Get metadata about a trained Keras block. 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)
Date when the model was trained
Layers of the neural network
Input size
33TensorFlow name
"x_input:0"TensorFlow type
"<dtype: 'float32'>"Output size
20TensorFlow name
"dense_1/Relu:0"TensorFlow type
"<dtype: 'float32'>"Labels for the output layer
Original labels in the dataset when features were generated, e.g. used to render the feature explorer.
The types of model that are available
Metrics for each of the available model types
The model's loss on the validation set after training
The model's accuracy on the validation set after training
Precision, recall, F1 and support scores
Custom, device-specific performance metrics
The name of the metric
The value of this metric for this model type
Only set for object detection projects
Only set for visual anomaly projects. 2D array of shape (n, n) with raw anomaly scores, where n varies based on the image input size and the specific visual anomaly algorithm used. The scores corresponds to each grid cell in the image's spatial matrix.
If this is set, then we're still profiling this model. Subscribe to job updates to see when it's done (afterward the metadata will be updated).
If this is set, then the profiling job failed (get the status by getting the job logs for 'profilingJobId').
Normalization that is applied to images. If this is not set then 0..1 is used. "0..1" gives you non-normalized pixels between 0 and 1. "-1..1" gives you non-normalized pixels between -1 and 1. "0..255" gives you non-normalized pixels between 0 and 255. "-128..127" gives you non-normalized pixels between -128 and 127. "torch" first scales pixels between 0 and 1, then applies normalization using the ImageNet dataset (same as torchvision.transforms.Normalize()). "bgr-subtract-imagenet-mean" scales to 0..255, reorders pixels to BGR, and subtracts the ImageNet mean from each channel.