a = np.array(self.validation_cost)[-n:]
signs = np.sign(a[1:] - a[:-1])
b = -sum(signs) == (n - 1) # b = True >>> going down continuously
if b and len(self.validation_cost) > n:
current_learning_rate = current_learning_rate * 1.05
return current_learning_rate
def _check_early_stopping(self):
n = 5 # consider the last n values
validation_cost_sequence = self.validation_cost
if len(validation_cost_sequence) <= n: return None
a = np.array(validation_cost_sequence)[-n:]
signs = np.sign(a[1:] - a[:-1])
b = sum(signs) == (n - 1) # b = True >>> going up
if b:
ix = validation_cost_sequence.index(
min(validation_cost_sequence)) # index of the best model
return ix
return b
#======= end of BatchGradientDescentEarlyStopping =============================================
md = BatchGradientDescentEarlyStopping(random_state=None, verbose=True)
md.fit(X, y)
probs = md.precict_probabilities(X)
y = md.predict(X)
acuracy = md.accuracy(X, y)
print("accuracy on the whole data set =", accuracy)
