def neuro_start(inputs, targets, training):
#initializes the neural network
network=neuro.setup_network(inputs)
#The number of repetitions that you will
#be training your network with
training_reps= training
#trains your neural network
neuro.train(network, inputs, targets, training_reps)
total = 0
correct = 0
for i in range(1,10):
for x in range(1,10):
pred = neuro.predict(network, [i+x])
#rounds the predicted value to either 0 or 1
#print '{} == pred {}'.format(i+x,pred)
pred = np.round(pred)
if (i+x)%2 == pred:
correct+= 1
total += 1
percent = (correct/total) * 100
#print 'with training: {}'.format(training_reps)
#print 'correct: {}\n total: {} \n {:.2f}%'.format(correct, total, (correct/total) * 100)
return training_reps, percent, correct, total
# inputs.pop()
# newT = []
# targets = open('myTest.csv', 'r')
# targets = targets.read()
# targets = targets.split('\n')
# if targets[-1] == '':
# targets.pop()
# for i in targets:
# newT.append(i.split(','))
# print(float(newT))
reps = 1000
network = []
netowrk = neuro.setup_network(inputs)
neuro.train(network, inputs, targets, reps)
neuro.writeNetworkToFile('myNetwork.net', network)
count = 0
myGrid = open('myNetwork.csv', 'w+')
def search(x, y):
if grid[x][y] == 2:
print('Solved... This is a Maze!')
return True
elif grid[x][y] == 1:
print('Wall! at %d, %d' % (x, y))
return False
elif grid[x][y] == 3:
print('Visiting %d, %d' % (x, y))
def main():
a, b, c, d = load_data_1()
m = neuro.create_model()
neuro.train(a, b, c, d, m)
neuro.ploting()