def create_data_generator(word_dict, batch_size, num_epochs=1, is_train=False):
"""样本生成器创建函数，用于创建样本生成器。
Args:
- batch_size: 训练和推理时传入网络的batch的大小
- num_epochs: 对数据集的遍历次数
- is_train: 训练/推理标志位
Return:
- data_generator_fn: 样本生成器函数"""
if is_train:
examples = [i for i in imdb.train(word_dict)()]
np.random.shuffle(examples)
else:
examples = [i for i in imdb.test(word_dict)()]
def data_generator_fn():
batch_x = []
batch_y = []
for i in range(num_epochs):
print('Training epoch {}:'.format(i))
for _x, _y in examples:
# 为了避免遭遇过长样本导致显存溢出，我们将句子长度截断到800
batch_x.append(_x[:800])
batch_y.append(_y)
if len(batch_x) == batch_size:
batch_x = array_normalize(batch_x, return_lod_tensor=False)
batch_y = array_normalize(batch_y)
yield [batch_x, batch_y]
batch_x = []
batch_y = []
return data_generator_fn
def get_model(args, is_train, main_prog, startup_prog):
if args.use_reader_op:
raise Exception(
"stacked_dynamic_lstm do not support reader op for now.")
lstm_size = 512
emb_dim = 512
crop_size = 1500
with fluid.program_guard(main_prog, startup_prog):
with fluid.unique_name.guard():
data = fluid.layers.data(name="words",
shape=[1],
lod_level=1,
dtype='int64')
sentence = fluid.layers.embedding(input=data,
size=[len(word_dict), emb_dim])
logit = lstm_net(sentence, lstm_size)
loss = fluid.layers.cross_entropy(input=logit,
label=fluid.layers.data(
name='label',
shape=[1],
dtype='int64'))
loss = fluid.layers.mean(x=loss)
# add acc
batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
batch_acc = fluid.layers.accuracy(input=logit, label=fluid.layers.data(name='label', \
shape=[1], dtype='int64'), total=batch_size_tensor)
if is_train:
adam = fluid.optimizer.Adam()
adam.minimize(loss)
if is_train:
reader = crop_sentence(imdb.train(word_dict), crop_size)
else:
reader = crop_sentence(imdb.test(word_dict), crop_size)
batched_reader = paddle.batch(paddle.reader.shuffle(reader,
buf_size=25000),
batch_size=args.batch_size * args.gpus)
return loss, adam, [batch_acc], batched_reader, None
# https://aistudio.baidu.com/aistudio/projectdetail/96357
# 导入必要的包
import paddle
import paddle.dataset.imdb as imdb
import paddle.fluid as fluid
import numpy as np
import os
word_dict = imdb.word_dict()
dict_dim = len(word_dict)
train_reader = paddle.batch(paddle.reader.shuffle(imdb.train(word_dict), 512), batch_size=128)
test_reader = paddle.batch(imdb.test(word_dict), batch_size=128)
# 定义长短期记忆网络
def lstm_net(ipt, input_dim):
# 以数据的IDs作为输入
emb = fluid.layers.embedding(input=ipt, size=[input_dim, 128], is_sparse=True)
# 第一个全连接层
fc1 = fluid.layers.fc(input=emb, size=128)
# 进行一个长短期记忆操作
lstm1, _ = fluid.layers.dynamic_lstm(input=fc1, # 返回：隐藏状态（hidden state），LSTM的神经元状态
size=128) # size=4*hidden_size
# 第一个最大序列池操作
fc2 = fluid.layers.sequence_pool(input=fc1, pool_type='max')
# 第二个最大序列池操作
lstm2 = fluid.layers.sequence_pool(input=lstm1, pool_type='max')
# 以softmax作为全连接的输出层，大小为2,也就是正负面
out = fluid.layers.fc(input=[fc2, lstm2], size=2, act='softmax')
return out
def get_model(args):
if args.use_reader_op:
raise Exception(
"stacked_dynamic_lstm do not support reader op for now.")
