(optimization, var_list, bound_list)
return new_list
Add weight constraints to an optimization step.

    Parameters
    ----------
    optimization : tf.Tensor
        The optimization routine that updates the parameters.
    var_list : list
        A list of variables that should be bounded.
    bound_list : list
        A list of bounds (lower, upper) for each variable in var_list.

    Returns
    -------
    assign_operations : list
        A list of assign operations that correspond to one step of the
        constrained optimization.
Add weight constraints to an optimization step.
Add weight constraints to an optimization step .
def add_weight_constraint(optimization, var_list, bound_list):
    """Add weight constraints to an optimization step.

    Parameters
    ----------
    optimization : tf.Tensor
        The optimization routine that updates the parameters.
    var_list : list
        A list of variables that should be bounded.
    bound_list : list
        A list of bounds (lower, upper) for each variable in var_list.

    Returns
    -------
    assign_operations : list
        A list of assign operations that correspond to one step of the
        constrained optimization.
    """
    with tf.control_dependencies([optimization]):
        new_list = []
        for var, bound in zip(var_list, bound_list):
            clipped_var = tf.clip_by_value(var, bound[0], bound[1])
            assign = tf.assign(var, clipped_var)
            new_list.append(assign)
    return new_list
def add_weight_constraint ( optimization , var_list , bound_list ) : with tf . control_dependencies ( [ optimization ] ) : new_list = [ ] for var , bound in zip ( var_list , bound_list ) : clipped_var = tf . clip_by_value ( var , bound [ 0 ] , bound [ 1 ] ) assign = tf . assign ( var , clipped_var ) new_list . append ( assign ) return new_list