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   -> 人工智能 -> PyTorch学习笔记(六)Residual Network -> 正文阅读

[人工智能]PyTorch学习笔记(六)Residual Network

# 导入相关库函数
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
# 配置设备
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 设置超参数
num_epochs = 80
batch_size = 100
learning_rate = 0.001
# 图像预处理模块
transform = transforms.Compose([
    transforms.Pad(4),
    transforms.RandomHorizontalFlip(),
    transforms.RandomCrop(32),
    transforms.ToTensor()])
# CIFAR-10数据集
train_dataset = torchvision.datasets.CIFAR10(root='../../data',
                                            train=True,
                                            transform=transform)
test_dataset = torchvision.datasets.CIFAR10(root='../../data',
                                           train=False,
                                           transform=transform)
# Data Loader
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                          batch_size=batch_size,
                                          shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                         batch_size=batch_size,
                                          shuffle=False)
# 3x3 卷积
def conv3x3(in_channels,out_channels,stride=1):
    return nn.Conv2d(in_channels,out_channels,kernel_size=3,
                    stride=stride,padding=1,bias=False)
# Residual block
class ResidualBlock(nn.Module):
    def __init__(self,in_channels,out_channels,stride=1,downsample=None):
        super(ResidualBlock,self).__init__()
        self.conv1 = conv3x3(in_channels,out_channels,stride)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(out_channels,out_channels)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.downsample = downsample
    
    def forward(self,x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        if self.downsample:
            residual = self.downsample(x)
        out += residual
        out = self.relu(out)
        return out
# ResNet
class ResNet(nn.Module):
    def __init__(self,block,layers,num_classes=10):
        super(ResNet,self).__init__()
        self.in_channels = 16
        self.conv = conv3x3(3,16)
        self.bn = nn.BatchNorm2d(16)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self.make_layer(block,16,layers[0])
        self.layer2 = self.make_layer(block,32,layers[1],2)
        self.layer3 = self.make_layer(block,64,layers[2],2)
        self.avg_pool = nn.AvgPool2d(8)
        self.fc = nn.Linear(64,num_classes)
    
    def make_layer(self,block,out_channels,blocks,stride=1):
        downsample = None
        if (stride != 1) or (self.in_channels != out_channels):
            downsample = nn.Sequential(conv3x3(self.in_channels,out_channels,stride=stride),
                                      nn.BatchNorm2d(out_channels))
        
        layers = []
        layers.append(block(self.in_channels,out_channels,stride,downsample))
        self.in_channels = out_channels
        for i in range(1,blocks):
            layers.append(block(out_channels,out_channels))
        return nn.Sequential(*layers)
    
    def forward(self,x):
        out = self.conv(x)
        out = self.bn(out)
