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提交 5258bcf3 编写于 作者: L Luo Tao
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implement more layers in v2

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doc/api/trainer_config_helpers/layers.rst
浏览文件 @ 5258bcf3
...@@ -139,24 +139,12 @@ lstmemory ...@@ -139,24 +139,12 @@ lstmemory
:members: lstmemory :members: lstmemory
:noindex: :noindex:
lstm_step_layer
---------------
.. automodule:: paddle.trainer_config_helpers.layers
:members: lstm_step_layer
:noindex:
grumemory grumemory
--------- ---------
.. automodule:: paddle.trainer_config_helpers.layers .. automodule:: paddle.trainer_config_helpers.layers
:members: grumemory :members: grumemory
:noindex: :noindex:
gru_step_layer
---------------
.. automodule:: paddle.trainer_config_helpers.layers
:members: gru_step_layer
:noindex:
Recurrent Layer Group Recurrent Layer Group
===================== =====================
...@@ -172,6 +160,18 @@ recurrent_group ...@@ -172,6 +160,18 @@ recurrent_group
:members: recurrent_group :members: recurrent_group
:noindex: :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 beam_search
------------ ------------
.. automodule:: paddle.trainer_config_helpers.layers .. automodule:: paddle.trainer_config_helpers.layers
...@@ -308,6 +308,12 @@ repeat_layer ...@@ -308,6 +308,12 @@ repeat_layer
:members: repeat_layer :members: repeat_layer
:noindex: :noindex:
rotate_layer
------------
.. automodule:: paddle.trainer_config_helpers.layers
:members: rotate_layer
:noindex:
seq_reshape_layer seq_reshape_layer
----------------- -----------------
.. automodule:: paddle.trainer_config_helpers.layers .. automodule:: paddle.trainer_config_helpers.layers
...@@ -462,6 +468,12 @@ ctc_layer ...@@ -462,6 +468,12 @@ ctc_layer
:members: ctc_layer :members: ctc_layer
:noindex: :noindex:
warp_ctc_layer
--------------
.. automodule:: paddle.trainer_config_helpers.layers
:members: warp_ctc_layer
:noindex:
nce_layer nce_layer
----------- -----------
.. automodule:: paddle.trainer_config_helpers.layers .. automodule:: paddle.trainer_config_helpers.layers
......
python/paddle/trainer_config_helpers/layers.py
浏览文件 @ 5258bcf3
...@@ -30,88 +30,28 @@ except ImportError: ...@@ -30,88 +30,28 @@ except ImportError:
import copy import copy
__all__ = [ __all__ = [
"full_matrix_projection", "full_matrix_projection", "AggregateLevel", "ExpandLevel",
"AggregateLevel", "identity_projection", "dotmul_projection", "dotmul_operator",
"ExpandLevel", "repeat_layer", "seq_reshape_layer", "table_projection", "mixed_layer",
"identity_projection", "data_layer", "embedding_layer", "fc_layer", "grumemory", "pooling_layer",
"dotmul_projection", "lstmemory", "last_seq", "first_seq", "cos_sim", "hsigmoid",
"dotmul_operator", "conv_projection", "regression_cost", 'classification_cost', "LayerOutput",
"repeat_layer", 'img_conv_layer', 'img_pool_layer', 'batch_norm_layer', 'img_cmrnorm_layer',
"seq_reshape_layer", 'addto_layer', 'concat_layer', 'seq_concat_layer', 'lstm_step_layer',
"table_projection", 'recurrent_group', 'memory', 'StaticInput', 'expand_layer', 'scaling_layer',
"mixed_layer", 'scaling_projection', 'power_layer', 'interpolation_layer',
"data_layer", 'bilinear_interp_layer', 'trans_layer', 'rotate_layer',
"embedding_layer", 'sum_to_one_norm_layer', 'get_output_layer', 'LayerType',
"fc_layer", 'context_projection', 'beam_search', 'maxid_layer', 'GeneratedInput',
"grumemory", 'SubsequenceInput', 'gru_step_layer', 'recurrent_layer',
"pooling_layer", 'BaseGeneratedInput', 'conv_operator', 'conv_shift_layer', 'tensor_layer',
"lstmemory", 'selective_fc_layer', 'sampling_id_layer', 'slope_intercept_layer',
"last_seq", 'trans_full_matrix_projection', 'linear_comb_layer', 'convex_comb_layer',
"first_seq", 'ctc_layer', 'warp_ctc_layer', 'crf_layer', 'crf_decoding_layer',
"cos_sim", 'nce_layer', 'cross_entropy_with_selfnorm', 'cross_entropy',
"hsigmoid", 'multi_binary_label_cross_entropy', 'sum_cost', 'rank_cost', 'lambda_cost',
"conv_projection", 'huber_cost', 'block_expand_layer', 'maxout_layer', 'out_prod_layer',
"regression_cost", 'print_layer', 'priorbox_layer', 'spp_layer', 'pad_layer', 'eos_layer'
'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',
] ]
...@@ -1287,6 +1227,12 @@ def last_seq(input, ...@@ -1287,6 +1227,12 @@ def last_seq(input,
""" """
Get Last Timestamp Activation of a sequence. 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 agg_level: Aggregated level
:param name: Layer name. :param name: Layer name.
