Spkeras

Spkeras can easily get and evaluate rate-based spiking neural networks (SNNs), by following steps:

• Pre-train Convolutional neural networks (CNNs) using Tensorflow-keras
• Convert CNNs into SNNs using Spkeras
• Evaluate SNNs and get parameters, e.g. weights, bias and thresholds

Built With

Spkeras has extension layers, e.g. CurrentBias and SpikeForward, following Tensorflow-keras design syntax.

• Tensorflow-keras

Getting Started

The package is tested in Python 3.7.6 and Tensorflow 2.3.1.

Prerequisites

1. Install tensorflow

pip install tensorflow

Installation

1. Clone the repo

git clone https://github.com/(Anonymous)/spkeras.git

Example

#load dataset and cnn model
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.models import load_model

(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train = x_train/255
x_test = x_test/255
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)

cnn_model = load_model('cnn_model.h5')

#Convert CNN into SNN
from spkeras.models import cnn_to_snn

#Current normalisation using cnn_to_snn
##Default: signed_bit=0, amp_factor=100, method=1, epsilon = 0.001

snn_model = cnn_to_snn(signed_bit=0)(cnn_model,x_train)

#Evaluate SNN accuracy
##Default: timesteps=256, thresholding=0.5, scaling_factor=1, noneloss=False, spike_ext=0
_,acc = snn_model.evaluate(x_test,y_test,timesteps=256)

#Count SNN spikes
##Default: timesteps=256, thresholding=0.5, scaling_factor=1, noneloss=False, spike_ext=0, mode=0
s_max,s = snn_model.SpikeCounter(x_train,timesteps=256)

#Count neuron numbers
##Default: mode = 0
n = snn_model.NeuronNumbers(mode=0)

Attributes

'''
--------------------------
cnn_to_snn
--------------------------
sigbed_bit: bitwidth of weights, default 0 (32-bit)
amp_factor: amplification factor, default 100
method    : default 1
epsilon   : 0.001
--------------------------
evaluate & SpikeCounter
--------------------------
timesteps   : inference time, default 256.
thresholding: default 0.5.
noneloss    : noneloss mode, default False.
spike_ext   : extra inference time, default 0. (-1 for unlimited inference time)
--------------------------
SpikeCounter
--------------------------
mode: set 1 to count number of neurons under different spikes, default 0
--------------------------
NeuronNumber
--------------------------
mode: set 1 to exclude average pooling layer, default 0
'''

Usage

For more examples, please refer to the Examples.

Restrictions

SpKeras only works with...

• ReLU as the activation type.

  ! model.add( Activation('relu') )

• Models with separately stacked Activation layers as below.

  + model.add( Conv2D(...) )
  + model.add( BatchNormalization() )
  + model.add( Activation('relu') )
  + model.add( Dropout(0.3) )

  or

  keras.models.Sequential( [
      keras.layers.Conv2D(...),
      keras.layers.Activation( 'relu' ),
      keras.layers.AveragePooling2D(),
      ...
  ] )

at the moment.

License

Distributed under the MIT License. See LICENSE for more information.

More details

paper