SpKeras 2.0

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

• Tensorflow-keras

New Features

• Works with Keras Functional API

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/Dengyu-Wu/spkeras.git

   Coding for SpKeras

   SpKeras will detect the Activation Layer in CNN to create SpikeActivation Layer. It means all activation function should stay inside Activation Layer, including Softmax and Sigmoid.

#Sequential model
model.add(Conv2D(64, (3, 3), padding='same')
model.add(BatchNormalization())
model.add(Activation('relu'))

#Functional API
x = Conv2D( 64, (3,3), padding="same")(inputs)
x = BatchNormalization()(x)
node = Activation("relu")(x)
x = Conv2D( 64, (3,3), padding="same")(node)
x = BatchNormalization()(x)
x = Activation("relu")(x)
x = add([x, node])

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
'''

Examples

For more examples, please refer to the Examples

License

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

Citation

For more details, please refer to the paper.

@article{wu2022little,
  title={A Little Energy Goes a Long Way: Build an Energy-Efficient, Accurate Spiking Neural Network From Convolutional Neural Network},
  author={Wu, Dengyu and Yi, Xinping and Huang, Xiaowei},
  journal={Frontiers in neuroscience},
  volume={16},
  year={2022}
  }