Visualización de deep learning
Monitorice el progreso del entrenamiento usando gráficas integradas de precisión y pérdida de red. Investigue redes entrenadas usando técnicas de visualización como Grad-CAM, sensibilidad de oclusión, LIME y Deep Dream.
Apps
Deep Network Designer | Diseñar, visualizar y entrenar redes de deep learning |
Objetos
trainingProgressMonitor | Monitor and plot training progress for deep learning custom training loops |
Funciones
Propiedades
ConfusionMatrixChart Properties | Confusion matrix chart appearance and behavior |
ROCCurve Properties | Receiver operating characteristic (ROC) curve appearance and behavior |
Temas
- Clasificar imágenes de una webcam mediante deep learning
Este ejemplo muestra cómo clasificar imágenes de una webcam en tiempo real usando una red neuronal convolucional profunda preentrenada, GoogLeNet.
- Monitorizar el progreso del entrenamiento de deep learning
Este ejemplo muestra cómo monitorizar el proceso de entrenamiento en redes de deep learning.
- Monitor Custom Training Loop Progress
Track and plot custom training loop progress.
- 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.
- Interpret Deep Network Predictions on Tabular Data Using LIME
This example shows how to use the locally interpretable model-agnostic explanations (LIME) technique to understand the predictions of a deep neural network classifying tabular data.
- 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
tsne
function to view activations in a trained network. - 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.
- 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.
- Visualizar activaciones de redes de LSTM
Este ejemplo muestra cómo investigar y visualizar las características aprendidas por las redes de LSTM extrayendo las activaciones.
- Visualize Features of a Convolutional Neural Network
This example shows how to visualize the features learned by convolutional neural networks.
- Deep Learning Visualization Methods
Learn about and compare deep learning visualization methods.
- ROC Curve and Performance Metrics
Use
rocmetrics
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.