cnn1 = sequential()
cnn1.add(cnn1.cnn(num_filters=10,filter_size = 3,input_shape = (28,28)))
cnn1.add(cnn1.max_pool(num_filters=8,filter_size=2))
cnn1.add(cnn1.dense(30))
cnn1.add(cnn1.dense(10))
for i in range(200):
  for x,y in zip(data,lables):
    cnn1.backProp(input_= x,lable= y,learn_rate=0.1)

count = 0
trueLables = 0
for y in range(1000):
    if np.argmax(cnn1.forawrdProp(data[y,:,:])) == lables[y]:
        trueLables = trueLables + 1
    count = count + 1
print("Accuracy is " + str(100*trueLables/count))