标签：Acc 50000 Loss CIFAR10 VGG19 epoch Epoch Train LeNet

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前面几篇文章介绍了MINIST，对这种简单图片的识别，LeNet-5可以达到99%的识别率。

CIFAR10是另一个著名的深度学习图像分类识别数据集，比MINIST更复杂，而且是RGB彩色图片。

看看较简单的LeNet-5可以达到多少准确率。网络结构基本和前面MINIST代码中的差不多，主要是输入图片的通道数不同，代码如下：

  1 # -*- coding:utf-8 -*-
  2 
  3 u"""LeNet卷积神经网络训练学习CIFAR10"""
  4 
  5 __author__ = 'zhengbiqing 460356155@qq.com'
  6 
  7 
  8 import torch as t
  9 import torchvision as tv
 10 import torch.nn as nn
 11 import torch.optim as optim
 12 import torchvision.transforms as transforms
 13 from torchvision.transforms import ToPILImage
 14 import torch.backends.cudnn as cudnn
 15 
 16 import datetime
 17 import argparse
 18 
 19 
 20 # 样本读取线程数
 21 WORKERS = 4
 22 
 23 # 网络参赛保存文件名
 24 PARAS_FN = 'cifar_lenet_params.pkl'
 25 
 26 # minist数据存放位置
 27 ROOT = '/home/zbq/PycharmProjects/cifar'
 28 
 29 # 目标函数
 30 loss_func = nn.CrossEntropyLoss()
 31 
 32 # 最优结果
 33 best_acc = 0
 34 
 35 
 36 # 定义网络模型
 37 class LeNet(nn.Module):
 38     def __init__(self):
 39         super(LeNet, self).__init__()
 40 
 41         # 卷积层
 42         self.cnn = nn.Sequential(
 43             # 卷积层1，3通道输入，6个卷积核，核大小5*5
 44             # 经过该层图像大小变为32-5+1，28*28
 45             # 经2*2最大池化，图像变为14*14
 46             nn.Conv2d(3, 6, 5),
 47             nn.ReLU(),
 48             nn.MaxPool2d(2),
 49 
 50             # 卷积层2，6输入通道，16个卷积核，核大小5*5
 51             # 经过该层图像变为14-5+1，10*10
 52             # 经2*2最大池化，图像变为5*5
 53             nn.Conv2d(6, 16, 5),
 54             nn.ReLU(),
 55             nn.MaxPool2d(2)
 56         )
 57 
 58         # 全连接层
 59         self.fc = nn.Sequential(
 60             # 16个feature，每个feature 5*5
 61             nn.Linear(16 * 5 * 5, 120),
 62             nn.ReLU(),
 63             nn.Linear(120, 84),
 64             nn.ReLU(),
 65             nn.Linear(84, 10)
 66         )
 67 
 68     def forward(self, x):
 69         x = self.cnn(x)
 70 
 71         # x.size()[0]: batch size
 72         x = x.view(x.size()[0], -1)
 73         x = self.fc(x)
 74 
 75         return x
 76 
 77 
 78 '''
 79 训练并测试网络
 80 net：网络模型
 81 train_data_load：训练数据集
 82 optimizer：优化器
 83 epoch：第几次训练迭代
 84 log_interval：训练过程中损失函数值和准确率的打印频率
 85 '''
 86 def net_train(net, train_data_load, optimizer, epoch, log_interval):
 87     net.train()
 88 
 89     begin = datetime.datetime.now()
 90 
 91     # 样本总数
 92     total = len(train_data_load.dataset)
 93 
 94     # 样本批次训练的损失函数值的和
 95     train_loss = 0
 96 
 97     # 识别正确的样本数
 98     ok = 0
 99 
100     for i, data in enumerate(train_data_load, 0):
101         img, label = data
102         img, label = img.cuda(), label.cuda()
103 
104         optimizer.zero_grad()
105 
106         outs = net(img)
107         loss = loss_func(outs, label)
108         loss.backward()
109         optimizer.step()
110 
111         # 累加损失值和训练样本数
112         train_loss += loss.item()
113         # total += label.size(0)
114 
115         _, predicted = t.max(outs.data, 1)
116         # 累加识别正确的样本数
117         ok += (predicted == label).sum()
118 
119         if (i + 1) % log_interval == 0:
120             # 训练结果输出
121 
122             # 损失函数均值
123             loss_mean = train_loss / (i + 1)
124 
125             # 已训练的样本数
126             traind_total = (i + 1) * len(label)
127 
128             # 准确度
129             acc = 100. * ok / traind_total
130 
131             # 一个迭代的进度百分比
132             progress = 100. * traind_total / total
133 
134             print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}  Acc: {:.6f}'.format(
135                 epoch, traind_total, total, progress, loss_mean, acc))
136 
137     end = datetime.datetime.now()
138     print('one epoch spend: ', end - begin)
139 
140 
141 '''
142 用测试集检查准确率
143 '''
144 def net_test(net, test_data_load, epoch):
145     net.eval()
146 
147     ok = 0
148 
149     for i, data in enumerate(test_data_load):
150         img, label = data
151         img, label = img.cuda(), label.cuda()
