# Visualization and Verification

Visualize neural network behavior, explain predictions, and verify robustness
using sequence and tabular data

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.

## Apps

Deep Network Designer | Design and visualize deep learning networks |

## Functions

## Properties

ConfusionMatrixChart Properties | Confusion matrix chart appearance and behavior |

ROCCurve Properties | Receiver operating characteristic (ROC) curve appearance and behavior (Since R2022b) |

## Topics

### Interpretability

**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`tsne`

function 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 progress of deep learning networks.**Monitor Custom Training Loop Progress**

Track and plot custom training loop progress.**ROC Curve and Performance Metrics**

Use`rocmetrics`

to examine the performance of a classification algorithm on a test data set.