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旅游做网站,全网品牌推广公司,网站运营管理员具体做什么,wordpress 内存占用模型神经网络采用下图我使用之后发现迭代多了之后一直最高是正确率65%左右，然后我自己添加了一些Relu激活函数和正则化，现在正确率可以有80%左右。模型代码 import torch from torch import nnclass YmModel(nn.Module):def __init__(self):super(…

模型

神经网络采用下图
在这里插入图片描述

我使用之后发现迭代多了之后一直最高是正确率65%左右，然后我自己添加了一些Relu激活函数和正则化，现在正确率可以有80%左右。

模型代码

import torch
from torch import nnclass YmModel(nn.Module):def __init__(self):super(YmModel, self).__init__()self.model = nn.Sequential(nn.Conv2d(3, 32, kernel_size=5, stride=1, padding=2),nn.BatchNorm2d(32),nn.ReLU(),nn.MaxPool2d(2),nn.Conv2d(32, 32, kernel_size=5, stride=1, padding=2),nn.BatchNorm2d(32),nn.ReLU(),nn.MaxPool2d(2),nn.Conv2d(32, 64, kernel_size=5, stride=1, padding=2),nn.BatchNorm2d(64),nn.ReLU(),nn.MaxPool2d(2),nn.Flatten(),nn.Linear(64 * 4 * 4, 512),nn.ReLU(),nn.Dropout(0.5),nn.Linear(512, 64),nn.ReLU(),nn.Dropout(0.5),nn.Linear(64, 10),)def forward(self, x):return self.model(x)

训练

有一点要说明的是，数据集中并没有验证集，你可以从训练集扣个1w张出来

import torch
import torchvision
from torchvision import transformsfrom models.YMModel import YmModel
from torch.utils.data import DataLoadertransform_train = transforms.Compose([transforms.RandomCrop(32, padding=4),transforms.RandomHorizontalFlip(),transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])# 数据集
train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, transform=transform_train, download=True)
test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, transform=torchvision.transforms.ToTensor(), download=True)train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64)
print(len(train_loader), len(test_loader))print(len(train_dataset), len(test_dataset))model = YmModel()
#迭代次数
train_epochs = 300
#优化器
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
# 损失函数
loss_fn = torch.nn.CrossEntropyLoss()train_epochs_step = 0
best_accuracy = 0.for epoch in range(train_epochs):model.train()print(f'Epoch is {epoch}')for images, labels in train_loader:outputs = model(images)loss = loss_fn(outputs, labels)optimizer.zero_grad()loss.backward()optimizer.step()if train_epochs_step % 100 == 0:print(f'Train_Epoch is {train_epochs_step}\t Loss is {loss.item()}')train_epochs_step += 1train_epochs_step = 0with torch.no_grad():loss_running_total = 0.acc_running_total = 0.for images, labels in test_loader:outputs = model(images)loss = loss_fn(outputs, labels)loss_running_total += loss.item()acc_running_total += (outputs.argmax(1) == labels).sum().item()acc_running_total /= len(test_dataset)if acc_running_total > best_accuracy:best_accuracy = acc_running_totaltorch.save(model.state_dict(), './best_model.pth')print('accuracy is {}'.format(acc_running_total))print('total loss is {}'.format(loss_running_total))print('best accuracy is {}'.format(best_accuracy))

验证

import osimport numpy as np
import torch
import torchvision
from PIL import Image
from torch.utils.data import DataLoader
from torchvision import transformsfrom models.TestColor import TextColor
from models.YMModel import YmModeltest_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, transform=torchvision.transforms.ToTensor(), download=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)classes = ('airplane', 'automobile', 'bird', 'cat', 'deer','dog', 'frog', 'horse', 'ship', 'truck')
model = YmModel()model.load_state_dict(torch.load('best_model.pth'))model.eval()
with torch.no_grad():correct = 0.for images, labels in test_loader:outputs = model(images)_, predicted = torch.max(outputs, 1)correct += (predicted == labels).sum().item()print('Accuracy : {}'.format(100 * correct / len(test_dataset)))
folder_path = './images'
files_names = os.listdir(folder_path)
transform_test = transforms.Compose([transforms.Resize((32, 32)),transforms.ToTensor(),
])for file_name in files_names:image_path = os.path.join(folder_path, file_name)image = Image.open(image_path)image = transform_test(image)image = np.reshape(image, [1, 3, 32, 32])output = model(image)_, predicted = torch.max(output, 1)source_name = os.path.splitext(file_name)[0]predicted_class = classes[predicted.item()]colors = TextColor.GREEN if predicted_class == source_name else TextColor.REDprint(f"Source is {TextColor.BLUE}{source_name}{TextColor.RESET}, and predicted is {colors}{predicted_class}{TextColor.RESET}")