Deep Learning Toolbox
Create, analyze, and train deep learning networks
Deep Learning Toolbox™ (formerly Neural Network Toolbox™) provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. Apps and plots help you visualize activations, edit network architectures, and monitor training progress.
For small training sets, you can perform transfer learning with pretrained deep network models (including SqueezeNet, Inception-v3, ResNet-101, GoogLeNet, and VGG-19) and models imported from TensorFlow™-Keras and Caffe.
To speed up training on large datasets, you can distribute computations and data across multicore processors and GPUs on the desktop (with Parallel Computing Toolbox™), or scale up to clusters and clouds, including Amazon EC2® P2, P3, and G3 GPU instances (with MATLAB Distributed Computing Server™).
Networks and Architectures
Use Deep Learning Toolbox to train deep learning networks for classification, regression, and feature learning on image, time-series, and text data.
Convolutional Neural Networks
Learn patterns in images to recognize objects, faces, and scenes. Construct and train convolutional neural networks (CNNs) to perform feature extraction and image recognition.
Long Short-Term Memory Networks
Learn long-term dependencies in sequential data including signal, audio, text, and other time-series data. Construct and train long short-term memory (LSTM) networks to perform classification and regression.
Working with LSTMs.
Network Architectures
Use various network structures such as series, directed acyclic graph (DAG), and recurrent architectures to build your deep learning network. DAG architectures offer more network topologies including those with skipped layers or layers connected in parallel.
Working with different network architectures.
Network Design and Analysis
Create, edit, visualize, and analyze deep learning networks with interactive apps.
Design Deep Learning Networks
Create a deep network from scratch using the Deep Network Designer app. Import a pretrained model, visualize the network structure, edit the layers, and tune parameters.
Deep Network Designer app.
Analyze Deep Learning Networks
Analyze your network architecture to detect and debug errors, warnings, and layer compatibility issues before training. Visualize the network topology and view details such as learnable parameters and activations.
Analyzing a deep learning network architecture.
Transfer Learning and Pretrained Models
Import pretrained models into MATLAB for inference.
Transfer Learning
Transfer learning is commonly used in deep learning applications. Access a pretrained network and use it as a starting point to learn a new task and quickly transfer learned features to a new task using a smaller number of training images.
Pretrained Models
Access the latest models from research with a single line of code. Import pretrained models including AlexNet, GoogLeNet, VGG-16, VGG-19, ResNet-101, Inception-v3, and SqueezeNet. See pretrained models for a complete list of models.
Analysis of deep neural network models.
Visualization
Visualize network topologies, training progress, and activations of the learned features in a deep learning network.
Network Visualization
Visualize a network topology with its layers and connections. Use the analyzeNetwork function to analyze the network architecture interactively.
Visualizing a deep learning network architecture.
Training Progress
View training progress in every iteration with plots of various metrics. Plot the validation metrics against the training metrics to visually analyze whether the network is overfitting.
Monitoring your model's training progress.
Network Activations
Extract activations corresponding to a layer, visualize the learned features, and train a machine learning classifier using the activations. Use the deepDreamImage function to understand and diagnose network behavior by synthesizing images that strongly activate network layers and highlighting the learned features.
Visualizing activations.
Framework Interoperability
Interoperate with deep learning frameworks from MATLAB.
ONNX Converter
Import and export ONNX models within MATLAB® for interoperability with other deep learning frameworks. ONNX enables models to be trained in one framework and transferred to another for inference.
Interoperate with deep learning frameworks.
TensorFlow-Keras Importer
Import models from TensorFlow-Keras into MATLAB for inference and transfer learning using the importKerasNetwork function.
Caffe Importer
Import models from Caffe Model Zoo into MATLAB for inference and transfer learning using the importCaffeNetwork function.
Importing models from Caffe Model Zoo into MATLAB.
Training Acceleration
Speed up deep learning training using GPU, cloud, and distributed computing.
GPU Acceleration
Speed up deep learning training and inference with high-performance NVIDIA® GPUs. You can perform training on a single workstation GPU or scale to multiple GPUs with DGX systems in data centers or on the cloud. You can use MATLAB with Parallel Computing Toolbox and most CUDA® enabled NVIDIA GPUs that have compute capability 3.0 or higher.
