API reference: skorch integration#

You can use a Neptune callback to capture model training metadata when using skorch.

`NeptuneLogger`#

Captures NeuralNetClassifier history and logs the metadata to Neptune.

Parameters

Name	Type	Default	Description
`run`	`Run`	-	An existing run reference, as returned by `neptune.init_run()`, or a namespace handler.
`log_on_batch_end`	`bool`, optional	`False`	Whether to log loss and other metrics on batch level.
`close_after_train`	`bool`, optional	`True`	Whether to close the run object once training finishes. Set to `False` if you want to continue logging to the same run or if you use it as a context manager.
`keys_ignored`	`str` or list of `str`	`None`	Key or list of keys that should not be logged to Neptune. Note that in addition to the keys provided by the user, keys such as those starting with `"event_"` or ending with `"_best"` are ignored by default.
`base_namespace`	`str`, optional	`"training"`	Namespace under which all metadata logged by the Neptune callback will be stored.

Examples

Create a NeptuneLogger callback:

import neptune

neptune_logger = NeptuneLogger(neptune.init_run(), close_after_train=False)

(Optional) Set the path to the checkpoints directory:

checkpoint_dirname = "./checkpoints"
checkpoint = Checkpoint(dirname=checkpoint_dirname)

Pass the callback to the net callbacks argument:

net = NeuralNetClassifier(
    ClassifierModule,
    max_epochs=20,
    lr=0.01,
    callbacks=[neptune_logger, checkpoint],
)

# Run training
net.fit(X, y)

Log additional metrics after training has finished:

from sklearn.metrics import roc_auc_score

y_pred = net.predict_proba(X)
auc = roc_auc_score(y, y_pred[:, 1])
neptune_logger.run["roc_auc_score"].append(auc)

Log charts, such as an ROC curve:

from scikitplot.metrics import plot_roc
import matplotlib.pyplot as plt

from neptune.types import File

fig, ax = plt.subplots(figsize=(16, 12))
plot_roc(y, y_pred, ax=ax)
neptune_logger.run["roc_curve"].upload(File.as_html(fig))

Log the net object after training:

net.save_params(f_params="basic_model.pkl")
neptune_logger.run["basic_model"].upload("basic_model.pkl")

Close the run if needed

If you set close_after_train=False, close the run when done:

neptune_logger.run.stop()

API reference: skorch integration#

NeptuneLogger#

`NeptuneLogger`#