diff --git a/python/paddle/fluid/io.py b/python/paddle/fluid/io.py index db88331040fa777c4eb8db628cbf6032d56eb951..0f69949018de71a330ac607c0b99f95dfc1494df 100644 --- a/python/paddle/fluid/io.py +++ b/python/paddle/fluid/io.py @@ -184,8 +184,6 @@ def is_belong_to_optimizer(var): @dygraph_not_support def get_program_parameter(program): """ - :api_attr: Static Graph - Get all the parameters from Program. Args: @@ -212,8 +210,6 @@ def get_program_parameter(program): @dygraph_not_support def get_program_persistable_vars(program): """ - :api_attr: Static Graph - Get all the persistable vars from Program. Args: @@ -292,9 +288,7 @@ def save_vars(executor, predicate=None, filename=None): """ - :api_attr: Static Graph - - This API saves specific variables in the `Program` to files. + Save specific variables in the `Program` to files. There are two ways to specify the variables to be saved: set variables in a list and assign it to the `vars`, or use the `predicate` function to select @@ -436,9 +430,7 @@ def save_vars(executor, @dygraph_not_support def save_params(executor, dirname, main_program=None, filename=None): """ - :api_attr: Static Graph - - This operator saves all parameters from the :code:`main_program` to + Save all parameters from the :code:`main_program` to the folder :code:`dirname` or file :code:`filename`. You can refer to :ref:`api_guide_model_save_reader_en` for more details. @@ -670,9 +662,7 @@ def _save_distributed_persistables(executor, dirname, main_program): @dygraph_not_support def save_persistables(executor, dirname, main_program=None, filename=None): """ - :api_attr: Static Graph - - This operator saves all persistable variables from :code:`main_program` to + Save all persistable variables from :code:`main_program` to the folder :code:`dirname` or file :code:`filename`. You can refer to :ref:`api_guide_model_save_reader_en` for more details. And then saves these persistables variables to the folder :code:`dirname` or file @@ -780,9 +770,6 @@ def load_vars(executor, Returns: None - Raises: - TypeError: If `main_program` is not an instance of Program nor None. - Examples: .. code-block:: python @@ -1247,8 +1234,6 @@ def save_inference_model(dirname, program_only=False, clip_extra=False): """ - :api_attr: Static Graph - Prune the given `main_program` to build a new program especially for inference, and then save it and all related parameters to given `dirname` . If you just want to save parameters of your trained model, please use the @@ -1279,7 +1264,7 @@ def save_inference_model(dirname, params_filename(str, optional): The name of file to save all related parameters. If it is set None, parameters will be saved in separate files . - export_for_deployment(bool): If True, programs are modified to only support + export_for_deployment(bool, optional): If True, programs are modified to only support direct inference deployment. Otherwise, more information will be stored for flexible optimization and re-training. Currently, only @@ -1290,14 +1275,7 @@ def save_inference_model(dirname, Default: False. Returns: - The fetch variables' name list - - Return Type: - list - - Raises: - ValueError: If `feed_var_names` is not a list of basestring, an exception is thrown. - ValueError: If `target_vars` is not a list of Variable, an exception is thrown. + list, The fetch variables' name list. Examples: .. code-block:: python @@ -1462,8 +1440,6 @@ def load_inference_model(dirname, params_filename=None, pserver_endpoints=None): """ - :api_attr: Static Graph - Load the inference model from a given directory. By this API, you can get the model structure(Inference Program) and model parameters. If you just want to load parameters of the pre-trained model, please use the :ref:`api_fluid_io_load_params` API. @@ -1501,8 +1477,6 @@ def load_inference_model(dirname, ``Variable`` (refer to :ref:`api_guide_Program_en`). It contains variables from which we can get inference results. - Raises: - ValueError: If `dirname` is not a existing directory. Examples: .. code-block:: python @@ -1659,12 +1633,6 @@ def get_parameter_value_by_name(name, executor, program=None): Returns: numpy.array: The parameter's