Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/luca-parisi/quantum_relu
QReLU and m-QReLU: Two novel quantum activation functions for Deep Learning in TensorFlow, Keras, and PyTorch
https://github.com/luca-parisi/quantum_relu
activation activation-functions deep-learning deep-neural-networks keras keras-classification-models keras-models keras-neural-networks keras-tensorflow neural-network neural-networks pytorch quantum quantum-algorithms quantum-machine-learning relu relu-activation relu-layer tensorflow torch
Last synced: 3 months ago
JSON representation
QReLU and m-QReLU: Two novel quantum activation functions for Deep Learning in TensorFlow, Keras, and PyTorch
- Host: GitHub
- URL: https://github.com/luca-parisi/quantum_relu
- Owner: luca-parisi
- License: other
- Created: 2020-10-19T07:36:23.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-09-02T22:19:10.000Z (5 months ago)
- Last Synced: 2024-11-02T08:31:24.527Z (3 months ago)
- Topics: activation, activation-functions, deep-learning, deep-neural-networks, keras, keras-classification-models, keras-models, keras-neural-networks, keras-tensorflow, neural-network, neural-networks, pytorch, quantum, quantum-algorithms, quantum-machine-learning, relu, relu-activation, relu-layer, tensorflow, torch
- Language: Python
- Homepage: https://bradscholars.brad.ac.uk/handle/10454/18613
- Size: 43.9 KB
- Stars: 11
- Watchers: 3
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
# QReLU and m-QReLU in TensorFlow, Keras, and PyTorch
## Two novel quantum activation functions
The Quantum ReLU **'QReLU'** and its modified version or **'m-QReLU'** are Python custom activation functions available for both shallow and deep neural networks in TensorFlow, Keras, and PyTorch for Machine Learning- and Deep Learning-based classification. They are distributed under the [CC BY 4.0 license](http://creativecommons.org/licenses/by/4.0/).
Details on these functions, implementations, and validations against gold standard activation functions for both shallow and deep neural networks are available at the papers: **[Parisi, L., 2020](https://arxiv.org/abs/2010.08031)** and **[Parisi, L., et al., 2022](https://www.sciencedirect.com/science/article/abs/pii/S0957417421012483)**.
### 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 create --name --file environment.yml`
### Usage
Run ` pip install -e .` to install the `src` package in editable mode.
You can use the `QuantumReLU` activation functions as a keras layer and set the `modified` attribute to either `False`
or `True` if using the QReLU or the m-QReLU respectively:
#### Example of usage in a sequential model in Keras with a `QuantumReLU` 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(QuantumReLU(modified=False)) # True if using the m-QReLU (instead of the QReLU)
model.add(layers.MaxPooling2D((2, 2)))
```
or
```python
model = keras.Sequential(
keras.Input(shape=(32, 32, 3)),
layers.Conv2D(32, kernel_size=(3, 3)),
QuantumReLU(modified=False), # True if using the m-QReLU (instead of the QReLU)
layers.MaxPooling2D(pool_size=(2, 2)),
]
)
```
#### Example of usage in a sequential model in PyTorch with a `QuantumReLU` layer between a convolutional layer and a pooling layer
```python
self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
self.relu1 = QuantumReLU(modified=False) # True if using the m-QReLU (instead of the QReLU)
self.pool1 = nn.MaxPool2d(kernel_size=2)
```
### 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 .`
### Unit testing
Run `pytest --cov-report term-missing --cov=src tests/` to execute all unit tests and view the report with the test
coverage in percentage and missing lines too.
### Citation request
If you use these activation functions, please cite the papers by **[Parisi, L., 2020](https://arxiv.org/abs/2010.08031)** and **[Parisi, L., et al., 2022](https://www.sciencedirect.com/science/article/abs/pii/S0957417421012483)**.