这个代码实现了预测和可视化
1 import os
2
3 # third-party library
4 import torch
5 import torch.nn as nn
6 import torch.utils.data as Data
7 import torchvision
8 import matplotlib.pyplot as plt
9
10 # torch.manual_seed(1) # reproducible
11
12 # Hyper Parameters
13 EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch
14 BATCH_SIZE = 50
15 LR = 0.001 # learning rate
16 DOWNLOAD_MNIST = False
17
18
19 # Mnist digits dataset
20 if not(os.path.exists('./mnist/')) or not os.listdir('./mnist/'):
21 # not mnist dir or mnist is empyt dir
22 DOWNLOAD_MNIST = True
23
24 train_data = torchvision.datasets.MNIST(
25 root='./mnist/',
26 train=True, # this is training data
27 transform=torchvision.transforms.ToTensor(), # 把数据压缩到0到1之间的numpy数据
28 # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
29 download=DOWNLOAD_MNIST,
30 )
31
32 # plot one example
33 print(train_data.train_data.size()) # (60000, 28, 28)
34 print(train_data.train_labels.size()) # (60000)
35 plt.imshow(train_data.train_data[0].numpy(), cmap='gray')
36 plt.title('%i' % train_data.train_labels[0])
37 plt.show()
38
39 # Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)
40 train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
41
42 # pick 2000 samples to speed up testing
43 test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)
44 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)
45 test_y = test_data.test_labels[:2000]
46
47
48 class CNN(nn.Module):
49 def __init__(self):
50 super(CNN, self).__init__()
51 self.conv1 = nn.Sequential( # input shape (1, 28, 28)
52 nn.Conv2d(
53 in_channels=1, # input height
54 out_channels=16, # n_filters
55 kernel_size=5, # filter size
56 stride=1, # filter movement/step
57 padding=2, # if want same width and length of this image after Conv2d, padding=(kernel_size-1)/2 if stride=1
58 ), # output shape (16, 28, 28)
59 nn.ReLU(), # activation
60 nn.MaxPool2d(kernel_size=2), # choose max value in 2x2 area, output shape (16, 14, 14)
61 )
62 self.conv2 = nn.Sequential( # input shape (16, 14, 14)
63 nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14)
64 nn.ReLU(), # activation
65 nn.MaxPool2d(2), # output shape (32, 7, 7)
66 )
67 self.out = nn.Linear(32 * 7 * 7, 10) # fully connected layer, output 10 classes
68
69 def forward(self, x):
70 x = self.conv1(x)
71 x = self.conv2(x)
72 x = x.view(x.size(0), -1) # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
73 output = self.out(x)
74 return output, x # return x for visualization
75
76
77 cnn = CNN()
78 print(cnn) # net architecture
79
80 optimizer = torch.optim.Adam(cnn.parameters(), lr=LR) # optimize all cnn parameters
81 loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted
82
83 # following function (plot_with_labels) is for visualization, can be ignored if not interested
84 from matplotlib import cm
85 try: from sklearn.manifold import TSNE; HAS_SK = True
86 except: HAS_SK = False; print('Please install sklearn for layer visualization')
87 def plot_with_labels(lowDWeights, labels):
88 plt.cla()
89 X, Y = lowDWeights[:, 0], lowDWeights[:, 1]
90 for x, y, s in zip(X, Y, labels):
91 c = cm.rainbow(int(255 * s / 9)); plt.text(x, y, s, backgroundcolor=c, fontsize=9)
92 plt.xlim(X.min(), X.max()); plt.ylim(Y.min(), Y.max()); plt.title('Visualize last layer'); plt.show(); plt.pause(0.01)
93
94 plt.ion()
95 # training and testing
96 for epoch in range(EPOCH):
97 for step, (b_x, b_y) in enumerate(train_loader): # gives batch data, normalize x when iterate train_loader
98
99 output = cnn(b_x)[0] # cnn output
100 loss = loss_func(output, b_y) # cross entropy loss
101 optimizer.zero_grad() # clear gradients for this training step
102 loss.backward() # backpropagation, compute gradients
103 optimizer.step() # apply gradients
104
105 if step % 50 == 0:
106 test_output, last_layer = cnn(test_x)
107 pred_y = torch.max(test_output, 1)[1].data.numpy()
108 accuracy = float((pred_y == test_y.data.numpy()).astype(int).sum()) / float(test_y.size(0))
109 print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)
110 if HAS_SK:
111 # Visualization of trained flatten layer (T-SNE)
