[人工智能]动手学深度学习笔记4-Softmax回归从0-1实现

import d2lzh as d2l
from mxnet import autograd,nd

#1-读取数据集
batch_size = 256
train_iter,test_iter = d2l.load_data_fashion_mnist(batch_size)

#2-初始化模型参数
num_inputs = 784  #像素块28*28=784
num_outputs = 10
W = nd.random.normal(scale=0.01,shape=(num_inputs,num_outputs))
b = nd.zeros(num_outputs)
W.attach_grad()
b.attach_grad()

#3-实现Softmax运算
def softmax(X):
    X_exp = X.exp()
    partition = X_exp.sum(axis=1,keepdims=True) #所有列相加
    return X_exp / partition
#得到的矩阵每行元素和为1，每行都是合法的概率分布

#4-定义模型
def net(X):
    return softmax(nd.dot(X.reshape((-1,num_inputs)),W)+b)

#5-定义损失函数
def cross_entropy(y_hat,y):
    return -nd.pick(y_hat,y).log()

#6-计算分类准确率
#准确率模型
def accuracy(y_hat,y):
    return (y_hat.argmax(axis=1)==y.astype('float32')).mean().asscalar()
#评价模型在数据集data_iter上的准确率
def evaluate_accuracy(data_iter,net):
    acc_sum,n=0.0,0
    for X,y in data_iter:
        y = y.astype('float32')
        acc_sum+=(net(X).argmax(axis=1)==y).sum().asscalar()
        n+=y.size
    return acc_sum /n

#7-训练模型
num_epochs,lr = 5,0.1
def tranin_ch3(net,train_iter,test_iter,loss,num_epochs,batch_size,params=None,lr=None,trainer=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:
            with autograd.record():
                y_hat = net(X)
                l = loss(y_hat,y).sum()
            l.backward()
            if trainer is None:
                d2l.sgd(params,lr,batch_size)
            else:
                trainer.step(batch_size)
            y = y.astype('float32')
            train_l_sum += l.asscalar()
            train_acc_sum += (y_hat.argmax(axis=1)==y).sum().asscalar()
            n += y.size
        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))
        

tranin_ch3(net,train_iter,test_iter,cross_entropy,num_epochs,batch_size,[W,b],lr)

epoch 1,loss 0.5729,train_acc 0.812,test_acc 0.820
epoch 2,loss 0.5288,train_acc 0.823,test_acc 0.832
epoch 3,loss 0.5041,train_acc 0.830,test_acc 0.838
epoch 4,loss 0.4901,train_acc 0.835,test_acc 0.840
epoch 5,loss 0.4778,train_acc 0.838,test_acc 0.842

#8-预测
for X,y in test_iter:
    break
true_labels = d2l.get_fashion_mnist_labels(y.asnumpy())
pred_labels = d2l.get_fashion_mnist_labels(net(X).argmax(axis=1).asnumpy())
titles = [true+'\n'+pred for true,pred in zip(true_labels,pred_labels)]
d2l.show_fashion_mnist(X[0:9],titles[0:9])