(self, instruction, label_column=None,
                            drop=None,
                            epochs=10,
                            preprocess=True,
                            random_state=49,
                            test_size=0.2,
                            top_k=5000,
                            batch_size=32,
                            buffer_size=1000,
                            embedding_dim=256,
                            units=512,
                            gpu=False,
                            generate_plots=True,
                            save_model_decoder=False,
                            save_path_decoder=os.getcwd(),
                            save_model_encoder=False,
                            save_path_encoder=os.getcwd())
Calls the body of the image_caption query which is located in the nlp_queries.py file
        :param instruction: The objective that you want to model (str).
        :param drop: A list of the dataset's columns to drop.
        :param epochs: Number of epochs (int).
        :param preprocess: Preprocess the data (bool).
        :param random_state: Initialize a pseudo-random number generator (int).
        :param top_k:  Number of most frequent words in the vocab to be used in tokenization (int).
        :param batch_size: The batch size for the dataset (int).
        :param buffer_size: The maximum number of elements to buffer (int).
        :param embedding_dim: The dimension of the word embedding mapping (int).
        :param units: The recurrent units in the decoder (int).
        :param test_size: test size (int) .
        :param gpu: Choose to use gpu (bool).
        :param generate_plots: Generate plots for the model (bool).
        :param save_model_decoder: Save the decoder (bool).
        :param save_path_decoder: Filepath of where to save the decoder (str).
        :param save_model_encoder: Save the encoder (bool).
        :param save_path_encoder: Filepath of where to save the encoder (str).


        :return: an updated model and history stored in the models dictionary
Calls the body of the image_caption query which is located in the nlp_queries.py file
        :param instruction: The objective that you want to model (str).
        :param drop: A list of the dataset's columns to drop.
        :param epochs: Number of epochs (int).
        :param preprocess: Preprocess the data (bool).
        :param random_state: Initialize a pseudo-random number generator (int).
        :param top_k:  Number of most frequent words in the vocab to be used in tokenization (int).
        :param batch_size: The batch size for the dataset (int).
        :param buffer_size: The maximum number of elements to buffer (int).
        :param embedding_dim: The dimension of the word embedding mapping (int).
        :param units: The recurrent units in the decoder (int).
        :param test_size: test size (int) .
        :param gpu: Choose to use gpu (bool).
        :param generate_plots: Generate plots for the model (bool).
        :param save_model_decoder: Save the decoder (bool).
        :param save_path_decoder: Filepath of where to save the decoder (str).
        :param save_model_encoder: Save the encoder (bool).
        :param save_path_encoder: Filepath of where to save the encoder (str).
Calls the body of the image_caption query which is located in the nlp_queries . py file : param instruction : The objective that you want to model ( str ) . : param drop : A list of the dataset s columns to drop . : param epochs : Number of epochs ( int ) . : param preprocess : Preprocess the data ( bool ) . : param random_state : Initialize a pseudo - random number generator ( int ) . : param top_k : Number of most frequent words in the vocab to be used in tokenization ( int ) . : param batch_size : The batch size for the dataset ( int ) . : param buffer_size : The maximum number of elements to buffer ( int ) . : param embedding_dim : The dimension of the word embedding mapping ( int ) . : param units : The recurrent units in the decoder ( int ) . : param test_size : test size ( int ) . : param gpu : Choose to use gpu ( bool ) . : param generate_plots : Generate plots for the model ( bool ) . : param save_model_decoder : Save the decoder ( bool ) . : param save_path_decoder : Filepath of where to save the decoder ( str ) . : param save_model_encoder : Save the encoder ( bool ) . : param save_path_encoder : Filepath of where to save the encoder ( str ) .
def image_caption_query(self, instruction, label_column=None,
                            drop=None,
                            epochs=10,
                            preprocess=True,
                            random_state=49,
                            test_size=0.2,
                            top_k=5000,
                            batch_size=32,
                            buffer_size=1000,
                            embedding_dim=256,
                            units=512,
                            gpu=False,
                            generate_plots=True,
                            save_model_decoder=False,
                            save_path_decoder=os.getcwd(),
                            save_model_encoder=False,
                            save_path_encoder=os.getcwd()):
        '''
        Calls the body of the image_caption query which is located in the nlp_queries.py file
        :param instruction: The objective that you want to model (str).
        :param drop: A list of the dataset's columns to drop.
        :param epochs: Number of epochs (int).
        :param preprocess: Preprocess the data (bool).
        :param random_state: Initialize a pseudo-random number generator (int).
        :param top_k:  Number of most frequent words in the vocab to be used in tokenization (int).
        :param batch_size: The batch size for the dataset (int).
        :param buffer_size: The maximum number of elements to buffer (int).
        :param embedding_dim: The dimension of the word embedding mapping (int).
        :param units: The recurrent units in the decoder (int).
        :param test_size: test size (int) .
        :param gpu: Choose to use gpu (bool).
        :param generate_plots: Generate plots for the model (bool).
        :param save_model_decoder: Save the decoder (bool).
        :param save_path_decoder: Filepath of where to save the decoder (str).
        :param save_model_encoder: Save the encoder (bool).
        :param save_path_encoder: Filepath of where to save the encoder (str).


        :return: an updated model and history stored in the models dictionary
        '''

        self.models["image_caption"] = image_caption_query(
            self, instruction=instruction, label_column=label_column,
            drop=drop,
            epochs=epochs,
            preprocess=preprocess,
            random_state=random_state,
            test_size=test_size,
            top_k=top_k,
            batch_size=batch_size,
            buffer_size=buffer_size,
            embedding_dim=embedding_dim,
            units=units,
            gpu=gpu,
            generate_plots=generate_plots,
            save_model_decoder=save_model_decoder,
            save_path_decoder=save_path_decoder,
            save_model_encoder=save_model_encoder,
            save_path_encoder=save_path_encoder)
        self.latest_model = 'image_caption'
        clearLog()
def image_caption_query ( self , instruction , label_column = None , drop = None , epochs = 10 , preprocess = True , random_state = 49 , test_size = 0.2 , top_k = 5000 , batch_size = 32 , buffer_size = 1000 , embedding_dim = 256 , units = 512 , gpu = False , generate_plots = True , save_model_decoder = False , save_path_decoder = os . getcwd ( ) , save_model_encoder = False , save_path_encoder = os . getcwd ( ) ) : self . models [ "image_caption" ] = image_caption_query ( self , instruction = instruction , label_column = label_column , drop = drop , epochs = epochs , preprocess = preprocess , random_state = random_state , test_size = test_size , top_k = top_k , batch_size = batch_size , buffer_size = buffer_size , embedding_dim = embedding_dim , units = units , gpu = gpu , generate_plots = generate_plots , save_model_decoder = save_model_decoder , save_path_decoder = save_path_decoder , save_model_encoder = save_model_encoder , save_path_encoder = save_path_encoder ) self . latest_model = 'image_caption' clearLog ( )