test_optimizer.py 文件源码

def __call__(self, input_data, weights):
        '''
        input_data in this case is a numpy array with batch_size on axis 1
        and weights is a matrix with 1 column
        '''
        if self.state is None:
            self.state = np.zeros_like(weights)

        gradient = - input_data.mean(axis=1)

        self.state[:] = self.state + np.square(gradient)

        weights[:] = weights \
            - gradient * self.learning_rate / (np.sqrt(self.state + self.epsilon))

        return weights