Built-In Training

Train deep learning networks using built-in training functions

After defining the network architecture, you can define training parameters using the trainingOptions function. You can then train the network using trainNetwork or trainnet. Use the trained network to predict class labels or numeric responses.

Apps

Deep Network Designer

Design, visualize, and train deep learning networks

Funktionen

alle erweitern

Training

`trainingOptions`	Options for training deep learning neural network
`trainNetwork`	Train neural network
`trainnet`	Train deep learning neural network (Seit R2023b)
`TrainingInfo`	Neural network training information (Seit R2023b)
`show`	Show training information plot (Seit R2023b)
`close`	Close training information plot (Seit R2023b)

Metrics

`accuracyMetric`	Deep learning accuracy metric (Seit R2023b)
`aucMetric`	Deep learning area under ROC curve (AUC) metric (Seit R2023b)
`fScoreMetric`	Deep learning F-score metric (Seit R2023b)
`precisionMetric`	Deep learning precision metric (Seit R2023b)
`recallMetric`	Deep learning recall metric (Seit R2023b)
`rmseMetric`	Deep learning root mean squared error metric (Seit R2023b)

Themen

App Training

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.

Command-Line Training

Create Simple Deep Learning Neural Network for Classification
This example shows how to create and train a simple convolutional neural network for deep learning classification.
Train Convolutional Neural Network for Regression
This example shows how to fit a regression model using convolutional neural networks to predict the angles of rotation of handwritten digits.
Time Series Forecasting Using Deep Learning
This example shows how to forecast time series data using a long short-term memory (LSTM) network.
Sequence Classification Using Deep Learning
This example shows how to classify sequence data using a long short-term memory (LSTM) network.
Sequence-to-Sequence Classification Using Deep Learning
This example shows how to classify each time step of sequence data using a long short-term memory (LSTM) network.
Sequence-to-Sequence Regression Using Deep Learning
This example shows how to predict the remaining useful life (RUL) of engines by using deep learning.
Sequence-to-One Regression Using Deep Learning
This example shows how to predict the frequency of a waveform using a long short-term memory (LSTM) neural network.
Create Custom Deep Learning Training Plot
This example shows how to create a custom training plot that updates at each iteration during training of deep learning neural networks using trainnet. (Seit R2023b)
Custom Stopping Criteria for Deep Learning Training
This example shows how to stop training of deep learning neural networks based on custom stopping criteria using trainnet. (Seit R2023b)

Enthaltene Beispiele

Transfer Learning with Deep Network Designer

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

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Train Network with Numeric Features

Create and train a simple neural network for deep learning feature data classification.

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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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Train Residual Network for Image Classification

Create a deep learning neural network with residual connections and train it on CIFAR-10 data. Residual connections are a popular element in convolutional neural network architectures. Using residual connections improves gradient flow through the network and enables training of deeper networks.

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Train Network on Image and Feature Data

Train a network that classifies handwritten digits using both image and feature input data.

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Multilabel Image Classification Using Deep Learning

Use transfer learning to train a deep learning model for multilabel image classification.

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Image-to-Image Regression in Deep Network Designer

Use Deep Network Designer to construct and train an image-to-image regression network for super resolution.

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Train Simple Semantic Segmentation Network in Deep Network Designer

Create and train a simple semantic segmentation network using Deep Network Designer.

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Transfer Learning with Pretrained Audio Networks in Deep Network Designer

Interactively fine-tune a pretrained network to classify new audio signals using Deep Network Designer.

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Create Simple Sequence Classification Network Using Deep Network Designer

Create a simple long short-term memory (LSTM) classification network using Deep Network Designer.

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Classify Videos Using Deep Learning

Create a network for video classification by combining a pretrained image classification model and an LSTM network.

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