SkalskiP

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)

SkalskiP

def forward_pass(self, a_prev: np.array) -> np.array:
    self._shape = a_prev.shape
    return np.ravel(a_prev).reshape(a_prev.shape[0], -1)

def backward_pass(self, da_curr: np.array) -> np.array:
    return da_curr.reshape(self._shape)

SkalskiP

def forward_pass(self, a_prev: np.array) -> np.array:
    self._z = np.maximum(0, a_prev)
    return self._z

def backward_pass(self, da_curr: np.array) -> np.array:
    dz = np.array(da_curr, copy=True)
    dz[self._z <= 0] = 0
    return dz

SkalskiP

fmpeg -i source-video.mp4 -r 1 -an -f image2 "frames/frame-%05d.jpg"