当前位置: 首页 > news >正文

陕西省交通建设公司网站推荐就业的培训机构

陕西省交通建设公司网站,推荐就业的培训机构,摄影作品投稿平台,注册公司费用多少CNN 网络适用于图片识别,卷积神经网络主要用于图片的处理识别。卷积神经网络,包括一下几部分,输入层、卷积层、池化层、全链接层和输出层。 使用 CIFAR-10 进行训练, CIFAR-10 中图片尺寸为 32 * 32。卷积层通过卷积核移动进行计…

CNN 网络适用于图片识别,卷积神经网络主要用于图片的处理识别。卷积神经网络,包括一下几部分,输入层、卷积层、池化层、全链接层和输出层。
在这里插入图片描述
使用 CIFAR-10 进行训练, CIFAR-10 中图片尺寸为 32 * 32。卷积层通过卷积核移动进行计算最终生成特征图。

在这里插入图片描述
通过池化层进行降维度
在这里插入图片描述

卷积网络结构从输入到输出, 3* 32*32 --> 10:

类型WeightBIAS
卷积(3, 12, 5)(12, 3, 5, 5)12
卷积(12, 12, 5)(12, 12, 5, 5)12
Norm1212
卷积(12, 24, 5)(24, 12, 5, 5)24
卷积(24 24, 5)(24, 24, 5, 5)24
Norm2424
Linear(10, 2400)10

训练分类模型

准备数据
from torchvision.datasets import CIFAR10
from torchvision.transforms import transforms
from torch.utils.data import DataLoader# Loading and normalizing the data.
# Define transformations for the training and test sets
transformations = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])# CIFAR10 dataset consists of 50K training images. We define the batch size of 10 to load 5,000 batches of images.
batch_size = 10
number_of_labels = 10 # Create an instance for training. 
# When we run this code for the first time, the CIFAR10 train dataset will be downloaded locally. 
train_set =CIFAR10(root="./data",train=True,transform=transformations,download=True)# Create a loader for the training set which will read the data within batch size and put into memory.
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=0)
print("The number of images in a training set is: ", len(train_loader)*batch_size)# Create an instance for testing, note that train is set to False.
# When we run this code for the first time, the CIFAR10 test dataset will be downloaded locally. 
test_set = CIFAR10(root="./data", train=False, transform=transformations, download=True)# Create a loader for the test set which will read the data within batch size and put into memory. 
# Note that each shuffle is set to false for the test loader.
test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=0)
print("The number of images in a test set is: ", len(test_loader)*batch_size)print("The number of batches per epoch is: ", len(train_loader))
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
创建网络
import torch
import torch.nn as nn
import torchvision
import torch.nn.functional as F# Define a convolution neural network
class Network(nn.Module):def __init__(self):super(Network, self).__init__()self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=5, stride=1, padding=1)self.bn1 = nn.BatchNorm2d(12)self.conv2 = nn.Conv2d(in_channels=12, out_channels=12, kernel_size=5, stride=1, padding=1)self.bn2 = nn.BatchNorm2d(12)self.pool = nn.MaxPool2d(2,2)self.conv4 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=5, stride=1, padding=1)self.bn4 = nn.BatchNorm2d(24)self.conv5 = nn.Conv2d(in_channels=24, out_channels=24, kernel_size=5, stride=1, padding=1)self.bn5 = nn.BatchNorm2d(24)self.fc1 = nn.Linear(24*10*10, 10)def forward(self, input):output = F.relu(self.bn1(self.conv1(input)))      output = F.relu(self.bn2(self.conv2(output)))     output = self.pool(output)                        output = F.relu(self.bn4(self.conv4(output)))     output = F.relu(self.bn5(self.conv5(output)))     output = output.view(-1, 24*10*10)output = self.fc1(output)return output# Instantiate a neural network model 
model = Network()

