LeNet-5

目录

一、知识点

二、代码

三、查看卷积层的feature map

1. 查看每层信息

​2. show_featureMap.py

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背景：LeNet-5是一个经典的CNN，由Yann LeCun在1998年提出，旨在解决手写数字识别问题。

一、知识点

1. iter()+next()

iter()：返回迭代器

next()：使用next()来获取下一条数据

data = [1, 2, 3] data_iter = iter(data) print(next(data_iter)) # 1 print(next(data_iter)) # 2 print(next(data_iter)) # 3

2. enumerate

enumerate(sequence,[start=0]) 函数用于将一个可遍历的数据对象组合为一个索引序列，同时列出数据和数据下标，一般用在 for 循环当中。

start--下标起始位置的值。 

data = ['zs', 'ls', 'ww'] print(list(enumerate(data))) # [(0, 'zs'), (1, 'ls'), (2, 'ww')]

3. torch.no_grad()

在该模块下，所有计算得出的tensor的requires_grad都自动设置为False。

当requires_grad设置为False时，在反向传播时就不会自动求导了，可以节约存储空间。

4. torch.max(input,dim)

input -- tensor类型

dim=0 -- 行比较

dim=1 -- 列比较

import torch data = torch.Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) x = torch.max(data, dim=0) print(x) # values=tensor([7., 8., 9.]), # indices=tensor([2, 2, 2]) x = torch.max(data, dim=1) print(x) # values=tensor([3., 6., 9.]), # indices=tensor([2, 2, 2])

5. torch.eq：对两个张量Tensor进行逐个元素的比较，若相同位置的两个元素相同，则返回True；若不同，返回False。

注意：item返回一个数。

import torch data1 = torch.tensor([1, 2, 3, 4, 5]) data2 = torch.tensor([2, 3, 3, 9, 5]) x = torch.eq(data1, data2) print(x) # tensor([False, False, True, False, True]) sum = torch.eq(data1, data2).sum() print(sum) # tensor(2) sum_item = torch.eq(data1, data2).sum().item() print(sum_item) # 2

6. squeeze(input,dim)函数

squeeze(0)：若第一维度值为1，则去除第一维度

squeeze(1)：若第二维度值为2，则去除第二维度

squeeze(-1)：去除最后维度值为1的维度

7. unsqueeze(input,dim)

增加大小为1的维度，即返回一个新的张量，对输入的指定位置插入维度 1且必须指明维度。

二、代码

model.py

import torch.nn as nn import torch.nn.functional as F class LeNet(nn.Module): def __init__(self): super(LeNet, self).__init__() self.conv1 = nn.Conv2d(3, 16, 5) # output(16,28,28) self.pool1 = nn.MaxPool2d(2, 2) # output(16,14,14) self.conv2 = nn.Conv2d(16, 32, 5) # output(32,10,10) self.pool2 = nn.MaxPool2d(2, 2) # output(32,5,5) self.fc1 = nn.Linear(32 * 5 * 5, 120) # output:120 self.fc2 = nn.Linear(120, 84) # output:84 self.fc3 = nn.Linear(84, 10) # output:10 def forward(self, x): x = F.relu(self.conv1(x)) x = self.pool1(x) x = F.relu(self.conv2(x)) x = self.pool2(x) x = x.view(-1, 32 * 5 * 5) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x

