生成Alexnet的网络图

为了生成AlexNet网络图，我们可以使用Graphviz这个工具。Graphviz是一个开源的可视化工具，可以将文本文件转换为图形。我们可以使用Python的graphviz库来生成AlexNet的网络图。以下是生成AlexNet网络图的步骤：

1. 安装graphviz库和pydotplus库

pip install graphviz
pip install pydotplus

2. 导入必要的库

import graphviz
import torch
import torch.nn as nn
import torchvision.models as models
from torch.autograd import Variable
from graphviz import Digraph
import numpy as np

3. 定义AlexNet网络

class AlexNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(192, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 6 * 6, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), 256 * 6 * 6)
        x = self.classifier(x)
        return x

4. 定义一个函数来生成网络图

def make_dot(var, params=None):
    """ Produces Graphviz representation of PyTorch autograd graph

    Blue nodes are the Variables that require grad, orange are Tensors
    saved for backward in torch.autograd.Function

    Args:
        var: output Variable
        params: dict of (name, Variable) to add names to node that
            require grad (TODO: make optional)
    """
    if params is not None:
        # 保存变量名
        param_map = {id(v): k for k, v in params.items()}

    node_attr = dict(style='filled',
                     shape='box',
                     align='left',
                     fontsize='12',
                     ranksep='0.1',
                     height='0.2')
    dot = Digraph(node_attr=node_attr, graph_attr=dict(size="12,12"))
    seen = set()

    def size_to_str(size):
        return '('+(', ').join(['%d' % v for v in size])+')'

    def add_nodes(var):
        if var not in seen:
            if torch.is_tensor(var):
                dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange')
            elif hasattr(var, 'variable'):
                u = var.variable
                name = param_map[id(u)] if params is not None else ''
                node_name = '%s\n %s' % (name, size_to_str(u.size()))
                dot.node(str(id(var)), node_name, fillcolor='lightblue')
            else:
                dot.node(str(id(var)), str(type(var).__name__))
            seen.add(var)
            if hasattr(var, 'next_functions'):
                for u in var.next_functions:
                    if u[0] is not None:
                        dot.edge(str(id(u[0])), str(id(var)))
                        add_nodes(u[0])
            if hasattr(var, 'saved_tensors'):
                for t in var.saved_tensors:
                    dot.edge(str(id(t)), str(id(var)))
                    add_nodes(t)

    add_nodes(var.grad_fn)
    return dot

5. 生成网络图

# 创建一个随机的输入张量
x = Variable(torch.randn(1, 3, 224, 224))

# 创建一个AlexNet模型
model = AlexNet()

# 生成网络图
y = model(x)
g = make_dot(y, params=dict(model.named_parameters()))

# 保存网络图
g.view()

运行以上代码后，将会生成一个AlexNet的网络图。你可以使用Graphviz的可视化工具来查看这个网络图。