def feed_token(model: keras.Sequential, token: int):
# Format inputs.
inputs = np.zeros((config.find('batch_size'), 1))
inputs[0, 0] = token
# Forward pass.
probs = model.predict_on_batch(inputs)[0]
probs = probs.squeeze()
return probs
def ramble(model: keras.Sequential, vectorizer: Vectorizer, seed='she'):
current_token = vectorizer.word_to_index[seed]
current_seq = [current_token]
model.reset_states()
model.summary()
probs_arr = []
# for t in range(config.max_seq_len):
for t in range(5):
# Format inputs.
inputs = np.zeros((config.find('batch_size'), 1))
assert inputs.shape[0] == 32, inputs.shape
inputs[0, 0] = current_token
# Forward pass.
probs = model.predict_on_batch(inputs)[0]
probs = probs.squeeze()
probs_arr.append(probs)
assert probs.shape == (vectorizer.vocab_size,
), f'{probs.shape} != {vectorizer.vocab_size}'
# Sample prediction.
current_token = probs.argmax()
current_seq.append(current_token)
text_rant = vectorizer.sequences_to_docs([[current_seq]])
assert len(text_rant) == 1, len(text_rant)
# Some post-processing prettification.
text_rant = util.lmap(str.strip, text_rant[0].split('.'))
util.print_box('Rant', list(text_rant))
sequences = beam_search_decoder(probs_arr, k=3)
sequences = [thing[0] for thing in sequences]
beam_rant = vectorizer.sequences_to_docs([sequences])
print('beam rant:', beam_rant)
