threshold = nn.Threshold(0., 0.0)
params = list(encoder.parameters()) + list(decoder.parameters())
optimizer = optim.Adam(params, lr=0.001)
s = 1
for e in range(100):
for i, v in enumerate(dloader):
optimizer.zero_grad()
images = Variable(v[0].cuda()).view(4, 32, 1, 28, 28)
########
#Encoder
########
hidden = encoder.get_init_states(32)
_, encoder_state = encoder(images.clone(), hidden)
########
#Decoder
########
#prepare inputs for decoder
rev_images = flip_var(images, 0)
dec_input = torch.cat((Variable(torch.zeros(
1, 32, 1, 28, 28).cuda()), rev_images[:-1, :, :, :, :]), 0)
decoder_out, _ = decoder(dec_input, encoder_state)
#######
#loss##
#######
seqs = batch
nextf_raw = seqs[:,10:,:,:,:].cuda()
#----cnn encoder----
prevf_raw = seqs[:,:10,:,:,:].contiguous().view(-1,1,64,64).cuda()
prevf_enc = cnn_encoder(prevf_raw).view(b_size,10,hidden_dim,hidden_spt,hidden_spt)
if teacher_forcing:
cnn_encoder_out = cnn_encoder(seqs[:,10:,:,:,:].contiguous().view(-1,1,64,64).cuda())
nextf_enc = cnn_encoder_out.view(b_size,10,hidden_dim,hidden_spt,hidden_spt)
#----lstm encoder---
hidden = lstm_encoder.get_init_states(b_size)
_, encoder_state = lstm_encoder(prevf_enc, hidden)
#----lstm decoder---
sample_prob = get_sample_prob(i) if teacher_forcing else 0
decoder_output_list = []
r_hist = []
for s in range(10):
if s == 0:
decoder_out, decoder_state = lstm_decoder(prevf_enc[:,-1:,:,:,:], encoder_state)
else:
r = np.random.rand()
r_hist.append(int(r > sample_prob)) #debug
