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[cherry pick] dygraph api doc for fluidDoc and api spec (#18302) 

add dygraph api doc for fluidDoc and api spec
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Showing with 391 addition and 69 deletion
paddle/fluid/API.spec
浏览文件 @ 129f2717
此差异已折叠。
paddle/fluid/pybind/imperative.cc
浏览文件 @ 129f2717
......@@ -162,22 +162,22 @@ void BindImperative(pybind11::module *m_ptr) {
1. :code:`sort_sum_gradient`, which will sum the gradient by the reverse order of trace.
Examples:
.. code-block:: python
import numpy as np
import paddle.fluid as fluid
from paddle.fluid import FC
x = np.ones([2, 2], np.float32)
with fluid.dygraph.guard():
inputs2 = []
for _ in range(10):
inputs2.append(fluid.dygraph.base.to_variable(x))
ret2 = fluid.layers.sums(inputs2)
loss2 = fluid.layers.reduce_sum(ret2)
backward_strategy = fluid.dygraph.BackwardStrategy()
backward_strategy.sort_sum_gradient = True
loss2.backward(backward_strategy)
.. code-block:: python
import numpy as np
import paddle.fluid as fluid
from paddle.fluid import FC
x = np.ones([2, 2], np.float32)
with fluid.dygraph.guard():
inputs2 = []
for _ in range(10):
inputs2.append(fluid.dygraph.base.to_variable(x))
ret2 = fluid.layers.sums(inputs2)
loss2 = fluid.layers.reduce_sum(ret2)
backward_strategy = fluid.dygraph.BackwardStrategy()
backward_strategy.sort_sum_gradient = True
loss2.backward(backward_strategy)
)DOC");
backward_strategy.def(py::init())
.def_property("sort_sum_gradient",
......
python/paddle/fluid/dygraph/nn.py
浏览文件 @ 129f2717
......@@ -42,7 +42,7 @@ class Conv2D(layers.Layer):
and W is the width of the filter. If the groups is greater than 1,
C will equal the number of input image channels divided by the groups.
Please refer to UFLDL's `convolution
<http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`_
<http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`
for more detials.
If bias attribution and activation type are provided, bias is added to the
output of the convolution, and the corresponding activation function is
......@@ -124,11 +124,7 @@ class Conv2D(layers.Layer):
Examples:
.. code-block:: python
with fluid.dygraph.guard():
conv2d = Conv2D( "conv2d", 2, 3)
data = to_variable( data )
conv = conv2d( data )
from paddle.fluid.dygraph.base import to_variable
import paddle.fluid as fluid
from paddle.fluid.dygraph import Conv2D
......@@ -306,6 +302,9 @@ class Conv3D(layers.Layer):
W_{out}&= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1
Args:
input (Variable): The input image with [N, C, D, H, W] format.
num_filters(int): The number of filter. It is as same as the output
image channel.
filter_size (int|tuple|None): The filter size. If filter_size is a tuple,
it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
Otherwise, the filter will be a square.
......@@ -356,7 +355,6 @@ class Conv3D(layers.Layer):
with fluid.dygraph.guard():
data = numpy.random.random((5, 3, 12, 32, 32)).astype('float32')
conv3d = fluid.dygraph.nn.Conv3D(
'Conv3D', num_filters=2, filter_size=3, act="relu")
ret = conv3d(fluid.dygraph.base.to_variable(data))
......@@ -508,6 +506,7 @@ class Conv3DTranspose(layers.Layer):
W_{out} &= (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1
Args:
input(Variable): The input image with [N, C, D, H, W] format.
num_filters(int): The number of the filter. It is as same as the output
image channel.
output_size(int|tuple|None): The output image size. If output size is a
......@@ -677,27 +676,30 @@ class Conv3DTranspose(layers.Layer):
class Pool2D(layers.Layer):
# TODO, should delete this class
"""
${comment}
The pooling2d operation calculates the output based on the input, pooling_type and ksize, strides,
paddings parameters.Input(X) and output(Out) are in NCHW format, where N is batch size, C is the number of channels,
H is the height of the feature, and W is the width of the feature.
Parameters(ksize, strides, paddings) are two elements. These two elements represent height and width, respectively.
The input(X) size and output(Out) size may be different.
Args:
name_scope(str) : The name of this class.
pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
it must contain two integers, (pool_size_Height, pool_size_Width).
Otherwise, the pool kernel size will be a square of an int.
pool_type: ${pooling_type_comment}
pool_type: (string), pooling type, can be "max" for max-pooling and "avg" for average-pooling
pool_stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
it must contain two integers, (pool_stride_Height, pool_stride_Width).
Otherwise, the pool stride size will be a square of an int.
pool_padding (int|list|tuple): The pool padding size. If pool padding size is a tuple,
it must contain two integers, (pool_padding_on_Height, pool_padding_on_Width).
