A tool for analyzing trained GQ-CNNs. Calculates statistics such as training/valiation errors and losses. Also plots Precision-Recall Curve and ROC, and saves sample TP/TN/FP/FN training/validation examples.

class gqcnn.GQCNNAnalyzer(config, verbose=True, plot_backend='pdf')

Bases: object

Analyzes a trained GQ-CNN model.

__init__(config, verbose=True, plot_backend='pdf')
  • config (dict) – Dictionary of analysis configuration parameters.

  • verbose (bool) – Whether or not to log analysis output to stdout.

  • plot_backend (str) – Matplotlib plotting backend to use, default is non-interactive “pdf” backend.

analyze(model_dir, output_dir, dataset_config=None)

Run analysis.

  • model_dir (str) – Path to the GQ-CNN model to analyze.

  • output_dir (str) – Path to save the analysis.

  • dataset_config (dict) – Dictionary to configure dataset used for training evaluation if different from one used during training.


  • autolab_core.BinaryClassificationResult – Result of analysis on training data.

  • autolab_core.BinaryClassificationResult – Result of analysis on validation data.