values. - Raises: - TypeError: If given `name` is not an instance of basestring. - TypeError: If the parameter with the given name doesn't exist. - AssertionError: If there is a variable named `name` in the - given program but it is not a Parameter. - Examples: .. code-block:: python @@ -2314,8 +2282,6 @@ def load_program_state(model_path, var_list=None): @static_only def set_program_state(program, state_dict): """ - :api_attr: Static Graph - Set program parameter from state_dict An exception will throw if shape or dtype of the parameters is not match. diff --git a/python/paddle/fluid/layers/loss.py b/python/paddle/fluid/layers/loss.py index 0ee2338ac1ae154d7fc04b37cd2d44c5c1c7c12a..61ab466c243623d945e27e97aa87d0a7ca37b779 100644 --- a/python/paddle/fluid/layers/loss.py +++ b/python/paddle/fluid/layers/loss.py @@ -311,7 +311,7 @@ def cross_entropy2(input, label, ignore_index=kIgnoreIndex): def square_error_cost(input, label): r""" - This op accepts input predictions and target label and returns the + Accept input predictions and target label and returns the squared error cost. For predictions label, and target label, the equation is: @@ -325,10 +325,8 @@ def square_error_cost(input, label): label (Tensor): Label tensor, the data type should be float32. Returns: - The tensor storing the element-wise squared error \ - difference between input and label. - - Return type: Tensor. + Tensor, The tensor storing the element-wise squared + error difference between input and label. Examples: diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index 03d21035bd510325ae78496abd5a24b30af09797..5b4fe640d01fc8080fd391be4f981f0a1a60b44b 100755 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -9359,14 +9359,14 @@ def crop(x, shape=None, offsets=None, name=None): Parameters: x (Variable): Tensor, data type can be float32 or float64. - shape (Variable|list/tuple of integers): The output shape is specified + shape (Variable|list/tuple of integers, optional): The output shape is specified by `shape`, which can be a Tensor or a list/tuple of integers. If it is a Tensor, it's rank must be the same as `x` , only it's shape will be used, and the value of it will be ignored. This way is suitable for the case that the output shape may be changed each iteration. If it is a list/tuple of integers, it's length must be the same as the rank of `x` - offsets (Variable|list/tuple of integers|None): Specifies the cropping + offsets (Variable|list/tuple of integers|None, optional): Specifies the cropping offsets at each dimension. It can be a Tensor or a list/tuple of integers. If it is a Tensor, it's rank must be the same as `x`. This way is suitable for the case that the offsets may be changed @@ -9377,13 +9377,7 @@ def crop(x, shape=None, offsets=None, name=None): None by default. Returns: - The cropped Tensor, which has the same rank and data type with `x` - - Return Type: - Variable - - Raises: - ValueError: If shape is not a list, tuple or Variable. + Tensor, The cropped Tensor, which has the same rank and data type with `x`. Examples: @@ -9721,7 +9715,8 @@ def pad2d(input, 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, a 4-D Tensor padded according to paddings and mode and data type is same as input. + Returns: + Tensor, a 4-D Tensor padded according to paddings and mode and data type is same as input. Examples: .. code-block:: text @@ -13282,14 +13277,10 @@ def space_to_depth(x, blocksize, name=None): to :ref:`api_guide_Name`. Usually name is no need to set and \ None by default. - Returns: The output, which should be 4 dims Tensor or LodTensor, with the shape \ + Returns: + Tensor, The output, which should be 4 dims Tensor or LodTensor, with the shape \ [batch, channel * blocksize * blocksize, height/blocksize, width/blocksize] - Return Type: Variable - - Raises: - TypeError: blocksize type must be int64. - Examples: .. code-block:: python diff --git a/python/paddle/fluid/layers/sequence_lod.py b/python/paddle/fluid/layers/sequence_lod.py index 4a213a7a146c86ba996872fb2f9a6e31494f23ce..6d8b0992ff0147380ce79ffc71d9bd9002a7bfa1 100644 --- a/python/paddle/fluid/layers/sequence_lod.py +++ b/python/paddle/fluid/layers/sequence_lod.py @@ -1,4 +1,4 @@ -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2022 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. @@ -54,9 +54,9 @@ def sequence_conv(input, act=None, name=None): r""" - :api_attr: Static Graph - **Notes: The Op only receives LoDTensor as input. If your input is Tensor, please use conv2d Op.