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Visualize and Verify Deep Neural Networks

Visualize network behavior, explain predictions, and verify robustness

Visualize deep networks during and after training. Monitor training progress using built-in plots of network accuracy and loss, or by specifying custom metrics. Investigate trained networks using visualization and interpretability techniques such as Grad-CAM, occlusion sensitivity, LIME, and deep dream.

You can use the Deep Network Designer app to build, visualize, and train deep learning networks interactively. You can then generate code to recreate network construction and training, and export trained networks to Simulink^®.

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, find adversarial examples, and detect out-of-distribution data.

Featured Examples

Explore Network Predictions Using Deep Learning Visualization Techniques

Investigate network predictions using deep learning visualization techniques.

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Grad-CAM Reveals the Why Behind Deep Learning Decisions

Use the gradient-weighted class activation mapping (Grad-CAM) technique to understand why a deep learning network makes its classification decisions. Grad-CAM, invented by Selvaraju and coauthors [1], uses the gradient of the classification score with respect to the convolutional features determined by the network in order to understand which parts of the image are most important for classification. This example uses the GoogLeNet pretrained network for images.

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Interpret Deep Learning Time-Series Classifications Using Grad-CAM

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.

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Understand Network Predictions Using LIME

Use locally interpretable model-agnostic explanations (LIME) to understand why a deep neural network makes a classification decision.

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Create Simple Image Classification Network Using Deep Network Designer

Create and train a simple convolutional neural network for deep learning classification using Deep Network Designer. Convolutional neural networks are essential tools for deep learning and are especially suited for image recognition.

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