Acceleration with GPUs.
Cloud Acceleration
Speed up deep learning training with cloud instances. Use high-performance GPU instances for the best results.
Support for Parallel Computing Toolbox and MATLAB Distributed Computing Server.
Distributed Computing
Run deep learning training across multiple processors on multiple servers on a network using MATLAB Distributed Computing Server.
Scaling up deep learning in parallel and in the cloud.
Code Generation and Deployment
Deploy trained networks to embedded systems or integrate them with a wide range of production environments.
Code Generation
Use GPU Coder™ to generate optimized CUDA code from MATLAB code for deep learning, embedded vision, and autonomous systems. Use MATLAB Coder™ to generate C/C++ code to deploy deep learning networks to Intel® Xeon® and ARM® Cortex®-A processors.
MATLAB Compiler Support
Use MATLAB Compiler™ and MATLAB Compiler SDK™ to deploy trained networks as C/C++ shared libraries, Microsoft® .NET assemblies, Java® classes, and Python® packages from MATLAB programs. You can also train a shallow network model in the deployed application or component.
Sharing standalone MATLAB programs with MATLAB Compiler.
Shallow Neural Networks
Use neural networks with a variety of supervised and unsupervised shallow neural network architectures.
Supervised Networks
Train supervised shallow neural networks to model and control dynamic systems, classify noisy data, and predict future events.
Shallow neural networks.
Unsupervised Networks
Find relationships within data and automatically define classification schemes by letting the shallow network continually adjust itself to new inputs. Use self-organizing, unsupervised networks, competitive layers, and self-organizing maps.
Self-organizing maps.
Stacked Autoencoders
Perform unsupervised feature transformation by extracting low-dimensional features from your data set using autoencoders. You can also use stacked autoencoders for supervised learning by training and stacking multiple encoders.
Stacked autoencoders.
Latest Features
Deep Network Designer
Edit and build deep learning networks
ONNX Support
Import and export models using the ONNX model format for interoperability with other deep learning frameworks
Network Analyzer
Visualize, analyze, and find problems in network architectures before training
TensorFlow-Keras
Import LSTM and BiLSTM layers from TensorFlow-Keras
Long Short-Term Memory (LSTM) Networks
Solve regression problems with LSTM networks and learn from full sequence context using bidirectional LSTM layers
Deep Learning Optimization
Improve network training using Adam, RMSProp, and gradient clipping
See the release notes for details on any of these features and corresponding functions.
MATLAB for Deep Learning
Design, build, and visualize deep learning networks
Have Questions?
Contact Shounak Mitra, Deep Learning Toolbox Technical Expert
Neural Network Toolbox™ では、浅いニューラル ネットワークと深いニューラル ネットワークの両方を作成、学習、可視化、シミュレーションするためのアルゴリズム、学習済みモデルおよびアプリが用意されています。分類、回帰、クラスタリング、次元削減、時系列予測、および動的システムのモデリングと制御を実行できます。
ディープラーニング ネットワークには、たたみ込みニューラル ネットワーク (ConvNet または CNN)、有向非循環グラフ (DAG) ネットワーク トポロジ、および画像の分類、回帰、特徴学習のためのオートエンコーダーが含まれます。時系列分類と回帰用として long short-term memory (LSTM) ディープラーニング ネットワークが用意されています。また、中間層とアクティベーションの可視化、ネットワーク アーキテクチャの変更、学習進行状況の監視を行うことができます。
小さい学習セットの場合、学習済みのディープ ネットワーク モデル (Inception-v3、ResNet-50、ResNet-101、GoogLeNet、AlexNet、VGG-16、VGG-19 など) や TensorFlow™ Keras または Caffe からインポートされたモデルを使って転移学習を実行することでディープラーニングを迅速に適用できます。
大規模なデータセットの場合、Parallel Computing Toolbox™ を使用してデスクトップ上のマルチコア プロセッサと GPU に計算やデータを分散させたり、MATLAB® Distributed Computing Server™ を使用して、Amazon EC2® P2、P3、および G3 GPU インスタンスなどのクラスターやクラウドにスケールアップして学習速度を向上させることができます。
ディープラーニング手法に関する無料の実践的な基礎講座については、ディープラーニング入門をご覧ください。
