def maxnorm_o_regularizer(scale):
    """Max-norm output regularization removes the neurons of current layer.
    Returns a function that can be used to apply max-norm regularization to each column of weight matrix.
    The implementation follows `TensorFlow contrib <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/regularizers.py>`__.

    Parameters
    ----------
    scale : float
        A scalar multiplier `Tensor`. 0.0 disables the regularizer.

    Returns
    ---------
    A function with signature `mn_o(weights, name=None)` that apply Lo regularization.

    Raises
    ---------
    ValueError : If scale is outside of the range [0.0, 1.0] or if scale is not a float.

    """
    if isinstance(scale, numbers.Integral):
        raise ValueError('scale cannot be an integer: %s' % scale)

    if isinstance(scale, numbers.Real):
        if scale < 0.:
            raise ValueError('Setting a scale less than 0 on a regularizer: %g' % scale)
        # if scale >= 1.:
        #   raise ValueError('Setting a scale greater than 1 on a regularizer: %g' %
        #                    scale)
        if scale == 0.:
            tl.logging.info('Scale of 0 disables regularizer.')
            return lambda _, name=None: None

    def mn_o(weights, name='maxnorm_o_regularizer'):
        """Applies max-norm regularization to weights."""
        with tf.name_scope(name) as scope:
            my_scale = ops.convert_to_tensor(scale, dtype=weights.dtype.base_dtype, name='scale')
            if tf.__version__ <= '0.12':
                standard_ops_fn = standard_ops.mul
            else:
                standard_ops_fn = standard_ops.multiply
            return standard_ops_fn(
                my_scale, standard_ops.reduce_sum(standard_ops.reduce_max(standard_ops.abs(weights), 0)), name=scope
            )

    return mn_o
def maxnorm_o_regularizer(scale):
    """Max-norm output regularization removes the neurons of current layer.
    Returns a function that can be used to apply max-norm regularization to each column of weight matrix.
    The implementation follows `TensorFlow contrib <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/regularizers.py>`__.

    Parameters
    ----------
    scale : float
        A scalar multiplier `Tensor`. 0.0 disables the regularizer.

    Returns
    ---------
    A function with signature `mn_o(weights, name=None)` that apply Lo regularization.

    Raises
    ---------
    ValueError : If scale is outside of the range [0.0, 1.0] or if scale is not a float.

    """
    if isinstance(scale, numbers.Integral):
        raise ValueError('scale cannot be an integer: %s' % scale)

    if isinstance(scale, numbers.Real):
        if scale < 0.:
            raise ValueError('Setting a scale less than 0 on a regularizer: %g' % scale)
        # if scale >= 1.:
        #   raise ValueError('Setting a scale greater than 1 on a regularizer: %g' %
        #                    scale)
        if scale == 0.:
            tl.logging.info('Scale of 0 disables regularizer.')
            return lambda _, name=None: None

    def mn_o(weights, name='maxnorm_o_regularizer'):
        """Applies max-norm regularization to weights."""
        with tf.name_scope(name) as scope:
            my_scale = ops.convert_to_tensor(scale, dtype=weights.dtype.base_dtype, name='scale')
            if tf.__version__ <= '0.12':
                standard_ops_fn = standard_ops.mul
            else:
                standard_ops_fn = standard_ops.multiply
            return standard_ops_fn(
                my_scale, standard_ops.reduce_sum(standard_ops.reduce_max(standard_ops.abs(weights), 0)), name=scope
            )

    return mn_o
def maxnorm_o_regularizer ( scale ) : if isinstance ( scale , numbers . Integral ) : raise ValueError ( 'scale cannot be an integer: %s' % scale ) if isinstance ( scale , numbers . Real ) : if scale < 0. : raise ValueError ( 'Setting a scale less than 0 on a regularizer: %g' % scale ) # if scale >= 1.: #   raise ValueError('Setting a scale greater than 1 on a regularizer: %g' % #                    scale) if scale == 0. : tl . logging . info ( 'Scale of 0 disables regularizer.' ) return lambda _ , name = None : None def mn_o ( weights , name = 'maxnorm_o_regularizer' ) : """Applies max-norm regularization to weights.""" with tf . name_scope ( name ) as scope : my_scale = ops . convert_to_tensor ( scale , dtype = weights . dtype . base_dtype , name = 'scale' ) if tf . __version__ <= '0.12' : standard_ops_fn = standard_ops . mul else : standard_ops_fn = standard_ops . multiply return standard_ops_fn ( my_scale , standard_ops . reduce_sum ( standard_ops . reduce_max ( standard_ops . abs ( weights ) , 0 ) ) , name = scope ) return mn_o
Max-norm output regularization removes the neurons of current layer.
    Returns a function that can be used to apply max-norm regularization to each column of weight matrix.
    The implementation follows `TensorFlow contrib <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/regularizers.py>`__.

    Parameters
    ----------
    scale : float
        A scalar multiplier `Tensor`. 0.0 disables the regularizer.

    Returns
    ---------
    A function with signature `mn_o(weights, name=None)` that apply Lo regularization.

    Raises
    ---------
    ValueError : If scale is outside of the range [0.0, 1.0] or if scale is not a float.
Max - norm output regularization removes the neurons of current layer . Returns a function that can be used to apply max - norm regularization to each column of weight matrix . The implementation follows TensorFlow contrib <https : // github . com / tensorflow / tensorflow / blob / master / tensorflow / contrib / layers / python / layers / regularizers . py > __ .