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58cd4fda
编写于
1月 22, 2018
作者:
Y
ying
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电子邮件补丁
差异文件
add wrapper for transpose operator.
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c6b78e56
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1 changed file
with
81 addition
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18 deletion
+81
-18
python/paddle/v2/fluid/layers/nn.py
python/paddle/v2/fluid/layers/nn.py
+81
-18
未找到文件。
python/paddle/v2/fluid/layers/nn.py
浏览文件 @
58cd4fda
...
...
@@ -22,13 +22,38 @@ from ..param_attr import ParamAttr
from
tensor
import
concat
__all__
=
[
'fc'
,
'embedding'
,
'dynamic_lstm'
,
'gru_unit'
,
'linear_chain_crf'
,
'crf_decoding'
,
'cos_sim'
,
'cross_entropy'
,
'square_error_cost'
,
'accuracy'
,
'chunk_eval'
,
'sequence_conv'
,
'conv2d'
,
'sequence_pool'
,
'pool2d'
,
'batch_norm'
,
'beam_search_decode'
,
'conv2d_transpose'
,
'sequence_expand'
,
'lstm_unit'
,
'reduce_sum'
,
'reduce_mean'
,
'reduce_max'
,
'reduce_min'
,
'sequence_first_step'
,
'sequence_last_step'
,
'dropout'
,
'split'
,
'l2_normalize'
,
'matmul'
,
'warpctc'
,
'sequence_reshape'
'fc'
,
'embedding'
,
'dynamic_lstm'
,
'gru_unit'
,
'linear_chain_crf'
,
'crf_decoding'
,
'cos_sim'
,
'cross_entropy'
,
'square_error_cost'
,
'accuracy'
,
'chunk_eval'
,
'sequence_conv'
,
'conv2d'
,
'sequence_pool'
,
'pool2d'
,
'batch_norm'
,
'beam_search_decode'
,
'conv2d_transpose'
,
'sequence_expand'
,
'lstm_unit'
,
'reduce_sum'
,
'reduce_mean'
,
'reduce_max'
,
'reduce_min'
,
'sequence_first_step'
,
'sequence_last_step'
,
'dropout'
,
'split'
,
'l2_normalize'
,
'matmul'
,
'warpctc'
,
'sequence_reshape'
,
]
...
...
@@ -43,14 +68,14 @@ def fc(input,
**Fully Connected Layer**
The fully connected layer can take multiple tensors as its inputs. It
creates a variable (one for each input tensor) called weights for each
input
tensor, which represents a fully connected weight matrix from each input
unit to each output unit. The fully connected layer multiplies each input
tensor with its coresponding weight to produce an output Tensor. If
multiple input tensors are given, the results of multiple multiplications
will be sumed up. If bias_attr is not None, a biases variable will be
created and added to the output. Finally, if activation is not None
,
it will be applied to the output as well.
creates a variable (one for each input tensor) called weights for each
input tensor, which represents a fully connected weight matrix from
each input unit to each output unit. The fully connected layer
multiplies each input tensor with its coresponding weight to produce
an output Tensor. If multiple input tensors are given, the results of
multiple multiplications will be sumed up. If bias_attr is not None,
a biases variable will be created and added to the output. Finally
,
i
f activation is not None, i
t will be applied to the output as well.
This process can be formulated as follows:
...
...
@@ -1971,3 +1996,41 @@ def sequence_reshape(input, new_dim):
outputs
=
{
'Out'
:
[
out
]},
attrs
=
{
'new_dim'
:
new_dim
})
return
out
def
transpose
(
input
,
perm
,
name
=
None
):
"""
**transpose Layer**
Permute the dimensions of `input` according to `perm`.
The `i`-th dimension of the returned tensor will correspond to the
perm[i]-th dimension of `input`.
Args:
input (Variable): (Tensor), A Tensor.
perm (list): A permutation of the dimensions of `input`.
Returns:
Variable: A transposed Tensor.
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[5, 10, 15], dtype='float32')
x_transposed = layers.transpose(input=x, perm=[1, 0, 2])
"""
if
len
(
perm
)
!=
len
(
input
.
shape
):
raise
ValueError
(
"Input(perm) is the permutation of dimensions of Input(input). "
"It's length shoud be equal to Input(input)'s rank."
)
helper
=
LayerHelper
(
'transpose'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
helper
.
input_dtype
())
helper
.
append_op
(
type
=
'transpose'
,
inputs
=
{
'X'
:
[
input
]},
outputs
=
{
'Out'
:
[
out
]},
attrs
=
{
'axis'
:
perm
})
return
out
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