PyTorch学习笔记

参考1：https://www.jianshu.com/p/d5af8aea4229

参考2：https://pytorch.org/docs/stable/index.html

 1、torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0,
dilation=1, groups=1, bias=True, padding_mode='zeros')

• in_channels (int) – Number of channels in the input image
• out_channels (int) – Number of channels produced by the convolution
• kernel_size (int or tuple) – Size of the convolving kernel
• stride (int or tuple, optional) – Stride of the convolution. Default: 1
• padding (int or tuple, optional) – Zero-padding added to both sides of the input. Default: 0
• padding_mode (string, optional) – zeros
• dilation (int or tuple, optional) – Spacing between kernel elements. Default: 1
• groups (int, optional) – Number of blocked connections from input channels to output channels. Default: 1
• bias (bool, optional) – If True, adds a learnable bias to the output. Default: True

torch.nn包仅支持对批量数据的处理,而不能对单个样本进行处理，例如,nn.Conv2d只接受4维的张量: nSamples * nChannels * Height * Width
input: (batch_size, channels, H_in, W_in)

2、class torch.nn.Linear(in_features, out_features, bias=True)

• in_features – size of each input sample
• out_features – size of each output sample
• bias – If set to False, the layer will not learn an additive bias. Default: True