def generate(seed: str = START, max_len: int = 50) -> str:
rnn1.reset_hidden_state() # Zresetuj obydwa ukryte stany
rnn2.reset_hidden_state()
output = [seed] # rozpocznij od podstawienia pod output określonej wartości seed
# Kontynuuj, aż trafisz na znak STOP lub do osiągnięcia maksymalnej długości
while output[-1] != STOP and len(output) < max_len:
# Użyj ostatniego znaku na wejściu
input = vocab.one_hot_encode(output[-1])
# Wygeneruj wyniki, używając modelu
predicted = model.forward(input)
# Przekonwertuj je na prawdopodobieństwa i pobierz losowy char_id
probabilities = softmax(predicted)
next_char_id = sample_from(probabilities)
# Dodaj odpowiedni znak do wartości wyjściowej
output.append(vocab.get_word(next_char_id))
# usuń znaki START i END i zwróć wygenerowane słowo
return ''.join(output[1:-1])
def generate(seed: str = START, max_len: int = 50) -> str:
rnn1.reset_hidden_state() # Reset both hidden states.
rnn2.reset_hidden_state()
output = [seed] # Start the output with the specified seed.
# Keep going until we produce the STOP character or reach the max length
while output[-1] != STOP and len(output) < max_len:
# Use the last character as the input
input = vocab.one_hot_encode(output[-1])
# Generate scores using the model
predicted = model.forward(input)
# Convert them to probabilities and draw a random char_id
probabilities = softmax(predicted)
next_char_id = sample_from(probabilities)
# Add the corresponding char to our output
output.append(vocab.get_word(next_char_id))
# Get rid of START and END characters and return the word.
return ''.join(output[1:-1])
def generate(seed: str = START, max_len: int = 50) -> str:
rnn1.reset_hidden_state() # Reset both hidden states.
rnn2.reset_hidden_state()
output = [seed] # Start the output with the specified seed.
# Keep going until we produce the STOP character or reach the max length
while output[-1] != STOP and len(output) < max_len:
# Use the last character as the input
input = vocab.one_hot_encode(output[-1])
# Generate scores using the model
predicted = model.forward(input)
# Convert them to probabilities and draw a random char_id
probabilities = softmax(predicted)
next_char_id = sample_from(probabilities)
# Add the corresponding char to our output
output.append(vocab.get_word(next_char_id))
# Get rid of START and END characters and return the word.
return ''.join(output[1:-1])