如果做网站报价,推广网站哪里好,公众号制作135,商丘市建设模型
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#xff0c;然后我自己添加了一些Relu激活函数和正则化#xff0c;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 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_size5, stride1, padding2),nn.BatchNorm2d(32),nn.ReLU(),nn.MaxPool2d(2),nn.Conv2d(32, 32, kernel_size5, stride1, padding2),nn.BatchNorm2d(32),nn.ReLU(),nn.MaxPool2d(2),nn.Conv2d(32, 64, kernel_size5, stride1, padding2),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, padding4),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, trainTrue, transformtransform_train, downloadTrue)
test_dataset torchvision.datasets.CIFAR10(root./data, trainFalse, transformtorchvision.transforms.ToTensor(), downloadTrue)train_loader DataLoader(train_dataset, batch_size64, shuffleTrue)
test_loader DataLoader(test_dataset, batch_size64)
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(), lr1e-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(fEpoch 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(fTrain_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, trainFalse, transformtorchvision.transforms.ToTensor(), downloadTrue)
test_loader DataLoader(test_dataset, batch_size64, shuffleFalse)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(fSource is {TextColor.BLUE}{source_name}{TextColor.RESET}, and predicted is {colors}{predicted_class}{TextColor.RESET})
结果 TextColor是自定义字体颜色的类image中就是自己的图片。 结果如下测试集的正确率有82.7%
