02-Lenet5 图像分类网络
图1 Lenet5 手写字符分类网络架构
Cifar10 数据集的Lenet5的框架实现（Pytorch）:
1 import torch 2 from torch import nn, optim 3 import torch.nn.functional as F 4 5 class Lenet5(nn.Module): 6 7 def __init__(self): 8 super(Lenet5, self).__init__() 9 10 self.conv1 = nn.Conv2d(3, 6, kernel_size=5) 11 self.conv2 = nn.Conv2d(6, 16, kernel_size=5) 12 self.pooling = nn.AvgPool2d(kernel_size=2, stride=2) 13 14 self.l1 = nn.Linear(400, 120) 15 self.l2 = nn.Linear(120, 84) 16 self.l3 = nn.Linear(84, 10) 17 18 def forward(self, x): 19 20 # x: [64, 3, 32, 32] 21 batch_size = x.size(0) 22 23 x = self.pooling(self.conv1(x)) 24 x = self.pooling(self.conv2(x)) 25 x = x.view(batch_size, -1) 26 x = F.relu(self.l1(x)) 27 x = F.relu(self.l2(x)) 28 29 return self.l3(x) 30 31 32 class Lenet5_new(nn.Module): 33 34 def __init__(self): 35 super(Lenet5_new, self).__init__() 36 37 self.conv_unit = nn.Sequential( 38 nn.Conv2d(3, 6, kernel_size=5), 39 nn.AvgPool2d(kernel_size=2, stride=2), 40 nn.Conv2d(6, 16, kernel_size=5), 41 nn.AvgPool2d(kernel_size=2, stride=2) 42 ) 43 44 self.classfy = nn.Sequential( 45 nn.Linear(400, 120), 46 nn.ReLU(), 47 nn.Linear(120, 84), 48 nn.ReLU(), 49 nn.Linear(84, 10) 50 ) 51 52 def forward(self, x): 53 54 # x: [64, 3, 32, 32] 55 batch_size = x.size(0) 56 57 x = self.conv_unit(x) 58 59 # print(x.shape) 60 61 x = x.view(batch_size, -1) 62 63 output = self.classfy(x) 64 65 return output 66 67 68 model = Lenet5_new() 69 x = torch.rand(64, 3, 32, 32) 70 print(model(x).shape)
classfyNet_main.py
1 import torch 2 from torch.utils.data import DataLoader 3 from torch import nn, optim 4 from torchvision import datasets, transforms 5 from torchvision.transforms.functional import InterpolationMode 6 7 from matplotlib import pyplot as plt 8 9 10 import time 11 12 from Lenet5 import Lenet5_new 13 from Resnet18 import ResNet18 14 from AlexNet import AlexNet 15 16 def main(): 17 18 print("Load datasets...") 19 20 # transforms.RandomHorizontalFlip(p=0.5)---以0.5的概率对图片做水平横向翻转 21 # transforms.ToTensor()---shape从(H,W,C)->(C,H,W), 每个像素点从(0-255)映射到(0-1):直接除以255 22 # transforms.Normalize---先将输入归一化到(0,1),像素点通过"(x-mean)/std",将每个元素分布到(-1,1) 23 transform_train = transforms.Compose([ 24 # transforms.Resize((224, 224), interpolation=InterpolationMode.BICUBIC), 25 transforms.RandomHorizontalFlip(p=0.5), 26 transforms.ToTensor(), 27 transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) 28 ]) 29 30 transform_test = transforms.Compose([ 31 # transforms.Resize((224, 224), interpolation=InterpolationMode.BICUBIC), 32 transforms.ToTensor(), 33 transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) 34 ]) 35 36 # 内置函数下载数据集 37 train_dataset = datasets.CIFAR10(root="./data/Cifar10/", train=True, 38 transform = transform_train, 39 download=True) 40 test_dataset = datasets.CIFAR10(root = "./data/Cifar10/", 41 train = False, 42 transform = transform_test, 43 download=True) 44 45 print(len(train_dataset), len(test_dataset)) 46 47 Batch_size = 128 48 train_loader = DataLoader(train_dataset, batch_size=Batch_size, shuffle = True, num_workers=4) 49 test_loader = DataLoader(test_dataset, batch_size = Batch_size, shuffle = False, num_workers=4) 50 51 # 设置CUDA 52 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") 53 54 # 初始化模型 55 # 直接更换模型就行，其他无需操作 56 model = Lenet5_new().to(device) 57 # model = ResNet18().to(device) 58 # model = AlexNet(num_classes=10, init_weights=True).to(device) 59 60 # 构造损失函数和优化器 61 criterion = nn.CrossEntropyLoss() # 多分类softmax构造损失 62 # opt = optim.SGD(model.parameters(), lr=0.01, momentum=0.8, weight_decay=0.001) 63 opt = optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=0.0005) 64 65 # 动态更新学习率 ------每隔step_size : lr = lr * gamma 66 schedule = optim.lr_scheduler.StepLR(opt, step_size=10, gamma=0.6, last_epoch=-1) 67 68 # 开始训练 69 print("Start Train...") 70 71 epochs = 100 72 73 loss_list = [] 74 train_acc_list =[] 75 test_acc_list = [] 76 epochs_list = [] 77 78 for epoch in range(0, epochs): 79 80 start = time.time() 81 82 model.train() 83 84 running_loss = 0.0 85 batch_num = 0 86 87 for i, (inputs, labels) in enumerate(train_loader): 88 89 inputs, labels = inputs.to(device), labels.to(device) 90 91 # 将数据送入模型训练 92 outputs = model(inputs) 93 # 计算损失 94 loss = criterion(outputs, labels).to(device) 95 96 # 重置梯度 97 opt.zero_grad() 98 # 计算梯度，反向传播 99 loss.backward() 100 # 根据反向传播的梯度值优化更新参数 101 opt.step() 102 103 # 100个batch的 loss 之和 104 running_loss += loss.item() 105 # loss_list.append(loss.item()) 106 batch_num+=1 107 108 109 epochs_list.append(epoch) 110 111 # 每一轮结束输出一下当前的学习率 lr 112 lr_1 = opt.param_groups[0]['lr'] 113 print("learn_rate:%.15f" % lr_1) 114 schedule.step() 115 116 end = time.time() 117 print('epoch = %d/100, batch_num = %d, loss = %.6f, time = %.3f' % (epoch+1, batch_num, running_loss/batch_num, end-start)) 118 running_loss=0.0 119 120 # 每个epoch训练结束，都进行一次测试验证 121 model.eval() 122 train_correct = 0.0 123 train_total = 0 124 125 test_correct = 0.0 126 test_total = 0 127 128 # 训练模式不需要反向传播更新梯度 129 with torch.no_grad(): 130 131 # print("=======================train=======================") 132 for inputs, labels in train_loader: 133 inputs, labels = inputs.to(device), labels.to(device) 134 outputs = model(inputs) 135 136 pred = outputs.argmax(dim=1) # 返回每一行中最大值元素索引 137 train_total += inputs.size(0) 138 train_correct += torch.eq(pred, labels).sum().item() 139 140 141 # print("=======================test=======================") 142 for inputs, labels in test_loader: 143 inputs, labels = inputs.to(device), labels.to(device) 144 outputs = model(inputs) 145 146 pred = outputs.argmax(dim=1) # 返回每一行中最大值元素索引 147 test_total += inputs.size(0) 148 test_correct += torch.eq(pred, labels).sum().item() 149 150 print("train_total = %d, Accuracy = %.5f %%, test_total= %d, Accuracy = %.5f %%" %(train_total, 100 * train_correct / train_total, test_total, 100 * test_correct / test_total)) 151 152 train_acc_list.append(100 * train_correct / train_total) 153 test_acc_list.append(100 * test_correct / test_total) 154 155 # print("Accuracy of the network on the 10000 test images:%.5f %%" % (100 * test_correct / test_total)) 156 # print("===============================================") 157 158 fig = plt.figure(figsize=(4, 4)) 159 160 plt.plot(epochs_list, train_acc_list, label='train_acc_list') 161 plt.plot(epochs_list, test_acc_list, label='test_acc_list') 162 plt.legend() 163 plt.title("train_test_acc") 164 plt.savefig('Lenet5_acc_epoch_{:04d}.png'.format(epochs)) 165 plt.close() 166 167 if __name__ == "__main__": 168 169 main()
