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体验新版 GitCode,发现更多精彩内容 >>
提交
5258bcf3
编写于
2月 24, 2017
作者:
L
Luo Tao
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
implement more layers in v2
上级
f25c9c5f
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
353 addition
and
159 deletion
+353
-159
doc/api/trainer_config_helpers/layers.rst
doc/api/trainer_config_helpers/layers.rst
+24
-12
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+113
-101
python/paddle/v2/__init__.py
python/paddle/v2/__init__.py
+2
-1
python/paddle/v2/layer.py
python/paddle/v2/layer.py
+190
-45
python/paddle/v2/pooling.py
python/paddle/v2/pooling.py
+24
-0
未找到文件。
doc/api/trainer_config_helpers/layers.rst
浏览文件 @
5258bcf3
...
...
@@ -139,24 +139,12 @@ lstmemory
:members: lstmemory
:noindex:
lstm_step_layer
---------------
.. automodule:: paddle.trainer_config_helpers.layers
:members: lstm_step_layer
:noindex:
grumemory
---------
.. automodule:: paddle.trainer_config_helpers.layers
:members: grumemory
:noindex:
gru_step_layer
---------------
.. automodule:: paddle.trainer_config_helpers.layers
:members: gru_step_layer
:noindex:
Recurrent Layer Group
=====================
...
...
@@ -172,6 +160,18 @@ recurrent_group
:members: recurrent_group
:noindex:
lstm_step_layer
---------------
.. automodule:: paddle.trainer_config_helpers.layers
:members: lstm_step_layer
:noindex:
gru_step_layer
---------------
.. automodule:: paddle.trainer_config_helpers.layers
:members: gru_step_layer
:noindex:
beam_search
------------
.. automodule:: paddle.trainer_config_helpers.layers
...
...
@@ -308,6 +308,12 @@ repeat_layer
:members: repeat_layer
:noindex:
rotate_layer
------------
.. automodule:: paddle.trainer_config_helpers.layers
:members: rotate_layer
:noindex:
seq_reshape_layer
-----------------
.. automodule:: paddle.trainer_config_helpers.layers
...
...
@@ -462,6 +468,12 @@ ctc_layer
:members: ctc_layer
:noindex:
warp_ctc_layer
--------------
.. automodule:: paddle.trainer_config_helpers.layers
:members: warp_ctc_layer
:noindex:
nce_layer
-----------
.. automodule:: paddle.trainer_config_helpers.layers
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
5258bcf3
...
...
@@ -30,88 +30,28 @@ except ImportError:
import
copy
__all__
=
[
"full_matrix_projection"
,
"AggregateLevel"
,
"ExpandLevel"
,
"identity_projection"
,
"dotmul_projection"
,
"dotmul_operator"
,
"repeat_layer"
,
"seq_reshape_layer"
,
"table_projection"
,
"mixed_layer"
,
"data_layer"
,
"embedding_layer"
,
"fc_layer"
,
"grumemory"
,
"pooling_layer"
,
"lstmemory"
,
"last_seq"
,
"first_seq"
,
"cos_sim"
,
"hsigmoid"
,
"conv_projection"
,
"regression_cost"
,
'classification_cost'
,
"LayerOutput"
,
'img_conv_layer'
,
'img_pool_layer'
,
'batch_norm_layer'
,
'img_cmrnorm_layer'
,
'addto_layer'
,
'concat_layer'
,
'seq_concat_layer'
,
'lstm_step_layer'
,
'recurrent_group'
,
'memory'
,
'StaticInput'
,
'expand_layer'
,
'scaling_layer'
,
'scaling_projection'
,
'power_layer'
,
