trainNetwork

Train neural network for deep learning

Syntax

net = trainNetwork(imds,layers,options)

net = trainNetwork(ds,layers,options)

net = trainNetwork(X,Y,layers,options)

net = trainNetwork(sequences,Y,layers,options)

net = trainNetwork(tbl,layers,options)

net = trainNetwork(tbl,responseName,layers,options)

[net,info] = trainNetwork(___)

Description

Use trainNetwork to train a convolutional neural network (ConvNet, CNN), a long short-term memory (LSTM) network, or a bidirectional LSTM (BiLSTM) network for deep learning classification and regression problems. You can train a network on either a CPU or a GPU. For image classification and image regression, you can train using multiple GPUs or in parallel. Using GPU, multi-GPU, and parallel options requires Parallel Computing Toolbox™. To use a GPU for deep learning, you must also have a CUDA^® enabled NVIDIA^® GPU with compute capability 3.0 or higher. Specify training options, including options for the execution environment, by using trainingOptions.

net = trainNetwork(imds,layers,options) trains a network for image classification problems. The image datastore imds stores the input image data, layers defines the network architecture, and options defines the training options.

net = trainNetwork(ds,layers,options) trains a network using the datastore ds. For networks with multiple inputs, use this syntax with a combined or transformed datastore.

net = trainNetwork(X,Y,layers,options) trains a network for image classification and regression problems. The numeric array X contains the predictor variables and Y contains the categorical labels or numeric responses.

net = trainNetwork(sequences,Y,layers,options) trains a network for sequence classification and regression problems (for example, an LSTM or BiLSTM network), where sequences contains sequence or time series predictors and Y contains the responses. For classification problems, Y is a categorical vector or a cell array of categorical sequences. For regression problems, Y is a matrix of targets or a cell array of numeric sequences.

net = trainNetwork(tbl,layers,options) trains a network for classification and regression problems. The table tbl contains numeric data or file paths to the data. The predictors must be in the first column of tbl. For information on the targets or response variables, see tbl.

net = trainNetwork(tbl,responseName,layers,options) trains a network for classification and regression problems. The predictors must be in the first column of tbl. The responseName argument specifies the response variables in tbl.

[net,info] = trainNetwork(___) also returns information on the training using any of the input arguments in the previous syntaxes.

Examples

Train Network for Image Classification

Load the data as an ImageDatastore object.

digitDatasetPath = fullfile(matlabroot,'toolbox','nnet', ...
    'nndemos','nndatasets','DigitDataset');
imds = imageDatastore(digitDatasetPath, ...
    'IncludeSubfolders',true, ...
    'LabelSource','foldernames');

The datastore contains 10,000 synthetic images of digits from 0 to 9. The images are generated by applying random transformations to digit images created with different fonts. Each digit image is 28-by-28 pixels. The datastore contains an equal number of images per category.

Display some of the images in the datastore.

figure
numImages = 10000;
perm = randperm(numImages,20);
for i = 1:20
    subplot(4,5,i);
    imshow(imds.Files{perm(i)});
end

Divide the datastore so that each category in the training set has 750 images and the testing set has the remaining images from each label.

numTrainingFiles = 750;
[imdsTrain,imdsTest] = splitEachLabel(imds,numTrainingFiles,'randomize');

splitEachLabel splits the image files in digitData into two new datastores, imdsTrain and imdsTest.

Define the convolutional neural network architecture.

layers = [ ...
    imageInputLayer([28 28 1])
    convolution2dLayer(5,20)
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer];

Set the options to the default settings for the stochastic gradient descent with momentum. Set the maximum number of epochs at 20, and start the training with an initial learning rate of 0.0001.

options = trainingOptions('sgdm', ...
    'MaxEpochs',20,...
    'InitialLearnRate',1e-4, ...
    'Verbose',false, ...
    'Plots','training-progress');

Train the network.

net = trainNetwork(imdsTrain,layers,options);

Run the trained network on the test set, which was not used to train the network, and predict the image labels (digits).