152 
153         outs = net(img)
154         _, pre = t.max(outs.data, 1)
155         ok += (pre == label).sum()
156 
157     acc = ok.item() * 100. / (len(test_data_load.dataset))
158     print('EPOCH:{}, ACC:{}\n'.format(epoch, acc))
159 
160     global best_acc
161     if acc > best_acc:
162         best_acc = acc
163 
164 
165 '''
166 显示数据集中一个图片
167 '''
168 def img_show(dataset, index):
169     classes = ('plane', 'car', 'bird', 'cat',
170                'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
171 
172     show = ToPILImage()
173 
174     data, label = dataset[index]
175     print('img is a ', classes[label])
176     show((data + 1) / 2).resize((100, 100)).show()
177 
178 
179 def main():
180     # 训练超参数设置，可通过命令行设置
181     parser = argparse.ArgumentParser(description='PyTorch CIFA10 LeNet Example')
182     parser.add_argument('--batch-size', type=int, default=64, metavar='N',
183                         help='input batch size for training (default: 64)')
184     parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
185                         help='input batch size for testing (default: 1000)')
186     parser.add_argument('--epochs', type=int, default=20, metavar='N',
187                         help='number of epochs to train (default: 20)')
188     parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
189                         help='learning rate (default: 0.01)')
190     parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
191                         help='SGD momentum (default: 0.9)')
192     parser.add_argument('--log-interval', type=int, default=100, metavar='N',
193                         help='how many batches to wait before logging training status (default: 100)')
194     parser.add_argument('--no-train', action='store_true', default=False,
195                         help='If train the Model')
196     parser.add_argument('--save-model', action='store_true', default=False,
197                         help='For Saving the current Model')
198     args = parser.parse_args()
199 
200     # 图像数值转换，ToTensor源码注释
201     """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
202         Converts a PIL Image or numpy.ndarray (H x W x C) in the range
203         [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0].
204         """
205     # 归一化把[0.0, 1.0]变换为[-1,1], ([0, 1] - 0.5) / 0.5 = [-1, 1]
206     transform = tv.transforms.Compose([
207         transforms.ToTensor(),
208         transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
209 
210     # 定义数据集
211     train_data = tv.datasets.CIFAR10(root=ROOT, train=True, download=True, transform=transform)
212     test_data = tv.datasets.CIFAR10(root=ROOT, train=False, download=False, transform=transform)
213 
214     train_load = t.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True, num_workers=WORKERS)
215     test_load = t.utils.data.DataLoader(test_data, batch_size=args.test_batch_size, shuffle=False, num_workers=WORKERS)
216 
217     net = LeNet().cuda()
218     print(net)
219 
220     # 如果不训练，直接加载保存的网络参数进行测试集验证
221     if args.no_train:
222         net.load_state_dict(t.load(PARAS_FN))
223         net_test(net, test_load, 0)
224         return
225 
226     optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum)
227 
228     start_time = datetime.datetime.now()
229 
230     for epoch in range(1, args.epochs + 1):
231         net_train(net, train_load, optimizer, epoch, args.log_interval)
232 
233         # 每个epoch结束后用测试集检查识别准确度
234         net_test(net, test_load, epoch)
235 
236     end_time = datetime.datetime.now()
237 
238     global best_acc
239     print('CIFAR10 pytorch LeNet Train: EPOCH:{}, BATCH_SZ:{}, LR:{}, ACC:{}'.format(args.epochs, args.batch_size, args.lr, best_acc))
240     print('train spend time: ', end_time - start_time)
241 
242     if args.save_model:
243         t.save(net.state_dict(), PARAS_FN)
244 
245 
246 if __name__ == '__main__':
247     main()

