[텐서플로] RNN LSTM 샘플 rnn_lstm.py

from keras import layers, models, datasets
from keras.preprocessing import sequence
 
class Data:
    def __init__(self, max_features=20000, maxlen=80):
        (x_train, y_train), (x_test, y_test) = datasets.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('Traing state')
        model.fit(
            data.x_train,
            data.y_train,
            batch_size=batch_size,
            epochs=epochs,
            validation_data=(data.x_test,
                             data.y_test),
            verbose=2)
        loss, acc = model.evaluate(
            data.x_test,
            data.y_test,
            batch_size=batch_size,
            verbose=2)
        print('Test performance: accuracy={0}, loss={1}'.format(acc, loss))
 
if __name__ == '__main__':
    m = Machine()
    m.run()