Automatically track Keras model training progress to Neptune
- Log parameters, metrics, losses, hardware utilization and monitor it live
- Log image predictions, performance charts (ROC CURVE) and model checkpoints
- Analyze and compare the results across multiple experiments and share them with others
Keras metrics, losses and hardware utilization logged to Neptune automatically
Experiment tracking tool with open source integration with Keras, to automatically log metrics.
Create your typical Keras training and validation code. Then, simply add
neptune_tensorboard.integrate_with_keras() to automatically track your metrics during training. Neptune visualizes them as interactive charts.
You can log additional information like ROC Curve or Confusion Matrix by implementing your custom logging callback as explained here.
import neptune neptune.init(api_token="ANONYMOUS", project_qualified_name='shared/keras-integration') import neptune_tensorboard neptune_tensorboard.integrate_with_keras() neptune.create_experiment(name='keras-example')
Support for deep learning libraries
- Start instantly with out-of-the-box integration.
- Track rich data (metrics, text, images, files and more).
- Save model weights.
- All charts visible for team members.
- Compare across multiple experiments and gain insight.
- Download charts from UI.
Backed up experiments history
- All visualizations are stored and secured.
- Keep all history – review when needed.
- Secured Intellectual Property (IP).
Register and try it out!