Train Network Using Custom Training Loop
This example shows how to train a network that classifies handwritten digits with a custom learning rate schedule.
You can train most types of neural networks using thetrainNetwork
andtrainingOptions
functions. If thetrainingOptions
function does not provide the options you need (for example, a custom learning rate schedule), then you can define your own custom training loop usingdlarray
anddlnetwork
objects for automatic differentiation. For an example showing how to retrain a pretrained deep learning network using thetrainNetwork
function, seeTransfer Learning Using Pretrained Network.
Training a deep neural network is an optimization task. By considering a neural network as a function , where is the network input, and is the set of learnable parameters, you can optimize so that it minimizes some loss value based on the training data. For example, optimize the learnable parameters such that for a given inputs with a corresponding targets , they minimize the error between the predictions and .
取决于使用的损失函数类型的任务. For example:
For classification tasks, you can minimize the cross entropy error between the predictions and targets.
For regression tasks, you can minimize the mean squared error between the predictions and targets.
You can optimize the objective using gradient descent: minimize the loss by iteratively updating the learnable parameters by taking steps towards the minimum using the gradients of the loss with respect to the learnable parameters. Gradient descent algorithms typically update the learnable parameters by using a variant of an update step of the form , where is the iteration number, is the learning rate, and denotes the gradients (the derivatives of the loss with respect to the learnable parameters).
This example trains a network to classify handwritten digits with thetime-based decaylearning rate schedule: for each iteration, the solver uses the learning rate given by , wheretis the iteration number, is the initial learning rate, andkis the decay.
Load Training Data
Load the digits data as an image datastore using theimageDatastore
function and specify the folder containing the image data.
dataFolder = fullfile(toolboxdir("nnet"),"nndemos","nndatasets","DigitDataset"); imds = imageDatastore(dataFolder,...IncludeSubfolders=true,....LabelSource="foldernames");
Partition the data into training and validation sets. Set aside 10% of the data for validation using thesplitEachLabel
function.
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.9,"randomize");
The network used in this example requires input images of size 28-by-28-by-1. To automatically resize the training images, use an augmented image datastore. Specify additional augmentation operations to perform on the training images: randomly translate the images up to 5 pixels in the horizontal and vertical axes. Data augmentation helps prevent the network from overfitting and memorizing the exact details of the training images.
inputSize = [28 28 1]; pixelRange = [-5 5]; imageAugmenter = imageDataAugmenter(...RandXTranslation=pixelRange,...RandYTranslation=pixelRange); augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain,DataAugmentation=imageAugmenter);
To automatically resize the validation images without performing further data augmentation, use an augmented image datastore without specifying any additional preprocessing operations.
augimdsValidation = augmentedImageDatastore(inputSize(1:2),imdsValidation);
Determine the number of classes in the training data.
classes = categories(imdsTrain.Labels); numClasses = numel(classes);
Define Network
Define the network for image classification.
For image input, specify an image input layer with input size matching the training data.
Do not normalize the image input, set the
Normalization
option of the input layer to"none"
.Specify three convolution-batchnorm-ReLU blocks.
Pad the input to the convolution layers such that the output has the same size by setting the
Padding
option to"same"
.For the first convolution layer specify 20 filters of size 5. For the remaining convolution layers specify 20 filters of size 3.
For classification, specify a fully connected layer with size matching the number of classes
To map the output to probabilities, include a softmax layer.
When training a network using a custom training loop, do not include an output layer.
layers = [ imageInputLayer(inputSize,Normalization="none") convolution2dLayer(5,20,Padding="same") batchNormalizationLayer reluLayer convolution2dLayer(3,20,Padding="same") batchNormalizationLayer reluLayer convolution2dLayer(3,20,Padding="same") batchNormalizationLayer reluLayer fullyConnectedLayer(numClasses) softmaxLayer];
Create adlnetwork
object from the layer array.
net = dlnetwork(layers)
net = dlnetwork with properties: Layers: [12×1 nnet.cnn.layer.Layer] Connections: [11×2 table] Learnables: [14×3 table] State: [6×3 table] InputNames: {'imageinput'} OutputNames: {'softmax'} Initialized: 1
Define Model Loss Function
Training a deep neural network is an optimization task. By considering a neural network as a function , where is the network input, and is the set of learnable parameters, you can optimize so that it minimizes some loss value based on the training data. For example, optimize the learnable parameters such that for a given inputs with a corresponding targets , they minimize the error between the predictions and .
Create the functionmodelLoss
, listed in theModel Loss Functionsection of the example, that takes as input thedlnetwork
object, a mini-batch of input data with corresponding targets, and returns the loss, the gradients of the loss with respect to the learnable parameters, and the network state.
Specify Training Options
Train for ten epochs with a mini-batch size of 128.
numEpochs = 10; miniBatchSize = 128;
Specify the options for SGDM optimization. Specify an initial learn rate of 0.01 with a decay of 0.01, and momentum 0.9.
initialLearnRate = 0.01; decay = 0.01; momentum = 0.9;
Train Model
Create aminibatchqueue
object that processes and manages mini-batches of images during training. For each mini-batch:
Use the custom mini-batch preprocessing function
preprocessMiniBatch
(defined at the end of this example) to convert the labels to one-hot encoded variables.Format the image data with the dimension labels
"SSCB"
(spatial, spatial, channel, batch). By default, theminibatchqueue
object converts the data todlarray
objects with underlying typesingle
. Do not format the class labels.Train on a GPU if one is available. By default, the
minibatchqueue
object converts each output to agpuArray
if a GPU is available. Using a GPU requires Parallel Computing Toolbox™ and a supported GPU device. For information on supported devices, seeGPU Support by Release(Parallel Computing Toolbox).
mbq = minibatchqueue(augimdsTrain,...MiniBatchSize=miniBatchSize,...MiniBatchFcn=@preprocessMiniBatch,...MiniBatchFormat=["SSCB"""]);
Initialize the training progress plot.
figure C = colororder; lineLossTrain = animatedline(Color=C(2,:)); ylim([0 inf]) xlabel("Iteration") ylabel("Loss") gridon
Initialize the velocity parameter for the SGDM solver.
velocity = [];
Train the network using a custom training loop. For each epoch, shuffle the data and loop over mini-batches of data. For each mini-batch:
Evaluate the model loss, gradients, and state using the
dlfeval
andmodelLoss
functions and update the network state.Determine the learning rate for the time-based decay learning rate schedule.
Update the network parameters using the
sgdmupdate
function.Display the training progress.
iteration = 0; start = tic;% Loop over epochs.forepoch = 1:numEpochs% Shuffle data.shuffle(mbq);% Loop over mini-batches.whilehasdata(mbq) iteration = iteration + 1;% Read mini-batch of data.[X,T] = next(mbq);% Evaluate the model gradients, state, and loss using dlfeval and the% modelLoss function and update the network state.[loss,gradients,state] = dlfeval(@modelLoss,net,X,T); net.State = state;% Determine learning rate for time-based decay learning rate schedule.learnRate = initialLearnRate/(1 + decay*iteration);% Update the network parameters using the SGDM optimizer.[net,velocity] = sgdmupdate(net,gradients,velocity,learnRate,momentum);% Display the training progress.D = duration(0,0,toc(start),Format="hh:mm:ss"); loss = double(loss); addpoints(lineLossTrain,iteration,loss) title("Epoch: "+ epoch +", Elapsed: "+ string(D)) drawnowendend
Test Model
Test the classification accuracy of the model by comparing the predictions on the validation set with the true labels.
After training, making predictions on new data does not require the labels. Createminibatchqueue
object containing only the predictors of the test data:
To ignore the labels for testing, set the number of outputs of the mini-batch queue to 1.
