Visualization and Verification
Visualize deep networks during and after training. Monitor training progress using built-in plots of network accuracy and loss. To investigate trained networks, you can use visualization techniques such as Grad-CAM.
Use deep learning verification methods to assess the properties of deep neural networks. For example, you can verify the robustness properties of a network, compute network output bounds, and find adversarial examples.
|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|
|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)|
- Visualize Activations of LSTM Network
This example shows how to investigate and visualize the features learned by LSTM networks by extracting the activations.
- Interpret Deep Learning Time-Series Classifications Using Grad-CAM
This example shows how to use the gradient-weighted class activation mapping (Grad-CAM) technique to understand the classification decisions of a 1-D convolutional neural network trained on time-series data.
- View Network Behavior Using tsne
This example shows how to use the
tsnefunction to view activations in a trained network.
- Deep Learning in MATLAB
Discover deep learning capabilities in MATLAB® using convolutional neural networks for classification and regression, including pretrained networks and transfer learning, and training on GPUs, CPUs, clusters, and clouds.
- Deep Learning Tips and Tricks
Learn how to improve the accuracy of deep learning networks.
Training Progress and Performance
- 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.
- ROC Curve and Performance Metrics
rocmetricsto examine the performance of a classification algorithm on a test data set.