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未验证 提交 c091dbdf 编写于 作者: Y Yu Yang 提交者： GitHub
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Merge pull request #7974 from reyoung/feature/unify_understand_sentiment 

Merge test_understand_sentiment together
上级 94965806 4fee15e8
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Showing with 78 addition and 298 deletion
python/paddle/v2/fluid/tests/book/test_understand_sentiment_dynamic_lstm.py → python/paddle/v2/fluid/tests/book/test_understand_sentiment.py
浏览文件 @ c091dbdf
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
# Copyright (c) 2018 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.
......@@ -12,9 +12,36 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import paddle.v2 as paddle
import unittest
import paddle.v2.fluid as fluid
import paddle.v2 as paddle
import contextlib
def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32,
hid_dim=32):
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")
conv_4 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=4,
act="tanh",
pool_type="sqrt")
prediction = fluid.layers.fc(input=[conv_3, conv_4],
size=class_dim,
act="softmax")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(x=cost)
adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
adam_optimizer.minimize(avg_cost)
accuracy = fluid.layers.accuracy(input=prediction, label=label)
return avg_cost, accuracy
def stacked_lstm_net(data,
......@@ -51,63 +78,77 @@ def stacked_lstm_net(data,
avg_cost = fluid.layers.mean(x=cost)
adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
adam_optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
return avg_cost, accuracy, accuracy.metrics[0]
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
def main():
BATCH_SIZE = 100
PASS_NUM = 5
accuracy = fluid.layers.accuracy(input=prediction, label=label)
return avg_cost, accuracy
word_dict = paddle.dataset.imdb.word_dict()
print "load word dict successfully"
def main(word_dict, net_method, use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
BATCH_SIZE = 128
PASS_NUM = 5
dict_dim = len(word_dict)
class_dim = 2
data = fluid.layers.data(
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(
cost, acc_out = net_method(
data, label, input_dim=dict_dim, class_dim=class_dim)
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=1000),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
exe.run(fluid.default_startup_program())
for pass_id in xrange(PASS_NUM):
accuracy.reset(exe)
for data in train_data():
cost_val, acc_val = exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[cost, acc_out])
pass_acc = accuracy.eval(exe)
print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
" pass_acc=" + str(pass_acc))
if cost_val < 1.0 and acc_val > 0.8:
exit(0)
exit(1)
print("cost=" + str(cost_val) + " acc=" + str(acc_val))
if cost_val < 0.4 and acc_val > 0.8:
return
raise AssertionError("Cost is too large for {0}".format(
net_method.__name__))
class TestUnderstandSentiment(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.word_dict = paddle.dataset.imdb.word_dict()
@contextlib.contextmanager
def new_program_scope(self):
prog = fluid.Program()
startup_prog = fluid.Program()
scope = fluid.core.Scope()
with fluid.scope_guard(scope):
with fluid.program_guard(prog, startup_prog):
yield
def test_conv_cpu(self):
with self.new_program_scope():
main(self.word_dict, net_method=convolution_net, use_cuda=False)
def test_stacked_lstm_cpu(self):
with self.new_program_scope():
main(self.word_dict, net_method=stacked_lstm_net, use_cuda=False)
def test_conv_gpu(self):
with self.new_program_scope():
main(self.word_dict, net_method=convolution_net, use_cuda=True)
def test_stacked_lstm_gpu(self):
with self.new_program_scope():
main(self.word_dict, net_method=stacked_lstm_net, use_cuda=True)
if __name__ == '__main__':
main()
unittest.main()
python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py 已删除 100644 → 0
浏览文件 @ 94965806
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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 __future__ import print_function
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32,
hid_dim=32):
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")
conv_4 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=4,
act="tanh",
pool_type="sqrt")
prediction = fluid.layers.fc(input=[conv_3, conv_4],
size=class_dim,
act="softmax")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(x=cost)
adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
adam_optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
return avg_cost, accuracy, accuracy.metrics[0]
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
def main():
BATCH_SIZE = 100
PASS_NUM = 5
word_dict = paddle.dataset.imdb.word_dict()
dict_dim = len(word_dict)
class_dim = 2
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
cost, accuracy, acc_out = convolution_net(
data, label, input_dim=dict_dim, class_dim=class_dim)
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=1000),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
exe.run(fluid.default_startup_program())
for pass_id in xrange(PASS_NUM):
accuracy.reset(exe)
for data in train_data():
cost_val, acc_val = exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[cost, acc_out])
pass_acc = accuracy.eval(exe)
print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
" pass_acc=" + str(pass_acc))
if cost_val < 1.0 and pass_acc > 0.8:
exit(0)
exit(1)
if __name__ == '__main__':
main()
python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py 已删除 100644 → 0
浏览文件 @ 94965806
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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 numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
from paddle.v2.fluid.layer_helper import LayerHelper
def lstm(x, c_pre_init, hidden_dim, forget_bias=None):
"""
This function helps create an operator for the LSTM (Long Short Term
Memory) cell that can be used inside an RNN.
