从0开始深度学习（12）——多层感知机的逐步实现
依然以Fashion-MNIST图像分类数据集为例，手动实现多层感知机和激活函数的编写，大部分代码均在从0开始深度学习（9）——softmax回归的逐步实现中实现过
1 读取数据
import torch
from torchvision import transforms
import torchvision
from torch.utils import data# 读取数据
def load_data_fashion_mnist(batch_size, resize=None): #@savetrans = [transforms.ToTensor()]if resize:trans.insert(0, transforms.Resize(resize))trans = transforms.Compose(trans)mnist_train = torchvision.datasets.FashionMNIST(root="D:/DL_Data/", train=True, transform=trans, download=False)mnist_test = torchvision.datasets.FashionMNIST(root="D:/DL_Data/", train=False, transform=trans, download=False)return (data.DataLoader(mnist_train, batch_size, shuffle=True,num_workers=12),data.DataLoader(mnist_test, batch_size, shuffle=False,num_workers=12))train_iter, test_iter = load_data_fashion_mnist(256, resize=28)
2 初始化模型参数
以单隐藏层的多层感知机为例，选择使用256个隐藏单元
from torch import nn# 初始化模型参数
num_inputs=784 # 28*28
num_outputs=10
num_hiddens=256 # 我们选择使用256个隐藏单元，注意，一般选择使用2的若干次幂，因为内存的特殊性，可以在计算上更高效w1 = nn.Parameter(torch.randn(num_inputs,num_hiddens,requires_grad=True)*0.01)
b1 = nn.Parameter(torch.zeros(num_hiddens,requires_grad=True))w2 = nn.Parameter(torch.randn(num_hiddens, num_outputs, requires_grad=True) * 0.01)
b2 = nn.Parameter(torch.zeros(num_outputs, requires_grad=True))params = [w1, b1, w2, b2]
3 激活函数、损失函数、建立模型
# 激活函数
def relu(x):a=torch.zeros_like(x) # 保证全零张量和x的形状一致，利于广播计算return torch.max(x,a)# 损失函数
loss = nn.CrossEntropyLoss(reduction='none')#建立模型
def net(x):x=x.reshape((-1,num_inputs))#展开H=relu(x@w1+b1)# @表示矩阵乘法return (H@w2+b2)
4 训练模型
优化器使用SGD
#训练，优化器使用sgd
num_epochs=5
lr=00.1
updater=torch.optim.SGD(params,lr=lr)def train_epoch(net, train_iter, loss, updater):if isinstance(net, torch.nn.Module):net.train() # 将模型设置为训练模式metric = Accumulator(3) # 训练损失总和、训练准确度总和、样本数for X, y in train_iter:y_hat = net(X)l = loss(y_hat, y).mean()if isinstance(updater, torch.optim.Optimizer):updater.zero_grad()l.backward()updater.step()else:l.backward()updater([w, b], lr, batch_size)metric.add(float(l) * y.numel(), compute_accuracy(y_hat, y), y.numel())return metric[0] / metric[2], metric[1] / metric[2]def train(net, train_iter, test_iter, loss, num_epochs, updater):for epoch in range(num_epochs):train_metrics = train_epoch(net, train_iter, loss, updater)test_acc = evaluate_accuracy(net, test_iter)print(f'Epoch {epoch + 1}: Train Loss {train_metrics[0]:.3f}, Train Acc {train_metrics[1]:.3f}, Test Acc {test_acc:.3f}')class Accumulator: #@save"""在n个变量上累加"""def __init__(self, n):self.data = [0.0] * ndef add(self, *args):self.data = [a + float(b) for a, b in zip(self.data, args)]def reset(self):self.data = [0.0] * len(self.data)def __getitem__(self, idx):return self.data[idx]def compute_accuracy(y_hat, y): # 预测值、真实值if len(y_hat.shape) > 1 and y_hat.shape[1] > 1:y_hat = y_hat.argmax(axis=1) # 找到一个样本中，对应的最大概率的类别cmp = y_hat.type(y.dtype) == y # 将预测值 y_hat 与真实标签 y 进行比较，生成一个布尔张量 cmpreturn float(cmp.type(y.dtype).sum())# 计算在指定数据集上模型的准确率
def evaluate_accuracy(net, data_iter): if isinstance(net, torch.nn.Module):net.eval() # 将模型设置为评估模式metric = Accumulator(2) # 累加多个变量的总和。这里初始化了一个包含两个元素的累加器，分别用来存储正确预测的数量和总的预测数量。with torch.no_grad():for X, y in data_iter:metric.add(compute_accuracy(net(X), y), y.numel())return metric[0] / metric[1]train(net, train_iter, test_iter, loss, num_epochs, updater)
5 预测
import matplotlib.pyplot as plt
# 定义 Fashion-MNIST 标签的文本描述
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]# 预测并显示结果
def predict(net, test_iter, n=6):for X, y in test_iter:break # 只取一个批次的数据trues = get_fashion_mnist_labels(y)preds = get_fashion_mnist_labels(net(X).argmax(axis=1))titles = [true + '\n' + pred for true, pred in zip(trues, preds)]n = min(n, X.shape[0])fig, axs = plt.subplots(1, n, figsize=(12, 3))for i in range(n):axs[i].imshow(X[i].permute(1, 2, 0).squeeze().numpy(), cmap='gray')axs[i].set_title(titles[i])axs[i].axis('off')plt.show()# 调用预测函数
predict(net, test_iter, n=6)