CNN卷积神经网络Python实现

import torch from torch import nn # ①定义互相关运算 def corr2d(X, K):"""计算二维互相关运算。"""# 获取K的形状 行为h,列为w h, w = K.shape # 生成全0的矩阵，行为X的行减去h加上1，列为X的列减去w加上1 Y = torch.zeros((X.shape[0] - h + 1, X.shape[1] - w + 1)) for i in range(Y.shape[0]): for j in range(Y.shape[1]): # 两层循环，相乘，求和 Y[i, j] = (X[i:i + h, j:j + w] * K).sum() # 返回Y return Y # ②实现二维卷积层 class Conv2D(nn.Module): def __init__(self, kernel_size): super().__init__() # 定义权重 self.weight = nn.Parameter(torch.rand(kernel_size)) # 定义偏移 self.bias = nn.Parameter(torch.zeros(1)) # 定义正向传播 def forward(self, x): return corr2d(x, self.weight) + self.bias if __name__ == '__main__': # 定义模型 conv2d = nn.Conv2d(1, 1, kernel_size=(1, 2), bias=False) # 定义X X = torch.ones((6, 8)) X[:, 2:6] = 0 # 定义K K = torch.tensor([[1.0, -1.0]]) # 计算Y Y = corr2d(X, K) X = X.reshape((1, 1, 6, 8)) Y = Y.reshape((1, 1, 6, 7)) # 训练10轮 for i in range(10): # 计算Y_hat Y_hat = conv2d(X) # 损失 l = (Y_hat - Y)**2 # 梯度归零 conv2d.zero_grad() # 后向传播 l.sum().backward() # 优化函数 学习率=3e-2 conv2d.weight.data[:] -= 3e-2 * conv2d.weight.grad if (i + 1) % 2 == 0: print(f'batch {i+1}, loss {l.sum():.3f}') # 经过10轮学习的权重为 print(conv2d.weight.data.reshape((1, 2)))

结果

batch 2, loss 1.463 batch 4, loss 0.358 batch 6, loss 0.106 batch 8, loss 0.037 batch 10, loss 0.014 tensor([[ 1.0066, -0.9830]]) Process finished with exit code 0