YPred = classify(net,imdsTest);
YTest = imdsTest.Labels;

Calculate the accuracy. The accuracy is the ratio of the number of true labels in the test data matching the classifications from classify to the number of images in the test data.

accuracy = sum(YPred == YTest)/numel(YTest)

accuracy = 0.9404

Train Network with Augmented Images

Train a convolutional neural network using augmented image data. Data augmentation helps prevent the network from overfitting and memorizing the exact details of the training images.

Load the sample data, which consists of synthetic images of handwritten digits.

[XTrain,YTrain] = digitTrain4DArrayData;

digitTrain4DArrayData loads the digit training set as 4-D array data. XTrain is a 28-by-28-by-1-by-5000 array, where:

• 28 is the height and width of the images.

• 1 is the number of channels.

• 5000 is the number of synthetic images of handwritten digits.

YTrain is a categorical vector containing the labels for each observation.

Set aside 1000 of the images for network validation.

idx = randperm(size(XTrain,4),1000);
XValidation = XTrain(:,:,:,idx);
XTrain(:,:,:,idx) = [];
YValidation = YTrain(idx);
YTrain(idx) = [];

Create an imageDataAugmenter object that specifies preprocessing options for image augmentation, such as resizing, rotation, translation, and reflection. Randomly translate the images up to three pixels horizontally and vertically, and rotate the images with an angle up to 20 degrees.

imageAugmenter = imageDataAugmenter( ...
    'RandRotation',[-20,20], ...
    'RandXTranslation',[-3 3], ...
    'RandYTranslation',[-3 3])

imageAugmenter = 
  imageDataAugmenter with properties:

           FillValue: 0
     RandXReflection: 0
     RandYReflection: 0
        RandRotation: [-20 20]
           RandScale: [1 1]
          RandXScale: [1 1]
          RandYScale: [1 1]
          RandXShear: [0 0]
          RandYShear: [0 0]
    RandXTranslation: [-3 3]
    RandYTranslation: [-3 3]

Create an augmentedImageDatastore object to use for network training and specify the image output size. During training, the datastore performs image augmentation and resizes the images. The datastore augments the images without saving any images to memory. trainNetwork updates the network parameters and then discards the augmented images.

imageSize = [28 28 1];
augimds = augmentedImageDatastore(imageSize,XTrain,YTrain,'DataAugmentation',imageAugmenter);

Specify the convolutional neural network architecture.

layers = [
    imageInputLayer(imageSize)
    
    convolution2dLayer(3,8,'Padding','same')
    batchNormalizationLayer
    reluLayer   
    
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer   
    
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,32,'Padding','same')
    batchNormalizationLayer
    reluLayer   
    
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer];

Specify training options for stochastic gradient descent with momentum.

opts = trainingOptions('sgdm', ...
    'MaxEpochs',15, ...
    'Shuffle','every-epoch', ...
    'Plots','training-progress', ...
    'Verbose',false, ...
    'ValidationData',{XValidation,YValidation});

Train the network. Because the validation images are not augmented, the validation accuracy is higher than the training accuracy.

net = trainNetwork(augimds,layers,opts);

Train Network for Image Regression

Load the sample data, which consists of synthetic images of handwritten digits. The third output contains the corresponding angles in degrees by which each image has been rotated.

Load the training images as 4-D arrays using digitTrain4DArrayData. The output XTrain is a 28-by-28-by-1-by-5000 array, where:

• 28 is the height and width of the images.

• 1 is the number of channels.

• 5000 is the number of synthetic images of handwritten digits.

YTrain contains the rotation angles in degrees.

