Visualization and Interpretability
Monitor training progress using built-in plots of network accuracy and loss. Investigate trained networks using visualization techniques such as Grad-CAM, occlusion sensitivity, LIME, and deep dream.
Deep Learning Visualization Methods
|Deep Network Designer
|Progetta, visualizza e addestra le reti di Deep Learning
|Monitor and plot training progress for deep learning custom training loops (Da R2022b)
|Deep learning accuracy metric (Da R2023b)
|Deep learning area under ROC curve (AUC) metric (Da R2023b)
|Deep learning F-score metric (Da R2023b)
|Deep learning precision metric (Da R2023b)
|Deep learning recall metric (Da R2023b)
|Deep learning root mean squared error metric (Da R2023b)
|Compute deep learning network layer activations
|Create confusion matrix chart for classification problem
|Sort classes of confusion matrix chart
|Receiver operating characteristic (ROC) curve and performance metrics for binary and multiclass classifiers (Da R2022b)
|Compute additional classification performance metrics (Da R2022b)
|Compute performance metrics for average receiver operating characteristic (ROC) curve in multiclass problem (Da R2022b)
|Plot receiver operating characteristic (ROC) curves and other performance curves (Da R2022b)
Training Progress and Performance
- Monitoraggio dei progressi dell’addestramento in Deep Learning
Questo esempio mostra come monitorare il processo di addestramento delle reti di Deep Learning.
- Monitor Custom Training Loop Progress
Track and plot custom training loop progress.
- Monitor GAN Training Progress and Identify Common Failure Modes
Learn how to diagnose and fix some of the most common failure modes in GAN training.
- ROC Curve and Performance Metrics
rocmetricsto examine the performance of a classification algorithm on a test data set.
- Compare Deep Learning Models Using ROC Curves
This example shows how to use receiver operating characteristic (ROC) curves to compare the performance of deep learning models.
- Define Custom Metric Function
Define custom deep learning metrics using functions.
- Define Custom Deep Learning Metric Object
Learn how to define custom deep learning metrics using custom classes.
- Define Custom F-Beta Score Metric Object
This example shows how to define a generic F-beta score metric.
- Explore Network Predictions Using Deep Learning Visualization Techniques
This example shows how to investigate network predictions using deep learning visualization techniques.
- Understand Network Predictions Using Occlusion
This example shows how to use occlusion sensitivity maps to understand why a deep neural network makes a classification decision.
- Interpret Deep Network Predictions on Tabular Data Using LIME
This example shows how to use the locally interpretable model-agnostic explanations (LIME) technique to understand the predictions of a deep neural network classifying tabular data.
- Investigate Spectrogram Classifications Using LIME
This example shows how to use locally interpretable model-agnostic explanations (LIME) to investigate the robustness of a deep convolutional neural network trained to classify spectrograms.
- Investigate Classification Decisions Using Gradient Attribution Techniques
This example shows how to use gradient attribution maps to investigate which parts of an image are most important for classification decisions made by a deep neural network.
- Investigate Network Predictions Using Class Activation Mapping
This example shows how to use class activation mapping (CAM) to investigate and explain the predictions of a deep convolutional neural network for image classification.
- Visualize Image Classifications Using Maximal and Minimal Activating Images
This example shows how to use a data set to find out what activates the channels of a deep neural network.
- View Network Behavior Using tsne
This example shows how to use the
tsnefunction to view activations in a trained network.
- Visualizzazione delle attivazioni della rete LSTM
Questo esempio mostra come analizzare e visualizzare le feature apprese dalle reti LSTM tramite l'estrazione delle attivazioni.
- Visualize Activations of a Convolutional Neural Network
This example shows how to feed an image to a convolutional neural network and display the activations of different layers of the network.
- Visualize Features of a Convolutional Neural Network
This example shows how to visualize the features learned by convolutional neural networks.
- Deep Learning Visualization Methods
Learn about and compare deep learning visualization methods.