class Controller():
def __init__(self):
self.nn = None#NN()
def set_file(self,file):
#print("seteo",file)
self.nn = NN()
self.nn.config(tickle.load(file))
return "Archivo seteado con éxito"
def classify(self,images):
msg = False
if self.nn:
imgs = []
for i in images:
imgs.append(cv2.imread(i,cv2.IMREAD_GRAYSCALE).flatten())
msg = str(self.nn.classify_image(imgs))
return msg
def get_input_size(self):
return "Input size: "+ str(self.nn.input_size)
def get_output_size(self):
return "Output size: "+str(self.nn.output_size)
def get_hidden_layers(self):
return "Hidden layers(Ws): "+str(self.nn.hidden_layers_size)
def get_learning_rate(self):
return "Learning rate: "+str(self.nn.learning_rate)
def get_dropout(self):
return "Dropout: "+str(self.nn.dropout)
try:
hyper_parameters = tickle.load(name)
print("*** Cargo la config ***")
except:
hyper_parameters["input_size"] = x[0].shape[0]
hyper_parameters["output_size"] = 10
hyper_parameters["hidden_layers_size"] = eval(sys.argv[1]) #[32,16]
hyper_parameters["learning_rate"] = float(sys.argv[2])
hyper_parameters["batch_size"] = batch_size
hyper_parameters["dropout"] = float(sys.argv[3])
print("*** Creo la config ***")
network = NN()
network.config(hyper_parameters)
def plot_graphic(name, loss, eficiencia):
range_of = list(range(len(eficiencia)))
fig1 = plt.figure(figsize=(8, 8))
plt.subplots_adjust(hspace=0.4)
p1 = plt.subplot(2, 1, 1)
l1 = plt.plot(range_of, eficiencia, 'g-')
xl = plt.xlabel('Epoch n')
yl = plt.ylabel('Exactitud(%)')
grd = plt.grid(True)
p2 = plt.subplot(2, 1, 2)
ll2 = plt.plot(range_of, loss, 'c-')
