ocrTrainingOptions
Description
The ocrTrainingOptions
object specifies training options for an
optical character recognition (OCR) model.
Creation
Description
creates an ocrOptions
= ocrTrainingOptions(Name=Value
)ocrTrainingOptions
object. Specify one or more properties
using name-value arguments to configure a set of training options, and then use them
with the trainOCR
function to train an OCR model. For example,
CharacterSetSource="base-model"
sets the source of the character
set to use during OCR training to the base model.
Properties
OutputLocation
— Folder in which to store the output model file
pwd
(default) | string scalar | character vector
CharacterSetSource
— Source of character set for text recognition training
"auto"
(default) | "base-model"
| "ground-truth-data"
Source of the character set for text recognition training, specified as
"auto"
, "base-model"
, or
"ground-truth-data"
.
"auto"
— Training uses the character set of the base model when resuming from a checkpoint. Otherwise, training selects a character set as defined by the ground truth data."base-model"
— Training uses the character set of the base model specified in thetrainOCR
function. Use this option to fine-tune the base model used on the ground truth data."ground-truth-data"
— To use the character set defined by the ground truth data specified in thetrainOCR
function.
MaxEpochs
— Maximum number of epochs used for training
5
(default) | positive integer
Maximum number of epochs used for training, specified as a positive integer.
InitialLearnRate
— Initial learning rate used for training
1.0000e-03 (default) | numeric scalar
Initial learning rate used for training, specified as a numeric scalar. Decreasing the initial learning rate can increase training time. However, increasing the rate too much can result in the training being unable to converge.
Verbose
— Progress information display
true
or 1
(default) | false
or 0
Progress information display, specified as a numeric or logical 1
(true
) or 0
(false
).
VerboseFrequency
— Number of iterations between progress displays
50
(default) | positive integer
Number of iterations between progress displays, specified as a positive integer.
This argument only applies when you set Verbose
to
true
.
CheckpointPath
— Path to save check point models
""
(default) | character vector | string
Path to save check point models, specified as a character vector or a string. When
you set CheckpointPath
to ""
, the trainOCR
function does not save checkpoints.
CheckpointFrequency
— Frequency for saving check point models
1
(default) | positive integer
Frequency for saving check point models, specified as a positive integer. This
argument only applies when you specify a path by using
CheckpointPath
.
CheckpointFrequencyUnit
— Unit of check point frequency
"epoch"
(default)
ValidationData
— Data to use for validation during training
[]
(default) | datastore object
Data to use for validation during training, specified as a datastore object with the
same format as the trainingData
input argument of the trainOCR
function.
ValidationFrequency
— Number of iterations between evaluations of validation metrics
50
(default) | positive integer
Number of iterations between evaluations of validation metrics, specified as a positive integer.
ValidationPatience
— Validation loss tolerance
Inf
(default) | positive integer
Validation loss tolerance, specified as Inf
or a positive
integer. This value specifies the consecutive number of iterations for which the
validation loss can be greater than or equal to the previous smallest loss before the
network stops training.
OutputNetwork
— Network returned when training ends
"auto"
(default) | "best-training-loss"
| "best-validation-loss"
| "last-iteration"
Network returned when training ends, specified as "auto"
,
"best-training-loss"
, "best-validation-loss"
, or
"last-iteration"
.
"auto"
— Returns"best-validation-loss"
when you specify theValidationData
property. Otherwise, returns"best-training-loss"
."best-training-loss"
— Returns the network at the iteration corresponding to the best training loss."best-validation-loss"
— Returns the network at the iteration corresponding to the best validation loss. To use this option, you must specify theValidationData
property."last-iteration"
— Returns the network corresponding to the last training iteration.
SolverName
— Name of solver to use for training
"adam"
(default) | "sgdm"
Name of the solver to use for training, specified as "adam"
or
"sgdm"
.
GradientDecayFactor
— Exponential decay rate for gradient moving average in the adam
solver
0.5
(default) | scalar in range [0, 1
)
Exponential decay rate for the gradient moving average in the
adam
solver, specified as a scalar in the range [0,
1
).
The GradientDecayFactor
corresponds to
β1, as defined by Kingma and Bar [1].
SquareGradientDecayFactor
— Exponential decay rate for squared gradient moving average in adam
solver
0.999
(default) | scalar in the range [0, 1
)
Exponential decay rate for the squared gradient moving average in the
adam
solver, specified by a scalar in the range [0,
1
).
The GradientDecayFactor
corresponds to
β1, as defined by Kingma and Bar [1].
Momentum
— Contribution of gradient step from previous iteration to current iteration of training
0.5
(default) | scalar in range [0, 1
]
Contribution of the gradient step from the previous iteration to the current
iteration of training, specified as a scalar in the range [0, 1
].
This argument applies only when using the sgdm
solver.
Shuffle
— Control shuffling of training data
"once"
(default) | "never"
Control shuffling of training data, specified as "once"
or
"never"
.
Examples
Specify Options For OCR Training
Create a directory to save the trained OCR model.
outputDir = "OCRModel"; if ~exist(outputDir, "dir") mkdir(outputDir); end
Create a directory to save checkpoints.
checkpointsDir = "Checkpoints"; if ~exist(checkpointsDir, "dir") mkdir(checkpointsDir); end
Create a set of options for training an OCR model.
Set the gradient decay factor for ADAM optimization to 0.9.
Use an initial learning rate of 20e-4.
Set the maximum number of epochs for training to 15.
Set the verbose frequency to 100 iterations.
Specify the output directory.
Specify the checkpoint path to enable saving checkpoints.
ocrOptions = ocrTrainingOptions(GradientDecayFactor=0.9,... InitialLearnRate=20e-4,... MaxEpochs=15,... VerboseFrequency=100,... OutputLocation=outputDir,... CheckpointPath=checkpointsDir)
ocrOptions = ocrTrainingOptions with properties: OutputLocation: "OCRModel" MaxEpochs: 15 InitialLearnRate: 0.0020 Verbose: 1 VerboseFrequency: 100 CheckpointPath: "Checkpoints" CheckpointFrequency: 1 CheckpointFrequencyUnit: "epoch" ValidationData: [] ValidationFrequency: 50 ValidationPatience: Inf OutputNetwork: "auto" SolverName: "adam" GradientDecayFactor: 0.9000 SquareGradientDecayFactor: 0.9990 Momentum: 0.5000 Shuffle: "once" CharacterSetSource: "auto"
References
[1] Kingma, Diederik P., and Jimmy Ba. "Adam: A Method for Stochastic Optimization". arXiv, January 29, 2017. https://doi.org/10.48550/arXiv.1412.6980.
Version History
Introduced in R2023a
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