Visualizzazione delle reti neurali profonde
Tracciare i progressi dell'addestramento, valutare la precisione, spiegare le previsioni e visualizzare le feature apprese da una rete di immagini
Monitorare i progressi dell'addestramento utilizzando i grafici integrati sulla precisione e sulla perdita della rete. Esaminare le reti addestrate utilizzando tecniche di visualizzazione come Grad-CAM, sensibilità all'occlusione, LIME e deep dream.
App
Deep Network Designer | Progettare e visualizzare reti di Deep Learning |
Funzioni
Proprietà
ConfusionMatrixChart Properties | Confusion matrix chart appearance and behavior |
ROCCurve Properties | Receiver operating characteristic (ROC) curve appearance and behavior (Da R2022b) |
Argomenti
Interpretabilità
- Deep Learning Visualization Methods
Learn about and compare deep learning visualization methods. - 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. - 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 thetsne
function to view activations in a trained network. - 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.
Progressi dell’addestramento e performance
- Classify Webcam Images Using Deep Learning
This example shows how to classify images from a webcam in real time using the pretrained deep convolutional neural network GoogLeNet. - Monitoraggio dei progressi dell’addestramento in Deep Learning
Questo esempio mostra come monitorare i progressi dell'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
Userocmetrics
to 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.