ijXFddlZddlmZddlmZddlmZGddeZy)N)ops)Callback) compile_utilsc8eZdZdZ dfd ZdZdZxZS)MonitorCallbackaBase class for callbacks that monitor a quantity and evaluates improvements. This class provides common functionality for callbacks that monitor a metric during training to determine whether a condition has been met, such as improvement over time. It encapsulates logic for selecting the comparison operation based on a `monitor` value and `mode`, and computing whether a new value is an improvement. It is intended to be subclassed by other callbacks like `ModelCheckpoint`, `EarlyStopping`, or `ReduceLROnPlateau`, and is not meant to be used directly. Arguments: monitor: Quantity to be monitored. Defaults to `"val_loss"`. mode: One of `{"auto", "min", "max"}`. In `min` mode, training will aim to minimize the monitored quantity; in `'max'` mode it will aim to maximize it.; in `"auto"` mode, the direction is automatically inferred from the name of the monitored quantity. Defaults to `"auto"`. baseline: Floating point initial "best" value of the metric to be monitored. If `None` (default), the first monitored value will be used. min_delta: Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement. Defaults to `0`. Raises: ValueError: If `mode='auto'` is selected and the direction of the metric cannot be inferred. ct||dvr3tj|jj d|ddd}||_||_||_t||_ d|_ y)N)autominmaxz mode 'z$' is unknown, fallback to auto mode.) stacklevelr ) super__init__warningswarn __class____name__monitormodebestabs min_delta monitor_op)selfrrbaselinerrs y/var/www/html/emotional.easysim.app/public_html/venv/lib/python3.12/site-packages/keras/src/callbacks/monitor_callback.pyrzMonitorCallback.__init__)sv  - - MM>>**+74&9    D   Yc|jdk(rtj|_nm|jdk(rtj|_nG|j j d}|dk(rtj|_t|jdrg}|jjD]H}t|tjtjfs.|j|jJ|D]Y}|j|k(st|ds |j dk(rtj|_Etj|_[|j/t#|j$j&d|j d |jtjk(r|xj(d zc_yy) Nr r val_lossmetrics _directionupz callback received monitor=z, but Keras isn't able to automatically determine whether that metric should be maximized or minimized. Pass `mode='max'` in order to monitor based on the highest metric value, or pass `mode='min'` in order to use the lowest value.)rrlessrgreaterr removeprefixhasattrmodelr! isinstancerCompileMetrics MetricsListextendnamer" ValueErrorrrr)r metric_name all_metricsms r_set_monitor_opzMonitorCallback._set_monitor_op>sj 99 !hhDO YY% !kkDO,,33F;Kf$"%((tzz9- ++6A!)88)55$**19956%;Avv,"1l3 ||t325++25(( ;& ~~../0#||n-EE ??chh & NNb N 'rcF|y|j||jz |S)NT)rr)r monitor_valuereference_values r_is_improvementzMonitorCallback._is_improvementes%  "}t~~=OOr)val_lossr Nr)r __module__ __qualname____doc__rr3r7 __classcell__)rs@rrrs(D  *%!NPrr)r keras.srcrkeras.src.callbacks.callbackrkeras.src.trainersrrrrrAs!1,`Ph`Pr