Skip to content
forked from Dengyu-Wu/spkeras

Conversion from CNNs to SNNs using Tensorflow-Keras

License

Notifications You must be signed in to change notification settings

TACPSLab/spkeras

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

39 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

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

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}
  }

About

Conversion from CNNs to SNNs using Tensorflow-Keras

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%