imdb_cnn_lstm.py

'''Train a recurrent convolutional network on the IMDB sentiment
classification task.
Gets to 0.8498 test accuracy after 2 epochs. 41s/epoch on K520 GPU.

在 IMDB 情感分类任务上训练一个循环卷积神经网络。
在两轮迭代后，的到了 0.8498 的测试准确率。 在 K520 GPU 上 41s/epoch。
'''
from __future__ import print_function
import numpy as np
np.random.seed(1337)  # for reproducibility

from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import LSTM
from keras.layers import Convolution1D, MaxPooling1D
from keras.datasets import imdb


# Embedding  词嵌入
max_features = 20000   # 词汇表大小
maxlen = 100           # 序列最大长度
embedding_size = 128   # 词向量维度
  
# Convolution  卷积
filter_length = 5    # 滤波器长度
nb_filter = 64       # 滤波器个数
pool_length = 4      # 池化长度

# LSTM
lstm_output_size = 70   # LSTM 层输出尺寸

# Training   训练参数
batch_size = 30   # 批数据量大小
nb_epoch = 2      # 迭代次数

'''
Note:
batch_size is highly sensitive.
Only 2 epochs are needed as the dataset is very small.

注意：
batch_size 这个值非常敏感。
由于数据集很小，所有2轮迭代足够了。
'''

# 载入模型
print('Loading data...')
(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features)
print(len(X_train), 'train sequences')
print(len(X_test), 'test sequences')

# 填充到固定长度 maxlen
print('Pad sequences (samples x time)')
X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
X_test = sequence.pad_sequences(X_test, maxlen=maxlen)
print('X_train shape:', X_train.shape)
print('X_test shape:', X_test.shape)

print('Build model...')
# 构建模型
model = Sequential()
# 词嵌入层
model.add(Embedding(max_features, embedding_size, input_length=maxlen))
# Dropout层
model.add(Dropout(0.25))

# 1D 卷积层，对词嵌入层输出做卷积操作
model.add(Convolution1D(nb_filter=nb_filter,
                        filter_length=filter_length,
                        border_mode='valid',
                        activation='relu',
                        kernel_initializer='glorot_uniform',
                        subsample_length=1))
# 池化层
model.add(MaxPooling1D(pool_length=pool_length))
# LSTM 循环层
model.add(LSTM(lstm_output_size))
# 全连接层，只有一个神经元，输入是否为正面情感值
model.add(Dense(1))
model.add(Activation('sigmoid'))  # sigmoid判断情感

model.summary()   # 模型概述

model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
# 训练
print('Train...')
model.fit(X_train, y_train, batch_size=batch_size, epochs=nb_epoch,
          validation_data=(X_test, y_test))

# 测试
score, acc = model.evaluate(X_test, y_test, batch_size=batch_size)
print('Test score:', score)
print('Test accuracy:', acc)