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Merge pull request #1502 from hedaoyuan/sentiment_new_api

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Training the understand sentiment model with the new API.
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hedaoyuan 9 years ago committed by GitHub
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247
demo/sentiment/train_v2.py
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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)
return cost
def stacked_lstm_net(input_dim,
class_dim=2,
emb_dim=128,
hid_dim=512,
stacked_num=3,
is_predict=False):
"""
A Wrapper for sentiment classification task.
This network uses bi-directional recurrent network,
consisting three LSTM layers. This configure is referred to
the paper as following url, but use fewer layrs.
http://www.aclweb.org/anthology/P15-1109
input_dim: here is word dictionary dimension.
class_dim: number of categories.
emb_dim: dimension of word embedding.
hid_dim: dimension of hidden layer.
stacked_num: number of stacked lstm-hidden layer.
is_predict: is predicting or not.
Some layers is not needed in network when predicting.
"""
assert stacked_num % 2 == 1
layer_attr = attrs.ExtraLayerAttribute(drop_rate=0.5)
fc_para_attr = attrs.ParameterAttribute(learning_rate=1e-3)
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(
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(
input=fc,
reverse=(i % 2) == 0,
act=relu,
bias_attr=bias_attr,
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)
lbl = layer.data("label", data_type.integer_value(2))
cost = layer.classification_cost(input=output, label=lbl)
return cost
if __name__ == '__main__':
# init
paddle.init(use_gpu=True, trainer_count=4)
# network config
print 'load dictionary...'
word_dict = imdb.word_dict()
dict_dim = len(word_dict)
class_dim = 2
# Please choose the way to build the network
# by uncommenting the corresponding line.
cost = convolution_net(dict_dim, class_dim=class_dim)
# cost = stacked_lstm_net(dict_dim, class_dim=class_dim, stacked_num=3)
# create parameters
parameters = paddle.parameters.create(cost)
# create optimizer
adam_optimizer = paddle.optimizer.Adam(
learning_rate=2e-3,
regularization=paddle.optimizer.L2Regularization(rate=8e-4),
model_average=paddle.optimizer.ModelAverage(average_window=0.5))
# End batch and end pass event handler
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "\nPass %d, Batch %d, Cost %f, %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics)
else:
sys.stdout.write('.')
sys.stdout.flush()
if isinstance(event, paddle.event.EndPass):
result = trainer.test(
reader=paddle.reader.batched(
lambda: imdb.test(word_dict), batch_size=128),
reader_dict={'word': 0,
'label': 1})
print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
# create trainer
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=adam_optimizer)
trainer.train(
reader=paddle.reader.batched(
paddle.reader.shuffle(
lambda: imdb.train(word_dict), buf_size=1000),
batch_size=100),
event_handler=event_handler,
reader_dict={'word': 0,
'label': 1},
num_passes=10)

5
python/paddle/v2/dataset/imdb.py
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@ -116,3 +116,8 @@ def test(word_idx):
return reader_creator(
re.compile("aclImdb/test/pos/.*\.txt$"),
re.compile("aclImdb/test/neg/.*\.txt$"), word_idx, 1000)
def word_dict():
return build_dict(
re.compile("aclImdb/((train)|(test))/((pos)|(neg))/.*\.txt$"), 150)

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