Dropout forward and backward

dropout.py

def forward_pass(self, a_prev: np.array, training: bool) -> np.array:
    if training:
        self._mask = (np.random.rand(*a_prev.shape) < self._keep_prob)
        return self._apply_mask(a_prev, self._mask)
    else:
        return a_prev

def backward_pass(self, da_curr: np.array) -> np.array:
    return self._apply_mask(da_curr, self._mask)