I thought both, PReLU and Leaky ReLU are
$$f(x) = \max(x, \alpha x) \qquad \text{ with } \alpha \in (0, 1)$$
Keras, however, has both functions in the docs.
Leaky ReLU
Source of LeakyReLU:
return K.relu(inputs, alpha=self.alpha)

Hence (see relu code)
$$f_1(x) = \max(0, x) - \alpha \max(0, -x)$$
PReLU
Source of PReLU:
def call(self, inputs, mask=None):
    pos = K.relu(inputs)
    if K.backend() == 'theano':
        neg = (K.pattern_broadcast(self.alpha, self.param_broadcast) *
               (inputs - K.abs(inputs)) * 0.5)
    else:
        neg = -self.alpha * K.relu(-inputs)
    return pos + neg

Hence
$$f_2(x) = \max(0, x) - \alpha \max(0, -x)$$
Question
Did I get something wrong? Aren't $f_1$ and $f_2$ equivalent to $f$ (assuming $\alpha \in (0, 1)$?)

Straight from wikipedia:




Leaky ReLUs allow a small, non-zero gradient when the unit is not active.
Parametric ReLUs take this idea further by making the coefficient of leakage into a parameter that is learned along with the other neural network parameters.

