diff --git a/paddle/fluid/operators/clip_by_norm_op.cc b/paddle/fluid/operators/clip_by_norm_op.cc index c87bded034e38..eae86a373be27 100644 --- a/paddle/fluid/operators/clip_by_norm_op.cc +++ b/paddle/fluid/operators/clip_by_norm_op.cc @@ -54,10 +54,19 @@ be linearly scaled to make the L2 norm of $Out$ equal to $max\_norm$, as shown in the following formula: $$ -Out = \frac{max\_norm * X}{norm(X)}, +Out = \\frac{max\\_norm * X}{norm(X)}, $$ where $norm(X)$ represents the L2 norm of $X$. + +Examples: + .. code-block:: python + + data = fluid.layer.data( + name='data', shape=[2, 4, 6], dtype='float32') + reshaped = fluid.layers.clip_by_norm( + x=data, max_norm=0.5) + )DOC"); } }; diff --git a/paddle/fluid/operators/uniform_random_batch_size_like_op.cc b/paddle/fluid/operators/uniform_random_batch_size_like_op.cc index 78fee77df8151..75d6181749e4e 100644 --- a/paddle/fluid/operators/uniform_random_batch_size_like_op.cc +++ b/paddle/fluid/operators/uniform_random_batch_size_like_op.cc @@ -35,10 +35,10 @@ class UniformRandomBatchSizeLikeOpMaker : public BatchSizeLikeOpMaker { protected: void Apply() override { AddComment(R"DOC( -Uniform random operator +UniformRandomBatchSizeLike operator. This operator initializes a tensor with the same batch_size as the Input tensor - with random values sampled from a uniform distribution. +with random values sampled from a uniform distribution. )DOC"); AddAttr("min", diff --git a/python/paddle/fluid/framework.py b/python/paddle/fluid/framework.py index f6438c82ac207..df0625649d2cf 100644 --- a/python/paddle/fluid/framework.py +++ b/python/paddle/fluid/framework.py @@ -1034,6 +1034,37 @@ def clone_variable(self, var): class Program(object): + """ + Python Program. Beneath it is a ProgramDesc, which is used for + create c++ Program. A program is a self-contained programing + language like container. It has at least one Block, when the + control flow op like conditional_block, while_op is included, + it will contains nested block. + Please reference the framework.proto for details. + + Notes: we have default_startup_program and default_main_program + by default, a pair of them will shared the parameters. + The default_startup_program only run once to initialize parameters, + default_main_program run in every minibatch and adjust the weights. + + Args: + None + + Returns: + Python Program + + Examples: + .. code-block:: python + + main_program = Program() + startup_program = Program() + with fluid.program_guard(main_program=main_program, startup_program=startup_program): + fluid.layers.data(name="x", shape=[-1, 784], dtype='float32') + fluid.layers.data(name="y", shape=[-1, 1], dtype='int32') + fluid.layers.fc(name="fc", shape=[10], dtype='float32', act="relu") + + """ + def __init__(self): self.desc = core.ProgramDesc() self.blocks = [Block(self, 0)] @@ -1099,6 +1130,8 @@ def get_desc(self): def clone(self, for_test=False): """Clone the Program object + Args: + for_test(bool): indicate whether clone for test. Set for_test to False when we want to clone the program for training. Set for_test to True when we want to clone the program for testing. @@ -1109,8 +1142,9 @@ def clone(self, for_test=False): the is_test attributes in these operators will be set to True for testing purposes, otherwise, they remain unchanged. - Returns(Program): - The cloned Program object. + Returns: + Program: The cloned Program object. + """ if for_test: p = self.inference_optimize() @@ -1228,6 +1262,7 @@ def sync_with_cpp(self): def copy_param_info_from(self, other): """ Copy the information of parameters from other program. + Args: other(Program): Other program @@ -1246,6 +1281,7 @@ def copy_param_info_from(self, other): def copy_data_info_from(self, other): """ Copy the information of data variables from other program. + Args: other(Program): Other program @@ -1299,6 +1335,7 @@ def __str__(self): def to_string(self, throw_on_error, with_details=False): """ To debug string. + Args: throw_on_error(bool): raise exception when self is not initialized when throw_on_error is True diff --git a/python/paddle/fluid/layers/control_flow.py b/python/paddle/fluid/layers/control_flow.py index 253a39671c8dc..b0534b251b2e4 100644 --- a/python/paddle/fluid/layers/control_flow.py +++ b/python/paddle/fluid/layers/control_flow.py @@ -902,8 +902,7 @@ def increment(x, value=1.0, in_place=True): in_place (bool): If the increment should be performed in-place. Returns: - Variable: The tensor variable storing the transformation of - element-wise increment of each value in the input. + Variable: The elementwise-incremented object. Examples: .. code-block:: python @@ -945,7 +944,7 @@ def array_write(x, i, array=None): Variable: The output LOD_TENSOR_ARRAY where the input tensor is written. Examples: - .. code-block::python + .. code-block:: python tmp = fluid.layers.zeros(shape=[10], dtype='int32') i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10) diff --git a/python/paddle/fluid/layers/metric.py b/python/paddle/fluid/layers/metric.py index 0a978eaa37402..58de1b6b9fe17 100644 --- a/python/paddle/fluid/layers/metric.py +++ b/python/paddle/fluid/layers/metric.py @@ -27,8 +27,32 @@ def accuracy(input, label, k=1, correct=None, total=None): """ + accuracy layer. + Refer to the https://en.wikipedia.org/wiki/Precision_and_recall + This function computes the accuracy using the input and label. - The output is the top k inputs and their indices. + If the correct label occurs in top k predictions, then correct will increment by one. + Note: the dtype of accuracy is determined by input. the input and label dtype can be different. + + Args: + input(Variable): The input of accuracy layer, which is the predictions of network. + Carry LoD information is supported. + label(Variable): The label of dataset. + k(int): The top k predictions for each class will be checked. + correct(Variable): The correct predictions count. + total(Variable): The total entries count. + + Returns: + Variable: The correct rate. + + Examples: + .. code-block:: python + + data = fluid.layers.data(name="data", shape=[-1, 32, 32], dtype="float32") + label = fluid.layers.data(name="data", shape=[-1,1], dtype="int32") + predict = fluid.layers.fc(input=data, size=10) + acc = fluid.layers.accuracy(input=predict, label=label, k=5) + """ helper = LayerHelper("accuracy", **locals()) topk_out, topk_indices = nn.topk(input, k=k) diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index ba4f6f230d94b..49e50a40a4193 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -794,11 +794,14 @@ def linear_chain_crf(input, label, param_attr=None): Args: input(${emission_type}): ${emission_comment} + input(${transition_type}): ${transition_comment} label(${label_type}): ${label_comment} param_attr(ParamAttr): The attribute of the learnable parameter. Returns: - ${log_likelihood_comment} + output(${emission_exps_type}): ${emission_exps_comment} \n + output(${transition_exps_type}): ${transition_exps_comment} \n + output(${log_likelihood_type}): ${log_likelihood_comment} """ helper = LayerHelper('linear_chain_crf', **locals()) @@ -1131,10 +1134,6 @@ def sequence_conv(input, Variable: output of sequence_conv """ - # FIXME(dzh) : want to unify the argument of python layer - # function. So we ignore some unecessary attributes. - # such as, padding_trainable, context_start. - helper = LayerHelper('sequence_conv', **locals()) dtype = helper.input_dtype() filter_shape = [filter_size * input.shape[1], num_filters] @@ -2068,15 +2067,37 @@ def layer_norm(input, def beam_search_decode(ids, scores, name=None): """ - ${beam_search_decode} + Beam Search Decode + + This layers is to pack the output of beam search layer into sentences and + associated scores. It is usually called after the beam search layer. + Typically, the output of beam search layer is a tensor of selected ids, with + a tensor of the score of each id. Beam search layer's output ids, however, + are generated directly during the tree search, and they are stacked by each + level of the search tree. Thus we need to reorganize them into sentences, + based on the score of each id. This layer takes the output of beam search + layer as input and repack them into sentences. Args: - ids (Variable): ${ids_comment} - scores (Variable): ${scores_comment} + ids (Variable): The selected ids, output of beam search layer. + scores (Variable): The associated scores of the ids, out put of beam + search layer. name (str): The name of this layer. It is optional. Returns: - tuple: a tuple of two output variable: sentence_ids, sentence_scores + tuple(Variable): a tuple of two output tensors: sentence_ids, sentence_scores. + sentence_ids is a tensor with shape [size, length], where size is the + beam size of beam search, and length is the length of each sentence. + Note that the length of sentences may vary. + sentence_scores is a tensor with the same shape as sentence_ids. + + Examples: + .. code-block:: python + + ids, scores = fluid.layers.beam_search( + pre_ids, ids, scores, beam_size, end_id) + sentence_ids, sentence_scores = fluid.layers.beam_search_decode( + ids, scores) """ helper = LayerHelper('beam_search_decode', **locals()) sentence_ids = helper.create_tmp_variable(dtype=ids.dtype) @@ -2957,7 +2978,7 @@ def split(input, num_or_sections, dim=-1, name=None): will be named automatically. Returns: - List: The list of segmented tensor variables. + list(Variable): The list of segmented tensor variables. Examples: .. code-block:: python @@ -3690,8 +3711,6 @@ def im2sequence(input, filter_size=1, stride=1, padding=0, name=None): Examples: - As an example: - .. code-block:: text Given: @@ -3735,7 +3754,7 @@ def im2sequence(input, filter_size=1, stride=1, padding=0, name=None): output.lod = [[4, 4]] - The simple usage is: + Examples: .. code-block:: python @@ -4220,9 +4239,7 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None): .. math:: - Output(i, x, y) = Input(i, x, y) / \left( - k + \alpha \sum\limits^{\min(C, c + n/2)}_{j = \max(0, c - n/2)} - (Input(j, x, y))^2 \right)^{\beta} + Output(i, x, y) = Input(i, x, y) / \\left(k + \\alpha \\sum\\limits^{\\min(C, c + n/2)}_{j = \\max(0, c - n/2)}(Input(j, x, y))^2\\right)^{\\beta} In the above equation: