程式碼註釋

'''Train a recurrent convolutional network on the IMDB sentiment
classification task.
為IMDB（資料集）情感分類任務訓練迴圈卷積網路
Gets to 0.8498 test accuracy after 2 epochs. 41s/epoch on K520 GPU.
2個週期後0.8498測試準確率。用時41秒/週期（K520 GPU）.
'''
from __future__ import print_function

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 Conv1D, MaxPooling1D
from keras.datasets import imdb

# Embedding
# （詞）嵌入
max_features = 20000
maxlen = 100
embedding_size = 128

# Convolution
# 卷積
kernel_size = 5
filters = 64
pool_size = 4

# LSTM
# 長短期記憶網路
lstm_output_size = 70

# Training
# 訓練
batch_size = 30
epochs = 2

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

print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features) 
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')

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...')

model = Sequential()
model.add(Embedding(max_features, embedding_size, input_length=maxlen))
model.add(Dropout(0.25))
model.add(Conv1D(filters,
                 kernel_size,
                 padding='valid',
                 activation='relu',
                 strides=1))
model.add(MaxPooling1D(pool_size=pool_size))
model.add(LSTM(lstm_output_size))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

print('Train...')
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          validation_data=(x_test, y_test))
score, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
print('Test score:', score)
print('Test accuracy:', acc)
 

程式碼執行

C:\ProgramData\Anaconda3\python.exe E:/keras-master/examples/imdb_cnn_lstm.py
Using TensorFlow backend.
Loading data...
25000 train sequences
25000 test sequences
Pad sequences (samples x time)
x_train shape: (25000, 100)
x_test shape: (25000, 100)
Build model...
Train...
Train on 25000 samples, validate on 25000 samples
Epoch 1/2

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21180/25000 [========================>.....] - ETA: 11s - loss: 0.3944 - acc: 0.8145
21210/25000 [========================>.....] - ETA: 11s - loss: 0.3944 - acc: 0.8146
21240/25000 [========================>.....] - ETA: 11s - loss: 0.3942 - acc: 0.8148
21270/25000 [========================>.....] - ETA: 11s - loss: 0.3941 - a