        out = self.relu(out)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.avg_pool(out)
        out = out.view(out.size(0),-1)
        out = self.fc(out)
        return out
model = ResNet(ResidualBlock,[2,2,2]).to(device)
# 损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(),lr=learning_rate)
# 更新学习率
def update_lr(optimizer,lr):
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr
# 训练模型
total_step = len(train_loader)
curr_lr = learning_rate
for epoch in range(num_epochs):
    for i,(images,labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)
        
        # 前向传播
        outputs = model(images)
        loss = criterion(outputs,labels)
        
        # 后向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        if (i+1) % 100 == 0:
            print("Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}"
                 .format(epoch+1,num_epochs,i+1,total_step,loss.item()))
Epoch [1/80], Step [100/500], Loss: 1.5762
Epoch [1/80], Step [200/500], Loss: 1.3403
Epoch [1/80], Step [300/500], Loss: 1.3554
Epoch [1/80], Step [400/500], Loss: 1.0875
Epoch [1/80], Step [500/500], Loss: 1.2314
Epoch [2/80], Step [100/500], Loss: 1.0771
Epoch [2/80], Step [200/500], Loss: 1.0226
Epoch [2/80], Step [300/500], Loss: 0.9251
Epoch [2/80], Step [400/500], Loss: 0.9837
Epoch [2/80], Step [500/500], Loss: 0.8946
Epoch [3/80], Step [100/500], Loss: 1.0241
Epoch [3/80], Step [200/500], Loss: 0.8404
Epoch [3/80], Step [300/500], Loss: 0.8677
Epoch [3/80], Step [400/500], Loss: 0.8813
Epoch [3/80], Step [500/500], Loss: 0.8162
Epoch [4/80], Step [100/500], Loss: 0.8872
Epoch [4/80], Step [200/500], Loss: 0.8941
Epoch [4/80], Step [300/500], Loss: 0.6441
Epoch [4/80], Step [400/500], Loss: 0.6321
Epoch [4/80], Step [500/500], Loss: 0.7459
Epoch [5/80], Step [100/500], Loss: 0.6637
Epoch [5/80], Step [200/500], Loss: 0.6761
Epoch [5/80], Step [300/500], Loss: 0.7153
Epoch [5/80], Step [400/500], Loss: 0.5910
Epoch [5/80], Step [500/500], Loss: 0.7322
Epoch [6/80], Step [100/500], Loss: 0.6090
Epoch [6/80], Step [200/500], Loss: 0.5780
Epoch [6/80], Step [300/500], Loss: 0.7058
Epoch [6/80], Step [400/500], Loss: 0.5379
Epoch [6/80], Step [500/500], Loss: 0.5864
Epoch [7/80], Step [100/500], Loss: 0.7285
Epoch [7/80], Step [200/500], Loss: 0.7091
Epoch [7/80], Step [300/500], Loss: 0.4751
Epoch [7/80], Step [400/500], Loss: 0.7019
Epoch [7/80], Step [500/500], Loss: 0.6529
Epoch [8/80], Step [100/500], Loss: 0.5066
Epoch [8/80], Step [200/500], Loss: 0.5977
Epoch [8/80], Step [300/500], Loss: 0.5677
Epoch [8/80], Step [400/500], Loss: 0.4757
Epoch [8/80], Step [500/500], Loss: 0.5904
Epoch [9/80], Step [100/500], Loss: 0.5766
Epoch [9/80], Step [200/500], Loss: 0.4707
Epoch [9/80], Step [300/500], Loss: 0.7031
Epoch [9/80], Step [400/500], Loss: 0.5224
Epoch [9/80], Step [500/500], Loss: 0.6124
Epoch [10/80], Step [100/500], Loss: 0.6083
Epoch [10/80], Step [200/500], Loss: 0.3922
Epoch [10/80], Step [300/500], Loss: 0.4919
Epoch [10/80], Step [400/500], Loss: 0.4444
Epoch [10/80], Step [500/500], Loss: 0.7481
Epoch [11/80], Step [100/500], Loss: 0.3925
Epoch [11/80], Step [200/500], Loss: 0.4807
