# library# standard libraryimport os # third-party libraryimport torchimport torch.nn as nnfrom torch.autograd import Variablefrom torch.utils.data import Dataset, DataLoaderimport torchvisionimport matplotlib.pyplot as pltfrom PIL import Imageimport numpy as np# torch.manual_seed(1) # reproducible # Hyper ParametersEPOCH = 1 # train the training data n times, to save time, we just train 1 epochBATCH_SIZE = 50LR = 0.001 # learning rate root = "./mnist/raw/" def default_loader(path): # return Image.open(path).convert('RGB') return Image.open(path) class MyDataset(Dataset): def __init__(self, txt, transform=None, target_transform=None, loader=default_loader): fh = open(txt, 'r') imgs = [] for line in fh: line = line.strip('/n') line = line.rstrip() words = line.split() imgs.append((words[0], int(words[1]))) self.imgs = imgs self.transform = transform self.target_transform = target_transform self.loader = loader fh.close() def __getitem__(self, index): fn, label = self.imgs[index] img = self.loader(fn) img = Image.fromarray(np.array(img), mode='L') if self.transform is not None: img = self.transform(img) return img,label def __len__(self): return len(self.imgs) train_data = MyDataset(txt= root + 'train.txt', transform = torchvision.transforms.ToTensor())train_loader = DataLoader(dataset = train_data, batch_size=BATCH_SIZE, shuffle=True) test_data = MyDataset(txt= root + 'test.txt', transform = torchvision.transforms.ToTensor())test_loader = DataLoader(dataset = test_data, batch_size=BATCH_SIZE) class CNN(nn.Module): def __init__(self): super(CNN, self).__init__() self.conv1 = nn.Sequential( # input shape (1, 28, 28) nn.Conv2d( in_channels=1, # input height out_channels=16, # n_filters kernel_size=5, # filter size stride=1, # filter movement/step padding=2, # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1 ), # output shape (16, 28, 28) nn.ReLU(), # activation nn.MaxPool2d(kernel_size=2), # choose max value in 2x2 area, output shape (16, 14, 14) ) self.conv2 = nn.Sequential( # input shape (16, 14, 14) nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14) nn.ReLU(), # activation nn.MaxPool2d(2), # output shape (32, 7, 7) ) self.out = nn.Linear(32 * 7 * 7, 10) # fully connected layer, output 10 classes def forward(self, x): x = self.conv1(x) x = self.conv2(x) x = x.view(x.size(0), -1) # flatten the output of conv2 to (batch_size, 32 * 7 * 7) output = self.out(x) return output, x # return x for visualization cnn = CNN()print(cnn) # net architecture optimizer = torch.optim.Adam(cnn.parameters(), lr=LR) # optimize all cnn parametersloss_func = nn.CrossEntropyLoss() # the target label is not one-hotted # training and testingfor epoch in range(EPOCH): for step, (x, y) in enumerate(train_loader): # gives batch data, normalize x when iterate train_loader b_x = Variable(x) # batch x b_y = Variable(y) # batch y output = cnn(b_x)[0] # cnn output loss = loss_func(output, b_y) # cross entropy loss optimizer.zero_grad() # clear gradients for this training step loss.backward() # backpropagation, compute gradients optimizer.step() # apply gradients if step % 50 == 0: cnn.eval() eval_loss = 0. eval_acc = 0. for i, (tx, ty) in enumerate(test_loader): t_x = Variable(tx) t_y = Variable(ty) output = cnn(t_x)[0] loss = loss_func(output, t_y) eval_loss += loss.data[0] pred = torch.max(output, 1)[1] num_correct = (pred == t_y).sum() eval_acc += float(num_correct.data[0]) acc_rate = eval_acc / float(len(test_data)) print('Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len(test_data)), acc_rate)) 
