python第35天打卡

import torch
import torch.nn as nn
import matplotlib.pyplot as plt
from torchinfo import summary
import numpy as np# 1. 定义示例模型
class CNN(nn.Module):def __init__(self):super().__init__()self.conv_layers = nn.Sequential(nn.Conv2d(3, 16, kernel_size=3),nn.ReLU(),nn.MaxPool2d(2),nn.Conv2d(16, 32, kernel_size=3),nn.ReLU(),nn.MaxPool2d(2))self.fc_layers = nn.Sequential(nn.Linear(32*6*6, 128),nn.ReLU(),nn.Linear(128, 10))def forward(self, x):x = self.conv_layers(x)x = torch.flatten(x, 1)x = self.fc_layers(x)return xmodel = CNN()# 2. 模型摘要可视化 (使用torchinfo)
print("\n=== 模型结构摘要 ===")
summary(model, input_size=(1, 3, 32, 32),  # (batch, channels, height, width)col_names=["input_size", "output_size", "num_params", "kernel_size"],verbose=1
)# 3. 权重分布可视化
def plot_weight_distribution(model):plt.figure(figsize=(12, 6))# 收集所有权重all_weights = []for name, param in model.named_parameters():if 'weight' in name:flattened = param.detach().cpu().numpy().flatten()all_weights.extend(flattened)# 绘制直方图plt.hist(all_weights, bins=150, alpha=0.7, color='blue', edgecolor='black')plt.title('Model Weights Distribution')plt.xlabel('Weight Value')plt.ylabel('Frequency (log scale)')plt.yscale('log')plt.grid(True, alpha=0.3)plt.show()print("\n=== 权重分布图 ===")
plot_weight_distribution(model)# 4. 逐层权重可视化 (额外方法)
def plot_layer_weights(model):plt.figure(figsize=(15, 10))for i, (name, param) in enumerate(model.named_parameters()):if 'weight' in name:plt.subplot(3, 3, i+1)layer_weights = param.detach().cpu().numpy().flatten()plt.hist(layer_weights, bins=100, alpha=0.7)plt.title(f'{name} weight distribution')plt.grid(True, alpha=0.2)plt.tight_layout()plt.show()print("\n=== 逐层权重分布 ===")
plot_layer_weights(model)# 5. 权重矩阵可视化 (额外方法)
def visualize_weight_matrix(model):plt.figure(figsize=(15, 4))# 获取第一个卷积层的权重conv1_weight = model.conv_layers[0].weight.detach().cpu()# 归一化权重用于显示min_val = torch.min(conv1_weight)max_val = torch.max(conv1_weight)normalized_weights = (conv1_weight - min_val) / (max_val - min_val)# 绘制权重矩阵for i in range(16):  # 显示前16个卷积核plt.subplot(2, 8, i+1)plt.imshow(normalized_weights[i].permute(1, 2, 0))  # CHW -> HWCplt.axis('off')plt.title(f'Kernel {i+1}')plt.suptitle('First Conv Layer Kernels', fontsize=16)plt.tight_layout()plt.show()print("\n=== 卷积核可视化 ===")
visualize_weight_matrix(model)