Image data, specified as one of the following:

For neural networks with multiple inputs, the datastore must be a TransformedDatastore or CombinedDatastore object.

Datastore

Datastores read mini-batches of images and responses. Datastores are best suited when you have data that does not fit in memory or when you want to apply augmentations or transformations to the data.

The list below lists the datastores that are directly compatible with trainNetwork for image data.

For example, you can create an image datastore using the imageDatastore function and use the names of the folders containing the images as labels by setting the 'LabelSource' option to 'foldernames'. Alternatively, you can specify the labels manually using the Labels property of the image datastore.

You can use other built-in datastores for training deep learning neural networks by using the transform and combine functions. These functions can convert the data read from datastores to the format required by trainNetwork.

For neural networks with multiple inputs, the datastore must be a TransformedDatastore or CombinedDatastore object.

The required format of the datastore output depends on the neural network architecture.

data = read(ds)

data =

  4×2 table

        Predictors        Response
    __________________    ________

    {224×224×3 double}       2    
    {224×224×3 double}       7    
    {224×224×3 double}       9    
    {224×224×3 double}       9  

data = read(ds)

data =

  4×2 cell array

    {224×224×3 double}    {[2]}
    {224×224×3 double}    {[7]}
    {224×224×3 double}    {[9]}
    {224×224×3 double}    {[9]}

data = read(ds)

data =

  4×3 cell array

    {224×224×3 double}    {128×128×3 double}    {[2]}
    {224×224×3 double}    {128×128×3 double}    {[2]}
    {224×224×3 double}    {128×128×3 double}    {[9]}
    {224×224×3 double}    {128×128×3 double}    {[9]}

The format of the predictors depends on the type of data.

For predictors returned in tables, the elements must contain a numeric scalar, a numeric row vector, or a 1-by-1 cell array containing the numeric array.

The format of the responses depends on the type of task.

For responses returned in tables, the elements must be a categorical scalar, a numeric scalar, a numeric row vector, or a 1-by-1 cell array containing a numeric array.

For more information, see Datastores for Deep Learning.

Numeric Array

For data that fits in memory and does not require additional processing like augmentation, you can specify a data set of images as a numeric array. If you specify images as a numeric array, then you must also specify the responses argument.

The size and shape of the numeric array depends on the type of image data.

Table

As an alternative to datastores or numeric arrays, you can also specify images and responses in a table. If you specify images as a table, then you can also specify which columns contain the responses using the responses argument.

When specifying images and responses in a table, each row in the table corresponds to an observation.

For image input, the predictors must be in the first column of the table, specified as one of the following:

The format of the responses depends on the type of task.

For neural networks with image input, if you do not specify responses, then the function, by default, uses the first column of tbl for the predictors and the subsequent columns as responses.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | table
Complex Number Support: Yes

Sequence or time series data, specified as one of the following:

Datastore

Datastores read mini-batches of sequences and responses. Datastores are best suited when you have data that does not fit in memory or when you want to apply transformations to the data.

The list below lists the datastores that are directly compatible with trainNetwork for sequence data.

You can use other built-in datastores for training deep learning neural 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 example, you can transform and combine data read from in-memory arrays and CSV files using ArrayDatastore and TabularTextDatastore objects, respectively.

The datastore must return data in a table or cell array. Custom mini-batch datastores must output tables.

data = read(ds)

data =

  4×2 table

        Predictors        Response
    __________________    ________

    {12×50 double}           2    
    {12×50 double}           7    
    {12×50 double}           9    
    {12×50 double}           9  

data = read(ds)

data =

  4×2 cell array

    {12×50 double}        {[2]}
    {12×50 double}        {[7]}
    {12×50 double}        {[9]}
    {12×50 double}        {[9]}

The format of the predictors depend on the type of data.

For predictors returned in tables, the elements must contain a numeric scalar, a numeric row vector, or a 1-by-1 cell array containing a numeric array.

The format of the responses depends on the type of task.

For responses returned in tables, the elements must be a categorical scalar, a numeric scalar, a numeric row vector, or a 1-by-1 cell array containing a numeric array.

For more information, see Datastores for Deep Learning.

