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fix API doc, solve conflict, test=develop, test=document_fix #20196

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merged 8 commits into from Oct 9, 2019
Merged

fix API doc, solve conflict, test=develop, test=document_fix #20196

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ac6c662
fix APIs,test=develop,test=document_fix
JesseyXujin Sep 25, 2019
66115c6
fix conflict, test=develop, test=document_fix
JesseyXujin Oct 8, 2019
8a8c7d9
fix confict, test=develop, test=document_fix
JesseyXujin Oct 8, 2019
442e3f3
fix confict, test=develop, test=document_fix
JesseyXujin Oct 8, 2019
35b79ab
fix API.spec, test=develop, test=document_fix
JesseyXujin Oct 8, 2019
d002c59
change fluid.layers.data to fluid.data,test=develop, test=document_fix
JesseyXujin Oct 8, 2019
f10bbaf
fix bug on example code, test=develop, test=document_fix
JesseyXujin Oct 8, 2019
aee127c
fix API.spec, test=develop, test=document_fix
JesseyXujin Oct 8, 2019
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Empty file modified .pre-commit-config.yaml 100644 → 100755
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12 changes: 6 additions & 6 deletions paddle/fluid/API.spec 100644 → 100755
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Expand Up @@ -132,7 +132,7 @@ paddle.fluid.layers.dynamic_lstm (ArgSpec(args=['input', 'size', 'h_0', 'c_0', '
paddle.fluid.layers.dynamic_lstmp (ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name', 'h_0', 'c_0', 'cell_clip', 'proj_clip'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None, None, None, None, None)), ('document', 'c37d51aad655c8a9f9b045c64717320a'))
paddle.fluid.layers.dynamic_gru (ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None, False)), ('document', '83617c165827e030636c80486d5de6f3'))
paddle.fluid.layers.gru_unit (ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False)), ('document', '33974b9bfa69f2f1eb85e6f956dff04e'))
paddle.fluid.layers.linear_chain_crf (ArgSpec(args=['input', 'label', 'param_attr', 'length'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'bc7a0fd2bb2b35dfd2f54947320e78fa'))
paddle.fluid.layers.linear_chain_crf (ArgSpec(args=['input', 'label', 'param_attr', 'length'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'e4089baa17d0c6f164cbe4534720473e'))
paddle.fluid.layers.crf_decoding (ArgSpec(args=['input', 'param_attr', 'label', 'length'], varargs=None, keywords=None, defaults=(None, None)), ('document', '933b7e268c4ffa3d5c3ef953a5ee9f0b'))
paddle.fluid.layers.cos_sim (ArgSpec(args=['X', 'Y'], varargs=None, keywords=None, defaults=None), ('document', '8e6ce424cf9e261ef32ee229c06a6e66'))
paddle.fluid.layers.cross_entropy (ArgSpec(args=['input', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100)), ('document', '789a141e97fd0b37241f630935936d08'))
Expand All @@ -144,7 +144,7 @@ paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size',
paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None, 'NCDHW')), ('document', '73a15322d460ef9aa90d4d237b0bc5d5'))
paddle.fluid.layers.sequence_pool (ArgSpec(args=['input', 'pool_type', 'is_test', 'pad_value'], varargs=None, keywords=None, defaults=(False, 0.0)), ('document', 'e90a93251c52dc4e6fb34fb3991b3f82'))
paddle.fluid.layers.sequence_softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', 'eaa9d0bbd3d4e017c8bc4ecdac483711'))
paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name', 'axis'], varargs=None, keywords=None, defaults=(False, None, -1)), ('document', 'cee673c79e3ff4582656a24e04f841e5'))
paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name', 'axis'], varargs=None, keywords=None, defaults=(False, None, -1)), ('document', '7ccaea1b93fe4f7387a6036692986c6b'))
paddle.fluid.layers.pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive', 'data_format'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True, 'NCHW')), ('document', '630cae697d46b4b575b15d56cf8be25a'))
paddle.fluid.layers.pool3d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive', 'data_format'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True, 'NCDHW')), ('document', 'db0035a3132b1dfb12e53c57591fb9f6'))
paddle.fluid.layers.adaptive_pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)), ('document', '52343203de40afe29607397e13aaf0d2'))
Expand Down Expand Up @@ -191,7 +191,7 @@ paddle.fluid.layers.layer_norm (ArgSpec(args=['input', 'scale', 'shift', 'begin_
paddle.fluid.layers.group_norm (ArgSpec(args=['input', 'groups', 'epsilon', 'param_attr', 'bias_attr', 'act', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(1e-05, None, None, None, 'NCHW', None)), ('document', '65231cc8281815124934b1439fbb750c'))
