Visualize Deep Neural Networks
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 Network Designer||Design, visualize, and train deep learning networks|
|Analyze deep learning network architecture|
|Monitor and plot training progress for deep learning custom training loops (Since R2022b)|
|Update information values for custom training loops (Since R2022b)|
|Record metric values for custom training loops (Since R2022b)|
|Group metrics in training plot (Since R2022b)|
|Plot neural network architecture|
|Compute deep learning network layer activations|
|Visualize network features using deep dream|
|Explain network predictions by occluding the inputs (Since R2019b)|
|Explain network predictions using LIME (Since R2020b)|
|Explain network predictions using Grad-CAM (Since R2021a)|
|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 (Since R2022b)|
|Compute additional classification performance metrics (Since R2022b)|
|Compute performance metrics for average receiver operating characteristic (ROC) curve in multiclass problem (Since R2022b)|
- 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 the
tsnefunction 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.
Training Progress and 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.
- Monitor Deep Learning Training Progress
This example shows how to monitor the training process of deep learning networks.
- 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.