API reference: Keras integration#

You can use a Neptune callback to capture model training metadata when using TensorFlow with Keras.

`NeptuneCallback`#

Captures model training metadata and logs them to Neptune.

Note

To use this package, you need to have Keras or TensorFlow 2 installed on your machine.

Parameters

Name	Type	Default	Description
`run`	`Run` or `Handler`	-	(required) An existing run reference, as returned by `neptune.init_run()`, or a namespace handler.
`base_namespace`	`str`, optional	`training`	Namespace under which all metadata logged by the Neptune callback will be stored.
`log_model_diagram`	`bool`, optional	`False`	Save the model visualization. Requires pydot to be installed.
`log_on_batch`	`bool`, optional	`False`	Log the metrics also for each batch, not only each epoch.
`log_model_summary`	`bool`, optional	`True`	Whether to log the model summary.

Examples#

Creating a Neptune run and callback#

Create a run:

import neptune

run = neptune.init_run()

If Neptune can't find your project name or API token

As a best practice, you should save your Neptune API token and project name as environment variables:

export NEPTUNE_API_TOKEN="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jv...Yh3Kb8"

export NEPTUNE_PROJECT="ml-team/classification"

Alternatively, you can pass the information when using a function that takes api_token and project as arguments:

run = neptune.init_run( # (1)!
    api_token="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jv...Yh3Kb8",  # your token here
    project="ml-team/classification",  # your full project name here
)

Also works for init_model(), init_model_version(), init_project(), and integrations that create Neptune runs underneath the hood, such as NeptuneLogger or NeptuneCallback.
API token: In the bottom-left corner, expand the user menu and select Get my API token.
Project name: You can copy the path from the project details ( → Edit project details).

If you haven't registered, you can log anonymously to a public project:

api_token=neptune.ANONYMOUS_API_TOKEN
project="common/quickstarts"

Make sure not to publish sensitive data through your code!

Instantiate the Neptune callback:

from neptune.integrations.tensorflow_keras import NeptuneCallback

neptune_callback = NeptuneCallback(run=run)

Pass the callback to the callbacks argument of model.fit()

model.fit(x_train, y_train, callbacks=[neptune_callback])

Logging with additional options#

import neptune
from neptune.integrations.tensorflow_keras import NeptuneCallback

run = neptune.init_run()

neptune_callback = NeptuneCallback(
    run=run,
    base_namespace="visualizations",  # optionally set a custom namespace name
    log_model_diagram=True,
    log_on_batch=True,
)

...