asin(self, name: '_str | None' = None) -> 'Tensor': r""" Arcsine Operator. .. math:: out = sin^-1(x) Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor. Same shape and data type as input (integer types are autocasted into float32) Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.asin(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [-0.41151685, -0.20135793, 0.10016742, 0.30469266]) """ ... def asin_(self, name=None): r""" Inplace version of ``asin`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_asin`. """ ... def asinh(self, name: '_str | None' = None) -> 'Tensor': r""" Asinh Activation Operator. .. math:: out = asinh(x) Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor. Output of Asinh operator, a Tensor with shape same as input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.asinh(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [-0.39003533, -0.19869010, 0.09983408, 0.29567307]) """ ... def asinh_(self, name=None): r""" Inplace version of ``asinh`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_asinh`. """ ... def astype(self, dtype: 'DTypeLike') -> 'Tensor': r""" Cast a Tensor to a specified data type if it differs from the current dtype; otherwise, return the original Tensor. Args: Returns: Tensor: a new Tensor with target dtype Examples: .. code-block:: python >>> import paddle >>> import numpy as np >>> original_tensor = paddle.ones([2, 2]) >>> print("original tensor's dtype is: {}".format(original_tensor.dtype)) original tensor's dtype is: paddle.float32 >>> new_tensor = original_tensor.astype('float32') >>> print("new tensor's dtype is: {}".format(new_tensor.dtype)) new tensor's dtype is: paddle.float32 """ ... def atan(self, name: '_str | None' = None) -> 'Tensor': r""" Arctangent Operator. .. math:: out = tan^{-1}(x) Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor. Same shape and dtype as input x (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.atan(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [-0.38050640, -0.19739556, 0.09966865, 0.29145682]) """ ... def atan2(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Element-wise arctangent of x/y with consideration of the quadrant. Equation: .. math:: atan2(x,y)=\left\{\begin{matrix} & tan^{-1}(\frac{x}{y}) & y > 0 \\ & tan^{-1}(\frac{x}{y}) + \pi & x>=0, y < 0 \\ & tan^{-1}(\frac{x}{y}) - \pi & x<0, y < 0 \\ & +\frac{\pi}{2} & x>0, y = 0 \\ & -\frac{\pi}{2} & x<0, y = 0 \\ &\text{undefined} & x=0, y = 0 \end{matrix}\right. Args: y (Tensor): An N-D Tensor, must have the same type as `x`. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor): An N-D Tensor, the shape and data type is the same with input (The output data type is float64 when the input data type is int). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-1, +1, +1, -1]).astype('float32') >>> x Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [-1, 1, 1, -1]) >>> y = paddle.to_tensor([-1, -1, +1, +1]).astype('float32') >>> y Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [-1, -1, 1, 1]) >>> out = paddle.atan2(x, y) >>> out Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [-2.35619450, 2.35619450, 0.78539819, -0.78539819]) """ ... def atan_(self, name=None): r""" Inplace version of ``atan`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_atan`. """ ... def atanh(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Atanh Activation Operator. .. math:: out = atanh(x) Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor. Output of Atanh operator, a Tensor with shape same as input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.atanh(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [-0.42364895, -0.20273255, 0.10033534, 0.30951962]) """ ... def atanh_(self, name=None): r""" Inplace version of ``atanh`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_atanh`. """ ... def atleast_1d(self, *inputs, name=None): r""" Convert inputs to tensors and return the view with at least 1-dimension. Scalar inputs are converted, one or high-dimensional inputs are preserved. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: One Tensor, if there is only one input. List of Tensors, if there are more than one inputs. Examples: .. code-block:: python >>> import paddle >>> # one input >>> x = paddle.to_tensor(123, dtype='int32') >>> out = paddle.atleast_1d(x) >>> print(out) Tensor(shape=[1], dtype=int32, place=Place(cpu), stop_gradient=True, [123]) >>> # more than one inputs >>> x = paddle.to_tensor(123, dtype='int32') >>> y = paddle.to_tensor([1.23], dtype='float32') >>> out = paddle.atleast_1d(x, y) >>> print(out) [Tensor(shape=[1], dtype=int32, place=Place(cpu), stop_gradient=True, [123]), Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [1.23000002])] >>> # more than 1-D input >>> x = paddle.to_tensor(123, dtype='int32') >>> y = paddle.to_tensor([[1.23]], dtype='float32') >>> out = paddle.atleast_1d(x, y) >>> print(out) [Tensor(shape=[1], dtype=int32, place=Place(cpu), stop_gradient=True, [123]), Tensor(shape=[1, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[1.23000002]])] """ ... def atleast_2d(self, *inputs, name=None): r""" Convert inputs to tensors and return the view with at least 2-dimension. Two or high-dimensional inputs are preserved. The following diagram illustrates the behavior of atleast_2d on different dimensional inputs for the following cases: 1. A 0-dim tensor input. 2. A 0-dim tensor and a 1-dim tensor input. 3. A 0-dim tensor and a 3-dim tensor input. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/atleast_2d.png :width: 600 :alt: legend of atleast_2d API :align: center In each case, the function returns the tensors (or a list of tensors) in views with at least 2 dimensions. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: One Tensor, if there is only one input. List of Tensors, if there are more than one inputs. Examples: .. code-block:: python >>> import paddle >>> # one input >>> x = paddle.to_tensor(123, dtype='int32') >>> out = paddle.atleast_2d(x) >>> print(out) Tensor(shape=[1, 1], dtype=int32, place=Place(cpu), stop_gradient=True, [[123]]) >>> # more than one inputs >>> x = paddle.to_tensor(123, dtype='int32') >>> y = paddle.to_tensor([1.23], dtype='float32') >>> out = paddle.atleast_2d(x, y) >>> print(out) [Tensor(shape=[1, 1], dtype=int32, place=Place(cpu), stop_gradient=True, [[123]]), Tensor(shape=[1, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[1.23000002]])] >>> # more than 2-D input >>> x = paddle.to_tensor(123, dtype='int32') >>> y = paddle.to_tensor([[[1.23]]], dtype='float32') >>> out = paddle.atleast_2d(x, y) >>> print(out) [Tensor(shape=[1, 1], dtype=int32, place=Place(cpu), stop_gradient=True, [[123]]), Tensor(shape=[1, 1, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[[1.23000002]]])] """ ... def atleast_3d(self, *inputs, name=None): r""" Convert inputs to tensors and return the view with at least 3-dimension. Three or high-dimensional inputs are preserved. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: One Tensor, if there is only one input. List of Tensors, if there are more than one inputs. Examples: .. code-block:: python >>> import paddle >>> # one input >>> x = paddle.to_tensor(123, dtype='int32') >>> out = paddle.atleast_3d(x) >>> print(out) Tensor(shape=[1, 1, 1], dtype=int32, place=Place(cpu), stop_gradient=True, [[[123]]]) >>> # more than one inputs >>> x = paddle.to_tensor(123, dtype='int32') >>> y = paddle.to_tensor([1.23], dtype='float32') >>> out = paddle.atleast_3d(x, y) >>> print(out) [Tensor(shape=[1, 1, 1], dtype=int32, place=Place(cpu), stop_gradient=True, [[[123]]]), Tensor(shape=[1, 1, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[[1.23000002]]])] >>> # more than 3-D input >>> x = paddle.to_tensor(123, dtype='int32') >>> y = paddle.to_tensor([[[[1.23]]]], dtype='float32') >>> out = paddle.atleast_3d(x, y) >>> print(out) [Tensor(shape=[1, 1, 1], dtype=int32, place=Place(cpu), stop_gradient=True, [[[123]]]), Tensor(shape=[1, 1, 1, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[[[1.23000002]]]])] """ ... def backward(self, grad_tensor: 'Tensor | None' = None, retain_graph: '_bool' = False, *, dump_backward_graph_path: '_str | None' = None) -> 'None': r""" Run backward of current Graph which starts from current Tensor. The new gradient will accumulate on previous gradient. You can clear gradient by ``Tensor.clear_grad()`` . Args: retain_graph(bool, optional): If False, the graph used to compute grads will be freed. If you would like to add more ops to the built graph after calling this method( :code:`backward` ), set the parameter :code:`retain_graph` to True, then the grads will be retained. Thus, setting it to False is much more memory-efficient. Defaults to False. dump_backward_graph_path(str, optional): Specifies the directory path for storing the debug file. If this parameter is specified, the backward-related graph (in dot format) and the debugging call stack information will be generated in this directory. Returns: None Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor(5., stop_gradient=False) >>> for i in range(5): ... y = paddle.pow(x, 4.0) ... y.backward() ... print("{}: {}".format(i, x.grad)) 0: 500.0 1: 1000.0 2: 1500.0 3: 2000.0 4: 2500.0 >>> x.clear_grad() >>> print("{}".format(x.grad)) 0.0 >>> grad_tensor=paddle.to_tensor(2.) >>> for i in range(5): ... y = paddle.pow(x, 4.0) ... y.backward(grad_tensor) ... print("{}: {}".format(i, x.grad)) 0: 1000.0 1: 2000.0 2: 3000.0 3: 4000.0 4: 5000.0 """ ... def baddbmm(self, x: 'Tensor', y: 'Tensor', beta: '_float' = 1.0, alpha: '_float' = 1.0, name: '_str | None' = None) -> 'Tensor': r""" **baddbmm** Perform batch matrix multiplication for input $x$ and $y$. $input$ is added to the final result. The equation is: .. math:: Out = alpha * x * y + beta * input $Input$, $x$ and $y$ can carry the LoD (Level of Details) information, or not. But the output only shares the LoD information with input $input$. Args: x (Tensor): The first input Tensor for batch matrix multiplication. y (Tensor): The second input Tensor for batch matrix multiplication. beta (float, optional): Coefficient of $input$, default is 1. alpha (float, optional): Coefficient of $x*y$, default is 1. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The output Tensor of baddbmm. Examples: .. code-block:: python >>> import paddle >>> x = paddle.ones([2, 2, 2]) >>> y = paddle.ones([2, 2, 2]) >>> input = paddle.ones([2, 2, 2]) >>> out = paddle.baddbmm(input=input, x=x, y=y, beta=0.5, alpha=5.0) >>> out Tensor(shape=[2, 2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[[10.50000000, 10.50000000], [10.50000000, 10.50000000]], [[10.50000000, 10.50000000], [10.50000000, 10.50000000]]]) """ ... def baddbmm_(self, x: 'Tensor', y: 'Tensor', beta: '_float' = 1.0, alpha: '_float' = 1.0, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``baddbmm`` API, the output Tensor will be inplaced with input ``input``. Please refer to :ref:`api_paddle_baddbmm`. """ ... def bernoulli_(self, p: '_float | Tensor' = 0.5, name: '_str | None' = None) -> 'Tensor': r""" This is the inplace version of api ``bernoulli``, which returns a Tensor filled with random values sampled from a bernoulli distribution. The output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_bernoulli`. Args: p (float|Tensor, optional): The success probability parameter of the output Tensor's bernoulli distribution. If ``p`` is float, all elements of the output Tensor shared the same success probability. If ``p`` is a Tensor, it has per-element success probabilities, and the shape should be broadcastable to ``x``. Default is 0.5 name(str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, A Tensor filled with random values sampled from the bernoulli distribution with success probability ``p`` . Examples: .. code-block:: python >>> import paddle >>> paddle.set_device('cpu') >>> paddle.seed(200) >>> x = paddle.randn([3, 4]) >>> x.bernoulli_() >>> print(x) Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[0., 1., 0., 1.], [1., 1., 0., 1.], [0., 1., 0., 0.]]) >>> x = paddle.randn([3, 4]) >>> p = paddle.randn([3, 1]) >>> x.bernoulli_(p) >>> print(x) Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[1., 1., 1., 1.], [0., 0., 0., 0.], [0., 0., 0., 0.]]) """ ... def bfloat16(self) -> 'Tensor': r""" Cast a Tensor to bfloat16 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with bfloat16 dtype """ ... def bincount(self, weights: 'Tensor | None' = None, minlength: '_int' = 0, name: '_str | None' = None) -> 'Tensor': r""" Computes frequency of each value in the input tensor. Args: weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None. minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0. name (str|None, optional): Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default is None. Returns: Tensor: The tensor of frequency. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 1, 4, 5]) >>> result1 = paddle.bincount(x) >>> print(result1) Tensor(shape=[6], dtype=int64, place=Place(cpu), stop_gradient=True, [0, 2, 1, 0, 1, 1]) >>> w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5]) >>> result2 = paddle.bincount(x, weights=w) >>> print(result2) Tensor(shape=[6], dtype=float32, place=Place(cpu), stop_gradient=True, [0. , 2.19999981, 0.40000001, 0. , 0.50000000, 0.50000000]) """ ... def bitwise_and_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``bitwise_and`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_bitwise_and`. """ ... def bitwise_invert(self, out: 'Tensor | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Apply ``bitwise_not`` (bitwise inversion) on Tensor ``x``. This is an alias to the ``paddle.bitwise_not`` function. .. math:: Out = \sim X Note: ``paddle.bitwise_invert`` is functionally equivalent to ``paddle.bitwise_not``. Args: out (Tensor|None, optional): Result of ``bitwise_invert``. It is a N-D Tensor with the same data type as the input Tensor. Default: None. name (str|None, optional): The default value is None. This property is typically not set by the user. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Result of ``bitwise_invert``. It is a N-D Tensor with the same data type as the input Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-5, -1, 1]) >>> res = x.bitwise_invert() >>> print(res) Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, [ 4, 0, -2]) """ ... def bitwise_invert_(self, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``bitwise_invert`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_bitwise_invert_`. """ ... def bitwise_left_shift(self, y: 'Tensor', is_arithmetic: '_bool' = True, out: 'Tensor | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Apply ``bitwise_left_shift`` on Tensor ``X`` and ``Y`` . .. math:: Out = X \ll Y .. note:: ``paddle.bitwise_left_shift`` supports broadcasting. If you want know more about broadcasting, please refer to please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: y (Tensor): Input Tensor of ``bitwise_left_shift`` . It is a N-D Tensor of uint8, int8, int16, int32, int64. is_arithmetic (bool, optional): A boolean indicating whether to choose arithmetic shift, if False, means logic shift. Default True. out (Tensor|None, optional): Result of ``bitwise_left_shift`` . It is a N-D Tensor with the same data type of input Tensor. Default: None. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Result of ``bitwise_left_shift`` . It is a N-D Tensor with the same data type of input Tensor. Examples: .. code-block:: python :name: bitwise_left_shift_example1 >>> import paddle >>> x = paddle.to_tensor([[1,2,4,8],[16,17,32,65]]) >>> y = paddle.to_tensor([[1,2,3,4,], [2,3,2,1]]) >>> paddle.bitwise_left_shift(x, y, is_arithmetic=True) Tensor(shape=[2, 4], dtype=int64, place=Place(gpu:0), stop_gradient=True, [[2 , 8 , 32 , 128], [64 , 136, 128, 130]]) .. code-block:: python :name: bitwise_left_shift_example2 >>> import paddle >>> x = paddle.to_tensor([[1,2,4,8],[16,17,32,65]]) >>> y = paddle.to_tensor([[1,2,3,4,], [2,3,2,1]]) >>> paddle.bitwise_left_shift(x, y, is_arithmetic=False) Tensor(shape=[2, 4], dtype=int64, place=Place(gpu:0), stop_gradient=True, [[2 , 8 , 32 , 128], [64 , 136, 128, 130]]) """ ... def bitwise_left_shift_(self, y: 'Tensor', is_arithmetic: '_bool' = True, out: 'Tensor | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``bitwise_left_shift`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_bitwise_left_shift`. """ ... def bitwise_not_(self, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``bitwise_not`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_bitwise_not`. """ ... def bitwise_or_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``bitwise_or`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_bitwise_or`. """ ... def bitwise_right_shift(self, y: 'Tensor', is_arithmetic: '_bool' = True, out: 'Tensor | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Apply ``bitwise_right_shift`` on Tensor ``X`` and ``Y`` . .. math:: Out = X \gg Y .. note:: ``paddle.bitwise_right_shift`` supports broadcasting. If you want know more about broadcasting, please refer to please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: y (Tensor): Input Tensor of ``bitwise_right_shift`` . It is a N-D Tensor of uint8, int8, int16, int32, int64. is_arithmetic (bool, optional): A boolean indicating whether to choose arithmetic shift, if False, means logic shift. Default True. out (Tensor|None, optional): Result of ``bitwise_right_shift`` . It is a N-D Tensor with the same data type of input Tensor. Default: None. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Result of ``bitwise_right_shift`` . It is a N-D Tensor with the same data type of input Tensor. Examples: .. code-block:: python :name: bitwise_right_shift_example1 >>> import paddle >>> x = paddle.to_tensor([[10,20,40,80],[16,17,32,65]]) >>> y = paddle.to_tensor([[1,2,3,4,], [2,3,2,1]]) >>> paddle.bitwise_right_shift(x, y, is_arithmetic=True) Tensor(shape=[2, 4], dtype=int64, place=Place(gpu:0), stop_gradient=True, [[5 , 5 , 5 , 5 ], [4 , 2 , 8 , 32]]) .. code-block:: python :name: bitwise_right_shift_example2 >>> import paddle >>> x = paddle.to_tensor([[-10,-20,-40,-80],[-16,-17,-32,-65]], dtype=paddle.int8) >>> y = paddle.to_tensor([[1,2,3,4,], [2,3,2,1]], dtype=paddle.int8) >>> paddle.bitwise_right_shift(x, y, is_arithmetic=False) # logic shift Tensor(shape=[2, 4], dtype=int8, place=Place(gpu:0), stop_gradient=True, [[123, 59 , 27 , 11 ], [60 , 29 , 56 , 95 ]]) """ ... def bitwise_right_shift_(self, y: 'Tensor', is_arithmetic: '_bool' = True, out: 'Tensor | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``bitwise_right_shift`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_bitwise_left_shift`. """ ... def bitwise_xor_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``bitwise_xor`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_bitwise_xor`. """ ... def block_diag(self, name: '_str | None' = None) -> 'Tensor': r""" Create a block diagonal matrix from provided tensors. Args: name (str|None, optional): Name for the operation (optional, default is None). Returns: Tensor, A ``Tensor``. The data type is same as ``inputs``. Examples: .. code-block:: python >>> import paddle >>> A = paddle.to_tensor([[4], [3], [2]]) >>> B = paddle.to_tensor([7, 6, 5]) >>> C = paddle.to_tensor(1) >>> D = paddle.to_tensor([[5, 4, 3], [2, 1, 0]]) >>> E = paddle.to_tensor([[8, 7], [7, 8]]) >>> out = paddle.block_diag([A, B, C, D, E]) >>> print(out) Tensor(shape=[9, 10], dtype=int64, place=Place(gpu:0), stop_gradient=True, [[4, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 7, 6, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 4, 3, 0, 0], [0, 0, 0, 0, 0, 2, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 8, 7], [0, 0, 0, 0, 0, 0, 0, 0, 7, 8]]) """ ... def bmm(self, y: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Applies batched matrix multiplication to two tensors. Both of the two input tensors must be three-dimensional and share the same batch size. If x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor. Args: y (Tensor): The input Tensor. name (str|None): A name for this layer(optional). If set None, the layer will be named automatically. Default: None. out(Tensor, optional): The output tensor. Returns: Tensor: The product Tensor. Examples: .. code-block:: python >>> import paddle >>> # In imperative mode: >>> # size x: (2, 2, 3) and y: (2, 3, 2) >>> x = paddle.to_tensor([[[1.0, 1.0, 1.0], ... [2.0, 2.0, 2.0]], ... [[3.0, 3.0, 3.0], ... [4.0, 4.0, 4.0]]]) >>> y = paddle.to_tensor([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]], ... [[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]]) >>> out = paddle.bmm(x, y) >>> print(out) Tensor(shape=[2, 2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[[6. , 6. ], [12., 12.]], [[45., 45.], [60., 60.]]]) """ ... def bool(self) -> 'Tensor': r""" Cast a Tensor to bool data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with bool dtype """ ... def broadcast_shape(self, y_shape: 'Sequence[_int]') -> 'list[_int]': r""" The function returns the shape of doing operation with broadcasting on tensors of x_shape and y_shape. Note: If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: y_shape (list[int]|tuple[int]): A shape of tensor. Returns: list[int], the result shape. Examples: .. code-block:: python >>> import paddle >>> shape = paddle.broadcast_shape([2, 1, 3], [1, 3, 1]) >>> shape [2, 3, 3] >>> # shape = paddle.broadcast_shape([2, 1, 3], [3, 3, 1]) >>> # ValueError (terminated with error message). """ ... def broadcast_shapes(self, *shapes: 'Sequence[_int]') -> 'list[_int]': r""" The function returns the shape of doing operation with broadcasting on tensors of shape list. Note: If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: Returns: list[int], the result shape. Examples: .. code-block:: python >>> import paddle >>> shape = paddle.broadcast_shapes([2, 1, 3], [1, 3, 1]) >>> shape [2, 3, 3] >>> # shape = paddle.broadcast_shapes([2, 1, 3], [3, 3, 1]) >>> # ValueError (terminated with error message). >>> shape = paddle.broadcast_shapes([5, 1, 3], [1, 4, 1], [1, 1, 3]) >>> shape [5, 4, 3] >>> # shape = paddle.broadcast_shapes([5, 1, 3], [1, 4, 1], [1, 2, 3]) >>> # ValueError (terminated with error message). """ ... def broadcast_tensors(self, name: '_str | None' = None) -> 'list[Tensor]': r""" Broadcast a list of tensors following broadcast semantics Note: If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor The following figure illustrates the process of broadcasting three tensors to the same dimensions. The dimensions of the three tensors are [4, 1, 3], [2, 3], and [4, 2, 1], respectively. During broadcasting, alignment starts from the last dimension, and for each dimension, either the sizes of the two tensors in that dimension are equal, or one of the tensors has a dimension of 1, or one of the tensors lacks that dimension. In the figure below, in the last dimension, Tensor3 has a size of 1, while Tensor1 and Tensor2 have sizes of 3; thus, this dimension is expanded to 3 for all tensors. In the second-to-last dimension, Tensor1 has a size of 2, and Tensor2 and Tensor3 both have sizes of 2; hence, this dimension is expanded to 2 for all tensors. In the third-to-last dimension, Tensor2 lacks this dimension, while Tensor1 and Tensor3 have sizes of 4; consequently, this dimension is expanded to 4 for all tensors. Ultimately, all tensors are expanded to [4, 2, 3]. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/broadcast.png :width: 800 :alt: Illustration of BroadCast :align: center Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: list(Tensor), The list of broadcasted tensors following the same order as ``input``. Examples: .. code-block:: python >>> import paddle >>> x1 = paddle.rand([1, 2, 3, 4]).astype('float32') >>> x2 = paddle.rand([1, 2, 1, 4]).astype('float32') >>> x3 = paddle.rand([1, 1, 3, 1]).astype('float32') >>> out1, out2, out3 = paddle.broadcast_tensors(input=[x1, x2, x3]) >>> # out1, out2, out3: tensors broadcasted from x1, x2, x3 with shape [1,2,3,4] """ ... def broadcast_to(self, shape: 'ShapeLike', name: '_str | None' = None) -> 'Tensor': r""" Broadcast the input tensor to a given shape. Both the number of dimensions of ``x`` and the number of elements in ``shape`` should be less than or equal to 6. The dimension to broadcast to must have a value 0. The following figure shows the process of broadcasting a one-dimensional tensor of shape [3] to a two-dimensional tensor of shape [2,3] based on the shape specified by 'shape'. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/broadcast_to.png :width: 500 :alt: broadcast_to API :align: center .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``size`` can be used as an alias for ``shape``. For example, ``broadcast_to(input=tensor_x, size=[2, 3], ...)`` is equivalent to ``broadcast_to(x=tensor_x, shape=[2, 3], ...)``. Args: shape (list|tuple|Tensor): The result shape after broadcasting. The data type is int32. If shape is a list or tuple, all its elements should be integers or 0-D or 1-D Tensors with the data type int32. If shape is a Tensor, it should be an 1-D Tensor with the data type int32. The value -1 in shape means keeping the corresponding dimension unchanged. alias: ``size``. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: N-D Tensor, A Tensor with the given shape. The data type is the same as ``x``. Examples: .. code-block:: python >>> import paddle >>> data = paddle.to_tensor([1, 2, 3], dtype='int32') >>> out = paddle.broadcast_to(data, shape=[2, 3]) >>> print(out) Tensor(shape=[2, 3], dtype=int32, place=Place(cpu), stop_gradient=True, [[1, 2, 3], [1, 2, 3]]) """ ... def bucketize(self, sorted_sequence: 'Tensor', out_int32: '_bool' = False, right: '_bool' = False, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" This API is used to find the index of the corresponding 1D tensor `sorted_sequence` in the innermost dimension based on the given `x`. Args: sorted_sequence (Tensor): An input 1-D tensor with type int32, int64, float32, float64. The value of the tensor monotonically increases in the innermost dimension. alias: ``boundaries``. out_int32 (bool, optional): Data type of the output tensor which can be int32, int64. The default value is False, and it indicates that the output data type is int64. right (bool, optional): Find the upper or lower bounds of the sorted_sequence range in the innermost dimension based on the given `x`. If the value of the sorted_sequence is nan or inf, return the size of the innermost dimension. The default value is False and it shows the lower bounds. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output tensor. Default: None. Returns: Tensor (the same sizes of the `x`), return the tensor of int32 if set :attr:`out_int32` is True, otherwise return the tensor of int64. Examples: .. code-block:: python >>> import paddle >>> sorted_sequence = paddle.to_tensor([2, 4, 8, 16], dtype='int32') >>> x = paddle.to_tensor([[0, 8, 4, 16], [-1, 2, 8, 4]], dtype='int32') >>> out1 = paddle.bucketize(x, sorted_sequence) >>> print(out1) Tensor(shape=[2, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 2, 1, 3], [0, 0, 2, 1]]) >>> out2 = paddle.bucketize(x, sorted_sequence, right=True) >>> print(out2) Tensor(shape=[2, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 3, 2, 4], [0, 1, 3, 2]]) >>> out3 = x.bucketize(sorted_sequence) >>> print(out3) Tensor(shape=[2, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 2, 1, 3], [0, 0, 2, 1]]) >>> out4 = x.bucketize(sorted_sequence, right=True) >>> print(out4) Tensor(shape=[2, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 3, 2, 4], [0, 1, 3, 2]]) """ ... def byte(self) -> 'Tensor': ... def cast(self, dtype: 'DTypeLike') -> 'Tensor': r""" Take in the Tensor :attr:`x` with :attr:`x.dtype` and cast it to the output with :attr:`dtype`. It's meaningless if the output dtype equals the input dtype, but it's fine if you do so. The following picture shows an example where a tensor of type float64 is cast to a tensor of type uint8. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/cast.png :width: 800 :alt: legend of reshape API :align: center Args: dtype (paddle.dtype|np.dtype|str): Data type of the output: bool, float16, float32, float64, int8, int32, int64, uint8. Returns: Tensor, A Tensor with the same shape as input's. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([2, 3, 4], 'float64') >>> y = paddle.cast(x, 'uint8') """ ... def cast_(self, dtype: 'DTypeLike') -> 'Tensor': r""" Inplace version of ``cast`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_cast`. """ ... def cauchy_(self, loc: 'Numeric' = 0, scale: 'Numeric' = 1, name: '_str | None' = None) -> 'paddle.Tensor': r""" Fills the tensor with numbers drawn from the Cauchy distribution. Args: loc (scalar, optional): Location of the peak of the distribution. The data type is float32 or float64. scale (scalar, optional): The half-width at half-maximum (HWHM). The data type is float32 or float64. Must be positive values. name(str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor: input tensor with numbers drawn from the Cauchy distribution. Examples: .. code-block:: python >>> import paddle >>> x = paddle.randn([3, 4]) >>> x.cauchy_(1, 2) >>> # doctest: +SKIP('random check') >>> print(x) Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 3.80087137, 2.25415039, 2.77960515, 7.64125967], [ 0.76541221, 2.74023032, 1.99383152, -0.12685823], [ 1.45228469, 1.76275957, -4.30458832, 34.74880219]]) """ ... def cdist(self, y: 'Tensor', p: '_float' = 2.0, compute_mode: "Literal['use_mm_for_euclid_dist_if_necessary', 'use_mm_for_euclid_dist', 'donot_use_mm_for_euclid_dist']" = 'use_mm_for_euclid_dist_if_necessary', name: '_str | None' = None) -> 'Tensor': r""" Compute the p-norm distance between each pair of the two collections of inputs. This function is equivalent to `scipy.spatial.distance.cdist(input,'minkowski', p=p)` if :math:`p \in (0, \infty)`. When :math:`p = 0` it is equivalent to `scipy.spatial.distance.cdist(input, 'hamming') * M`. When :math:`p = \infty`, the closest scipy function is `scipy.spatial.distance.cdist(xn, lambda x, y: np.abs(x - y).max())`. Args: y (Tensor): A tensor with shape :math:`B \times R \times M`. p (float, optional): The value for the p-norm distance to calculate between each vector pair. Default: :math:`2.0`. compute_mode (str, optional): The mode for compute distance. - ``use_mm_for_euclid_dist_if_necessary`` , for p = 2.0 and (P > 25 or R > 25), it will use matrix multiplication to calculate euclid distance if possible. - ``use_mm_for_euclid_dist`` , for p = 2.0, it will use matrix multiplication to calculate euclid distance. - ``donot_use_mm_for_euclid_dist`` , it will not use matrix multiplication to calculate euclid distance. Default: ``use_mm_for_euclid_dist_if_necessary``. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor, the dtype is same as input tensor. If x has shape :math:`B \times P \times M` and y has shape :math:`B \times R \times M` then the output will have shape :math:`B \times P \times R`. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[0.9041, 0.0196], [-0.3108, -2.4423], [-0.4821, 1.059]], dtype=paddle.float32) >>> y = paddle.to_tensor([[-2.1763, -0.4713], [-0.6986, 1.3702]], dtype=paddle.float32) >>> distance = paddle.cdist(x, y) >>> print(distance) Tensor(shape=[3, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[3.11927032, 2.09589314], [2.71384072, 3.83217239], [2.28300953, 0.37910119]]) """ ... def cdouble(self) -> 'Tensor': r""" Cast a Tensor to complex128 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with complex128 dtype """ ... def ceil(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Ceil Operator. Computes ceil of x element-wise. .. math:: out = \left \lceil x \right \rceil Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output tensor. Default: None. Returns: Tensor. Output of Ceil operator, a Tensor with shape same as input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.ceil(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [-0., -0., 1. , 1. ]) """ ... def ceil_(self, name=None): r""" Inplace version of ``ceil`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_ceil`. """ ... def cfloat(self) -> 'Tensor': r""" Cast a Tensor to complex64 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with complex64 dtype """ ... def char(self) -> 'Tensor': r""" Cast a Tensor to int8 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with int8 dtype """ ... def cholesky(self, upper: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Computes the Cholesky decomposition of one symmetric positive-definite matrix or batches of symmetric positive-definite matrices. If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` , and the returned matrix :math:`U` is upper-triangular. Otherwise, the decomposition has the form :math:`A = LL^{T}` , and the returned matrix :math:`L` is lower-triangular. Args: upper (bool, optional): The flag indicating whether to return upper or lower triangular matrices. Default: False. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, A Tensor with same shape and data type as `x`. It represents triangular matrices generated by Cholesky decomposition. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2023) >>> a = paddle.rand([3, 3], dtype="float32") >>> a_t = paddle.transpose(a, [1, 0]) >>> x = paddle.matmul(a, a_t) + 1e-03 >>> out = paddle.linalg.cholesky(x, upper=False) >>> print(out) Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[1.04337072, 0. , 0. ], [1.06467664, 0.17859250, 0. ], [1.30602181, 0.08326444, 0.22790681]]) """ ... def cholesky_inverse(self, upper: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Using the Cholesky factor `U` to calculate the inverse matrix of a symmetric positive definite matrix, returns the matrix `inv`. If `upper` is `False`, `U` is lower triangular matrix: .. math:: inv = (UU^{T})^{-1} If `upper` is `True`, `U` is upper triangular matrix: .. math:: inv = (U^{T}U)^{-1} Args: upper (bool, optional): If `upper` is `False`, `x` is lower triangular matrix, or is upper triangular matrix. Default: `False`. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor. Computes the inverse matrix. Examples: .. code-block:: python >>> import paddle >>> # lower triangular matrix >>> x = paddle.to_tensor([[3.,.0,.0], [5.,3.,.0], [-1.,1.,2.]]) >>> out = paddle.linalg.cholesky_inverse(x) >>> print(out) Tensor(shape=[3, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True, [[ 0.61728382, -0.25925916, 0.22222219], [-0.25925916, 0.13888884, -0.08333331], [ 0.22222218, -0.08333331, 0.25000000]]) >>> # upper triangular matrix >>> out = paddle.linalg.cholesky_inverse(x.T, upper=True) >>> print(out) Tensor(shape=[3, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True, [[ 0.61728382, -0.25925916, 0.22222219], [-0.25925916, 0.13888884, -0.08333331], [ 0.22222218, -0.08333331, 0.25000000]]) """ ... def cholesky_solve(self, y: 'Tensor', upper: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B. Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs is also batches. Args: y (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions. Its data type should be float32 or float64. upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The solution of the system of equations. Its data type is the same as that of `x`. Examples: .. code-block:: python >>> import paddle >>> u = paddle.to_tensor([[1, 1, 1], ... [0, 2, 1], ... [0, 0,-1]], dtype="float64") >>> b = paddle.to_tensor([[0], [-9], [5]], dtype="float64") >>> out = paddle.linalg.cholesky_solve(b, u, upper=True) >>> print(out) Tensor(shape=[3, 1], dtype=float64, place=Place(cpu), stop_gradient=True, [[-2.50000000], [-7. ], [ 9.50000000]]) """ ... def chunk(self, chunks: '_int', axis: '_int | Tensor' = 0, name: '_str | None' = None) -> 'list[Tensor]': r""" Split the input tensor into multiple sub-Tensors. Here are some examples to explain it. - 1. Given a 3-D tensor x with a shape [3, 3, 3], if we split the first dimension into three equal parts, it will output a list containing three 3-D tensors with a shape of [1, 3, 3]. - 2. Given a 3-D tensor x with a shape [3, 3, 3], if we split the second dimension into three equal parts, it will output a list containing three 3-D tensors with a shape of [3, 1, 3]. - 3. Given a 3-D tensor x with a shape [3, 3, 3], if we split the third dimension into three equal parts, it will output a list containing three 3-D tensors with a shape of [3, 3, 1]. The following figure illustrates the first example. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/chunk.png :width: 800 :alt: legend of reshape API :align: center .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and the parameter name ``dim`` can be used as an alias for ``axis``. For example, ``chunk(input=tensor_x, dim=1)`` is equivalent to ``chunk(x=tensor_x, axis=1)``. Args: chunks(int): The number of tensor to be split along the certain axis. axis (int|Tensor, optional): The axis along which to split, it can be a integer or a ``0-D Tensor`` with shape [] and data type ``int32`` or ``int64``. If :math::`axis < 0`, the axis to split along is :math:`rank(x) + axis`. Default is 0. alias: ``dim``. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: list(Tensor), The list of segmented Tensors. Examples: .. code-block:: python >>> import paddle >>> x = paddle.rand([3, 9, 5]) >>> out0, out1, out2 = paddle.chunk(x, chunks=3, axis=1) >>> # out0.shape [3, 3, 5] >>> # out1.shape [3, 3, 5] >>> # out2.shape [3, 3, 5] >>> # axis is negative, the real axis is (rank(x) + axis) which real >>> # value is 1. >>> out0, out1, out2 = paddle.chunk(x, chunks=3, axis=-2) >>> # out0.shape [3, 3, 5] >>> # out1.shape [3, 3, 5] >>> # out2.shape [3, 3, 5] """ ... def clamp(self, min: '_float | None' = None, max: '_float | None' = None, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" This operator clip all elements in input into the range [ min, max ] and return a resulting tensor as the following equation: .. math:: Out = MIN(MAX(x, min), max) .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``. For example, ``clip(input=tensor_x)`` is equivalent to ``clip(x=tensor_x)``. Args: min (float|int|Tensor, optional): The lower bound with type ``float`` , ``int`` or a ``0-D Tensor`` with shape [] and type ``bfloat16``, ``float16``, ``float32``, ``float64``, ``int32``. max (float|int|Tensor, optional): The upper bound with type ``float``, ``int`` or a ``0-D Tensor`` with shape [] and type ``bfloat16``, ``float16``, ``float32``, ``float64``, ``int32``. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output Tensor. If set, the result will be stored in this Tensor. Default: None. Returns: Tensor: A Tensor with the same data shape as input. If either min or max is a floating-point value/Tensor, the output tensor will have a data type of ``float32``. Otherwise, the output tensor will inherit the same data type as the input. Examples: .. code-block:: python >>> import paddle >>> x1 = paddle.to_tensor([[1.2, 3.5], [4.5, 6.4]], 'float32') >>> out1 = paddle.clip(x1, min=3.5, max=5.0) >>> out1 Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[3.50000000, 3.50000000], [4.50000000, 5. ]]) >>> out2 = paddle.clip(x1, min=2.5) >>> out2 Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[2.50000000, 3.50000000], [4.50000000, 6.40000010]]) """ ... def clamp_(self, min: '_float | None' = None, max: '_float | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``clip`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_clip`. """ ... def clear_grad(self) -> 'None': r""" The alias of clear_gradient(). """ ... def coalesce(self, name: '_str | None' = None) -> 'Tensor': r""" the coalesced operator include sorted and merge, after coalesced, the indices of x is sorted and unique. Parameters: x (Tensor): the input SparseCooTensor. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: return the SparseCooTensor after coalesced. Examples: .. code-block:: python >>> import paddle >>> indices = [[0, 0, 1], [1, 1, 2]] >>> values = [1.0, 2.0, 3.0] >>> sp_x = paddle.sparse.sparse_coo_tensor(indices, values) >>> sp_x = sp_x.coalesce() >>> print(sp_x.indices()) Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 1], [1, 2]]) >>> print(sp_x.values()) Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [3., 3.]) """ ... def combinations(self, r: '_int' = 2, with_replacement: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Compute combinations of length r of the given tensor. The behavior is similar to python's itertools.combinations when with_replacement is set to False, and itertools.combinations_with_replacement when with_replacement is set to True. Args: r (int, optional): number of elements to combine, default value is 2. with_replacement (bool, optional): whether to allow duplication in combination, default value is False. name (str|None, optional): Name for the operation (optional, default is None).For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor). Tensor concatenated by combinations, same dtype with x. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3], dtype='int32') >>> res = paddle.combinations(x) >>> print(res) Tensor(shape=[3, 2], dtype=int32, place=Place(gpu:0), stop_gradient=True, [[1, 2], [1, 3], [2, 3]]) """ ... def complex128(self) -> 'Tensor': r""" Cast a Tensor to complex128 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with complex128 dtype """ ... def complex64(self) -> 'Tensor': r""" Cast a Tensor to complex64 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with complex64 dtype """ ... def concat(self, axis: '_int | Tensor' = 0, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Concatenates the input along the axis. It doesn't support 0-D Tensor because it requires a certain axis, and 0-D Tensor doesn't have any axis. The image illustrates a typical case of the concat operation. Two three-dimensional tensors with shapes [2, 3, 4] are concatenated along different axes, resulting in tensors of different shapes. The effects of concatenation along various dimensions are clearly visible. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/concat.png :width: 500 :alt: legend of concat API :align: center .. note:: Alias Support: The parameter name ``tensors`` can be used as an alias for ``x``, and ``dim`` can be used as an alias for ``axis``. For example, ``concat(tensors=tensor_x, dim=1, ...)`` is equivalent to ``concat(x=tensor_x, axis=1, ...)``. Args: axis (int|Tensor, optional): Specify the axis to operate on the input Tensors. Tt should be integer or 0-D int Tensor with shape []. The effective range is [-R, R), where R is Rank(x). When ``axis < 0``, it works the same way as ``axis+R``. Default is 0. alias: ``dim``. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output Tensor. If set, the result will be stored in this Tensor. Default is None. Returns: Tensor, A Tensor with the same data type as ``x``. Examples: .. code-block:: python >>> import paddle >>> x1 = paddle.to_tensor([[1, 2, 3], ... [4, 5, 6]]) >>> x2 = paddle.to_tensor([[11, 12, 13], ... [14, 15, 16]]) >>> x3 = paddle.to_tensor([[21, 22], ... [23, 24]]) >>> zero = paddle.full(shape=[1], dtype='int32', fill_value=0) >>> # When the axis is negative, the real axis is (axis + Rank(x)) >>> # As follow, axis is -1, Rank(x) is 2, the real axis is 1 >>> out1 = paddle.concat(x=[x1, x2, x3], axis=-1) >>> out2 = paddle.concat(x=[x1, x2], axis=0) >>> out3 = paddle.concat(x=[x1, x2], axis=zero) >>> print(out1) Tensor(shape=[2, 8], dtype=int64, place=Place(cpu), stop_gradient=True, [[1 , 2 , 3 , 11, 12, 13, 21, 22], [4 , 5 , 6 , 14, 15, 16, 23, 24]]) >>> print(out2) Tensor(shape=[4, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[1 , 2 , 3 ], [4 , 5 , 6 ], [11, 12, 13], [14, 15, 16]]) >>> print(out3) Tensor(shape=[4, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[1 , 2 , 3 ], [4 , 5 , 6 ], [11, 12, 13], [14, 15, 16]]) """ ... def cond(self, p: '_float | _POrder | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``. Args: p (float|string, optional): Order of the norm. Supported values are `fro`, `nuc`, `1`, `-1`, `2`, `-2`, `inf`, `-inf`. Default value is `None`, meaning that the order of the norm is `2`. name (str, optional): The default value is `None`. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: computing results of condition number, its data type is the same as input Tensor ``x``. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2023) >>> x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]]) >>> # compute conditional number when p is None >>> out = paddle.linalg.cond(x) >>> print(out) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.41421378) >>> # compute conditional number when order of the norm is 'fro' >>> out_fro = paddle.linalg.cond(x, p='fro') >>> print(out_fro) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 3.16227770) >>> # compute conditional number when order of the norm is 'nuc' >>> out_nuc = paddle.linalg.cond(x, p='nuc') >>> print(out_nuc) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 9.24264145) >>> # compute conditional number when order of the norm is 1 >>> out_1 = paddle.linalg.cond(x, p=1) >>> print(out_1) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 2.) >>> # compute conditional number when order of the norm is -1 >>> out_minus_1 = paddle.linalg.cond(x, p=-1) >>> print(out_minus_1) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.) >>> # compute conditional number when order of the norm is 2 >>> out_2 = paddle.linalg.cond(x, p=2) >>> print(out_2) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.41421378) >>> # compute conditional number when order of the norm is -1 >>> out_minus_2 = paddle.linalg.cond(x, p=-2) >>> print(out_minus_2) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.70710671) >>> # compute conditional number when order of the norm is inf >>> out_inf = paddle.linalg.cond(x, p=float("inf")) >>> print(out_inf) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 2.) >>> # compute conditional number when order of the norm is -inf >>> out_minus_inf = paddle.linalg.cond(x, p=-float("inf")) >>> print(out_minus_inf) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.) >>> a = paddle.randn([2, 4, 4]) >>> print(a) Tensor(shape=[2, 4, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[[ 0.06132207, 1.11349595, 0.41906244, -0.24858207], [-1.85169315, -1.50370061, 1.73954511, 0.13331604], [ 1.66359663, -0.55764782, -0.59911072, -0.57773495], [-1.03176904, -0.33741450, -0.29695082, -1.50258386]], [[ 0.67233968, -1.07747352, 0.80170447, -0.06695852], [-1.85003340, -0.23008066, 0.65083790, 0.75387722], [ 0.61212337, -0.52664012, 0.19209868, -0.18707706], [-0.00711021, 0.35236868, -0.40404350, 1.28656745]]]) >>> a_cond_fro = paddle.linalg.cond(a, p='fro') >>> print(a_cond_fro) Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [6.37173700 , 35.15114594]) >>> b = paddle.randn([2, 3, 4]) >>> print(b) Tensor(shape=[2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[[ 0.03306439, 0.70149767, 0.77064633, -0.55978841], [-0.84461296, 0.99335045, -1.23486686, 0.59551388], [-0.63035583, -0.98797107, 0.09410731, 0.47007179]], [[ 0.85850012, -0.98949534, -1.63086998, 1.07340240], [-0.05492965, 1.04750168, -2.33754158, 1.16518629], [ 0.66847134, -1.05326962, -0.05703246, -0.48190674]]]) >>> b_cond_2 = paddle.linalg.cond(b, p=2) >>> print(b_cond_2) Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [2.86566353, 6.85834455]) """ ... def conj(self, name: '_str | None' = None) -> 'Tensor': r""" This function computes the conjugate of the Tensor elementwisely. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor): The conjugate of input. The shape and data type is the same with input. If the elements of tensor is real type such as float32, float64, int32 or int64, the out is the same with input. Examples: .. code-block:: python >>> import paddle >>> data = paddle.to_tensor([[1+1j, 2+2j, 3+3j], [4+4j, 5+5j, 6+6j]]) >>> data Tensor(shape=[2, 3], dtype=complex64, place=Place(cpu), stop_gradient=True, [[(1+1j), (2+2j), (3+3j)], [(4+4j), (5+5j), (6+6j)]]) >>> conj_data = paddle.conj(data) >>> conj_data Tensor(shape=[2, 3], dtype=complex64, place=Place(cpu), stop_gradient=True, [[(1-1j), (2-2j), (3-3j)], [(4-4j), (5-5j), (6-6j)]]) """ ... def copysign(self, y: 'Tensor | _float', name: '_str | None' = None) -> 'Tensor': r""" Create a new floating-point tensor with the magnitude of input ``x`` and the sign of ``y``, elementwise. Equation: .. math:: copysign(x_{i},y_{i})=\left\{\begin{matrix} & -|x_{i}| & if \space y_{i} <= -0.0\\ & |x_{i}| & if \space y_{i} >= 0.0 \end{matrix}\right. Args: y (Tensor|float): contains value(s) whose signbit(s) are applied to the magnitudes in input. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor), the output tensor. The data type is the same as the input tensor. Examples: .. code-block:: python :name: example1 >>> import paddle >>> x = paddle.to_tensor([1, 2, 3], dtype='float64') >>> y = paddle.to_tensor([-1, 1, -1], dtype='float64') >>> out = paddle.copysign(x, y) >>> print(out) Tensor(shape=[3], dtype=float64, place=Place(gpu:0), stop_gradient=True, [-1., 2., -3.]) .. code-block:: python :name: example2 >>> import paddle >>> x = paddle.to_tensor([1, 2, 3], dtype='float64') >>> y = paddle.to_tensor([-2], dtype='float64') >>> res = paddle.copysign(x, y) >>> print(res) Tensor(shape=[3], dtype=float64, place=Place(gpu:0), stop_gradient=True, [-1., -2., -3.]) .. code-block:: python :name: example_zero1 >>> import paddle >>> x = paddle.to_tensor([1, 2, 3], dtype='float64') >>> y = paddle.to_tensor([0.0], dtype='float64') >>> out = paddle.copysign(x, y) >>> print(out) Tensor(shape=[3], dtype=float64, place=Place(gpu:0), stop_gradient=True, [1., 2., 3.]) .. code-block:: python :name: example_zero2 >>> import paddle >>> x = paddle.to_tensor([1, 2, 3], dtype='float64') >>> y = paddle.to_tensor([-0.0], dtype='float64') >>> out = paddle.copysign(x, y) >>> print(out) Tensor(shape=[3], dtype=float64, place=Place(gpu:0), stop_gradient=True, [-1., -2., -3.]) """ ... def copysign_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``copysign`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_copysign`. """ ... def corrcoef(self, rowvar: '_bool' = True, name: '_str | None' = None) -> 'Tensor': r""" A correlation coefficient matrix indicate the correlation of each pair variables in the input matrix. For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the correlation coefficient matrix element Rij is the correlation of xi and xj. The element Rii is the covariance of xi itself. The relationship between the correlation coefficient matrix `R` and the covariance matrix `C`, is .. math:: R_{ij} = \frac{ C_{ij} } { \sqrt{ C_{ii} * C_{jj} } } The values of `R` are between -1 and 1. Args: x (Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below. rowvar (bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True. name (str|None, optional): Name of the output. It's used to print debug info for developers. Details: :ref:`api_guide_Name`. Default: None. Returns: The correlation coefficient matrix of the variables. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2023) >>> xt = paddle.rand((3,4)) >>> print(paddle.linalg.corrcoef(xt)) Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0.99999988, -0.47689581, -0.89559376], [-0.47689593, 1. , 0.16345492], [-0.89559382, 0.16345496, 1. ]]) """ ... def cos(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Cosine Operator. Computes cosine of x element-wise. Input range is `(-inf, inf)` and output range is `[-1,1]`. .. math:: out = cos(x) Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output tensor. If set, the result will be stored in this tensor. Default is None. Returns: Tensor. Output of Cos operator, a Tensor with shape same as input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.cos(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [0.92106098, 0.98006660, 0.99500418, 0.95533651]) """ ... def cos_(self, name=None): r""" Inplace version of ``cos`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_cos`. """ ... def cosh(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Cosh Activation Operator. Input range `(-inf, inf)`, output range `(1, inf)`. .. math:: out = \frac{exp(x)+exp(-x)}{2} Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output Tensor. If set, the result will be stored in this Tensor. Default: None. Returns: Tensor. Output of Cosh operator, a Tensor with shape same as input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.cosh(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [1.08107233, 1.02006674, 1.00500417, 1.04533851]) """ ... def cosh_(self, name=None): r""" Inplace version of ``cosh`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_cosh`. """ ... def count_nonzero(self, axis: '_int | Sequence[_int] | None' = None, keepdim: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Counts the number of non-zero values in the tensor x along the specified axis. Args: axis (int|list|tuple, optional): The dimensions along which the sum is performed. If :attr:`None`, sum all elements of :attr:`x` and return a Tensor with a single element, otherwise must be in the range :math:`[-rank(x), rank(x))`. If :math:`axis[i] < 0`, the dimension to reduce is :math:`rank + axis[i]`. keepdim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result Tensor will have one fewer dimension than the :attr:`x` unless :attr:`keepdim` is true, default value is False. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Results of count operation on the specified axis of input Tensor `x`, it's data type is `'int64'`. Examples: .. code-block:: python >>> import paddle >>> # x is a 2-D Tensor: >>> x = paddle.to_tensor([[0., 1.1, 1.2], [0., 0., 1.3], [0., 0., 0.]]) >>> out1 = paddle.count_nonzero(x) >>> out1 Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True, 3) >>> out2 = paddle.count_nonzero(x, axis=0) >>> out2 Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, [0, 1, 2]) >>> out3 = paddle.count_nonzero(x, axis=0, keepdim=True) >>> out3 Tensor(shape=[1, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 1, 2]]) >>> out4 = paddle.count_nonzero(x, axis=1) >>> out4 Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, [2, 1, 0]) >>> out5 = paddle.count_nonzero(x, axis=1, keepdim=True) >>> out5 Tensor(shape=[3, 1], dtype=int64, place=Place(cpu), stop_gradient=True, [[2], [1], [0]]) >>> # y is a 3-D Tensor: >>> y = paddle.to_tensor([[[0., 1.1, 1.2], [0., 0., 1.3], [0., 0., 0.]], ... [[0., 2.5, 2.6], [0., 0., 2.4], [2.1, 2.2, 2.3]]]) >>> out6 = paddle.count_nonzero(y, axis=[1, 2]) >>> out6 Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True, [3, 6]) >>> out7 = paddle.count_nonzero(y, axis=[0, 1]) >>> out7 Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, [1, 3, 5]) """ ... def cov(self, rowvar: '_bool' = True, ddof: '_bool' = True, fweights: 'Tensor | None' = None, aweights: 'Tensor | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Estimate the covariance matrix of the input variables, given data and weights. A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix. For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the covariance matrix element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself. Parameters: x (Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below. rowvar (bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True. ddof (bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True. fweights (Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None. aweights (Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None. name (str|None, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name` . Returns: Tensor: The covariance matrix Tensor of the variables. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2023) >>> xt = paddle.rand((3, 4)) >>> paddle.linalg.cov(xt) >>> print(xt) Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.86583614, 0.52014720, 0.25960937, 0.90525323], [0.42400089, 0.40641287, 0.97020894, 0.74437362], [0.51785129, 0.73292869, 0.97786582, 0.04315904]]) """ ... def cpu(self) -> 'Tensor': ... def create_parameter(self, dtype: 'DTypeLike', name: '_str | None' = None, attr: 'ParamAttrLike | None' = None, is_bias: '_bool' = False, default_initializer: 'paddle.nn.initializer.Initializer | None' = None) -> 'paddle.Tensor': r""" This function creates a parameter. The parameter is a learnable variable, which can have gradient, and can be optimized. Note: This is a very low-level API. This API is useful when you create operator by your self, instead of using layers. Args: dtype (str): Data type of the parameter. It can be set as 'float16', 'float32', 'float64'. name(str|None, optional): For detailed information, please refer to :ref:`api_guide_Name` . Usually name is no need to set and None by default. attr (ParamAttr|None, optional): Attribute object of the specified argument. For detailed information, please refer to :ref:`api_paddle_ParamAttr` None by default, which means that ParamAttr will be initialized as it is. is_bias (bool, optional): This can affect which default initializer is chosen when default_initializer is None. If is_bias, initializer.Constant(0.0) will be used. Otherwise, Xavier() will be used. default_initializer (Initializer|None, optional): Initializer for the parameter Returns: The created parameter. Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> W = paddle.create_parameter(shape=[784, 200], dtype='float32') """ ... def create_tensor(self, name: '_str | None' = None, persistable: '_bool' = False) -> 'paddle.Tensor': r""" Create a variable, which will hold a Tensor with data type dtype. Args: name(string, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` persistable(bool): Set the persistable flag of the create tensor. default value is False. Returns: Variable: The tensor to be created according to dtype. Examples: .. code-block:: python >>> import paddle >>> tensor = paddle.tensor.create_tensor(dtype='float32') """ ... def cross(self, y: 'Tensor', axis: '_int' = 9, name: '_str | None' = None) -> 'Tensor': r""" Computes the cross product between two tensors along an axis. Inputs must have the same shape, and the length of their axes should be equal to 3. If `axis` is not given, it defaults to the first axis found with the length 3. Args: y (Tensor): The second input tensor, the data type is float16, float32, float64, int32, int64, complex64, complex128. axis (int, optional): The axis along which to compute the cross product. It defaults to be 9 which indicates using the first axis found with the length 3. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor. A Tensor with same data type as `x`. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1.0, 1.0, 1.0], ... [2.0, 2.0, 2.0], ... [3.0, 3.0, 3.0]]) >>> y = paddle.to_tensor([[1.0, 1.0, 1.0], ... [1.0, 1.0, 1.0], ... [1.0, 1.0, 1.0]]) ... >>> z1 = paddle.cross(x, y) >>> print(z1) Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[-1., -1., -1.], [ 2., 2., 2.], [-1., -1., -1.]]) >>> z2 = paddle.cross(x, y, axis=1) >>> print(z2) Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]) """ ... def cuda(self, device_id: '_int | None' = None, blocking: '_bool' = True) -> 'Tensor': ... def cummax(self, axis: '_int | None' = None, dtype: 'DTypeLike' = 'int64', name: '_str | None' = None) -> 'tuple[Tensor, Tensor]': r""" The cumulative max of the elements along a given axis. Note: The first element of the result is the same as the first element of the input. Args: axis (int, optional): The dimension to accumulate along. -1 means the last dimension. The default (None) is to compute the cummax over the flattened array. dtype (str|paddle.dtype|np.dtype, optional): The data type of the indices tensor, can be int32, int64. The default value is int64. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor), The result of cummax operation. The dtype of cummax result is same with input x. indices (Tensor), The corresponding index results of cummax operation. Examples: .. code-block:: python >>> import paddle >>> data = paddle.to_tensor([-1, 5, 0, -2, -3, 2]) >>> data = paddle.reshape(data, (2, 3)) >>> value, indices = paddle.cummax(data) >>> value Tensor(shape=[6], dtype=int64, place=Place(cpu), stop_gradient=True, [-1, 5, 5, 5, 5, 5]) >>> indices Tensor(shape=[6], dtype=int64, place=Place(cpu), stop_gradient=True, [0, 1, 1, 1, 1, 1]) >>> value, indices = paddle.cummax(data, axis=0) >>> value Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[-1, 5, 0], [-1, 5, 2]]) >>> indices Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 0, 0], [0, 0, 1]]) >>> value, indices = paddle.cummax(data, axis=-1) >>> value Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[-1, 5, 5], [-2, -2, 2]]) >>> indices Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 1, 1], [0, 0, 2]]) >>> value, indices = paddle.cummax(data, dtype='int64') >>> assert indices.dtype == paddle.int64 """ ... def cummin(self, axis: '_int | None' = None, dtype: 'DTypeLike' = 'int64', name: '_str | None' = None) -> 'tuple[Tensor, Tensor]': r""" The cumulative min of the elements along a given axis. Note: The first element of the result is the same as the first element of the input. Args: axis (int, optional): The dimension to accumulate along. -1 means the last dimension. The default (None) is to compute the cummin over the flattened array. dtype (str|paddle.dtype|np.dtype, optional): The data type of the indices tensor, can be int32, int64. The default value is int64. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor), The result of cummin operation. The dtype of cummin result is same with input x. indices (Tensor), The corresponding index results of cummin operation. Examples: .. code-block:: python >>> import paddle >>> data = paddle.to_tensor([-1, 5, 0, -2, -3, 2]) >>> data = paddle.reshape(data, (2, 3)) >>> value, indices = paddle.cummin(data) >>> value Tensor(shape=[6], dtype=int64, place=Place(cpu), stop_gradient=True, [-1, -1, -1, -2, -3, -3]) >>> indices Tensor(shape=[6], dtype=int64, place=Place(cpu), stop_gradient=True, [0, 0, 0, 3, 4, 4]) >>> value, indices = paddle.cummin(data, axis=0) >>> value Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[-1, 5, 0], [-2, -3, 0]]) >>> indices Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 0, 0], [1, 1, 0]]) >>> value, indices = paddle.cummin(data, axis=-1) >>> value Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[-1, -1, -1], [-2, -3, -3]]) >>> indices Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 0, 0], [0, 1, 1]]) >>> value, indices = paddle.cummin(data, dtype='int64') >>> assert indices.dtype == paddle.int64 """ ... def cumprod(self, dim: '_int | None' = None, *, dtype: 'DTypeLike | None' = None, out: 'Tensor | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Compute the cumulative product of the input tensor x along a given dimension dim. .. note:: The first element of the result is the same as the first element of the input. Alias Support: The parameter name ``input`` can be used as an alias for ``x``. Args: dim (int|None, optional): the dimension along which the input tensor will be accumulated. It need to be in the range of [-x.rank, x.rank) or None, where x.rank means the dimensions of the input tensor x and -1 means the last dimension. The default (None) is to compute the cumprod over the flattened array. dtype (str|core.VarDesc.VarType|core.DataType|np.dtype, optional): The data type of the output tensor, can be bfloat16, float16, float32, float64, int32, int64, complex64, complex128. If specified, the input tensor is casted to dtype before the operation is performed. This is useful for preventing data type overflows. The default value is None. out (Tensor|None, optional): The output tensor. Default: None. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, the result of cumprod operator. Examples: .. code-block:: python >>> import paddle >>> data = paddle.arange(12) >>> data = paddle.reshape(data, (3, 4)) >>> data Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0 , 1 , 2 , 3 ], [4 , 5 , 6 , 7 ], [8 , 9 , 10, 11]]) >>> y = paddle.cumprod(data, dim=0) >>> y Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0 , 1 , 2 , 3 ], [0 , 5 , 12 , 21 ], [0 , 45 , 120, 231]]) >>> y = paddle.cumprod(data, dim=-1) >>> y Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0 , 0 , 0 , 0 ], [4 , 20 , 120 , 840 ], [8 , 72 , 720 , 7920]]) >>> y = paddle.cumprod(data, dim=1, dtype='float64') >>> y Tensor(shape=[3, 4], dtype=float64, place=Place(cpu), stop_gradient=True, [[0. , 0. , 0. , 0. ], [4. , 20. , 120. , 840. ], [8. , 72. , 720. , 7920.]]) >>> assert y.dtype == paddle.float64 """ ... def cumprod_(self, dim: '_int | None' = None, dtype: 'DTypeLike | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``cumprod`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_cumprod`. """ ... def cumsum(self, axis: '_int | None' = None, dtype: 'DTypeLike | None' = None, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" The cumulative sum of the elements along a given axis. Note: The first element of the result is the same as the first element of the input. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``dim`` can be used as an alias for ``axis``. For example, ``cumsum(input=tensor_x, dim=1, ...)`` is equivalent to ``cumsum(x=tensor_x, axis=1, ...)``. Args: axis (int, optional): The dimension to accumulate along. -1 means the last dimension. The default (None) is to compute the cumsum over the flattened array. alias: ``dim``. dtype (str|paddle.dtype|np.dtype|None, optional): The data type of the output tensor, can be bfloat16, float16, float32, float64, int32, int64, complex64, complex128. By default, it is int64 if the input x is int8/int16/int32; otherwise, it is None. If it is not None, the input tensor is casted to dtype before the operation is performed. This is useful for preventing data type overflows. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output tensor. If provided, the result will be stored in this tensor. Returns: Tensor, the result of cumsum operator. Examples: .. code-block:: python >>> import paddle >>> data = paddle.arange(12) >>> data = paddle.reshape(data, (3, 4)) >>> y = paddle.cumsum(data) >>> y Tensor(shape=[12], dtype=int64, place=Place(cpu), stop_gradient=True, [0 , 1 , 3 , 6 , 10, 15, 21, 28, 36, 45, 55, 66]) >>> y = paddle.cumsum(data, axis=0) >>> y Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0 , 1 , 2 , 3 ], [4 , 6 , 8 , 10], [12, 15, 18, 21]]) >>> y = paddle.cumsum(data, axis=-1) >>> y Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0 , 1 , 3 , 6 ], [4 , 9 , 15, 22], [8 , 17, 27, 38]]) >>> y = paddle.cumsum(data, dtype='float64') >>> assert y.dtype == paddle.float64 """ ... def cumsum_(self, axis: '_int | None' = None, dtype: 'DTypeLike | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``cumprod`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_cumprod`. """ ... def cumulative_trapezoid(self, x: 'Tensor | None' = None, dx: '_float | None' = None, axis: '_int' = -1, name: '_str | None' = None) -> 'Tensor': r""" Integrate along the given axis using the composite trapezoidal rule. Use the cumsum method Args: x (Tensor|None, optional): The sample points corresponding to the :attr:`y` values, the same type as :attr:`y`. It is known that the size of :attr:`y` is `[d_1, d_2, ... , d_n]` and :math:`axis=k`, then the size of :attr:`x` can only be `[d_k]` or `[d_1, d_2, ... , d_n ]`. If :attr:`x` is None, the sample points are assumed to be evenly spaced :attr:`dx` apart. The default is None. dx (float|None, optional): The spacing between sample points when :attr:`x` is None. If neither :attr:`x` nor :attr:`dx` is provided then the default is :math:`dx = 1`. axis (int, optional): The axis along which to integrate. The default is -1. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, Definite integral of :attr:`y` is N-D tensor as approximated along a single axis by the trapezoidal rule. The result is an N-D tensor. Examples: .. code-block:: python >>> import paddle >>> y = paddle.to_tensor([4, 5, 6], dtype='float32') >>> paddle.cumulative_trapezoid(y) Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [4.50000000, 10. ]) >>> paddle.cumulative_trapezoid(y, dx=2.) >>> # Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, >>> # [9. , 20.]) >>> y = paddle.to_tensor([4, 5, 6], dtype='float32') >>> x = paddle.to_tensor([1, 2, 3], dtype='float32') >>> paddle.cumulative_trapezoid(y, x) Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [4.50000000, 10. ]) >>> y = paddle.to_tensor([1, 2, 3], dtype='float64') >>> x = paddle.to_tensor([8, 6, 4], dtype='float64') >>> paddle.cumulative_trapezoid(y, x) Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=True, [-3., -8.]) >>> y = paddle.arange(6).reshape((2, 3)).astype('float32') >>> paddle.cumulative_trapezoid(y, axis=0) Tensor(shape=[1, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[1.50000000, 2.50000000, 3.50000000]]) >>> paddle.cumulative_trapezoid(y, axis=1) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.50000000, 2. ], [3.50000000, 8. ]]) """ ... def deg2rad(self, name: '_str | None' = None) -> 'Tensor': r""" Convert each of the elements of input x from degrees to angles in radians. .. math:: deg2rad(x)=\pi * x / 180 Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor): An N-D Tensor, the shape and data type is the same with input (The output data type is float32 when the input data type is int). Examples: .. code-block:: python >>> import paddle >>> x1 = paddle.to_tensor([180.0, -180.0, 360.0, -360.0, 90.0, -90.0]) >>> result1 = paddle.deg2rad(x1) >>> result1 Tensor(shape=[6], dtype=float32, place=Place(cpu), stop_gradient=True, [3.14159274, -3.14159274, 6.28318548, -6.28318548, 1.57079637, -1.57079637]) >>> x2 = paddle.to_tensor(180) >>> result2 = paddle.deg2rad(x2) >>> result2 Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 3.14159274) """ ... def diag(self, offset: '_int' = 0, padding_value: '_int' = 0, name: '_str | None' = None) -> 'paddle.Tensor': r""" If ``x`` is a vector (1-D tensor), a 2-D square tensor with the elements of ``x`` as the diagonal is returned. If ``x`` is a matrix (2-D tensor), a 1-D tensor with the diagonal elements of ``x`` is returned. The argument ``offset`` controls the diagonal offset: If ``offset`` = 0, it is the main diagonal. If ``offset`` > 0, it is superdiagonal. If ``offset`` < 0, it is subdiagonal. Args: offset (int, optional): The diagonal offset. A positive value represents superdiagonal, 0 represents the main diagonal, and a negative value represents subdiagonal. padding_value (int|float, optional): Use this value to fill the area outside the specified diagonal band. Only takes effect when the input is a 1-D Tensor. The default value is 0. name(str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor, a square matrix or a vector. The output data type is the same as input data type. Examples: .. code-block:: python :name: diag-example-1 >>> import paddle >>> paddle.disable_static() >>> x = paddle.to_tensor([1, 2, 3]) >>> y = paddle.diag(x) >>> print(y) Tensor(shape=[3, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[1, 0, 0], [0, 2, 0], [0, 0, 3]]) >>> y = paddle.diag(x, offset=1) >>> print(y) Tensor(shape=[4, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 1, 0, 0], [0, 0, 2, 0], [0, 0, 0, 3], [0, 0, 0, 0]]) >>> y = paddle.diag(x, padding_value=6) >>> print(y) Tensor(shape=[3, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[1, 6, 6], [6, 2, 6], [6, 6, 3]]) .. code-block:: python :name: diag-example-2 >>> import paddle >>> paddle.disable_static() >>> x = paddle.to_tensor([[1, 2, 3], [4, 5, 6]]) >>> y = paddle.diag(x) >>> print(y) Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True, [1, 5]) >>> y = paddle.diag(x, offset=1) >>> print(y) Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True, [2, 6]) >>> y = paddle.diag(x, offset=-1) >>> print(y) Tensor(shape=[1], dtype=int64, place=Place(cpu), stop_gradient=True, [4]) """ ... def diag_embed(self, offset: '_int' = 0, dim1: '_int' = -2, dim2: '_int' = -1) -> 'paddle.Tensor': r""" Creates a tensor whose diagonals of certain 2D planes (specified by dim1 and dim2) are filled by ``input``. By default, a 2D plane formed by the last two dimensions of the returned tensor will be selected. The argument ``offset`` determines which diagonal is generated: - If offset = 0, it is the main diagonal. - If offset > 0, it is above the main diagonal. - If offset < 0, it is below the main diagonal. Args: offset(int, optional): Which diagonal to consider. Default: 0 (main diagonal). dim1(int, optional): The first dimension with respect to which to take diagonal. Default: -2. dim2(int, optional): The second dimension with respect to which to take diagonal. Default: -1. Returns: Tensor, the output data type is the same as input data type. Examples: .. code-block:: python >>> import paddle >>> diag_embed_input = paddle.arange(6) >>> diag_embed_output1 = paddle.diag_embed(diag_embed_input) >>> print(diag_embed_output1) Tensor(shape=[6, 6], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0], [0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 4, 0], [0, 0, 0, 0, 0, 5]]) >>> diag_embed_output2 = paddle.diag_embed(diag_embed_input, offset=-1, dim1=0,dim2=1 ) >>> print(diag_embed_output2) Tensor(shape=[7, 7], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0], [0, 0, 0, 0, 0, 5, 0]]) >>> diag_embed_input_2dim = paddle.reshape(diag_embed_input,[2,3]) >>> print(diag_embed_input_2dim) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 1, 2], [3, 4, 5]]) >>> diag_embed_output3 = paddle.diag_embed(diag_embed_input_2dim,offset= 0, dim1=0, dim2=2 ) >>> print(diag_embed_output3) Tensor(shape=[3, 2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[[0, 0, 0], [3, 0, 0]], [[0, 1, 0], [0, 4, 0]], [[0, 0, 2], [0, 0, 5]]]) """ ... def diagflat(self, offset: '_int' = 0, name: '_str | None' = None) -> 'paddle.Tensor': r""" If ``x`` is a vector (1-D tensor), a 2-D square tensor with the elements of ``x`` as the diagonal is returned. If ``x`` is a tensor (more than 1-D), a 2-D square tensor with the elements of flattened ``x`` as the diagonal is returned. The argument ``offset`` controls the diagonal offset. If ``offset`` = 0, it is the main diagonal. If ``offset`` > 0, it is superdiagonal. If ``offset`` < 0, it is subdiagonal. Args: offset (int, optional): The diagonal offset. A positive value represents superdiagonal, 0 represents the main diagonal, and a negative value represents subdiagonal. Default: 0 (main diagonal). name(str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor, a square matrix. The output data type is the same as input data type. Examples: .. code-block:: python :name: diagflat-example-1 >>> import paddle >>> x = paddle.to_tensor([1, 2, 3]) >>> y = paddle.diagflat(x) >>> print(y) Tensor(shape=[3, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[1, 0, 0], [0, 2, 0], [0, 0, 3]]) >>> y = paddle.diagflat(x, offset=1) >>> print(y) Tensor(shape=[4, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 1, 0, 0], [0, 0, 2, 0], [0, 0, 0, 3], [0, 0, 0, 0]]) >>> y = paddle.diagflat(x, offset=-1) >>> print(y) Tensor(shape=[4, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 0, 0, 0], [1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0]]) .. code-block:: python :name: diagflat-example-2 >>> import paddle >>> x = paddle.to_tensor([[1, 2], [3, 4]]) >>> y = paddle.diagflat(x) >>> print(y) Tensor(shape=[4, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0], [0, 0, 0, 4]]) >>> y = paddle.diagflat(x, offset=1) >>> print(y) Tensor(shape=[5, 5], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 1, 0, 0, 0], [0, 0, 2, 0, 0], [0, 0, 0, 3, 0], [0, 0, 0, 0, 4], [0, 0, 0, 0, 0]]) >>> y = paddle.diagflat(x, offset=-1) >>> print(y) Tensor(shape=[5, 5], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 0, 0, 0, 0], [1, 0, 0, 0, 0], [0, 2, 0, 0, 0], [0, 0, 3, 0, 0], [0, 0, 0, 4, 0]]) """ ... def diagonal(self, offset: '_int' = 0, axis1: '_int' = 0, axis2: '_int' = 1, name: '_str | None' = None) -> 'Tensor': r""" Computes the diagonals of the input tensor x. If ``x`` is 2D, returns the diagonal. If ``x`` has larger dimensions, diagonals be taken from the 2D planes specified by axis1 and axis2. By default, the 2D planes formed by the first and second axis of the input tensor x. The argument ``offset`` determines where diagonals are taken from input tensor x: - If offset = 0, it is the main diagonal. - If offset > 0, it is above the main diagonal. - If offset < 0, it is below the main diagonal. .. note:: Alias Support: 1. The parameter name ``input`` can be used as an alias for ``x``. 2. The parameter name ``dim1`` can be used as an alias for ``axis1``. 3. The parameter name ``dim2`` can be used as an alias for ``axis2``. Args: offset (int, optional): Which diagonals in input tensor x will be taken. Default: 0 (main diagonals). axis1 (int, optional): The first axis with respect to take diagonal. Default: 0. Alias: ``dim1``. axis2 (int, optional): The second axis with respect to take diagonal. Default: 1. Alias: ``dim2``. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: a partial view of input tensor in specify two dimensions, the output data type is the same as input data type. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2023) >>> x = paddle.rand([2, 2, 3],'float32') >>> print(x) Tensor(shape=[2, 2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[[0.86583614, 0.52014720, 0.25960937], [0.90525323, 0.42400089, 0.40641287]], [[0.97020894, 0.74437362, 0.51785129], [0.73292869, 0.97786582, 0.04315904]]]) >>> out1 = paddle.diagonal(x) >>> print(out1) Tensor(shape=[3, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.86583614, 0.73292869], [0.52014720, 0.97786582], [0.25960937, 0.04315904]]) >>> out2 = paddle.diagonal(x, offset=0, axis1=2, axis2=1) >>> print(out2) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.86583614, 0.42400089], [0.97020894, 0.97786582]]) >>> out3 = paddle.diagonal(x, offset=1, axis1=0, axis2=1) >>> print(out3) Tensor(shape=[3, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.90525323], [0.42400089], [0.40641287]]) >>> out4 = paddle.diagonal(x, offset=0, axis1=1, axis2=2) >>> print(out4) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.86583614, 0.42400089], [0.97020894, 0.97786582]]) """ ... def diagonal_scatter(self, y: 'Tensor', offset: '_int' = 0, axis1: '_int' = 0, axis2: '_int' = 1, name: '_str | None' = None) -> 'Tensor': r""" Embed the values of Tensor ``y`` into Tensor ``x`` along the diagonal elements of Tensor ``x``, with respect to ``axis1`` and ``axis2``. This function returns a tensor with fresh storage. The argument ``offset`` controls which diagonal to consider: - If ``offset`` = 0, it is the main diagonal. - If ``offset`` > 0, it is above the main diagonal. - If ``offset`` < 0, it is below the main diagonal. Note: ``y`` should have the same shape as :ref:`paddle.diagonal `. The image below demonstrates the example: A 2D tensor with a shape of [2, 3] is ``diagonal_scatter`` along its main diagonal (``offset = 0``) within ``axis1 = 0`` and ``axis2 = 1`` using a 1D tensor filled with ones. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/diagonal_scatter.png :width: 500 :alt: legend of diagonal_scatter API Args: y (Tensor): ``y`` is the Tensor to embed into ``x`` offset (int, optional): which diagonal to consider. Default: 0 (main diagonal). axis1 (int, optional): first axis with respect to which to take diagonal. Default: 0. axis2 (int, optional): second axis with respect to which to take diagonal. Default: 1. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, Tensor with diagonal embedded with ``y``. Examples: .. code-block:: python >>> import paddle >>> x = paddle.arange(6.0).reshape((2, 3)) >>> y = paddle.ones((2,)) >>> out = x.diagonal_scatter(y) >>> print(out) Tensor(shape=[2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True, [[1., 1., 2.], [3., 1., 5.]]) """ ... def diff(self, n: '_int' = 1, axis: '_int' = -1, prepend: 'Tensor | None' = None, append: 'Tensor | None' = None, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Computes the n-th forward difference along the given axis. The first-order differences is computed by using the following formula: .. math:: out[i] = x[i+1] - x[i] Higher-order differences are computed by using paddle.diff() recursively. The number of n supports any positive integer value. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``dim`` can be used as an alias for ``axis``. For example, ``diff(input=tensor_x, dim=1, ...)`` is equivalent to ``diff(x=tensor_x, axis=1, ...)``. Args: n (int, optional): The number of times to recursively compute the difference. Supports any positive integer value. Default:1 axis (int, optional): The axis to compute the difference along. Default:-1 alias: ``dim``. prepend (Tensor|None, optional): The tensor to prepend to input along axis before computing the difference. It's dimensions must be equivalent to that of x, and its shapes must match x's shape except on axis. append (Tensor|None, optional): The tensor to append to input along axis before computing the difference, It's dimensions must be equivalent to that of x, and its shapes must match x's shape except on axis. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output tensor. If provided, the result will be stored in this tensor. Returns: Tensor: The output tensor with same dtype with x. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 4, 5, 2]) >>> out = paddle.diff(x) >>> out Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, [ 3, 1, -3]) >>> x_2 = paddle.to_tensor([1, 4, 5, 2]) >>> out = paddle.diff(x_2, n=2) >>> out Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True, [ -2, -4]) >>> y = paddle.to_tensor([7, 9]) >>> out = paddle.diff(x, append=y) >>> out Tensor(shape=[5], dtype=int64, place=Place(cpu), stop_gradient=True, [ 3, 1, -3, 5, 2]) >>> out = paddle.diff(x, n=2, append=y) >>> out Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True, [-2, -4, 8, -3]) >>> out = paddle.diff(x, n=3, append=y) >>> out Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, [-2 , 12, -11]) >>> z = paddle.to_tensor([[1, 2, 3], [4, 5, 6]]) >>> out = paddle.diff(z, axis=0) >>> out Tensor(shape=[1, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[3, 3, 3]]) >>> out = paddle.diff(z, axis=1) >>> out Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[1, 1], [1, 1]]) """ ... def digamma(self, name: '_str | None' = None) -> 'Tensor': r""" Calculates the digamma of the given input tensor, element-wise. .. math:: Out = \Psi(x) = \frac{ \Gamma^{'}(x) }{ \Gamma(x) } Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, the digamma of the input Tensor, the shape and data type is the same with input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> data = paddle.to_tensor([[1, 1.5], [0, -2.2]], dtype='float32') >>> res = paddle.digamma(data) >>> res Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[-0.57721591, 0.03648996], [ nan , 5.32286835]]) """ ... def digamma_(self, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``digamma`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_digamma`. """ ... def dim(self) -> '_int': ... def dist(self, y: 'Tensor', p: '_float' = 2, name: '_str | None' = None) -> 'Tensor': r""" Returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure of distance. The shapes of x and y must be broadcastable. The definition is as follows, for details, please refer to the `Introduction to Tensor <../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor>`_: - Each input has at least one dimension. - Match the two input dimensions from back to front, the dimension sizes must either be equal, one of them is 1, or one of them does not exist. Where, z = x - y, the shapes of x and y are broadcastable, then the shape of z can be obtained as follows: 1. If the number of dimensions of x and y are not equal, prepend 1 to the dimensions of the tensor with fewer dimensions. For example, The shape of x is [8, 1, 6, 1], the shape of y is [7, 1, 5], prepend 1 to the dimension of y. x (4-D Tensor): 8 x 1 x 6 x 1 y (4-D Tensor): 1 x 7 x 1 x 5 2. Determine the size of each dimension of the output z: choose the maximum value from the two input dimensions. z (4-D Tensor): 8 x 7 x 6 x 5 If the number of dimensions of the two inputs are the same, the size of the output can be directly determined in step 2. When p takes different values, the norm formula is as follows: When p = 0, defining $0^0=0$, the zero-norm of z is simply the number of non-zero elements of z. .. math:: ||z||_{0}=\lim_{p \\rightarrow 0}\sum_{i=1}^{m}|z_i|^{p} When p = inf, the inf-norm of z is the maximum element of the absolute value of z. .. math:: ||z||_\infty=\max_i |z_i| When p = -inf, the negative-inf-norm of z is the minimum element of the absolute value of z. .. math:: ||z||_{-\infty}=\min_i |z_i| Otherwise, the p-norm of z follows the formula, .. math:: ||z||_{p}=(\sum_{i=1}^{m}|z_i|^p)^{\\frac{1}{p}} Args: y (Tensor): 1-D to 6-D Tensor, its data type is bfloat16, float16, float32 or float64. p (float, optional): The norm to be computed, its data type is float32 or float64. Default: 2. name (str|None, optional): The default value is `None`. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Tensor that is the p-norm of (x - y). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[3, 3],[3, 3]], dtype="float32") >>> y = paddle.to_tensor([[3, 3],[3, 1]], dtype="float32") >>> out = paddle.dist(x, y, 0) >>> print(out) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.) >>> out = paddle.dist(x, y, 2) >>> print(out) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 2.) >>> out = paddle.dist(x, y, float("inf")) >>> print(out) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 2.) >>> out = paddle.dist(x, y, float("-inf")) >>> print(out) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.) """ ... def div(self, y: 'Tensor', name: '_str | None' = None, *, rounding_mode: '_str | None' = None, out: 'Tensor | None' = None) -> 'Tensor': r""" Divide two tensors element-wise. The equation is: .. math:: out = x / y Note: ``paddle.divide`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``other`` can be used as an alias for ``y``. For example, ``divide(input=tensor_x, other=tensor_y)`` is equivalent to ``divide(x=tensor_x, y=tensor_y)``. Args: y (Tensor): the input tensor, it's data type should be bool, bfloat16, float16, float32, float64, int8, int16, int32, int64, uint8, complex64, complex128. alias: ``other``. rounding_mode (str|None, optional): The rounding mode. Can be None (default), "trunc" (truncate toward zero), or "floor" (round down toward negative infinity). out (Tensor, optional): The output tensor. Default: None. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: N-D Tensor. A location into which the result is stored. If x, y have different shapes and are "broadcastable", the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape, its shape is the same as x and y. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([2, 3, 4], dtype='float64') >>> y = paddle.to_tensor([1, 5, 2], dtype='float64') >>> z = paddle.divide(x, y) >>> print(z) Tensor(shape=[3], dtype=float64, place=Place(cpu), stop_gradient=True, [2. , 0.60000000, 2. ]) """ ... def div_(self, y: 'Tensor', name: '_str | None' = None, *, rounding_mode: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``divide`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_divide`. """ ... def dot(self, y: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" This operator calculates inner product for vectors. Note: Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix is the batch dimension, which means that the vectors of multiple batches are dotted. .. note:: Alias Support: 1. The parameter name ``input`` can be used as an alias for ``x``. 2. The parameter name ``other`` can be used as an alias for ``y``. Parameters: x (Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``, ``complex64``, ``complex128`` Alias: ``input``. y (Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``, ``complex64``, ``complex128`` Alias: ``other``. name (str|None, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name` Keyword args: out (Tensor|None, optional): The output tensor. Returns: Tensor: the calculated result Tensor. Examples: .. code-block:: python >>> import paddle >>> # 1-D Tensor * 1-D Tensor >>> x = paddle.to_tensor([1, 2, 3]) >>> y = paddle.to_tensor([4, 5, 6]) >>> z = paddle.dot(x, y) >>> print(z) Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True, 32) >>> # 2-D Tensor * 2-D Tensor >>> x = paddle.to_tensor([[1, 2, 3], [2, 4, 6]]) >>> y = paddle.to_tensor([[4, 5, 6], [4, 5, 6]]) >>> z = paddle.dot(x, y) >>> print(z) Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True, [32, 64]) """ ... def double(self) -> 'Tensor': r""" Cast a Tensor to float64 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with float64 dtype """ ... def dsplit(self, num_or_indices: '_int | Sequence[_int]', name: '_str | None' = None) -> 'list[Tensor]': r""" ``dsplit`` Full name Depth Split, splits the input Tensor into multiple sub-Tensors along the depth axis, which is equivalent to ``paddle.tensor_split`` with ``axis=2``. Note: Make sure that the number of Tensor dimensions transformed using ``paddle.dsplit`` must be no less than 3. In the following figure, Tenser ``x`` has shape [4, 4, 4], and after ``paddle.dsplit(x, num_or_indices=2)`` transformation, we get ``out0`` and ``out1`` sub-Tensors whose shapes are both [4, 4, 2] : .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/dsplit/dsplit.png Args: num_or_indices (int|list|tuple): If ``num_or_indices`` is an int ``n``, ``x`` is split into ``n`` sections. If ``num_or_indices`` is a list or tuple of integer indices, ``x`` is split at each of the indices. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: list[Tensor], The list of segmented Tensors. Examples: .. code-block:: pycon >>> import paddle >>> # x is a Tensor of shape [7, 6, 8] >>> x = paddle.rand([7, 6, 8]) >>> out0, out1 = paddle.dsplit(x, num_or_indices=2) >>> print(out0.shape) paddle.Size([7, 6, 4]) >>> print(out1.shape) paddle.Size([7, 6, 4]) >>> out0, out1, out2 = paddle.dsplit(x, num_or_indices=[1, 4]) >>> print(out0.shape) paddle.Size([7, 6, 1]) >>> print(out1.shape) paddle.Size([7, 6, 3]) >>> print(out2.shape) paddle.Size([7, 6, 4]) """ ... def eig(self, name: '_str | None' = None) -> 'tuple[Tensor, Tensor]': r""" Performs the eigenvalue decomposition of a square matrix or a batch of square matrices. Note: - If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`api_paddle_linalg_eigh` instead, which is much faster. - If only eigenvalues is needed, please use :ref:`api_paddle_linalg_eigvals` instead. - If the matrix is of any shape, please use :ref:`api_paddle_linalg_svd`. - This API is only supported on CPU device. - The output datatype is always complex for both real and complex input. Args: name (str|None, optional): The default value is `None`. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Eigenvalues(Tensor): A tensor with shape math:`[*, N]` refers to the eigen values. Eigenvectors(Tensor): A tensor with shape math:`[*, N, N]` refers to the eigen vectors. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1.6707249, 7.2249975, 6.5045543], ... [9.956216, 8.749598, 6.066444 ], ... [4.4251957, 1.7983172, 0.370647 ]]) >>> w, v = paddle.linalg.eig(x) >>> print(v) Tensor(shape=[3, 3], dtype=complex64, place=Place(cpu), stop_gradient=True, [[ (0.5061365365982056+0j) , (0.7971761226654053+0j) , (0.1851806491613388+0j) ], [ (0.8308236598968506+0j) , (-0.3463813066482544+0j) , (-0.6837005615234375+0j) ], [ (0.23142573237419128+0j), (-0.49449989199638367+0j), (0.7058765292167664+0j) ]]) >>> print(w) Tensor(shape=[3], dtype=complex64, place=Place(cpu), stop_gradient=True, [ (16.50470733642578+0j) , (-5.503481388092041+0j) , (-0.21026138961315155+0j)]) """ ... def eigvals(self, name: '_str | None' = None) -> 'Tensor': r""" Compute the eigenvalues of one or more general matrices. Warning: The gradient kernel of this operator does not yet developed. If you need back propagation through this operator, please replace it with paddle.linalg.eig. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`. The eigenvalues are complex-valued even when `x` is real. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2023) >>> x = paddle.rand(shape=[3, 3], dtype='float64') >>> print(x) Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True, [[0.86583615, 0.52014721, 0.25960938], [0.90525323, 0.42400090, 0.40641288], [0.97020893, 0.74437359, 0.51785128]]) >>> print(paddle.linalg.eigvals(x)) Tensor(shape=[3], dtype=complex128, place=Place(cpu), stop_gradient=True, [ (1.788956694280852+0j) , (0.16364484879581526+0j), (-0.14491322408727625+0j)]) """ ... def eigvalsh(self, UPLO: "Literal['L', 'U']" = 'L', name: '_str | None' = None) -> 'Tensor': r""" Computes the eigenvalues of a complex Hermitian (conjugate symmetric) or a real symmetric matrix. Args: UPLO(str, optional): Lower triangular part of a ('L', default) or the upper triangular part ('U'). name(str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The tensor eigenvalues in ascending order. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, -2j], [2j, 5]]) >>> out_value = paddle.eigvalsh(x, UPLO='L') >>> print(out_value) Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [0.17157286, 5.82842731]) """ ... def eq(self, y: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" This layer returns the truth value of :math:`x == y` elementwise. Note: The output has no gradient. Args: y (Tensor): Tensor, data type is bool, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128. alias: ``other`` name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): Output tensor. If provided, the result will be stored in this tensor. Returns: Tensor: output Tensor, it's shape is the same as the input's Tensor, and the data type is bool. The result of this op is stop_gradient. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3]) >>> y = paddle.to_tensor([1, 3, 2]) >>> result1 = paddle.equal(x, y) >>> print(result1) Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True, [True , False, False]) """ ... def equal_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``equal`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_equal`. """ ... def equal_all(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Returns the truth value of :math:`x == y`. True if two inputs have the same elements, False otherwise. Note: The output has no gradient. Args: y(Tensor): Tensor, data type is bool, float32, float64, int32, int64. name(str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: output Tensor, data type is bool, value is [False] or [True]. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3]) >>> y = paddle.to_tensor([1, 2, 3]) >>> z = paddle.to_tensor([1, 4, 3]) >>> result1 = paddle.equal_all(x, y) >>> print(result1) Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True, True) >>> result2 = paddle.equal_all(x, z) >>> print(result2) Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True, False) """ ... def erf(self, name: '_str | None' = None) -> 'Tensor': r""" The error function. For more details, see `Error function `_. Equation: .. math:: out = \frac{2}{\sqrt{\pi}} \int_{0}^{x}e^{- \eta^{2}}d\eta Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor. The output of Erf, dtype: float32 or float64 (integer types are autocasted into float32), shape: the same as the input. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.erf(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [-0.42839241, -0.22270259, 0.11246292, 0.32862678]) """ ... def erfinv(self, name: '_str | None' = None) -> 'Tensor': r""" The inverse error function of x. Please refer to :ref:`api_paddle_erf` .. math:: erfinv(erf(x)) = x. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor), an N-D Tensor, the shape and data type is the same with input (integer types are autocasted into float32). Example: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([0, 0.5, -1.], dtype="float32") >>> out = paddle.erfinv(x) >>> out Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [ 0. , 0.47693631, -inf. ]) """ ... def erfinv_(self, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``erfinv`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_erfinv`. """ ... def exp(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Computes exp of x element-wise with a natural number `e` as the base. .. math:: out = e^x .. note:: Alias Support: 1. The parameter name ``input`` can be used as an alias for ``x``. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output tensor. Default: None. Returns: Tensor. Output of Exp operator, a Tensor with shape same as input. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.exp(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [0.67032003, 0.81873077, 1.10517097, 1.34985888]) """ ... def exp_(self, name=None): r""" Inplace version of ``exp`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_exp`. """ ... def expand(self, shape: 'ShapeLike', name: '_str | None' = None) -> 'Tensor': r""" Expand the input tensor to a given shape. Both the number of dimensions of ``x`` and the number of elements in ``shape`` should be less than or equal to 6. And the number of dimensions of ``x`` should be less than the number of elements in ``shape``. The dimension to expand must have a value 0. The image illustrates a typical case of the expand operation. The Original Tensor is a 1D tensor with shape ``[3]`` and values [1, 2, 3]. Using the ``paddle.expand`` method with the parameter ``shape = [2, 3]``, it is broadcasted and expanded into a 2D tensor with shape ``[2, 3]`` .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/expand.png :width: 500 :alt: legend of expand API :align: center .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x`` and ``size`` can be used as an alias for ``shape``. ``shape`` can be a variable number of arguments. For example: ``paddle.expand(tensor_x, shape=[3, 4], name=None)`` ``tensor_x.expand([3, 4]) -> paddle.expand(tensor_x, [3, 4])`` ``tensor_x.expand(3, 4) -> paddle.expand(tensor_x, 3, 4)`` ``tensor_x.expand(size=[3, 4]) -> paddle.expand(tensor_x, size=[3, 4])`` Args: shape (list|tuple|Tensor|variable number of arguments): The result shape after expanding. The data type is int32. If shape is a list or tuple, all its elements should be integers or 0-D or 1-D Tensors with the data type int32. If shape is a Tensor, it should be an 1-D Tensor with the data type int32. The value -1 in shape means keeping the corresponding dimension unchanged. ``shape`` can be a variable number of arguments. alias: ``size``. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: N-D Tensor, A Tensor with the given shape. The data type is the same as ``x``. Examples: .. code-block:: python >>> import paddle >>> data = paddle.to_tensor([1, 2, 3], dtype='int32') >>> out = paddle.expand(data, shape=[2, 3]) >>> print(out) Tensor(shape=[2, 3], dtype=int32, place=Place(cpu), stop_gradient=True, [[1, 2, 3], [1, 2, 3]]) """ ... def expand_as(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Expand the input tensor ``x`` to the same shape as the input tensor ``y``. Both the number of dimensions of ``x`` and ``y`` must be less than or equal to 6, and the number of dimensions of ``y`` must be greater than or equal to that of ``x``. The dimension to expand must have a value of 0. The following diagram illustrates how a one-dimensional tensor is transformed into a tensor with a shape of [2,3] through the expand_as operation. The target tensor has a shape of [2,3], and through expand_as, the one-dimensional tensor is expanded into a tensor with a shape of [2,3]. .. image:: https://raw.githubusercontent.com/PaddlePaddle/docs/develop/docs/images/api_legend/expand_as.png :width: 800 :alt: expand_as API :align: center Args: y (Tensor): The input tensor that gives the shape to expand to. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: N-D Tensor, A Tensor with the same shape as ``y``. The data type is the same as ``x``. Examples: .. code-block:: python >>> import paddle >>> data_x = paddle.to_tensor([1, 2, 3], 'int32') >>> data_y = paddle.to_tensor([[1, 2, 3], [4, 5, 6]], 'int32') >>> out = paddle.expand_as(data_x, data_y) >>> print(out) Tensor(shape=[2, 3], dtype=int32, place=Place(cpu), stop_gradient=True, [[1, 2, 3], [1, 2, 3]]) """ ... def expm1(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Expm1 Operator. Computes expm1 of x element-wise with a natural number :math:`e` as the base. .. math:: out = e^x - 1 .. note:: Alias Support: 1. The parameter name ``input`` can be used as an alias for ``x``. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output tensor. Default: None. Returns: Tensor. Output of Expm1 operator, a Tensor with shape same as input. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.expm1(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [-0.32967997, -0.18126924, 0.10517092, 0.34985882]) """ ... def exponential_(self, lam: '_float' = 1.0, name: '_str | None' = None) -> 'Tensor': r""" This inplace OP fill input Tensor ``x`` with random number from a Exponential Distribution. ``lam`` is :math:`\lambda` parameter of Exponential Distribution. .. math:: f(x) = \lambda e^{-\lambda x} .. note:: Alias Support: The parameter name ``lambd`` can be used as an alias for ``lam``. For example, ``exponential_(tensor_x, lambd=1.0, ...)`` is equivalent to ``exponential_(tensor_x, lam=1.0, ...)``. Args: lam(float, optional): :math:`\lambda` parameter of Exponential Distribution. Default, 1.0. alias: ``lambd``. name(str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, Input Tensor ``x``. Examples: .. code-block:: python >>> import paddle >>> paddle.set_device('cpu') >>> paddle.seed(100) >>> x = paddle.empty([2,3]) >>> x.exponential_() >>> # doctest: +SKIP("Random output") Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.80643415, 0.23211166, 0.01169797], [0.72520679, 0.45208144, 0.30234432]]) >>> # doctest: -SKIP """ ... def fill_(self, value: '_float') -> 'Tensor': r""" **Notes**: **This API is ONLY available in Dygraph mode** This function fill the Tensor with value inplace. Args: value (int|float): ``value`` is the value to be filled in x Returns: x(Tensor), Tensor x filled with value inplace Examples: .. code-block:: python >>> import paddle >>> tensor = paddle.to_tensor([0, 1, 2, 3, 4]) >>> tensor.fill_(0) >>> print(tensor.tolist()) [0, 0, 0, 0, 0] """ ... def fill_diagonal_(self, value: '_float', offset: '_int' = 0, wrap: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Note: This API is ONLY available in Dygraph mode. This function fill the value into the x Tensor's diagonal inplace. Args: value(int|float): ``value`` is the value to filled in x offset(int,optional): the offset to the main diagonal. Default: 0 (main diagonal). wrap(bool,optional): the diagonal 'wrapped' after N columns for tall matrices. name(str|None,optional): Name for the operation (optional, default is None) Returns: Tensor, Tensor with diagonal filled with value. Examples: .. code-block:: python >>> import paddle >>> x = paddle.ones((4, 3)) * 2 >>> x.fill_diagonal_(1.0) >>> print(x.tolist()) [[1.0, 2.0, 2.0], [2.0, 1.0, 2.0], [2.0, 2.0, 1.0], [2.0, 2.0, 2.0]] """ ... def fill_diagonal_tensor(self, y: 'Tensor', offset: '_int' = 0, dim1: '_int' = 0, dim2: '_int' = 1, name: '_str | None' = None) -> 'Tensor': r""" This function fill the source Tensor y into the x Tensor's diagonal. Args: y (Tensor): ``y`` is the Tensor to filled in x dim1 (int,optional): first dimension with respect to which to fill diagonal. Default: 0. dim2 (int,optional): second dimension with respect to which to fill diagonal. Default: 1. offset (int,optional): the offset to the main diagonal. Default: 0 (main diagonal). name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, Tensor with diagonal filled with y. Examples: .. code-block:: python >>> import paddle >>> x = paddle.ones((4, 3)) * 2 >>> y = paddle.ones((3,)) >>> nx = x.fill_diagonal_tensor(y) >>> print(nx.tolist()) [[1.0, 2.0, 2.0], [2.0, 1.0, 2.0], [2.0, 2.0, 1.0], [2.0, 2.0, 2.0]] """ ... def fill_diagonal_tensor_(self, y: 'Tensor', offset: '_int' = 0, dim1: '_int' = 0, dim2: '_int' = 1, name: '_str | None' = None) -> 'Tensor': r""" Note: This API is ONLY available in Dygraph mode. This function fill the source Tensor y into the x Tensor's diagonal inplace. Args: y (Tensor): ``y`` is the Tensor to filled in x dim1 (int,optional): first dimension with respect to which to fill diagonal. Default: 0. dim2 (int,optional): second dimension with respect to which to fill diagonal. Default: 1. offset (int,optional): the offset to the main diagonal. Default: 0 (main diagonal). name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, Tensor with diagonal filled with y. Examples: .. code-block:: python >>> import paddle >>> x = paddle.ones((4, 3)) * 2 >>> y = paddle.ones((3,)) >>> x.fill_diagonal_tensor_(y) >>> print(x.tolist()) [[1.0, 2.0, 2.0], [2.0, 1.0, 2.0], [2.0, 2.0, 1.0], [2.0, 2.0, 2.0]] """ ... def flatten(self, start_axis: '_int' = 0, stop_axis: '_int' = -1, name: '_str | None' = None) -> 'Tensor': r""" Flattens a contiguous range of axes in a tensor according to start_axis and stop_axis. Note: The output Tensor will share data with origin Tensor and doesn't have a Tensor copy in ``dygraph`` mode. If you want to use the Tensor copy version, please use `Tensor.clone` like ``flatten_clone_x = x.flatten().clone()``. For Example: .. code-block:: text Case 1: Given X.shape = (3, 100, 100, 4) and start_axis = 1 end_axis = 2 We get: Out.shape = (3, 100 * 100, 4) Case 2: Given X.shape = (3, 100, 100, 4) and start_axis = 0 stop_axis = -1 We get: Out.shape = (3 * 100 * 100 * 4) .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, the parameter name ``start_dim`` can be used as an alias for ``start_axis`` , and the parameter name ``end_dim`` can be used as an alias for ``stop_axis``. For example, ``flatten(input=tensor_x, start_dim=0, end_dim=-1)`` is equivalent to ``flatten(x=tensor_x, start_axis=0, stop_axis=-1)``. Args: start_axis (int): the start axis to flatten alias: ``start_dim``. stop_axis (int): the stop axis to flatten alias: ``end_dim``. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, A tensor with the contents of the input tensor, whose input axes are flattened by indicated :attr:`start_axis` and :attr:`end_axis`, and data type is the same as input :attr:`x`. Examples: .. code-block:: pycon >>> import paddle >>> image_shape = (2, 3, 4, 4) >>> x = paddle.arange(end=image_shape[0] * image_shape[1] * image_shape[2] * image_shape[3]) >>> img = paddle.reshape(x, image_shape) >>> out = paddle.flatten(img, start_axis=1, stop_axis=2) >>> print(out.shape) paddle.Size([2, 12, 4]) >>> # out shares data with img in dygraph mode >>> img[0, 0, 0, 0] = -1 >>> print(out[0, 0, 0]) Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True, -1) """ ... def flatten_(self, start_axis: '_int' = 0, stop_axis: '_int' = -1, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``flatten`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_flatten`. """ ... def flip(self, axis: 'Sequence[_int] | _int', name: '_str | None' = None) -> 'Tensor': r""" Reverse the order of a n-D tensor along given axis in axis. The image below illustrates how ``flip`` works. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/flip.png :width: 500 :alt: legend of flip API :align: center Args: axis (list|tuple|int): The axis(axes) to flip on. Negative indices for indexing from the end are accepted. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, Tensor or DenseTensor calculated by flip layer. The data type is same with input x. Examples: .. code-block:: python >>> # doctest: +SKIP("This has diff in xdoctest env") >>> import paddle >>> image_shape=(3, 2, 2) >>> img = paddle.arange(image_shape[0] * image_shape[1] * image_shape[2]).reshape(image_shape) >>> tmp = paddle.flip(img, [0,1]) >>> print(tmp) Tensor(shape=[3, 2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[[10, 11], [8 , 9 ]], [[6 , 7 ], [4 , 5 ]], [[2 , 3 ], [0 , 1 ]]]) >>> out = paddle.flip(tmp,-1) >>> print(out) Tensor(shape=[3, 2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[[11, 10], [9 , 8 ]], [[7 , 6 ], [5 , 4 ]], [[3 , 2 ], [1 , 0 ]]]) """ ... def float(self) -> 'Tensor': r""" Cast a Tensor to float32 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with float32 dtype """ ... def float16(self) -> 'Tensor': r""" Cast a Tensor to float16 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with float16 dtype """ ... def float32(self) -> 'Tensor': r""" Cast a Tensor to float32 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with float32 dtype """ ... def float64(self) -> 'Tensor': r""" Cast a Tensor to float64 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with float64 dtype """ ... def floor(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Floor Activation Operator. Computes floor of x element-wise. .. math:: out = \lfloor x \rfloor Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output tensor. If set, the result will be stored in this tensor. Default is None. Returns: Tensor. Output of Floor operator, a Tensor with shape same as input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.floor(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [-1., -1., 0., 0.]) """ ... def floor_(self, name=None): r""" Inplace version of ``floor`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_floor`. """ ... def floor_divide(self, y: 'Number | Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Floor divide two tensors element-wise and rounds the quotinents to the nearest integer toward negative infinite. The equation is: .. math:: out = floor(x / y) - :math:`x`: Multidimensional Tensor. - :math:`y`: Multidimensional Tensor. Note: ``paddle.floor_divide`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: y (Tensor｜Number): the input tensor or number, it's data type should be uint8, int8, int32, int64, float32, float64, float16, bfloat16. alias: ``other``. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output tensor. Default: None. Returns: N-D Tensor. A location into which the result is stored. It's dimension equals with $x$. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([2, 3, 8, 7]) >>> y = paddle.to_tensor([1, 5, 3, 3]) >>> z = paddle.floor_divide(x, y) >>> print(z) Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True, [2, 0, 2, 2]) >>> x = paddle.to_tensor([2, 3, 8, 7]) >>> y = paddle.to_tensor([1, -5, -3, -3]) >>> z = paddle.floor_divide(x, y) >>> print(z) Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True, [2, -1, -3, -3]) """ ... def floor_divide_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``floor_divide`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_floor_divide`. """ ... def floor_mod(self, y: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Mod two tensors element-wise. The equation is: .. math:: out = x \% y .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``other`` can be used as an alias for ``y``. Note: ``paddle.remainder`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor And `mod`, `floor_mod` are all functions with the same name Args: y (Tensor): the input tensor, it's data type should be bfloat16, float16, float32, float64, int32, int64. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output tensor. If set, the result will be stored in this tensor. Default is None. Returns: N-D Tensor. A location into which the result is stored. If x, y have different shapes and are "broadcastable", the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape, its shape is the same as x and y. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([2, 3, 8, 7]) >>> y = paddle.to_tensor([1, 5, 3, 3]) >>> z = paddle.remainder(x, y) >>> print(z) Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True, [0, 3, 2, 1]) >>> z = paddle.floor_mod(x, y) >>> print(z) Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True, [0, 3, 2, 1]) >>> z = paddle.mod(x, y) >>> print(z) Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True, [0, 3, 2, 1]) """ ... def floor_mod_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``floor_mod_`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_floor_mod_`. """ ... def fmax(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Compares the elements at the corresponding positions of the two tensors and returns a new tensor containing the maximum value of the element. If one of them is a nan value, the other value is directly returned, if both are nan values, then the first nan value is returned. The equation is: .. math:: out = fmax(x, y) Note: ``paddle.fmax`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: y (Tensor): the input tensor, it's data type should be bfloat16, float16, float32, float64, int32, int64. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: N-D Tensor. A location into which the result is stored. If x, y have different shapes and are "broadcastable", the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape, its shape is the same as x and y. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 2], [7, 8]]) >>> y = paddle.to_tensor([[3, 4], [5, 6]]) >>> res = paddle.fmax(x, y) >>> print(res) Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[3, 4], [7, 8]]) >>> x = paddle.to_tensor([[1, 2, 3], [1, 2, 3]]) >>> y = paddle.to_tensor([3, 0, 4]) >>> res = paddle.fmax(x, y) >>> print(res) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[3, 2, 4], [3, 2, 4]]) >>> x = paddle.to_tensor([2, 3, 5], dtype='float32') >>> y = paddle.to_tensor([1, float("nan"), float("nan")], dtype='float32') >>> res = paddle.fmax(x, y) >>> print(res) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [2., 3., 5.]) >>> x = paddle.to_tensor([5, 3, float("inf")], dtype='float32') >>> y = paddle.to_tensor([1, -float("inf"), 5], dtype='float32') >>> res = paddle.fmax(x, y) >>> print(res) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [5. , 3. , inf.]) """ ... def fmin(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Compares the elements at the corresponding positions of the two tensors and returns a new tensor containing the minimum value of the element. If one of them is a nan value, the other value is directly returned, if both are nan values, then the first nan value is returned. The equation is: .. math:: out = fmin(x, y) Note: ``paddle.fmin`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: y (Tensor): the input tensor, it's data type should be bfloat16, float16, float32, float64, int32, int64. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: N-D Tensor. A location into which the result is stored. If x, y have different shapes and are "broadcastable", the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape, its shape is the same as x and y. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 2], [7, 8]]) >>> y = paddle.to_tensor([[3, 4], [5, 6]]) >>> res = paddle.fmin(x, y) >>> print(res) Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[1, 2], [5, 6]]) >>> x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]]) >>> y = paddle.to_tensor([3, 0, 4]) >>> res = paddle.fmin(x, y) >>> print(res) Tensor(shape=[1, 2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[[1, 0, 3], [1, 0, 3]]]) >>> x = paddle.to_tensor([2, 3, 5], dtype='float32') >>> y = paddle.to_tensor([1, float("nan"), float("nan")], dtype='float32') >>> res = paddle.fmin(x, y) >>> print(res) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [1., 3., 5.]) >>> x = paddle.to_tensor([5, 3, float("inf")], dtype='float64') >>> y = paddle.to_tensor([1, -float("inf"), 5], dtype='float64') >>> res = paddle.fmin(x, y) >>> print(res) Tensor(shape=[3], dtype=float64, place=Place(cpu), stop_gradient=True, [ 1. , -inf., 5. ]) """ ... def frac(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" This API is used to return the fractional portion of each element in input. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output tensor. Default: None. Returns: Tensor: The output Tensor of frac. Examples: .. code-block:: python >>> import paddle >>> input = paddle.to_tensor([[12.22000003, -1.02999997], ... [-0.54999995, 0.66000003]]) >>> output = paddle.frac(input) >>> output Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0.22000003, -0.02999997], [-0.54999995, 0.66000003]]) """ ... def frac_(self, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``frac`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_frac`. """ ... def frexp(self, name: '_str | None' = None) -> 'tuple[Tensor, Tensor]': r""" The function used to decompose a floating point number into mantissa and exponent. Args: name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: - mantissa (Tensor), A mantissa Tensor. The shape and data type of mantissa tensor and exponential tensor are the same as those of input. - exponent (Tensor), A exponent Tensor. The shape and data type of mantissa tensor and exponential tensor are the same as those of input. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 2, 3, 4]], dtype="float32") >>> mantissa, exponent = paddle.tensor.math.frexp(x) >>> mantissa Tensor(shape=[1, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.50000000, 0.50000000, 0.75000000, 0.50000000]]) >>> exponent Tensor(shape=[1, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[1., 2., 2., 3.]]) """ ... def gammainc(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Computes the regularized lower incomplete gamma function. .. math:: P(x, y) = \frac{1}{\Gamma(x)} \int_{0}^{y} t^{x-1} e^{-t} dt Args: y (Tensor): The positive parameter Tensor. Must be one of the following types: float32, float64. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, the gammainc of the input Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([0.5, 0.5, 0.5, 0.5, 0.5], dtype="float32") >>> y = paddle.to_tensor([0, 1, 10, 100, 1000], dtype="float32") >>> out = paddle.gammainc(x, y) >>> print(out) Tensor(shape=[5], dtype=float32, place=Place(cpu), stop_gradient=True, [0. , 0.84270084, 0.99999225, 1. , 1. ]) """ ... def gammainc_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``gammainc`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_gammainc`. """ ... def gammaincc(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Computes the regularized upper incomplete gamma function. .. math:: Q(x, y) = \frac{1}{\Gamma(x)} \int_{y}^{\infty} t^{x-1} e^{-t} dt Args: y (Tensor): The positive parameter Tensor. Must be one of the following types: float32, float64. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, the gammaincc of the input Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([0.5, 0.5, 0.5, 0.5, 0.5], dtype="float32") >>> y = paddle.to_tensor([0, 1, 10, 100, 1000], dtype="float32") >>> out = paddle.gammaincc(x, y) >>> print(out) Tensor(shape=[5], dtype=float32, place=Place(cpu), stop_gradient=True, [1. , 0.15729916, 0.00000774, 0. , 0. ]) """ ... def gammaincc_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``gammaincc`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_gammaincc`. """ ... def gammaln(self, name: '_str | None' = None) -> 'Tensor': r""" Calculates the logarithm of the absolute value of the gamma function elementwisely. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, The values of the logarithm of the absolute value of the gamma at the given tensor x. Examples: .. code-block:: python >>> import paddle >>> x = paddle.arange(1.5, 4.5, 0.5) >>> out = paddle.gammaln(x) >>> print(out) Tensor(shape=[6], dtype=float32, place=Place(cpu), stop_gradient=True, [-0.12078224, 0. , 0.28468287, 0.69314718, 1.20097363, 1.79175949]) """ ... def gammaln_(self, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``gammaln`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_gammaln`. """ ... def gather(self, index: 'Tensor', axis: 'Tensor | _int | None' = None, name: '_str | None' = None, out: 'Tensor | None' = None) -> 'Tensor': r""" This function has two functionalities, depending on the parameters passed: 1. ``gather(Tensor input, int dim, Tensor index, Tensor out = None)``: PyTorch compatible gather, calls a non-broadcast `paddle.take_along_axis`. Check out :ref:`api_paddle_take_along_axis` and also `[torch has more parameters] torch.scatter `_ Note that ``sparse_grad`` param of PyTorch is currently not supported by Paddle, therefore do not pass this param (behavior is equivalent to ``sparse_grad = False``). Also, dim allows for Tensor input, the same as PyTorch. However, when the first 3 params are all of Tensor types, there will be ambiguity between these two functionalities. Currently, original gather pass is more actively selected. Try avoiding using Tensor dim as input therefore. 2. ``gather(Tensor x, Tensor index, int axis, str name = None, Tensor out = None)``: The original paddle.gather, see the following docs. Output is obtained by gathering entries of ``axis`` of ``x`` indexed by ``index`` and concatenate them together. .. code-block:: text Given: x = [[1, 2], [3, 4], [5, 6]] index = [1, 2] axis=[0] Then: out = [[3, 4], [5, 6]] Args: index (Tensor): The index input tensor with rank=0 or rank=1. Data type is int32 or int64. axis (Tensor|int|None, optional): The axis of input to be gathered, it's can be int or a Tensor with data type is int32 or int64. The default value is None, if None, the ``axis`` is 0. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: output (Tensor), If the index is a 1-D tensor, the output is a tensor with the same shape as ``x``. If the index is a 0-D tensor, the output will reduce the dimension where the axis pointing. Examples: .. code-block:: python >>> import paddle >>> input = paddle.to_tensor([[1,2],[3,4],[5,6]]) >>> index = paddle.to_tensor([0,1]) >>> output = paddle.gather(input, index, axis=0) >>> print(output) Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[1, 2], [3, 4]]) """ ... def gather_nd(self, index: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" This function is actually a high-dimensional extension of :code:`gather` and supports for simultaneous indexing by multiple axes. :attr:`index` is a K-dimensional integer tensor, which is regarded as a (K-1)-dimensional tensor of :attr:`index` into :attr:`input`, where each element defines a slice of params: .. math:: output[(i_0, ..., i_{K-2})] = input[index[(i_0, ..., i_{K-2})]] Obviously, :code:`index.shape[-1] <= input.rank` . And, the output tensor has shape :code:`index.shape[:-1] + input.shape[index.shape[-1]:]` . .. code-block:: text Given: x = [[[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]] x.shape = (2, 3, 4) * Case 1: index = [[1]] gather_nd(x, index) = [x[1, :, :]] = [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]] * Case 2: index = [[0,2]] gather_nd(x, index) = [x[0, 2, :]] = [8, 9, 10, 11] * Case 3: index = [[1, 2, 3]] gather_nd(x, index) = [x[1, 2, 3]] = [23] Args: index (Tensor): The index input with rank > 1, index.shape[-1] <= input.rank. Its dtype should be int32, int64. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: output (Tensor), A tensor with the shape index.shape[:-1] + input.shape[index.shape[-1]:] Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[[1, 2], [3, 4], [5, 6]], ... [[7, 8], [9, 10], [11, 12]]]) >>> index = paddle.to_tensor([[0, 1]]) >>> output = paddle.gather_nd(x, index) >>> print(output) Tensor(shape=[1, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[3, 4]]) """ ... def gcd(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Computes the element-wise greatest common divisor (GCD) of input |x| and |y|. Both x and y must have integer types. Note: gcd(0,0)=0, gcd(0, y)=|y| If x.shape != y.shape, they must be broadcastable to a common shape (which becomes the shape of the output). Args: y (Tensor): An N-D Tensor, the data type is int32, int64. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor): An N-D Tensor, the data type is the same with input. Examples: .. code-block:: python >>> import paddle >>> x1 = paddle.to_tensor(12) >>> x2 = paddle.to_tensor(20) >>> paddle.gcd(x1, x2) Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True, 4) >>> x3 = paddle.arange(6) >>> paddle.gcd(x3, x2) Tensor(shape=[6], dtype=int64, place=Place(cpu), stop_gradient=True, [20, 1 , 2 , 1 , 4 , 5]) >>> x4 = paddle.to_tensor(0) >>> paddle.gcd(x4, x2) Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True, 20) >>> paddle.gcd(x4, x4) Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True, 0) >>> x5 = paddle.to_tensor(-20) >>> paddle.gcd(x1, x5) Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True, 4) """ ... def gcd_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``gcd`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_gcd`. """ ... def ge(self, y: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Returns the truth value of :math:`x >= y` elementwise, which is equivalent function to the overloaded operator `>=`. Note: The output has no gradient. Args: y (Tensor): Second input to compare which is N-D tensor. The input data type should be bool, bfloat16, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128. Alias: ``other``. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output tensor. If set, the result will be stored in this tensor. Default is None. Returns: Tensor: The output shape is same as input :attr:`x`. The output data type is bool. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3]) >>> y = paddle.to_tensor([1, 3, 2]) >>> result1 = paddle.greater_equal(x, y) >>> print(result1) Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True, [True , False, True ]) """ ... def geometric_(self, probs: '_float | paddle.Tensor', name: '_str | None' = None) -> 'paddle.Tensor': r""" Fills the tensor with numbers drawn from the Geometric distribution. Args: probs (float|Tensor): Probability parameter. The value of probs must be positive. When the parameter is a tensor, probs is probability of success for each trial. name(str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor: input tensor with numbers drawn from the Geometric distribution. Examples: .. code-block:: python >>> import paddle >>> x = paddle.randn([3, 4]) >>> x.geometric_(0.3) >>> # doctest: +SKIP('random check') >>> print(x) Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[2.42739224, 4.78268528, 1.23302543, 3.76555204], [1.38877118, 0.16075331, 0.16401523, 2.47349310], [1.72872102, 2.76533413, 0.33410925, 1.63351011]]) """ ... def get_device(self) -> '_int': r""" Return the device id where the Tensor is located. Returns: int: The device id of the Tensor. Returns -1 for CPU tensors; for GPU tensors, returns the CUDA device id (e.g., 0 for `gpu:0`). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3], place=paddle.CPUPlace()) >>> x.get_device() -1 >>> # doctest: +REQUIRES(env:GPU) >>> y = paddle.to_tensor([1, 2, 3], place=paddle.CUDAPlace(0)) >>> y.get_device() 0 """ ... def gradient(self) -> 'npt.NDArray[Any] | tuple[npt.NDArray[Any], npt.NDArray[Any]] | None': r""" Warning: API "paddle.base.dygraph.tensor_patch_methods.gradient" is deprecated since 2.1.0, and will be removed in future versions. Reason: Please use tensor.grad, which returns the tensor value of the gradient. .. warning:: This API will be deprecated in the future, it is recommended to use :code:`x.grad` which returns the tensor value of the gradient. Get the Gradient of Current Tensor. Returns: ndarray: Numpy value of the gradient of current Tensor Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor(5., stop_gradient=False) >>> y = paddle.pow(x, 4.0) >>> y.backward() >>> print("grad of x: {}".format(x.gradient())) grad of x: 500.0 """ ... def greater(self, y: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Returns the truth value of :math:`x > y` elementwise, which is equivalent function to the overloaded operator `>`. Note: The output has no gradient. Args: y (Tensor): Second input to compare which is N-D tensor. The input data type should be bool, bfloat16, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128. Alias: ``other``. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output tensor. If provided, the result will be stored in this tensor. Returns: Tensor: The output shape is same as input :attr:`x`. The output data type is bool. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3]) >>> y = paddle.to_tensor([1, 3, 2]) >>> result1 = paddle.gt(x, y) >>> print(result1) Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True, [False, False, True ]) """ ... def greater_equal_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``greater_equal`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_greater_equal`. """ ... def greater_than(self, y: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Returns the truth value of :math:`x > y` elementwise, which is equivalent function to the overloaded operator `>`. Note: The output has no gradient. Args: y (Tensor): Second input to compare which is N-D tensor. The input data type should be bool, bfloat16, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128. Alias: ``other``. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output tensor. If provided, the result will be stored in this tensor. Returns: Tensor: The output shape is same as input :attr:`x`. The output data type is bool. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3]) >>> y = paddle.to_tensor([1, 3, 2]) >>> result1 = paddle.greater_than(x, y) >>> print(result1) Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True, [False, False, True ]) """ ... def greater_than_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``greater_than`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_greater_than`. """ ... def half(self) -> 'Tensor': r""" Cast a Tensor to float16 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with float16 dtype """ ... def heaviside(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Computes the Heaviside step function determined by corresponding element in y for each element in x. The equation is .. math:: heaviside(x, y)= \left\{ \begin{array}{lcl} 0,& &\text{if} \ x < 0, \\ y,& &\text{if} \ x = 0, \\ 1,& &\text{if} \ x > 0. \end{array} \right. Note: ``paddle.heaviside`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: y (Tensor): The tensor that determines a Heaviside step function, it's data type should be bfloat16, float16, float32, float64, int32 or int64. name (str|None, optional): Name for the operation (optional, default is None). Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: N-D Tensor. A location into which the result is stored. If x and y have different shapes and are broadcastable, the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape, its shape is the same as x and y. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.5, 0, 0.5]) >>> y = paddle.to_tensor([0.1]) >>> paddle.heaviside(x, y) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [0. , 0.10000000, 1. ]) >>> x = paddle.to_tensor([[-0.5, 0, 0.5], [-0.5, 0.5, 0]]) >>> y = paddle.to_tensor([0.1, 0.2, 0.3]) >>> paddle.heaviside(x, y) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[0. , 0.20000000, 1. ], [0. , 1. , 0.30000001]]) """ ... def histogram(self, bins: '_int' = 100, min: '_float' = 0.0, max: '_float' = 0.0, weight: 'Tensor | None' = None, density: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max. If min and max are both zero, the minimum and maximum values of the data are used. Args: bins (int, optional): number of histogram bins. Default: 100. min (float, optional): lower end of the range (inclusive). Default: 0.0. max (float, optional): upper end of the range (inclusive). Default: 0.0. weight (Tensor, optional): If provided, it must have the same shape as input. Each value in input contributes its associated weight towards the bin count (instead of 1). Default: None. density (bool, optional): If False, the result will contain the count (or total weight) in each bin. If True, the result is the value of the probability density function over the bins, normalized such that the integral over the range of the bins is 1. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor, shape is (nbins,), the counts or density of the histogram. Examples: .. code-block:: python >>> import paddle >>> inputs = paddle.to_tensor([1, 2, 1]) >>> result = paddle.histogram(inputs, bins=4, min=0, max=3) >>> print(result) Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True, [0, 2, 1, 0]) """ ... def histogram_bin_edges(self, bins: '_int' = 100, min: '_float' = 0.0, max: '_float' = 0.0, name: '_str | None' = None) -> 'Tensor': r""" Computes only the edges of the bins used by the histogram function. If min and max are both zero, the minimum and maximum values of the data are used. Args: bins (int, optional): number of histogram bins. min (float, optional): lower end of the range (inclusive). Default: 0.0. max (float, optional): upper end of the range (inclusive). Default: 0.0. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor, the values of the bin edges. The output data type will be float32. Examples: .. code-block:: python >>> import paddle >>> inputs = paddle.to_tensor([1, 2, 1]) >>> result = paddle.histogram_bin_edges(inputs, bins=4, min=0, max=3) >>> print(result) Tensor(shape=[5], dtype=float32, place=Place(cpu), stop_gradient=True, [0. , 0.75000000, 1.50000000, 2.25000000, 3. ]) """ ... def histogramdd(self, bins: 'Tensor | list[_int] | _int' = 10, ranges: 'Sequence[_float] | None' = None, density: '_bool' = False, weights: 'Tensor | None' = None, name: '_str | None' = None) -> 'tuple[Tensor, list[Tensor]]': r""" Computes a multi-dimensional histogram of the values in a tensor. Interprets the elements of an input tensor whose innermost dimension has size `N` as a collection of N-dimensional points. Maps each of the points into a set of N-dimensional bins and returns the number of points (or total weight) in each bin. input `x` must be a tensor with at least 2 dimensions. If input has shape `(M, N)`, each of its `M` rows defines a point in N-dimensional space. If input has three or more dimensions, all but the last dimension are flattened. Each dimension is independently associated with its own strictly increasing sequence of bin edges. Bin edges may be specified explicitly by passing a sequence of 1D tensors. Alternatively, bin edges may be constructed automatically by passing a sequence of integers specifying the number of equal-width bins in each dimension. Args: bins (list[Tensor], list[int], or int): If list[Tensor], defines the sequences of bin edges. If list[int], defines the number of equal-width bins in each dimension. If int, defines the number of equal-width bins for all dimensions. ranges (sequence[float]|None, optional): Defines the leftmost and rightmost bin edges in each dimension. If is None, set the minimum and maximum as leftmost and rightmost edges for each dimension. density (bool, optional): If False (default), the result will contain the count (or total weight) in each bin. If True, each count (weight) is divided by the total count (total weight), then divided by the volume of its associated bin. weights (Tensor, optional): By default, each value in the input has weight 1. If a weight tensor is passed, each N-dimensional coordinate in input contributes its associated weight towards its bin's result. The weight tensor should have the same shape as the input tensor excluding its innermost dimension N. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: N-dimensional Tensor containing the values of the histogram. ``bin_edges(Tensor[])``, sequence of N 1D Tensors containing the bin edges. Examples: .. code-block:: python :name: example >>> import paddle >>> x = paddle.to_tensor([[0., 1.], [1., 0.], [2.,0.], [2., 2.]]) >>> bins = [3,3] >>> weights = paddle.to_tensor([1., 2., 4., 8.]) >>> paddle.histogramdd(x, bins=bins, weights=weights) (Tensor(shape=[3, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True, [[0., 1., 0.], [2., 0., 0.], [4., 0., 8.]]), [Tensor(shape=[4], dtype=float32, place=Place(gpu:0), stop_gradient=True, [0. , 0.66666669, 1.33333337, 2. ]), Tensor(shape=[4], dtype=float32, place=Place(gpu:0), stop_gradient=True, [0. , 0.66666669, 1.33333337, 2. ])]) .. code-block:: python :name: examp2 >>> import paddle >>> y = paddle.to_tensor([[0., 0.], [1., 1.], [2., 2.]]) >>> bins = [2,2] >>> ranges = [0., 1., 0., 1.] >>> density = True >>> paddle.histogramdd(y, bins=bins, ranges=ranges, density=density) (Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True, [[2., 0.], [0., 2.]]), [Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True, [0. , 0.50000000, 1. ]), Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True, [0. , 0.50000000, 1. ])]) """ ... def householder_product(self, tau: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Computes the first n columns of a product of Householder matrices. This function can get the vector :math:`\omega_{i}` from matrix `x` (m x n), the :math:`i-1` elements are zeros, and the i-th is `1`, the rest of the elements are from i-th column of `x`. And with the vector `tau` can calculate the first n columns of a product of Householder matrices. :math:`H_i = I_m - \tau_i \omega_i \omega_i^H` Args: tau (Tensor): A tensor with shape (*, k) where * is zero or more batch dimensions. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor, the dtype is same as input tensor, the Q in QR decomposition. :math:`out = Q = H_1H_2H_3...H_k` Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[-1.1280, 0.9012, -0.0190], ... [ 0.3699, 2.2133, -1.4792], ... [ 0.0308, 0.3361, -3.1761], ... [-0.0726, 0.8245, -0.3812]]) >>> tau = paddle.to_tensor([1.7497, 1.1156, 1.7462]) >>> Q = paddle.linalg.householder_product(x, tau) >>> print(Q) Tensor(shape=[4, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True, [[-0.74969995, -0.02181768, 0.31115776], [-0.64721400, -0.12367040, -0.21738708], [-0.05389076, -0.37562513, -0.84836429], [ 0.12702821, -0.91822827, 0.36892807]]) """ ... def hsplit(self, num_or_indices: '_int | Sequence[_int]', name: '_str | None' = None) -> 'list[Tensor]': r""" ``hsplit`` Full name Horizontal Split, splits the input Tensor into multiple sub-Tensors along the horizontal axis, in the following two cases: 1. When the dimension of x is equal to 1, it is equivalent to ``paddle.tensor_split`` with ``axis=0``; .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/hsplit/hsplit-1.png 2. when the dimension of x is greater than 1, it is equivalent to ``paddle.tensor_split`` with ``axis=1``. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/hsplit/hsplit-2.png Args: num_or_indices (int|list|tuple): If ``num_or_indices`` is an int ``n``, ``x`` is split into ``n`` sections. If ``num_or_indices`` is a list or tuple of integer indices, ``x`` is split at each of the indices. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: list[Tensor], The list of segmented Tensors. Examples: .. code-block:: pycon >>> import paddle >>> # x is a Tensor of shape [8] >>> x = paddle.rand([8]) >>> out0, out1 = paddle.hsplit(x, num_or_indices=2) >>> print(out0.shape) paddle.Size([4]) >>> print(out1.shape) paddle.Size([4]) >>> # x is a Tensor of shape [7, 8] >>> x = paddle.rand([7, 8]) >>> out0, out1 = paddle.hsplit(x, num_or_indices=2) >>> print(out0.shape) paddle.Size([7, 4]) >>> print(out1.shape) paddle.Size([7, 4]) >>> out0, out1, out2 = paddle.hsplit(x, num_or_indices=[1, 4]) >>> print(out0.shape) paddle.Size([7, 1]) >>> print(out1.shape) paddle.Size([7, 3]) >>> print(out2.shape) paddle.Size([7, 4]) """ ... def hypot(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Calculate the length of the hypotenuse of a right-angle triangle. The equation is: .. math:: out = {\sqrt{x^2 + y^2}} Args: y (Tensor): The input Tensor, the data type is float32, float64, int32 or int64. name (str|None, optional): Name for the operation (optional, default is None).For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor): An N-D Tensor. If x, y have different shapes and are "broadcastable", the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape, its shape is the same as x and y. And the data type is float32 or float64. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([3], dtype='float32') >>> y = paddle.to_tensor([4], dtype='float32') >>> res = paddle.hypot(x, y) >>> print(res) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [5.]) """ ... def hypot_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``hypot`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_hypot`. """ ... def i0(self, name: '_str | None' = None) -> 'Tensor': r""" The function used to calculate modified bessel function of order 0. Equation: .. math:: I_0(x) = \sum^{\infty}_{k=0}\frac{(x^2/4)^k}{(k!)^2} Args: name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: - out (Tensor), A Tensor. the value of the modified bessel function of order 0 at x (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([0, 1, 2, 3, 4], dtype="float32") >>> paddle.i0(x) Tensor(shape=[5], dtype=float32, place=Place(cpu), stop_gradient=True, [0.99999994 , 1.26606596 , 2.27958512 , 4.88079262 , 11.30192089]) """ ... def i0_(self, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``i0`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_i0`. """ ... def i0e(self, name: '_str | None' = None) -> 'Tensor': r""" The function used to calculate exponentially scaled modified Bessel function of order 0. Equation: .. math:: I_0(x) = \sum^{\infty}_{k=0}\frac{(x^2/4)^k}{(k!)^2} \\ I_{0e}(x) = e^{-|x|}I_0(x) Args: name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: - out (Tensor), A Tensor. the value of the exponentially scaled modified Bessel function of order 0 at x (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([0, 1, 2, 3, 4], dtype="float32") >>> print(paddle.i0e(x)) Tensor(shape=[5], dtype=float32, place=Place(cpu), stop_gradient=True, [0.99999994, 0.46575963, 0.30850831, 0.24300036, 0.20700191]) """ ... def i1(self, name: '_str | None' = None) -> 'Tensor': r""" The function is used to calculate modified bessel function of order 1. Args: name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: - out (Tensor), A Tensor. the value of the modified bessel function of order 1 at x (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([0, 1, 2, 3, 4], dtype="float32") >>> print(paddle.i1(x)) Tensor(shape=[5], dtype=float32, place=Place(cpu), stop_gradient=True, [0. , 0.56515908, 1.59063685, 3.95337057, 9.75946712]) """ ... def i1e(self, name: '_str | None' = None) -> 'Tensor': r""" The function is used to calculate exponentially scaled modified Bessel function of order 1. Args: x (Tensor): The input tensor, it's data type should be float32, float64, uint8, int8, int16, int32, int64. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: - out (Tensor), A Tensor. the value of the exponentially scaled modified Bessel function of order 1 at x (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([0, 1, 2, 3, 4], dtype="float32") >>> print(paddle.i1e(x)) Tensor(shape=[5], dtype=float32, place=Place(cpu), stop_gradient=True, [0. , 0.20791042, 0.21526928, 0.19682673, 0.17875087]) """ ... def imag(self, name: '_str | None' = None) -> 'Tensor': r""" Returns a new tensor containing imaginary values of input tensor. Args: name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: Tensor: a tensor containing imaginary values of the input tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor( ... [[1 + 6j, 2 + 5j, 3 + 4j], [4 + 3j, 5 + 2j, 6 + 1j]]) >>> print(x) Tensor(shape=[2, 3], dtype=complex64, place=Place(cpu), stop_gradient=True, [[(1+6j), (2+5j), (3+4j)], [(4+3j), (5+2j), (6+1j)]]) >>> imag_res = paddle.imag(x) >>> print(imag_res) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[6., 5., 4.], [3., 2., 1.]]) >>> imag_t = x.imag() >>> print(imag_t) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[6., 5., 4.], [3., 2., 1.]]) """ ... def increment(self, value: '_float' = 1.0, name: '_str | None' = None) -> 'Tensor': r""" The API is usually used for control flow to increment the data of :attr:`x` by an amount :attr:`value`. Notice that the number of elements in :attr:`x` must be equal to 1. Args: value (float, optional): The amount to increment the data of :attr:`x`. Default: 1.0. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, the elementwise-incremented tensor with the same shape and data type as :attr:`x`. Examples: .. code-block:: python >>> import paddle >>> data = paddle.zeros(shape=[1], dtype='float32') >>> counter = paddle.increment(data) >>> counter Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [1.]) """ ... def index_add(self, index: 'Tensor', axis: '_int', value: 'Tensor', alpha: 'Number' = 1, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Adds the elements of the input tensor with value tensor by selecting the indices in the order given in index. Args: index (Tensor): The 1-D Tensor containing the indices to index. The data type of ``index`` must be int32 or int64. axis (int): The dimension in which we index. alias: ``dim``. value (Tensor): The tensor used to add the elements along the target axis. alias: ``source``. alpha (Number, optional): Scaling factor for value. Default: 1. out (Tensor, optional): The output tensor. Default: None. name(str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor, same dimension and dtype with x. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> input_tensor = paddle.to_tensor(paddle.ones((3, 3)), dtype="float32") >>> index = paddle.to_tensor([0, 2], dtype="int32") >>> value = paddle.to_tensor([[1, 1, 1], [1, 1, 1]], dtype="float32") >>> outplace_res = paddle.index_add(input_tensor, index, 0, value) >>> print(outplace_res) Tensor(shape=[3, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True, [[2., 2., 2.], [1., 1., 1.], [2., 2., 2.]]) """ ... def index_add_(self, index: 'Tensor', axis: '_int', value: 'Tensor', alpha: '_int' = 1, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``index_add`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_index_add`. """ ... def index_fill(self, index: 'Tensor', axis: '_int', value: '_float', name: '_str | None' = None): r""" Fill the elements of the input tensor with value by the specific axis and index. As shown below, a ``[3, 3]`` 2D tensor is updated via the index_fill operation. With ``axis=0``, ``index=[0, 2]`` and ``value=-1``, the 1st and 3rd row elements become ``-1``. The resulting tensor, still [3, 3], has updated values. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/index_fill.png :width: 500 :alt: Illustration of Case 2 :align: center Args: index (Tensor): The 1-D Tensor containing the indices to index. The data type of ``index`` must be int32 or int64. axis (int): The dimension along which to index. value (int|float): The tensor used to fill with. name(str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor, same dimension and dtype with x. Examples: .. code-block:: python >>> import paddle >>> input_tensor = paddle.to_tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype='int64') >>> index = paddle.to_tensor([0, 2], dtype="int32") >>> value = -1 >>> res = paddle.index_fill(input_tensor, index, 0, value) >>> print(input_tensor) Tensor(shape=[3, 3], dtype=int64, place=Place(gpu:0), stop_gradient=True, [[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> print(res) Tensor(shape=[3, 3], dtype=int64, place=Place(gpu:0), stop_gradient=True, [[-1, -1, -1], [ 4, 5, 6], [-1, -1, -1]]) """ ... def index_fill_(self, index: 'Tensor', axis: '_int', value: '_float', name: '_str | None' = None): r""" Inplace version of ``index_fill`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_index_fill`. """ ... def index_put(self, indices: 'Sequence[Tensor]', value: 'Tensor', accumulate: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Puts values from the tensor values into the tensor x using the indices specified in indices (which is a tuple of Tensors). The expression paddle.index_put_(x, indices, values) is equivalent to tensor[indices] = values. Returns x. If accumulate is True, the elements in values are added to x. If accumulate is False, the behavior is undefined if indices contain duplicate elements. Args: indices (list[Tensor]|tuple[Tensor]): The tuple of Tensor containing the indices to index. The data type of ``tensor in indices`` must be int32, int64 or bool. value (Tensor): The tensor used to be assigned to x. accumulate (bool, optional): Whether the elements in values are added to x. Default: False. name(str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor, same dimension and dtype with x. Examples: .. code-block:: python >>> import paddle >>> x = paddle.zeros([3, 3]) >>> value = paddle.ones([3]) >>> ix1 = paddle.to_tensor([0,1,2]) >>> ix2 = paddle.to_tensor([1,2,1]) >>> indices=(ix1,ix2) >>> out = paddle.index_put(x,indices,value) >>> print(x) Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]) >>> print(out) Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[0., 1., 0.], [0., 0., 1.], [0., 1., 0.]]) """ ... def index_put_(self, indices: 'Sequence[Tensor]', value: 'Tensor', accumulate: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``index_put`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_index_put`. """ ... def index_sample(self, index: 'Tensor') -> 'Tensor': r""" **IndexSample Layer** IndexSample OP returns the element of the specified location of X, and the location is specified by Index. .. code-block:: text Given: X = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]] Index = [[0, 1, 3], [0, 2, 4]] Then: Out = [[1, 2, 4], [6, 8, 10]] Args: index (Tensor): The index input tensor with 2-D shape, first dimension should be same with X. Data type is int32 or int64. Returns: Tensor, The output is a tensor with the same shape as index. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1.0, 2.0, 3.0, 4.0], ... [5.0, 6.0, 7.0, 8.0], ... [9.0, 10.0, 11.0, 12.0]], dtype='float32') >>> index = paddle.to_tensor([[0, 1, 2], ... [1, 2, 3], ... [0, 0, 0]], dtype='int32') >>> target = paddle.to_tensor([[100, 200, 300, 400], ... [500, 600, 700, 800], ... [900, 1000, 1100, 1200]], dtype='int32') >>> out_z1 = paddle.index_sample(x, index) >>> print(out_z1.numpy()) [[1. 2. 3.] [6. 7. 8.] [9. 9. 9.]] >>> # Use the index of the maximum value by topk op >>> # get the value of the element of the corresponding index in other tensors >>> top_value, top_index = paddle.topk(x, k=2) >>> out_z2 = paddle.index_sample(target, top_index) >>> print(top_value.numpy()) [[ 4. 3.] [ 8. 7.] [12. 11.]] >>> print(top_index.numpy()) [[3 2] [3 2] [3 2]] >>> print(out_z2.numpy()) [[ 400 300] [ 800 700] [1200 1100]] """ ... def index_select(self, index: 'Tensor', axis: '_int' = 0, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Returns a new tensor which indexes the ``input`` tensor along dimension ``axis`` using the entries in ``index`` which is a Tensor. The returned tensor has the same number of dimensions as the original ``x`` tensor. The dim-th dimension has the same size as the length of ``index``; other dimensions have the same size as in the ``x`` tensor. .. note:: Alias and Order Support: 1. The parameter name ``input`` can be used as an alias for ``x``. 2. The parameter name ``dim`` can be used as an alias for ``axis``. 3. This API also supports the PyTorch argument order ``(input, dim, index)`` for positional arguments, which will be converted to the Paddle order ``(x, index, axis)``. For example, ``paddle.index_select(input=x, dim=1, index=idx)`` is equivalent to ``paddle.index_select(x=x, axis=1, index=idx)``, and ``paddle.index_select(x, 1, idx)`` is equivalent to ``paddle.index_select(x, idx, axis=1)``. Args: index (Tensor): The 1-D Tensor containing the indices to index. The data type of ``index`` must be int32 or int64. axis (int, optional): The dimension in which we index. Default: if None, the ``axis`` is 0. alias: ``dim``. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Keyword Args: out (Tensor|None, optional): The output tensor. Default: None. Returns: Tensor, A Tensor with same data type as ``x``. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1.0, 2.0, 3.0, 4.0], ... [5.0, 6.0, 7.0, 8.0], ... [9.0, 10.0, 11.0, 12.0]]) >>> index = paddle.to_tensor([0, 1, 1], dtype='int32') >>> out_z1 = paddle.index_select(x=x, index=index) >>> print(out_z1.numpy()) [[1. 2. 3. 4.] [5. 6. 7. 8.] [5. 6. 7. 8.]] >>> out_z2 = paddle.index_select(x=x, index=index, axis=1) >>> print(out_z2.numpy()) [[ 1. 2. 2.] [ 5. 6. 6.] [ 9. 10. 10.]] """ ... def indices(self, /): r""" Note: **This API is only available for SparseCooTensor.** Returns the indices of non zero elements in input SparseCooTensor. Returns: DenseTensor Examples: .. code-block:: python >>> import paddle >>> indices = [[0, 1, 2], [1, 2, 0]] >>> values = [1.0, 2.0, 3.0] >>> dense_shape = [3, 3] >>> coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape) >>> coo.indices() Tensor(shape=[2, 3], dtype=int64, place=Place(gpu:0), stop_gradient=True, [[0, 1, 2], [1, 2, 0]]) """ ... def inner(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inner product of two input Tensor. Ordinary inner product for 1-D Tensors, in higher dimensions a sum product over the last axes. Args: y (Tensor): An N-D Tensor or a Scalar Tensor. If its not a scalar Tensor, its last dimensions must match x's. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The inner-product Tensor, the output shape is x.shape[:-1] + y.shape[:-1]. Examples: .. code-block:: python >>> import paddle >>> x = paddle.arange(1, 7).reshape((2, 3)).astype('float32') >>> y = paddle.arange(1, 10).reshape((3, 3)).astype('float32') >>> out = paddle.inner(x, y) >>> print(out) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[14. , 32. , 50. ], [32. , 77. , 122.]]) """ ... def int(self) -> 'Tensor': r""" Cast a Tensor to int32 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with int32 dtype """ ... def int16(self) -> 'Tensor': r""" Cast a Tensor to int16 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with int16 dtype """ ... def int32(self) -> 'Tensor': r""" Cast a Tensor to int32 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with int32 dtype """ ... def int64(self) -> 'Tensor': r""" Cast a Tensor to int64 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with int64 dtype """ ... def int8(self) -> 'Tensor': r""" Cast a Tensor to int8 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with int8 dtype """ ... def inverse(self, name: '_str | None' = None) -> 'Tensor': r""" Takes the inverse of the square matrix. A square matrix is a matrix with the same number of rows and columns. The input can be a square matrix (2-D Tensor) or batches of square matrices. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A Tensor holds the inverse of x. The shape and data type is the same as x. Examples: .. code-block:: python >>> import paddle >>> mat = paddle.to_tensor([[2, 0], [0, 2]], dtype='float32') >>> inv = paddle.inverse(mat) >>> print(inv) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.50000000, 0. ], [0. , 0.50000000]]) """ ... def is_complex(self) -> '_bool': r""" Return whether x is a tensor of complex data type(complex64 or complex128). .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``. For example, ``input=tensor_x`` is equivalent to ``x=tensor_x``. Args: input: An alias for ``x`` , with identical behavior. Returns: bool: True if the data type of the input is complex data type, otherwise false. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1 + 2j, 3 + 4j]) >>> print(paddle.is_complex(x)) True >>> x = paddle.to_tensor([1.1, 1.2]) >>> print(paddle.is_complex(x)) False >>> x = paddle.to_tensor([1, 2, 3]) >>> print(paddle.is_complex(x)) False """ ... def is_empty(self, name: '_str | None' = None) -> 'Tensor': r""" Test whether a Tensor is empty. Args: name (str|None, optional): The default value is ``None`` . Normally users don't have to set this parameter. For more information, please refer to :ref:`api_guide_Name` . Returns: Tensor: A bool scalar Tensor. True if 'x' is an empty Tensor. Examples: .. code-block:: python >>> import paddle >>> input = paddle.rand(shape=[4, 32, 32], dtype='float32') >>> res = paddle.is_empty(x=input) >>> print(res) Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True, False) """ ... def is_floating_point(self) -> '_bool': r""" Returns whether the dtype of `x` is one of paddle.float64, paddle.float32, paddle.float16, and paddle.bfloat16. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``. For example, ``is_floating_point(input=tensor_x)`` is equivalent to ``is_floating_point(x=tensor_x)``. Args: Returns: bool: True if the dtype of `x` is floating type, otherwise false. Examples: .. code-block:: python >>> import paddle >>> x = paddle.arange(1., 5., dtype='float32') >>> y = paddle.arange(1, 5, dtype='int32') >>> print(paddle.is_floating_point(x)) True >>> print(paddle.is_floating_point(y)) False """ ... def is_integer(self) -> '_bool': r""" Return whether x is a tensor of integral data type. Args: Returns: bool: True if the data type of the input is integer data type, otherwise false. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1 + 2j, 3 + 4j]) >>> print(paddle.is_integer(x)) False >>> x = paddle.to_tensor([1.1, 1.2]) >>> print(paddle.is_integer(x)) False >>> x = paddle.to_tensor([1, 2, 3]) >>> print(paddle.is_integer(x)) True """ ... def is_tensor(self) -> 'TypeGuard[Tensor]': r""" Tests whether input object is a paddle.Tensor. .. note:: Alias Support: The parameter name ``obj`` can be used as an alias for ``x``. For example, ``is_tensor(obj=tensor_x)`` is equivalent to ``is_tensor(x=tensor_x)``. Args: Returns: A boolean value. True if ``x`` is a paddle.Tensor, otherwise False. Examples: .. code-block:: python >>> import paddle >>> input1 = paddle.rand(shape=[2, 3, 5], dtype='float32') >>> check = paddle.is_tensor(input1) >>> print(check) True >>> input3 = [1, 4] >>> check = paddle.is_tensor(input3) >>> print(check) False """ ... def isclose(self, y: 'Tensor', rtol: '_float' = 1e-05, atol: '_float' = 1e-08, equal_nan: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Check if all :math:`x` and :math:`y` satisfy the condition: .. math:: \left| x - y ight| \leq atol + rtol imes \left| y ight| elementwise, for all elements of :math:`x` and :math:`y`. The behaviour of this operator is analogous to :math:`numpy.isclose`, namely that it returns :math:`True` if two tensors are elementwise equal within a tolerance. Args: y(Tensor): The input tensor, it's data type should be float16, float32, float64, complex64, complex128. rtol(float, optional): The relative tolerance. Default: :math:`1e-5` . atol(float, optional): The absolute tolerance. Default: :math:`1e-8` . equal_nan(bool, optional): If :math:`True` , then two :math:`NaNs` will be compared as equal. Default: :math:`False` . name (str|None, optional): Name for the operation. For more information, please refer to :ref:`api_guide_Name`. Default: None. Returns: Tensor: The output tensor, it's data type is bool. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([10000., 1e-07]) >>> y = paddle.to_tensor([10000.1, 1e-08]) >>> result1 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08, ... equal_nan=False, name="ignore_nan") >>> print(result1) Tensor(shape=[2], dtype=bool, place=Place(cpu), stop_gradient=True, [True , False]) >>> result2 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08, ... equal_nan=True, name="equal_nan") >>> print(result2) Tensor(shape=[2], dtype=bool, place=Place(cpu), stop_gradient=True, [True , False]) >>> x = paddle.to_tensor([1.0, float('nan')]) >>> y = paddle.to_tensor([1.0, float('nan')]) >>> result1 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08, ... equal_nan=False, name="ignore_nan") >>> print(result1) Tensor(shape=[2], dtype=bool, place=Place(cpu), stop_gradient=True, [True , False]) >>> result2 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08, ... equal_nan=True, name="equal_nan") >>> print(result2) Tensor(shape=[2], dtype=bool, place=Place(cpu), stop_gradient=True, [True, True]) """ ... def isfinite(self, name: '_str | None' = None) -> 'Tensor': r""" Return whether every element of input tensor is finite number or not. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``. For example, ``isfinite(input=tensor_x)`` is equivalent to ``isfinite(x=tensor_x)``. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: `Tensor`, the bool result which shows every element of `x` whether it is finite number or not. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([float('-inf'), -2, 3.6, float('inf'), 0, float('-nan'), float('nan')]) >>> out = paddle.isfinite(x) >>> out Tensor(shape=[7], dtype=bool, place=Place(cpu), stop_gradient=True, [False, True , True , False, True , False, False]) """ ... def isin(self, test_x: 'Tensor', assume_unique: '_bool' = False, invert: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Tests if each element of `x` is in `test_x`. .. note:: Alias Support: The parameter name ``elements`` can be used as an alias for ``x``, and the parameter name ``test_elements`` can be used as an alias for ``test_x``. For example, ``isin(elements=tensor1, test_elements=tensor2)`` is equivalent to ``isin(x=tensor1, test_x=tensor2)``. Args: test_x (Tensor): Tensor values against which to test for each input element. Supported data type: 'bfloat16', 'float16', 'float32', 'float64', 'int32', 'int64'. alias: ``test_elements``. assume_unique (bool, optional): If True, indicates both `x` and `test_x` contain unique elements, which could make the calculation faster. Default: False. invert (bool, optional): Indicate whether to invert the boolean return tensor. If True, invert the results. Default: False. name (str|None, optional): Name for the operation (optional, default is None).For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor), The output Tensor with the same shape as `x`. Examples: .. code-block:: python >>> import paddle >>> paddle.set_device('cpu') >>> x = paddle.to_tensor([-0., -2.1, 2.5, 1.0, -2.1], dtype='float32') >>> test_x = paddle.to_tensor([-2.1, 2.5], dtype='float32') >>> res = paddle.isin(x, test_x) >>> print(res) Tensor(shape=[5], dtype=bool, place=Place(cpu), stop_gradient=True, [False, True, True, False, True]) >>> x = paddle.to_tensor([-0., -2.1, 2.5, 1.0, -2.1], dtype='float32') >>> test_x = paddle.to_tensor([-2.1, 2.5], dtype='float32') >>> res = paddle.isin(x, test_x, invert=True) >>> print(res) Tensor(shape=[5], dtype=bool, place=Place(cpu), stop_gradient=True, [True, False, False, True, False]) >>> # Set `assume_unique` to True only when `x` and `test_x` contain unique values, otherwise the result may be incorrect. >>> x = paddle.to_tensor([0., 1., 2.]*20).reshape([20, 3]) >>> test_x = paddle.to_tensor([0., 1.]*20) >>> correct_result = paddle.isin(x, test_x, assume_unique=False) >>> print(correct_result) Tensor(shape=[20, 3], dtype=bool, place=Place(cpu), stop_gradient=True, [[True , True , False], [True , True , False], [True , True , False], [True , True , False], [True , True , False], [True , True , False], [True , True , False], [True , True , False], [True , True , False], [True , True , False], [True , True , False], [True , True , False], [True , True , False], [True , True , False], [True , True , False], [True , True , False], [True , True , False], [True , True , False], [True , True , False], [True , True , False]]) >>> incorrect_result = paddle.isin(x, test_x, assume_unique=True) >>> print(incorrect_result) Tensor(shape=[20, 3], dtype=bool, place=Place(gpu:0), stop_gradient=True, [[True , True , True ], [True , True , True ], [True , True , True ], [True , True , True ], [True , True , True ], [True , True , True ], [True , True , True ], [True , True , True ], [True , True , True ], [True , True , True ], [True , True , True ], [True , True , True ], [True , True , True ], [True , True , True ], [True , True , True ], [True , True , True ], [True , True , True ], [True , True , True ], [True , True , True ], [True , True , False]]) """ ... def isinf(self, name: '_str | None' = None) -> 'Tensor': r""" Return whether every element of input tensor is `+/-INF` or not. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``. For example, ``isinf(input=tensor_x)`` is equivalent to ``isinf(x=tensor_x)``. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: `Tensor`, the bool result which shows every element of `x` whether it is `+/-INF` or not. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([float('-inf'), -2, 3.6, float('inf'), 0, float('-nan'), float('nan')]) >>> out = paddle.isinf(x) >>> out Tensor(shape=[7], dtype=bool, place=Place(cpu), stop_gradient=True, [True , False, False, True , False, False, False]) """ ... def isnan(self, name: '_str | None' = None) -> 'Tensor': r""" Return whether every element of input tensor is `NaN` or not. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``. For example, ``isnan(input=tensor_x)`` is equivalent to ``isnan(x=tensor_x)``. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: `Tensor`, the bool result which shows every element of `x` whether it is `NaN` or not. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([float('-inf'), -2, 3.6, float('inf'), 0, float('-nan'), float('nan')]) >>> out = paddle.isnan(x) >>> out Tensor(shape=[7], dtype=bool, place=Place(cpu), stop_gradient=True, [False, False, False, False, False, True , True ]) """ ... def isneginf(self, name: '_str | None' = None) -> 'Tensor': r""" Tests if each element of input is negative infinity or not. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor), The output Tensor. Each element of output indicates whether the input element is negative infinity or not. Examples: .. code-block:: python >>> import paddle >>> paddle.set_device('cpu') >>> x = paddle.to_tensor([-0., float('inf'), -2.1, -float('inf'), 2.5], dtype='float32') >>> res = paddle.isneginf(x) >>> print(res) Tensor(shape=[5], dtype=bool, place=Place(cpu), stop_gradient=True, [False, False, False, True, False]) """ ... def isposinf(self, name: '_str | None' = None) -> 'Tensor': r""" Tests if each element of input is positive infinity or not. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor), The output Tensor. Each element of output indicates whether the input element is positive infinity or not. Examples: .. code-block:: python >>> import paddle >>> paddle.set_device('cpu') >>> x = paddle.to_tensor([-0., float('inf'), -2.1, -float('inf'), 2.5], dtype='float32') >>> res = paddle.isposinf(x) >>> print(res) Tensor(shape=[5], dtype=bool, place=Place(cpu), stop_gradient=True, [False, True, False, False, False]) """ ... def isreal(self, name: '_str | None' = None) -> 'Tensor': r""" Tests if each element of input is a real number or not. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor), The output Tensor. Each element of output indicates whether the input element is a real number or not. Examples: .. code-block:: python >>> import paddle >>> paddle.set_device('cpu') >>> x = paddle.to_tensor([-0., -2.1, 2.5], dtype='float32') >>> res = paddle.isreal(x) >>> print(res) Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True, [True, True, True]) >>> x = paddle.to_tensor([(-0.+1j), (-2.1+0.2j), (2.5-3.1j)]) >>> res = paddle.isreal(x) >>> print(res) Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True, [False, False, False]) >>> x = paddle.to_tensor([(-0.+1j), (-2.1+0j), (2.5-0j)]) >>> res = paddle.isreal(x) >>> print(res) Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True, [False, True, True]) """ ... def istft(self, n_fft: '_int', hop_length: '_int | None' = None, win_length: '_int | None' = None, window: 'Tensor | None' = None, center: '_bool' = True, normalized: '_bool' = False, onesided: '_bool' = True, length: '_int | None' = None, return_complex: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Inverse short-time Fourier transform (ISTFT). Reconstruct time-domain signal from the giving complex input and window tensor when nonzero overlap-add (NOLA) condition is met: .. math:: \sum_{t = -\infty}^{\infty} \text{window}^2[n - t \times H]\ \neq \ 0, \ \text{for } all \ n Where: - :math:`t`: The :math:`t`-th input window. - :math:`N`: Value of `n_fft`. - :math:`H`: Value of `hop_length`. Result of `istft` expected to be the inverse of `paddle.signal.stft`, but it is not guaranteed to reconstruct a exactly realizable time-domain signal from a STFT complex tensor which has been modified (via masking or otherwise). Therefore, `istft` gives the `[Griffin-Lim optimal estimate] `_ (optimal in a least-squares sense) for the corresponding signal. Args: n_fft (int): The size of Fourier transform. hop_length (int|None, optional): Number of steps to advance between adjacent windows from time-domain signal and `0 < hop_length < win_length`. Default: `None` ( treated as equal to `n_fft//4`) win_length (int|None, optional): The size of window. Default: `None` (treated as equal to `n_fft`) window (Tensor|None, optional): A 1-dimensional tensor of size `win_length`. It will be center padded to length `n_fft` if `win_length < n_fft`. It should be a real-valued tensor if `return_complex` is False. Default: `None`(treated as a rectangle window with value equal to 1 of size `win_length`). center (bool, optional): It means that whether the time-domain signal has been center padded. Default: `True`. normalized (bool, optional): Control whether to scale the output by :math:`1/sqrt(n_{fft})`. Default: `False` onesided (bool, optional): It means that whether the input STFT tensor is a half of the conjugate symmetry STFT tensor transformed from a real-valued signal and `istft` will return a real-valued tensor when it is set to `True`. Default: `True`. length (int|None, optional): Specify the length of time-domain signal. Default: `None`( treated as the whole length of signal). return_complex (bool, optional): It means that whether the time-domain signal is real-valued. If `return_complex` is set to `True`, `onesided` should be set to `False` cause the output is complex. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: A tensor of least squares estimation of the reconstructed signal(s) with shape `[..., seq_length]` Examples: .. code-block:: pycon >>> import numpy as np >>> import paddle >>> from paddle.signal import stft, istft >>> paddle.seed(0) >>> # STFT >>> x = paddle.randn([8, 48000], dtype=paddle.float64) >>> y = stft(x, n_fft=512) >>> print(y.shape) paddle.Size([8, 257, 376]) >>> # ISTFT >>> x_ = istft(y, n_fft=512) >>> print(x_.shape) paddle.Size([8, 48000]) >>> np.allclose(x, x_) True """ ... def item(self, *args: '_int') -> '_float | _bool | _complex': r""" Convert element at specific position in Tensor into Python scalars. If the position is not specified, the Tensor must be a single-element Tensor. Args: Returns(Python scalar): A Python scalar, whose dtype is corresponds to the dtype of Tensor. Raises: ValueError: If the Tensor has more than one element, there must be coordinates. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor(1) >>> print(x.item()) 1 >>> print(type(x.item())) >>> x = paddle.to_tensor(1.0) >>> print(x.item()) 1.0 >>> print(type(x.item())) >>> x = paddle.to_tensor(True) >>> print(x.item()) True >>> print(type(x.item())) >>> x = paddle.to_tensor(1+1j) >>> print(x.item()) (1+1j) >>> print(type(x.item())) >>> x = paddle.to_tensor([[1.1, 2.2, 3.3]]) >>> print(x.item(2)) 3.299999952316284 >>> print(x.item(0, 2)) 3.299999952316284 """ ... def kron(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Compute the Kronecker product of two tensors, a composite tensor made of blocks of the second tensor scaled by the first. Assume that the rank of the two tensors, $X$ and $Y$ are the same, if necessary prepending the smallest with ones. If the shape of $X$ is [$r_0$, $r_1$, ..., $r_N$] and the shape of $Y$ is [$s_0$, $s_1$, ..., $s_N$], then the shape of the output tensor is [$r_{0}s_{0}$, $r_{1}s_{1}$, ..., $r_{N}s_{N}$]. The elements are products of elements from $X$ and $Y$. The equation is: $$ output[k_{0}, k_{1}, ..., k_{N}] = X[i_{0}, i_{1}, ..., i_{N}] * Y[j_{0}, j_{1}, ..., j_{N}] $$ where $$ k_{t} = i_{t} * s_{t} + j_{t}, t = 0, 1, ..., N $$ Args: y (Tensor): the second operand of kron op, data type: bfloat16, float16, float32, float64, int32 or int64. Its data type should be the same with x. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The output of kron, data type: bfloat16, float16, float32, float64, int32 or int64. Its data is the same with x. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 2], [3, 4]], dtype='int64') >>> y = paddle.to_tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype='int64') >>> out = paddle.kron(x, y) >>> out Tensor(shape=[6, 6], dtype=int64, place=Place(cpu), stop_gradient=True, [[1 , 2 , 3 , 2 , 4 , 6 ], [4 , 5 , 6 , 8 , 10, 12], [7 , 8 , 9 , 14, 16, 18], [3 , 6 , 9 , 4 , 8 , 12], [12, 15, 18, 16, 20, 24], [21, 24, 27, 28, 32, 36]]) """ ... def kthvalue(self, k: '_int', axis: '_int | None' = None, keepdim: '_bool' = False, name: '_str | None' = None) -> 'tuple[Tensor, Tensor]': r""" Find values and indices of the k-th smallest at the axis. Args: k (int): The k for the k-th smallest number to look for along the axis. axis (int, optional): Axis to compute indices along. The effective range is [-R, R), where R is x.ndim. when axis < 0, it works the same way as axis + R. The default is None. And if the axis is None, it will computed as -1 by default. keepdim (bool, optional): Whether to keep the given axis in output. If it is True, the dimensions will be same as input x and with size one in the axis. Otherwise the output dimensions is one fewer than x since the axis is squeezed. Default is False. name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: tuple(Tensor), return the values and indices. The value data type is the same as the input `x`. The indices data type is int64. Examples: .. code-block:: python >>> import paddle >>> x = paddle.randn((2,3,2)) >>> print(x) >>> # doctest: +SKIP('Different environments yield different output.') Tensor(shape=[2, 3, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[[ 0.11855337, -0.30557564], [-0.09968963, 0.41220093], [ 1.24004936, 1.50014710]], [[ 0.08612321, -0.92485696], [-0.09276631, 1.15149164], [-1.46587241, 1.22873247]]]) >>> # doctest: -SKIP >>> y = paddle.kthvalue(x, 2, 1) >>> print(y) >>> # doctest: +SKIP('Different environments yield different output.') (Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0.11855337, 0.41220093], [-0.09276631, 1.15149164]]), Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 1], [1, 1]])) >>> # doctest: -SKIP """ ... def lcm(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Computes the element-wise least common multiple (LCM) of input |x| and |y|. Both x and y must have integer types. Note: lcm(0,0)=0, lcm(0, y)=0 If x.shape != y.shape, they must be broadcastable to a common shape (which becomes the shape of the output). Args: y (Tensor): An N-D Tensor, the data type is int32, int64. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor): An N-D Tensor, the data type is the same with input. Examples: .. code-block:: python >>> import paddle >>> x1 = paddle.to_tensor(12) >>> x2 = paddle.to_tensor(20) >>> paddle.lcm(x1, x2) Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True, 60) >>> x3 = paddle.arange(6) >>> paddle.lcm(x3, x2) Tensor(shape=[6], dtype=int64, place=Place(cpu), stop_gradient=True, [0, 20, 20, 60, 20, 20]) >>> x4 = paddle.to_tensor(0) >>> paddle.lcm(x4, x2) Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True, 0) >>> paddle.lcm(x4, x4) Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True, 0) >>> x5 = paddle.to_tensor(-20) >>> paddle.lcm(x1, x5) Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True, 60) """ ... def lcm_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``lcm`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_lcm`. """ ... def ldexp(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Compute the result of multiplying x by 2 to the power of y. The equation is: .. math:: out = x * 2^{y} Args: y (Tensor): A Tensor of exponents, typically integers. name (str|None, optional): Name for the operation (optional, default is None).For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor): An N-D Tensor. If x, y have different shapes and are "broadcastable", the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape, its shape is the same as x and y. And the data type is float32 or float64. Examples: .. code-block:: python >>> import paddle >>> # example1 >>> x = paddle.to_tensor([1, 2, 3], dtype='float32') >>> y = paddle.to_tensor([2, 3, 4], dtype='int32') >>> res = paddle.ldexp(x, y) >>> print(res) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [4. , 16., 48.]) >>> # example2 >>> x = paddle.to_tensor([1, 2, 3], dtype='float32') >>> y = paddle.to_tensor([2], dtype='int32') >>> res = paddle.ldexp(x, y) >>> print(res) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [4. , 8. , 12.]) """ ... def ldexp_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``polygamma`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_polygamma`. """ ... def le(self, y: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Returns the truth value of :math:`x <= y` elementwise, which is equivalent function to the overloaded operator `<=`. Note: The output has no gradient. Args: y (Tensor): Second input to compare which is N-D tensor. The input data type should be bool, bfloat16, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128. Alias: ``other``. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output tensor. If set, the result will be stored in this tensor. Default is None. Returns: Tensor: The output shape is same as input :attr:`x`. The output data type is bool. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3]) >>> y = paddle.to_tensor([1, 3, 2]) >>> result1 = paddle.less_equal(x, y) >>> print(result1) Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True, [True , True , False]) """ ... def lerp(self, y: 'Tensor', weight: '_float | Tensor', name: '_str | None' = None) -> 'Tensor': r""" Does a linear interpolation between x and y based on weight. Equation: .. math:: lerp(x, y, weight) = x + weight * (y - x). Args: y (Tensor): An N-D Tensor with ending points, the data type is bfloat16, float16, float32, float64. weight (float|Tensor): The weight for the interpolation formula. When weight is Tensor, the data type is bfloat16, float16, float32, float64. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor): An N-D Tensor, the shape and data type is the same with input. Example: .. code-block:: python >>> import paddle >>> x = paddle.arange(1., 5., dtype='float32') >>> y = paddle.empty([4], dtype='float32') >>> y.fill_(10.) >>> out = paddle.lerp(x, y, 0.5) >>> out Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [5.50000000, 6. , 6.50000000, 7. ]) """ ... def lerp_(self, y: 'Tensor', weight: '_float | Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``lerp`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_lerp`. """ ... def less(self, y: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Returns the truth value of :math:`x < y` elementwise, which is equivalent function to the overloaded operator `<`. Note: The output has no gradient. Args: y (Tensor): Second input to compare which is N-D tensor. The input data type should be bool, bfloat16, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128. Alias: ``other`` name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output tensor. If set, the result will be stored in this tensor. Default is None. Returns: Tensor: The output shape is same as input :attr:`x`. The output data type is bool. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3]) >>> y = paddle.to_tensor([1, 3, 2]) >>> result1 = paddle.less_than(x, y) >>> print(result1) Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True, [False, True , False]) """ ... def less_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``less_`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_less`. """ ... def less_equal_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``less_equal`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_less_equal`. """ ... def less_than_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``less_than`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_less_than`. """ ... def lgamma(self, name: '_str | None' = None) -> 'Tensor': r""" Calculates the lgamma of the given input tensor, element-wise. This operator performs elementwise lgamma for input $X$. :math:`out = log\Gamma(x)` Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, the lgamma of the input Tensor, the shape and data type is the same with input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.lgamma(x) >>> out Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [1.31452453, 1.76149762, 2.25271273, 1.09579790]) """ ... def lgamma_(self, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``lgamma`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_lgamma`. """ ... def log(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Calculates the natural log of the given input Tensor, element-wise. .. math:: Out = \ln(x) Args: name (str|None): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` out (Tensor, optional): The output Tensor. If set, the result will be stored in this tensor. Default is None. Returns: Tensor: The natural log of the input Tensor computed element-wise. Examples: .. code-block:: python >>> import paddle >>> x = [[2, 3, 4], [7, 8, 9]] >>> x = paddle.to_tensor(x, dtype='float32') >>> print(paddle.log(x)) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.69314718, 1.09861231, 1.38629436], [1.94591010, 2.07944155, 2.19722462]]) """ ... def log10(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Calculates the log to the base 10 of the given input tensor, element-wise. .. math:: Out = \log_{10}x Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output Tensor. If set, the result will be stored in this Tensor. Default: None. Returns: Tensor: The log to the base 10 of the input Tensor computed element-wise. Examples: .. code-block:: python >>> import paddle >>> # example 1: x is a float >>> x_i = paddle.to_tensor([[1.0], [10.0]]) >>> res = paddle.log10(x_i) >>> res Tensor(shape=[2, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.], [1.]]) >>> # example 2: x is float32 >>> x_i = paddle.full(shape=[1], fill_value=10, dtype='float32') >>> paddle.to_tensor(x_i) >>> res = paddle.log10(x_i) >>> res Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [1.]) >>> # example 3: x is float64 >>> x_i = paddle.full(shape=[1], fill_value=10, dtype='float64') >>> paddle.to_tensor(x_i) >>> res = paddle.log10(x_i) >>> res Tensor(shape=[1], dtype=float64, place=Place(cpu), stop_gradient=True, [1.]) """ ... def log10_(self, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``log10`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_log10`. """ ... def log1p(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Calculates the natural log of the given input tensor plus 1, element-wise. .. math:: Out = \ln(x+1) Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output Tensor. If set, the result will be stored in this Tensor. Default: None. Returns: Tensor: The natural log of the input Tensor plus 1 computed element-wise. Examples: .. code-block:: python >>> import paddle >>> data = paddle.to_tensor([[0], [1]], dtype='float32') >>> res = paddle.log1p(data) >>> res Tensor(shape=[2, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[0. ], [0.69314718]]) """ ... def log1p_(self, name: '_str | None' = None) -> 'None': r""" Inplace version of ``log1p`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_log1p`. """ ... def log2(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Calculates the log to the base 2 of the given input tensor, element-wise. .. math:: Out = \log_2x Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output Tensor. If set, the result will be stored in this Tensor. Default: None. Returns: Tensor: The log to the base 2 of the input Tensor computed element-wise. Examples: .. code-block:: python >>> import paddle >>> # example 1: x is a float >>> x_i = paddle.to_tensor([[1.0], [2.0]]) >>> res = paddle.log2(x_i) >>> res Tensor(shape=[2, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.], [1.]]) >>> # example 2: x is float32 >>> x_i = paddle.full(shape=[1], fill_value=2, dtype='float32') >>> paddle.to_tensor(x_i) >>> res = paddle.log2(x_i) >>> res Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [1.]) >>> # example 3: x is float64 >>> x_i = paddle.full(shape=[1], fill_value=2, dtype='float64') >>> paddle.to_tensor(x_i) >>> res = paddle.log2(x_i) >>> res Tensor(shape=[1], dtype=float64, place=Place(cpu), stop_gradient=True, [1.]) """ ... def log2_(self, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``log2`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_log2`. """ ... def log_(self, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``log`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_log`. """ ... def log_normal_(self, mean: '_float' = 1.0, std: '_float' = 2.0, name: '_str | None' = None) -> 'Tensor': r""" This inplace version of api ``log_normal``, which returns a Tensor filled with random values sampled from a log normal distribution. The output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_log_normal`. Args: mean (float|int, optional): Mean of the output tensor, default is 1.0. std (float|int, optional): Standard deviation of the output tensor, default is 2.0. name(str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, A Tensor filled with random values sampled from a log normal distribution with the underlying normal distribution's ``mean`` and ``std`` . Examples: .. code-block:: python >>> import paddle >>> paddle.seed(200) >>> x = paddle.randn([3, 4]) >>> x.log_normal_() >>> print(x) Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[3.99360156 , 0.11746082 , 12.14813519, 4.74383831 ], [0.36592522 , 0.09426476 , 31.81549835, 0.61839998 ], [1.33314908 , 12.31954002, 36.44527435, 1.69572163 ]]) """ ... def logaddexp(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Elementwise LogAddExp Operator. Add of exponentiations of the inputs The equation is: .. math:: Out=log(X.exp()+Y.exp()) $X$ the tensor of any dimension. $Y$ the tensor whose dimensions must be less than or equal to the dimensions of $X$. There are two cases for this operator: 1. The shape of $Y$ is the same with $X$. 2. The shape of $Y$ is a continuous subsequence of $X$. For case 2: 1. Broadcast $Y$ to match the shape of $X$, where axis is the start dimension index for broadcasting $Y$ onto $X$. 2. If $axis$ is -1 (default), $axis$=rank($X$)-rank($Y$). 3. The trailing dimensions of size 1 for $Y$ will be ignored for the consideration of subsequence, such as shape($Y$) = (2, 1) => (2). For example: .. code-block:: text shape(X) = (2, 3, 4, 5), shape(Y) = (,) shape(X) = (2, 3, 4, 5), shape(Y) = (5,) shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5), with axis=-1(default) or axis=2 shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0 shape(X) = (2, 3, 4, 5), shape(Y) = (2, 1), with axis=0 Args: y (Tensor): Tensor of any dimensions. Its dtype should be int32, int64, bfloat16, float16, float32, float64. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: N-D Tensor. A location into which the result is stored. It's dimension equals with x. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-1, -2, -3], 'float64') >>> y = paddle.to_tensor([-1], 'float64') >>> z = paddle.logaddexp(x, y) >>> print(z) Tensor(shape=[3], dtype=float64, place=Place(cpu), stop_gradient=True, [-0.30685282, -0.68673831, -0.87307199]) """ ... def logcumsumexp(self, axis: '_int | None' = None, dtype: 'DTypeLike | None' = None, name: '_str | None' = None) -> 'Tensor': r""" The logarithm of the cumulative summation of the exponentiation of the elements along a given axis. For summation index j given by `axis` and other indices i, the result is .. math:: logcumsumexp(x)_{ij} = log \sum_{i=0}^{j}exp(x_{ij}) Note: The first element of the result is the same as the first element of the input. Args: axis (int, optional): The dimension to do the operation along. -1 means the last dimension. The default (None) is to compute the cumsum over the flattened array. dtype (str|core.VarDesc.VarType|core.DataType|np.dtype, optional): The data type of the output tensor, can be float16, float32, float64. If specified, the input tensor is casted to dtype before the operation is performed. This is useful for preventing data type overflows. The default value is None. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, the result of logcumsumexp operator (integer input types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> data = paddle.arange(12, dtype='float64') >>> data = paddle.reshape(data, (3, 4)) >>> y = paddle.logcumsumexp(data) >>> y Tensor(shape=[12], dtype=float64, place=Place(cpu), stop_gradient=True, [0. , 1.31326169 , 2.40760596 , 3.44018970 , 4.45191440 , 5.45619332 , 6.45776285 , 7.45833963 , 8.45855173 , 9.45862974 , 10.45865844, 11.45866900]) >>> y = paddle.logcumsumexp(data, axis=0) >>> y Tensor(shape=[3, 4], dtype=float64, place=Place(cpu), stop_gradient=True, [[0. , 1. , 2. , 3. ], [4.01814993 , 5.01814993 , 6.01814993 , 7.01814993 ], [8.01847930 , 9.01847930 , 10.01847930, 11.01847930]]) >>> y = paddle.logcumsumexp(data, axis=-1) >>> y Tensor(shape=[3, 4], dtype=float64, place=Place(cpu), stop_gradient=True, [[0. , 1.31326169 , 2.40760596 , 3.44018970 ], [4. , 5.31326169 , 6.40760596 , 7.44018970 ], [8. , 9.31326169 , 10.40760596, 11.44018970]]) >>> y = paddle.logcumsumexp(data, dtype='float64') >>> assert y.dtype == paddle.float64 """ ... def logical_and(self, y: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Compute element-wise logical AND on ``x`` and ``y``, and return ``out``. ``out`` is N-dim boolean ``Tensor``. Each element of ``out`` is calculated by .. math:: out = x \&\& y Note: ``paddle.logical_and`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor .. note:: Alias Support: 1. The parameter name ``input`` can be used as an alias for ``x``. 2. The parameter name ``other`` can be used as an alias for ``y``. Args: y (Tensor): the input tensor, it's data type should be one of bool, int8, int16, int32, int64, bfloat16, float16, float32, float64, complex64, complex128. Alias: ``other``. out(Tensor|None, optional): The ``Tensor`` that specifies the output of the operator, which can be any ``Tensor`` that has been created in the program. The default value is None, and a new ``Tensor`` will be created to save the output. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: N-D Tensor. A location into which the result is stored. It's dimension equals with ``x``. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([True]) >>> y = paddle.to_tensor([True, False, True, False]) >>> res = paddle.logical_and(x, y) >>> print(res) Tensor(shape=[4], dtype=bool, place=Place(cpu), stop_gradient=True, [True , False, True , False]) """ ... def logical_and_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``logical_and`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_logical_and`. """ ... def logical_not(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" ``logical_not`` operator computes element-wise logical NOT on ``x``, and returns ``out``. ``out`` is N-dim boolean ``Variable``. Each element of ``out`` is calculated by .. math:: out = !x Note: ``paddle.logical_not`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor .. note:: Alias Support: 1. The parameter name ``input`` can be used as an alias for ``x``. Args: out(Tensor|None): The ``Tensor`` that specifies the output of the operator, which can be any ``Tensor`` that has been created in the program. The default value is None, and a new ``Tensor` will be created to save the output. name(str|None, optional): The default value is None. Normally there is no need for users to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: N-D Tensor. A location into which the result is stored. It's dimension equals with ``x``. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([True, False, True, False]) >>> res = paddle.logical_not(x) >>> print(res) Tensor(shape=[4], dtype=bool, place=Place(cpu), stop_gradient=True, [False, True , False, True ]) """ ... def logical_not_(self, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``logical_not`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_logical_not`. """ ... def logical_or(self, y: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" ``logical_or`` operator computes element-wise logical OR on ``x`` and ``y``, and returns ``out``. ``out`` is N-dim boolean ``Tensor``. Each element of ``out`` is calculated by .. math:: out = x || y Note: ``paddle.logical_or`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor .. note:: Alias Support: 1. The parameter name ``input`` can be used as an alias for ``x``. 2. The parameter name ``other`` can be used as an alias for ``y``. Args: y (Tensor): the input tensor, it's data type should be one of bool, int8, int16, int32, int64, bfloat16, float16, float32, float64, complex64, complex128. Alias: ``other``. out(Tensor|None, optional): The ``Variable`` that specifies the output of the operator, which can be any ``Tensor`` that has been created in the program. The default value is None, and a new ``Tensor`` will be created to save the output. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: N-D Tensor. A location into which the result is stored. It's dimension equals with ``x``. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([True, False], dtype="bool").reshape([2, 1]) >>> y = paddle.to_tensor([True, False, True, False], dtype="bool").reshape([2, 2]) >>> res = paddle.logical_or(x, y) >>> print(res) Tensor(shape=[2, 2], dtype=bool, place=Place(cpu), stop_gradient=True, [[True , True ], [True , False]]) """ ... def logical_or_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``logical_or`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_logical_or`. """ ... def logical_xor(self, y: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" ``logical_xor`` operator computes element-wise logical XOR on ``x`` and ``y``, and returns ``out``. ``out`` is N-dim boolean ``Tensor``. Each element of ``out`` is calculated by .. math:: out = (x || y) \&\& !(x \&\& y) Note: ``paddle.logical_xor`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor .. note:: Alias Support: 1. The parameter name ``input`` can be used as an alias for ``x``. 2. The parameter name ``other`` can be used as an alias for ``y``. Args: y (Tensor): the input tensor, it's data type should be one of bool, int8, int16, int32, int64, bfloat16, float16, float32, float64, complex64, complex128. Alias: ``other``. out(Tensor|None, optional): The ``Tensor`` that specifies the output of the operator, which can be any ``Tensor`` that has been created in the program. The default value is None, and a new ``Tensor`` will be created to save the output. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: N-D Tensor. A location into which the result is stored. It's dimension equals with ``x``. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([True, False], dtype="bool").reshape([2, 1]) >>> y = paddle.to_tensor([True, False, True, False], dtype="bool").reshape([2, 2]) >>> res = paddle.logical_xor(x, y) >>> print(res) Tensor(shape=[2, 2], dtype=bool, place=Place(cpu), stop_gradient=True, [[False, True ], [True , False]]) """ ... def logical_xor_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``logical_xor`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_logical_xor`. """ ... def logit(self, eps: '_float | None' = None, name: '_str | None' = None) -> 'Tensor': r""" This function generates a new tensor with the logit of the elements of input x. x is clamped to [eps, 1-eps] when eps is not zero. When eps is zero and x < 0 or x > 1, the function will yields NaN. .. math:: logit(x) = ln(\frac{x}{1 - x}) where .. math:: x_i= \left\{\begin{array}{rcl} x_i & &\text{if } eps == Default \\ eps & &\text{if } x_i < eps \\ x_i & &\text{if } eps <= x_i <= 1-eps \\ 1-eps & &\text{if } x_i > 1-eps \end{array}\right. Args: eps (float|None, optional): the epsilon for input clamp bound. Default is None. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out(Tensor): A Tensor with the same data type and shape as ``x`` (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([0.2635, 0.0106, 0.2780, 0.2097, 0.8095]) >>> out1 = paddle.logit(x) >>> out1 Tensor(shape=[5], dtype=float32, place=Place(cpu), stop_gradient=True, [-1.02785587, -4.53624487, -0.95440406, -1.32673466, 1.44676447]) """ ... def logit_(self, eps: '_float | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``logit`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_logit`. """ ... def logsumexp(self, axis: '_int | Sequence[_int] | None' = None, keepdim: '_bool' = False, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Calculates the log of the sum of exponentials of ``x`` along ``axis`` . .. math:: logsumexp(x) = \log\sum exp(x) Args: axis (int|list|tuple|None, optional): The axis along which to perform logsumexp calculations. ``axis`` should be int, list(int) or tuple(int). If ``axis`` is a list/tuple of dimension(s), logsumexp is calculated along all element(s) of ``axis`` . ``axis`` or element(s) of ``axis`` should be in range [-D, D), where D is the dimensions of ``x`` . If ``axis`` or element(s) of ``axis`` is less than 0, it works the same way as :math:`axis + D` . If ``axis`` is None, logsumexp is calculated along all elements of ``x``. Default is None. keepdim (bool, optional): Whether to reserve the reduced dimension(s) in the output Tensor. If ``keep_dim`` is True, the dimensions of the output Tensor is the same as ``x`` except in the reduced dimensions(it is of size 1 in this case). Otherwise, the shape of the output Tensor is squeezed in ``axis`` . Default is False. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Keyword Args: out (Tensor|optional): The output tensor. Returns: Tensor, results of logsumexp along ``axis`` of ``x``, with the same data type as ``x`` (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[-1.5, 0., 2.], [3., 1.2, -2.4]]) >>> out1 = paddle.logsumexp(x) >>> out1 Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 3.46912265) >>> out2 = paddle.logsumexp(x, 1) >>> out2 Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [2.15317822, 3.15684605]) """ ... def long(self) -> 'Tensor': r""" Cast a Tensor to int64 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with int64 dtype """ ... def lstsq(self, y: 'Tensor', rcond: '_float | None' = None, driver: "Literal['gels', 'gelsy', 'gelsd', 'gelss'] | None" = None, name: '_str | None' = None) -> 'tuple[Tensor, Tensor, Tensor, Tensor]': r""" Computes a solution to the least squares problem of a system of linear equations. Args: y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y`` should be one of float32, float64. rcond(float, optional): The default value is None. A float pointing number used to determine the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the machine precision of x_dtype. driver(str, optional): The default value is None. The name of LAPACK method to be used. For CPU inputs the valid values are 'gels', 'gelsy', 'gelsd, 'gelss'. For CUDA input, the only valid driver is 'gels'. If ``driver`` is None, 'gelsy' is used for CPU inputs and 'gels' for CUDA inputs. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``). ``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals`` is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in ('gelsy', 'gelsd', 'gelss'), otherwise return an empty tensor. ``singular_values`` is a tensor with shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when ``driver`` in ('gelsd', 'gelss'), otherwise return an empty tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]]) >>> y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]]) >>> results = paddle.linalg.lstsq(x, y, driver="gelsd") >>> print(results[0]) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0.78350395, -0.22165027, -0.62371236], [-0.11340097, 0.78866047, 1.14948535]]) >>> print(results[1]) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [19.81443405, 10.43814468, 30.56185532]) >>> print(results[2]) Tensor(shape=[], dtype=int32, place=Place(cpu), stop_gradient=True, 2) >>> print(results[3]) Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [9.03455734, 1.54167950]) >>> x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]]) >>> y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]]) >>> results = paddle.linalg.lstsq(x, y, driver="gels") >>> print(results[0]) Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0.39386186, 0.10230169, 0.93606132], [ 0.10741688, -0.29028130, 0.11892584], [-0.05115093, 0.51918161, -0.19948851]]) >>> print(results[1]) Tensor(shape=[0], dtype=float32, place=Place(cpu), stop_gradient=True, []) """ ... def lu(self, pivot=True, get_infos=False, name=None) -> 'tuple[Tensor, Tensor] | tuple[Tensor, Tensor, Tensor]': r""" Computes the LU factorization of an N-D(N>=2) matrix x. Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and upper triangular matrix U are combined to a single LU matrix. Pivoting is done if pivot is set to True. P mat can be get by pivots: .. code-block:: text ones = eye(rows) #eye matrix of rank rows for i in range(cols): swap(ones[i], ones[pivots[i]]) return ones Args: X (Tensor): the tensor to factor of N-dimensions(N>=2). Its data type should be float32, float64, complex64, or complex128. pivot (bool, optional): controls whether pivoting is done. Default: True. get_infos (bool, optional): if set to True, returns an info IntTensor. Default: False. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: factorization (Tensor), LU matrix, the factorization of input X. pivots (IntTensor), the pivots of size(\*(N-2), min(m,n)). `pivots` stores all the intermediate transpositions of rows. The final permutation `perm` could be reconstructed by this, details refer to upper example. infos (IntTensor, optional), if `get_infos` is `True`, this is a tensor of size (\*(N-2)) where non-zero values indicate whether factorization for the matrix or each minibatch has succeeded or failed. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64') >>> lu,p,info = paddle.linalg.lu(x, get_infos=True) >>> print(lu) Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True, [[5. , 6. ], [0.20000000, 0.80000000], [0.60000000, 0.50000000]]) >>> print(p) Tensor(shape=[2], dtype=int32, place=Place(cpu), stop_gradient=True, [3, 3]) >>> print(info) Tensor(shape=[], dtype=int32, place=Place(cpu), stop_gradient=True, 0) >>> P,L,U = paddle.linalg.lu_unpack(lu,p) >>> print(P) Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True, [[0., 1., 0.], [0., 0., 1.], [1., 0., 0.]]) >>> print(L) Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True, [[1. , 0. ], [0.20000000, 1. ], [0.60000000, 0.50000000]]) >>> print(U) Tensor(shape=[2, 2], dtype=float64, place=Place(cpu), stop_gradient=True, [[5. , 6. ], [0. , 0.80000000]]) >>> # one can verify : X = P @ L @ U ; """ ... def lu_unpack(self, y: 'Tensor', unpack_ludata: '_bool' = True, unpack_pivots: '_bool' = True, name: '_str | None' = None) -> 'tuple[Tensor, Tensor, Tensor]': r""" Unpack L U and P to single matrix tensor . unpack L and U matrix from LU, unpack permutation matrix P from Pivots . P mat can be get by pivots: .. code-block:: text ones = eye(rows) #eye matrix of rank rows for i in range(cols): swap(ones[i], ones[pivots[i]]) Args: y (Tensor): Pivots get from paddle.linalg.lu. Its data type should be int32. unpack_ludata (bool, optional): whether to unpack L and U from x. Default: True. unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivots. Default: True. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: P (Tensor), Permutation matrix P of lu factorization. L (Tensor), The lower triangular matrix tensor of lu factorization. U (Tensor), The upper triangular matrix tensor of lu factorization. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64') >>> lu,p,info = paddle.linalg.lu(x, get_infos=True) >>> print(lu) Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True, [[5. , 6. ], [0.20000000, 0.80000000], [0.60000000, 0.50000000]]) >>> print(p) Tensor(shape=[2], dtype=int32, place=Place(cpu), stop_gradient=True, [3, 3]) >>> print(info) Tensor(shape=[], dtype=int32, place=Place(cpu), stop_gradient=True, 0) >>> P,L,U = paddle.linalg.lu_unpack(lu,p) >>> print(P) Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True, [[0., 1., 0.], [0., 0., 1.], [1., 0., 0.]]) >>> print(L) Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True, [[1. , 0. ], [0.20000000, 1. ], [0.60000000, 0.50000000]]) >>> print(U) Tensor(shape=[2, 2], dtype=float64, place=Place(cpu), stop_gradient=True, [[5. , 6. ], [0. , 0.80000000]]) >>> # one can verify : X = P @ L @ U ; """ ... def masked_fill(self, mask: 'Tensor', value: 'Numeric', name: '_str | None' = None) -> 'Tensor': r""" Fills elements of self tensor with value where mask is True. The shape of mask must be broadcastable with the shape of the underlying tensor. The following figure shows an example: consider a 3x3 matrix `x`,where all elements have a value of 1, and a matrix `mask` of the same size, and `value` is 3. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/masked_fill.png :width: 700 :align: center Args: mask (Tensor): The boolean tensor indicate the position to be filled. The data type of mask must be bool. value (Scalar or 0-D Tensor): The value used to fill the target tensor. Supported data types are float, double, int, int64_t,float16 and bfloat16. name(str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, same dimension and dtype with x. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> x = paddle.ones((3, 3), dtype="float32") >>> mask = paddle.to_tensor([[True, True, False]]) >>> print(mask) Tensor(shape=[1, 3], dtype=bool, place=Place(gpu:0), stop_gradient=True, [[True , True , False]]) >>> out = paddle.masked_fill(x, mask, 2) >>> print(out) Tensor(shape=[3, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True, [[2., 2., 1.], [2., 2., 1.], [2., 2., 1.]]) """ ... def masked_fill_(self, mask: 'Tensor', value: 'Numeric', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``masked_fill`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_masked_fill`. """ ... def masked_scatter(self, mask: 'Tensor', value: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Copies elements from `value` into `x` tensor at positions where the `mask` is True. Elements from source are copied into `x` starting at position 0 of `value` and continuing in order one-by-one for each occurrence of `mask` being True. The shape of `mask` must be broadcastable with the shape of the underlying tensor. The `value` should have at least as many elements as the number of ones in `mask`. The image illustrates a typical case of the masked_scatter operation. 1. Tensor ``value``: Contains the data to be filled into the target tensor. Only the parts where the mask is True will take values from the value tensor, while the rest will be ignored; 2. Tensor ``mask``: Specifies which positions should extract values from the value tensor and update the target tensor. True indicates the corresponding position needs to be updated; 3. Tensor ``origin``: The input tensor, where only the parts satisfying the mask will be replaced, and the rest remains unchanged; Result: After the ``masked_scatter`` operation, the parts of the ``origin`` tensor where the ``mask`` is ``True`` are updated with the corresponding values from the ``value`` tensor, while the parts where the ``mask`` is ``False`` remain unchanged, forming the final updated tensor. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/masked_scatter.png :width: 500 :alt: legend of masked_scatter API :align: center Args: mask (Tensor): The boolean tensor indicate the position to be filled. The data type of mask must be bool. value (Tensor): The value used to fill the target tensor. Supported data types are same as x. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, A reshaped Tensor with the same data type as ``x``. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2048) >>> x = paddle.randn([2, 2]) >>> print(x) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[-1.24725831, 0.03843464], [-0.31660911, 0.04793844]]) >>> mask = paddle.to_tensor([[True, True], [False, False]]) >>> value = paddle.to_tensor([1, 2, 3, 4, 5,], dtype="float32") >>> out = paddle.masked_scatter(x, mask, value) >>> print(out) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[1, 2], [-0.31660911, 0.04793844]]) """ ... def masked_scatter_(self, mask: 'Tensor', value: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``masked_scatter`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_masked_scatter`. """ ... def masked_select(self, mask: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Returns a new 1-D tensor which indexes the input tensor according to the ``mask`` which is a tensor with data type of bool. Note: ``paddle.masked_select`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: mask (Tensor): The Tensor containing the binary mask to index with, it's data type is bool. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. out (Tensor|None, optional): The output tensor. Default: None. Returns: Tensor, A 1-D Tensor which is the same data type as ``x``. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1.0, 2.0, 3.0, 4.0], ... [5.0, 6.0, 7.0, 8.0], ... [9.0, 10.0, 11.0, 12.0]]) >>> mask = paddle.to_tensor([[True, False, False, False], ... [True, True, False, False], ... [True, False, False, False]]) >>> out = paddle.masked_select(x, mask) >>> print(out.numpy()) [1. 5. 6. 9.] """ ... def matmul(self, y: 'Tensor', transpose_x: '_bool' = False, transpose_y: '_bool' = False, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Applies matrix multiplication to two tensors. `matmul` follows the complete broadcast rules, and its behavior is consistent with `np.matmul`. Currently, the input tensors' number of dimensions can be any, `matmul` can be used to achieve the `dot`, `matmul` and `batchmatmul`. The actual behavior depends on the shapes of :math:`x`, :math:`y` and the flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically: - If a transpose flag is specified, the last two dimensions of the tensor are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`. The multiplication behavior depends on the dimensions of `x` and `y`. Specifically: - If both tensors are 1-dimensional, the dot product result is obtained. - If both tensors are 2-dimensional, the matrix-matrix product is obtained. - If the `x` is 1-dimensional and the `y` is 2-dimensional, a `1` is prepended to its dimension in order to conduct the matrix multiply. After the matrix multiply, the prepended dimension is removed. - If the `x` is 2-dimensional and `y` is 1-dimensional, the matrix-vector product is obtained. - If both arguments are at least 1-dimensional and at least one argument is N-dimensional (where N > 2), then a batched matrix multiply is obtained. If the first argument is 1-dimensional, a 1 is prepended to its dimension in order to conduct the batched matrix multiply and removed after. If the second argument is 1-dimensional, a 1 is appended to its dimension for the purpose of the batched matrix multiple and removed after. The non-matrix (exclude the last two dimensions) dimensions are broadcasted according the broadcast rule. For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor, out will be a (j, k, n, p) tensor. Args: y (Tensor): The input tensor which is a Tensor. transpose_x (bool, optional): Whether to transpose :math:`x` before multiplication. Default is False. transpose_y (bool, optional): Whether to transpose :math:`y` before multiplication. Default is False. name (str|None, optional): If set None, the layer will be named automatically. For more information, please refer to :ref:`api_guide_Name`. Default is None. out (Tensor, optional): The output tensor. If set, the result will be stored in this tensor. Default is None. Returns: Tensor: The output Tensor. Examples: .. code-block:: pycon >>> import paddle >>> # vector * vector >>> x = paddle.rand([10]) >>> y = paddle.rand([10]) >>> z = paddle.matmul(x, y) >>> print(z.shape) paddle.Size([]) >>> # matrix * vector >>> x = paddle.rand([10, 5]) >>> y = paddle.rand([5]) >>> z = paddle.matmul(x, y) >>> print(z.shape) paddle.Size([10]) >>> # batched matrix * broadcasted vector >>> x = paddle.rand([10, 5, 2]) >>> y = paddle.rand([2]) >>> z = paddle.matmul(x, y) >>> print(z.shape) paddle.Size([10, 5]) >>> # batched matrix * batched matrix >>> x = paddle.rand([10, 5, 2]) >>> y = paddle.rand([10, 2, 5]) >>> z = paddle.matmul(x, y) >>> print(z.shape) paddle.Size([10, 5, 5]) >>> # batched matrix * broadcasted matrix >>> x = paddle.rand([10, 1, 5, 2]) >>> y = paddle.rand([1, 3, 2, 5]) >>> z = paddle.matmul(x, y) >>> print(z.shape) paddle.Size([10, 3, 5, 5]) """ ... def matrix_power(self, n: '_int', name: '_str | None' = None) -> 'tuple[Tensor, _int]': r""" Computes the n-th power of a square matrix or a batch of square matrices. Let :math:`X` be a square matrix or a batch of square matrices, :math:`n` be an exponent, the equation should be: .. math:: Out = X ^ {n} Specifically, - If `n > 0`, it returns the matrix or a batch of matrices raised to the power of `n`. - If `n = 0`, it returns the identity matrix or a batch of identity matrices. - If `n < 0`, it returns the inverse of each matrix (if invertible) raised to the power of `abs(n)`. Args: n (int): The exponent. It can be any positive, negative integer or zero. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: - Tensor, The n-th power of the matrix (or the batch of matrices) `x`. Its data type should be the same as that of `x`. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 2, 3], ... [1, 4, 9], ... [1, 8, 27]], dtype='float64') >>> print(paddle.linalg.matrix_power(x, 2)) Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True, [[6. , 34. , 102.], [14. , 90. , 282.], [36. , 250., 804.]]) >>> print(paddle.linalg.matrix_power(x, 0)) Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True, [[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]) >>> print(paddle.linalg.matrix_power(x, -2)) Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True, [[ 12.91666667, -12.75000000, 2.83333333 ], [-7.66666667 , 8. , -1.83333333 ], [ 1.80555556 , -1.91666667 , 0.44444444 ]]) """ ... def matrix_transpose(self, name: '_str | None' = None) -> 'paddle.Tensor': r""" Transpose the last two dimensions of the input tensor `x`. Note: If `n` is the number of dimensions of `x`, `paddle.matrix_transpose(x)` is equivalent to `x.transpose([0, 1, ..., n-2, n-1])`. Args: name (str|None, optional): The name of this layer. For more information, please refer to :ref:`api_guide_Name`. Default is None. Returns: Tensor: A new tensor with the same shape as `x`, except that the last two dimensions are transposed. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.ones(shape=[2, 3, 5]) >>> x_transposed = paddle.matrix_transpose(x) >>> print(x_transposed.shape) paddle.Size([2, 5, 3]) """ ... def max(self, axis: '_int | Sequence[_int] | None' = None, keepdim: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Computes the maximum of tensor elements over the given axis. Note: The difference between max and amax is: If there are multiple maximum elements, amax evenly distributes gradient between these equal values, while max propagates gradient to all of them. Args: axis (int|list|tuple|None, optional): The axis along which the maximum is computed. If :attr:`None`, compute the maximum over all elements of `x` and return a Tensor with a single element, otherwise must be in the range :math:`[-x.ndim(x), x.ndim(x))`. If :math:`axis[i] < 0`, the axis to reduce is :math:`x.ndim + axis[i]`. keepdim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result tensor will have one fewer dimension than the `x` unless :attr:`keepdim` is true, default value is False. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, results of maximum on the specified axis of input tensor, it's data type is the same as `x`. Examples: .. code-block:: python >>> import paddle >>> # data_x is a Tensor with shape [2, 4] >>> # the axis is a int element >>> x = paddle.to_tensor([[0.2, 0.3, 0.5, 0.9], ... [0.1, 0.2, 0.6, 0.7]], ... dtype='float64', stop_gradient=False) >>> result1 = paddle.max(x) >>> result1.backward() >>> result1 Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=False, 0.90000000) >>> x.grad Tensor(shape=[2, 4], dtype=float64, place=Place(cpu), stop_gradient=False, [[0., 0., 0., 1.], [0., 0., 0., 0.]]) >>> x.clear_grad() >>> result2 = paddle.max(x, axis=0) >>> result2.backward() >>> result2 Tensor(shape=[4], dtype=float64, place=Place(cpu), stop_gradient=False, [0.20000000, 0.30000000, 0.60000000, 0.90000000]) >>> x.grad Tensor(shape=[2, 4], dtype=float64, place=Place(cpu), stop_gradient=False, [[1., 1., 0., 1.], [0., 0., 1., 0.]]) >>> x.clear_grad() >>> result3 = paddle.max(x, axis=-1) >>> result3.backward() >>> result3 Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=False, [0.90000000, 0.70000000]) >>> x.grad Tensor(shape=[2, 4], dtype=float64, place=Place(cpu), stop_gradient=False, [[0., 0., 0., 1.], [0., 0., 0., 1.]]) >>> x.clear_grad() >>> result4 = paddle.max(x, axis=1, keepdim=True) >>> result4.backward() >>> result4 Tensor(shape=[2, 1], dtype=float64, place=Place(cpu), stop_gradient=False, [[0.90000000], [0.70000000]]) >>> x.grad Tensor(shape=[2, 4], dtype=float64, place=Place(cpu), stop_gradient=False, [[0., 0., 0., 1.], [0., 0., 0., 1.]]) >>> # data_y is a Tensor with shape [2, 2, 2] >>> # the axis is list >>> y = paddle.to_tensor([[[1.0, 2.0], [3.0, 4.0]], ... [[5.0, 6.0], [7.0, 8.0]]], ... dtype='float64', stop_gradient=False) >>> result5 = paddle.max(y, axis=[1, 2]) >>> result5.backward() >>> result5 Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=False, [4., 8.]) >>> y.grad Tensor(shape=[2, 2, 2], dtype=float64, place=Place(cpu), stop_gradient=False, [[[0., 0.], [0., 1.]], [[0., 0.], [0., 1.]]]) >>> y.clear_grad() >>> result6 = paddle.max(y, axis=[0, 1]) >>> result6.backward() >>> result6 Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=False, [7., 8.]) >>> y.grad Tensor(shape=[2, 2, 2], dtype=float64, place=Place(cpu), stop_gradient=False, [[[0., 0.], [0., 0.]], [[0., 0.], [1., 1.]]]) """ ... def maximum(self, y: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Compare two tensors and returns a new tensor containing the element-wise maxima. The equation is: .. math:: out = max(x, y) Note: ``paddle.maximum`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: y (Tensor): the input tensor, it's data type should be bfloat16, float16, float32, float64, int32, int64. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out(Tensor, optional): The output tensor. Returns: N-D Tensor. A location into which the result is stored. If x, y have different shapes and are "broadcastable", the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape, its shape is the same as x and y. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 2], [7, 8]]) >>> y = paddle.to_tensor([[3, 4], [5, 6]]) >>> res = paddle.maximum(x, y) >>> print(res) Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[3, 4], [7, 8]]) >>> x = paddle.to_tensor([[1, 2, 3], [1, 2, 3]]) >>> y = paddle.to_tensor([3, 0, 4]) >>> res = paddle.maximum(x, y) >>> print(res) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[3, 2, 4], [3, 2, 4]]) >>> x = paddle.to_tensor([2, 3, 5], dtype='float32') >>> y = paddle.to_tensor([1, float("nan"), float("nan")], dtype='float32') >>> res = paddle.maximum(x, y) >>> print(res) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [2. , nan, nan]) >>> x = paddle.to_tensor([5, 3, float("inf")], dtype='float32') >>> y = paddle.to_tensor([1, -float("inf"), 5], dtype='float32') >>> res = paddle.maximum(x, y) >>> print(res) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [5. , 3. , inf.]) """ ... def mean(self, axis: '_int | Sequence[_int] | None' = None, keepdim: '_bool' = False, name: '_str | None' = None, *, dtype: 'DTypeLike | None' = None, out: 'Tensor | None' = None) -> 'Tensor': r""" Computes the mean of the input tensor's elements along ``axis``. Args: axis (int|list|tuple|None, optional): The axis along which to perform mean calculations. ``axis`` should be int, list(int) or tuple(int). If ``axis`` is a list/tuple of dimension(s), mean is calculated along all element(s) of ``axis`` . ``axis`` or element(s) of ``axis`` should be in range [-D, D), where D is the dimensions of ``x`` . If ``axis`` or element(s) of ``axis`` is less than 0, it works the same way as :math:`axis + D` . If ``axis`` is None, mean is calculated over all elements of ``x``. Default is None. alias: ``dim`` keepdim (bool, optional): Whether to reserve the reduced dimension(s) in the output Tensor. If ``keepdim`` is True, the dimensions of the output Tensor is the same as ``x`` except in the reduced dimensions(it is of size 1 in this case). Otherwise, the shape of the output Tensor is squeezed in ``axis`` . Default is False. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. dtype (str): The desired data type of returned tensor. Default: None. out(Tensor|None, optional): The output tensor. Default: None. Returns: Tensor, results of average along ``axis`` of ``x``, with the same data type as ``x``. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[[1., 2., 3., 4.], ... [5., 6., 7., 8.], ... [9., 10., 11., 12.]], ... [[13., 14., 15., 16.], ... [17., 18., 19., 20.], ... [21., 22., 23., 24.]]]) >>> out1 = paddle.mean(x) >>> print(out1.numpy()) 12.5 >>> out2 = paddle.mean(x, axis=-1) >>> print(out2.numpy()) [[ 2.5 6.5 10.5] [14.5 18.5 22.5]] >>> out3 = paddle.mean(x, axis=-1, keepdim=True) >>> print(out3.numpy()) [[[ 2.5] [ 6.5] [10.5]] [[14.5] [18.5] [22.5]]] >>> out4 = paddle.mean(x, axis=[0, 2]) >>> print(out4.numpy()) [ 8.5 12.5 16.5] >>> out5 = paddle.mean(x, dtype='float64') >>> out5 Tensor(shape=[], dtype=float64, place=Place(gpu:0), stop_gradient=True, 12.50000000) """ ... def median(self, axis=None, keepdim=False, mode='avg', name=None, *, out=None): r""" Compute the median along the specified axis. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``dim`` can be used as an alias for ``axis``. When an alias replacement occurs, the default parameter for mode setting is min instead of avg. For example, ``median(input=tensor_x, dim=1, ...)`` is equivalent to ``median(x=tensor_x, axis=1, ...)``. Args: axis (int|None, optional): The axis along which to perform median calculations ``axis`` should be int. alias: ``dim``. ``axis`` should be in range [-D, D), where D is the dimensions of ``x`` . If ``axis`` is less than 0, it works the same way as :math:`axis + D`. If ``axis`` is None, median is calculated over all elements of ``x``. Default is None. keepdim (bool, optional): Whether to reserve the reduced dimension(s) in the output Tensor. If ``keepdim`` is True, the dimensions of the output Tensor is the same as ``x`` except in the reduced dimensions(it is of size 1 in this case). Otherwise, the shape of the output Tensor is squeezed in ``axis`` . Default is False. mode (str, optional): Whether to use mean or min operation to calculate the median values when the input tensor has an even number of elements in the dimension ``axis``. Support 'avg' and 'min'. Default is 'avg'. When an alias replacement occurs, the default parameter for mode setting is min instead of avg. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor or tuple of Tensor. If ``mode`` == 'avg', the result will be the tensor of median values; If ``mode`` == 'min' and ``axis`` is None, the result will be the tensor of median values; If ``mode`` == 'min' and ``axis`` is not None, the result will be a tuple of two tensors containing median values and their indices. When ``mode`` == 'avg', if data type of ``x`` is float64, data type of median values will be float64, otherwise data type of median values will be float32. When ``mode`` == 'min', the data type of median values will be the same as ``x``. The data type of indices will be int64. Examples: .. code-block:: python >>> import paddle >>> import numpy as np >>> x = paddle.arange(12).reshape([3, 4]) >>> print(x) Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0 , 1 , 2 , 3 ], [4 , 5 , 6 , 7 ], [8 , 9 , 10, 11]]) >>> y1 = paddle.median(x) >>> print(y1) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 5.50000000) >>> y2 = paddle.median(x, axis=0) >>> print(y2) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [4., 5., 6., 7.]) >>> y3 = paddle.median(x, axis=1) >>> print(y3) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [1.50000000, 5.50000000, 9.50000000]) >>> y4 = paddle.median(x, axis=0, keepdim=True) >>> print(y4) Tensor(shape=[1, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[4., 5., 6., 7.]]) >>> y5 = paddle.median(x, mode='min') >>> print(y5) Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True, 5) >>> median_value, median_indices = paddle.median(x, axis=1, mode='min') >>> print(median_value) Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, [1, 5, 9]) >>> print(median_indices) Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, [1, 1, 1]) >>> # cases containing nan values >>> x = paddle.to_tensor(np.array([[1,float('nan'),3,float('nan')],[1,2,3,4],[float('nan'),1,2,3]])) >>> y6 = paddle.median(x, axis=-1, keepdim=True) >>> print(y6) Tensor(shape=[3, 1], dtype=float64, place=Place(cpu), stop_gradient=True, [[nan ], [2.50000000], [nan ]]) >>> median_value, median_indices = paddle.median(x, axis=1, keepdim=True, mode='min') >>> print(median_value) Tensor(shape=[3, 1], dtype=float64, place=Place(cpu), stop_gradient=True, [[nan], [2. ], [nan]]) >>> print(median_indices) Tensor(shape=[3, 1], dtype=int64, place=Place(cpu), stop_gradient=True, [[1], [1], [0]]) """ ... def min(self, axis: '_int | Sequence[_int] | None' = None, keepdim: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Computes the minimum of tensor elements over the given axis Note: The difference between min and amin is: If there are multiple minimum elements, amin evenly distributes gradient between these equal values, while min propagates gradient to all of them. Args: axis (int|list|tuple|None, optional): The axis along which the minimum is computed. If :attr:`None`, compute the minimum over all elements of `x` and return a Tensor with a single element, otherwise must be in the range :math:`[-x.ndim, x.ndim)`. If :math:`axis[i] < 0`, the axis to reduce is :math:`x.ndim + axis[i]`. keepdim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result tensor will have one fewer dimension than the `x` unless :attr:`keepdim` is true, default value is False. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, results of minimum on the specified axis of input tensor, it's data type is the same as input's Tensor. Examples: .. code-block:: python >>> import paddle >>> # data_x is a Tensor with shape [2, 4] >>> # the axis is a int element >>> x = paddle.to_tensor([[0.2, 0.3, 0.5, 0.9], ... [0.1, 0.2, 0.6, 0.7]], ... dtype='float64', stop_gradient=False) >>> result1 = paddle.min(x) >>> result1.backward() >>> result1 Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=False, 0.10000000) >>> x.grad Tensor(shape=[2, 4], dtype=float64, place=Place(cpu), stop_gradient=False, [[0., 0., 0., 0.], [1., 0., 0., 0.]]) >>> x.clear_grad() >>> result2 = paddle.min(x, axis=0) >>> result2.backward() >>> result2 Tensor(shape=[4], dtype=float64, place=Place(cpu), stop_gradient=False, [0.10000000, 0.20000000, 0.50000000, 0.70000000]) >>> x.grad Tensor(shape=[2, 4], dtype=float64, place=Place(cpu), stop_gradient=False, [[0., 0., 1., 0.], [1., 1., 0., 1.]]) >>> x.clear_grad() >>> result3 = paddle.min(x, axis=-1) >>> result3.backward() >>> result3 Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=False, [0.20000000, 0.10000000]) >>> x.grad Tensor(shape=[2, 4], dtype=float64, place=Place(cpu), stop_gradient=False, [[1., 0., 0., 0.], [1., 0., 0., 0.]]) >>> x.clear_grad() >>> result4 = paddle.min(x, axis=1, keepdim=True) >>> result4.backward() >>> result4 Tensor(shape=[2, 1], dtype=float64, place=Place(cpu), stop_gradient=False, [[0.20000000], [0.10000000]]) >>> x.grad Tensor(shape=[2, 4], dtype=float64, place=Place(cpu), stop_gradient=False, [[1., 0., 0., 0.], [1., 0., 0., 0.]]) >>> # data_y is a Tensor with shape [2, 2, 2] >>> # the axis is list >>> y = paddle.to_tensor([[[1.0, 2.0], [3.0, 4.0]], ... [[5.0, 6.0], [7.0, 8.0]]], ... dtype='float64', stop_gradient=False) >>> result5 = paddle.min(y, axis=[1, 2]) >>> result5.backward() >>> result5 Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=False, [1., 5.]) >>> y.grad Tensor(shape=[2, 2, 2], dtype=float64, place=Place(cpu), stop_gradient=False, [[[1., 0.], [0., 0.]], [[1., 0.], [0., 0.]]]) >>> y.clear_grad() >>> result6 = paddle.min(y, axis=[0, 1]) >>> result6.backward() >>> result6 Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=False, [1., 2.]) >>> y.grad Tensor(shape=[2, 2, 2], dtype=float64, place=Place(cpu), stop_gradient=False, [[[1., 1.], [0., 0.]], [[0., 0.], [0., 0.]]]) """ ... def minimum(self, y: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Compare two tensors and return a new tensor containing the element-wise minima. The equation is: .. math:: out = min(x, y) Note: ``paddle.minimum`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: y (Tensor): the input tensor, it's data type should be bfloat16, float16, float32, float64, int32, int64. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out(Tensor, optional): The output tensor. Returns: Tensor. If x, y have different shapes and are "broadcastable", the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape, its shape is the same as x and y. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 2], [7, 8]]) >>> y = paddle.to_tensor([[3, 4], [5, 6]]) >>> res = paddle.minimum(x, y) >>> print(res) Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[1, 2], [5, 6]]) >>> x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]]) >>> y = paddle.to_tensor([3, 0, 4]) >>> res = paddle.minimum(x, y) >>> print(res) Tensor(shape=[1, 2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[[1, 0, 3], [1, 0, 3]]]) >>> x = paddle.to_tensor([2, 3, 5], dtype='float32') >>> y = paddle.to_tensor([1, float("nan"), float("nan")], dtype='float32') >>> res = paddle.minimum(x, y) >>> print(res) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [1. , nan, nan]) >>> x = paddle.to_tensor([5, 3, float("inf")], dtype='float64') >>> y = paddle.to_tensor([1, -float("inf"), 5], dtype='float64') >>> res = paddle.minimum(x, y) >>> print(res) Tensor(shape=[3], dtype=float64, place=Place(cpu), stop_gradient=True, [ 1. , -inf., 5. ]) """ ... def mm(self, mat2: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Applies matrix multiplication to two tensors. Currently, the input tensors' rank can be any, but when the rank of any inputs is bigger than 3, this two inputs' rank should be equal. Also note that if the raw tensor :math:`x` or :math:`mat2` is rank-1 and nontransposed, the prepended or appended dimension :math:`1` will be removed after matrix multiplication. Args: mat2 (Tensor): The input tensor which is a Tensor. Support data types: bfloat16, float16, float32, float64, int8, int32, int64, complex64, complex128. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The product Tensor, with same data type of the input Tensor. :: * example 1: input: [B, ..., M, K], mat2: [B, ..., K, N] out: [B, ..., M, N] * example 2: input: [B, M, K], mat2: [B, K, N] out: [B, M, N] * example 3: input: [B, M, K], mat2: [K, N] out: [B, M, N] * example 4: input: [M, K], mat2: [K, N] out: [M, N] * example 5: input: [B, M, K], mat2: [K] out: [B, M] * example 6: input: [K], mat2: [K] out: [1] Examples: .. code-block:: python >>> import paddle >>> input = paddle.arange(1, 7).reshape((3, 2)).astype('float32') >>> mat2 = paddle.arange(1, 9).reshape((2, 4)).astype('float32') >>> out = paddle.mm(input, mat2) >>> out Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[11., 14., 17., 20.], [23., 30., 37., 44.], [35., 46., 57., 68.]]) """ ... def mode(self, axis: '_int' = -1, keepdim: '_bool' = False, name: '_str | None' = None) -> 'tuple[Tensor, Tensor]': r""" Used to find values and indices of the modes at the optional axis. Args: axis (int, optional): Axis to compute indices along. The effective range is [-R, R), where R is x.ndim. when axis < 0, it works the same way as axis + R. Default is -1. keepdim (bool, optional): Whether to keep the given axis in output. If it is True, the dimensions will be same as input x and with size one in the axis. Otherwise the output dimensions is one fewer than x since the axis is squeezed. Default is False. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: tuple (Tensor), return the values and indices. The value data type is the same as the input `x`. The indices data type is int64. Examples: .. code-block:: python >>> import paddle >>> tensor = paddle.to_tensor([[[1,2,2],[2,3,3]],[[0,5,5],[9,9,0]]], dtype=paddle.float32) >>> res = paddle.mode(tensor, 2) >>> print(res) (Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[2., 3.], [5., 9.]]), Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[2, 2], [2, 1]])) """ ... def moveaxis(self, source: '_int | Sequence[_int]', destination: '_int | Sequence[_int]', name: '_str | None' = None) -> 'Tensor': r""" Move the axis of tensor from ``source`` position to ``destination`` position. Other axis that have not been moved remain their original order. Args: source(int|tuple|list): ``source`` position of axis that will be moved. Each element must be unique and integer. destination(int|tuple|list): ``destination`` position of axis that has been moved. Each element must be unique and integer. name(str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, A new tensor whose axis have been moved. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.ones([3, 2, 4]) >>> outshape = paddle.moveaxis(x, [0, 1], [1, 2]).shape >>> print(outshape) paddle.Size([4, 3, 2]) >>> x = paddle.ones([2, 3]) >>> outshape = paddle.moveaxis(x, 0, 1).shape # equivalent to paddle.t(x) >>> print(outshape) paddle.Size([3, 2]) """ ... def msort(self, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Sorts the input along the given axis = 0, and returns the sorted output tensor. The sort algorithm is ascending. Args: out(Tensor, optional): The output tensor. Returns: Tensor, sorted tensor(with the same shape and data type as ``input``). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[[5,8,9,5], ... [0,0,1,7], ... [6,9,2,4]], ... [[5,2,4,2], ... [4,7,7,9], ... [1,7,0,6]]], ... dtype='float32') >>> out1 = paddle.msort(input=x) >>> print(out1.numpy()) [[[5. 2. 4. 2.] [0. 0. 1. 7.] [1. 7. 0. 4.]] [[5. 8. 9. 5.] [4. 7. 7. 9.] [6. 9. 2. 6.]]] >>> out2 = paddle.empty_like(x) >>> paddle.msort(input=x, out=out2) >>> print(out2.numpy()) [[[5. 2. 4. 2.] [0. 0. 1. 7.] [1. 7. 0. 4.]] [[5. 8. 9. 5.] [4. 7. 7. 9.] [6. 9. 2. 6.]]] """ ... def mul(self, y: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" multiply two tensors element-wise. The equation is: .. math:: out = x * y Note: Supported shape of :attr:`x` and :attr:`y` for this operator: 1. `x.shape` == `y.shape`. 