def set_save(self, save):
previous_neurons = 0
index = 0
index_weights = 0
self.layers = []
for i in range(len(save['neurons'])):
# Create and populate layers.
layer = Layer(index, self.neuro_options)
layer.populate(save['neurons'][i], previous_neurons)
for j in range(len(layer.neurons)):
for k in range(len(layer.neurons[j].weights)):
# Apply neurons weights to each Neuron.
layer.neurons[j].weights[k] = save['weights'][index_weights]
index_weights += 1 # Increment index of flat array.
previous_neurons = save['neurons'][i]
index += 1
self.layers.append(layer)
def perceptron_generation(self, input, hiddens, output):
index = 0
previous_neurons = 0
layer = Layer(index, self.neuro_options)
# Number of Inputs will be set to 0 since it is an input layer.
layer.populate(input, previous_neurons)
# number of input is size of previous layer.
previous_neurons = input
self.layers.append(layer)
index += 1
for i in range(len(hiddens)):
# Repeat same process as first layer for each hidden layer.
layer = Layer(index, self.neuro_options)
layer.populate(hiddens[i], previous_neurons)
previous_neurons = hiddens[i]
self.layers.append(layer)
index += 1
layer = Layer(index, self.neuro_options)
# Number of input is equal to the size of the last hidden layer.
layer.populate(output, previous_neurons)
self.layers.append(layer)
