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华建建设集团网站,搜索引擎seo优化平台,wordpress后台爆破,推广下载app拿佣金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…

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 ImageFolderclass 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 = 0n_total = 0with 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_totaldef 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 0.1print("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:breakpredict = 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 torchdevice = 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 ImageFolderdevice = 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 xdef 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 = 0n_total = 0with 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 += 1n_total += 1return n_correct / n_totaldef 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:breakx = 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()