2. `x.shape` could be the continuous subsequence of `y.shape`. ``paddle.mul`` supports broadcasting. If you would like to know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: y (Tensor): the input tensor, its data type should be one of bfloat16, float16, float32, float64, int32, int64, bool, complex64, complex128. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output tensor. If set, the result will be stored in this tensor. Default is None. Returns: N-D Tensor. A location into which the result is stored. If :attr:`x`, :attr:`y` have different shapes and are "broadcastable", the resulting tensor shape is the shape of :attr:`x` and :attr:`y` after broadcasting. If :attr:`x`, :attr:`y` have the same shape, its shape is the same as :attr:`x` and :attr:`y`. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 2], [3, 4]]) >>> y = paddle.to_tensor([[5, 6], [7, 8]]) >>> res = paddle.mul(x, y) >>> print(res) Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[5 , 12], [21, 32]]) >>> x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]]) >>> y = paddle.to_tensor([2]) >>> res = paddle.mul(x, y) >>> print(res) Tensor(shape=[1, 2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[[2, 4, 6], [2, 4, 6]]]) """ ... def mul_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``multiply`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_multiply`. """ ... def multi_dot(self, name: '_str | None' = None) -> 'Tensor': r""" Multi_dot is an operator that calculates multiple matrix multiplications. Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not support batched inputs. The input tensor in [x] must be 2-D except for the first and last can be 1-D. If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it is treated as a column vector of shape(n, 1). If the first and last tensor are 2-D matrix, then the output is also 2-D matrix, otherwise the output is a 1-D vector. Multi_dot will select the lowest cost multiplication order for calculation. The cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c. Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively, we can calculate the cost of different multiplication orders as follows: - Cost((AB)C) = 20x5x100 + 20x100x10 = 30000 - Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000 In this case, multiplying B and C first, then multiply A, which is 5 times faster than sequential calculation. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The output Tensor. Examples: .. code-block:: pycon >>> import paddle >>> # A * B >>> A = paddle.rand([3, 4]) >>> B = paddle.rand([4, 5]) >>> out = paddle.linalg.multi_dot([A, B]) >>> print(out.shape) paddle.Size([3, 5]) >>> # A * B * C >>> A = paddle.rand([10, 5]) >>> B = paddle.rand([5, 8]) >>> C = paddle.rand([8, 7]) >>> out = paddle.linalg.multi_dot([A, B, C]) >>> print(out.shape) paddle.Size([10, 7]) """ ... def multigammaln(self, p: '_int', name: '_str | None' = None) -> 'Tensor': r""" This function computes the log of multivariate gamma, also sometimes called the generalized gamma. Args: p (int): The dimension of the space of integration. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor): The values of the log multivariate gamma at the given tensor x. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([2.5, 3.5, 4, 6.5, 7.8, 10.23, 34.25]) >>> p = 2 >>> out = paddle.multigammaln(x, p) >>> print(out) Tensor(shape=[7], dtype=float32, place=Place(cpu), stop_gradient=True, [0.85704780 , 2.46648574 , 3.56509781 , 11.02241898 , 15.84497833 , 26.09257698 , 170.68318176]) """ ... def multigammaln_(self, p: '_int', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``multigammaln_`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_multigammaln`. """ ... def multinomial(self, num_samples: '_int' = 1, replacement: '_bool' = False, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Returns a Tensor filled with random values sampled from a Multinomial distribution. The input ``x`` is a tensor with probabilities for generating the random number. Each element in ``x`` should be larger or equal to 0, but not all 0. ``replacement`` indicates whether it is a replaceable sample. If ``replacement`` is True, a category can be sampled more than once. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``. For example, ``multinomial(input=tensor_x, ...)`` is equivalent to ``multinomial(x=tensor_x, ...)``. Args: num_samples(int, optional): Number of samples, default is 1. replacement(bool, optional): Whether it is a replaceable sample, default is False. name(str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output Tensor. If set, the result will be stored in this Tensor. Default is None. Returns: Tensor, A Tensor filled with sampled category index after ``num_samples`` times samples. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(100) # on CPU device >>> x = paddle.rand([2,4]) >>> print(x) >>> # doctest: +SKIP("Random output") Tensor(shape=[2, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.55355281, 0.20714243, 0.01162981, 0.51577556], [0.36369765, 0.26091650, 0.18905126, 0.56219709]]) >>> # doctest: -SKIP >>> paddle.seed(200) # on CPU device >>> out1 = paddle.multinomial(x, num_samples=5, replacement=True) >>> print(out1) >>> # doctest: +SKIP("Random output") Tensor(shape=[2, 5], dtype=int64, place=Place(cpu), stop_gradient=True, [[3, 3, 0, 0, 0], [3, 3, 3, 1, 0]]) >>> # doctest: -SKIP >>> # out2 = paddle.multinomial(x, num_samples=5) >>> # InvalidArgumentError: When replacement is False, number of samples >>> # should be less than non-zero categories >>> paddle.seed(300) # on CPU device >>> out3 = paddle.multinomial(x, num_samples=3) >>> print(out3) >>> # doctest: +SKIP("Random output") Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[3, 0, 1], [3, 1, 0]]) >>> # doctest: -SKIP """ ... def multiplex(self, index: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Based on the given index parameter, the OP selects a specific row from each input Tensor to construct the output Tensor. If the input of this OP contains :math:`m` Tensors, where :math:`I_{i}` means the i-th input Tensor, :math:`i` between :math:`[0,m)` . And :math:`O` means the output, where :math:`O[i]` means the i-th row of the output, then the output satisfies that :math:`O[i] = I_{index[i]}[i]` . For Example: .. code-block:: text Given: inputs = [[[0,0,3,4], [0,1,3,4], [0,2,4,4], [0,3,3,4]], [[1,0,3,4], [1,1,7,8], [1,2,4,2], [1,3,3,4]], [[2,0,3,4], [2,1,7,8], [2,2,4,2], [2,3,3,4]], [[3,0,3,4], [3,1,7,8], [3,2,4,2], [3,3,3,4]]] index = [[3],[0],[1],[2]] out = [[3,0,3,4], # out[0] = inputs[index[0]][0] = inputs[3][0] = [3,0,3,4] [0,1,3,4], # out[1] = inputs[index[1]][1] = inputs[0][1] = [0,1,3,4] [1,2,4,2], # out[2] = inputs[index[2]][2] = inputs[1][2] = [1,2,4,2] [2,3,3,4]] # out[3] = inputs[index[3]][3] = inputs[2][3] = [2,3,3,4] Args: index (Tensor): Used to select some rows in the input Tensor to construct an index of the output Tensor. It is a 2-D Tensor with data type int32 or int64 and shape [M, 1], where M is the number of input Tensors. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Output of multiplex OP, with data type being float32, float64, int32, int64. Examples: .. code-block:: python >>> import paddle >>> img1 = paddle.to_tensor([[1, 2], [3, 4]], dtype=paddle.float32) >>> img2 = paddle.to_tensor([[5, 6], [7, 8]], dtype=paddle.float32) >>> inputs = [img1, img2] >>> index = paddle.to_tensor([[1], [0]], dtype=paddle.int32) >>> res = paddle.multiplex(inputs, index) >>> print(res) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[5., 6.], [3., 4.]]) """ ... def multiply(self, y: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" multiply two tensors element-wise. The equation is: .. math:: out = x * y Note: Supported shape of :attr:`x` and :attr:`y` for this operator: 1. `x.shape` == `y.shape`. 2. `x.shape` could be the continuous subsequence of `y.shape`. ``paddle.multiply`` supports broadcasting. If you would like to know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: y (Tensor): the input tensor, its data type should be one of bfloat16, float16, float32, float64, int32, int64, bool, complex64, complex128. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: N-D Tensor. A location into which the result is stored. If :attr:`x`, :attr:`y` have different shapes and are "broadcastable", the resulting tensor shape is the shape of :attr:`x` and :attr:`y` after broadcasting. If :attr:`x`, :attr:`y` have the same shape, its shape is the same as :attr:`x` and :attr:`y`. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 2], [3, 4]]) >>> y = paddle.to_tensor([[5, 6], [7, 8]]) >>> res = paddle.multiply(x, y) >>> print(res) Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[5 , 12], [21, 32]]) >>> x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]]) >>> y = paddle.to_tensor([2]) >>> res = paddle.multiply(x, y) >>> print(res) Tensor(shape=[1, 2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[[2, 4, 6], [2, 4, 6]]]) """ ... def mv(self, vec: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Performs a matrix-vector product of the matrix x and the vector vec. Args: vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x should be one of float32, float64. name (str|None, optional): Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default is None. Returns: Tensor: The tensor which is produced by x and vec. Examples: .. code-block:: python >>> # x: [M, N], vec: [N] >>> # paddle.mv(x, vec) # out: [M] >>> import paddle >>> x = paddle.to_tensor([[2, 1, 3], [3, 0, 1]]).astype("float64") >>> vec = paddle.to_tensor([3, 5, 1]).astype("float64") >>> out = paddle.mv(x, vec) >>> print(out) Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=True, [14., 10.]) """ ... def nan_to_num(self, nan: '_float' = 0.0, posinf: '_float | None' = None, neginf: '_float | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Replaces NaN, positive infinity, and negative infinity values in input tensor. Args: nan (float, optional): the value to replace NaNs with. Default is 0. posinf (float|None, optional): if a Number, the value to replace positive infinity values with. If None, positive infinity values are replaced with the greatest finite value representable by input’s dtype. Default is None. neginf (float|None, optional): if a Number, the value to replace negative infinity values with. If None, negative infinity values are replaced with the lowest finite value representable by input’s dtype. Default is None. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Results of nan_to_num operation input Tensor ``x``. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([float('nan'), 0.3, float('+inf'), float('-inf')], dtype='float32') >>> out1 = paddle.nan_to_num(x) >>> out1 Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [ 0. , 0.30000001 , 340282346638528859811704183484516925440., -340282346638528859811704183484516925440.]) >>> out2 = paddle.nan_to_num(x, nan=1) >>> out2 Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [ 1. , 0.30000001 , 340282346638528859811704183484516925440., -340282346638528859811704183484516925440.]) >>> out3 = paddle.nan_to_num(x, posinf=5) >>> out3 Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [ 0. , 0.30000001 , 5. , -340282346638528859811704183484516925440.]) >>> out4 = paddle.nan_to_num(x, nan=10, neginf=-99) >>> out4 Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [ 10. , 0.30000001 , 340282346638528859811704183484516925440., -99. ]) """ ... def nan_to_num_(self, nan: '_float' = 0.0, posinf: '_float | None' = None, neginf: '_float | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``nan_to_num`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_nan_to_num`. """ ... def nanmean(self, axis: '_int | Sequence[_int] | None' = None, keepdim: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Compute the arithmetic mean along the specified axis, ignoring NaNs. Args: axis (int|list|tuple, optional):The axis along which to perform nanmean calculations. ``axis`` should be int, list(int) or tuple(int). If ``axis`` is a list/tuple of dimension(s), nanmean is calculated along all element(s) of ``axis`` . ``axis`` or element(s) of ``axis`` should be in range [-D, D), where D is the dimensions of ``x`` . If ``axis`` or element(s) of ``axis`` is less than 0, it works the same way as :math:`axis + D` . If ``axis`` is None, nanmean is calculated over all elements of ``x``. Default is None. keepdim (bool, optional): Whether to reserve the reduced dimension(s) in the output Tensor. If ``keepdim`` is True, the dimensions of the output Tensor is the same as ``x`` except in the reduced dimensions(it is of size 1 in this case). Otherwise, the shape of the output Tensor is squeezed in ``axis`` . Default is False. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, results of arithmetic mean along ``axis`` of ``x``, with the same data type as ``x``. Examples: .. code-block:: python >>> import paddle >>> # x is a 2-D Tensor: >>> x = paddle.to_tensor([[float('nan'), 0.3, 0.5, 0.9], ... [0.1, 0.2, float('-nan'), 0.7]]) >>> out1 = paddle.nanmean(x) >>> out1 Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.44999996) >>> out2 = paddle.nanmean(x, axis=0) >>> out2 Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [0.10000000, 0.25000000, 0.50000000, 0.79999995]) >>> out3 = paddle.nanmean(x, axis=0, keepdim=True) >>> out3 Tensor(shape=[1, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.10000000, 0.25000000, 0.50000000, 0.79999995]]) >>> out4 = paddle.nanmean(x, axis=1) >>> out4 Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [0.56666666, 0.33333334]) >>> out5 = paddle.nanmean(x, axis=1, keepdim=True) >>> out5 Tensor(shape=[2, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.56666666], [0.33333334]]) >>> # y is a 3-D Tensor: >>> y = paddle.to_tensor([[[1, float('nan')], [3, 4]], ... [[5, 6], [float('-nan'), 8]]]) >>> out6 = paddle.nanmean(y, axis=[1, 2]) >>> out6 Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [2.66666675, 6.33333349]) >>> out7 = paddle.nanmean(y, axis=[0, 1]) >>> out7 Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [3., 6.]) """ ... def nanmedian(self, axis=None, keepdim=False, mode='avg', name=None): r""" Compute the median along the specified axis, while ignoring NaNs. If the valid count of elements is a even number, the average value of both elements in the middle is calculated as the median. Args: axis (None|int|list|tuple, optional): The axis along which to perform median calculations ``axis`` should be int or list of int. ``axis`` should be in range [-D, D), where D is the dimensions of ``x`` . If ``axis`` is less than 0, it works the same way as :math:`axis + D`. If ``axis`` is None, median is calculated over all elements of ``x``. Default is None. keepdim (bool, optional): Whether to reserve the reduced dimension(s) in the output Tensor. If ``keepdim`` is True, the dimensions of the output Tensor is the same as ``x`` except in the reduced dimensions(it is of size 1 in this case). Otherwise, the shape of the output Tensor is squeezed in ``axis`` . Default is False. mode (str, optional): Whether to use mean or min operation to calculate the nanmedian values when the input tensor has an even number of non-NaN elements along the dimension ``axis``. Support 'avg' and 'min'. Default is 'avg'. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor or tuple of Tensor. If ``mode`` == 'min' and ``axis`` is int, the result will be a tuple of two tensors (nanmedian value and nanmedian index). Otherwise, only nanmedian value will be returned. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[float('nan'), 2. , 3. ], [0. , 1. , 2. ]]) >>> y1 = x.nanmedian() >>> print(y1.numpy()) 2.0 >>> y2 = x.nanmedian(0) >>> print(y2.numpy()) [0. 1.5 2.5] >>> y3 = x.nanmedian(0, keepdim=True) >>> print(y3.numpy()) [[0. 1.5 2.5]] >>> y4 = x.nanmedian((0, 1)) >>> print(y4.numpy()) 2.0 >>> y5 = x.nanmedian(mode='min') >>> print(y5.numpy()) 2.0 >>> y6, y6_index = x.nanmedian(0, mode='min') >>> print(y6.numpy()) [0. 1. 2.] >>> print(y6_index.numpy()) [1 1 1] >>> y7, y7_index = x.nanmedian(1, mode='min') >>> print(y7.numpy()) [2. 1.] >>> print(y7_index.numpy()) [1 1] >>> y8 = x.nanmedian((0,1), mode='min') >>> print(y8.numpy()) 2.0 """ ... def nanquantile(self, q: '_float | Sequence[_float] | Tensor', axis: 'list[_int] | _int | None' = None, keepdim: '_bool' = False, interpolation: '_Interpolation' = 'linear') -> 'Tensor': r""" Compute the quantile of the input as if NaN values in input did not exist. If all values in a reduced row are NaN, then the quantiles for that reduction will be NaN. Args: q (int|float|list|Tensor): The q for calculate quantile, which should be in range [0, 1]. If q is a list or a 1-D Tensor, each element of q will be calculated and the first dimension of output is same to the number of ``q`` . If q is a 0-D Tensor, it will be treated as an integer or float. axis (int|list, optional): The axis along which to calculate quantile. ``axis`` should be int or list of int. ``axis`` should be in range [-D, D), where D is the dimensions of ``x`` . If ``axis`` is less than 0, it works the same way as :math:`axis + D`. If ``axis`` is a list, quantile is calculated over all elements of given axes. If ``axis`` is None, quantile is calculated over all elements of ``x``. Default is None. keepdim (bool, optional): Whether to reserve the reduced dimension(s) in the output Tensor. If ``keepdim`` is True, the dimensions of the output Tensor is the same as ``x`` except in the reduced dimensions(it is of size 1 in this case). Otherwise, the shape of the output Tensor is squeezed in ``axis`` . Default is False. interpolation (str, optional): The interpolation method to use when the desired quantile falls between two data points. Must be one of linear, higher, lower, midpoint and nearest. Default is linear. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, results of quantile along ``axis`` of ``x``. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor( ... [[0, 1, 2, 3, 4], ... [5, 6, 7, 8, 9]], ... dtype="float32") >>> x[0,0] = float("nan") >>> y1 = paddle.nanquantile(x, q=0.5, axis=[0, 1]) >>> print(y1) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 5.) >>> y2 = paddle.nanquantile(x, q=0.5, axis=1) >>> print(y2) Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [2.50000000, 7. ]) >>> y3 = paddle.nanquantile(x, q=[0.3, 0.5], axis=0) >>> print(y3) Tensor(shape=[2, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[5. , 2.50000000, 3.50000000, 4.50000000, 5.50000000], [5. , 3.50000000, 4.50000000, 5.50000000, 6.50000000]]) >>> y4 = paddle.nanquantile(x, q=0.8, axis=1, keepdim=True) >>> print(y4) Tensor(shape=[2, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[3.40000000], [8.20000000]]) >>> nan = paddle.full(shape=[2, 3], fill_value=float("nan")) >>> y5 = paddle.nanquantile(nan, q=0.8, axis=1, keepdim=True) >>> print(y5) Tensor(shape=[2, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[nan], [nan]]) """ ... def nansum(self, axis: '_int | Sequence[_int] | None' = None, dtype: 'DTypeLike | None' = None, keepdim: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Computes the sum of tensor elements over the given axis, treating Not a Numbers (NaNs) as zero. Args: axis (int|list|tuple, optional): The dimensions along which the nansum is performed. If :attr:`None`, nansum all elements of :attr:`x` and return a Tensor with a single element, otherwise must be in the range :math:`[-rank(x), rank(x))`. If :math:`axis[i] < 0`, the dimension to reduce is :math:`rank + axis[i]`. dtype (str|paddle.dtype|np.dtype, optional): The dtype of output Tensor. The default value is None, the dtype of output is the same as input Tensor `x`. keepdim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result Tensor will have one fewer dimension than the :attr:`x` unless :attr:`keepdim` is true, default value is False. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Results of summation operation on the specified axis of input Tensor `x`, Examples: .. code-block:: python >>> import paddle >>> # x is a Tensor with following elements: >>> # [[nan, 0.3, 0.5, 0.9] >>> # [0.1, 0.2, -nan, 0.7]] >>> # Each example is followed by the corresponding output tensor. >>> x = paddle.to_tensor([[float('nan'), 0.3, 0.5, 0.9], ... [0.1, 0.2, float('-nan'), 0.7]],dtype="float32") >>> out1 = paddle.nansum(x) >>> out1 Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 2.69999981) >>> out2 = paddle.nansum(x, axis=0) >>> out2 Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [0.10000000, 0.50000000, 0.50000000, 1.59999990]) >>> out3 = paddle.nansum(x, axis=-1) >>> out3 Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [1.70000005, 1. ]) >>> out4 = paddle.nansum(x, axis=1, keepdim=True) >>> out4 Tensor(shape=[2, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[1.70000005], [1. ]]) >>> # y is a Tensor with shape [2, 2, 2] and elements as below: >>> # [[[1, nan], [3, 4]], >>> # [[5, 6], [-nan, 8]]] >>> # Each example is followed by the corresponding output tensor. >>> y = paddle.to_tensor([[[1, float('nan')], [3, 4]], ... [[5, 6], [float('-nan'), 8]]]) >>> out5 = paddle.nansum(y, axis=[1, 2]) >>> out5 Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [8. , 19.]) >>> out6 = paddle.nansum(y, axis=[0, 1]) >>> out6 Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [9. , 18.]) """ ... def narrow(self, dim: '_int', start: '_int | Tensor', length: '_int') -> 'Tensor': r""" Returns a narrowed slice of input along a single axis. This operator selects the index range [start, start + length) on dimension dim and keeps all the dimensions unchanged. Args: dim (int): Dimension to narrow. Supports negative indexing. start (int|Tensor): Start index on ``dim``. Can be a Python int or a 0-D int Tensor (int32 or int64). Negative values are supported. length (int): Number of elements to select from ``start``. Must be non-negative. Returns: Tensor: A tensor that is a narrowed view of ``input``. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 2, 3, 4],[5, 6, 7, 8]], dtype='int64') >>> y1 = paddle.narrow(x, dim=1, start=1, length=2) >>> print(y1) Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[2, 3], [6, 7]]) >>> y2 = paddle.narrow(x, dim=-1, start=-3, length=3) >>> print(y2) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[2, 3, 4], [6, 7, 8]]) >>> s = paddle.to_tensor(0, dtype='int64') >>> y3 = paddle.narrow(x, dim=1, start=s, length=2) >>> print(y3) Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[1, 2], [5, 6]]) """ ... def ndimension(self) -> '_int': ... def ne(self, y: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Returns the truth value of :math:`x != y` elementwise, which is equivalent function to the overloaded operator `!=`. Note: The output has no gradient. Args: y (Tensor): Second input to compare which is N-D tensor. The input data type should be bool, bfloat16, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128. Alias: ``other``. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output tensor. If set, the result will be stored in this tensor. Default is None. Returns: Tensor: The output shape is same as input :attr:`x`. The output data type is bool. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3]) >>> y = paddle.to_tensor([1, 3, 2]) >>> result1 = paddle.not_equal(x, y) >>> print(result1) Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True, [False, True , True ]) """ ... def neg(self, name: '_str | None' = None) -> 'Tensor': r""" This function computes the negative of the Tensor elementwisely. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor): The negative of input Tensor. The shape and data type are the same with input Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.neg(x) >>> out Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [ 0.40000001, 0.20000000, -0.10000000, -0.30000001]) """ ... def neg_(self, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``neg`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_neg`. """ ... def negative(self, name: '_str | None' = None) -> 'Tensor': r""" Returns the negated version of the input Tensor. This is used in `Tensor.__neg__`, applying the unary `-` operator to the tensor. .. math:: Out = -X Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A tensor with the same shape and data type as the input tensor. The returned tensor is the negative. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-1, 0, 1]) >>> out = paddle.negative(x) >>> print(out) Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, [1, 0, -1]) """ ... def new_empty(self, size: 'ShapeLike', *, dtype: 'DTypeLike | None' = None, device: 'PlaceLike | None' = None, requires_grad: '_bool' = False, pin_memory: '_bool' = False) -> 'Tensor': r""" Create a new uninitialized Tensor of the specified shape. Args: size (ShapeLike): Shape of the new Tensor. dtype (DTypeLike, optional): Desired data type of the new Tensor. Defaults to `var.dtype`. device (PlaceLike, optional): Device on which to place the new Tensor. Defaults to `var.place`. requires_grad (bool, optional): Whether to track gradients. Default: False. pin_memory (bool, optional): Whether to pin memory. Default: False. Returns: Tensor: A new uninitialized Tensor with the specified shape. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.ones([2, 2]) >>> y = x.new_empty(3, 3) # type: ignore >>> y.shape paddle.Size([3, 3]) """ ... def new_full(self, size: 'ShapeLike', fill_value: '_bool | _float | paddle.Tensor', *, dtype: 'DTypeLike | None' = None, device: 'PlaceLike | None' = None, requires_grad: '_bool' = False, pin_memory: '_bool' = False) -> 'Tensor': r""" Create a new Tensor of specified shape and fill it with a given value. Args: size (ShapeLike): Shape of the new Tensor. fill_value (bool | float | Tensor): Value to fill the Tensor with. dtype (DTypeLike, optional): Desired data type of the new Tensor. Defaults to `var.dtype`. device (PlaceLike, optional): Device on which to place the new Tensor. Defaults to `var.place`. requires_grad (bool, optional): Whether to track gradients. Default: False. pin_memory (bool, optional): Whether to pin memory. Default: False. Returns: Tensor: A new Tensor filled with `fill_value`. Examples: .. code-block:: python >>> import paddle >>> x = paddle.ones([2, 2]) >>> y = x.new_full([3, 3], 5.0) >>> y.numpy() array([[5., 5., 5.], [5., 5., 5.], [5., 5., 5.]], dtype=float32) """ ... def new_ones(self, size: 'ShapeLike', *, dtype: 'DTypeLike | None' = None, device: 'PlaceLike | None' = None, requires_grad: '_bool' = False, pin_memory: '_bool' = False) -> 'Tensor': r""" Create a new Tensor of the specified shape filled with ones. Args: size (ShapeLike): Shape of the new Tensor. dtype (DTypeLike, optional): Desired data type of the new Tensor. Defaults to `var.dtype`. device (PlaceLike, optional): Device on which to place the new Tensor. Defaults to `var.place`. requires_grad (bool, optional): Whether to track gradients. Default: False. pin_memory (bool, optional): Whether to pin memory. Default: False. Returns: Tensor: A new Tensor filled with ones. Examples: .. code-block:: python >>> import paddle >>> x = paddle.zeros([2, 2]) >>> y = x.new_ones(3, 3) # type: ignore >>> y.numpy() array([[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]], dtype=float32) """ ... def new_zeros(self, size: 'ShapeLike', *, dtype: 'DTypeLike | None' = None, device: 'PlaceLike | None' = None, requires_grad: '_bool' = False, pin_memory: '_bool' = False) -> 'Tensor': r""" Create a new Tensor of the specified shape filled with zeros. Args: size (ShapeLike): Shape of the new Tensor. dtype (DTypeLike, optional): Desired data type of the new Tensor. Defaults to `var.dtype`. device (PlaceLike, optional): Device on which to place the new Tensor. Defaults to `var.place`. requires_grad (bool, optional): Whether to track gradients. Default: False. pin_memory (bool, optional): Whether to pin memory. Default: False. Returns: Tensor: A new Tensor filled with zeros. Examples: .. code-block:: python >>> import paddle >>> x = paddle.ones([2, 2]) >>> y = x.new_zeros(3, 3) # type: ignore >>> y.numpy() array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]], dtype=float32) """ ... def nextafter(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Return the next floating-point value after input towards other, elementwise. The shapes of input and other must be broadcastable. Args: y (Tensor): An N-D Tensor, the data type is float32, float64. name(str, optional):Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor): An N-D Tensor, the shape and data type is the same with input. Examples: .. code-block:: python >>> import paddle >>> out = paddle.nextafter(paddle.to_tensor([1.0,2.0]),paddle.to_tensor([2.0,1.0])) >>> out Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [1.00000012, 1.99999988]) """ ... def nonzero(self, as_tuple=False, *, out: 'Tensor | None' = None): r""" Return a tensor containing the indices of all non-zero elements of the `input` tensor. If as_tuple is True, return a tuple of 1-D tensors, one for each dimension in `input`, each containing the indices (in that dimension) of all non-zero elements of `input`. Given a n-Dimensional `input` tensor with shape [x_1, x_2, ..., x_n], If as_tuple is False, we can get a output tensor with shape [z, n], where `z` is the number of all non-zero elements in the `input` tensor. If as_tuple is True, we can get a 1-D tensor tuple of length `n`, and the shape of each 1-D tensor is [z, 1]. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``. For example, ``nonzero(input=tensor_x)`` is equivalent to ``nonzero(x=tensor_x)``. Args: as_tuple (bool, optional): Return type, Tensor or tuple of Tensor. out (Tensor|None, optional): The output tensor. Default: None. Returns: Tensor or tuple of Tensor, The data type is int64. Examples: .. code-block:: python >>> import paddle >>> x1 = paddle.to_tensor([[1.0, 0.0, 0.0], ... [0.0, 2.0, 0.0], ... [0.0, 0.0, 3.0]]) >>> x2 = paddle.to_tensor([0.0, 1.0, 0.0, 3.0]) >>> out_z1 = paddle.nonzero(x1) >>> print(out_z1) Tensor(shape=[3, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 0], [1, 1], [2, 2]]) >>> out_z1_tuple = paddle.nonzero(x1, as_tuple=True) >>> for out in out_z1_tuple: ... print(out) Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, [0, 1, 2]) Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, [0, 1, 2]) >>> out_z2 = paddle.nonzero(x2) >>> print(out_z2) Tensor(shape=[2, 1], dtype=int64, place=Place(cpu), stop_gradient=True, [[1], [3]]) >>> out_z2_tuple = paddle.nonzero(x2, as_tuple=True) >>> for out in out_z2_tuple: ... print(out) Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True, [1, 3]) """ ... def norm(self, p: '_float | _POrder | None' = None, axis: '_int | list[_int] | tuple[_int, _int] | None' = None, keepdim: '_bool' = False, out: 'paddle.Tensor | None' = None, dtype: 'paddle._typing.DTypeLike | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Returns the matrix norm (the Frobenius norm, the nuclear norm and p-norm) or vector norm (the 1-norm, the Euclidean or 2-norm, and in general the p-norm) of a given tensor. Whether the function calculates the vector norm or the matrix norm is determined as follows: - If axis is of type int, calculate the vector norm. - If axis is a two-dimensional array, calculate the matrix norm. - If axis is None, x is compressed into a one-dimensional vector and the vector norm is calculated. Paddle supports the following norms: +----------------+--------------------------------+--------------------------------+ | porder | norm for matrices | norm for vectors | +================+================================+================================+ | None(default) | frobenius norm | 2_norm | +----------------+--------------------------------+--------------------------------+ | fro | frobenius norm | not support | +----------------+--------------------------------+--------------------------------+ | nuc | nuclear norm | not support | +----------------+--------------------------------+--------------------------------+ | inf | max(sum(abs(x), dim=1)) | max(abs(x)) | +----------------+--------------------------------+--------------------------------+ | -inf | min(sum(abs(x), dim=1)) | min(abs(x)) | +----------------+--------------------------------+--------------------------------+ | 0 | not support | sum(x != 0) | +----------------+--------------------------------+--------------------------------+ | 1 | max(sum(abs(x), dim=0)) | as below | +----------------+--------------------------------+--------------------------------+ | -1 | min(sum(abs(x), dim=0)) | as below | +----------------+--------------------------------+--------------------------------+ | 2 |The maximum singular value | as below | | |of a matrix consisting of axis. | | +----------------+--------------------------------+--------------------------------+ | -2 |The minimum singular value | as below | | |of a matrix consisting of axis. | | +----------------+--------------------------------+--------------------------------+ | other int | not support | sum(abs(x)^{porder})^ | | or float | | {(1 / porder)} | +----------------+--------------------------------+--------------------------------+ .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``dim`` can be used as an alias for ``axis``. For example, ``norm(input=tensor_x, dim=1, ...)`` is equivalent to ``norm(x=tensor_x, axis=1, ...)``. Args: p (int|float|string|None, optional): Order of the norm. Supported values are `fro`, `nuc`, `0`, `±1`, `±2`, `±inf` and any real number yielding the corresponding p-norm. Default value is None. axis (int|list|tuple, optional): The axis on which to apply norm operation. If axis is int or list(int)/tuple(int) with only one element, the vector norm is computed over the axis. If `axis < 0`, the dimension to norm operation is rank(input) + axis. If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis. Default value is `None`. alias: ``dim``. keepdim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result tensor will have fewer dimension than the :attr:`input` unless :attr:`keepdim` is true, default value is False. out (Tensor, optional): The output tensor. Ignored out = None. dtype (DTypeLike | None, optional): The data type of the output tensor. If specified, the input tensor is casted to `dtype` while performing the operation. Default value is None. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: results of norm operation on the specified axis of input tensor, it's data type is the same as input's Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.arange(24, dtype="float32").reshape([2, 3, 4]) - 12 >>> print(x) Tensor(shape=[2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[[-12., -11., -10., -9. ], [-8. , -7. , -6. , -5. ], [-4. , -3. , -2. , -1. ]], [[ 0. , 1. , 2. , 3. ], [ 4. , 5. , 6. , 7. ], [ 8. , 9. , 10., 11.]]]) >>> # compute frobenius norm along last two dimensions. >>> out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1]) >>> print(out_fro) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [17.43559647, 16.91153526, 16.73320007, 16.91153526]) >>> # compute 2-order vector norm along last dimension. >>> out_pnorm = paddle.linalg.norm(x, p=2, axis=-1) >>> print(out_pnorm) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[21.11871147, 13.19090557, 5.47722578 ], [3.74165750 , 11.22497177, 19.13112640]]) >>> # compute 2-order norm along [0,1] dimension. >>> out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1]) >>> print(out_pnorm) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [15.75857544, 14.97978878, 14.69693947, 14.97978973]) >>> # compute inf-order norm >>> out_pnorm = paddle.linalg.norm(x, p=float("inf")) >>> print(out_pnorm) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 12.) >>> out_pnorm = paddle.linalg.norm(x, p=float("inf"), axis=0) >>> print(out_pnorm) Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[12., 11., 10., 9. ], [8. , 7. , 6. , 7. ], [8. , 9. , 10., 11.]]) >>> # compute -inf-order norm >>> out_pnorm = paddle.linalg.norm(x, p=-float("inf")) >>> print(out_pnorm) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.) >>> out_pnorm = paddle.linalg.norm(x, p=-float("inf"), axis=0) >>> print(out_pnorm) Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[0., 1., 2., 3.], [4., 5., 6., 5.], [4., 3., 2., 1.]]) """ ... def normal_(self, mean: '_complex' = 0.0, std: '_float' = 1.0, name: '_str | None' = None) -> 'Tensor': r""" This is the inplace version of api ``normal``, which returns a Tensor filled with random values sampled from a normal distribution. The output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_normal`. Args: mean (float|int|complex, optional): Mean of the output tensor, default is 0.0. std (float|int, optional): Standard deviation of the output tensor, default is 1.0. name(str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, A Tensor filled with random values sampled from a normal distribution with ``mean`` and ``std`` . Examples: .. code-block:: python >>> import paddle >>> x = paddle.randn([3, 4]) >>> x.normal_() >>> # doctest: +SKIP('random check') >>> print(x) Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0.06132207, 1.11349595, 0.41906244, -0.24858207], [-1.85169315, -1.50370061, 1.73954511, 0.13331604], [ 1.66359663, -0.55764782, -0.59911072, -0.57773495]]) """ ... def not_equal_(self, y: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``not_equal`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_not_equal`. """ ... def numel(self, name: '_str | None' = None) -> 'Tensor': r""" Returns the number of elements for a tensor, which is a 0-D int64 Tensor with shape []. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The number of elements for the input Tensor, whose shape is []. Examples: .. code-block:: python >>> import paddle >>> x = paddle.full(shape=[4, 5, 7], fill_value=0, dtype='int32') >>> numel = paddle.numel(x) >>> print(numel.numpy()) 140 """ ... def ormqr(self, tau: 'Tensor', y: 'Tensor', left: '_bool' = True, transpose: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Calculate the product of a normal matrix and a householder matrix. Compute the product of the matrix C (given by y) with dimensions (m, n) and a matrix Q, where Q is generated by the Householder reflection coefficient (x, tau). Returns a Tensor. Args: tau (Tensor): Shape (\*, min(mn, k)), where \* indicates that the length of the Tensor on axis 0 is 0 or greater, and its type is the same as input. y (Tensor): Shape (\*m,n), where \* indicates that the length of the Tensor on axis 0 is 0 or greater, and its type is the same as input. left (bool, optional): Determines the order in which the matrix product operations are operated. If left is true, the order of evaluation is op(Q) \* y, otherwise, the order of evaluation is y \* op(Q). Default value: True. transpose (bool, optional): If true, the matrix Q is conjugated and transposed, otherwise, the conjugate transpose transformation is not performed. Default value: False. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor. Data type and dimension are equals with :attr:`y`. Examples: .. code-block:: python >>> import paddle >>> import numpy as np >>> from paddle import linalg >>> input = paddle.to_tensor([[-114.6, 10.9, 1.1], [-0.304, 38.07, 69.38], [-0.45, -0.17, 62]]) >>> tau = paddle.to_tensor([1.55, 1.94, 3.0]) >>> y = paddle.to_tensor([[-114.6, 10.9, 1.1], [-0.304, 38.07, 69.38], [-0.45, -0.17, 62]]) >>> output = linalg.ormqr(input, tau, y) >>> print(output) Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 63.82712936 , -13.82312393 , -116.28614044], [-53.65926361 , -28.15783691 , -70.42700958 ], [-79.54292297 , 24.00182915 , -41.34253311 ]]) """ ... def outer(self, y: 'Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Outer product of two Tensors. Input is flattened if not already 1-dimensional. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``vec2`` can be used as an alias for ``y``. For example, ``outer(input=tensor_x, vec2=tensor_y, ...)`` is equivalent to ``outer(x=tensor_x, y=tensor_y, ...)``. Args: y (Tensor): An N-D Tensor or a Scalar Tensor. alias: ``vec2``. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output Tensor. If set, the result will be stored in this Tensor. Returns: Tensor: The outer-product Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.arange(1, 4).astype('float32') >>> y = paddle.arange(1, 6).astype('float32') >>> out = paddle.outer(x, y) >>> print(out) Tensor(shape=[3, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[1. , 2. , 3. , 4. , 5. ], [2. , 4. , 6. , 8. , 10.], [3. , 6. , 9. , 12., 15.]]) """ ... def pca_lowrank(self, q: '_int | None' = None, center: '_bool' = True, niter: '_int' = 2, name: '_str | None' = None) -> 'tuple[Tensor, Tensor, Tensor]': r""" Performs linear Principal Component Analysis (PCA) on a low-rank matrix or batches of such matrices. Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies: .. math:: X = U * diag(S) * V^{T} Args: q (int, optional): a slightly overestimated rank of :math:`X`. Default value is :math:`q=min(6,N,M)`. center (bool, optional): if True, center the input tensor. Default value is True. niter (int, optional): number of iterations to perform. Default: 2. name (str|None, optional): Name for the operation. For more information, please refer to :ref:`api_guide_Name`. Default: None. Returns: - Tensor U, is N x q matrix. - Tensor S, is a vector with length q. - Tensor V, is M x q matrix. tuple (U, S, V): which is the nearly optimal approximation of a singular value decomposition of a centered matrix :math:`X`. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2023) >>> x = paddle.randn((5, 5), dtype='float64') >>> U, S, V = paddle.linalg.pca_lowrank(x) >>> print(U) Tensor(shape=[5, 5], dtype=float64, place=Place(cpu), stop_gradient=True, [[ 0.80131563, 0.11962647, 0.27667179, -0.25891214, 0.44721360], [-0.12642301, 0.69917551, -0.17899393, 0.51296394, 0.44721360], [ 0.08997135, -0.69821706, -0.20059228, 0.51396579, 0.44721360], [-0.23871837, -0.02815453, -0.59888153, -0.61932365, 0.44721360], [-0.52614559, -0.09243040, 0.70179595, -0.14869394, 0.44721360]]) >>> print(S) Tensor(shape=[5], dtype=float64, place=Place(cpu), stop_gradient=True, [2.60101614, 2.40554940, 1.49768346, 0.19064830, 0.00000000]) >>> print(V) Tensor(shape=[5, 5], dtype=float64, place=Place(cpu), stop_gradient=True, [[ 0.58339481, -0.17143771, 0.00522143, 0.57976310, 0.54231640], [ 0.22334335, 0.72963474, -0.30148399, -0.39388750, 0.41438019], [ 0.05416913, 0.34666487, 0.93549758, 0.00063507, 0.04162998], [-0.39519094, 0.53074980, -0.16687419, 0.71175586, -0.16638919], [-0.67131070, -0.19071018, 0.07795789, -0.04615811, 0.71046714]]) """ ... def permute(self, dims: 'Sequence[_int]') -> 'Tensor': r""" Permute the dimensions of a tensor. Args: *dims (tuple|list|int): The desired ordering of dimensions. Supports passing as variable-length arguments (e.g., permute(x, 1, 0, 2)) or as a single list/tuple (e.g., permute(x, [1, 0, 2])). Returns: Tensor: A tensor with permuted dimensions. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.randn([2, 3, 4]) >>> y = paddle.permute(x, (1, 0, 2)) >>> print(y.shape) paddle.Size([3, 2, 4]) >>> y = x.permute([1, 0, 2]) >>> print(y.shape) paddle.Size([3, 2, 4]) """ ... def pin_memory(self, blocking: '_bool' = True) -> 'Tensor': ... def pinv(self, rcond: '_float | Tensor' = 1e-15, hermitian: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Calculate pseudo inverse via SVD(singular value decomposition) of one matrix or batches of regular matrix. .. math:: if hermitian == False: x = u * s * vt (SVD) out = v * 1/s * ut else: x = u * s * ut (eigh) out = u * 1/s * u.conj().transpose(-2,-1) If x is hermitian or symmetric matrix, svd will be replaced with eigh. Args: rcond (Tensor|float, optional): the tolerance value to determine when is a singular value zero. Default:1e-15. hermitian (bool, optional): indicates whether x is Hermitian if complex or symmetric if real. Default: False. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The tensor with same data type with x. it represents pseudo inverse of x. Its shape should be (*, n, m). Examples: .. code-block:: python >>> import paddle >>> x = paddle.arange(15).reshape((3, 5)).astype('float64') >>> input = paddle.to_tensor(x) >>> out = paddle.linalg.pinv(input) >>> print(input) Tensor(shape=[3, 5], dtype=float64, place=Place(cpu), stop_gradient=True, [[0. , 1. , 2. , 3. , 4. ], [5. , 6. , 7. , 8. , 9. ], [10., 11., 12., 13., 14.]]) >>> print(out) Tensor(shape=[5, 3], dtype=float64, place=Place(cpu), stop_gradient=True, [[-0.22666667, -0.06666667, 0.09333333], [-0.12333333, -0.03333333, 0.05666667], [-0.02000000, -0.00000000, 0.02000000], [ 0.08333333, 0.03333333, -0.01666667], [ 0.18666667, 0.06666667, -0.05333333]]) # one can verify : x * out * x = x ; # or out * x * out = x ; """ ... def polar(self, angle: 'paddle.Tensor', name: '_str | None' = None, *, out: 'paddle.Tensor | None' = None) -> 'paddle.Tensor': r""" Return a Cartesian coordinates corresponding to the polar coordinates complex tensor given the ``abs`` and ``angle`` component. Args: angle (Tensor): The angle component. The data type should be the same as ``abs``. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. out (Tensor, optional): The output tensor. If set, the result will be stored in this tensor. Default is None. Returns: Tensor, The output tensor. The data type is 'complex64' or 'complex128', with the same precision as ``abs`` and ``angle``. Note: ``paddle.polar`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Examples: .. code-block:: python >>> import paddle >>> import numpy as np >>> abs = paddle.to_tensor([1, 2], dtype=paddle.float64) >>> angle = paddle.to_tensor([np.pi / 2, 5 * np.pi / 4], dtype=paddle.float64) >>> out = paddle.polar(abs, angle) >>> print(out) Tensor(shape=[2], dtype=complex128, place=Place(cpu), stop_gradient=True, [ (6.123233995736766e-17+1j) , (-1.4142135623730954-1.414213562373095j)]) """ ... def polygamma(self, n: '_int', name: '_str | None' = None) -> 'Tensor': r""" Calculates the polygamma of the given input tensor, element-wise. The equation is: .. math:: \Phi^n(x) = \frac{d^n}{dx^n} [\ln(\Gamma(x))] Args: n (int): Order of the derivative. Must be integral. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: - out (Tensor), A Tensor. the polygamma of the input Tensor, the shape and data type is the same with input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> data = paddle.to_tensor([2, 3, 25.5], dtype='float32') >>> res = paddle.polygamma(data, 1) >>> print(res) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [0.64493412, 0.39493406, 0.03999467]) """ ... def polygamma_(self, n: '_int', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``polygamma`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_polygamma`. """ ... def pow(self, y: '_float | Tensor', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Compute the power of Tensor elements. The equation is: .. math:: out = x^{y} Note: ``paddle.pow`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, The parameter name ``exponent`` can be used as an alias for ``y``. For example, ``pow(input=2, exponent=1.1)`` is equivalent to ``pow(x=2, y=1.1)``. Args: input: An alias for ``x`` , with identical behavior. y (float|int|Tensor): If it is an N-D Tensor, its data type should be the same as `x`. exponent: An alias for ``y`` , with identical behavior. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output tensor. If set, the result will be stored in this tensor. Default is None. Returns: N-D Tensor. A location into which the result is stored. Its dimension and data type are the same as `x`. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3], dtype='float32') >>> # example 1: y is a float or int >>> res = paddle.pow(x, 2) >>> print(res) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [1., 4., 9.]) >>> res = paddle.pow(x, 2.5) >>> print(res) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [1. , 5.65685415 , 15.58845711]) >>> # example 2: y is a Tensor >>> y = paddle.to_tensor([2], dtype='float32') >>> res = paddle.pow(x, y) >>> print(res) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [1., 4., 9.]) """ ... def pow_(self, y: '_float | Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``pow`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_pow`. """ ... def prod(self, axis: '_int | Sequence[_int] | None' = None, keepdim: '_bool' = False, dtype: 'DTypeLike | None' = None, out: 'Tensor | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Compute the product of tensor elements over the given axis. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``dim`` can be used as an alias for ``axis``. For example, ``prod(input=tensor_x, dim=1, ...)`` is equivalent to ``prod(x=tensor_x, axis=1, ...)``. Args: axis (int|list|tuple|None, optional): The axis along which the product is computed. If :attr:`None`, multiply all elements of `x` and return a Tensor with a single element, otherwise must be in the range :math:`[-x.ndim, x.ndim)`. If :math:`axis[i]<0`, the axis to reduce is :math:`x.ndim + axis[i]`. Default is None. alias: ``dim``. keepdim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result tensor will have one fewer dimension than the input unless `keepdim` is true. Default is False. dtype (str|core.VarDesc.VarType|core.DataType|np.dtype, optional): The desired date type of returned tensor, can be bfloat16, float16, float32, float64, int32, int64. If specified, the input tensor is casted to dtype before operator performed. This is very useful for avoiding data type overflows. The default value is None, the dtype of output is the same as input Tensor `x`. out (Tensor|None, optional): The output tensor. Default: None. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, result of product on the specified dim of input tensor. Examples: .. code-block:: python >>> import paddle >>> # the axis is a int element >>> x = paddle.to_tensor([[0.2, 0.3, 0.5, 0.9], ... [0.1, 0.2, 0.6, 0.7]]) >>> out1 = paddle.prod(x) >>> out1 Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.00022680) >>> out2 = paddle.prod(x, -1) >>> out2 Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [0.02700000, 0.00840000]) >>> out3 = paddle.prod(x, 0) >>> out3 Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [0.02000000, 0.06000000, 0.30000001, 0.63000000]) >>> out4 = paddle.prod(x, 0, keepdim=True) >>> out4 Tensor(shape=[1, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.02000000, 0.06000000, 0.30000001, 0.63000000]]) >>> out5 = paddle.prod(x, 0, dtype='int64') >>> out5 Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True, [0, 0, 0, 0]) >>> # the axis is list >>> y = paddle.to_tensor([[[1.0, 2.0], [3.0, 4.0]], ... [[5.0, 6.0], [7.0, 8.0]]]) >>> out6 = paddle.prod(y, [0, 1]) >>> out6 Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [105., 384.]) >>> out7 = paddle.prod(y, (1, 2)) >>> out7 Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [24. , 1680.]) """ ... def put_along_axis(self, indices: 'Tensor', values: '_float | Tensor', axis: '_int', reduce: "Literal['assign', 'add', 'mul', 'multiply', 'mean', 'amin', 'amax']" = 'assign', include_self: '_bool' = True, broadcast: '_bool' = True) -> 'Tensor': r""" Put values into the destination array by given indices matrix along the designated axis. Args: indices (Tensor) : Indices to put along each 1d slice of arr. This must match the dimension of arr, and need to broadcast against arr if broadcast is 'True'. Supported data type are int32 and int64. values (scalar|Tensor) : The value element(s) to put. The data types should be same as arr. axis (int) : The axis to put 1d slices along. reduce (str, optional): The reduce operation, default is 'assign', support 'add', 'assign', 'mul', 'multiply', 'mean', 'amin' and 'amax'. include_self (bool, optional): whether to reduce with the elements of arr, default is 'True'. broadcast (bool, optional): whether to broadcast indices, default is 'True'. Returns: Tensor, The indexed element, same dtype with arr Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[10, 30, 20], [60, 40, 50]]) >>> index = paddle.to_tensor([[0]]) >>> value = 99 >>> axis = 0 >>> result = paddle.put_along_axis(x, index, value, axis) >>> print(result) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[99, 99, 99], [60, 40, 50]]) >>> index = paddle.zeros((2,2)).astype("int32") >>> value=paddle.to_tensor([[1,2],[3,4]]).astype(x.dtype) >>> result = paddle.put_along_axis(x, index, value, 0, "add", True, False) >>> print(result) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[14, 36, 20], [60, 40, 50]]) >>> result = paddle.put_along_axis(x, index, value, 0, "mul", True, False) >>> print(result) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[30 , 240, 20 ], [60 , 40 , 50 ]]) >>> result = paddle.put_along_axis(x, index, value, 0, "mean", True, False) >>> print(result) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[4 , 12, 20], [60, 40, 50]]) >>> result = paddle.put_along_axis(x, index, value, 0, "amin", True, False) >>> print(result) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[1 , 2 , 20], [60, 40, 50]]) >>> result = paddle.put_along_axis(x, index, value, 0, "amax", True, False) >>> print(result) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[10, 30, 20], [60, 40, 50]]) >>> result = paddle.put_along_axis(x, index, value, 0, "add", False, False) >>> print(result) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[4 , 6 , 20], [60, 40, 50]]) """ ... def put_along_axis_(self, indices: 'Tensor', values: '_float | Tensor', axis: '_int', reduce: "Literal['assign', 'add', 'mul', 'multiply', 'mean', 'amin', 'amax']" = 'assign', include_self: '_bool' = True, broadcast: '_bool' = True): r""" Inplace version of ``put_along_axis`` API, the output Tensor will be inplaced with input ``arr``. Please refer to :ref:`api_paddle_put_along_axis`. """ ... def qr(self, mode='reduced', name=None) -> 'Tensor | tuple[Tensor, Tensor]': r""" Computes the QR decomposition of one matrix or batches of matrices (backward is unsupported now). Args: mode (str, optional): A flag to control the behavior of qr. Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`: If mode = "reduced", qr op will return reduced Q and R matrices, which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`. If mode = "complete", qr op will return complete Q and R matrices, which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`. If mode = "r", qr op will only return reduced R matrix, which means R's shape is `[..., K, N]`. Default: "reduced". name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: If mode = "reduced" or mode = "complete", qr will return a two tensor-tuple, which represents Q and R. If mode = "r", qr will return a tensor which represents R. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64') >>> q, r = paddle.linalg.qr(x) >>> print(q) Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True, [[-0.16903085, 0.89708523], [-0.50709255, 0.27602622], [-0.84515425, -0.34503278]]) >>> print(r) Tensor(shape=[2, 2], dtype=float64, place=Place(cpu), stop_gradient=True, [[-5.91607978, -7.43735744], [ 0. , 0.82807867]]) >>> # one can verify : X = Q * R ; """ ... def quantile(self, q: '_float | Sequence[_float] | Tensor', axis: '_int | list[_int] | None' = None, keepdim: '_bool' = False, interpolation: '_Interpolation' = 'linear', name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Compute the quantile of the input along the specified axis. If any values in a reduced row are NaN, then the quantiles for that reduction will be NaN. Args: q (int|float|list|Tensor): The q for calculate quantile, which should be in range [0, 1]. If q is a list or a 1-D Tensor, each element of q will be calculated and the first dimension of output is same to the number of ``q`` . If q is a 0-D Tensor, it will be treated as an integer or float. axis (int|list, optional): The axis along which to calculate quantile. ``axis`` should be int or list of int. ``axis`` should be in range [-D, D), where D is the dimensions of ``x`` . If ``axis`` is less than 0, it works the same way as :math:`axis + D`. If ``axis`` is a list, quantile is calculated over all elements of given axes. If ``axis`` is None, quantile is calculated over all elements of ``x``. Default is None. keepdim (bool, optional): Whether to reserve the reduced dimension(s) in the output Tensor. If ``keepdim`` is True, the dimensions of the output Tensor is the same as ``x`` except in the reduced dimensions(it is of size 1 in this case). Otherwise, the shape of the output Tensor is squeezed in ``axis`` . Default is False. interpolation (str, optional): The interpolation method to use when the desired quantile falls between two data points. Must be one of linear, higher, lower, midpoint and nearest. Default is linear. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output tensor. Default: None. Returns: Tensor, results of quantile along ``axis`` of ``x``. Examples: .. code-block:: python >>> import paddle >>> y = paddle.arange(0, 8 ,dtype="float32").reshape([4, 2]) >>> print(y) Tensor(shape=[4, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[0., 1.], [2., 3.], [4., 5.], [6., 7.]]) >>> y1 = paddle.quantile(y, q=0.5, axis=[0, 1]) >>> print(y1) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 3.50000000) >>> y2 = paddle.quantile(y, q=0.5, axis=1) >>> print(y2) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [0.50000000, 2.50000000, 4.50000000, 6.50000000]) >>> y3 = paddle.quantile(y, q=[0.3, 0.5], axis=0) >>> print(y3) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[1.80000000, 2.80000000], [3. , 4. ]]) >>> y[0,0] = float("nan") >>> y4 = paddle.quantile(y, q=0.8, axis=1, keepdim=True) >>> print(y4) Tensor(shape=[4, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[nan ], [2.80000000], [4.80000000], [6.80000000]]) """ ... def rad2deg(self, name: '_str | None' = None) -> 'Tensor': r""" Convert each of the elements of input x from angles in radians to degrees. Equation: .. math:: rad2deg(x)=180/ \pi * x Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor): An N-D Tensor, the shape and data type is the same with input (The output data type is float32 when the input data type is int). Examples: .. code-block:: python >>> import paddle >>> import math >>> x1 = paddle.to_tensor([3.142, -3.142, 6.283, -6.283, 1.570, -1.570]) >>> result1 = paddle.rad2deg(x1) >>> result1 Tensor(shape=[6], dtype=float32, place=Place(cpu), stop_gradient=True, [ 180.02334595, -180.02334595, 359.98937988, -359.98937988, 89.95437622 , -89.95437622 ]) >>> x2 = paddle.to_tensor(math.pi/2) >>> result2 = paddle.rad2deg(x2) >>> result2 Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 90.) >>> x3 = paddle.to_tensor(1) >>> result3 = paddle.rad2deg(x3) >>> result3 Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 57.29578018) """ ... def random_(self, from_: '_int' = 0, to: '_int | None' = None, *, generator: 'None' = None) -> 'Tensor': r""" Fills self tensor with numbers sampled from the discrete uniform distribution over [from, to - 1]. If not specified, the values are usually only bounded by self tensor’s data type. However, for floating point types, if unspecified, range will be [0, 2^mantissa] to ensure that every value is representable. Args: to (int|None, optional): The upper bound on the range of random values to generate. Default is None. generator (None): Placeholder for random number generator (currently not implemented, reserved for future use). Returns: Tensor, A Tensor filled with random integers from a discrete uniform distribution in the range [``from``, ``to``). Examples: .. code-block:: python >>> import paddle >>> x = paddle.zeros([3], dtype=paddle.int32) >>> x.random_(0, 10) """ ... def rank(self) -> 'Tensor': r""" Returns the number of dimensions for a tensor, which is a 0-D int32 Tensor. Args: Returns: Tensor, the output data type is int32.: The 0-D tensor with the dimensions of the input Tensor. Examples: .. code-block:: python >>> import paddle >>> input = paddle.rand((3, 100, 100)) >>> rank = paddle.rank(input) >>> print(rank.numpy()) 3 """ ... def ravel(self) -> 'Tensor': r""" Return a contiguous flattened tensor. A copy is made only if needed. Note: The output Tensor will share data with origin Tensor and doesn't have a Tensor copy in ``dygraph`` mode. If you want to use the Tensor copy version, please use `Tensor.clone` like ``ravel_clone_x = x.ravel().clone()``. For example: .. code-block:: text Case 1: Given X.shape = (3, 100, 100, 4) We get: Out.shape = (3 * 100 * 100 * 4) Args: Returns: Tensor: A tensor with the contents of the input tensor, whose input axes are flattened by indicated :attr:`start_axis` and :attr:`stop_axis`, and data type is the same as input :attr:`x`. Examples: .. code-block:: pycon >>> import paddle >>> image_shape = (2, 3, 4, 4) >>> x = paddle.arange(end=image_shape[0] * image_shape[1] * image_shape[2] * image_shape[3]) >>> img = paddle.reshape(x, image_shape) >>> out = paddle.ravel(img) >>> print(out.shape) paddle.Size([96]) """ ... def real(self, name: '_str | None' = None) -> 'Tensor': r""" Returns a new Tensor containing real values of the input Tensor. Args: name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: Tensor: a Tensor containing real values of the input Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor( ... [[1 + 6j, 2 + 5j, 3 + 4j], [4 + 3j, 5 + 2j, 6 + 1j]]) >>> print(x) Tensor(shape=[2, 3], dtype=complex64, place=Place(cpu), stop_gradient=True, [[(1+6j), (2+5j), (3+4j)], [(4+3j), (5+2j), (6+1j)]]) >>> real_res = paddle.real(x) >>> print(real_res) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[1., 2., 3.], [4., 5., 6.]]) >>> real_t = x.real() >>> print(real_t) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[1., 2., 3.], [4., 5., 6.]]) """ ... def reciprocal(self, name: '_str | None' = None) -> 'Tensor': r""" Reciprocal Activation Operator. .. math:: out = \frac{1}{x} Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor. Output of Reciprocal operator, a Tensor with shape same as input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.reciprocal(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [-2.50000000, -5. , 10. , 3.33333325]) """ ... def reciprocal_(self, name=None): r""" Inplace version of ``reciprocal`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_reciprocal`. """ ... def reconstruct_from_(self, /, other): r""" Reconstruct the self with other Tensor. It is a deep copy of 'self = other'. Returns: None. Examples: .. code-block:: python >>> import paddle >>> t1 = paddle.to_tensor([1.0], stop_gradient=False) >>> t2 = paddle.to_tensor([2.0], stop_gradient=True) >>> t1.reconstruct_from_(t2) >>> print(t1) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [2.]) """ ... def reduce_as(self, target: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Computes the sum of tensor elements make the shape of its result equal to the shape of target. Args: target (Tensor): An N-D Tensor, the length of x shape must greater than or equal to the length of target shape. The data type is bool, float16, float32, float64, int8, uint8, int16, uint16, int32, int64, complex64 or complex128. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The sum of the input tensor x along some axis has the same shape as the shape of the input tensor target, if `x.dtype='bool'`, `x.dtype='int32'`, it's data type is `'int64'`, otherwise it's data type is the same as `x`. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 2, 3, 4], [5, 6, 7, 8]]) >>> x Tensor(shape=[2, 4], dtype=int64, place=Place(gpu:0), stop_gradient=True, [[1, 2, 3, 4], [5, 6, 7, 8]]) >>> target = paddle.to_tensor([1, 2, 3, 4]) >>> target Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True, [1, 2, 3, 4]) >>> res = paddle.reduce_as(x, target) >>> res Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True, [6 , 8 , 10, 12]) """ ... def register_hook(self, hook: 'Callable[[Tensor], Tensor | None]') -> 'TensorHookRemoveHelper': r""" Registers a backward hook for current Tensor. The hook will be called every time the gradient Tensor of current Tensor is computed. The hook should not modify the input gradient Tensor, but it can optionally return a new gradient Tensor which will be used in place of current Tensor's gradient. The hook should have the following signature: hook(grad) -> Tensor or None Args: Returns: TensorHookRemoveHelper: A helper object that can be used to remove the registered hook by calling `remove()` method. Examples: .. code-block:: python >>> import paddle >>> # hook function return None >>> def print_hook_fn(grad): ... print(grad) ... >>> # hook function return Tensor >>> def double_hook_fn(grad): ... grad = grad * 2 ... return grad ... >>> x = paddle.to_tensor([0., 1., 2., 3.], stop_gradient=False) >>> y = paddle.to_tensor([4., 5., 6., 7.], stop_gradient=False) >>> z = paddle.to_tensor([1., 2., 3., 4.]) >>> # one Tensor can register multiple hooks >>> h = x.register_hook(print_hook_fn) >>> x.register_hook(double_hook_fn) >>> w = x + y >>> # register hook by lambda function >>> w.register_hook(lambda grad: grad * 2) >>> o = z.matmul(w) >>> o.backward() >>> # print_hook_fn print content in backward Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=False, [2., 4., 6., 8.]) >>> print("w.grad:", w.grad) w.grad: None >>> print("x.grad:", x.grad) x.grad: Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=False, [4. , 8. , 12., 16.]) >>> print("y.grad:", y.grad) y.grad: Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=False, [2., 4., 6., 8.]) >>> # remove hook >>> h.remove() """ ... def renorm(self, p: '_float', axis: '_int', max_norm: '_float') -> 'Tensor': r""" **renorm** This operator is used to calculate the p-norm along the axis, suppose the input-shape on axis dimension has the value of T, then the tensor is split into T parts, the p-norm should be calculated for each part, if the p-norm for part i is larger than max-norm, then each element in part i should be re-normalized at the same scale so that part-i' p-norm equals max-norm exactly, otherwise part-i stays unchanged. Args: p (float): The power of the norm operation. axis (int): the dimension to slice the tensor. max-norm (float): the maximal norm limit. Returns: Tensor: the renorm Tensor. Examples: .. code-block:: python >>> import paddle >>> input = [[[2.0, 2.0, -2.0], [3.0, 0.3, 3.0]], ... [[2.0, -8.0, 2.0], [3.1, 3.7, 3.0]]] >>> x = paddle.to_tensor(input,dtype='float32') >>> y = paddle.renorm(x, 1.0, 2, 2.05) >>> print(y) Tensor(shape=[2, 2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[[ 0.40594056, 0.29285714, -0.41000000], [ 0.60891086, 0.04392857, 0.61500001]], [[ 0.40594056, -1.17142856, 0.41000000], [ 0.62920785, 0.54178572, 0.61500001]]]) """ ... def renorm_(self, p: '_float', axis: '_int', max_norm: '_float') -> 'Tensor': r""" Inplace version of ``renorm`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_renorm`. """ ... def repeat(self, repeats: '_int | Sequence[_int] | Tensor') -> 'Tensor': r""" Repeat elements of a tensor along specified dimensions. Args: *repeats (int|list|tuple|Tensor): The number of times to repeat along each dimension. Can be a single integer (applies to the first dimension only), or multiple integers (one per dimension). Returns: Tensor: The repeated tensor with expanded dimensions. Note: When using a single integer, it only repeats along the first dimension. The total number of repeat values must match the number of dimensions in the tensor when using multiple values. Examples: .. code-block:: python >>> import paddle >>> # Example 1: 1D tensor - single repeat >>> x = paddle.to_tensor([1, 2, 3]) >>> out = x.repeat(2) >>> print(out) Tensor(shape=[6], dtype=int64, place=Place(cpu), stop_gradient=True, [1, 2, 3, 1, 2, 3]) >>> # Example 2: 2D tensor - single repeat value >>> x = paddle.to_tensor([[1, 2], [3, 4]]) >>> out = x.repeat(2) >>> print(out) Tensor(shape=[2, 4], dtype=int64, place=Place(gpu:0), stop_gradient=True, [[1, 2, 1, 2], [3, 4, 3, 4]]) >>> # Example 3: 2D tensor - multiple repeats >>> x = paddle.to_tensor([[1, 2], [3, 4]]) >>> out = x.repeat([2, 3]) >>> print(out) Tensor(shape=[4, 6], dtype=int64, place=Place(gpu:0), stop_gradient=True, [[1, 2, 1, 2, 1, 2], [3, 4, 3, 4, 3, 4], [1, 2, 1, 2, 1, 2], [3, 4, 3, 4, 3, 4]]) >>> # Example 4: 3D tensor - mixed repeats >>> x = paddle.to_tensor([[[1, 2], [3, 4]]]) >>> out = x.repeat([2, 1, 3]) >>> print(out) Tensor(shape=[2, 2, 6], dtype=int64, place=Place(gpu:0), stop_gradient=True, [[[1, 2, 1, 2, 1, 2], [3, 4, 3, 4, 3, 4]], [[1, 2, 1, 2, 1, 2], [3, 4, 3, 4, 3, 4]]]) """ ... def repeat_interleave(self, repeats: '_int | Tensor', axis: '_int | None' = None, name: '_str | None' = None, *, output_size: '_int | None' = None) -> 'Tensor': r""" Returns a new tensor which repeats the ``x`` tensor along dimension ``axis`` using the entries in ``repeats`` which is a int or a Tensor. The image illustrates a typical case of the repeat_interleave operation. Given a tensor ``[[1, 2, 3], [4, 5, 6]]``, with the repeat counts ``repeats = [3, 2, 1]`` and parameter ``axis = 1``, it means that the elements in the 1st column are repeated 3 times, the 2nd column is repeated 2 times, and the 3rd column is repeated 1 time. The final output is a 2D tensor: ``[[1, 1, 1, 2, 2, 3], [4, 4, 4, 5, 5, 6]]``. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/repeat_interleave.png :width: 500 :alt: legend of repeat_interleave API :align: center .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``dim`` can be used as an alias for ``axis``. For example, ``repeat_interleave(input=tensor_x, dim=1, ...)`` is equivalent to ``repeat_interleave(x=tensor_x, axis=1, ...)``. Args: repeats (Tensor|int): The number of repetitions for each element. repeats is broadcasted to fit the shape of the given axis. axis (int|None, optional): The dimension in which we manipulate. Default: None, the output tensor is flatten. alias: ``dim``. name(str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. output_size (int, optional): Total output size for the given axis (e.g. sum of repeats). If given, it will avoid stream synchronization needed to calculate output shape of the tensor. Returns: Tensor, A Tensor with same data type as ``x``. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 2, 3], [4, 5, 6]]) >>> repeats = paddle.to_tensor([3, 2, 1], dtype='int32') >>> out = paddle.repeat_interleave(x, repeats, 1) >>> print(out) Tensor(shape=[2, 6], dtype=int64, place=Place(cpu), stop_gradient=True, [[1, 1, 1, 2, 2, 3], [4, 4, 4, 5, 5, 6]]) >>> out = paddle.repeat_interleave(x, 2, 0) >>> print(out) Tensor(shape=[4, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[1, 2, 3], [1, 2, 3], [4, 5, 6], [4, 5, 6]]) >>> out = paddle.repeat_interleave(x, 2, None) >>> print(out) Tensor(shape=[12], dtype=int64, place=Place(cpu), stop_gradient=True, [1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6]) """ ... def requires_grad_(self, requires_grad: '_bool' = True) -> 'Tensor': r""" Set whether this Tensor requires gradient computation. Args: """ ... def reshape(self, shape: 'ShapeLike', name: '_str | None' = None) -> 'Tensor': r""" Changes the shape of ``x`` without changing its data. Note that the output Tensor will share data with origin Tensor and doesn't have a Tensor copy in ``dygraph`` mode. If you want to use the Tensor copy version, please use `Tensor.clone` like ``reshape_clone_x = x.reshape([-1]).clone()``. Some tricks exist when specifying the target shape. - 1. -1 means the value of this dimension is inferred from the total element number of x and remaining dimensions. Thus one and only one dimension can be set -1. - 2. 0 means the actual dimension value is going to be copied from the corresponding dimension of x. The index of 0s in shape can not exceed the dimension of x. Here are some examples to explain it. - 1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [6, 8], the reshape operator will transform x into a 2-D tensor with shape [6, 8] and leaving x's data unchanged. - 2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape specified is [2, 3, -1, 2], the reshape operator will transform x into a 4-D tensor with shape [2, 3, 4, 2] and leaving x's data unchanged. In this case, one dimension of the target shape is set to -1, the value of this dimension is inferred from the total element number of x and remaining dimensions. - 3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [-1, 0, 3, 2], the reshape operator will transform x into a 4-D tensor with shape [2, 4, 3, 2] and leaving x's data unchanged. In this case, besides -1, 0 means the actual dimension value is going to be copied from the corresponding dimension of x. The following figure illustrates the first example -- a 3D tensor of shape [2, 4, 6] is transformed into a 2D tensor of shape [6, 8], during which the order and values of the elements in the tensor remain unchanged. The elements in the two subdiagrams correspond to each other, clearly demonstrating how the reshape API works. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/reshape.png :width: 800 :alt: legend of reshape API :align: center Args: shape (list|tuple|Tensor): Define the target shape. At most one dimension of the target shape can be -1. The data type is ``int32`` . If ``shape`` is a list or tuple, each element of it should be integer or Tensor with shape []. If ``shape`` is a Tensor, it should be an 1-D Tensor . name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, A reshaped Tensor with the same data type as ``x``. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.rand([2, 4, 6], dtype="float32") >>> positive_four = paddle.full([1], 4, "int32") >>> out = paddle.reshape(x, [-1, 0, 3, 2]) >>> print(out.shape) paddle.Size([2, 4, 3, 2]) >>> out = paddle.reshape(x, shape=[positive_four, 12]) >>> print(out.shape) paddle.Size([4, 12]) >>> shape_tensor = paddle.to_tensor([8, 6], dtype=paddle.int32) >>> out = paddle.reshape(x, shape=shape_tensor) >>> print(out.shape) paddle.Size([8, 6]) >>> # out shares data with x in dygraph mode >>> x[0, 0, 0] = 10.0 >>> print(out[0, 0]) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 10.) """ ... def reshape_(self, shape: 'ShapeLike', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``reshape`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_tensor_reshape`. """ ... def resize_(self, shape: 'Sequence[_int]', fill_zero: '_bool' = False, name: '_str | None' = None) -> 'paddle.Tensor': r""" Resize ``x`` with specified ``shape``. Args: shape (list|tuple): Define the target shape. Each element of it should be integer. fill_zero (bool, optional): If the size of specified ``shape`` is greater than the original Tensor size, the new Tensor will be filled with zero if ``fill_zero`` is True. Default: False, which means the filled value will be undetermined. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, the resized Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1., 2., 3.]) >>> x.resize_([2, 1]) >>> print(x) Tensor(shape=[2, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[1.], [2.]]) >>> x = paddle.to_tensor([1., 2., 3.]) >>> x.resize_([2, 3], fill_zero=True) >>> print(x) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[1., 2., 3.], [0., 0., 0.]]) """ ... def retain_grads(self, /): r""" Enables this Tensor to have their grad populated during backward(). It is a no-op for leaf tensors. Returns: None. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1.0, 2.0, 3.0]) >>> x.stop_gradient = False >>> y = x + x >>> y.retain_grads() >>> loss = y.sum() >>> loss.backward() >>> print(y.grad) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=False, [1., 1., 1.]) >>> x = paddle.to_tensor([1.0, 2.0, 3.0]) >>> x.stop_gradient = False >>> y = x + x >>> y.retain_grads() >>> loss = y.sum() >>> loss.backward() >>> print(y.grad) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=False, [1., 1., 1.]) """ ... def roll(self, shifts: '_int | Sequence[_int]', axis: '_int | Sequence[_int] | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Roll the `x` tensor along the given axis(axes). With specific 'shifts', Elements that roll beyond the last position are re-introduced at the first according to 'shifts'. If a axis is not specified, the tensor will be flattened before rolling and then restored to the original shape. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and the parameter name ``dim`` can be used as an alias for ``axis``. For example, ``roll(input=tensor_x, dim=1)`` is equivalent to ``roll(x=tensor_x, axis=1)``. Args: shifts (int|list|tuple): The number of places by which the elements of the `x` tensor are shifted. axis (int|list|tuple, optional): axis(axes) along which to roll. Default: None alias: ``dim``. name(str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . The image below shows a 2D tensor `[[1,2,3],[4,5,6],[7,8,9]]` being transformed into tensors with different shapes through the roll operation. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/roll.png :width: 700 :align: center :alt: legend of roll API Returns: Tensor, A Tensor with same data type as `x`. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1.0, 2.0, 3.0], ... [4.0, 5.0, 6.0], ... [7.0, 8.0, 9.0]]) >>> out_z1 = paddle.roll(x, shifts=1) >>> print(out_z1.numpy()) [[9. 1. 2.] [3. 4. 5.] [6. 7. 8.]] >>> out_z2 = paddle.roll(x, shifts=1, axis=0) >>> print(out_z2.numpy()) [[7. 8. 9.] [1. 2. 3.] [4. 5. 6.]] >>> out_z3 = paddle.roll(x, shifts=1, axis=1) >>> print(out_z3.numpy()) [[3. 1. 2.] [6. 4. 5.] [9. 7. 8.]] """ ... def rot90(self, k: '_int' = 1, axes: 'Sequence[_int]' = [0, 1], name: '_str | None' = None) -> 'Tensor': r""" Rotate a n-D tensor by 90 degrees. The rotation direction and times are specified by axes and the absolute value of k. Rotation direction is from axes[0] towards axes[1] if k > 0, and from axes[1] towards axes[0] for k < 0. Args: k (int, optional): Direction and number of times to rotate, default value: 1. axes (list|tuple, optional): Axes to rotate, dimension must be 2. default value: [0, 1]. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: Tensor, Tensor or DenseTensor calculated by rot90 layer. The data type is same with input x. Examples: .. code-block:: python >>> import paddle >>> data = paddle.arange(4) >>> data = paddle.reshape(data, (2, 2)) >>> print(data.numpy()) [[0 1] [2 3]] >>> y = paddle.rot90(data, 1, [0, 1]) >>> print(y.numpy()) [[1 3] [0 2]] >>> y= paddle.rot90(data, -1, [0, 1]) >>> print(y.numpy()) [[2 0] [3 1]] >>> data2 = paddle.arange(8) >>> data2 = paddle.reshape(data2, (2,2,2)) >>> print(data2.numpy()) [[[0 1] [2 3]] [[4 5] [6 7]]] >>> y = paddle.rot90(data2, 1, [1, 2]) >>> print(y.numpy()) [[[1 3] [0 2]] [[5 7] [4 6]]] """ ... def round(self, decimals: '_int' = 0, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Round the values in the input to the nearest integer value. .. code-block:: text input: x.shape = [4] x.data = [1.2, -0.9, 3.4, 0.9] output: out.shape = [4] out.data = [1., -1., 3., 1.] .. note:: Alias Support: 1. The parameter name ``input`` can be used as an alias for ``x``. Args: decimals(int): Rounded decimal place (default: 0). name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output tensor. Default: None. Returns: Tensor. Output of Round operator, a Tensor with shape same as input. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.5, -0.2, 0.6, 1.5]) >>> out = paddle.round(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [-0., -0., 1., 2.]) """ ... def round_(self, decimals=0, name=None): r""" Inplace version of ``round`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_round`. """ ... def rsqrt(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Rsqrt Activation Operator. Please make sure input is legal in case of numeric errors. .. math:: out = \frac{1}{\sqrt{x}} Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output tensor. If set, the result will be stored in this tensor. Default is None. Returns: Tensor. Output of Rsqrt operator, a Tensor with shape same as input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([0.1, 0.2, 0.3, 0.4]) >>> out = paddle.rsqrt(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [3.16227770, 2.23606801, 1.82574177, 1.58113885]) """ ... def rsqrt_(self, name=None): r""" Inplace version of ``rsqrt`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_rsqrt`. """ ... def scale(self, scale: '_float | Tensor' = 1.0, bias: '_float' = 0.0, bias_after_scale: '_bool' = True, act: '_str | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Scale operator. Putting scale and bias to the input Tensor as following: ``bias_after_scale`` is True: .. math:: Out=scale*X+bias ``bias_after_scale`` is False: .. math:: Out=scale*(X+bias) Args: scale (float|Tensor): The scale factor of the input, it should be a float number or a 0-D Tensor with shape [] and data type as float32. bias (float): The bias to be put on the input. bias_after_scale (bool): Apply bias addition after or before scaling. It is useful for numeric stability in some circumstances. act (str|None, optional): Activation applied to the output such as tanh, softmax, sigmoid, relu. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Output Tensor of scale operator, with shape and data type same as input. Examples: .. code-block:: python >>> # scale as a float32 number >>> import paddle >>> data = paddle.arange(6).astype("float32").reshape([2, 3]) >>> print(data) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[0., 1., 2.], [3., 4., 5.]]) >>> res = paddle.scale(data, scale=2.0, bias=1.0) >>> print(res) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[1. , 3. , 5. ], [7. , 9. , 11.]]) .. code-block:: python >>> # scale with parameter scale as a Tensor >>> import paddle >>> data = paddle.arange(6).astype("float32").reshape([2, 3]) >>> print(data) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[0., 1., 2.], [3., 4., 5.]]) >>> factor = paddle.to_tensor([2], dtype='float32') >>> res = paddle.scale(data, scale=factor, bias=1.0) >>> print(res) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[1. , 3. , 5. ], [7. , 9. , 11.]]) """ ... def scale_(self, scale: '_float' = 1.0, bias: '_float' = 0.0, bias_after_scale: '_bool' = True, act: '_str | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``scale`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_scale`. """ ... def scatter(self, index: 'Tensor', updates: 'Tensor', overwrite: '_bool' = True, name: '_str | None' = None, out: 'Tensor | None' = None) -> 'Tensor': r""" This function has two functionalities, depending on the parameters passed: 1. ``scatter(Tensor input, int dim, Tensor index, Tensor src (alias value), *, str reduce = None, Tensor out = None)``: PyTorch compatible scatter, calls a non-broadcast `paddle.put_along_axis`. Check out :ref:`api_paddle_put_along_axis` and also `[torch has more parameters] torch.scatter `_ 2. ``scatter(Tensor x, Tensor index, Tensor updates, bool overwrite, str name = None)``: The original paddle.scatter, see the following docs. **Scatter Layer** Output is obtained by updating the input on selected indices based on updates. As shown in the figure, when ``overwrite`` is set to ``True``, the output for the same index is updated in overwrite mode, where ``x[index[i]]`` is directly replaced with ``update[i]`` sequentially; When ``overwrite`` is set to ``False``, the output for the same index is updated in accumulation mode. In this mode, ``x[index[i]]`` is first initialized with elements set to 0. Then, ``update[i]`` is sequentially added to ``x[index[i]]`` to produce the output. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/scatter.png :alt: Legend - scatter behavior display .. code-block:: pycon :name: scatter-example-1 >>> import paddle >>> # input: >>> x = paddle.to_tensor([[1, 1], [2, 2], [3, 3]], dtype='float32') >>> index = paddle.to_tensor([2, 1, 0, 1], dtype='int64') >>> # shape of updates should be the same as x >>> # shape of updates with dim > 1 should be the same as input >>> updates = paddle.to_tensor([[1, 1], [2, 2], [3, 3], [4, 4]], dtype='float32') >>> overwrite = False >>> # calculation: >>> if not overwrite: ... for i in range(len(index)): ... x[index[i]] = paddle.zeros([2]) >>> for i in range(len(index)): ... if overwrite: ... x[index[i]] = updates[i] ... else: ... x[index[i]] += updates[i] >>> # output: >>> out = paddle.to_tensor([[3, 3], [6, 6], [1, 1]]) >>> print(out.shape) paddle.Size([3, 2]) **NOTICE**: The order in which updates are applied is nondeterministic, so the output will be nondeterministic if index contains duplicates. Args: index (Tensor): The index is a 1-D or 0-D Tensor. Data type can be int32, int64. The length of index cannot exceed updates's length, and the value in index cannot exceed input's length. updates (Tensor): Update input with updates parameter based on index. When the index is a 1-D tensor, the updates shape should be the same as input, and dim value with dim > 1 should be the same as input. When the index is a 0-D tensor, the updates should be a (N-1)-D tensor, the ith dim of the updates should be equal with the (i+1)th dim of the input. overwrite (bool, optional): The mode that updating the output when there are same indices.If True, use the overwrite mode to update the output of the same index,if False, use the accumulate mode to update the output of the same index. Default value is True. name(str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: Tensor, The output is a Tensor with the same shape as x. Examples: .. code-block:: python :name: scatter-example-2 >>> import paddle >>> x = paddle.to_tensor([[1, 1], [2, 2], [3, 3]], dtype='float32') >>> index = paddle.to_tensor([2, 1, 0, 1], dtype='int64') >>> updates = paddle.to_tensor([[1, 1], [2, 2], [3, 3], [4, 4]], dtype='float32') >>> output1 = paddle.scatter(x, index, updates, overwrite=False) >>> print(output1) Tensor(shape=[3, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[3., 3.], [6., 6.], [1., 1.]]) >>> output2 = paddle.scatter(x, index, updates, overwrite=True) >>> # CPU device: >>> # [[3., 3.], >>> # [4., 4.], >>> # [1., 1.]] >>> # GPU device maybe have two results because of the repeated numbers in index >>> # result 1: >>> # [[3., 3.], >>> # [4., 4.], >>> # [1., 1.]] >>> # result 2: >>> # [[3., 3.], >>> # [2., 2.], >>> # [1., 1.]] """ ... def scatter_(self, *args: 'Any', **kwargs: 'Any') -> 'Tensor': r""" Inplace version of ``scatter`` API, the output Tensor will be inplaced with input. Please refer to :ref:`api_paddle_tensor_scatter`. """ ... def scatter_add(self, dim: '_int', index: 'Tensor', src: 'Tensor') -> 'Tensor': r""" Scatter the values of the source tensor to the target tensor according to the given indices, and perform a add operation along the designated axis. Args: dim (int) : The axis to scatter 1d slices along. index (Tensor) : Indices to scatter along each 1d slice of input. This must match the dimension of input, Supported data type are int32 and int64. src (Tensor) : The value element(s) to scatter. The data types should be same as input. Returns: Tensor, The indexed element, same dtype with input Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[10, 20, 30], [40, 50, 60]]) >>> indices = paddle.zeros((2,3)).astype("int32") >>> values = paddle.to_tensor([[1, 2, 3],[4, 5, 6]]).astype(x.dtype) >>> result = paddle.scatter_add(x, 0, indices, values) >>> print(result) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[15, 27, 39], [40, 50, 60]]) """ ... def scatter_add_(self, dim: '_int', index: 'Tensor', src: 'Tensor') -> 'Tensor': r""" Inplace version of ``scatter_add`` API, the output Tensor will be inplaced with input ``input``. Please refer to :ref:`api_paddle_scatter_add`. """ ... def scatter_nd(self, updates: 'Tensor', shape: 'ShapeLike', name: '_str | None' = None) -> 'Tensor': r""" **Scatter_nd Layer** Output is obtained by scattering the :attr:`updates` in a new tensor according to :attr:`index` . This op is similar to :code:`scatter_nd_add`, except the tensor of :attr:`shape` is zero-initialized. Correspondingly, :code:`scatter_nd(index, updates, shape)` is equal to :code:`scatter_nd_add(paddle.zeros(shape, updates.dtype), index, updates)` . If :attr:`index` has repeated elements, then the corresponding updates are accumulated. Because of the numerical approximation issues, the different order of repeated elements in :attr:`index` may cause different results. The specific calculation method can be seen :code:`scatter_nd_add` . This op is the inverse of the :code:`gather_nd` op. Args: updates (Tensor): The updated value of scatter_nd op. Its dtype should be float32, float64. It must have the shape index.shape[:-1] + shape[index.shape[-1]:] shape(tuple|list|Tensor): Shape of output tensor. name (str|None, optional): The output Tensor name. If set None, the layer will be named automatically. Returns: output (Tensor), The output is a tensor with the same type as :attr:`updates` . Examples: .. code-block:: python >>> import paddle >>> index = paddle.to_tensor([[1, 1], ... [0, 1], ... [1, 3]], dtype="int64") >>> updates = paddle.rand(shape=[3, 9, 10], dtype='float32') >>> shape = [3, 5, 9, 10] >>> output = paddle.scatter_nd(index, updates, shape) """ ... def scatter_nd_add(self, index: 'Tensor', updates: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" Output is obtained by applying sparse addition to a single value or slice in a Tensor. :attr:`x` is a Tensor with ndim :math:`R` and :attr:`index` is a Tensor with ndim :math:`K` . Thus, :attr:`index` has shape :math:`[i_0, i_1, ..., i_{K-2}, Q]` where :math:`Q \leq R` . :attr:`updates` is a Tensor with ndim :math:`K - 1 + R - Q` and its shape is :math:`index.shape[:-1] + x.shape[index.shape[-1]:]` . According to the :math:`[i_0, i_1, ..., i_{K-2}]` of :attr:`index` , add the corresponding :attr:`updates` slice to the :attr:`x` slice which is obtained by the last one dimension of :attr:`index` . .. code-block:: text Given: * Case 1: x = [0, 1, 2, 3, 4, 5] index = [[1], [2], [3], [1]] updates = [9, 10, 11, 12] we get: output = [0, 22, 12, 14, 4, 5] * Case 2: x = [[65, 17], [-14, -25]] index = [[], []] updates = [[[-1, -2], [1, 2]], [[3, 4], [-3, -4]]] x.shape = (2, 2) index.shape = (2, 0) updates.shape = (2, 2, 2) we get: output = [[67, 19], [-16, -27]] Args: index (Tensor): The index input with ndim > 1 and index.shape[-1] <= x.ndim. Its dtype should be int32 or int64 as it is used as indexes. updates (Tensor): The updated value of scatter_nd_add op, and it must have the same dtype as x. It must have the shape index.shape[:-1] + x.shape[index.shape[-1]:]. name (str|None, optional): The output tensor name. If set None, the layer will be named automatically. Returns: output (Tensor), The output is a tensor with the same shape and dtype as x. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.rand(shape=[3, 5, 9, 10], dtype='float32') >>> updates = paddle.rand(shape=[3, 9, 10], dtype='float32') >>> index = paddle.to_tensor([[1, 1], [0, 1], [1, 3]], dtype='int64') >>> output = paddle.scatter_nd_add(x, index, updates) >>> print(output.shape) paddle.Size([3, 5, 9, 10]) """ ... def scatter_reduce(self, dim: '_int', index: 'Tensor', src: 'Tensor', reduce: "Literal['sum', 'prod', 'mean', 'amin', 'amax']", *, include_self: '_bool' = True) -> 'Tensor': r""" Scatter the values of the source tensor to the target tensor according to the given indices, and perform a reduction operation along the designated axis. Args: dim (int) : The axis to scatter 1d slices along. index (Tensor) : Indices to scatter along each 1d slice of input. This must match the dimension of input, Supported data type are int32 and int64. src (Tensor) : The value element(s) to scatter. The data types should be same as input. reduce (str): The reduce operation, support 'sum', 'prod', 'mean', 'amin', 'amax'. include_self (bool, optional): whether to reduce with the elements of input, default is 'True'. Returns: Tensor, The indexed element, same dtype with input Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[10, 20, 30], [40, 50, 60]]) >>> indices = paddle.zeros((2,3)).astype("int32") >>> values = paddle.to_tensor([[1, 2, 3],[4, 5, 6]]).astype(x.dtype) >>> result = paddle.scatter_reduce(x, 0, indices, values, "sum", include_self=True) >>> print(result) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[15, 27, 39], [40, 50, 60]]) >>> result = paddle.scatter_reduce(x, 0, indices, values, "prod", include_self=True) >>> print(result) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[40 , 200, 540], [40 , 50 , 60 ]]) >>> result = paddle.scatter_reduce(x, 0, indices, values, "mean", include_self=True) >>> print(result) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[5 , 9 , 13], [40, 50, 60]]) """ ... def select_scatter(self, values: 'Tensor', axis: '_int', index: '_int', name: '_str | None' = None) -> 'Tensor': r""" Embeds the values of the values tensor into x at the given index of axis. Args: values (Tensor) : The tensor to embed into x. Supported data types are `bool`, `float16`, `float32`, `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`, `bfloat16`, `complex64`, `complex128`. axis (int) : the dimension to insert the slice into. index (int) : the index to select with. name (str|None, optional): Name for the operation (optional, default is None). Returns: Tensor, same dtype and shape with x Examples: .. code-block:: python >>> import paddle >>> x = paddle.zeros((2,3,4)).astype("float32") >>> values = paddle.ones((2,4)).astype("float32") >>> res = paddle.select_scatter(x,values,1,1) >>> print(res) Tensor(shape=[2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[[0., 0., 0., 0.], [1., 1., 1., 1.], [0., 0., 0., 0.]], [[0., 0., 0., 0.], [1., 1., 1., 1.], [0., 0., 0., 0.]]]) """ ... def set_(self, source: 'paddle.Tensor | None' = None, shape: 'Sequence[_int] | None' = None, stride: 'Sequence[_int] | None' = None, offset: '_int' = 0, name: '_str | None' = None) -> 'paddle.Tensor': r""" set x with specified source Tensor's underlying storage, shape, stride and offset. Note that the ``x`` will share the same data with ``source`` Tensor. Args: source (Tensor|None, optional): Define the target Tensor to use. The data type supports `bfloat16`, ``float16``, ``float32``, ``float64``, ``bool``, ``int8``, ``int16``, ``int32``, ``int64``, ``uint8``, ``complex64`` or ``complex128``. Default: None, which means to set ``x`` with an empty source tensor. shape (list|tuple|None, optional): Define the target shape. Each element of it should be integer. Default: None, which means it will use the specified ``source``'s shape as default value. stride (list|tuple|None, optional): Define the target stride. Each element of it should be integer. Default: None, and when ``shape`` is also None, it will use the specified ``source``'s stride as default value; when ``shape`` is specified, it will use the default stride corresponding to the specified ``shape``. offset (int, optional): Define the target offset from x's holder. Default: 0. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, the Tensor with the same data type as ``x``. Examples: .. code-block:: python >>> import paddle >>> src = paddle.to_tensor([[11., 22., 33.]]) >>> src2 = paddle.to_tensor([11., 22., 33., 44., 55., 66.]) >>> x = paddle.to_tensor([1., 2., 3., 4., 5.]) >>> x.set_() >>> print(x) Tensor(shape=[0], dtype=float32, place=Place(cpu), stop_gradient=True, []) >>> x = paddle.to_tensor([1., 2., 3., 4., 5.]) >>> x.set_(src) >>> print(x) Tensor(shape=[1, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[11., 22., 33.]]) >>> print(x._is_shared_buffer_with(src)) True >>> x = paddle.to_tensor([1., 2., 3., 4., 5.]) >>> x.set_(src, shape=[2, 1]) >>> print(x) Tensor(shape=[2, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[11.], [22.]]) >>> x = paddle.to_tensor([1., 2., 3., 4., 5.]) >>> x.set_(src2, shape=[3], stride=[2]) >>> print(x) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [11., 33., 55.]) >>> x = paddle.to_tensor([1., 2., 3., 4., 5.]) >>> x.set_(src2, shape=[5], offset=4) >>> print(x) Tensor(shape=[5], dtype=float32, place=Place(cpu), stop_gradient=True, [22., 33., 44., 55., 66.]) """ ... def set_tensor(self): ... def set_value(self, value: 'Tensor | npt.NDArray[Any] | dict[_str, _int] | _str') -> 'None': r""" **Notes**: **This API is ONLY available in Dygraph mode** Set a new value for this Variable. Args: Examples: .. code-block:: python >>> import paddle.base as base >>> import paddle >>> from paddle.nn import Linear >>> import numpy as np >>> data = np.ones([3, 1024], dtype='float32') >>> with base.dygraph.guard(): ... linear = Linear(1024, 4) ... t = paddle.to_tensor(data) ... linear(t) # call with default weight ... custom_weight = np.random.randn(1024, 4).astype("float32") ... linear.weight.set_value(custom_weight) # change existing weight ... out = linear(t) # call with different weight """ ... def sgn(self, name: '_str | None' = None) -> 'Tensor': r""" For complex tensor, this API returns a new tensor whose elements have the same angles as the corresponding elements of input and absolute values of one. For other float dtype tensor, this API returns sign of every element in `x`: 1 for positive, -1 for negative and 0 for zero, same as paddle.sign. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A sign Tensor for real input, or normalized Tensor for complex input, shape and data type are same as input. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.to_tensor([[3 + 4j, 7 - 24j, 0, 1 + 2j], [6 + 8j, 3, 0, -2]]) >>> paddle.sgn(x) Tensor(shape=[2, 4], dtype=complex64, place=Place(cpu), stop_gradient=True, [[ (0.60000002+0.80000001j), (0.28000000-0.95999998j), (0.00000000+0.00000000j), (0.44721359+0.89442718j)], [ (0.60000002+0.80000001j), (1.00000000+0.00000000j), (0.00000000+0.00000000j), (-1.00000000+0.00000000j)]]) """ ... def shard_index(self, index_num: '_int', nshards: '_int', shard_id: '_int', ignore_value: '_int' = -1) -> 'Tensor': r""" Reset the values of `input` according to the shard it belongs to. Every value in `input` must be a non-negative integer, and the parameter `index_num` represents the integer above the maximum value of `input`. Thus, all values in `input` must be in the range [0, index_num) and each value can be regarded as the offset to the beginning of the range. The range is further split into multiple shards. Specifically, we first compute the `shard_size` according to the following formula, which represents the number of integers each shard can hold. So for the i'th shard, it can hold values in the range [i*shard_size, (i+1)*shard_size). :: shard_size = (index_num + nshards - 1) // nshards For each value `v` in `input`, we reset it to a new value according to the following formula: :: v = v - shard_id * shard_size if shard_id * shard_size <= v < (shard_id + 1) * shard_size else ignore_value That is, the value `v` is set to the new offset within the range represented by the shard `shard_id` if it in the range. Otherwise, we reset it to be `ignore_value`. As shown below, a ``[2, 1]`` 2D tensor is updated with the ``shard_index`` operation. Given ``index_num = 20``, ``nshards = 2``, and ``shard_id = 0``, the shard size is ``shard_size = (20 + 2 - 1) // 2 = 10``. For each label element: if its value is in [0, 10), it's adjusted to its offset; e.g., 1 becomes 1 - 0 * 10 = 1. Otherwise, it's set to the default ignore_value of -1, like 16 becoming -1. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/shard_index.png :width: 500 :alt: Illustration of Case 2 :align: center Args: index_num (int): An integer represents the integer above the maximum value of `input`. nshards (int): The number of shards. shard_id (int): The index of the current shard. ignore_value (int, optional): An integer value out of sharded index range. The default value is -1. Returns: Tensor. Examples: .. code-block:: python >>> import paddle >>> label = paddle.to_tensor([[16], [1]], "int64") >>> shard_label = paddle.shard_index(input=label, ... index_num=20, ... nshards=2, ... shard_id=0) >>> print(shard_label.numpy()) [[-1] [ 1]] """ ... def short(self) -> 'Tensor': r""" Cast a Tensor to int16 data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with int16 dtype """ ... def sigmoid(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'paddle.Tensor': r""" Sigmoid Activation. .. math:: out = \\frac{1}{1 + e^{-x}} .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``. For example, ``sigmoid(input=tensor_x)`` is equivalent to ``sigmoid(x=tensor_x)``. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Keyword Args: out (Tensor|optional): The output tensor. Returns: Tensor. Output of Sigmoid operator, a Tensor with shape same as input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> import paddle.nn.functional as F >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = F.sigmoid(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [0.40131235, 0.45016602, 0.52497917, 0.57444251]) """ ... def sigmoid_(self, name=None): r""" Inplace version of ``sigmoid`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_sigmoid`. """ ... def sign(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Returns sign of every element in `x`: For real numbers, 1 for positive, -1 for negative and 0 for zero. For complex numbers, the return value is a complex number with unit magnitude. If a complex number element is zero, the result is 0+0j. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output tensor. If set, the result will be stored in this tensor. Default is None. Returns: Tensor: The output sign tensor with identical shape and data type to the input :attr:`x`. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([3.0, 0.0, -2.0, 1.7], dtype='float32') >>> out = paddle.sign(x=x) >>> out Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [ 1., 0., -1., 1.]) """ ... def signbit(self, name: '_str | None' = None) -> 'Tensor': r""" Tests if each element of input has its sign bit set or not. Args: name (str|None, optional): Name for the operation (optional, default is None).For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor): The output Tensor. The sign bit of the corresponding element of the input tensor, True means negative, False means positive. Examples: .. code-block:: python :name: signbit-example-1 >>> import paddle >>> paddle.set_device('cpu') >>> x = paddle.to_tensor([-0., 1.1, -2.1, 0., 2.5], dtype='float32') >>> res = paddle.signbit(x) >>> print(res) Tensor(shape=[5], dtype=bool, place=Place(cpu), stop_gradient=True, [True, False, True, False, False]) .. code-block:: python :name: signbit-example-2 >>> import paddle >>> paddle.set_device('cpu') >>> x = paddle.to_tensor([-5, -2, 3], dtype='int32') >>> res = paddle.signbit(x) >>> print(res) Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True, [True , True , False]) """ ... def sin(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Sine Activation Operator. .. math:: out = sin(x) Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output tensor. If set, the result will be stored in this tensor. Default is None. Returns: Tensor. Output of Sin operator, a Tensor with shape same as input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.sin(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [-0.38941833, -0.19866933, 0.09983342, 0.29552022]) """ ... def sin_(self, name=None): r""" Inplace version of ``sin`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_sin`. """ ... def sinc(self, name: '_str | None' = None) -> 'Tensor': r""" Calculate the normalized sinc of ``x`` elementwise. .. math:: out_i = \left\{ \begin{aligned} &1 & \text{ if $x_i = 0$} \\ &\frac{\sin(\pi x_i)}{\pi x_i} & \text{ otherwise} \end{aligned} \right. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: out (Tensor), The Tensor of elementwise-computed normalized sinc result. Examples: .. code-block:: python >>> import paddle >>> paddle.set_device('cpu') >>> paddle.seed(100) >>> x = paddle.rand([2,3], dtype='float32') >>> res = paddle.sinc(x) >>> print(res) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.56691176, 0.93089867, 0.99977750], [0.61639023, 0.79618412, 0.89171958]]) """ ... def sinc_(self, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``sinc`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_sinc`. """ ... def sinh(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Sinh Activation Operator. .. math:: out = sinh(x) Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor. Output of Sinh operator, a Tensor with shape same as input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.sinh(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [-0.41075233, -0.20133601, 0.10016675, 0.30452031]) """ ... def sinh_(self, name=None): r""" Inplace version of ``sinh`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_sinh`. """ ... def slice(self, axes: 'Sequence[_int | Tensor]', starts: 'Sequence[_int | Tensor] | Tensor', ends: 'Sequence[_int | Tensor] | Tensor') -> 'Tensor': r""" This operator produces a slice of ``input`` along multiple axes. Similar to numpy: https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html Slice uses ``axes``, ``starts`` and ``ends`` attributes to specify the start and end dimension for each axis in the list of axes and Slice uses this information to slice the input data tensor. If a negative value is passed to ``starts`` or ``ends`` such as :math:`-i`, it represents the reverse position of the axis :math:`i-1` (here 0 is the initial position). If the value passed to ``starts`` or ``ends`` is greater than n (the number of elements in this dimension), it represents n. For slicing to the end of a dimension with unknown size, it is recommended to pass in INT_MAX. The size of ``axes`` must be equal to ``starts`` and ``ends``. Following examples will explain how slice works: .. code-block:: text Case1: Given: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] Then: result = [ [5, 6, 7], ] Case2: Given: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [0, 1] ends = [-1, 1000] # -1 denotes the reverse 0th position of dimension 0. Then: result = [ [2, 3, 4], ] # result = data[0:1, 1:4] The following figure illustrates the first case -- a 2D tensor of shape [2, 4] is transformed into a 2D tensor of shape [1, 3] through a slicing operation. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/slice.png :width: 500 :alt: legend of slice API :align: center Args: axes (list|tuple): The data type is ``int32`` . Axes that `starts` and `ends` apply to . starts (list|tuple|Tensor): The data type is ``int32`` . If ``starts`` is a list or tuple, each element of it should be integer or 0-D int Tensor with shape []. If ``starts`` is an Tensor, it should be an 1-D Tensor. It represents starting indices of corresponding axis in ``axes``. ends (list|tuple|Tensor): The data type is ``int32`` . If ``ends`` is a list or tuple, each element of it should be integer or 0-D int Tensor with shape []. If ``ends`` is an Tensor, it should be an 1-D Tensor . It represents ending indices of corresponding axis in ``axes``. Returns: Tensor, A ``Tensor``. The data type is same as ``input``. Examples: .. code-block:: python >>> import paddle >>> input = paddle.rand(shape=[4, 5, 6], dtype='float32') >>> # example 1: >>> # attr starts is a list which doesn't contain tensor. >>> axes = [0, 1, 2] >>> starts = [-3, 0, 2] >>> ends = [3, 2, 4] >>> sliced_1 = paddle.slice(input, axes=axes, starts=starts, ends=ends) >>> # sliced_1 is input[1:3, 0:2, 2:4]. >>> # example 2: >>> # attr starts is a list which contain tensor. >>> minus_3 = paddle.full([1], -3, "int32") >>> sliced_2 = paddle.slice(input, axes=axes, starts=[minus_3, 0, 2], ends=ends) >>> # sliced_2 is input[1:3, 0:2, 2:4]. """ ... def slice_scatter(self, value: 'Tensor', axes: 'Sequence[_int]', starts: 'Sequence[_int]', ends: 'Sequence[_int]', strides: 'Sequence[_int]', name: '_str | None' = None) -> 'Tensor': r""" Embeds the `value` tensor into `x` along multiple axes. Returns a new tensor instead of a view. The size of `axes` must be equal to `starts` , `ends` and `strides`. Args: value (Tensor) : The tensor to embed into x. Supported data types are `bool`, `float16`, `float32`, `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`, `bfloat16`, `complex64`, `complex128`. axes (list|tuple) : the dimensions to insert the value. starts (list|tuple) : the start indices of where to insert. ends (list|tuple) : the stop indices of where to insert. strides (list|tuple) : the steps for each insert. name (str|None, optional): Name for the operation (optional, default is None). Returns: Tensor, same dtype and shape with x Examples: .. code-block:: python >>> import paddle >>> x = paddle.zeros((3, 9)) >>> value = paddle.ones((3, 2)) >>> res = paddle.slice_scatter(x, value, axes=[1], starts=[2], ends=[6], strides=[2]) >>> print(res) Tensor(shape=[3, 9], dtype=float32, place=Place(cpu), stop_gradient=True, [[0., 0., 1., 0., 1., 0., 0., 0., 0.], [0., 0., 1., 0., 1., 0., 0., 0., 0.], [0., 0., 1., 0., 1., 0., 0., 0., 0.]]) >>> # broadcast `value` got the same result >>> x = paddle.zeros((3, 9)) >>> value = paddle.ones((3, 1)) >>> res = paddle.slice_scatter(x, value, axes=[1], starts=[2], ends=[6], strides=[2]) >>> print(res) Tensor(shape=[3, 9], dtype=float32, place=Place(cpu), stop_gradient=True, [[0., 0., 1., 0., 1., 0., 0., 0., 0.], [0., 0., 1., 0., 1., 0., 0., 0., 0.], [0., 0., 1., 0., 1., 0., 0., 0., 0.]]) >>> # broadcast `value` along multiple axes >>> x = paddle.zeros((3, 3, 5)) >>> value = paddle.ones((1, 3, 1)) >>> res = paddle.slice_scatter(x, value, axes=[0, 2], starts=[1, 0], ends=[3, 4], strides=[1, 2]) >>> print(res) Tensor(shape=[3, 3, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[[0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.]], [[1., 0., 1., 0., 0.], [1., 0., 1., 0., 0.], [1., 0., 1., 0., 0.]], [[1., 0., 1., 0., 0.], [1., 0., 1., 0., 0.], [1., 0., 1., 0., 0.]]]) """ ... def slogdet(self, name: '_str | None' = None) -> 'Tensor': r""" Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant. The determinant can be computed with ``sign * exp`` (logabsdet). Supports input of float, double, complex64, complex128. Notes: 1. For matrices that have zero determinant, this returns ``(0, -inf)``. 2. For matrices with complex value, the :math:`abs(det)` is the modulus of the determinant, and therefore :math:`sign = det / abs(det)`. Args: name (str|None, optional): Name of the output.It's used to print debug info for developers. Details: :ref:`api_guide_Name`. Default is None. Returns: y (Tensor), A tensor containing the sign of the determinant and the natural logarithm of the absolute value of determinant, respectively. The output shape is :math:`(2, *)`, where math:`*` is one or more batch dimensions of the input `x`. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2023) >>> x = paddle.randn([3, 3, 3]) >>> A = paddle.linalg.slogdet(x) >>> print(A) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[-1. , 1. , 1. ], [ 0.25681755, -0.25061053, -0.10809582]]) """ ... def softmax(self, dim: '_int | None' = None, dtype: 'DTypeLike | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" This operator implements PyTorch compatible softmax. The calculation process is as follows: 1. The dimension :attr:`dim` of ``input`` will be permuted to the last. 2. Then ``input`` will be logically flattened to a 2-D matrix. The matrix's second dimension(row length) is the same as the dimension :attr:`axis` of ``input``, and the first dimension(column length) is the product of all other dimensions of ``input``. For each row of the matrix, the softmax operator squashes the K-dimensional(K is the width of the matrix, which is also the size of ``input``'s dimension :attr:`dim`) vector of arbitrary real values to a K-dimensional vector of real values in the range [0, 1] that add up to 1. 3. After the softmax operation is completed, the inverse operations of steps 1 and 2 are performed to restore the two-dimensional matrix to the same dimension as the ``input`` . It computes the exponential of the given dimension and the sum of exponential values of all the other dimensions in the K-dimensional vector input. Then the ratio of the exponential of the given dimension and the sum of exponential values of all the other dimensions is the output of the softmax operator. For each row :math:`i` and each column :math:`j` in the matrix, we have: .. math:: softmax[i, j] = \frac{\exp(input[i, j])}{\sum_j(exp(input[i, j])} Example: .. code-block:: text Case 1: Input: input.shape = [2, 3, 4] input.data = [[[2.0, 3.0, 4.0, 5.0], [3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 8.0, 9.0]], [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [6.0, 7.0, 8.0, 9.0]]] Attrs: dim = -1 Output: out.shape = [2, 3, 4] out.data = [[[0.0320586 , 0.08714432, 0.23688282, 0.64391426], [0.0320586 , 0.08714432, 0.23688282, 0.64391426], [0.07232949, 0.19661193, 0.19661193, 0.53444665]], [[0.0320586 , 0.08714432, 0.23688282, 0.64391426], [0.0320586 , 0.08714432, 0.23688282, 0.64391426], [0.0320586 , 0.08714432, 0.23688282, 0.64391426]]] Case 2: Input: input.shape = [2, 3, 4] input.data = [[[2.0, 3.0, 4.0, 5.0], [3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 8.0, 9.0]], [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [6.0, 7.0, 8.0, 9.0]]] Attrs: dim = 1 Output: out.shape = [2, 3, 4] out.data = [[[0.00657326, 0.00657326, 0.01714783, 0.01714783], [0.01786798, 0.01786798, 0.04661262, 0.04661262], [0.97555875, 0.97555875, 0.93623955, 0.93623955]], [[0.00490169, 0.00490169, 0.00490169, 0.00490169], [0.26762315, 0.26762315, 0.26762315, 0.26762315], [0.72747516, 0.72747516, 0.72747516, 0.72747516]]] Parameters: input (Tensor): The input Tensor with data type bfloat16, float16, float32, float64. dim (int, optional): The dim along which to perform softmax calculations. It should be in range [-D, D), where D is the rank of ``input`` . If ``dim`` < 0, it works the same way as :math:`dim + D` . Default is None. dtype (str, optional): The data type of the output tensor, can be bfloat16, float16, float32, float64. out (Tensor, optional): The output Tensor. Returns: A Tensor with the same shape and data type (use ``dtype`` if it is specified) as input. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[[2.0, 3.0, 4.0, 5.0], ... [3.0, 4.0, 5.0, 6.0], ... [7.0, 8.0, 8.0, 9.0]], ... [[1.0, 2.0, 3.0, 4.0], ... [5.0, 6.0, 7.0, 8.0], ... [6.0, 7.0, 8.0, 9.0]]],dtype='float32') >>> out1 = paddle.compat.nn.functional.softmax(x, -1) >>> out2 = paddle.compat.nn.functional.softmax(x, -1, dtype='float64') >>> #out1's data type is float32; out2's data type is float64 >>> #out1 and out2's value is as follows: >>> print(out1) >>> print(out2) Tensor(shape=[2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[[0.03205860, 0.08714432, 0.23688284, 0.64391428], [0.03205860, 0.08714432, 0.23688284, 0.64391428], [0.07232949, 0.19661194, 0.19661194, 0.53444666]], [[0.03205860, 0.08714432, 0.23688284, 0.64391428], [0.03205860, 0.08714432, 0.23688284, 0.64391428], [0.03205860, 0.08714432, 0.23688284, 0.64391428]]]) Tensor(shape=[2, 3, 4], dtype=float64, place=Place(cpu), stop_gradient=True, [[[0.03205860, 0.08714432, 0.23688282, 0.64391426], [0.03205860, 0.08714432, 0.23688282, 0.64391426], [0.07232949, 0.19661193, 0.19661193, 0.53444665]], [[0.03205860, 0.08714432, 0.23688282, 0.64391426], [0.03205860, 0.08714432, 0.23688282, 0.64391426], [0.03205860, 0.08714432, 0.23688282, 0.64391426]]]) """ ... def solve(self, y: 'Tensor', left: '_bool' = True, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'. Let :math:`X` be a square matrix or a batch of square matrices, :math:`Y` be a vector/matrix or a batch of vectors/matrices. When `left` is True, the equation should be: .. math:: Out = X^-1 * Y When `left` is False, the equation should be: .. math:: Out = Y * X^-1 Specifically, this system of linear equations has one solution if and only if input 'X' is invertible. Args: y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be ``[*, M, K]``, where ``*`` is zero or more batch dimensions. Its data type should be float32 or float64. Alias: ``B``. left (bool, optional): Whether to solve the system :math:`X * Out = Y` or :math:`Out * X = Y`. Default: True. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output tensor. Default: None. Returns: Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'. Its data type should be the same as that of `x`. Examples: .. code-block:: python >>> # a square system of linear equations: >>> # 3*X0 + X1 = 9 >>> # X0 + 2*X1 = 8 >>> import paddle >>> x = paddle.to_tensor([[3, 1],[1, 2]], dtype="float64") >>> y = paddle.to_tensor([9, 8], dtype="float64") >>> out = paddle.linalg.solve(x, y) >>> print(out) Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=True, [2., 3.]) """ ... def sort(self, axis: '_int' = -1, descending: '_bool' = False, stable: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Sorts the input along the given axis, and returns the sorted output tensor. The default sort algorithm is ascending, if you want the sort algorithm to be descending, you must set the :attr:`descending` as True. Args: axis (int, optional): Axis to compute indices along. The effective range is [-R, R), where R is Rank(x). when axis<0, it works the same way as axis+R. Default is -1. descending (bool, optional) : Descending is a flag, if set to true, algorithm will sort by descending order, else sort by ascending order. Default is false. stable (bool, optional): Whether to use stable sorting algorithm or not. When using stable sorting algorithm, the order of equivalent elements will be preserved. Default is False. name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor, sorted tensor(with the same shape and data type as ``x``). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[[5,8,9,5], ... [0,0,1,7], ... [6,9,2,4]], ... [[5,2,4,2], ... [4,7,7,9], ... [1,7,0,6]]], ... dtype='float32') >>> out1 = paddle.sort(x=x, axis=-1) >>> out2 = paddle.sort(x=x, axis=0) >>> out3 = paddle.sort(x=x, axis=1) >>> print(out1.numpy()) [[[5. 