运行结果如下：

Files already downloaded and verified
LeNet(
  (cnn): Sequential(
    (0): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))
    (1): ReLU()
    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (3): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
    (4): ReLU()
    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (fc): Sequential(
    (0): Linear(in_features=400, out_features=120, bias=True)
    (1): ReLU()
    (2): Linear(in_features=120, out_features=84, bias=True)
    (3): ReLU()
    (4): Linear(in_features=84, out_features=10, bias=True)
  )
)
Train Epoch: 1 [6400/50000 (13%)]    Loss: 2.297558  Acc: 10.000000
Train Epoch: 1 [12800/50000 (26%)]    Loss: 2.219855  Acc: 16.000000
Train Epoch: 1 [19200/50000 (38%)]    Loss: 2.117518  Acc: 20.000000
Train Epoch: 1 [25600/50000 (51%)]    Loss: 2.030452  Acc: 23.000000
Train Epoch: 1 [32000/50000 (64%)]    Loss: 1.956154  Acc: 26.000000
Train Epoch: 1 [38400/50000 (77%)]    Loss: 1.894052  Acc: 29.000000
Train Epoch: 1 [44800/50000 (90%)]    Loss: 1.845520  Acc: 31.000000
one epoch spend:  0:00:02.007186
EPOCH:1, ACC:43.86

Train Epoch: 2 [6400/50000 (13%)]    Loss: 1.497962  Acc: 44.000000
Train Epoch: 2 [12800/50000 (26%)]    Loss: 1.471271  Acc: 45.000000
Train Epoch: 2 [19200/50000 (38%)]    Loss: 1.458853  Acc: 46.000000
Train Epoch: 2 [25600/50000 (51%)]    Loss: 1.445787  Acc: 47.000000
Train Epoch: 2 [32000/50000 (64%)]    Loss: 1.436431  Acc: 47.000000
Train Epoch: 2 [38400/50000 (77%)]    Loss: 1.425798  Acc: 47.000000
Train Epoch: 2 [44800/50000 (90%)]    Loss: 1.415501  Acc: 48.000000
one epoch spend:  0:00:01.879316
EPOCH:2, ACC:53.16

Train Epoch: 3 [6400/50000 (13%)]    Loss: 1.288907  Acc: 52.000000
Train Epoch: 3 [12800/50000 (26%)]    Loss: 1.293646  Acc: 53.000000
Train Epoch: 3 [19200/50000 (38%)]    Loss: 1.284784  Acc: 53.000000
Train Epoch: 3 [25600/50000 (51%)]    Loss: 1.281050  Acc: 53.000000
Train Epoch: 3 [32000/50000 (64%)]    Loss: 1.281222  Acc: 53.000000
Train Epoch: 3 [38400/50000 (77%)]    Loss: 1.269620  Acc: 54.000000
Train Epoch: 3 [44800/50000 (90%)]    Loss: 1.262982  Acc: 54.000000
one epoch spend:  0:00:01.928787
EPOCH:3, ACC:54.31

Train Epoch: 4 [6400/50000 (13%)]    Loss: 1.157912  Acc: 58.000000
Train Epoch: 4 [12800/50000 (26%)]    Loss: 1.157038  Acc: 58.000000
Train Epoch: 4 [19200/50000 (38%)]    Loss: 1.164880  Acc: 58.000000
Train Epoch: 4 [25600/50000 (51%)]    Loss: 1.169460  Acc: 58.000000
Train Epoch: 4 [32000/50000 (64%)]    Loss: 1.169655  Acc: 58.000000
Train Epoch: 4 [38400/50000 (77%)]    Loss: 1.169239  Acc: 58.000000
Train Epoch: 4 [44800/50000 (90%)]    Loss: 1.159252  Acc: 58.000000
one epoch spend:  0:00:01.928551
EPOCH:4, ACC:60.15

Train Epoch: 5 [6400/50000 (13%)]    Loss: 1.081296  Acc: 61.000000
Train Epoch: 5 [12800/50000 (26%)]    Loss: 1.073868  Acc: 61.000000
Train Epoch: 5 [19200/50000 (38%)]    Loss: 1.086076  Acc: 61.000000
Train Epoch: 5 [25600/50000 (51%)]    Loss: 1.088019  Acc: 61.000000
Train Epoch: 5 [32000/50000 (64%)]    Loss: 1.083983  Acc: 61.000000
Train Epoch: 5 [38400/50000 (77%)]    Loss: 1.088050  Acc: 61.000000
Train Epoch: 5 [44800/50000 (90%)]    Loss: 1.087298  Acc: 61.000000
one epoch spend:  0:00:01.898825
EPOCH:5, ACC:59.84

