0013-pytorch入门-自定义letnet-5网络

绝地灬酷狼 2023-01-07 10:27 112阅读 0赞

'''
    定义包含一些可学习的参数(或者叫权重)神经网络模型；
    
    在数据集上迭代；
    
    通过神经网络处理输入；
    
    计算损失(输出结果和正确值的差值大小)；
    
    将梯度反向传播回网络的参数；
    
    更新网络的参数，主要使用如下简单的更新原则：weight = weight - learning_rate * gradient
    '''
    #定义网络
    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    '''
    CNN计算
    
    (H - k +2 * P) / S + 1
    (W - k +2 * P) / S + 1
    
    LetNet-5 
    input: 32*32*3
    
    out_conv1 = (32-5)+1 = 28 
    max_pool1 = 28 / 2 = 14
    out_conv2 = (14 - 5) + 1 = 10
    max_pool2 = 10 / 2 = 5
    
    定义一个神经网络
    https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html#sphx-glr-beginner-blitz-neural-networks-tutorial-py
    '''
    class Net(nn.Module):
        def __init__(self):
            super(Net, self).__init__()
            #  conv1层，输入的灰度图，所以 in_channels=1, out_channels=6 说明使用了6个滤波器/卷积核，
            # kernel_size=5卷积核大小5x5
            self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5)
            # conv2层， 输入通道in_channels 要等于上一层的 out_channels
            self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5)
            # an affine operarion: y = Wx + b
            # 全连接层fc1,因为32x32图像输入到fc1层时候，feature map为：5x5x16
            # 因此，全连接层的输入特征维度为16*5*5，  因为上一层conv2的out_channels=16
            # out_features=84,输出维度为84，代表该层为84个神经元
            self.fc1 = nn.Linear(16*5*5, 120)
            self.fc2 = nn.Linear(in_features=120, out_features=84)
            self.fc3 = nn.Linear(in_features=84, out_features=10)
    
        def forward(self, x):
            # Max pooling over a (2, 2) window
            x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
            # If the size is a square you can only specify a single number
            x = F.max_pool2d(F.relu(self.conv2(x)), 2)
            # 特征图转换为一个１维的向量
            x = x.view(-1, self.num_flat_features(x))
            x = F.relu(self.fc1(x))
            x = F.relu(self.fc2(x))
            x = self.fc3(x)
            return x
    
        def num_flat_features(self, x):
            size = x.size()[1:]     # all dimensions except the batch dimension
            num_features = 1
            for s in size:
                num_features *= s
            return num_features
    
    
    net = Net()
    print(net)
    
    params = list(net.parameters())
    print(params[0].size())
    
    #在上面net模型是"Lenet"期望输入是：32*32
    input = torch.randn(1,1,32,32)
    out = net(input)
    print(out)
    
    #损失函数
    #一个损失函数接受一对作为输入，计算一个值来评估网络的输出和目标值相差多少
    output = net(input)
    target = torch.rand(10)
    target = target.view(1,-1)
    cri = nn.MSELoss()
    loss = cri(output,target)
    print(loss)