5. 8. 9.] [0. 0. 1. 7.] [2. 4. 6. 9.]] [[2. 2. 4. 5.] [4. 7. 7. 9.] [0. 1. 6. 7.]]] >>> print(out2.numpy()) [[[5. 2. 4. 2.] [0. 0. 1. 7.] [1. 7. 0. 4.]] [[5. 8. 9. 5.] [4. 7. 7. 9.] [6. 9. 2. 6.]]] >>> print(out3.numpy()) [[[0. 0. 1. 4.] [5. 8. 2. 5.] [6. 9. 9. 7.]] [[1. 2. 0. 2.] [4. 7. 4. 6.] [5. 7. 7. 9.]]] """ ... def split(self, num_or_sections: '_int | Sequence[_int]', axis: '_int | Tensor' = 0, name: '_str | None' = None) -> 'list[Tensor]': r""" Split the input tensor into multiple sub-Tensors. Args: num_or_sections (int|list|tuple): If ``num_or_sections`` is an int, then ``num_or_sections`` indicates the number of equal sized sub-Tensors that the ``x`` will be divided into. If ``num_or_sections`` is a list or tuple, the length of it indicates the number of sub-Tensors and the elements in it indicate the sizes of sub-Tensors' dimension orderly. The length of the list must not be larger than the ``x`` 's size of specified ``axis``. axis (int|Tensor, optional): The axis along which to split, it can be a integer or a ``0-D Tensor`` with shape [] and data type ``int32`` or ``int64``. If :math::`axis < 0`, the axis to split along is :math:`rank(x) + axis`. Default is 0. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: list(Tensor), The list of segmented Tensors. Examples: .. code-block:: pycon >>> import paddle >>> # x is a Tensor of shape [3, 9, 5] >>> x = paddle.rand([3, 9, 5]) >>> out0, out1, out2 = paddle.split(x, num_or_sections=3, axis=1) >>> print(out0.shape) paddle.Size([3, 3, 5]) >>> print(out1.shape) paddle.Size([3, 3, 5]) >>> print(out2.shape) paddle.Size([3, 3, 5]) >>> out0, out1, out2 = paddle.split(x, num_or_sections=[2, 3, 4], axis=1) >>> print(out0.shape) paddle.Size([3, 2, 5]) >>> print(out1.shape) paddle.Size([3, 3, 5]) >>> print(out2.shape) paddle.Size([3, 4, 5]) >>> out0, out1, out2 = paddle.split(x, num_or_sections=[2, 3, -1], axis=1) >>> print(out0.shape) paddle.Size([3, 2, 5]) >>> print(out1.shape) paddle.Size([3, 3, 5]) >>> print(out2.shape) paddle.Size([3, 4, 5]) >>> # axis is negative, the real axis is (rank(x) + axis)=1 >>> out0, out1, out2 = paddle.split(x, num_or_sections=3, axis=-2) >>> print(out0.shape) paddle.Size([3, 3, 5]) >>> print(out1.shape) paddle.Size([3, 3, 5]) >>> print(out2.shape) paddle.Size([3, 3, 5]) """ ... def split_with_sizes(self, split_sizes: 'list[_int]', dim: '_int' = 0) -> 'list[paddle.Tensor]': r""" Splits the input tensor into multiple sub tensors according to given split sizes. Args: split_sizes (list[int]): A list of non negative integers specifying the sizes of each split along dimension ``dim``. The sum of all elements in this list must equal the size of ``self`` along ``dim``. dim (int, optional): The dimension along which to split the tensor. Defaults to 0. Returns: list[Tensor]: A list of sub tensors resulting from splitting ``self`` along the specified dimension. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) >>> # Split into two parts along the first dimension, of sizes 1 and 2 >>> splits = paddle.Tensor.split_with_sizes(x, [1, 2], dim=0) >>> print(splits) """ ... def sqrt(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Sqrt Activation Operator. .. math:: out=\sqrt{x}=x^{1/2} Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor. Output of Sqrt operator, a Tensor with shape same as input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([0.1, 0.2, 0.3, 0.4]) >>> out = paddle.sqrt(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [0.31622776, 0.44721359, 0.54772258, 0.63245553]) """ ... def sqrt_(self, name=None): r""" Inplace version of ``sqrt`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_sqrt`. """ ... def square(self, name: '_str | None' = None) -> 'Tensor': r""" Square each elements of the inputs. .. math:: out = x^2 Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor. Output of Square operator, a Tensor with shape same as input. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.square(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [0.16000001, 0.04000000, 0.01000000, 0.09000000]) """ ... def square_(self, name=None): r""" Inplace version of ``square`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_square`. """ ... def squeeze(self, axis: '_int | Sequence[_int] | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Squeeze the dimension(s) of size 1 of input tensor x's shape. Note that the output Tensor will share data with origin Tensor and doesn't have a Tensor copy in ``dygraph`` mode. If you want to use the Tensor copy version, please use `Tensor.clone` like ``squeeze_clone_x = x.squeeze().clone()``. If axis is provided, it will remove the dimension(s) by given axis that of size 1. If the dimension of given axis is not of size 1, the dimension remain unchanged. If axis is not provided, all dims equal of size 1 will be removed. .. code-block:: text Case1: Input: x.shape = [1, 3, 1, 5] # If axis is not provided, all dims equal of size 1 will be removed. axis = None Output: out.shape = [3, 5] Case2: Input: x.shape = [1, 3, 1, 5] # If axis is provided, it will remove the dimension(s) by given axis that of size 1. axis = 0 Output: out.shape = [3, 1, 5] Case4: Input: x.shape = [1, 3, 1, 5] # If the dimension of one given axis (3) is not of size 1, the dimension remain unchanged. axis = [0, 2, 3] Output: out.shape = [3, 5] Case4: Input: x.shape = [1, 3, 1, 5] # If axis is negative, axis = axis + ndim (number of dimensions in x). axis = [-2] Output: out.shape = [1, 3, 5] .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``dim`` can be used as an alias for ``axis``. For example, ``squeeze(input=tensor_x, dim=1)`` is equivalent to ``squeeze(x=tensor_x, axis=1)``. Args: axis (int|list|tuple, optional): An integer or list/tuple of integers, indicating the dimensions to be squeezed. Default is None. The range of axis is :math:`[-ndim(x), ndim(x))`. If axis is negative, :math:`axis = axis + ndim(x)`. If axis is None, all the dimensions of x of size 1 will be removed. alias: ``dim``. name (str|None, optional): Please refer to :ref:`api_guide_Name`, Default None. Returns: Tensor, Squeezed Tensor with the same data type as input Tensor. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.rand([5, 1, 10]) >>> output = paddle.squeeze(x, axis=1) >>> print(x.shape) paddle.Size([5, 1, 10]) >>> print(output.shape) paddle.Size([5, 10]) >>> # output shares data with x in dygraph mode >>> x[0, 0, 0] = 10.0 >>> print(output[0, 0]) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 10.) """ ... def squeeze_(self, axis: '_int | Sequence[_int] | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``squeeze`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_tensor_squeeze`. """ ... def stack(self, axis: '_int' = 0, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Stacks all the input tensors ``x`` along ``axis`` dimension. All tensors must be of the same shape and same dtype. For example, given N tensors of shape [A, B], if ``axis == 0``, the shape of stacked tensor is [N, A, B]; if ``axis == 1``, the shape of stacked tensor is [A, N, B], etc. It also supports the operation with zero-size tensors which contain 0 in their shape. See the examples below. .. code-block:: text Case 1: Input: x[0].shape = [1, 2] x[0].data = [ [1.0 , 2.0 ] ] x[1].shape = [1, 2] x[1].data = [ [3.0 , 4.0 ] ] x[2].shape = [1, 2] x[2].data = [ [5.0 , 6.0 ] ] Attrs: axis = 0 Output: Out.dims = [3, 1, 2] Out.data =[ [ [1.0, 2.0] ], [ [3.0, 4.0] ], [ [5.0, 6.0] ] ] Case 2: Input: x[0].shape = [1, 2] x[0].data = [ [1.0 , 2.0 ] ] x[1].shape = [1, 2] x[1].data = [ [3.0 , 4.0 ] ] x[2].shape = [1, 2] x[2].data = [ [5.0 , 6.0 ] ] Attrs: axis = 1 or axis = -2 # If axis = -2, axis = axis+ndim(x[0])+1 = -2+2+1 = 1. Output: Out.shape = [1, 3, 2] Out.data =[ [ [1.0, 2.0] [3.0, 4.0] [5.0, 6.0] ] ] Case 3: Input: x[0].shape = [0, 1, 2] x[0].data = [] x[1].shape = [0, 1, 2] x[1].data = [] Attrs: axis = 0 Output: Out.shape = [2, 0, 1, 2] Out.data = [] Case 4: Input: x[0].shape = [0, 1, 2] x[0].data = [] x[1].shape = [0, 1, 2] x[1].data = [] Attrs: axis = 1 Output: Out.shape = [0, 2, 1, 2] Out.data = [] The image below demonstrates the Case 1: three 2-dimensional tensors with shape [1, 2] are stacked in the dimension of axis=0 to form a 3-dimensional tensor with shape [3, 1, 2] . .. figure:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/stack/stack-0.png :width: 1000 :alt: Legend 1 :align: center Args: axis (int, optional): The axis along which all inputs are stacked. ``axis`` range is ``[-(R+1), R+1)``, where ``R`` is the number of dimensions of the first input tensor ``x[0]``. If ``axis < 0``, ``axis = axis+R+1``. The default value of axis is 0. Alias: ``dim``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor, optional): The output tensor. If set, the output will be written to this tensor. Returns: Tensor, The stacked tensor with same data type as input. Examples: .. code-block:: pycon >>> import paddle >>> x1 = paddle.to_tensor([[1.0, 2.0]]) >>> x2 = paddle.to_tensor([[3.0, 4.0]]) >>> x3 = paddle.to_tensor([[5.0, 6.0]]) >>> out = paddle.stack([x1, x2, x3], axis=0) >>> print(out.shape) paddle.Size([3, 1, 2]) >>> print(out) Tensor(shape=[3, 1, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[[1., 2.]], [[3., 4.]], [[5., 6.]]]) >>> out = paddle.stack([x1, x2, x3], axis=-2) >>> print(out.shape) paddle.Size([1, 3, 2]) >>> print(out) Tensor(shape=[1, 3, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[[1., 2.], [3., 4.], [5., 6.]]]) >>> # zero-size tensors >>> x1 = paddle.ones([0, 1, 2]) >>> x2 = paddle.ones([0, 1, 2]) >>> out = paddle.stack([x1, x2], axis=0) >>> print(out.shape) paddle.Size([2, 0, 1, 2]) >>> print(out) Tensor(shape=[2, 0, 1, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[], []]) >>> out = paddle.stack([x1, x2], axis=1) >>> print(out.shape) paddle.Size([0, 2, 1, 2]) >>> print(out) Tensor(shape=[0, 2, 1, 2], dtype=float32, place=Place(cpu), stop_gradient=True, []) """ ... def stanh(self, scale_a: '_float' = 0.67, scale_b: '_float' = 1.7159, name: '_str | None' = None) -> 'Tensor': r""" stanh activation. .. math:: out = b * \frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}} Parameters: x (Tensor): The input Tensor with data type bfloat16, float16, float32, float64, uint8, int8, int16, int32, int64. scale_a (float, optional): The scale factor a of the input. Default is 0.67. scale_b (float, optional): The scale factor b of the output. Default is 1.7159. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Tensor with the same shape and data type as ``x`` (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0]) >>> out = paddle.stanh(x, scale_a=0.67, scale_b=1.72) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [1.00616539, 1.49927628, 1.65933096, 1.70390463]) """ ... def std(self, axis: '_int | Sequence[_int] | None' = None, unbiased: '_bool' = True, keepdim: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Computes the standard-deviation of ``x`` along ``axis`` . Args: axis (int|list|tuple|None, optional): The axis along which to perform standard-deviation calculations. ``axis`` should be int, list(int) or tuple(int). If ``axis`` is a list/tuple of dimension(s), standard-deviation is calculated along all element(s) of ``axis`` . ``axis`` or element(s) of ``axis`` should be in range [-D, D), where D is the dimensions of ``x`` . If ``axis`` or element(s) of ``axis`` is less than 0, it works the same way as :math:`axis + D` . If ``axis`` is None, standard-deviation is calculated over all elements of ``x``. Default is None. unbiased (bool, optional): Whether to use the unbiased estimation. If ``unbiased`` is True, the standard-deviation is calculated via the unbiased estimator. If ``unbiased`` is True, the divisor used in the computation is :math:`N - 1`, where :math:`N` represents the number of elements along ``axis`` , otherwise the divisor is :math:`N`. Default is True. keepdim (bool, optional): Whether to reserve the reduced dimension(s) in the output Tensor. If ``keepdim`` is True, the dimensions of the output Tensor is the same as ``x`` except in the reduced dimensions(it is of size 1 in this case). Otherwise, the shape of the output Tensor is squeezed in ``axis`` . Default is False. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, results of standard-deviation along ``axis`` of ``x``, with the same data type as ``x``. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1.0, 2.0, 3.0], [1.0, 4.0, 5.0]]) >>> out1 = paddle.std(x) >>> print(out1.numpy()) 1.6329932 >>> out2 = paddle.std(x, unbiased=False) >>> print(out2.numpy()) 1.490712 >>> out3 = paddle.std(x, axis=1) >>> print(out3.numpy()) [1. 2.081666] """ ... def stft(self, n_fft: '_int', hop_length: '_int | None' = None, win_length: '_int | None' = None, window: 'Tensor | None' = None, center: '_bool' = True, pad_mode: "Literal['reflect', 'constant']" = 'reflect', normalized: '_bool' = False, onesided: '_bool | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Short-time Fourier transform (STFT). The STFT computes the discrete Fourier transforms (DFT) of short overlapping windows of the input using this formula: .. math:: X_t[f] = \sum_{n = 0}^{N-1} \text{window}[n]\ x[t \times H + n]\ e^{-{2 \pi j f n}/{N}} Where: - :math:`t`: The :math:`t`-th input window. - :math:`f`: Frequency :math:`0 \leq f < \text{n_fft}` for `onesided=False`, or :math:`0 \leq f < \lfloor \text{n_fft} / 2 \rfloor + 1` for `onesided=True`. - :math:`N`: Value of `n_fft`. - :math:`H`: Value of `hop_length`. Args: n_fft (int): The number of input samples to perform Fourier transform. hop_length (int|None, optional): Number of steps to advance between adjacent windows and `0 < hop_length`. Default: `None` (treated as equal to `n_fft//4`) win_length (int|None, optional): The size of window. Default: `None` (treated as equal to `n_fft`) window (Tensor|None, optional): A 1-dimensional tensor of size `win_length`. It will be center padded to length `n_fft` if `win_length < n_fft`. Default: `None` ( treated as a rectangle window with value equal to 1 of size `win_length`). center (bool, optional): Whether to pad `x` to make that the :math:`t \times hop\_length` at the center of :math:`t`-th frame. Default: `True`. pad_mode (str, optional): Choose padding pattern when `center` is `True`. See `paddle.nn.functional.pad` for all padding options. Default: `"reflect"` normalized (bool, optional): Control whether to scale the output by `1/sqrt(n_fft)`. Default: `False` onesided (bool, optional): Control whether to return half of the Fourier transform output that satisfies the conjugate symmetry condition when input is a real-valued tensor. It can not be `True` if input is a complex tensor. Default: `None` name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: The complex STFT output tensor with shape `[..., n_fft//2 + 1, num_frames]` (real-valued input and `onesided` is `True`) or `[..., n_fft, num_frames]` (`onesided` is `False`) Examples: .. code-block:: pycon >>> import paddle >>> from paddle.signal import stft >>> # real-valued input >>> x = paddle.randn([8, 48000], dtype=paddle.float64) >>> y1 = stft(x, n_fft=512) >>> print(y1.shape) paddle.Size([8, 257, 376]) >>> y2 = stft(x, n_fft=512, onesided=False) >>> print(y2.shape) paddle.Size([8, 512, 376]) >>> # complex input >>> x = paddle.randn([8, 48000], dtype=paddle.float64) + \ ... paddle.randn([8, 48000], dtype=paddle.float64)*1j >>> print(x.shape) paddle.Size([8, 48000]) >>> print(x.dtype) paddle.complex128 >>> y1 = stft(x, n_fft=512, center=False, onesided=False) >>> print(y1.shape) paddle.Size([8, 512, 372]) """ ... def stride(self, dim=None, /): r""" Returns the stride of self tensor. Stride is the jump necessary to go from one element to the next one in the specified dimension dim. A tuple of all strides is returned when no argument is passed in. Otherwise, an integer value is returned as the stride in the particular dimension dim. Args: Returns: int or tuple: The stride of the tensor. If dim is None, returns a tuple of all strides. If dim is specified, returns the stride in that dimension. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 2, 3], [4, 5, 6]]) >>> x.stride() [3, 1] >>> x.stride(0) 3 >>> x.stride(1) 1 >>> x.stride(-1) 1 """ ... def strided_slice(self, axes: 'Sequence[_int | Tensor]', starts: 'Sequence[_int | Tensor] | Tensor', ends: 'Sequence[_int | Tensor] | Tensor', strides: 'Sequence[_int | Tensor] | Tensor', name: '_str | None' = None) -> 'Tensor': r""" This operator produces a slice of ``x`` along multiple axes. Similar to numpy: https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html Slice uses ``axes``, ``starts`` and ``ends`` attributes to specify the start and end dimension for each axis in the list of axes and Slice uses this information to slice the input data tensor. If a negative value is passed to ``starts`` or ``ends`` such as :math:`-i`, it represents the reverse position of the axis :math:`i-1` th(here 0 is the initial position). The ``strides`` represents steps of slicing and if the ``strides`` is negative, slice operation is in the opposite direction. If the value passed to ``starts`` or ``ends`` is greater than n (the number of elements in this dimension), it represents n. For slicing to the end of a dimension with unknown size, it is recommended to pass in INT_MAX. The size of ``axes`` must be equal to ``starts`` , ``ends`` and ``strides``. Following examples will explain how strided_slice works: .. code-block:: text Case1: Given: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] strides = [1, 1] Then: result = [ [5, 6, 7], ] Case2: Given: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [0, 1] ends = [2, 0] strides = [1, -1] Then: result = [ [8, 7, 6], ] Case3: Given: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [0, 1] ends = [-1, 1000] strides = [1, 3] Then: result = [ [2], ] Args: axes (list|tuple): The data type is ``int32`` . Axes that `starts` and `ends` apply to. It's optional. If it is not provides, it will be treated as :math:`[0,1,...,len(starts)-1]`. starts (list|tuple|Tensor): The data type is ``int32`` . If ``starts`` is a list or tuple, the elements of it should be integers or Tensors with shape []. If ``starts`` is an Tensor, it should be an 1-D Tensor. It represents starting indices of corresponding axis in ``axes``. ends (list|tuple|Tensor): The data type is ``int32`` . If ``ends`` is a list or tuple, the elements of it should be integers or Tensors with shape []. If ``ends`` is an Tensor, it should be an 1-D Tensor. It represents ending indices of corresponding axis in ``axes``. strides (list|tuple|Tensor): The data type is ``int32`` . If ``strides`` is a list or tuple, the elements of it should be integers or Tensors with shape []. If ``strides`` is an Tensor, it should be an 1-D Tensor. It represents slice step of corresponding axis in ``axes``. name(str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: Tensor, A ``Tensor`` with the same dimension as ``x``. The data type is same as ``x``. Examples: .. code-block:: python >>> import paddle >>> x = paddle.zeros(shape=[3,4,5,6], dtype="float32") >>> # example 1: >>> # attr starts is a list which doesn't contain Tensor. >>> axes = [1, 2, 3] >>> starts = [-3, 0, 2] >>> ends = [3, 2, 4] >>> strides_1 = [1, 1, 1] >>> strides_2 = [1, 1, 2] >>> sliced_1 = paddle.strided_slice(x, axes=axes, starts=starts, ends=ends, strides=strides_1) >>> # sliced_1 is x[:, 1:3:1, 0:2:1, 2:4:1]. >>> # example 2: >>> # attr starts is a list which contain tensor Tensor. >>> minus_3 = paddle.full(shape=[1], fill_value=-3, dtype='int32') >>> sliced_2 = paddle.strided_slice(x, axes=axes, starts=[minus_3, 0, 2], ends=ends, strides=strides_2) >>> # sliced_2 is x[:, 1:3:1, 0:2:1, 2:4:2]. """ ... def sub(self, y: 'Tensor', name: '_str | None' = None, *, alpha: 'Number' = 1, out: 'Tensor | None' = None) -> 'Tensor': r""" Subtract two tensors element-wise. The equation is: .. math:: out = x - y Note: ``paddle.subtract`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: y (Tensor): the input tensor, it's data type should be bfloat16, float16, float32, float64, int16, int32, int64, complex64, complex128. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. alpha (Number, optional): Scaling factor for Y. Default: 1. out (Tensor, optional): The output tensor. Default: None. Returns: N-D Tensor. A location into which the result is stored. If x, y have different shapes and are "broadcastable", the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape, its shape is the same as x and y. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 2], [7, 8]]) >>> y = paddle.to_tensor([[5, 6], [3, 4]]) >>> res = paddle.subtract(x, y) >>> print(res) Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[-4, -4], [ 4, 4]]) >>> x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]]) >>> y = paddle.to_tensor([1, 0, 4]) >>> res = paddle.subtract(x, y) >>> print(res) Tensor(shape=[1, 2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[[ 0, 2, -1], [ 0, 2, -1]]]) >>> x = paddle.to_tensor([2, float('nan'), 5], dtype='float32') >>> y = paddle.to_tensor([1, 4, float('nan')], dtype='float32') >>> res = paddle.subtract(x, y) >>> print(res) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [1. , nan, nan]) >>> x = paddle.to_tensor([5, float('inf'), -float('inf')], dtype='float64') >>> y = paddle.to_tensor([1, 4, 5], dtype='float64') >>> res = paddle.subtract(x, y) >>> print(res) Tensor(shape=[3], dtype=float64, place=Place(cpu), stop_gradient=True, [ 4. , inf., -inf.]) """ ... def sub_(self, y: 'Tensor', name: '_str | None' = None, *, alpha: 'Number' = 1) -> 'Tensor': r""" Inplace version of ``subtract`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_subtract`. """ ... def sum(self, axis: '_int | Sequence[_int] | None' = None, dtype: 'DTypeLike | None' = None, keepdim: '_bool' = False, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Computes the sum of tensor elements over the given dimension. .. note:: Parameter order support: When passing positional parameters, it is possible to support swapping the positional order of dtype and axis. For example, ``sum(x, axis, keepdim, dtype)`` is equivalent to ``sum(x, axis, dtype, keepdim)``. Alias Support: The parameter name ``input`` can be used as an alias for ``x`` and the parameter name ``dim`` can be used as an alias for ``axis``. For example, ``sum(input=tensor_x, dim=1)`` is equivalent to ``sum(x=tensor_x, axis=1)``. Args: axis (int|list|tuple|None, optional): The dimensions along which the sum is performed. If :attr:`None`, sum all elements of :attr:`x` and return a Tensor with a single element, otherwise must be in the range :math:`[-rank(x), rank(x))`. If :math:`axis[i] < 0`, the dimension to reduce is :math:`rank + axis[i]`. alias: ``dim``. dtype (str|paddle.dtype|np.dtype, optional): The dtype of output Tensor. The default value is None, the dtype of output is the same as input Tensor `x`. keepdim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result Tensor will have one fewer dimension than the :attr:`x` unless :attr:`keepdim` is true, default value is False. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output tensor. Default: None. Returns: Tensor: Results of summation operation on the specified axis of input Tensor `x`, if `x.dtype='bool'`, `x.dtype='int32'`, it's data type is `'int64'`, otherwise it's data type is the same as `x`. Examples: .. code-block:: python >>> import paddle >>> # x is a Tensor with following elements: >>> # [[0.2, 0.3, 0.5, 0.9] >>> # [0.1, 0.2, 0.6, 0.7]] >>> # Each example is followed by the corresponding output tensor. >>> x = paddle.to_tensor([[0.2, 0.3, 0.5, 0.9], ... [0.1, 0.2, 0.6, 0.7]]) >>> out1 = paddle.sum(x) >>> out1 Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 3.50000000) >>> out2 = paddle.sum(x, axis=0) >>> out2 Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [0.30000001, 0.50000000, 1.10000002, 1.59999990]) >>> out3 = paddle.sum(x, axis=-1) >>> out3 Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [1.89999998, 1.60000002]) >>> out4 = paddle.sum(x, axis=1, keepdim=True) >>> out4 Tensor(shape=[2, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[1.89999998], [1.60000002]]) >>> # y is a Tensor with shape [2, 2, 2] and elements as below: >>> # [[[1, 2], [3, 4]], >>> # [[5, 6], [7, 8]]] >>> # Each example is followed by the corresponding output tensor. >>> y = paddle.to_tensor([[[1, 2], [3, 4]], ... [[5, 6], [7, 8]]]) >>> out5 = paddle.sum(y, axis=[1, 2]) >>> out5 Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True, [10, 26]) >>> out6 = paddle.sum(y, axis=[0, 1]) >>> out6 Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True, [16, 20]) >>> # x is a Tensor with following elements: >>> # [[True, True, True, True] >>> # [False, False, False, False]] >>> # Each example is followed by the corresponding output tensor. >>> x = paddle.to_tensor([[True, True, True, True], ... [False, False, False, False]]) >>> out7 = paddle.sum(x) >>> out7 Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True, 4) >>> out8 = paddle.sum(x, axis=0) >>> out8 Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True, [1, 1, 1, 1]) >>> out9 = paddle.sum(x, axis=1) >>> out9 Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True, [4, 0]) """ ... def svd_lowrank(self, q: '_int | None' = None, niter: '_int' = 2, M: 'Tensor | None' = None, name: '_str | None' = None) -> 'tuple[Tensor, Tensor, Tensor]': r""" Return the singular value decomposition (SVD) on a low-rank matrix or batches of such matrices. If :math:`X` is the input matrix or a batch of input matrices, the output should satisfies: .. math:: X \approx U * diag(S) * V^{H} When :math:`M` is given, the output should satisfies: .. math:: X - M \approx U * diag(S) * V^{H} Args: q (int, optional): A slightly overestimated rank of :math:`X`. Default value is None, which means the overestimated rank is 6. niter (int, optional): The number of iterations to perform. Default: 2. M (Tensor, optional): The input tensor's mean. Its shape should be `[..., 1, M]`. Default value is None. name (str|None, optional): Name for the operation. For more information, please refer to :ref:`api_guide_Name`. Default: None. Returns: - Tensor U, is N x q matrix. - Tensor S, is a vector with length q. - Tensor V, is M x q matrix. tuple (U, S, V): which is the nearly optimal approximation of a singular value decomposition of the matrix :math:`X` or :math:`X - M`. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2024) >>> x = paddle.randn((5, 5), dtype='float64') >>> U, S, V = paddle.linalg.svd_lowrank(x) >>> print(U) Tensor(shape=[5, 5], dtype=float64, place=Place(cpu), stop_gradient=True, [[-0.03586982, -0.17211503, 0.31536566, -0.38225676, -0.85059629], [-0.38386839, 0.67754925, 0.23222694, 0.51777188, -0.26749766], [-0.85977150, -0.28442378, -0.41412094, -0.08955629, -0.01948348], [ 0.18611503, 0.56047358, -0.67717019, -0.39286761, -0.19577062], [ 0.27841082, -0.34099254, -0.46535957, 0.65071250, -0.40770727]]) >>> print(S) Tensor(shape=[5], dtype=float64, place=Place(cpu), stop_gradient=True, [4.11253399, 3.03227120, 2.45499752, 1.25602436, 0.45825337]) >>> print(V) Tensor(shape=[5, 5], dtype=float64, place=Place(cpu), stop_gradient=True, [[ 0.46401347, 0.50977695, -0.08742316, -0.11140428, -0.71046833], [-0.48927226, -0.35047624, 0.07918771, 0.45431083, -0.65200463], [-0.20494730, 0.67097011, -0.05427719, 0.66510472, 0.24997083], [-0.69645001, 0.40237917, 0.09360970, -0.58032322, -0.08666357], [ 0.13512270, 0.07199989, 0.98710572, 0.04529277, 0.01134594]]) """ ... def swapaxes(self, perm: 'Sequence[_int]', name: '_str | None' = None) -> 'Tensor': r""" Permute the data dimensions of `input` according to `perm`. The `i`-th dimension of the returned tensor will correspond to the perm[i]-th dimension of `input`. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``dim0`` & ``dim1`` can replace ``perm``. For example, ``transpose(input=x, dim0=0, dim1=1)`` is equivalent to ``transpose(x=x, perm=[1, 0, 2])``. Args: perm (list|tuple): Permute the input according to the data of perm. name (str|None, optional): The name of this layer. For more information, please refer to :ref:`api_guide_Name`. Default is None. Returns: Tensor: A transposed n-D Tensor, with data type being bool, float32, float64, int32, int64. Examples: .. code-block:: text # The following codes in this code block are pseudocode, designed to show the execution logic and results of the function. x = to_tensor([[[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12]] [[13 14 15 16] [17 18 19 20] [21 22 23 24]]]) shape(x): return [2,3,4] # Example 1 perm0 = [1,0,2] y_perm0 = transpose(x, perm0) # Permute x by perm0 # dim:0 of y_perm0 is dim:1 of x # dim:1 of y_perm0 is dim:0 of x # dim:2 of y_perm0 is dim:2 of x # The above two lines can also be understood as exchanging the zeroth and first dimensions of x y_perm0.data = [[[ 1 2 3 4] [13 14 15 16]] [[ 5 6 7 8] [17 18 19 20]] [[ 9 10 11 12] [21 22 23 24]]] shape(y_perm0): return [3,2,4] # Example 2 perm1 = [2,1,0] y_perm1 = transpose(x, perm1) # Permute x by perm1 # dim:0 of y_perm1 is dim:2 of x # dim:1 of y_perm1 is dim:1 of x # dim:2 of y_perm1 is dim:0 of x # The above two lines can also be understood as exchanging the zeroth and second dimensions of x y_perm1.data = [[[ 1 13] [ 5 17] [ 9 21]] [[ 2 14] [ 6 18] [10 22]] [[ 3 15] [ 7 19] [11 23]] [[ 4 16] [ 8 20] [12 24]]] shape(y_perm1): return [4,3,2] Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.randn([2, 3, 4]) >>> x_transposed = paddle.transpose(x, perm=[1, 0, 2]) >>> print(x_transposed.shape) paddle.Size([3, 2, 4]) """ ... def t(self, name: '_str | None' = None) -> 'Tensor': r""" Transpose <=2-D tensor. 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to the paddle.transpose function which perm dimensions set 0 and 1. Args: name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64. Examples: .. code-block:: pycon :name: code-example >>> import paddle >>> # Example 1 (0-D tensor) >>> x = paddle.to_tensor([0.79]) >>> out = paddle.t(x) >>> print(out) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [0.79000002]) >>> # Example 2 (1-D tensor) >>> x = paddle.to_tensor([0.79, 0.84, 0.32]) >>> out2 = paddle.t(x) >>> print(out2) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [0.79000002, 0.83999997, 0.31999999]) >>> print(paddle.t(x).shape) paddle.Size([3]) >>> # Example 3 (2-D tensor) >>> x = paddle.to_tensor([[0.79, 0.84, 0.32], [0.64, 0.14, 0.57]]) >>> print(x.shape) paddle.Size([2, 3]) >>> out3 = paddle.t(x) >>> print(out3) Tensor(shape=[3, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.79000002, 0.63999999], [0.83999997, 0.14000000], [0.31999999, 0.56999999]]) >>> print(paddle.t(x).shape) paddle.Size([3, 2]) """ ... def t_(self, name=None): r""" Inplace version of ``t`` API, the output Tensor will be inplaced with input ``input``. Please refer to :ref:`api_paddle_t`. """ ... def take(self, index: 'Tensor', mode: "Literal['raise', 'wrap', 'clip']" = 'raise', name: '_str | None' = None) -> 'Tensor': r""" Returns a new tensor with the elements of input tensor x at the given index. The input tensor is treated as if it were viewed as a 1-D tensor. The result takes the same shape as the index. Args: index (Tensor): An N-D Tensor, its data type should be int32, int64. mode (str, optional): Specifies how out-of-bounds index will behave. the candidates are ``'raise'``, ``'wrap'`` and ``'clip'``. - ``'raise'``: raise an error (default); - ``'wrap'``: wrap around; - ``'clip'``: clip to the range. ``'clip'`` mode means that all indices that are too large are replaced by the index that addresses the last element. Note that this disables indexing with negative numbers. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, Tensor with the same shape as index, the data type is the same with input. Examples: .. code-block:: python >>> import paddle >>> x_int = paddle.arange(0, 12).reshape([3, 4]) >>> x_float = x_int.astype(paddle.float64) >>> idx_pos = paddle.arange(4, 10).reshape([2, 3]) # positive index >>> idx_neg = paddle.arange(-2, 4).reshape([2, 3]) # negative index >>> idx_err = paddle.arange(-2, 13).reshape([3, 5]) # index out of range >>> paddle.take(x_int, idx_pos) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[4, 5, 6], [7, 8, 9]]) >>> paddle.take(x_int, idx_neg) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[10, 11, 0 ], [1 , 2 , 3 ]]) >>> paddle.take(x_float, idx_pos) Tensor(shape=[2, 3], dtype=float64, place=Place(cpu), stop_gradient=True, [[4., 5., 6.], [7., 8., 9.]]) >>> x_int.take(idx_pos) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[4, 5, 6], [7, 8, 9]]) >>> paddle.take(x_int, idx_err, mode='wrap') Tensor(shape=[3, 5], dtype=int64, place=Place(cpu), stop_gradient=True, [[10, 11, 0 , 1 , 2 ], [3 , 4 , 5 , 6 , 7 ], [8 , 9 , 10, 11, 0 ]]) >>> paddle.take(x_int, idx_err, mode='clip') Tensor(shape=[3, 5], dtype=int64, place=Place(cpu), stop_gradient=True, [[0 , 0 , 0 , 1 , 2 ], [3 , 4 , 5 , 6 , 7 ], [8 , 9 , 10, 11, 11]]) """ ... def take_along_axis(self, indices: 'Tensor', axis: '_int', broadcast: '_bool' = True, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Take values from the input array by given indices matrix along the designated axis. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``arr``, and ``dim`` can be used as an alias for ``axis``. For example, ``repeat_interleave(input=tensor_arr, dim=1, ...)`` is equivalent to ``repeat_interleave(arr=tensor_arr, axis=1, ...)``. Args: indices (Tensor) : Indices to take along each 1d slice of arr. This must match the dimension of arr, and need to broadcast against arr. Supported data type are int32 and int64. axis (int) : The axis to take 1d slices along. alias: ``dim``. broadcast (bool, optional): whether the indices broadcast. out (Tensor, optional): The output Tensor. If set, the output will be written to this Tensor. Returns: Tensor, The indexed element, same dtype with arr. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1, 2, 3], [4, 5, 6], [7,8,9]]) >>> index = paddle.to_tensor([[0]]) >>> axis = 0 >>> result = paddle.take_along_axis(x, index, axis) >>> print(result) Tensor(shape=[1, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[1, 2, 3]]) """ ... def tan(self, name: '_str | None' = None) -> 'Tensor': r""" Tangent Operator. Computes tangent of x element-wise. Input range is `(k*pi-pi/2, k*pi+pi/2)` and output range is `(-inf, inf)`. .. math:: out = tan(x) Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor. Output of Tan operator, a Tensor with shape same as input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.tan(x) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [-0.42279324, -0.20271003, 0.10033467, 0.30933627]) """ ... def tan_(self, name=None): r""" Inplace version of ``tan`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_tan`. """ ... def tanh(self, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Tanh Activation Operator. .. math:: out = \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}} .. note:: Alias Support: 1. The parameter name ``input`` can be used as an alias for ``x``. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output tensor. Default: None. Returns: Output of Tanh operator, a Tensor with same data type and shape as input (integer types are autocasted into float32). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) >>> out = paddle.tanh(x) >>> out Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [-0.37994900, -0.19737528, 0.09966799, 0.29131261]) """ ... def tanh_(self, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``tanh`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_tanh`. """ ... def tensor_split(self, num_or_indices: '_int | Sequence[_int]', axis: '_int | Tensor' = 0, name: '_str | None' = None) -> 'list[Tensor]': r""" Split the input tensor into multiple sub-Tensors along ``axis``, allowing not being of equal size. In the following figure, the shape of Tenser x is [6], and after paddle.tensor_split(x, num_or_indices=4) transformation, we get four sub-Tensors out0, out1, out2, and out3 : .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/tensor_split/tensor_split-1_en.png since the length of x in axis = 0 direction 6 is not divisible by num_or_indices = 4, the size of the first int(6 % 4) part after splitting will be int(6 / 4) + 1 and the size of the remaining parts will be int(6 / 4). .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, ``indices_or_sections`` can be used as an alias for ``num_or_indices``, and ``dim`` can be used as an alias for ``axis``. For example, ``tensor_split(input=tensor_x, indices=[2,4], dim=1, ...)`` is equivalent to ``tensor_split(x=tensor_x, num_or_indices=[2,4], axis=1, ...)``. Args: num_or_indices (int|list|tuple): If ``num_or_indices`` is an int ``n``, ``x`` is split into ``n`` sections along ``axis``. If ``x`` is divisible by ``n``, each section will be ``x.shape[axis] / n``. If ``x`` is not divisible by ``n``, the first ``int(x.shape[axis] % n)`` sections will have size ``int(x.shape[axis] / n) + 1``, and the rest will be ``int(x.shape[axis] / n). If ``num_or_indices`` is a list or tuple of integer indices, ``x`` is split along ``axis`` at each of the indices. For instance, ``num_or_indices=[2, 4]`` with ``axis=0`` would split ``x`` into ``x[:2]``, ``x[2:4]`` and ``x[4:]`` along axis 0. alias: ``indices`` or ``sections`` axis (int|Tensor, optional): The axis along which to split, it can be a integer or a ``0-D Tensor`` with shape [] and data type ``int32`` or ``int64``. If :math::`axis < 0`, the axis to split along is :math:`rank(x) + axis`. Default is 0. alias: ``dim`` name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: list[Tensor], The list of segmented Tensors. Examples: .. code-block:: pycon :name: tensor-split-example-1 >>> import paddle >>> # evenly split >>> # x is a Tensor of shape [8] >>> x = paddle.rand([8]) >>> out0, out1 = paddle.tensor_split(x, num_or_indices=2) >>> print(out0.shape) paddle.Size([4]) >>> print(out1.shape) paddle.Size([4]) .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/tensor_split/tensor_split-2.png .. code-block:: pycon :name: tensor-split-example-2 >>> import paddle >>> # not evenly split >>> # x is a Tensor of shape [8] >>> x = paddle.rand([8]) >>> out0, out1, out2 = paddle.tensor_split(x, num_or_indices=3) >>> print(out0.shape) paddle.Size([3]) >>> print(out1.shape) paddle.Size([3]) >>> print(out2.shape) paddle.Size([2]) .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/tensor_split/tensor_split-3_en.png .. code-block:: pycon :name: tensor-split-example-3 >>> import paddle >>> # split with indices >>> # x is a Tensor of shape [8] >>> x = paddle.rand([8]) >>> out0, out1, out2 = paddle.tensor_split(x, num_or_indices=[2, 3]) >>> print(out0.shape) paddle.Size([2]) >>> print(out1.shape) paddle.Size([1]) >>> print(out2.shape) paddle.Size([5]) .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/tensor_split/tensor_split-4.png .. code-block:: pycon :name: tensor-split-example-4 >>> import paddle >>> # split along axis >>> # x is a Tensor of shape [7, 8] >>> x = paddle.rand([7, 8]) >>> out0, out1 = paddle.tensor_split(x, num_or_indices=2, axis=1) >>> print(out0.shape) paddle.Size([7, 4]) >>> print(out1.shape) paddle.Size([7, 4]) .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/tensor_split/tensor_split-5.png .. code-block:: pycon :name: tensor-split-example-5 >>> import paddle >>> # split along axis with indices >>> # x is a Tensor of shape [7, 8] >>> x = paddle.rand([7, 8]) >>> out0, out1, out2 = paddle.tensor_split(x, num_or_indices=[2, 3], axis=1) >>> print(out0.shape) paddle.Size([7, 2]) >>> print(out1.shape) paddle.Size([7, 1]) >>> print(out2.shape) paddle.Size([7, 5]) .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/tensor_split/tensor_split-6.png """ ... def tensordot(self, y: 'Tensor', axes: '_int | NestedSequence[_int] | Tensor' = 2, name: '_str | None' = None) -> 'Tensor': r""" This function computes a contraction, which sum the product of elements from two tensors along the given axes. Args: y (Tensor): The right tensor for contraction with the same data type as ``x``. axes (int|tuple|list|Tensor, optional): The axes to contract for ``x`` and ``y``, defaulted to integer ``2``. 1. It could be a non-negative integer ``n``, in which the function will sum over the last ``n`` axes of ``x`` and the first ``n`` axes of ``y`` in order. 2. It could be a 1-d tuple or list with data type ``int``, in which ``x`` and ``y`` will be contracted along the same given axes. For example, ``axes`` =[0, 1] applies contraction along the first two axes for ``x`` and the first two axes for ``y``. 3. It could be a tuple or list containing one or two 1-d tuple|list|Tensor with data type ``int``. When containing one tuple|list|Tensor, the data in tuple|list|Tensor specified the same axes for ``x`` and ``y`` to contract. When containing two tuple|list|Tensor, the first will be applied to ``x`` and the second to ``y``. When containing more than two tuple|list|Tensor, only the first two axis sequences will be used while the others will be ignored. 4. It could be a tensor, in which the ``axes`` tensor will be translated to a python list and applied the same rules described above to determine the contraction axes. Note that the ``axes`` with Tensor type is ONLY available in Dygraph mode. name(str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Return: Output (Tensor), The contraction result with the same data type as ``x`` and ``y``. In general, :math:`output.ndim = x.ndim + y.ndim - 2 \times n_{axes}`, where :math:`n_{axes}` denotes the number of axes to be contracted. NOTES: 1. This function supports tensor broadcast, the size in the corresponding dimensions of ``x`` and ``y`` should be equal, or applies to the broadcast rules. 2. This function also supports axes expansion, when the two given axis sequences for ``x`` and ``y`` are of different lengths, the shorter sequence will expand the same axes as the longer one at the end. For example, if ``axes`` =[[0, 1, 2, 3], [1, 0]], the axis sequence for ``x`` is [0, 1, 2, 3], while the corresponding axis sequences for ``y`` will be expanded from [1, 0] to [1, 0, 2, 3]. Examples: .. code-block:: python >>> import paddle >>> from typing import Literal >>> data_type: Literal["float64"] = 'float64' >>> # For two 2-d tensor x and y, the case axes=0 is equivalent to outer product. >>> # Note that tensordot supports empty axis sequence, so all the axes=0, axes=[], axes=[[]], and axes=[[],[]] are equivalent cases. >>> x = paddle.arange(4, dtype=data_type).reshape([2, 2]) >>> y = paddle.arange(4, dtype=data_type).reshape([2, 2]) >>> z = paddle.tensordot(x, y, axes=0) >>> print(z) Tensor(shape=[2, 2, 2, 2], dtype=float64, place=Place(cpu), stop_gradient=True, [[[[0., 0.], [0., 0.]], [[0., 1.], [2., 3.]]], [[[0., 2.], [4., 6.]], [[0., 3.], [6., 9.]]]]) >>> # For two 1-d tensor x and y, the case axes=1 is equivalent to inner product. >>> x = paddle.arange(10, dtype=data_type) >>> y = paddle.arange(10, dtype=data_type) >>> z1 = paddle.tensordot(x, y, axes=1) >>> z2 = paddle.dot(x, y) >>> print(z1) Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=True, 285.) >>> print(z2) Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=True, 285.) >>> # For two 2-d tensor x and y, the case axes=1 is equivalent to matrix multiplication. >>> x = paddle.arange(6, dtype=data_type).reshape([2, 3]) >>> y = paddle.arange(12, dtype=data_type).reshape([3, 4]) >>> z1 = paddle.tensordot(x, y, axes=1) >>> z2 = paddle.matmul(x, y) >>> print(z1) Tensor(shape=[2, 4], dtype=float64, place=Place(cpu), stop_gradient=True, [[20., 23., 26., 29.], [56., 68., 80., 92.]]) >>> print(z2) Tensor(shape=[2, 4], dtype=float64, place=Place(cpu), stop_gradient=True, [[20., 23., 26., 29.], [56., 68., 80., 92.]]) >>> # When axes is a 1-d int list, x and y will be contracted along the same given axes. >>> # Note that axes=[1, 2] is equivalent to axes=[[1, 2]], axes=[[1, 2], []], axes=[[1, 2], [1]], and axes=[[1, 2], [1, 2]]. >>> x = paddle.arange(24, dtype=data_type).reshape([2, 3, 4]) >>> y = paddle.arange(36, dtype=data_type).reshape([3, 3, 4]) >>> z = paddle.tensordot(x, y, axes=[1, 2]) >>> print(z) Tensor(shape=[2, 3], dtype=float64, place=Place(cpu), stop_gradient=True, [[506. , 1298., 2090.], [1298., 3818., 6338.]]) >>> # When axes is a list containing two 1-d int list, the first will be applied to x and the second to y. >>> x = paddle.arange(60, dtype=data_type).reshape([3, 4, 5]) >>> y = paddle.arange(24, dtype=data_type).reshape([4, 3, 2]) >>> z = paddle.tensordot(x, y, axes=([1, 0], [0, 1])) >>> print(z) Tensor(shape=[5, 2], dtype=float64, place=Place(cpu), stop_gradient=True, [[4400., 4730.], [4532., 4874.], [4664., 5018.], [4796., 5162.], [4928., 5306.]]) >>> # Thanks to the support of axes expansion, axes=[[0, 1, 3, 4], [1, 0, 3, 4]] can be abbreviated as axes= [[0, 1, 3, 4], [1, 0]]. >>> x = paddle.arange(720, dtype=data_type).reshape([2, 3, 4, 5, 6]) >>> y = paddle.arange(720, dtype=data_type).reshape([3, 2, 4, 5, 6]) >>> z = paddle.tensordot(x, y, axes=[[0, 1, 3, 4], [1, 0]]) >>> print(z) Tensor(shape=[4, 4], dtype=float64, place=Place(cpu), stop_gradient=True, [[23217330., 24915630., 26613930., 28312230.], [24915630., 26775930., 28636230., 30496530.], [26613930., 28636230., 30658530., 32680830.], [28312230., 30496530., 32680830., 34865130.]]) """ ... def tile(self, repeat_times: 'TensorOrTensors | Sequence[_int]', name: '_str | None' = None) -> 'Tensor': r""" Construct a new Tensor by repeating ``x`` the number of times given by ``repeat_times``. After tiling, the value of the i'th dimension of the output is equal to ``x.shape[i]*repeat_times[i]``. Both the number of dimensions of ``x`` and the number of elements in ``repeat_times`` should be less than or equal to 6. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``dims`` can be used as an alias for ``repeat_times``. For example, ``tile(input=x, dims=repeat_times)`` is equivalent to ``tile(x=x, repeat_times=repeat_times)``. Args: repeat_times (list|tuple|Tensor): The number of repeating times. If repeat_times is a list or tuple, all its elements should be integers or 1-D Tensors with the data type int32. If repeat_times is a Tensor, it should be an 1-D Tensor with the data type int32. alias: ``dims``. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: N-D Tensor. The data type is the same as ``x``. The size of the i-th dimension is equal to ``x[i] * repeat_times[i]``. Examples: .. code-block:: python >>> import paddle >>> data = paddle.to_tensor([1, 2, 3], dtype='int32') >>> out = paddle.tile(data, repeat_times=[2, 1]) >>> print(out) Tensor(shape=[2, 3], dtype=int32, place=Place(cpu), stop_gradient=True, [[1, 2, 3], [1, 2, 3]]) >>> out = paddle.tile(data, repeat_times=(2, 2)) >>> print(out) Tensor(shape=[2, 6], dtype=int32, place=Place(cpu), stop_gradient=True, [[1, 2, 3, 1, 2, 3], [1, 2, 3, 1, 2, 3]]) >>> repeat_times = paddle.to_tensor([1, 2], dtype='int32') >>> out = paddle.tile(data, repeat_times=repeat_times) >>> print(out) Tensor(shape=[1, 6], dtype=int32, place=Place(cpu), stop_gradient=True, [[1, 2, 3, 1, 2, 3]]) """ ... def to(self, *args, **kwargs): r""" Performs Tensor dtype and/or device conversion. A paddle.dtype and place are inferred from the arguments of ``self.to(*args, **kwargs)``.There are three ways to call `to`: 1. to(dtype, blocking=True) 2. to(device, dtype=None, blocking=True) 3. to(other, blocking=True) **Notes**: **If the self Tensor already has the correct dtype and device, then self is returned. Otherwise, the returned tensor is a copy of self with the desired dtype and device.** Returns: Tensor: self Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1,2,3]) >>> print(x) Tensor(shape=[3], dtype=int64, place=Place(gpu:0), stop_gradient=True, [1, 2, 3]) >>> x = x.to("cpu") >>> print(x.place) Place(cpu) >>> x = x.to("float32") >>> print(x.dtype) paddle.float32 >>> x = x.to("gpu", "int16") >>> print(x) Tensor(shape=[3], dtype=int16, place=Place(gpu:0), stop_gradient=True, [1, 2, 3]) >>> y = paddle.to_tensor([4,5,6]) >>> y Tensor(shape=[3], dtype=int64, place=Place(gpu:0), stop_gradient=True, [4, 5, 6]) >>> y = y.to(x) >>> print(y) Tensor(shape=[3], dtype=int16, place=Place(gpu:0), stop_gradient=True, [4, 5, 6]) """ ... def to_dense(self) -> 'Tensor': r""" **Notes**: **This API is ONLY available in Dygraph mode** Convert the current SparseTensor(COO or CSR) to DenseTensor. Returns: Tensor: A DenseTensor Examples: .. code-block:: python >>> import paddle >>> indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]] >>> values = [1, 2, 3, 4, 5] >>> dense_shape = [3, 4] >>> sparse_x = paddle.sparse.sparse_coo_tensor(paddle.to_tensor(indices, dtype='int64'), paddle.to_tensor(values, dtype='float32'), shape=dense_shape) >>> dense_x = sparse_x.to_dense() >>> print(dense_x) Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[0., 1., 0., 2.], [0., 0., 3., 0.], [4., 5., 0., 0.]]) """ ... def to_sparse_coo(self, sparse_dim: '_int') -> 'Tensor': r""" **Notes**: **This API is ONLY available in Dygraph mode** Convert the current DenseTensor to SparseTensor in COO format. When the input is already a SparseCooTensor, this function will directly return the input itself without performing any conversion. Returns: Tensor: A SparseCooTensor Examples: .. code-block:: python >>> import paddle >>> dense_x = [[0, 1, 0, 2], [0, 0, 3, 4]] >>> dense_x = paddle.to_tensor(dense_x, dtype='float32') >>> sparse_x = dense_x.to_sparse_coo(sparse_dim=2) >>> print(sparse_x) Tensor(shape=[2, 4], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 0, 1, 1], [1, 3, 2, 3]], values=[1., 2., 3., 4.]) """ ... def to_sparse_csr(self, /): r""" Note: **This API is only available for DenseTensor or SparseCooTensor.** Convert input Tensor to SparseCsrTensor. When input is SparseCooTensor, will convert `COO` to `CSR` . When input is DenseTensor, will convert `Dense` to `CSR` . When input is SparseCsrTensor, the function will directly return the input itself without performing any conversion. Returns: SparseCsrTensor Examples: .. code-block:: python >>> import paddle >>> indices = [[0, 1, 2], [1, 2, 0]] >>> values = [1.0, 2.0, 3.0] >>> dense_shape = [3, 3] >>> coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape) >>> coo.to_sparse_csr() Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True, crows=[0, 1, 2, 3], cols=[1, 2, 0], values=[1., 2., 3.]) """ ... def tolist(self) -> 'NestedList[_int | _float | _complex]': r""" Note: This API is ONLY available in Dygraph mode. This function translate the paddle.Tensor to python list. Args: Returns: list, A list that contain the same value of current Tensor. Examples: .. code-block:: python >>> import paddle >>> t = paddle.to_tensor([0,1,2,3,4]) >>> expectlist = t.tolist() >>> print(expectlist) [0, 1, 2, 3, 4] >>> expectlist = paddle.tolist(t) >>> print(expectlist) [0, 1, 2, 3, 4] """ ... def top_p_sampling(self, ps: 'Tensor', threshold: 'Tensor | None' = None, topp_seed: 'Tensor | None' = None, seed: '_int' = -1, k: '_int' = 0, mode: "Literal['truncated', 'non-truncated']" = 'truncated', return_top: '_bool' = False, name: '_str | None' = None) -> 'tuple[Tensor, Tensor]': r""" Get the TopP scores and ids. Args: ps(Tensor): A 1-D Tensor with type float32, float16 and bfloat16, used to specify the top_p corresponding to each query. threshold(Tensor|None, optional): A 1-D Tensor with type float32, float16 and bfloat16, used to avoid sampling low score tokens. topp_seed(Tensor|None, optional): A 1-D Tensor with type int64, used to specify the random seed for each query. seed(int, optional): the random seed. Default is -1, k(int): the number of top_k scores/ids to be returned. Default is 0. mode(str): The mode to choose sampling strategy. If the mode is `truncated`, sampling will truncate the probability at top_p_value. If the mode is `non-truncated`, it will not be truncated. Default is `truncated`. return_top(bool): Whether to return the top_k scores and ids. Default is False. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: tuple(Tensor), return the values and indices. The value data type is the same as the input `x`. The indices data type is int64. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> paddle.seed(2023) >>> x = paddle.randn([2,3]) >>> print(x) Tensor(shape=[2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True, [[-0.32012719, -0.07942779, 0.26011357], [ 0.79003978, -0.39958701, 1.42184138]]) >>> paddle.seed(2023) >>> ps = paddle.randn([2]) >>> print(ps) Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True, [-0.32012719, -0.07942779]) >>> value, index = paddle.tensor.top_p_sampling(x, ps) >>> print(value) Tensor(shape=[2, 1], dtype=float32, place=Place(gpu:0), stop_gradient=True, [[0.26011357], [1.42184138]]) >>> print(index) Tensor(shape=[2, 1], dtype=int64, place=Place(gpu:0), stop_gradient=True, [[2], [2]]) """ ... def topk(self, k: '_int | Tensor', axis: '_int | None' = None, largest: '_bool' = True, sorted: '_bool' = True, name: '_str | None' = None, *, out: 'tuple[Tensor, Tensor] | None' = None) -> 'TopKRetType': r""" Return values and indices of the k largest or smallest at the optional axis. If the input is a 1-D Tensor, finds the k largest or smallest values and indices. If the input is a Tensor with higher rank, this operator computes the top k values and indices along the :attr:`axis`. Args: k (int, Tensor): The number of top elements to look for along the axis. axis (int|None, optional): Axis to compute indices along. The effective range is [-R, R), where R is x.ndim. when axis < 0, it works the same way as axis + R. Default is -1. largest (bool, optional) : largest is a flag, if set to true, algorithm will sort by descending order, otherwise sort by ascending order. Default is True. sorted (bool, optional): controls whether to return the elements in sorted order, default value is True. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: tuple(Tensor), return the values and indices. The value data type is the same as the input `x`. The indices data type is int64. Examples: .. code-block:: python >>> import paddle >>> data_1 = paddle.to_tensor([1, 4, 5, 7]) >>> value_1, indices_1 = paddle.topk(data_1, k=1) >>> print(value_1) Tensor(shape=[1], dtype=int64, place=Place(cpu), stop_gradient=True, [7]) >>> print(indices_1) Tensor(shape=[1], dtype=int64, place=Place(cpu), stop_gradient=True, [3]) >>> data_2 = paddle.to_tensor([[1, 4, 5, 7], [2, 6, 2, 5]]) >>> value_2, indices_2 = paddle.topk(data_2, k=1) >>> print(value_2) Tensor(shape=[2, 1], dtype=int64, place=Place(cpu), stop_gradient=True, [[7], [6]]) >>> print(indices_2) Tensor(shape=[2, 1], dtype=int64, place=Place(cpu), stop_gradient=True, [[3], [1]]) >>> value_3, indices_3 = paddle.topk(data_2, k=1, axis=-1) >>> print(value_3) Tensor(shape=[2, 1], dtype=int64, place=Place(cpu), stop_gradient=True, [[7], [6]]) >>> print(indices_3) Tensor(shape=[2, 1], dtype=int64, place=Place(cpu), stop_gradient=True, [[3], [1]]) >>> value_4, indices_4 = paddle.topk(data_2, k=1, axis=0) >>> print(value_4) Tensor(shape=[1, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[2, 6, 5, 7]]) >>> print(indices_4) Tensor(shape=[1, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[1, 1, 0, 0]]) """ ... def trace(self, offset: '_int' = 0, axis1: '_int' = 0, axis2: '_int' = 1, name: '_str | None' = None) -> 'Tensor': r""" Computes the sum along diagonals of the input tensor x. If ``x`` is 2D, returns the sum of diagonal. If ``x`` has larger dimensions, then returns an tensor of diagonals sum, diagonals be taken from the 2D planes specified by axis1 and axis2. By default, the 2D planes formed by the first and second axes of the input tensor x. The argument ``offset`` determines where diagonals are taken from input tensor x: - If offset = 0, it is the main diagonal. - If offset > 0, it is above the main diagonal. - If offset < 0, it is below the main diagonal. - Note that if offset is out of input's shape indicated by axis1 and axis2, 0 will be returned. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``. For example, ``trace(input=x)`` is equivalent to ``trace(x=x)``. Args: offset (int, optional): Which diagonals in input tensor x will be taken. Default: 0 (main diagonals). axis1 (int, optional): The first axis with respect to take diagonal. Default: 0. axis2 (int, optional): The second axis with respect to take diagonal. Default: 1. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: the output data type is the same as input data type. Examples: .. code-block:: pycon >>> import paddle >>> case1 = paddle.randn([2, 3]) >>> case2 = paddle.randn([3, 10, 10]) >>> case3 = paddle.randn([3, 10, 5, 10]) >>> data1 = paddle.trace(case1) >>> data1.shape paddle.Size([]) >>> data2 = paddle.trace(case2, offset=1, axis1=1, axis2=2) >>> data2.shape paddle.Size([3]) >>> data3 = paddle.trace(case3, offset=-3, axis1=1, axis2=-1) >>> data3.shape paddle.Size([3, 5]) """ ... def transpose_(self, perm, name=None): r""" Inplace version of ``transpose`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_transpose`. """ ... def trapezoid(self, x: 'Tensor | None' = None, dx: '_float | None' = None, axis: '_int' = -1, name: '_str | None' = None) -> 'Tensor': r""" Integrate along the given axis using the composite trapezoidal rule. Use the sum method. Args: x (Tensor|None, optional): The sample points corresponding to the :attr:`y` values, the same type as :attr:`y`. It is known that the size of :attr:`y` is `[d_1, d_2, ... , d_n]` and :math:`axis=k`, then the size of :attr:`x` can only be `[d_k]` or `[d_1, d_2, ... , d_n ]`. If :attr:`x` is None, the sample points are assumed to be evenly spaced :attr:`dx` apart. The default is None. dx (float|None, optional): The spacing between sample points when :attr:`x` is None. If neither :attr:`x` nor :attr:`dx` is provided then the default is :math:`dx = 1`. axis (int, optional): The axis along which to integrate. The default is -1. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, Definite integral of :attr:`y` is N-D tensor as approximated along a single axis by the trapezoidal rule. If :attr:`y` is a 1D tensor, then the result is a float. If N is greater than 1, then the result is an (N-1)-D tensor. Examples: .. code-block:: python >>> import paddle >>> y = paddle.to_tensor([4, 5, 6], dtype='float32') >>> paddle.trapezoid(y) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 10.) >>> paddle.trapezoid(y, dx=2.) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 20.) >>> y = paddle.to_tensor([4, 5, 6], dtype='float32') >>> x = paddle.to_tensor([1, 2, 3], dtype='float32') >>> paddle.trapezoid(y, x) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 10.) >>> y = paddle.to_tensor([1, 2, 3], dtype='float64') >>> x = paddle.to_tensor([8, 6, 4], dtype='float64') >>> paddle.trapezoid(y, x) Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=True, -8.) >>> y = paddle.arange(6).reshape((2, 3)).astype('float32') >>> paddle.trapezoid(y, axis=0) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [1.50000000, 2.50000000, 3.50000000]) >>> paddle.trapezoid(y, axis=1) Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [2., 8.]) """ ... def tril(self, diagonal: '_int' = 0, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Returns the lower triangular part of a matrix (2-D tensor) or batch of matrices :attr:`x`, the other elements of the result tensor are set to 0. The lower triangular part of the matrix is defined as the elements on and below the diagonal. Args: diagonal (int, optional): The diagonal to consider, default value is 0. If :attr:`diagonal` = 0, all elements on and below the main diagonal are retained. A positive value includes just as many diagonals above the main diagonal, and similarly a negative value excludes just as many diagonals below the main diagonal. The main diagonal are the set of indices :math:`\{(i, i)\}` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` where :math:`d_{1}, d_{2}` are the dimensions of the matrix. name(str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. out(Tensor, optional): The output tensor. Returns: Tensor: Results of lower triangular operation by the specified diagonal of input tensor x, it's data type is the same as x's Tensor. Examples: .. code-block:: python >>> import paddle >>> data = paddle.arange(1, 13, dtype="int64").reshape([3,-1]) >>> print(data) Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[1 , 2 , 3 , 4 ], [5 , 6 , 7 , 8 ], [9 , 10, 11, 12]]) >>> tril1 = paddle.tril(data) >>> print(tril1) Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[1 , 0 , 0 , 0 ], [5 , 6 , 0 , 0 ], [9 , 10, 11, 0 ]]) >>> # example 2, positive diagonal value >>> tril2 = paddle.tril(data, diagonal=2) >>> print(tril2) Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[1 , 2 , 3 , 0 ], [5 , 6 , 7 , 8 ], [9 , 10, 11, 12]]) >>> # example 3, negative diagonal value >>> tril3 = paddle.tril(data, diagonal=-1) >>> print(tril3) Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0 , 0 , 0 , 0 ], [5 , 0 , 0 , 0 ], [9 , 10, 0 , 0 ]]) """ ... def tril_(self, diagonal: '_int' = 0, name: '_str | None' = None) -> 'paddle.Tensor | None': r""" Inplace version of ``tril`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_tril`. """ ... def triu(self, diagonal: '_int' = 0, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Return the upper triangular part of a matrix (2-D tensor) or batch of matrices :attr:`x`, the other elements of the result tensor are set to 0. The upper triangular part of the matrix is defined as the elements on and above the diagonal. Args: diagonal (int, optional): The diagonal to consider, default value is 0. If :attr:`diagonal` = 0, all elements on and above the main diagonal are retained. A positive value excludes just as many diagonals above the main diagonal, and similarly a negative value includes just as many diagonals below the main diagonal. The main diagonal are the set of indices :math:`\{(i, i)\}` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` where :math:`d_{1}, d_{2}` are the dimensions of the matrix. name(str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. out(Tensor, optional): The output tensor. Returns: Tensor: Results of upper triangular operation by the specified diagonal of input tensor x, it's data type is the same as x's Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.arange(1, 13, dtype="int64").reshape([3,-1]) >>> print(x) Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[1 , 2 , 3 , 4 ], [5 , 6 , 7 , 8 ], [9 , 10, 11, 12]]) >>> # example 1, default diagonal >>> triu1 = paddle.tensor.triu(x) >>> print(triu1) Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[1 , 2 , 3 , 4 ], [0 , 6 , 7 , 8 ], [0 , 0 , 11, 12]]) >>> # example 2, positive diagonal value >>> triu2 = paddle.tensor.triu(x, diagonal=2) >>> print(triu2) Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 0, 3, 4], [0, 0, 0, 8], [0, 0, 0, 0]]) >>> # example 3, negative diagonal value >>> triu3 = paddle.tensor.triu(x, diagonal=-1) >>> print(triu3) Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[1 , 2 , 3 , 4 ], [5 , 6 , 7 , 8 ], [0 , 10, 11, 12]]) """ ... def triu_(self, diagonal: '_int' = 0, name: '_str | None' = None) -> 'paddle.Tensor | None': r""" Inplace version of ``triu`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_triu`. """ ... def true_divide(self, other: 'Tensor', *, out: 'Tensor | None' = None) -> 'Tensor': r""" Alias for paddle.divide with rounding_mode=None. Please refer to :ref:`api_paddle_divide`. """ ... def trunc(self, name: '_str | None' = None) -> 'Tensor': r""" This API is used to returns a new tensor with the truncated integer values of input. Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The output Tensor of trunc. Examples: .. code-block:: python >>> import paddle >>> input = paddle.to_tensor([[0.1, 1.5], [-0.2, -2.4]], 'float32') >>> output = paddle.trunc(input) >>> output Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0., 1.], [-0., -2.]]) """ ... def trunc_(self, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``trunc`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_trunc`. """ ... def type_as(self, other: 'Tensor') -> 'Tensor': ... def unbind(self, axis: '_int' = 0) -> 'list[Tensor]': r""" Removes a tensor dimension, then split the input tensor into multiple sub-Tensors. .. note:: Alias Support: The parameter name ``dim`` can be used as an alias for ``axis``. For example, ``unbind(input=tensor_x, dim=0)`` is equivalent to ``unbind(input=tensor_x, axis=0)``. Args: axis (int, optional): A 0-D Tensor with shape [] and type is ``int32|int64``. The dimension along which to unbind. If :math:`axis < 0`, the dimension to unbind along is :math:`rank(input) + axis`. Default is 0. alias: ``dim``. Returns: list(Tensor), The list of segmented Tensor variables. Examples: .. code-block:: python >>> import paddle >>> # input is a Tensor which shape is [3, 4, 5] >>> input = paddle.rand([3, 4, 5]) >>> [x0, x1, x2] = paddle.unbind(input, axis=0) >>> # x0.shape [4, 5] >>> # x1.shape [4, 5] >>> # x2.shape [4, 5] >>> [x0, x1, x2, x3] = paddle.unbind(input, axis=1) >>> # x0.shape [3, 5] >>> # x1.shape [3, 5] >>> # x2.shape [3, 5] >>> # x3.shape [3, 5] """ ... def unflatten(self, axis: '_int', shape: 'ShapeLike', name: '_str | None' = None) -> 'Tensor': r""" Expand a certain dimension of the input x Tensor into a desired shape. The figure below shows the shape of a [2, 6] Tensor after applying ``unflatten(X, axis=1, shape=(2, 3))``, with data ranging from 0 to 11 in sequence. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/unflatten.png :width: 500 :alt: Illustration of unflatten :align: center .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``. Alias Support: The parameter name ``dim`` can be used as an alias for ``axis``. Alias Support: The parameter name ``sizes`` can be used as an alias for ``shape``. Args: axis (int): :attr:`axis` to be unflattened, specified as an index into `x.shape`. Alias: ``dim``. shape (list|tuple|Tensor): Unflatten :attr:`shape` on the specified :attr:`axis`. At most one dimension of the target :attr:`shape` can be -1. If the input :attr:`shape` does not contain -1 , the product of all elements in ``shape`` should be equal to ``x.shape[axis]``. The data type is `int` . If :attr:`shape` is a list or tuple, the elements of it should be integers or Tensors with shape []. If :attr:`shape` is an Tensor, it should be an 1-D Tensor. Alias: ``sizes``. name(str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor, return the unflatten tensor of :attr:`x`. Examples: .. code-block:: python >>> import paddle >>> x = paddle.randn(shape=[4, 6, 8]) >>> shape = [2, 3] >>> axis = 1 >>> res = paddle.unflatten(x, axis, shape) >>> print(res.shape) paddle.Size([4, 2, 3, 8]) >>> x = paddle.randn(shape=[4, 6, 8]) >>> shape = (-1, 2) >>> axis = -1 >>> res = paddle.unflatten(x, axis, shape) >>> print(res.shape) paddle.Size([4, 6, 4, 2]) >>> x = paddle.randn(shape=[4, 6, 8]) >>> shape = paddle.to_tensor([2, 2]) >>> axis = 0 >>> res = paddle.unflatten(x, axis, shape) >>> print(res.shape) paddle.Size([2, 2, 6, 8]) """ ... def unfold(self, axis: '_int', size: '_int', step: '_int', name: '_str | None' = None) -> 'Tensor': r""" View x with specified shape, stride and offset, which contains all slices of size from x in the dimension axis. Note that the output Tensor will share data with origin Tensor and doesn't have a Tensor copy in ``dygraph`` mode. Args: axis (int): The axis along which the input is unfolded. Alias: ``dimension``. size (int): The size of each slice that is unfolded. step (int): The step between each slice. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, A unfold Tensor with the same data type as ``x``. Examples: .. code-block:: python >>> import paddle >>> paddle.base.set_flags({"FLAGS_use_stride_kernel": True}) >>> x = paddle.arange(9, dtype="float64") >>> out = paddle.unfold(x, 0, 2, 4) >>> print(out) Tensor(shape=[2, 2], dtype=float64, place=Place(cpu), stop_gradient=True, [[0., 1.], [4., 5.]]) """ ... def uniform_(self, min: '_float' = 0, max: '_float' = 1.0, seed: '_int' = 0, name: '_str | None' = None) -> 'Tensor': r""" This is the inplace version of OP ``uniform``, which returns a Tensor filled with random values sampled from a uniform distribution. The output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_uniform`. Args: min(float|int, optional): The lower bound on the range of random values to generate, ``min`` is included in the range. Default is 0. Alias: ``from``. max(float|int, optional): The upper bound on the range of random values to generate, ``max`` is excluded in the range. Default is 1.0. Alias: ``to``. seed(int, optional): Random seed used for generating samples. If seed is 0, it will use the seed of the global default generator (which can be set by paddle.seed). Note that if seed is not 0, this operator will always generate the same random numbers every time. Default is 0. name(str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, The input tensor x filled with random values sampled from a uniform distribution in the range [``min``, ``max``). Examples: .. code-block:: python >>> import paddle >>> # example: >>> x = paddle.ones(shape=[3, 4]) >>> x.uniform_() >>> # doctest: +SKIP("Random output") Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[-0.50484276, 0.49580324, 0.33357990, -0.93924278], [ 0.39779735, 0.87677515, -0.24377221, 0.06212139], [-0.92499518, -0.96244860, 0.79210341, -0.78228098]]) >>> # doctest: -SKIP """ ... def unique(self, return_index=False, return_inverse=False, return_counts=False, axis=None, dtype='int64', sorted=True, name=None): r""" Returns the unique elements of `x` in ascending order. Args: return_index(bool, optional): If True, also return the indices of the input tensor that result in the unique Tensor. return_inverse(bool, optional): If True, also return the indices for where elements in the original input ended up in the returned unique tensor. return_counts(bool, optional): If True, also return the counts for each unique element. axis(int, optional): The axis to apply unique. If None, the input will be flattened. Default: None. dtype(str|paddle.dtype|np.dtype, optional): The date type of `indices` or `inverse` tensor: int32 or int64. Default: int64. sorted(bool, optional): Does not affect the return result, same as PyTorch. name(str|None, optional): Name for the operation. For more information, please refer to :ref:`api_guide_Name`. Default: None. Returns: tuple (out, indices, inverse, counts). `out` is the unique tensor for `x`. `indices` is \ provided only if `return_index` is True. `inverse` is provided only if `return_inverse` \ is True. `counts` is provided only if `return_counts` is True. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([2, 3, 3, 1, 5, 3]) >>> unique = paddle.unique(x) >>> print(unique) Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True, [1, 2, 3, 5]) >>> _, indices, inverse, counts = paddle.unique(x, return_index=True, return_inverse=True, return_counts=True) >>> print(indices) Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True, [3, 0, 1, 4]) >>> print(inverse) Tensor(shape=[6], dtype=int64, place=Place(cpu), stop_gradient=True, [1, 2, 2, 0, 3, 2]) >>> print(counts) Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True, [1, 1, 3, 1]) >>> x = paddle.to_tensor([[2, 1, 3], [3, 0, 1], [2, 1, 3]]) >>> unique = paddle.unique(x) >>> print(unique) Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True, [0, 1, 2, 3]) >>> unique = paddle.unique(x, axis=0) >>> print(unique) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[2, 1, 3], [3, 0, 1]]) """ ... def unique_consecutive(self, return_inverse: '_bool' = False, return_counts: '_bool' = False, axis: '_int | None' = None, dtype: 'DTypeLike' = 'int64', name: '_str | None' = None) -> 'tuple[Tensor, Tensor, Tensor]': r""" Eliminates all but the first element from every consecutive group of equivalent elements. Note: This function is different from :ref:`api_paddle_unique` in the sense that this function only eliminates consecutive duplicate values. This semantics is similar to :ref:`api_paddle_unique` in C++. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``dim`` can be used as an alias for ``axis``. For example, ``unique_consecutive(input=tensor_x, dim=1, ...)`` is equivalent to ``unique_consecutive(x=tensor_x, axis=1, ...)``. Args: return_inverse(bool, optional): If True, also return the indices for where elements in the original input ended up in the returned unique consecutive tensor. Default is False. return_counts(bool, optional): If True, also return the counts for each unique consecutive element. Default is False. axis(int, optional): The axis to apply unique consecutive. If None, the input will be flattened. Default is None. dtype(str|paddle.dtype|np.dtype, optional):The data type `inverse` tensor: int32 or int64. Default: int64. name(str|None, optional): Name for the operation. For more information, please refer to :ref:`api_guide_Name`. Default is None. Returns: - out (Tensor), the unique consecutive tensor for x. - inverse (Tensor), the element of the input tensor corresponds to the index of the elements in the unique consecutive tensor for x. inverse is provided only if return_inverse is True. - counts (Tensor), the counts of the every unique consecutive element in the input tensor. counts is provided only if return_counts is True. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 1, 2, 2, 3, 1, 1, 2]) >>> output = paddle.unique_consecutive(x) # >>> print(output) Tensor(shape=[5], dtype=int64, place=Place(cpu), stop_gradient=True, [1, 2, 3, 1, 2]) >>> _, inverse, counts = paddle.unique_consecutive(x, return_inverse=True, return_counts=True) >>> print(inverse) Tensor(shape=[8], dtype=int64, place=Place(cpu), stop_gradient=True, [0, 0, 1, 1, 2, 3, 3, 4]) >>> print(counts) Tensor(shape=[5], dtype=int64, place=Place(cpu), stop_gradient=True, [2, 2, 1, 2, 1]) >>> x = paddle.to_tensor([[2, 1, 3], [3, 0, 1], [2, 1, 3], [2, 1, 3]]) >>> output = paddle.unique_consecutive(x, axis=0) # >>> print(output) Tensor(shape=[3, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[2, 1, 3], [3, 0, 1], [2, 1, 3]]) >>> x = paddle.to_tensor([[2, 1, 3], [3, 0, 1], [2, 1, 3], [2, 1, 3]]) >>> output = paddle.unique_consecutive(x, axis=0) # >>> print(output) Tensor(shape=[3, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[2, 1, 3], [3, 0, 1], [2, 1, 3]]) """ ... def unsqueeze(self, axis: '_int | Sequence[Tensor | _int] | Tensor', name: '_str | None' = None) -> 'Tensor': r""" Insert single-dimensional entries to the shape of input Tensor ``x``. Takes one required argument axis, a dimension or list of dimensions that will be inserted. Dimension indices in axis are as seen in the output tensor. Note that the output Tensor will share data with origin Tensor and doesn't have a Tensor copy in ``dygraph`` mode. If you want to use the Tensor copy version, please use `Tensor.clone` like ``unsqueeze_clone_x = x.unsqueeze(-1).clone()``. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``dim`` can be used as an alias for ``axis``. For example, ``unsqueeze(input=tensor_x, dim=1)`` is equivalent to ``unsqueeze(x=tensor_x, axis=1)``. Args: axis (int|list|tuple|Tensor): Indicates the dimensions to be inserted. The data type is ``int32`` . If ``axis`` is a list or tuple, each element of it should be integer or 0-D Tensor with shape []. If ``axis`` is a Tensor, it should be an 1-D Tensor . If ``axis`` is negative, ``axis = axis + ndim(x) + 1``. alias: ``dim``. name (str|None, optional): Name for this layer. Please refer to :ref:`api_guide_Name`, Default None. Returns: Tensor, Unsqueezed Tensor with the same data type as input Tensor. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.rand([5, 10]) >>> print(x.shape) paddle.Size([5, 10]) >>> out1 = paddle.unsqueeze(x, axis=0) >>> print(out1.shape) paddle.Size([1, 5, 10]) >>> out2 = paddle.unsqueeze(x, axis=[0, 2]) >>> print(out2.shape) paddle.Size([1, 5, 1, 10]) >>> axis = paddle.to_tensor([0, 1, 2]) >>> out3 = paddle.unsqueeze(x, axis=axis) >>> print(out3.shape) paddle.Size([1, 1, 1, 5, 10]) >>> # out1, out2, out3 share data with x in dygraph mode >>> x[0, 0] = 10.0 >>> print(out1[0, 0, 0]) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 10.) >>> print(out2[0, 0, 0, 0]) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 10.) >>> print(out3[0, 0, 0, 0, 0]) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 10.) """ ... def unsqueeze_(self, axis: '_int | Sequence[_int] | Tensor', name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``unsqueeze`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_tensor_unsqueeze`. """ ... def unstack(self, axis: '_int' = 0, num: '_int | None' = None) -> 'list[Tensor]': r""" This layer unstacks input Tensor :code:`x` into several Tensors along :code:`axis`. If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x)`. If :code:`num` is None, it would be inferred from :code:`x.shape[axis]`, and if :code:`x.shape[axis]` <= 0 or is unknown, :code:`ValueError` is raised. Args: axis (int, optional): The axis along which the input is unstacked. num (int|None, optional): The number of output variables. Returns: list(Tensor), The unstacked Tensors list. The list elements are N-D Tensors of data types float32, float64, int32, int64, complex64, complex128. Examples: .. code-block:: python >>> import paddle >>> x = paddle.ones(name='x', shape=[2, 3, 5], dtype='float32') # create a tensor with shape=[2, 3, 5] >>> y = paddle.unstack(x, axis=1) # unstack with second axis, which results 3 tensors with shape=[2, 5] """ ... def value(self) -> 'Tensor': ... def values(self) -> 'Tensor': r""" **Notes**: **This API is ONLY available in Dygraph mode** Get the values of current SparseTensor(COO or CSR). Returns: Tensor: A DenseTensor Examples: .. code-block:: python >>> import paddle >>> indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]] >>> values = [1, 2, 3, 4, 5] >>> dense_shape = [3, 4] >>> sparse_x = paddle.sparse.sparse_coo_tensor(paddle.to_tensor(indices, dtype='int32'), paddle.to_tensor(values, dtype='float32'), shape=dense_shape) >>> print(sparse_x.values()) Tensor(shape=[5], dtype=float32, place=Place(cpu), stop_gradient=True, [1., 2., 3., 4., 5.]) """ ... def vander(self, n: '_int | None' = None, increasing: '_bool' = False, name: '_str | None' = None) -> 'Tensor': r""" Generate a Vandermonde matrix. The columns of the output matrix are powers of the input vector. Order of the powers is determined by the increasing Boolean parameter. Specifically, when the increment is "false", the ith output column is a step-up in the order of the elements of the input vector to the N - i - 1 power. Such a matrix with a geometric progression in each row is named after Alexandre-Theophile Vandermonde. Args: n (int|None): Number of columns in the output. If n is not specified, a square array is returned (n = len(x)). increasing(bool): Order of the powers of the columns. If True, the powers increase from left to right, if False (the default) they are reversed. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor, A vandermonde matrix with shape (len(x), N). If increasing is False, the first column is :math:`x^{(N-1)}`, the second :math:`x^{(N-2)}` and so forth. If increasing is True, the columns are :math:`x^0`, :math:`x^1`, ..., :math:`x^{(N-1)}`. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1., 2., 3.], dtype="float32") >>> out = paddle.vander(x) >>> out Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[1., 1., 1.], [4., 2., 1.], [9., 3., 1.]]) >>> out1 = paddle.vander(x,2) >>> out1 Tensor(shape=[3, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[1., 1.], [2., 1.], [3., 1.]]) >>> out2 = paddle.vander(x, increasing = True) >>> out2 Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[1., 1., 1.], [1., 2., 4.], [1., 3., 9.]]) >>> real = paddle.to_tensor([2., 4.]) >>> imag = paddle.to_tensor([1., 3.]) >>> complex = paddle.complex(real, imag) >>> out3 = paddle.vander(complex) >>> out3 Tensor(shape=[2, 2], dtype=complex64, place=Place(cpu), stop_gradient=True, [[(2+1j), (1+0j)], [(4+3j), (1+0j)]]) """ ... def var(self, axis: '_int | Sequence[_int] | None' = None, unbiased: '_bool | None' = None, keepdim: '_bool' = False, name: '_str | None' = None, *, correction: '_float' = 1, out: 'Tensor | None' = None) -> 'Tensor': r""" Computes the variance of ``x`` along ``axis`` . .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``dim`` can be used as an alias for ``axis``. For example, ``var(input=tensor_x, dim=1, ...)`` is equivalent to ``var(x=tensor_x, axis=1, ...)``. Args: axis (int|list|tuple|None, optional): The axis along which to perform variance calculations. ``axis`` should be int, list(int) or tuple(int). alias: ``dim``. - If ``axis`` is a list/tuple of dimension(s), variance is calculated along all element(s) of ``axis`` . ``axis`` or element(s) of ``axis`` should be in range [-D, D), where D is the dimensions of ``x`` . - If ``axis`` or element(s) of ``axis`` is less than 0, it works the same way as :math:`axis + D` . - If ``axis`` is None, variance is calculated over all elements of ``x``. Default is None. unbiased (bool, optional): Whether to use the unbiased estimation. If ``unbiased`` is True, the divisor used in the computation is :math:`N - 1`, where :math:`N` represents the number of elements along ``axis`` , otherwise the divisor is :math:`N`. Default is True. keep_dim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result tensor will have one fewer dimension than the input unless keep_dim is true. Default is False. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. correction (int|float, optional): Difference between the sample size and sample degrees of freedom. Defaults to 1 (Bessel's correction). If unbiased is specified, this parameter is ignored. out (Tensor|None, optional): Output tensor. Default is None. Returns: Tensor, results of variance along ``axis`` of ``x``, with the same data type as ``x``. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1.0, 2.0, 3.0], [1.0, 4.0, 5.0]]) >>> out1 = paddle.var(x) >>> print(out1.numpy()) 2.6666667 >>> out2 = paddle.var(x, axis=1) >>> print(out2.numpy()) [1. 4.3333335] """ ... def view(self, shape_or_dtype: 'Sequence[_int] | DTypeLike', name: '_str | None' = None) -> 'Tensor': r""" View x with specified shape or dtype. Note that the output Tensor will share data with origin Tensor and doesn't have a Tensor copy in ``dygraph`` mode. .. note:: Alias Support: The parameter name ``size`` and ``dtype`` can be used as an alias for ``shape_or_dtype``. ``shape_or_dtype`` can be a variable number of arguments. For example: ``tensor_x.view(dtype=paddle.float32)`` ``tensor_x.view(size=[-1, 1, 3])`` ``tensor_x.view(-1, 1, 3)`` Args: shape_or_dtype (list|tuple|np.dtype|str|VarType|variable number of arguments): Define the target shape or dtype. If list or tuple, shape_or_dtype represents shape, each element of it should be integer. If np.dtype or str or VarType, shape_or_dtype represents dtype, it can be bool, float16, float32, float64, int8, int32, int64, uint8. ``shape_or_dtype`` can be a variable number of arguments. alias: ``size`` or ``dtype``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, A viewed Tensor with the same data as ``x``. Examples: .. code-block:: pycon >>> import paddle >>> paddle.base.set_flags({"FLAGS_use_stride_kernel": True}) >>> x = paddle.rand([2, 4, 6], dtype="float32") >>> out = paddle.view(x, [8, 6]) >>> print(out.shape) paddle.Size([8, 6]) >>> import paddle >>> paddle.base.set_flags({"FLAGS_use_stride_kernel": True}) >>> x = paddle.rand([2, 4, 6], dtype="float32") >>> out = paddle.view(x, "uint8") >>> print(out.shape) paddle.Size([2, 4, 24]) >>> import paddle >>> paddle.base.set_flags({"FLAGS_use_stride_kernel": True}) >>> x = paddle.rand([2, 4, 6], dtype="float32") >>> out = paddle.view(x, [8, -1]) >>> print(out.shape) paddle.Size([8, 6]) >>> import paddle >>> paddle.base.set_flags({"FLAGS_use_stride_kernel": True}) >>> x = paddle.rand([2, 4, 6], dtype="float32") >>> out = paddle.view(x, paddle.uint8) >>> print(out.shape) paddle.Size([2, 4, 24]) """ ... def view_as(self, other: 'Tensor', name: '_str | None' = None) -> 'Tensor': r""" View x with other's shape. Note that the output Tensor will share data with origin Tensor and doesn't have a Tensor copy in ``dygraph`` mode. The following figure shows a view_as operation - a three-dimensional tensor with a shape of [2, 4, 6] is transformed into a two-dimensional tensor with a shape of [8, 6] through the view_as operation. We can clearly see the corresponding relationship between the elements before and after the transformation. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/view_as.png :width: 800 :alt: legend of view_as API :align: center Args: other (Tensor): The result tensor has the same size as other. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, A viewed Tensor with the same shape as ``other``. Examples: .. code-block:: pycon >>> import paddle >>> paddle.base.set_flags({"FLAGS_use_stride_kernel": True}) >>> x = paddle.rand([2, 4, 6], dtype="float32") >>> y = paddle.rand([8, 6], dtype="float32") >>> out = paddle.view_as(x, y) >>> print(out.shape) paddle.Size([8, 6]) """ ... def view_as_complex(self) -> 'Tensor': r""" Return a complex tensor that is a view of the input real tensor . The data type of the input tensor is 'float32' or 'float64', and the data type of the returned tensor is 'complex64' or 'complex128', respectively. The shape of the input tensor is ``(* ,2)``, (``*`` means arbitrary shape), i.e. the size of the last axis should be 2, which represent the real and imag part of a complex number. The shape of the returned tensor is ``(*,)``. The complex tensor is a view of the input real tensor, meaning that it shares the same memory with real tensor. The image below demonstrates the case that a real 3D-tensor with shape [2, 3, 2] is transformed into a complex 2D-tensor with shape [2, 3]. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/as_complex.png :width: 500 :alt: Illustration of as_complex :align: center Args: Returns: Tensor, The output. Data type is 'complex64' or 'complex128', sharing the same memory with input. Examples: .. code-block:: python >>> import paddle >>> x = paddle.arange(12, dtype=paddle.float32).reshape([2, 3, 2]) >>> y = paddle.as_complex(x) >>> print(y) Tensor(shape=[2, 3], dtype=complex64, place=Place(cpu), stop_gradient=True, [[1j , (2+3j) , (4+5j) ], [(6+7j) , (8+9j) , (10+11j)]]) """ ... def view_as_real(self) -> 'Tensor': r""" Return a real tensor that is a view of the input complex tensor. The data type of the input tensor is 'complex64' or 'complex128', and the data type of the returned tensor is 'float32' or 'float64', respectively. When the shape of the input tensor is ``(*, )``, (``*`` means arbitrary shape), the shape of the output tensor is ``(*, 2)``, i.e. the shape of the output is the shape of the input appended by an extra ``2``. The real tensor is a view of the input complex tensor, meaning that it shares the same memory with complex tensor. Args: Returns: Tensor, The output. Data type is 'float32' or 'float64', sharing the same memory with input. Examples: .. code-block:: python >>> import paddle >>> x = paddle.arange(12, dtype=paddle.float32).reshape([2, 3, 2]) >>> y = paddle.as_complex(x) >>> z = paddle.as_real(y) >>> print(z) Tensor(shape=[2, 3, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[[0. , 1. ], [2. , 3. ], [4. , 5. ]], [[6. , 7. ], [8. , 9. ], [10., 11.]]]) """ ... def vsplit(self, num_or_indices: '_int | Sequence[_int]', name: '_str | None' = None) -> 'list[Tensor]': r""" ``vsplit`` Full name Vertical Split, splits the input Tensor into multiple sub-Tensors along the vertical axis, which is equivalent to ``paddle.tensor_split`` with ``axis=0``. 1. When the number of Tensor dimensions is equal to 2: .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/vsplit/vsplit-1.png 2. When the number of Tensor dimensions is greater than 2: .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/vsplit/vsplit-2.png Note: Make sure that the number of Tensor dimensions transformed using ``paddle.vsplit`` must be not less than 2. Args: num_or_indices (int|list|tuple): If ``num_or_indices`` is an int ``n``, ``x`` is split into ``n`` sections. If ``num_or_indices`` is a list or tuple of integer indices, ``x`` is split at each of the indices. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: list[Tensor], The list of segmented Tensors. Examples: .. code-block:: pycon >>> import paddle >>> # x is a Tensor of shape [8, 6, 7] >>> x = paddle.rand([8, 6, 7]) >>> out0, out1 = paddle.vsplit(x, num_or_indices=2) >>> print(out0.shape) paddle.Size([4, 6, 7]) >>> print(out1.shape) paddle.Size([4, 6, 7]) >>> out0, out1, out2 = paddle.vsplit(x, num_or_indices=[1, 4]) >>> print(out0.shape) paddle.Size([1, 6, 7]) >>> print(out1.shape) paddle.Size([3, 6, 7]) >>> print(out2.shape) paddle.Size([4, 6, 7]) """ ... def where(self, x: 'Tensor | _float | None' = None, y: 'Tensor | _float | None' = None, name: '_str | None' = None, *, out: 'Tensor | None' = None) -> 'Tensor': r""" Return a Tensor of elements selected from either :attr:`x` or :attr:`y` according to corresponding elements of :attr:`condition`. Concretely, .. math:: out_i = \begin{cases} x_i, & \text{if} \ condition_i \ \text{is} \ True \\ y_i, & \text{if} \ condition_i \ \text{is} \ False \\ \end{cases}. Notes: ``numpy.where(condition)`` is identical to ``paddle.nonzero(condition, as_tuple=True)``, please refer to :ref:`api_paddle_nonzero`. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``other`` can be used as an alias for ``y``. For example, ``paddle.where(condition, input=x, other=y)`` can be written as ``paddle.where(condition, x=x, y=y)``. Args: x (Tensor|scalar|None, optional): A Tensor or scalar to choose when the condition is True with data type of bfloat16, float16, float32, float64, int32 or int64. Either both or neither of x and y should be given. alias: ``input``. y (Tensor|scalar|None, optional): A Tensor or scalar to choose when the condition is False with data type of bfloat16, float16, float32, float64, int32 or int64. Either both or neither of x and y should be given. alias: ``other``. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. out (Tensor|None, optional): The output tensor. If set, the result will be stored to this tensor. Default is None. Returns: Tensor, A Tensor with the same shape as :attr:`condition` and same data type as :attr:`x` and :attr:`y`. If :attr:`x` and :attr:`y` have different data types, type promotion rules will be applied (see `Auto Type Promotion `_). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([0.9383, 0.1983, 3.2, 1.2]) >>> y = paddle.to_tensor([1.0, 1.0, 1.0, 1.0]) >>> out = paddle.where(x>1, x, y) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [1. , 1. , 3.20000005, 1.20000005]) >>> out = paddle.where(x>1) >>> print(out) (Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True, [2, 3]),) """ ... def where_(self, x: 'Tensor | _float | None' = None, y: 'Tensor | _float | None' = None, name: '_str | None' = None) -> 'Tensor': r""" Inplace version of ``where`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_where`. """ ... def zero_(self) -> 'Tensor': r""" **Notes**: **This API is ONLY available in Dygraph mode** This function fill the Tensor with zero inplace. Args: Returns: x (Tensor), Tensor x filled with zero inplace Examples: .. code-block:: python >>> import paddle >>> tensor = paddle.to_tensor([0, 1, 2, 3, 4]) >>> tensor.zero_() >>> print(tensor.tolist()) [0, 0, 0, 0, 0] """ ... @overload def __init__(self) -> None: ... @overload def __init__( self, dtype, dims, name: _str, type, persistable: _bool ) -> None: ... @overload def __init__( self, value: npt.NDArray[Any], place, persistable: _bool, zero_copy: _bool, name: _str, stop_gradient: _bool, ) -> None: ... @overload def __init__(self, value: npt.NDArray[Any]) -> None: ... @overload def __init__( self, value: Tensor, dims, name: _str, process_mesh, placements ) -> None: ... @overload def __init__(self, value: Tensor, place, name: _str) -> None: ... @overload def __init__(self, *args: Any, **kwargs: Any) -> None: """ ref: paddle/fluid/pybind/eager.cc We should have init function with signature: 1. def __init__ () 2. def __init__ ( dtype: paddle::framework::proto::VarType::Type, dims: vector, name: std::string, type: paddle::framework::proto::VarType::DenseTensor, persistable: bool) 3. (multi-place) (should have at least one parameter, one parameter equals to case 4, zero parameter equals to case 1) def __init__ ( value: ndarray, place: paddle::platform::Place, persistable: bool, zero_copy: bool, name: std::string, stop_gradient: bool) 4. def __init__ ( value: ndarray) 5. def __init__ ( tensor: Tensor) 6. (multi-place) (should have at least one parameter, one parameter equals to case 5, zero parameter equals to case 1.) def __init__ ( global_tensor: Tensor, place: paddle::platform::Place, name: std::string, process_mesh: phi::distributed::ProcessMesh placements: std::vector) 7. (multi-place) (should have at least one parameter, one parameter equals to case 5, zero parameter equals to case 1.) def __init__ ( local_tensor: Tensor, global_dims: vector, name: std::string, process_mesh: phi::distributed::ProcessMesh placements: std::vector) 8. (multi-place) (should have at least one parameter, one parameter similar to case 5, zero parameter equals to case 1.) def __init__ ( tensor: FrameworkTensor, place: paddle::platform::Place, name: std::string) """ ... # rich comparison def __eq__(self, y: _typing.TensorLike) -> Tensor: # type: ignore[override] r""" Return self==value. """ ... def __ge__(self, y: _typing.TensorLike) -> Tensor: r""" Return self>=value. """ ... def __gt__(self, y: _typing.TensorLike) -> Tensor: r""" Return self>value. """ ... def __lt__(self, y: _typing.TensorLike) -> Tensor: r""" Return self Tensor: r""" Return self<=value. """ ... def __ne__(self, y: _typing.TensorLike) -> Tensor: # type: ignore[override] r""" Return self!=value. """ ... # binary arithmetic operations def __add__(self, y: _typing.TensorLike) -> Tensor: ... def __sub__(self, y: _typing.TensorLike) -> Tensor: ... def __mul__(self, y: _typing.TensorLike) -> Tensor: ... def __matmul__(self, y: _typing.TensorLike) -> Tensor: ... def __truediv__(self, y: _typing.TensorLike) -> Tensor: ... def __floordiv__(self, y: _typing.TensorLike) -> Tensor: ... def __mod__(self, y: _typing.TensorLike) -> Tensor: ... def __pow__(self, y: _typing.TensorLike) -> Tensor: ... def __and__(self, y: _typing.TensorLike) -> Tensor: r""" Apply ``bitwise_and`` on Tensor ``X`` and ``Y`` . .. math:: Out = X \& Y Note: ``paddle.bitwise_and`` supports broadcasting. If you want know more about broadcasting, please refer to please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: y (Tensor): Input Tensor of ``bitwise_and`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64. out (Tensor|None, optional): Result of ``bitwise_and`` . It is a N-D Tensor with the same data type of input Tensor. Default: None. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Result of ``bitwise_and`` . It is a N-D Tensor with the same data type of input Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-5, -1, 1]) >>> y = paddle.to_tensor([4, 2, -3]) >>> res = paddle.bitwise_and(x, y) >>> print(res) Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, [0, 2, 1]) """ ... def __ror__(self, y: _typing.TensorLike) -> Tensor: ... def __rxor__(self, y: _typing.TensorLike) -> Tensor: ... def __div__(self, y: _typing.TensorLike) -> Tensor: ... def __radd__(self, y: _typing.TensorLike) -> Tensor: ... # type: ignore def __rsub__(self, y: _typing.TensorLike) -> Tensor: ... # type: ignore def __rmul__(self, y: _typing.TensorLike) -> Tensor: ... # type: ignore def __rmatmul__(self, y: _typing.TensorLike) -> Tensor: ... # type: ignore def __rtruediv__(self, y: _typing.TensorLike) -> Tensor: ... # type: ignore def __rmod__(self, y: _typing.TensorLike) -> Tensor: ... # type: ignore def __rpow__(self, y: _typing.TensorLike) -> Tensor: ... # type: ignore def __rdiv__(self, y: _typing.TensorLike) -> Tensor: ... # type: ignore def __rfloordiv__(self, y: _typing.TensorLike) -> Tensor: ... # type: ignore def __rand__(self, y: _typing.TensorLike) -> Tensor: ... # type: ignore # type cast def __bool__(self) -> _bool: ... def __float__(self) -> _float: ... def __int__(self) -> _int: ... def __long__(self) -> _float: ... def __nonzero__(self) -> _bool: ... def __complex__(self) -> _complex: ... # emulating container types def __getitem__( self, item: _typing.TensorIndex, ) -> Tensor: ... def __setitem__( self, item: _typing.TensorIndex, value: Tensor | npt.NDArray[Any] | _complex | _bool, ) -> None: ... def __len__(self) -> _int: ... # emulating numeric types def __index__(self) -> _int: ... # unary arithmetic operations def __invert__(self) -> Tensor: r""" Apply ``bitwise_not`` on Tensor ``X``. .. math:: Out = \sim X Note: ``paddle.bitwise_not`` supports broadcasting. If you want know more about broadcasting, please refer to please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: out (Tensor|None, optional): Result of ``bitwise_not`` . It is a N-D Tensor with the same data type of input Tensor. Default: None. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Result of ``bitwise_not`` . It is a N-D Tensor with the same data type of input Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-5, -1, 1]) >>> res = paddle.bitwise_not(x) >>> print(res) Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, [ 4, 0, -2]) """ ... def __neg__(self) -> Tensor: ... def __pos__(self) -> Tensor: r""" Returns the input Tensor as it is. This is used in `Tensor.__pos__`, applying the unary `+` operator to the tensor. .. math:: Out = +X Args: name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A tensor with the same shape and data type as the input tensor. The returned tensor is the same. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([-1, 0, 1]) >>> out = paddle.positive(x) >>> print(out) Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, [-1, 0, 1]) """ ... # basic def __hash__(self) -> _int: ... def clear_gradient(self, set_to_zero: _bool = True) -> None: r""" Only for Tensor that has gradient, normally we use this for Parameters since other temporary Tensor doesn't has gradient. The Gradient of current Tensor will be set to ``0`` elementwise or ``None``. Args: Returns: None. Examples: .. code-block:: python >>> import paddle >>> input = paddle.uniform([10, 2]) >>> linear = paddle.nn.Linear(2, 3) >>> out = linear(input) >>> out.backward() >>> print("Before clear_gradient, linear.weight.grad: {}".format(linear.weight.grad)) >>> # doctest: +SKIP("Random output") Before clear_gradient, linear.weight.grad: Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=False, [[-0.03178465, -0.03178465, -0.03178465], [-0.98546225, -0.98546225, -0.98546225]]) >>> # doctest: -SKIP >>> linear.weight.clear_gradient() >>> print("After clear_gradient, linear.weight.grad: {}".format(linear.weight.grad)) After clear_gradient, linear.weight.grad: Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=False, [[0., 0., 0.], [0., 0., 0.]]) """ ... def clone(self) -> Tensor: r""" Returns a new Tensor, which is clone of origin Tensor, and it remains in the current graph. It will always have a Tensor copy. In addition, the cloned Tensor provides gradient propagation. Returns: Tensor, The cloned Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor(1.0, stop_gradient=False) >>> clone_x = x.clone() >>> clone_x.retain_grads() >>> y = clone_x**2 >>> y.backward() >>> print(clone_x.stop_gradient) False >>> print(clone_x.grad) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=False, 2.) >>> print(x.stop_gradient) False >>> print(x.grad) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=False, 2.) >>> x = paddle.to_tensor(1.0) >>> clone_x = x.clone() >>> clone_x.stop_gradient = False >>> z = clone_x**3 >>> z.backward() >>> print(clone_x.stop_gradient) False >>> print(clone_x.grad) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=False, 3.) >>> print(x.stop_gradient) True >>> print(x.grad) None """ ... def cols(self) -> Tensor: r""" Note: **This API is only available for SparseCsrTensor.** Returns the column index of non zero elements in input SparseCsrTensor. Returns: DenseTensor Examples: .. code-block:: python >>> import paddle >>> crows = [0, 2, 3, 5] >>> cols = [1, 3, 2, 0, 1] >>> values = [1, 2, 3, 4, 5] >>> dense_shape = [3, 4] >>> csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape) >>> csr.cols() Tensor(shape=[5], dtype=int64, place=Place(gpu:0), stop_gradient=True, [1, 3, 2, 0, 1]) """ ... def contiguous(self) -> Tensor: r""" Returns a contiguous in memory tensor containing the same data as current Tensor. If self tensor is already contiguous, this function returns the current Tensor. Returns: Tensor, The contiguous Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3]) >>> y = x[1] >>> y = y.contiguous() >>> print(y) Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True, 2) """ ... def copy_(self) -> Tensor: ... def crows(self) -> Tensor: r""" Note: **This API is only available for SparseCsrTensor.** Returns the compressed row index of non zero elements in input SparseCsrTensor. Returns: DenseTensor Examples: .. code-block:: python >>> import paddle >>> crows = [0, 2, 3, 5] >>> cols = [1, 3, 2, 0, 1] >>> values = [1, 2, 3, 4, 5] >>> dense_shape = [3, 4] >>> csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape) >>> csr.crows() Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True, [0, 2, 3, 5]) """ ... @property def data(self) -> Tensor: ... @data.setter def data(self, value: Tensor) -> None: r""" data Tensor's self. Returns: Tensor: self. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor(1.) >>> print(x) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.) >>> print(x.data) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.) >>> x.data = paddle.to_tensor(2.) >>> print(x) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 2.) >>> print(x.data) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 2.) """ ... def data_ptr(self) -> _int: r""" Returns the address of the first element of current Tensor. Returns: int, The address of the first element of current Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3]) >>> print(x.data_ptr()) >>> # doctest: +SKIP('return the address') 93220864 >>> # doctest: -SKIP """ ... def dense_dim(self) -> _int: r""" Returns the number of dense dimensions of sparse Tensor. Note: **If self is not sparse Tensor, return len(self.shape).** Returns: int, dense dim of self Tensor Examples: .. code-block:: python >>> import paddle >>> import numpy as np >>> indices = [[0, 1, 1], [2, 0, 2]] >>> values = np.array([[3, 4], [5, 6], [7, 8]]) >>> dense_shape = [2, 3, 2] >>> coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape) >>> coo.dense_dim() 1 >>> crows = [0, 2, 3, 5] >>> cols = [1, 3, 2, 0, 1] >>> values = [1, 2, 3, 4, 5] >>> dense_shape = [3, 4] >>> csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape) >>> csr.dense_dim() 0 >>> dense = paddle.to_tensor([[1, 2, 3]]) >>> dense.dense_dim() >>> 2 """ ... def detach(self) -> Tensor: r""" Returns a new Tensor, detached from the current graph. It will share data with origin Tensor and always doesn't have a Tensor copy. In addition, the detached Tensor doesn't provide gradient propagation. Returns: Tensor, The detached Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1.0], stop_gradient=False) >>> detach_x = x.detach() >>> detach_x[0] = 10.0 >>> print(x) Tensor(shape=[1], dtype=float32, place=CPUPlace, stop_gradient=False, [10.]) >>> y = x**2 >>> y.backward() >>> print(x.grad) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=False, [20.]) >>> print(detach_x.grad) # None, 'stop_gradient=True' by default None >>> detach_x.stop_gradient = False # Set stop_gradient to be False, supported auto-grad >>> z = detach_x**3 >>> z.backward() >>> print(x.grad) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=False, [20.]) >>> print(detach_x.grad) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=False, [300.]) >>> # Due to sharing of data with origin Tensor, There are some unsafe operations: >>> # y = 2 * x >>> # detach_x[:] = 5.0 >>> # y.backward() >>> # It will raise Error: >>> # one of the variables needed for gradient computation has been modified by an inplace operation. """ ... def detach_(self) -> Tensor: r""" Detach self from the current graph, and returns self Tensor. In addition, the detached Tensor doesn't provide gradient propagation. Returns: Tensor, The detached Tensor. """ ... @property def dtype(self) -> paddle.dtype: r""" dtype Tensor's data type. Returns: paddle dtype: dtype. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3]) >>> print(x.dtype) paddle.int64 """ ... def element_size(self) -> _int: r""" Returns the size in bytes of an element in the Tensor. Returns: int, The size in bytes of an element in the Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor(1, dtype='bool') >>> x.element_size() 1 >>> x = paddle.to_tensor(1, dtype='float16') >>> x.element_size() 2 >>> x = paddle.to_tensor(1, dtype='float32') >>> x.element_size() 4 >>> x = paddle.to_tensor(1, dtype='float64') >>> x.element_size() 8 >>> x = paddle.to_tensor(1, dtype='complex128') >>> x.element_size() 16 """ ... def get_map_tensor(self) -> Tensor: ... def get_selected_rows(self) -> None: ... def get_strides(self) -> list[_int]: r""" Returns the strides of current Tensor. Returns: List, the strides of current Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3]) >>> y = x[1] >>> print(y.get_strides()) [] """ ... def get_tensor(self) -> Tensor: r""" Returns the underline tensor in the origin Tensor. Returns: Underline tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1.0], stop_gradient=False) >>> underline_x = x.get_tensor() >>> print(underline_x) - place: Place(cpu) - shape: [1] - layout: NCHW - dtype: float32 - data: [1] """ ... @property def grad(self) -> Tensor | None: ... @grad.setter def grad(self, value: Tensor | None) -> None: r""" grad Tensor's grad Tensor. Returns: Tensor: grad Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor(1.0, stop_gradient=False) >>> y = x**2 >>> y.backward() >>> print(x.grad) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=False, 2.) >>> x.grad = paddle.to_tensor(3.0) >>> print(x.grad) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=False, 3.) """ ... @property def grad_(self) -> Tensor | None: ... @grad_.setter def grad_(self, value: Tensor) -> None: ... @property def grad_fn(self) -> Any: ... def is_contiguous(self) -> _bool: r""" Whether the Tensor is contiguous. Returns: Bool, Whether the Tensor is contiguous. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3]) >>> y = x[1] >>> print(y.is_contiguous()) """ ... def is_coalesced(self) -> _bool: r""" Check whether the Tensor is a coalesced SparseCooTensor. If not it will return False. Tensor types other than SparseCooTensor are not supported. Notes: It will return always False for a newly created SparseCooTensor. Args: Returns: bool: True if the Tensor is a coalesced SparseCooTensor, and False otherwise. Examples: .. code-block:: python >>> import paddle >>> indices = [[0, 0, 1], [1, 1, 2]] >>> values = [1.0, 2.0, 3.0] >>> x = paddle.sparse.sparse_coo_tensor(indices, values) >>> x.is_coalesced() False >>> x = x.coalesce() >>> x.is_coalesced() True >>> indices = [[0, 1, 1], [1, 0, 2]] >>> values = [1.0, 2.0, 3.0] >>> x = paddle.sparse.sparse_coo_tensor(indices, values) >>> x.is_coalesced() False """ ... def is_dense(self) -> _bool: r""" Whether the Tensor is a Dense Tensor. Returns: Whether the Tensor is a Dense Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1.0], stop_gradient=False) >>> print(x.is_dense()) True """ ... def is_dist(self) -> _bool: r""" Whether the Tensor is a Distributed Tensor. Returns: Whether the Tensor is a Distributed Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1.0], stop_gradient=False) >>> print(x.is_dist()) False """ ... @property def is_leaf(self) -> _bool: r""" is_leaf Whether a Tensor is leaf Tensor. For the Tensor whose stop_gradient is ``True`` , it will be leaf Tensor. For the Tensor whose stop_gradient is ``False`` , it will be leaf Tensor too if it is created by user. Returns: bool: Whether a Tensor is leaf Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor(1.) >>> print(x.is_leaf) True >>> x = paddle.to_tensor(1., stop_gradient=True) >>> y = x + 1 >>> print(x.is_leaf) True >>> print(y.is_leaf) True >>> x = paddle.to_tensor(1., stop_gradient=False) >>> y = x + 1 >>> print(x.is_leaf) True >>> print(y.is_leaf) False """ ... def is_same_shape(self, y: Tensor) -> _bool: r""" Return the results of shape comparison between two Tensors, check whether x.shape equal to y.shape. Any two type Tensor among DenseTensor/SparseCooTensor/SparseCsrTensor are supported. Args: y (Tensor): The input tensor. It can be DenseTensor/SparseCooTensor/SparseCsrTensor. Returns: bool: True for same shape and False for different shape. Examples: .. code-block:: python >>> import paddle >>> x = paddle.rand([2, 3, 8]) >>> y = paddle.rand([2, 3, 8]) >>> y = y.to_sparse_csr() >>> z = paddle.rand([2, 5]) >>> x.is_same_shape(y) True >>> x.is_same_shape(z) False """ ... def is_selected_rows(self) -> _bool: ... def is_sparse(self) -> _bool: r""" Returns whether the input Tensor is SparseCooTensor or SparseCsrTensor. When input is SparseCooTensor/SparseCsrTensor, will return True. When input is DenseTensor, will return False. Returns: bool Examples: .. code-block:: python >>> import paddle >>> indices = [[0, 1, 2], [1, 2, 0]] >>> values = [1.0, 2.0, 3.0] >>> dense_shape = [3, 3] >>> coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape) >>> coo.is_sparse() True """ ... def is_sparse_coo(self) -> _bool: r""" Returns whether the input Tensor is SparseCooTensor. When input is SparseCooTensor, will return True. When input is DenseTensor/SparseCsrTensor, will return False. Returns: bool Examples: .. code-block:: python >>> import paddle >>> indices = [[0, 1, 2], [1, 2, 0]] >>> values = [1.0, 2.0, 3.0] >>> dense_shape = [3, 3] >>> coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape) >>> coo.is_sparse_coo() True """ ... def is_sparse_csr(self) -> _bool: r""" Returns whether the input Tensor is SparseCsrTensor. When input is SparseCsrTensor, will return True. When input is DenseTensor/SparseCooTensor, will return False. Returns: bool Examples: .. code-block:: python >>> import paddle >>> crows = [0, 2, 3, 5] >>> cols = [1, 3, 2, 0, 1] >>> values = [1, 2, 3, 4, 5] >>> dense_shape = [3, 4] >>> csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape) >>> csr.is_sparse_csr() True """ ... @property def layout(self) -> _typing.DataLayoutND: r""" layout Tensor's memory layout. Returns: Layout: layout. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3]) >>> print(x.layout) NCHW """ ... @property def name(self) -> _str: ... @name.setter def name(self, value: _str) -> None: r""" name Tensor's name. Returns: str: Tensor's name. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor(1.) >>> print(x.name) generated_tensor_0 >>> x.name = 'test_tensor_name' >>> print(x.name) test_tensor_name """ ... @property def ndim(self) -> _int: ... def nnz(self) -> _int: r""" Note: **This API is only available for SparseCooTensor or SparseCsrTensor.** Returns the total number of non zero elements in input SparseCooTensor/SparseCsrTensor. Returns: int Examples: .. code-block:: python >>> import paddle >>> indices = [[0, 1, 2], [1, 2, 0]] >>> values = [1.0, 2.0, 3.0] >>> dense_shape = [3, 3] >>> coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape) >>> coo.nnz() 3 """ ... @property def num_shard(self) -> _int: r""" num_shard Tensor's num_shard. Returns: int64_t: Tensor's num_shard. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:DISTRIBUTED) >>> import paddle >>> import paddle.distributed as dist >>> from paddle.base import core >>> mesh = dist.ProcessMesh([[2, 4, 5], [0, 1, 3]], dim_names=["x", "y"]) >>> a = paddle.to_tensor([[1,2,3], ... [5,6,7]]) >>> d_tensor = paddle.Tensor(a, [core.Shard(0), core.Shard(1)]) >>> print(d_tensor.num_shard) # 4 """ ... def numpy(self) -> npt.NDArray[Any]: r""" Returns a numpy array shows the value of current Tensor. Returns: ndarray, The numpy value of current Tensor, dtype is same as current Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1.0, 2.0, 3.0], ... [4.0, 5.0, 6.0]]) >>> x.numpy() array([[1., 2., 3.], [4., 5., 6.]], dtype=float32) """ ... @property def offset(self) -> _int: r""" offset The address of the first element relative to the offset of the video memory. Returns: int: offset. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3]) >>> y = x[1] >>> print(y.offset) 8 """ ... @property def persistable(self) -> _bool: ... @persistable.setter def persistable(self, value: _bool) -> None: r""" persistable Tensor's persistable. Returns: bool: persistable. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor(1.0, stop_gradient=False) >>> print(x.persistable) False >>> x. persistable = True >>> print(x.persistable) True """ ... @property def place(self) -> paddle.core.Place: r""" place The device Tensor's memory locate. Returns: Place: place. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3]) >>> print(x.place) Place(cpu) """ ... @property def placements(self) -> list[paddle.distributed.Placement] | None: r""" placements Get placements property from shard tensor. Returns: List[core.Placement]: the process mesh of shard tensor Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:DISTRIBUTED) >>> import paddle >>> import paddle.distributed as dist >>> from paddle.base import core >>> mesh = dist.ProcessMesh([[2, 4, 5], [0, 1, 3]], dim_names=["x", "y"]) >>> a = paddle.to_tensor([[1,2,3], ... [5,6,7]]) >>> d_tensor = dist.shard_tensor(a, mesh, [core.Shard(0), core.Shard(1)]) >>> print(d_tensor.placements) """ ... @property def process_mesh(self) -> paddle.distributed.ProcessMesh | None: r""" process_mesh Get process_mesh property from shard tensor. Returns: core.ProcessMesh: the process mesh of shard tensor Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:DISTRIBUTED) >>> import paddle >>> import paddle.distributed as dist >>> from paddle.base import core >>> mesh = dist.ProcessMesh([[2, 4, 5], [0, 1, 3]], dim_names=["x", "y"]) >>> a = paddle.to_tensor([[1,2,3], ... [5,6,7]]) >>> d_tensor = dist.shard_tensor(a, mesh, [core.Shard(0), core.Shard(1)]) >>> print(d_tensor.process_mesh) """ ... def rows(self) -> list[_int]: ... def set_string_list(self, value: _str) -> None: ... def set_vocab(self, value: dict[_str, _int]) -> None: ... @property def shape(self) -> paddle.Size: r""" shape Tensor's shape. Returns: List: shape. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.to_tensor(1.0, stop_gradient=False) >>> print(x.shape) paddle.Size([]) """ ... @property def size(self) -> _int: ... def sparse_dim(self) -> _int: r""" Returns the number of sparse dimensions of sparse Tensor. Note: **If self is not sparse Tensor, return 0.** Returns: int, sparse dim of self Tensor Examples: .. code-block:: python >>> import paddle >>> indices = [[0, 1, 2], [1, 2, 0]] >>> values = [1.0, 2.0, 3.0] >>> dense_shape = [3, 3] >>> coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape) >>> coo.sparse_dim() 2 >>> crows = [0, 2, 3, 5] >>> cols = [1, 3, 2, 0, 1] >>> values = [1, 2, 3, 4, 5] >>> dense_shape = [3, 4] >>> csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape) >>> csr.sparse_dim() 2 >>> dense = paddle.to_tensor([1, 2, 3]) >>> dense.sparse_dim() 0 """ ... @property def stop_gradient(self) -> _bool: ... @stop_gradient.setter def stop_gradient(self, value: _bool) -> None: r""" stop_gradient Tensor's stop_gradient. Returns: bool: Tensor's stop_gradient. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor(1.) >>> print(x.stop_gradient) True >>> x.stop_gradient = False >>> print(x.stop_gradient) False """ ... @property def strides(self) -> list[_int]: r""" strides Tensor's strides. Returns: List: strides. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1, 2, 3]) >>> y = x[1] >>> print(y.strides) [] """ ... @property def type(self) -> Any: r""" type Tensor's type. Returns: VarType: Tensor's type. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor(1.) >>> print(x.type) VarType.DENSE_TENSOR """ ... # virtual methods def __iter__(self) -> Iterator[Tensor]: ... # For iterating over the tensor # private methods def _grad_ivar(self) -> Tensor | None: ... # annotation: ${tensor_alias} __str__ = __repr__ bitwise_and = __and__ bitwise_not = __invert__ bitwise_or = __or__ bitwise_xor = __xor__ clip = clamp clip_ = clamp_ divide = div divide_ = div_ equal = eq greater_equal = ge gt = greater less_equal = le less_than = less lt = less mod = floor_mod mod_ = floor_mod_ multiply_ = mul_ not_equal = ne remainder = floor_mod remainder_ = floor_mod_ reverse = flip subtract = sub subtract_ = sub_ swapdims = swapaxes take_along_dim = take_along_axis transpose = swapaxes uint8 = byte class Tensor(AbstractTensor, AbstractEagerParamBase): # annotation: ${tensor_docstring} r""" Tensor is the basic data structure in PaddlePaddle. There are some ways to create a Tensor: - Use the existing ``data`` to create a Tensor, please refer to :ref:`api_paddle_to_tensor`. - Create a Tensor with a specified ``shape``, please refer to :ref:`api_paddle_ones`, :ref:`api_paddle_zeros`, :ref:`api_paddle_full`. - Create a Tensor with the same ``shape`` and ``dtype`` as other Tensor, please refer to :ref:`api_paddle_ones_like`, :ref:`api_paddle_zeros_like`, :ref:`api_paddle_full_like`. """ __qualname__: Literal["Tensor"] # annotation: ${tensor_end}