import os
os.chdir('..')
xys = []
nums = [0, 1, 3, 2, 6, 14, 10, 11, 9, 8, 12, 4, 5, 7, 15, 13]
num2 = [0, 1, 5, 4, 12, 8, 9, 13, 15, 11, 10, 14, 6, 7, 3, 2]
num3 = [0, 4, 12, 8, 9, 13, 5, 1, 3, 7, 15, 11, 10, 14, 6, 2]
num4 = [0, 8, 10, 2, 3, 7, 6, 4, 5, 1, 9, 13, 12, 14, 15, 11]
for i in range(16):
x = np.binary_repr(i, 4)
x = [int(j) for j in x]
y = np.binary_repr(num4[i], 4)
y = [int(j) for j in y]
xys.append(
(np.array(x).astype(np.float32), np.array(y).astype(np.float32)))
nn = NeuralNet(Layer(4, activation_type=TANH),
Layer(64, activation_type=SIGMOID), Output(4))
nn.predict_values(xys)
fun = lambda: nn.train(xys,
batch_size=len(xys),
num_epochs=100,
print_every=100,
predict_after_every_batch=False)
fun()
print("**************FINAL PREDICTION***************************")
nn.predict_values(xys)
