import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from torchvision import transforms

class Rnn(nn.Module):
  def __init__(self, in_dim, hidden_dim, n_layer, n_classes):
    super(Rnn, self).__init__()
    self.n_layer = n_layer
    self.hidden_dim = hidden_dim
    self.lstm = nn.LSTM(in_dim, hidden_dim, n_layer, batch_first=True)
    self.classifier = nn.Linear(hidden_dim, n_classes)

  def forward(self, x):
    out, (h_n, c_n) = self.lstm(x)
    # 此時(shí)可以從out中獲得最終輸出的狀態(tài)h
    # x = out[:, -1, :]
    x = h_n[-1, :, :]
    x = self.classifier(x)
    return x

transform = transforms.Compose([
  transforms.ToTensor(),
  transforms.Normalize([0.5], [0.5]),
])

trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True)

testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False)

net = Rnn(28, 10, 2, 10)

net = net.to('cpu')
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9)

# Training
def train(epoch):
  print('\nEpoch: %d' % epoch)
  net.train()
  train_loss = 0
  correct = 0
  total = 0
  for batch_idx, (inputs, targets) in enumerate(trainloader):
    inputs, targets = inputs.to('cpu'), targets.to('cpu')
    optimizer.zero_grad()
    outputs = net(torch.squeeze(inputs, 1))
    loss = criterion(outputs, targets)
    loss.backward()
    optimizer.step()

    train_loss += loss.item()
    _, predicted = outputs.max(1)
    total += targets.size(0)
    correct += predicted.eq(targets).sum().item()

    print(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
      % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))

def test(epoch):
  global best_acc
  net.eval()
  test_loss = 0
  correct = 0
  total = 0
  with torch.no_grad():
    for batch_idx, (inputs, targets) in enumerate(testloader):
      inputs, targets = inputs.to('cpu'), targets.to('cpu')
      outputs = net(torch.squeeze(inputs, 1))
      loss = criterion(outputs, targets)

      test_loss += loss.item()
      _, predicted = outputs.max(1)
      total += targets.size(0)
      correct += predicted.eq(targets).sum().item()

      print(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
        % (test_loss/(batch_idx+1), 100.*correct/total, correct, total))




for epoch in range(200):
  train(epoch)
  test(epoch)