Once an initial inference has been run with a model, sample dataset, and target, you can view performance results on the Configurations Page.
The components specified below provide visual representation of a model performance on a selected dataset and help find potential bottlenecks and areas for improvement:
The Model Analyzer is used for generating estimated performance information on neural networks. The tool analyzes of the following characteristics:
|Parameter||Explanation||Unit of Measurement|
|Flop||Total number of floating-point operations required to infer a model. Summed up over known layers only.||Number of operations|
|Iop||Total number of integer operations required to infer a model. Summed up over known layers only.||Number of operations|
|Total number of weights||Total number of trainable network parameters excluding custom constants. Summed up over known layers only.||Number of weights|
|Minimum Memory Consumption||Theoretical minimum of memory used by a network for inference given that the memory is reused as much as possible. Minimum Memory Consumption does not depend on weights.||Number of activations|
|Maximum Memory Consumption||Theoretical maximum of memory used by a network for inference given that the memory is not reused, which means all internal feature maps are stored in the memory simultaneously. Maximum Memory Consumption does not depend on weights.||Number of activations|
|Sparsity||Percentage of zero weights||Percentage|
Model analysis data is collected when the model is imported. All parameters depend on the size of a batch. Currently, information is gathered on the default model batch.
To view analysis data, click Details next to the name of a model in the table:
The details appear on the right: