<gh_stars>0
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.utils.data as Data
import torchvision


EPOCH = 1
BATCH_SIZE = 50
LR = 0.001
DOWNLOAD_MINST = False

train_data = torchvision.datasets.MNIST(
    root = './mnist',
    train = True,
    transform=torchvision.transforms.ToTensor(),
    download=DOWNLOAD_MINST
)



train_loader = Data.DataLoader(
    dataset =  train_data,
    batch_size=BATCH_SIZE,
    shuffle= True,
    num_workers=2,
)

test_data = torchvision.datasets.MNIST(
    root = './mnist',
    train=False
)

test_x = Variable(torch.unsqueeze(test_data.test_data,dim = 1),volatile = True).type(torch.FloatTensor)[:2000]/255.
test_y = test_data.test_labels[:2000]

class CNN(nn.Module):
    def __init__(self):
        super(CNN,self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(             #(1,28,28)
                in_channels=1,
                out_channels=16,
                kernel_size=5,
                stride=1,
                padding=2,   #if stride = 1 padding = (kernel_size -1)/2 = 2
            ),                #(16,28,28)
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2),         #(16,14,14)
        )

        self.conv2 = nn.Sequential(
            nn.Conv2d(16,32,5,1,2),          #(16,14,14)
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2),         #(32,7,7)
        )
        self.out = nn.Linear(32*7*7,10)

    def forward(self,x):
        x = self.conv1(x)
        x = self.conv2(x)        #(batch,32,7,7)
        x = x.view(x.size(0),-1)   #(batch,32*7*7)
        output = self.out(x)
        return output

cnn = CNN()

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

for epoch in range(EPOCH):
    for step,(x,y) in enumerate(train_loader):
        b_x = Variable(x)
        b_y = Variable(y)

        output = cnn(b_x)
        loss = loss_func(output,b_y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if step %50 ==0:
            test_output = cnn(test_x)
            pred_y = torch.max(test_output,1)[1].data.squeeze()
            accuracy = sum(pred_y == test_y)/float(test_y.size(0))
            print ('Epoch: ',epoch,'|train loss:%.4f'%loss.data[0],'|test accuracy:%.2f' %accuracy)

test_output = cnn(test_x[:10])
pred_y = torch.max(test_output,1)[1].data.numpy()
print (pred_y)
print(test_y[:10].numpy())

