Using a saved model for prediction

During the training, the model is exported every n epochs (you can set n in the config file, by default n=5). The exported models are SavedModel TensorFlow objects, which need to be loaded in order to be used.

Assuming that the output folder is named output_dir, the exported models will be saved in output_dir/export/<timestamp> with different timestamps for each export. Each <timestamp> folder contains a saved_model.pb file and a variables folder.

The saved_model.pb contains the graph definition of your model and the variables folder contains the saved variables (where the weights are stored). You can find more information about SavedModel on the TensorFlow dedicated page.

In order to easily handle the loading of the exported models, a PredictionModel class is provided and you can use the trained model to transcribe new image segments in the following way :

import tensorflow as tf
from tf_crnn.loader import PredictionModel

model_directory = 'output/export/<timestamp>/'
image_filename = 'data/images/b04-034-04-04.png'

with tf.Session() as session:
    model = PredictionModel(model_directory, signature='filename')
    prediction = model.predict(image_filename)