2f1f42eeb68c64ff991c0ae2e8253a9305e90f74,autokeras/hypermodel/processor.py,Normalize,fit,#Normalize#Any#Any#,82

Before Change

    def fit(self, hp, data):
        axis = tuple(range(len(data.shape) - 1))
        self.mean = np.mean(data,
                            axis=axis,
                            keepdims=True).flatten()
        self.std = np.std(data,
                          axis=axis,
                          keepdims=True).flatten()

After Change

        self.std = None

    def fit(self, hp, data):
        shape = utils.dataset_shape(data)
        axis = tuple(range(len(shape) - 1))

        def sum_up(old_state, new_elem):
            return old_state + new_elem

        def sum_up_square(old_state, new_elem):
            return old_state + tf.square(new_elem)

        num_instance = data.reduce(np.float64(0), lambda x, _: x + 1)
        total_sum = data.reduce(np.float64(0), sum_up) / num_instance
        self.mean = tf.reduce_mean(total_sum, axis=axis)

        total_sum_square = data.reduce(np.float64(0), sum_up_square) / num_instance
        square_mean = tf.reduce_mean(total_sum_square, axis=axis)
        self.std = tf.sqrt(square_mean - tf.square(self.mean))

    def transform(self, hp, data):
         Transform the test data, perform normalization.

Instances