:type name: basestring :type name: basestring
...@@ -1325,6 +1271,12 @@ def first_seq(input, ...@@ -1325,6 +1271,12 @@ def first_seq(input,
""" """
Get First Timestamp Activation of a sequence. 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 agg_level: aggregation level
:param name: Layer name. :param name: Layer name.
:type name: basestring :type name: basestring
...@@ -1425,7 +1377,7 @@ def repeat_layer(input, num_repeats, name=None, layer_attr=None): ...@@ -1425,7 +1377,7 @@ def repeat_layer(input, num_repeats, name=None, layer_attr=None):
.. code-block:: python .. code-block:: python
expand = repeat_layer(layer, 4) expand = repeat_layer(input=layer, num_repeats=4)
:param input: Input layer :param input: Input layer
:type input: LayerOutput :type input: LayerOutput
...@@ -1797,6 +1749,12 @@ def cos_sim(a, b, scale=1, size=1, name=None, layer_attr=None): ...@@ -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 Note that the above computation is for one sample. Multiple samples are
processed in one batch. processed in one batch.
The example usage is:
.. code-block:: python
cos = cos_sim(a=layer1, b=layer2, size=3)
:param name: layer name :param name: layer name
:type name: basestring :type name: basestring
:param a: input layer a :param a: input layer a
...@@ -1958,6 +1916,16 @@ def img_conv_layer(input, ...@@ -1958,6 +1916,16 @@ def img_conv_layer(input,
pieces. First 256/4 = 64 channels will process by first 32 filters. The pieces. First 256/4 = 64 channels will process by first 32 filters. The
rest channels will be processed by rest group of filters. 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. :param name: Layer name.
:type name: basestring :type name: basestring
:param input: Layer Input. :param input: Layer Input.
...@@ -2097,6 +2065,34 @@ def img_pool_layer(input, ...@@ -2097,6 +2065,34 @@ def img_pool_layer(input,
.. _pooling: http://ufldl.stanford.edu/tutorial/supervised/Pooling/ .. _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. :param padding: pooling padding width.
:type padding: int :type padding: int
:param padding_y: pooling padding height. It's equal to padding by default. :param padding_y: pooling padding height. It's equal to padding by default.
...@@ -2123,19 +2119,6 @@ def img_pool_layer(input, ...@@ -2123,19 +2119,6 @@ def img_pool_layer(input,
:param ceil_mode: Wether to use ceil mode to calculate output height and with. :param ceil_mode: Wether to use ceil mode to calculate output height and with.
Defalut is True. If set false, Otherwise use floor. 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 :type ceil_mode: bool
:return: LayerOutput object. :return: LayerOutput object.
:rtype: LayerOutput :rtype: LayerOutput
...@@ -2197,6 +2180,15 @@ def spp_layer(input, ...@@ -2197,6 +2180,15 @@ def spp_layer(input,
The details please refer to The details please refer to
`Kaiming He's paper <https://arxiv.org/abs/1406.4729>`_. `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. :param name: layer name.
:type name: basestring :type name: basestring
:param input: layer's input. :param input: layer's input.
...@@ -2285,6 +2277,12 @@ def img_cmrnorm_layer(input, ...@@ -2285,6 +2277,12 @@ def img_cmrnorm_layer(input,
The details please refer to The details please refer to
`Alex's paper <http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf>`_. `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. :param name: layer name.
:type name: None|basestring :type name: None|basestring
:param input: layer's input. :param input: layer's input.
...@@ -2340,6 +2338,12 @@ def batch_norm_layer(input, ...@@ -2340,6 +2338,12 @@ def batch_norm_layer(input,
The details of batch normalization please refer to this The details of batch normalization please refer to this
`paper <http://arxiv.org/abs/1502.03167>`_. `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. :param name: layer name.
:type name: basestring :type name: basestring
:param input: batch normalization input. Better be linear activation. :param input: batch normalization input. Better be linear activation.
