多层感知机

较底层实现

import torch
import numpy as np
import matplotlib.pylab as plt
import sys
import torchvision
import torchvision.transforms as transforms

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())

print(type(mnist_train))
print(len(mnist_train), len(mnist_test))
feature, label = mnist_train[0]  # 可下标获取数据
print(feature.shape, label)  # Channel x Height x Width

# 获取标签函数
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]

# 显示函数
def show_fashion_mnist(images, labels):
    display.set_matplotlib_formats('svg')
    plt.rcParams['figure.figsize'] = (3.5, 2.5)
    # 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()

    
def load_data_fashion_mnist(batch_size):
    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
    
batch_size = 256
train_iter, test_iter = load_data_fashion_mnist(batch_size)



#### 设置网络参数


num_inputs, num_outputs, num_hiddens = 784, 10, 256

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)

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

def net(X):
    X = X.view((-1, num_inputs))
    H = relu(torch.matmul(X, W1) + b1)
    return torch.matmul(H, W2) + b2

def sgd(params, lr, batch_size):
    for param in params:
        param.data -= lr * param.grad / batch_size   # 更新时用.data 以免操作计入计算图

loss = torch.nn.CrossEntropyLoss()

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

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)             ## 前向得到网络输出
            l = loss(y_hat, y).sum()   ## 计算loss

            # 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()               ## 反向计算梯度
            if optimizer is None:
                sgd(params, lr, batch_size)
            else:
                optimizer.step()  # “softmax回归的简洁实现”一节将用到


            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, lr = 5, 100.0
train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params, lr)

步骤基本上与前面是一样的，需要改的只是网络参数，激活函数，网络结构。

torch封装实现

import torch.nn as nn
from torch.nn import init

num_inputs, num_outputs, num_hiddens = 784, 10, 256

# net = nn.Sequential(
#         FlattenLayer(),
#         nn.Linear(num_inputs, num_hiddens),
#         nn.ReLU(),
#         nn.Linear(num_hiddens, num_outputs), 
#         )

class mul_net(nn.Module):
    def __init__(self, num_inputs, num_outputs, num_hiddens):
        super().__init__()
        self.linear = nn.Linear(num_inputs, num_hiddens)
        self.relu = nn.ReLU()
        self.hidden = nn.Linear(num_hiddens, num_outputs)
        
    def forward(self, x):
        y = self.linear(x.view(x.shape[0], -1))
        y = self.relu(y)
    return self.hidden(y)

net = mul_net( num_inputs, num_outputs, num_hiddens)
    
for params in net.parameters():
    init.normal_(params, mean=0, std=0.01)

batch_size = 256
train_iter, test_iter = load_data_fashion_mnist(batch_size)
loss = torch.nn.CrossEntropyLoss()

optimizer = torch.optim.SGD(net.parameters(), lr=0.5)

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