loss和acc变化
1 torch.Size([64, 10]) 2 Load datasets... 3 Files already downloaded and verified 4 Files already downloaded and verified 5 50000 10000 6 Start Train... 7 learn_rate:0.010000000000000 8 epoch = 1/100, batch_num = 391, loss = 1.727464, time = 36.498 9 train_total = 50000, Accuracy = 52.45200 %, test_total= 10000, Accuracy = 52.38000 % 10 learn_rate:0.010000000000000 11 epoch = 2/100, batch_num = 391, loss = 1.216604, time = 36.500 12 train_total = 50000, Accuracy = 63.83800 %, test_total= 10000, Accuracy = 63.70000 % 13 learn_rate:0.010000000000000 14 epoch = 3/100, batch_num = 391, loss = 1.008766, time = 36.392 15 train_total = 50000, Accuracy = 71.96200 %, test_total= 10000, Accuracy = 70.04000 % 16 learn_rate:0.010000000000000 17 epoch = 4/100, batch_num = 391, loss = 0.873215, time = 37.154 18 train_total = 50000, Accuracy = 76.05800 %, test_total= 10000, Accuracy = 73.51000 % 19 learn_rate:0.010000000000000 20 epoch = 5/100, batch_num = 391, loss = 0.792505, time = 36.425 21 train_total = 50000, Accuracy = 77.78200 %, test_total= 10000, Accuracy = 75.44000 % 22 learn_rate:0.010000000000000 23 epoch = 6/100, batch_num = 391, loss = 0.719424, time = 36.860 24 train_total = 50000, Accuracy = 79.40400 %, test_total= 10000, Accuracy = 76.35000 % 25 learn_rate:0.010000000000000 26 epoch = 7/100, batch_num = 391, loss = 0.664876, time = 36.796 27 train_total = 50000, Accuracy = 80.93000 %, test_total= 10000, Accuracy = 77.69000 % 28 learn_rate:0.010000000000000 29 epoch = 8/100, batch_num = 391, loss = 0.622452, time = 36.943 30 train_total = 50000, Accuracy = 82.91200 %, test_total= 10000, Accuracy = 78.87000 % 31 learn_rate:0.010000000000000 32 epoch = 9/100, batch_num = 391, loss = 0.587154, time = 36.859 33 train_total = 50000, Accuracy = 84.36000 %, test_total= 10000, Accuracy = 80.54000 % 34 learn_rate:0.010000000000000 35 epoch = 10/100, batch_num = 391, loss = 0.540577, time = 36.299 36 train_total = 50000, Accuracy = 84.67400 %, test_total= 10000, Accuracy = 79.98000 % 37 learn_rate:0.006000000000000 38 epoch = 11/100, batch_num = 391, loss = 0.452174, time = 36.273 39 train_total = 50000, Accuracy = 88.88400 %, test_total= 10000, Accuracy = 83.02000 % 40 learn_rate:0.006000000000000 41 epoch = 12/100, batch_num = 391, loss = 0.423968, time = 36.287 42 train_total = 50000, Accuracy = 89.21800 %, test_total= 10000, Accuracy = 82.94000 % 43 learn_rate:0.006000000000000 44 epoch = 13/100, batch_num = 391, loss = 0.397755, time = 36.149 45 train_total = 50000, Accuracy = 89.88200 %, test_total= 10000, Accuracy = 83.39000 % 46 learn_rate:0.006000000000000 47 epoch = 14/100, batch_num = 391, loss = 0.385166, time = 36.782 48 train_total = 50000, Accuracy = 91.02200 %, test_total= 10000, Accuracy = 83.24000 % 49 learn_rate:0.006000000000000 50 epoch = 15/100, batch_num = 391, loss = 0.362211, time = 36.290 51 train_total = 50000, Accuracy = 91.95400 %, test_total= 10000, Accuracy = 84.20000 % 52 learn_rate:0.006000000000000 53 epoch = 16/100, batch_num = 391, loss = 0.346161, time = 36.188 54 train_total = 50000, Accuracy = 92.11200 %, test_total= 10000, Accuracy = 84.13000 % 55 learn_rate:0.006000000000000 56 epoch = 17/100, batch_num = 391, loss = 0.334280, time = 36.307 57 train_total = 50000, Accuracy = 92.40800 %, test_total= 10000, Accuracy = 84.22000 % 58 learn_rate:0.006000000000000 59 epoch = 18/100, batch_num = 391, loss = 0.326841, time = 36.467 60 train_total = 50000, Accuracy = 92.81800 %, test_total= 10000, Accuracy = 84.10000 % 61 learn_rate:0.006000000000000 62 epoch = 19/100, batch_num = 391, loss = 0.309976, time = 36.388 63 train_total = 50000, Accuracy = 93.51000 %, test_total= 10000, Accuracy = 85.18000 % 64 learn_rate:0.006000000000000 65 epoch = 20/100, batch_num = 391, loss = 0.298686, time = 36.942 66 train_total = 50000, Accuracy = 94.38400 %, test_total= 10000, Accuracy = 84.86000 % 67 learn_rate:0.003600000000000 68 epoch = 21/100, batch_num = 391, loss = 0.240491, time = 35.967 69 train_total = 50000, Accuracy = 95.48800 %, test_total= 10000, Accuracy = 85.79000 % 70 learn_rate:0.003600000000000 71 epoch = 22/100, batch_num = 391, loss = 0.217680, time = 36.585 72 train_total = 50000, Accuracy = 96.28200 %, test_total= 10000, Accuracy = 86.05000 % 73 learn_rate:0.003600000000000 74 epoch = 23/100, batch_num = 391, loss = 0.211073, time = 36.719 75 train_total = 50000, Accuracy = 96.30000 %, test_total= 10000, Accuracy = 86.04000 % 76 learn_rate:0.003600000000000 77 epoch = 24/100, batch_num = 391, loss = 0.200940, time = 36.871 78 train_total = 50000, Accuracy = 97.05000 %, test_total= 10000, Accuracy = 86.03000 % 79 learn_rate:0.003600000000000 80 epoch = 25/100, batch_num = 391, loss = 0.185021, time = 37.046 81 train_total = 50000, Accuracy = 97.22600 %, test_total= 10000, Accuracy = 86.32000 % 82 learn_rate:0.003600000000000 83 epoch = 26/100, batch_num = 391, loss = 0.187093, time = 36.523 84 train_total = 50000, Accuracy = 97.37600 %, test_total= 10000, Accuracy = 86.35000 % 85 learn_rate:0.003600000000000 86 epoch = 27/100, batch_num = 391, loss = 0.179190, time = 36.332 87 train_total = 50000, Accuracy = 96.98600 %, test_total= 10000, Accuracy = 86.22000 % 88 learn_rate:0.003600000000000 89 epoch = 28/100, batch_num = 391, loss = 0.167953, time = 36.809 90 train_total = 50000, Accuracy = 97.56800 %, test_total= 10000, Accuracy = 86.54000 % 91 learn_rate:0.003600000000000 92 epoch = 29/100, batch_num = 391, loss = 0.167085, time = 36.486 93 train_total = 50000, Accuracy = 97.80400 %, test_total= 10000, Accuracy = 86.57000 % 94 learn_rate:0.003600000000000 95 epoch = 30/100, batch_num = 391, loss = 0.162673, time = 36.913 96 train_total = 50000, Accuracy = 97.79400 %, test_total= 10000, Accuracy = 86.37000 % 97 learn_rate:0.002160000000000 