API reference: Keras integration#
You can use a Neptune callback to capture model training metadata when using TensorFlow with Keras.
Related
- For an in-depth tutorial, see Integrations ≫ Keras integration guide.
- neptune-tensorflow-keras repo on GitHub
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:
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
)
- API token: In the bottom-left corner, expand the user menu and select Get my API token.
- Project name: in the top-right menu: → Edit project details.
If you haven't registered, you can also log anonymously to a public project (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()