NLP CNN_nlm cnn-优快云博客

本文链接：https://blog.youkuaiyun.com/return_2/article/details/116526048

这篇博客介绍了如何使用卷积神经网络（CNN）处理图像数据，特别是针对MNIST手写数字识别任务。文章通过PyTorch库展示了CNN模型的构建过程，包括卷积层、池化层和全连接层的使用，并提供了训练和测试模型的代码示例。最终，模型在测试集上得到了较高的准确率。

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CNN 卷积神经网络

为什么CNN常用于图像处理：

局部性
平移性
可缩性

CNN简易图

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局部性与平移性会在卷积层得到体现。
可缩性会在池化层得到体现。

CNN-卷积层（参数，不同维度下的特征提取）

卷积核的通道数和输入数据的通道数一致，卷积核个数等于输出通道数
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CNN-池化层

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摊平（Flatten）

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在test-CNN中卷积核宽度是与词向量的维度一致
用卷积核进行卷积时，不仅考虑了词义而且考虑了词序及上下文

CNN实现MNIST

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision import datasets
from matplotlib import pyplot as plt

# tensor type :(batch,channel,w,h)

# 1. Prepare Datases
# data transform to desired form
transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize(0.1307,0.3081)])
# load data
train_data = datasets.MNIST(root='./data/mnist/',train=True,transform=transform)
test_data = datasets.MNIST(root='./data/mnist/',train=False,transform=transform)
# mini-batch
train_loader = DataLoader(dataset=train_data,shuffle=True,batch_size=64,num_workers=2)
test_loader = DataLoader(dataset=test_data,shuffle=False,batch_size=64,num_workers=2)

# 2. Design Model Using class
class MNISTModel(nn.Module):
    def __init__(self):
        super(MNISTModel,self).__init__()
        self.conv1 = nn.Conv2d(1,10,5) # 10,12,12
        self.conv2 = nn.Conv2d(10,20,5) # 20,4,4
        self.conv3 = nn.Conv2d(20,40,1) # 40,2,2
        self.pool = nn.MaxPool2d(2)
        self.linear = nn.Linear(160,10)
        self.relu = nn.ReLU()

    def forward(self,x):
        batch_size = x.size(0)
        x = self.relu(self.pool(self.conv1(x)))
        x = self.relu(self.pool(self.conv2(x)))
        x = self.relu(self.pool(self.conv3(x)))
        x = x.view(batch_size,-1)
        x = self.linear(x)
        return x

model = MNISTModel()
# using GPU
device = torch.device('cuda')
model.to(device)

# 3.optmizor and loss function
loss_f = nn.CrossEntropyLoss()
opti = optim.SGD(model.parameters(),lr=0.01,momentum=0.5)

# 4.Traing cycle
def train(epoch):
    loss_num = 0.0
    for index,data in enumerate(train_loader):
        inputs , target = data
        inputs , target = inputs.to(device),target.to(device)

        outputs = model.forward(inputs)

        loss = loss_f(outputs,target)

        opti.zero_grad()
        loss.backward()
        opti.step()

        loss_num += loss.item()

        if index % 100 == 99:
            print(f'epoch:{epoch+1},index:{index+1},loss:{loss_num / 100}')
            loss_num = 0


def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            images, labels = images.to(device), labels.to(device)
            outputs = model.forward(images)
            # return (max,maxindex)
            _, predicted = torch.max(outputs.data, dim=1)

            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print(f'accuracy on test set: {100 * correct / total} % ')
    return correct / total

if __name__ == '__main__':
    epoch_list = []
    acc_list = []

    for epoch in range(10):
        train(epoch)
        acc = test()
        epoch_list.append(epoch)
        acc_list.append(acc)

    plt.plot(epoch_list, acc_list)
    plt.ylabel('accuracy')
    plt.xlabel('epoch')
    plt.show()