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)
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)
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)
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)