Independently Recurrent Neural Networks

Simple TensorFlow implementation of Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN by Shuai Li et al. The author's original implementation in Theano and Lasagne can be found in Sunnydreamrain/IndRNN_Theano_Lasagne.

Summary

In IndRNNs, neurons in recurrent layers are independent from each other. The basic RNN calculates the hidden state h with h = act(W * input + U * state + b). IndRNNs use an element-wise vector multiplication u * state meaning each neuron has a single recurrent weight connected to its last hidden state.

The IndRNN

• can be used efficiently with ReLU activation functions making it easier to stack multiple recurrent layers without saturating gradients
• allows for better interpretability, as neurons in the same layer are independent from each other
• prevents vanishing and exploding gradients by regulating each neuron's recurrent weight

Usage

Copy ind_rnn_cell.py into your project.

from ind_rnn_cell import IndRNNCell

# Regulate each neuron's recurrent weight as recommended in the paper
recurrent_max = pow(2, 1 / TIME_STEPS)

cell = MultiRNNCell([IndRNNCell(128, recurrent_max_abs=recurrent_max),
                     IndRNNCell(128, recurrent_max_abs=recurrent_max)])
output, state = tf.nn.dynamic_rnn(cell, input_data, dtype=tf.float32)
...

Experiments in the paper

Addition Problem

See examples/addition_rnn.py for a script reproducing the "Adding Problem" from the paper. Below are the results reproduced with the addition_rnn.py code.

Sequential MNIST

See examples/sequential_mnist.py for a script reproducing the Sequential MNIST experiment. I let it run for two days and stopped it after 60,000 training steps with a

• Training error rate of 0.7%
• Validation error rate of 1.1%
• Test error rate of 1.1%

Requirements

• Python 3.4+
• TensorFlow 1.5+