【PyTorch系列】PyTorch - SOFTMAX回归的从零开始实现

学习教材：
动手学深度学习 PYTORCH 版(DEMO)

(https://github.com/ShusenTang/Dive-into-DL-PyTorch)
PDF 制作by [Marcus Yang](https://github.com/chenyang1999)

直接代码：

'''导入所需的包'''
import torch
from torch import nn
from torch.nn import init
import numpy as np
import torchvision



# 下载并读取数据集
def load_data_fashion_mnist(batch_size, resize=None, root='~/Datasets/FashionMNIST'):
    """Download the fashion mnist dataset and then load intomemory."""
    trans = []
    if resize:
        trans.append(torchvision.transforms.Resize(size=resize))
    trans.append(torchvision.transforms.ToTensor())

    transform = torchvision.transforms.Compose(trans)
    mnist_train = torchvision.datasets.FashionMNIST(root=root, train=True, download=True, transform=transform)
    mnist_test = torchvision.datasets.FashionMNIST(root=root,
                                                   train=False, download=True, transform=transform)
    train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=4)
    test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=4)

    return train_iter, test_iter


''' 获取和读取数据 '''
batch_size = 256
train_iter, test_iter = load_data_fashion_mnist(batch_size)

''' 定义和初始化模型'''
num_inputs = 784
num_outputs = 10


# 定义线性模型
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

# 定义一个FlatternLayer,用作对x的形状转换的这个功能。
class FlattenLayer(nn.Module):
    def __init__(self):
        super(FlattenLayer, self).__init__()

    def forward(self, x):  # x shape:(batch,*,*)
        return x.view(x.shape[0], -1)


net = LinearNet(num_inputs, num_outputs)
from collections import OrderedDcit

net = nn.Sequential(
    OrderedDict([
        ('flatten', FlattenLayer()),
        ('linear', nn.Linear(num_inputs, num_outputs))
    ])
)

# 然后我们使用均值为0，标准差为0.01的正态随机初始化模型的权重参数。
init.normal_(net.linear.weight, mean=0, std=0.01)
init.constant_(net.linear.bias, val=0)

# pytorch提供一个包含softmax和交叉熵损失函数计算的函数，它的数值稳定性更高。
loss = nn.CrossEntropyLoss()

# 使用学习率为0.1的小批量随机梯度下降算法作为优化函数
optimizer = torch.optim.SGD(net.parameters(), lr=0.1)


def sgd(params, lr, batch_size):
    for param in params:
        param.data -= lr * param.grad / batch_size


def evaluate_accuracy(data_iter, net):
    acc_sum, n = 0.0, 0
    for X, y in data_iter:
        acc_sum += (net(X).argmax(dim=1) == y).float().sum().item()
        n += y.shape[0]
    return acc_sum / n


# 训练模型
def train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params=None, lr=None, optimizer=None):
    for epoch in range(num_epochs):
        train_l_sum, train_acc_sum, n = 0.0, 0.0, 0
        for X, y in train_iter:
            y_hat = net(X)
            l = loss(y_hat, y).sum()
            # 梯度清零
            if optimizer is not None:
                optimizer.zero_grad()
            elif params is not None and params[0].grad is not None:
                for param in params:
                    param.grad.data.zero_()

            l.backward()
            if optimizer is None:
                sgd(params, lr, batch_size)
            else:
                optimizer.step()
            train_l_sum += l.item()
            train_acc_sum += (y_hat.argmax(dim=1) == y).sum().item()
            n += y.shape[0]
        test_acc = evaluate_accuracy(test_iter, net)
        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f' % (
        epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc))


num_epochs = 5
train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None, None, optimizer)