paddle.fluid.layers.spectral_norm (ArgSpec(args=['weight', 'dim', 'power_iters', 'eps', 'name'], varargs=None, keywords=None, defaults=(0, 1, 1e-12, None)), ('document', '9461e67095a6fc5d568fb2ce8fef66ff'))
paddle.fluid.layers.softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode', 'return_softmax', 'axis'], varargs=None, keywords=None, defaults=(False, -100, True, False, -1)), ('document', '54e1675aa0364f4a78fa72804ec0f413'))
paddle.fluid.layers.smooth_l1 (ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'ecb75c1b00c4c76c98b482f633b7a10c'))
paddle.fluid.layers.smooth_l1 (ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'cbe8940643ac80ef75e1abdfbdb09e88'))
paddle.fluid.layers.one_hot (ArgSpec(args=['input', 'depth', 'allow_out_of_range'], varargs=None, keywords=None, defaults=(False,)), ('document', 'ec4115591be842868c86b2e5334245c6'))
paddle.fluid.layers.autoincreased_step_counter (ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1)), ('document', '98e7927f09ee2270535b29f048e481ec'))
paddle.fluid.layers.reshape (ArgSpec(args=['x', 'shape', 'actual_shape', 'act', 'inplace', 'name'], varargs=None, keywords=None, defaults=(None, None, False, None)), ('document', 'ca73fdc4551c5765c92eb00f24874289'))
Expand Down Expand Up @@ -229,7 +229,7 @@ paddle.fluid.layers.margin_rank_loss (ArgSpec(args=['label', 'left', 'right', 'm
paddle.fluid.layers.elu (ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', '9af1926c06711eacef9e82d7a9e4d308'))
paddle.fluid.layers.relu6 (ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(6.0, None)), ('document', '538fc860b2a1734e118b94e4a1a3ee67'))
paddle.fluid.layers.pow (ArgSpec(args=['x', 'factor', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', 'ca34f88ff61cf2a7f4c97a493d6000d0'))
paddle.fluid.layers.stanh (ArgSpec(args=['x', 'scale_a', 'scale_b', 'name'], varargs=None, keywords=None, defaults=(0.6666666666666666, 1.7159, None)), ('document', '1e1efad868714425da15c785dfb533a1'))
paddle.fluid.layers.stanh (ArgSpec(args=['x', 'scale_a', 'scale_b', 'name'], varargs=None, keywords=None, defaults=(0.67, 1.7159, None)), ('document', 'd3f742178a7263adf5929153d104883d'))
paddle.fluid.layers.hard_sigmoid (ArgSpec(args=['x', 'slope', 'offset', 'name'], varargs=None, keywords=None, defaults=(0.2, 0.5, None)), ('document', '607d79ca873bee40eed1c79a96611591'))
paddle.fluid.layers.swish (ArgSpec(args=['x', 'beta', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', 'e0dc7bc66cba939033bc028d7a62c5f4'))
paddle.fluid.layers.prelu (ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '1fadca6622c70bd33cc260817f4ff191'))
Expand Down Expand Up @@ -436,8 +436,8 @@ paddle.fluid.layers.retinanet_detection_output (ArgSpec(args=['bboxes', 'scores'
paddle.fluid.layers.distribute_fpn_proposals (ArgSpec(args=['fpn_rois', 'min_level', 'max_level', 'refer_level', 'refer_scale', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'be432c9b5f19ccba7aca38789ead29e4'))
paddle.fluid.layers.box_decoder_and_assign (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'box_score', 'box_clip', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5203935538d06a6d47b8630ad80cb2b0'))
paddle.fluid.layers.collect_fpn_proposals (ArgSpec(args=['multi_rois', 'multi_scores', 'min_level', 'max_level', 'post_nms_top_n', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '808fcca082e0040e2b77dbc53a0cf9d5'))
paddle.fluid.layers.accuracy (ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None)), ('document', 'ef799022a6040597462ae2b3d2f1c407'))
paddle.fluid.layers.auc (ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1)), ('document', '34b4575807f955f7e8698b8dead23858'))
paddle.fluid.layers.accuracy (ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None)), ('document', 'b691b7be425e281bd36897b514b2b064'))
paddle.fluid.layers.auc (ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1)), ('document', 'f567351321d358165650a38be57b90db'))
paddle.fluid.layers.exponential_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', 'eaf430c5a0380fb11bfe9a8922cd6295'))
paddle.fluid.layers.natural_exp_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', 'aa3146f64d5d508e4e50687603aa7b15'))
paddle.fluid.layers.inverse_time_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', 'ea37a3a8a0b3ce2254e7bc49a0951dbe'))
Expand Down
10 changes: 6 additions & 4 deletions paddle/fluid/operators/activation_op.cc 100644 → 100755
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Expand Up @@ -502,10 +502,12 @@ Pow Activation Operator.
class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "Input of STanh operator");
