diff --git a/README.md b/README.md index 3a77d8b3c..85e2fbbe0 100644 --- a/README.md +++ b/README.md @@ -1,132 +1,13 @@ -# FER May Hakathon +## Emotion Detection FER Hackathon - Vedanth -Facial Emotion Detection Hackathon Project, Create a model and test it uing 5 to 10 sec videos to detect emotions +Team Name : FutureX +Team Captain : Vedanth Aggarwal +Members : Vedanth Aggarwal -# Facial Emotion Recognition +Model Building : Advanced CNN architectures + Tranfer Learning -
-
+Deployment : Streamlit + OpenCV -![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54) -![TensorFlow](https://img.shields.io/badge/TensorFlow-%23FF6F00.svg?style=for-the-badge&logo=TensorFlow&logoColor=white) -![Jupyter Notebook](https://img.shields.io/badge/jupyter-%23FA0F00.svg?style=for-the-badge&logo=jupyter&logoColor=white) +The videos are the files with names like recording, angry, happy_multiface - -
- -
-
- Logo -
-

-
- DDS logo -
-# Decoding Data Science in partnership with Falcons.ai -

- -Objective: Develop an efficient facial emotion classification system employing OpenCV/Tensorflow to identify facial emotions within video streams. The goal is to achieve a high level of accuracy, low latency, and minimal computational overhead. - -Similar to:
- -[![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/Msk1drgWEdY/0.jpg)](https://www.youtube.com/watch?v=Msk1drgWEdY) - -Data Source: A video dataset or a combination of image datasets featuring the target objects in states of emotion. - -Kaggle : https://www.kaggle.com/datasets/msambare/fer2013 - -Preprocessing (if needed): Standardize or augment the images/video frames to improve model generalization, if necessary, while preserving the aspect ratio and critical features. - -Model Selection & Training: -1. Using the FER dataset(partial). -2. Train a custom model using the prepared dataset and analyze the performance. -3. Deploy Streamlit and OpenCV to allow users a web ui in which to upload a video and have the video frames analyzed by your model. - -Expecation - -The expectations are for the following: -1) The code used to train the model. -2) The model you trained. -3) The Code used to run the UI and upload the video for inference. - -This problem set provides a clear path to address image analysis issues using OpenCV, with a focus on Facial Emotion Classification in video streams. It allows researchers or students to hone in on critical aspects such as data preprocessing, model selection, hyperparameter tuning, performance evaluation, and results interpretation. -

- --------------- Fully functional Jupyternotebook will be added upon hack-a-thon challenge completion -------------- - - -

-

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-
- - -## Usage -
-

- To use the notebook with relative ease please follow the steps below: -
-

- -1. Ensure all of the required libraries are installed. - -2. Load the libraries. - -3. Run the cells and the cloud images will be generated and saved in the "clouds" directory. - -

-
-

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-
- - - - - -## Contributing - -Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**. - -If you want, feel free to fork this repository. You can also simply open an issue with the tag "enhancement". -Don't forget to give the project a star! Thanks again! - -1. Fork the Project -2. Create your Feature Branch (`git checkout -b feature/YourFeature`) -3. Commit your Changes (`git commit -m 'Add some YourFeature'`) -4. Push to the Branch (`git push origin feature/YourFeature`) -5. Open a Pull Request -
- - -

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- - - - -## License - -![](https://img.shields.io/badge/License-MIT-blue) - -

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- - - - -## Contact - -Project Link: [https://github.com/Falcons-ai/fer_dds_challenge] - - -

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- - - -Contributing -Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated. - -If you want, feel free to fork this repository. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again! - -Fork the Project -Create your Feature Branch (git checkout -b feature/YourFeature) -Commit your Changes (git commit -m 'Add some YourFeature') -Push to the Branch (git push origin feature/YourFeature) -Open a Pull Request +*Project works with multiple different people in a video ( can detect many face emotions simultaneously ) diff --git a/Recording 2024-06-04 104505.mp4 b/Recording 2024-06-04 104505.mp4 new file mode 100644 index 000000000..220345d33 Binary files /dev/null and b/Recording 2024-06-04 104505.mp4 differ diff --git a/Recording 2024-06-04 104648.mp4 b/Recording 2024-06-04 104648.mp4 new file mode 100644 index 000000000..34a6a9643 Binary files /dev/null and b/Recording 2024-06-04 104648.mp4 differ diff --git a/angry.mp4 b/angry.mp4 new file mode 100644 index 000000000..c3c3e4b28 Binary files /dev/null and b/angry.mp4 differ diff --git a/haarcascades/haarcascade_frontalface_default.xml b/haarcascades/haarcascade_frontalface_default.xml new file mode 100644 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6 15 16 4 3. + <_> + + <_> + 2 11 16 12 -1. + <_> + 2 15 16 4 3. + <_> + + <_> + 14 12 7 9 -1. + <_> + 14 15 7 3 3. + <_> + + <_> + 10 1 3 21 -1. + <_> + 10 8 3 7 3. + <_> + + <_> + 13 11 9 4 -1. + <_> + 13 13 9 2 2. + <_> + + <_> + 3 10 17 9 -1. + <_> + 3 13 17 3 3. + <_> + + <_> + 13 8 8 15 -1. + <_> + 13 13 8 5 3. + <_> + + <_> + 3 8 8 15 -1. + <_> + 3 13 8 5 3. + <_> + + <_> + 11 14 10 8 -1. + <_> + 16 14 5 4 2. + <_> + 11 18 5 4 2. + <_> + + <_> + 0 18 22 6 -1. + <_> + 0 18 11 3 2. + <_> + 11 21 11 3 2. + <_> + + <_> + 0 16 24 4 -1. + <_> + 0 16 12 4 2. + <_> + + <_> + 6 20 12 3 -1. + <_> + 12 20 6 3 2. + <_> + + <_> + 18 12 6 12 -1. + <_> + 21 12 3 6 2. + <_> + 18 18 3 6 2. + <_> + + <_> + 0 12 6 12 -1. + <_> + 0 12 3 6 2. + <_> + 3 18 3 6 2. + <_> + + <_> + 15 17 9 6 -1. + <_> + 15 19 9 2 3. + <_> + + <_> + 1 6 22 10 -1. + <_> + 1 6 11 5 2. + <_> + 12 11 11 5 2. + <_> + + <_> + 15 17 9 6 -1. + <_> + 15 19 9 2 3. + <_> + + <_> + 0 18 18 2 -1. + <_> + 0 19 18 1 2. + <_> + + <_> + 3 15 19 3 -1. + <_> + 3 16 19 1 3. + <_> + + <_> + 0 13 18 3 -1. + <_> + 0 14 18 1 3. + <_> + + <_> + 15 17 9 6 -1. + <_> + 15 19 9 2 3. + <_> + + <_> + 0 17 9 6 -1. + <_> + 0 19 9 2 3. + <_> + + <_> + 12 17 9 6 -1. + <_> + 12 19 9 2 3. + <_> + + <_> + 3 17 9 6 -1. + <_> + 3 19 9 2 3. + <_> + + <_> + 16 2 3 20 -1. + <_> + 17 2 1 20 3. + <_> + + <_> + 0 13 24 8 -1. + <_> + 0 17 24 4 2. + <_> + + <_> + 9 1 6 22 -1. + <_> + 12 1 3 11 2. + <_> + 9 12 3 11 2. + diff --git a/happy_multiface.mp4 b/happy_multiface.mp4 new file mode 100644 index 000000000..ce42fa8db Binary files /dev/null and b/happy_multiface.mp4 differ diff --git a/image_utilities.ipynb b/image_utilities.ipynb new file mode 100644 index 000000000..9d9c3038d --- /dev/null +++ b/image_utilities.ipynb @@ -0,0 +1,682 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Image Classification Dataset preparation Operations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Load the Data Directory\n", + "\n", + "- Ensure that your data directory contains subfolders named according to each class of images. For example:\n", + "\n", + " ```\n", + " data_directory/\n", + " ├── class1/\n", + " ├── class2/\n", + " └── class3/\n", + " ```\n", + "\n", + "### 2. Convert all .png Files to .jpg Files\n", + "\n", + "- Use a script to iterate through each class folder and convert all `.png` images to `.jpg` format.\n", + "\n", + "### 3. Convert all .webp Files to .jpg Files\n", + "\n", + "- Use a similar script to convert `.webp` images to `.jpg` format.\n", + "\n", + "### 4. Convert .jpeg files to .jpg files\n", + "\n", + "- Use a similar script to convert `.jpeg` images to `.jpg` format.\n", + "\n", + "\n", + "### 5. Resize all images to ensure consistency\n", + "\n", + "- Use a the function to resize all images equally.\n", + "\n", + "\n", + "### 6. Create Extra Images (Rotated and Black/White)\n", + "\n", + "- Augment the dataset by creating rotated (90, 180, 270 degrees) and black-and-white versions of each image.\n", + "\n", + "### 7. Rename All Images in a Directory\n", + "\n", + "- Ensure that all images have unique and consistent filenames.\n", + "\n", + "### 8. Verify the .jpg Images in a Directory\n", + "\n", + "- Check all images to ensure they are in `.jpg` format and valid.\n", + "\n", + "### 9. Remove Any Truncated Images in a Directory\n", + "\n", + "- Remove any images that are truncated or cannot be opened.\n", + "\n", + "### 10. Split Your Data into Training and Validation Directories\n", + "\n", + "- Split the dataset into training and validation sets." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Find all sub-Directories in a directory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "def find_subdirectories(directory):\n", + " \"\"\"\n", + " Find all subdirectories within a given directory and add them to a list.\n", + " If no subdirectories are found, return the passed-in directory itself.\n", + " \n", + " Parameters:\n", + " - directory: The path to the main directory.\n", + " \n", + " Returns:\n", + " - A list of subdirectory paths if subdirectories are found,\n", + " otherwise, return the passed-in directory itself.\n", + " \"\"\"\n", + " subdirectories = [os.path.join(directory, d) for d in os.listdir(directory) if os.path.isdir(os.path.join(directory, d))]\n", + " if subdirectories:\n", + " return subdirectories\n", + " else:\n", + " return [directory]\n", + "\n", + "# Example usage:\n", + "main_directory = './images'\n", + "subdirectories = sorted(find_subdirectories(main_directory))\n", + "print(subdirectories)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Convert all .png files to .jpg" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from PIL import Image\n", + "\n", + "def convert_png_to_jpg(directory):\n", + " \"\"\"\n", + " Convert PNG images to JPG format in the specified directory.\n", + "\n", + " Parameters:\n", + " directory (str): The path to the directory containing PNG images.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + " for file in os.listdir(directory):\n", + " if file.endswith(\".png\"):\n", + " png_path = os.path.join(directory, file)\n", + " jpg_path = os.path.join(directory, file.replace(\".png\", \".jpg\"))\n", + " img = Image.open(png_path)\n", + " img.convert(\"RGB\").save(jpg_path, \"JPEG\")\n", + " os.remove(png_path)\n", + "\n", + "# Example usage\n", + "# main_directory = './images2/test'\n", + "# convert_png_to_jpg(main_directory)\n", + "\n", + "# Loop through all sub directories in the data directory\n", + "for i in subdirectories:\n", + " print(\"converting files in :\" + i)\n", + " convert_png_to_jpg(i)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loop through a dir and convert .webp to .jpg" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from PIL import Image\n", + "\n", + "def convert_webp_to_jpg(folder_path):\n", + " \"\"\"\n", + " Converts all .webp images in the specified folder to .jpg format.\n", + " \n", + " Parameters:\n", + " - folder_path: The path to the folder containing .webp images.\n", + " \"\"\"\n", + " # Get the list of files in the folder\n", + " file_list = os.listdir(folder_path)\n", + "\n", + " # Iterate through the files\n", + " for file_name in file_list:\n", + " # Check if the file is a .webp image\n", + " if file_name.endswith(\".webp\"):\n", + " # Create the full file path\n", + " file_path = os.path.join(folder_path, file_name)\n", + " # Open the .webp image\n", + " with Image.open(file_path) as image:\n", + " # Convert the image to RGB mode (if needed)\n", + " if image.mode == \"RGBA\":\n", + " image = image.convert(\"RGB\")\n", + " # Convert the image to .jpg format\n", + " jpg_file_path = file_path.replace(\".webp\", \".jpg\")\n", + " image.save(jpg_file_path, \"JPEG\")\n", + " os.remove(file_path)\n", + "\n", + "# Example usage\n", + "# main_directory = './images2/test'\n", + "# convert_webp_to_jpg(main_directory)\n", + "# Loop through all sub directories in the data directory\n", + "for i in subdirectories:\n", + " print(\"converting files in :\"+i)\n", + " convert_webp_to_jpg(i)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loop through a dir and convert jpeg file extensions to .jpg " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from PIL import Image\n", + "\n", + "def convert_jpeg_to_jpg(directory):\n", + " \"\"\"\n", + " Convert JPEG images to JPG format in the specified directory.\n", + "\n", + " Parameters:\n", + " directory (str): The path to the directory containing JPEG images.