98 epoch = 31/100, batch_num = 391, loss = 0.130083, time = 36.981 99 train_total = 50000, Accuracy = 98.81000 %, test_total= 10000, Accuracy = 86.85000 % 100 learn_rate:0.002160000000000 101 epoch = 32/100, batch_num = 391, loss = 0.116471, time = 36.890 102 train_total = 50000, Accuracy = 98.83800 %, test_total= 10000, Accuracy = 86.92000 % 103 learn_rate:0.002160000000000 104 epoch = 33/100, batch_num = 391, loss = 0.108332, time = 36.568 105 train_total = 50000, Accuracy = 99.09200 %, test_total= 10000, Accuracy = 86.70000 % 106 learn_rate:0.002160000000000 107 epoch = 34/100, batch_num = 391, loss = 0.101327, time = 37.373 108 train_total = 50000, Accuracy = 99.20800 %, test_total= 10000, Accuracy = 87.11000 % 109 learn_rate:0.002160000000000 110 epoch = 35/100, batch_num = 391, loss = 0.096986, time = 36.520 111 train_total = 50000, Accuracy = 99.27200 %, test_total= 10000, Accuracy = 87.04000 % 112 learn_rate:0.002160000000000 113 epoch = 36/100, batch_num = 391, loss = 0.096955, time = 36.757 114 train_total = 50000, Accuracy = 99.29600 %, test_total= 10000, Accuracy = 87.03000 % 115 learn_rate:0.002160000000000 116 epoch = 37/100, batch_num = 391, loss = 0.094020, time = 37.187 117 train_total = 50000, Accuracy = 99.34000 %, test_total= 10000, Accuracy = 87.11000 % 118 learn_rate:0.002160000000000 119 epoch = 38/100, batch_num = 391, loss = 0.087212, time = 36.777 120 train_total = 50000, Accuracy = 99.46800 %, test_total= 10000, Accuracy = 87.08000 % 121 learn_rate:0.002160000000000 122 epoch = 39/100, batch_num = 391, loss = 0.084191, time = 36.623 123 train_total = 50000, Accuracy = 99.43000 %, test_total= 10000, Accuracy = 86.95000 % 124 learn_rate:0.002160000000000 125 epoch = 40/100, batch_num = 391, loss = 0.086169, time = 37.182 126 train_total = 50000, Accuracy = 99.55600 %, test_total= 10000, Accuracy = 87.01000 % 127 learn_rate:0.001296000000000 128 epoch = 41/100, batch_num = 391, loss = 0.071286, time = 37.197 129 train_total = 50000, Accuracy = 99.75000 %, test_total= 10000, Accuracy = 87.50000 % 130 learn_rate:0.001296000000000 131 epoch = 42/100, batch_num = 391, loss = 0.063353, time = 37.097 132 train_total = 50000, Accuracy = 99.74000 %, test_total= 10000, Accuracy = 87.33000 % 133 learn_rate:0.001296000000000 134 epoch = 43/100, batch_num = 391, loss = 0.061826, time = 36.941 135 train_total = 50000, Accuracy = 99.75000 %, test_total= 10000, Accuracy = 87.45000 % 136 learn_rate:0.001296000000000 137 epoch = 44/100, batch_num = 391, loss = 0.059741, time = 36.205 138 train_total = 50000, Accuracy = 99.86400 %, test_total= 10000, Accuracy = 87.17000 % 139 learn_rate:0.001296000000000 140 epoch = 45/100, batch_num = 391, loss = 0.056397, time = 36.835 141 train_total = 50000, Accuracy = 99.83000 %, test_total= 10000, Accuracy = 87.44000 % 142 learn_rate:0.001296000000000 143 epoch = 46/100, batch_num = 391, loss = 0.054009, time = 36.849 144 train_total = 50000, Accuracy = 99.78800 %, test_total= 10000, Accuracy = 87.40000 % 145 learn_rate:0.001296000000000 146 epoch = 47/100, batch_num = 391, loss = 0.053588, time = 36.394 147 train_total = 50000, Accuracy = 99.88600 %, test_total= 10000, Accuracy = 87.60000 % 148 learn_rate:0.001296000000000 149 epoch = 48/100, batch_num = 391, loss = 0.053057, time = 37.785 150 train_total = 50000, Accuracy = 99.89200 %, test_total= 10000, Accuracy = 87.46000 % 151 learn_rate:0.001296000000000 152 epoch = 49/100, batch_num = 391, loss = 0.045930, time = 36.526 153 train_total = 50000, Accuracy = 99.89600 %, test_total= 10000, Accuracy = 87.61000 % 154 learn_rate:0.001296000000000 155 epoch = 50/100, batch_num = 391, loss = 0.049549, time = 36.887 156 train_total = 50000, Accuracy = 99.89000 %, test_total= 10000, Accuracy = 87.41000 % 157 learn_rate:0.000777600000000 158 epoch = 51/100, batch_num = 391, loss = 0.045231, time = 36.780 159 train_total = 50000, Accuracy = 99.93200 %, test_total= 10000, Accuracy = 87.55000 % 160 learn_rate:0.000777600000000 161 epoch = 52/100, batch_num = 391, loss = 0.039894, time = 36.363 162 train_total = 50000, Accuracy = 99.93800 %, test_total= 10000, Accuracy = 87.54000 % 163 learn_rate:0.000777600000000 164 epoch = 53/100, batch_num = 391, loss = 0.038822, time = 37.128 165 train_total = 50000, Accuracy = 99.95600 %, test_total= 10000, Accuracy = 87.62000 % 166 learn_rate:0.000777600000000 167 epoch = 54/100, batch_num = 391, loss = 0.037930, time = 36.859 168 train_total = 50000, Accuracy = 99.94400 %, test_total= 10000, Accuracy = 87.74000 % 169 learn_rate:0.000777600000000 170 epoch = 55/100, batch_num = 391, loss = 0.039034, time = 36.948 171 train_total = 50000, Accuracy = 99.96000 %, test_total= 10000, Accuracy = 87.50000 % 172 learn_rate:0.000777600000000 173 epoch = 56/100, batch_num = 391, loss = 0.036626, time = 36.273 174 train_total = 50000, Accuracy = 99.95200 %, test_total= 10000, Accuracy = 87.74000 % 175 learn_rate:0.000777600000000 176 epoch = 57/100, batch_num = 391, loss = 0.035077, time = 36.407 177 train_total = 50000, Accuracy = 99.95000 %, test_total= 10000, Accuracy = 87.69000 % 178 learn_rate:0.000777600000000 179 epoch = 58/100, batch_num = 391, loss = 0.037595, time = 36.269 180 train_total = 50000, Accuracy = 99.96600 %, test_total= 10000, Accuracy = 87.78000 % 181 learn_rate:0.000777600000000 182 epoch = 59/100, batch_num = 391, loss = 0.034734, time = 37.324 183 train_total = 50000, Accuracy = 99.97800 %, test_total= 10000, Accuracy = 87.60000 % 184 learn_rate:0.000777600000000 185 epoch = 60/100, batch_num = 391, loss = 0.033837, time = 36.963 186 train_total = 50000, Accuracy = 99.94800 %, test_total= 10000, Accuracy = 87.67000 % 187 learn_rate:0.000466560000000 188 epoch = 61/100, batch_num = 391, loss = 0.031147, time = 37.240 189 train_total = 50000, Accuracy = 99.97200 %, test_total= 10000, Accuracy = 87.96000 % 190 learn_rate:0.000466560000000 191 epoch = 62/100, batch_num = 391, loss = 0.032038, time = 37.097 192 train_total = 50000, Accuracy = 99.97600 %, test_total= 10000, Accuracy = 87.85000 % 193 learn_rate:0.000466560000000 194 epoch = 63/100, batch_num = 391, loss = 0.031524, time = 36.390 195 train_total = 50000, Accuracy = 99.98000 %, test_total= 10000, Accuracy = 87.85000 % 196 learn_rate:0.000466560000000 197 epoch = 64/100, batch_num = 391, loss = 0.029830, time = 36.537 198 train_total = 50000, Accuracy = 99.98200 %, test_total= 10000, Accuracy = 87.90000 % 199 learn_rate:0.000466560000000 200 epoch = 65/100, batch_num = 391, loss = 0.028459, time = 37.747 201 train_total = 50000, Accuracy = 99.98200 %, test_total= 10000, Accuracy = 87.77000 % 202 learn_rate:0.000466560000000 203 epoch = 66/100, batch_num = 391, loss = 0.027390, time = 36.716 204 train_total = 50000, Accuracy = 99.97200 %, test_total= 10000, Accuracy = 87.98000 % 205 learn_rate:0.000466560000000 206 epoch = 67/100, batch_num = 391, loss = 0.027200, time = 36.640 207 train_total = 50000, Accuracy = 99.97600 %, test_total= 10000, Accuracy = 87.69000 % 208 learn_rate:0.000466560000000 209 epoch = 68/100, batch_num = 391, loss = 0.029614, time = 36.406 210 train_total = 50000, Accuracy = 99.98600 %, test_total= 10000, Accuracy = 87.81000 % 211 learn_rate:0.000466560000000 212 epoch = 69/100, batch_num = 391, loss = 0.026783, time = 36.637 213 train_total = 50000, Accuracy = 99.98800 %, test_total= 10000, Accuracy = 87.83000 % 214 learn_rate:0.000466560000000 215 epoch = 70/100, batch_num = 391, loss = 0.029514, time = 36.458 216 train_total = 50000, Accuracy = 99.98200 %, test_total= 10000, Accuracy = 87.92000 % 217 learn_rate:0.000279936000000 218 epoch = 71/100, batch_num = 391, loss = 0.026174, time = 36.751 219 train_total = 50000, Accuracy = 99.98200 %, test_total= 10000, Accuracy = 87.89000 % 220 learn_rate:0.000279936000000 221 epoch = 72/100, batch_num = 391, loss = 0.024701, time = 36.821 222 train_total = 50000, Accuracy = 99.98800 %, test_total= 10000, Accuracy = 87.77000 % 223 learn_rate:0.000279936000000 224 epoch = 73/100, batch_num = 391, loss = 0.025671, time = 37.318 225 train_total = 50000, Accuracy = 99.99400 %, test_total= 10000, Accuracy = 87.88000 % 226 learn_rate:0.000279936000000 227 epoch = 74/100, batch_num = 391, loss = 0.024448, time = 36.599 228 train_total = 50000, Accuracy = 99.98600 %, test_total= 10000, Accuracy = 87.83000 % 229 learn_rate:0.000279936000000 230 epoch = 75/100, batch_num = 391, loss = 0.025472, time = 37.062 231 train_total = 50000, Accuracy = 99.99000 %, test_total= 10000, Accuracy = 87.78000 % 232 learn_rate:0.000279936000000 233 epoch = 76/100, batch_num = 391, loss = 0.024742, time = 36.788 234 train_total = 50000, Accuracy = 99.98400 %, test_total= 10000, Accuracy = 87.94000 % 235 learn_rate:0.000279936000000 236 epoch = 77/100, batch_num = 391, loss = 0.024732, time = 36.909 237 train_total = 50000, Accuracy = 99.98600 %, test_total= 10000, Accuracy = 88.00000 % 238 learn_rate:0.000279936000000 239 epoch = 78/100, batch_num = 391, loss = 0.023314, time = 37.003 240 train_total = 50000, Accuracy = 99.99200 %, test_total= 10000, Accuracy = 87.75000 % 241 learn_rate:0.000279936000000 242 epoch = 79/100, batch_num = 391, loss = 0.023640, time = 37.048 243 train_total = 50000, Accuracy = 99.99000 %, test_total= 10000, Accuracy = 87.82000 % 244 learn_rate:0.000279936000000 245 epoch = 80/100, batch_num = 391, loss = 0.023612, time = 36.823 246 train_total = 50000, Accuracy = 99.98800 %, test_total= 10000, Accuracy = 88.00000 % 247 learn_rate:0.000167961600000 248 epoch = 81/100, batch_num = 391, loss = 0.023991, time = 37.118 249 train_total = 50000, Accuracy = 99.99400 %, test_total= 10000, Accuracy = 87.88000 % 250 learn_rate:0.000167961600000 251 epoch = 82/100, batch_num = 391, loss = 0.022168, time = 37.438 252 train_total = 50000, Accuracy = 99.98800 %, test_total= 10000, Accuracy = 88.00000 % 253 learn_rate:0.000167961600000 254 epoch = 83/100, batch_num = 391, loss = 0.022910, time = 36.649 255 train_total = 50000, Accuracy = 99.99800 %, test_total= 10000, Accuracy = 88.02000 % 256 learn_rate:0.000167961600000 257 epoch = 84/100, batch_num = 391, loss = 0.022220, time = 36.602 258 train_total = 50000, Accuracy = 99.99200 %, test_total= 10000, Accuracy = 88.05000 % 259 learn_rate:0.000167961600000 260 epoch = 85/100, batch_num = 391, loss = 0.022573, time = 36.785 261 train_total = 50000, Accuracy = 99.99200 %, test_total= 10000, Accuracy = 87.92000 % 262 learn_rate:0.000167961600000 263 epoch = 86/100, batch_num = 391, loss = 0.022758, time = 36.705 264 train_total = 50000, Accuracy = 99.99600 %, test_total= 10000, Accuracy = 87.89000 % 265 learn_rate:0.000167961600000 266 epoch = 87/100, batch_num = 391, loss = 0.022738, time = 36.700 267 train_total = 50000, Accuracy = 99.99400 %, test_total= 10000, Accuracy = 88.02000 % 268 learn_rate:0.000167961600000 269 epoch = 88/100, batch_num = 391, loss = 0.020802, time = 36.288 270 train_total = 50000, Accuracy = 99.99400 %, test_total= 10000, Accuracy = 87.84000 % 271 learn_rate:0.000167961600000 272 epoch = 89/100, batch_num = 391, loss = 0.022297, time = 36.614 273 train_total = 50000, Accuracy = 99.99400 %, test_total= 10000, Accuracy = 87.89000 % 274 learn_rate:0.000167961600000 275 epoch = 90/100, batch_num = 391, loss = 0.021643, time = 36.498 276 train_total = 50000, Accuracy = 99.99600 %, test_total= 10000, Accuracy = 88.01000 % 277 learn_rate:0.000100776960000 278 epoch = 91/100, batch_num = 391, loss = 0.022491, time = 36.515 279 train_total = 50000, Accuracy = 99.99400 %, test_total= 10000, Accuracy = 87.90000 % 280 learn_rate:0.000100776960000 281 epoch = 92/100, batch_num = 391, loss = 0.020951, time = 36.592 282 train_total = 50000, Accuracy = 99.99800 %, test_total= 10000, Accuracy = 87.95000 % 283 learn_rate:0.000100776960000 284 epoch = 93/100, batch_num = 391, loss = 0.020818, time = 37.168 285 train_total = 50000, Accuracy = 99.99800 %, test_total= 10000, Accuracy = 87.95000 % 286 learn_rate:0.000100776960000 287 epoch = 94/100, batch_num = 391, loss = 0.020871, time = 36.718 288 train_total = 50000, Accuracy = 99.99600 %, test_total= 10000, Accuracy = 87.99000 % 289 learn_rate:0.000100776960000 290 epoch = 95/100, batch_num = 391, loss = 0.021837, time = 36.537 291 train_total = 50000, Accuracy = 99.99200 %, test_total= 10000, Accuracy = 88.04000 % 