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...

dataset需要实现def __getitem__(self, index) -> T_co: # 基于一个索引返回一个训练样本(x, y)构成的训练对 raise NotImplementedError("Subclasses of Dataset should implement __getitem__.") def __len__(self): # 将数据放进去，返回数据总大小 return len(self.img_labels) return self.tensors[0].size(0)import os import pandas as pd from torchvision.io import read_image # 从磁盘中读取训练数据、__getitem__中能够根据idx返回相对...

torch.gather(input, dim, index, *, sparse_grad=False, out=None) → [Tensor]Gathers values along an axis specified by dim.Parametersinput (Tensor) – the source tensordim (int) – the axis along which to indexindex (LongTensor) – the indices of elements to gatherKeyword Argumentssparse_grad (bool, optional) – If True, gradient w.r.t. input will be a sparse tensor.out (Tensor, optional) ...