Numeric or Cell Array

For data that fits in memory and does not require additional processing like custom transformations, you can specify a single sequence as a numeric array or a data set of sequences as a cell array of numeric arrays. If you specify sequences as a cell or numeric array, then you must also specify the responses argument.

For cell array input, the cell array must be an N-by-1 cell array of numeric arrays, where N is the number of observations. The size and shape of the numeric array representing a sequence depends on the type of sequence data.

The trainNetwork function supports neural networks with at most one sequence input layer.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | cell
Complex Number Support: Yes

Feature data, specified as one of the following:

Datastore

Datastores read mini-batches of feature data and responses. Datastores are best suited when you have data that does not fit in memory or when you want to apply transformations to the data.

The list below lists the datastores that are directly compatible with trainNetwork for feature data.

You can use other built-in datastores for training deep learning neural 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.

For neural networks with multiple inputs, the datastore must be a TransformedDatastore or CombinedDatastore object.

The datastore must return data in a table or a cell array. Custom mini-batch datastores must output tables. The format of the datastore output depends on the neural network architecture.

data = read(ds)

data =

  4×2 table

        Predictors        Response
    __________________    ________

    {24×1 double}            2    
    {24×1 double}            7    
    {24×1 double}            9    
    {24×1 double}            9  

data = read(ds)

data =

  4×2 cell array

    {24×1 double}    {[2]}
    {24×1 double}    {[7]}
    {24×1 double}    {[9]}
    {24×1 double}    {[9]}

data = read(ds)

data =

  4×3 cell array

    {24×1 double}    {28×1 double}    {[2]}
    {24×1 double}    {28×1 double}    {[2]}
    {24×1 double}    {28×1 double}    {[9]}
    {24×1 double}    {28×1 double}    {[9]}

The predictors must be c-by-1 column vectors, where c is the number of features.

The format of the responses depends on the type of task.

For more information, see Datastores for Deep Learning.

Table

For feature data that fits in memory and does not require additional processing like custom transformations, you can specify feature data and responses as a table.

Each row in the table corresponds to an observation. The arrangement of predictors and responses in the table columns depends on the type of task.

For classification neural networks with feature input, if you do not specify the responses argument, then the function, by default, uses the first (numColumns - 1) columns of tbl for the predictors and the last column for the labels, where numFeatures is the number of features in the input data.

For regression neural networks with feature input, if you do not specify the responseNames argument, then the function, by default, uses the first numFeatures columns for the predictors and the subsequent columns for the responses, where numFeatures is the number of features in the input data.

Numeric Array

For feature data that fits in memory and does not require additional processing like custom transformations, you can specify feature data as a numeric array. If you specify feature data as a numeric array, then you must also specify the responses argument.

The numeric array must be an N-by-numFeatures numeric array, where N is the number of observations and numFeatures is the number of features of the input data.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | table
Complex Number Support: Yes

Mixed data and responses, specified as one of the following:

You can use other built-in datastores for training deep learning neural 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.

The datastore must return data in a table or a cell array. Custom mini-batch datastores must output tables. The format of the datastore output depends on the neural network architecture.

data = read(ds)

data =

  4×3 cell array

    {24×1 double}    {28×1 double}    {[2]}
    {24×1 double}    {28×1 double}    {[2]}
    {24×1 double}    {28×1 double}    {[9]}
    {24×1 double}    {28×1 double}    {[9]}

For image, sequence, and feature predictor input, the format of the predictors must match the formats described in the images, sequences, or features argument descriptions, respectively. Similarly, the format of the responses must match the formats described in the images, sequences, or features argument descriptions that corresponds to the type of task.

The trainNetwork function supports neural networks with at most one sequence input layer.

For an example showing how to train a neural network with multiple inputs, see Train Network on Image and Feature Data.

Responses.

When the input data is a numeric array or a cell array, specify the responses as one of the following.

When the input data is a table, you can optionally specify which columns of the table contains the responses as one of the following:

When the input data is a numeric array or a cell array, then the format of the responses depends on the type of task.

Neural network layers, specified as a Layer array or a LayerGraph object.

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

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

The trainNetwork function supports neural networks with at most one sequence input layer.

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

Training options, specified as a TrainingOptionsSGDM, TrainingOptionsRMSProp, or TrainingOptionsADAM object returned by the trainingOptions function.