3.7_softmax-regression-pytorch.py

# -*- coding:utf-8 –*-
'''
@Author: lkk
@Date: 2019-12-17 15:52:28
@LastEditTime: 2019-12-17 17:50:04
@LastEditors: lkk
@Description: 
'''
import torch
from torch import nn
from torch.nn import init
import numpy as np
import d2lzh_pytorch as d2l

batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
num_input = 784
num_output = 10
num_epochs = 5


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):  # x shape: (batch, 1, 28, 28)
        y = self.linear(x.view(x.shape[0], -1))
        return y


net = LinearNet(num_input, num_output)
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)

d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None, None, optimizer)