论坛网站搭建,陕西省城乡建设网站,万网账户,济南制作公司网站可视化feature maps以及kernel weights#xff0c;使用alexnet模型进行演示。
1. 查看中间层特征矩阵
alexnet模型#xff0c;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):d…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_classes1000,init_weightsFalse, *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,padding2)self.pool2 nn.MaxPool2d(3,2)self.conv3 nn.Conv2d(128,192,3,padding1)self.conv4 nn.Conv2d(192,192,3,padding1)self.conv5 nn.Conv2d(192,128,3,padding1)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,inplaceTrue))x self.conv2(x)outputs.append(x)x self.pool2(F.relu(x,inplaceTrue))x self.conv3(x)outputs.append(x)x F.relu(x,inplaceTrue)x F.relu(self.conv4(x),inplaceTrue)x self.pool3(F.relu(self.conv5(x),inplaceTrue))x x.view(-1,128*6*6)x F.dropout(x,p0.5)x F.relu(self.fc1(x),inplaceTrue)x F.dropout(x,p0.5)x F.relu(self.fc2(x),inplaceTrue)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, modefan_out, nonlinearityrelu)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_classes5)
weights torch.load(./alexnet_weight_20.pth, map_locationcpu)
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,i1) # i1: 每个图的索引plt.imshow(im[:,:,i],cmapgray)plt.show()结果 2. 查看卷积核参数
import matplotlib.pyplot as plt
import numpy as np
import torchfrom AlexNet.model import AlexNet# 实例化模型
model AlexNet(num_classes5)
weights torch.load(./alexnet_weight_20.pth, map_locationcpu)
model.load_state_dict(weights)weights_keys model.state_dict().keys()
for key in weights_keys:if num_batches_tracked in key:continueweight_t model.state_dict()[key].numpy()weight_mean weight_t.mean()weight_std weight_t.std(ddof1)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,bins50)plt.title(key)plt.show()结果
