num_inputs = 2 #feature number
num_examples = 1000 #訓(xùn)練樣本個(gè)數(shù)
true_w = torch.tensor([[2],[-3.4]]) #真實(shí)的權(quán)重值
true_b = torch.tensor(4.2) #真實(shí)的bias
samples = torch.normal(0,1,(num_examples,num_inputs))
noise = torch.normal(0,0.01,(num_examples,1))
labels = samples.matmul(true_w) + true_b + noise

class LinearNet(nn.Module):
	def __init__(self,in_features):
		super().__init__()
		self.fc = nn.Linear(in_features=2,out_features=1)
	def forward(self,t):
		t = self.fc(t)
		return t

import torch.utils.data as Data
dataset = Data.TensorDataset(samples,labels)#類(lèi)似于zip，把兩個(gè)張量打包
data_loader = Data.DataLoader(dataset,batch_size=100,shuffle=True)

network = LinearNet(2)
optimizer = optim.SGD(network.paramters(),lr=0.05)

for epoch in range(10):
    total_loss = 0
    for data,label in data_loader:
        predict = network(data)
        loss = F.mse_loss(predict,label)
        total_loss += loss.item()
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    print(
        'epoch',epoch,
        'loss',total_loss,
        'weight',network.weight,
        'bias',network.bias
    )

import torchvision
import torchvision.transforms as transforms
train_set = torchvision.datasets.FashionMNIST(
	root='./data',
	train=True,
	download=True,
	transform=transforms.ToTensor()
)

len(train_set),len(test_set)
> (60000,10000)

import matplotlib.pyplot as plt
plt.figure(figsize=(10,10))
for i,(image,lable) in enumerate(train_set,start=1):
	plt.subplot(1,10,i)
	plt.imshow(image.squeeze())
	plt.title(train_set.classes[lable])
	plt.axis('off')
	if i == 10:
		break
plt.show()

train_loader = torch.utils.data.DataLoader(train_set,batch_size=100,shuffle=True,num_workers=4)
test_loader = torch.utils.data.DataLoader(test_set,batch_size=100,shuffle=False,num_workers=1)

def net(samples,w,b):
	samples = samples.flatten(start_dim=1) #將c,h,w三個(gè)軸展成一個(gè)feature軸，長(zhǎng)度為28 * 28
	samples = torch.exp(samples)#全體元素取以e為底的指數(shù)
	partial_sum = samples.sum(dim=1,keepdim=True) 
	samples = samples / partial_sum #歸一化，得概率，這里還應(yīng)用了廣播機(jī)制
	return samples.matmul(w) + b	

def net(samples,w,b):
	samples = samples.flatten(start_dim=1) #將c,h,w三個(gè)軸展成一個(gè)feature軸，長(zhǎng)度為28 * 28
	samples = torch.exp(samples)#全體元素取以e為底的指數(shù)
	partial_sum = samples.sum(dim=1,keepdim=True) 
	samples = samples / partial_sum #歸一化，得概率，這里還應(yīng)用了廣播機(jī)制
	return samples.matmul(w) + b	

import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as Data
import torchvision.transforms as transforms
import torch.optim as optim
import torch.nn.init as init
class SoftmaxNet(nn.Module):
    def __init__(self,in_features,out_features):
        super().__init__()
        self.fc = nn.Linear(in_features=in_features,out_features=out_features)
    def forward(self,t):
        t = t.flatten(start_dim=1)
        t = self.fc(t)
        return t
train_set = torchvision.datasets.FashionMNIST(
    root='E:\project\python\jupyterbook\data',
    train=True,
    download=True,
    transform=transforms.ToTensor()
)
test_set = torchvision.datasets.FashionMNIST(
    root='E:\project\python\jupyterbook\data',
    train=False,
    download=True,
    transform=transforms.ToTensor()
)
train_loader = Data.DataLoader(
    train_set,
    batch_size=100,
    shuffle=True,
    #num_workers=2
)
test_loader = Data.DataLoader(
    test_set,
    batch_size=100,
    shuffle=False,
    #num_workers=2
)
@torch.no_grad()
def get_correct_nums(predict,labels):
    return predict.argmax(dim=1).eq(labels).sum().item()
@torch.no_grad()
def evaluate(test_loader,net,total_num):
    correct = 0
    for image,label in test_loader:
        predict = net(image)
        correct += get_correct_nums(predict,label)
        pass
    return correct / total_num
network = SoftmaxNet()
optimizer = optim.SGD(network.parameters(),lr=0.05)
for epoch in range(10):
    total_loss = 0
    total_correct = 0
    for image,label in train_loader:
        predict = network(image)
        loss = F.cross_entropy(predict,label)
        total_loss += loss.item()
        total_correct += get_correct_nums(predict,label)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        pass
    print(
        'epoch',epoch,
        'loss',total_loss,
        'train_acc',total_correct / len(train_set),
        'test_acc',evaluate(test_loader,network,len(test_set))
    )