'interpolation_layer'
,
'bilinear_interp_layer'
,
'trans_layer'
,
'rotate_layer'
,
'sum_to_one_norm_layer'
,
'get_output_layer'
,
'LayerType'
,
'context_projection'
,
'beam_search'
,
'maxid_layer'
,
'GeneratedInput'
,
'SubsequenceInput'
,
'gru_step_layer'
,
'recurrent_layer'
,
'BaseGeneratedInput'
,
'conv_operator'
,
'conv_shift_layer'
,
'tensor_layer'
,
'selective_fc_layer'
,
'sampling_id_layer'
,
'slope_intercept_layer'
,
'trans_full_matrix_projection'
,
'linear_comb_layer'
,
'convex_comb_layer'
,
'ctc_layer'
,
'warp_ctc_layer'
,
'crf_layer'
,
'crf_decoding_layer'
,
'nce_layer'
,
'cross_entropy_with_selfnorm'
,
'cross_entropy'
,
'multi_binary_label_cross_entropy'
,
'sum_cost'
,
'rank_cost'
,
'lambda_cost'
,
'huber_cost'
,
'block_expand_layer'
,
'maxout_layer'
,
'out_prod_layer'
,
'print_layer'
,
'priorbox_layer'
,
'spp_layer'
,
'pad_layer'
,
"full_matrix_projection"
,
"AggregateLevel"
,
"ExpandLevel"
,
"identity_projection"
,
"dotmul_projection"
,
"dotmul_operator"
,
"repeat_layer"
,
"seq_reshape_layer"
,
"table_projection"
,
"mixed_layer"
,
"data_layer"
,
"embedding_layer"
,
"fc_layer"
,
"grumemory"
,
"pooling_layer"
,
"lstmemory"
,
"last_seq"
,
"first_seq"
,
"cos_sim"
,
"hsigmoid"
,
"conv_projection"
,
"regression_cost"
,
'classification_cost'
,
"LayerOutput"
,
'img_conv_layer'
,
'img_pool_layer'
,
'batch_norm_layer'
,
'img_cmrnorm_layer'
,
'addto_layer'
,
'concat_layer'
,
'seq_concat_layer'
,
'lstm_step_layer'
,
'recurrent_group'
,
'memory'
,
'StaticInput'
,
'expand_layer'
,
'scaling_layer'
,
'scaling_projection'
,
'power_layer'
,
'interpolation_layer'
,
'bilinear_interp_layer'
,
'trans_layer'
,
'rotate_layer'
,
'sum_to_one_norm_layer'
,
'get_output_layer'
,
'LayerType'
,
'context_projection'
,
'beam_search'
,
'maxid_layer'
,
'GeneratedInput'
,
'SubsequenceInput'
,
'gru_step_layer'
,
'recurrent_layer'
,
'BaseGeneratedInput'
,
'conv_operator'
,
'conv_shift_layer'
,
'tensor_layer'
,
'selective_fc_layer'
,
'sampling_id_layer'
,
'slope_intercept_layer'
,
'trans_full_matrix_projection'
,
'linear_comb_layer'
,
'convex_comb_layer'
,
'ctc_layer'
,
'warp_ctc_layer'
,
'crf_layer'
,
'crf_decoding_layer'
,
'nce_layer'
,
'cross_entropy_with_selfnorm'
,
'cross_entropy'
,
'multi_binary_label_cross_entropy'
,
'sum_cost'
,
'rank_cost'
,
'lambda_cost'
,
'huber_cost'
,
'block_expand_layer'
,
'maxout_layer'
,
'out_prod_layer'
,
'print_layer'
,
'priorbox_layer'
,
'spp_layer'
,
'pad_layer'
,
'eos_layer'
]
...
...
@@ -1287,6 +1227,12 @@ def last_seq(input,
"""
Get Last Timestamp Activation of a sequence.
The simple usage is:
.. code-block:: python
seq = last_seq(input=layer)
:param agg_level: Aggregated level
:param name: Layer name.
:type name: basestring
...
...
@@ -1325,6 +1271,12 @@ def first_seq(input,
"""
Get First Timestamp Activation of a sequence.
The simple usage is:
.. code-block:: python
seq = first_seq(input=layer)
:param agg_level: aggregation level
:param name: Layer name.
:type name: basestring
...
...
