[人工智能]12.卷积神经网络的学习

'''
Author: 365JHWZGo
Description: 12.cnn study
Date: 2021-10-28 19:21:45
FilePath: \pytorch\pytorch\cnn\day10-1.py
LastEditTime: 2021-10-28 23:26:28
LastEditors: 365JHWZGo
'''
#导包
import torch
import torchvision
from torch.autograd import Variable
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.utils.data as Data

torch.manual_seed(1)

#hyper parameters
BATCH_SIZE = 50
LR = 0.001
DOWNLOAD_MNIST = False
EPOCH = 1

#create dataset
train_data = torchvision.datasets.MNIST(
    root='./mnist',
    train=True,
    transform=torchvision.transforms.ToTensor(),
    download=DOWNLOAD_MNIST
)
train_loader = Data.DataLoader(
    dataset= train_data,
    batch_size=BATCH_SIZE,
    shuffle=True,
    num_workers=2    
)

test_data = torchvision.datasets.MNIST(
    root = './mnist',
    train = False
)
test_loader = Data.DataLoader(
    dataset= test_data,
    batch_size=BATCH_SIZE,
    shuffle=True,
    num_workers=2    
)

#create some data
test_x = torch.unsqueeze(test_data.test_data,dim=1).type(torch.FloatTensor)[:2000]/255.
test_y = test_data.test_labels[:2000]

#create network
class CNN(nn.Module):
    def __init__(self):
        super(CNN,self).__init__()
        self.conv1=nn.Sequential(
            nn.Conv2d(
                in_channels=1,
                out_channels=16,
                kernel_size=5,
                stride = 1,
                padding = 2
            ),
            nn.ReLU(),
            nn.MaxPool2d(
                kernel_size=2
            )
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(
                16,32,5,1,2
            ),
            nn.ReLU(),
            nn.MaxPool2d(
                kernel_size=2
            )
        )
        self.out = nn.Linear(32*7*7,10) #因为0-9有十个数字

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.view(x.size(0),-1)
        output = self.out(x)
        return output

#create an instance
cnn = CNN()

#optimizer
optimizer = torch.optim.Adam(cnn.parameters(),lr=LR)

#loss_func
loss_func = nn.CrossEntropyLoss()

#training
if __name__ == '__main__':
    for epoch in range(EPOCH):
        for step,(batch_x,batch_y) in enumerate(train_loader):
            batch_x = Variable(batch_x)
            batch_y = Variable(batch_y)

            output = cnn(batch_x)
            loss = loss_func(output,batch_y)

            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            if step%50==0:
                out_y = cnn(test_x)
                pred_y = torch.max(out_y,1)[1].data.numpy().squeeze()
                accuracy = float((pred_y==test_y.data.numpy()).astype(int).sum())/float(test_y.size(0))        
                print(
                    'epoch:',epoch,
                    'loss:%.4f'%loss,
                    'accuracy:%.2f'%accuracy
                )