可视化feature maps以及kernel weights，使用alexnet模型进行演示。

1. 查看中间层特征矩阵

alexnet模型，修改了向前传播

import torch from torch import nn from torch.nn import functional as F  # 对花图像数据进行分类 class AlexNet(nn.Module):     def __init__(self,num_classes=1000,init_weights=False, *args, **kwargs) -> None:         super().__init__(*args, **kwargs)         self.conv1 = nn.Conv2d(3,48,11,4,2)         self.pool1 = nn.MaxPool2d(3,2)         self.conv2 = nn.Conv2d(48,128,5,padding=2)         self.pool2 = nn.MaxPool2d(3,2)         self.conv3 = nn.Conv2d(128,192,3,padding=1)         self.conv4 = nn.Conv2d(192,192,3,padding=1)         self.conv5 = nn.Conv2d(192,128,3,padding=1)         self.pool3 = nn.MaxPool2d(3,2)          self.fc1 = nn.Linear(128*6*6,2048)         self.fc2 = nn.Linear(2048,2048)         self.fc3 = nn.Linear(2048,num_classes)         # 是否进行初始化         # 其实我们并不需要对其进行初始化，因为在pytorch中，对我们对卷积及全连接层，自动使用了凯明初始化方法进行了初始化         if init_weights:             self._initialize_weights()      def forward(self,x):         outputs = []  # 定义一个列表，返回我们要查看的哪一层的输出特征矩阵         x = self.conv1(x)         outputs.append(x)         x = self.pool1(F.relu(x,inplace=True))         x = self.conv2(x)         outputs.append(x)         x = self.pool2(F.relu(x,inplace=True))         x = self.conv3(x)         outputs.append(x)         x = F.relu(x,inplace=True)         x = F.relu(self.conv4(x),inplace=True)         x = self.pool3(F.relu(self.conv5(x),inplace=True))         x = x.view(-1,128*6*6)         x = F.dropout(x,p=0.5)         x = F.relu(self.fc1(x),inplace=True)         x = F.dropout(x,p=0.5)         x = F.relu(self.fc2(x),inplace=True)         x = self.fc3(x)          # for name,module in self.named_children():         #     x = module(x)         #     if name == ["conv1","conv2","conv3"]:         #         outputs.append(x)         return outputs      # 初始化权重     def _initialize_weights(self):         for m in self.modules():             if isinstance(m,nn.Conv2d):                 # 凯明初始化 - 何凯明                 nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')                 if m.bias is not None:                     nn.init.constant_(m.bias, 0)             elif isinstance(m,nn.Linear):                 nn.init.normal_(m.weight, 0,0.01)  # 使用正态分布给权重赋值进行初始化                 nn.init.constant_(m.bias,0) 

拿到向前传播的结果，对特征图进行可视化，这里，我们使用训练好的模型，直接加载模型参数。

注意，要使用与训练时相同的数据预处理。

import matplotlib.pyplot as plt from torchvision import transforms import alexnet_model import torch from PIL import Image import numpy as np from alexnet_model import AlexNet  # AlexNet 数据预处理 transform = transforms.Compose([     transforms.Resize((224, 224)),     transforms.ToTensor(),     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ])  device = torch.device("mps" if torch.backends.mps.is_available() else "cpu") # 实例化模型 model = AlexNet(num_classes=5) weights = torch.load("./alexnet_weight_20.pth", map_location="cpu") model.load_state_dict(weights)  image = Image.open("./images/yjx.jpg") image = transform(image) image = image.unsqueeze(0)  with torch.no_grad():     output = model(image)  for feature_map in output:     # (N,C,W,H) -> (C,W,H)     im = np.squeeze(feature_map.detach().numpy())     # (C,W,H) -> (W,H,C)     im = np.transpose(im,[1,2,0])     plt.figure()     # 展示当前层的前12个通道     for i in range(12):         ax = plt.subplot(3,4,i+1) # i+1: 每个图的索引         plt.imshow(im[:,:,i],cmap='gray')     plt.show() 

结果：

2. 查看卷积核参数

import matplotlib.pyplot as plt import numpy as np import torch  from AlexNet.model import AlexNet  # 实例化模型 model = AlexNet(num_classes=5) weights = torch.load("./alexnet_weight_20.pth", map_location="cpu") model.load_state_dict(weights)  weights_keys = model.state_dict().keys() for key in weights_keys:     if "num_batches_tracked" in key:         continue     weight_t = model.state_dict()[key].numpy()     weight_mean = weight_t.mean()     weight_std = weight_t.std(ddof=1)     weight_min = weight_t.min()     weight_max = weight_t.max()     print("mean is {}, std is {}, min is {}, max is {}".format(weight_mean, weight_std, weight_min, weight_max))      weight_vec = np.reshape(weight_t,[-1])     plt.hist(weight_vec,bins=50)     plt.title(key)     plt.show() 

结果：