synth = RNN.synthesize(make_one_hot([char_to_ind['.']], K), 1000)
text = ""
for column in synth.T:
text += ind_to_char[np.argmax(column)]
print(text.encode('ascii', 'ignore').decode('ascii'))
exit()
losses = []
f = open(
'synthesized-' + str(
datetime.datetime.fromtimestamp(
time.time()).strftime('%Y-%m-%d %H:%M:%S')), 'w+')
for epoch in range(n_epoch):
print("\t\t---NEW EPOCH--- number: %d" % (epoch + last_epoch))
RNN.h0 = np.zeros((m, 1))
for X_seq, Y_seq in get_batch():
step += 1
loss = RNN.train(X_seq, Y_seq)
smooth_loss = 0.999 * smooth_loss + 0.001 * loss if smooth_loss != -1 else loss
losses.append(smooth_loss)
if step % 500 == 0:
f.write(
'\n\tSynthesized text at iteration: %d with smooth loss: %f\n'
% (step, smooth_loss))
text = synthesize(X_seq)
f.write(text.encode('ascii', 'ignore').decode('ascii'))
f.write('\n')
f.flush()
elif step % 100 == 0:
