Callbacks provide a flexible way to customize the behavior of your NeuralNet training without the need to write subclasses.

You will often find callbacks writing to or reading from the history attribute. Therefore, if you would like to log the net’s behavior or do something based on the past behavior, consider using net.history.

This page will not explain all existing callbacks. For that, please look at skorch.callbacks.

Callback base class

The base class for each callback is Callback. If you would like to write your own callbacks, you should inherit from this class. A guide and practical example on how to write your own callbacks is shown in this notebook. In general, remember this:

  • They should inherit from the base class.

  • They should implement at least one of the on_ methods provided by the parent class (see below).

  • As argument, the methods first get the NeuralNet instance, and, where appropriate, the local data (e.g. the data from the current batch). The method should also have **kwargs in the signature for potentially unused arguments.

Callback methods to override

The following methods could potentially be overriden when implementing your own callbacks.


If you have attributes that should be reset when the model is re-initialized, those attributes should be set in this method.

on_train_begin(net, X, y)

Called once at the start of the training process (e.g. when calling fit).

on_train_end(net, X, y)

Called once at the end of the training process.

on_epoch_begin(net, dataset_train, dataset_valid)

Called once at the start of the epoch, i.e. possibly several times per fit call. Gets training and validation data as additional input.

on_epoch_end(net, dataset_train, dataset_valid)

Called once at the end of the epoch, i.e. possibly several times per fit call. Gets training and validation data as additional input.

on_batch_begin(net, batch, training)

Called once before each batch of data is processed, i.e. possibly several times per epoch. Gets batch data as additional input. Also includes a bool indicating if this is a training batch or not.

on_batch_end(net, batch, training, loss, y_pred)

Called once after each batch of data is processed, i.e. possibly several times per epoch. Gets batch data as additional input.

on_grad_computed(net, named_parameters, Xi, yi)

Called once per batch after gradients have been computed but before an update step was performed. Gets the module parameters as additional input as well as the batch data. Useful if you want to tinker with gradients.

Setting callback parameters

You can set specific callback parameters using the ususal set_params interface on the network by using the callbacks__ prefix and the callback’s name. For example to change the name of the accuracy of the validation set shown during training, you would do:

net = NeuralNetClassifier(...)
net.set_params(callbacks__valid_acc__name="accuracy of valid set")

Changes will be applied on initialization and callbacks that are changed using set_params will be re-initialized.

The name you use to address the callback can be chosen during initialization of the network and defaults to the class name. If there is a conflict, the conflicting names will be made unique by appending a count suffix starting at 1, e.g. EpochScoring_1, EpochScoring_2, etc.

Deactivating callbacks

If you would like to (temporarily) deactivate a callback, you can do so by setting its parameter to None. E.g., if you have a callback called ‘my_callback’, you can deactivate it like this:

net = NeuralNet(
        callbacks=[('my_callback', MyCallback())],
# now deactivate 'my_callback':

This also works with default callbacks.

Deactivating callbacks can be especially useful when you do a parameter search (say with sklearn GridSearchCV). If, for instance, you use a callback for learning rate scheduling (e.g. via LRScheduler) and want to test its usefulness, you can compare the performance once with and once without the callback.

To completely disable all callbacks, including default callbacks, set callbacks="disable".


skorch provides two callbacks that calculate scores by default, EpochScoring and BatchScoring. They work basically in the same way, except that EpochScoring calculates scores after each epoch and BatchScoring after each batch. Use the former if averaging of batch-wise scores is imprecise (say for AUC score) and the latter if you are very tight for memory.

In general, these scoring callbacks are useful when the default scores determined by the NeuralNet are not enough. They allow you to easily add new metrics to be logged during training. For an example of how to add a new score to your model, look at this notebook.

The first argument to both callbacks is name and should be a string. This determines the column name of the score shown by the PrintLog after each epoch.

Next comes the scoring parameter. For eager sklearn users, this should be familiar, since it works exactly the same as in sklearn GridSearchCV, RandomizedSearchCV, cross_val_score(), etc. For those who are unfamiliar, here is a short explanation:

  • If you pass a string, sklearn makes a look-up for a score with that name. Examples would be 'f1' and 'roc_auc'.

  • If you pass None, the model’s score method is used. By default, NeuralNet doesn’t provide a score method, but you can easily implement your own by subclassing it. If you do, it should take X and y (the target) as input and return a scalar as output. NeuralNetClassifier and NeuralNetRegressor have the same score methods as normal sklearn classifiers and regressors.

  • Finally, you can pass a function/callable. In that case, this function should have the signature func(net, X, y) and return a scalar.

More on sklearn's model evaluation can be found in this notebook.

The lower_is_better parameter determines whether lower scores should be considered as better (e.g. log loss) or worse (e.g. accuracy). This information is used to write a <name>_best value to the net’s history. E.g., if your score is f1 score and is called 'f1', you should set lower_is_better=False. The history will then contain an entry for 'f1', which is the score itself, and an entry for 'f1_best', which says whether this is the as of yet best f1 score.

on_train is a bool that is used to indicate whether training or validation data should be used to determine the score. By default, it is set to validation.

Finally, you may have to provide your own target_extractor. This should be a function or callable that is applied to the target before it is passed to the scoring function. The main reason why we need this is that sometimes, the target is not of a form expected by sklearn and we need to process it before passing it on.

On top of the two described scoring callbacks, skorch also provides PassthroughScoring. This callback does not actually calculate any new scores. Instead it uses an existing score that is calculated for each batch (the train loss, for example) and determines the average of this score, which is then written to the epoch level of the net’s history. This is very useful if the score was already calculated and logged on the batch level and you’re interested to see the averaged score on the epoch level.

For this callback, you only need to provide the name of the score in the history. Moreover, you may again specify if lower_is_better and if the score should be calculated on_train or not.


Both BatchScoring and PassthroughScoring honor the batch size when calculating the average. This can make a difference when not all batch sizes are equal, which is typically the case because the last batch of an epoch contains fewer samples than the rest.


The Checkpoint callback creates a checkpoint of your model after each epoch that met certain criteria. By default, the condition is that the validation loss has improved, however you may change this by specifying the monitor parameter. It can take three types of arguments:

  • None: The model is saved after each epoch;

  • string: The model checks whether the last entry in the model history for that key is truthy. This is useful in conjunction with scores determined by a scoring callback. They write a <score>_best entry to the history, which can be used for checkpointing. By default, the Checkpoint callback looks at 'valid_loss_best';

  • function or callable: In that case, the function should take the NeuralNet instance as sole input and return a bool as output.

To specify where and how your model is saved, change the arguments starting with f_:

  • f_params: to save model parameters

  • f_optimizer: to save optimizer state

  • f_history: to save training history

  • f_pickle: to pickle the entire model object.

Please refer to Saving and Loading for more information about restoring your network from a checkpoint.

Learning rate schedulers

The LRScheduler callback allows the use of the various learning rate schedulers defined in torch.optim.lr_scheduler, such as ReduceLROnPlateau, which allows dynamic learning rate reduction based on a given value to monitor, or CyclicLR, which cycles the learning rate between two boundaries with a constant frequency.

Here’s a network that uses a callback to set a cyclic learning rate:

from skorch.callbacks import LRScheduler
from torch.optim.lr_scheduler import CyclicLR

net = NeuralNet(

As with other callbacks, you can use set_params to set parameters, and thus search learning rate scheduler parameters using GridSearchCV or similar. An example:

from sklearn.model_selection import GridSearchCV

search = GridSearchCV(
    param_grid={'callbacks__lr_scheduler__max_lr': [0.01, 0.1, 1.0]},