Softmax回归从零开始实现（李沐动手学）

Softmax回归从零开始实现（李沐动手学）

依然是pycharm环境，图像显示部分和jupyter不一样
简洁实现：https://blog.csdn.net/tongjingqi_/article/details/122766549

import torch
import matplotlib.pyplot as plt
from IPython import display
from d2l import torch as d2l
d2l.use_svg_display()#!!!!
# help(d2l.use_svg_display())
batch_size=256
train_iter,test_iter=d2l.load_data_fashion_mnist(batch_size)
next(iter(test_iter))
num_inputs=784
num_outputs=10
W=torch.normal(0,0.01,size=(num_inputs,num_outputs),requires_grad=True)
b=torch.zeros(num_outputs,requires_grad=True)

X=torch.tensor([[1.0,2.0,3.0],[4.0,5.0,6.0]])
print(X.sum(0,keepdim=True),X.sum(1,keepdim=True))

def softmax(X):
    X_exp=torch.exp(X)
    partition=X_exp.sum(1,keepdim=True)
    return X_exp/partition

X=torch.normal(0,1,(2,5))
X_prob=softmax(X)
print(X_prob,X_prob.sum(1))
def net(X):
    return softmax(torch.matmul(X.reshape(-1,W.shape[0]),W)+b)

y=torch.tensor([0,2])
y_hat=torch.tensor([[0.1,0.3,0.6],[0.3,0.2,0.5]])
print(y_hat[[0,1],y])
def cross_entropy(y_hat,y):
    return -torch.log(y_hat[range(y_hat.shape[0]),y])

print(cross_entropy(y_hat,y))
print(y_hat.shape,len(y_hat.shape),y_hat.shape[1])
def accuracy(y_hat,y):
    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())

print(accuracy(y_hat,y)/len(y))

def evaluate_accuracy(net,data_iter):
    if isinstance(net,torch.nn.Module):
        net.eval()
    metric=Accumulator(2)
    with torch.no_grad():
        for X,y in data_iter:
            metric.add(accuracy(net(X),y),y.numel())
    return metric[0]/metric[1]

class Accumulator:
    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]*self.data
    def __getitem__(self, item):
        return self.data[item]

print(evaluate_accuracy(net,test_iter))

def train_epoch_ch3(net,train_iter,loss,updater):
    if isinstance(net,torch.nn.Module):
        net.train()
    metric=Accumulator(3)
    for X,y in train_iter:
        y_hat=net(X)
        l=loss(y_hat,y)
        if isinstance(updater,torch.optim.Optimizer):
            updater.zero_grad()
            l.mean.backward()
            updater.step()
        else:
            l.sum().backward()
            updater(X.shape[0])
        metric.add(float(l.sum()),accuracy(y_hat,y),y.numel())
    return metric[0]/metric[2],metric[1]/metric[2]
class Animator: #@save
    """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)):
        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)
        display.clear_output(wait=True)

        plt.pause(0.01)

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

lr=0.1
def updater(batch_size):
    return d2l.sgd([W,b],lr,batch_size)

num_epochs=10
train_ch3(net,train_iter,test_iter,cross_entropy,num_epochs,updater)
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)]
    d2l.show_images(X[0:n].reshape((n, 28, 28)), 1, n, titles=titles[0:n])
predict_ch3(net,test_iter)
plt.show()