残差神经网络训练sklearn手写体数据集（pytorch）

import torch
from torch.autograd import Variable
from sklearn import datasets
from torch import nn


digits = datasets.load_digits()

X = digits.images
y = digits.target

X_train = X[:1700, :, :]
Y_train = y[:1700]

X_test = X[1700:, :, :]
y_test = y[1700:]


X_train = torch.Tensor(X_train).unsqueeze(0).unsqueeze(0).view(-1, 1, 8, 8)
Y_train = torch.Tensor(Y_train).long()

X_test = torch.Tensor(X_test).unsqueeze(0).unsqueeze(0).view(-1, 1, 8, 8)
y_test = torch.Tensor(y_test).long()




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(1, 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])


loss_func = torch.nn.CrossEntropyLoss()
opt = torch.optim.Adam(model.parameters(), lr=0.001)

loss_count = []

for epoch in range(1):
    for i in range(500):
        batch_x = Variable(X_train, requires_grad=True)             # torch.Size([128, 1, 28, 28])
        batch_y = Variable(Y_train).view(-1)    # torch.Size([128])

        out = model(batch_x)              # torch.Size([128,10])
        # 获取损失
        loss = loss_func(out, batch_y)
        # 使用优化器优化损失
        opt.zero_grad()   # 清空上一步残余更新参数值
        loss.backward()   # 误差反向传播，计算参数更新值
        opt.step()        # 将参数更新值施加到net的parmeters上

        loss_count.append(loss)
        print('{}:\t'.format(i), loss.item())
        torch.save(model, r'C:\Users\Administrator\PycharmProjects\data\cnn')