Table Of Contents
Table Of Contents

Data iterators for common data formats.


NDArrayIter(data[, label, batch_size, …]) Returns an iterator for mx.nd.NDArray, numpy.ndarray, h5py.Dataset mx.nd.sparse.CSRNDArray or scipy.sparse.csr_matrix.
CSVIter(*args, **kwargs) b”Returns the CSV file iterator.nnIn this function, the data_shape parameter is used to set the shape of each line of the input data.nIf a row in an input file is 1,2,3,4,5,6` and data_shape is (3,2), that rownwill be reshaped, yielding the array [[1,2],[3,4],[5,6]] of shape (3,2).nnBy default, the CSVIter has round_batch parameter set to True.
LibSVMIter(*args, **kwargs) b”Returns the LibSVM iterator which returns data with csrnstorage type.
MNISTIter(*args, **kwargs) b’Iterating on the MNIST dataset.nnOne can download the dataset from in src/io/’
ImageDetRecordIter(*args, **kwargs) b’Create iterator for image detection dataset packed in recordio.’
ImageRecordIter(*args, **kwargs) b’Iterates on image RecordIO filesnnReads batches of images from .rec RecordIO files.
ImageRecordIter_v1(*args, **kwargs) b’Iterating on image RecordIO filesnn..
ImageRecordUInt8Iter(*args, **kwargs) b’Iterating on image RecordIO filesnnThis iterator is identical to ImageRecordIter except for using uint8 asnthe data type instead of float.nnnnDefined in src/io/’
ImageRecordUInt8Iter_v1(*args, **kwargs) b’Iterating on image RecordIO filesnn..

Helper classes and functions

DataBatch(data[, label, pad, index, …]) A data batch.
DataDesc DataDesc is used to store name, shape, type and layout information of the data or the label.
DataIter([batch_size]) The base class for an MXNet data iterator.
MXDataIter(handle[, data_name, label_name]) A python wrapper a C++ data iterator.
PrefetchingIter(iters[, rename_data, …]) Performs pre-fetch for other data iterators.
ResizeIter(data_iter, size[, reset_internal]) Resize a data iterator to a given number of batches.