使用pytorch利用神经网络原理进行图片的训练(持续学习中....)

news2024/11/18 9:32:58

1.做这件事的目的
语言只是工具,使用python训练图片数据,最终会得到.pth的训练文件,java有使用这个文件进行图片识别的工具,顺便整合,我觉得Neo4J正确率太低了,草莓都能识别成为苹果,而且速度慢,不能持续识别视频帧

2.什么是神经网络?(其实就是数学的排列组合最终得到统计结果的概率)

1.先把二维数组转为一维
2.通过公式得到节点个数和值
3…同2
4.通过节点得到概率(softmax归一化公式)
5.对比模型的和差值=原始概率-目标结果概率
6.不断优化原来模型的概率
5.激活函数,激活某个节点的函数,可以引入非线性的(因为所有问题不可能是线性的比如很少图片识别一定可以识别出绝对的正方形,他可能中间有一定弯曲或者线在中心短开了)

在这里插入图片描述

3.训练的代码
//环境python3.8 最好使用conda进行版本管理,不然每个版本都可能不兼容,到处碰壁

 #安装依赖
 pip install numpy torch torchvision matplotlib

#文件夹结构,图片一定要是28x28的
在这里插入图片描述

import torch
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import MNIST
import matplotlib.pyplot as plt
from torchvision.datasets.folder import ImageFolder

class Net(torch.nn.Module):

    def __init__(self):
        super().__init__()
        self.fc1 = torch.nn.Linear(28 * 28, 64)
        self.fc2 = torch.nn.Linear(64, 64)
        self.fc3 = torch.nn.Linear(64, 64)
        self.fc4 = torch.nn.Linear(64, 10)

    def forward(self, x):
        x = torch.nn.functional.relu(self.fc1(x))
        x = torch.nn.functional.relu(self.fc2(x))
        x = torch.nn.functional.relu(self.fc3(x))
        x = torch.nn.functional.log_softmax(self.fc4(x), dim=1)
        return x

#导入数据
def get_data_loader(is_train):
     #张量,多维数组
    to_tensor = transforms.Compose([transforms.ToTensor()])
     # 下载数据集 下载目录
    data_set = MNIST("", is_train, transform=to_tensor, download=True)
     #一个批次15张,顺序打乱
    return DataLoader(data_set, batch_size=15, shuffle=True)

def get_image_loader(folder_path):
    to_tensor = transforms.Compose([transforms.ToTensor()])
    data_set = ImageFolder(folder_path, transform=to_tensor)
    return DataLoader(data_set, batch_size=1)

#评估准确率
def evaluate(test_data, net):
    n_correct = 0
    n_total = 0
    with torch.no_grad():
        #按批次取数据
        for (x, y) in test_data:
            #计算神经网络预测值
            outputs = net.forward(x.view(-1, 28 * 28))

            for i, output in enumerate(outputs):
                #比较预测结果和测试集结果
                if torch.argmax(output) == y[i]:
                    #统计正确预测结果数
                    n_correct += 1
                #统计全部预测结果
                n_total += 1
        #返回准确率=正确/全部的
    return n_correct / n_total


def main():
    #加载训练集
    train_data = get_data_loader(is_train=True)
    #加载测试集
    test_data = get_data_loader(is_train=False)
    #初始化神经网络
    net = Net()
    #打印测试网络的准确率 0.1
    print("initial accuracy:", evaluate(test_data, net))
    #训练神经网络
    optimizer = torch.optim.Adam(net.parameters(), lr=0.001)
    #重复利用数据集 2次
    for epoch in range(100):
        for (x, y) in train_data:
            #初始化 固定写法
            net.zero_grad()
            #正向传播
            output = net.forward(x.view(-1, 28 * 28))
            #计算差值
            loss = torch.nn.functional.nll_loss(output, y)
            #反向误差传播
            loss.backward()
            #优化网络参数
            optimizer.step()
        print("epoch", epoch, "accuracy:", evaluate(test_data, net))
    # #使用3张图片进行预测
    # for (n, (x, _)) in enumerate(test_data):
    #     if n > 3:
    #         break
    #     predict = torch.argmax(net.forward(x[0].view(-1, 28 * 28)))
    #     plt.figure(n)
    #     plt.imshow(x[0].view(28, 28))
    #     plt.title("prediction: " + str(int(predict)))
    # plt.show()
    image_loader = get_image_loader("aa")

