Pytorch中怎么实现softmax回归

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如果采用自定义方式搭建网络方式：

import torchimport torchvisionimport torchvision.transforms as transformsimport matplotlib.pyplot as pltimport timeimport sysimport numpy as npimport  requests#设置训练集和测试集，下载在本地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 = 256if sys.platform.startswith('win'):    num_workers = 0  # 0表示不用额外的进程来加速读取数据else:    num_workers = 4train_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_inputs=784#图片为1*28*28，单通道，所以输入为28*28=784num_outputs=10#最终的类别为10个类别，所以输出是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)#使得w,b可以反向传播w.requires_grad_(requires_grad=True)b.requires_grad_(requires_grad=True)def cross_entropy(y_hat, y):    return - torch.log(y_hat.gather(1, y.view(-1, 1)))def accuracy(y_hat,y):    return (y_hat.argmax(dim=1)==y).float().mean().item()'''这里注意下正确率计算的写法；对于y_hat,返回的是batch_size*10的一个矩阵，argemax(dim=1)为选择一个维度上的最大数的索引；所以y_hat.argmax(dim=1)返回一个batch_size*1的向量；针对于返回的向量和y进行诸位比较，平均化取值，即可得到该批次的正确率'''def sgd(params, lr, batch_size):  # 本函数已保存在d2lzh_pytorch包中方便以后使用    for param in params:        param.data -= lr * param.grad / batch_size # 注意这里更改param时用的param.datadef evalute_accuracy(data_iter,net):    acc_sum,n=0.0,0    for X,y in data_iter:        acc_sum+=(net(X).argmax(dim=1)==y).float().sum().item()        n+=y.shape[0]    return acc_sum/nnum_epochs,lr=30,0.1def softmax(X):    X_exp = X.exp()    partition = X_exp.sum(dim=1, keepdim=True)    return X_exp / partition  # 这里应用了广播机制def net(X):    return softmax(torch.mm(X.view((-1, num_inputs)), w) + b)def train(net,train_iter,test_iter,loss,num_epochs,batch_size,params=None,lr=None,optimizer=None):    for epoch in range(num_epochs):        train_l_sum,train_acc_sum,n=0.0,0.0,0        for X,y in train_iter:            y_hat=net(X)            l=loss(y_hat,y).sum()            if optimizer is not None:                optimizer.zero_grad()            elif params is not None and params[0].grad is not None:                for param in params:                    param.grad.data.zero_()            l.backward()            if optimizer is None:                sgd(params, lr, batch_size)            else:                optimizer.step()  # "softmax回归的简洁实现"一节将用到            train_l_sum+=l.item()            train_acc_sum+=(y_hat.argmax(dim=1)==y).sum().item()            n+=y.shape[0]        test_acc=evalute_accuracy(test_iter,net);        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f'              % (epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc))if __name__ == '__main__':    print(mnist_train)    X, y = [], []    '''    for i in range(10):        X.append(mnist_train[i][0])        y.append(mnist_train[i][1])    show_fashion_mnist(X, get_fashion_mnist_labels(y))        '''    train(net, train_iter, test_iter, cross_entropy, num_epochs, batch_size, [w, b], lr)

如果采用典型的层数搭建函数，能有更加简洁的实现版本：

import torchimport torchvisionimport torchvision.transforms as transformsfrom torch import nnfrom collections import OrderedDictimport matplotlib.pyplot as pltimport timeimport sysimport numpy as npimport  requests#设置训练集和测试集，下载在本地from torch.nn import initmnist_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)test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False)num_inputs=784#图片为1*28*28，单通道，所以输入为28*28=784num_outputs=10#最终的类别为10个类别，所以输出是10；class LinearNet(nn.Module):    def __init__(self,num_inputs,num_outputs):        super(LinearNet,self).__init__()        self.linear=nn.Linear(num_inputs,num_outputs)    def forward(self,x):        y=self.linear(x.view(x.shape[0],-1))        return ydef evalute_accuracy(data_iter,net):    acc_sum,n=0.0,0    for X,y in data_iter:        acc_sum+=(net(X).argmax(dim=1)==y).float().sum().item()        n+=y.shape[0]    return acc_sum/nclass FlattenLayer(nn.Module):    def __init__(self):        super(FlattenLayer, self).__init__()    def forward(self,x):        return x.view(x.shape[0],-1)    #把1*28*28转化为1*784net=LinearNet(num_inputs,num_outputs)#使用orderdict来进行网络结构搭建#第一层flatten层把1*28*28转化为1*784#第二层为实际工作层net=nn.Sequential(    OrderedDict(        [            ('flatten',FlattenLayer()),            ('linear',nn.Linear(num_inputs,num_outputs))        ]    ))init.normal_(net.linear.weight, mean=0, std=0.01)init.constant_(net.linear.bias, val=0)loss=nn.CrossEntropyLoss()optimizer=torch.optim.SGD(net.parameters(),lr=0.1)num_epochs=10def train(net,train_iter,test_iter,loss,num_epochs,batch_size,params=None,lr=None,optimizer=None):    for epoch in range(num_epochs):        train_l_sum,train_acc_sum,n=0.0,0.0,0        for X,y in train_iter:            y_hat=net(X)            l=loss(y_hat,y).sum()            if optimizer is not None:                optimizer.zero_grad()            elif params is not None and params[0].grad is not None:                for param in params:                    param.grad.data.zero_()            l.backward()            optimizer.step()  # "softmax回归的简洁实现"一节将用到            train_l_sum+=l.item()            train_acc_sum+=(y_hat.argmax(dim=1)==y).sum().item()            n+=y.shape[0]        test_acc=evalute_accuracy(test_iter,net);        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f'              % (epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc))if __name__ == '__main__':    print(mnist_train)    X, y = [], []    '''    for i in range(10):        X.append(mnist_train[i][0])        y.append(mnist_train[i][1])    show_fashion_mnist(X, get_fashion_mnist_labels(y))        '''    train(net, train_iter, test_iter, loss, num_epochs, batch_size,None,None,optimizer)

以上就是Pytorch中怎么实现softmax回归，小编相信有部分知识点可能是我们日常工作会见到或用到的。希望你能通过这篇文章学到更多知识。更多详情敬请关注行业资讯频道。