Example snippet on how to generate text using TF Keras model.

meditations_generation.py

meditations = "From "  # initialize our meditations text
# convert to indices
input_eval = tf.expand_dims([char2idx[c] for x in meditations], 0)
# initialize states
model.reset_states()
# loop through, generating 100K characters
for i in range(100000):
    y_hat = model(input_eval)  # make a prediction
    y_hat = tf.squeeze(y_hat, 0)  # remove batch dimension
    predicted_idx = tf.random.categorical(y_hat, num_samples=1)[-1,0].numpy()
    # convert predicted index to char and append to text
    text += idx2char[predicted_idx]
    # pass predicted value as next input to model (i + 1)
    input_eval = tf.expand_dims([predicted_idx], 0)
# now we print our generated Meditations
print(meditations)