softmax回归

import torch
n = 10
c = torch.zeros(n)

%matplotlib inline
import random
import torch
from d2l import torch as d2l

def deta_iter(batch_size,feature,labels):
    num_examples =len(feature)
    indices = list(range(num_examples))
    random.shuffle(indices)
    for i in range(0,num_examples,batch_size):
        batch_indices = torch.tensor(
            indices[i:min(i+batch_size,num_examples)]
        )
        yield feature(batch_indices),labels[batch_indices]

Softmax regression