【卷积神经网络】例题XO - Python实现 - PyTorch版本

题目及理论讲解：

【卷积神经网络】例题XO - Python实现 - HBU_DAVID - 博客园 (cnblogs.com)

PyTorch实现代码：

# https://blog.csdn.net/qq_26369907/article/details/88366147 # https://zhuanlan.zhihu.com/p/405242579 import numpy as np import torch import torch.nn as nn  x = torch.tensor([[[[-1, -1, -1, -1, -1, -1, -1, -1, -1],                     [-1, 1, -1, -1, -1, -1, -1, 1, -1],                     [-1, -1, 1, -1, -1, -1, 1, -1, -1],                     [-1, -1, -1, 1, -1, 1, -1, -1, -1],                     [-1, -1, -1, -1, 1, -1, -1, -1, -1],                     [-1, -1, -1, 1, -1, 1, -1, -1, -1],                     [-1, -1, 1, -1, -1, -1, 1, -1, -1],                     [-1, 1, -1, -1, -1, -1, -1, 1, -1],                     [-1, -1, -1, -1, -1, -1, -1, -1, -1]]]], dtype=torch.float) print(x.shape) print(x)  print(--------------- 卷积  ---------------) conv1 = nn.Conv2d(1, 1, (3, 3), 1)  # in_channel , out_channel , kennel_size , stride conv1.weight.data = torch.Tensor([[[[1, -1, -1],                                     [-1, 1, -1],                                     [-1, -1, 1]]                                    ]]) conv2 = nn.Conv2d(1, 1, (3, 3), 1)  # in_channel , out_channel , kennel_size , stride conv2.weight.data = torch.Tensor([[[[1, -1, 1],                                     [-1, 1, -1],                                     [1, -1, 1]]                                    ]]) conv3 = nn.Conv2d(1, 1, (3, 3), 1)  # in_channel , out_channel , kennel_size , stride conv3.weight.data = torch.Tensor([[[[-1, -1, 1],                                     [-1, 1, -1],                                     [1, -1, -1]]                                    ]])  feature_map1 = conv1(x) feature_map2 = conv2(x) feature_map3 = conv3(x)  print(feature_map1 / 9) print(feature_map2 / 9) print(feature_map3 / 9)  print(--------------- 池化  ---------------) max_pool = nn.MaxPool2d(2, padding=0, stride=2)  # Pooling zeroPad = nn.ZeroPad2d(padding=(0, 1, 0, 1))  # pad 0 , Left Right Up Down  feature_map_pad_0_1 = zeroPad(feature_map1) feature_pool_1 = max_pool(feature_map_pad_0_1) feature_map_pad_0_2 = zeroPad(feature_map2) feature_pool_2 = max_pool(feature_map_pad_0_2) feature_map_pad_0_3 = zeroPad(feature_map3) feature_pool_3 = max_pool(feature_map_pad_0_3)  print(feature_pool_1.size()) print(feature_pool_1 / 9) print(feature_pool_2 / 9) print(feature_pool_3 / 9)  print(--------------- 激活  ---------------) activation_function = nn.ReLU()  feature_relu1 = activation_function(feature_map1) feature_relu2 = activation_function(feature_map2) feature_relu3 = activation_function(feature_map3) print(feature_relu1 / 9) print(feature_relu2 / 9) print(feature_relu3 / 9)