print('[train]')
print('going to train {} iterations'.format(jump * n_epoch))
# loop starts
while epoch <= n_epoch:
# initialize hidden state to 0
state = make_initial_state()
# train dataset
train_data = dg.generate_seq(whole_len)
for i in six.moves.range(jump):
# forward propagation
h_batch = sae.encode(chainer.Variable(mod.asarray([train_data['image'][i]], dtype='float32'))).data[0]
tmp = train_data['direction'][i] + h_batch.tolist()
x_batch = mod.asarray([tmp], dtype='float32')
t_batch = sae.encode(chainer.Variable(mod.asarray([train_data['image'][i + 1]], dtype = 'float32'))).data
state, loss_i, acc_i = forward_one_step(x_batch, t_batch, state)
accum_loss += loss_i
cur_log_perp += loss_i.data.reshape(())
# truncated BPTT
if (i + 1) % bprop_len == 0:
optimizer.zero_grads()
accum_loss.backward()
accum_loss.unchain_backward() # truncate
accum_loss = chainer.Variable(mod.zeros((), dtype=np.float32))
optimizer.clip_grads(grad_clip) # gradient clip
