Pytorch：Softmax_Classifier/MINIST

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#深度学习

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该博客介绍了如何使用PyTorch搭建深度学习模型对MNIST手写数字数据集进行分类。首先，通过transforms将数据转换为Tensor并归一化。然后，定义了一个包含多个全连接层的神经网络模型，并使用CrossEntropyLoss作为损失函数。训练过程中，采用SGD优化器，设置动量为0.5。在每个训练周期结束后，会打印出损失值。最后，测试模型的准确性。

1. softmax

使最后输出结果是一个概率分布，和为1.
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2. Cross Entropy

 CrossEntropyLoss = LogSoftmax + NLLLoss

损失函数，其中已经包括了求softmax。
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3. MNIST数据集分类

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transform=transforms.Compose([transforms.ToTensor(),#Convert the PIL Image to Tensor.
                              transforms.Normalize((0.1307,),(0.3081,))])#The parameters are mean and std respectively.

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Design model

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import numpy as np
import torch
from torch.utils.data import DataLoader #For constructing DataLoader
from torchvision import transforms #For constructing DataLoader
from torchvision import datasets #For constructing DataLoader
import torch.nn.functional as F #For using function relu()
import torch.optim as optim #For constructing Optimizer

batch_size=64
transform=transforms.Compose([transforms.ToTensor(),#Convert the PIL Image to Tensor.
                              transforms.Normalize((0.1307,),(0.3081,))])#The parameters are mean and std respectively.

train_dataset = datasets.MNIST(root='../dataset/mnist',train=True,transform=transform,download=True)
test_dataset = datasets.MNIST(root='../dataset/mnist',train=False,transform=transform,download=True)
train_loader = DataLoader(dataset=train_dataset,batch_size=batch_size,shuffle=True)
test_loader = DataLoader(dataset=test_dataset,batch_size=batch_size,shuffle=False)

class Net(torch.nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        self.linear1=torch.nn.Linear(784,512)
        self.linear2=torch.nn.Linear(512,256)
        self.linear3=torch.nn.Linear(256,128)
        self.linear4=torch.nn.Linear(128,64)
        self.linear5=torch.nn.Linear(64,10)

    def forward(self, x):
        x=x.view(-1,784)#把每一张图片的像素都拼接起来（n张图*784）
        x=F.relu(self.linear1(x))
        x=F.relu(self.linear2(x))
        x=F.relu(self.linear3(x))
        x=F.relu(self.linear4(x))
        return self.linear5(x)#最后一层就是10层，不做变换

model=Net()

criterion=torch.nn.CrossEntropyLoss()
optimizer=torch.optim.SGD(model.parameters(),lr=0.01,momentum=0.5)#SGD with momentum

def train(epoch):
    running_loss=0.0
    for batch_idex,data in enumerate(train_loader,0):
        inputs,target=data
        optimizer.zero_grad()

        #forward + backward + update
        outputs=model(inputs)
        loss=criterion(outputs,target)
        loss.backward()
        optimizer.step()

        running_loss+=loss.item()
        if batch_idex%300==299:
            print('[%d,%5d loss:%.3f]' % (epoch+1,batch_idex+1,running_loss/300))
            running_loss=0.0

def test():
    correct=0
    total=0
    with torch.no_grad():
        for data in test_loader:
            images,labels=data
            outputs=model(images)
           # predicted=torch.max(outputs.data,dim=1)
            predicted=torch.argmax(outputs.data,dim=1)
            total+=labels.size(0)
            correct += (predicted == labels).sum().item()

    print('Accuracy on test set: %d %%' % (100 * correct / total))
    #print(correct)

if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()