该9步过程可以看作是一个矩阵跟另外一个矩阵的矩阵相乘将每步input拉直，例如左上角第一个深蓝色区域拉为332001312行向量，再将Kernel拉为$(012220012)^T$列向量相乘得到第一个最终结果的左上角第一个数值12.0So，上边九步运算可以视为行数为9的矩阵和列数为9的矩阵进行矩阵乘法，再将相乘的结果reshape为欲得到的结果Other Method实现一个长度为25的内积目前Kernel只是3×3的范围大小，若是将Kernel填充一下，eg.左上角第一幅图，Kernel只有9个数，但是我们可以脑补一下浅蓝色部分都填充为0，每一步都将Kernel填充浅蓝色部分为0，那么，原问题就变为25行向量和25列向量相乘。Coding希望把region_vector都放入到region_matrix中，再将region_matrix与Kernel_mat...

蓝色的input_feature 5*5深蓝色小字部分kernel_size 3*3绿色部分out_feature 3*3stride = 1padding = 0channel = 1padding = 1stride = 2底部input_channels = 2顶端绿色为out_channels = 3kernels = 2*3 = 6（倒数第二行）input = input_feature_map # 卷积输入特征图 kernel = conv_layer.weight.data # 卷积核 input = torch.randn(5, 5) # 卷积输入特征图 kernel = torch.randn(3, 3) # 卷积核 bias = torch.randn(1) # 卷积偏置项，默认输出通道数目＝1 # Func1 用原始的矩阵运算...

22、Pytorch nn.Conv2dCLASS torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', device=None, dtype=None)[ ](https://pytorch.org/docs/stable/_modules/torch/nn/modules/conv.html#Conv2d)torch import torch.nn as nn import torch.nn.functional as F conv_layer = nn.Conv2d(in_channels=1, out_channels=1, kernel_s...

MODULECLASStorch.nn.Module(args, kwargs*)[SOURCE]层、模型的父类Base class for all neural network modules.Your models should also subclass this class.Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self): ...

TransformsData does not always come in its final processed form that is required for training machine learning algorithms. We use transforms to perform some manipulation of the data and make it suitable for training.All TorchVision datasets have two parameters -transform to modify the features and target_transform to modify the labels - that accept callables containing the transform...