def test(self, x_test, y_test):
print('\nTesting...')
acc = 0
xs, ys = Matrix.shuffle(x_test, y_test)
for i in range(len(xs)):
guess = self.predict(xs[i])
if Matrix.arg_max(guess) == Matrix.arg_max(ys[i]):
acc += 1
acc /= len(xs)
print('Done testing! accuracy: {:.5f}%'.format(acc * 100))
return acc
def main():
nn = NeuralNetwork()
nn.set_input_nodes(2)
nn.add_layer(4, 'sigmoid')
nn.add_layer(4, 'sigmoid')
nn.add_layer(2, 'sigmoid')
nn.set_lr(0.1)
x_train = []
y_train = []
for i in range(1000):
x_train.append([])
x_train[i].append(round(random.random()))
x_train[i].append(round(random.random()))
y_train.append(get_label(x_train[i]))
print(len(x_train), len(x_train[0]))
nn.train(x_train, y_train, 3)
x_test = []
y_test = []
for i in range(1000):
x_test.append([])
x_test[i].append(round(random.random()))
x_test[i].append(round(random.random()))
y_test.append(get_label(x_test[i]))
nn.test(x_test, y_test)
print()
print('0 0 = {}'.format(Matrix.arg_max(nn.predict([0, 0]))))
print('0 1 = {}'.format(Matrix.arg_max(nn.predict([0, 1]))))
print('1 0 = {}'.format(Matrix.arg_max(nn.predict([1, 0]))))
print('1 1 = {}'.format(Matrix.arg_max(nn.predict([1, 1]))))
# nn.save('XOR.pickle')
# model = NeuralNetwork.load('XOR.pickle')
nn.save('model.json', format=True)
model = NeuralNetwork.load('model.json')
model.train(x_train, y_train, 3)
print()
print('0 0 = {}'.format(Matrix.arg_max(model.predict([0, 0]))))
print('0 1 = {}'.format(Matrix.arg_max(model.predict([0, 1]))))
print('1 0 = {}'.format(Matrix.arg_max(model.predict([1, 0]))))
print('1 1 = {}'.format(Matrix.arg_max(model.predict([1, 1]))))
