Keras information
Last updated
Last updated
Get information about a Keras block, such as its dependencies. Use the impulse blocks to find the learnId.
Project ID
Learn Block ID, use the impulse functions to retrieve the ID
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
Whether the block is trained
The type of learning block (anomaly, keras, keras-transfer-image, keras-transfer-kws, keras-object-detection, keras-regression). Each behaves differently.
The Keras script. This script might be empty if the mode is visual.
Minimum confidence rating required for the neural network. Scores below this confidence are tagged as uncertain.
The mode (visual or expert) to use for editing this network.
The visual layers (if in visual mode) for the neural network. This will be an empty array when in expert mode.
Number of training cycles. If in expert mode this will be 0.
Learning rate (between 0 and 1). If in expert mode this will be 0.
The batch size used during training.
The default batch size if a value is not configured.
Python-formatted tuple of input axes
Train/test split (between 0 and 1)
Whether to automatically balance class weights, use this for skewed datasets.
Use learned optimizer and ignore learning rate.
The data augmentation policy to use with image input
Whether to profile the i8 model (might take a very long time)
If set, skips creating embeddings and measuring memory (used in tests)
This metadata key is used to prevent group data leakage between train and validation datasets.
Whether the 'Advanced training settings' UI element should be expanded.
Whether the 'Augmentation training settings' UI element should be expanded.
Training parameters, this list depends on the list of parameters that the model exposes.
Capacity level for visual anomaly detection. Determines which set of default configurations to use. The higher capacity, the higher number of (Gaussian) components, and the more adapted the model becomes to the original distribution