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}
}
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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