Epoch [11/80], Step [300/500], Loss: 0.5437
Epoch [11/80], Step [400/500], Loss: 0.3982
Epoch [11/80], Step [500/500], Loss: 0.6027
Epoch [12/80], Step [100/500], Loss: 0.4001
Epoch [12/80], Step [200/500], Loss: 0.4891
Epoch [12/80], Step [300/500], Loss: 0.5112
Epoch [12/80], Step [400/500], Loss: 0.3395
Epoch [12/80], Step [500/500], Loss: 0.4576
Epoch [13/80], Step [100/500], Loss: 0.4057
Epoch [13/80], Step [200/500], Loss: 0.3944
Epoch [13/80], Step [300/500], Loss: 0.4909
Epoch [13/80], Step [400/500], Loss: 0.4984
Epoch [13/80], Step [500/500], Loss: 0.4071
Epoch [14/80], Step [100/500], Loss: 0.4293
Epoch [14/80], Step [200/500], Loss: 0.3345
Epoch [14/80], Step [300/500], Loss: 0.3877
Epoch [14/80], Step [400/500], Loss: 0.5184
Epoch [14/80], Step [500/500], Loss: 0.4583
Epoch [15/80], Step [100/500], Loss: 0.3694
Epoch [15/80], Step [200/500], Loss: 0.3034
Epoch [15/80], Step [300/500], Loss: 0.4626
Epoch [15/80], Step [400/500], Loss: 0.4211
Epoch [15/80], Step [500/500], Loss: 0.4108
Epoch [16/80], Step [100/500], Loss: 0.3813
Epoch [16/80], Step [200/500], Loss: 0.3624
Epoch [16/80], Step [300/500], Loss: 0.3195
Epoch [16/80], Step [400/500], Loss: 0.6363
Epoch [16/80], Step [500/500], Loss: 0.3853
Epoch [17/80], Step [100/500], Loss: 0.2451
Epoch [17/80], Step [200/500], Loss: 0.4081
Epoch [17/80], Step [300/500], Loss: 0.4094
Epoch [17/80], Step [400/500], Loss: 0.5220
Epoch [17/80], Step [500/500], Loss: 0.4002
Epoch [18/80], Step [100/500], Loss: 0.3539
Epoch [18/80], Step [200/500], Loss: 0.3408
Epoch [18/80], Step [300/500], Loss: 0.4528
Epoch [18/80], Step [400/500], Loss: 0.4958
Epoch [18/80], Step [500/500], Loss: 0.3533
Epoch [19/80], Step [100/500], Loss: 0.3593
Epoch [19/80], Step [200/500], Loss: 0.3446
Epoch [19/80], Step [300/500], Loss: 0.5390
Epoch [19/80], Step [400/500], Loss: 0.3758
Epoch [19/80], Step [500/500], Loss: 0.5264
Epoch [20/80], Step [100/500], Loss: 0.3963
Epoch [20/80], Step [200/500], Loss: 0.3253
Epoch [20/80], Step [300/500], Loss: 0.2982
Epoch [20/80], Step [400/500], Loss: 0.4432
Epoch [20/80], Step [500/500], Loss: 0.4277
Epoch [21/80], Step [100/500], Loss: 0.3551
Epoch [21/80], Step [200/500], Loss: 0.4178
Epoch [21/80], Step [300/500], Loss: 0.3620
Epoch [21/80], Step [400/500], Loss: 0.4359
Epoch [21/80], Step [500/500], Loss: 0.4085
Epoch [22/80], Step [100/500], Loss: 0.3407
Epoch [22/80], Step [200/500], Loss: 0.3673
Epoch [22/80], Step [300/500], Loss: 0.2835
Epoch [22/80], Step [400/500], Loss: 0.4101
Epoch [22/80], Step [500/500], Loss: 0.3882
Epoch [23/80], Step [100/500], Loss: 0.3227
Epoch [23/80], Step [200/500], Loss: 0.4617
Epoch [23/80], Step [300/500], Loss: 0.2973
Epoch [23/80], Step [400/500], Loss: 0.3678
Epoch [23/80], Step [500/500], Loss: 0.3429
Epoch [24/80], Step [100/500], Loss: 0.1978
Epoch [24/80], Step [200/500], Loss: 0.2038
Epoch [24/80], Step [300/500], Loss: 0.2736
Epoch [24/80], Step [400/500], Loss: 0.2299
Epoch [24/80], Step [500/500], Loss: 0.3331
Epoch [25/80], Step [100/500], Loss: 0.3207
Epoch [25/80], Step [200/500], Loss: 0.3646
Epoch [25/80], Step [300/500], Loss: 0.3261
Epoch [25/80], Step [400/500], Loss: 0.3122