lstm_size = 512
emb_dim = 512
crop_size = 1500
data = fluid.layers.data(
name="words", shape=[1], lod_level=1, dtype='int64')
sentence = fluid.layers.embedding(
input=data, size=[len(word_dict), emb_dim])
sentence = fluid.layers.fc(input=sentence, size=lstm_size, act='tanh')
rnn = fluid.layers.DynamicRNN()
with rnn.block():
word = rnn.step_input(sentence)
prev_hidden = rnn.memory(value=0.0, shape=[lstm_size])
prev_cell = rnn.memory(value=0.0, shape=[lstm_size])
def gate_common(
ipt,
hidden,
size, ):
gate0 = fluid.layers.fc(input=ipt, size=size, bias_attr=True)
gate1 = fluid.layers.fc(input=hidden, size=size, bias_attr=False)
gate = fluid.layers.sums(input=[gate0, gate1])
return gate
forget_gate = fluid.layers.sigmoid(
x=gate_common(word, prev_hidden, lstm_size))
input_gate = fluid.layers.sigmoid(
x=gate_common(word, prev_hidden, lstm_size))
output_gate = fluid.layers.sigmoid(
x=gate_common(word, prev_hidden, lstm_size))
cell_gate = fluid.layers.tanh(
x=gate_common(word, prev_hidden, lstm_size))
cell = fluid.layers.sums(input=[
fluid.layers.elementwise_mul(
x=forget_gate, y=prev_cell), fluid.layers.elementwise_mul(
x=input_gate, y=cell_gate)
])
hidden = fluid.layers.elementwise_mul(
x=output_gate, y=fluid.layers.tanh(x=cell))
rnn.update_memory(prev_cell, cell)
rnn.update_memory(prev_hidden, hidden)
rnn.output(hidden)
last = fluid.layers.sequence_pool(rnn(), 'last')
logit = fluid.layers.fc(input=last, size=2, act='softmax')
loss = fluid.layers.cross_entropy(
input=logit,
label=fluid.layers.data(
name='label', shape=[1], dtype='int64'))
loss = fluid.layers.mean(x=loss)
# add acc
batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
batch_acc = fluid.layers.accuracy(input=logit, label=fluid.layers.data(name='label', \
shape=[1], dtype='int64'), total=batch_size_tensor)
inference_program = fluid.default_main_program().clone()
with fluid.program_guard(inference_program):
inference_program = fluid.io.get_inference_program(
target_vars=[batch_acc, batch_size_tensor])
adam = fluid.optimizer.Adam()
train_reader = batch(
paddle.reader.shuffle(
crop_sentence(imdb.train(word_dict), crop_size), buf_size=25000),
batch_size=args.batch_size * args.gpus)
test_reader = batch(
paddle.reader.shuffle(
crop_sentence(imdb.test(word_dict), crop_size), buf_size=25000),
batch_size=args.batch_size)
return loss, inference_program, adam, train_reader, test_reader, batch_acc
def get_model(args):
lstm_size = 512
emb_dim = 512
crop_size = 1500
data = fluid.layers.data(
name="words", shape=[1], lod_level=1, dtype='int64')
sentence = fluid.layers.embedding(
input=data, size=[len(word_dict), emb_dim])
sentence = fluid.layers.fc(input=sentence, size=lstm_size, act='tanh')
rnn = fluid.layers.DynamicRNN()
with rnn.block():
word = rnn.step_input(sentence)
prev_hidden = rnn.memory(value=0.0, shape=[lstm_size])
prev_cell = rnn.memory(value=0.0, shape=[lstm_size])
def gate_common(
ipt,
hidden,
size, ):
gate0 = fluid.layers.fc(input=ipt, size=size, bias_attr=True)
gate1 = fluid.layers.fc(input=hidden, size=size, bias_attr=False)
gate = fluid.layers.sums(input=[gate0, gate1])
return gate
forget_gate = fluid.layers.sigmoid(
x=gate_common(word, prev_hidden, lstm_size))
input_gate = fluid.layers.sigmoid(
x=gate_common(word, prev_hidden, lstm_size))
output_gate = fluid.layers.sigmoid(
x=gate_common(word, prev_hidden, lstm_size))
cell_gate = fluid.layers.tanh(
x=gate_common(word, prev_hidden, lstm_size))
cell = fluid.layers.sums(input=[
fluid.layers.elementwise_mul(
x=forget_gate, y=prev_cell), fluid.layers.elementwise_mul(
x=input_gate, y=cell_gate)
])
hidden = fluid.layers.elementwise_mul(
x=output_gate, y=fluid.layers.tanh(x=cell))
rnn.update_memory(prev_cell, cell)
rnn.update_memory(prev_hidden, hidden)
rnn.output(hidden)
last = fluid.layers.sequence_pool(rnn(), 'last')
logit = fluid.layers.fc(input=last, size=2, act='softmax')
loss = fluid.layers.cross_entropy(
input=logit,
label=fluid.layers.data(
name='label', shape=[1], dtype='int64'))
loss = fluid.layers.mean(x=loss)
# add acc
batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
batch_acc = fluid.layers.accuracy(input=logit, label=fluid.layers.data(name='label', \
shape=[1], dtype='int64'), total=batch_size_tensor)
inference_program = fluid.default_main_program().clone()
with fluid.program_guard(inference_program):
inference_program = fluid.io.get_inference_program(
target_vars=[batch_acc, batch_size_tensor])
adam = fluid.optimizer.Adam()
train_reader = batch(
paddle.reader.shuffle(
crop_sentence(imdb.train(word_dict), crop_size), buf_size=25000),
batch_size=args.batch_size)
test_reader = batch(
paddle.reader.shuffle(
crop_sentence(imdb.test(word_dict), crop_size), buf_size=25000),
batch_size=args.batch_size)
return loss, inference_program, adam, train_reader, test_reader, batch_acc
