MINIST深度学习识别：python全连接神经网络和pytorch LeNet CNN网络训练实现及比较（二）

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在前一篇文章MINIST深度学习识别：python全连接神经网络和pytorch LeNet CNN网络训练实现及比较（一）中，直接用python创建全连接神经网络模型进行深度学习训练，这样可以对神经网络有较为深刻的认识。

但是在实际应用中，一般都是采用各种深度学习框架来开展人工智能项目，以下就采用pytorch来实现前一篇文章中的全连接神经网络（784-300-10）。

  1 # -*- coding:utf-8 -*-
  2 
  3 u"""pytorch LineNet神经网络训练学习MINIST"""
  4 
  5 __author__ = 'zhengbiqing [e-mail]'
  6 
  7 
  8 import torch as t
  9 import torchvision as tv
 10 import torch.nn as nn
 11 import torch.nn.functional as F
 12 import torchvision.transforms as transforms
 13 from torch.autograd import Variable
 14 import matplotlib.pyplot as plt
 15 import datetime
 16 
 17 
 18 #是否训练网络
 19 TRAIN = True
 20 
 21 #是否保存网络
 22 SAVE_PARA = False
 23 
 24 #学习率和训练次数
 25 LR = 0.05
 26 EPOCH = 10
 27 
 28 #训练每批次的样本数
 29 BATCH_SZ = 16
 30 
 31 #样本读取线程数
 32 WORKERS = 4
 33 
 34 #网络参赛保存文件名
 35 PARAS_FN = 'minist_linenet_params.pkl'
 36 
 37 #minist数据存放位置
 38 ROOT = '/home/zbq/pytorch/minist'
 39 
 40 
 41 #定义网络模型
 42 class LineNet(nn.Module):
 43     def __init__(self):
 44         super(LineNet, self).__init__()
 45 
 46         self.fc = nn.Sequential(
 47             nn.Linear(28*28, 300),
 48             nn.ReLU(),
 49             nn.Linear(300, 10)
 50         )
 51 
 52     def forward(self, x):
 53         #x是2维tensor，转换为1维向量
 54         x = x.view(x.size()[0], -1)
 55         x = self.fc(x)
 56         return x
 57 
 58 
 59 '''
 60 训练并测试网络
 61 net：网络模型
 62 train_data_load：训练数据集
 63 test_data_load：测试数据集
 64 epochs：训练迭代次数
 65 save：是否保存训练结果
 66 '''
 67 def net_train(net, train_data_load, test_data_load, epochs, save):
 68     start_time = datetime.datetime.now()
 69 
 70     loss_list = []
 71 
 72     for epoch in range(epochs):
 73         for i, data in enumerate(train_data_load, 0):
 74             img, label = data
 75             img, label = Variable(img), Variable(label)
 76             img, label = img.cuda(), label.cuda()
 77 
 78             optimizer.zero_grad()
 79 
 80             pre = net(img)
 81             loss = loss_func(pre, label)
 82             loss.backward()
 83 
 84             optimizer.step()
 85 
 86             #显示损失函数值的变化
 87             loss_data = loss.data.item()
 88             if i % 1000 == 999:
 89                 print('epoch:{epoch} i:{i} loss:{loss}'.format(epoch=epoch, i=i, loss=loss_data))
 90 
 91             if i % 100 == 99:
 92                 loss_list.append(loss_data)
 93 
 94         # 每个epoch结束后用测试集检查识别准确度
 95         net_test(epoch, test_data_load)
 96 
 97     print('MINIST pytorch LineNet Train: EPOCH:{epochs}, BATCH_SZ:{batch_sz}, LR:{lr}'.format(epochs=epochs, batch_sz=BATCH_SZ, lr=LR))
 98     print('train spend time: ', datetime.datetime.now() - start_time)
 99 
100     if save:
101         t.save(net.state_dict(), PARAS_FN)
102 
103     #显示目标函数值的变化曲线
104     plt.plot(loss_list)
105     plt.show()
106 
107 
108 '''
109 用测试集检查准确率
110 '''
111 def net_test(epoch, test_data_load):
112     ok = 0
113 
114     for i, data in enumerate(test_data_load):
115         img, label = data
116         img, label = Variable(img), Variable(label)
117         img, label = img.cuda(), label.cuda()
118 
119         outs = net(img)
120         _, pre = t.max(outs.data, 1)
121         ok += (pre == label).sum()
122 
123     acc = ok.item() * 100 / (len(test_data_load) * BATCH_SZ)
124 
125     print('EPOCH:{epoch}, ACC:{acc}\n'.format(epoch=epoch, acc=acc))
126 
127 
128 #图像数值转换，ToTensor源码注释
129 """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
130     Converts a PIL Image or numpy.ndarray (H x W x C) in the range
131     [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0].
132     """
133 #归一化，把[0.0, 1.0]变换为[-1,1], ([0, 1] - 0.5) / 0.5 = [-1, 1]
134 transform = tv.transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])
135 
136 #定义数据集
137 train_data = tv.datasets.MNIST(root=ROOT, train=True, download=True, transform=transform)
138 test_data = tv.datasets.MNIST(root=ROOT, train=False, download=False, transform=transform)
139 
140 train_load = t.utils.data.DataLoader(train_data, batch_size=BATCH_SZ, shuffle=True, num_workers=WORKERS)
141 test_load = t.utils.data.DataLoader(test_data, batch_size=BATCH_SZ, shuffle=False, num_workers=WORKERS)
142 
143 print('train data num:', len(train_data), ', test data num:', len(test_data))
144 
145 
146 net = LineNet()
147 net.cuda()
148 
149 loss_func = nn.CrossEntropyLoss()
150 optimizer = t.optim.SGD(net.parameters(), lr=LR)
151 
152 if TRAIN:
153     net_train(net, train_load, test_load, EPOCH, SAVE_PARA)
154 else:
155     net.load_state_dict(t.load(PARAS_FN))
156     net_test(0, test_load)

