李沐《动手学深度学习》 Softmax回归实现
前言
本篇是在看完李沐老师的Softmax回归课程后，做的总结笔记，并且针对实现代码根据个人的习惯进行了一些调整，本篇博文仅作记录，完整的学习笔记可以留言分享。
李沐老师在课程中是使用的Jupyter运行的代码，然而我本人更加习惯在IDLE或者VScode中运行代码，所以就将整个实现拆成了两个文件，一个是用于获取数据集，绘制图像等操作的准备文件，命名为Prepare_for_Softmax.py，另一个就是用于实现Softmax回归的Softmax_Regression文件
一些注意点记录
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数据集建议手动下载，默认下载太卡
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前往数据集下载网址直接下载需要的四个数据集。
下载1: train-images-idx3-ubyte.gz
下载2: train-labels-idx1-ubyte.gz
下载3: t10k-images-idx3-ubyte.gz
下载4: t10k-labels-idx1-ubyte.gz
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数据集存储位置在脚本目录的上级目录中，默认文件名叫做data，直接将下载好的文件放到data\FashionMNIST\raw下即可，且不用解压，第一次运行时在加载本地数据集时会自动进行解压并读取
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前期的准备工作Prepare_for_Softmax.py
import torch
from IPython import display
from d2l import torch as d2l
# 数据集获取
def get_dataloader_workers():
"""使用4个进程来读取数据"""
return 8
def load_data_fashion_mnist(batch_size, resize=None):
"""下载Fashion-MNIST数据集，然后将其加载到内存中"""
trans = [transforms.ToTensor()]
if resize:
trans.insert(0, transforms.Resize(resize))# 这边是为后续如果要调整图片做的安排
trans = transforms.Compose(trans)
mnist_train = torchvision.datasets.FashionMNIST(
root="../data", train=True, transform=trans, download=False)
mnist_test = torchvision.datasets.FashionMNIST(
root="../data", train=False, transform=trans, download=False)
return (data.DataLoader(mnist_train, batch_size, shuffle=True,
num_workers=get_dataloader_workers()),
data.DataLoader(mnist_test, batch_size, shuffle=False,
num_workers=get_dataloader_workers()))
# 图表绘制
class Animator:
"""在动画中绘制数据"""
def __init__(self, xlabel=None, ylabel=None, legend=None, xlim=None,
ylim=None, xscale='linear', yscale='linear',
fmts=('-', 'm--', 'g-.', 'r:'), nrows=1, ncols=1,
figsize=(3.5, 2.5)):
# 增量地绘制多条线
if legend is None:
legend = []
d2l.use_svg_display()
self.fig, self.axes = d2l.plt.subplots(nrows, ncols, figsize=figsize)
if nrows * ncols == 1:
self.axes = [self.axes, ]
# 使用lambda函数捕获参数
self.config_axes = lambda: d2l.set_axes(
self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)
self.X, self.Y, self.fmts = None, None, fmts
def add(self, x, y):
# 向图表中添加多个数据点
if not hasattr(y, "__len__"):
y = [y]
n = len(y)
if not hasattr(x, "__len__"):
x = [x] * n
if not self.X:
self.X = [[] for _ in range(n)]
if not self.Y:
self.Y = [[] for _ in range(n)]
for i, (a, b) in enumerate(zip(x, y)):
if a is not None and b is not None:
self.X[i].append(a)
self.Y[i].append(b)
self.axes[0].cla()
for x, y, fmt in zip(self.X, self.Y, self.fmts):
self.axes[0].plot(x, y, fmt)
self.config_axes()
display.display(self.fig)
plt.draw()
plt.pause(0.001)
display.clear_output(wait=True)
# 训练部分
class Accumulator:
"""在n个变量上累加"""
def __init__(self, n):
self.data = [0.0] * n
def 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 accuracy(y_hat, y):
"""计算预测正确的数量"""
if len(y_hat.shape) > 1 and y_hat.shape[1] > 1:
y_hat = y_hat.argmax(axis=1)
cmp = y_hat.type(y.dtype) == y
return float(cmp.type(y.dtype).sum())
def evaluate_accuracy(net, data_iter):
"""计算在指定数据集上模型的精度"""
if isinstance(net, torch.nn.Module):
net.eval() # 将模型设置为评估模式
metric = Accumulator(2) # 正确预测数、预测总数
with torch.no_grad():
for X, y in data_iter:
metric.add(accuracy(net(X), y), y.numel())
return metric[0] / metric[1]
def train_epoch_ch3(net, train_iter, loss, updater):
"""训练模型一个迭代周期"""
# 将模型设置为训练模式
if isinstance(net, torch.nn.Module):
net.train()
# 训练损失总和、训练准确度总和、样本数
metric = Accumulator(3)
for X, y in train_iter:
# 计算梯度并更新参数
y_hat = net(X)
l = loss(y_hat, y)
if isinstance(updater, torch.optim.Optimizer):
# 使用PyTorch内置的优化器和损失函数
updater.zero_grad()
l.mean().backward()
updater.step()
else:
# 使用定制的优化器和损失函数
l.sum().backward()
updater(X.shape[0])
metric.add(float(l.sum()), accuracy(y_hat, y), y.numel())
# 返回训练损失和训练精度
return metric[0] / metric[2], metric[1] / metric[2]
def train_ch3(net, train_iter, test_iter, loss, num_epochs, updater):
"""训练模型"""
animator = Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0.3, 0.9],
legend=['train loss', 'train acc', 'test acc'])
for epoch in range(num_epochs):
train_metrics = train_epoch_ch3(net, train_iter, loss, updater)
test_acc = evaluate_accuracy(net, test_iter)
animator.add(epoch + 1, train_metrics + (test_acc,))
train_loss, train_acc = train_metrics
assert train_loss < 0.5, train_loss
assert train_acc <= 1 and train_acc > 0.7, train_acc
assert test_acc <= 1 and test_acc > 0.7, test_acc
- 那么下面就是具体的回归函数的实现Softmax_Regression.py
import torch
from torch import nn
from d2l import torch as d2l
from Prepare_for_Softmax import *
# 引入数据集，fashion_mnist
batch_size = 256
train_iter,test_iter = d2l.load_data_fashion_mnist(batch_size)
# 初始化模型参数
net = nn.Sequential(nn.Flatten(),nn.Linear(784,10))
def init_weights(m):
if type(m) == nn.Linear:
nn.init.normal_(m.weight,std=0.01)
net.apply(init_weights)
# Softmax实现
loss = nn.CrossEntropyLoss(reduction='none')
# 算法优化,采用小批量梯度下降
trainer = torch.optim.SGD(net.parameters(),lr=0.1)
# 训练模型
num_epochs = 10
d2l.train_ch3(net,train_iter,test_iter,loss,num_epochs,trainer)
d2l.plt.show()
def predict_ch3(net, test_iter, n=6):
"""预测标签"""
for X, y in test_iter:
break
trues = d2l.get_fashion_mnist_labels(y)
preds = d2l.get_fashion_mnist_labels(net(X).argmax(axis=1))
titles = [true +'\n' + pred for true, pred in zip(trues, preds)]
#print(titles)
d2l.show_images(
X[0:n].reshape((n, 28, 28)), 1, n, titles=titles[0:n])
d2l.plt.show()
predict_ch3(net,test_iter)
- 绘制出来的动态曲线
- 最终的分类情况
小结
- 本次只是针对教程中的代码进行了一个复现，一轮顺下来感觉只能说是掌握了6，70%，中间还是有不少地方掌握不是很深刻，后续还是要反复多敲几次代码实现
