The CIFAR-10 and CIFAR-100 are labeled subsets of the80 million tiny imagesdataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. The CIFAR-10 dataset The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There ar...
If you're going to use this dataset, please cite the tech report at the bottom of this page. Version Size md5sumCIFAR-10python version163MB c58f30108f718f92721af3b95e74349aCIFAR-10Matlab version175MB70270af85842c9e89bb428ec9976c926CIFAR-10binary version (suitableforC programs)162MB c32a1...
If you're going to use this dataset, please cite the tech report at the bottom of this page. Version Size md5sumCIFAR-10python version163MB c58f30108f718f92721af3b95e74349aCIFAR-10Matlab version175MB70270af85842c9e89bb428ec9976c926CIFAR-10binary version (suitableforC programs)162MB c32a1...
0.5)) ])train_dataset = dsets.CIFAR10(root='/ml/pycifar', # 选择数据的根目录train=True, # 选择训练集transform=transform,download=True)test_dataset = dsets.CIFAR10(root='/ml/pycifar',train=False,# 选择测试集transform=transform,download=True)trainloader = DataLoader(train_dataset,batch_s...
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:9: DeprecationWarning: Warning: API "paddle.dataset.cifar.test10" is deprecated since 2.0.0, and will be removed in future versions. Please use "paddle.vision.datasets.Cifar10" instead. reason: Please ...
In [3] BATCH_SIZE = 128 # 每次取数据的个数 #将训练数据和测试数据读入内存 train_reader = paddle.batch( paddle.reader.shuffle(paddle.dataset.cifar.train10(), #获取cifa10训练数据 buf_size=128 * 100), #在buf_size的空间内进行乱序 batch_size=BATCH_SIZE) #batch_size:每个批次读入的训练数据...
total = total_size self.update((block_num - self.last_block) * block_size) self.last_block = block_num if not isfile('cifar-10-python.tar.gz'): with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar: urlretrieve( 'https://www.cs.toronto....
test_dataset = dsets.CIFAR10(root='/ml/pycifar', train=False,# 选择测试集 transform=transform, download=True) trainloader = DataLoader(train_dataset, batch_size=4, # 每个batch载入的图片数量 shuffle=True, num_workers=2) #载入训练数据所需的子任务数 ...
())small_cifar10 = []for i in range(2560):small_cifar10.append(cifar10_train[i])cifar10_train_loader = data.DataLoader(dataset= small_cifar10, batch_size=batch_size , shuffle=False)cifar10_train_loader = list(cifar10_train_loader)class AlexNet(nn.Module):def __init__(self, dropout...
testloader = DataLoader(test_dataset, batch_size=4, # 每个batch载入的图片数量 shuffle=False, num_workers=2) #载入训练数据所需的子任务数 cifar10_classes = ('plane','car','bird','cat','deer', 'dog','frog','horse','ship','truck') ...