292 learn_rate:0.000100776960000 293 epoch = 96/100, batch_num = 391, loss = 0.021248, time = 36.726 294 train_total = 50000, Accuracy = 99.99800 %, test_total= 10000, Accuracy = 87.97000 % 295 learn_rate:0.000100776960000 296 epoch = 97/100, batch_num = 391, loss = 0.021883, time = 37.247 297 train_total = 50000, Accuracy = 99.99800 %, test_total= 10000, Accuracy = 87.92000 % 298 learn_rate:0.000100776960000 299 epoch = 98/100, batch_num = 391, loss = 0.021112, time = 36.765 300 train_total = 50000, Accuracy = 100.00000 %, test_total= 10000, Accuracy = 87.98000 % 301 learn_rate:0.000100776960000 302 epoch = 99/100, batch_num = 391, loss = 0.021158, time = 36.582 303 train_total = 50000, Accuracy = 99.99000 %, test_total= 10000, Accuracy = 87.93000 % 304 learn_rate:0.000100776960000 305 epoch = 100/100, batch_num = 391, loss = 0.020427, time = 36.319 306 train_total = 50000, Accuracy = 99.99600 %, test_total= 10000, Accuracy = 87.960
1 torch.Size([64, 10]) 2 Load datasets... 3 Files already downloaded and verified 4 Files already downloaded and verified 5 50000 10000 6 Start Train... 7 learn_rate:0.010000000000000 8 epoch = 1/100, batch_num = 391, loss = 1.903144, time = 8.799 9 train_total = 50000, Accuracy = 40.93600 %, test_total= 10000, Accuracy = 40.76000 % 10 learn_rate:0.010000000000000 11 epoch = 2/100, batch_num = 391, loss = 1.565806, time = 8.417 12 train_total = 50000, Accuracy = 48.31000 %, test_total= 10000, Accuracy = 47.85000 % 13 learn_rate:0.010000000000000 14 epoch = 3/100, batch_num = 391, loss = 1.441674, time = 8.476 15 train_total = 50000, Accuracy = 50.90600 %, test_total= 10000, Accuracy = 50.16000 % 16 learn_rate:0.010000000000000 17 epoch = 4/100, batch_num = 391, loss = 1.381594, time = 8.434 18 train_total = 50000, Accuracy = 52.56000 %, test_total= 10000, Accuracy = 51.30000 % 19 learn_rate:0.010000000000000 20 epoch = 5/100, batch_num = 391, loss = 1.327557, time = 8.507 21 train_total = 50000, Accuracy = 54.97600 %, test_total= 10000, Accuracy = 52.81000 % 22 learn_rate:0.010000000000000 23 epoch = 6/100, batch_num = 391, loss = 1.290775, time = 8.591 24 train_total = 50000, Accuracy = 55.57200 %, test_total= 10000, Accuracy = 53.24000 % 25 learn_rate:0.010000000000000 26 epoch = 7/100, batch_num = 391, loss = 1.261222, time = 8.110 27 train_total = 50000, Accuracy = 56.98000 %, test_total= 10000, Accuracy = 54.04000 % 28 learn_rate:0.010000000000000 29 epoch = 8/100, batch_num = 391, loss = 1.238212, time = 8.579 30 train_total = 50000, Accuracy = 57.30400 %, test_total= 10000, Accuracy = 54.24000 % 31 learn_rate:0.010000000000000 32 epoch = 9/100, batch_num = 391, loss = 1.221999, time = 8.290 33 train_total = 50000, Accuracy = 58.77600 %, test_total= 10000, Accuracy = 54.90000 % 34 learn_rate:0.010000000000000 35 epoch = 10/100, batch_num = 391, loss = 1.192803, time = 8.462 36 train_total = 50000, Accuracy = 59.51800 %, test_total= 10000, Accuracy = 55.77000 % 37 learn_rate:0.006000000000000 38 epoch = 11/100, batch_num = 391, loss = 1.131696, time = 8.475 39 train_total = 50000, Accuracy = 60.72000 %, test_total= 10000, Accuracy = 56.26000 % 40 learn_rate:0.006000000000000 41 epoch = 12/100, batch_num = 391, loss = 1.112754, time = 8.660 42 train_total = 50000, Accuracy = 61.81400 %, test_total= 10000, Accuracy = 56.88000 % 43 learn_rate:0.006000000000000 44 epoch = 13/100, batch_num = 391, loss = 1.100870, time = 8.282 45 train_total = 50000, Accuracy = 62.78600 %, test_total= 10000, Accuracy = 57.10000 % 46 learn_rate:0.006000000000000 47 epoch = 14/100, batch_num = 391, loss = 1.090827, time = 8.212 48 train_total = 50000, Accuracy = 62.81200 %, test_total= 10000, Accuracy = 57.30000 % 49 learn_rate:0.006000000000000 50 epoch = 15/100, batch_num = 391, loss = 1.083375, time = 8.078 51 train_total = 50000, Accuracy = 63.52600 %, test_total= 10000, Accuracy = 56.98000 % 52 learn_rate:0.006000000000000 53 epoch = 16/100, batch_num = 391, loss = 1.066339, time = 8.124 54 train_total = 50000, Accuracy = 63.56000 %, test_total= 10000, Accuracy = 57.35000 % 55 learn_rate:0.006000000000000 56 epoch = 17/100, batch_num = 391, loss = 1.060602, time = 8.204 57 train_total = 50000, Accuracy = 63.54000 %, test_total= 10000, Accuracy = 57.29000 % 58 learn_rate:0.006000000000000 59 epoch = 18/100, batch_num = 391, loss = 1.052601, time = 8.214 60 train_total = 50000, Accuracy = 63.76200 %, test_total= 10000, Accuracy = 57.38000 % 61 learn_rate:0.006000000000000 62 epoch = 19/100, batch_num = 391, loss = 1.046312, time = 8.268 63 train_total = 50000, Accuracy = 63.50800 %, test_total= 10000, Accuracy = 56.43000 % 64 learn_rate:0.006000000000000 65 epoch = 20/100, batch_num = 391, loss = 1.043756, time = 8.192 66 train_total = 50000, Accuracy = 64.93400 %, test_total= 10000, Accuracy = 57.73000 % 67 learn_rate:0.003600000000000 68 epoch = 21/100, batch_num = 391, loss = 0.992392, time = 8.288 69 train_total = 50000, Accuracy = 66.18200 %, test_total= 10000, Accuracy = 58.13000 % 70 learn_rate:0.003600000000000 71 epoch = 22/100, batch_num = 391, loss = 0.978301, time = 8.277 72 train_total = 50000, Accuracy = 66.18000 %, test_total= 10000, Accuracy = 58.04000 % 73 learn_rate:0.003600000000000 74 epoch = 23/100, batch_num = 391, loss = 0.974397, time = 28.932 75 (pytorch-CycleGAN-and-pix2pix) cd /opt/data/zp ; /usr/bin/env /root/miniconda3/envs/pytorch-CycleGAN-and-pix2pix/bin/python /root/.vscode-server/extensions/ms-python.python-2022.20.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher 46134 -- /opt/data/zp/ClassifyNet/classfyNet_train.py 76 torch.Size([64, 10]) 77 Load datasets... 78 Files already downloaded and verified 79 Files already downloaded and verified 80 50000 10000 81 Start Train... 