@@ -1425,7 +1377,7 @@ def repeat_layer(input, num_repeats, name=None, layer_attr=None):
.. code-block:: python
expand = repeat_layer(
layer,
4)
expand = repeat_layer(
input=layer, num_repeats=
4)
:param input: Input layer
:type input: LayerOutput
...
...
@@ -1797,6 +1749,12 @@ def cos_sim(a, b, scale=1, size=1, name=None, layer_attr=None):
Note that the above computation is for one sample. Multiple samples are
processed in one batch.
The example usage is:
.. code-block:: python
cos = cos_sim(a=layer1, b=layer2, size=3)
:param name: layer name
:type name: basestring
:param a: input layer a
...
...
@@ -1958,6 +1916,16 @@ def img_conv_layer(input,
pieces. First 256/4 = 64 channels will process by first 32 filters. The
rest channels will be processed by rest group of filters.
The example usage is:
.. code-block:: python
conv = img_conv_layer(input=data, filter_size=1, filter_size_y=1,
num_channels=8,
num_filters=16, stride=1,
bias_attr=False,
act=ReluActivation())
:param name: Layer name.
:type name: basestring
:param input: Layer Input.
...
...
@@ -2097,6 +2065,34 @@ def img_pool_layer(input,
.. _pooling: http://ufldl.stanford.edu/tutorial/supervised/Pooling/
- ceil_mode=True:
.. math::
w = 1 + int(ceil(input\_width + 2 * padding - pool\_size) / float(stride))
h = 1 + int(ceil(input\_height + 2 * padding\_y - pool\_size\_y) / float(stride\_y))
- ceil_mode=False:
.. math::
w = 1 + int(floor(input\_width + 2 * padding - pool\_size) / float(stride))
h = 1 + int(floor(input\_height + 2 * padding\_y - pool\_size\_y) / float(stride\_y))
The example usage is:
.. code-block:: python
maxpool = img_pool_layer(input=conv,
pool_size=3,
pool_size_y=5,
num_channels=8,
stride=1,
stride_y=2,
padding=1,
padding_y=2,
pool_type=MaxPooling())
:param padding: pooling padding width.
:type padding: int
:param padding_y: pooling padding height. It's equal to padding by default.
...
...
@@ -2123,19 +2119,6 @@ def img_pool_layer(input,
:param ceil_mode: Wether to use ceil mode to calculate output height and with.
Defalut is True. If set false, Otherwise use floor.
- ceil_mode=True:
.. math::
w = 1 + int(ceil(input_width + 2 * padding - pool_size) / float(stride))
h = 1 + int(ceil(input_height + 2 * padding_y - pool_size_y) / float(stride_y))
- ceil_mode=False:
.. math::
w = 1 + int(floor(input_width + 2 * padding - pool_size) / float(stride))
h = 1 + int(floor(input_height + 2 * padding_y - pool_size_y) / float(stride_y))
:type ceil_mode: bool
:return: LayerOutput object.
:rtype: LayerOutput
...
...
@@ -2197,6 +2180,15 @@ def spp_layer(input,
The details please refer to
`Kaiming He's paper <https://arxiv.org/abs/1406.4729>`_.
The example usage is:
.. code-block:: python
spp = spp_layer(input=data,
pyramid_height=2,
num_channels=16,
pool_type=MaxPooling())
:param name: layer name.
:type name: basestring
:param input: layer's input.
...
...
@@ -2285,6 +2277,12 @@ def img_cmrnorm_layer(input,
The details please refer to
`Alex's paper <http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf>`_.
The example usage is:
.. code-block:: python
norm = img_cmrnorm_layer(input=net, size=5)
:param name: layer name.
:type name: None|basestring
:param input: layer's input.
...
...
@@ -2340,6 +2338,12 @@ def batch_norm_layer(input,
The details of batch normalization please refer to this
`paper <http://arxiv.org/abs/1502.03167>`_.
The example usage is:
.. code-block:: python
norm = batch_norm_layer(input=net, act=ReluActivation())
:param name: layer name.
:type name: basestring
:param input: batch normalization input. Better be linear activation.
...
...
