ActTensor-tf 1.0.0

Description:

ActTensortf 1.0.0

ActTensor: Activation Functions for TensorFlow

What is it?
ActTensor is a Python package that provides state-of-the-art activation functions which facilitate using them in Deep Learning projects in an easy and fast manner.
Why not using tf.keras.activations?
As you may know, TensorFlow only has a few defined activation functions and most importantly it does not include newly-introduced activation functions. Wrting another one requires time and energy; however, this package has most of the widely-used, and even state-of-the-art activation functions that are ready to use in your models.
Requirements
numpy
tensorflow
setuptools
keras
wheel

Where to get it?
The source code is currently hosted on GitHub at:
https://github.com/pouyaardehkhani/ActTensor
Binary installers for the latest released version are available at the Python
Package Index (PyPI)
# PyPI
pip install ActTensor-tf

License
MIT
How to use?
import tensorflow as tf
import numpy as np
from ActTensor_tf import ReLU # name of the layer

functional api
inputs = tf.keras.layers.Input(shape=(28,28))
x = tf.keras.layers.Flatten()(inputs)
x = tf.keras.layers.Dense(128)(x)
# wanted class name
x = ReLU()(x)
output = tf.keras.layers.Dense(10,activation='softmax')(x)

model = tf.keras.models.Model(inputs = inputs,outputs=output)

sequential api
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128),
# wanted class name
ReLU(),
tf.keras.layers.Dense(10, activation = tf.nn.softmax)])

NOTE:

The main function of the activation layers are also availabe but it maybe defined as different name. Check this for more information.

from ActTensor_tf import relu

Activations
Classes and Functions are available in ActTensor_tf



Activation Name
Class Name
Function Name




SoftShrink
SoftShrink
softSHRINK


HardShrink
HardShrink
hard_shrink


GLU
GLU
-


Bilinear
Bilinear
-


ReGLU
ReGLU
-


GeGLU
GeGLU
-


SwiGLU
SwiGLU
-


SeGLU
SeGLU
-


ReLU
ReLU
relu


Identity
Identity
identity


Step
Step
step


Sigmoid
Sigmoid
sigmoid


HardSigmoid
HardSigmoid
hard_sigmoid


LogSigmoid
LogSigmoid
log_sigmoid


SiLU
SiLU
silu


PLinear
ParametricLinear
parametric_linear


Piecewise-Linear
PiecewiseLinear
piecewise_linear


Complementary Log-Log
CLL
cll


Bipolar
Bipolar
bipolar


Bipolar-Sigmoid
BipolarSigmoid
bipolar_sigmoid


Tanh
Tanh
tanh


TanhShrink
TanhShrink
tanhshrink


LeCun's Tanh
LeCunTanh
leCun_tanh


HardTanh
HardTanh
hard_tanh


TanhExp
TanhExp
tanh_exp


Absolute
ABS
Abs


Squared-ReLU
SquaredReLU
squared_relu


P-ReLU
ParametricReLU
Parametric_ReLU


R-ReLU
RandomizedReLU
Randomized_ReLU


LeakyReLU
LeakyReLU
leaky_ReLU


ReLU6
ReLU6
relu6


Mod-ReLU
ModReLU
Mod_ReLU


Cosine-ReLU
CosReLU
Cos_ReLU


Sin-ReLU
SinReLU
Sin_ReLU


Probit
Probit
probit


Cos
Cos
Cosine


Gaussian
Gaussian
gaussian


Multiquadratic
Multiquadratic
Multi_quadratic


Inverse-Multiquadratic
InvMultiquadratic
Inv_Multi_quadratic


SoftPlus
SoftPlus
softPlus


Mish
Mish
mish


SMish
Smish
smish


P-SMish
ParametricSmish
Parametric_Smish


Swish
Swish
swish


ESwish
ESwish
eswish


HardSwish
HardSwish
hardSwish


GCU
GCU
gcu


CoLU
CoLU
colu


PELU
PELU
pelu


SELU
SELU
selu


CELU
CELU
celu


ArcTan
ArcTan
arcTan


Shifted-SoftPlus
ShiftedSoftPlus
Shifted_SoftPlus


Softmax
Softmax
softmax


Logit
Logit
logit


GELU
GELU
gelu


Softsign
Softsign
softsign


ELiSH
ELiSH
elish


HardELiSH
HardELiSH
hardELiSH


Serf
Serf
serf


ELU
ELU
elu


Phish
Phish
phish


QReLU
QReLU
qrelu


MQReLU
MQReLU
mqrelu


FReLU
FReLU
frelu






Which activation functions it supports?


