SimpleRNN.py

D = 512

#Simple RNN
T = train_X.shape[1]
i = Input(shape=(T,))
x = Embedding(V, D)(i)
x = Dropout(0.2)(x)
x = SimpleRNN(150)(x)
x = Dense(V, activation="softmax")(x)
rnn_model = Model(i, x)
rnn_model.summary()

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

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

rnn_r = rnn_model.fit(train_X, train_y, epochs=50)