@@ -3903,13 +3907,13 @@ def conv_shift_layer(a, b, name=None, layer_attr=None):
.. code-block:: python
conv_shift = conv_shift_layer(
input=[layer1, layer2]
)
conv_shift = conv_shift_layer(
a=layer1, b=layer2
)
:param name: layer name
:type name: basestring
:param a: Input layer a.
:type a: LayerOutput
:param b: input layer b
:param b: input layer b
.
:type b: LayerOutput
:param layer_attr: layer's extra attribute.
:type layer_attr: ExtraLayerAttribute
...
...
@@ -4001,8 +4005,8 @@ def tensor_layer(a,
@
wrap_act_default
()
@
layer_support
()
def
selective_fc_layer
(
input
,
select
,
size
,
select
=
None
,
act
=
None
,
name
=
None
,
pass_generation
=
False
,
...
...
@@ -4029,6 +4033,7 @@ def selective_fc_layer(input,
:type input: LayerOutput|list|tuple
:param select: The select layer. The output of select layer should be a
sparse binary matrix, and treat as the mask of selective fc.
If is None, acts exactly like fc_layer.
:type select: LayerOutput
:param size: The layer dimension.
:type size: int
...
...
@@ -4257,7 +4262,7 @@ def block_expand_layer(input,
.. code-block:: python
block_expand = block_expand_layer(input,
block_expand = block_expand_layer(input
=layer
,
num_channels=128,
stride_x=1,
stride_y=1,
...
...
@@ -4461,7 +4466,7 @@ def warp_ctc_layer(input,
- You can set 'blank' to any value ranged in [0, num_classes], which
should be consistent as that used in your labels.
- As a native 'softmax' activation is interated to the warp-ctc library,
'linear' activation is expected instead in the 'input' layer.
'linear' activation is expected instead in the 'input' layer.
The simple usage:
...
...
@@ -4594,6 +4599,13 @@ def crf_decoding_layer(input,
this layer will also calculate error. output.value[i] is 1 for incorrect
decoding or 0 for correct decoding.
The simple usage:
.. code-block:: python
crf_decoding = crf_decoding_layer(input=input,
size=label_dim)
:param input: The first input layer.
:type input: LayerOutput
:param size: size of this layer.
...
...
python/paddle/v2/__init__.py
浏览文件 @
5258bcf3
...
...
@@ -19,11 +19,12 @@ import trainer
import
event
import
data_type
import
attr
import
pooling
import
py_paddle.swig_paddle
as
api
__all__
=
[
'optimizer'
,
'layer'
,
'activation'
,
'parameters'
,
'init'
,
'trainer'
,
'event'
,
'data_type'
,
'attr'
'event'
,
'data_type'
,
'attr'
,
'pooling'
]
...
...
python/paddle/v2/layer.py
浏览文件 @
5258bcf3
...
...
@@ -76,12 +76,20 @@ from paddle.trainer_config_helpers.default_decorators import wrap_name_default
import
data_type
import
activation
import
attr
import
pooling
__all__
=
[
'parse_network'
,
'data'
,
'fc'
,
'max_id'
,
'classification_cost'
,
'cross_entropy_cost'
,
'cross_entropy_with_selfnorm_cost'
,
'regression_cost'
,
'parse_network'
,
'data'
,
'fc'
,
'conv_shift'
,
'img_conv'
,
'img_pool'
,
'spp'
,
'maxout'
,
'img_cmrnorm'
,
'batch_norm'
,
'sum_to_one_norm'
,
'recurrent'
,
'lstmemory'
,
'grumemory'
,
'pool'
,
'last_seq'
,
'first_seq'
,
'concat'
,
'seq_concat'
,
'block_expand'
,
'expand'
,
'repeat'
,
'seq_reshape'
,
'addto'
,
'linear_comb'
,
'interpolation'
,
'bilinear_interp'
,
'power'
,
'scaling'
,
'slope_intercept'
,
'tensor'
,
'cos_sim'
,
'trans'
,
'max_id'
,
'sampling_id'
,
'pad'
,
'classification_cost'
,
'cross_entropy_cost'
,
'cross_entropy_with_selfnorm_cost'
,
'regression_cost'
,
'multi_binary_label_cross_entropy_cost'
,
'rank_cost'
,
'lambda_cost'
,
'sum_cost'
,
'huber_cost'
'sum_cost'
,
'huber_cost'
,
'crf'
,
'crf_decoding'
,
'ctc'
,
'warp_ctc'
,
'nce'
,
'hsigmoid'
,
'eos'
]
...
