# In[28]: history = model.fit(dataset.repeat(), epochs=EPOCHS, steps_per_epoch=steps_per_epoch, callbacks=[checkpoint_callback]) # In[29]: tf.train.latest_checkpoint(checkpoint_dir) model = build_model(vocab_size, embedding_dim, rnn_units, batch_size=1) model.load_weights(tf.train.latest_checkpoint(checkpoint_dir)) model.build(tf.TensorShape([1, None])) model.summary() def generate_text(model, start_string): # Evaluation step (generating text using the learned model) # Number of characters to generate num_generate = 1000 # Converting our start string to numbers (vectorizing) input_eval = [char2idx[s] for s in start_string] input_eval = tf.expand_dims(input_eval, 0) # Empty string to store our results text_generated = [] # Low temperatures results in more predictable text.
