EPOCHS,
SAVE_AT,
LEARNING_RATE,
BATCH_SIZE,
VERBOSE,
LOSS)
tokenizer = Tokenizer()
encoder_input_data, decoder_input_data, decoder_output_data = create_training_data() # parsing the dataset and creating conversation pairs
encoder_input_data, decoder_input_data, decoder_output_data = tokenizer.tokenize_and_pad_training_data(encoder_input_data,
decoder_input_data,
decoder_output_data) # tokenizing and padding those pairs
tokenizer.save_tokenizer(f'tokenizer-vocab_size-{VOCAB_SIZE}') # saving tokenizer for layer use
Seq2SeqModel = seq2seq() # creating the seq2seq model
optimizer = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE, clipnorm=1.0, clipvalue=0.5)
Seq2SeqModel.compile(optimizer=optimizer, loss=LOSS, metrics=['accuracy'])
Seq2SeqModel.summary()
def train(model, encoder_input_data, decoder_input_data, decoder_output_data, epochs, batch_size, verbose, save_at):
with tf.device('/device:GPU:0' if utils.check_cuda else '/cpu:0'):
for epoch in range(1, epochs+1):
print(f'Epochs {epoch}/{epochs}')
model.fit([encoder_input_data, decoder_input_data], decoder_output_data, batch_size=batch_size, epochs=1, verbose=verbose)
if epoch % SAVE_AT == 0:
model.save_weights(f'seq2seq-weights-{epochs}-epochs-{LEARNING_RATE}-learning_rate.h5')
