for t, word in enumerate(d):
decoder_targets_one_hot[i, t, word] = 1
# =================================
encoder_inputs = Input(shape=(None, max_input_len))
encoder = GRU(latent_dim, return_state=True)
encoder_outputs, state_h = encoder(encoder_inputs)
decoder_inputs = Input(shape=(None, max_out_len))
decoder_gru = GRU(latent_dim, return_sequences=True)
decoder_outputs = decoder_gru(decoder_inputs, initial_state=state_h)
decoder_dense = Dense(max_out_len, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.Flatten()
# ================================================
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
from keras.utils import plot_model
plot_model(model,
to_file='model_GRU.png',
show_shapes=True,
show_layer_names=True)
history = model.fit([encoder_input_sequences, decoder_input_sequences],
decoder_targets_one_hot,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
validation_split=0.2)
# =============================================