Otherwise, the pool padding size will be a square of an int.
global_pooling (bool): ${global_pooling_comment}
use_cudnn (bool): ${use_cudnn_comment}
ceil_mode (bool): ${ceil_mode_comment}
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
global_pooling (bool): (bool, default false) Whether to use the global pooling. If global_pooling = true,
kernel size and paddings will be ignored
use_cudnn (bool): (bool, default True) Onlyceil_mode (bool) - (bool, default false) Whether to use the ceil
function to calculate output height and width. False is the default.
If it is set to False, the floor function will be used.
exclusive (bool): Whether to exclude padding points in average pooling
mode, default is true
......@@ -713,14 +715,18 @@ class Pool2D(layers.Layer):
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32')
pool2d = fluid.Pool2D("pool2d",pool_size=2,
import paddle.fluid as fluid
import numpy
with fluid.dygraph.guard():
data = numpy.random.random((3, 32, 32)).astype('float32')
pool2d = fluid.dygraph.Pool2D("pool2d",pool_size=2,
pool_type='max',
pool_stride=1,
global_pooling=False)
pool2d_res = pool2d(data)
pool2d_res = pool2d(data)
"""
def __init__(self,
......@@ -836,7 +842,7 @@ class FC(layers.Layer):
out.shape = (1, 2)
Args:
name(str): The name of this class.
name_scope(str): The name of this class.
size(int): The number of output units in this layer.
num_flatten_dims (int, default 1): The fc layer can accept an input tensor with more than
two dimensions. If this happens, the multidimensional tensor will first be flattened
......@@ -861,11 +867,12 @@ class FC(layers.Layer):
Examples:
.. code-block:: python
from paddle.fluid.dygraph.base import to_variable
import paddle.fluid as fluid
from paddle.fluid.dygraph import FC
import numpy as np
data = np.random.uniform( -1, 1, [30, 10, 32] ).astype('float32')
with fluid.dygraph.guard():
fc = FC( "fc", 64, num_flatten_dims=2)
......@@ -1011,7 +1018,7 @@ class BatchNorm(layers.Layer):
y_i &\\gets \\gamma \\hat{x_i} + \\beta
Args:
input(variable): The rank of input variable can be 2, 3, 4, 5.
name_scope(str): The name of this class.
act(string, Default None): Activation type, linear|relu|prelu|...
is_test (bool, Default False): A flag indicating whether it is in
test phrase or not.
......@@ -1032,8 +1039,6 @@ class BatchNorm(layers.Layer):
is not set, the bias is initialized zero. Default: None.
data_layout(string, default NCHW): NCHW|NHWC
in_place(bool, Default False): Make the input and output of batch norm reuse memory.
name(string, Default None): A name for this layer(optional). If set None, the layer
will be named automatically.
moving_mean_name(string, Default None): The name of moving_mean which store the global Mean.
moving_variance_name(string, Default None): The name of the moving_variance which store the global Variance.
do_model_average_for_mean_and_var(bool, Default False): Do model average for mean and variance or not.
......@@ -1050,12 +1055,12 @@ class BatchNorm(layers.Layer):
Variable: A tensor variable which is the result after applying batch normalization on the input.
Examples:
.. code-block:: python
fc = fluid.FC('fc', size=200, param_attr='fc1.w')
hidden1 = fc(x)
batch_norm = fluid.BatchNorm("batch_norm", 10)
hidden2 = batch_norm(hidden1)
fc = fluid.FC('fc', size=200, param_attr='fc1.w')
hidden1 = fc(x)
batch_norm = fluid.BatchNorm("batch_norm", 10)
hidden2 = batch_norm(hidden1)
"""
def __init__(self,
......@@ -1193,11 +1198,13 @@ class Embedding(layers.Layer):
Args:
name_scope: See base class.
size(tuple|list): The shape of the look up table parameter. It should have two elements which indicate the size of the dictionary of embeddings and the size of each embedding vector respectively.
size(tuple|list): The shape of the look up table parameter. It should have two elements which indicate the size
of the dictionary of embeddings and the size of each embedding vector respectively.
is_sparse(bool): The flag indicating whether to use sparse update.
is_distributed(bool): Whether to run lookup table from remote parameter server.
padding_idx(int|long|None): If :attr:`None`, it makes no effect to lookup. Otherwise the given :attr:`padding_idx` indicates padding the output with zeros whenever lookup encounters it in :attr:`input`. If :math:`padding_idx < 0`, the :attr:`padding_idx` to use in lookup is :math:`size[0] + dim`.
padding_idx(int|long|None): If :attr:`None`, it makes no effect to lookup.
