import torch
from d2l import torch as d2l
from torch import nn
from softmax_normal import train_ch3


batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)


# softmax回归的输出层是一个全连接层
# PyTorch不会隐式地调整输入形状
# 因此，定义展平层()在线性层前调整网络输入的形状
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)
# 在交叉熵损失函数中传递未归一化的预测，并同时计算softmax及其对数
loss = nn.CrossEntropyLoss()
# 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)
# 调用之前定义的函数来训练模型
num_epochs = 10
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)