[XTrain,~,YTrain] = digitTrain4DArrayData;

Display 20 random training images using imshow.

figure
numTrainImages = numel(YTrain);
idx = randperm(numTrainImages,20);
for i = 1:numel(idx)
    subplot(4,5,i)    
    imshow(XTrain(:,:,:,idx(i)))
end

Specify the convolutional neural network architecture. For regression problems, include a regression layer at the end of the network.

layers = [ ...
    imageInputLayer([28 28 1])
    convolution2dLayer(12,25)
    reluLayer
    fullyConnectedLayer(1)
    regressionLayer];

Specify the network training options. Set the initial learn rate to 0.001.

options = trainingOptions('sgdm', ...
    'InitialLearnRate',0.001, ...
    'Verbose',false, ...
    'Plots','training-progress');

Train the network.

net = trainNetwork(XTrain,YTrain,layers,options);

Test the performance of the network by evaluating the prediction accuracy of the test data. Use predict to predict the angles of rotation of the validation images.

[XTest,~,YTest] = digitTest4DArrayData;
YPred = predict(net,XTest);

Evaluate the performance of the model by calculating the root-mean-square error (RMSE) of the predicted and actual angles of rotation.

rmse = sqrt(mean((YTest - YPred).^2))

rmse = single
    6.0360

Train Network for Sequence Classification

Train a deep learning LSTM network for sequence-to-label classification.

Load the Japanese Vowels data set as described in [1] and [2]. XTrain is a cell array containing 270 sequences of varying length with a feature dimension of 12. Y is a categorical vector of labels 1,2,...,9. The entries in XTrain are matrices with 12 rows (one row for each feature) and a varying number of columns (one column for each time step).

[XTrain,YTrain] = japaneseVowelsTrainData;

Visualize the first time series in a plot. Each line corresponds to a feature.

figure
plot(XTrain{1}')
title("Training Observation 1")
numFeatures = size(XTrain{1},1);
legend("Feature " + string(1:numFeatures),'Location','northeastoutside')

Define the LSTM network architecture. Specify the input size as 12 (the number of features of the input data). Specify an LSTM layer to have 100 hidden units and to output the last element of the sequence. Finally, specify nine classes by including a fully connected layer of size 9, followed by a softmax layer and a classification layer.

inputSize = 12;
numHiddenUnits = 100;
numClasses = 9;

layers = [ ...
    sequenceInputLayer(inputSize)
    lstmLayer(numHiddenUnits,'OutputMode','last')
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer]

layers = 
  5x1 Layer array with layers:

     1   ''   Sequence Input          Sequence input with 12 dimensions
     2   ''   LSTM                    LSTM with 100 hidden units
     3   ''   Fully Connected         9 fully connected layer
     4   ''   Softmax                 softmax
     5   ''   Classification Output   crossentropyex

Specify the training options. Specify the solver as 'adam' and 'GradientThreshold' as 1. Set the mini-batch size to 27 and set the maximum number of epochs to 100.

Because the mini-batches are small with short sequences, the CPU is better suited for training. Set 'ExecutionEnvironment' to 'cpu'. To train on a GPU, if available, set 'ExecutionEnvironment' to 'auto' (the default value).

maxEpochs = 100;
miniBatchSize = 27;

options = trainingOptions('adam', ...
    'ExecutionEnvironment','cpu', ...
    'MaxEpochs',maxEpochs, ...
    'MiniBatchSize',miniBatchSize, ...
    'GradientThreshold',1, ...
    'Verbose',false, ...
    'Plots','training-progress');

Train the LSTM network with the specified training options.

net = trainNetwork(XTrain,YTrain,layers,options);

Load the test set and classify the sequences into speakers.

[XTest,YTest] = japaneseVowelsTestData;

Classify the test data. Specify the same mini-batch size used for training.

YPred = classify(net,XTest,'MiniBatchSize',miniBatchSize);

Calculate the classification accuracy of the predictions.

acc = sum(YPred == YTest)./numel(YTest)

acc = 0.9541

Input Arguments

imds — Image datastore

ImageDatastore object

Image datastore, specified as an ImageDatastore object.

ImageDatastore allows batch reading of JPG or PNG image files using prefetching. If you use a custom function for reading the images, then ImageDatastore does not prefetch.

Tip

Use augmentedImageDatastore for efficient preprocessing of images for deep learning including image resizing.

Do not use the readFcn option of imageDatastore as this option is usually significantly slower.

ds — Datastore

datastore

Datastore for out-of-memory data and preprocessing.

