b9927f30cb9c92040c30d0fe8c735d8baea167b2,tensornets/layers.py,,gconvbn,#,59

Before Change

        g = f // c
        x = tf.reshape(x, tf.concat([tf.shape(x)[:-1],
                                     tf.constant([g, c, c])], axis=0))
        x = tf.reduce_sum(x, axis=-2)
        x = reshape(x, tf.concat([tf.shape(x)[:-2],
                                  tf.constant([f])], axis=0), name="gconv")
        return batch_norm(x)


def sconvbn(*args, **kwargs):

After Change

        c = args[-1]
        f = x.shape[-1].value // c
        g = f // c
        kernel = np.zeros((1, 1, f * c, f), np.float32)
        for i in range(f):
            start = (i // c) * c * c + i % c
            end = start + c * c
            kernel[:, :, start:end:c, i] = 1.

Instances