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Deep Networks for Images

Create deep neural networks and train from scratch

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 trainingOptions function. 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 trainingOptions function.

App

Deep Network DesignerDesign, visualize, and train deep learning networks

Funzioni

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trainingOptionsOptions for training deep learning neural network
trainNetworkTrain deep learning neural network
analyzeNetworkAnalyze deep learning network architecture

Input Layers

imageInputLayerImage input layer
image3dInputLayer3-D image input layer

Convolution and Fully Connected Layers

convolution2dLayer2-D convolutional layer
convolution3dLayer3-D convolutional layer
groupedConvolution2dLayer2-D grouped convolutional layer
transposedConv2dLayerTransposed 2-D convolution layer
transposedConv3dLayerTransposed 3-D convolution layer
fullyConnectedLayerFully connected layer

Activation Layers

reluLayerRectified Linear Unit (ReLU) layer
leakyReluLayerLeaky Rectified Linear Unit (ReLU) layer
clippedReluLayerClipped Rectified Linear Unit (ReLU) layer
eluLayerExponential linear unit (ELU) layer
tanhLayerHyperbolic tangent (tanh) layer
swishLayerSwish layer
geluLayerGaussian error linear unit (GELU) layer
functionLayerFunction layer

Normalization, Dropout, and Cropping Layers

batchNormalizationLayerBatch normalization layer
groupNormalizationLayerGroup normalization layer
instanceNormalizationLayerInstance normalization layer
layerNormalizationLayerLayer normalization layer
crossChannelNormalizationLayer Channel-wise local response normalization layer
dropoutLayerDropout layer
crop2dLayer2-D crop layer
crop3dLayer3-D crop layer

Pooling and Unpooling Layers

averagePooling2dLayerAverage pooling layer
averagePooling3dLayer3-D average pooling layer
globalAveragePooling2dLayer2-D global average pooling layer
globalAveragePooling3dLayer3-D global average pooling layer
globalMaxPooling2dLayerGlobal max pooling layer
globalMaxPooling3dLayer3-D global max pooling layer
maxPooling2dLayerMax pooling layer
maxPooling3dLayer3-D max pooling layer
maxUnpooling2dLayerMax unpooling layer

Combination Layers

additionLayerAddition layer
multiplicationLayerMultiplication layer
concatenationLayerConcatenation layer
depthConcatenationLayerDepth concatenation layer

Output Layers

sigmoidLayerSigmoid layer
softmaxLayerSoftmax layer
classificationLayerClassification output layer
regressionLayerRegression output layer
layerGraphGraph of network layers for deep learning
plotPlot neural network architecture
addLayersAdd layers to layer graph or network
removeLayersRemove layers from layer graph or network
replaceLayerReplace layer in layer graph or network
connectLayersConnect layers in layer graph or network
disconnectLayersDisconnect layers in layer graph or network
DAGNetworkDirected acyclic graph (DAG) network for deep learning
resnetLayersCreate 2-D residual network
resnet3dLayersCreate 3-D residual network
isequalCheck equality of deep learning layer graphs or networks
isequalnCheck equality of deep learning layer graphs or networks ignoring NaN values
classifyClassify data using trained deep learning neural network
predictPredict responses using trained deep learning neural network
activationsCompute deep learning network layer activations
confusionchartCreate confusion matrix chart for classification problem
sortClassesSort classes of confusion matrix chart

Blocchi

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PredictPredict responses using a trained deep learning neural network
Image ClassifierClassify data using a trained deep learning neural network

Argomenti