imdb_cnn.py

'''This example demonstrates the use of Convolution1D for text classification.
Gets to 0.89 test accuracy after 2 epochs.
90s/epoch on Intel i5 2.4Ghz CPU.
10s/epoch on Tesla K40 GPU.
'''

from __future__ import print_function

from keras.preprocessing import sequence
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import Conv1D, GlobalMaxPooling1D, Input
from keras.datasets import imdb

# set parameters:
max_features = 5000
maxlen = 400
batch_size = 32
embedding_dims = 50
filters = 250
kernel_size = 3
hidden_dims = 250
epochs = 2

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('y_train shape:', y_train.shape)

print('Build model...')

x = Input(shape=(maxlen, ))
embed = Embedding(max_features, embedding_dims, input_length=maxlen)(x)
print(embed.shape)
dropout = Dropout(0.2)(embed)

conv1 = Conv1D(filters,
                 kernel_size,
                 padding='valid',
                 activation='relu',
                 strides=1)(dropout)
pool = GlobalMaxPooling1D()(conv1)
x_out = Dense(hidden_dims, )(pool)
dropout = Dropout(0.2)(x_out)

out = Activation('relu')(dropout)
x_out = Dense(1, activation='sigmoid')(out)

model = Model(x, x_out)

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

print(model.summary())
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          validation_data=(x_test, y_test))