pytorch 8 CNN 卷积神经网络
2019-02-26 21:47
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版权声明:本文为博主原创文章,未经博主允许不得转载。 https://blog.csdn.net/weixin_42419002/article/details/88859729
pytorch 8 CNN 卷积神经网络
[code]# library # standard library import os # third-party library import torch import torch.nn as nn 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 = False # Mnist digits dataset if not(os.path.exists('./mnist/')) or not os.listdir('./mnist/'): # not mnist dir or mnist is empyt dir DOWNLOAD_MNIST = True 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, ) # plot one example 每个图片都是灰度图片,高度为1,长宽为28;RGB图片的高度为3。 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) # pick 2000 samples to speed up testing test_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, # 灰度图片的高度为1,input height out_channels=16, # 16个卷积,之后高度为从1变成6,长宽不变,n_filters kernel_size=5, # 5*5宽度的卷积,filter size stride=1, # 步幅为1,filter movement/step padding=2, # 周围填充2圈0,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(), # 激活时,图片长宽高不变,activation nn.MaxPool2d(kernel_size=2), # 4合1的池化,之后图片的高度不变,长宽减半,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) # 考虑bach之后的数据输出是(batch, 32, 7, 7) x = x.view(x.size(0), -1) # 保持bach不变,将数据展开成一行的,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, (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 data test_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')
打印网络结构
[code]print(cnn) # net architecture > CNN( > (conv1): Sequential( > (0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)) > (1): ReLU() > (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) > ) > (conv2): Sequential( > (0): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)) > (1): ReLU() > (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) > ) > (out): Linear(in_features=1568, out_features=10, bias=True) > )
打印训练过程
[code]Epoch: 0 | train loss: 2.3170 | test accuracy: 0.11 Epoch: 0 | train loss: 0.2893 | test accuracy: 0.83 Epoch: 0 | train loss: 0.4333 | test accuracy: 0.90 ....... Epoch: 0 | train loss: 0.1268 | test accuracy: 0.98
从测试数据中打印10个预测
[code]# print 10 predictions from test data print(pred_y, 'prediction number') print(test_y[:10].numpy(), 'real number') > [7 2 1 0 4 1 4 9 5 9] prediction number > [7 2 1 0 4 1 4 9 5 9] real number
END
posted @ 2019-02-26 21:47 YangZhaonan 阅读(...) 评论(...) 编辑 收藏
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