trainable=False)# cifar10 数据文件夹data_dir='/home/your_name/TensorFlow/cifar10/data/cifar-10-batches-bin/'# 训练时的日志logs文件,没有这个目录要先建一个train_dir='/home/your_name/TensorFlow/cifar10/cifar10_train/'# 加载 images,labelsimages,labels=my_cifar10_input.inputs(data_dir,BATCH_...
train=True,download=True)# 定义数据增强的转换方法data_transforms=[# 1. 随机水平翻转transforms.Compose([transforms.RandomHorizontalFlip(p=1)]),# 2. 随机旋转transforms.Compose([transforms.RandomRotation(10)]),# 3. 随机裁剪transforms.Compose([transforms.Random...
batch_size=32cifar_train =datasets.CIFAR10('cifar',True,transform=transforms.Compose([ transforms.Resize((32,32)),# 缩放到 32 * 32 大小transforms.ToTensor(),# 转化为Tensor]),download=True) cifar_train=DataLoader(cifar_train,batch_size=batch_size,shuffle=True) cifar_test=datasets.CIFAR10('ci...
train() for batchidx, (x, label) in enumerate(cifar_train): #x, label = x.to(device), label.to(device) logits = model(x) # logits: [b, 10] # label: [b] loss = criteon(logits, label) # backward optimizer.zero_grad() loss.backward() optimizer.step() print('train:', epoch...
train_data = torchvision.datasets.CIFAR10("data1", train=True, transform=torchvision.transforms.ToTensor(), download=True) test_data = torchvision.datasets.CIFAR10("data1", train=False, transform=torchvision.transforms.ToTensor(), download=True) ...
Datasetfromtorch.autogradimportVariableimportosimportcv2ascvimportnumpyasnp# 参数说明max_epoch=100# 迭代次数test_epoch=5display=100train_batch_size=64val_batch_size=32# 训练图片数据路径train_data_dir='./data/cifar-10/train/'# 测试图片数据路径test_data_dir='./data/cifar-10/test/'# 模型保存...
tf.gfile.DeleteRecursively(FLAGS.train_dir) tf.gfile.MakeDirs(FLAGS.train_dir)train() 開發者ID:ringringyi,項目名稱:DOTA_models,代碼行數:8,代碼來源:cifar10_train.py 示例2: main ▲點讚 5▼ # 需要導入模塊: import cifar10 [as 別名]# 或者: from cifar10 importtrain[as 別名]defmain(argv=No...
preprocessed_train_data=preprocess_data(train_data) 通过上述预处理步骤,我们已经准备好CIFAR-10数据集,可以开始构建深度学习模型。 构建深度学习模型 在图像识别任务中,卷积神经网络(CNN)是最常用的深度学习模型之一。我们将构建一个简单的CNN模型来识别CIFAR-10数据集中的图像。
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:6: DeprecationWarning: Warning: API "paddle.dataset.cifar.train10" is deprecated since 2.0.0, and will be removed in future versions. Please use "paddle.vision.datasets.Cifar10" instead. reason: Please ...
train_ds, train_valid_ds = [paddlevision.datasets.DatasetFolder( os.path.join(data_dir, 'train_valid_test', folder), transform=transform_train) for folder in ['train', 'train_valid']] valid_ds, test_ds = [paddlevision.datasets.DatasetFolder( os.path.join(data_dir, 'train_valid_test...