Deep Networks for Images
Create new deep networks for image classification and regression tasks by defining the network architecture and training the network from scratch.
After defining the network architecture, you can define training
parameters using the
You can then train the network using
trainNetwork. Use the
trained network to predict class labels or numeric responses.
You can train a convolutional neural network on a CPU, a GPU, multiple
CPUs or GPUs, or in parallel on a cluster or in the cloud. Training on a
GPU or in parallel requires Parallel Computing Toolbox™. Using a GPU requires a supported GPU device (for
information on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox)).
Specify the execution environment using the
|Deep Network Designer||Design, visualize, and train deep learning networks|
Convolution and Fully Connected Layers
|2-D convolutional layer|
|3-D convolutional layer|
|2-D grouped convolutional layer|
|Transposed 2-D convolution layer|
|Transposed 3-D convolution layer|
|Fully connected layer|
|Rectified Linear Unit (ReLU) layer|
|Leaky Rectified Linear Unit (ReLU) layer|
|Clipped Rectified Linear Unit (ReLU) layer|
|Exponential linear unit (ELU) layer|
|Hyperbolic tangent (tanh) layer|
|Gaussian error linear unit (GELU) layer|
Normalization, Dropout, and Cropping Layers
|Batch normalization layer|
|Group normalization layer|
|Instance normalization layer|
|Layer normalization layer|
|Channel-wise local response normalization layer|
|2-D crop layer|
|3-D crop layer|
Pooling and Unpooling Layers
|Average pooling layer|
|3-D average pooling layer|
|2-D global average pooling layer|
|3-D global average pooling layer|
|Global max pooling layer|
|3-D global max pooling layer|
|Max pooling layer|
|3-D max pooling layer|
|Max unpooling layer|
|Depth concatenation layer|
|Graph of network layers for deep learning|
|Plot neural network architecture|
|Add layers to layer graph or network|
|Remove layers from layer graph or network|
|Replace layer in layer graph or network|
|Connect layers in layer graph or network|
|Disconnect layers in layer graph or network|
|Directed acyclic graph (DAG) network for deep learning|
|Create 2-D residual network|
|Create 3-D residual network|
|Check equality of deep learning layer graphs or networks|
|Check equality of deep learning layer graphs or networks ignoring
|Classify data using trained deep learning neural network|
|Predict responses using trained deep learning neural network|
|Compute deep learning network layer activations|
|Create confusion matrix chart for classification problem|
|Sort classes of confusion matrix chart|
- Create Simple Deep Learning 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.
- List of Deep Learning Layers
Discover all the deep learning layers in MATLAB®.
- Specify Layers of Convolutional Neural Network
Learn about the layers of a convolutional neural network (ConvNet), and the order they appear in a ConvNet.
- Build Networks with Deep Network Designer
Interactively build and edit deep learning networks in Deep Network Designer.
- Set Up Parameters and Train Convolutional Neural Network
Learn how to set up training parameters for a convolutional neural network.
- Deep Learning in MATLAB
Discover deep learning capabilities in MATLAB using convolutional neural networks for classification and regression, including pretrained networks and transfer learning, and training on GPUs, CPUs, clusters, and clouds.
- Deep Learning Tips and Tricks
Learn how to improve the accuracy of deep learning networks.
- Data Sets for Deep Learning
Discover data sets for various deep learning tasks.
- Example Deep Learning Networks Architectures
This example shows how to define simple deep learning neural networks for various tasks.