...
@@ -130,11 +138,8 @@ class Layer(object):
raise
NotImplementedError
()
def
__convert_to_v2__
(
method_name
,
name_prefix
,
parent_names
):
if
name_prefix
is
not
None
:
wrapper
=
wrap_name_default
(
name_prefix
=
name_prefix
)
else
:
wrapper
=
None
def
__convert_to_v2__
(
method_name
,
parent_names
):
wrapper
=
wrap_name_default
(
name_prefix
=
method_name
)
class
V2LayerImpl
(
Layer
):
def
__init__
(
self
,
name
=
None
,
**
kwargs
):
...
...
@@ -192,44 +197,92 @@ class DataLayerV2(Layer):
data
=
DataLayerV2
fc
=
__convert_to_v2__
(
'fc_layer'
,
name_prefix
=
'fc'
,
parent_names
=
[
'input'
])
max_id
=
__convert_to_v2__
(
'maxid_layer'
,
name_prefix
=
'maxid'
,
parent_names
=
[
'input'
])
classification_cost
=
__convert_to_v2__
(
'classification_cost'
,
name_prefix
=
'classification_cost'
,
parent_names
=
[
'input'
,
'label'
,
'weight'
])
regression_cost
=
__convert_to_v2__
(
'regression_cost'
,
name_prefix
=
'regression_cost'
,
parent_names
=
[
'input'
,
'label'
,
'weight'
])
cross_entropy_cost
=
__convert_to_v2__
(
'cross_entropy'
,
name_prefix
=
'cross_entropy'
,
parent_names
=
[
'input'
,
'label'
])
cross_entropy_with_selfnorm_cost
=
__convert_to_v2__
(
'cross_entropy_with_selfnorm'
,
name_prefix
=
'cross_entropy_with_selfnorm'
,
parent_names
=
[
'input'
,
'label'
])
multi_binary_label_cross_entropy_cost
=
__convert_to_v2__
(
'multi_binary_label_cross_entropy'
,
name_prefix
=
'multi_binary_label_cross_entropy'
,
parent_names
=
[
'input'
,
'label'
])
rank_cost
=
__convert_to_v2__
(
'rank_cost'
,
name_prefix
=
'rank_cost'
,
parent_names
=
[
'left'
,
'right'
,
'label'
,
'weight'
])
lambda_cost
=
__convert_to_v2__
(
'lambda_cost'
,
name_prefix
=
'lambda_cost'
,
parent_names
=
[
'input'
,
'score'
])
sum_cost
=
__convert_to_v2__
(
'sum_cost'
,
name_prefix
=
'sum_cost'
,
parent_names
=
[
'input'
])
huber_cost
=
__convert_to_v2__
(
'huber_cost'
,
name_prefix
=
'huber_cost'
,
parent_names
=
[
'input'
,
'label'
])
AggregateLevel
=
conf_helps
.
layers
.
AggregateLevel
ExpandLevel
=
conf_helps
.
layers
.