train.py

import torch import torchvision import torch.nn as nn import torch.optim as optim import torchvision.transforms as transforms from model import LeNet def main(): # preprocess data transform = transforms.Compose([ # Converts a PIL Image or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] transforms.ToTensor(), # (mean[1],...,mean[n])`` and std: ``(std[1],..,std[n]) transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) # 训练集 如果数据集已经下载了，则download=False train_data = torchvision.datasets.CIFAR10('./data', train=True, transform=transform, download=False) train_loader = torch.utils.data.DataLoader(train_data, batch_size=36, shuffle=True, num_workers=0) # 验证集 val_data = torchvision.datasets.CIFAR10('./data', train=False, download=False, transform=transform) val_loader = torch.utils.data.DataLoader(val_data, batch_size=10000, shuffle=False, num_workers=0) # 返回迭代器 val_data_iter = iter(val_loader) val_image, val_label = next(val_data_iter) net = LeNet() loss_function = nn.CrossEntropyLoss() optimizer = optim.Adam(net.parameters(), lr=0.001) # loop over the dataset multiple times for epoch in range(5): epoch_loss = 0 for step, data in enumerate(train_loader, start=0): # get the inputs from train_loader;data is a list of[inputs,labels] inputs, labels = data # 在处理每一个batch时并不需要与其他batch的梯度混合起来累积计算，因此需要对每个batch调用一遍zero_grad()将参数梯度设置为0 optimizer.zero_grad() # 1.forward outputs = net(inputs) # 2.loss loss = loss_function(outputs, labels) # 3.backpropagation loss.backward() # 4.update x by optimizer optimizer.step() # print statistics # 使用item()取出的元素值的精度更高 epoch_loss += loss.item() # print every 500 mini-batches if step % 500 == 499: with torch.no_grad(): outputs = net(val_image) predict_y = torch.max(outputs, dim=1)[1] # [0]取每行最大值，[1]取每行最大值的索引 val_accuracy = torch.eq(predict_y, val_label).sum().item() / val_label.size(0) print('[epoch：%d step：%5d] train_loss:%.3f test_accuracy:%.3f' % ( epoch + 1, step + 1, epoch_loss / 500, val_accuracy)) epoch_loss = 0 print('Train finished!') sava_path = './model/LeNet.pth' torch.save(net.state_dict(), sava_path) if __name__ == '__main__': main()

predict.py

import torch import torchvision.transforms as transforms from PIL import Image from model import LeNet def main(): transform = transforms.Compose([ transforms.Resize((32, 32)), transforms.ToTensor(), # CHW格式 transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] net = LeNet() net.load_state_dict(torch.load('./model/LeNet.pth')) image = Image.open('./predict/2.png') # HWC格式 image = transform(image) image = torch.unsqueeze(image, dim=0) # 在第0维加一个维度 #[N,C,H,W] N:Batch批处理大小 with torch.no_grad(): outputs = net(image) predict = torch.max(outputs, dim=1)[1] print(classes[predict]) if __name__ == '__main__': main()

2.png

 

三、查看卷积层的feature map 1. 查看每层信息 for i in net.children(): print(i) 2. show_featureMap.py import torch import torch.nn as nn from model import LeNet import torchvision import torchvision.transforms as transforms from PIL import Image import matplotlib.pyplot as plt def main(): transform = transforms.Compose([ transforms.Resize((32, 32)), transforms.ToTensor(), # CHW格式 transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) image = Image.open('./predict/2.png') # HWC格式 image = transform(image) image = torch.unsqueeze(image, dim=0) # 在第0维加一个维度 #[N,C,H,W] N:Batch批处理大小 net = LeNet() net.load_state_dict(torch.load('./model/LeNet.pth')) conv_weights = [] # 模型权重 conv_layers = [] # 模型卷积层 counter = 0 # 模型里有多少个卷积层 # 1.将卷积层以及对应权重放入列表中 model_children = list(net.children()) for i in range(len(model_children)): if type(model_children[i]) == nn.Conv2d: counter += 1 conv_weights.append(model_children[i].weight) conv_layers.append(model_children[i]) outputs = [] names = [] for layer in conv_layers[0:]: # 2.每个卷积层对image进行计算 image = layer(image) outputs.append(image) names.append(str(layer)) # 3.进行维度转换 print(outputs[0].shape) # torch.Size([1, 16, 28, 28]) 1-batch 16-channel 28-H 28-W print(outputs[0].squeeze(0).shape) # torch.Size([16, 28, 28]) 去除第0维 # 将16颜色通道的feature map加起来，变为一张28×28的feature map，sum将所有灰度图映射到一张 print(torch.sum(outputs[0].squeeze(0), 0).shape) # torch.Size([28, 28]) processed_data = [] for feature_map in outputs: feature_map = feature_map.squeeze(0) # torch.Size([16, 28, 28]) gray_scale = torch.sum(feature_map, 0) # torch.Size([28, 28]) # 取所有灰度图的平均值 gray_scale = gray_scale / feature_map.shape[0] processed_data.append(gray_scale.data.numpy()) # 4.可视化特征图 figure = plt.figure() for i in range(len(processed_data)): x = figure.add_subplot(1, 2, i + 1) x.imshow(processed_data[i]) x.set_title(names[i].split('(')[0]) plt.show() if __name__ == '__main__': main()