Neptune + Keras integration

Experiment tracking for Keras-trained models.

What will you get with this integration?

Log, organize, visualize, and compare ML experiments in a single place
Monitor model training live
Version and query production-ready models and associated metadata (e.g., datasets)
Collaborate with the team and across the organization

What will be logged to Neptune?

hyperparameters for every run,
learning curves for losses and metrics during training,
hardware consumption and stdout/stderr output during training,
TensorFlow tensors as images to see model predictions live,
training code and Git commit information,
model weights,
other metadata

Example charts in the Neptune UI with logged accuracy and loss

Resources

Example

On the command line:

pip install neptune-tensorflow-keras

In Python:

import neptune
from neptune.integrations.tensorflow_keras import NeptuneCallback
from neptune import ANONYMOUS_API_TOKEN

# Start a run
run = neptune.init_run(
    project="common/tf-keras-integration",
    api_token=ANONYMOUS_API_TOKEN,
)

# Create a NeptuneCallback instance
neptune_cbk = NeptuneCallback(run=run, base_namespace="metrics")

# Pass the callback to model.fit()
model.fit(
    x_train,
    y_train,
    epochs=5,
    batch_size=64,
    callbacks=[neptune_cbk],
)

# Stop the run
run.stop()

Support

If you got stuck or simply want to talk to us, here are your options:

Check our FAQ page
You can submit bug reports, feature requests, or contributions directly to the repository.
Chat! When in the Neptune application click on the blue message icon in the bottom-right corner and send a message. A real person will talk to you ASAP (typically very ASAP),
You can just shoot us an email at support@neptune.ai