print("")
status()
calculate_confusion_matrix(m, k)
plt.imshow(m, cmap='gray', interpolation='none')
plt.draw()
plt.pause(1)
plt.savefig(str(k) + ".png")
with open("treinamento.txt", "r") as filestream:
for l in filestream:
c = l.split(",")
for i in range(0, instances):
c[i] = float(c[i])
#c[-1] = float(c[-1].strip('\n'))/1000
c[-1] = dict[c[-1].strip('\n').upper()]
data.append(c)
for k in range(epoch):
for line in data:
p = net.propagation(line[:-1])
net.back_propagation(line[-1])
#print("-- Interaction: ", j, " Expected - ",line[-1], " - ", p, "\t", end="")
#for t in range(0, classes):
# print(net.output_layer.z[t],"\t", end="")
#print("")
j += 1
if k % 100 == 0: test(k)
if k % 100 == 0: print("-- Interaction: ", k)
#input()
