main.py

# Text classification on IMDB dataset using Keras and bi-LSTM network

import numpy as np
from keras.datasets import imdb
from keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional
from keras.models import Sequential
from keras.preprocessing import sequence
from matplotlib import pyplot

n_unique_words = 10000  # cut texts after this number of words
maxlen = 200
batch_size = 128

(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=n_unique_words)
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
y_train = np.array(y_train)
y_test = np.array(y_test)

model = Sequential()
model.add(Embedding(n_unique_words, 128, input_length=maxlen))
model.add(Bidirectional(LSTM(64)))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

history = model.fit(x_train, y_train, batch_size=batch_size, epochs=12, validation_data=[x_test, y_test])
print(history.history['loss'])
print(history.history['accuracy'])

pyplot.plot(history.history['loss'])
pyplot.plot(history.history['accuracy'])
pyplot.title('Model Loss vs Accuracy')
pyplot.xlabel('Epoch')
pyplot.legend(['Loss', 'Accuracy'], loc='upper right')
pyplot.show()