AddOutput("Out", "Output of STanh operator");
AddAttr<float>("scale_a", "The scale parameter of a for the input")
.SetDefault(2.0f / 3.0f);
AddInput("X",
"Input of STanh operator."
" A LoDTensor or Tensor with type float32, float64.");
AddOutput("Out", "Output of STanh operator. A Tensor with type float32.");
AddAttr<float>("scale_a", "The scale parameter of a for the input. ")
.SetDefault(0.67f);
AddAttr<float>("scale_b", "The scale parameter of b for the input")
.SetDefault(1.7159f);
AddComment(R"DOC(
Expand Down
30 changes: 17 additions & 13 deletions paddle/fluid/operators/linear_chain_crf_op.cc 100644 → 100755
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Expand Up @@ -22,14 +22,14 @@ namespace operators {
class LinearChainCRFOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput(
"Emission",
"(LoDTensor/Tensor<float>). When a LoDTensor input, A 2-D LoDTensor"
" with shape [N x D], where N is the size of the "
"mini-batch and D is the total tag number. The unscaled emission "
"weight matrix for the linear chain CRF. When a Tensor input,"
"A Tensor with shape [N x S x D], where N is batch size,"
"S is max length of sequences, D is the total tag number.");
AddInput("Emission",
"(LoDTensor/Tensor<float>). When a LoDTensor input,A 2-D LoDTensor"
" with shape [N x D], where N is the size of the "
"mini-batch and D is the total tag number. The unscaled emission "
"weight matrix for the linear chain CRF. When a Tensor input,"
"A Tensor with shape [N x S x D], where N is batch number,"
"S is max length of sequences, D is the total tag number."
"A LoDTensor or Tensor with type float32, float64.");
AddInput("Transition",
"(Tensor, default Tensor<float>) A 2-D Tensor with shape "
"[(D + 2) x D]. The learnable parameter for the linear_chain_crf "
Expand All @@ -38,10 +38,12 @@ class LinearChainCRFOpMaker : public framework::OpProtoAndCheckerMaker {
"(LoDTensor/Tensor<int64_t>), when a LoDTensor input, "
"[N x 1], where N is the total element number in a mini-batch. "
"when a Tensor input, [N x S], where N is batch number. "
"S is max length of sequences. The ground truth.");
"S is max length of sequences. The ground truth."
"A LoDTensor or Tensor with int64.");
AddInput("Length",
"(Tensor, default Tensor<int64_t>) A Tensor with shape "
"[M x 1], where M is the sequence number in a mini-batch.")
"[M x 1], where M is the sequence number in a mini-batch."
"A Tensor with type int64.")
.AsDispensable();
AddOutput(
"Alpha",
Expand All @@ -60,22 +62,24 @@ class LinearChainCRFOpMaker : public framework::OpProtoAndCheckerMaker {
"(Tensor, default Tensor<float>), the same shape with Emission. "
"The exponentials of Input(Emission). This is an intermediate "
"computational result in forward computation, and will be reused in "
"backward computation.")
"backward computation."
"A LoDTensor or Tensor with type float32, float64.")
.AsIntermediate();
AddOutput(
"TransitionExps",
"(Tensor, default Tensor<float>) A 2-D Tensor with shape "
"[(D + 2) x D]. The exponentials of Input(Transition). This is an "
"intermediate computational result in forward computation, and "
"will be reused in backward computation.")
"will be reused in backward computation."
"A LoDTensor or Tensor with type float32, float64.")
.AsIntermediate();
AddOutput(
"LogLikelihood",
"(Tensor, default Tensor<float>) The logarithm of the conditional "
"likelihood of each training sample in a mini-batch. This is a 2-D "
"tensor with shape [S x 1], where S is the sequence number in a "
"mini-batch. Note: S is equal to the sequence number in a mini-batch. "
"The output is no longer a LoDTensor.");
"A Tensor with type float32, float64.");
AddComment(R"DOC(
Conditional Random Field defines an undirected probabilistic graph with nodes
denoting random variables and edges denoting dependencies between these
Expand Down
62 changes: 46 additions & 16 deletions python/paddle/fluid/layers/metric_op.py
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Expand Up @@ -37,25 +37,38 @@ def accuracy(input, label, k=1, correct=None, total=None):
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.
input(Variable): The input of accuracy layer, which is the predictions of network. A LoDTensor or Tensor with type float32,float64.
label(Variable): The label of dataset. LoDTensor or Tensor with type int32,int64.
k(int): The top k predictions for each class will be checked. Data type is int64 or int32.
correct(Variable): The correct predictions count. A Tensor with type int64 or int32.
total(Variable): The total entries count. A tensor with type int64 or int32.