For networks with a single input, the table or cell array returned by the datastore has two columns that specify the network inputs and expected responses, respectively.

For networks with multiple inputs, the datastore must be a combined or transformed datastore that returns a cell array with (numInputs+1) columns containing the predictors and the responses, where numInputs is the number of network inputs and numResponses is the number of responses. For i less than or equal to numInputs, the ith element of the cell array corresponds to the input layers.InputNames(i), where layers is the layer graph defining the network architecture. The last column of the cell array corresponds to the responses.

The table below lists the datastores that are directly compatible with trainNetwork. You can use other built-in datastores for training deep learning networks by using the transform and combine functions. These functions can convert the data read from datastores to the table or cell array format required by trainNetwork. For more information, see Datastores for Deep Learning.

Type of Datastore	Description
CombinedDatastore	Horizontally concatenate the data read from two or more underlying datastores.
TransformedDatastore	Transform batches of read data from an underlying datastore according to your own preprocessing pipeline.
AugmentedImageDatastore	Apply random affine geometric transformations, including resizing, rotation, reflection, shear, and translation, for training deep neural networks.
PixelLabelImageDatastore	Apply identical affine geometric transformations to images and corresponding ground truth labels for training semantic segmentation networks (requires Computer Vision Toolbox™).
RandomPatchExtractionDatastore	Extract pairs of random patches from images or pixel label images (requires Image Processing Toolbox™). You optionally can apply identical random affine geometric transformations to the pairs of patches.
DenoisingImageDatastore	Apply randomly generated Gaussian noise for training denoising networks (requires Image Processing Toolbox).
Custom mini-batch datastore	Create mini-batches of sequence, time series, or text data. For details, see Develop Custom Mini-Batch Datastore.

X — Image data

numeric array

Image data, specified as a numeric array. The size of the array depends on the type of image input:

Input	Description
2-D images	A h-by-w-by-c-by-N numeric array, where h, w, and c are the height, width, and number of channels of the images, respectively, and N is the number of images.
3-D images	A h-by-w-by-d-by-c-by-N numeric array, where h, w, d, and c are the height, width, depth, and number of channels of the images, respectively, and N is the number of images.

If the array contains NaNs, then they are propagated through the network.

sequences — Sequence or time series data

cell array of numeric arrays | numeric array | datastore

Sequence or time series data, specified as an N-by-1 cell array of numeric arrays, where N is the number of observations, a numeric array representing a single sequence, or a datastore.

For cell array or numeric array input, the dimensions of the numeric arrays containing the sequences depend on the type of data.

Input	Description
Vector sequences	c-by-s matrices, where c is the number of features of the sequences and s is the sequence length.
2-D image sequences	h-by-w-by-c-by-s arrays, where h, w, and c correspond to the height, width, and number of channels of the images, respectively, and s is the sequence length.
3-D image sequences	h-by-w-by-d-by-c-by-s, where h, w, d, and c correspond to the height, width, depth, and number of channels of the 3-D images, respectively, and s is the sequence length.

For datastore input, the datastore must return data as a cell array of sequences or a table whose first column contains sequences. The dimensions of the sequence data must correspond to the table above.

Y — Responses

categorical vector of labels | numeric array | cell array of categorical sequences | cell array of numeric sequences

Responses, specified as a categorical vector of labels, a numeric array, a cell array of categorical sequences, or cell array of numeric sequences. The format of Y depends on the type of task. Responses must not contain NaNs.

Classification

Task	Format
Image classification	N-by-1 categorical vector of labels, where N is the number of observations.
Sequence-to-label classification
Sequence-to-sequence classification	N-by-1 cell array of categorical sequences of labels, where N is the number of observations. Each sequence has the same number of time steps as the corresponding input sequence after applying the SequenceLength option to each mini-batch independently.

For sequence-to-sequence classification problems with one observation, sequences can also be a vector. In this case, Y must be a categorical sequence of labels.

