optimizer.py 文件源码

def apply_update(self, weights, gradient):
        """Update the running averages of gradients and weight updates,
            and compute the Adadelta update for this step."""
        if self.running_g2 is None:
            self.running_g2 = [ np.zeros_like(g) for g in gradient ]
        if self.running_dx2 is None:
            self.running_dx2 = [ np.zeros_like(g) for g in gradient ]

        self.running_g2 = self.running_average_square( self.running_g2, gradient )
        new_weights = []
        updates = []
        for w, g, g2, dx2 in zip(weights, gradient, self.running_g2, self.running_dx2):
            update = np.multiply( np.divide( self.sqrt_plus_epsilon(dx2), self.sqrt_plus_epsilon(g2) ), g )
            new_weights.append( np.subtract( w, update ) )
            updates.append(update)
        self.running_dx2 = self.running_average_square( self.running_dx2, updates )
        return new_weights