def main():
train_X = np.asarray([[0.2, -0.3]])
train_Y = np.asarray([[0.0, 1.0, 0.0]])
net = AtomicNetwork(cost="mse", atomic_input=AtomicInput(2))
net.append(Atomic(2, 5, activation="sigmoid"))
net.append(Atomic(5, 3, activation="sigmoid"))
print(net.predict(train_X))
print(net.cost(train_X, train_Y))
print(net.cost_gradient(train_X, train_Y))
def main():
train_X = np.asarray([[0.2, -0.3]])
train_Y = np.asarray([[0.0, 1.0, 0.0]])
net = AtomicNetwork(cost="mse", atomic_input=AtomicInput(2))
net.append(Atomic(2, 5, activation="sigmoid"))
net.append(Atomic(5, 3, activation="sigmoid"))
print(net.predict(train_X))
print(net.cost(train_X, train_Y))
print(net.cost_gradient(train_X, train_Y))
net.fit(train_X, train_Y, AtomicSGD(mini_batch_size=1, n_epochs=10))
