Modern Computer Architecture and Organization
Modern Computer Architecture and Organization, by Jim Ledin. Published by Packt Publishing.
Chapter 6, Exercise 3
The hyperbolic tangent is often used as an activation function in ANNs. The hyperbolic tangent function is defined as follows:
Given a neuron with inputs from three preceding neurons as depicted in Figure 6.4, compute the neuron’s output with the hyperbolic tangent as the activation function F(x) using the following preceding neuron outputs and path weights:
| Neuron | Neuron output | Weight |
|---|---|---|
| N1 | 0.6 | 0.4 |
| N2 | -0.3 | 0.8 |
| N3 | 0.5 | -0.2 |
Answer
See the python file Ex__3_activation_func.py for the code.
To run the code, assuming python is installed and is in your path, execute the command python Ex__3_activation_func.py
This is the output produced by the program:
C:\>Ex__3_activation_func.py
Neuron output = -0.099668