This is a torch module with This is a simple CNN with three convolutional
layers and two dense layers. It works well with the default dimensions
32x192 from the captcha_transform_image() function. It also uses batch
normalization in the forward method. This function can be used either
to fit a Captcha model using the luz workflow suggested in
vignette("advanced") or as a base code to develop custom models.
Usage
net_captcha(
input_dim,
output_ndigits,
output_vocab_size,
vocab,
transform,
dropout = c(0.25, 0.25),
dense_units = 400
)Arguments
- input_dim
(integer, integer): image input dimensions.
- output_ndigits
number of tokens for each Captcha.
- output_vocab_size
number of unique token values.
- vocab
token labels.
- transform
input transform function (for prediction purposes)
- dropout
(float, float) AlexNet dropout values.
- dense_units
Number of dense units
Value
object of classes CAPTCHA-CNN and nn_module. It works
as a predictive function and as the input to a luz fitting workflow.
Examples
if (torch::torch_is_installed()) {
# raw image
captcha_file <- system.file(
"examples/captcha/tjmg.jpeg",
package = "captcha"
)
# initializes model. The output_ndigits, output_vocab_size and vocab
# parameters are compatible to the TJMG Captcha.
model <- net_captcha(
input_dim = c(32, 192),
output_ndigits = 5,
output_vocab_size = 10,
vocab = 0:9,
transform = captcha_transform_image,
dropout = c(.25, .25),
dense_units = 400
)
model
transformed <- model$transform(captcha_file)
# tensor of size 1 x output_ndigits x output_vocab_size
prediction <- model(transformed)
dim(prediction)
# get the predicted labels
# the model is awful, because it is not fitted yet.
indices <- as.numeric(torch::torch_argmax(prediction, 3))
label <- paste(model$vocab[indices], collapse = "")
}