deeptexthelper.py

X = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))

y = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))
for i in range(0, int(len(data)/SEQ_LENGTH)):
    X_sequence = data[i*SEQ_LENGTH:(i+1)*SEQ_LENGTH]
    X_sequence_ix = [char_to_ix[value] for value in X_sequence]
    input_sequence = np.zeros((SEQ_LENGTH, VOCAB_SIZE))
    for j in range(SEQ_LENGTH):
        input_sequence[j][X_sequence_ix[j]] = 1.
    X[i] = input_sequence

    y_sequence = data[i*SEQ_LENGTH+1:(i+1)*SEQ_LENGTH+1]
    y_sequence_ix = [char_to_ix[value] for value in y_sequence]
    target_sequence = np.zeros((SEQ_LENGTH, VOCAB_SIZE))
    for j in range(SEQ_LENGTH):
        target_sequence[j][y_sequence_ix[j]] = 1.
    y[i] = target_sequence
    
def generate_text(model, length):
    ix = [np.random.randint(VOCAB_SIZE)]
    y_char = [ix_to_char[ix[-1]]]
    X = np.zeros((1, length, VOCAB_SIZE))
    for i in range(length):
        X[0, i, :][ix[-1]] = 1
        print(ix_to_char[ix[-1]], end="")
        ix = np.argmax(model.predict(X[:, :i+1, :])[0], 1)
        y_char.append(ix_to_char[ix[-1]])
    return ('').join(y_char)
  
nb_epoch = 0
while nb_epoch < 10:
    print('\n\n')
    model.fit(X, y, batch_size=BATCH_SIZE, verbose=1, nb_epoch=1)
    nb_epoch += 1
    generate_text(model, GENERATE_LENGTH)
    if nb_epoch % 10 == 0:
        model.save_weights('checkpoint_{}_epoch_{}.hdf5'.format(HIDDEN_DIM, nb_epoch))