Otherwise the given :attr:`padding_idx` indicates padding the output with zeros whenever lookup encounters
it in :attr:`input`. If :math:`padding_idx < 0`, the :attr:`padding_idx` to use in lookup is :math:`size[0] + dim`.
param_attr(ParamAttr): Parameters for this layer
dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc
......@@ -1209,15 +1216,19 @@ class Embedding(layers.Layer):
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.dygraph.base as base
import numpy as np
inp_word = np.array([[[1]]]).astype('int64')
dict_size = 20
with fluid.dygraph.guard():
emb = fluid.Embedding(
emb = fluid.dygraph.Embedding(
name_scope='embedding',
size=[dict_size, 32],
param_attr='emb.w',
is_sparse=False)
static_rlt3 = emb2(base.to_variable(inp_word))
static_rlt3 = emb(base.to_variable(inp_word))
"""
def __init__(self,
......@@ -1228,7 +1239,6 @@ class Embedding(layers.Layer):
padding_idx=None,
param_attr=None,
dtype='float32'):
super(Embedding, self).__init__(name_scope, dtype)
self._size = size
self._is_sparse = is_sparse
......@@ -1481,6 +1491,26 @@ class GRUUnit(layers.Layer):
Returns:
tuple: The hidden value, reset-hidden value and gate values.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.dygraph.base as base
import numpy
lod = [[2, 4, 3]]
D = 5
T = sum(lod[0])
hidden_input = numpy.random.rand(T, D).astype('float32')
with fluid.dygraph.guard():
x = numpy.random.random((3, 32, 32)).astype('float32')
gru = fluid.dygraph.GRUUnit('gru', size=D * 3)
dy_ret = gru(
base.to_variable(input), base.to_variable(hidden_input))
"""
def __init__(self,
......@@ -1544,15 +1574,12 @@ class NCE(layers.Layer):
Compute and return the noise-contrastive estimation training loss. See
`Noise-contrastive estimation: A new estimation principle for unnormalized
statistical models
<http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf>`_.
<http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf>`.
By default this operator uses a uniform distribution for sampling.
Args:
name_scope (str): See base class.
num_total_classes (int): Total number of classes in all samples
sample_weight (Variable|None): A Variable of shape [batch_size, 1]
storing a weight for each sample. The default weight for each
sample is 1.0.
param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
of nce. If it is set to None or one attribute of ParamAttr, nce
will create ParamAttr as param_attr. If the Initializer of the param_attr
......@@ -1563,8 +1590,6 @@ class NCE(layers.Layer):
will create ParamAttr as bias_attr. If the Initializer of the bias_attr
is not set, the bias is initialized zero. Default: None.
num_neg_samples (int): The number of negative classes. The default value is 10.
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically. Default: None.
sampler (str): The sampler used to sample class from negtive classes.
It can be 'uniform', 'log_uniform' or 'custom_dist'.
default: 'uniform'.
......@@ -1789,8 +1814,6 @@ class PRelu(layers.Layer):
element:each element has a weight
param_attr(ParamAttr|None): The parameter attribute for the learnable
weight (alpha).
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The output tensor with the same shape as input.
......@@ -1799,14 +1822,18 @@ class PRelu(layers.Layer):
.. code-block:: python
inp_np = np.ones([5, 200, 100, 100]).astype('float32')
with fluid.dygraph.guard():
mode = 'channel'
prelu = fluid.PRelu(
'prelu',
mode=mode,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(1.0)))
dy_rlt = prelu(fluid.dygraph.base.to_variable(inp_np))
import paddle.fluid as fluid
import numpy as np
inp_np = np.ones([5, 200, 100, 100]).astype('float32')
with fluid.dygraph.guard():
mode = 'channel'
prelu = fluid.PRelu(
'prelu',
mode=mode,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(1.0)))
dy_rlt = prelu(fluid.dygraph.base.to_variable(inp_np))
"""
def __init__(self, name_scope, mode, param_attr=None):
......@@ -2329,7 +2356,6 @@ class GroupNorm(layers.Layer):
If it is set to None, the bias is initialized zero. Default: None.
act(str): Activation to be applied to the output of group normalizaiton.
data_layout(string|NCHW): Only NCHW is supported.
dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc
Returns:
Variable: A tensor variable which is the result after applying group normalization on the input.
......@@ -2536,7 +2562,9 @@ class TreeConv(layers.Layer):
out(Variable): (Tensor) The feature vector of subtrees. The shape of the output tensor is [max_tree_node_size, output_size, num_filters]. The output tensor could be a new feature vector for next tree convolution layers
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy
......@@ -2587,6 +2615,7 @@ class TreeConv(layers.Layer):
is_bias=False)
def forward(self, nodes_vector, edge_set):
if self._name:
out = self.create_variable(
name=self._name, dtype=self._dtype, persistable=False)
......
tools/print_signatures.py
浏览文件 @ 129f2717
......@@ -28,7 +28,7 @@ import hashlib
member_dict = collections.OrderedDict()
experimental_namespace = {"paddle.fluid.dygraph", "paddle.fluid.LoDTensorset"}
experimental_namespace = {"paddle.fluid.LoDTensorset"}
def md5(doc):
......
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