Regression

Task	Format
2-D image regression	N-by-R matrix, where N is the number of images and R is the number of responses. h-by-w-by-c-by-N numeric array, where h, w, and c are the height, width, and number of channels of the images, respectively, and N is the number of images.
3-D image regression	N-by-R matrix, where N is the number of images and R is the number of responses. h-by-w-by-d-by-c-by-N numeric array, where h, w, d, and c are the height, width, depth, and number of channels of the images, respectively, and N is the number of images.
Sequence-to-one regression	N-by-R matrix, where N is the number of sequences and R is the number of responses.
Sequence-to-sequence regression	N-by-1 cell array of numeric sequences, where N is the number of sequences. The sequences are matrices with R rows, where R is the number of responses. Each sequence has the same number of time steps as the corresponding input sequence after applying the SequenceLength option to each mini-batch independently.

For sequence-to-sequence regression problems with one observation, sequences can be a matrix. In this case, Y must be a matrix of responses.

Normalizing the responses often helps to stabilize and speed up training of neural networks for regression. For more information, see Train Convolutional Neural Network for Regression.

tbl — Input data

table

Input data, specified as a table containing predictors in the first column and responses in the remaining column or columns. Each row in the table corresponds to an observation.

The arrangement of predictors and responses in the table columns depends on the type of problem.

Classification

Task	Predictors	Responses
Image classification	Absolute or relative file path to an image, specified as a character vector Image specified as a 3-D numeric array	Categorical label
Sequence-to-label classification	Absolute or relative file path to a MAT file containing sequence or time series data. The MAT file must contain a time series represented by a matrix with rows corresponding to data points and columns corresponding to time steps.	Categorical label
Sequence-to-sequence classification		Absolute or relative file path to a MAT file. The MAT file must contain a time series represented by a categorical vector, with entries corresponding to labels for each time step.

For classification problems, if you do not specify responseName, then the function, by default, uses the responses in the second column of tbl.

Regression

Task	Predictors	Responses
Image regression	Absolute or relative file path to an image, specified as a character vector Image specified as a 3-D numeric array	One or more columns of scalar values Numeric vector 1-by-1 cell array containing a 3-D numeric array
Sequence-to-one regression	Absolute or relative file path to a MAT file containing sequence or time series data. The MAT file must contain a time series represented by a matrix with rows corresponding to data points and columns corresponding to time steps.	One or more columns of scalar values Numeric vector
Sequence-to-sequence regression		Absolute or relative file path to a MAT file. The MAT file must contain a time series represented by a matrix, where rows correspond to responses and columns correspond to time steps.

For regression problems, if you do not specify responseName, then the function, by default, uses the remaining columns of tbl. Normalizing the responses often helps to stabilize and speed up training of neural networks for regression. For more information, see Train Convolutional Neural Network for Regression.

Responses cannot contain NaNs. If the predictor data contains NaNs, then they are propagated through the training. However, in most cases, the training fails to converge.

Data Types: table

responseName — Names of response variables in the input table

character vector | cell array of character vectors | string array

Names of the response variables in the input table, specified as a character vector, cell array of character vectors, or a string array. For problems with one response, responseName is the corresponding variable name in tbl. For regression problems with multiple response variables, responseName is an array of the corresponding variable names in tbl.

Data Types: char | cell | string

layers — Network layers

Layer array | LayerGraph object

Network layers, specified as a Layer array or a LayerGraph object.

To create a network with all layers connected sequentially, you can use a Layer array as the input argument. In this case, the returned network is a SeriesNetwork object.

A directed acyclic graph (DAG) network has a complex structure in which layers can have multiple inputs and outputs. To create a DAG network, specify the network architecture as a LayerGraph object and then use that layer graph as the input argument to trainNetwork.

For a list of built-in layers, see List of Deep Learning Layers.

options — Training options

TrainingOptionsSGDM | TrainingOptionsRMSProp | TrainingOptionsADAM

Training options, specified as a TrainingOptionsSGDM, TrainingOptionsRMSProp, or TrainingOptionsADAM object returned by the trainingOptions function. To specify solver and other options for network training, use trainingOptions.

