import torch from torch import nn import d2l net = nn.Sequential( nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=1), nn.ReLU(), nn.MaxPool2d(kernel_size=3, stride=2), nn.Conv2d(96, 256, kernel_size=5, padding=2), nn.ReLU(), nn.MaxPool2d(kernel_size=3, stride=2), nn.Conv2d(256, 384, kernel_size=3, padding=1), nn.ReLU(), nn.Conv2d(384, 384, kernel_size=3, padding=1), nn.ReLU(), nn.Conv2d(384, 256, kernel_size=3, padding=1), nn.ReLU(), nn.MaxPool2d(kernel_size=3, stride=2), nn.Flatten(), nn.Linear(6400, 4096), nn.ReLU(), nn.Dropout(p=0.5), nn.Linear(4096, 4096), nn.ReLU(), nn.Dropout(p=0.5), nn.Linear(4096, 10)) X = torch.randn((1, 3, 224, 224),dtype=torch.float32) for layer in net: X=layer(X) print(layer.__class__.__name__,'output shape:\t',X.shape) batch_size = 32 train_iter, test_iter = d2l.load_data_cifar10(batch_size, resize=224) lr, num_epochs = 0.01, 50 d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())，代码中的参数值具体调整到多少，可以提高准确率到90%

提高准确率到90%需要进行一系列的参数调整和网络结构改进，以下是一些建议：

1. 将学习率(lr)适当调低，可以提高训练的稳定性，如将lr调整为0.001或更小的值。

2. 增加训练轮数(num_epochs)，可以让模型更充分地学习数据，如将num_epochs调整为100或更多的值。

3. 增加网络深度或宽度，可以提高模型的表达能力，如增加卷积层或全连接层的数量或通道数。

4. 使用更先进的优化器，如Adam或RMSprop，可以加速模型的收敛速度，提高训练效率。

5. 数据增强，如随机裁剪、翻转、旋转等，可以增加数据的多样性，提高模型的泛化能力。

6. 尝试使用预训练的模型或迁移学习，可以利用已有的大规模数据集和模型，加速模型的训练和提高准确率。

需要注意的是，不同的问题和数据集可能需要不同的调参策略，需要根据具体情况进行调整。

相关问题

为代码添加注释：import torch from torch import nn from d2l import torch as d2l net = nn.Sequential( nn.Conv2d(1, 6, kernel_size=5, padding=2), nn.Sigmoid(), nn.AvgPool2d(kernel_size=2, stride=2), nn.Conv2d(6, 16, kernel_size=5), nn.Sigmoid(), nn.AvgPool2d(kernel_size=2, stride=2), nn.Flatten(), nn.Linear(16 * 5 * 5, 120), nn.Sigmoid(), nn.Linear(120, 84), nn.Sigmoid(), nn.Linear(84, 10))

# 导入需要的库
import torch
from torch import nn
from d2l import torch as d2l

# 建立神经网络模型
net = nn.Sequential(
# 第一层卷积层
nn.Conv2d(1, 6, kernel_size=5, padding=2),
nn.Sigmoid(),
nn.AvgPool2d(kernel_size=2, stride=2),
# 第二层卷积层
nn.Conv2d(6, 16, kernel_size=5),
nn.Sigmoid(),
nn.AvgPool2d(kernel_size=2, stride=2),
# 全连接层
nn.Flatten(),
nn.Linear(16 * 5 * 5, 120),
nn.Sigmoid(),
nn.Linear(120, 84),
nn.Sigmoid(),
nn.Linear(84, 10)
)

