Ai公益深度学习
softmax的基本概念 下面的连接是我自己记录的softmax和交叉熵的笔记。 模型的训练和预测 softmax丛林开始实现 import torchvision import numpy as np import sys sys . path . append ( "/home/kesci/input" ) import d2lzh1981 as d2l print ( torch . __version__ ) print ( torchvision . __version__ ) 获取训练集数据和测试集数据 batch_size = 256 train_iter , test_iter = d2l . load_data_fashion_mnist ( batch_size ) 模型参数初始化 num_inputs = 784 print ( 28 * 28 ) num_outputs = 10 W = torch . tensor ( np . random . normal ( 0 , 0.01 , ( num_inputs , num_outputs ) ) , dtype = torch . float ) b = torch . zeros ( num_outputs , dtype = torch . float ) W . requires_grad_ (