Output Arguments

net — Trained network

SeriesNetwork object | DAGNetwork object

Trained network, returned as a SeriesNetwork object or a DAGNetwork object.

If you train the network using a Layer array as the layers input argument, then net is a SeriesNetwork object. If you train the network using a LayerGraph object as the input argument, then net is a DAGNetwork object.

info — Training information

structure

Training information, returned as a structure, where each field is a scalar or a numeric vector with one element per training iteration.

For classification problems, info contains the following fields:

• TrainingLoss — Loss function values

• TrainingAccuracy — Training accuracies

• ValidationLoss — Loss function values

• ValidationAccuracy — Validation accuracies

• BaseLearnRate — Learning rates

• FinalValidationLoss — Final validation loss

• FinalValidationAccuracy — Final validation accuracy

For regression problems, info contains the following fields:

• TrainingLoss — Loss function values

• TrainingRMSE — Training RMSE values

• ValidationLoss — Loss function values

• ValidationRMSE — Validation RMSE values

• BaseLearnRate — Learning rates

• FinalValidationLoss — Final validation loss

• FinalValidationRMSE — Final validation RMSE

The structure only contains the fields ValidationLoss, ValidationAccuracy, ValidationRMSE , FinalValidationLoss , FinalValidationAccuracy and FinalValidationRMSE when options specifies validation data. The 'ValidationFrequency' option of trainingOptions determines which iterations the software calculates validation metrics. For iterations when the software does not calculate validation metrics, the corresponding values in the structure are NaN.

If your network contains batch normalization layers, then the final validation metrics are often different from the validation metrics evaluated during training. This is because batch normalization layers in the final network perform different operations than during training.

More About

Save Checkpoint Networks and Resume Training

Deep Learning Toolbox™ enables you to save networks as .mat files after each epoch during training. This periodic saving is especially useful when you have a large network or a large data set, and training takes a long time. If the training is interrupted for some reason, you can resume training from the last saved checkpoint network. If you want trainNetwork to save checkpoint networks, then you must specify the name of the path by using the 'CheckpointPath' name-value pair argument of trainingOptions. If the path that you specify does not exist, then trainingOptions returns an error.

trainNetwork automatically assigns unique names to checkpoint network files. In the example name, net_checkpoint__351__2018_04_12__18_09_52.mat, 351 is the iteration number, 2018_04_12 is the date, and 18_09_52 is the time at which trainNetwork saves the network. You can load a checkpoint network file by double-clicking it or using the load command at the command line. For example:

load net_checkpoint__351__2018_04_12__18_09_52.mat

You can then resume training by using the layers of the network as an input argument to trainNetwork. For example:

trainNetwork(XTrain,YTrain,net.Layers,options)

You must manually specify the training options and the input data, because the checkpoint network does not contain this information. For an example, see Resume Training from Checkpoint Network.

Floating-Point Arithmetic

All functions for deep learning training, prediction, and validation in Deep Learning Toolbox perform computations using single-precision, floating-point arithmetic. Functions for deep learning include trainNetwork, predict, classify, and activations. The software uses single-precision arithmetic when you train networks using both CPUs and GPUs.

References

[1] Kudo, M., J. Toyama, and M. Shimbo. "Multidimensional Curve Classification Using Passing-Through Regions." Pattern Recognition Letters. Vol. 20, No. 11–13, pp. 1103–1111.

[2] Kudo, M., J. Toyama, and M. Shimbo. Japanese Vowels Data Set. https://archive.ics.uci.edu/ml/datasets/Japanese+Vowels

Extended Capabilities

Automatic Parallel Support

Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.

To run computation in parallel, set the 'ExecutionEnvironment' option to 'multi-gpu' or 'parallel'.

Use trainingOptions to set the 'ExecutionEnvironment' and supply the options to trainNetwork. If you do not set 'ExecutionEnvironment', then trainNetwork runs on a GPU if available.

For details, see Scale Up Deep Learning in Parallel and in the Cloud.