1.导入必备的包
1 import torch
2 import numpy as np
3 from torchvision.datasets import mnist
4 from torch import nn
5 from torch.autograd import Variable
6 import matplotlib.pyplot as plt
7 import torch.nn.functional as F
8 from torch.utils.data import DataLoader
9 %matplotlib inline
2.定义mnist数据的格式变换
1 def data_transform(x):
2 x = np.array(x, dtype = 'float32') / 255
3 x = (x - 0.5) /0.5
4 x = x.reshape((-1, ))
5 x = torch.from_numpy(x)
6 return x
3.下载数据集，定义数据迭代器
1 trainset = mnist.MNIST('./dataset/mnist', train=True, transform=data_transform, download=True)
2 testset = mnist.MNIST('./dataset/mnist', train = False, transform=data_transform, download=True)】
3 train_data = DataLoader(trainset, batch_size=64, shuffle=True)
4 test_data = DataLoader(testset, batch_size=128, shuffle=False)
4.定义全连接神经网络（多层感知机）（若是CNN卷积神经网络，则在网络中添加几个卷积层即可）
1 class MLP(nn.Module):
2 def __init__(self):
3 super(MLP, self).__init__()
#CNN网络就是加几个卷积层，再修改fc1的输入为16*5*5
#self.conv1 = nn.Conv2d(3, 6, 5)
#self.pool = nn.MaxPool2d(2, 2)
#self.conv2 = nn.Conv2d(6, 16, 5)
4 self.fc1 = nn.Linear(28*28, 500)
5 self.fc2 = nn.Linear(500, 250)
6 self.fc3 = nn.Linear(250, 125)
7 self.fc4 = nn.Linear(125, 10)
8
9 def forward(self, x):
10 x = F.relu(self.fc1(x))
11 x = F.relu(self.fc2(x))
12 x = F.relu(self.fc3(x))
13 x = self.fc4(x)
14 return x
15
16 mlp = MLP()
5.定义损失函数和优化器
#交叉熵损失
1 criterion = nn.CrossEntropyLoss()
#随机梯度下降
2 optimizer = torch.optim.SGD(mlp.parameters(), 1e-3)
6.开始训练和测试
1 losses = []
2 acces = []
3 eval_losses = []
4 eval_acces = []
5
6 for e in range(20):
7 train_loss = 0
8 train_acc = 0
#开始训练
9 mlp.train()
10 for im, label in train_data:
11 im = Variable(im)
12 label = Variable(label)
13 # 前向传播
14 out = mlp(im)
15 loss = criterion(out, label)
16 # 反向传播
17 optimizer.zero_grad()
18 loss.backward()
19 optimizer.step()
20 # 记录误差
21 train_loss += loss.item()
22 # 计算分类的准确率
23 _, pred = out.max(1)
24 num_correct = (pred == label).sum().item()
25 acc = num_correct / im.shape[0]
26 train_acc += acc
27
28 losses.append(train_loss / len(train_data))
29 acces.append(train_acc / len(train_data))
30 # 在测试集上检验效果
31 eval_loss = 0
32 eval_acc = 0
33 mlp.eval() # 将模型改为预测模式
34 for im, label in test_data:
35 im = Variable(im)
36 label = Variable(label)
37 out = mlp(im)
38 loss = criterion(out, label)
39 # 记录误差
40 eval_loss += loss.item()
41 # 记录准确率
42 _, pred = out.max(1)
43 num_correct = (pred == label).sum().item()
44 acc = num_correct / im.shape[0]
45 eval_acc += acc
46
47 eval_losses.append(eval_loss / len(test_data))
48 eval_acces.append(eval_acc / len(test_data))
49 print('epoch: {}, Train Loss: {:.6f}, Train Acc: {:.6f}, Eval Loss: {:.6f}, Eval Acc: {:.6f}'
50 .format(e, train_loss / len(train_data), train_acc / len(train_data),
51 eval_loss / len(test_data), eval_acc / len(test_data)))
7.测试结果
epoch: 0, Train Loss: 2.287240, Train Acc: 0.124150, Eval Loss: 2.265074, Eval Acc: 0.237540
epoch: 1, Train Loss: 2.237043, Train Acc: 0.385861, Eval Loss: 2.197773, Eval Acc: 0.524921
epoch: 2, Train Loss: 2.138911, Train Acc: 0.555487, Eval Loss: 2.050214, Eval Acc: 0.554292
epoch: 3, Train Loss: 1.901877, Train Acc: 0.563833, Eval Loss: 1.688784, Eval Acc: 0.592662
epoch: 4, Train Loss: 1.439467, Train Acc: 0.625483, Eval Loss: 1.178063, Eval Acc: 0.704905
epoch: 5, Train Loss: 1.022494, Train Acc: 0.745586, Eval Loss: 0.869467, Eval Acc: 0.778184
epoch: 6, Train Loss: 0.795575, Train Acc: 0.790528, Eval Loss: 0.702586, Eval Acc: 0.808347
epoch: 7, Train Loss: 0.665018, Train Acc: 0.816031, Eval Loss: 0.601074, Eval Acc: 0.831586
epoch: 8, Train Loss: 0.583082, Train Acc: 0.834588, Eval Loss: 0.535897, Eval Acc: 0.843750
epoch: 9, Train Loss: 0.527930, Train Acc: 0.848231, Eval Loss: 0.490443, Eval Acc: 0.857694
epoch: 10, Train Loss: 0.488764, Train Acc: 0.858925, Eval Loss: 0.456138, Eval Acc: 0.866396
epoch: 11, Train Loss: 0.459293, Train Acc: 0.868220, Eval Loss: 0.430784, Eval Acc: 0.873220
epoch: 12, Train Loss: 0.436398, Train Acc: 0.874117, Eval Loss: 0.413343, Eval Acc: 0.875890
epoch: 13, Train Loss: 0.418043, Train Acc: 0.880031, Eval Loss: 0.396967, Eval Acc: 0.880340
epoch: 14, Train Loss: 0.403195, Train Acc: 0.884029, Eval Loss: 0.385431, Eval Acc: 0.885483
epoch: 15, Train Loss: 0.390613, Train Acc: 0.887327, Eval Loss: 0.372552, Eval Acc: 0.889537
epoch: 16, Train Loss: 0.379947, Train Acc: 0.890275, Eval Loss: 0.363168, Eval Acc: 0.891812
epoch: 17, Train Loss: 0.370701, Train Acc: 0.893557, Eval Loss: 0.355597, Eval Acc: 0.894482
epoch: 18, Train Loss: 0.362498, Train Acc: 0.896572, Eval Loss: 0.348329, Eval Acc: 0.897844
epoch: 19, Train Loss: 0.354748, Train Acc: 0.898121, Eval Loss: 0.340272, Eval Acc: 0.899921
8.训练损失和训练精度曲线
1 plt.title('train loss')
2 plt.plot(np.arange(len(losses)), losses)
1 plt.plot(np.arange(len(acces)), acces)
2 plt.title('train acc')
