在train-labels-idx1-ubyte中是第5-8字节中第1个字节和第4个字节交换,第2个字节和第3个字节交换,才能满足要求。
上述代码在__init__中实现了从MNIST目录中读取所有数据(包括训练集和测试集)并且对数据类型进行适当转化(幸好MNIST数据集不大) trainDataset = DealDataset('.\\mnist_dataset', "train-images-idx3-ubyte.gz","train-labels-idx1-ubyte.gz",transform=transforms.Compose([ transforms.ToTensor(), transforms.Nor...
label='/home/zhaopace/MXNet/mxnet/example/adversary/data/t10k-labels-idx1-ubyte', batch_size= 128, data_shape= (784, ) ) Second: 符号式编程, 生成一个两层的MLP #Declare a two-layer MLPdata = mx.symbol.Variable('data')#data layerfc1 = mx.symbol.FullyConnected(data=data, num_hidden...
下载下面4个链接的压缩包。 https://apache-mxnet.s3.cn-north-1.amazonaws.com.cn/gluon/dataset/fashion-mnist/t10k-labels-idx1-ubyte.gz https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/t10k-images-idx3-ubyte.gz https://apache-mxnet.s3.cn-north-1.amaz...
wget -nv ${url_prefix}train-images-idx3-ubyte.gz -P data/MNIST/raw/ wget -nv ${url_prefix}train-labels-idx1-ubyte.gz -P data/MNIST/raw/ wget -nv ${url_prefix}t10k-images-idx3-ubyte.gz -P data/MNIST/raw/ wget -nv ${url_prefix}t10k-labels-idx1-ubyte.gz -P data/MNIST/...
ubyte' self.label_file = 'train-labels-idx1-ubyte' self.train_data, self.train_labels = load_data(self.folder, self.data_file, self.label_file) self.transforms = transforms def __getitem__(self, index): img, target = self.train_data[index], int(self.train_labels[index]) if self....
path.join(path, 'train-images-idx3-ubyte'), N=60000) self.y_l = self.load_labels( filename=os.path.join(path, 'train-labels-idx1-ubyte'), N=60000) self.x_t = self.load_images( filename=os.path.join(path, 't10k-images-idx3-ubyte'), N=10000) self.y_t = self.load_...
from src.utils import load_data X_test = load_data(os.path.join(data_folder, "t10k-images-idx3-ubyte.gz"), False) y_test = load_data( os.path.join(data_folder, "t10k-labels-idx1-ubyte.gz"), True ).reshape(-1) Pick 30 random samples from the test set and write them to ...
labels_path = os.path.join(path, '%s-labels-idx1-ubyte' % kind) images_path = os.path.join(path, '%s-images-idx3-ubyte' % kind) with open(labels_path, 'rb') as lbpath: magic, n = struct.unpack('>II', lbpath.read(8)) labels = np.fromfile(lbpath, dtype=np....
└── MNIST ├── processed │ ├── test.pt │ └── training.pt └── raw ├── t10k-images-idx3-ubyte ├── t10k-labels-idx1-ubyte ├── train-images-idx3-ubyte └── train-labels-idx1-ubyte 将压缩包放在mnist/row,文件夹下运行train_set = mnist.MNIST('./data', ...