inputs_v = seq_out[step_id:(step_id+bptt_steps)]
elif len(seq_out)>0:
for b_id in xrange(batch_size):
if p_sub > np.random.random_sample():
for seq_id in xrange(bptt_steps):
inputs_v[seq_id][b_id, :] = seq_out[step_id + seq_id][b_id, :]
feed_dict = {k: v for k, v in zip(inputs, inputs_v) }
feed_dict[state] = state_v
feed_dict.update({k: v for k, v in zip(targets, targets_v)})
outputs_v, state_v, loss_v, _ = sess.run([outputs, finstate, loss, train_step], feed_dict)
new_seq_out += outputs_v
losses.append(loss_v)
seq_out = new_seq_out
batch_ids += forecast_step
gc.collect()
print "Epoch {}, learning rate {}".format(e, ep_lrate), "train loss {}".format(sum(losses)/float(len(losses)))
if e % 100 == 0 and e > 0:
print "Generating sample"
generated = generate()
generated = np.asarray(generated).reshape(len(generated), input_size)
cm = ConvModel.deserialize()
waveform = cm.restore_hidden(generated, sess = sess)
cm.save_waveform_as(waveform, 0, env.run("{}_generated.wav".format(e)))
print "Saving model {}".format(saver.save(sess, model_fname))