class ASPP(nn.Module) def init(self, dim_in, dim_out, rate=1, bn_mom=0.1) super(ASPP, self).init() self.branch1 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 1, 1, padding=0, dilation=rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), ) self.branch2 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, 1, padding=4 rate, dilation=4 rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), ) self.branch3 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, 1, padding=8 rate, dilation=8 rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), ) self.branch4 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, 1, padding=12 rate, dilation=12 rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), ) self.branch5 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, 1, padding=16 rate, dilation=16 rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), ) self.branch6 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, 1, padding=20 rate, dilation=20 rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True) ) self.branch7 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, 1, padding=24 rate, dilation=24 rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True) ) self.branch8_conv = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=True) self.branch8_bn = nn.BatchNorm2d(dim_out, momentum=bn_mom) self.branch8_relu = nn.ReLU(inplace=True) self.conv_cat = nn.Sequential( nn.Conv2d(dim_out 8, dim_out, 1, 1, padding=0, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), ) def forward(self, x) [b, c, row, col] = x.size() conv1x1 = self.branch1(x) conv3x3_1 = self.branch2(x) conv3x3_2 = self.branch3(x) conv3x3_3 = self.branch4(x) conv3x3_4 = self.branch5(x) conv3x3_5 = self.branch6(x) conv3x3_6 = self.branch7(x) global_feature = torch.mean(x, 2, True) global_feature = torch.mean(global_feature, 3, True) global_feature = self.branch8_conv(global_feature) global_feature = self.branch8_bn(global_feature) global_feature = self.branch8_relu(global_feature) global_feature = F.interpolate(global_feature, (row, col), None, 'bilinear', True) feature_cat = torch.cat([conv1x1, conv3x3_1, conv3x3_2, conv3x3_3, conv3x3_4, conv3x3_5, conv3x3_6, global_feature], dim=1) result = self.conv_cat(feature_cat) return result用深度可分离卷积代替这段代码的3×3卷积

可以将代码中的3x3卷积替换为深度可分离卷积，即将`nn.Conv2d`替换为`nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=in_channels, bias=True)`，其中`groups=in_channels`，表示每个输入通道都对应一个卷积核，实现深度可分离卷积。具体替换代码如下：

class ASPP(nn.Module):
    def __init__(self, dim_in, dim_out, rate=1, bn_mom=0.1):
        super(ASPP, self).__init__()

        self.branch1 = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, 1, 1, padding=0, dilation=rate, bias=True),
            nn.BatchNorm2d(dim_out, momentum=bn_mom),
            nn.ReLU(inplace=True),
        )

        self.branch2 = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, 3, 1, padding=4 * rate, dilation=rate, groups=dim_in, bias=True),
            nn.BatchNorm2d(dim_out, momentum=bn_mom),
            nn.ReLU(inplace=True),
        )

        self.branch3 = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, 3, 1, padding=8 * rate, dilation=rate, groups=dim_in, bias=True),
            nn.BatchNorm2d(dim_out, momentum=bn_mom),
            nn.ReLU(inplace=True),
        )

        self.branch4 = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, 3, 1, padding=12 * rate, dilation=rate, groups=dim_in, bias=True),
            nn.BatchNorm2d(dim_out, momentum=bn_mom),
            nn.ReLU(inplace=True),
        )

        self.branch5 = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, 3, 1, padding=16 * rate, dilation=rate, groups=dim_in, bias=True),
            nn.BatchNorm2d(dim_out, momentum=bn_mom),
            nn.ReLU(inplace=True),
        )

        self.branch6 = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, 3, 1, padding=20 * rate, dilation=rate, groups=dim_in, bias=True),
            nn.BatchNorm2d(dim_out, momentum=bn_mom),
            nn.ReLU(inplace=True),
        )

        self.branch7 = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, 3, 1, padding=24 * rate, dilation=rate, groups=dim_in, bias=True),
            nn.BatchNorm2d(dim_out, momentum=bn_mom),
            nn.ReLU(inplace=True),
        )

        self.branch8_conv = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=True)
        self.branch8_bn = nn.BatchNorm2d(dim_out, momentum=bn_mom)
        self.branch8_relu = nn.ReLU(inplace=True)

        self.conv_cat = nn.Sequential(
            nn.Conv2d(dim_out * 8, dim_out, 1, 1, padding=0, bias=True),
            nn.BatchNorm2d(dim_out, momentum=bn_mom),
            nn.ReLU(inplace=True),
        )

    def forward(self, x):
        [b, c, row, col] = x.size()

        conv1x1 = self.branch1(x)
        conv3x3_1 = self.branch2(x)
        conv3x3_2 = self.branch3(x)
        conv3x3_3 = self.branch4(x)
        conv3x3_4 = self.branch5(x)
        conv3x3_5 = self.branch6(x)
        conv3x3_6 = self.branch7(x)

        global_feature = torch.mean(x, 2, True)
        global_feature = torch.mean(global_feature, 3, True)
        global_feature = self.branch8_conv(global_feature)
        global_feature = self.branch8_bn(global_feature)
        global_feature = self.branch8_relu(global_feature)
        global_feature = F.interpolate(global_feature, (row, col), None, 'bilinear', True)

        feature_cat = torch.cat([conv1x1, conv3x3_1, conv3x3_2, conv3x3_3, conv3x3_4, conv3x3_5, conv3x3_6, global_feature], dim=1)
        result = self.conv_cat(feature_cat)

        return result