There are two components to working with data in neon. The first is a data iterator (NervanaDataIterator), that feeds the model with minibatches of data during training or evaluation. The second is a dataset (Dataset) class, which handles the loading and preprocessing of the data. When working with your own data, the latter is optional although highly recommended.

Data iterators are python iterables in that they implement the __iter__ method, which returns a new minibatch of data with each call.

• If your data is small enough to fit into memory:

• For image data or other data in the form of numpy arrays, use ArrayIterator.
• For specific modalities, neon includes specialized iterators (Text, Image Captioning, Q&A)
• If your data is too large:
• For data in the HDF5 format, use the HDF5Iterator to load chunks of data to send to the model. This approach is flexible for any type of data.
• For other types of data, use the macrobatching DataLoader, a specialized loader that loads macrobatches of data into memory, and then splits the macrobatches into minibatches to feed the model. This can be used for images, audio, video datasets and is recommended for large datasets or high-performance applications.

## ArrayIterator¶

The ArrayIterator class provides for iteration over minibatches of data that has been preloaded into memory as numpy arrays. This iterator supports classification, regression, and autoencoder tasks.

### Classification¶

Below is an example of a classification task with images where we load in 10,000 images. Each image is 32x32 pixels with 3 color channels (R, G, B), for a total of $$32\times32\times3=3,072$$ features.

from neon.data import ArrayIterator
import numpy as np

"""
X are the features and y are the labels.
The data in X must have shape (# examples, feature size)
"""
X = np.random.rand(10000,3072) # X.shape = (10000, 3072)
"""

For classification, the labels y must have shape (# examples, 1). y must also
consist of integers from 0 to nclass-1, where nclass is the number of categories.
"""
y = np.random.randint(0,10,10000) # y.shape = (10000, )

"""
The features X and labels y are passed to ArrayIterator be loaded into the backend
nclass, the number of classes, is set to 10
lshape, the local shape of the features, is set to (3,32,32) to represent
the the image dimensions: 32x32 pixels with 3 channels
"""
train = ArrayIterator(X=X, y=y, nclass=10, lshape=(3,32,32))


Importantly, the labels $$y$$ for classification should be integers from $$0$$ to $$K-1$$, where $$K$$ is the number of classes. These labels are stored in the backend in a one-hot representation. This means that if we have $$N$$ labels with $$K$$ classes, the labels will be stored in a $$N \times K$$ binary matrix. Each column will be all zeros except at the $$k$$-th element, which will be one. For example,

$\begin{split}y = (0,0,1,3,2,2) \rightarrow \left( \begin{array}{cccccc} 1 & 1 & 0 & 0 & 0 & 0\\ 0 & 0& 1 & 0 & 0 & 0 \\ 0 & 0& 0 & 0 & 1 & 1\\ 0 & 0& 0 & 1 & 0 & 0 \end{array} \right).\end{split}$

### Regression¶

In regression, the model output for each training example is a vector $$\hat{y}$$ that is compared against a desired vector $$y$$ with a cost function (such as mean squared error). Below is a simple example implementing linear regression.

We first create the iterator. By default, ArrayIterator assumes classification, so for regression we must set make_onehot = False to turn off the one-hot representation.

from neon.data import ArrayIterator
import numpy as np

X = np.random.rand(1000, 1)
y = 2*X + 1 + 0.01*np.random.randn(1000, 1)  # y = 2X+1 with some gaussian noise
train = ArrayIterator(X=X, y=y, make_onehot=False)


We then fit a linear model with a bias term using stochastic gradient descent:

from neon.initializers import Gaussian
from neon.layers import Linear, Bias
from neon.layers import GeneralizedCost
from neon.transforms import SumSquared
from neon.models import Model
from neon.callbacks.callbacks import Callbacks

# Linear layer with one unit and a bias layer
init_norm = Gaussian(loc=0.0, scale=0.01)
layers = [Linear(1, init=init_norm), Bias(init=init_norm)]

mlp = Model(layers=layers)

# Loss function is the squared difference
cost = GeneralizedCost(costfunc=SumSquared())

# Learning rules

# run fit
mlp.fit(train, optimizer=optimizer, num_epochs=10, cost=cost,
callbacks=Callbacks(mlp))

# print weights
slope = mlp.get_description(True)['model']['config']['layers'][0]['params']['W']
print "slope = ", slope
bias_weight = mlp.get_description(True)['model']['config']['layers'][1]['params']['W']
print "bias = ", bias_weight


After training, the weights match what we expect:

slope =  [[ 2.01577163]]
bias =  [[ 1.01664519]]


### Autoencoders¶

Autoencoders are a special case of regression where the desired outputs $$y$$ are the input features $$X$$. For convenience, you can exclude passing the labels $$y$$ to the iterator:

# Example construction of ArrayIterator for Autoencoder task with MNIST
from neon.data import MNIST

mnist = MNIST()

(X_train, y_train), (X_test, y_test), nclass = mnist.load_data()

# Set input and target to X_train
train = ArrayIterator(X_train, lshape=(1, 28, 28))


For the full example, see examples/conv_autoencoder.py.