112 tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
113 plot_only = 500
114 low_dim_embs = tsne.fit_transform(last_layer.data.numpy()[:plot_only, :])
115 labels = test_y.numpy()[:plot_only]
116 plot_with_labels(low_dim_embs, labels)
117 plt.ioff()
118
119 # print 10 predictions from test data
120 test_output, _ = cnn(test_x[:10])
121 pred_y = torch.max(test_output, 1)[1].data.numpy()
122 print(pred_y, 'prediction number')
123 print(test_y[:10].numpy(), 'real number')去掉可视化进行代码简化
1 import os
2 # third-party library
3 import torch
4 import torch.nn as nn
5 import torch.utils.data as Data
6 import torchvision
7 import matplotlib.pyplot as plt
8
9 EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch
10 BATCH_SIZE = 50
11 LR = 0.001 # learning rate
12
13 train_data = torchvision.datasets.MNIST(
14 root='./mnist/', #下载后的存放目录
15 train=True, # this is training data
16 transform=torchvision.transforms.ToTensor(), # 把数据压缩到0到1之间的numpy数据，如果原始数据是rgb数据（0-255）则变为黑白数据，并使numpy数据变为tensor数据 # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
17 download=True#不存在该数据就设置为True进行下载，存在则改为False
18 )
19
20 # plot one example
21 print(train_data.train_data.size()) # (60000, 28, 28)，六万图片
22 print(train_data.train_labels.size()) # (60000)，六万标签
23 plt.imshow(train_data.train_data[0].numpy(), cmap='gray')#展现第一个训练数据图片
24 plt.title('%i' % train_data.train_labels[0])
25 plt.show()
26
27 # Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)
28 train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
29
30 # pick 2000 samples to speed up testing
31 test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)
32 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)
33 test_y = test_data.test_labels[:2000]
34
35 class CNN(nn.Module):
36 def __init__(self):
37 super(CNN, self).__init__()
38 self.conv1 = nn.Sequential( # input shape (1, 28, 28)，考虑batch是（batch，1,28,28）
39 nn.Conv2d(
40 in_channels=1, # input height
41 out_channels=16, # n_filters
42 kernel_size=5, # filter size
43 stride=1, # filter movement/step
44 padding=2, # if want same width and length of this image after Conv2d, padding=(kernel_size-1)/2 if stride=1
45 ), # output shape (16, 28, 28)
46 nn.ReLU(), # activation
47 nn.MaxPool2d(kernel_size=2), # choose max value in 2x2 area, output shape (16, 14, 14)
48 )
49 self.conv2 = nn.Sequential( # input shape (16, 14, 14)
50 nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14)
51 nn.ReLU(), # activation
52 nn.MaxPool2d(2), # output shape (32, 7, 7)
53 )
54 self.out = nn.Linear(32 * 7 * 7, 10) # fully connected layer, output 10 classes
55
56 def forward(self, x):
57 x = self.conv1(x)
58 x = self.conv2(x)
59 x = x.view(x.size(0), -1) # flatten the output of conv2 to (batch_size, 32 * 7 * 7)，只有tensor对象才可以使用x.size(0)
60 output = self.out(x)
61 return output, x # return x for visualization
62
63
64 cnn = CNN()
65 #print(cnn) # net architecture
66
67 optimizer = torch.optim.Adam(cnn.parameters(), lr=LR) # optimize all cnn parameters
68 loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted
69
70 # training and testing
71 for epoch in range(EPOCH):
72 for step, (b_x, b_y) in enumerate(train_loader): # gives batch data, normalize x when iterate train_loader
73
74 output = cnn(b_x)[0] # cnn output
75 loss = loss_func(output, b_y) # cross entropy loss
76 optimizer.zero_grad() # clear gradients for this training step
77 loss.backward() # backpropagation, compute gradients
78 optimizer.step() # apply gradients
79
80 if step % 50 == 0:
81 test_output = cnn(test_x)[0]
82 print("----------------")
83 #print(test_output.shape) #2000*10
84 #print(torch.max(test_output, 1)) #返回的每一行中最大值和其下标
85 pred_y = torch.max(test_output, 1)[1].data.numpy() #返回的是每个样本对应0-9数字可能性最大的概率对应的下标
86 accuracy = float((pred_y == test_y.data.numpy()).astype(int).sum()) / float(test_y.size(0))
87 print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)
88
89 # print 10 predictions from test data
90 test_output, _ = cnn(test_x[:10])
91 pred_y = torch.max(test_output, 1)[1].data.numpy()
92 print(pred_y, 'prediction number')
93 print(test_y[:10].numpy(), 'real number')
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