ExpandLevel
layer_list
=
[
# [V2LayerImpl, V1_method_name, parent_names]
# fully connected layers
[
'fc'
,
'fc_layer'
,
[
'input'
]],
# conv layers
[
'conv_shift'
,
'conv_shift_layer'
,
[
'a'
,
'b'
]],
[
'img_conv'
,
'img_conv_layer'
,
[
'input'
]],
# image pooling layers
[
'img_pool'
,
'img_pool_layer'
,
[
'input'
]],
[
'spp'
,
'spp_layer'
,
[
'input'
]],
[
'maxout'
,
'maxout_layer'
,
[
'input'
]],
# norm layers
[
'img_cmrnorm'
,
'img_cmrnorm_layer'
,
[
'input'
]],
[
'batch_norm'
,
'batch_norm_layer'
,
[
'input'
]],
[
'sum_to_one_norm'
,
'sum_to_one_norm_layer'
,
[
'input'
]],
# recurrent layers
[
'recurrent'
,
'recurrent_layer'
,
[
'input'
]],
[
'lstmemory'
,
'lstmemory'
,
[
'input'
]],
[
'grumemory'
,
'grumemory'
,
[
'input'
]],
# aggregate layers
[
'pool'
,
'pooling_layer'
,
[
'input'
]],
[
'last_seq'
,
'last_seq'
,
[
'input'
]],
[
'first_seq'
,
'first_seq'
,
[
'input'
]],
[
'concat'
,
'concat_layer'
,
[
'input'
]],
[
'seq_concat'
,
'seq_concat_layer'
,
[
'a'
,
'b'
]],
# reshaping layers
[
'block_expand'
,
'block_expand_layer'
,
[
'input'
]],
[
'expand'
,
'expand_layer'
,
[
'input'
,
'expand_as'
]],
[
'repeat'
,
'repeat_layer'
,
[
'input'
]],
[
'rotate'
,
'rotate_layer'
,
[
'input'
]],
[
'seq_reshape'
,
'seq_reshape_layer'
,
[
'input'
]],
# math layers
[
'addto'
,
'addto_layer'
,
[
'input'
]],
[
'linear_comb'
,
'linear_comb_layer'
,
[
'weights'
,
'vectors'
]],
[
'interpolation'
,
'interpolation_layer'
,
[
'input'
,
'weight'
]],
[
'bilinear_interp'
,
'bilinear_interp_layer'
,
[
'input'
]],
[
'power'
,
'power_layer'
,
[
'input'
,
'weight'
]],
[
'scaling'
,
'scaling_layer'
,
[
'input'
,
'weight'
]],
[
'slope_intercept'
,
'slope_intercept_layer'
,
[
'input'
]],
[
'tensor'
,
'tensor_layer'
,
[
'a'
,
'b'
]],
[
'cos_sim'
,
'cos_sim'
,
[
'a'
,
'b'
]],
[
'trans'
,
'trans_layer'
,
[
'input'
]],
# sampling layers
[
'max_id'
,
'maxid_layer'
,
[
'input'
]],
[
'sampling_id'
,
'sampling_id_layer'
,
[
'input'
]],
# slicing and joining layers
[
'pad'
,
'pad_layer'
,
[
'input'
]],
# cost layers
[
'classification_cost'
,
'classification_cost'
,
[
'input'
,
'label'
,
'weight'
]
],
[
'regression_cost'
,
'regression_cost'
,
[
'input'
,
'label'
,
'weight'
]],
[
'cross_entropy_cost'
,
'cross_entropy'
,
[
'input'
,
'label'
]],
[
'cross_entropy_with_selfnorm_cost'
,
'cross_entropy_with_selfnorm'
,
[
'input'
,
'label'
]
],
[
'multi_binary_label_cross_entropy_cost'
,
'multi_binary_label_cross_entropy'
,
[
'input'
,
'label'
]
],
[
'rank_cost'
,
'rank_cost'
,
[
'left'
,
'right'
,
'label'
,
'weight'
]],
[
'lambda_cost'
,
'lambda_cost'
,
[
'input'
,
'score'
]],
[
'sum_cost'
,
'sum_cost'
,
[
'input'
]],
[
'huber_cost'
,
'huber_cost'
,
[
'input'
,
'label'
]],
[
'crf'
,
'crf_layer'
,
[
'input'
,
'label'
]],
[
'crf_decoding'
,
'crf_decoding_layer'
,
[
'input'
]],
[
'ctc'
,
'ctc_layer'
,
[
'input'
,
'label'
]],
[
'warp_ctc'
,
'warp_ctc_layer'
,
[
'input'
,
'label'
]],
[
'nce'
,
'nce_layer'
,
[
'input'
,
'label'
]],
[
'hsigmoid'
,
'hsigmoid'
,
[
'input'
,
'label'
]],
# check layers
[
'eos'
,
'eos_layer'
,
[
'input'
]]
]
for
l
in
layer_list
:
globals
()[
l
[
0
]]
=
__convert_to_v2__
(
l
[
1
],
l
[
2
])
if
__name__
==
'__main__'
:
pixel
=
data
(
name
=
'pixel'
,
type
=
data_type
.
