Regression Example 1ΒΆ

The next code listing builds a neural network for regression. Here is what to do to make the code works for regression:

  1. Set the problem_type parameter in the pygad.nn.train() and pygad.nn.predict() functions to the string "regression".

pygad.nn.train(...,
               problem_type="regression")

predictions = pygad.nn.predict(..., 
                               problem_type="regression")

  1. Set the activation function for the output layer to the string "None".

output_layer = pygad.nn.DenseLayer(num_neurons=num_outputs, previous_layer=hidden_layer1, activation_function="None")

  1. Calculate the prediction error according to your preferred error function. Here is how the mean absolute error is calculated.

abs_error = numpy.mean(numpy.abs(predictions - data_outputs))
print(f"Absolute error : {abs_error}.")

Here is the complete code. Yet, there is no algorithm used to train the network and thus the network is expected to give bad results. Later, the pygad.gann module is used to train either a regression or classification networks.

import numpy
import pygad.nn

# Preparing the NumPy array of the inputs.
data_inputs = numpy.array([[2, 5, -3, 0.1],
                           [8, 15, 20, 13]])

# Preparing the NumPy array of the outputs.
data_outputs = numpy.array([0.1, 
                            1.5])

# The number of inputs (i.e. feature vector length) per sample
num_inputs = data_inputs.shape[1]
# Number of outputs per sample
num_outputs = 1

HL1_neurons = 2

# Building the network architecture.
input_layer = pygad.nn.InputLayer(num_inputs)
hidden_layer1 = pygad.nn.DenseLayer(num_neurons=HL1_neurons, previous_layer=input_layer, activation_function="relu")
output_layer = pygad.nn.DenseLayer(num_neurons=num_outputs, previous_layer=hidden_layer1, activation_function="None")

# Training the network.
pygad.nn.train(num_epochs=100,
               last_layer=output_layer,
               data_inputs=data_inputs,
               data_outputs=data_outputs,
               learning_rate=0.01,
               problem_type="regression")

# Using the trained network for predictions.
predictions = pygad.nn.predict(last_layer=output_layer, 
                         data_inputs=data_inputs, 
                         problem_type="regression")

# Calculating some statistics
abs_error = numpy.mean(numpy.abs(predictions - data_outputs))
print(f"Absolute error : {abs_error}.")