Epoch [25/80], Step [500/500], Loss: 0.3622
Epoch [26/80], Step [100/500], Loss: 0.3574
Epoch [26/80], Step [200/500], Loss: 0.2704
Epoch [26/80], Step [300/500], Loss: 0.3022
Epoch [26/80], Step [400/500], Loss: 0.2779
Epoch [26/80], Step [500/500], Loss: 0.3574
Epoch [27/80], Step [100/500], Loss: 0.2380
Epoch [27/80], Step [200/500], Loss: 0.1288
Epoch [27/80], Step [300/500], Loss: 0.2354
Epoch [27/80], Step [400/500], Loss: 0.4427
Epoch [27/80], Step [500/500], Loss: 0.3255
Epoch [28/80], Step [100/500], Loss: 0.2964
Epoch [28/80], Step [200/500], Loss: 0.3254
Epoch [28/80], Step [300/500], Loss: 0.1881
Epoch [28/80], Step [400/500], Loss: 0.4440
Epoch [28/80], Step [500/500], Loss: 0.3123
Epoch [29/80], Step [100/500], Loss: 0.3065
Epoch [29/80], Step [200/500], Loss: 0.2815
Epoch [29/80], Step [300/500], Loss: 0.2467
Epoch [29/80], Step [400/500], Loss: 0.3063
Epoch [29/80], Step [500/500], Loss: 0.3652
Epoch [30/80], Step [100/500], Loss: 0.3264
Epoch [30/80], Step [200/500], Loss: 0.2425
Epoch [30/80], Step [300/500], Loss: 0.2608
Epoch [30/80], Step [400/500], Loss: 0.4155
Epoch [30/80], Step [500/500], Loss: 0.3214
Epoch [31/80], Step [100/500], Loss: 0.1212
Epoch [31/80], Step [200/500], Loss: 0.2171
Epoch [31/80], Step [300/500], Loss: 0.2945
Epoch [31/80], Step [400/500], Loss: 0.2927
Epoch [31/80], Step [500/500], Loss: 0.3075
Epoch [32/80], Step [100/500], Loss: 0.1831
Epoch [32/80], Step [200/500], Loss: 0.2182
Epoch [32/80], Step [300/500], Loss: 0.3482
Epoch [32/80], Step [400/500], Loss: 0.3035
Epoch [32/80], Step [500/500], Loss: 0.2285
Epoch [33/80], Step [100/500], Loss: 0.1738
Epoch [33/80], Step [200/500], Loss: 0.2407
Epoch [33/80], Step [300/500], Loss: 0.3299
Epoch [33/80], Step [400/500], Loss: 0.2976
Epoch [33/80], Step [500/500], Loss: 0.2273
Epoch [34/80], Step [100/500], Loss: 0.3169
Epoch [34/80], Step [200/500], Loss: 0.2362
Epoch [34/80], Step [300/500], Loss: 0.1517
Epoch [34/80], Step [400/500], Loss: 0.3536
Epoch [34/80], Step [500/500], Loss: 0.2798
Epoch [35/80], Step [100/500], Loss: 0.2591
Epoch [35/80], Step [200/500], Loss: 0.1770
Epoch [35/80], Step [300/500], Loss: 0.2246
Epoch [35/80], Step [400/500], Loss: 0.2181
Epoch [35/80], Step [500/500], Loss: 0.3327
Epoch [36/80], Step [100/500], Loss: 0.2776
Epoch [36/80], Step [200/500], Loss: 0.1536
Epoch [36/80], Step [300/500], Loss: 0.3514
Epoch [36/80], Step [400/500], Loss: 0.1732
Epoch [36/80], Step [500/500], Loss: 0.1783
Epoch [37/80], Step [100/500], Loss: 0.1768
Epoch [37/80], Step [200/500], Loss: 0.2327
Epoch [37/80], Step [300/500], Loss: 0.3117
Epoch [37/80], Step [400/500], Loss: 0.3398
Epoch [37/80], Step [500/500], Loss: 0.2773
Epoch [38/80], Step [100/500], Loss: 0.1481
Epoch [38/80], Step [200/500], Loss: 0.1423
Epoch [38/80], Step [300/500], Loss: 0.2493
Epoch [38/80], Step [400/500], Loss: 0.3462
Epoch [38/80], Step [500/500], Loss: 0.2133
Epoch [39/80], Step [100/500], Loss: 0.1774
Epoch [39/80], Step [200/500], Loss: 0.2748
Epoch [39/80], Step [300/500], Loss: 0.1728
Epoch [39/80], Step [400/500], Loss: 0.2278
Epoch [39/80], Step [500/500], Loss: 0.2479
Epoch [40/80], Step [100/500], Loss: 0.1808
