How to use

In order to use the capabilities of evolutionary_keras in a project is necessary to use the model-classes provided. These classes inherit from the Keras model class and transparently defer to them whenever a Gradient Descent algorithm is used.

As an example, let us consider a project in which we have some neural network constructed with an input layer input_layer and an output layer output_layer. The Keras model would usually be constructed as:

from keras.models import Model
my_model = Model(input_layer, output_layer)

Using evolutionary_keras is as easy as doing:

from evolutionary_keras.models import EvolModel
my_model = EvolModel(input_layer, output_layer)

From that point onwards my_model behaves exactly as a normal Keras model implementing the same methods and attributes as well as allowing the usage of Evolutionary Optimizers. For instance, the example belows utilizes the Nodal Genetic Algorithm (NGA):


Which will use the default parameters of the Nodal Genetic Algorithm (NGA). Subsequent calls to methods such as will use the NGA algorithm to train.

For a more fine-grained usage we can also import the optimizer and instantiate it ourselves:

from evolutionary_keras.optimizers import NGA
my_nga = NGA(population_size = 42, mutation_rate = 0.2)