|Deep learning network for custom training loops|
|Reset state parameters of neural network|
|Plot neural network architecture|
|Add input layer to network|
|Add layers to layer graph or network|
|Remove layers from layer graph or network|
|Connect layers in layer graph or network|
|Disconnect layers in layer graph or network|
|Replace layer in layer graph or network|
|Print network summary|
|Initialize learnable and state parameters of a
|Deep learning network data layout for learnable parameter initialization|
|Compute deep learning network output for training|
|Compute deep learning network output for inference|
|Update parameters using adaptive moment estimation (Adam)|
|Update parameters using root mean squared propagation (RMSProp)|
|Update parameters using stochastic gradient descent with momentum (SGDM)|
|Update parameters using custom function|
|Create mini-batches for deep learning|
|Encode data labels into one-hot vectors|
|Decode probability vectors into class labels|
|Pad or truncate sequence data to same length|
|Monitor and plot training progress for deep learning custom training loops|
|Deep learning array for customization|
|Compute gradients for custom training loops using automatic differentiation|
|Evaluate deep learning model for custom training loops|
|Dimension labels of |
|Find dimensions with specified label|
|Extract data from |
|Check if object is |
|(To be removed) Convert deep learning model function to a layer graph|
|Deep learning convolution|
|Deep learning transposed convolution|
|Long short-term memory|
|Gated recurrent unit|
|Embed discrete data|
|Sum all weighted input data and apply a bias|
|Deep learning solution of nonstiff ordinary differential equation (ODE)|
|Apply rectified linear unit activation|
|Apply leaky rectified linear unit activation|
|Apply Gaussian error linear unit (GELU) activation|
|Normalize data across all observations for each channel independently|
|Cross channel square-normalize using local responses|
|Normalize data across grouped subsets of channels for each observation independently|
|Normalize across each channel for each observation independently|
|Normalize data across all channels for each observation independently|
|Pool data to average values over spatial dimensions|
|Pool data to maximum value|
|Unpool the output of a maximum pooling operation|
|Apply softmax activation to channel dimension|
|应用 sigmoid 激活|
|Accelerate deep learning function for custom training loops|
|Accelerated deep learning function|
|Clear accelerated deep learning function trace cache|
- Train Deep Learning Model in MATLAB
Learn how to training deep learning models in MATLAB®.
- Train Network Using Custom Training Loop
This example shows how to train a network that classifies handwritten digits with a custom learning rate schedule.
- Specify Training Options in Custom Training Loop
Learn how to specify common training options in a custom training loop.
- Define Model Loss Function for Custom Training Loop
Learn how to define a model loss function for a custom training loop.
- Update Batch Normalization Statistics in Custom Training Loop
This example shows how to update the network state in a custom training loop.
- Make Predictions Using dlnetwork Object
This example shows how to make predictions using a
dlnetworkobject by splitting data into mini-batches.
- Monitor Custom Training Loop Progress
Track and plot custom training loop progress.
- Train Network with Multiple Outputs
This example shows how to train a deep learning network with multiple outputs that predict both labels and angles of rotations of handwritten digits.
- Classify Videos Using Deep Learning with Custom Training Loop
This example shows how to create a network for video classification by combining a pretrained image classification model and a sequence classification network.
- Train Image Classification Network Robust to Adversarial Examples
This example shows how to train a neural network that is robust to adversarial examples using fast gradient sign method (FGSM) adversarial training.
- Train Neural ODE Network
This example shows how to train an augmented neural ordinary differential equation (ODE) network.
- Train Robust Deep Learning Network with Jacobian Regularization
This example shows how to train a neural network that is robust to adversarial examples using a Jacobian regularization scheme .
- Solve Ordinary Differential Equation Using Neural Network
This example shows how to solve an ordinary differential equation (ODE) using a neural network.
- Assemble Multiple-Output Network for Prediction
This example shows how to assemble a multiple output network for prediction.
- Run Custom Training Loops on a GPU and in Parallel
Speed up custom training loops by running on a GPU, in parallel using multiple GPUs, or on a cluster.
- Train Network Using Model Function
This example shows how to create and train a deep learning network by using functions rather than a layer graph or a
- Update Batch Normalization Statistics Using Model Function
This example shows how to update the network state in a network defined as a function.
- Make Predictions Using Model Function
This example shows how to make predictions using a model function by splitting data into mini-batches.
- Initialize Learnable Parameters for Model Function
Learn how to initialize learnable parameters for custom training loops using a model function.
- Train Latent ODE Network with Irregularly Sampled Time-Series Data
This example shows how to train a latent ordinary differential equation (ODE) autoencoder with time-series data that is sampled at irregular time intervals.
- Multivariate Time Series Anomaly Detection Using Graph Neural Network
This example shows how to detect anomalies in multivariate time series data using a graph neural network (GNN).
- List of Functions with dlarray Support
View the list of functions that support
- Automatic Differentiation Background
Learn how automatic differentiation works.
- Use Automatic Differentiation In Deep Learning Toolbox
How to use automatic differentiation in deep learning.
- Deep Learning Function Acceleration for Custom Training Loops
Accelerate model functions and model loss functions for custom training loops by caching and reusing traces.
- Accelerate Custom Training Loop Functions
This example shows how to accelerate deep learning custom training loop and prediction functions.
- Check Accelerated Deep Learning Function Outputs
This example shows how to check that the outputs of accelerated functions match the outputs of the underlying function.
- Evaluate Performance of Accelerated Deep Learning Function
This example shows how to evaluate the performance gains of using an accelerated function.