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + " # Get a list of all files in the directory\n", + " files = os.listdir(directory)\n", + " # Iterate through each file in the directory\n", + " for file in files:\n", + " # Check if the file is a JPEG image\n", + " if file.lower().endswith(\".jpeg\"):\n", + " # Open the JPEG image\n", + " with Image.open(os.path.join(directory, file)) as img:\n", + " # Construct the output file path\n", + " output_file = os.path.join(directory, os.path.splitext(file)[0] + \".jpg\")\n", + " # Save the image as JPG format\n", + " img.convert(\"RGB\").save(output_file, \"JPEG\")\n", + " \n", + " # Delete the original JPEG file\n", + " os.remove(os.path.join(directory, file))\n", + "\n", + "# Specify the directory containing JPEG images\n", + "# dir_path = './test'\n", + "# convert_jpeg_to_jpg(dir_path)\n", + "for i in subdirectories:\n", + " print(\"converting files in :\" + i)\n", + " convert_jpeg_to_jpg(i)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ensue all training images are the same size" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from PIL import Image\n", + "import tqdm\n", + "\n", + "def resize_images_in_subdirectories(directory, size=(48, 48)):\n", + " \"\"\"\n", + " Resize all images in all subdirectories of the given directory.\n", + "\n", + " Parameters:\n", + " - directory: The path to the main directory containing subdirectories.\n", + " - size: The target size of the resized images. Default is (48, 48).\n", + " \"\"\"\n", + " for subdir in os.listdir(directory):\n", + " subdirectory_path = os.path.join(directory, subdir)\n", + " if os.path.isdir(subdirectory_path):\n", + " print(subdirectory_path)\n", + " for filename in tqdm.tqdm(os.listdir(subdirectory_path)): \n", + " file_path = os.path.join(subdirectory_path, filename)\n", + " if os.path.isfile(file_path):\n", + " try:\n", + " with Image.open(file_path) as img:\n", + " img_resized = img.resize(size, Image.LANCZOS)\n", + " img_resized.save(file_path)\n", + " except IOError:\n", + " print(f'Cannot open or process the file: {file_path}')\n", + "\n", + "# Example usage:\n", + "# main_directory = './images'\n", + "resize_images_in_subdirectories(main_directory)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create rotated and black/white images of each image in a directory\n", + "1 X 8" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from PIL import Image\n", + "\n", + "def process_images(image_dir):\n", + " \"\"\"\n", + " Process images in the given directory:\n", + " - Create rotated copies (90, 180, 270 degrees).\n", + " - Create black and white copies of all images including rotated ones.\n", + " \n", + " Parameters:\n", + " - image_dir: Directory containing the images to process.\n", + " \"\"\"\n", + " if not os.path.isdir(image_dir):\n", + " raise ValueError(f\"{image_dir} is not a valid directory\")\n", + " \n", + " for file in os.listdir(image_dir):\n", + " file_path = os.path.join(image_dir, file)\n", + " if os.path.isfile(file_path):\n", + " try:\n", + " # Open the image\n", + " with Image.open(file_path) as img:\n", + " # Process original image\n", + " process_image(img, file, image_dir)\n", + " \n", + " # Create rotated copies\n", + " for angle in [90, 180, 270]:\n", + " rotated_img = img.rotate(angle)\n", + " process_image(rotated_img, f\"{os.path.splitext(file)[0]}_rot{angle}{os.path.splitext(file)[1]}\", image_dir)\n", + "\n", + " except Exception as e:\n", + " print(f\"Failed to process {file}: {e}\")\n", + "\n", + "def process_image(img, file_name, image_dir):\n", + " \"\"\"\n", + "\n", + " Save the given image and its black and white version in the specified directory.\n", + " \n", + " Parameters:\n", + " - img: The image to process.\n", + " - file_name: The base file name for the image.\n", + " - image_dir: The directory to save the images.\n", + " \"\"\"\n", + " # Save the original (or rotated) image\n", + " img.save(os.path.join(image_dir, file_name))\n", + " #print(f\"Saved image: {os.path.join(image_dir, file_name)}\")\n", + " \n", + " # Create and save the black and white version\n", + " bw_img = img.convert(\"L\")\n", + " bw_file_name = f\"{os.path.splitext(file_name)[0]}_bw{os.path.splitext(file_name)[1]}\"\n", + " bw_img.save(os.path.join(image_dir, bw_file_name))\n", + " #print(f\"Saved black and white image: {os.path.join(image_dir, bw_file_name)}\")\n", + "\n", + "# Example usage:\n", + "# image_directory = './images/'\n", + "# process_images(image_directory)\n", + "# Loop through all sub directories in the data directory\n", + "for i in subdirectories:\n", + " print(\"Creating 7X more images in:\"+i)\n", + " process_images(i)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Rename All Images in a diirectory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def suffle_image_names(dir_name):\n", + " \"\"\"\n", + " Shuffle the names of image files in the specified directory by adding a random triple-digit number prefix.\n", + "\n", + " Parameters:\n", + " dir_name (str): The path to the directory containing image files.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + " import os\n", + " import random\n", + " import string\n", + " \n", + " # Specify the directory containing your image files\n", + " directory_path = dir_name \n", + " # Function to generate a random triple-digit number\n", + " def generate_random_number():\n", + " return str(random.randint(100000, 200000))\n", + " # Iterate through the files in the directory\n", + " for filename in os.listdir(directory_path):\n", + " # Check if the file is an image (you can adjust the file extensions as needed)\n", + " if filename.lower().endswith(('.jpg')):\n", + " # Generate a random triple-digit number\n", + " random_number = \"00\"+generate_random_number()\n", + " # Get the file extension\n", + " file_extension = os.path.splitext(filename)[1]\n", + " # Create the new filename with the random number\n", + " new_filename = random_number + file_extension\n", + " while os.path.exists(os.path.join(directory_path, new_filename)):\n", + " random_number = \"00\"+generate_random_number()\n", + " new_filename = random_number + file_extension\n", + " \n", + " old_file_path = os.path.join(directory_path, filename)\n", + " new_file_path = os.path.join(directory_path, new_filename)\n", + " \n", + " # Rename the file\n", + " os.rename(old_file_path, new_file_path)\n", + " #print(f'Renamed: {filename} -> {new_filename}')\n", + "\n", + "# Example usage\n", + "# main_directory = './images/test'\n", + "# suffle_image_names(main_directory)\n", + "for i in subdirectories:\n", + " print(\"Renaming images in:\"+i)\n", + " suffle_image_names(i)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Verify .jpg images in a dir" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def verify_jpg_images(folder_path):\n", + " \"\"\"\n", + " Verify the integrity of JPG images in the specified folder.\n", + "\n", + " This function iterates through all files in the folder specified by `folder_path`.\n", + " It checks if each file has a '.jpg' extension and attempts to open and verify its integrity using Pillow.\n", + " If a file is found to be broken or corrupt, it prints a message indicating the issue.\n", + "\n", + " Parameters:\n", + " folder_path (str): The path to the folder containing JPG images.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + " import os\n", + " from PIL import Image\n", + "\n", + " # Get the list of files in the folder\n", + " file_list = os.listdir(folder_path)\n", + "\n", + " # Iterate through the files\n", + " for file_name in file_list:\n", + " # Check if the file is a .jpg image\n", + " if file_name.endswith(\".jpg\"):\n", + " # Create the full file path\n", + " file_path = os.path.join(folder_path, file_name)\n", + " \n", + " try:\n", + " # Open the .jpg image\n", + " image = Image.open(file_path)\n", + " image.verify()\n", + " except (IOError, SyntaxError) as e:\n", + " # Handle the broken or corrupt .jpg image\n", + " print(f\"Fixing broken or corrupt image: {file_path}\")\n", + " #os.remove(file_path)\n", + " continue\n", + " \n", + " # Print the valid file path\n", + " #print(f\"Valid image: {file_path}\")\n", + "\n", + "\n", + "# Example usage\n", + "# main_directory = './images/test'\n", + "# verify_jpg_images(main_directory) \n", + "for i in subdirectories:\n", + " print(\"Verifying images in:\"+i)\n", + " verify_jpg_images(i) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loop through a dir for truncated images" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def find_truncated_images(directory):\n", + " \"\"\"\n", + " Find truncated images in the specified directory.\n", + "\n", + " This function iterates through all files in the directory specified by `directory`.\n", + " It attempts to open each file using Pillow's `Image.open()` method and loads the image data.\n", + " If an exception occurs during the loading process, it indicates that the image is truncated or corrupt,\n", + " and the filename is added to the list of truncated images.\n", + "\n", + " Parameters:\n", + " directory (str): The path to the directory containing images.\n", + "\n", + " Returns:\n", + " list: A list of filenames of truncated images found in the directory.\n", + " \"\"\"\n", + " import os\n", + " from tqdm import tqdm\n", + " from PIL import Image\n", + " truncated_images = []\n", + " for file in tqdm(os.listdir(directory)):\n", + " file_path = os.path.join(directory, file)\n", + " if os.path.isfile(file_path):\n", + " try:\n", + " # try to open that image and load the data\n", + " Image.open(file_path).load()\n", + " except Exception as e:\n", + " truncated_images.append(file)\n", + " return truncated_images\n", + "\n", + "\n", + "def get_subdirs(dir_path):\n", + " \"\"\"\n", + " Get the subfolder names of a given directory and find truncated images within each subfolder.\n", + "\n", + " This function iterates through the subdirectories of the specified directory (`dir_path`).\n", + " For each subdirectory, it finds truncated images using the `find_truncated_images` function,\n", + " prints the number of truncated images found, and lists their filenames.\n", + "\n", + " Parameters:\n", + " dir_path (str): The path to the directory containing subfolders.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + " import os\n", + " # Get the subfolder names of a given directory.\n", + " classes = sorted([f.name for f in os.scandir(dir_path) if f.is_dir()])\n", + " for name in classes:\n", + " print(name)\n", + " truncated_images = find_truncated_images(dir_path + name)\n", + " print(\"Truncated Images:\")\n", + " print(len(truncated_images))\n", + " for image in truncated_images:\n", + " fout = dir_path + \"/\" + name + \"/\" + image\n", + " print(fout) \n", + "\n", + "# Example usage\n", + "main_directory = './images/'\n", + "get_subdirs(main_directory) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Split your data into train and test directories at 80%" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "import random\n", + "\n", + "def split_dataset(base_dir, train_dir, val_dir, val_ratio=0.2):\n", + " \"\"\"\n", + " Directory structure prior to split\n", + " |-------> Data Dir\n", + " |\n", + " class1 class2 class3 etc\n", + "\n", + " Splits the dataset into training and validation sets and moves original data.\n", + " \n", + " Parameters:\n", + " - base_dir: Directory containing the class subdirectories.\n", + " - train_dir: Directory where the training set will be created.\n", + " - val_dir: Directory where the validation set will be created.\n", + " - val_ratio: Ratio of files to be used for validation.