深度学习02 神经网络实现手写数字案例

目录

下载手写数字图像(图像+标签)

展示手写数字图片

数据打包

判断当前设备是否支持GPU

建立神经网络模型

设置训练集与测试集

创建损失函数、优化器

开始训练

---

下载手写数字图像(图像+标签)

training_data=datasets.MNIST(root='data',train=True,download=True,transform=ToTensor(),
)
test_data=datasets.MNIST(root='data',train=False,download=True,transform=ToTensor(),
)

展示手写数字图片

from matplotlib import pyplot as plt
figure=plt.figure()
for i in range(16):img,label=training_data[i+59000]figure.add_subplot(4,4,i+1)plt.title(label)plt.axis('off')plt.imshow(img.squeeze(),cmap='gray')a=img.squeeze()
plt.show()

数据打包

train_dataloader=DataLoader(training_data,batch_size=64)
test_dataloader=DataLoader(test_data,batch_size=64)

判断当前设备是否支持GPU

device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
print(device)

建立神经网络模型

class  Neturalwork(nn.Module):def __init__(self):super().__init__()self.flatten=nn.Flatten()self.hidden1=nn.Linear(28*28,128)self.hidden2=nn.Linear(128,256)self.out=nn.Linear(256,10)def forward(self,x):x=self.flatten(x)x=self.hidden1(x)x=torch.sigmoid(x)x=self.hidden2(x)x=torch.sigmoid(x)x=self.out(x)return x
​
model=Neturalwork().to(device)
print(model)

 

设置训练集与测试集

def train(dataloader,model,loss_fn,optimizer):model.train()batch_size_num=1for x,y in dataloader:x,y=x.to(device),y.to(device)pred=model.forward(x)loss=loss_fn(pred,y)
​optimizer.zero_grad()loss.backward()optimizer.step()loss_value=loss.item()if batch_size_num%100==0:print(f'loss:{loss_value:>7f} [number:{batch_size_num}]')batch_size_num+=1
​def test(dataloader,model,loss_fn):size=len(dataloader.dataset)num_batches=len(dataloader)model.eval()test_loss,correct=0,0with torch.no_grad():for x,y in dataloader:x,y=x.to(device),y.to(device)pred=model.forward(x)test_loss+=loss_fn(pred,y).item()correct+=(pred.argmax(1)==y).type(torch.float).sum().item()a=(pred.argmax(1)==y)b=(pred.argmax(1)==y).type(torch.float)test_loss/=num_batchescorrect/=sizeprint(f'Test result:\n Accuracy:{(100*correct)}%,Avg loss:{test_loss}')

创建损失函数、优化器

loss_fn=nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)  # 学习率可以根据需要调整

开始训练

epochs=15
for t in range(epochs):print(f'EPOCH {t+1}\n-----------')train(train_dataloader,model,loss_fn,optimizer)
print('结束')
test(test_dataloader,model,loss_fn)