（1）简介
使用Mnist数据集，使用CNN网络实现分类任务，其中网络架构为（输入层-两个隐藏层-全连接层）。
（2）代码
# library# standard libraryimport os# third-party libraryimport torchimport torch.nn as nnimport torch.utils.data as Dataimport torchvisionimport matplotlib.pyplot as plt# 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 rateDOWNLOAD_MNIST = False# Mnist digits datasetif not(os.path.exists(‘./mnist/’)) or not os.listdir(‘./mnist/’): # not mnist dir or mnist is empyt dir DOWNLOAD_MNIST = Truetrain_data = torchvision.datasets.MNIST( root=’./mnist/’, train=True, # this is training data transform=torchvision.transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0] download=DOWNLOAD_MNIST,)# plot one exampleprint(train_data.train_data.size()) # (60000, 28, 28)print(train_data.train_labels.size()) # (60000)plt.imshow(train_data.train_data[0].numpy(), cmap=’gray’)plt.title(‘%i’ % train_data.train_labels[0])plt.show()# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)# pick 2000 samples to speed up testingtest_data = torchvision.datasets.MNIST(root=’./mnist/’, train=False)test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[:2000]/255. # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)test_y = test_data.test_labels[:2000]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 Conv2d, 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 2×2 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 visualizationcnn = CNN()print(cnn) # net architectureoptimizer = torch.optim.RMSprop(cnn.parameters(), lr=LR) # optimize all cnn parametersloss_func = nn.CrossEntropyLoss() # the target label is not one-hotted# following function (plot_with_labels) is for visualization, can be ignored if not interestedfrom matplotlib import cmtry: from sklearn.manifold import TSNE; HAS_SK = Trueexcept: HAS_SK = False; print(‘Please install sklearn for layer visualization’)def plot_with_labels(lowDWeights, labels): plt.cla() X, Y = lowDWeights[:, 0], lowDWeights[:, 1] for x, y, s in zip(X, Y, labels): c = cm.rainbow(int(255 * s / 9)); plt.text(x, y, s, backgroundcolor=c, fontsize=9) plt.xlim(X.min(), X.max()); plt.ylim(Y.min(), Y.max()); plt.title(‘Visualize last layer’); plt.show(); plt.pause(0.01)plt.ion()# training and testingfor epoch in range(EPOCH): for step, (b_x, b_y) in enumerate(train_loader): # gives batch data, normalize x when iterate train_loader 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: test_output, last_layer = cnn(test_x) pred_y = torch.max(test_output, 1)[1].data.numpy() accuracy = float((pred_y == test_y.data.numpy()).astype(int).sum()) / float(test_y.size(0)) print(‘Epoch: ‘, epoch, ‘| train loss: %.4f’ % loss.data.numpy(), ‘| test accuracy: %.2f’ % accuracy) if HAS_SK: # Visualization of trained flatten layer (T-SNE) tsne = TSNE(perplexity=30, n_components=2, init=’pca’, n_iter=5000) plot_only = 500 low_dim_embs = tsne.fit_transform(last_layer.data.numpy()[:plot_only, :]) labels = test_y.numpy()[:plot_only] plot_with_labels(low_dim_embs, labels)plt.ioff()# print 10 predictions from test datatest_output, _ = cnn(test_x[:10])pred_y = torch.max(test_output, 1)[1].data.numpy()print(pred_y, ‘prediction number’)print(test_y[:10].numpy(), ‘real number’)
（3）结果
注：代码主要参考：??https://github.com/MorvanZhou??
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