Adaptive Computation Time

This is an implementation of Adaptive Computation Time (Graves, 2016) in PyTorch.

Introduction

Adaptive Computation Time is a drop-in replacement for RNNs structures that allows the model to process multiple time steps on a single input token. More information can be found in the paper, or in this blog post.

Requirements

• Python 3.6
• PyTorch 0.3.0
• matplotlib, argparse

Experiments

I am still in the process of replicating the experiments described in the paper.

• [x] Bit Parity
• [x] Logical Gates
• [ ] Addition
• [ ] Sorting
• [ ] Word Prediction

Usage

1. Git clone this repository

2. Train/Evaluate the model on a given task/parameter setting:

   • E.g.

   python run_train.py \
     --task=parity \
     --use_act=False \
     --model_save_path="outputs/models/parity/rnn"
   

   python run_train.py \
     --task=parity \
     --use_act=True \
     --act_ponder_penalty=0.001 \
     --model_save_path="outputs/models/parity/act_0.001"