def synthesize(X_seq):
x0 = X_seq[:, 0:1]
synth = RNN.synthesize(x0, 200)
text = ""
for column in synth.T:
text += ind_to_char[np.argmax(column)]
return text
# np.random.seed(400) # TODO: remove
# compare_gradients()
RNN = RNN(K, m, eta, seq_length, init='xavier')
save = True
smooth_loss = -1
step = -1
last_epoch = 0
if save:
smooth_loss, step, last_epoch = RNN.load()
print('last smooth_loss: %f \t last step: %d \t last epoch: %d' %
(smooth_loss, step, last_epoch))
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():
