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算法|基于pytorch手动实现softmax回归

算法神经网络pytorch深度学习数学工具使用softmax手动实现

【算法|基于pytorch手动实现softmax回归】softmax回归的难点主要是对于交叉熵损失函数的设计和理解，可以看我的另一篇博客：（https://blog.csdn.net/weixin_41806489/article/details/109190353）。其他的就是流程作业了。

import torch import torchvision from torch import nn import numpy as np import torchvision.transforms as transforms mnist_train = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST', train=True, download=True, transform=transforms.ToTensor()) mnist_test = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST', train=False, download=True, transform=transforms.ToTensor())batch_size = 256train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, # num_workers=num_workers )test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, # num_workers=num_workers )num_input=28*28#输入的维度 num_output=10#预测的维度 def softMaxReg(X,w,b): #将x的维度重新拉平为[:,num_input],w的维度为[num_input,num_outpput] linValue = https://www.it610.com/article/torch.mm(X, w) + b exp_linValue = linValue.exp() sum_exp_linValue = exp_linValue.sum(axis=1) softmax_value = exp_linValue / (sum_exp_linValue).view(-1, 1) return softmax_value # 交叉熵损失函数 def cross_entropy(y_hat, y): return - torch.log(y_hat.gather(1, y.view(-1, 1))) def sgd(params,lr,batch_size): for param in params: param.data-=lr*param.grad/batch_size w=torch.tensor(np.random.normal(0,0.01,(num_input,num_output)),dtype=torch.float32) b=torch.zeros(num_output,dtype=torch.float32) w.requires_grad_(requires_grad=True) b.requires_grad_(requires_grad=True)net=softMaxRegloss=cross_entropy#pytorch中的交叉熵损失函数已经包含了softmax计算，所以直接输入原始的线性结果就行，出来的就是概率 import torch.optim as optim # optimizer=optim.SGD([w,b],lr=0.03) lr=0.03 num_epochs=10 for epoch in range(1,num_epochs+1): for Xx,yy in train_iter: Xx = Xx.view(-1, num_input) output=net(Xx,w,b) l=loss(output,yy).sum()/len(yy) #反向传播求梯度，梯度之后更新， l.backward() sgd([w,b],lr,batch_size) w.grad.data.zero_() b.grad.data.zero_() # 训练集上的正确率 allTrain = 0 rightTrain = 0 allTest = 0 rightTest = 0 for train_x, train_y in train_iter: allTrain += len(train_y) train_x = train_x.view(-1, num_input) trainOut = net(train_x,w,b) correct = torch.softmax(trainOut.view(trainOut.size()[0], -1), dim=1).argmax(dim=1) == train_y rightTrain += sum(correct).item() # 测试集上的正确率 for test_x, test_y in test_iter: allTest += len(test_y) test_x = test_x.view(-1, num_input) testOut = net(test_x,w,b) correct = torch.softmax(testOut.view(testOut.size()[0], -1), dim=1).argmax(dim=1) == test_y rightTest += sum(correct).item() print("epoch%d,损失:%f,训练集上正确率%f,测试集上的正确率%f" % (epoch, l.item(), rightTrain / allTrain, rightTest / allTest))

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