###仅为自己练习，没有其他用途
1 # library 2 # standard library 3 import os 4 5 # third-party library 6 import torch 7 import torch.nn as nn 8 import torch.utils.data as Data 9 import torchvision 10 import matplotlib.pyplot as plt 11 12 # torch.manual_seed(1) # reproducible 13 14 # Hyper Parameters 15 EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch 16 BATCH_SIZE = 50 17 LR = 0.001 # learning rate 18 DOWNLOAD_MNIST = False 19 20 21 # Mnist digits dataset 22 if not(os.path.exists('./mnist/')) or not os.listdir('./mnist/'): 23 # not mnist dir or mnist is empyt dir 24 DOWNLOAD_MNIST = True 25 26 train_data = torchvision.datasets.MNIST( 27 root='./mnist/', 28 train=True, # this is training data 29 transform=torchvision.transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to 30 # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0] 31 download=DOWNLOAD_MNIST, 32 ) 33 34 # # plot one example 35 # print(train_data.train_data.size()) # (60000, 28, 28) 36 # print(train_data.train_labels.size()) # (60000) 37 # plt.imshow(train_data.train_data[0].numpy(), cmap='gray') 38 # plt.title('%i' % train_data.train_labels[0]) 39 # plt.show() 40 41 # Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28) 42 train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True) 43 # pick 2000 samples to speed up testing 44 test_data = torchvision.datasets.MNIST(root='./mnist/', train=False) 45 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) 46 test_y = test_data.test_labels[:2000] 47 48 49 class CNN(nn.Module): 50 def __init__(self): 51 super(CNN, self).__init__() 52 self.conv1 = nn.Sequential( # input shape (1, 28, 28) 53 nn.Conv2d( 54 in_channels=1, # input height 55 out_channels=16, # n_filters 56 kernel_size=5, # filter size 57 stride=1, # filter movement/step 58 padding=2, # if want same width and length of this image after Conv2d, padding=(kernel_size-1)/2 if stride=1 59 ), # output shape (16, 28, 28) 60 nn.ReLU(), # activation 61 nn.MaxPool2d(kernel_size=2), # choose max value in 2x2 area, output shape (16, 14, 14) 62 ) 63 self.conv2 = nn.Sequential( # input shape (16, 14, 14) 64 nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14) 65 nn.ReLU(), # activation 66 nn.MaxPool2d(2), # output shape (32, 7, 7) 67 ) 68 self.out = nn.Linear(32 * 7 * 7, 10) # fully connected layer, output 10 classes 69 70 def forward(self, x): 71 x = self.conv1(x) 72 x = self.conv2(x) 73 x = x.view(x.size(0), -1) # flatten the output of conv2 to (batch_size, 32 * 7 * 7) 74 output = self.out(x) 75 return output, x # return x for visualization 76 77 78 cnn = CNN() 79 print(cnn) # net architecture 80 81 optimizer = torch.optim.Adam(cnn.parameters(), lr=LR) # optimize all cnn parameters 82 loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted 83 84 # following function (plot_with_labels) is for visualization, can be ignored if not interested 85 from matplotlib import cm 86 try: from sklearn.manifold import TSNE; HAS_SK = True 87 except: HAS_SK = False; print('Please install sklearn for layer visualization') 88 def plot_with_labels(lowDWeights, labels): 89 plt.cla() 90 X, Y = lowDWeights[:, 0], lowDWeights[:, 1] 91 for x, y, s in zip(X, Y, labels): 92 c = cm.rainbow(int(255 * s / 9)); plt.text(x, y, s, backgroundcolor=c, fontsize=9) 93 plt.xlim(X.min(), X.max()); plt.ylim(Y.min(), Y.max()); plt.title('Visualize last layer'); plt.show(); plt.pause(0.01) 94 95 plt.ion() 96 # training and testing 97 for epoch in range(EPOCH): 98 for step, (b_x, b_y) in enumerate(train_loader): # gives batch data, normalize x when iterate train_loader 99 100 output = cnn(b_x)[0] # cnn output 101 loss = loss_func(output, b_y) # cross entropy loss 102 optimizer.zero_grad() # clear gradients for this training step 103 loss.backward() # backpropagation, compute gradients 104 optimizer.step() # apply gradients 105 106 if step % 50 == 0: 107 test_output, last_layer = cnn(test_x) 108 pred_y = torch.max(test_output, 1)[1].data.numpy() 109 accuracy = float((pred_y == test_y.data.numpy()).astype(int).sum()) / float(test_y.size(0)) 110 print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy) 111 if HAS_SK: 112 # Visualization of trained flatten layer (T-SNE) 113 tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000) 114 plot_only = 500 115 low_dim_embs = tsne.fit_transform(last_layer.data.numpy()[:plot_only, :]) 116 labels = test_y.numpy()[:plot_only] 117 plot_with_labels(low_dim_embs, labels) 118 plt.ioff() 119 120 # print 10 predictions from test data 121 test_output, _ = cnn(test_x[:10]) 122 pred_y = torch.max(test_output, 1)[1].data.numpy() 123 print(pred_y, 'prediction number') 124 print(test_y[:10].numpy(), 'real number')