Deep Learning Note7 softmax回归的简洁实现

作者： 作者的头像   Agoni7z ,  2024-08-16 02:28:25 ,  所有人可见 ,  阅读 11

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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)

# 初始化模型参数
# PyTorch不会隐式地调整输入的形状
# 因此，我们在线性层前定义了展平层(flatten)来调整网络输入的形状
net = nn.Sequential(nn.Flatten(), nn.Linear(28 * 28, 10))


def init_weights(m):
    if type(m) == nn.Linear:
        nn.init.normal_(m.weight, std=0.01)


net.apply(init_weights)

# 损失函数
loss = nn.CrossEntropyLoss(reduction='none')

# 优化算法
trainer = torch.optim.SGD(net.parameters(), lr=0.1)

# 训练
num_epochs = 10
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)
d2l.plt.show()

351604_4e3136780a-屏幕截图-2024-05-05-005514.png

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Agoni7z   3个月前         踩      回复 
def train_ch3(net, train_iter, test_iter, loss, num_epochs, updater):
    """训练模型"""
    animator = Animator(xlable='epoch', xlim=[1, num_epochs], ylim=[0.3, 0.9],
                        legend=['train loss', 'train acc', 'test acc'])
    for epoch in range(num_epochs):
        train_metrics = train_epoch_ch3(net, train_iter, loss, updater)
        test_acc = evaluate_accuracy(net, test_iter)
        animator.add(epoch + 1, train_metrics + (test_acc,))
    train_loss, train_acc = train_metrics
    assert train_loss < 0.5, train_loss
    assert train_acc <= 1 and train_acc > 0.7, train_acc
    assert test_acc <= 1 and test_acc > 0.7, test_acc