dense_vector
(
784
))
pixel
=
data
(
name
=
'pixel'
,
type
=
data_type
.
dense_vector
(
128
))
label
=
data
(
name
=
'label'
,
type
=
data_type
.
integer_value
(
10
))
weight
=
data
(
name
=
'weight'
,
type
=
data_type
.
dense_vector
(
10
))
word
=
data
(
name
=
'word'
,
type
=
data_type
.
integer_value
(
12
))
score
=
data
(
name
=
'score'
,
type
=
data_type
.
dense_vector
(
1
))
hidden
=
fc
(
input
=
pixel
,
...
...
@@ -237,7 +290,90 @@ if __name__ == '__main__':
act
=
activation
.
Sigmoid
(),
param_attr
=
attr
.
Param
(
name
=
'hidden'
))
inference
=
fc
(
input
=
hidden
,
size
=
10
,
act
=
activation
.
Softmax
())
print
parse_network
(
inference
)
# test conv layers
conv1
=
conv_shift
(
a
=
pixel
,
b
=
score
)
conv2
=
img_conv
(
input
=
pixel
,
filter_size
=
1
,
filter_size_y
=
1
,
num_channels
=
8
,
num_filters
=
16
,
act
=
activation
.
Linear
())
print
parse_network
(
conv1
,
conv2
)
# test image pooling layers
maxpool
=
img_pool
(
input
=
conv2
,
pool_size
=
2
,
num_channels
=
16
,
padding
=
1
,
pool_type
=
pooling
.
Max
())
spp
=
spp
(
input
=
conv2
,
pyramid_height
=
2
,
num_channels
=
16
,
pool_type
=
pooling
.
Max
())
maxout
=
maxout
(
input
=
conv2
,
num_channels
=
16
,
groups
=
4
)
print
parse_network
(
maxpool
,
spp
,
maxout
)
# test norm layers
norm1
=
img_cmrnorm
(
input
=
maxpool
,
size
=
5
)
norm2
=
batch_norm
(
input
=
maxpool
)
norm3
=
sum_to_one_norm
(
input
=
maxpool
)
print
parse_network
(
norm1
,
norm2
,
norm3
)
# test recurrent layers
recurrent
=
recurrent
(
input
=
word
)
lstm
=
lstmemory
(
input
=
word
)
gru
=
grumemory
(
input
=
word
)
print
parse_network
(
recurrent
,
lstm
,
gru
)
# test aggregate layers
pool
=
pool
(
input
=
pixel
,
pooling_type
=
pooling
.
Avg
(),
agg_level
=
AggregateLevel
.
EACH_SEQUENCE
)
last_seq
=
last_seq
(
input
=
pixel
)
first_seq
=
first_seq
(
input
=
pixel
)
concat
=
concat
(
input
=
[
last_seq
,
first_seq
])
seq_concat
=
seq_concat
(
a
=
last_seq
,
b
=
first_seq
)
print
parse_network
(
pool
,
last_seq
,
first_seq
,
concat
,
seq_concat
)
# test reshaping layers
block_expand
=
block_expand
(
input
=
maxout
,
num_channels
=
4
,
stride_x
=
1
,
block_x
=
1
)
expand
=
expand
(
input
=
last_seq
,
expand_as
=
pixel
,
expand_level
=
ExpandLevel
.
FROM_TIMESTEP
)
repeat
=
repeat
(
input
=
last_seq
,
num_repeats
=
4
)
reshape
=
seq_reshape
(
input
=
last_seq
,
reshape_size
=
4
)
rotate
=
rotate
(
input
=
pixel
,
height
=
16
,
width
=
49
)
print
parse_network
(
block_expand
,
expand
,
repeat
,
reshape
,
rotate
)
# test math layers
addto
=
addto
(
input
=
[
last_seq
,
first_seq
])
linear_comb
=
linear_comb
(
weights
=
weight
,
vectors
=
hidden
,
size
=
10
)
interpolation
=
interpolation
(
input
=
[
hidden
,
hidden
],
weight
=
score
)
bilinear
=
bilinear_interp
(
input
=
conv2
,
out_size_x
=
4
,
out_size_y
=
4
)
power
=
power
(
input
=
conv1
,
weight
=
score
)
scaling
=
scaling
(
input
=
conv1
,
weight
=
score
)
slope
=
slope_intercept
(
input
=
conv1
)
tensor
=
tensor
(
a
=
last_seq
,
b
=
first_seq
,
size
=
1000
)
cos_sim
=
cos_sim
(
a
=
last_seq
,
b
=
first_seq
)
trans
=
trans
(
input
=
tensor
)
print
parse_network
(
addto
,
linear_comb
,
interpolation
,
bilinear
,
power
,
scaling
,
slope
,
tensor
,
cos_sim
,
trans
)
# test sampling layers
maxid
=
max_id
(
input
=
inference
)
sampling_id
=
sampling_id
(
input
=
inference
)
print
parse_network
(
maxid
,
sampling_id
)
# test slicing and joining layers
pad
=
pad
(
input
=
maxpool
,
pad_c
=
[
2
,
3
],
pad_h
=
[
1
,
2
],
pad_w
=
[
3
,
1
])
print
parse_network
(
pad
)
# test cost layers
cost1
=
classification_cost
(
input
=
inference
,
label
=
label
)
cost2
=
classification_cost
(
input
=
inference
,
label
=
label
,
weight
=
weight
)
cost3
=
cross_entropy_cost
(
input
=
inference
,
label
=
label
)
...
...
@@ -249,9 +385,18 @@ if __name__ == '__main__':
cost9
=
lambda_cost
(
input
=
inference
,
score
=
score
)
cost10
=
sum_cost
(
input
=
inference
)
cost11
=
huber_cost
(
input
=
score
,
label
=
label
)
print
parse_network
(
cost1
,
cost2
)
print
parse_network
(
cost3
,
cost4
)
print
parse_network
(
cost5
,
cost6
)
print
parse_network
(
cost7
,
cost8
,
cost9
,
cost10
,
cost11
)
print
parse_network
(
inference
,
maxid
)
crf
=
crf
(
input
=
inference
,
label
=
label
)
crf_decoding
=
crf_decoding
(
input
=
inference
,
size
=
3
)
ctc
=
ctc
(
input
=
inference
,
label
=
label
)
warp_ctc
=
warp_ctc
(
input
=
pixel
,
label
=
label
)
nce
=
nce
(
input
=
inference
,
label
=
label
,
num_classes
=
3
)
hsigmoid
=
hsigmoid
(
input
=
inference
,
label
=
label
,
num_classes
=
3
)
print
parse_network
(
crf
,
crf_decoding
,
ctc
,
warp_ctc
,
nce
,
hsigmoid
)
# test check layers
eos
=
eos
(
input
=
maxid
,
eos_id
=
5
)
print
parse_network
(
eos
)
python/paddle/v2/pooling.py
0 → 100644
浏览文件 @
5258bcf3
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from
paddle.trainer_config_helpers.poolings
import
*
__all__
=
[
"Max"
,
"CudnnMax"
,
"Avg"
,
"CudnnAvg"
,
"Sum"
,
"SquareRootN"
]
Max
=
MaxPooling
CudnnMax
=
CudnnMaxPooling
Avg
=
AvgPooling
CudnnAvg
=
CudnnAvgPooling
Sum
=
SumPooling
SquareRootN
=
SquareRootNPooling
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