"""
helper = LayerHelper('lstm_unit', **locals())
rnn = fluid.layers.StaticRNN()
with rnn.step():
c_pre = rnn.memory(init=c_pre_init)
x_t = rnn.step_input(x)
before_fc = fluid.layers.concat(input=[x_t, c_pre], axis=1)
after_fc = fluid.layers.fc(input=before_fc, size=hidden_dim * 4)
dtype = x.dtype
c = helper.create_tmp_variable(dtype)
h = helper.create_tmp_variable(dtype)
helper.append_op(
type='lstm_unit',
inputs={"X": after_fc,
"C_prev": c_pre},
outputs={"C": c,
"H": h},
attrs={"forget_bias": forget_bias})
rnn.update_memory(c_pre, c)
rnn.output(h)
return rnn()
def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50):
data = fluid.layers.data(
name="words",
shape=[seq_len * batch_size, 1],
append_batch_size=False,
dtype="int64",
lod_level=1)
label = fluid.layers.data(
name="label",
shape=[batch_size, 1],
append_batch_size=False,
dtype="int64")
emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
emb = fluid.layers.reshape(x=emb, shape=[batch_size, seq_len, emb_dim])
emb = fluid.layers.transpose(x=emb, perm=[1, 0, 2])
c_pre_init = fluid.layers.fill_constant(
dtype=emb.dtype, shape=[batch_size, emb_dim], value=0.0)
c_pre_init.stop_gradient = False
layer_1_out = lstm(emb, c_pre_init=c_pre_init, hidden_dim=emb_dim)
layer_1_out = fluid.layers.transpose(x=layer_1_out, perm=[1, 0, 2])
prediction = fluid.layers.fc(input=layer_1_out,
size=class_dim,
act="softmax")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(x=cost)
adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
adam_optimizer.minimize(avg_cost)
acc = fluid.layers.accuracy(input=prediction, label=label)
return avg_cost, acc
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
def chop_data(data, chop_len=80, batch_size=50):
data = [(x[0][:chop_len], x[1]) for x in data if len(x[0]) >= chop_len]
return data[:batch_size]
def prepare_feed_data(data, place):
tensor_words = to_lodtensor(map(lambda x: x[0], data), place)
label = np.array(map(lambda x: x[1], data)).astype("int64")
label = label.reshape([len(label), 1])
tensor_label = fluid.LoDTensor()
tensor_label.set(label, place)
return tensor_words, tensor_label
def main():
BATCH_SIZE = 100
PASS_NUM = 5
word_dict = paddle.dataset.imdb.word_dict()
print "load word dict successfully"
dict_dim = len(word_dict)
class_dim = 2
cost, acc = lstm_net(dict_dim=dict_dim, class_dim=class_dim)
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=BATCH_SIZE * 10),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
for pass_id in xrange(PASS_NUM):
for data in train_data():
chopped_data = chop_data(data)
tensor_words, tensor_label = prepare_feed_data(chopped_data, place)
outs = exe.run(fluid.default_main_program(),
feed={"words": tensor_words,
"label": tensor_label},
fetch_list=[cost, acc])
cost_val = np.array(outs[0])
acc_val = np.array(outs[1])
print("cost=" + str(cost_val) + " acc=" + str(acc_val))
if acc_val > 0.7:
exit(0)
exit(1)
if __name__ == '__main__':
main()
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