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API reference: Keras integration#

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

Related

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.

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="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jvYh3Kb8"
export NEPTUNE_PROJECT="ml-team/classification"

You can, however, also pass them as arguments when initializing Neptune:

run = neptune.init_run(
    api_token="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jvYh3Kb8",  # your token here
    project="ml-team/classification",  # your full project name here
)
  • Find and copy your API token by clicking your avatar and selecting Get my API token.
  • Find and copy your project name in the project Settings → Properties.

If you haven't registered, you can also log anonymously to a public project (make sure not to publish sensitive data through your code!):

run = neptune.init_run(
    api_token=neptune.ANONYMOUS_API_TOKEN,
    project="common/quickstarts",
)

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,
)

...