Pytorch实现CNN卷积神经网络

柔情只为你懂 2022-06-11 07:39 255阅读 0赞

## 前言 ##

Pytorch实现CNN卷积神将网络，以MNIST数据集为例

## 环境 ##

Mac OS

Python 3

## 代码 ##

import torch
    import torch.nn as nn
    from torch.autograd import Variable
    import torch.utils.data as Data
    import torchvision
    import matplotlib.pyplot as plt
    
    torch.manual_seed(1)    # reproducible
    
    # Hyper Parameters
    EPOCH = 1               # train the training data n times, to save time, we just train 1 epoch
    BATCH_SIZE = 50
    LR = 0.001              # learning rate
    DOWNLOAD_MNIST = True   # set to False if you have downloaded
    
    
    # Mnist digits dataset
    train_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,                        # download it if you don't have it
    )
    
    # plot one example
    print(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)
    
    # convert test data into Variable, pick 2000 samples to speed up testing
    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.   # 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 (1, 28, 28)
                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 parameters
    loss_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)
    
    plt.ion()
    # training and testing
    for 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:
                test_output, last_layer = 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)
                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')