提交 1d2025c9 编写于 作者: H hedaoyuan

Use the sequence_conv_pool define inside the networks.py

上级 d194ce73
# 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.
import sys
from os.path import join as join_path
import paddle.trainer_config_helpers.attrs as attrs
from paddle.trainer_config_helpers.poolings import MaxPooling
import paddle.v2.layer as layer
import paddle.v2.activation as activation
import paddle.v2.data_type as data_type
import paddle.v2.dataset.imdb as imdb
import paddle.v2 as paddle
def sequence_conv_pool(input,
input_size,
context_len,
hidden_size,
name=None,
context_start=None,
pool_type=None,
context_proj_layer_name=None,
context_proj_param_attr=False,
fc_layer_name=None,
fc_param_attr=None,
fc_bias_attr=None,
fc_act=None,
pool_bias_attr=None,
fc_attr=None,
context_attr=None,
pool_attr=None):
"""
Text convolution pooling layers helper.
Text input => Context Projection => FC Layer => Pooling => Output.
:param name: name of output layer(pooling layer name)
:type name: basestring
:param input: name of input layer
:type input: LayerOutput
:param context_len: context projection length. See
context_projection's document.
:type context_len: int
:param hidden_size: FC Layer size.
:type hidden_size: int
:param context_start: context projection length. See
context_projection's context_start.
:type context_start: int or None
:param pool_type: pooling layer type. See pooling_layer's document.
:type pool_type: BasePoolingType.
:param context_proj_layer_name: context projection layer name.
None if user don't care.
:type context_proj_layer_name: basestring
:param context_proj_param_attr: context projection parameter attribute.
None if user don't care.
:type context_proj_param_attr: ParameterAttribute or None.
:param fc_layer_name: fc layer name. None if user don't care.
:type fc_layer_name: basestring
:param fc_param_attr: fc layer parameter attribute. None if user don't care.
:type fc_param_attr: ParameterAttribute or None
:param fc_bias_attr: fc bias parameter attribute. False if no bias,
None if user don't care.
:type fc_bias_attr: ParameterAttribute or None
:param fc_act: fc layer activation type. None means tanh
:type fc_act: BaseActivation
:param pool_bias_attr: pooling layer bias attr. None if don't care.
False if no bias.
:type pool_bias_attr: ParameterAttribute or None.
:param fc_attr: fc layer extra attribute.
:type fc_attr: ExtraLayerAttribute
:param context_attr: context projection layer extra attribute.
:type context_attr: ExtraLayerAttribute
:param pool_attr: pooling layer extra attribute.
:type pool_attr: ExtraLayerAttribute
:return: output layer name.
:rtype: LayerOutput
"""
# Set Default Value to param
context_proj_layer_name = "%s_conv_proj" % name \
if context_proj_layer_name is None else context_proj_layer_name
with layer.mixed(
name=context_proj_layer_name,
size=input_size * context_len,
act=activation.Linear(),
layer_attr=context_attr) as m:
m += layer.context_projection(
input=input,
context_len=context_len,
context_start=context_start,
padding_attr=context_proj_param_attr)
fc_layer_name = "%s_conv_fc" % name \
if fc_layer_name is None else fc_layer_name
fl = layer.fc(name=fc_layer_name,
input=m,
size=hidden_size,
act=fc_act,
layer_attr=fc_attr,
param_attr=fc_param_attr,
bias_attr=fc_bias_attr)
return layer.pooling(
name=name,
input=fl,
pooling_type=pool_type,
bias_attr=pool_bias_attr,
layer_attr=pool_attr)
def convolution_net(input_dim,
class_dim=2,
emb_dim=128,
hid_dim=128,
is_predict=False):
data = layer.data("word", data_type.integer_value_sequence(input_dim))
emb = layer.embedding(input=data, size=emb_dim)
conv_3 = sequence_conv_pool(
input=emb, input_size=emb_dim, context_len=3, hidden_size=hid_dim)
conv_4 = sequence_conv_pool(
input=emb, input_size=emb_dim, context_len=4, hidden_size=hid_dim)
output = layer.fc(input=[conv_3, conv_4],
size=class_dim,
act=activation.Softmax())
lbl = layer.data("label", data_type.integer_value(2))
cost = layer.classification_cost(input=output, label=lbl)
data = paddle.layer.data("word",
paddle.data_type.integer_value_sequence(input_dim))
emb = paddle.layer.embedding(input=data, size=emb_dim)
conv_3 = paddle.networks.sequence_conv_pool(
input=emb, context_len=3, hidden_size=hid_dim)
conv_4 = paddle.networks.sequence_conv_pool(
input=emb, context_len=4, hidden_size=hid_dim)
output = paddle.layer.fc(input=[conv_3, conv_4],
size=class_dim,
act=paddle.activation.Softmax())
lbl = paddle.layer.data("label", paddle.data_type.integer_value(2))
cost = paddle.layer.classification_cost(input=output, label=lbl)
return cost
......@@ -152,24 +66,28 @@ def stacked_lstm_net(input_dim,
lstm_para_attr = attrs.ParameterAttribute(initial_std=0., learning_rate=1.)