Train Epoch: 6 [6400/50000 (13%)]    Loss: 0.979352  Acc: 65.000000
Train Epoch: 6 [12800/50000 (26%)]    Loss: 1.005338  Acc: 64.000000
Train Epoch: 6 [19200/50000 (38%)]    Loss: 1.019300  Acc: 63.000000
Train Epoch: 6 [25600/50000 (51%)]    Loss: 1.022704  Acc: 63.000000
Train Epoch: 6 [32000/50000 (64%)]    Loss: 1.021217  Acc: 63.000000
Train Epoch: 6 [38400/50000 (77%)]    Loss: 1.022035  Acc: 63.000000
Train Epoch: 6 [44800/50000 (90%)]    Loss: 1.024987  Acc: 63.000000
one epoch spend:  0:00:01.926922
EPOCH:6, ACC:60.04

Train Epoch: 7 [6400/50000 (13%)]    Loss: 0.952975  Acc: 66.000000
Train Epoch: 7 [12800/50000 (26%)]    Loss: 0.965437  Acc: 65.000000
Train Epoch: 7 [19200/50000 (38%)]    Loss: 0.964711  Acc: 65.000000
Train Epoch: 7 [25600/50000 (51%)]    Loss: 0.962520  Acc: 65.000000
Train Epoch: 7 [32000/50000 (64%)]    Loss: 0.964768  Acc: 65.000000
Train Epoch: 7 [38400/50000 (77%)]    Loss: 0.966530  Acc: 65.000000
Train Epoch: 7 [44800/50000 (90%)]    Loss: 0.971995  Acc: 65.000000
one epoch spend:  0:00:01.858537
EPOCH:7, ACC:62.63

Train Epoch: 8 [6400/50000 (13%)]    Loss: 0.901441  Acc: 67.000000
Train Epoch: 8 [12800/50000 (26%)]    Loss: 0.896776  Acc: 68.000000
Train Epoch: 8 [19200/50000 (38%)]    Loss: 0.898365  Acc: 68.000000
Train Epoch: 8 [25600/50000 (51%)]    Loss: 0.898383  Acc: 68.000000
Train Epoch: 8 [32000/50000 (64%)]    Loss: 0.909455  Acc: 67.000000
Train Epoch: 8 [38400/50000 (77%)]    Loss: 0.910068  Acc: 67.000000
Train Epoch: 8 [44800/50000 (90%)]    Loss: 0.914733  Acc: 67.000000
one epoch spend:  0:00:01.849259
EPOCH:8, ACC:62.99

Train Epoch: 9 [6400/50000 (13%)]    Loss: 0.842184  Acc: 69.000000
Train Epoch: 9 [12800/50000 (26%)]    Loss: 0.853178  Acc: 69.000000
Train Epoch: 9 [19200/50000 (38%)]    Loss: 0.863828  Acc: 69.000000
Train Epoch: 9 [25600/50000 (51%)]    Loss: 0.868452  Acc: 69.000000
Train Epoch: 9 [32000/50000 (64%)]    Loss: 0.870991  Acc: 69.000000
Train Epoch: 9 [38400/50000 (77%)]    Loss: 0.874963  Acc: 69.000000
Train Epoch: 9 [44800/50000 (90%)]    Loss: 0.878533  Acc: 68.000000
one epoch spend:  0:00:01.954615
EPOCH:9, ACC:62.5

Train Epoch: 10 [6400/50000 (13%)]    Loss: 0.837819  Acc: 70.000000
Train Epoch: 10 [12800/50000 (26%)]    Loss: 0.823905  Acc: 70.000000
Train Epoch: 10 [19200/50000 (38%)]    Loss: 0.833733  Acc: 70.000000
Train Epoch: 10 [25600/50000 (51%)]    Loss: 0.838861  Acc: 70.000000
Train Epoch: 10 [32000/50000 (64%)]    Loss: 0.841117  Acc: 70.000000
Train Epoch: 10 [38400/50000 (77%)]    Loss: 0.849762  Acc: 69.000000
Train Epoch: 10 [44800/50000 (90%)]    Loss: 0.850071  Acc: 69.000000
one epoch spend:  0:00:01.812348
EPOCH:10, ACC:63.48

