Method forward_pass

mla/neuralnet/layers/basic.py:117–125

(self, X)

        self._mask = None

    def forward_pass(self, X):
        assert self.p > 0
        if self.is_training:
            self._mask = np.random.uniform(size=X.shape) > self.p
            y = X * self._mask
        else:
            y = X * (1.0 - self.p)

        return y

    def backward_pass(self, delta):
        return delta * self._mask