网络训练结果准确率基本在97%～98%，和前一篇MINIST深度学习识别：python全连接神经网络和pytorch LeNet CNN网络训练实现及比较（一）相同网络结构的全连接神经网络相当，但是因为这里采用GPU运算，训练时间降低到1/8。

此外，借助pytorch，代码更简单。

运行结果如下：

train data num: 60000 , test data num: 10000
epoch:0 i:999 loss:0.3457891643047333
epoch:0 i:1999 loss:0.09639787673950195
epoch:0 i:2999 loss:0.27898865938186646
EPOCH:0, ACC:94.84

epoch:1 i:999 loss:0.33745211362838745
epoch:1 i:1999 loss:0.11106520891189575
epoch:1 i:2999 loss:0.21725007891654968
EPOCH:1, ACC:96.42

epoch:2 i:999 loss:0.3825737535953522
epoch:2 i:1999 loss:0.02866300940513611
epoch:2 i:2999 loss:0.11832481622695923
EPOCH:2, ACC:96.77

epoch:3 i:999 loss:0.11886310577392578
epoch:3 i:1999 loss:0.012149035930633545
epoch:3 i:2999 loss:0.030409961938858032
EPOCH:3, ACC:97.2

epoch:4 i:999 loss:0.008915185928344727
epoch:4 i:1999 loss:0.008089780807495117
epoch:4 i:2999 loss:0.0005310177803039551
EPOCH:4, ACC:97.6

epoch:5 i:999 loss:0.02993696928024292
epoch:5 i:1999 loss:0.01784616708755493
epoch:5 i:2999 loss:0.10544028878211975
EPOCH:5, ACC:97.6

epoch:6 i:999 loss:0.008486062288284302
epoch:6 i:1999 loss:0.0334945023059845
epoch:6 i:2999 loss:0.00291365385055542
EPOCH:6, ACC:97.37

epoch:7 i:999 loss:0.0062919557094573975
epoch:7 i:1999 loss:0.0003241896629333496
epoch:7 i:2999 loss:0.0006818175315856934
EPOCH:7, ACC:97.23

epoch:8 i:999 loss:0.0007421970367431641
epoch:8 i:1999 loss:0.005641639232635498
epoch:8 i:2999 loss:0.005949795246124268
EPOCH:8, ACC:97.7

epoch:9 i:999 loss:0.024028539657592773
epoch:9 i:1999 loss:0.005388796329498291
epoch:9 i:2999 loss:0.0029097795486450195
EPOCH:9, ACC:97.39

MINIST pytorch LineNet Train: EPOCH:10, BATCH_SZ:16, LR:0.05
train spend time:  0:00:43.183836

损失函数值变化曲线为：