lstm_imdb_dropout_adam.py

from keras.preprocessing import sequence
from keras.datasets import imdb
from keras import layers, models


class Data:
    def __init__(self, max_features=20000, maxlen=80):
        (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
        x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
        x_test = sequence.pad_sequences(x_test, maxlen=maxlen)

        self.x_train, self.y_train = x_train, y_train
        self.x_test, self.y_test = x_test, y_test


class RNN_LSTM(models.Model):
    def __init__(self, max_features, maxlen):
        x = layers.Input((maxlen,))
        h = layers.Embedding(max_features, 128)(x)
        h = layers.LSTM(128, dropout=0.2, recurrent_dropout=0.2)(h)
        y = layers.Dense(1, activation="sigmoid")(h)
        super().__init__(x, y)
        self.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])


class Machine:
    def __init__(self, max_features=20000, maxlen=80):
        self.data = Data(max_features, maxlen)
        self.model = RNN_LSTM(max_features, maxlen)

    def run(self, epochs=3, batch_size=32):
        data = self.data
        model = self.model
        print('Training stage')
        print('==============')
        model.fit(data.x_train, data.y_train, 
                batch_size=batch_size, epochs=epochs,
                validation_data=(data.x_test, data.y_test))
        score, acc = model.evaluate(data.x_test, data.y_test,
                batch_size=batch_size)
        print('Test performance: accuracy={0}, loss={1}'.format(acc, score))


if __name__ == '__main__':
    m = Machine()
    m.run()