Pytorch 学习（四）：Pytorch 实现 Softmax 回归

Pytorch 实现 Softmax 回归

本方法参考自《动手学深度学习》（Pytorch版）github项目

一般步骤

• 构建数据集和以 batch 为单位的数据读取
• 构建模型及其初始化、损失函数、下降方法
• 训练网络及评估

方法一：造*法

从零搭建 Softmax 回归有三个关键点

1. Softmax 方法实现
2. 交叉熵函数实现
3. 精度评估实现

Softmax 方法实现

def softmax(x):
  x_exp = x.exp()  # m * n
  partition = x_exp.sum(dim=1, keepdim=True)  # 按列累加, m * 1
  return x_exp / partition  # 广播机制, [m * n] / [m * 1] = [m * n]

交叉熵实现

• y_hat 为网络输出，size 是 batch * c，其中 c 为类别数，每一行表示一个图片的 softmax 分类概率，一共 batch_size 行。
• y 为真值，size 是 batch * 1，1 为该图片所属类。
• gather 函数将 y_hat 每一行（dim=1）的 y 值处概率取出来

def cross_entropy(y_hat, y):
  return - torch.log(y_hat.gather(1, y.view(-1, 1)))

精度评估

def accuracy(y_hat, y):
  return (y_hat.argmax(dim=1) == y).float().mean().item()

完整代码

import torch
import torchvision
import numpy as np
import d2lzh_pytorch as d2l

batch_size = 256
# 数据获取
print('load fashion-mnist start...')
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)

# 模型参数初始化
num_inputs = 28 * 28  # 784
num_outputs = 10

w = torch.tensor(np.random.normal(0, 0.01, (num_inputs, num_outputs)), dtype=torch.float32)
b = torch.zeros(num_outputs, dtype=torch.float32)

w.requires_grad_(True)
b.requires_grad_(True)

# 定义模型
def softmax(X):
    X_exp = X.exp()
    partition = X_exp.sum(dim=1, keepdim=True)
    return X_exp / partition

def net(X):
    return softmax(torch.mm(X.view(-1, num_inputs), w) + b)

# 定义损失函数
def cross_entropy(y_hat, y):
    return - torch.log(y_hat.gather(1, y.view(-1, 1)))

# 定义精度评估函数
def accuracy(y_hat, y):
    return (y_hat.argmax(dim=1) == y).float().mean().item()

def evaluate_accuracy(data_iter, net):
    acc_sum, n = 0.0, 0
    for X, y in data_iter:
        y_hat = net(X)
        acc = (y_hat.argmax(dim=1) == y).float().sum().item()
        acc_sum += acc
        n += y.shape[0]
    return acc_sum / n

# 训练模型
num_epochs = 5
lr = 0.1

def train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params=None, lr=None, optimizer=None):
    for epo in range(1, num_epochs + 1):
        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()
            # 梯度下降
            if optimizer is not None:
                optimizer.step()
            else:
                d2l.sgd(params, lr, batch_size)

            train_l_sum += l.item()
            train_acc_sum += (y_hat.argmax(dim=1) == y).float().sum().item()
            n += y.shape[0]
        test_acc = evaluate_accuracy(test_iter, net)
        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f'
              % (epo, train_l_sum / n, train_acc_sum / n, test_acc))

train_ch3(net, train_iter, test_iter, cross_entropy, num_epochs, batch_size, [w, b], lr)

方法二：我是调包侠

import torch
import torch.nn as nn
import torch.nn.init as init
import d2lzh_pytorch as d2l
from collections import OrderedDict
import torch.optim as optim

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

# 构建网络
class FlattenLayer(nn.Module):
    def __init__(self):
        super(FlattenLayer, self).__init__()

    def forward(self, x):  # transform (batch, c, h, w) to (batch, c * h * w)
        return x.view(x.shape[0], -1)

class LinearNet(nn.Module):
    def __init__(self, n_input, n_output):
        super(LinearNet, self).__init__()
        self.flatten = FlattenLayer()
        self.linear = nn.Linear(n_input, n_output)

    def forward(self, x):
        return self.linear(self.flatten(x))

num_features = 28 * 28  # 784
num_labels = 10
net = LinearNet(num_features, num_labels)
# 等价于用 Sequential 创建网络
net2 = nn.Sequential(
    OrderedDict([
        ('flatten', FlattenLayer()),
        ('linear', nn.Linear(num_features, num_labels))
    ])
)
print(net)
print(net2)

# 参数初始化
init.normal_(net.linear.weight, mean=0, std=0.01)
init.constant_(net.linear.bias, val=0)

# 损失函数
loss = nn.CrossEntropyLoss()

# 下降器
optimizer = optim.SGD(net.parameters(), lr=0.1)

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