Basic_IMDB_movie_review.py

 
from keras.datasets import imdb
import numpy
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
from keras.layers.convolutional import Convolution1D
from keras.layers.convolutional import MaxPooling1D


(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = numpy.concatenate((X_train, X_test), axis=0)
y = numpy.concatenate((y_train, y_test), axis=0)

seed = 7
numpy.random.seed(seed)

(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=top_words)

max_words = 500
X_train = sequence.pad_sequences(X_train, maxlen=max_words)
X_test = sequence.pad_sequences(X_test, maxlen=max_words)

model = Sequential()
model.add(Embedding(top_words, 32, input_length=max_words))
model.add(Convolution1D(nb_filter=32, filter_length=3, border_mode='same', activation='relu'))
model.add(MaxPooling1D(pool_length=2))
model.add(Flatten())
model.add(Dense(250, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())

model.fit(X_train, y_train, validation_data=(X_test, y_test), nb_epoch=2, batch_size=128, verbose=1)

scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))