Soft Shrink:








Hard Shrink:








GLU:







Source Paper : Language Modeling with Gated Convolutional Networks


Bilinear:


Source Paper : Parameter Efficient Deep Neural Networks with Bilinear Projections



ReGLU:
ReGLU is an activation function which is a variant of GLU.




Source Paper : GLU Variants Improve Transformer



GeGLU:
GeGLU is an activation function which is a variant of GLU.




Source Paper : GLU Variants Improve Transformer



SwiGLU:
SwiGLU is an activation function which is a variant of GLU.




Source Paper : GLU Variants Improve Transformer



SeGLU:
SeGLU is an activation function which is a variant of GLU.


ReLU:







Source Paper : Nair, Vinod, and Geoffrey E. Hinton. "Rectified linear units improve restricted boltzmann machines." In Icml. 2010.



Identity:
f(x)=x







Step:








Sigmoid:







Source Paper : Han, Jun, and Claudio Moraga. "The influence of the sigmoid function parameters on the speed of backpropagation learning." In International workshop on artificial neural networks, pp. 195-201. Springer, Berlin, Heidelberg, 1995.



Hard Sigmoid:







Source Paper : Courbariaux, Matthieu, Yoshua Bengio, and Jean-Pierre David. "Binaryconnect: Training deep neural networks with binary weights during propagations." Advances in neural information processing systems 28 (2015).



Log Sigmoid:








SiLU:







Source Paper : Elfwing, Stefan, Eiji Uchibe, and Kenji Doya. "Sigmoid-weighted linear units for neural network function approximation in reinforcement learning." Neural Networks 107 (2018): 3-11.



ParametricLinear:
f(x)=a∗x


PiecewiseLinear:
Choose some xmin and xmax, which is our "range". Everything less than than this range will be 0, and everything greater than this range will be 1. Anything else is linearly-interpolated between.










Complementary Log-Log (CLL):







Source Paper : Gomes, Gecynalda S. da S., and Teresa B. Ludermir. "Complementary log-log and probit: activation functions implemented in artificial neural networks." In 2008 Eighth International Conference on Hybrid Intelligent Systems, pp. 939-942. IEEE, 2008.



Bipolar:








Bipolar Sigmoid:







Source Paper : Mansor, Mohd Asyraf, and Saratha Sathasivam. "Activation function comparison in neural-symbolic integration." In AIP Conference Proceedings, vol. 1750, no. 1, p. 020013. AIP Publishing LLC, 2016.



Tanh:







Source Paper : Harrington, Peter de B. "Sigmoid transfer functions in backpropagation neural networks." Analytical Chemistry 65, no. 15 (1993): 2167-2168.



Tanh Shrink:








LeCunTanh:







Source Paper : LeCun, Yann A., Léon Bottou, Genevieve B. Orr, and Klaus-Robert Müller. "Efficient backprop." In Neural networks: Tricks of the trade, pp. 9-48. Springer, Berlin, Heidelberg, 2012.



Hard Tanh:








TanhExp:







Source Paper : Liu, Xinyu, and Xiaoguang Di. "TanhExp: A smooth activation function with high convergence speed for lightweight neural networks." IET Computer Vision 15, no. 2 (2021): 136-150.



ABS:








SquaredReLU:







Source Paper : So, David, Wojciech Mańke, Hanxiao Liu, Zihang Dai, Noam Shazeer, and Quoc V. Le. "Searching for Efficient Transformers for Language Modeling." Advances in Neural Information Processing Systems 34 (2021): 6010-6022.



ParametricReLU (PReLU):







Source Paper : He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification." In Proceedings of the IEEE international conference on computer vision, pp. 1026-1034. 2015.



RandomizedReLU (RReLU):








Source Paper : Xu, Bing, Naiyan Wang, Tianqi Chen, and Mu Li. "Empirical evaluation of rectified activations in convolutional network." arXiv preprint arXiv:1505.00853 (2015).



LeakyReLU:








ReLU6:







Source Paper : Howard, Andrew G., Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. "Mobilenets: Efficient convolutional neural networks for mobile vision applications." arXiv preprint arXiv:1704.04861 (2017).



ModReLU:







Source Paper : Arjovsky, Martin, Amar Shah, and Yoshua Bengio. "Unitary evolution recurrent neural networks." In International conference on machine learning, pp. 1120-1128. PMLR, 2016.



CosReLU:








SinReLU:








Probit:








Cosine:








Gaussian:








Multiquadratic:
Choose some point (x,y).