### Specialized ArrayIterators¶

Neon includes specialized iterators that subclass from NervanaDataIterator for specific modalities where the entire dataset can be directly loaded into memory.

Name Description
neon.data.Text Iterator for processing and feeding text data
neon.data.ImageCaption Iterator for feeding an image and a sentence for each training example
neon.data.QA Data iterator for taking a Q&A dataset, which has already been vectorized, and feeding data to training

For more information on usage of these iterators, see the API documentation.

### Sequence data¶

For sequence data, where data are fed to the model across multiple time steps, the shape of the input data can depend on your usage.

• Often, data such as sentences are encoded as a vector sequence of integers, where each integer corresponds to a word in the vocabulary. This encoding is often used in conjunction with embedding layers. In this case, the input data should be formatted to have shape $$(T, N)$$, where $$T$$ is the number of time steps and $$N$$ is the batch size. The embedding layer takes this input and provides as output to a subsequent recurrent neural network data of shape $$(F, T * N)$$, where $$F$$ is the number of features (in this case, the embedding dimension). For an example, see imdb_lstm.py.
• When the sequence data uses a one-hot encoding, the input data should be formatted to have shape $$(F, T*N)$$. For example, if sentences use a one-hot encoding with 50 possible characters, and each sentence is 60-characters long, the input data will have shape $$(F=50, 60*N)$$. See the Text class, or the char_lstm.py example.
• Time series data should be formatted to have shape $$(F, T * N)$$, where $$F$$ is the number of features. For an example, see timeseries_lstm.py.

## HDF5Iterator¶

For datasets that are too large to fit in memory the HDF5Iterator class can be used. This uses an HDF5 formatted data file to store the input and target data arrays so the data size is not limited by on-host and/or on-device memory capacity. To use the HDF5Iterator, the data arrays need to be stored in an HDF5 file with the following format:

• The input data is in an HDF5 dataset named input and the target output, if needed, in a dataset named output. The data arrays are of the same format as the arrays used to initialize the ArrayIterator class.
• The input data class also requires an attribute named lshape which specifies the shape of the flattened input data array. For mean subtraction, an additional dataset named mean can be included in the HDF5 file which includes either a channel-wise mean vector or a complete mean image to subtract from the input data.

For alternate target label formats, such as converting the targets to a one-hot vector, or for autoencoder data, the HDF5IteratorOneHot and HDF5IteratorAutoencoder subclasses are included. These subclasses demonstrate how to extend the HDF5Iterator to handle different input and target data formats or transformations.

See the example, examples/mnist_hdf5.py, for how to format the HDF5 data file for use with the HDF5Iterator class.

If your data is too large to load directly into memory, use a macrobatching approach. In macrobatching, the data is loaded in smaller batches, then split further into minibatches to feed the model. neon supports macrobatching with image, audio, and video datasets using the DataLoader class.

DataLoader was created to provide a way to feed images from disk to neon with minimal latency. The module takes advantage of the high compressibility of images to conserve disk space and disk to host memory IO. DataLoader uses a multithreaded library to hide the latency of decoding images, applying augmentation and/or transformations, and transferring the resulting outputs to device memory (if necessary). The module also adds optional functionality for applying transformations to images (scale, flip, and rotation).

### Data format¶

The DataLoader supports several ways to organize the data:

1. CSV manifest files
2. General directory structure
3. Macrobatched data

### CSV manifest files¶

The most common approach is to provide training and validation .csv files, each containing file path and label indexes (for classification). The manifest file should contain a header line (that is ignored). Subsequent lines will have one record per line, formatted as:

filename, label
<path_to_image_1>,<label_1>
<path_to_image_2>,<label_2>
...
<path_to_image_N>,<label_N>


For example:

filename, label
/image_dir/faces/naveen_rao.jpg, 0
/image_dir/faces/arjun_bansal.jpg, 0
/image_dir/faces/amir_khosrowshahi.jpg, 0
/image_dir/fruits/apple.jpg, 1
/image_dir/fruits/pear.jpg, 1
/image_dir/animals/lion.jpg, 2
/image_dir/animals/tiger.jpg, 2
...
/image_dir/vehicles/toyota.jpg, 3


The manifest file is shuffled if the shuffle parameter to the DataLoader constructor is set to True

If the specified paths are not absolute (i.e. starts with ‘/’), then the path will be assumed to be relative to the location of the csv file.

For example, see the examples/whale_calls.py script.

#### General Directory Structure¶

This option presumes that your data is provided as a directory of images, that are organized in a hierarchy as follows:

In this organization, there are $$K=4$$ categories, with each category containing a variable number of images. The DataLoader will write out CSV files mapping the file location to an integer corresponding to the category label index. Note that to generate training/validation splits, the user should provide separate directories for training and testing. Alternatively, use the generated manifest file and partition into separate manifest files.

