[人工智能] PyTorch学习笔记（六）Residual Network

开发: C++知识库 Java知识库 JavaScript Python PHP知识库人工智能区块链大数据移动开发嵌入式开发工具数据结构与算法开发测试游戏开发网络协议系统运维
教程: HTML教程 CSS教程 JavaScript教程 Go语言教程 JQuery教程 VUE教程 VUE3教程 Bootstrap教程 SQL数据库教程 C语言教程 C++教程 Java教程 Python教程 Python3教程 C#教程
数码: 电脑笔记本显卡显示器固态硬盘硬盘耳机手机 iphone vivo oppo 小米华为单反装机图拉丁

-> 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')

人工智能最新文章

2022吴恩达机器学习课程——第二课（神经网

第十五章规则学习

FixMatch: Simplifying Semi-Supervised Le

数据挖掘Java——Kmeans算法的实现

大脑皮层的分割方法

【翻译】GPT-3是如何工作的

论文笔记:TEACHTEXT: CrossModal Generaliz

python从零学（六）

详解Python 3.x 导入(import)

【答读者问27】backtrader不支持最新版本的

加:2021-07-24 11:26:17 更:2021-07-24 11:27:02

360图书馆购物三丰科技阅读网日历万年历 2024年11日历

-2024/11/17 20:37:31-

图片自动播放器
↓图片自动播放器↓

TxT小说阅读器
↓语音阅读,小说下载,古典文学↓

一键清除垃圾
↓轻轻一点,清除系统垃圾↓

图片批量下载器
↓批量下载图片,美女图库↓

网站联系: qq:121756557 email:121756557@qq.com IT数码