Ejemplo n.º 1
0
 def dialog(network, learning_data):
     layers = network.get_layers()
     if Layers.get_type(layers[0]) == Layers.KOHONEN_LAYER:
         if len(layers) == 2 and Layers.get_type(layers[1]) == Layers.GENERAL_LAYER:
             return CounterPropagationLearningDialog(network)
         else:
             return KohonenLearningDialog(network)
     else:
         return BackPropagationLearningDialog(network, learning_data)
Ejemplo n.º 2
0
 def from_network(network):
     model = NetworkModel(empty=True)
     model.set_network_input_neurons(network.inputs_count())
     for layer in network.get_layers():
         layer_model = model.add_layer()
         neurons = layer.get_neurons()
         layer_model.set_layer_type(Layers.get_type(layer))
         layer_model.set_neurons(len(neurons))
         for neuron_index in range(len(neurons)):
             neuron = neurons[neuron_index]
             layer_model.set_psp_for_neuron(neuron_index, neuron.get_psp().NAME)
             layer_model.set_activation_for_neuron(neuron_index, neuron.get_activator().NAME)
             layer_model.set_bias_for_neuron(neuron_index, neuron.get_bias().get_weight())
             inputs = neuron.get_inputs()
             for input_index in range(len(inputs)):
                 layer_model.set_weight_for_connection(neuron_index, input_index,
                     round(neuron.get_input_edge(input_index).get_weight(), 3))
     return model