ij(\ddlZddlmZddlmZddlmZedGddeZy)N) keras_export)MonitorCallback)io_utilszkeras.callbacks.EarlyStoppingcReZdZdZ dfd ZddZddZddZdZxZ S) EarlyStoppingax Stop training when a monitored metric has stopped improving. Assuming the goal of a training is to minimize the loss. With this, the metric to be monitored would be `'loss'`, and mode would be `'min'`. A `model.fit()` training loop will check at end of every epoch whether the loss is no longer decreasing, considering the `min_delta` and `patience` if applicable. Once it's found no longer decreasing, `model.stop_training` is marked True and the training terminates. The quantity to be monitored needs to be available in `logs` dict. To make it so, pass the loss or metrics at `model.compile()`. Args: monitor: Quantity to be monitored. Defaults to `"val_loss"`. 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`. patience: Number of epochs with no improvement after which training will be stopped. Defaults to `0`. verbose: Verbosity mode, 0 or 1. Mode 0 is silent, and mode 1 displays messages when the callback takes an action. Defaults to `0`. mode: One of `{"auto", "min", "max"}`. In `min` mode, training will stop when the quantity monitored has stopped decreasing; in `"max"` mode it will stop when the quantity monitored has stopped increasing; in `"auto"` mode, the direction is automatically inferred from the name of the monitored quantity. Defaults to `"auto"`. baseline: Baseline value for the monitored quantity. If not `None`, training will stop if the model doesn't show improvement over the baseline. Defaults to `None`. restore_best_weights: Whether to restore model weights from the epoch with the best value of the monitored quantity. If `False`, the model weights obtained at the last step of training are used. An epoch will be restored regardless of the performance relative to the `baseline`. If no epoch improves on `baseline`, training will run for `patience` epochs and restore weights from the best epoch in that set. Defaults to `False`. start_from_epoch: Number of epochs to wait before starting to monitor improvement. This allows for a warm-up period in which no improvement is expected and thus training will not be stopped. Defaults to `0`. Example: >>> callback = keras.callbacks.EarlyStopping(monitor='loss', ... patience=3) >>> # This callback will stop the training when there is no improvement in >>> # the loss for three consecutive epochs. >>> model = keras.models.Sequential([keras.layers.Dense(10)]) >>> model.compile(keras.optimizers.SGD(), loss='mse') >>> history = model.fit(np.arange(100).reshape(5, 20), np.zeros(5), ... epochs=10, batch_size=1, callbacks=[callback], ... verbose=0) >>> len(history.history['loss']) # Only 4 epochs are run. 4 c t ||||||_||_||_d|_d|_||_d|_||_ y)N) min_deltar) super__init__patienceverbosebaselinewait stopped_epochrestore_best_weights best_weightsstart_from_epoch) selfmonitorr r r moderrr __class__s w/var/www/html/emotional.easysim.app/public_html/venv/lib/python3.12/site-packages/keras/src/callbacks/early_stopping.pyr zEarlyStopping.__init__BsU $)<      $8!  0cJd|_d|_d|_d|_d|_y)Nr)rrbestr best_epochrlogss ron_train_beginzEarlyStopping.on_train_beginWs'   rc|j|j|j|}|||jkry|jr2|j &|j j|_||_|xjdz c_ |j||jri||_ ||_|jr|j j|_|j|j||jrd|_ y|j|jk\r|dkDr||_d|j _yyy)NrT) monitor_op_set_monitor_opget_monitor_valuerrrmodel get_weightsrr_is_improvementrrr r stop_training)repochrcurrents r on_epoch_endzEarlyStopping.on_epoch_end_s ?? "  "((. ?ed&;&;;   $ $):):)B!% 6 6 8D #DO Q    3DI#DO(($(JJ$:$:$<!}}$(<(<)  99 %%!)!&D '+DJJ $+4 %rct|jdkDr5|jdkDr&tjd|jdzd|jrh|j [|jdkDr&tjd|j dzd|jj|j yyy)NrzEpoch r!z: early stoppingz8Restoring model weights from the end of the best epoch: .) rr r print_msgrrrr% set_weightsrs r on_train_endzEarlyStopping.on_train_ends    !dllQ&6   ++a/00@A   $ $):):)F||a""2*+1. JJ " "4#4#4 5*G $rc |xsi}|j|j}|Mtjd|jddj t |j d|S)Nz&Early stopping conditioned on metric `z1` which is not available. Available metrics are: ,) stacklevel)getrwarningswarnjoinlistkeys)rr monitor_values rr$zEarlyStopping.get_monitor_valuesmzr.  MMrGs5-@$-.ROR/Rr