简述

• 获取和读取数据;这里还是采用Fashion-MNIST数据集
• 定义模型参数,这里的输入层是28*28,隐藏层设为256,输出层为10
• 定义模型,激活函数使用ReLU,然后再经过一层线性层
• 定义损失函数,采用交叉熵损失函数
• 最后采用小批量随机梯度下降进行训练优化

• 注: 由于SoftmaxCrossEntropyLoss在反向传播的时候相对于沿batch维求和了，而PyTorch默认的是求平均，所以用PyTorch计算得到的loss比mxnet小很多（大概是maxnet计算得到的1/batch_size这个量级），所以反向传播得到的梯度也小很多，所以为了得到差不多的学习效果，我们把学习率设置成100.0。(之所以这么大，应该是因为d2lzh_pytorch里面的sgd函数在更新的时候除以了batch_size，其实PyTorch在计算loss的时候已经除过一次了，sgd这里应该不用除了)

完整代码

d2lzh_pytorch.py

import random
from IPython import display
import matplotlib.pyplot as plt
import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import time
import sys
import torch.nn as nn


def use_svg_display():
    # 用矢量图显示
    display.set_matplotlib_formats('svg')

def set_figsize(figsize=(3.5, 2.5)):
    use_svg_display()
    # 设置图的尺寸
    plt.rcParams['figure.figsize'] = figsize

'''给定batch_size, feature, labels,做数据的打乱并生成指定大小的数据集'''
def data_iter(batch_size, features, labels):
    num_examples = len(features)
    indices = list(range(num_examples))
    random.shuffle(indices)
    for i in range(0, num_examples, batch_size): #(start, staop, step)
        j = torch.LongTensor(indices[i: min(i + batch_size, num_examples)]) #最后一次可能没有一个batch
        yield features.index_select(0, j), labels.index_select(0, j)

'''定义线性回归的模型'''
def linreg(X, w, b):
    return torch.mm(X, w) + b

'''定义线性回归的损失函数'''
def squared_loss(y_hat, y):
    return (y_hat - y.view(y_hat.size())) ** 2 / 2

'''线性回归的优化算法 —— 小批量随机梯度下降法'''
def sgd(params, lr, batch_size):
    for param in params:
        param.data -= lr * param.grad / batch_size #这里使用的是param.data

'''MINIST,可以将数值标签转成相应的文本标签'''
def get_fashion_mnist_labels(labels):
    text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',
                   'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']
    return [text_labels[int(i)] for i in labels]

'''定义一个可以在一行里画出多张图像和对应标签的函数'''
def show_fashion_mnist(images, labels):
    use_svg_display()
    # 这里的_表示我们忽略（不使用）的变量
    _, figs = plt.subplots(1, len(images), figsize=(12, 12))
    for f, img, lbl in zip(figs, images, labels):
        f.imshow(img.view((28, 28)).numpy())
        f.set_title(lbl)
        f.axes.get_xaxis().set_visible(False)
        f.axes.get_yaxis().set_visible(False)
    plt.show()

'''获取并读取Fashion-MNIST数据集;该函数将返回train_iter和test_iter两个变量'''
def load_data_fashion_mnist(batch_size):
    mnist_train = torchvision.datasets.FashionMNIST(root='Datasets/FashionMNIST', train=True, download=True,
                                                    transform=transforms.ToTensor())
    mnist_test = torchvision.datasets.FashionMNIST(root='Datasets/FashionMNIST', train=False, download=True,
                                                   transform=transforms.ToTensor())
    if sys.platform.startswith('win'):
        num_workers = 0  # 0表示不用额外的进程来加速读取数据
    else:
        num_workers = 4
    train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers)
    test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers)

    return train_iter, test_iter

'''评估模型net在数据集data_iter的准确率'''
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,softmax'''
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))

'''对x的形状转换'''
class FlattenLayer(nn.Module):
    def __init__(self):
        super(FlattenLayer, self).__init__()
    def forward(self, x):
        return x.view(x.shape[0], -1)

main.py

import torch
import numpy as np
import sys
sys.path.append("..")
import d2lzh_pytorch as d2l

# 获取和读取数据
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)

# 定义模型参数
num_inputs, num_hiddens, num_outputs = 784, 256, 10
w1 = torch.tensor(np.random.normal(0, 0.01, (num_inputs, num_hiddens)), dtype=torch.float)
b1 = torch.zeros(num_hiddens, dtype=torch.float)
w2 = torch.tensor(np.random.normal(0, 0.01, (num_hiddens, num_outputs)), dtype=torch.float)
b2 = torch.zeros(num_outputs, dtype=torch.float)
params = [w1, b1, w2, b2]
for param in params:
    param.requires_grad_(requires_grad=True)

# 定义ReLU函数
def relu(X):
    return torch.max(input = X, other = torch.tensor(0.0))

# 定义模型
def net(X):
    X = X.view(-1, num_inputs)
    H = relu(torch.mm(X, w1) + b1)
    return torch.mm(H, w2) + b2

# 定义损失函数
loss = torch.nn.CrossEntropyLoss()

# 训练模型
num_epochs, lr = 5, 100.0
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params, lr)