def create_training_split(x, y, num_hidden_neurons, lr, epochs):
shuffled = np.hstack((x, y))
np.random.shuffle(shuffled)
h_index = int(shuffled.shape[0] * .8)
x_training_split = shuffled[:h_index, :-1]
y_training_split = shuffled[:h_index, -1:]
x_test_split = shuffled[h_index:, :-1]
y_test_split = shuffled[h_index:, -1:]
nn = NeuralNetwork(x_training_split, y_training_split, num_hidden_neurons)
nn.train(epochs)
print("Training accuracy, precision:", nn.accuracy_precision())
nn.test_feedforward(x_test_split, y_test_split)
print("Testing accuracy, precision:", nn.accuracy_precision())
def test_or_nn(verbose=0):
x, y = create_or_nn_data()
nn = NeuralNetwork(x, y, 4, 1)
nn.feedforward()
if verbose > 0:
print("OR 1 " + str(nn.loss()))
print("NN output " + str(nn._output))
print(nn.accuracy_precision())
assert nn.loss() < .04
def test_ttt_nn(verbose=0):
x, y = load_tictactoe_csv("tic-tac-toe-1.csv")
nn = NeuralNetwork(x, y, 4, .1)
nn.load_4_layer_ttt_network()
nn.feedforward()
if verbose > 0:
print("NN 1 " + str(nn.loss()))
print("NN output " + str(nn._output))
print(nn.accuracy_precision())
assert nn.loss() < .02
def test_nn_6():
x, y = load_tictactoe_csv("tic-tac-toeFull.csv")
nn = NeuralNetwork(x, y, 20, .002)
nn.train(100000)
# print("test 12 loss:", nn.loss())
print("test 12 comparison:")
print("num hidden neurons:", 20)
print("num epochs:", 100000)
print("learning rate:", .002)
create_training_split(x, y, 30, .02, 100000)
print("100/0 split accuracy, precision:", nn.accuracy_precision())
print("-------")
assert nn.loss() < .5
