https://github.com/luca-parisi/hyper_sinh

hyper-sinh: An Accurate and Reliable Activation Function from Shallow to Deep Learning in TensorFlow, Keras, and PyTorch
https://github.com/luca-parisi/hyper_sinh

activation-functions cnn cnn-classification cnn-keras cnn-model deep-learning deep-neural-networks keras keras-neural-networks keras-tensorflow machine-learning machinelearning-python pytorch sinh tensorflow tensorflow-models torch

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hyper-sinh: An Accurate and Reliable Activation Function from Shallow to Deep Learning in TensorFlow, Keras, and PyTorch

• Host: GitHub
• URL: https://github.com/luca-parisi/hyper_sinh
• Owner: luca-parisi
• License: other
• Created: 2021-02-06T16:29:01.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2024-09-12T22:41:15.000Z (14 days ago)
• Last Synced: 2024-09-18T07:48:38.223Z (9 days ago)
• Topics: activation-functions, cnn, cnn-classification, cnn-keras, cnn-model, deep-learning, deep-neural-networks, keras, keras-neural-networks, keras-tensorflow, machine-learning, machinelearning-python, pytorch, sinh, tensorflow, tensorflow-models, torch
• Language: Python
• Homepage: https://www.sciencedirect.com/science/article/pii/S2666827021000566
• Size: 36.1 KB
• Stars: 2
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# hyper-sinh in TensorFlow, Keras, and PyTorch

## An Accurate and Reliable Function from Shallow to Deep Learning

The **'hyper-sinh'** is a Python custom activation function available for both shallow and deep neural networks in TensorFlow, Keras, and PyTorch for Machine Learning- and Deep Learning-based classification. It is distributed under the [CC BY 4.0 license](http://creativecommons.org/licenses/by/4.0/).

Details on this function, implementation and validation against gold standard activation functions for both shallow and deep neural networks are available at the following paper: **[Parisi et al., 2021a](https://www.sciencedirect.com/science/article/pii/S2666827021000566)**. 

### Dependencies

The dependencies are included in the `environment.yml` file. 

Run the following command to install the required version of Python (v3.9.16) and all dependencies in a conda virtual 

environment (replace `` with your environment name):

- `conda env create --name  --file environment.yml`

### Usage

You can use the custom `HyperSinh` activation function in Keras or PyTorch as a layer:

#### Example of usage in a sequential model in Keras with a `HyperSinh` layer between a convolutional layer and a pooling layer

Either

```python

model = models.Sequential()

model.add(layers.Conv2D(32, (3, 3), input_shape=(32, 32, 3)))

model.add(HyperSinh()) 

model.add(layers.MaxPooling2D((2, 2)))

```

or

```python

model = keras.Sequential(

        keras.Input(shape=(32, 32, 3)),

        layers.Conv2D(32, kernel_size=(3, 3)),

        HyperSinh(),

        layers.MaxPooling2D(pool_size=(2, 2)),

    ]

)

```

#### Example of usage in a sequential model in PyTorch with a `HyperSinh` layer between a convolutional layer and a pooling layer

```python

self.conv1 = nn.Conv2d(1, OUT_CHANNEL_CONV1, kernel_size=KERNEL_SIZE_CONV)

self.hyper_sinh1 = HyperSinh()

self.pool1 = nn.MaxPool2d(kernel_size=KERNEL_SIZE_MAX_POOL)

```

### Linting

`isort` is used to ensure a consistent order of imports, whilst `autopep8` to ensure adherence of the codes to PEP-8, 

via the following two commands respectively:

- `isort `

- `autopep8 --in-place --recursive .`

### Citation request

If you are using this function, please cite the papers by:

* **[Parisi et al., 2020](https://arxiv.org/abs/2011.07661)**.

* **[Parisi et al., 2021a](https://www.sciencedirect.com/science/article/pii/S2666827021000566)**.

* **[Parisi et al., 2021b](https://www.wseas.org/multimedia/journals/computerresearch/2021/a025118-001(2021).pdf)**.