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HDF5 interface for Tensorflow.
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README.rst

tftables allows convenient access to HDF5 files with Tensorflow. A class for reading batches of data out of arrays or tables is provided. A secondary class wraps both the primary reader and a Tensorflow FIFOQueue for straight-forward streaming of data from HDF5 files into Tensorflow operations.

The library is backed by multitables for high-speed reading of HDF5 datasets. multitables is based on PyTables (tables), so this library can make use of any compression algorithms that PyTables supports.

Licence

This software is distributed under the MIT licence. See the LICENSE.txt file for details.

Installation

pip install tftables

Alternatively, to install from HEAD, run

pip install git+https://github.com/ghcollin/tftables.git

You can also download or clone the repository and run

python setup.py install

tftables depends on multitables, numpy and tensorflow. The package is compatible with the latest versions of python 2 and 3.

Quick start

An example of accessing a table in a HDF5 file.

import tftables
import tensorflow as tf

with tf.device('/cpu:0'):
    # This function preprocesses the batches before they
    # are loaded into the internal queue.
    # You can cast data, or do one-hot transforms.
    # If the dataset is a table, this function is required.
    def input_transform(tbl_batch):
        labels = tbl_batch['label']
        data = tbl_batch['data']

        truth = tf.to_float(tf.one_hot(labels, num_labels, 1, 0))
        data_float = tf.to_float(data)

        return truth, data_float

    # Open the HDF5 file and create a loader for a dataset.
    # The batch_size defines the length (in the outer dimension)
    # of the elements (batches) returned by the reader.
    # Takes a function as input that pre-processes the data.
    loader = tftables.load_dataset(filename='path/to/h5_file.h5',
                                   dataset_path='/internal/h5/path',
                                   input_transform=input_transform,
                                   batch_size=20)

# To get the data, we dequeue it from the loader.
# Tensorflow tensors are returned in the same order as input_transformation
truth_batch, data_batch = loader.dequeue()

# The placeholder can then be used in your network
result = my_network(truth_batch, data_batch)

with tf.Session() as sess:

    # This context manager starts and stops the internal threads and
    # processes used to read the data from disk and store it in the queue.
    with loader.begin(sess):
        for _ in range(num_iterations):
            sess.run(result)

If the dataset is an array instead of a table. Then input_transform can be omitted if no pre-processing is required. If only a single pass through the dataset is desired, then you should pass cyclic=False to load_dataset.

Examples

See the unit tests for complete examples.

Examples

See the how-to for more in-depth documentation, and the unit tests for complete examples.

Documentation

Online documentation is available. A how to gives a basic overview of the library.

Offline documentation can be built from the docs folder using sphinx.

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