(fluid.layers.** :ref:`api_fluid_layers_conv2d` ). + Note: + Only receives LoDTensor as input. If your input is Tensor, please use conv2d Op.(fluid.layers.** :ref:`api_fluid_layers_conv2d` ). This operator receives input sequences with variable length and other convolutional configuration parameters(num_filters, filter_size) to apply the convolution operation. @@ -179,11 +179,9 @@ def sequence_conv(input, def sequence_softmax(input, use_cudnn=False, name=None): r""" - :api_attr: Static Graph - **Note**: - - **The input type of the OP must be LoDTensor. For Tensor, use:** :ref:`api_fluid_layers_softmax` + Note: + The input type of the OP must be LoDTensor. For Tensor, use:** :ref:`api_fluid_layers_softmax` A LoD-tensor can be regarded as several sequences, and this op apply softmax algo on each sequence. The shape of input Tensor can be :math:`[N, 1]` or :math:`[N]`, where :math:`N` @@ -264,9 +262,9 @@ def sequence_softmax(input, use_cudnn=False, name=None): def sequence_pool(input, pool_type, is_test=False, pad_value=0.0): r""" - :api_attr: Static Graph - **Notes: The Op only receives LoDTensor as input. If your input is Tensor, please use pool2d Op.(fluid.layers.** :ref:`api_fluid_layers_pool2d` ). + Note: + Only receives LoDTensor as input. If your input is Tensor, please use pool2d Op.(fluid.layers.** :ref:`api_fluid_layers_pool2d` ). This operator only supports LoDTensor as input. It will apply specified pooling operation on the input LoDTensor. It pools features of all time-steps of each @@ -381,9 +379,9 @@ def sequence_pool(input, pool_type, is_test=False, pad_value=0.0): @templatedoc() def sequence_concat(input, name=None): """ - :api_attr: Static Graph - **Notes: The Op only receives LoDTensor as input. If your input is Tensor, please use concat Op.(fluid.layers.** :ref:`api_fluid_layers_concat` ). + Note: + Only receives LoDTensor as input. If your input is Tensor, please use concat Op.(fluid.layers.** :ref:`api_fluid_layers_concat` ). This operator only supports LoDTensor as input. It concatenates the multiple LoDTensor from input by the LoD information, and outputs the concatenated LoDTensor. @@ -445,9 +443,8 @@ def sequence_concat(input, name=None): def sequence_first_step(input): """ - :api_attr: Static Graph - This operator only supports LoDTensor as input. Given the input LoDTensor, it will + Only supports LoDTensor as input. Given the input LoDTensor, it will select first time-step feature of each sequence as output. .. code-block:: text @@ -503,9 +500,8 @@ def sequence_first_step(input): def sequence_last_step(input): """ - :api_attr: Static Graph - This operator only supports LoDTensor as input. Given the input LoDTensor, it will + Only supports LoDTensor as input. Given the input LoDTensor, it will select last time-step feature of each sequence as output. .. code-block:: text @@ -562,7 +558,6 @@ def sequence_last_step(input): def sequence_slice(input, offset, length, name=None): """ - :api_attr: Static Graph **Sequence Slice Layer** @@ -653,7 +648,6 @@ def sequence_slice(input, offset, length, name=None): def sequence_expand(x, y, ref_level=-1, name=None): r""" - :api_attr: Static Graph Sequence Expand Layer. This layer will expand the input variable ``x`` \ according to specified level ``ref_level`` lod of ``y``. Please note that \ @@ -662,11 +656,13 @@ def sequence_expand(x, y, ref_level=-1, name=None): of ``y``. If the lod level of ``x`` is 0, then the first dim of ``x`` should \ be equal to the size of ``ref_level`` of ``y``. The rank of **x** is at least 2. \ When rank of ``x`` is greater than 2, then it would be viewed as a 2-D tensor. + + Note: - Please note that the input ``x`` should be LodTensor or Tensor, \ + Please note that the input ``x`` should be LodTensor or Tensor, \ and input ``y`` must be