Provides a basic interface for fitting custom models from a fully labeled
data. Annotation can be done manually using the captcha_annotate()
function presented earlier or with another method developed by the user.
The model uses a convolutional neural network architecture, similar
to the LeNet-5 model.
Usage
captcha_fit_model(
dir,
dir_valid = NULL,
prop_valid = 0.2,
dropout = 0.25,
dense_units = 200,
decay = 0.99,
batch_size = 40,
epochs = 100
)Arguments
- dir
directory where the classified images are
- dir_valid
(optional) directory to validation files
- prop_valid
proportion of total images considered to validation. Defaults to 0.2.
- dropout
dropout hyperparameter. Default 0.25.
- dense_units
number of dense units to use after convolution steps. Defaults to 200.
- decay
Weight decay applied each epoch.
- batch_size
Mini batch size. Default 40.
- epochs
Number of epochs to use. Default 100. The model uses early stopping, so it is possible that the procedure ends before the total number of epochs actually run.
Value
fitted model of class luz_module_fitted.
The modeling step has some assumptions about the file names.
Images must be in a folder and have the pattern
path/to/file/<id>_<lab>.<ext>, where:
<id>: can be any name, preferably without accents or other special characters, to avoid encoding issues. It usually contains a name for the type and a hash to identify the image uniquely. Note: When annotating a file, the id must be unique, as two Captchas can have the same label.<lab>: is the Captcha label. It is a string of characters between[a-zA-Z0-9], which can be case-sensitive if necessary. All labels must have the same length.<ext>: file extension. It can be.png,.jpegor.jpg. The operations also work for the.svgformat, but it may have problems due to the image's transparency.
An important note is that the model stops fitting after 20 iterations
without significant increment of accuracy (chosen as 1%; for more
details, see vignette("advanced").