o 2h@sHddlZddlmZddlmZddlmZedGdddeZdS)N) keras_export)MonitorCallback)io_utilszkeras.callbacks.EarlyStoppingcsXeZdZdZ        dfdd Zdd d Zdd d Zdd dZddZZ 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 val_lossrautoNFc sFtj|||d||_||_||_d|_d|_||_d|_||_ dS)N) min_deltar) super__init__patienceverbosebaselinewait stopped_epochrestore_best_weights best_weightsstart_from_epoch) selfmonitorrr r moder rr __class__]/var/www/html/chatgem/venv/lib/python3.10/site-packages/keras/src/callbacks/early_stopping.pyr Bs  zEarlyStopping.__init__cCsd|_d|_d|_d|_dS)Nr)rrr best_epochrlogsrrron_train_beginWs zEarlyStopping.on_train_begincCs|jdur |||}|dus||jkrdS|jr*|jdur*|j|_||_|j d7_ | ||j rX||_ ||_|jrG|j|_|j dusS| ||j rVd|_ dS|j |j krk|dkrm||_d|j_dSdSdS)NrT) monitor_op_set_monitor_opget_monitor_valuerrrmodel get_weightsrr_is_improvementbestr r r stop_training)repochrcurrentrrr on_epoch_end^s.     zEarlyStopping.on_epoch_endcCsx|jdkr|jdkrtd|jdd|jr8|jdur:|jdkr/td|jdd|j|jdSdSdS)NrzEpoch rz: early stoppingz8Restoring model weights from the end of the best epoch: .) rr r print_msgrrrr" set_weightsrrrr on_train_ends zEarlyStopping.on_train_endc CsJ|pi}||j}|dur#tjd|jddt|dd|S)Nz&Early stopping conditioned on metric `z1` which is not available. Available metrics are: ,) stacklevel)getrwarningswarnjoinlistkeys)rr monitor_valuerrrr!s  zEarlyStopping.get_monitor_value)rrrrrNFr)N) __name__ __module__ __qualname____doc__r rr)r-r! __classcell__rrrrrs:   "r)r2keras.src.api_exportr$keras.src.callbacks.monitor_callbackrkeras.src.utilsrrrrrrs