import torch 
import torch.nn as nn 
from torch.autograd import Variable 
import torch.utils.data as Data 
import torchvision 
import time
#import matplotlib.pyplot as plt 
 
torch.manual_seed(1) 
 
EPOCH = 1 
BATCH_SIZE = 50 
LR = 0.001 
DOWNLOAD_MNIST = False 
if_use_gpu = 1
 
# 獲取訓(xùn)練集dataset 
training_data = torchvision.datasets.MNIST( 
       root='./mnist/', # dataset存儲(chǔ)路徑 
       train=True, # True表示是train訓(xùn)練集，F(xiàn)alse表示test測(cè)試集 
       transform=torchvision.transforms.ToTensor(), # 將原數(shù)據(jù)規(guī)范化到（0,1）區(qū)間 
       download=DOWNLOAD_MNIST, 
       ) 
 
# 打印MNIST數(shù)據(jù)集的訓(xùn)練集及測(cè)試集的尺寸 
print(training_data.train_data.size()) 
print(training_data.train_labels.size()) 
# torch.Size([60000, 28, 28]) 
# torch.Size([60000]) 
 
#plt.imshow(training_data.train_data[0].numpy(), cmap='gray') 
#plt.title('%i' % training_data.train_labels[0]) 
#plt.show() 
 
# 通過(guò)torchvision.datasets獲取的dataset格式可直接可置于DataLoader 
train_loader = Data.DataLoader(dataset=training_data, batch_size=BATCH_SIZE, 
                shuffle=True) 
 
# 獲取測(cè)試集dataset 

test_data = torchvision.datasets.MNIST( 
       root='./mnist/', # dataset存儲(chǔ)路徑 
       train=False, # True表示是train訓(xùn)練集，F(xiàn)alse表示test測(cè)試集 
       transform=torchvision.transforms.ToTensor(), # 將原數(shù)據(jù)規(guī)范化到（0,1）區(qū)間 
       download=DOWNLOAD_MNIST, 
       ) 
# 取前全部10000個(gè)測(cè)試集樣本 
test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1).float(), requires_grad=False)
#test_x = test_x.cuda()
## (~, 28, 28) to (~, 1, 28, 28), in range(0,1) 
test_y = test_data.test_labels
#test_y = test_y.cuda() 
class CNN(nn.Module): 
  def __init__(self): 
    super(CNN, self).__init__() 
    self.conv1 = nn.Sequential( # (1,28,28) 
           nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, 
                stride=1, padding=2), # (16,28,28) 
    # 想要con2d卷積出來(lái)的圖片尺寸沒(méi)有變化, padding=(kernel_size-1)/2 
           nn.ReLU(), 
           nn.MaxPool2d(kernel_size=2) # (16,14,14) 
           ) 
    self.conv2 = nn.Sequential( # (16,14,14) 
           nn.Conv2d(16, 32, 5, 1, 2), # (32,14,14) 
           nn.ReLU(), 
           nn.MaxPool2d(2) # (32,7,7) 
           ) 
    self.out = nn.Linear(32*7*7, 10) 
 
  def forward(self, x): 
    x = self.conv1(x) 
    x = self.conv2(x) 
    x = x.view(x.size(0), -1) # 將（batch，32,7,7）展平為（batch，32*7*7） 
    output = self.out(x) 
    return output 
 
cnn = CNN() 
if if_use_gpu:
  cnn = cnn.cuda()

optimizer = torch.optim.Adam(cnn.parameters(), lr=LR) 
loss_function = nn.CrossEntropyLoss() 
 


for epoch in range(EPOCH): 
  start = time.time() 
  for step, (x, y) in enumerate(train_loader): 
    b_x = Variable(x, requires_grad=False) 
    b_y = Variable(y, requires_grad=False) 
    if if_use_gpu:
      b_x = b_x.cuda()
      b_y = b_y.cuda()
 
    output = cnn(b_x) 
    loss = loss_function(output, b_y) 
    optimizer.zero_grad() 
    loss.backward() 
    optimizer.step() 
 
    if step % 100 == 0: 
      print('Epoch:', epoch, '|Step:', step, 
         '|train loss:%.4f'%loss.data[0]) 
  duration = time.time() - start 
  print('Training duation: %.4f'%duration)
  
cnn = cnn.cpu()
test_output = cnn(test_x) 
pred_y = torch.max(test_output, 1)[1].data.squeeze()
accuracy = sum(pred_y == test_y) / test_y.size(0) 
print('Test Acc: %.4f'%accuracy)