para_attr = [fc_para_attr, lstm_para_attr]
bias_attr = attrs.ParameterAttribute(initial_std=0., l2_rate=0.)
relu = activation.Relu()
linear = activation.Linear()
data = layer.data("word", data_type.integer_value_sequence(input_dim))
emb = layer.embedding(input=data, size=emb_dim)
fc1 = layer.fc(input=emb, size=hid_dim, act=linear, bias_attr=bias_attr)
lstm1 = layer.lstmemory(
relu = paddle.activation.Relu()
linear = paddle.activation.Linear()
data = paddle.layer.data("word",
paddle.data_type.integer_value_sequence(input_dim))
emb = paddle.layer.embedding(input=data, size=emb_dim)
fc1 = paddle.layer.fc(input=emb,
size=hid_dim,
act=linear,
bias_attr=bias_attr)
lstm1 = paddle.layer.lstmemory(
input=fc1, act=relu, bias_attr=bias_attr, layer_attr=layer_attr)
inputs = [fc1, lstm1]
for i in range(2, stacked_num + 1):
fc = layer.fc(input=inputs,
size=hid_dim,
act=linear,
param_attr=para_attr,
bias_attr=bias_attr)
lstm = layer.lstmemory(
fc = paddle.layer.fc(input=inputs,
size=hid_dim,
act=linear,
param_attr=para_attr,
bias_attr=bias_attr)
lstm = paddle.layer.lstmemory(
input=fc,
reverse=(i % 2) == 0,
act=relu,
......@@ -177,16 +95,16 @@ def stacked_lstm_net(input_dim,
layer_attr=layer_attr)
inputs = [fc, lstm]
fc_last = layer.pooling(input=inputs[0], pooling_type=MaxPooling())
lstm_last = layer.pooling(input=inputs[1], pooling_type=MaxPooling())
output = layer.fc(input=[fc_last, lstm_last],
size=class_dim,
act=activation.Softmax(),
bias_attr=bias_attr,
param_attr=para_attr)
fc_last = paddle.layer.pooling(input=inputs[0], pooling_type=MaxPooling())
lstm_last = paddle.layer.pooling(input=inputs[1], pooling_type=MaxPooling())
output = paddle.layer.fc(input=[fc_last, lstm_last],
size=class_dim,
act=paddle.activation.Softmax(),
bias_attr=bias_attr,
param_attr=para_attr)
lbl = layer.data("label", data_type.integer_value(2))
cost = layer.classification_cost(input=output, label=lbl)
lbl = paddle.layer.data("label", paddle.data_type.integer_value(2))
cost = paddle.layer.classification_cost(input=output, label=lbl)
return cost
......@@ -196,7 +114,7 @@ if __name__ == '__main__':
# network config
print 'load dictionary...'
word_dict = imdb.word_dict()
word_dict = paddle.dataset.imdb.word_dict()
dict_dim = len(word_dict)
class_dim = 2
......@@ -226,7 +144,8 @@ if __name__ == '__main__':
if isinstance(event, paddle.event.EndPass):
result = trainer.test(
reader=paddle.reader.batched(
lambda: imdb.test(word_dict), batch_size=128),
lambda: paddle.dataset.imdb.test(word_dict),
batch_size=128),
reader_dict={'word': 0,
'label': 1})
print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
......@@ -239,7 +158,7 @@ if __name__ == '__main__':
trainer.train(
reader=paddle.reader.batched(
paddle.reader.shuffle(
lambda: imdb.train(word_dict), buf_size=1000),
lambda: paddle.dataset.imdb.train(word_dict), buf_size=1000),
batch_size=100),
event_handler=event_handler,
reader_dict={'word': 0,
......
......@@ -93,6 +93,8 @@ def __convert_to_v2__(method_name, parent_names, is_default_name=True):
name = kwargs.get('name', None)
super(V2LayerImpl, self).__init__(name, parent_layers)
if kwargs.has_key('size'):
self.size = kwargs['size']
self.__other_kwargs__ = other_kwargs
if wrapper is not None:
......
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