library(keras)
# vectorize texts then save for use in prediction
tokenizer <- text_tokenizer(num_words = 10000) %>%
fit_text_tokenizer(tokenizer, texts)
save_text_tokenizer(tokenizer, "tokenizer")
# (train model, etc.)
# ...later in another session
tokenizer <- load_text_tokenizer("tokenizer")
# (use tokenizer to preprocess data for prediction) save_text_tokenizer
Save a text tokenizer to an external file
Description
Enables persistence of text tokenizers alongside saved models.
Usage
save_text_tokenizer(object, filename)
load_text_tokenizer(filename) Arguments
| Arguments | Description |
|---|---|
| object | Text tokenizer fit with fit_text_tokenizer() |
| filename | File to save/load |
Details
You should always use the same text tokenizer for training and prediction. In many cases however prediction will occur in another session with a version of the model loaded via load_model_hdf5(). In this case you need to save the text tokenizer object after training and then reload it prior to prediction.
Examples
See Also
Other text tokenization: fit_text_tokenizer(), sequences_to_matrix(), text_tokenizer(), texts_to_matrix(), texts_to_sequences_generator(), texts_to_sequences()