定义损失函数

使用交叉熵函数作为损失函数,交叉熵分为两种

  • 二分类交叉熵函数
    在这里插入图片描述
  • 多分类交叉熵函数
    在这里插入图片描述
loss_fn = nn.CrossEntropyLoss()
optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001)
模型训练
from torch.autograd import Variable# Function to save the model
def saveModel():path = "./myFirstModel.pth"torch.save(model.state_dict(), path)# Function to test the model with the test dataset and print the accuracy for the test images
def testAccuracy():model.eval()accuracy = 0.0total = 0.0device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")with torch.no_grad():for data in test_loader:images, labels = data# run the model on the test set to predict labelsoutputs = model(images.to(device))# the label with the highest energy will be our prediction_, predicted = torch.max(outputs.data, 1)total += labels.size(0)accuracy += (predicted == labels.to(device)).sum().item()# compute the accuracy over all test imagesaccuracy = (100 * accuracy / total)return(accuracy)# Training function. We simply have to loop over our data iterator and feed the inputs to the network and optimize.
def train(num_epochs):best_accuracy = 0.0# Define your execution devicedevice = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")print("The model will be running on", device, "device")# Convert model parameters and buffers to CPU or Cudamodel.to(device)for epoch in range(num_epochs):  # loop over the dataset multiple timesrunning_loss = 0.0running_acc = 0.0for i, (images, labels) in enumerate(train_loader, 0):# get the inputsimages = Variable(images.to(device))labels = Variable(labels.to(device))# zero the parameter gradientsoptimizer.zero_grad()# predict classes using images from the training setoutputs = model(images)# compute the loss based on model output and real labelsloss = loss_fn(outputs, labels)# backpropagate the lossloss.backward()# adjust parameters based on the calculated gradientsoptimizer.step()# Let's print statistics for every 1,000 imagesrunning_loss += loss.item()     # extract the loss valueif i % 1000 == 999:    # print every 1000 (twice per epoch) print('[%d, %5d] loss: %.3f' %(epoch + 1, i + 1, running_loss / 1000))# zero the lossrunning_loss = 0.0# Compute and print the average accuracy fo this epoch when tested over all 10000 test imagesaccuracy = testAccuracy()print('For epoch', epoch+1,'the test accuracy over the whole test set is %d %%' % (accuracy))# we want to save the model if the accuracy is the bestif accuracy > best_accuracy:saveModel()best_accuracy = accuracy
测试模型
import matplotlib.pyplot as plt
import numpy as np# Function to show the images
def imageshow(img):img = img / 2 + 0.5     # unnormalizenpimg = img.numpy()plt.imshow(np.transpose(npimg, (1, 2, 0)))plt.show()# Function to test the model with a batch of images and show the labels predictions
def testBatch():# get batch of images from the test DataLoader  images, labels = next(iter(test_loader))# show all images as one image gridimageshow(torchvision.utils.make_grid(images))# Show the real labels on the screen print('Real labels: ', ' '.join('%5s' % classes[labels[j]] for j in range(batch_size)))# Let's see what if the model identifiers the  labels of those exampleoutputs = model(images)# We got the probability for every 10 labels. The highest (max) probability should be correct label_, predicted = torch.max(outputs, 1)# Let's show the predicted labels on the screen to compare with the real onesprint('Predicted: ', ' '.join('%5s' % classes[predicted[j]] for j in range(batch_size)))
执行模型
if __name__ == "__main__":# Let's build our modeltrain(5)print('Finished Training')# Test which classes performed welltestAccuracy()# Let's load the model we just created and test the accuracy per labelmodel = Network()path = "myFirstModel.pth"model.load_state_dict(torch.load(path))# Test with batch of imagestestBatch()

在这里插入图片描述

总结

pytorch 搭建一个 CNN 模型比较简单,5 轮训练之后,效果就可以达到 60%,10 张图片中预测对了 6 张。

http://www.dinnco.com/news/81370.html

相关文章:

  • vs音乐网站开发实例郑州seo技术服务顾问
  • 佛山网站建设是哪个好网络营销方式
  • 手机网站诊断分析西安seo网站关键词优化
  • 上海做网站大的公司有哪些搜狗站长工具综合查询
  • 文章视频类网站怎么做测试seo 培训教程
  • 无棣网站定制免费b站在线观看人数在哪儿
  • 网站建设安全架构整站优化价格
  • 宁波网站建设详细方案模板搜索引擎优化方式
  • wordpress企业源码太原seo排名优化软件
  • 如何保护我做的网站模板怎样联系百度客服
  • 宝塔面板怎么做多个网站广州网站seo公司
  • 网站热力图怎么做b站视频推广网站动漫
  • 做润滑油网站图片推广官网
  • 快速建站平台源码搜多多搜索引擎入口
  • 做网站商业欺骗赔多少万能搜索网站
  • 西安网站制作托百度指数app
  • 镇江网站推广网络营销的渠道
  • 网站建设专业知识新手怎么做销售
  • 企业网站设计制作教程seo综合诊断工具
  • 微信公众号制作网站百度收录是什么意思
  • 沃航科技网站开发海外广告优化师
  • 旅游网站做seosem和seo哪个工作好
  • 推荐几个响应式网站做参考推广平台有哪些?
  • 网站开发邮件安徽网站开发哪家好
  • 花钱做推广广告哪个网站好seo推广小分享
  • 电商网站建设系统seo推广哪家服务好
  • 企业移动网站建设商反向链接查询
  • 263企业会议邮箱登录入口seo外链推广工具
  • 帮别人做网站涉嫌诈骗网站的推广
  • 哪些网站可以做ppt阿里巴巴怎么优化关键词排名