南昌企业网站建设公司,找人做网站毕业设计,桂林生活网手机版,百度指数官网查询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) 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
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), dim1)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, transformto_tensor, downloadTrue)#一个批次15张,顺序打乱return DataLoader(data_set, batch_size15, shuffleTrue)def get_image_loader(folder_path):to_tensor transforms.Compose([transforms.ToTensor()])data_set ImageFolder(folder_path, transformto_tensor)return DataLoader(data_set, batch_size1)#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_trainTrue)#加载测试集test_data get_data_loader(is_trainFalse)#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(), lr0.001)#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 torch1.9.0cu102 -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), dim1)return xdef get_data_loader(is_train):to_tensor transforms.Compose([transforms.ToTensor()])data_set MNIST(, is_train, transformto_tensor, downloadTrue)return DataLoader(data_set, batch_size15, shuffleTrue)def get_image_loader(folder_path):to_tensor transforms.Compose([transforms.ToTensor()])data_set ImageFolder(folder_path, transformto_tensor)return DataLoader(data_set, batch_size1)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_trainTrue)test_data get_data_loader(is_trainFalse)net Net().to(device)print(initial accuracy:, evaluate(test_data, net))optimizer torch.optim.Adam(net.parameters(), lr0.001)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()
