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Deep Network Designer App

Interactively create and train deep learning networks

Build, visualize, and train deep learning networks interactively. Use the start page to load pretrained image classification networks suitable for transfer learning. Analyze your network to check that you have defined the architecture correctly and detect problems before training. Import and visualize training data, specify training options, and track network training using animated plots of training progress. Generate code to recreate network construction and training, and export trained networks to Simulink^®.

This flow chart shows common workflows for building and training deep learning models in the Deep Network Designer app. You can train your network in Deep Network Designer or export your untrained network for training at the command line.

Deep learning workflow. Step 1. Design network. Step 2. Import data. Step 3. Select training options. Step 4. Train network. Step 5. Export network.

Apps

Deep Network Designer

Design, visualize, and train deep learning networks

Functions

`trainingOptions`	Options for training deep learning neural network
`trainNetwork`	Train neural network
`trainnet`	Train deep learning neural network (Since R2023b)

Topics

Build Networks with Deep Network Designer
Interactively build and edit deep learning networks in Deep Network Designer.
Train Networks Using Deep Network Designer
Interactively train deep learning networks in Deep Network Designer.
Import Data into Deep Network Designer
Import and visualize data in Deep Network Designer.
Import Custom Layer into Deep Network Designer
This example shows how to import a custom classification output layer with the sum of squares error (SSE) loss and add it to a pretrained network in Deep Network Designer.
Generate MATLAB Code from Deep Network Designer
Generate MATLAB^® code to recreate designing and training a network in Deep Network Designer.
Generate Experiment Using Deep Network Designer
Use Experiment Manager to tune the hyperparameters of a network trained in Deep Network Designer.
View Autogenerated Custom Layers Using Deep Network Designer
This example shows how to import a pretrained TensorFlow™ network and view the autogenerated layers in Deep Network Designer.

Featured Examples

Get Started with Deep Network Designer

Use Deep Network Designer to adapt a pretrained GoogLeNet network to classify a new collection of images. This process is called transfer learning and is usually much faster and easier than training a new network, because you can apply learned features to a new task using a smaller number of training images. To prepare a network for transfer learning interactively, use Deep Network Designer.

Open Live Script

Transfer Learning with Deep Network Designer

Interactively fine-tune a pretrained deep learning network to learn a new image classification task.

Open Live Script

Train Network for Time Series Forecasting Using Deep Network Designer

Forecast time series data by training a long short-term memory (LSTM) network in Deep Network Designer.

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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.

Open Live Script