mdrnn 0.3.0

Custom Keras layers for implementing multi-dimensional recurrent neural networks (MDRNNs)

Multi-Directional Multi-Dimensional Recurrent Neural Networks

A library built on top of TensorFlow implementing the model described in Alex Graves's paper https://arxiv.org/pdf/0705.2011.pdf. The library comes with a set of custom Keras layers. Each layer can be seamlessly used in Keras to build a model and train it as usual.

Status: under development

This repository is in its early stages. The code presented here is not stable yet and it wasn't extensively tested. Use it at your own risk

Features

Layers available now:

• MDRNN: layer analogous to Keras SimpleRNN layer for processing multi-dimensional inputs
• MDLSTM: analogous to Keras LSTM layer
• MultiDirectional: layer-wrapper analogous to Keras Bidirectional for creating multi-directional multi-dimensional RNN

Layers currently under development (coming soon):

• MDGRU: analogous to Keras GRU layer

Additional features:

• easy to use with Keras
• Keras-like API for each layer
• option to choose order/direction in which to process inputs
• computations are run on CPU

Installation

Install the package from PyPI:

pip install mdrnn

Alternatively, clone the repository and install dependencies:

git clone <repo_url>
cd <repo_directory>
pip install -r requirements.txt

Quick Start

Create a 2-dimensional RNN:

from mdrnn import MDRNN, MDLSTM, MultiDirectional
import numpy as np
import tensorflow as tf
rnn = MDRNN(units=16, input_shape=(5, 4, 10), activation='tanh', return_sequences=True)
output = rnn(np.zeros((1, 5, 4, 10)))

Build a Keras model consisting of 1 MDRNN layer and train it:

model = tf.keras.Sequential()
model.add(MDRNN(units=16, input_shape=(2, 3, 6), activation='tanh'))
model.add(tf.keras.layers.Dense(units=10, activation='softmax'))
model.compile(loss='categorical_crossentropy', metrics=['acc'])
model.summary()
x = np.zeros((10, 2, 3, 6))
y = np.zeros((10, 10,))
model.fit(x, y)

Similarly, create and train a multi-directional MDRNN

x = np.zeros((10, 2, 3, 6))
y = np.zeros((10, 40,))

model = tf.keras.Sequential()
model.add(tf.keras.layers.Input(shape=(2, 3, 6)))
model.add(MultiDirectional(MDRNN(10, input_shape=[2, 3, 6])))

model.compile(loss='categorical_crossentropy', metrics=['acc'])
model.summary()

model.fit(x, y, epochs=1)

Similarly, create and train a multi-directional multi-dimensional LSTM (MDLSTM)

x = np.zeros((10, 2, 3, 6))
y = np.zeros((10, 40,))

model = tf.keras.Sequential()
model.add(tf.keras.layers.Input(shape=(2, 3, 6)))
model.add(MultiDirectional(MDLSTM(10, input_shape=[2, 3, 6])))

model.compile(loss='categorical_crossentropy', metrics=['acc'])
model.summary()

model.fit(x, y, epochs=1)

Requirements

• TensorFlow version >= 2.0

References

[1] A. Graves, S. Ferńandez, and J. Schmidhuber. Multidimensional recurrent neural networks.

[2] A. Graves and J. Schmidhuber. Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks.