For most deep learning tasks, you can use a pretrained network and adapt it to your own data. For an example showing how to use transfer learning to retrain a convolutional neural network to classify a new set of images, see Train Deep Learning Network to Classify New Images. Alternatively, you can create and train networks from scratch using layerGraph objects with the trainNetwork and trainingOptions functions.
If the trainingOptions function does not provide the training options that you need for your task, then you can create a custom training loop using automatic differentiation. To learn more, see Define Custom Training Loops.
If Deep Learning Toolbox™ does not provide the layers you need for your task (including output layers that specify loss functions), then you can create a custom layer. To learn more, see Define Custom Deep Learning Layers. For loss functions that cannot be specified using an output layer, you can specify the loss in a custom training loop. To learn more, see Specify Loss Functions. For networks that cannot be created using layer graphs, you can define custom networks as a function. To learn more, see Define Custom Networks.
Custom training loops, loss functions, and networks use automatic differentiation to automatically compute the model gradients. To learn more, see Automatic Differentiation Background.
For most tasks, you can control the training algorithm details using the trainingOptions and trainNetwork functions. If the trainingOptions function does not provide the options you need for your task (for example, a custom learn rate schedule), then you can define your own custom training loop using automatic differentiation.
For an example showing how to train a network with a custom learn rate schedule, see Train Network Using Custom Training Loop.
To update the learnable parameters, you must first calculate the gradients of the loss with respect to the learnable parameters.
Create a function of the form gradients =
modelGradients(dlnet,dlX,dlT), where dlnet is the
network, dlX are the input predictors, dlT are
the targets, and gradients are the returned gradients.
Optionally, you can pass extra arguments to the gradients function (for example, if
the loss function requires extra information), or return extra arguments (for
example, metrics for plotting the training progress). For models defined as a
function, you do not need to pass a network as an input argument.
To use automatic differentiation, you call dlgradient to
compute the gradient of the function, and dlfeval to set up or
update the computational graph. These functions use a dlarray to
manage the data structures and enable tracing of evaluation.
To update the network weights, you can use the following functions:
| Function | Description |
|---|---|
adamupdate | Update parameters using adaptive moment estimation (Adam) |
rmspropupdate | Update parameters using root mean squared propagation (RMSProp) |
sgdmupdate | Update parameters using stochastic gradient descent with momentum (SGDM) |
dlupdate | Update parameters using custom function |
For an example showing how to create a model gradients function to train a generative adversarial network (GAN) that generates images, see Train Generative Adversarial Network (GAN).
When using dlnetwork objects, do not use output layers, instead
you must calculate the loss manually in the model gradients function. You can use
the following functions to compute the loss:
| Function | Description |
|---|---|
softmax | The softmax activation operation applies the softmax function to the channel dimension of the input data. |
sigmoid | The sigmoid activation operation applies the sigmoid function to the input data. |
crossentropy | The cross-entropy operation computes the cross-entropy loss between network predictions and target values for single-label and multi-label classification tasks. |
mse | The half mean squared error operation computes the half mean squared error loss between network predictions and target values for regression tasks. |
Alternatively, you can use custom loss function by creating a function of the form
loss = myLoss(Y,T), where Y is the network
predictions, T are the targets, and loss is
the returned loss.
Use the loss value when computing gradients for updating the network weights.
For an example showing how to create a model gradients function to train a generative adversarial network (GAN) that generates images using a custom loss function, see Train Generative Adversarial Network (GAN).
For most tasks, you can use a pretrained network or define your own network as a layer
graph. To learn more about pretrained networks, see Pretrained Deep Neural Networks.
For a list of layers supported by dlnetwork objects, see Supported Layers.
For architectures that cannot be created using layer graphs, you can define a custom
model as a function of the form [dlY1,...,dlYM] =
model(dlX1,...,dlXN,parameters), where dlX1,...,dlXN
correspond to the input data for the N model inputs,
parameters contains the network parameters, and
dlY1,...,dlYM correspond to the M model
outputs. To train a custom network, use a custom training loop
If you define a custom network as a function, then the model function must support
automatic differentiation. You can use the following deep learning operations. The
functions listed here are only a subset. For a complete list of functions that support
dlarray
input, see List of Functions with dlarray Support.
| Function | Description |
|---|---|
avgpool | The average pooling operation performs downsampling by dividing the input into pooling regions and computing the average value of each region. |
batchnorm | The batch normalization operation normalizes each input channel
across a mini-batch. To speed up training of convolutional neural networks and reduce the
sensitivity to network initialization, use batch normalization between convolution and nonlinear
operations such as relu. |
crossentropy | The cross-entropy operation computes the cross-entropy loss between network predictions and target values for single-label and multi-label classification tasks. |
crosschannelnorm | The cross-channel normalization operation uses local responses
in different channels to normalize each activation. Cross-channel normalization typically
follows a relu operation.
Cross-channel normalization is also known as local response normalization. |
dlconv | The convolution operation applies sliding filters to the input data. Use 1-D and 2-D filters with ungrouped or grouped convolutions and 3-D filters with ungrouped convolutions. |
dltranspconv | The transposed convolution operation upsamples feature maps. |
fullyconnect | The fully connect operation multiplies the input by a weight matrix and then adds a bias vector. |
gru | The gated recurrent unit (GRU) operation allows a network to learn dependencies between time steps in time series and sequence data. |
leakyrelu | The leaky rectified linear unit (ReLU) activation operation performs a nonlinear threshold operation, where any input value less than zero is multiplied by a fixed scale factor. |
lstm | The long short-term memory (LSTM) operation allows a network to learn long-term dependencies between time steps in time series and sequence data. |
maxpool | The maximum pooling operation performs downsampling by dividing the input into pooling regions and computing the maximum value of each region. |
maxunpool | The maximum unpooling operation unpools the output of a maximum pooling operation by upsampling and padding with zeros. |
mse | The half mean squared error operation computes the half mean squared error loss between network predictions and target values for regression tasks. |
relu | The rectified linear unit (ReLU) activation operation performs a nonlinear threshold operation, where any input value less than zero is set to zero. |
sigmoid | The sigmoid activation operation applies the sigmoid function to the input data. |
softmax | The softmax activation operation applies the softmax function to the channel dimension of the input data. |
dlarray | dlfeval | dlgradient | dlnetwork