    for (n, (x, _)) in enumerate(image_loader):
        if n > 2:
            break
        predict = torch.argmax(net.forward(x.view(-1, 28 * 28)))
        plt.figure(n)
        plt.imshow(x[0].permute(1, 2, 0))
        plt.title("prediction: " + str(int(predict)))
    plt.show()


if __name__ == "__main__":
    main()

#运行结果弹框出现图片和识别结果

4.测试电脑的cuda是否安装成功,不成功不能运行下面的代码

import torch

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Using device:', device)
print('CUDA version:', torch.version.cuda)
print('PyTorch version:', torch.__version__)

5.在gpu上运行,需要去官网下载cuda安装
https://developer.nvidia.com/cuda-toolkit-archive
#并且需要安装和torch对应的版本,我的电脑是1660ti的所以安装了10.2的cuda
#安装torchgpu版本

pip install torch==1.9.0+cu102 -f
https://download.pytorch.org/whl/cu102/torch_stable.html

import torch
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import MNIST
import matplotlib.pyplot as plt
from torchvision.datasets.folder import ImageFolder

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

class Net(torch.nn.Module):

    def __init__(self):
        super().__init__()
        self.fc1 = torch.nn.Linear(28 * 28, 64)
        self.fc2 = torch.nn.Linear(64, 64)
        self.fc3 = torch.nn.Linear(64, 64)
        self.fc4 = torch.nn.Linear(64, 10)

    def forward(self, x):
        x = torch.nn.functional.relu(self.fc1(x))
        x = torch.nn.functional.relu(self.fc2(x))
        x = torch.nn.functional.relu(self.fc3(x))
        x = torch.nn.functional.log_softmax(self.fc4(x), dim=1)
        return x

def get_data_loader(is_train):
    to_tensor = transforms.Compose([transforms.ToTensor()])
    data_set = MNIST("", is_train, transform=to_tensor, download=True)
    return DataLoader(data_set, batch_size=15, shuffle=True)

def get_image_loader(folder_path):
    to_tensor = transforms.Compose([transforms.ToTensor()])
    data_set = ImageFolder(folder_path, transform=to_tensor)
    return DataLoader(data_set, batch_size=1)

def evaluate(test_data, net):
    n_correct = 0
    n_total = 0
    with torch.no_grad():
        for (x, y) in test_data:
            x, y = x.to(device), y.to(device)
            outputs = net.forward(x.view(-1, 28 * 28))
            for i, output in enumerate(outputs):
                if torch.argmax(output.cpu()) == y[i].cpu():
                    n_correct += 1
                n_total += 1
    return n_correct / n_total

def main():
    train_data = get_data_loader(is_train=True)
    test_data = get_data_loader(is_train=False)
    net = Net().to(device)
    print("initial accuracy:", evaluate(test_data, net))
    optimizer = torch.optim.Adam(net.parameters(), lr=0.001)
    for epoch in range(100):
        for (x, y) in train_data:
            x, y = x.to(device), y.to(device)
            net.zero_grad()
            output = net.forward(x.view(-1, 28 * 28))
            loss = torch.nn.functional.nll_loss(output, y)
            loss.backward()
            optimizer.step()
        print("epoch", epoch, "accuracy:", evaluate(test_data, net))
    image_loader = get_image_loader("aa")

    for (n, (x, _)) in enumerate(image_loader):
        if n > 2:
            break
        x = x.to(device)
        predict = torch.argmax(net.forward(x.view(-1, 28 * 28)).cpu())
        plt.figure(n)
        plt.imshow(x[0].permute(1, 2, 0).cpu())
        plt.title("prediction: " + str(int(predict)))
    plt.show()

if __name__ == "__main__":
    main()