笔记|深度学习基础--多层感知机（MLP）深度学习|pytorch|深度学习|神经

深度学习基础–多层感知机（MLP）最近在阅读一本书籍–Dive-into-DL-Pytorch（动手学深度学习），链接：https://github.com/newmonkey/Dive-into-DL-PyTorch，自身觉得受益匪浅，在此记录下自己的学习历程。
本篇主要记录关于多层感知机(multilayer perceptron, MLP)的知识。多层感知机是在单层神经网络的基础上引入一个或多个隐藏层。
以单层神经网路SOFTMAX回归为例子。给定一个小批量样本X，假设输出层的softmax回归的权重和偏差参数分别为Wo和bo，输出层的输出记为O，则softmax回归的计算表达式为：

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在上述的SOFTMAX回归中，我们在输入层与输出层间引入一个隐藏层，形成多层感知机。假设隐藏层的输出记为H，隐藏层的权重参数和偏差参数分别为Wh和bh，?表示激活函数。则这个多层感知机的计算表达式为：

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上述式子联立可得：

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利用pytorch实现上述的多层感知机：
0 引入相关的包

import torch from torch import nn from torch.nn import init import numpy as np import torchvision import torchvision.transforms as transforms

1 获取数据集采用的是Fashion-MNIST数据集。

def load_data_fashion_mnist(batch_size, root='~/Datasets/FashionMNIST'): transform = transforms.ToTensor() 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) 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_iterbatch_size = 256 train_iter, test_iter = load_data_fashion_mnist(batch_size)

2 定义和初始化模型采用ReLU函数作为激活函数。

num_inputs, num_outputs, num_hiddens = 784, 10, 256class FlattenLayer(torch.nn.Module): def __init__(self): super(FlattenLayer, self).__init__() def forward(self, x): # x shape: (batch, *, *, ...) return x.view(x.shape[0], -1)net = nn.Sequential( FlattenLayer(), nn.Linear(num_inputs, num_hiddens), nn.ReLU(), nn.Linear(num_hiddens, num_outputs), ) for params in net.parameters(): init.normal_(params, mean=0, std=0.01)

3 定义损失函数仍是采用SOFTMAX回归使用的交叉熵损失函数

loss = torch.nn.CrossEntropyLoss()

4 定义优化算法采用?批量随机梯度下降（SGD）为优化算法。

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

5 训练模型迭代周期设置为5，训练模型。

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 / ndef sgd(params, lr, batch_size): for param in params: param.data -= lr * param.grad / batch_size # 注意这里更改param时用的param.datadef 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) #结果 #epoch 1, loss 0.0031, train acc 0.709, test acc 0.798 #epoch 2, loss 0.0019, train acc 0.819, test acc 0.819 #epoch 3, loss 0.0017, train acc 0.844, test acc 0.840 #epoch 4, loss 0.0015, train acc 0.856, test acc 0.820 #epoch 5, loss 0.0014, train acc 0.864, test acc 0.832

【笔记|深度学习基础--多层感知机（MLP）】END！