Returns:
Variable: The correct rate.
Variable: The correct rate. A Tensor with type float32.

Examples:
.. code-block:: python

import paddle.fluid as fluid
data = fluid.layers.data(name="data", shape=[-1, 32, 32], dtype="float32")
label = fluid.layers.data(name="label", shape=[-1,1], dtype="int32")
predict = fluid.layers.fc(input=data, size=10)
accuracy_out = fluid.layers.accuracy(input=predict, label=label, k=5)
import paddle.fluid as fluid
import numpy as np

data = fluid.data(name="input", shape=[-1, 32, 32], dtype="float32")
label = fluid.data(name="label", shape=[-1,1], dtype="int")
fc_out = fluid.layers.fc(input=data, size=10)
predict = fluid.layers.softmax(input=fc_out)
result = fluid.layers.accuracy(input=predict, label=label, k=5)

place = fluid.CPUPlace()
exe = fluid.Executor(place)

exe.run(fluid.default_startup_program())
x = np.random.rand(3, 32, 32).astype("float32")
y = np.array([[1],[0],[1]])
output= exe.run(feed={"input": x,"label": y},
fetch_list=[result[0]])
print(output)

#[array([0.6666667], dtype=float32)]
"""
helper = LayerHelper("accuracy", **locals())
topk_out, topk_indices = nn.topk(input, k=k)
Expand Down Expand Up @@ -105,8 +118,10 @@ def auc(input,
[0, 1]. Each row is sorted in descending order. This
input should be the output of topk. Typically, this
Variable indicates the probability of each label.
A LoDTensor or Tensor with type float32,float64.
label(Variable): A 2D int Variable indicating the label of the training
data. The height is batch size and width is always 1.
A LoDTensor or Tensor with type int32,int64.
curve(str): Curve type, can be 'ROC' or 'PR'. Default 'ROC'.
num_thresholds(int): The number of thresholds to use when discretizing
the roc curve. Default 200.
Expand All @@ -118,15 +133,30 @@ def auc(input,
Variable: A tuple representing the current AUC.
The return tuple is auc_out, batch_auc_out, [
batch_stat_pos, batch_stat_neg, stat_pos, stat_neg ]
Data type is Tensor, supporting float32, float64.

Examples:
.. code-block:: python

import paddle.fluid as fluid
data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
label = fluid.layers.data(name="label", shape=[1], dtype="int32")
predict = fluid.layers.fc(input=data, size=2)
auc_out = fluid.layers.auc(input=predict, label=label)
import numpy as np

data = fluid.data(name="input", shape=[-1, 32,32], dtype="float32")
label = fluid.data(name="label", shape=[1], dtype="int")
fc_out = fluid.layers.fc(input=data, size=2)
predict = fluid.layers.softmax(input=fc_out)
result=fluid.layers.auc(input=predict, label=label)

place = fluid.CPUPlace()
exe = fluid.Executor(place)

exe.run(fluid.default_startup_program())
x = np.random.rand(3,32,32).astype("float32")
y = np.array([1,0,1])
output= exe.run(feed={"input": x,"label": y},
fetch_list=[result[0]])
print(output)
#[array([0.5])]
"""
helper = LayerHelper("auc", **locals())
auc_out = helper.create_variable_for_type_inference(dtype="float64")
Expand Down