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Refine test_understand_sentiment_conv.py #6383

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Refine test_understand_sentiment_conv.py #6383

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1d7e1a3
Refine test_understand_sentiment_conv.py
Dec 7, 2017
e855423
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
Dec 22, 2017
642879c
Refine understand_sentiment_dynamic_lstm
Dec 25, 2017
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12 changes: 8 additions & 4 deletions python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,21 +4,25 @@
import paddle.v2.fluid as fluid


def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32,
hid_dim=32):
def convolution_net(data,
label,
input_dim,
class_dim=2,
emb_dim=128,
hid_dim=128):
emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=3,
act="tanh",
pool_type="sqrt")
pool_type="max")
conv_4 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=4,
act="tanh",
pool_type="sqrt")
pool_type="max")
prediction = fluid.layers.fc(input=[conv_3, conv_4],
size=class_dim,
act="softmax")
Expand Down
86 changes: 73 additions & 13 deletions python/paddle/v2/fluid/tests/book/test_understand_sentiment_dynamic_lstm.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,9 @@
import math
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
from paddle.v2.fluid.param_attr import ParamAttr
from paddle.v2.fluid.initializer import NormalInitializer


def stacked_lstm_net(data,
Expand All @@ -9,32 +12,85 @@ def stacked_lstm_net(data,
class_dim=2,
emb_dim=128,
hid_dim=512,
stacked_num=3):
stacked_num=3,
batch_size=100):
assert stacked_num % 2 == 1

emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
# add bias attr

# TODO(qijun) linear act
fc1 = fluid.layers.fc(input=emb, size=hid_dim)
lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)
emb = fluid.layers.embedding(
input=data,
size=[input_dim, emb_dim],
param_attr=ParamAttr(
initializer=NormalInitializer(
loc=0., scale=1.0 / math.sqrt(input_dim)), ))

fc1 = fluid.layers.fc(input=emb,
size=hid_dim,
bias_attr=ParamAttr(initializer=NormalInitializer(
loc=0., scale=0.)),
param_attr=ParamAttr(
name='fc1',
initializer=NormalInitializer(
loc=0., scale=1.0 / math.sqrt(emb_dim)), ))
lstm1, cell1 = fluid.layers.dynamic_lstm(
input=fc1,
size=hid_dim,
candidate_activation='relu',
bias_attr=ParamAttr(initializer=NormalInitializer(
loc=0., scale=0.)),
param_attr=ParamAttr(
initializer=NormalInitializer(
loc=0., scale=1.0 / math.sqrt(emb_dim)), ))

inputs = [fc1, lstm1]

for i in range(2, stacked_num + 1):
fc = fluid.layers.fc(input=inputs, size=hid_dim)
fc = fluid.layers.fc(input=inputs,
size=hid_dim,
bias_attr=ParamAttr(initializer=NormalInitializer(
loc=0., scale=0.)),
param_attr=[
ParamAttr(
learning_rate=1e-3,
initializer=NormalInitializer(
loc=0., scale=1.0 /
math.sqrt(hid_dim))), ParamAttr(
learning_rate=1.,
initializer=NormalInitializer(
loc=0., scale=0.), )
])
lstm, cell = fluid.layers.dynamic_lstm(
input=fc, size=hid_dim, is_reverse=(i % 2) == 0)
input=fc,
size=hid_dim,
is_reverse=(i % 2) == 0,
candidate_activation='relu',
bias_attr=ParamAttr(initializer=NormalInitializer(
loc=0., scale=0.)),
param_attr=ParamAttr(
initializer=NormalInitializer(
loc=0., scale=1.0 / math.sqrt(emb_dim)), ))
inputs = [fc, lstm]

fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max')

prediction = fluid.layers.fc(input=[fc_last, lstm_last],
size=class_dim,
act='softmax')
prediction = fluid.layers.fc(
input=[fc_last, lstm_last],
size=class_dim,
bias_attr=ParamAttr(initializer=NormalInitializer(
loc=0., scale=0.)),
param_attr=[
ParamAttr(
learning_rate=1e-3,
initializer=NormalInitializer(
loc=0., scale=1.0 / math.sqrt(hid_dim)), ), ParamAttr(
learning_rate=1.,
initializer=NormalInitializer(
loc=0., scale=0.), )
],
act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(x=cost)
avg_cost = fluid.layers.scale(x=avg_cost, scale=float(batch_size))
adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
adam_optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
Expand Down Expand Up @@ -69,7 +125,11 @@ def main():
name="words", shape=[1], dtype="int64", lod_level=1)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
cost, accuracy, acc_out = stacked_lstm_net(
data, label, input_dim=dict_dim, class_dim=class_dim)
data,
label,
input_dim=dict_dim,
class_dim=class_dim,
batch_size=BATCH_SIZE)

train_data = paddle.batch(
paddle.reader.shuffle(
Expand Down