LodTensor. - Following examples will explain how sequence_expand works: + **Following examples will explain how sequence_expand works:** .. code-block:: text @@ -722,12 +718,11 @@ def sequence_expand(x, y, ref_level=-1, name=None): to :ref:`api_guide_Name`. Usually name is no need to set and \ None by default. - Returns: The expanded variable which is a LoDTensor, with dims ``[N, K]``. \ + Returns: + Tensor, The expanded variable which is a LoDTensor, with dims ``[N, K]``. \ ``N`` depends on the lod info of ``x`` and ``y``. \ The data type is same as input. - Return Type: Variable - Examples: .. code-block:: python @@ -791,7 +786,6 @@ def sequence_expand(x, y, ref_level=-1, name=None): def sequence_expand_as(x, y, name=None): r""" - :api_attr: Static Graph Sequence Expand As Layer. This OP will expand the input variable ``x`` \ according to the zeroth level lod of ``y``. Current implementation requires \ @@ -800,7 +794,8 @@ def sequence_expand_as(x, y, name=None): the expanded LodTensor has the same lod info as ``y``. The expanded result \ has nothing to do with ``x``'s lod, so the lod of Input(X) is not considered. - Please note that the input ``x`` should be LodTensor or Tensor, \ + Note: + Please note that the input ``x`` should be LodTensor or Tensor, \ and input ``y`` must be LodTensor. Following examples will explain how sequence_expand_as works: @@ -845,12 +840,11 @@ def sequence_expand_as(x, y, name=None): to :ref:`api_guide_Name`. Usually name is no need to set and \ None by default. - Returns: The expanded variable which is a LoDTensor with the dims ``[N, K]``. \ + Returns: + Tensor, The expanded variable which is a LoDTensor with the dims ``[N, K]``. \ ``N`` depends on the lod of ``y``, and the lod level must be 1. \ The data type is same as input. - Return Type: Variable - Examples: .. code-block:: python @@ -913,7 +907,6 @@ def sequence_expand_as(x, y, name=None): def sequence_pad(x, pad_value, maxlen=None, name=None): r""" - :api_attr: Static Graph This layer padding the sequences in a same batch to a common length (according to ``maxlen``). The padding value is defined by ``pad_value``, and will be @@ -921,6 +914,7 @@ def sequence_pad(x, pad_value, maxlen=None, name=None): the LodTensor ``Out`` is the padded sequences, and LodTensor ``Length`` is the length information of input sequences. For removing padding data (unpadding operation), See :ref:`api_fluid_layers_sequence_unpad`. + Note: Please note that the input ``x`` should be LodTensor. .. code-block:: text @@ -978,13 +972,12 @@ def sequence_pad(x, pad_value, maxlen=None, name=None): to :ref:`api_guide_Name`. Usually name is no need to set and \ None by default. - Returns: A Python tuple (Out, Length): the 1st is a 0 level LodTensor \ + Returns: + tuple, A Python tuple (Out, Length): the 1st is a 0 level LodTensor \ ``Out``, with the shape ``[batch_size, maxlen, K]``; the second is the original \ sequences length infor ``Length``, which should be a 0-level 1D LodTensor. \ The size of ``Length`` is equal to batch size, and the data type is int64. - Return Type: tuple - Examples: .. code-block:: python @@ -1031,13 +1024,11 @@ def sequence_pad(x, pad_value, maxlen=None, name=None): def sequence_unpad(x, length, name=None): """ - :api_attr: Static Graph - **Note**: - - **The input of the OP is Tensor and the output is LoDTensor. For padding operation, See:** :ref:`api_fluid_layers_sequence_pad` + Note: + The input of the OP is Tensor and the output is LoDTensor. For padding operation, See:** :ref:`api_fluid_layers_sequence_pad` - The OP removes the padding data from the input based on the length information and returns a LoDTensor. + Remove the padding data from the input based on the length information and returns a LoDTensor. .. code-block:: text @@ -1109,11 +1100,11 @@ def sequence_unpad(x, length, name=None): def sequence_reshape(input, new_dim): """ - :api_attr: Static Graph - **Notes: The Op only receives LoDTensor as input. If your input is Tensor, please use reshape Op.(fluid.layers.** :ref:`api_fluid_layers_reshape` ). + Note: + Only receives LoDTensor as input. If your input is Tensor, please use reshape Op.(fluid.layers.