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体验新版 GitCode,发现更多精彩内容 >>
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38898c28
编写于
3月 15, 2019
作者:
T
Tao Luo
提交者:
GitHub
3月 15, 2019
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Merge pull request #16212 from Aurelius84/develop
improve layers.fc api doc
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b77ebb2a
2d1e76fb
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2
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2 changed file
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44 addition
and
13 deletion
+44
-13
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-1
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+43
-12
未找到文件。
paddle/fluid/API.spec
浏览文件 @
38898c28
...
@@ -68,7 +68,7 @@ paddle.fluid.initializer.MSRAInitializer.__init__ (ArgSpec(args=['self', 'unifor
...
@@ -68,7 +68,7 @@ paddle.fluid.initializer.MSRAInitializer.__init__ (ArgSpec(args=['self', 'unifor
paddle.fluid.initializer.force_init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '6d0f3e22c90d9d500d36ff57daf056ee'))
paddle.fluid.initializer.force_init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '6d0f3e22c90d9d500d36ff57daf056ee'))
paddle.fluid.initializer.init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'a6d7011ca3d8c0d454dac3a56eae0c29'))
paddle.fluid.initializer.init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'a6d7011ca3d8c0d454dac3a56eae0c29'))
paddle.fluid.initializer.NumpyArrayInitializer.__init__ (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.initializer.NumpyArrayInitializer.__init__ (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.layers.fc (ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, False, None)), ('document', '
1929058262994f212620599c63aea6bd
'))
paddle.fluid.layers.fc (ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, False, None)), ('document', '
424e898365195e3ccbc2e7dc8b63605e
'))
paddle.fluid.layers.embedding (ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32')), ('document', '89c2c55a0b0656b106064048e068e77a'))
paddle.fluid.layers.embedding (ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32')), ('document', '89c2c55a0b0656b106064048e068e77a'))
paddle.fluid.layers.dynamic_lstm (ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None)), ('document', 'dfbb624f85015df29e994ca6999e8ff6'))
paddle.fluid.layers.dynamic_lstm (ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None)), ('document', 'dfbb624f85015df29e994ca6999e8ff6'))
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', 'b4b608b986eb9617aa0525e1be21d32d'))
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', 'b4b608b986eb9617aa0525e1be21d32d'))
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
38898c28
...
@@ -205,16 +205,23 @@ def fc(input,
...
@@ -205,16 +205,23 @@ def fc(input,
**Fully Connected Layer**
**Fully Connected Layer**
This function creates a fully connected layer in the network. It can take
This function creates a fully connected layer in the network. It can take
multiple tensors as its inputs. It creates a variable called weights for
one or multiple tensors as its inputs(input can be a list of Variable, see
each input tensor, which represents a fully connected weight matrix from
Args in detail). It creates a variable called weights for each input tensor,
each input unit to each output unit. The fully connected layer multiplies
which represents a fully connected weight matrix from each input unit to
each input tensor with its coresponding weight to produce an output Tensor.
each output unit. The fully connected layer multiplies each input tensor
If multiple input tensors are given, the results of multiple multiplications
with its corresponding weight to produce an output Tensor with shape [M, `size`],
will be sumed up. If bias_attr is not None, a bias variable will be created
where M is batch size. If multiple input tensors are given, the results of
and added to the output. Finally, if activation is not None, it will be applied
multiple output tensors with shape [M, `size`] will be summed up. If bias_attr
to the output as well.
is not None, a bias variable will be created and added to the output.
Finally, if activation is not None, it will be applied to the output as well.
When the input is single tensor:
This process can be formulated as follows:
.. math::
Out = Act({XW + b})
When the input are multiple tensors:
.. math::
.. math::
...
@@ -222,13 +229,31 @@ def fc(input,
...
@@ -222,13 +229,31 @@ def fc(input,
In the above equation:
In the above equation:
* :math:`N`: Number of the input.
* :math:`N`: Number of the input.
N equals to len(input) if input is list of Variable.
* :math:`X_i`: The input tensor.
* :math:`X_i`: The i
-th i
nput tensor.
* :math:`W
`: The weights created by this laye
r.
* :math:`W
_i`: The i-th weights matrix corresponding i-th input tenso
r.
* :math:`b`: The bias parameter created by this layer (if needed).
* :math:`b`: The bias parameter created by this layer (if needed).
* :math:`Act`: The activation function.
* :math:`Act`: The activation function.
* :math:`Out`: The output tensor.
* :math:`Out`: The output tensor.
See below for an example.
.. code-block:: text
Given:
data_1.data = [[[0.1, 0.2],
[0.3, 0.4]]]
data_1.shape = (1, 2, 2) # 1 is batch_size
data_2 = [[[0.1, 0.2, 0.3]]]
data_2.shape = (1, 1, 3)
out = fluid.layers.fc(input=[data_1, data_2], size=2)
Then:
out.data = [[0.18669507, 0.1893476]]
out.shape = (1, 2)
Args:
Args:
input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of
input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of
the input tensor(s) is at least 2.
the input tensor(s) is at least 2.
...
@@ -260,8 +285,14 @@ def fc(input,
...
@@ -260,8 +285,14 @@ def fc(input,
Examples:
Examples:
.. code-block:: python
.. code-block:: python
# when input is single tensor
data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
fc = fluid.layers.fc(input=data, size=1000, act="tanh")
fc = fluid.layers.fc(input=data, size=1000, act="tanh")
# when input are multiple tensors
data_1 = fluid.layers.data(name="data_1", shape=[32, 32], dtype="float32")
data_2 = fluid.layers.data(name="data_2", shape=[24, 36], dtype="float32")
fc = fluid.layers.fc(input=[data_1, data_2], size=1000, act="tanh")
"""
"""
helper
=
LayerHelper
(
"fc"
,
**
locals
())
helper
=
LayerHelper
(
"fc"
,
**
locals
())
...
...
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