\n", + " \"\"\"\n", + " # Ensure the output directories exist\n", + " os.makedirs(train_dir, exist_ok=True)\n", + " os.makedirs(val_dir, exist_ok=True)\n", + "\n", + " # Create the original_data directory\n", + " original_data_dir = os.path.join(base_dir, 'original_data')\n", + " os.makedirs(original_data_dir, exist_ok=True)\n", + "\n", + " # Get the list of class subdirectories, excluding 'train', 'validation', and 'original_data'\n", + " classes = [d for d in os.listdir(base_dir) if os.path.isdir(os.path.join(base_dir, d)) \n", + " and d not in ['train', 'validation', 'original_data']]\n", + "\n", + " for cls in classes:\n", + " class_dir = os.path.join(base_dir, cls)\n", + " train_class_dir = os.path.join(train_dir, cls)\n", + " val_class_dir = os.path.join(val_dir, cls)\n", + " \n", + " # Ensure the class subdirectories exist in train and val directories\n", + " os.makedirs(train_class_dir, exist_ok=True)\n", + " os.makedirs(val_class_dir, exist_ok=True)\n", + " \n", + " # Get the list of files in the class directory\n", + " files = [f for f in os.listdir(class_dir) if os.path.isfile(os.path.join(class_dir, f))]\n", + " \n", + " # Shuffle the files\n", + " random.shuffle(files)\n", + " \n", + " # Split the files\n", + " val_count = int(len(files) * val_ratio)\n", + " val_files = files[:val_count]\n", + " train_files = files[val_count:]\n", + " \n", + " # Copy files to the train and val directories\n", + " for f in train_files:\n", + " shutil.copy(os.path.join(class_dir, f), os.path.join(train_class_dir, f))\n", + " \n", + " for f in val_files:\n", + " shutil.copy(os.path.join(class_dir, f), os.path.join(val_class_dir, f))\n", + " \n", + " print(f'Class {cls}: {len(train_files)} training files, {len(val_files)} validation files')\n", + "\n", + " # Move the original class directories to the original_data directory\n", + " for cls in classes:\n", + " original_class_dir = os.path.join(base_dir, cls)\n", + " new_location = os.path.join(original_data_dir, cls)\n", + " shutil.move(original_class_dir, new_location)\n", + " \n", + " print(f'Moved original class directories to {original_data_dir}')\n", + "\n", + "# Example usage:\n", + "data_dir = \"images\"\n", + "train_dir = os.path.join(data_dir, \"train\")\n", + "val_dir = os.path.join(data_dir, \"validation\")\n", + "split_dataset(data_dir, train_dir, val_dir)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Congratulations, Now you are ready to train your model" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/machinelearning_vedanth.ipynb b/machinelearning_vedanth.ipynb new file mode 100644 index 000000000..c754ce06d --- /dev/null +++ b/machinelearning_vedanth.ipynb @@ -0,0 +1,9233 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kVUtxix8t55H" + }, + "source": [ + "#Downloading Fer2013 Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xZBDlw82t-H4" + }, + "source": [ + "## Method 1 : Opendatasets library" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nusp4a7RUfmq", + "outputId": "fe8b81ef-41e2-42b9-e0d3-a66fa7f3bac8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting opendatasets\n", + " Downloading opendatasets-0.1.22-py3-none-any.whl (15 kB)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from opendatasets) (4.66.4)\n", + "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (from opendatasets) (1.6.14)\n", + "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from opendatasets) (8.1.7)\n", + "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (1.16.0)\n", + "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2024.2.2)\n", + "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.8.2)\n", + "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.31.0)\n", + "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (8.0.4)\n", + "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.0.7)\n", + "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (6.1.0)\n", + "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle->opendatasets) (0.5.1)\n", + "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle->opendatasets) (1.3)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle->opendatasets) (3.3.2)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle->opendatasets) (3.7)\n", + "Installing collected packages: opendatasets\n", + "Successfully installed opendatasets-0.1.22\n", + "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n", + "Your Kaggle username: vedanthaggarwal\n", + "Your Kaggle Key: ··········\n", + "Dataset URL: https://www.kaggle.com/datasets/msambare/fer2013\n", + "Downloading fer2013.zip to ./fer2013\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 60.3M/60.3M [00:00<00:00, 233MB/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "!pip install opendatasets\n", + "import opendatasets as od\n", + "dataset_url = 'https://www.kaggle.com/datasets/msambare/fer2013'\n", + "od.download(dataset_url)\n", + "\n", + "# {\"username\":\"vedanthaggarwal\",\"key\":\"c868f308d5ea632cd9181796a112e745\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sPEBEXW7uLdD" + }, + "source": [ + "## Method 2 : Kaggle API" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7ltslGQ35IY9", + "outputId": "0edc4a9e-8c1f-4b02-885d-7b380b1ef7db" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "cp: cannot create regular file '/root/.kaggle/': Not a directory\n" + ] + } + ], + "source": [ + "!mkdir -p ./kaggle\n", + "!cp kaggle.json ~/.kaggle/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vORZSTi8uZP-", + "outputId": "0d74fcff-ab1e-4306-c6db-b2ff57f54bd2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", + " inflating: /content/train/sad/Training_65242339.jpg \n", + " inflating: /content/train/sad/Training_65267116.jpg \n", + " inflating: /content/train/sad/Training_65275626.jpg \n", + " inflating: /content/train/sad/Training_6529266.jpg \n", + " inflating: /content/train/sad/Training_65329617.jpg \n", + " 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/content/train/surprise/Training_99827442.jpg \n", + " inflating: /content/train/surprise/Training_99916297.jpg \n", + " inflating: /content/train/surprise/Training_99924420.jpg \n", + " inflating: /content/train/surprise/Training_99937001.jpg \n", + " inflating: /content/train/surprise/Training_99951755.jpg \n", + " inflating: /content/train/surprise/Training_99984132.jpg \n" + ] + } + ], + "source": [ + "!kaggle datasets download -d msambare/fer2013\n", + "!unzip /content/fer2013.zip -d /content/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0DT8tNaSXYJH", + "outputId": "6106ca63-77cf-41c9-af27-0118a0a8779f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "angry disgust\tfear happy neutral sad surprise\n" + ] + } + ], + "source": [ + "# Inspect data\n", + "\n", + "!ls fer2013/train" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nari1XZrudtB" + }, + "source": [ + "# Exploring & Visualizing Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wAGLhaizujxw" + }, + "source": [ + "##Directory Structure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ubk55YQYXYLc", + "outputId": "44156f5f-f88d-43a5-a1ab-447aa960e747" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Directorites 2 / Images 0 / in fer2013\n", + "Directorites 7 / Images 0 / in fer2013/train\n", + "Directorites 0 / Images 4965 / in fer2013/train/neutral\n", + "Directorites 0 / Images 4097 / in fer2013/train/fear\n", + "Directorites 0 / Images 4830 / in fer2013/train/sad\n", + "Directorites 0 / Images 3995 / in fer2013/train/angry\n", + "Directorites 0 / Images 7215 / in fer2013/train/happy\n", + "Directorites 0 / Images 3171 / in fer2013/train/surprise\n", + "Directorites 0 / Images 436 / in fer2013/train/disgust\n", + "Directorites 7 / Images 0 / in fer2013/test\n", + "Directorites 0 / Images 1233 / in fer2013/test/neutral\n", + "Directorites 0 / Images 1024 / in fer2013/test/fear\n", + "Directorites 0 / Images 1247 / in fer2013/test/sad\n", + "Directorites 0 / Images 958 / in fer2013/test/angry\n", + "Directorites 0 / Images 1774 / in fer2013/test/happy\n", + "Directorites 0 / Images 831 / in fer2013/test/surprise\n", + "Directorites 0 / Images 111 / in fer2013/test/disgust\n" + ] + } + ], + "source": [ + "# Explore directories of dataset\n", + "import os\n", + "\n", + "for dirpath,dirnames,filenames in os.walk('fer2013'):\n", + " print(f'Directorites {len(dirnames)} / Images {len(filenames)} / in {dirpath}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1AZ6dCbzXYNs", + "outputId": "3a80eda6-e701-4556-cef4-dd4243d12ff4" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise'],\n", + " dtype='" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Function to view random image from dataset\n", + "\n", + "import matplotlib.image as mpimg\n", + "import random\n", + "\n", + "def view_rand_img(target_dir,target_class):\n", + " target_folder = target_dir + '/' + target_class\n", + " rand_img = random.sample(os.listdir(target_folder),1)\n", + " img = mpimg.imread(target_folder + \"/\" + rand_img[0])\n", + "\n", + " plt.imshow(img/255.,cmap='gray',interpolation='bilinear')\n", + " plt.title(target_class)\n", + " plt.axis('off')\n", + " print(img.shape) # show image shape\n", + " return img\n", + "\n", + "img = view_rand_img('fer2013/train','angry')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "a2O3TXxVXYR-", + "outputId": "908019ec-52b0-42a2-ef79-581648d01467" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "uint8\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "#tf.constant(img)\n", + "print(img.dtype)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ox7YvwfWxC0-" + }, + "source": [ + "### 1 image for each emotion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 837 + }, + "id": "oE6TeRqKiFS2", + "outputId": "0311f31e-db39-472b-a16d-81ca8a3ec364" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['sad', 'surprise', 'disgust', 'fear', 'neutral', 'angry', 'happy']\n", + "train/sad\n", + "['Training_8041120.jpg', 'Training_84458220.jpg', 'Training_36073435.jpg', 'Training_82221340.jpg', 'Training_13299816.jpg', 'Training_12885734.jpg', 'Training_40023543.jpg', 'Training_55472250.jpg', 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TqD/zs5+dv+uuu1pXXXVVa9CgQa2+ffu2fu3Xfq3V2dnZqDt+/PgD/gR0/rnoj370o61TTz21NXDgwNYxxxzTmjFjRutP/uRPGj9t32q1WitXrmxddtllrREjRrRe/epXt0aPHt164xvf2Lrpppu6ecYkvZLdddddrZNPPrl11FFHtSZNmtT63Oc+1/rQhz500J+dr51HvvzlL7dmzJjR6t27d2v27Nmtb33rW61f/uVfbs2YMaNRb/v27a1f/uVfbh177LGtQYMGtd797ne3Fi5c2Fin7dixo/Vf/+t/bc2YMaPVp0+f1oABA1qnnXZa65/+6Z8a+9qyZUvrkksuafXr168VEf4EvaRWq3XgNVfNeufpp59u/e7v/m5r5MiRrWOOOaZ1+umnt+65555uret0aOjVaqV/CyZJ0svg+uuvjyuvvDLuu+++OOWUU17u5kjSS2Lu3LnR0dFRZDlKkvRyM0NIkiRJepGeffbZeO655xpld955Zzz88MNxzjnnvDyNkiTpIMwQkiRJkl6kjRs3xvnnnx//5b/8lxg1alQ89thj8bnPfS5GjBgR73nPe17u5kmSVPCBkCRJkvQiDRo0KE4++eT4whe+ENu3b48+ffrEJZdcEv/7f//vGDJkyMvdPEmSCmYISZIkSZIktRkzhCRJkiRJktqMD4QkSZIkSZLajA+EJEmSJEmS2kx1qPRXvvKVouyFF15obL/qVeXzpV69ehVlTz/9dFH2ne98p7G9cePGog4F8j3++ONFWf7JzwEDBhR1+vbtW5Q9++yzje2jjjqqqNO/f/+ijI573759RVlXfy8i4uijjy7KBg0a1Ng+5phjutx3RESOh9q7d29Rh64PyfUoeuqpp54qyp5//vmi7NWvfnVje8eOHUWdzZs3F2V0/ffs2dPY/ulPf1rUoes4ePDgxvbOnTuLOlu2bCnKJk+eXJT169evsb1p06aizowZM4qyWbNmFWW5T1Dfuuaaa4oyHdj48eOLsmeeeaaxnftkBJ/7mjFLcwuZOnVqUZb76tKlS4s673znO4uyCy+8sLFNY5H6JY3PPn36NLZ/8IMfFHVWrlxZlHV0dBRlRxxxRGP74YcfLupQW/M8O2bMmKIOjcVjjz22KMtzxJNPPlnUeeKJJ4oyuk/l65P7UUR5/4ngeSnvn/4eleW/SXMxteHII8vbfe73NFboHkH7z/cpulf+7//9v4syHVztPfqVoKatp556alH2iU98orFN47h27ZX7OY0Pmu979+5dlOU1bu19Is97NPYInb88R9O+aB6iuT23i9DYps/lc5O3D9SuXI/mfzoPdO/N+6J5ls5Dzbmhv3fKKacUZTqwk08+uSjL66OZM2cWdWgtQX0p37dpTU19N6//I8rxT+OM+niuR983CPXV3Faau2rv93n/9PfocxnNbzR+6NzU7Ktmbokoj5vqULtq5qBaua3092rXl1u3bu3y71E7H3jggaJs+PDhje1Pf/rTRZ2JEyd2+ff8F0KSJEmSJEltxgdCkiRJkiRJbcYHQpIkSZIkSW2mOkOI3p3O7yTS+4GdnZ1F2Z133lmU5bwWel+8Npuj5h1Oevcvv7dO+6H3SnOGTETEiBEjGtv0/mbte981+T0171zTu5o5L+RA9fLfpKwjKqt575euK+UkUV5Uzq3YsGFDUYdyOHKe0ujRo4s61HfXrVtXlE2bNq2xTe/Er1ixoiijfjNq1KjGNp0/dQ+9J537Kr2rW/tuc02mF82fNMflPK38bnBExHHHHVeU5Xlj+/btRR0aPzT2cht2795d1KF5g87zo48+2timMUXHM3369Mb2yJEjizo0NignLdejOXzgwIFFGaHrmFEfoXOT52d695yuWS6jrAu6F+cspYiy79JcSRlvdL7oGunwVbNOoLFAOR812Vw9VZvBVJMrUbuvfB5o/Pc0T4PWkjXZJtQuUnvfq6lDZTTH1KBz39N20b5yn6vJV9HBUR/P342oDuV30rp6woQJjW36zkbXmu7juV5Njk1Ez/sJ7Suv46hObXZOTzN9elLnQHL7a8dwTXZSzZrqQGU19ylS82yB+hZlVuV20Tq75vtzRJmxWZsznPkvhCRJkiRJktqMD4QkSZIkSZLajA+EJEmSJEmS2kx1hhC905ffNaZ32370ox8VZZs3by7Kcn4PZRTQ+4f0bl7OxaD36SjvpiZDiD5HeRr5PULKb6B3wek9wn379nXZLsojqXn3k84pvXue90X7ps+R/O7nzp07izp0nrdu3VqU5TyNnCkUwdkcuQ9SVkd+1zkiYv369UVZvj70Dum2bduKsuXLlxdlOVeIcobUPXQ9cv+i95GpP+c5IoLHXkZzRE3Gw7x584o6lNWS5xfqzzTWKS8i5/xQ5gO1feXKlUVZzvQ5//zzizpTpkwpyvL8T2OY3punuXHAgAGNbcpNos/V5AxQG2rlz9J5puuYrzVlK1B/pvtNLqN5iuZ6uj/n+z/NlTp81PR9mnNmzJjR5edozUb7or6Z8yBq6kTwGjd/trYNuV7t5+ic5rFMc29NnhOhv0fzHrWVsjlq2pXnWtoPzVV0PPmztXkn9DfzuaA1qLpn0qRJRdmQIUMa27SeoWtN/TJnblJ/o7nkpcrXIdS3aPzX5APV5AzR5yLKeaM2J+fFZAZ1pXa9VHOMtRlCNRlPte3K56Z23u1ppk/OyIrgfK08hvKat5b/QkiSJEmSJKnN+EBIkiRJkiSpzfhASJIkSZIkqc34QEiSJEmSJKnNVIdK1wTzLViwoKhDAdK9e/cuynJgGIW30udyQBm1lUJfx4wZU5TlYCYK73vmmWeKsppAWjoeCkql4K8cXDVs2LCiztChQ4uyHGRLQX2ErnU+Rgo7pXNTExhIgWsUlEr1MgqMpXOaw5p37NhR1Mlh0fS5iDIUmwJqKXCdAlzXrFnT2J48eXJRR91DQeO5j9MYpnFA4dC5X9aG8tHf7OjoaGyffPLJRZ2aoGHaN7WdwvR27drV2KawY0Lj5eyzz25sz58/v6hDY6rmb9Ln6B6RA/0oLJQCK6ksqw3qrrl/0pxH5zSHflOdHOYdwXNQvtY0VujeRXNX3tcvMpxSr0w1ay8Kms1jjcZoTeAy7avmRzIi6tYX1AYKEc37ojZQ22v2T3XoeGgeyu2gY6Z1IoWi5nbVHk9eH9EPrtQEfFO92kBfkttfe9/TgY0dO7Yoy4G348aNK+rQepzGUF7vU5+n71k1wezUb2r60osJgn4pQ6VzGdWpDdyvaQON/9z+2jD/mmOkz9UEdUf0fG2S91U7F9fM63ntH8E/mkVOPPHExnZPQ6xdsUmSJEmSJLUZHwhJkiRJkiS1GR8ISZIkSZIktRkfCEmSJEmSJLWZ6lBpCthcunRpY5uCwCjki4K5cnBlbaDbhg0birJRo0Y1tnOIWUTEwIEDi7IcZEjtpLAmCk7MoZ50/ij4k0LxclvpeKhdOWCVAqooQJBCsXJZDhCNiNi6dWtRRsdTEwRI/YbKMjqnFCqWrzUF7lLYIZ3DHFpHodIUdkbnPoe1Usiruqcm0LM2lJPUBNRRsCHNCdOmTWts0zxFodIZ9Tc6D7SvHD5Mn6O5/rTTTivKXvva1za26UcA6DznED4KzaewYzqneTxS8DSF2NJ1zfuqPc8kh/BTODTJ4dM0f1J/o7kr37toztu4cWNRRseY91X7IwY6NNWE/NKPdwwfPrzLfdG+adxSH6sJ+a8ty/MQrSVozsn7ovUs/b2a+YsCSknNvEBBsPQ5+nGYPH+NGDGiqENroXzPofNAbSD5WtN8TPeXmiDw2vu/Doz6fb5PTJw4sahDY53W9nn804/K0Jii61+jJrSY5ggaszUB6LUhyTVhzfR9tuZztcdTU6/2eEhPA6prQqtr5gOqRz+kRN/FaY7L62z6e3Pnzi3KNm3aVJTRM4ie8F8ISZIkSZIktRkfCEmSJEmSJLUZHwhJkiRJkiS1GR8ISZIkSZIktZnqUOmdO3cWZTkAN4ckRXCYEgUg5RAxCtjcvXt3UTZ58uQu909Bpv369SvKcogcBY1SGYXP5TBYCgemkE8KN87hY7Vhd/mc0rWg81ATpkWfy2HeERyAtXr16sY2BQ8SCjLLwbJ0Tqnv5n1RQC2FllFYa0ZBehQ+Rn08n1cK71X3UHBmzRiisUj7yn2H6tD1p3D4WbNmNbZpTiU55Ld2TFFQY+6/FNxKIc+nn356UTZs2LDGNs39NcHMFNRHx0hhx7ke7as2oLBmTiUUIFkTbEj7z+2vbTvdPzO6rrWhrzmQujb8VocmmkNzv5g3b15Rh8Zovq9SOCzNq1RW087a4Pc8jnr6Yxf09yjstrOzsyjLoe60JqAfB6AxmtevtXNHDrKn/dO6Mc//EeVx0z2IjmfSpElFWV7b1wbZU1tznzNU+sWj7z35PkTrLFr30HjJ/ZnmA1on0Hoso33VhBZT36oNYa6pUxvMXBNQXbMvantNUPOB2tVTNT8WQG2oLctq7hG1YeF0vvJ8Q+HqNH9edNFFRRl9l+gJ/4WQJEmSJElSm/GBkCRJkiRJUpvxgZAkSZIkSVKbqc4Q2rp1a1GWs1lq3suNqMu3oHeWp0+fXpRRbk1+r5jybqhd+R1V+hxlCNF7y/lc5OyJA+2r5r18ei+T3mXMx0PXpzZrJLeB3q+l/Y8YMaIoy+9FbtmypahD/W3lypVdllG/ofOVj5tyf+jdZjrGmr9Xm4mQrz/lDKl7asYZXVcas1QvX296t5n60ujRo4uyPHetX7++qEN5V7n/1mSRRXCuTM50oDF13nnnFWU0F+csNRoblA2Qx0ttNgSd+/wueO177dSuvC+aNwhlFuTcFJoP6D32fF+iOZzK6PrnrAa6/3R0dBRl1G9ytgn1Gx0+qD9ldP/fvHlzUZbHO83Z1KdpvOd+V3PPjuCxlucdagPtP9+3aWxTvuIjjzxSlG3fvr3Lv0fni44n3ztoX5T7QnNhnjtypmgEr1/yOaR8DZovH3zwwaIs36Mpc4PuS3S+avKo1D2U6Xnqqac2tqdMmVLUWbhwYVFG+Vp5vUR9qTZDMq8LatcJNVk9NE/1NNuI9t/TejXfJWszhGryjmrPKZ2vfJ5rs5p6mmVYsx6jfkRzJV3r3C76PkDr3jFjxhRleRzUriUz/4WQJEmSJElSm/GBkCRJkiRJUpvxgZAkSZIkSVKb8YGQJEmSJElSm6kOlc7BdhFl+GgOEI3g4EEKa837oqAxCl0aOnRoUTZ8+PDGNgWsUhhUbhcFM1Ho1zHHHFOU5TAt+nsUzNW/f/+iLKNgLmpXTUgetaumHgV15ZDUiLowWAoCpOs6ceLEoiwHjS9fvryos27duqIsh1ZTOykwloK5cj0KI6frSm3NIYwUIKzuoT6exwYFwdH4pHr5+tM1o7lx7NixRVkeVzSGqSwHfNKcREHANGZzPQrlnDRpUlFW8wMCdB7y3B9Rngc67zQH0fjMAYW1gfgUbJjHJ93fagIrqV5t4GLul3Q8dP0p4Df3XQrzp7mRQhLz/ZPu8zp81ASl0g9BUNBwXrPRvmnOodDa3M9rg0ZpDZXHB413Grc5OHvFihVFHVqX0Pw1efLkxjYFIufzF8Hh0HnuoHmiNrw7t6Mm0D+inE9qfzCAQurXrl3b2KZg6zVr1hRltObM96+aEFYd3Jw5c4qy/IMUFK5Oa32aN/LYo3sO9cvaIPOafXXVpgOV0Tohl9Hfo/t97doho/OQy3oaIB1RzrMvJhA7r9Ho3NS2qybYnub6mu/1tfebvP+adfCB/mZPfzgl818ISZIkSZIktRkfCEmSJEmSJLUZHwhJkiRJkiS1GR8ISZIkSZIktZnq1DQKa8soVJTCjSgcLIeGUjAvBQhSvRwiTcF5NeFgFGRKgU4USFqDzg3tKwel1YTkRpSBsRRaRUFqtcFfGbWdzmE+bjoPdH2of82aNauxnQMYIzgQffXq1Y3t++67r6jz4x//uCijAMTcn2fPnl3UGTJkSFG2YcOGouzxxx9vbNMxq3soHLwm0Lc2qD2PK6pz1llnFWUDBgzosq00d+3YsaPLfdHnqIzmhNx+Cnik/kwhprn/0vihIMiaoFGaWygIMM9LFBZaE4gYURfUT5+j85xRWCztK6MQ2NpjzCj8nuapHGIeUba/pu06NNQGs+Y1Wr4/R/D6L4eZU9+hUPxx48YVZXmeo7mE1o2kJlSa1hfLli1rbNMx03inuTDP93TeaX6ZOnVqUZbvEzR30I8i1Mwx1EfoHprPYW2gK81p+fpPmDChqNPZ2VmU7dmzpyjL15qOWd1zwgknFGV5jVvzfTCCQ8Xz/ZjGD30vofVevt40b9SEHVOfp79Hx5j7eG2gc02IcG2Qds2+an5chf4mrZ/o+yzNJflv1gQ1R/A4zsdY+wMleV90rmidXfNdonZep/XY8ccf39imH6yp4b8QkiRJkiRJajM+EJIkSZIkSWozPhCSJEmSJElqMz4QkiRJkiRJajPVodIUIplDlyhgiQLqKGApByBTuCUFhtH+c1hfTXgX1aOwK0LhY7T/mjo1Ydf5XEXUhVZRQDG1vaeBrnQ8NYFuVKcmvC2i7Jd0bii8N4fd5SByamdExD333FOU5eDMgQMHFnXuv//+omz48OFFWQ47pONR91CAZB5nFPBJY5FC6/J4mTRpUlGHAvFrxhmhsUfzYEZ9KYe5RtSFK9aGQ+ewPjqnNK7zXELherVBgBnNLXROa34sgPoD7b8mMJoCF6ksz0vUBrrWNT9GQMGdOQT0QPvX4aH2RzKon+f76nnnnVfUoXDolStXNrZ37txZ1KF+SPXWrFnT2Ka5kUI3aX2Ux+gDDzxQ1Nm8eXNRln+EheYqCmHdtWtXUZbvXzSOKfg//3BGRBl4SvMxhaLS+iifV7rHUVlej9XM2RF8vvJcSH2EztewYcOKstyfe/rjKvoP1L9qxgbdh2gNkK8/XbOefs+qCVeOKNtPx1P7oxU1P3ZSu+7J6wL6HM31eU3wYo4n76s2qJ2uTz5Gmg9oXVoTIF773TW3i9a8tdcs93EKV6fnLitWrCjK8mdnzpxZ1Jk7d25RlvkvhCRJkiRJktqMD4QkSZIkSZLajA+EJEmSJEmS2kx1hhC9M5jLKMeG3hmk94rz+3T0Th/lMNRkP/T0XWB6h5DeR6b3HWv+Jp2vmvdd6XP0jmK/fv0a2/S+I72rS5kUuQ10bqgNNe/h1n6O/uagQYMa2/Sue2dnZ1GW380cMWJEUWfChAlFGeUf5HfpKV+D3n+mnKw8XmqyYdR9Ne9JE5qDhgwZ0tgePXp0UYf6Jc0beWzTWKQ5NX+Osme2bNlSlNF72K997Wsb25TL8L3vfa+qXR0dHV22i/p4nj8p14LODd0PauYgajvNNzXvwNN733Tcef80F9e8j16bKUf7yu+eU5+k+wbtP7e/dkzp8JHz82h+ITlPb+PGjUWdxYsXF2XUX3OGEN3baZ1FfTqjMUq5Qjt27GhsU7YFzV9UludfOuYFCxYUZfQ38/XJ83NExPHHH1+U0fyb10I03mk+zvk9dE+lHD6ql3Of6PzR/JXXxlSvNktLB7Zs2bKiLGdi0T2V1uzbt28vynKfo+tPfZfGf75H136vy/2E+nzN/ZJQ22u/z+a5t/Z+TOcwo/UMfTeuyRqszW/Mf7OmnRF1mZF0raldNahd1HfzPYKuT03OVEQ511M2aA3/hZAkSZIkSVKb8YGQJEmSJElSm/GBkCRJkiRJUpvxgZAkSZIkSVKbqQ6VphCuHHhEIXYU6ERBdjkcuDa0qiaEmdQExtWGN9eEYtHfqy3L4WB0nuk85BBRCjamc0pyUCqFRdI1o/NF9bKa0LeI8loPGDCgqENhhDVhlxR2S30rB6dRiOXUqVOLshzwGBHxyCOPNLZrr48OjK5/Rv2G+iDNZ3nuorDwoUOHFmU0v+RAutqw49xPKJRx9erVXf69iIhx48Y1tilU+p577inKaF467bTTGttjx44t6tD8mY+nZn6LqAtcrJ13KYw0o9BEOqc1Ifl0jLSvPA/WzKcH2n8uo2tNbad5KffnmkBJHbroXpjXGJs2bSrqLFmypCjbtm1bY5sCV3ft2tXl5yIiFi5c2NimkP8cYh3BP3aSQ11POeWUog7N7Y8++mhj+7HHHivqbN68uSij8Z4DkHMQfATfS/J9KaIcyzQn/OhHPyrKaH7M98x834jg41m0aFFjmwLEaX6ZOHFiUZbXaLSmonbRD4bkPkHnT92zcuXKoix/l6Af3KAfb6GA6tmzZze2a78P0r29JmiYxlle99T8gEQEr1Xy9yWaB2mdVbOGou9idIy539Nako6R1r01a5Oa73UR5XHv3Lmz6nM1a8faHzbK/ZLuSTQ/03eQ3H5aL1G7aoLz6RlLDf+FkCRJkiRJUpvxgZAkSZIkSVKb8YGQJEmSJElSm/GBkCRJkiRJUpupDpUmOciqNlR6w4YNRdmoUaMa2xScRAFLNagNNUFWFFBF+6K21oQWU9AUHSOFQWcUzJb3Re2kMpKDuXLY4oFQeFs+7trrSgFrOZirNjg7t5+uBYWkkRwOSSGGFGJJoWg5QLImJFcHR6FyPakTwXNcNmzYsKKM+lfNuKZxRnNXnosp2G7NmjVFGYXi3XvvvV22gc7Xww8/XJRt2bKlsX3mmWcWdY477riiLI89agONawoCzG2tDQunsprg5JogaNpXDts8UFm+t9SGUdeg/k3zLoVk5gB0uifp8EFjLffXr371q0Wd9evXF2UzZszoct90D6V5Ye3atY1tCtOnuWPkyJFFWZ4DaBzPmTOnKMtB07SWWLp0aVG2ffv2oqxmvNP+89xL9SiEme5LFLidr8f8+fOLOhTC+4Mf/KCxTes/uj70wxz5BzzoRxgoYJWOJ+vpHKr/QOuLvF7OYz+C1y9bt24tynIfp+8bdE+j/pXX3vRDIzXhzTR31QY65/FC81tN4DK1tafrJVoH0XqW2pWPm84f/ZAFHU8OkM/XK4KPh/pgnnspOJv6Ur4edMzULurPOSQ9/xhCBP8gAp2bvNaiebCG/0JIkiRJkiSpzfhASJIkSZIkqc34QEiSJEmSJKnNvKgMofy+I72/R+8f0nvSuR69z01ZBvROd03eDWUgdNWmCD5GKsvtor9H7zbSvvL7mpTNUPNuK71DSu9JUxtqzlfNeYgo3yuld+Lp3Ux6LzKXUR+h85z7Lr0vTGX0vvOOHTsa2w888ECXdSI4OyVf25rMGh0c5XnlPkd1KL8lv8ccUWYpTJ48uahDeVGUr1CTW0XvNufch927dxd1KEuDxsstt9zS2KZ5I2c3HKhdOS+EcoYoxyLPJTXz/IHq5XbVZANF8LnJ9agOqcmxo5wB+lw+bnqfn+4HlHdE1zajewTNS3kM1exbrzy1uXW0Jnj00Ucb29RP6B5aM65ofUFru5zfQ2sJmqNpPsl/k8YQze35PFAdGrc0r+ZzOGXKlKIOrS+oLH+W1iAXX3xxUUZroZzDQzkZlKeRr8e0adOKOrRm27hxY1GW5xj63lCb8Znv7c5fL17NeKEMmXvuuacoo2yrbdu2NbZp/UR9l9qV10y0Xurbt29RltcXNE/R/ZjWSzV5qrR/Op48zvbs2VPUobVXPkaau/Ice6B25TmBsrtonFFZ/q5H15rWlz/60Y+KstyXxo4dW9SheSmv9eleSdlwlFGUzxfNn4MGDSrKVqxYUZTldvQ0/8x/ISRJkiRJktRmfCAkSZIkSZLUZnwgJEmSJEmS1GZ8ICRJkiRJktRmqkOlKZgvBzNRHSqjAKw1a9Z02QYKeaKQ1xzCt2HDhqIOhcrlkEQK/eppGQUwUgAqna8cNEzHTHJAGQWW1QZN55BHChCj0Gc6xhyeRWGOFEZI+8/nlULf6LhzkB21YeLEiUUZBRt2dHQ0tin0i/obBY3lsDYKNlb3UFhbDtOj8Ul9YsiQIUXZ9OnTG9sUdJrDDyM4kC6HD1K7KKAwB/+tWrWqqEN9ieabHPpPIfYUBEnnJo8hmg8oADufQxrDFMpKIba5jI6ZzjP9zVxW+4MFVK8mjJLuLfn+SffT2lDefG4oXJtQn8hzHIWA6tBEcxX183xPozq0fsnrkDvuuKOoM2bMmKJszpw5RdmZZ57Z2KY+Tfui8Z7HEdWhfp6Ph+Y9KqM5bfbs2Y1tGu933XVXUUbrpbyuonUWBTNTgGu+jvQ5Ovdz585tbFOQNoVK14T814Z30/HkPk7nT92Tf1Qiorz/0rrkoYceKspobZ/HEAWp0zpu2LBhZWMTahf1r+XLlze2t2zZUtShOY/6fQ4tpjmC+iWtvfJcldsZweuSfH2oDq1B6ftM/i5J6xlaS+ZQ/oiy/fQ5uj40Z0+dOrWxnefYCO4jee6l74ObNm0qyuh7dj6HFGJOcxcFc+f5kn40oYb/QkiSJEmSJKnN+EBIkiRJkiSpzfhASJIkSZIkqc34QEiSJEmSJKnNVIdK14TiURgxqQk3poAqCiikoLkcBkbhUxSKNWjQoIO2KYIDSSlwMZdREGBtmHIOA6W/R6Fl+W9SOBgF7lKYWr62NQGlEXyea8JU6brS9c/Xg85NTXAahT7nsOiIiJkzZxZly5Yta2xTkB6FqVH4WP6b1N/UPTTOcn+m60/BuRRGmgPcaO6qDajMc2NtCF8ex0uWLCnqUJBmDjGPKAP+aXwSCnTMn6UgRbpv5H3R3FLzQwcR5XikoFPaF/WJGjRP5RDziHJupPsNXZ/cn/fs2VPUofNF5zmX1YaYk/xZWjPo8EF9Jd9/6YcZ3vjGNxZluU/TD4jQuoT6eb6vnn766UUdWo/RvvIcQKGlFICc90XzOH2OxnJeE9J9icK16RjzNaNAf1q/1MxDdF+iuTaXUT+i++WoUaOKsnxeaa6ifkPtyveq2vueDozGVA5OJnQfpz6Y5xsaP3QfonttDmam9T+1IY9PGncLFiwoyr773e8WZXksUB+k8U/fS/Jai+YuCh/OayE6Zjp/9F0yfzf6yU9+UtTZvHlzUUbf4/J1pDl10qRJRRmFQ+c+SN8R1q5dW5TlOTv/AEsEryUpHDqHpNP1obmL1tB5vPR07eW3TUmSJEmSpDbjAyFJkiRJkqQ24wMhSZIkSZKkNuMDIUmSJEmSpDZTHSpNIV85kJKCoChAuiagmoKAKWi4Jnx6ypQpRZ2RI0cWZRQsVYOCn3LoFrW9Nsg2h1RRcF5N0DQFvFEQGF0zKsvoulJoYe43FMJFx0ihgoMHD+6yXTUovI1CWCloLoeRU9gZhbBRiFjuJzQO1D01QbkU+kYhzCeffHJRVhM+TGOP9k/9K1u3bl2XZTQnUbgq9a88jimocdu2bUUZjc88B1HbTzjhhKIsnxuap2h80nyTgw0pzJ32T3N2Pl90/ugaUuh/DqOk80z3pBzySHMg/T2ag3I/oX5D/Zv2pcNX7Y9p5L546aWXFnVqwtNpPqY1CK0TVq1a1dimUFH68Q4Kg81ByRScTPeX/DdpPNLcQXPTypUrG9t036AfO6Brlo+RwpVpjqZ5NV9rWrtSIG0O2K/50Y8IXl/mc099iwL9Se0PP6gejc8civvggw8Wdej7Jq2rc9+heYPGNQUZ53o0R1D/yv2Sgppp/NMaKn8Pob+3YcOGouzee+8tymbMmNHYPu2007r8exHluaf5jb53U1vz9chzWQSPu+OOO64omz17dmObrg+hY8zzHh0Pyd/ZaP6kH1IgeW1H81v+gZeIiOOPP74oy98vevqDKP4LIUmSJEmSpDbjAyFJkiRJkqQ24wMhSZIkSZKkNlMdmkPvTlOGQ0bv2NE7ffn9OXpfnHI46F3TWbNmNbYp04Hef6fMiK7aGcHvyebjpvdfKR9i+/btRVl+N5ve1ab3yvN5pmtBx0PyMa5du7ao84Mf/KAoq3lXd/78+UUdumb0PuXcuXMb25RjQuc+vxdL78nSu8f0vmvOC6Drs379+qKM+k1+P3jatGlFHXUPXbP8/jG9j0zvMVP2WJ6D6F1q6s8DBgwoynLfWbx4cVGH+tKWLVsa2yeeeGJR59xzzy3KKDtn6dKljW0aG/SO8vTp04uynCGxfPnyog5lPOTzQGOR5kq6H9TkRdG9hebnfG2p7ZTnMX78+KIsz9k0F9N9Nx83vZc/c+bMooz6eL7+lOdB15/yjvJ5pvlNr3x0vQmtofK9kMYHfS6vJ2g8Dh8+vCijsTx27NiDtimCxxWt/3Jb6d5ek51B+6bsL7pP5DmN1nE1eWcR5f2L6tB6ie5Vuf10rSk7pSark9b/Nd83KHuIzheV5c/WjgMdGF3/fB0nTZpU1BkyZEhRRn0ijz0aU9Qvd+3aVZQtWrSosU1ZMDWZpTRH0PeScePGFWV5/U95QbT/173udV3+TVq70ljPawK6j9P6j9ZoOS+KciwpS3XFihVF2ZIlSxrbdH3oHkHnK49/Ws9QG3LGG313rVlnRZTzIJ3TfMwRZZZSRDlX7dixo6hTw38hJEmSJEmS1GZ8ICRJkiRJktRmfCAkSZIkSZLUZnwgJEmSJEmS1GaqQ6Up8CgH7lFYNAVNUVhbDhqj4DkKjKOQrxyURwGFFCqXQ8soHIoCtiiYL4cKbtu2rahTGzSdj/vFhENnFLhF4W2rV69ubFOQ6YMPPliUURB0DpGjwMLjjz++KKOQr3zc1AepLJ97On8UFlkTuNi3b9+qz1EA7hNPPNHYpoBadQ8FG+agaQr9pRA+CsDLY50CEamM+kkOYb///vuLOhSImMNIc7D+gdpA/T6H9VEQNAVu03yZ53VqQw6ejiivRw7NjuDxSSHc+ZpROGltaHW+D1LbaV8kh91Sf6BA3Nyf6X6a5+sIPve579JYofsuhSTm9lNwrw5vud899thjRR36AYzcD2vmkgieC3NYK41R2hf1/bx2oPUZrQnz/YXCr2lNQG3IQan0Iwm0ZqO5PR8PzaEUwltzzWieoLJ8DmvX+nQ8FFCe0fWh4877MlT6xaPvF/l6U0ju1KlTizL6DpqvLY0puv40HvOPadDa64wzzijKcnAy9V2ap26//faibN26dY3tQYMGFXVOOumkoiwH6UeUx1jz4x0R5fxCcwvNQXSe89qLrg+Fa9N8k39M5eGHHy7q0HcqutZ5bNMzApqL83dX+n5Gcwt9b8h/k75v0BqKvnvntV3ND2QR/4WQJEmSJElSm/GBkCRJkiRJUpvxgZAkSZIkSVKb8YGQJEmSJElSm6kOlaYQvhyeRaFvFARGIXI57I7CtGrCYSPK0GUKh6OyHOh79913F3Uo0ImO8dxzz21sT5gwoahDgXgUkr1nz57Gdm1INoVbZRSITceTQx4pVJbCx3IQWER5LujcEApTy4GRFGxG5zmXURAc9REKKMthbdS/c/BcBIfp5TBdCiNT99Acka8ZzTc0fqhP5D5IY5iC03fs2FGU3XvvvY1tClKncMATTjihsT1lypSiDoWrU9Dw/Pnzu6yzYcOGoozGXi6j8O4ZM2YUZXl8UsggzYMU6NzZ2dnl5yi0evfu3UVZ7kt5bo7g8Z/bEFHOqZMnTy7qUGhhvg/S3ELXmu7PuT9TWDTti+bUfG7oPOjwQfNjHt+nnXZaUYf6RS6jtR6NNbo/5vmK5my639P8lUM96XO0JqB7Tlb7gx553NauZ6mt+ZpRyCsdD52brvZdW0b7pvUszUP5fFEdQn2w5kdy1D3Ul/K9nELfZ86cWZTdc889RVnu4zVrvQjuX9OnT29sL1mypKjzwAMPFGV5jUbjjr4/0zrxlFNOaWyPHj26qFP7XSWvC2i+oX3lOYHOVc1aL6L8oQlqA62zaL13zjnnNLbpeyr9EMjWrVuLshzyTSHMOcw/ovwOSsdDAeJ0/fO8Tj8qQmu2Rx55pCh7y1ve0tg2VFqSJEmSJElVfCAkSZIkSZLUZnwgJEmSJEmS1GZ8ICRJkiRJktRmqlMfKcApB4bVhk9SvRzyRoFOFOBEoWV5XxTUt3jx4qLsxz/+cWP7jjvuKOpQyBMFOOWgNAoM3bx5c1FGAaE5kIrOKYWp0vnKKDCMjie3i4JZKRyOwsFy+ylwi8IIKTAuh6lRuBodTw5TezHBkzmYe+zYsUUdCgyj65ODZuk8q3voPOd+SfMIzUEjR44synJgNPV5CkT9wQ9+UJQtXLiwsV0bBJ3HHvU3Okaa13PYIfXn2sDNPF9SuDYFyOY5nEKZKSyQ5vocKkn7onB9Ch7NIbY0T9G5obk+B1lv2rSpqDNq1KiibNy4cY1t6g8Ukk7HmOdGurfQ9aF+s3fv3sY2BVTrlY/6AN0faazlH5ugNQHJ91XqczngOYLn9jzea35UIqIudL12nZDbRXMvrXtqfryF2lC79s7nmdYzNT9GElGux+gYa9uaUdg1yfMQHTNdn5oftqG2q3tWr15dlHV0dHT5OfoxBeoTNetj6uO0Dpk2bVpj+93vfndRZ9u2bUVZDtKneZHmwUmTJhVluc9Rf679Hpzr0THXzBt0PPT3SD6eYcOGFXVorVoTUE39YerUqUUZHWOeE6gO9a28hqK1JN1H8tooojweOqe0ZqMfo8lh2nT/rOG/EJIkSZIkSWozPhCSJEmSJElqMz4QkiRJkiRJajPVGUL0zm1+P5DeNaT36agsvw9I75nSu4b0jmVuB72/t3z58qJsxowZB91PRMTtt99elE2YMKEoy++j0jukdDz0Dnl+v5HaRe9J53o1+RoRnA/xxBNPNLbpHU96BzJ/jtpB2UD0Hjtls+R+SW2nd25zdhKdGyojuR7lfkyfPr0oo/dW77zzzsY2ZType+h99JzpddJJJxV18rvhEfzecr6O69evL+rcf//9RdmiRYuKspoxS2Mvj6GanLbaMsqjqc1lyP2Xxjq9J52zemgOp/vIxIkTi7I8b6xbt66oQ+c0tyGizBCgOWnevHlF2Zo1a4qyfA+i99HpPOfzRfNuTaZARDlf0ucog43k99ap7To00TqL5DUUrS9q8icoEzHnJESU66yIco6ubTutVbLaTMw81ui+QXN7TZ4i/T36HM2PNEdnNG5p3s5lNfuOKI+H/h4dY00ZrZdqM7Fq8lvUPUuWLCnKcs4LXTPKQKF1yK5duxrblPtC+6c5KGf6UGYZZdS89rWvbWzTWoLGIrUr16PviDQ30vjPcxCNH5qX8pqAxj61gcZxXh/RmKJjpDVhnjdqc9loXzUZQnSM+R5B9wy6rrSvXI++u9Lai87h7t27G9u1c3Hmik2SJEmSJKnN+EBIkiRJkiSpzfhASJIkSZIkqc34QEiSJEmSJKnNVCcPUVBWDjeiQK/aoOkc6DZ48OCiDgVgUVlGYZqXXHJJl/XmzJlT1LnyyiuLsuHDhxdlOfAqhz4dqF0UipVRAFbNea4NNqMA7BywlsPcIjh8rCYAl+oMGTKkKKOg6VxGAWI14W3Uj+haUIhYDvAaMWJEUYfK6LgXL17c2N6wYUNRR91zwQUXFGU5MJrmGxqzNPZy4DEFKa5du7Yoo2DLPG9QQDUF1OXxT2OdQusomC+3q+YHBSK4P2cUMksBkqtWrWpsU6h0v379ijK6jiNHjjzodgTPXRQ+nefs2uOh8Ol87mtCbSPK80z9YcCAAUUZ9Ykc1ElzbG2odJ5nqV36z0Nj9KUMyqUQ0XHjxjW2KTCU2pX7fm1oKf34SN4/zdm0VqUQ2dwuakNNYHRPPxdRjltqO8299DdzGbWh9gcDcj2aO2hfuQ/SvETnoTYcugZ9ruZ41D01awK6FrRmp76a1wW1PwRD/Tmvj+i7BLU1j8faH/QgeZ1Aa7baH+bIa5WasUj737NnT1Gndu7Kx039If9QRwTPCTXHQ2vCmmtG6DrmtVBtGDWdm3y/oWMm9MMpeR1HAew1/BdCkiRJkiRJbcYHQpIkSZIkSW3GB0KSJEmSJEltxgdCkiRJkiRJbaY6VJoCdmuCTAkFLOUgKwrmpBApCtiioM+MQrHyvigsmkKkqKwmkK425HXnzp2NbTo3dE5rAvcoXIuOJ59TCnOkUEYKLcvBYhQ8WRsgmPtcTVjgi0H7yueLAvEo7Jau9eTJkxvbOVxX3UchbPk6bt68uWpf+/btK8ryNdq0aVNRhwKDaQ7KY4jGAZXluYT6IAX6URkFFNZ8jv5mLqMg/c7OzqIsny+aT2lM0dyf25qDbw/0uXvvvbcoW758eVGWLVq0qCjbsmVLUZbvNyeffHJRh+bZHFBPgdVURnNjPq80DujeQtcjB3zWBDfq0FUT6k7rxpr7OK2NaNzSHJDRvET7p3q5T1M4LI2F/CMCdK7oxzuoXp4nasdVzdxOaz36XG34dE/q0PcGahfJfak2ZLpmndjTwGr9B7oP5fFCfYT6BNXLY5Z+AIHGC5XlH2KgPkjzWU0YOd1DaUzlv1nbB+k7YR7rdE6pLB8jtbM29D/PlzTvUhvoe30+NzRP0fmia5avUc0PD9Hnao+H6uWxQfcyGj90rfN3Ajp/NfwXQpIkSZIkSW3GB0KSJEmSJEltxgdCkiRJkiRJbaY6Q6gmo6Y274Lezcz7qnnvjz4XUb4PSO/c0XuR9E5iRu/m1byPTPumdy7pGPM7ibXvO+a20r7pnVt6PzS/AzlkyJCiTm1uRUb9oTZDKL+jSnXo/c38OepvpOY858ynCM5JmT17dlGW85ToWqt76NznPkfzAV3rDRs2FGUbN25sbNP76ZTLUzP2arJaIso+TmOY2lAzfxIaLzRmc3+mDI6a7A6aw2vLcm4RzTfHHXdcUUZz3IIFCxrblA21devWomzatGlFWX5nnPKCqA35OtK1oKwrmpcGDRrU2Kb7G/VTWg/UZgjqPweNR9LTrJTcdyLq+ib1sVxG80TOzjrQvvJ8QmOhNleoZu1F95d8D6B8CMpEGjt2bFGWswZrsjrocxHl3Efnj+6FNflj1C7qg3nuoLmE9kV9KZfRvmr3X5P7qe6h/pXPM2Uurlu3riijfLuc/Udro2HDhhVlNXMe1aE1VP6b9F2PxmdP76G1uV95/9SGmu89tXlOVC/Pl7XZbXTu8zqE6tTey/K5qc26y22g+Y3W/1Qvr0spX3PmzJlF2Y4dO4qynFlHuak1/BdCkiRJkiRJbcYHQpIkSZIkSW3GB0KSJEmSJEltxgdCkiRJkiRJbaY6VJrCtHJAFIXR1QT1RZTBfxQ0RcFPNYFxFLhVE2xIKEyL5EAqCgKjc0pykBWFBVJgWA6yonDF2hC+XEahbxTKR0FZuV3UdtoX9YkcIkvB1rT/HPxVGyhYE4A9derUog71eerPOWiyX79+Ve3SgVHIb+4n1LcoQHrLli1d/r3awL3HH3+8y8/S/EmB0Xls1waDUhtyW6kOzV1z5swpyoYPH16UZXv27Oly/xQWTeeU5sYc1kxjeNeuXUXZqFGjirKhQ4c2tmlOpdA/qpevUQ7IjODjyfNGTdhqBN/fcn+jNtD9kwKqa/6eXnny/aunIdP0WRq3NWso6ocUKl0zr9asNyN43ZODs2nuoHtHDptfvXp1UYfuSxSKO3LkyMY2heTWBsbmsUyfq13j5n7T07Uk1SF07nPZS/nDNrWh7DqwJUuWFGU5KJfus4TWNHkc14YW0xoq778mqDmivM9R36LPUb08ZmltRPMzfe/J45g+R3285nO1P/qUzzNdn23bthVltHbI8yBdn9rxn+8JVKfm2QXdR6hv0f7z2o7uedTnqS/lHzaonVMz/4WQJEmSJElSm/GBkCRJkiRJUpvxgZAkSZIkSVKb8YGQJEmSJElSm6kOlR4yZEhRloM4KbyLQoVzUF9EGT5IYVq1wU858Ko2aKomXLE2cLEmvC8HG0dwWFc+X3Qt6HP5+uRw6oiI3bt3F2U5oCqiDGalACxSE25K56Y2cDsHeNE5rQlKzSGQVCeCr3/+m3R9asK1IyIGDBjQ2KawOHUPhZHmsbFx48aiDo0NGkM5HJDC9SgslK5/7nM0p1KQXd4/9V0aG1RGfzObP39+UUbzeg4opMA9Gnv5GGkeocBlOqf5s3mMRfAcREHT+W8OHjy4qEOhr3TNMprzauZ1qpPn6wOh65/V9sEcuEjnVK98NIbovlcTsEx9k8Zf7ocUNEsBmyS3lcYVzY9UlucvGi/0ubyepXvJihUrijKaQzOa9+ieQ4He+dzUrmdr1ssUZErXP/cbCv2uCYKlejXB07VeTLi6/h39IMWqVasa26997WuLOjT+ae2Q74W0zqI20Lq65odmasKhqZ9SH6S5JM8T9F2M1hzUrnz/rT2e3C6qUxu4nttA34MIBe7XfLZ2DZXV/uhLXvdQf6N9Ud/N8zgdH831Y8aMKcryfSOPsYiIU089tSjL/BdCkiRJkiRJbcYHQpIkSZIkSW3GB0KSJEmSJEltxgdCkiRJkiRJbaY69ZFCuHLgFYVPUQAWhSflADcKlaIQrhxQHFEXdlcTDkYhXFRGchso4G/16tVFGYVk55Die+65p6hDAYUzZszosg0UkktBZrkehYpRmDLVyyGyFD5KIVzU/hzoSEFgdK1zWBf1LWoDhZ3n/jx06NCiDvXTHFhJ+xo+fHhRR92zYcOGLsso2JjGIvXnHGRH/YY+R30ih9ZRGCn1r9xWmj8poI6OMbd/9uzZRR0Khq0JsqZrQeHN27Zta2z37du3qEMhsxQqmOcNOuZaea6i+ZPCSPv161eU5XmJgicpEDF/jupQu+i4cz0aBzSn1uzrxZxnvbLU/qBHrkefo36exxWNbVpfUIBnnodoPqZ7L83HNX2a1g55HNH4p7md1sY1QbP0OZoza344pTZUvKeBznn+on3XBvPmsp62nfZlqPSLN2/evKIsjykK5q25j9O+tm/fXtShEF66z9V8t6N5o6bf0BxBbc3zC7WJgudp7spzKn3PorL8wwA137voc4Su64QJE4qyHTt2FGWbNm3q8u/RuaF5I7ef+iD1kdwu2jfdb2rQnEfrOLqX5OcsCxcu7FEb/BdCkiRJkiRJbcYHQpIkSZIkSW3GB0KSJEmSJEltpjpDiORsAXq3jd5jpnfl8ruFOUMigt/po7+Z3+ujv0e5MvRecdbT94rp/feNGzcWZcuWLSvK8nuLlA9C75Dndz/pfcdhw4YVZdOmTSvK8jmkc0rnj941ze2g9/Jr85vye6R0fXbu3FmU5fbX5kyRfJ5r+zy9j5zzVHr6Pqr+A13/3C+pnw4cOLAoo2uW322m/lyTr0P7qs3EymiuJDQn5PeRKffn7rvvLsooayj3X7oW9L74yJEjG9sjRowo6qxdu7Yoo3en8/miuZgyS2jM5jyP2jmC5q68fxrrlA+U+w31N8plor6U90X9ht5jr2kX9S0dPqg/5Yy90aNHF3VoXOUyyqykuZfWHLmMcito7l2wYEFR9uCDDza2ac1Gc0DOWKNjpmwTmpvy/EjZIx0dHUUZjducNUIZInSeqV4+7tp1NtXLaO6oudaEjqcmQ9QMoReP8g63bNnS2Kb7P93vaR2SryPd99asWVOU0Rq9JkOIchgzmluo7ZRbk9delCGa10YRdeOT+jx9Ln+foXbSeonGbJ6DKOuI5jz6Xp/XJjQP0jHS2jsfN617cp5nRHkuaP6h+yLdB/N9qafZUBHl9aDs5hr+CyFJkiRJkqQ24wMhSZIkSZKkNuMDIUmSJEmSpDbjAyFJkiRJkqQ2Ux0qTSHPOcySAoprw4dzgCsF4lFgGAVS5aDh2rC7XFYTBHog+bP0OQpOo7+Zz9f06dOLOnQecvgUheRRYFhNoDMFdeXzTm2IKIOyKLyL2prDzggFCFLYWS6jILjaoGlqf0bhYCSHCq9atarqczowCljLc9eoUaOKOjlAOCJi8+bNRVkOQKXgQQqCz9c6ohyPNH5obsz9l8YntYv6c56XKBDxnnvuKcooHDLPG1SH5q5JkyY1tmn85ODWiIiVK1cWZcuXL29s0/mjUP6aIHCaW2guITXh+nR9cqAz3RcJzc85yJrmMgq7pvtGbldNAKcODXQ/pnGU14kzZsyo2n+ea2lupHBo6tO5bNGiRUWdm2++uSijQPqxY8c2tikkl8ZHnvdqf7yD5rkcGE0hubQ+p+DXjNZ6dM+pCXnuaah0bfg8fW/I+68No6Z6eV+0llT3UNj5+vXrG9u0vpg1a1ZRRvfVHA5N9y9aE9DaIV9v+ns0pnK/6ezsLOrkY47gdU8e/zTn0bqEyvLYprmSxkae6+k+Xvt9Kc8ldDx0zWheyusLGp/0oyX0N3Nb6f5G6/+aH+GgUOlx48YVZfnc1PxAVgTfI/J1pGcLNZzxJEmSJEmS2owPhCRJkiRJktqMD4QkSZIkSZLajA+EJEmSJEmS2kx1qDSFouaQXwqtohAuCsDKoXsU1kRBgxMmTCjKcmgdhd3VhM9RSBaFT1G4Vf5sbUAxhbDl80ptoH3lMC36HIWp5vCuiPI6UuhXbQhfPvc5lDeCQ0vpOuZjrG1DDgejz1EZBWnmc0MhbPQ5umY5HJLGnbqH5ogcPkdBnRQWSkGAe/bsaWxv2bKlqEPXkQKqcx+nADkKrcvjhYK0aVzT2MvHSKF8tK9HHnmkKMtjiMJPaV85TJ2uIc3hFLCcw5rpnA4aNKgoo/Gf7xEUykjHUxOuSp+jOS/PJXSvrA07zPWoDt3Xaf85JJHu83rlo3UCob6f5zkao9Sfcl+59957izo0p1G46d13393YvvPOO4s65JxzzinKcrg9BaDSvT3Pq2PGjCnq0L2E5pwcgJt/gCWCg2ypXl6b0JxTEzQbUc5NNMfVBDrXhj7XBEHXBlTXtLV2HOjAqH/l7w4UBEzjmu7tud/T36N7GgVZ5zmI1kY1P1pT+0M9ND4zWoPS92Bqa1630TgjNaHvdG+n/ef2U8A3/XAOzbP5eQOtjeh80Y91ZLWh0vkYaT1La0mS5yDq33RO6Qdwcvupv9XwXwhJkiRJkiS1GR8ISZIkSZIktRkfCEmSJEmSJLUZHwhJkiRJkiS1mepQaZJD/ijQacSIEUUZBX/l4DwKZqKQJwoozIFkVIfKcmAYhY/2NGhu/fr1RRkFZ1E42MaNGxvbObzzQO3q169fY5sCqymgbPXq1UVZDkqjoK7Ro0cXZZMnTy7KMuo3NSGsEWVwWg4nj4jo27dvUZaDf3MwcAT3U+o3GQV60ecoMOyBBx5obFPgnrqH+nieSyg4mcpoHOdQvO3btxd1aoLtIurC4Wj85+A/CjqlcMCJEycWZTkknQIeKRCVjjufGwpcz2GBERHLly8/6H4i+N5y2mmnFWU7duzocl8UWk1trfmxABrXdD1yn6gN4M3Xg+ZF6m81Adi18xS1Nf9AgaHSh4aXMjw3j1vqm/T38nxC970f/OAHRRn1wxzWP2PGjKIOzR3Tpk0ryvLageZeCrvOYyGvbw+0L6qX16V0PI899lhRRsHvef80f9G4rZmbasOo85xD+6bP0f5zWe2+yEsVzKr/QPehfL9fsGBBUYeCc+kHcPK9nL6z1QaU5zUhjc+aHxCiz9Eah+TvQrT2oiBj2n8+F3T+6Ht27ve16xJaV+XQbwrzpnsEjb18buha137Xz2v7mvVZRLk2psBy6rt0HfN8Qz9sQ2tQuh75fNF33hr+CyFJkiRJkqQ24wMhSZIkSZKkNuMDIUmSJEmSpDZTnSGU352LKN/Dy7ksEfwe3syZM4uy/A4cZcHQu5n0nvTAgQMb2/SeH71jl9/Nq3lnOaLuHUh6V5vaRbkl+XjoPVY6z48++mhjm3JyKLOI3rnO54bey/zCF75QlM2bN6/LMroWw4cPL8roHOZ3M3NeSAS/E5/zm6hOzmCK4Gudrwfti8bPfffdV5Tdf//9jW3qb+oe6jd5LqHMihUrVhRlNMft2rXrJWtXfmeYrn9NHhG9Z0z7orkkt4Gy22hf9I56ns9obqH33/O+aF6kzA+azzLKOqLrmufdiDL/go6n9j32fB3pPfbaLKisNtsoHw/dY2v7d77nUb/RK0/uFy8mU2jp0qWN7drstDwP0TjOOYYRfL+ndU5G2YYjR44synI+EK03aU2Q8y1qM+pqskZofUHrJZon8vWgzCKaOyjnke4dGeUw5v3TvEQZIj3NEKJ91WbN6MWh8Z/HC42pvA6OqMsHpf5GbaB6Gd33aI7Ix0P3+pr8UyqjttP3RsqjzfM43Y8pjyi3n9Z1dG5ydltEOWfT+oJyjGgOynlEdE7pc3Q/y3MCnVOaG3O+Ls3FNXNeRPmdsOa7fwTnEeVrRuemhv9CSJIkSZIkqc34QEiSJEmSJKnN+EBIkiRJkiSpzfhASJIkSZIkqc1Uh0pTMFMO76PAqIULFxZlI0aMKMqOP/74xjaFaY4bN64oywGoEWXAErWLwrooaC6j8ClqQw6RojoUDkWBVLldFOhF4WA5cG/GjBlFnRNOOKEoo2DDJUuWNLb/7d/+rahz9913d/m5iDIcbP78+UUdCkmkgK0cplYbZJr7M4WDUdgZhc/mc09jJYeRRUR86lOfKso2bNjQ2B4yZEhRR93z8MMPF2U5JJP6DQWWUgBiRmGbNNYprK8m0JUC4/KcR32X5hYK3MxtGDp0aJd/L4LnxhwgT+GHFN6X/ya1oWbujyjnero+dP3pHpH/Zm2ANN3Pch+kz9WEJNL9rTbsMB8j1aG+SwGvua10n9fhg+aO9evXN7YpmH/27NlFWV7jUEAp/cgDzR25v1IYNd1XaT7OayFqA80TOTCUAotp/qJzWrOvUaNGFWVbt24tyjIa77U/plITRk5lef6lOvT3asJha9tA57lmX+oeuqfl80z39k2bNlWV5TFLaxD6PkPfoU466aTG9sqVK4s6NT8qQ+Mzr+sjItatW1eU5RD7sWPHdlknou77Ja296BhzPbo+NA/S9+f8PY4+l78PRvD32fzDL7QWHzNmTFFGP1qU+yVdM1qz5bmevpPSfYT2nz9L55nK6AeKcn+ueZZB/BdCkiRJkiRJbcYHQpIkSZIkSW3GB0KSJEmSJEltxgdCkiRJkiRJbaY6VJpCkXLAEoXrUQjXN7/5zaKso6Ojsf3a1762qEMheTVBnBSc19PwUQooo2PMgXQ5ECuCQ7EoFC0HGd9xxx1FHQpXPPHEExvbFDT65S9/uaoNt99+e2ObghopAI2Cee+9997GNgXgUlAqBUHmgC06D7SvHLhGfXf79u1FGYVKf+ITn2hsU8AbXX8KtsvngkLF1D107nNgHPVTmiMo0C33pRwWHMFBczUoVJj6am7DMcccU9ShUMEf//jHRVme46gNEyZMKMqmTJlSlOX+nOf5CJ6f8/Wh+w+dZwoCzGhepzmP5rh8XqntdD+gYOZ8HekYa0Jma0NTqT9ndDx0nul85WBbOh698tUG+tL1zX2MgmDnzp3b5d+kuYrWCRRSmucY2hcFjdJx53FEc2FN0GxNYPWB5L9J14LOTf7RF9pXTWh9BJ+bfP1r+02uR22g+2yN2n5KasKB1T3UB/P3EFoT0Bqa1t553Ub3JQrYpXr5ewJ9n8mh+RFlkDHNLXQ8tL7IfW7jxo1FnVWrVhVlFDSd12jUhunTpxdl+XtJ7Q8iUcByXo8tX768al80l+S+RN/16DsurV9yGR0jyfcDOu/9+/cvyqhP5Dmbjoe+89J5zqHl9B2hhv9CSJIkSZIkqc34QEiSJEmSJKnN+EBIkiRJkiSpzfhASJIkSZIkqc1Uh0pTWFMOa6VAytqguRxuPGnSpKp9UTBzDmKiz1GQXT7G2kBXCi3L9Sh4kELr1q1bV5R99rOfbWxTMBeFyN56662N7eHDhxd1pk2bVpTlkLSIiPe+972NbQrqWrp0aVFWEyJGwVkUpkznOfc5CpmsDUnMRo4cWZTdddddRVkOn6ZwMBobdIw53KymnTq4mgB5Ch6nYEsKKM3XlgJ9af4k+W/SfFMT5kr9jY5x4cKFRVme1ykkj8IOad7IoXsU1Ehlua0UwEiBlTQv0dyb0fxJYYe5HbXBo3Qvyf2E+g3Nefnc0PxGoZk1fYnOH4Xr14T5Uh/UoamngcEPPfRQUef888/v8nPUVymYeerUqUVZ7tM0rijQme73ec1E44P2n+eOmlDmA+0rf5Y+R/un9V6e2+k803WlsozC+kmeC+l4aubsWjT3kpr1v7qH1j05YLd2HND6JYfW0/1y9+7dRRl9b9y2bVtjm344g358JP/Y0bhx44o6FD5M4cB5fNIYprnr4Ycf7rKMwrvp+uRzSOePxjrd7/O5ob9Hx9jZ2VmU5XNIQfqkZs1Ox0NzfV4n0jml60rfcfO+6P5GYddUlvdVG5Kd+S+EJEmSJEmS2owPhCRJkiRJktqMD4QkSZIkSZLaTHWGUM07vfR+IH2O3vPMeRA5/yYi4m1ve1tRRvkgu3btamzT+5v07nTOXaB3FOldanoHNh8jvY+Y36+M4ByexYsXN7ZPPPHEos5rXvOaoiy3f/To0UUdymqi9yl7+l7+L/3SLxVl+b1fyqiYPHly1f4XLVrU2Kb3RSkfJL9zn99rjuDz8JOf/KQoy/2e+jztn965zdkDdMzqHjrP+X1dGsM0R1CfyGOBsgfonV6aL3NeD2Xn0HyW2//UU08Vdagv0Thbv359Y5tyzTZv3tzl5yLK96kpB2D69OlFWc4GoXew6ZpRNkC+/jQ+KS+IzmHNvqiPUFm+/lSHzleeu6h/Ux+kfeVMBLquVEZ9KY8Dmnd1+KjJlaGMMsr+yvMxZQ8SmgNyhl9NVkdEuW6MKNdMlDNE83Hu+zSP07il9WzNWoXWl3R98rilOY7mNMooqslAq8kVojovZX5PT/OvavPhdGCUi5LPPd1Lar835nsaXTNqw9q1a4uyPCeMGDGiqDNq1KiiLOcp5iyiiLqsnohybK9evbqoM2PGjKLskksuKcpWrVrV2K7NXMxtpTmP0FySzyHlUdJ33vwdMaKcZ2k9Q9mJ1K68Hq/JGY0oc19pXUr3G+rj+TzT8waaU6ld+Vz0dO3lvxCSJEmSJElqMz4QkiRJkiRJajM+EJIkSZIkSWozPhCSJEmSJElqM9Wh0v369SvKcvAbBYFReBuFyOWAvTvvvLOoM2fOnKLs/PPPL8pySDEFZ1EwXw6WooBSCn6iAKccUkp/jwLQKHTr137t17qsM2/evC73P3z48KIOBY1R4GJuP32OwrRyOGxEGehFQdCEgnlzv6RrRmU55JEC3ihAOodYR0T07t27sU1hZ7Uhr/kc0rVW9+SQ0YhyTqDQP5q7aI7L15HGOvUvCijMcwm1gcZB7uN0PNTfKKAwt/+WW26p2lcOKI6I2LBhQ2ObAlhznYiI+++/v7E9bdq0os6UKVOKMjpfeaxTKB+p/QGBntSJKM8hBQiSmvsuoftgDsCkUEbqu1Qv318oGFKHj5p+TmObQkRnzpzZ2Kb+RetGCofNAZ40X1LQMJUtWLCgsU3rYJonau7bEydOLMroRz7yvEDtrAmfJzXB4AfaV77+PZ0v6XN0PDQ/9jR8uuZzL2Wwdbuq6RN0XWt/CCjvvzaMmvaVw4dr7+N5rO/evbvLfR+oXbmMArFzWHQEBz/nHxqi73W0JshtoO+3dF0pJD/vn34sgH5cia5/blfNj/LUon3RXJ/X7DTP0+doPsv3JfreQMdD1yz31Z7OXf4LIUmSJEmSpDbjAyFJkiRJkqQ24wMhSZIkSZKkNuMDIUmSJEmSpDZTHSpNYV05aI5C/yiQqrOzsyjLwWIUBPzpT3+6KMsBghERI0aMaGzn0N+IMmg0ogyMrg2VpeCnHCJFYdQnnnhiUbZx48aiLAe/jh49uqizYsWKoiyHZHd0dBR1KGiagmbz9dm1a1dRh4LTNm/eXJTlwEgKV6S+RNdj/PjxXbaLAraGDh3a2KZQsa9//etFGQXg5QBfCmCkAGGSw8HoeNQ9FA5YE2xYG2yZ5xIKAqTrT0GAecxSQB3JY6/2eMhJJ53U2Kbx89hjjxVlFLCXxz+F0VNZDqim8NiFCxcWZTQ35rFO8wgF29MclI+R7lO1gX55nqB+SnNJnqtqwhwjOOQ534tz/4vg80D3qbz/73//+0UdHT6oX2TUpx955JGiLK8BaAxRQPWWLVuKsjz/UlgnoXkhl9H4qAlcpnNF8xeFaecf5qA2UHA2leXPvpiA/XxMNeeP1Jy/iJ4HZ9fuv6Y/q3vo+udwYPr+RN/rSB7bdA1pfUHfx/J9lPoIzUv5GKnt9IMb9MMfee6icGWaI+gHcHK49bBhw4o6NKbyWojWkvQ9mMK0c5A1zeG0L/qBotxP6Ds8rYXoGHM9agN9X85lVIeeN9RcazrPtWvCrPaHRjJnQEmSJEmSpDbjAyFJkiRJkqQ24wMhSZIkSZKkNlOdIUTvLed8IHofmd5lo3e66b3IjN4Z/NM//dOiLOfivOlNbyrqvP71ry/Kcvsph4HeUc3vxEaU75rSe4WUIbJt27aiLH+W2kXvi+csIHqPkTKe6Frkd2Br3+emjI18/akNdL5o/zljherQ+7v5/c1bb721qLNq1aqijI47l+XMkgjOKKHznPsX9S11D43ZPF7ovW/qSzXzFOVyUdmAAQOKspy5Q+2i94rzO9HU38aOHdvl5yLK437Na15TtS/KhsvHSDljNK/nzKDt27cXdShfi8qmTJnS2Kb30+ldfTr3+Rhrcjoi+DzXZA3R5/J9iu6xW7duLcro+mQ0Vui+TvNgzpWiNujwUZPzQnUeeuihouzMM89sbFN22pIlS4oyGn95nUM5hrVZE3mdQOuzmjFKa72cfxjBWWl5fUR5dHSvorI8/9Zm9dC8UDN/0edqsnpoDq1Zc9ZkHUXwcefPUqaHuqemj9B9trYP5nq0XqZ9Uf/K96vaPL38N6lv0bqxJnuWvivT+oW+E+a5ZN26dUUdmj9zv6ecNjoPlCGUy2h8Ug4PnfvabMYaeV/UBymPMp97ys2k77OkNierRu5z9F2/hv9CSJIkSZIkqc34QEiSJEmSJKnN+EBIkiRJkiSpzfhASJIkSZIkqc1Uh0pTSFEOznsxAZt5/xQqTIFUFBi2adOmxva3vvWtos7UqVOLsokTJ3bZBgqCouPJwVIUULdz586ijMJNc5gitYs+t2zZssZ2bTAghZ1RQGE2ZsyYooyOO/cJ6lu1AWK5D1KYLgVB7tmzp7FNAdLULgoRy32QAnApmI3C1HKIHAXGqnsoFDcHf1N4HwX1kRw0RwGiQ4YMKcoofC4H/9LnKCQ193saw9SfN2zYUJRlFCB9wgknFGU0NvLx0DmloOk1a9Y0tul4aF814c00t9Dn6N6V+wmFRdYG+uW/SX+Pxn8+X3kui+A+QvL8TOeUyugc5vsZhUzq8FEb1putXLmyKMs//JDDnCN4fTlz5syiLK9DaG6ndRzt/6STTury79HxfP3rX29s33bbbUWd5cuXF2XDhg0ryvJcOGfOnKLOtGnTijJaE+bxTus6WmeT/NmaoOYD1ctoXV+zFqL1Jv09KsvHQ21Q99CaIK9V6F5C4dBUlu+1NIbp3k7rqnwfpfsezSW5L9GYon5JP7iTj5H2Rd//aC2Z20VrFTpGWmtldM1ovOQfFaFxR22gsZ4/S3Vqx2z+LM2VNBfn7w304wd0rek65nsQXR9aG9O9MV+P2mDrzH8hJEmSJEmS1GZ8ICRJkiRJktRmfCAkSZIkSZLUZnwgJEmSJEmS1GZe0tS0muCkCA7PzOFgFLhFYZ30N3OQFYUK33jjjUXZu971rsY2hUNRGR1jDtOjMK3aYK4cZFYbuJ2Ds6jtFAyZQ3IP1K6aNlBAWb7+FARHbaXzlYO4cqB4BF//HMxF4brDhw8vyuh4tm7d2timttM1Gz9+fFGWQ8QoEFndM2jQoKIszyUUPEjjgK5jTag0zRF0bfNYpyBFCuHL+6J+Sm2gOfWBBx5obG/cuLGoM2vWrKKM+nNuf03oc0TEhAkTGtvr1q0r6tSGCuY5ju4/FN5H9fK+agMEa4Ja6XN0vnJYM81vtK+aMG36HPXTCy+8sCi76KKLGttf/epXizo6fNSEA1O/p364cOHCxjaFJFNALc2Pe/fubWxTWCfN7RTE+eCDDza2c/h1BP+Yxpvf/ObG9rx584o6Dz30UFH2/e9/vyjL4dM03mmOPu+884qyfH+h9R/NOTTPZTR31MyF1AYqq/mbNWHREXw8+d5Rc8w6OFrb79q1q7FdM49EcJ/I16j2mlEYcP7eSPMGzTe5P9OaisYU/QBObgOtcWp/7Ci3g8KIqV15vFAdahfN63kNRWuJ2hD7/DepP9TuK8/1NIfTj/7k61Pb32q+s9OPTFF/q/lOTd95avgvhCRJkiRJktqMD4QkSZIkSZLajA+EJEmSJEmS2owPhCRJkiRJktpMdaj0nj17yg+nEDYK6qNANwpYykFJtQFYNeFzFCD26KOPFmX/8A//0Ni+4oorijoUWkVtzaFVFFhIbacg0xxcRYFe1K4cikzXggKqKByMgmwzCgKn487nKwcdRnAwG4Ww5TDFlStXFnUo7Hbx4sWNbTqnFOi1du3aomzEiBGNbQqepPNA8nHXhqTpwGje6OzsbGxTKB+Nawo2HTt2bGObAt3o+lOQeW4HzQc5VDiiDGakfVPA4+jRo4uyPI7zWIkoQ2AjeMzmewKdv5owQgrvGzJkSFE2dOjQLtuVQ2cjePxTv6mpUxu4mMtofssBnBHl9a+dw2t+2IDCaV/zmtcUZV/4wheKstx3L7vssqKODm81gcHUN3OQ/amnnlrUmTx5clFGa868nqAgUyqrCZtfs2ZNUYfmgLwvmv9p3TN//vyibNy4cY1tmkPpxzToHjB16tTGNl2f2kD6ms9RWVYbKl0T4F8b6F9TVhtsrQOj7175/kVjkcYUrV/yNaPrSms76kt5XG3fvr2oU/tjR121M4L7ag4WpmOmeaNmfNb+SFLeV+13kJo2EDoPNBfXhErTtaB25e92tG7MAdL0OTo3td/185xNdWgtSeMlf+eg7641nPEkSZIkSZLajA+EJEmSJEmS2owPhCRJkiRJktpMdYYQvWuYDR48uCij9zBrshno3TzK5qD3Q3PeDeUw5PyjiIgf/ehHje38/nhExK/92q8VZTXnht5HpHdBa3IkKIOJ0HvlNejc5DJ6t5XeW6Tjzu/90zue9J4kvb+/YsWKxvakSZOKOnQ8+f16uharV68uyii3JKNrOHHixC4/F1GOIXqHVN2zefPmoixnok2bNq2oQ/PUqFGjirIxY8Y0tuk95tq8sPzOPWVW0ZyaURYMZc1Qf543b15je+TIkUWd9evXF2U0PnNeAOX31OQD5ZymCM4noPOc53EaU3TNqF4+h3ROCV3rPE/kXCuqQ+2qzSegNuQ+TjlDb3rTm4qybdu2FWX5vnvccccVdXR4y/2uNlcizx1btmwp6tAcTXlquQ10/6ecB5qH8vigNS6tHfKYpLURzTk0p+VsE2onzdGUd5LXVdQu2j/NMfna1uaW1aA21GRn1uak1OQR9TQTRf+BMgrzeFy6dGlR5/HHHy/KOjo6uvx71E9rM6rou2RWk5Na2waal/Larrbv0ryUv6PVzG8R5Tqhdn1B+89toPUSfc+mstzW2pyxmgwh+k5N83Oez2rzgmjdm88zfeeltlMmbs5A6um8678QkiRJkiRJajM+EJIkSZIkSWozPhCSJEmSJElqMz4QkiRJkiRJajPVodIULJVDuCgIjFDgUQ7YorCmPn36VLUrhzpRnZpg5ocffrgooxC+17zmNUXZaaed1timMFoKI8yByxHluaHwKQoMzcdIodx0nikwOtejQLTakK98Pag/UKDz1q1bi7Jx48Y1tmuDzXNoGbWdrjWdw9zWESNGFHWGDx9elFEQZA5Oo3BNdQ+Nl9wvc3BnBAe60bXNQaD0OQoMprGXg+woEHvnzp1FWUZjikLmKaw5t4v67rBhw4qyE044oSjL4d0UKkhjL89ddK4oZJTOfb5P0dxPoX8U6JyDk+l46Nznz0WU8xLdP+ncZDT3UxsoQDK3i4IUH3rooaJs9uzZRVkO+F24cGFR56qrrirKdGiidVUN6tN53N5///1Fnbe//e1FGY2ZvC8Ki6V5gvp+nito7qDzkNdxdH+hdVbNWpXOH413Wr/kdRv9vZofSYko5186pzV9pHauql1f1nyO5PZTcK66h37kJf/QDI1hWvfSPTqH6RK6jvT9pSaEmX6gKKP5pjbQOX923bp1RR36PkOB+/nHTmrHdW4DrS9oTNHx5GtGP0ZR+8NTeT6juaXmhzMiynUbzf01PwRAn6N+Smv2mtB6CmWnZwk1P9RUw38hJEmSJEmS1GZ8ICRJkiRJktRmfCAkSZIkSZLUZnwgJEmSJEmS1GaqQ6Up0JmC8jIKWCI5OK8mXDmCA8NyGYU30+dySNUll1xS1KGgqX/4h38oym699dbG9hve8Iaizrx584qyIUOGFGX5XFCgKwXn5iBWOg8UikXyuaFAL9pXTfAXBcht2LChKKMwrRzynMOiIzjQN19HOu9PPPFEUUahcvl45s6dW9SZPHlyUUYhb7mtFFCn7qHAvRxkSAGfFPpN1zHPJRQgTP1ywoQJRdmiRYsa28uWLSvq0ByUQ+QoXG/x4sVFGYWw535PwY0UKkjnMIdwU9gdhf7luZ7OHx0jBU3nMro+NK5zIDa1tSZ4+kBlFEid1QRn0xxL97eac0/7+t73vleUUR/M4eMUIGmo9OGjp6G7FCKcPfjgg0XZ6173uqKM1qW5n9N9ltYqtK88/mgNSvvPx0hjvTbINO+LxjHNxxTEn9tKc2/N2pjq1azFqYz6Q20oKp2vnsrtoGut7qExla//zJkzizq09qbw4bx/+o5D15Huq3k8Ut+tCQfu6Ogo6tQEvFO7KMSevustWLCgKFu5cmVjm34Qha5PRueB5jNa4+T1Ea2pakO/8xxHcx61i9aJuYzaQNcsz590Leg8UBuyHAIewaHStM7O7erpvOi/EJIkSZIkSWozPhCSJEmSJElqMz4QkiRJkiRJajM+EJIkSZIkSWoz1alpFFCXA28p9JfC4ShMLwdU59DXiPpAvxyUR5+jkKccukWhVRSuR8FPCxcubGx//vOfL+rcd999Rdm5555blE2fPv2g2xEcZFYbzJfRcdcEQdYGv+ZgtjVr1hR16HgoMC7vn4559+7dRVkO6qYQcwqQo32deuqpjW0KlaZw9c2bNxdlJ510UmObguDUPccdd1xRlsO7Kbxv6tSpRRkFuuWwQwqVo/mT5sEVK1Y0tmn81PRVGq+rVq0qymjuqglqpzmVxmc+bgoxpH3l8EEK/aNAP2pr3teOHTu6rBPBYzbflyiUldpAIZZ0L8no+udzQfuhc0NtyPuq/WEACrEcO3ZsY/vee+8t6ujwQf0irx3ofkxjOX+Oxt6jjz5alJ155plFWf4szb00P9L4yPN9bVhn/hzdNwjNhTVrL1ITSF/7OVIzD1Hba46H+khN+HTNnBpRF6ZtqPSLR9cs32PyjxFE8A9u0JyQQ4vpR1jonkb9K3+XrB0/uQ30nZS+U9FcUtN/aa1a872HflyH5qW8jnspg+fpWtC5qflRjJ7+qEFEua6ic1PThpofBongtXH+sRb6ka6aZyXUjh5/9+/RpyRJkiRJknTI8oGQJEmSJElSm/GBkCRJkiRJUpupfkmWMn3yu4w5/yKC38Ok9+Jy7gK9X0lllLGQ69H7e5TzkTOQKGviiSeeKMoGDx5clE2cOLGxvWzZsqLOhg0birK1a9cWZTnfgrJnKLcmHw+9E03Xh+rl60PvBtO5oSyLfNz5XcoIPqf07mxu66ZNm4o69C5ofm+Z3iHt169fUTZy5MiiLL/vvHHjxqIO5bdQFszWrVsb29u2bSvqqHvomg0ZMqSxTXPLpEmTijKaB/P7u/TONWVBUXZWvv6U8UPjLI9jGiv0Dv6DDz5YlI0fP76xTe8j09ig/pzPc20+WT6e/J5+BM8tNK/nsZ0znyL4XXCaN/L+KS+A3qWnern9dC+jz+XrQZ8jNZkvNA7ofk35Wm94wxsa2zTn6fBRk7FQK88B1Fcffvjhouw1r3lNUZb764vJ78iZZ3TMVJbHJO2b5lA6f/mz1Haav2qOm+aOnl5DUpMFROemdq3a1b5rPxfx0maU6N/V5OlQ5h7l0VKmZ76X09qrNse25nP0XTLXozUijUXKO8r9nvozjX8aL/lv1ub+5DLKGarNNcv1aJ6iMuo3uYz6DX2uZi6hNWFP88/o7+Vczojy3kL9gdbUdL5yn6A+WMN/ISRJkiRJktRmfCAkSZIkSZLUZnwgJEmSJEmS1GZ8ICRJkiRJktRmerVMTpMkSZIkSWor/gshSZIkSZKkNuMDIUmSJEmSpDbjAyFJkiRJkqQ24wMhSZIkSZKkNuMDIUmSJEmSpDbjAyFJkiRJkqQ24wMhSZIkSZKkNuMDIUmSJEmSpDbjAyFJkiRJkqQ24wMhSZIkSZKkNuMDIUmSJEmSpDbjAyFJkiRJkqQ24wMhSZIkSZKkNuMDIUmSJEmSpDbjAyFJkiRJkqQ24wMhSZIkSZKkNuMDIUmSJEmSpDbjAyFJkiRJkqQ24wMhSZIkSZKkNuMDIUmSJEmSpDbjAyFJkiRJkqQ24wMhSZIkSZKkNuMDIUmSJEmSpDbjAyG95K644oqYMGHCy90MSZKkg7r22mujV69esWPHjpe7KZIk/afzgVAb2rRpU1x77bWxYMGCl7spkiRJkiTpZeADoTa0adOm+PCHP+wDIUmSJEmS2pQPhNSlJ5988uVugiR12xNPPPFyN0GSJEl6xfKB0MvoZ++tr1ixIq644ooYOHBgDBgwIK688sriIcwNN9wQJ598chxzzDExePDgeMc73hHr169v1JkwYUJcccUVxd8555xz4pxzzomIiDvvvDPmzZsXERFXXnll9OrVK3r16hXXX3/9/rqzZ8+OBx54IM4666w49thj4w/+4A8iIuKb3/xmXHLJJTFq1Kjo3bt3TJ48Of74j/84nn/++Zf2xEg6JK1duzauvvrqmD59ehxzzDExZMiQeNvb3hZr1qxp1Lv++uujV69ecffdd8cHP/jB6OjoiD59+sRb3/rW2L59e6PuCy+8ENdee22MGjUqjj322Dj33HNj8eLFxXz3s33eddddcfXVV8ewYcNizJgx8b3vfS969eoV//zP/1y09x/+4R+iV69ecc899/wiToekQ8ju3bsPuhb74he/GOedd14MGzYsevfuHbNmzYrPfvazxX4mTJgQb3zjG+O73/1uzJ07N44++uiYNWtWfP3rX2/U+9mc9f3vfz/e/e53x5AhQ6J///5x2WWXxa5du/bXu/zyy2Po0KHx7LPPFn/rwgsvjOnTp7+EZ0GS1G58IPQKcOmll8a+ffviuuuui0svvTSuv/76+PCHP7z/v//Jn/xJXHbZZTF16tT4xCc+ER/4wAfijjvuiLPOOit2797drb81c+bM+MhHPhIREVdddVV86Utfii996Utx1lln7a/T2dkZF198ccydOzf+4i/+Is4999yI+PfFS9++feODH/xg/OVf/mWcfPLJ8Ud/9Efxe7/3ey/+JEg65N13333xox/9KN7xjnfE//t//y/e8573xB133BHnnHMO/kvD97///fHwww/Hhz70oXjve98b//Iv/xLve9/7GnV+//d/Pz784Q/HKaecEn/2Z38WU6dOjYsuuuiA//rn6quvjsWLF++fm84555wYO3Zs3HjjjUXdG2+8MSZPnhyvfe1rX5oTIOmQ1dVa7LOf/WyMHz8+/uAP/iA+/vGPx9ixY+Pqq6+OT3/608W+li9fHm9/+9vj4osvjuuuuy6OPPLIeNvb3ha33XZbUfd973tfLFmyJK699tq47LLL4sYbb4y3vOUt0Wq1IiLi13/916OzszNuvfXWxue2bNkS//Zv/xb/5b/8l5f4TEiS2kpLL5sPfehDrYho/cZv/Eaj/K1vfWtryJAhrVar1VqzZk3riCOOaP3Jn/xJo86jjz7aOvLIIxvl48ePb11++eXF3zn77LNbZ5999v7t++67rxURrS9+8YtYNyJan/vc54r/9uSTTxZl7373u1vHHnts6+mnn95fdvnll7fGjx9PhyzpMEZzxD333NOKiNbf//3f7y/74he/2IqI1vnnn9964YUX9pf/t//231pHHHFEa/fu3a1Wq9XasmVL68gjj2y95S1vaezz2muvbUVEY7772T7POOOM1nPPPdeo//u///ut3r17799vq9Vqbdu2rXXkkUe2PvShD72YQ5Z0iKtZi7VaPL9ddNFFrUmTJjXKxo8f34qI1te+9rX9ZXv27GmNHDmydeKJJ+4v+9mcdfLJJ7eeeeaZ/eUf+9jHWhHR+uY3v9lqtVqt559/vjVmzJjW29/+9sbf+cQnPtHq1atXa9WqVT04akmS/p3/QugV4D3veU9j+8wzz4zOzs7Yu3dvfP3rX48XXnghLr300tixY8f+/xsxYkRMnTo1vve9773k7endu3dceeWVRfkxxxyz///ft29f7NixI84888x48skn47HHHnvJ2yHp0PLzc8Szzz4bnZ2dMWXKlBg4cGA8+OCDRf2rrroqevXqtX/7zDPPjOeffz7Wrl0bERF33HFHPPfcc3H11Vc3Pvf+97//gG34rd/6rTjiiCMaZZdddln89Kc/jZtuuml/2Ve+8pV47rnn/F/XJUXEwddiEc35bc+ePbFjx444++yzY9WqVbFnz57GZ0eNGhVvfetb92//7FWwhx56KLZs2dKoe9VVV8WrX/3q/dvvfe9748gjj4xbbrklIiJe9apXxa/92q/Ft771rdi3b9/+ejfeeGPMnz8/Jk6c+CKPXJLUznwg9Aowbty4xvagQYMiImLXrl2xfPnyaLVaMXXq1Ojo6Gj835IlS2Lbtm0veXtGjx4dRx11VFG+aNGieOtb3xoDBgyI/v37R0dHx/4vU3kxJKn9PPXUU/FHf/RHMXbs2Ojdu3cMHTo0Ojo6Yvfu3ThHHGzui4j9D4amTJnSqDd48OD9dTP6cjRjxoyYN29e47WxG2+8MV7zmtcU+5bUnrqaj+6+++44//zzo0+fPjFw4MDo6OjYn7GY57cpU6Y0HnZHREybNi0ioshUmzp1amO7b9++MXLkyEa9yy67LJ566qn9WWhLly6NBx54IH7913+9B0cqSdJ/OPLlboCi+F+zf6bVasULL7wQvXr1im9/+9tYr2/fvvv//7z4+Jnnn3/+gH+D/Pz/CvYzu3fvjrPPPjv69+8fH/nIR2Ly5Mlx9NFHx4MPPhj/43/8j3jhhReq9y/p8PT+978/vvjFL8YHPvCBeO1rXxsDBgyIXr16xTve8Q6cIw429/UUzV8R//6F6nd+53diw4YN8dOf/jTuvffe+NSnPtXjvyPp8HKw+WjlypXxute9LmbMmBGf+MQnYuzYsXHUUUfFLbfcEn/+53/+C18DzZo1K04++eS44YYb4rLLLosbbrghjjrqqLj00kt/oX9XknT484HQK9zkyZOj1WrFxIkT9/+vSwcyaNAgDJleu3ZtTJo0af/2gR4cHcydd94ZnZ2d8fWvf70RQL169epu70vS4emmm26Kyy+/PD7+8Y/vL3v66ae7HX7/M+PHj4+IiBUrVjT+5U9nZ2fjV3hqvOMd74gPfvCD8Y//+I/x1FNPxatf/ep4+9vf3qN2SWov//Iv/xI//elP41vf+lbjXxId6LX9FStWRKvVaqy3li1bFhH//itkP2/58uX7f7wjIuLxxx+PzZs3xxve8IZGvcsuuyw++MEPxubNm+Mf/uEf4pJLLjngv5SUJKmWr4y9wv1//9//F0cccUR8+MMfLv5X81arFZ2dnfu3J0+eHPfee28888wz+8tuvvnm4ufp+/TpExHRrS9pP/tfzn6+Dc8880x85jOfqd6HpMPbEUccUcxTn/zkJ+P555/v0f5e97rXxZFHHln8tHNP/mXP0KFD4+KLL44bbrghbrzxxnj9618fQ4cO7VG7JLUXWgPt2bMnvvjFL2L9TZs27X+9KyJi79698fd///cxd+7cGDFiRKPuX/3VXzV+Uv6zn/1sPPfcc3HxxRc36r3zne+MXr16xe/8zu/EqlWrzD+TJL0k/BdCr3CTJ0+Oj370o/H7v//7sWbNmnjLW94S/fr1i9WrV8c///M/x1VXXRXXXHNNRES8613viptuuile//rXx6WXXhorV66MG264ISZPnlzsc+DAgfG5z30u+vXrF3369InTTjvtoMGE8+fPj0GDBsXll18ev/3bvx29evWKL33pSy/q1Q5Jh5c3vvGN8aUvfSkGDBgQs2bNinvuuSduv/32GDJkSI/2N3z48Pid3/md+PjHPx6/9Eu/FK9//evj4Ycfjm9/+9sxdOjQbv9rx8suuyx+5Vd+JSIi/viP/7hHbZLUfi688MI46qij4k1velO8+93vjscffzz++q//OoYNGxabN28u6k+bNi1+8zd/M+67774YPnx4/O3f/m1s3boVHyA988wz8brXvS4uvfTSWLp0aXzmM5+JM844I37pl36pUa+joyNe//rXx1e/+tUYOHBgXHLJJb+w45UktQ//hdAh4Pd+7/fia1/7WrzqVa+KD3/4w3HNNdfEt771rbjwwgsbC4aLLrooPv7xj8eyZcviAx/4QNxzzz1x8803x5gxYxr7e/WrXx1/93d/F0cccUS85z3viXe+851x1113HbQNQ4YMiZtvvjlGjhwZ/+t//a/4v//3/8YFF1wQH/vYx34hxyzp0POXf/mXcdlll8WNN94Yv/u7vxubN2+O22+/vZF11l3/5//8n/jDP/zDuO++++Kaa66JFStWxHe/+91otVpx9NFHd2tfb3rTm2LQoEExYMCA4suWJB3I9OnT46abbopevXrFNddcE5/73Ofiqquuit/5nd/B+lOnTo2vfOUrccstt8Tv/d7vxbPPPhtf+cpX4qKLLirqfupTn4qZM2fGH/3RH8X1118f73znO+Ob3/wmPvC+7LLLIiLi0ksvjd69e7+0BylJaku9Wv4TD0nSIWT37t0xaNCg+OhHPxr/83/+z+rPPffcczFq1Kh405veFH/zN3/zC2yhpHY1YcKEmD17dtx8880HrXf99dfHlVdeGffdd1+ccsopVfv+5je/GW95y1vi+9//fpx55pkvRXMlSW3OfyEkSXrFeuqpp4qyv/iLv4iIiHPOOadb+/rGN74R27dv3/+/skvSoeSv//qvY9KkSXHGGWe83E2RJB0mzBCSJL1ifeUrX4nrr78+3vCGN0Tfvn3jhz/8YfzjP/5jXHjhhXH66adX7ePHP/5xPPLII/HHf/zHceKJJ8bZZ5/9C261JL10vvzlL8cjjzwS//qv/xp/+Zd/2aNfi5UkifhASJL0inX88cfHkUceGR/72Mdi7969+4OmP/rRj1bv47Of/WzccMMNMXfu3Lj++ut/cY2VpF+Ad77zndG3b9/4zd/8zbj66qtf7uZIkg4jZghJkiRJkiS1GTOEJEmSJEmS2owPhCRJkiRJktqMD4QkSZIkSZLaTHWo9Je+9KUu67zwwgtF2atf/eqi7Cc/+UlRduuttza2n3766aLO888/X5QdccQRRdlPf/rTxnb//v2LOvQLDbn9zzzzTFHnySefLMqeeOKJoiyjqKYjjyxP/6teVT6jq2kXfe6oo47qsg6dZ9KnT58u2/Dss88WZTW/hEH9htpK++rdu3djm/oI7eu555476HYEHyPJ7a+5hhERb3zjG4uyG2+8sbFNfeToo4+uapf+Xe28kX35y18uyr797W8XZYMHD25s33nnnUUdGhu7d+/usg39+vUryvK4jijHBvUbQvNzbiuNO9r/448/XpTlvkrz4DHHHNPl/mmeymM/IuKEE04oyvLxrFixoqgzcODAoozO89q1axvbo0ePLurs2rWrKNu4cWNR1tnZ2diunVPzXELXguYzOp4hQ4Y0trdv3171Oeo3GfWbDRs2dPk5Nf3t3/5tUZbH0bHHHlvUofmlZq2yb9++ouzmm2/u8nMR5XxF90Laf54nOjo6ijojRowoyui4a9Y9eY0YETF9+vSi7KSTTmps79ixo6izbdu2LttA45HG9p49e4qyPK/SWoLuE3S+BgwY0Nju27dvUYfmIZp/89+sPTe579K5ofsztSvXy8cXwf3tkUceKcryZ2lff/AHf1CUSdLhwH8hJEmSJEmS1GZ8ICRJkiRJktRmfCAkSZIkSZLUZqozhObPn1+U5XeBKTOA3gW+6aabirKczUPvfdP74pQ/kd9JpjZQBkL+XG0OR09zcijbhMry/ul9ccrTyGqzbWoyQ+icUrYNtSu/v0/vhtO5p33ltlJGSc05peOhv0cZUjVtoPN87733FmXXX399Y3v27NlFnXPOOacoU/fk/AbqI1u3bi3KqE/86Ec/amw/9dRTRR3qN1SWsyeoL1H+Rc6CoPFD+6rp97XZYDT+85xKeTQ1+Wo0z48bN64oGzVqVFGWxx5da8qZGDNmTFGW0fmj7A6SzytlMNXcp+j8UT+lc79z584uP1eb55GvP8156j66vrlfUJ+meYgyhHI/v//++4s6uZ9EcH5WbgeNK1qH5PFOdSgTqSaHjealCRMmFGU03nPOF50/amsuoxxLys6htV0uy7lfEbw2JrlP0NjuadYkZe7Q/vO8sHLlyqIOnS/q43lfdF+iewcd4969exvbdJ4l6XDlvxCSJEmSJElqMz4QkiRJkiRJajM+EJIkSZIkSWoz1RlCw4cPL8oo3yC7++67i7LVq1cXZfk9eXofmd6vpneUM8pmIDnTgTIQ+vTpU5RRNkPOWKjJsTlQWd5XbX5Pfk+a9k3vUpP8jj9llNRkA0SU2SmUUUJ9i85zPqbaHKucPUB5C5TdQOc5t5+Oh95jp1yGO+64o7FN40DdU5OTQNeC+uCjjz5alNVkiOUsioi6nATKmaA+MXjw4C4/R32cxlTOBtmwYUPVvmoyymqzjXIWEOUF5dykCM7AyXM2jeH169cXZePHjy/KBg4c2NimLB2a66kP5vsS9SOaS2ryr+i61uTF0fWhMmprbkftvUUHR9cyl1G2TW0/3LVrV2ObMl1o3UPrqrx/yrapWTfSvmms0Zpw2LBhje1BgwYVdaZMmVKU1fRpGkNDhw4tyvJx0zHT/E9zWr7WdF0pV47ksVybuUjtz+eL+khNH6S1EWVD0dye20/Xh9pO99CNGzd2uS9JOly5YpMkSZIkSWozPhCSJEmSJElqMz4QkiRJkiRJajM+EJIkSZIkSWoz1aHSP/jBD4qyHA739NNPF3U+85nPFGUUGDhkyJDGNoURUlAq7YvqZbT/HIpHYZr0OQpqzGGHObgxoj4IMAe4UtAoBeDl80DHUxsEmNUGoOYAaapHIa8UNFgTeErXgo4xt5+CB6k/U7v27dvX2KagRkJtXbhwYWP7zDPPrNqXDozmg9xv6PqvXbu2KOvo6CjKckg+jfXa/pXHXp4XIyImT55clOXAzR07dhR1aHzS2Js0aVJjmwKkt2zZUpTRfJaDhakOjYORI0cedDuiDHiO4Hkwh7JSSC+1oSa0uib0OYLnrpoA8W3bthVluS9RsG5tKG++/lSHwqHpfNXez9Q91KdzGc0ldD2oT+cA39pAX5rTcl+htu/Zs6coy+ODxhDNvTWBzjlk+kD7p3kurx1y2H0Ez6E5MJraSeeP1l41IfKkpt/QOo7mIfqb+VrXzoUZnZtVq1YVZbUh3BnNvRQqvWbNmsY2jSlJOlz5L4QkSZIkSZLajA+EJEmSJEmS2owPhCRJkiRJktqMD4QkSZIkSZLaTHWoNIXp5nDOjRs3FnUo3JRC+HIQb21YNAVe5mDMwYMHF3UopDSHFlJo6fDhw4syCvXMIYydnZ1FHTpGCuHL4ZAU1Lx9+/aiLIfiUmAohQrStc7hfRQ8WRMgTX+TAgvpWlMYbA0K18x9kIIH6RhJvv7UTjoeCn3M59BgwxePAnDzfEN15s6dW5Tdd999RVm+RhQqTuOMrn+uN2LEiKIOhUrnMUTBrTTv1oSw01xJY4NCWfM8SHUoVHTQoEGNbZoj6Hgo9D/fIyjMlUJzaczmuZHGOs15VC8fUw6nj+A+kvsbtZP6ILWhJmSW7ht0PXJfMmT6pUHXLfcBuk9Q36H10t69exvbdN0o0Jfkv0nzBI3RvPaisU3zBPXNPB5o7VUTsB5RBlLTfEznJo8FOu90fWrWf3R9atfGuV21AfjU1nwOa9uQ7wHUH+j6bNq0qSgbM2ZMUVbTBgqjzsdN63NJOlz5L4QkSZIkSZLajA+EJEmSJEmS2owPhCRJkiRJktqMD4QkSZIkSZLaTHWoNMlBcw8//HBR54knnijKKMg016NgOwrKpMC46dOnN7Znz55d1Bk7dmxRlsNTKVS6JkCQyii8j9SE8FFg7NatW4uyHMK3YsWKog4F51G44qhRoxrba9asKerUhjDnUFf6exQ+S30ihw/S+du1a1eX+6JAxByuG8HhnTlotiYsOIKDE/P1WLhwYVFH3UPnPofiDhgwoKgzZcqUooz6ZR7bNUH3ETyf5YDNmTNnFnUoVDr3uWXLlhV1qF0UiJ/bkMd+RMTKlSur9p/HNoU3z5kzpyjLAbJ0rmrl60+Boj29ZjQnURn1r3zuqc62bduKsvxjAUuWLCnq0HxGZbnvUuA2hdjS9ch98MVcM/0HGjM1Pz5A9yHqm8uXL29s03qG0Noh/01aL1EQcM09lH60hM5NHlcTJkwo6px44olV7cqB3rSeJfk+QfMsqflBAhqjNT+cEMFzQM3nalAbataz9CMmdM/evHlzUZbPa20QNK3t8vxrqLSkduK/EJIkSZIkSWozPhCSJEmSJElqMz4QkiRJkiRJajPVGUL0XnF+b3316tVFHXqHmPJ0cl4DvY9M72GPHj26KDv77LMb2xMnTizqUG5Nzq2gTAd635mOMb+rT8dD77/X5OTk/IAIPqf5PXzKo9i3b19RRu9v53NB7/NTtlHOaqH958ySiIidO3cWZfROdz4X1E8pQyjvK+dyRETs3bu3KKPjye/l0/kjOZ8gorxm69evr9qXDqwm04n6DY0pKsuZDjWZVRHclwYNGtTYprwgGi9ZbV7Qa17zmqIsZ6fR+Rs8eHCXbYgoxwbNu8OHDy/KanIzaPzQnJrPRe34pL+Z73l0TmtyQCLK60jXlbLuciYKZaQsWLCgKKM5ribrjs5DzX2qNotGB0f3vZzrRHVoDqBrmdcFNK46OzuLMsphGTJkSGN73LhxRR2a93LeIY3j8ePHF2VnnXVWUXbSSSc1tjs6Ooo6dG6ov+bxTvlj9Ll8bmpzBWvLstr95+OuzU6j/We1uZ8Z5V/ROaU1YV6/Uo4lnQe6jjnvasuWLUUdSTpc+S+EJEmSJEmS2owPhCRJkiRJktqMD4QkSZIkSZLajA+EJEmSJEmS2kx1qDSFsD322GON7ZUrVxZ1cqhoBIcP5xBBCqOjQNJZs2YVZTNnzmxsU9gdhenl/dd+jsJAc/spJI/2RfL+6VpQ6F8OCB0xYkRRh9r1xBNPdLn/UaNGFXWeeuqpoozCLvO5oVDBHE4ZEbFjx46irCa4lOrkfVGI5aJFi4qyngai0zmlgM98bmj8qHtonOXzWhv6TCHseTzSfEDh4BR2mQOWKXCZ9p/DjSlomMKuhw0bVpTlAHka19OnTy/KKIx03bp1jW0aZxTMnMP7KeCfxhmNqfxZmitpDqLjrgmLJXStc5+jttM5zXMEhQDTOa0JC6Yg1Zrw+4hynOXgY/UM3QPyjynQXEV9gK5J/vEEulfRPEQhz1kOi47g45kyZUpj+7TTTivqnHvuuUUZzV95XqAxVBssn4OGa+XxQXNV7Q8ZZLX7ojGa+wnNObVzWs3ahM5zntupv9Fan35MJc/bdP6onTTf5/nLUHxJ7cR/ISRJkiRJktRmfCAkSZIkSZLUZnwgJEmSJEmS1GZ8ICRJkiRJktRmqkOlKbRw4cKFjW0KLaXPkRxkR2F0Q4cOLcpGjhxZlOXg0hxiR38vogytqw19JjkwlPZFZTXhtrVBwzUBhRSuR/LfpMA9+nsUSJqDLSnoksIOKSQzX0c6p9SuvC8KyV6yZElRRuc+t7U2lLEm9NFgw1+MPL/UBnzWBOxS360NSR88eHBjm/o89YkcdkwB8jR/Ulk+F1SH5o3Ro0cXZePGjWtsUyh3PuaIcg6vOeYIDr/N5zkHKUfw9aF7Vw6apvDT2rk+H1PNfBBR3s+oT1KQbkdHR1GWQ//pWhO6T+VA6vPOO69qXzo46hf5+tJ4pHVPDpCOKMckBdJTeDOFAW/cuLGxnX/gIyLi4osvLsrmzp3b2KbxTm2nstw3KYSffpiDymrCp6mtNes/WvfQuMpq70sUwpzvTXTfq11z5LmvNhQ/92ea4+h46Nzk7xwUAk4/SEA/bpCPe+DAgUUdSTpc+S+EJEmSJEmS2owPhCRJkiRJktqMD4QkSZIkSZLajA+EJEmSJEmS2kx1qDQFG+ZQOQqjo7BDCurLZRRQR2UU6pnDRulzdDy5jEL56HMU3lgT3kcBxXRucuheDjY90L5y+CCFGFLb6Zzm/VMQIJ1namvuE9RvKIyQQmTz+aLzQOe+JiyS9kVBmhSAmNE5pevRkzo6OBqzNaHSFJI8ffr0oiyHFlOAKIWy0t/Mob7U52ns5eMZP3581d+j0OocNEzjk0LYafwPGjSosU3BxhT6mkM/a8ZrRMT3v//9ouz+++9vbNN5OP7444syGnu5HWPHji3qUIgp9cGaAFnqSzn8lu5TtUG6eV6ivkXzIP2YQw4GPv/884s66r7awPuM7jnbtm0ryvK1pJD31atXF2XUx37jN36jsX3hhRcWdWqCy6nP5XkpgsdoXjvUrktprNWsS+n65DbQGoHmtJr9164JatdVGc0BNUH5dM1ofZnbQAHPdG5oDs2B6NRH6Hho7s3jhdZ6knS48l8ISZIkSZIktRkfCEmSJEmSJLUZHwhJkiRJkiS1meoMIXqHOL8fTO8s07vA9P5uLqP3heldbWpXfpe5JmcoonyvnNpO70TXtrUGvUOe37Gufee6Br1LT/J5rn0Hn8py9kBtVlNNu6hvUb/M7+Hv3r27qEM5DfSOeu5L9I4/vcde00dq3vnXwVEfrDF16tSi7IMf/GBR9uCDDza2f/SjHxV1aA6iXI7+/fs3tmvyqSLKfj9ixIiiTm1fyvuiNtSOz5ynQ2ORxmweQ2vWrCnq3HPPPUXZ+vXri7L8NynjJ+cMRfBx58wlGtcTJ04syvJ1jSjHP80HdD/I7aLrSsc4ZsyYomzx4sWNbcoBob40YcKEoiwfN+XHqftozOR7E2WBUWYU3b/yNad5adKkSUXZr/7qrxZlc+bMaWzTPEH31Zr1C+WW0X07j0n6e3RPoLbWrAlpLUTjNqN1Qk3eIc1LtXN7bit9jtZj1NY9e/Y0tmvWRhHluaHMKurPdA/NOT+7du0q6tAx0nXM57X2HidJhwP/hZAkSZIkSVKb8YGQJEmSJElSm/GBkCRJkiRJUpvxgZAkSZIkSVKbqQ6VplC5HMJG4aCEAv3yZymsk+zbt68oy2FwFCpX0wYKc6TPUchzDgesDVwmeV8UdkfXJ4d60jmlwEUKQM0hfDnMj9p5oLZmOWQ6gvsStT8HYlIoak2gM/Wj2nDoXI/OKZ0vCofM7TJU+sWjPpjHHvUbMn/+/KIsB3VSADIFdVIIZx4LAwcOLOrUjAMKv6dxRvVq+iDNjRT6WXOet23bVpTdcccdje0c3B3BbR85cmRRlkNLBw0aVNShkFEKNs3Xg64PzRs117pmPogo53UKsKXjGTZsWFE2c+bMxvbChQuLOhQqPXz48KIs90sKi1X30b0phylTn6bxTmMtz4+zZs0q6rz5zW8uyih0P7eVxijNJ7mM+j2FN9PaKx9PbTgw7T/PVzT30jouj8na+zi1Nf9NmkNpfVHz4y3Utwid59znaM6h487HQ22nfkPz3N69exvbOeg6gq8r3b/y/imYX5IOV/4LIUmSJEmSpDbjAyFJkiRJkqQ24wMhSZIkSZKkNuMDIUmSJEmSpDZTHSpNoXITJ05sbFOIZE3oc0Rd6B4Fc/bt27cooxC5jAJQc7solLFPnz5FGQUN5oC62iDongbg0udyGYWWUiAinb9Vq1Y1tleuXFnUmTx5clE2ZsyYoiyHD1KAJPUHCmvOZdTfcvBgRHkuKIyWQhJrQh/pnFKIIe0rnws6ZnVPTXh7bSA+9dUczDt06NCizl/91V8VZRMmTCjK8hx39913F3VoXsqBwRQyS32Qxnqeu2jeoLFRE4q8adOmok4OkI6IuPfeexvbY8eOLeocd9xxRRnNN/lc0L2sJoA1ImLIkCGNbToPNAdRv6GynqB20rWmAOzcVzs6OrqsE8F9MN9TlyxZUtS58MILizIdHAXl5jFK143uLxTM+9rXvraxneezCO4DOdia9k9zwtatW6vamtE6iz6X12i0Lq0NQM71qA6tQXO92mtRs06k81Cz5qXP1v74AM2ZeY6htQqFmOcfWKB1fe2PyuTzTGtqOjc0Z+b5mOZLSTpc+S+EJEmSJEmS2owPhCRJkiRJktqMD4QkSZIkSZLaTHWGEL2bm9/NpvfY161bV5TRu9M556Mm4yeC83Tyvug9dpLfGaZ3qSmbgY4n/016j5nOKb2jnuvR5+gd71xGmRW17crnuTZXhN4Fz8dI15DeK9+xY0dRlt+Bp/e+6RjzvqjtOS8kImLDhg1FWW4/9RE6X0888URRlq9R7bv0OrCaXK4Xk/GS86col4H65XnnnVeUdXZ2NrZvvfXWog6Nl9znxo0bV9Q5/vjji7IRI0YUZaNGjWpsU24ajSmau3JeyMKFC4s6W7ZsKcpOOOGExjblBfXv378oo/Ocy2gs0hxO53nnzp2NbcqnokyRmhyr2pyRfG+k+ZrmILpHDB8+vLFN8xtlsOTPRZT5UIsWLSrqqPsoqyf3AZpzaIxOnTq1KBs/fnxjm/phbQ5L7vu1GXj5fkwZhQMGDCjKaHxk1H/zMUfwmvOpp55qbPfr16/LvxdRzh21a9ea+1BtviLNAXlNS3M7nQdah+S/SX+P9p/nXzoPNet6kvOJIuryqSLKY6T1vyQdrvwXQpIkSZIkSW3GB0KSJEmSJEltxgdCkiRJkiRJbcYHQpIkSZIkSW2mOlSagtlySCUFFj744INV+8+BbhS4S6GYFGxYE1BNAcU5UJM+RyF5FASYw0cpCJBC8ijIOAcZUpAitSGH/u3du7eos2/fvqqyfL7Wrl1b1LnvvvuKMrqOef90bmrDrnMA7umnn17UobDzHCJN/SjvOyJizZo1RdnGjRsb2xTwWaumXeoeGmd5vqFATAoap3kwl9E1e/vb316Ubd++vSh76KGHGts//vGPizoUipzbkPcTEfGd73ynKJs/f35RdvbZZze2hw0bVtShcFUK9FyxYkVjm+YWCrvOocV0DakNFEa/bNmyxjZd67FjxxZlNBfnOZXmKUJzQp7Ha0JTI+pCUmuCxyPKvkTBvXT9KdA73yNmzpzZZTvVNeoXuT/RuKL1CwWe5/5EaxzqTzTP5Tkz3xsjIhYvXlyUrV+/vrGdA8ojONiY5oDc73IwNLUzImL06NFFWV6b1P6oSE3YNQVB07XO7c9B/QdqF617Bg0a1NiuDaimstxvan8AI//N2s/V3PfoHlTzIzYR5Zii7xuSdLjyXwhJkiRJkiS1GR8ISZIkSZIktRkfCEmSJEmSJLUZHwhJkiRJkiS1merEWgphy4FuFCpNQXAUUJdDkSk4c9y4cUXZscceW5Tl8GEKMbz99tuLsm3btjW2awN9Kax5165djW0K7xs4cGBRdsoppxRlOfh12rRpRR0KFcyBzrt37y7qULsoMDqH2y5YsKCoQyG51G9yn6BrTZ+jINOVK1c2tinYet68eUXZxIkTu2wDXR8KsaTAzYyCEyn0M1+Pmn3r4ChwPY8XqkNlNcG/NCdRwO6cOXOKstwPf/KTnxR1agKQKex4yZIlRRnNjXkePPnkk4s606dPL8poDspBnTksOoLHQQ70pPGzfPnyoozm7BxaTfPgokWLirI8h0dEjB8/vrFNIb0UWEthzbkezUE1QapUh+Z1Ojd5Tp07d25RhwKx6VrPmjWrsU0B/Oo+Wl/kexMF51LAOo2j3AeoP1GfpntTvh//6Ec/KupQSPk555zT2L7jjjuKOt/73veKssGDBxdlF1xwQWP71FNPLerksPsIHjNZZ2dnUVYzRmm80LWgH87I8xDNX2PGjCnK6P61atWqLv8erc9JDqimeZyOO6sNP6c5NM9ptaHSdG7yvmrDriXpcOC/EJIkSZIkSWozPhCSJEmSJElqMz4QkiRJkiRJajM+EJIkSZIkSWoz1aHSFMKWw+coLHDSpElFGQUSFw2DAMyhQ4cWZRRad9dddzW2v//97xd1KAgwHyMdM4XwUbhibn8O4IvggGLa/6ZNmxrbFJI3efLkoiyfGzqnFJJI4dA5oJKOh8IB6XhyYCwFVtYE1EZEdHR0NLZrw3RXr17d2D7xxBOLOnQ8I0eO7HL/FE6ZjzmiDP2OiOjTp09je8aMGUUdvfRorFO/rPksfY7CqOlvnnHGGY3tLVu2FHVqxgaN9ZNOOqkoy3MLlVE/pWBTCv3Poa80V1J4Z27/1q1bizo0t5xwwglFWR5Tb3zjG4s6dB6uv/76oiwHbtN8kIOnDyQHz9J8Q30pX2s6D9RHKHA134PyuTrQvih4Nl8zOh51X836omZ9FsHzUN4/BfrSfPLUU08VZfk+94Y3vKGoQ/1p586dje3TTjutqEMh/LQWyu2n80f9nO7R+/bta2zTXEXzY74etT+uQuuXjRs3NrYpXJnaQKH7ef6ieW/Hjh1FGd2H8o+b5GDwiIgRI0YUZflcUGg6nRtaL+c5hs4DoX6T+0nt/V+SDgf+CyFJkiRJkqQ24wMhSZIkSZKkNuMDIUmSJEmSpDZTnSFE733nHAl6V3vgwIFFGWUL5PeIKS/m2GOPLco2b95clO3atauxTe820/vi+X3q/P54RJmlE8HvXOdzQzkP9B4zye/qU0YNZQjk9+SpDfT+dn7PPKJ8n5repafzRe99jx49urFNfYvK6J3unC1Cn6NMj7wvOmbKxKIcq3yMlNVB1yefh4iIqVOnNranTJlS1FH35KyWiLKfULZGrZqsAapDYyjPjbNmzSrqLFy4sCjL/YuywWh+puPOWUDUd2m8UL/P8z9lVpA8N65YsaKoc/rppxdlt912W1H2ta99rbF93XXXFXVe97rXFWXz588vynK2BY1hmp/p3OdrTTkj1G9yf6b7CM31NdlWlB9Dx0Nl+W/u2bOnqKPuo8yY3HfoelB/orLcnyi/hT6X8/siyjmNsodo7TVx4sTGdm3GD82hq1atamzXzEsHamte01AbaA7NY42uD60lqSznCg0ZMqSoQ+s4yoy78cYbG9s5bzOC5+gLLrigKMvrnttvv72o89a3vrUoy+t4yk2ifCrqE7kNlHVE+6JrXXPNJOlw5b8QkiRJkiRJajM+EJIkSZIkSWozPhCSJEmSJElqMz4QkiRJkiRJajPVodIUzHrPPfc0tmfPnl3UOe2004qyRx55pCjLoZsUbkllFD6dg3kpFHXp0qVFWQ7mo7C7DRs2FGUUwpgDNp944omiDqFQwYxCBSnYMIdRUujzzp07i7Icyk1ltSHJ27dvL8oWL17c2KbQP5KDJyPKcGAKWK05NxQWSfuiYPP+/fs3tim8lfZF52vUqFGNbQrTVfdQ0Hiez6g/0+dI/mxNiHUE98u8Lwoxp8/dd999jW0KBj377LOLspogVQoopsB1ml9yECiNn5qgWwqGp3nquOOOK8ry36T5c+3atUUZBbXm61gb8E/nOc8JFJBL80ZNfyP0Yw75vkvh13SPpftZ7nMjR46sapcOjq5bvl/RdSPUD1/Kz+V7H40F6q95HNHai9pAgdF5HNHfo7lq/fr1RVkef7QGJbkNNcHTEbyWzGV0bmisUcDyiSee2Nim4PcFCxYUZTRnvvnNb25sX3/99UWd73znO0XZm970psY2rXlpLqRQ6Ty30/mjsVHzoyW0L0k6XPkvhCRJkiRJktqMD4QkSZIkSZLajA+EJEmSJEmS2owPhCRJkiRJktpMdcJgTVDmD3/4w6LOzJkzizIKqMsBoRRaSuGAgwcPLhubUFggHU8Ot6O/R5+j8L4cWjh9+vSiDoUk7969uyjLAXsUpkrhozmEj8JBKXCPgl9zqOuaNWuKOiNGjCjKBg4cWJT90z/9U2ObzjOFyFJb8zUbN25cUYf6Wz5fOWT6QO2iMMLcVymEl8pIPka61uoeCqisDYyukcMoad80b1CoaEbzIFm5cmVj+4orrijqUH/+5je/WZRdcMEFjW0KnqdgU5qLcwgrzREUFpvnKjp/NFdSvZNPPrnLz23evLkoo/OVQ99pTqLwUwoozUGz9Pfoc/kYa/sbyeeexgrNXRQEnn8coPaHFHRwNeG2dM+mvknXN/dDWkvQXFVzf6TPUbtyGR0zzRO0r/xZCpAmHR0dRVmeh2rvGzt27GhsU0B9bUB1DhWnH+qgtlMY+ZQpUxrbZ511VlGHQqUpdD//OMzkyZOLOvQjLPlHRUi/fv2KMpqH8jHSvEc/GFKztq8N65ekw4H/QkiSJEmSJKnN+EBIkiRJkiSpzfhASJIkSZIkqc34QEiSJEmSJKnNVIdKU0BdDv6tCVeO4MC4HIpI4X30OSrLgZcURjho0KCiLNejOhQ0d9tttxVl2Vvf+taijAKXKQA7h6LmEMgIDtzLIZ+E2nD88ccXZRMmTGhsL1mypKhDwZYUipv7De1r0qRJRRldj9WrVze2Kdg2BylGlMdN4ZSdnZ1FGZ3THCJLwZ10HihgN4+z4cOHF3XUPT0NkKbrSCGpNB5rPkd9Ls9BFN5LfSKPWRort9xyS1F26623FmVjx45tbM+YMaOos2fPnqKMAj1zfx49enRRh4LT87xO9xE6fxS4SqHYGc3rFDSfQ2apDvU3CsnN/YvaQH0r16PzUBsM/MwzzzS26RpSODTVy/N/TRiyeiZfNwoopuB3Wgvlspqw+wPJ+6I+TffQ3FdovUmfozk6ozUiBb/TOcxh0LV9OgfX03iksnxdI8q5fOPGjUWdpUuXFmX0gy5PPvlkY5vC9OnHTmhdlc89zRNTp04tynL7aY6jtSTtPwdGU/+mvkTrZUOkJbUz/4WQJEmSJElSm/GBkCRJkiRJUpvxgZAkSZIkSVKbqc4Qonency4KZc9QRs3IkSOLsmXLljW26X1ueo+Z3g/P71xTfktNFgxlT9D7yL/+679elOV3mXP2RAS/xzxv3ryiLOf3UH5Hfpc6onw3m963p6yR/J55RJllMm7cuKIO5XesWbOmKMvXg/oIHeO2bduKsiFDhjS2TzzxxKJOzkSJKHM3KKMk5wBE8DvquQ/W5H5EcP/KfXDMmDFFHXUPZUH179+/sU25L3Qde5qvQZkr1JfyGKV2UYbQmWee2dimXJ4TTjihKLvqqquKsjzWV65cWdTJ4y6CjydnQVAmEs1deX6mvK18DSMihg4dWpRt3br1oPuO4Hl9+vTpRVnOQKI5leZ1OjcZtasm/4r6Vm0eUZ4Ha+b+CM6nyWWUbaTuo76Z+x31AbqWdK/NaF+E1jQ1+Y10PHl9Sfk6ND5q9k9rScrqyXlBEWVuDY0rGu95fqT18+OPP17VhnwuRo0aVdShtcS6deuKsvxZakPt/JjXaHSM1EfyHErZQHR96N6by2iuojJqa55re5o9KEmHIv+FkCRJkiRJUpvxgZAkSZIkSVKb8YGQJEmSJElSm/GBkCRJkiRJUpupDpWmgMgc1kdhpzkkN4JDfnM9CnSjoEEKFcz1qA4Fmc6YMaOxvWnTpqIOhbVSW3MwI/09Ol85cI/2VRt2l4OSKaiPQlEpVDDXo4BvCqOkfeXgRGoD7X/mzJlF2ZQpUxrb1N8o9DGXUVhkDsSN4PBpOsaaOkuXLi3KclAunQd1z5IlS4qyWbNmNbYpXL02QDr3+54GTxMKGd24cWNRlkM/qZ8+9thjRRkdd94/9UEKdO7o6CjKcqgojXUKus1ldP+h+wHNs/lvUqAoXTPqNzVzMR0jzb01of81Ic8U+EoB0nTc+W+uWrWqqEM/AlETKLxixYqijrqvZo1D44P6Dq05ct+nsUBjjdB9NKMxmsdRbUAxzUP5fFGoMM2PNK/m80rnj65PXl/Q8dQEG0eUgdH0QxMU8k/Xf8eOHY1tCtwmtIbK9w6a46gNeb6iz1HQNM2POcie5kK6/jQ/GiotqZ35L4QkSZIkSZLajA+EJEmSJEmS2owPhCRJkiRJktqMD4QkSZIkSZLaTHWoNAXn5ZA3CvSlkDcKKKRwwIwCVinwNIcWUtspMLhfv36NbQq/ziF2ERykmP8mBQjWhmR3dnY2tilAkoIgKYQxo7BAuo65rRRiOGzYsKKMzlfuE9RHKHiSznM+7scff7yoQ8dYg8IOa8Ih6TxQGOXKlSuLsnxtKUhR3UOhkjmok+YW6uM0pvJn6e9ReOf27du7rJfHfgT3y9zvKfyegjopODOHCFPwNH2OxkZG8y7NEevXr29s0ximsUFzRJ7PRo0aVdSh8NM9e/YUZVu3bm1s0/EQutZZvv9E1IX55jZF8LWoCeClz9XOxXm81AYR6+BovOdzSz/oQOOK1hc5YJfq0HinPlCz9iJ5zqTxTusE2n9NwP6GDRuKMjrPI0aMaGzTPYHGR94X/agEnT+a7/P4zsHQEeUPokTwfJ/PK7WLzjPVy/ccGu81PzRCAc9UVhPWT/dnuj7Ub3JbXXtJaif+CyFJkiRJkqQ24wMhSZIkSZKkNuMDIUmSJEmSpDZTnSFE75Dnd2xr3z2nPJWZM2c2ttesWVPU2b17d1FGeTr5PWnK9KD3g3P76Z1oyuqhNuT3kSlDpCYTh9pB73jTMeY21L6rTdcxZ0vQO/6U50THnd/7pmtB76zT9cifpXZRWX6/nupQBgPlDOTrQdlThK5/ziygbBN1D42pnCtBOTkLFiwoyk455ZSibNWqVY1tyqihcbBp06aibODAgY1tGhs0DnL2BI1ryr+guWTSpEmNbcqoofNFY2jx4sWN7ZNPPrmos2LFiqIsX598XiIiHn300aIsZw9FREycOLGxTbkWdJ+isZ7PM/WttWvXFmU0jocOHdplu6hsy5YtXe6b7gfbtm0ryvJcRfcyQvln48ePP+i2eobGcp4DqK/W3jvy/b42v6UmM6YmZ4j+Ju2byijzKs8dlKdG/XzKlClFWR6j9PcoczHPtZRjRG2gcZvrLVu2rKiT5+yIiDFjxhRlORetNieRrmO+HtRH6Bjzvqif0r5ojs7fCaiP1GYi5f3T3CtJhyv/hZAkSZIkSVKb8YGQJEmSJElSm/GBkCRJkiRJUpvxgZAkSZIkSVKbqQ6Vrgmao4A6CigeN25cUZbDOjdu3FjUoVBMCjLNoYUUDlwTUEzBdhQ0R4F7xx57bGO7NtCZAvbyMfbr16+oc8wxx3TZVgrlo3DY7du3F2X5HNYEQ0ZwSGJWG+hKZfnc1AZB51DBxx9/vKhDQZB79+4tynL76RrSuZk3b15Rlq/t8uXLizrqHpq7Jk+e3NimQHQKI6Xrn+eSmmDQCJ438mcHDBhQ1KF5MAdZ5+DhCO6DJ5xwQpdtpX3RfDNhwoSibOHChY1t+mEAGp+5XXSu6DzQ/Lxu3brGNoXA0j2Jrln//v0b2zmc/kDtmjZtWlGW5w3qpxSIunPnzsZ2Ds2OKO8/EeU9NqJsP81dORg8ojwPEWVfpfuUuo/WUPn+S9eNxgzda2vQvmrq0TqL9kVzU0ZBwDX3+zxeIsofb4jg8Z5/3IR+EIXWDrmttG86ZhozOfidjofmaPpxi/zjBtRvqI9QW/NcWxtQnVF/oDJal2bUdron0Lydz0VPx4okHYr8F0KSJEmSJEltxgdCkiRJkiRJbcYHQpIkSZIkSW3GB0KSJEmSJEltpjpUmlBgYLZnz56ijAIvZ8yY0dheu3ZtUWfZsmVF2UknnVSU5cBLCqjr1atX2diKOhQ0R+chl1FANQUNUlkO5qtpO9Wj814bIJjDGylwlwJQKcA1B2LSuak9xp6em/w5CqysDbHMZRSuOGXKlKKMrnXu9xRirO6h/pz7SW2APNWrCQemYNiaYHtqA4VW53B1akPt8dSEhe7YsaMoo7E+dOjQxjZdC9p/HlN0/igQPwdIU1tz0HUEh13TfSOH0VI4KbWLxnGeB+mcUshsvmb0wwrUdgooz/dnmt9o/wMHDizK8rmgOuo+6vv5Xkv9kO4vdH/M453miZ6W0fxSsx6jfVN4c82PitC8tGrVqqKM1i85aDiHTEdwIH3eF629tm7dWpTRucnzCYVFb9q0qSij85XPDf04AN1faJ1I1zajuT1f29rA8pofEaDzTH2ExkauV/ODKJJ0uPBfCEmSJEmSJLUZHwhJkiRJkiS1GR8ISZIkSZIktRkfCEmSJEmSJLWZ6lBpCpXLQYYUfkhhncuXLy/KchD0zJkzizqrV68uyiiYL4dZUpApHU8OkcsBfAdCx51D6ygkkVA45IYNGxrbI0eOLOrk8xdRBoRSGyhUsCZccf369UUdCkClYL7cLgp9rA3vzu2iAMFch8qoDgUiUjhsbtewYcOKOoT6ZW4/HY+652Mf+1hR9oEPfKCxfdZZZxV1KICVAjDzuKIwckLXNodB01ikQMw8B1HbaW6hoM48l9DYOOecc4oy+pt5vND4oSDo3C5qZ20Y/cSJE7v8XN++fYuyRx55pCjL12PMmDFFHQo2pbk3Xw8KgaU5NffBzs7Ook5HR0dRlu8jEeW9ku55tffiHH5L87peGrmf0488UEA49cM859D8Qv2C5sKaH3mg+17+m9QGOh6S78e0xhk+fHhRRmuO/GMQVGfLli1FWb4eVIfmNAqkz+eZ5gkqo/VL3j99jq4ZrePy/mkdXPODDtSPaI6mPp7r5dD/A6kJV5ekduK/EJIkSZIkSWozPhCSJEmSJElqMz4QkiRJkiRJajPVGUL0TnfOU6CMCkLvSed9UV4M5TzQe9ijR4/ucl/0jnJ+h7g2QyS/N09ldP7o/e3Nmzd32a7BgwcXdait+d15uj6U81CTUbJz586izpo1a4oyyh/JarOa6J34ffv2NbYps4pyS3I9uhY7duyoakPuu7WZVdSX8nvy9Dl1D+WMfeMb32hsn3766UUdyu+huWT8+PGN7WXLlhV1qF9SZsGQIUMa25R/MXTo0KJs7dq1XbaTcjNWrVpVlM2aNauxTefhW9/6VlFGeTe5HdSfKRsiZ27U5LRF8PkaNWpUY3vq1KlFHRrXlEfx6KOPNrbpPA8YMKAoo3tE7pe0LzrunCGXr31ExPbt24syuj75XlLb9nHjxhVleb6szVJT9+X1S02+yoHkcUTZZpQhU3NP6+m9sCZfLYLHe/4sjSuaQ+l85XNDbaf1S847ojaceuqpRRmtCfK6is4NzVV0PLVr9Bp0bbOa9QvdBymHj9ZjNRlCdP+i+b7meCTpcOUMKEmSJEmS1GZ8ICRJkiRJktRmfCAkSZIkSZLUZnwgJEmSJEmS1GaqQ6VJTQgzhWJSsF0OyqPgZAqppNDivC8KI6Q25PZTkCKh487tyiGDERyISAF4OWCPggcpHDaHHVIYIQXgUlkOn6bznsO8I/gc5uOmcFgqo79Zc57pWucwato3tZ36c77+FCBJfWTTpk1FWe6rtYHbOjAKTl+0aFFje/Xq1UWdmTNnFmUU1JmDM0866aSiDo09GusZjfUpU6Z0WXbHHXcUdSg0c9KkSUVZDoynvptDnyN4Psth7XQe6Jzmfk9jke4HFCqa69Hx0LyRw6gjymu2fv36og7NJTksPILny5p25XMzZsyYLvcTwXNXbivddymAla51Ph6aw/XSyGuvfD+L4HUPBfhmdN2oD9C+ch+jOYf6Yd5/bfhxzRxKY4jaVXOvpbYPHDiwKMs/gEJrAponKBw8j8n+/fsXdWh+pHm1JnC7Ft2baurkfkP9iO4T9EMm+frX9IcIDq3O15+utSQdrvwXQpIkSZIkSW3GB0KSJEmSJEltxgdCkiRJkiRJbcYHQpIkSZIkSW2mOjWtJgiQQgxr95VDESnYkALjdu/eXZTt2bOnsU2hshQsmgP2XspAXwr4o3DAlStXFmXf/va3G9tjx44t6lBZPofbt28v6jzyyCNFGYX35dBCCnSdP39+UTZgwICiLAcG5uDZCL7+VC8HM1IYIQVU1oTdUpAm9ZsdO3Z0WSf3yQOVZbVjSge2fPnyomzo0KGN7XvvvbeoQ4HLdG0zCiylEF6SAzZpXzQ28pw6ffr0os6tt95alNGYyiHZFHRK6Bhrgjlpnq0JgqYw1+HDh3fZhpofIojg8Z9DUun60D2J/mY+xprA+ojy+ufrRXUi+Dzn+1JNaHoE95t8/WtCZ9W1mnUI3ePo3kFlOQCZUL8gef6ifk9jJref+texxx5blFFgdF6r0PqP+ia1K/d9GqM18z2NRzoemodqgsDp+lCfoLFcg/afzyH1LZpDcxnVoZB0CoLOa2gK16Y+Ql5MwLYkHer8F0KSJEmSJEltxgdCkiRJkiRJbcYHQpIkSZIkSW3GB0KSJEmSJEltpjpUmoLtagJWKUyPQviytWvXFmUUkkdy2CiFN9cEyFEYXU3QaER5bii4kc4fBbPm80UhuZs2bSrK8rmvqXOgstwuCvimIEAKGsz7ohBWutZUlkMYKZSRAiprggZrgtTpb65ataqoU9Pn9YtBgZi5Xy5YsKCo86u/+qtFWc11rA0s7emcSv0yj9lRo0YVdSho+sEHHyzKcohtbZAqHXdHR0dje+TIkVWfy9eM5sXBgwcXZTTP5jFLf48Cqula5HmD/h7NLXS+aK6qaUNNwG9NgHRE2Qdr7wf0YwG5Hn1O3Udrldwvau5LEXV9k4KHacxQvdyfqF0k36PpXk/noWb/NSH8B9p/v379GtsUUF3TVgo7prB+Grf5mtXMGxF1gfc079XO7fm8Ut+ituYy+hytCel+mYO56frQvEeB3lnteZakw4HfUiVJkiRJktqMD4QkSZIkSZLajA+EJEmSJEmS2kx1hlBPsy3ofWQqy+/50t/bu3dvUUb5MHPmzGls17zHTPuifAjKxKHjpoydjDJ3cuZGRMS8efMa25T7Qecmv5tN74FT2ykfYtiwYY3tfI4jIoYOHVqUkfw36VrX5h/kevQ+/1NPPdVlm6gNte