InvMultiquadratic:








SoftPlus:







Source Paper : Dugas, Charles, Yoshua Bengio, François Bélisle, Claude Nadeau, and René Garcia. "Incorporating second-order functional knowledge for better option pricing." Advances in neural information processing systems 13 (2000).



Mish:







Source Paper : Misra, Diganta. "Mish: A self regularized non-monotonic neural activation function." arXiv preprint arXiv:1908.08681 4, no. 2 (2019): 10-48550.



Smish:








ParametricSmish (PSmish):










Swish:







Source Paper : Ramachandran, Prajit, Barret Zoph, and Quoc V. Le. "Searching for activation functions." arXiv preprint arXiv:1710.05941 (2017).



ESwish:







Source Paper : Alcaide, Eric. "E-swish: Adjusting activations to different network depths." arXiv preprint arXiv:1801.07145 (2018).



Hard Swish:







Source Paper : Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for mobilenetv3." In Proceedings of the IEEE/CVF international conference on computer vision, pp. 1314-1324. 2019.



GCU:







Source Paper : Noel, Mathew Mithra, Advait Trivedi, and Praneet Dutta. "Growing cosine unit: A novel oscillatory activation function that can speedup training and reduce parameters in convolutional neural networks." arXiv preprint arXiv:2108.12943 (2021).



CoLU:







Source Paper : Vagerwal, Advait. "Deeper Learning with CoLU Activation." arXiv preprint arXiv:2112.12078 (2021).



PELU:







Source Paper : Trottier, Ludovic, Philippe Giguere, and Brahim Chaib-Draa. "Parametric exponential linear unit for deep convolutional neural networks." In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 207-214. IEEE, 2017.



SELU:

where α≈1.6733 & λ≈1.0507






Source Paper : Klambauer, Günter, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter. "Self-normalizing neural networks." Advances in neural information processing systems 30 (2017).



CELU:







Source Paper : Barron, Jonathan T. "Continuously differentiable exponential linear units." arXiv preprint arXiv:1704.07483 (2017).



ArcTan:








ShiftedSoftPlus:







Source Paper : Schütt, Kristof, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller. "Schnet: A continuous-filter convolutional neural network for modeling quantum interactions." Advances in neural information processing systems 30 (2017).



Softmax:




Source Paper : Gold, Steven, and Anand Rangarajan. "Softmax to softassign: Neural network algorithms for combinatorial optimization." Journal of Artificial Neural Networks 2, no. 4 (1996): 381-399.



Logit:








GELU:








Softsign:








ELiSH:







Source Paper : Basirat, Mina, and Peter M. Roth. "The quest for the golden activation function." arXiv preprint arXiv:1808.00783 (2018).



Hard ELiSH:







Source Paper : Basirat, Mina, and Peter M. Roth. "The quest for the golden activation function." arXiv preprint arXiv:1808.00783 (2018).



Serf:







Source Paper : Nag, Sayan, and Mayukh Bhattacharyya. "SERF: Towards better training of deep neural networks using log-Softplus ERror activation Function." arXiv preprint arXiv:2108.09598 (2021).



ELU:







Source Paper : Clevert, Djork-Arné, Thomas Unterthiner, and Sepp Hochreiter. "Fast and accurate deep network learning by exponential linear units (elus)." arXiv preprint arXiv:1511.07289 (2015).



Phish:







Source Paper : Naveen, Philip. "Phish: A novel hyper-optimizable activation function." (2022).



QReLU:







Source Paper : Parisi, Luca, Daniel Neagu, Renfei Ma, and Felician Campean. "QReLU and m-QReLU: Two novel quantum activation functions to aid medical diagnostics." arXiv preprint arXiv:2010.08031 (2020).



m-QReLU:







Source Paper : Parisi, Luca, Daniel Neagu, Renfei Ma, and Felician Campean. "QReLU and m-QReLU: Two novel quantum activation functions to aid medical diagnostics." arXiv preprint arXiv:2010.08031 (2020).



FReLU:







Source Paper : Qiu, Suo, Xiangmin Xu, and Bolun Cai. "FReLU: flexible rectified linear units for improving convolutional neural networks." In 2018 24th international conference on pattern recognition (icpr), pp. 1223-1228. IEEE, 2018.

Cite this repository
@software{Pouya_ActTensor_2022,
author = {Pouya, Ardehkhani and Pegah, Ardehkhani},
license = {MIT},
month = {7},
title = {{ActTensor}},
url = {https://github.com/pouyaardehkhani/ActTensor},
version = {1.0.0},
year = {2022}
}