Train Epoch: 11 [6400/50000 (13%)]    Loss: 0.781857  Acc: 72.000000
Train Epoch: 11 [12800/50000 (26%)]    Loss: 0.773329  Acc: 72.000000
Train Epoch: 11 [19200/50000 (38%)]    Loss: 0.785191  Acc: 72.000000
Train Epoch: 11 [25600/50000 (51%)]    Loss: 0.797921  Acc: 71.000000
Train Epoch: 11 [32000/50000 (64%)]    Loss: 0.802146  Acc: 71.000000
Train Epoch: 11 [38400/50000 (77%)]    Loss: 0.804404  Acc: 71.000000
Train Epoch: 11 [44800/50000 (90%)]    Loss: 0.805919  Acc: 71.000000
one epoch spend:  0:00:01.881838
EPOCH:11, ACC:63.72

Train Epoch: 12 [6400/50000 (13%)]    Loss: 0.734165  Acc: 74.000000
Train Epoch: 12 [12800/50000 (26%)]    Loss: 0.739923  Acc: 74.000000
Train Epoch: 12 [19200/50000 (38%)]    Loss: 0.753080  Acc: 73.000000
Train Epoch: 12 [25600/50000 (51%)]    Loss: 0.755026  Acc: 73.000000
Train Epoch: 12 [32000/50000 (64%)]    Loss: 0.758760  Acc: 73.000000
Train Epoch: 12 [38400/50000 (77%)]    Loss: 0.765208  Acc: 72.000000
Train Epoch: 12 [44800/50000 (90%)]    Loss: 0.774539  Acc: 72.000000
one epoch spend:  0:00:01.856290
EPOCH:12, ACC:63.71

Train Epoch: 13 [6400/50000 (13%)]    Loss: 0.709528  Acc: 75.000000
Train Epoch: 13 [12800/50000 (26%)]    Loss: 0.713831  Acc: 74.000000
Train Epoch: 13 [19200/50000 (38%)]    Loss: 0.720146  Acc: 74.000000
Train Epoch: 13 [25600/50000 (51%)]    Loss: 0.723680  Acc: 74.000000
Train Epoch: 13 [32000/50000 (64%)]    Loss: 0.730473  Acc: 73.000000
Train Epoch: 13 [38400/50000 (77%)]    Loss: 0.742575  Acc: 73.000000
Train Epoch: 13 [44800/50000 (90%)]    Loss: 0.744857  Acc: 73.000000
one epoch spend:  0:00:01.808256
EPOCH:13, ACC:61.71

Train Epoch: 14 [6400/50000 (13%)]    Loss: 0.700821  Acc: 74.000000
Train Epoch: 14 [12800/50000 (26%)]    Loss: 0.691082  Acc: 75.000000
Train Epoch: 14 [19200/50000 (38%)]    Loss: 0.693119  Acc: 75.000000
Train Epoch: 14 [25600/50000 (51%)]    Loss: 0.706147  Acc: 74.000000
Train Epoch: 14 [32000/50000 (64%)]    Loss: 0.710033  Acc: 74.000000
Train Epoch: 14 [38400/50000 (77%)]    Loss: 0.717097  Acc: 74.000000
Train Epoch: 14 [44800/50000 (90%)]    Loss: 0.724987  Acc: 74.000000
one epoch spend:  0:00:01.797417
EPOCH:14, ACC:63.15

Train Epoch: 15 [6400/50000 (13%)]    Loss: 0.624073  Acc: 77.000000
Train Epoch: 15 [12800/50000 (26%)]    Loss: 0.637354  Acc: 77.000000
Train Epoch: 15 [19200/50000 (38%)]    Loss: 0.646385  Acc: 76.000000
Train Epoch: 15 [25600/50000 (51%)]    Loss: 0.662080  Acc: 76.000000
Train Epoch: 15 [32000/50000 (64%)]    Loss: 0.668658  Acc: 76.000000
Train Epoch: 15 [38400/50000 (77%)]    Loss: 0.679682  Acc: 75.000000
Train Epoch: 15 [44800/50000 (90%)]    Loss: 0.688876  Acc: 75.000000
one epoch spend:  0:00:01.916400
EPOCH:15, ACC:62.81

Train Epoch: 16 [6400/50000 (13%)]    Loss: 0.611007  Acc: 78.000000
Train Epoch: 16 [12800/50000 (26%)]    Loss: 0.612629  Acc: 78.000000
Train Epoch: 16 [19200/50000 (38%)]    Loss: 0.622980  Acc: 77.000000
Train Epoch: 16 [25600/50000 (51%)]    Loss: 0.638267  Acc: 77.000000
Train Epoch: 16 [32000/50000 (64%)]    Loss: 0.650756  Acc: 76.000000
Train Epoch: 16 [38400/50000 (77%)]    Loss: 0.656675  Acc: 76.000000
Train Epoch: 16 [44800/50000 (90%)]    Loss: 0.665181  Acc: 75.000000
one epoch spend:  0:00:01.878367
EPOCH:16, ACC:61.64

Train Epoch: 17 [6400/50000 (13%)]    Loss: 0.591583  Acc: 78.000000
Train Epoch: 17 [12800/50000 (26%)]    Loss: 0.601943  Acc: 78.000000
Train Epoch: 17 [19200/50000 (38%)]    Loss: 0.612084  Acc: 78.000000
Train Epoch: 17 [25600/50000 (51%)]    Loss: 0.619225  Acc: 77.000000
Train Epoch: 17 [32000/50000 (64%)]    Loss: 0.633562  Acc: 77.000000
Train Epoch: 17 [38400/50000 (77%)]    Loss: 0.641217  Acc: 77.000000
Train Epoch: 17 [44800/50000 (90%)]    Loss: 0.648393  Acc: 76.000000
one epoch spend:  0:00:01.894760
EPOCH:17, ACC:61.44

Train Epoch: 18 [6400/50000 (13%)]    Loss: 0.553651  Acc: 80.000000
Train Epoch: 18 [12800/50000 (26%)]    Loss: 0.569668  Acc: 79.000000
Train Epoch: 18 [19200/50000 (38%)]    Loss: 0.584057  Acc: 78.000000
Train Epoch: 18 [25600/50000 (51%)]    Loss: 0.598776  Acc: 78.000000
Train Epoch: 18 [32000/50000 (64%)]    Loss: 0.610767  Acc: 78.000000
Train Epoch: 18 [38400/50000 (77%)]    Loss: 0.617563  Acc: 77.000000
Train Epoch: 18 [44800/50000 (90%)]    Loss: 0.628669  Acc: 77.000000
one epoch spend:  0:00:01.925175
EPOCH:18, ACC:62.46

Train Epoch: 19 [6400/50000 (13%)]    Loss: 0.554530  Acc: 79.000000
Train Epoch: 19 [12800/50000 (26%)]    Loss: 0.574952  Acc: 78.000000
Train Epoch: 19 [19200/50000 (38%)]    Loss: 0.576819  Acc: 79.000000
Train Epoch: 19 [25600/50000 (51%)]    Loss: 0.584052  Acc: 78.000000
Train Epoch: 19 [32000/50000 (64%)]    Loss: 0.590673  Acc: 78.000000
Train Epoch: 19 [38400/50000 (77%)]    Loss: 0.599807  Acc: 78.000000
Train Epoch: 19 [44800/50000 (90%)]    Loss: 0.607849  Acc: 78.000000
one epoch spend:  0:00:01.827582
EPOCH:19, ACC:62.16

Train Epoch: 20 [6400/50000 (13%)]    Loss: 0.534505  Acc: 80.000000
Train Epoch: 20 [12800/50000 (26%)]    Loss: 0.547133  Acc: 80.000000
Train Epoch: 20 [19200/50000 (38%)]    Loss: 0.557482  Acc: 79.000000
Train Epoch: 20 [25600/50000 (51%)]    Loss: 0.567949  Acc: 79.000000
Train Epoch: 20 [32000/50000 (64%)]    Loss: 0.579047  Acc: 79.000000
Train Epoch: 20 [38400/50000 (77%)]    Loss: 0.591825  Acc: 78.000000
Train Epoch: 20 [44800/50000 (90%)]    Loss: 0.598099  Acc: 78.000000
one epoch spend:  0:00:01.846124
EPOCH:20, ACC:62.47

CIFAR10 pytorch LeNet Train: EPOCH:20, BATCH_SZ:64, LR:0.01, ACC:63.72
train spend time:  0:00:46.669295

Process finished with exit code 0

训练的lenet准确度在63%左右，远低于MINIST的99%，简单的LeNet对较复杂的图片准确度不高。

标签：Acc,50000,Loss,CIFAR10,VGG19,epoch,Epoch,Train,LeNet
来源： https://www.cnblogs.com/zhengbiqing/p/10424693.html

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