Specify the same mini-batch size used for training.
Preprocess the predictors using the
preprocessMiniBatchPredictors
function, listed at the end of the example.For the single output of the datastore, specify the mini-batch format
"SSCB"
(spatial, spatial, channel, batch).
numOutputs = 1; mbqTest = minibatchqueue(augimdsValidation,numOutputs,...MiniBatchSize=miniBatchSize,...MiniBatchFcn = @preprocessMiniBatchPredictors,...MiniBatchFormat="SSCB");
Loop over the mini-batches and classify the images usingmodelPredictions
function, listed at the end of the example.
YTest = modelPredictions(net,mbqTest,classes);
Evaluate the classification accuracy.
TTest = imdsValidation.Labels; accuracy = mean(TTest == YTest)
accuracy = 0.7210
Visualize the predictions in a confusion chart.
figure confusionchart(TTest,YTest)
Large values on the diagonal indicate accurate predictions for the corresponding class. Large values on the off-diagonal indicate strong confusion between the corresponding classes.
Supporting Functions
Model Loss Function
ThemodelLoss
function takes adlnetwork
objectnet
, a mini-batch of input dataX
with corresponding targetsT
and returns the loss, the gradients of the loss with respect to the learnable parameters innet
, and the network state. To compute the gradients automatically, use thedlgradient
function.
function[loss,gradients,state] = modelLoss(net,X,T)% Forward data through network.[Y,state] = forward(net,X);% Calculate cross-entropy loss.loss = crossentropy(Y,T);% Calculate gradients of loss with respect to learnable parameters.gradients = dlgradient(loss,net.Learnables);end
Model Predictions Function
ThemodelPredictions
function takes adlnetwork
objectnet
, aminibatchqueue
of input datambq
, and the network classes, and computes the model predictions by iterating over all data in theminibatchqueue
object. The function uses theonehotdecode
function to find the predicted class with the highest score.
functionY = modelPredictions(net,mbq,classes) Y = [];% Loop over mini-batches.whilehasdata(mbq) X = next(mbq);% Make prediction.scores = predict(net,X);% Decode labels and append to output.labels = onehotdecode(scores,classes,1)'; Y = [Y; labels];endend
Mini Batch Preprocessing Function
ThepreprocessMiniBatch
function preprocesses a mini-batch of predictors and labels using the following steps:
Preprocess the images using the
preprocessMiniBatchPredictors
function.从传入细胞arra提取标签数据y and concatenate into a categorical array along the second dimension.
One-hot encode the categorical labels into numeric arrays. Encoding into the first dimension produces an encoded array that matches the shape of the network output.
function[X,T] = preprocessMiniBatch(dataX,dataT)% Preprocess predictors.X = preprocessMiniBatchPredictors(dataX);% Extract label data from cell and concatenate.T = cat(2,dataT{1:end});% One-hot encode labels.T = onehotencode(T,1);end
Mini-Batch Predictors Preprocessing Function
ThepreprocessMiniBatchPredictors
function preprocesses a mini-batch of predictors by extracting the image data from the input cell array and concatenate into a numeric array. For grayscale input, concatenating over the fourth dimension adds a third dimension to each image, to use as a singleton channel dimension.
functionX = preprocessMiniBatchPredictors(dataX)% Concatenate.X = cat(4,dataX{1:end});end
See Also
dlarray
|dlgradient
|dlfeval
|dlnetwork
|forward
|adamupdate
|predict
|minibatchqueue
|onehotencode
|onehotdecode
Related Topics
- Train Generative Adversarial Network (GAN)
- Define Model Loss Function for Custom Training Loop
- Update Batch Normalization Statistics in Custom Training Loop
- Define Custom Training Loops, Loss Functions, and Networks
- Specify Training Options in Custom Training Loop
- List of Deep Learning Layers
- List of Functions with dlarray Support