深度学习识别CIFAR10:pytorch训练LeNet、AlexNet、VGG19实现及比较(一)

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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:

 # -*- coding:utf-8 -*-

 u"""LeNet卷积神经网络训练学习CIFAR10"""

 __author__ = 'zhengbiqing 460356155@qq.com'

 import torch as t
import torchvision as tv
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torchvision.transforms import ToPILImage
import torch.backends.cudnn as cudnn import datetime
import argparse # 样本读取线程数
WORKERS = 4 # 网络参赛保存文件名
PARAS_FN = 'cifar_lenet_params.pkl' # minist数据存放位置
ROOT = '/home/zbq/PycharmProjects/cifar' # 目标函数
loss_func = nn.CrossEntropyLoss() # 最优结果
best_acc = 0 # 定义网络模型
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__() # 卷积层
self.cnn = nn.Sequential(
# 卷积层1,3通道输入,6个卷积核,核大小5*5
# 经过该层图像大小变为32-5+1,28*28
# 经2*2最大池化,图像变为14*14
nn.Conv2d(3, 6, 5),
nn.ReLU(),
nn.MaxPool2d(2), # 卷积层2,6输入通道,16个卷积核,核大小5*5
# 经过该层图像变为14-5+1,10*10
# 经2*2最大池化,图像变为5*5
nn.Conv2d(6, 16, 5),
nn.ReLU(),
nn.MaxPool2d(2)
) # 全连接层
self.fc = nn.Sequential(
# 16个feature,每个feature 5*5
nn.Linear(16 * 5 * 5, 120),
nn.ReLU(),
nn.Linear(120, 84),
nn.ReLU(),
nn.Linear(84, 10)
) def forward(self, x):
x = self.cnn(x) # x.size()[0]: batch size
x = x.view(x.size()[0], -1)
x = self.fc(x) return x '''
训练并测试网络
net:网络模型
train_data_load:训练数据集
optimizer:优化器
epoch:第几次训练迭代
log_interval:训练过程中损失函数值和准确率的打印频率
'''
def net_train(net, train_data_load, optimizer, epoch, log_interval):
net.train() begin = datetime.datetime.now() # 样本总数
total = len(train_data_load.dataset) # 样本批次训练的损失函数值的和
train_loss = 0 # 识别正确的样本数
ok = 0 for i, data in enumerate(train_data_load, 0):
img, label = data
img, label = img.cuda(), label.cuda() optimizer.zero_grad() outs = net(img)
loss = loss_func(outs, label)
loss.backward()
optimizer.step() # 累加损失值和训练样本数
train_loss += loss.item()
# total += label.size(0) _, predicted = t.max(outs.data, 1)
# 累加识别正确的样本数
ok += (predicted == label).sum() if (i + 1) % log_interval == 0:
# 训练结果输出 # 损失函数均值
loss_mean = train_loss / (i + 1) # 已训练的样本数
traind_total = (i + 1) * len(label) # 准确度
acc = 100. * ok / traind_total # 一个迭代的进度百分比
progress = 100. * traind_total / total print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f} Acc: {:.6f}'.format(
epoch, traind_total, total, progress, loss_mean, acc)) end = datetime.datetime.now()
print('one epoch spend: ', end - begin) '''
用测试集检查准确率
'''
def net_test(net, test_data_load, epoch):
net.eval() ok = 0 for i, data in enumerate(test_data_load):
img, label = data
img, label = img.cuda(), label.cuda() outs = net(img)
_, pre = t.max(outs.data, 1)
ok += (pre == label).sum() acc = ok.item() * 100. / (len(test_data_load.dataset))
print('EPOCH:{}, ACC:{}\n'.format(epoch, acc)) global best_acc
if acc > best_acc:
best_acc = acc '''
显示数据集中一个图片
'''
def img_show(dataset, index):
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck') show = ToPILImage() data, label = dataset[index]
print('img is a ', classes[label])
show((data + 1) / 2).resize((100, 100)).show() def main():
# 训练超参数设置,可通过命令行设置
parser = argparse.ArgumentParser(description='PyTorch CIFA10 LeNet Example')
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=20, metavar='N',
help='number of epochs to train (default: 20)')
parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum (default: 0.9)')
parser.add_argument('--log-interval', type=int, default=100, metavar='N',
help='how many batches to wait before logging training status (default: 100)')
parser.add_argument('--no-train', action='store_true', default=False,
help='If train the Model')
parser.add_argument('--save-model', action='store_true', default=False,
help='For Saving the current Model')
args = parser.parse_args() # 图像数值转换,ToTensor源码注释
"""Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
Converts a PIL Image or numpy.ndarray (H x W x C) in the range
[0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0].
"""
# 归一化把[0.0, 1.0]变换为[-1,1], ([0, 1] - 0.5) / 0.5 = [-1, 1]
transform = tv.transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) # 定义数据集
train_data = tv.datasets.CIFAR10(root=ROOT, train=True, download=True, transform=transform)
test_data = tv.datasets.CIFAR10(root=ROOT, train=False, download=False, transform=transform) train_load = t.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True, num_workers=WORKERS)
test_load = t.utils.data.DataLoader(test_data, batch_size=args.test_batch_size, shuffle=False, num_workers=WORKERS) net = LeNet().cuda()
print(net) # 如果不训练,直接加载保存的网络参数进行测试集验证
if args.no_train:
net.load_state_dict(t.load(PARAS_FN))
net_test(net, test_load, 0)
return optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum) start_time = datetime.datetime.now() for epoch in range(1, args.epochs + 1):
net_train(net, train_load, optimizer, epoch, args.log_interval) # 每个epoch结束后用测试集检查识别准确度
net_test(net, test_load, epoch) end_time = datetime.datetime.now() global best_acc
print('CIFAR10 pytorch LeNet Train: EPOCH:{}, BATCH_SZ:{}, LR:{}, ACC:{}'.format(args.epochs, args.batch_size, args.lr, best_acc))
print('train spend time: ', end_time - start_time) if args.save_model:
t.save(net.state_dict(), PARAS_FN) if __name__ == '__main__':
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。

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