# spkeras

**Repository Path**: zdevt/spkeras

## Basic Information

- **Project Name**: spkeras
- **Description**: No description available
- **Primary Language**: Verilog
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-04-07
- **Last Updated**: 2026-04-07

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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## SpKeras 2.0
<img src="./doc/Spkeras_Framework.png">

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](https://www.tensorflow.org/guide/keras/sequential_model)

## New Features
* Works with Keras Functional API

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## Getting Started
The package is tested in Python 3.7.6 and Tensorflow 2.3.1.

### Prerequisites

1. Install tensorflow
```sh
pip install tensorflow
```

### Installation

1. Clone the repo
```sh
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. 

```python
#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
```python
#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
```python
'''
--------------------------
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
'''
```
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## Examples

_For more examples, please refer to the [Examples](./examples/)_

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## License

Distributed under the MIT License. See [LICENSE](./LICENSE) for more information.

<!-- Citation -->
## Citation
For more details, please refer to the <a href="https://doi.org/10.3389/fnins.2022.759900">paper</a>.

```
@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}
  }
```