The mathematical definition of the Tanh activation function is

and its derivative is defined as

The Tanh function and its derivative for a batch of inputs (a 2D array with nRows=nSamples and nColumns=nNodes) can be implemented in the following manner:

**Tanh simplest implementation**

import numpy as np def Tanh(x): return np.tanh(x)

**Tanh derivative simplest implementation**

import numpy as np def Tanh_grad(x): return 1.-np.tanh(x)**2 # sech^2{x}

However, these implementations can be further accelerated (sped-up) by using Numba (https://numba.pydata.org/). Numba is a Just-in-time (JIT) compiler that

translates a subset of Python and NumPy code into fast machine code.

To use numba, install it as:

pip install numba

Also, make sure that your numpy is compatible with Numba or not, although usually pip takes care of that. You can get the info here: https://pypi.org/project/numba/

Accelerating the above functions using Numba is quite simple. Just modify them in the following manner:

**Tanh NUMBA implementation**

from numba import njit @njit(cache=True,fastmath=True) def Tanh(x): return np.tanh(x)

**Tanh derivative NUMBA implementation**

from numba import njit @njit(cache=True,fastmath=True) def Tanh_grad(x): return 1.-np.tanh(x)**2 # sech^2{x}

This is quite fast and competitive with Tensorflow and PyTorch (https://github.com/manassharma07/crysx_nn/blob/main/benchmarks_tests/Performance_Activation_Functions_CPU.ipynb).

It is in fact also used in the CrysX-Neural Network library (crysx_nn)

Furthermore, the above implementations can be further accelerated using Cupy (CUDA), if using single precision (float32) is not a problem.

CuPy is an open-source array library for GPU-accelerated computing with Python. CuPy utilizes CUDA Toolkit libraries to make full use of the GPU architecture.

The Cupy implementations look as follows:

import cupy as cp def Tanh_cupy(x): return cp.tanh(x)

def Tanh_grad_cupy(x): return 1.-cp.tanh(x)**2 # sech^2{x}

The above code is also used in the crysx_nn library.

To see how the crysx_nn implementations of Tanh compare with TensorFlow and PyTorch, click here.

I hope you found this information useful.

If you did, then don’t forget to check out my other posts on Machine Learning and efficient implementations of activation/loss functions in Python.

Ph.D. researcher at Friedrich-Schiller University Jena, Germany. I’m a physicist specializing in computational material science. I write efficient codes for simulating light-matter interactions at atomic scales. I like to develop Physics, DFT, and Machine Learning related apps and software from time to time. Can code in most of the popular languages. I like to share my knowledge in Physics and applications using this Blog and a YouTube channel.