softmax回归简洁实现
1.通过深度学习框架的高级API能够使实现softmax回归更容易
import torch
from torch import nn
from d2l import torch as d2l
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
2.softmax回归输出层是一个全连接层
net = nn.Sequential(nn.Flatten(), nn.Linear(784, 10))
def init_weights(m):
if type(m) == nn.Linear:
nn.init.normal_(m.weight, std=0.01)
net.apply(init_weights);
3.在交叉熵损失函数中传递为归一化的预测，计算softmax及对数
loss = nn.CrossEntropyLoss(reduction='none')
4.使用学习率为0.1的小批量随机梯度下降作为优化算法
trainer = torch.optim.SGD(net.parameters(), lr=0.1)