model.py

import os
import pickle
import numpy as np
from tqdm.notebook import tqdm
import tensorflow
from tensorflow import keras
from keras.applications.vgg16 import VGG16, preprocess_input
from keras.utils import load_img, img_to_array
from keras.preprocessing.text import Tokenizer
from keras.utils import pad_sequences
from keras.models import Model
from keras.utils import to_categorical, plot_model
from keras.layers import Input, Dense, LSTM, Embedding, Dropout, add

def Caption_model(max_length, vocab_size):
    # encoder model
    inputs1 = Input(shape=(4096,))
    fe1 = Dropout(0.4)(inputs1)
    fe2 = Dense(256, activation='relu')(fe1)

    inputs2 = Input(shape=(max_length,))
    se1 = Embedding(vocab_size, 256, mask_zero=True)(inputs2)
    se2 = Dropout(0.4)(se1)
    se3 = LSTM(256)(se2)

    # decoder model
    decoder1 = add([fe2, se3])
    decoder2 = Dense(256, activation='relu')(decoder1)
    outputs = Dense(vocab_size, activation='softmax')(decoder2)

    model = Model(inputs=[inputs1, inputs2], outputs=outputs)
    model.compile(loss='categorical_crossentropy', optimizer='adam')
    return model