()
return layout
Create the necessary ops to include autosummaries in TensorBoard report.
    Note: This should be done only once per graph.
Create the necessary ops to include autosummaries in TensorBoard report.
    Note: This should be done only once per graph.
Create the necessary ops to include autosummaries in TensorBoard report . Note : This should be done only once per graph .
def finalize_autosummaries() -> None:
    """Create the necessary ops to include autosummaries in TensorBoard report.
    Note: This should be done only once per graph.
    """
    global _finalized
    tfutil.assert_tf_initialized()

    if _finalized:
        return None

    _finalized = True
    tfutil.init_uninitialized_vars([var for vars_list in _vars.values() for var in vars_list])

    # Create summary ops.
    with tf.device(None), tf.control_dependencies(None):
        for name, vars_list in _vars.items():
            name_id = name.replace("/", "_")
            with tfutil.absolute_name_scope("Autosummary/" + name_id):
                moments = tf.add_n(vars_list)
                moments /= moments[0]
                with tf.control_dependencies([moments]):  # read before resetting
                    reset_ops = [tf.assign(var, tf.zeros(3, dtype=_dtype)) for var in vars_list]
                    with tf.name_scope(None), tf.control_dependencies(reset_ops):  # reset before reporting
                        mean = moments[1]
                        std = tf.sqrt(moments[2] - tf.square(moments[1]))
                        tf.summary.scalar(name, mean)
                        if enable_custom_scalars:
                            tf.summary.scalar("xCustomScalars/" + name + "/margin_lo", mean - std)
                            tf.summary.scalar("xCustomScalars/" + name + "/margin_hi", mean + std)

    # Setup layout for custom scalars.
    layout = None
    if enable_custom_scalars:
        cat_dict = OrderedDict()
        for series_name in sorted(_vars.keys()):
            p = series_name.split("/")
            cat = p[0] if len(p) >= 2 else ""
            chart = "/".join(p[1:-1]) if len(p) >= 3 else p[-1]
            if cat not in cat_dict:
                cat_dict[cat] = OrderedDict()
            if chart not in cat_dict[cat]:
                cat_dict[cat][chart] = []
            cat_dict[cat][chart].append(series_name)
        categories = []
        for cat_name, chart_dict in cat_dict.items():
            charts = []
            for chart_name, series_names in chart_dict.items():
                series = []
                for series_name in series_names:
                    series.append(layout_pb2.MarginChartContent.Series(
                        value=series_name,
                        lower="xCustomScalars/" + series_name + "/margin_lo",
                        upper="xCustomScalars/" + series_name + "/margin_hi"))
                margin = layout_pb2.MarginChartContent(series=series)
                charts.append(layout_pb2.Chart(title=chart_name, margin=margin))
            categories.append(layout_pb2.Category(title=cat_name, chart=charts))
        layout = summary_lib.custom_scalar_pb(layout_pb2.Layout(category=categories))
    return layout
def finalize_autosummaries ( ) -> None : global _finalized tfutil . assert_tf_initialized ( ) if _finalized : return None _finalized = True tfutil . init_uninitialized_vars ( [ var for vars_list in _vars . values ( ) for var in vars_list ] ) # Create summary ops. with tf . device ( None ) , tf . control_dependencies ( None ) : for name , vars_list in _vars . items ( ) : name_id = name . replace ( "/" , "_" ) with tfutil . absolute_name_scope ( "Autosummary/" + name_id ) : moments = tf . add_n ( vars_list ) moments /= moments [ 0 ] with tf . control_dependencies ( [ moments ] ) : # read before resetting reset_ops = [ tf . assign ( var , tf . zeros ( 3 , dtype = _dtype ) ) for var in vars_list ] with tf . name_scope ( None ) , tf . control_dependencies ( reset_ops ) : # reset before reporting mean = moments [ 1 ] std = tf . sqrt ( moments [ 2 ] - tf . square ( moments [ 1 ] ) ) tf . summary . scalar ( name , mean ) if enable_custom_scalars : tf . summary . scalar ( "xCustomScalars/" + name + "/margin_lo" , mean - std ) tf . summary . scalar ( "xCustomScalars/" + name + "/margin_hi" , mean + std ) # Setup layout for custom scalars. layout = None if enable_custom_scalars : cat_dict = OrderedDict ( ) for series_name in sorted ( _vars . keys ( ) ) : p = series_name . split ( "/" ) cat = p [ 0 ] if len ( p ) >= 2 else "" chart = "/" . join ( p [ 1 : - 1 ] ) if len ( p ) >= 3 else p [ - 1 ] if cat not in cat_dict : cat_dict [ cat ] = OrderedDict ( ) if chart not in cat_dict [ cat ] : cat_dict [ cat ] [ chart ] = [ ] cat_dict [ cat ] [ chart ] . append ( series_name ) categories = [ ] for cat_name , chart_dict in cat_dict . items ( ) : charts = [ ] for chart_name , series_names in chart_dict . items ( ) : series = [ ] for series_name in series_names : series . append ( layout_pb2 . MarginChartContent . Series ( value = series_name , lower = "xCustomScalars/" + series_name + "/margin_lo" , upper = "xCustomScalars/" + series_name + "/margin_hi" ) ) margin = layout_pb2 . MarginChartContent ( series = series ) charts . append ( layout_pb2 . Chart ( title = chart_name , margin = margin ) ) categories . append ( layout_pb2 . Category ( title = cat_name , chart = charts ) ) layout = summary_lib . custom_scalar_pb ( layout_pb2 . Layout ( category = categories ) ) return layout