Epoch [40/80], Step [200/500], Loss: 0.2273
Epoch [40/80], Step [300/500], Loss: 0.2186
Epoch [40/80], Step [400/500], Loss: 0.2461
Epoch [40/80], Step [500/500], Loss: 0.3028
Epoch [41/80], Step [100/500], Loss: 0.2703
Epoch [41/80], Step [200/500], Loss: 0.2342
Epoch [41/80], Step [300/500], Loss: 0.2019
Epoch [41/80], Step [400/500], Loss: 0.4073
Epoch [41/80], Step [500/500], Loss: 0.1835
Epoch [42/80], Step [100/500], Loss: 0.1370
Epoch [42/80], Step [200/500], Loss: 0.2080
Epoch [42/80], Step [300/500], Loss: 0.0908
Epoch [42/80], Step [400/500], Loss: 0.2554
Epoch [42/80], Step [500/500], Loss: 0.1956
Epoch [43/80], Step [100/500], Loss: 0.1763
Epoch [43/80], Step [200/500], Loss: 0.2144
Epoch [43/80], Step [300/500], Loss: 0.2851
Epoch [43/80], Step [400/500], Loss: 0.2409
Epoch [43/80], Step [500/500], Loss: 0.2242
Epoch [44/80], Step [100/500], Loss: 0.1618
Epoch [44/80], Step [200/500], Loss: 0.1487
Epoch [44/80], Step [300/500], Loss: 0.1841
Epoch [44/80], Step [400/500], Loss: 0.1820
Epoch [44/80], Step [500/500], Loss: 0.2557
Epoch [45/80], Step [100/500], Loss: 0.2867
Epoch [45/80], Step [200/500], Loss: 0.2042
Epoch [45/80], Step [300/500], Loss: 0.2000
Epoch [45/80], Step [400/500], Loss: 0.1579
Epoch [45/80], Step [500/500], Loss: 0.2681
Epoch [46/80], Step [100/500], Loss: 0.1551
Epoch [46/80], Step [200/500], Loss: 0.2198
Epoch [46/80], Step [300/500], Loss: 0.2958
Epoch [46/80], Step [400/500], Loss: 0.2145
Epoch [46/80], Step [500/500], Loss: 0.2579
Epoch [47/80], Step [100/500], Loss: 0.1394
Epoch [47/80], Step [200/500], Loss: 0.2146
Epoch [47/80], Step [300/500], Loss: 0.1710
Epoch [47/80], Step [400/500], Loss: 0.1590
Epoch [47/80], Step [500/500], Loss: 0.1470
Epoch [48/80], Step [100/500], Loss: 0.1962
Epoch [48/80], Step [200/500], Loss: 0.2232
Epoch [48/80], Step [300/500], Loss: 0.1686
Epoch [48/80], Step [400/500], Loss: 0.3067
Epoch [48/80], Step [500/500], Loss: 0.2685
Epoch [49/80], Step [100/500], Loss: 0.1417
Epoch [49/80], Step [200/500], Loss: 0.1506
Epoch [49/80], Step [300/500], Loss: 0.2252
Epoch [49/80], Step [400/500], Loss: 0.2574
Epoch [49/80], Step [500/500], Loss: 0.1232
Epoch [50/80], Step [100/500], Loss: 0.2177
Epoch [50/80], Step [200/500], Loss: 0.1809
Epoch [50/80], Step [300/500], Loss: 0.2496
Epoch [50/80], Step [400/500], Loss: 0.1618
Epoch [50/80], Step [500/500], Loss: 0.1933
Epoch [51/80], Step [100/500], Loss: 0.2316
Epoch [51/80], Step [200/500], Loss: 0.3021
Epoch [51/80], Step [300/500], Loss: 0.1793
Epoch [51/80], Step [400/500], Loss: 0.1902
Epoch [51/80], Step [500/500], Loss: 0.2463
Epoch [52/80], Step [100/500], Loss: 0.1417
Epoch [52/80], Step [200/500], Loss: 0.1784
Epoch [52/80], Step [300/500], Loss: 0.1849
Epoch [52/80], Step [400/500], Loss: 0.3067
Epoch [52/80], Step [500/500], Loss: 0.1606
Epoch [53/80], Step [100/500], Loss: 0.2153
Epoch [53/80], Step [200/500], Loss: 0.1975
Epoch [53/80], Step [300/500], Loss: 0.1823
Epoch [53/80], Step [400/500], Loss: 0.1709
Epoch [53/80], Step [500/500], Loss: 0.1117
Epoch [54/80], Step [100/500], Loss: 0.0943
Epoch [54/80], Step [200/500], Loss: 0.1369
Epoch [54/80], Step [300/500], Loss: 0.1761
