lstm.py

# LSTM
D = 768

T = train_X.shape[1]
i = Input(shape=(T,))
x = Embedding(V + 1, D)(i)
x = Dropout(0.1)(x)
x = LSTM(100, return_sequences=True)(x)
x = LSTM(100)(x)
x = Dropout(0.1)(x)
x = Dense(V, activation="softmax")(x)
lstm_model = Model(i, x)
lstm_model.summary()


adam = tf.keras.optimizers.Adam(0.001)

lstm_model.compile(optimizer=adam, metrics=["accuracy"], loss="categorical_crossentropy")

lstm_r = lstm_model.fit(train_X, train_y, epochs=120)