82 learn_rate:0.010000000000000 83 epoch = 1/100, batch_num = 391, loss = 1.881741, time = 9.116 84 train_total = 50000, Accuracy = 43.08400 %, test_total= 10000, Accuracy = 43.72000 % 85 learn_rate:0.010000000000000 86 epoch = 2/100, batch_num = 391, loss = 1.541906, time = 8.130 87 train_total = 50000, Accuracy = 48.50200 %, test_total= 10000, Accuracy = 47.63000 % 88 learn_rate:0.010000000000000 89 epoch = 3/100, batch_num = 391, loss = 1.430356, time = 8.277 90 train_total = 50000, Accuracy = 50.41000 %, test_total= 10000, Accuracy = 49.71000 % 91 learn_rate:0.010000000000000 92 epoch = 4/100, batch_num = 391, loss = 1.370210, time = 8.568 93 train_total = 50000, Accuracy = 53.12000 %, test_total= 10000, Accuracy = 52.20000 % 94 learn_rate:0.010000000000000 95 epoch = 5/100, batch_num = 391, loss = 1.329338, time = 8.578 96 train_total = 50000, Accuracy = 55.14600 %, test_total= 10000, Accuracy = 53.29000 % 97 learn_rate:0.010000000000000 98 epoch = 6/100, batch_num = 391, loss = 1.289808, time = 9.173 99 train_total = 50000, Accuracy = 55.66800 %, test_total= 10000, Accuracy = 53.90000 % 100 learn_rate:0.010000000000000 101 epoch = 7/100, batch_num = 391, loss = 1.258256, time = 8.447 102 train_total = 50000, Accuracy = 57.02200 %, test_total= 10000, Accuracy = 54.16000 % 103 learn_rate:0.010000000000000 104 epoch = 8/100, batch_num = 391, loss = 1.234633, time = 8.180 105 train_total = 50000, Accuracy = 57.67600 %, test_total= 10000, Accuracy = 54.95000 % 106 learn_rate:0.010000000000000 107 epoch = 9/100, batch_num = 391, loss = 1.212396, time = 8.370 108 train_total = 50000, Accuracy = 57.52800 %, test_total= 10000, Accuracy = 53.96000 % 109 learn_rate:0.010000000000000 110 epoch = 10/100, batch_num = 391, loss = 1.200719, time = 8.711 111 train_total = 50000, Accuracy = 59.38400 %, test_total= 10000, Accuracy = 55.71000 % 112 learn_rate:0.006000000000000 113 epoch = 11/100, batch_num = 391, loss = 1.135619, time = 8.319 114 train_total = 50000, Accuracy = 61.45800 %, test_total= 10000, Accuracy = 56.57000 % 115 learn_rate:0.006000000000000 116 epoch = 12/100, batch_num = 391, loss = 1.113530, time = 8.651 117 train_total = 50000, Accuracy = 62.41400 %, test_total= 10000, Accuracy = 57.09000 % 118 learn_rate:0.006000000000000 119 epoch = 13/100, batch_num = 391, loss = 1.100080, time = 8.482 120 train_total = 50000, Accuracy = 61.96600 %, test_total= 10000, Accuracy = 56.98000 % 121 learn_rate:0.006000000000000 122 epoch = 14/100, batch_num = 391, loss = 1.094360, time = 8.200 123 train_total = 50000, Accuracy = 62.18000 %, test_total= 10000, Accuracy = 55.90000 % 124 learn_rate:0.006000000000000 125 epoch = 15/100, batch_num = 391, loss = 1.080716, time = 8.655 126 train_total = 50000, Accuracy = 62.75600 %, test_total= 10000, Accuracy = 56.38000 % 127 learn_rate:0.006000000000000 128 epoch = 16/100, batch_num = 391, loss = 1.074441, time = 8.350 129 train_total = 50000, Accuracy = 62.81600 %, test_total= 10000, Accuracy = 56.71000 % 130 learn_rate:0.006000000000000 131 epoch = 17/100, batch_num = 391, loss = 1.068547, time = 8.665 132 train_total = 50000, Accuracy = 63.60000 %, test_total= 10000, Accuracy = 56.86000 % 133 learn_rate:0.006000000000000 134 epoch = 18/100, batch_num = 391, loss = 1.059618, time = 8.439 135 train_total = 50000, Accuracy = 63.92000 %, test_total= 10000, Accuracy = 57.31000 % 136 learn_rate:0.006000000000000 137 epoch = 19/100, batch_num = 391, loss = 1.051635, time = 8.454 138 train_total = 50000, Accuracy = 64.84200 %, test_total= 10000, Accuracy = 57.17000 % 139 learn_rate:0.006000000000000 140 epoch = 20/100, batch_num = 391, loss = 1.042078, time = 8.425 141 train_total = 50000, Accuracy = 64.29800 %, test_total= 10000, Accuracy = 57.26000 % 142 learn_rate:0.003600000000000 143 epoch = 21/100, batch_num = 391, loss = 0.996652, time = 8.580 144 train_total = 50000, Accuracy = 65.97600 %, test_total= 10000, Accuracy = 57.42000 % 145 learn_rate:0.003600000000000 146 epoch = 22/100, batch_num = 391, loss = 0.985630, time = 8.581 147 train_total = 50000, Accuracy = 66.27000 %, test_total= 10000, Accuracy = 57.55000 % 148 learn_rate:0.003600000000000 149 epoch = 23/100, batch_num = 391, loss = 0.979711, time = 8.486 150 train_total = 50000, Accuracy = 66.27600 %, test_total= 10000, Accuracy = 57.93000 % 151 learn_rate:0.003600000000000 152 epoch = 24/100, batch_num = 391, loss = 0.973961, time = 8.152 153 train_total = 50000, Accuracy = 67.05800 %, test_total= 10000, Accuracy = 58.58000 % 154 learn_rate:0.003600000000000 155 epoch = 25/100, batch_num = 391, loss = 0.967450, time = 8.439 156 train_total = 50000, Accuracy = 66.76400 %, test_total= 10000, Accuracy = 57.62000 % 157 learn_rate:0.003600000000000 158 epoch = 26/100, batch_num = 391, loss = 0.964060, time = 8.368 159 train_total = 50000, Accuracy = 67.17000 %, test_total= 10000, Accuracy = 57.92000 % 160 learn_rate:0.003600000000000 161 epoch = 27/100, batch_num = 391, loss = 0.963552, time = 8.569 162 train_total = 50000, Accuracy = 66.95400 %, test_total= 10000, Accuracy = 57.78000 % 163 learn_rate:0.003600000000000 164 epoch = 28/100, batch_num = 391, loss = 0.955293, time = 8.372 165 train_total = 50000, Accuracy = 67.13200 %, test_total= 10000, Accuracy = 57.93000 % 166 learn_rate:0.003600000000000 167 epoch = 29/100, batch_num = 391, loss = 0.953944, time = 8.723 168 train_total = 50000, Accuracy = 67.40800 %, test_total= 10000, Accuracy = 57.66000 % 169 learn_rate:0.003600000000000 170 epoch = 30/100, batch_num = 391, loss = 0.946184, time = 8.704 171 train_total = 50000, Accuracy = 66.98400 %, test_total= 10000, Accuracy = 57.68000 % 172 learn_rate:0.002160000000000 173 epoch = 31/100, batch_num = 391, loss = 0.911537, time = 8.487 174 train_total = 50000, Accuracy = 68.91000 %, test_total= 10000, Accuracy = 58.46000 % 175 learn_rate:0.002160000000000 176 epoch = 32/100, batch_num = 391, loss = 0.904129, time = 8.937 177 train_total = 50000, Accuracy = 69.27000 %, test_total= 10000, Accuracy = 58.22000 % 178 learn_rate:0.002160000000000 179 epoch = 33/100, batch_num = 391, loss = 0.900249, time = 8.957 180 train_total = 50000, Accuracy = 68.78000 %, test_total= 10000, Accuracy = 58.16000 % 181 learn_rate:0.002160000000000 182 epoch = 34/100, batch_num = 391, loss = 0.898092, time = 8.350 183 train_total = 50000, Accuracy = 69.28800 %, test_total= 10000, Accuracy = 58.27000 % 184 learn_rate:0.002160000000000 185 epoch = 35/100, batch_num = 391, loss = 0.893488, time = 8.472 186 train_total = 50000, Accuracy = 69.46000 %, test_total= 10000, Accuracy = 58.42000 % 187 learn_rate:0.002160000000000 188 epoch = 36/100, batch_num = 391, loss = 0.891810, time = 8.247 189 train_total = 50000, Accuracy = 69.02400 %, test_total= 10000, Accuracy = 57.93000 % 190 learn_rate:0.002160000000000 191 epoch = 37/100, batch_num = 391, loss = 0.889165, time = 8.261 192 train_total = 50000, Accuracy = 69.48800 %, test_total= 10000, Accuracy = 57.91000 % 193 learn_rate:0.002160000000000 194 epoch = 38/100, batch_num = 391, loss = 0.886049, time = 8.376 195 train_total = 50000, Accuracy = 69.72000 %, test_total= 10000, Accuracy = 58.19000 % 196 learn_rate:0.002160000000000 197 epoch = 39/100, batch_num = 391, loss = 0.884485, time = 8.196 198 train_total = 50000, Accuracy = 69.58200 %, test_total= 10000, Accuracy = 58.38000 % 199 learn_rate:0.002160000000000 200 epoch = 40/100, batch_num = 391, loss = 0.880268, time = 8.774 201 train_total = 50000, Accuracy = 70.25200 %, test_total= 10000, Accuracy = 58.46000 % 202 learn_rate:0.001296000000000 203 epoch = 41/100, batch_num = 391, loss = 0.857334, time = 8.379 204 train_total = 50000, Accuracy = 70.68600 %, test_total= 10000, Accuracy = 58.88000 % 205 learn_rate:0.001296000000000 206 epoch = 42/100, batch_num = 391, loss = 0.850778, time = 8.457 207 train_total = 50000, Accuracy = 70.79600 %, test_total= 10000, Accuracy = 58.17000 % 208 learn_rate:0.001296000000000 209 epoch = 43/100, batch_num = 391, loss = 0.848947, time = 8.526 210 train_total = 50000, Accuracy = 70.77800 %, test_total= 10000, Accuracy = 58.23000 % 211 learn_rate:0.001296000000000 212 epoch = 44/100, batch_num = 391, loss = 0.844374, time = 8.536 213 train_total = 50000, Accuracy = 71.18200 %, test_total= 10000, Accuracy = 58.37000 % 214 learn_rate:0.001296000000000 215 epoch = 45/100, batch_num = 391, loss = 0.843636, time = 8.250 216 train_total = 50000, Accuracy = 71.09200 %, test_total= 10000, Accuracy = 58.09000 % 217 learn_rate:0.001296000000000 218 epoch = 46/100, batch_num = 391, loss = 0.843857, time = 8.493 219 train_total = 50000, Accuracy = 71.13600 %, test_total= 10000, Accuracy = 58.37000 % 220 learn_rate:0.001296000000000 221 epoch = 47/100, batch_num = 391, loss = 0.838642, time = 8.833 222 train_total = 50000, Accuracy = 71.01200 %, test_total= 10000, Accuracy = 58.11000 % 223 learn_rate:0.001296000000000 224 epoch = 48/100, batch_num = 391, loss = 0.835518, time = 8.447 225 train_total = 50000, Accuracy = 71.29600 %, test_total= 10000, Accuracy = 58.26000 % 226 learn_rate:0.001296000000000 227 epoch = 49/100, batch_num = 391, loss = 0.834901, time = 8.236 228 train_total = 50000, Accuracy = 71.18800 %, test_total= 10000, Accuracy = 58.16000 % 229 learn_rate:0.001296000000000 230 epoch = 50/100, batch_num = 391, loss = 0.833575, time = 8.534 231 train_total = 50000, Accuracy = 71.38200 %, test_total= 10000, Accuracy = 57.92000 % 232 learn_rate:0.000777600000000 233 epoch = 51/100, batch_num = 391, loss = 0.815790, time = 8.231 234 train_total = 50000, Accuracy = 71.89600 %, test_total= 10000, Accuracy = 58.02000 % 235 learn_rate:0.000777600000000 236 epoch = 52/100, batch_num = 391, loss = 0.814659, time = 8.546 237 train_total = 50000, Accuracy = 71.98800 %, test_total= 10000, Accuracy = 58.58000 % 238 learn_rate:0.000777600000000 239 epoch = 53/100, batch_num = 391, loss = 0.809388, time = 8.359 240 train_total = 50000, Accuracy = 72.16800 %, test_total= 10000, Accuracy = 58.11000 % 241 learn_rate:0.000777600000000 242 epoch = 54/100, batch_num = 391, loss = 0.809077, time = 8.655 243 train_total = 50000, Accuracy = 72.12800 %, test_total= 10000, Accuracy = 58.21000 % 244 learn_rate:0.000777600000000 245 epoch = 55/100, batch_num = 391, loss = 0.807538, time = 8.357 246 train_total = 50000, Accuracy = 72.08800 %, test_total= 10000, Accuracy = 58.19000 % 247 learn_rate:0.000777600000000 248 epoch = 56/100, batch_num = 391, loss = 0.805878, time = 8.417 249 train_total = 50000, Accuracy = 72.36600 %, test_total= 10000, Accuracy = 58.24000 % 250 learn_rate:0.000777600000000 251 epoch = 57/100, batch_num = 391, loss = 0.806363, time = 8.566 252 train_total = 50000, Accuracy = 72.29200 %, test_total= 10000, Accuracy = 57.90000 % 253 learn_rate:0.000777600000000 254 epoch = 58/100, batch_num = 391, loss = 0.804692, time = 8.337 255 train_total = 50000, Accuracy = 72.53200 %, test_total= 10000, Accuracy = 58.08000 % 256 learn_rate:0.000777600000000 257 epoch = 59/100, batch_num = 391, loss = 0.802078, time = 8.637 258 train_total = 50000, Accuracy = 72.31000 %, test_total= 10000, Accuracy = 57.96000 % 259 learn_rate:0.000777600000000 260 epoch = 60/100, batch_num = 391, loss = 0.803284, time = 8.555 261 train_total = 50000, Accuracy = 72.21600 %, test_total= 10000, Accuracy = 57.83000 % 262 learn_rate:0.000466560000000 263 epoch = 61/100, batch_num = 391, loss = 0.790608, time = 8.665 264 train_total = 50000, Accuracy = 72.56400 %, test_total= 10000, Accuracy = 57.97000 % 265 learn_rate:0.000466560000000 266 epoch = 62/100, batch_num = 391, loss = 0.785791, time = 8.336 267 train_total = 50000, Accuracy = 72.92800 %, test_total= 10000, Accuracy = 58.23000 % 268 learn_rate:0.000466560000000 269 epoch = 63/100, batch_num = 391, loss = 0.785719, time = 8.434 270 train_total = 50000, Accuracy = 72.76200 %, test_total= 10000, Accuracy = 57.86000 % 271 learn_rate:0.000466560000000 272 epoch = 64/100, batch_num = 391, loss = 0.784822, time = 8.561 273 train_total = 50000, Accuracy = 72.68200 %, test_total= 10000, Accuracy = 58.07000 % 274 learn_rate:0.000466560000000 275 epoch = 65/100, batch_num = 391, loss = 0.785787, time = 8.412 276 train_total = 50000, Accuracy = 72.73800 %, test_total= 10000, Accuracy = 57.91000 % 277 learn_rate:0.000466560000000 278 epoch = 66/100, batch_num = 391, loss = 0.784818, time = 8.434 279 train_total = 50000, Accuracy = 72.96200 %, test_total= 10000, Accuracy = 58.12000 % 280 learn_rate:0.000466560000000 281 epoch = 67/100, batch_num = 391, loss = 0.781922, time = 8.366 282 train_total = 50000, Accuracy = 72.92600 %, test_total= 10000, Accuracy = 57.74000 % 283 learn_rate:0.000466560000000 284 epoch = 68/100, batch_num = 391, loss = 0.783252, time = 8.267 285 