for epoch in range(epochs):
optimizer = adjust_learning_rate(optimizer, epoch)
batch_num = len(train_data) / batch_size
## training epoch
total_acc = 0.0
total_loss = 0.0
total = 0.0
for i in range(batch_num):
train_inputs = train_data[i * batch_size:(i + 1) * batch_size]
#TODO: itt hagytam abba
#test_labels = [ for l in train_data[i*batch_size: (i+1)*batch_size]]
model.zero_grad()
model.batch_size = len(train_labels)
model.hidden = model.init_hidden()
output = model(train_inputs.t())
sys.exit(0)
loss = loss_function(output, Variable(train_labels))
loss.backward()
optimizer.step()
# calc training acc
_, predicted = torch.max(output.data, 1)
total_acc += (predicted == train_labels).sum()
total += len(train_labels)
total_loss += loss.data[0]
train_loss_.append(total_loss / total)
