Пример #1
0
    sim.launch(nbr_try, step_propagation, step_statictics, step_learn)

    sim.plot(12, 'FoN_rms', [
        'FoN_rms', 'SoN_rms', 'SoN_rms_input', 'SoN_rms_hidden',
        'SoN_rms_output'
    ], [
        "FoN", "SoN", "SoN input layer", "SoN hidden layer", "SoN output layer"
    ], [3, 3, 2, 2, 2], moregraph1)

    sim.plot(12, 'FoN_rms',
             ['FoN_err', 'SoN_err_input', 'SoN_err_hidden', 'SoN_err_output'],
             [
                 "FoN ( winner take all )",
                 "SoN input layer ( x > 0.5 => activation )",
                 "SoN hidden layer ( | x - o | <= 0.3 )",
                 "SoN output layer ( winner take all )"
             ], [3, 2, 2, 2], moregraph2)

    sim.custom_plot([Simulation.DISCRETIZE, Simulation.PROTOTYPE])

    #Representations hidden
    graph_networkhidden(sim.networks[0]['FoN'], width, (8, 8, 1))

    #Representations
    on = []
    hn = []
    for net in sim.networks:
        on.append(net['FoN'].outputNeurons)
        hn.append(net['FoN'].hiddenNeurons)
    graph_network(on, hn, width)
Пример #2
0
    sim.launch(nbr_try, step_propagation, step_statictics, step_learn)

    sim.plot(point, 'FoN_rms', [
        'FoN_rms', 'SoN_rms', 'SoN_rms_input', 'SoN_rms_hidden',
        'SoN_rms_output'
    ], [
        "FoN", "SoN", "SoN input layer", "SoN hidden layer", "SoN output layer"
    ], [3, 3, 2, 2, 2], moregraph1)

    sim.plot(point, 'FoN_rms',
             ['FoN_err', 'SoN_err_input', 'SoN_err_hidden', 'SoN_err_output'],
             [
                 "FoN ( winner take all )",
                 "SoN input layer ( x > 0.5 => activation )",
                 "SoN hidden layer ( | x - o | <= 0.3 )",
                 "SoN output layer ( winner take all )"
             ], [3, 2, 2, 2], moregraph2)

    sim.custom_plot([Simulation.DISCRETIZE, Simulation.PROTOTYPE])

    #Representations hidden
    graph_networkhidden(sim.networks[0]['FoN'], Hwidth, 151)

    #Representations
    on = []
    hn = []
    for net in sim.networks:
        on.append(net['FoN'].outputNeurons)
        hn.append(net['FoN'].hiddenNeurons)
    graph_network(on, hn, width)
Пример #3
0
    
    sim = Simulation(nbr_epoch, width, data, nbr_network, [FoN, SoN])
    sim.dgraph(['FoN_rms', 'SoN_rms', 'SoN_rms_input', 'SoN_rms_hidden', 'SoN_rms_output', 'FoN_err',
                'SoN_err_input', 'SoN_err_hidden', 'SoN_err_output'], [Simulation.DISCRETIZE])
    sim.launch(nbr_try, step_propagation, step_statictics, step_learn)
    
    sim.plot(12, 'FoN_rms', ['FoN_rms', 'SoN_rms', 'SoN_rms_input', 'SoN_rms_hidden', 'SoN_rms_output'],
             ["FoN" , "SoN", "SoN input layer", "SoN hidden layer", "SoN output layer"],
             [3, 3, 2, 2 , 2 ], moregraph1)
    
    
    sim.plot(12, 'FoN_rms', ['FoN_err', 'SoN_err_input', 'SoN_err_hidden', 'SoN_err_output'],
             ["FoN ( winner take all )" , "SoN input layer ( x > 0.5 => activation )", "SoN hidden layer ( | x - o | <= 0.3 )",
                        "SoN output layer ( winner take all )"],
             [3, 2, 2, 2 ], moregraph2)
    
    sim.custom_plot([Simulation.DISCRETIZE, Simulation.PROTOTYPE])
    
    #Representations hidden
    graph_networkhidden(sim.networks[0]['FoN'], width, (8,8,1))
    
    
    #Representations
    on = []
    hn = []
    for net in sim.networks:
        on.append(net['FoN'].outputNeurons)
        hn.append(net['FoN'].hiddenNeurons)
    graph_network(on, hn, width)
    
Пример #4
0
    sim = Simulation(nbr_epoch, width, data, nbr_network, [FoN, SoN])
    sim.dgraph(['FoN_rms', 'SoN_rms', 'SoN_rms_input', 'SoN_rms_hidden', 'SoN_rms_output', 'FoN_err',
                'SoN_err_input', 'SoN_err_hidden', 'SoN_err_output'], [Simulation.DISCRETIZE])
    sim.launch(nbr_try, step_propagation, step_statictics, step_learn)
    
    sim.plot(point, 'FoN_rms', ['FoN_rms', 'SoN_rms', 'SoN_rms_input', 'SoN_rms_hidden', 'SoN_rms_output'],
             ["FoN" , "SoN", "SoN input layer", "SoN hidden layer", "SoN output layer"],
             [3, 3, 2, 2 , 2 ], moregraph1)
    
    
    sim.plot(point, 'FoN_rms', ['FoN_err', 'SoN_err_input', 'SoN_err_hidden', 'SoN_err_output'],
             ["FoN ( winner take all )" , "SoN input layer ( x > 0.5 => activation )", "SoN hidden layer ( | x - o | <= 0.3 )",
                        "SoN output layer ( winner take all )"],
             [3, 2, 2 , 2 ], moregraph2)
    
    sim.custom_plot([Simulation.DISCRETIZE, Simulation.PROTOTYPE])
    
    #Representations hidden
    graph_networkhidden(sim.networks[0]['FoN'], Hwidth, 151)
    
    #Representations
    on = []
    hn = []
    for net in sim.networks:
        on.append(net['FoN'].outputNeurons)
        hn.append(net['FoN'].hiddenNeurons)
    graph_network(on, hn, width)