Epoch [54/80], Step [400/500], Loss: 0.1823
Epoch [54/80], Step [500/500], Loss: 0.2230
Epoch [55/80], Step [100/500], Loss: 0.1253
Epoch [55/80], Step [200/500], Loss: 0.1470
Epoch [55/80], Step [300/500], Loss: 0.2650
Epoch [55/80], Step [400/500], Loss: 0.2928
Epoch [55/80], Step [500/500], Loss: 0.2083
Epoch [56/80], Step [100/500], Loss: 0.1393
Epoch [56/80], Step [200/500], Loss: 0.1190
Epoch [56/80], Step [300/500], Loss: 0.1761
Epoch [56/80], Step [400/500], Loss: 0.1921
Epoch [56/80], Step [500/500], Loss: 0.1909
Epoch [57/80], Step [100/500], Loss: 0.1711
Epoch [57/80], Step [200/500], Loss: 0.2736
Epoch [57/80], Step [300/500], Loss: 0.1435
Epoch [57/80], Step [400/500], Loss: 0.1843
Epoch [57/80], Step [500/500], Loss: 0.1662
Epoch [58/80], Step [100/500], Loss: 0.0726
Epoch [58/80], Step [200/500], Loss: 0.0474
Epoch [58/80], Step [300/500], Loss: 0.3009
Epoch [58/80], Step [400/500], Loss: 0.1847
Epoch [58/80], Step [500/500], Loss: 0.2121
Epoch [59/80], Step [100/500], Loss: 0.1617
Epoch [59/80], Step [200/500], Loss: 0.0809
Epoch [59/80], Step [300/500], Loss: 0.1406
Epoch [59/80], Step [400/500], Loss: 0.1325
Epoch [59/80], Step [500/500], Loss: 0.3449
Epoch [60/80], Step [100/500], Loss: 0.1996
Epoch [60/80], Step [200/500], Loss: 0.0681
Epoch [60/80], Step [300/500], Loss: 0.2138
Epoch [60/80], Step [400/500], Loss: 0.2091
Epoch [60/80], Step [500/500], Loss: 0.1212
Epoch [61/80], Step [100/500], Loss: 0.0661
Epoch [61/80], Step [200/500], Loss: 0.2304
Epoch [61/80], Step [300/500], Loss: 0.1157
Epoch [61/80], Step [400/500], Loss: 0.1931
Epoch [61/80], Step [500/500], Loss: 0.2525
Epoch [62/80], Step [100/500], Loss: 0.1343
Epoch [62/80], Step [200/500], Loss: 0.2773
Epoch [62/80], Step [300/500], Loss: 0.1402
Epoch [62/80], Step [400/500], Loss: 0.1830
Epoch [62/80], Step [500/500], Loss: 0.1188
Epoch [63/80], Step [100/500], Loss: 0.2578
Epoch [63/80], Step [200/500], Loss: 0.1356
Epoch [63/80], Step [300/500], Loss: 0.1527
Epoch [63/80], Step [400/500], Loss: 0.1002
Epoch [63/80], Step [500/500], Loss: 0.1767
Epoch [64/80], Step [100/500], Loss: 0.1269
Epoch [64/80], Step [200/500], Loss: 0.2103
Epoch [64/80], Step [300/500], Loss: 0.1291
Epoch [64/80], Step [400/500], Loss: 0.1776
Epoch [64/80], Step [500/500], Loss: 0.0795
Epoch [65/80], Step [100/500], Loss: 0.1605
Epoch [65/80], Step [200/500], Loss: 0.2012
Epoch [65/80], Step [300/500], Loss: 0.1505
Epoch [65/80], Step [400/500], Loss: 0.0554
Epoch [65/80], Step [500/500], Loss: 0.2361
Epoch [66/80], Step [100/500], Loss: 0.1018
Epoch [66/80], Step [200/500], Loss: 0.2111
Epoch [66/80], Step [300/500], Loss: 0.1561
Epoch [66/80], Step [400/500], Loss: 0.1095
Epoch [66/80], Step [500/500], Loss: 0.1387
Epoch [67/80], Step [100/500], Loss: 0.1832
Epoch [67/80], Step [200/500], Loss: 0.1073
Epoch [67/80], Step [300/500], Loss: 0.1876
Epoch [67/80], Step [400/500], Loss: 0.1999
Epoch [67/80], Step [500/500], Loss: 0.1774
Epoch [68/80], Step [100/500], Loss: 0.1364
Epoch [68/80], Step [200/500], Loss: 0.1086
Epoch [68/80], Step [300/500], Loss: 0.1324
Epoch [68/80], Step [400/500], Loss: 0.1146
Epoch [68/80], Step [500/500], Loss: 0.1608