** :ref:`api_fluid_layers_reshape` ). - This operator only supports LoDTensor as input. Given :attr:`new_dim` , + Only supports LoDTensor as input. Given :attr:`new_dim` , it will compute new shape according to original length of each sequence, original dimensions and :attr:`new_dim` . Then it will output a new LoDTensor containing :attr:`new_dim` . Currently it only supports 1-level LoDTensor. @@ -1172,11 +1163,9 @@ def sequence_reshape(input, new_dim): def sequence_scatter(input, index, updates, name=None): """ - :api_attr: Static Graph - **Note**: - - **The index and updates parameters of the OP must be LoDTensor.** + Note: + The index and updates parameters of the OP must be LoDTensor. Plus the updates data to the corresponding input according to the index. @@ -1264,7 +1253,6 @@ def sequence_scatter(input, index, updates, name=None): def sequence_enumerate(input, win_size, pad_value=0, name=None): r""" - :api_attr: Static Graph Generate a new sequence for the input index sequence with \ shape ``[d_1, win_size]``, which enumerates all the \ @@ -1300,12 +1288,11 @@ def sequence_enumerate(input, win_size, pad_value=0, name=None): to :ref:`api_guide_Name`. Usually name is no need to set and \ None by default. - Returns: The enumerate sequence variable which is a LoDTensor with \ + Returns: + Tensor, The enumerate sequence variable which is a LoDTensor with \ shape ``[d_1, win_size]`` and 1-level lod info. \ The data type is same as ``input``. - Return Type: Variable - Examples: .. code-block:: python @@ -1371,12 +1358,11 @@ def sequence_mask(x, maxlen=None, dtype='int64', name=None): to :ref:`api_guide_Name`. Usually name is no need to set and \ None by default. - Returns: The output sequence mask. Tensor with shape [d_1, d_2, ..., d_n, maxlen] \ - and data type of :code:`dtype`. The data type should be bool, float32, float64, int8, \ + Returns: + Tensor, The output sequence mask. Tensor with shape [d_1, d_2, ..., d_n, maxlen] + and data type of :code:`dtype`. The data type should be bool, float32, float64, int8, int32 or int64. - Return Type: Tensor - Examples: .. code-block:: python @@ -1398,9 +1384,10 @@ def sequence_mask(x, maxlen=None, dtype='int64', name=None): @templatedoc() def sequence_reverse(x, name=None): """ - **Notes: The Op only receives LoDTensor as input. If your input is Tensor, please use reverse Op.(fluid.layers.** :ref:`api_fluid_layers_reverse` ). + Note: + Only receives LoDTensor as input. If your input is Tensor, please use reverse Op.(fluid.layers.** :ref:`api_fluid_layers_reverse` ). - This operator only supports LoDTensor as input. It will reverse each sequence for input LoDTensor. + Only supports LoDTensor as input. It will reverse each sequence for input LoDTensor. Currently it only supports 1-level LoDTensor. This operator is very useful when building a reverse :ref:`api_fluid_layers_DynamicRNN` network. diff --git a/python/paddle/fluid/metrics.py b/python/paddle/fluid/metrics.py index 9ee27e0c3cfe9f7b162efade04e261dffece3d06..4bc6be7e093876855dda0b1aeef08edcbb3cac1f 100644 --- a/python/paddle/fluid/metrics.py +++ b/python/paddle/fluid/metrics.py @@ -172,7 +172,7 @@ class MetricBase(object): None Return types: - None + None """ raise NotImplementedError( diff --git a/python/paddle/nn/functional/common.py b/python/paddle/nn/functional/common.py index 90762e7e439dd7dd5034724ab2b8571b4e599c48..ba75fb23b8b18b344c486e7f739f008a19c8a9e8 100644 --- a/python/paddle/nn/functional/common.py +++ b/python/paddle/nn/functional/common.py @@ -44,7 +44,7 @@ __all__ = [] def unfold(x, kernel_sizes, strides=1, paddings=0, dilations=1, name=None): r""" - This op returns a col buffer of sliding local blocks of input x, also known + Return a col buffer of sliding local blocks of input x, also known as im2col for batched 2D image tensors. For each block under the convolution filter, all element will be rearranged as a column. While the convolution filter sliding over the input feature map, a series of such columns will be formed. @@ -91,15 +91,12 @@ def unfold(x, kernel_sizes, strides=1, paddings=0, dilations=1, name=None): Returns: - The tensor corresponding to the sliding local blocks. + Tensor, The tensor corresponding to the sliding local blocks. The output shape is [N, Cout, Lout] as decriabled above. Cout is the total number of values within each block, and Lout is the total number of such blocks. The data type of output is the same as the input :math:`x` - Return Type: - Tensor - Examples: .. code-block:: python @@ -1321,21 +1318,21 @@ def pad(x, pad, mode='constant', value=0, data_format="NCHW", name=None): pad_right). 