class Perceptron(Neuron):
def __init__(self, learning_rate: float, X: np.array, Y: np.array,
func: str):
super().__init__(learning_rate, X, Y)
self._activer = Activation(func)
def run(self, steps=1001, show=100):
for step in range(steps):
cost = 0
for x_n, y_n in zip(self._data, self._label):
y_pred = self._predict(x_n)
y_pred = self._activer.chooser(y_pred)
diff = y_n - int(y_pred)
self._update_w_online(x_n, diff)
self._update_bias(diff)
cost += diff**2
if step % show == 0:
print('step {0}: {1}'.format(step, cost))
self.print_status(step)
class Sigmoid(Neuron):
def __init__(self, learning_rate: float, X: np.array, Y: np.array,
func: str):
super().__init__(learning_rate, X, Y)
self._activer = Activation(func)
def run(self, steps=1001, show=100):
for step in range(steps):
z = np.dot(self._data, self._w.T) + self._bias
y_pred = self._activer.chooser(z)
error = self._label - y_pred
self._update_w_offline(self._data, error.T)
self._update_bias(error.sum())
if step % show == 0:
cost = np.mean(-self._label * np.log(y_pred) -
(1 - self._label) * np.log(1 - y_pred))
print('step {0}: {1}'.format(step, cost))
self.print_status(step)
