HTML Captioning with VGG16 and LSTM

This project demonstrates a neural network model that generates HTML captions for images. It leverages the VGG16 model for image feature extraction and a Long Short-Term Memory (LSTM) network for sequence generation.

Table of Contents

• Introduction
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Model Architecture
• Training
• Generating Captions
• Contributing
• Acknowledgements

Introduction

This project focuses on generating HTML captions for images using a combination of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The VGG16 model, pre-trained on ImageNet, is used to extract features from the images. These features are then passed to an LSTM network that generates the corresponding HTML captions.

Getting Started

Prerequisites

• Python 3.7 or higher
• Jupyter Notebook or Google Colab
• Keras
• TensorFlow
• NumPy
• Matplotlib

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/html-captioning.git
   cd html-captioning

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate    # On Windows: venv\Scripts\activate

3. Install the required packages:

   pip install -r requirements.txt

Usage

1. Load and preprocess the data:

   from keras.preprocessing.image import img_to_array, load_img
   import numpy as np
   import yfinance as yf
   
   images = []
   for i in range(2):
       images.append(img_to_array(load_img('/content/screenshot.png', target_size=(224, 224))))
   images = np.array(images, dtype=float)
   images = preprocess_input(images)

2. Define and train the model:

   from keras.applications.vgg16 import VGG16, preprocess_input
   from keras.models import Model
   from keras.layers import Embedding, TimeDistributed, RepeatVector, LSTM, concatenate, Input, Reshape, Dense
   from keras.optimizers import RMSprop
   
   # Load the VGG16 model
   VGG = VGG16(weights='imagenet', include_top=True)
   features = VGG.predict(images)
   
   # Define the model architecture
   vgg_feature = Input(shape=(1000,))
   vgg_feature_dense = Dense(5)(vgg_feature)
   vgg_feature_repeat = RepeatVector(3)(vgg_feature_dense)
   
   language_input = Input(shape=(3, 3))
   language_model = LSTM(5, return_sequences=True)(language_input)
   
   decoder = concatenate([vgg_feature_repeat, language_model])
   decoder = LSTM(5, return_sequences=False)(decoder)
   decoder_output = Dense(3, activation='softmax')(decoder)
   
   model = Model(inputs=[vgg_feature, language_input], outputs=decoder_output)
   model.compile(loss='categorical_crossentropy', optimizer=RMSprop())

3. Train the model:

   html_input = np.array([[[0., 0., 0.], [0., 0., 0.], [1., 0., 0.]], [[0., 0., 0.], [1., 0., 0.], [0., 1., 0.]]])
   next_words = np.array([[0., 1., 0.], [0., 0., 1.]])
   
   model.fit([features, html_input], next_words, batch_size=2, shuffle=False, epochs=1000)

Model Architecture

The model consists of the following components:

• VGG16 Model: Extracts features from the input images.
• Dense Layer: Reduces the dimensionality of the extracted features.
• RepeatVector Layer**: Repeats the feature vector to match the length of the caption.
• LSTM Layers: Processes the input sequence and the repeated feature vector.
• Dense Output Layer: Predicts the next word in the sequence.

Training

The model is trained using a small dataset of HTML captions. Each image is associated with a sequence of HTML tokens, which are used to train the LSTM network. The model is optimized using the RMSprop optimizer and categorical cross-entropy loss.

Generating Captions

After training, the model can generate HTML captions for new images. The process involves:

1. Preprocessing the image using VGG16.
2. Feeding the preprocessed image and a start token into the model.
3. Predicting the next token in the sequence.
4. Repeating the process until the end token is generated.

Example: python start_token = [1., 0., 0.] sentence = np.zeros((1, 3, 3)) sentence[0][2] = start_token

second_word = model.predict([np.array([features[1]]), sentence]) sentence[0][1] = start_token sentence[0][2] = np.round(second_word)

third_word = model.predict([np.array([features[1]]), sentence]) sentence[0][0] = start_token sentence[0][1] = np.round(second_word) sentence[0][2] = np.round(third_word)

vocabulary = ["start", "

Hello World!

", "end"] html = "" for i in sentence[0]: html += vocabulary[np.argmax(i)] + ' '

from IPython.core.display import display, HTML display(HTML(html[6:49]))

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or new features.

Acknowledgements

We would like to extend our gratitude to the following resources and communities:

• Keras: For providing a user-friendly API for building and training neural network models.
• TensorFlow: For offering a robust platform for machine learning and deep learning.
• VGG16 Model: For the pre-trained model used for image feature extraction.
• Google Colab: For providing an accessible platform to develop and test machine learning models.

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