2. If the input dimension is 4, then the pad has the form (pad_left, pad_right, pad_top, pad_bottom). 3. If the input dimension is 5, then the pad has the form (pad_left, pad_right, pad_top, pad_bottom, pad_front, pad_back). - mode (str): Four modes: 'constant' (default), 'reflect', 'replicate', 'circular'. + mode (str, optional): Four modes: 'constant' (default), 'reflect', 'replicate', 'circular'. When in 'constant' mode, this op uses a constant value to pad the input tensor. When in 'reflect' mode, uses reflection of the input boundaries to pad the input tensor. When in 'replicate' mode, uses input boundaries to pad the input tensor. When in 'circular' mode, uses circular input to pad the input tensor. Default is 'constant' - value (float32): The value to fill the padded areas in 'constant' mode . Default is 0.0 - data_format (str): An string from: "NCL", "NLC", NHWC", "NCHW", "NCDHW", "NDHWC". Specify the data format of + value (float32, optional): The value to fill the padded areas in 'constant' mode . Default is 0.0 + data_format (str, optional): An string from: "NCL", "NLC", NHWC", "NCHW", "NCDHW", "NDHWC". Specify the data format of the input data. Default is "NCHW" 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: a Tensor padded according to pad and mode and data type is same as input. - Return Type: Tensor + Returns: + Tensor, a Tensor padded according to pad and mode and data type is same as input. Examples: .. code-block:: text @@ -1549,12 +1546,13 @@ def zeropad2d(x, padding, data_format="NCHW", name=None): padding(int | Tensor | List[int] | Tuple[int]): The padding size with data type int. The input dimension should be 4 and pad has the form (pad_left, pad_right, pad_top, pad_bottom). - data_format(str): An string from: "NHWC", "NCHW". Specify the data format of + data_format(str, optional): An string from: "NHWC", "NCHW". Specify the data format of the input data. Default: "NCHW". name(str, optional): The default value is None. Normally there is no need for user to set this property. - Returns：Tensor，padded with 0 according to pad and data type is same as input. + Returns： + Tensor, padded with 0 according to pad and data type is same as input. Examples: .. code-block:: python @@ -1587,11 +1585,11 @@ def cosine_similarity(x1, x2, axis=1, eps=1e-8): Parameters: x1 (Tensor): First input. float32/double. x2 (Tensor): Second input. float32/double. - axis (int): Dimension of vectors to compute cosine similarity. Default is 1. - eps(float): Small value to avoid division by zero. Default is 1e-8. + axis (int, optional): Dimension of vectors to compute cosine similarity. Default is 1. + eps(float, optional): Small value to avoid division by zero. Default is 1e-8. - Returns: a Tensor representing cosine similarity between x1 and x2 along axis. - Return Type: Tensor + Returns: + Tensor, a Tensor representing cosine similarity between x1 and x2 along axis. Examples: .. code-block:: text @@ -1614,16 +1612,14 @@ def cosine_similarity(x1, x2, axis=1, eps=1e-8): import paddle import paddle.nn as nn - import numpy as np - np.random.seed(0) - x1 = np.random.rand(2,3) - x2 = np.random.rand(2,3) - x1 = paddle.to_tensor(x1) - x2 = paddle.to_tensor(x2) + paddle.seed(1) + x1 = paddle.randn(shape=[2, 3]) + x2 = paddle.randn(shape=[2, 3]) + result = paddle.nn.functional.cosine_similarity(x1, x2, axis=0) print(result) - # [0.99806249 0.9817672 0.94987036] + # [0.97689527, 0.99996042, -0.55138415] """ w12 = sum(paddle.multiply(x1, x2), axis=axis) diff --git a/python/paddle/nn/functional/extension.py b/python/paddle/nn/functional/extension.py index 6191d015e2a201a6c09f3c1af2baa1e1391309eb..995ba1905884263964660597e688bd56d0ebc0c8 100644 --- a/python/paddle/nn/functional/extension.py +++ b/python/paddle/nn/functional/extension.py @@ -191,12 +191,11 @@ def sequence_mask(x, maxlen=None, dtype='int64', name=None): to :ref:`api_guide_Name`. Usually name is no need to set and \ None by default. - Returns: The output sequence mask. Tensor with shape [d_1, d_2, ..., d_n, maxlen] \ + Returns: + Tensor, The output sequence mask. Tensor with shape [d_1, d_2, ..., d_n, maxlen] \ and data type of :code:`dtype`. The data type should be bool, float32, float64, int8, \ int32 or int64. - Return Type: Tensor - Examples: .. code-block:: python diff --git a/python/paddle/nn/functional/loss.py b/python/paddle/nn/functional/loss.py index 33471120441fbacba6a6ac2ac0eda3f2b9acc634..4c6db73abccc5b3b53bfad9b15279a465d683c49 100755 --- a/python/paddle/nn/functional/loss.py +++ b/python/paddle/nn/functional/loss.py @@ -396,10 +396,8 @@ def square_error_cost(input, label): label (Tensor): Label tensor, the data type should be float32. Returns: - The tensor storing the element-wise squared error \ - difference between input and label. - - Return type: Tensor. + Tensor, The tensor storing the element-wise squared error + difference between input and label. Examples: @@ -981,7 +979,7 @@ def hsigmoid_loss(input, def smooth_l1_loss(input, label, reduction='mean', delta=1.0, name=None): r""" - This operator calculates smooth_l1_loss. Creates a criterion that uses a squared + Calculate smooth_l1_loss. Creates a criterion that uses a squared term if the absolute element-wise error falls below 1 and an L1 term otherwise. In some cases it can prevent exploding gradients and it is more robust and less sensitivity to outliers. Also known as the Huber loss: @@ -1020,9 +1018,7 @@ def smooth_l1_loss(input, label, reduction='mean', delta=1.0, name=None): None). For more information, please refer to :ref:`api_guide_Name`. Returns: - The tensor variable storing the smooth_l1_loss of input and label. - - Return type: Tensor. + Tensor, The tensor variable storing the smooth_l1_loss of input and label. Examples: .. code-block:: python @@ -1081,7 +1077,7 @@ def margin_ranking_loss(input, name=None): r""" - This op the calcluate the margin rank loss between the input, other and label, use the math function as follows. + Calcluate the margin rank loss between the input, other and label, use the math function as follows. .. math:: margin\_rank\_loss = max(0, -label * (input - other) + margin) @@ -1106,7 +1102,8 @@ def margin_ranking_loss(input, reduction (str, optional): Indicate the reduction to apply to the loss, the candicates are ``'none'``, ``'mean'``, ``'sum'``.If :attr:`reduction` is ``'none'``, the unreduced loss is returned; If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned. If :attr:`reduction` is ``'sum'``, the reduced sum loss is returned. Default is ``'mean'``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. - Returns: Tensor, if :attr:`reduction` is ``'mean'`` or ``'sum'``, the out shape is :math:`[1]`, otherwise the shape is the same as `input` .The same dtype as input tensor. + Returns: + Tensor, if :attr:`reduction` is ``'mean'`` or ``'sum'``, the out shape is :math:`[1]`, otherwise the shape is the same as `input` .The same dtype as input tensor. Examples: @@ -1540,7 +1537,7 @@ def kl_div(input, label, reduction='mean', name=None): def mse_loss(input, label, reduction='mean', name=None): r""" - This op accepts input predications and label and returns the mean square error. + Accept input predications and label and returns the mean square error. If :attr:`reduction` is set to ``'none'``, loss is calculated as: @@ -1570,9 +1567,7 @@ def mse_loss(input, label, reduction='mean', name=None): Returns: - Tensor: The tensor tensor storing the mean square error difference of input and label. - - Return type: Tensor. + Tensor, The tensor tensor storing the mean square error difference of input and label. Examples: diff --git a/python/paddle/nn/layer/loss.py b/python/paddle/nn/layer/loss.py index f3ad33c6e57e12dfb99a1b0515a21f7f5e250f6c..635315b0027e19761e4cceccbb889fededa65060 100644 --- a/python/paddle/nn/layer/loss.py +++ b/python/paddle/nn/layer/loss.py @@ -1177,16 +1177,16 @@ class SmoothL1Loss(Layer): None). For more information, please refer to :ref:`api_guide_Name`. Call Parameters: - input (Tensor): Input tensor, the data type is float32 or float64. Shape is - (N, C), where C is number of classes, and if shape is more than 2D, this - is (N, C, D1, D2,..., Dk), k >= 1. - label (Tensor): Label tensor, the data type is float32 or float64. The shape of label - is the same as the shape of input. - Returns: - The tensor storing the smooth_l1_loss of input and label. + input (Tensor): Input tensor, the data type is float32 or float64. Shape is (N, C), + where C is number of classes, and if shape is more than 2D, + this is (N, C, D1, D2,..., Dk), k >= 1. + + label (Tensor): Label tensor, the data type is float32 or float64. + The shape of label is the same as the shape of input. - Return type: Tensor. + Returns: + Tensor, The tensor storing the smooth_l1_loss of input and label. Examples: .. code-block:: python diff --git a/python/paddle/static/io.py b/python/paddle/static/io.py index ae6248980ec165eb3457a66a4d2f9fea42a9492d..f0019df696b9713908a1fde1156db88b0f24ad7e 100644 --- a/python/paddle/static/io.py +++ b/python/paddle/static/io.py @@ -469,8 +469,11 @@ def save_inference_model(path_prefix, feed_vars, fetch_vars, executor, executor(Executor): The executor that saves the inference model. You can refer to :ref:`api_guide_executor_en` for more details. kwargs: Supported keys including 'program' and "clip_extra". Attention please, kwargs is used for backward compatibility mainly. - - program(Program): specify a program if you don't want to use default main program. - - clip_extra(bool): set to True if you want to clip extra information for every operator. + + - program(Program): specify a program if you don't want to use default main program. + + - clip_extra(bool): set to True if you want to clip extra information for every operator. + Returns: None diff --git a/python/paddle/tensor/creation.py b/python/paddle/tensor/creation.py index 06b8d9db98845fa16ec6037c303c9d94f73bea6f..857175120fc59c98060dbd0240ee5c7b6db47382 100644 --- a/python/paddle/tensor/creation.py +++ b/python/paddle/tensor/creation.py @@ -1628,7 +1628,8 @@ def clone(x, name=None): x (Tensor): The input Tensor. name(str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. - Returns: A Tensor copied from ``input`` . + Returns: + Tensor, A Tensor copied from ``input``. Examples: .. code-block:: python @@ -1661,7 +1662,7 @@ def _memcpy(input, place=None, output=None): be created as :attr:`output`. Default: None. Returns: - Tensor: A tensor with the same shape, data type and value as :attr:`input`. + Tensor, A tensor with the same shape, data type and value as :attr:`input`. Examples: .. code-block:: python diff --git a/python/paddle/tensor/manipulation.py b/python/paddle/tensor/manipulation.py index 2147ed57d9f261febcf418b3c685580f7b57f3f9..e0e34abff365eddec718ec098d77e81be5668a0a 100755 --- a/python/paddle/tensor/manipulation.py +++ b/python/paddle/tensor/manipulation.py @@ -599,7 +599,7 @@ def crop(x, shape=None, offsets=None, name=None): Parameters: x (Tensor): 1-D to 6-D Tensor, the data type is float32, float64, int32 or int64. - shape (list|tuple|Tensor): The output shape is specified + shape (list|tuple|Tensor, optional): The output shape is specified by `shape`. Its data type is int32. If a list/tuple, it's length must be the same as the dimension size of `x`. If a Tensor, it should be a 1-D Tensor. When it is a list, each element can be an integer or a Tensor of shape: [1]. diff --git a/python/paddle/utils/cpp_extension/cpp_extension.py b/python/paddle/utils/cpp_extension/cpp_extension.py index 73642e1a004cac0f48977651b8a08be48ae8f84e..0367e9ed3e3544aae1bf4b3afce29f60d55546d7 100644 --- a/python/paddle/utils/cpp_extension/cpp_extension.py +++ b/python/paddle/utils/cpp_extension/cpp_extension.py @@ -139,7 +139,8 @@ def setup(**attr): compiler using dict type with ``{'cxx': [...], 'nvcc': [...]}`` . Default is None. **attr(dict, optional): Specify other arguments same as ``setuptools.setup`` . - Returns: None + Returns: + None """ cmdclass = attr.get('cmdclass', {})