Pytorch 学习（五）：Pytorch 实现多层感知机（MLP）

Pytorch 实现多层感知机（MLP）

本方法总结自《动手学深度学习》（Pytorch版）github项目

部分内容延续 Pytorch 学习（四）：Pytorch 实现 Softmax 回归实现方法

实现多层感知器（Multlayer Perceptron）同样遵循以下步骤：

数据集读取
模型搭建和参数初始化
损失函数和下降器构建
模型训练

方法一：从零开始实现

import torch
import torch.nn as nn
import numpy as np
import d2lzh_pytorch as d2l

# 各层节点数
num_i = 28 * 28
num_h = 256
num_o = 10

# 构建数据
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)

# 参数初始化
w1 = torch.tensor(np.random.normal(0, 0.01, (num_i, num_h)), dtype=torch.float32, requires_grad=True)
b1 = torch.zeros(num_h, requires_grad=True)
w2 = torch.tensor(np.random.normal(0, 0.01, (num_h, num_o)), dtype=torch.float32, requires_grad=True)
b2 = torch.zeros(num_o, requires_grad=True)
params = [w1, b1, w2, b2]

# 激活函数
def relu(x):
    return torch.max(x, torch.tensor(0.0))

# 模型构建
def net(x):
    x = x.view(-1, num_i)
    h = relu(x.mm(w1) + b1)
    o = h.mm(w2) + b2
    return o

# 损失函数
loss = nn.CrossEntropyLoss()

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

方法二：能调包就不实现

import torch
import torch.nn as nn
import torch.nn.init as init
import torch.optim as optim
import d2lzh_pytorch as d2l

# node number of MLP Layer
num_i, num_h, num_o = 28 * 28, 256, 10

# data load
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)

# network build
class MLP(nn.Module):
    def __init__(self, n_i, n_h, n_o):
        super(MLP, self).__init__()
        self.flatten = d2l.FlattenLayer()
        self.linear1 = nn.Linear(n_i, n_h)
        self.relu = nn.ReLU()
        self.linear2 = nn.Linear(n_h, n_o)

    def forward(self, input):
        return self.linear2(self.relu(self.linear1(self.flatten(input))))

net = MLP(num_i, num_h, num_o)
for param in net.parameters():
    init.normal_(param, mean=0, std=0.01)

# loss
loss = nn.CrossEntropyLoss()

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

# train
num_epochs = 5
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, optimizer=optimizer)

Pytorch 学习（五）：Pytorch 实现多层感知机（MLP）

Pytorch 实现多层感知机（MLP）

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