cnn_classification.py

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


# set parameters:
batch_size = 32
embedding_dims = 200
filters = 250
kernel_size = 3
epochs = 2

# load the dataset but only keep the top n words, zero the rest
top_words = 5000
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=top_words)
# pad dataset to a maximum review length in words
max_words = 500
x_train = sequence.pad_sequences(x_train, maxlen=max_words)
x_test = sequence.pad_sequences(x_test, maxlen=max_words)

# Build model
sentence = Input(batch_shape=(None, max_words), dtype='int32', name='sentence')
embedding_layer = Embedding(top_words, embedding_dims, input_length=max_words)
sent_embed = embedding_layer(sentence)
conv_layer = Conv1D(filters, kernel_size, padding='valid', activation='relu')
sent_conv = conv_layer(sent_embed)
sent_pooling = GlobalMaxPooling1D()(sent_conv)
sent_repre = Dense(250)(sent_pooling)
sent_repre = Activation('relu')(sent_repre)
sent_repre = Dense(1)(sent_repre)
pred = Activation('sigmoid')(sent_repre)
model = Model(inputs=sentence, outputs=pred)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# fit the model
model.fit(x_train, y_train, batch_size=batch_size,
          epochs=epochs, verbose=1,validation_data=(x_test, y_test))
scores = model.evaluate(x_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))