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import 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/'):  # 如果已经下载可以不用下载    # 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 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 visualizationcnn = CNN()print(cnn)  # net architectureoptimizer = torch.optim.Adam(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 interested# from matplotlib import cm# try: from sklearn.manifold import TSNE; HAS_SK = True# except: 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)# training and testingif __name__ == '__main__': # win10必加    # plt.ion()    for 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.squeeze()                accuracy = float(sum(pred_y == test_y)) / 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 data    test_output, _ = cnn(test_x[:10])    pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()    print(pred_y, 'prediction number')    print(test_y[:10].numpy(), 'real number')

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