def restore_model(checkpoint_filename, video_retriever_generator, selector,
extractor):
"""This function restores a model from a tf checkpoint
Json filename is asserted to be the same as checpoint filename, but with json file extension
-Recovers the model's parameters via the json file
-Builds the model using this parameters
-Prepare the tensorflow graph and get neural network operations
"""
json_filename = checkpoint_filename + '.json'
with open(json_filename, 'r', encoding='utf-8') as json_file:
params = json.load(json_file)
builder = ModelBuilder(params["training_videos_names"],
params["testing_videos_names"],
params["n_captions_per_video"],
params["feature_n_frames"],
video_retriever_generator, selector, extractor)
model = params["model"]
builder.create_model(model["enc_units"], model["dec_units"],
model["rnn_layers"], model["embedding_dims"],
model["learning_rate"], model["dropout_rate"],
model["bi_encoder"])
builder.prepare_training(params["batch_size"])
model_saver = ModelSaver(os.path.dirname(checkpoint_filename),
os.path.basename(checkpoint_filename))
return builder, model_saver, params