...@@ -3903,13 +3907,13 @@ def conv_shift_layer(a, b, name=None, layer_attr=None): ...@@ -3903,13 +3907,13 @@ def conv_shift_layer(a, b, name=None, layer_attr=None):
.. code-block:: python .. code-block:: python
conv_shift = conv_shift_layer(input=[layer1, layer2]) conv_shift = conv_shift_layer(a=layer1, b=layer2)
:param name: layer name :param name: layer name
:type name: basestring :type name: basestring
:param a: Input layer a. :param a: Input layer a.
:type a: LayerOutput :type a: LayerOutput
:param b: input layer b :param b: input layer b.
:type b: LayerOutput :type b: LayerOutput
:param layer_attr: layer's extra attribute. :param layer_attr: layer's extra attribute.
:type layer_attr: ExtraLayerAttribute :type layer_attr: ExtraLayerAttribute
...@@ -4001,8 +4005,8 @@ def tensor_layer(a, ...@@ -4001,8 +4005,8 @@ def tensor_layer(a,
@wrap_act_default() @wrap_act_default()
@layer_support() @layer_support()
def selective_fc_layer(input, def selective_fc_layer(input,
select,
size, size,
select=None,
act=None, act=None,
name=None, name=None,
pass_generation=False, pass_generation=False,
...@@ -4029,6 +4033,7 @@ def selective_fc_layer(input, ...@@ -4029,6 +4033,7 @@ def selective_fc_layer(input,
:type input: LayerOutput|list|tuple :type input: LayerOutput|list|tuple
:param select: The select layer. The output of select layer should be a :param select: The select layer. The output of select layer should be a
sparse binary matrix, and treat as the mask of selective fc. sparse binary matrix, and treat as the mask of selective fc.
If is None, acts exactly like fc_layer.
:type select: LayerOutput :type select: LayerOutput
:param size: The layer dimension. :param size: The layer dimension.
:type size: int :type size: int
...@@ -4257,7 +4262,7 @@ def block_expand_layer(input, ...@@ -4257,7 +4262,7 @@ def block_expand_layer(input,
.. code-block:: python .. code-block:: python
block_expand = block_expand_layer(input, block_expand = block_expand_layer(input=layer,
num_channels=128, num_channels=128,
stride_x=1, stride_x=1,
stride_y=1, stride_y=1,
...@@ -4594,6 +4599,13 @@ def crf_decoding_layer(input, ...@@ -4594,6 +4599,13 @@ def crf_decoding_layer(input,
this layer will also calculate error. output.value[i] is 1 for incorrect this layer will also calculate error. output.value[i] is 1 for incorrect
decoding or 0 for correct decoding. 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. :param input: The first input layer.
:type input: LayerOutput :type input: LayerOutput
:param size: size of this layer. :param size: size of this layer.
......
python/paddle/v2/__init__.py
浏览文件 @ 5258bcf3
...@@ -19,11 +19,12 @@ import trainer ...@@ -19,11 +19,12 @@ import trainer
import event import event
import data_type import data_type
import attr import attr
import pooling
import py_paddle.swig_paddle as api import py_paddle.swig_paddle as api
__all__ = [ __all__ = [
'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', '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 ...@@ -76,12 +76,20 @@ from paddle.trainer_config_helpers.default_decorators import wrap_name_default
import data_type import data_type
import activation import activation
import attr import attr
import pooling
__all__ = [ __all__ = [
'parse_network', 'data', 'fc', 'max_id', 'classification_cost', 'parse_network', 'data', 'fc', 'conv_shift', 'img_conv', 'img_pool', 'spp',
'cross_entropy_cost', 'cross_entropy_with_selfnorm_cost', 'regression_cost', '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', '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): ...@@ -130,11 +138,8 @@ class Layer(object):
raise NotImplementedError() raise NotImplementedError()
def __convert_to_v2__(method_name, name_prefix, parent_names): def __convert_to_v2__(method_name, parent_names):
if name_prefix is not None: wrapper = wrap_name_default(name_prefix=method_name)
wrapper = wrap_name_default(name_prefix=name_prefix)
else:
wrapper = None
class V2LayerImpl(Layer): class V2LayerImpl(Layer):
def __init__(self, name=None, **kwargs): def __init__(self, name=None, **kwargs):
...@@ -192,44 +197,92 @@ class DataLayerV2(Layer): ...@@ -192,44 +197,92 @@ class DataLayerV2(Layer):
data = DataLayerV2 data = DataLayerV2
fc = __convert_to_v2__('fc_layer', name_prefix='fc', parent_names=['input']) AggregateLevel = conf_helps.layers.AggregateLevel
max_id = __convert_to_v2__( ExpandLevel = conf_helps.layers.ExpandLevel
'maxid_layer', name_prefix='maxid', parent_names=['input'])
classification_cost = __convert_to_v2__( layer_list = [
'classification_cost', # [V2LayerImpl, V1_method_name, parent_names]
name_prefix='classification_cost', # fully connected layers
parent_names=['input', 'label', 'weight']) ['fc', 'fc_layer', ['input']],
regression_cost = __convert_to_v2__( # conv layers
'regression_cost', ['conv_shift', 'conv_shift_layer', ['a', 'b']],
name_prefix='regression_cost', ['img_conv', 'img_conv_layer', ['input']],
parent_names=['input', 'label', 'weight']) # image pooling layers
cross_entropy_cost = __convert_to_v2__( ['img_pool', 'img_pool_layer', ['input']],
'cross_entropy', ['spp', 'spp_layer', ['input']],
name_prefix='cross_entropy', ['maxout', 'maxout_layer', ['input']],
parent_names=['input', 'label']) # norm layers
cross_entropy_with_selfnorm_cost = __convert_to_v2__( ['img_cmrnorm', 'img_cmrnorm_layer', ['input']],
'cross_entropy_with_selfnorm', ['batch_norm', 'batch_norm_layer', ['input']],
name_prefix='cross_entropy_with_selfnorm', ['sum_to_one_norm', 'sum_to_one_norm_layer', ['input']],
parent_names=['input', 'label']) # recurrent layers
multi_binary_label_cross_entropy_cost = __convert_to_v2__( ['recurrent', 'recurrent_layer', ['input']],
'multi_binary_label_cross_entropy', ['lstmemory', 'lstmemory', ['input']],
name_prefix='multi_binary_label_cross_entropy', ['grumemory', 'grumemory', ['input']],
parent_names=['input', 'label']) # aggregate layers
rank_cost = __convert_to_v2__( ['pool', 'pooling_layer', ['input']],
'rank_cost', ['last_seq', 'last_seq', ['input']],
name_prefix='rank_cost', ['first_seq', 'first_seq', ['input']],
parent_names=['left', 'right', 'label', 'weight']) ['concat', 'concat_layer', ['input']],
lambda_cost = __convert_to_v2__( ['seq_concat', 'seq_concat_layer', ['a', 'b']],
'lambda_cost', name_prefix='lambda_cost', parent_names=['input', 'score']) # reshaping layers
sum_cost = __convert_to_v2__( ['block_expand', 'block_expand_layer', ['input']],
'sum_cost', name_prefix='sum_cost', parent_names=['input']) ['expand', 'expand_layer', ['input', 'expand_as']],
huber_cost = __convert_to_v2__( ['repeat', 'repeat_layer', ['input']],
'huber_cost', name_prefix='huber_cost', parent_names=['input', 'label']) ['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__': 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)) label = data(name='label', type=data_type.integer_value(10))
weight = data(name='weight', type=data_type.dense_vector(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)) score = data(name='score', type=data_type.dense_vector(1))
hidden = fc(input=pixel, hidden = fc(input=pixel,
...@@ -237,7 +290,90 @@ if __name__ == '__main__': ...@@ -237,7 +290,90 @@ if __name__ == '__main__':
act=activation.Sigmoid(), act=activation.Sigmoid(),
param_attr=attr.Param(name='hidden')) param_attr=attr.Param(name='hidden'))
inference = fc(input=hidden, size=10, act=activation.Softmax()) 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) 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) cost1 = classification_cost(input=inference, label=label)
cost2 = classification_cost(input=inference, label=label, weight=weight) cost2 = classification_cost(input=inference, label=label, weight=weight)
cost3 = cross_entropy_cost(input=inference, label=label) cost3 = cross_entropy_cost(input=inference, label=label)
...@@ -249,9 +385,18 @@ if __name__ == '__main__': ...@@ -249,9 +385,18 @@ if __name__ == '__main__':
cost9 = lambda_cost(input=inference, score=score) cost9 = lambda_cost(input=inference, score=score)
cost10 = sum_cost(input=inference) cost10 = sum_cost(input=inference)
cost11 = huber_cost(input=score, label=label) cost11 = huber_cost(input=score, label=label)
print parse_network(cost1, cost2)
print parse_network(cost3, cost4) print parse_network(cost3, cost4)
print parse_network(cost5, cost6) print parse_network(cost5, cost6)
print parse_network(cost7, cost8, cost9, cost10, cost11) 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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