Copyright 2018 National Technology & Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation version 3 of the License only. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. Tf not, see https://www.gnu.org/licenses.

This package provides a framework for training deep spiking neural networks using keras. Whetstone is designed to be extendable, modular, and easy-to-use.

Before an initial release, you must clone the git repo and install the package manually.

The easiest way to this is:

1. Find a location where you have write permissions and would like a copy of the Whetstone package.
2. git clone --branch master https://github.com/SNL-NERL/Whetstone.git This will create a new sub-directory called Whetstone which will contain all the relevant code.
3. In the new directory, run pip install . to install the package.

Typical install requires less than 1 minute.

Whetstone has been tested on Ubuntu Linux 14.04 and 16.04 and is written using Python.

Testing was performed using Python 2.7.6, though the code should be generally compatible with Python 3.

A CUDA-capable GPU is not required by highly recommended for larger network models. Testing was performed using NVIDIA Titan Xp and NVIDIA V100 GPUs.

Average runtime varies wildly depending on system setup, dataset, and network complexity. The example listed below will take approximately 1 minute using a Titan Xp GPU.

• Keras 2.1.2
• Tensorflow 1.3.0
• Numpy 1.14.5

Note: Generally, the version numbers do not need to be exactly those listed. However, care should be taken particularly with newer versions of Keras due to changes in default parameter values. E.g. K.epsilon()

Some example scripts require

• opencv-python 3.4.1.15
• Keras-rl 0.4.2

Documentation is available at docs.

The code below can be used to train a simple densely connected spiking network for classifying mnist.

This and additional examples, as well as benchmarking/logging scripts, can be found in the examples directory.

from __future__ import print_function
"""
Light-weight demo of SimpleSharpener, Spiking_BRelu, and Softmax_Decode for a fully connected net on mnist.
"""

import os
import numpy as np
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.utils import to_categorical
from keras.layers import Dense
from keras.optimizers import Adadelta
from whetstone.layers import Spiking_BRelu, Softmax_Decode, key_generator
from whetstone.callbacks import SimpleSharpener, WhetstoneLogger

numClasses = 10
(x_train, y_train),(x_test, y_test) = mnist.load_data()

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

y_train = to_categorical(y_train, numClasses)
y_test = to_categorical(y_test, numClasses)

x_train = np.reshape(x_train, (60000,28*28))
x_test = np.reshape(x_test, (10000,28*28))

key = key_generator(num_classes=10, width=100)

model = Sequential()
model.add(Dense(256, input_shape=(28*28,)))
model.add(Spiking_BRelu())
model.add(Dense(64))
model.add(Spiking_BRelu())
model.add(Dense(100))
model.add(Spiking_BRelu())
model.add(Softmax_Decode(key))

simple = SimpleSharpener(start_epoch=5, steps=5, epochs=True, bottom_up=True)

# Create a new directory to save the logs in.
log_dir = './simple_logs'
if not os.path.exists(log_dir):
    os.makedirs(log_dir)

logger = WhetstoneLogger(logdir=log_dir, sharpener=simple)

model.compile(loss='categorical_crossentropy', optimizer=Adadelta(lr=4.0, rho=0.95, epsilon=1e-8, decay=0.0), metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=128, epochs=21, callbacks=[simple, logger])

print(model.evaluate(x_test, y_test))