### Macrobatches¶

Macrobatches are simply archive files that package together many data files (jpegs) to take advantage of disk locality. The container for these macrobatches is designed to be compatible with the GNU tool cpio.

During runtime, the DataLoader will generate macrobatches from the data, if they do not exist. These macrobatches can then be used as direct input on subsequent training runs.

Alternatively, users can pre-generate macrobatches using the neon.util.batch_writer.py script. Macrobatch datasets can be generated with this script from four types of raw image sources:

1. General directory structure

Assuming the same directory structure as mentioned above, the following command illustrates how to invoke batch_writer.py in this scenario:

python neon/data/batch_writer.py  --data_dir /usr/local/data/macrobatch_out \
--image_dir /usr/local/data/raw_images \
--set_type directory \
--target_size 256 \
--macro_size 5000 \
--file_pattern "*.jpg"


In this command, the images will be loaded from /usr/local/data/raw_images and the macrobatches written to /usr/local/data/macrobatch_out. Images that are larger than the target_size=256 will be scaled down (e.g. a 512x768 image will be rescaled to 256x384, but a 128x128 will be untouched). Each macrobatch will have at most macro_size=5000 images.

1. CSV Manifest file

For data formatted as a CSV Manifest file (see above), the batch writer can then be invoked by calling:

python neon/data/batch_writer.py  --data_dir /usr/local/data/macrobatch_out \
--image_dir /location/of/csv_files \
--set_type csv

1. ImageNet 1K tar files

The ImageNet task is recognition task is described on the ILSVRC website. The 1.3M training images, 50K validation images, and development kit are provided as TAR archives. Because the images are organized in a way that makes them unamenable to the generalized directory structure described above, we provide some special handling to properly unpack the TARs and correctly associate the category names to the integer labels. ImageNet macrobatches can be created using the following command:

python neon/data/batch_writer.py  --data_dir /usr/local/data/macrobatch_out \
--image_dir /usr/local/data/I1K_tar_location \
--set_type i1k


In this command, the file_pattern, target_size, and macro_size arguments are handled as defaults. The only difference are the set_type argument and the image_dir argument. The image_dir should contain the three TAR files that are provided by ILSVRC:

ILSVRC2012_img_train.tar
ILSVRC2012_img_val.tar
ILSVRC2012_devkit_t12.tar.gz


Ensure that the disk where data_dir is located has sufficient space to hold the resulting macrobatches as well as space for the unpacked images (these can be deleted once the macrobatches have been written). Since the dataset is relatively large, an SSD can greatly speed up the batch writing process.

1. CIFAR-10 numpy arrays (pickled)

The CIFAR10 dataset is provided as a pickled set of numpy arrays containing the uncompressed pixel buffers of each image. This dataset is small enough to easily fit in host memory. However, the ArrayIterator module does not allow for random flipping, cropping, or shuffling. We therefore added the ability to write out CIFAR10 data as macrobatches to work with ImageLoader :

python neon/data/batch_writer.py  --data_dir /usr/local/data/macrobatch_out \
--set_type cifar10 \
--target_size 40


CIFAR10 images are 32x32, so if the target_size argument is omitted, then the images will be written out as 32x32. However, in many scenarios, one might wish to zero-pad the images so that random cropping can be done without further reducing the feature map size. Setting target_size to the desired padded image size instructs the batch writer to center the image in the target feature map size and pad the border with the means of that image along each channel. See numpy.pad for more details.

Because CIFAR images are so small, we have found that JPEG encoding of the images can negatively impact the accuracy of classification algorithms, so in this case we use lossless PNG encoding as the format to dump into the macrobatches.

The DataLoader constructor takes several arguments (see the API), including a media_params, which specifies the type of media being loaded and provides additional parameters. For images, an example invocation is:

shape = dict(channel_count=3, height=32, width=32)
train_params = ImageParams(center=False, aspect_ratio=110, **shape)
shuffle=True, target_size=1, nclasses=10)


For images, transformations specified by ImageParams will be performed on-the-fly. For supported transformations, see the ImageParams documentation.

For audio, use the AudioParams object to provide the needed parameters to the data loader:

common_params = dict(sampling_freq=2000, clip_duration=2000, frame_duration=80, overlap_percent=50)
train_params = AudioParams(random_scale_percent=5, **common_params)
index_file=train_idx, target_size=1, nclasses=2)


Here, several important metadata are supplied, such as the sampling frequency and the maximum duration of audio clips, as well as FFT-related parameters for generating the spectrogram such as frame_duration and overlap_percent. For more information, see the AudioParams documentation and the whale_calls.py and music_genres.py scripts.

For video, use the VideoParams class. We first define image parameters for the frames, and those parameters are then supplied to constructor the VideoParams object. For example:

shape = dict(channel_count=3, height=112, width=112, scale_min=128, scale_max=128)
frame_params = ImageParams(center=False, flip=True, **shape)
trainParams = VideoParams(frame_params=frame_params, frames_per_clip=16)

neon maintains backwards compatibility with the old ImageLoader class. For more details on how to use the old system, see documentation for neon v1.4.0.