train_total = 50000, Accuracy = 72.90200 %, test_total= 10000, Accuracy = 58.28000 % 286 learn_rate:0.000466560000000 287 epoch = 69/100, batch_num = 391, loss = 0.780409, time = 8.257 288 train_total = 50000, Accuracy = 73.00600 %, test_total= 10000, Accuracy = 58.09000 % 289 learn_rate:0.000466560000000 290 epoch = 70/100, batch_num = 391, loss = 0.779665, time = 8.263 291 train_total = 50000, Accuracy = 73.15200 %, test_total= 10000, Accuracy = 57.68000 % 292 learn_rate:0.000279936000000 293 epoch = 71/100, batch_num = 391, loss = 0.772377, time = 8.195 294 train_total = 50000, Accuracy = 73.33800 %, test_total= 10000, Accuracy = 58.04000 % 295 learn_rate:0.000279936000000 296 epoch = 72/100, batch_num = 391, loss = 0.773185, time = 8.299 297 train_total = 50000, Accuracy = 73.28200 %, test_total= 10000, Accuracy = 58.02000 % 298 learn_rate:0.000279936000000 299 epoch = 73/100, batch_num = 391, loss = 0.768606, time = 8.174 300 train_total = 50000, Accuracy = 73.31600 %, test_total= 10000, Accuracy = 58.09000 % 301 learn_rate:0.000279936000000 302 epoch = 74/100, batch_num = 391, loss = 0.770491, time = 8.152 303 train_total = 50000, Accuracy = 73.20600 %, test_total= 10000, Accuracy = 57.86000 % 304 learn_rate:0.000279936000000 305 epoch = 75/100, batch_num = 391, loss = 0.768286, time = 8.529 306 train_total = 50000, Accuracy = 73.25600 %, test_total= 10000, Accuracy = 58.00000 % 307 learn_rate:0.000279936000000 308 epoch = 76/100, batch_num = 391, loss = 0.768315, time = 8.657 309 train_total = 50000, Accuracy = 73.34600 %, test_total= 10000, Accuracy = 57.93000 % 310 learn_rate:0.000279936000000 311 epoch = 77/100, batch_num = 391, loss = 0.766712, time = 8.247 312 train_total = 50000, Accuracy = 73.37200 %, test_total= 10000, Accuracy = 58.08000 % 313 learn_rate:0.000279936000000 314 epoch = 78/100, batch_num = 391, loss = 0.767419, time = 8.479 315 train_total = 50000, Accuracy = 73.49000 %, test_total= 10000, Accuracy = 58.13000 % 316 learn_rate:0.000279936000000 317 epoch = 79/100, batch_num = 391, loss = 0.766828, time = 8.333 318 train_total = 50000, Accuracy = 73.30600 %, test_total= 10000, Accuracy = 57.98000 % 319 learn_rate:0.000279936000000 320 epoch = 80/100, batch_num = 391, loss = 0.770401, time = 8.462 321 train_total = 50000, Accuracy = 73.43200 %, test_total= 10000, Accuracy = 57.80000 % 322 learn_rate:0.000167961600000 323 epoch = 81/100, batch_num = 391, loss = 0.763245, time = 8.375 324 train_total = 50000, Accuracy = 73.50800 %, test_total= 10000, Accuracy = 57.94000 % 325 learn_rate:0.000167961600000 326 epoch = 82/100, batch_num = 391, loss = 0.761148, time = 8.566 327 train_total = 50000, Accuracy = 73.72600 %, test_total= 10000, Accuracy = 57.76000 % 328 learn_rate:0.000167961600000 329 epoch = 83/100, batch_num = 391, loss = 0.761816, time = 8.432 330 train_total = 50000, Accuracy = 73.53400 %, test_total= 10000, Accuracy = 57.83000 % 331 learn_rate:0.000167961600000 332 epoch = 84/100, batch_num = 391, loss = 0.757929, time = 8.460 333 train_total = 50000, Accuracy = 73.50400 %, test_total= 10000, Accuracy = 57.93000 % 334 learn_rate:0.000167961600000 335 epoch = 85/100, batch_num = 391, loss = 0.759568, time = 8.428 336 train_total = 50000, Accuracy = 73.59200 %, test_total= 10000, Accuracy = 57.94000 % 337 learn_rate:0.000167961600000 338 epoch = 86/100, batch_num = 391, loss = 0.757942, time = 8.664 339 train_total = 50000, Accuracy = 73.47000 %, test_total= 10000, Accuracy = 58.00000 % 340 learn_rate:0.000167961600000 341 epoch = 87/100, batch_num = 391, loss = 0.757922, time = 8.465 342 train_total = 50000, Accuracy = 73.43600 %, test_total= 10000, Accuracy = 57.97000 % 343 learn_rate:0.000167961600000 344 epoch = 88/100, batch_num = 391, loss = 0.759980, time = 8.493 345 train_total = 50000, Accuracy = 73.47600 %, test_total= 10000, Accuracy = 57.73000 % 346 learn_rate:0.000167961600000 347 epoch = 89/100, batch_num = 391, loss = 0.758584, time = 8.558 348 train_total = 50000, Accuracy = 73.72200 %, test_total= 10000, Accuracy = 57.84000 % 349 learn_rate:0.000167961600000 350 epoch = 90/100, batch_num = 391, loss = 0.758065, time = 8.492 351 train_total = 50000, Accuracy = 73.70200 %, test_total= 10000, Accuracy = 57.85000 % 352 learn_rate:0.000100776960000 353 epoch = 91/100, batch_num = 391, loss = 0.755028, time = 8.441 354 train_total = 50000, Accuracy = 73.57600 %, test_total= 10000, Accuracy = 57.88000 % 355 learn_rate:0.000100776960000 356 epoch = 92/100, batch_num = 391, loss = 0.754445, time = 8.338 357 train_total = 50000, Accuracy = 73.58000 %, test_total= 10000, Accuracy = 57.79000 % 358 learn_rate:0.000100776960000 359 epoch = 93/100, batch_num = 391, loss = 0.754118, time = 8.328 360 train_total = 50000, Accuracy = 73.62800 %, test_total= 10000, Accuracy = 57.87000 % 361 learn_rate:0.000100776960000 362 epoch = 94/100, batch_num = 391, loss = 0.756821, time = 8.832 363 train_total = 50000, Accuracy = 73.69800 %, test_total= 10000, Accuracy = 57.89000 % 364 learn_rate:0.000100776960000 365 epoch = 95/100, batch_num = 391, loss = 0.755418, time = 8.750 366 train_total = 50000, Accuracy = 73.53200 %, test_total= 10000, Accuracy = 57.87000 % 367 learn_rate:0.000100776960000 368 epoch = 96/100, batch_num = 391, loss = 0.753379, time = 8.438 369 train_total = 50000, Accuracy = 73.56800 %, test_total= 10000, Accuracy = 57.82000 % 370 learn_rate:0.000100776960000 371 epoch = 97/100, batch_num = 391, loss = 0.753533, time = 8.443 372 train_total = 50000, Accuracy = 73.52200 %, test_total= 10000, Accuracy = 57.89000 % 373 learn_rate:0.000100776960000 374 epoch = 98/100, batch_num = 391, loss = 0.753787, time = 8.337 375 train_total = 50000, Accuracy = 73.44000 %, test_total= 10000, Accuracy = 57.83000 % 376 learn_rate:0.000100776960000 377 epoch = 99/100, batch_num = 391, loss = 0.755190, time = 8.276 378 train_total = 50000, Accuracy = 73.69800 %, test_total= 10000, Accuracy = 57.89000 % 379 learn_rate:0.000100776960000 380 epoch = 100/100, batch_num = 391, loss = 0.754359, time = 8.253 381 train_total = 50000, Accuracy = 73.69200 %, test_total= 10000, Accuracy = 57.83000 %
图2 Lenet5_acc_epoch_100