Epoch [69/80], Step [100/500], Loss: 0.1190
Epoch [69/80], Step [200/500], Loss: 0.2497
Epoch [69/80], Step [300/500], Loss: 0.2121
Epoch [69/80], Step [400/500], Loss: 0.1431
Epoch [69/80], Step [500/500], Loss: 0.0936
Epoch [70/80], Step [100/500], Loss: 0.2063
Epoch [70/80], Step [200/500], Loss: 0.1734
Epoch [70/80], Step [300/500], Loss: 0.0849
Epoch [70/80], Step [400/500], Loss: 0.1122
Epoch [70/80], Step [500/500], Loss: 0.1814
Epoch [71/80], Step [100/500], Loss: 0.1795
Epoch [71/80], Step [200/500], Loss: 0.1698
Epoch [71/80], Step [300/500], Loss: 0.1683
Epoch [71/80], Step [400/500], Loss: 0.1997
Epoch [71/80], Step [500/500], Loss: 0.2391
Epoch [72/80], Step [100/500], Loss: 0.1597
Epoch [72/80], Step [200/500], Loss: 0.0688
Epoch [72/80], Step [300/500], Loss: 0.1460
Epoch [72/80], Step [400/500], Loss: 0.1132
Epoch [72/80], Step [500/500], Loss: 0.0775
Epoch [73/80], Step [100/500], Loss: 0.0952
Epoch [73/80], Step [200/500], Loss: 0.0728
Epoch [73/80], Step [300/500], Loss: 0.1084
Epoch [73/80], Step [400/500], Loss: 0.1674
Epoch [73/80], Step [500/500], Loss: 0.2439
Epoch [74/80], Step [100/500], Loss: 0.1990
Epoch [74/80], Step [200/500], Loss: 0.0840
Epoch [74/80], Step [300/500], Loss: 0.1093
Epoch [74/80], Step [400/500], Loss: 0.1223
Epoch [74/80], Step [500/500], Loss: 0.2627
Epoch [75/80], Step [100/500], Loss: 0.1768
Epoch [75/80], Step [200/500], Loss: 0.1167
Epoch [75/80], Step [300/500], Loss: 0.1619
Epoch [75/80], Step [400/500], Loss: 0.1418
Epoch [75/80], Step [500/500], Loss: 0.2167
Epoch [76/80], Step [100/500], Loss: 0.2540
Epoch [76/80], Step [200/500], Loss: 0.2036
Epoch [76/80], Step [300/500], Loss: 0.0924
Epoch [76/80], Step [400/500], Loss: 0.2102
Epoch [76/80], Step [500/500], Loss: 0.0761
Epoch [77/80], Step [100/500], Loss: 0.2176
Epoch [77/80], Step [200/500], Loss: 0.1518
Epoch [77/80], Step [300/500], Loss: 0.1040
Epoch [77/80], Step [400/500], Loss: 0.1389
Epoch [77/80], Step [500/500], Loss: 0.0750
Epoch [78/80], Step [100/500], Loss: 0.1723
Epoch [78/80], Step [200/500], Loss: 0.0972
Epoch [78/80], Step [300/500], Loss: 0.1122
Epoch [78/80], Step [400/500], Loss: 0.0907
Epoch [78/80], Step [500/500], Loss: 0.2186
Epoch [79/80], Step [100/500], Loss: 0.1585
Epoch [79/80], Step [200/500], Loss: 0.1186
Epoch [79/80], Step [300/500], Loss: 0.0886
Epoch [79/80], Step [400/500], Loss: 0.1035
Epoch [79/80], Step [500/500], Loss: 0.1829
Epoch [80/80], Step [100/500], Loss: 0.1647
Epoch [80/80], Step [200/500], Loss: 0.2264
Epoch [80/80], Step [300/500], Loss: 0.1059
Epoch [80/80], Step [400/500], Loss: 0.1147
Epoch [80/80], Step [500/500], Loss: 0.1452
# 测试模型
model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for images,labels in test_loader:
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images)
        _, predicted =torch.max(outputs.data,1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    
    print("Accuracy of the model on the test images: {}%."
         .format(100*correct/total))
Accuracy of the model on the test images: 87.33%.
# 保存模型
torch.save(model.state_dict(),'resnet.ckpt')
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