/S5/3Tu+61mQU140DdQ9dj586dje2FCxcWdTZs2FCUjR07tijLfYKyNUhPr21N/6I6p5xySlG2fv36omz79u2NbcploEyMJ598siibNGlSl5/bsmVLUZYzJGjfe/bsKcpoHszXo/a85/yQiDJzg/4eZVbQWM/zGc031JdqclNoX3TcuYzub/Q56l9ZTdaRukb3jjx/0X2P7pd5bEdETJgwoUdtoD6d+yv1AWpXvofS8Wzbtq0oo/txzoepzW+ksZbXF7Q+o/OXj5vGVU2OYUQ51mqzbXo6/mjeo/bnMspqomOkdW9GOZk03+d8ULq/9DSrj/qWJB2u/BdCkiRJkiRJbcYHQpIkSZIkSW3GB0KSJEmSJEltxgdCkiRJkiRJbaY6VJqC2XJo3UsZwkahmBQYR0F2NaGb9Lma8D4K16PjzvvPIakHQsc9atSoxjaF661du7YoW7NmTWObgq4pJDEHp1K9QYMGFXVqQywpCDyjc0ohifka1QYu0nnOqI/UhK5SYCGFctaEPlKfV/fkkNGIiLPOOqux/du//dtFHRoHnZ2dRVnNdaR90bXN+6Kg05pATOqnNP7PPPPMouzmm28+aJsiePxT8HOeizdv3lzU2bRpU1GW52IKuh83blxRRudr/Pjxje3HHnusqLNx48ai7JZbbinKpk+f3tgeOXJkUYfOF537fP1rw67zMdJcVhsOnYOBa4KnD1RW0wZ1H4W65/tqbUAx3Y/zuKUQZrp/0VjLfbrmPhtRztG1AcUUPpznppkzZ1bti9ZC+bO7du0q6tx9991FWQ5mrvlBjAPJc0fv3r2LOjTn0Do4n2faF7WL6uV1HM3/dB0zWuutXr26KKNxQPf2jPou9fEcdu0PekhqJ/4LIUmSJEmSpDbjAyFJkiRJkqQ24wMhSZIkSZKkNuMDIUmSJEmSpDZTHSpNakLXakMFKbS6Zl81odLUzppQaWoTtYFC+HL4LIURHnPMMUXZmDFjuqw3adKkok4OB6V90THT5yhccejQoY1tCpSk4Gz6mzkgtvackhxISAGFFN6b/yYFMFJQI4VD5n5SG8JKfzO3y2DWF+/Tn/50UXb++ec3tulaPPHEE0UZXcd8jbZt21bUocDNiRMnFmUUgJlRH6/pg4Tmm9NPP72xTeHKNHfR38zhoBTKPXz48KLskksuaWzXBoP+27/9W1H2t3/7t43tVatWFXVonqLw/hUrVjS2x44dW9Q54YQTijIKlc79hgJSafw/9dRTjW2aw2lOrek3VIf2Rde6Juxa3UeBvjkAt9bKlSuLsvnz5ze26d5L/bemD9C4pePJ6wsKjN+yZUtRRvN2Hh9bt24t6hD6YY4pU6Y0tufNm1fUWbRoUVH29re/vbH9wAMPFHVuuummoqzmB0poDp0wYUJR1r9//6Isj/ccfh3B8+revXu7bGvNvTGi7BN0PLSWzNeitg00P9LarmZNKEmHK1dskiRJkiRJbcYHQpIkSZIkSW3GB0KSJEmSJEltxgdCkiRJkiRJbaY6VLqnAdL0uZp6tZ8jOYSPAuQovC8H4FGQIqHA2BxI+thjjxV1KKA4By5HRGzfvr2xvWnTpqp9jR49urFNQZQUgEth2sOGDWtsT548uahDYZEUKpgDIwcOHFjUoZBXumY5AJPOQ02AOAUI0t+rUTsOKOw6qwlb18Gdd955RVkOyaR+Q9eMxnoOaqWxfvzxxxdlFARaM89Sv8xhrhT6TvMg/b1Zs2Y1tvP8ExHxgx/8oCij8M48/mmOePDBB4uyHEZNoaa5TkTE5s2bi7IcDj5nzpyiTp8+fYoyOod5nqB25eDpCA7Ozn+TglSpDXl+qf2xBbo+NaHSFAxM81L+LLVd3UfrkByUnIOUIyIGDRpUlO3YsaMoy/2VxgfdH2vuabUhv7mvUNgxjSsKt89zDP04AP1wBrXr61//emObzh/dO/7gD/6gsU1B3TRuBw8eXJRt2LChsT19+vSizpAhQ4oyWl/kew7Nx3QeaF6g+0lGc0C+ZjSPU4D4iBEjirKa7w21ch+ncyNJhyv/hZAkSZIkSVKb8YGQJEmSJElSm/GBkCRJkiRJUpupzhDqaZYJvSdNmQT5feea98wPVC/nVuQ8nwN9Lr+Hf+yxxxZ16DxQTk5+H5mygahs586dRVk+N/PmzSvqUF5APvfr1q0r6owaNaooo3fiH3/88cb2I4880uXfi+CcFMojyGoynmhfdH2ov+V90efovf+eokwPapdeejT+87WluWXXrl1FWc5ziIi49957G9uUA5FzeQ4k5zJQv6S2vpQZb9m5555blFF+BOUK5ZwcmlMpI+XWW29tbOfMlAO1gc797NmzG9s5Wy2CxyeV5f337du3qENzOM2p+bN0HmjOy+2qzT+jfpPnIOpv1G9o/zVzsV4aOcumpp9E8D3tnnvuaWzPnDmzqEPZadQHcl+hHBvq53lfNC9R/6WyCy+8sLG9du3aog6V0VjO8xXNHXltFFGuJWm9SeOWrk+ev2gOpbXE+PHji7Kc30P7onbRucnHWJvDl8t++MMfFnUo44nknB86z9Tfau6hL+X6T5Je6fwXQpIkSZIkSW3GB0KSJEmSJEltxgdCkiRJkiRJbcYHQpIkSZIkSW2mOtWWwuFygCCFT9YEWUaUYXAUDlcbNJhRyGcO16N9UUge/T0Krcvhg2PGjCnqUBA0BeBu2bKlsV0Tyk1yAF8Eh1Fu27atKOvXr19je+DAgUUdOjfDhg3rsh3bt28v6uTAwgi+Hjn4j4I0Kfwyn+d9+/YVdWhfJPdL+nsUgNvTkF91D43/3O8pNLWzs7Moe/jhh4uy8847r7H9mte8prtNPKCaUM6IulBWKqPg31yPQkYvuOCCoozm1Ntvv73LOlOnTi3K8t+8//77izp79+4tykaMGFGU5fB+Op4cfh1RN45pztu9e3dRRvNZ/mzNHB5RzoP9+/cv6tB9hObPfJ+tuc9HcL/JZTTnqftobsp9n9YgdL3JqlWrGts01s4888yqfeVrTuuLmn5Ox0xrDgqHfvDBBxvbb3jDG4o6Y8eOLcpojObzTP0+B3xHlO2ncUXrixwgHVHOJw888EBRZ8qUKUUZ9YkcIl3zgxsRde2neZXakH98IPe/CD4eamu+HrU/wkBzez431B8k6XDlvxCSJEmSJElqMz4QkiRJkiRJajM+EJIkSZIkSWozPhCSJEmSJElqM9Wh0jXhoxT6RoGUVJbDLWlfNWHUEWXI5uOPP17UoeDPHD5H+6aQPCobPHhwY5sC6p544omijMLucjsoLJKC+XJQMl3DHKQXUYawRkR0dHQ0til4kEJEa0KSKWiWQiWp/flv0vmj8NmtW7c2tun6DBkypCija0b9MqN2UVnNvtQ9q1ev7rIOhYrfcsstRRkFrv/6r/96Y5vGQc38SfUoELNmXy8mnDyHhdaG0Z9++ulFWQ6C/frXv17UoeM5/vjjG9s0Xz/00ENFGYVDjxo1qrG9YMGCog4dT82PJNB8QMHWtK/cT6jf1ITR01xJ8wjNcXnupf5G4bcUIFvzowzqPjqP+VpS/yK0psn3x+9+97tFnXHjxhVl9IMR+Z5Gf4/u97m/Up/r27dvUUbHne/tNI9Pnjy5KKN5bvr06UVZRnNtHrc0T1C4Nt2H7rvvvsZ27bWmHy2hNUdG8wR9LreV5iE6nm9/+9uN7VNOOaWoU3uMI0eObGzTWrw2ODtfI+qnknS48l8ISZIkSZIktRkfCEmSJEmSJLUZHwhJkvT/t3c3O1pU7RrH1zvxCwkKSDcgKl8aETFIOuJXojEmqIlGEocegDHGQ/AAPAQ9Ax2a+BWjqIkxYqIoaHdoQLobWmwQNWoc7T3Yo7ruP/Rq3HkH1P83qzvrKdazatWqotJ1PZIkSdLIdAeW0Dvd/5968nvonWt6PzhzK+jdZsqkyPePe3IfWuO+5vvI9J459Z3euc7PUhvq6/r16wfb//zzT2lDaJwzh4m+T+YmtcbvaudcojwKqvXkMFFe0K+//lpqOYY0fvQ+es87+ITGtOecokwPrQxlcOT8euONN0qbr7/+utToXD906NBg+4UXXihtKEOC8m5yTlxpFhDl8tDcpfmV6xKtebSmUlZTzvvnn3++tKGMjxyvqamp0oay4ZaWlkot809OnTpV2hw9erTUKD9kbm5usJ0ZFq21tnfv3lKjfJJcj+n49OT3UOYbrZ+0/uexpflNa3hPJtpvv/1W2mjl6F6lJ7+RzlG6zuXcpH/v/fffL7WDBw+WWvaD1r2eLCA6X2gdp5y/hYWFwXaes621tri4WGo99zQ0zpQ1k+cf3bNRfuP8/Hyp5TqXGZmt1YzH1njdznsaul+i70jjlWsTrVW0tue8zAy+1mpuUmv8fXJcaY1bvXp1qdG1Q5LGzL8QkiRJkiRJGhkfCEmSJEmSJI2MD4QkSZIkSZJGxgdCkiRJkiRJI9MdKk1hkxnyRuGTFKZLwYYZbtr7OepX1jJcubXWTpw4UWoZptcbBE3hjdT/5frZGgfg5b4oMJQCkLP/FLhHgYhU6+kDBdlSEHTOGwqLpABB6n+GVvaGUWcAIoXDUnAq9Sv3RfOUAhdpLuV5QPvSytC58cEHHwy2P/7449KGzv8DBw6U2rZt2wbbFFpM84bO9YmJicE2zcGeOdEbRk3hmjlXKZSdQpgvXLhQahmwvHnz5tLmscceK7X33ntvsJ0/FNBaa/v37y+1Tz75pNTynKJQVhrn7du3l1r2f8uWLaVN748F5PGnNhT6ndcNmlu0FvfUaP2k6xtdI3Jfx44dK220cufPny+1XNPo+kL3F9QuaxT6/Msvv5Ta4cOHS+3BBx8stURh43mPRveSFMJPYcp5HtEad/PNN5fazp07Sy3X0ZmZmdKmJwCb+kBh13T+5f43bNhQ2tC1pCcgnu69KFScwqfzO9HaS9eJJ554YrBNa3vvD6fkeUD7ojlP8yvReilJVyv/tylJkiRJkjQyPhCSJEmSJEkaGR8ISZIkSZIkjYwPhCRJkiRJkkamO1S6N6Qy9QZBZxBnT5gmfa61GiJMgXu0/wxFpXBFCqilgML83jQOFDxNfb3hhhsG27fffntpQ8F5GZRKgYW9xycDBCn0LwOeW+M5kkGGFBZNNQo3zWNNwbbUrxxnmg+9eo411Qwt/O+gkOe33nprsE3z7cUXXyy11157rdTyONJ8o+O/sLBQanlebd26ddl/r7V6rlMflpaWSo3Wjezr6dOnS5svv/yy1B544IFSy9BlCjGldTDX1DNnziy779Zae/TRR0stx/T+++8vbSiolWqbNm1atl/T09OlRmt2HkeaIxR+mutzb4g9yf1TeDAdsww/b62GH9MPN2jl6FqbwegU1EzB8nRt7wmoXrt2banNzs6W2uTk5GCbgpppHco1gO6p6Bq9bt26Usu1j84PGi8KQc+x6Q1wzz7QD1vQ5/Jer7X6Henco/vGngDxc+fOlTYUgE0/LPDpp58Otuk6S/fLa9asGWzTGtrz4x20fxo/+j4kjwf930KSrlb+hZAkSZIkSdLI+EBIkiRJkiRpZHwgJEmSJEmSNDI+EJIkSZIkSRqZ7tS03jC9Hj2B0RR+eO2115ZaBtS1VsPnrr/++q7Pzc/PD7YpTJWCmSnQ75Zbblm2D/R9KAAxP0vBeTSmOQ4UUJphtK3xsc6ASgqsJDRe2a+LFy+WNhRuSgGI+dnff/+9tKGxyTGl73Olc57CD3sDCnPe/5uwa/2fN998s9QWFxcH26+88kpp8/LLL5caza88h2juUignrRs///zzYJvCVSlINcPUKUiTzn8K/cxzdmZmprTZsWNHqe3Zs6fUMpCYguFpDcoQ28OHD5c2Petua/X70Pp51113lVqGJLfW2pEjRwbbtCbRjxHQ+Z/7X7VqVWlD3yfHkALEKSSd1rhcPynwt+ca21prJ0+eHGxTGLVWLtcEQvOrN1g870MocJnWLwp1z2Bhmr90fuS9F93/0flOtQx+p3+vdx3KewC6J6DA6ER9oLGh+8QMEKdQaVoD6PhngDONw9zcXKl98cUXpZbXk127dnXtK9cF+iEQuu7RdZXmSeo9D3pC/iXpauWKJ0mSJEmSNDI+EJIkSZIkSRoZHwhJkiRJkiSNTHeGEOUP9LxzS+9c9+z/mmuuKW0oY4Hke9jUr/Xr15da5idQhgTti8Yms0a2b99e2uS74a3xeOU719QHqmWOBPWTsiYofyTfw6bPEXq3PceG8gkyj6I1zjbId+Bp/Gje9GQiUX4P1XLsKYuAjg+1y/33vCOvyzt+/Hipvfrqq4Ptl156qbTpneOZw0LzlM6Ds2fPllpm+uzbt6+0oUyRzFegc4rmLq1BlDWU9u7dW2qUk5FjQ5liZMOGDYNtypSgXIvHH3+81HJNoEwk+s50/LP/lANH40Braq5LlB/Ss9ZTrhkda5qDCwsLg20a5zwWrXGeR+bH7Ny5s7TRytE5k9cFynn6448/uvaf+UO0JmSmV2t8Xc1zLedEa63dc889pZbrFWUiUW5ZT64QnR+UndaTzUiZO7R25L9Jx5DuOahfeU7SukQ1uk7kOM/OzpY233zzTalt3bq11DKrie6XKUfsp59+GmxTlhLNN8qazPGieyoa5577Pfo/iCRdrfwLIUmSJEmSpJHxgZAkSZIkSdLI+EBIkiRJkiRpZHwgJEmSJEmSNDLdodIkg9l6A5epXYYkUlAfhf5RaF0GfVIwLwUnZngm7TsDkVvj8MYM3aRgQ/qOFAaan6XARZIBjxRa2hsqnWF9vWMzPz9fahcuXBhsZ5h3axxaSuOV37E3CDrnJbUh1C4Dt//NeXAlbXR5r7/+eqlNTU0NtimMnIJAKdgygzrpnPr2229LbXFxsdSefvrpUuvpQwZ6UqDoxMREqVEo8t9//z3Y3rZtW2lDofy0buS40tjkv9daXat2795d2kxPT5faZ599VmoHDx4cbFPwaK5Jl5L96l031q1bt+y+aF2nOZhj2vNDBK21dvr06VLLYOAMim2NjxkFoue49sxlLY+Ob84dCjKne46eHzKgYPFcL1vja1Peo/3www+lDf1gRAYLU0DxTTfdVGp57aU+0H0PXY/p/Mt7RwoapvUrx5n60Lt25HekHwzIoOZL9SuPx4cffljaPPnkk6W2f//+UsvjSPOGrgk9Yef0wwx0H59h6jS/SU87miOSdLXyf5uSJEmSJEkj4wMhSZIkSZKkkfGBkCRJkiRJ0sj4QEiSJEmSJGlkukOlKdiwJ3StN0w3axS4R6F/FJyYMjizNQ4H/PPPPwfbFGxHYa0UbJiOHz9eahQOSMGJGaZH4Xokx5CC9Oi4UnhzhpRSaOmZM2dKjYJzM8CVwqgJ9asn9JHmTYY8/psg6JyD1Ib60BOu3nusdWkPP/xwqWVQbp77l6pR2Hkeo++//760OXXqVKnde++9pbZnz57BNgVI05zIeX/dddeVNhSkT2tXhpFm2H5rPJ9pTcg1p3ddz3aTk5OlTY5Va629++67pfb5558Ptg8cOFDa0LreE65Px4K+Dx2PHC+ab7Q25ppN6/rc3Fyp0dq4cePGy+67NQ6npR8LuO+++wbbFEaulaNg8Zx3FA6/evXqUqN5nnOTApAptJqC0jPQme7PaA3tuR73rqF5XtE52nveZj/oWNB9QtZoHKhG6+Nff/012D5x4kRpQ8d1dna21I4ePTrYzlDm1vgHA3rGcPPmzaUNXRPyR0RoHCh4mu6Nc97Q8em97+35v4QkXa38CyFJkiRJkqSR8YGQJEmSJEnSyPhASJIkSZIkaWR8ICRJkiRJkjQy3aHSFMzWEypNQW30OQrT62lDQZk9baiWYXoUbEfBnxQ+evbs2cE2hStTiCEFNWYYLAWUUkhejhe1oeNKtfyOFBZIYZE0hhQOnXpDH3vCNWm8sl3vnKR5k8G81Hcae/o+2Y+ec0yXRwGlGT5JwbkU+k7H45133hlsU7DxM888U2oPPfRQqeWco77T+ZmBmxlE2hrPwQzXbq3OXwqV7t1/znFqQ+dsrhG0LlL46Y4dO0rt0KFDy35u3759V9QvCuWm70jh0Dn21IbWyvzcuXPnuvqwdu3aUkt0vab907x89tlnB9tfffVVaUNzXpfXEypN1yoKkT9//nyp5TWNQqVpnaB1Ie8L7rzzztKG+prrKl1DaR4eO3as1O6+++7BNl1ne+8Js1+09tLnaLx60P3Sd999N9iemZkpbShUetOmTaU2NTU12D558mRpQ8ef7qHymkPXRlpzMuycfryFAtEptDrnCa1fvT8YkmtmBqRL0tXMvxCSJEmSJEkaGR8ISZIkSZIkjYwPhCRJkiRJkkamO0OI3rnN93Xp/Wp6p7dn//TeL9Xo3eZ8Z5xyGCgfYs2aNYPt2267rbSh7Bx6pzv7QO+G0/v8c3Nzy/aV3vHuyXiid+kJ5YPkGNI49OYR5TGj70PH7MYbbyy1zCOgedrzvel9ccrhoP3nWPS+x075I5k9QOOnlaG5mrkcS0tLpQ3lWHz00Uel9vbbbw+2n3rqqdLmueeeKzXK4Mg5R+cizaU8h6jvlJNE7TKvh+Zg7/rckxdG515+jvaza9euUqM1NTMq8nhdyuTkZKnlXKKxofwQ6n9mBtGxphyrixcvDrbpuG7ZsqXU6FqZ+Se05tH+d+/eXWo5BzNHT1eG8mHuuOOOwTbl8qxatarUaM1JdB7T/RLN8+wHXXs3btxYajnH6J6A1gk633/88cfB9q233lraUI5Yz/Wertm0hub1pTdXbHp6utQyA21iYqK0eeSRR0qNjv+RI0cG29T3nnt9+iytL3TMci7RvTGtX3l/3lpdf+n70LlBxzHXdjOEJI2JfyEkSZIkSZI0Mj4QkiRJkiRJGhkfCEmSJEmSJI2MD4QkSZIkSZJG5j//05v6LEmSJEmSpKuCfyEkSZIkSZI0Mj4QkiRJkiRJGhkfCEmSJEmSJI2MD4QkSZIkSZJGxgdCkiRJkiRJI+MDIUmSJEmSpJHxgZAkSZIkSdLI+EBIkiRJkiRpZHwgJEmSJEmSNDL/C+JsZ+ZxezDrAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize 1 image from each emotion\n", + "\n", + "train_dir = 'train'\n", + "emotions = os.listdir(train_dir)\n", + "print(emotions)\n", + "plt.figure(figsize=(15,10))\n", + "for i, emotion in enumerate(emotions, 1):\n", + " folder = os.path.join(train_dir, emotion)\n", + " print(folder)\n", + " print(os.listdir(folder))\n", + " img_path = os.path.join(folder, os.listdir(folder)[2])\n", + " img = plt.imread(img_path)\n", + " plt.subplot(3, 4, i)\n", + " plt.imshow(img, cmap='gray')\n", + " plt.title(emotion)\n", + " plt.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uZyEhpooxFsp" + }, + "source": [ + "### Few images for selected emotion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 + }, + "id": "Cz44wbcpiFVQ", + "outputId": "35cc16cf-21f7-432a-b2b1-aaf1f3383eac" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot few images from respective emotion directory\n", + "\n", + "def plot_images_from_directory(directory_path, class_name, num_images=9):\n", + " # Retrieve list of all file names in the directory\n", + " image_filenames = os.listdir(directory_path)\n", + "\n", + " # If there are fewer images than requested, we'll just show them all\n", + " if len(image_filenames) < num_images:\n", + " print(f\"Only found {len(image_filenames)} images in {directory_path}, displaying them all.\")\n", + " num_images = len(image_filenames)\n", + "\n", + " # Randomly select 'num_images' number of file names\n", + " selected_images = random.sample(image_filenames, num_images)\n", + "\n", + " # Plotting the images\n", + " fig, axes = plt.subplots(3, 3, figsize=(5, 5)) # Adjust the size as needed\n", + " axes = axes.ravel()\n", + "\n", + " for i, image_file in enumerate(selected_images):\n", + " image_path = os.path.join(directory_path, image_file)\n", + " # image = Image.open(image_path)\n", + " image = load_img(image_path)\n", + " axes[i].imshow(image)\n", + " axes[i].set_title(f\"Image: {class_name}\")\n", + " axes[i].axis('off') # Hide the axis\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "# Placeholder for the directory path\n", + "angry_directory_path = 'train/angry' # Replace with your directory path\n", + "plot_images_from_directory(angry_directory_path, class_name = 'Angry')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6sJFQze2xKr4" + }, + "source": [ + "# ImageDataGenerators" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a9o7QDlFyKq7" + }, + "source": [ + "## Non augmented data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "Uc-kzumfXYUB", + "outputId": "8ea9740f-2351-4bfb-a1bd-946dc21fed2f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 28709 images belonging to 7 classes.\n", + "Found 7178 images belonging to 7 classes.\n" + ] + } + ], + "source": [ + "# Non augmented data\n", + "import tensorflow as tf\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "\n", + "tf.random.set_seed(42)\n", + "\n", + "train_datagen = ImageDataGenerator(rescale=1./255)\n", + "valid_datagen = ImageDataGenerator(rescale=1./255)\n", + "\n", + "train_dir = 'fer2013/train'\n", + "test_dir = 'fer2013/test'\n", + "\n", + "train_data_nonaug = train_datagen.flow_from_directory(train_dir,batch_size=32,target_size=(48,48),\n", + " class_mode = 'categorical',seed=42)\n", + "test_data_nonaug = valid_datagen.flow_from_directory(test_dir,batch_size=32,target_size=(48,48),\n", + " class_mode = 'categorical',seed=42)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U-cZA2avyMlR" + }, + "source": [ + "## Augmented data ( Better results )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GLs0YD5QfYjq", + "outputId": "84b81c74-66d0-4eb3-c0da-c5f0b97af4ab" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Augmented data\n", + "Found 28709 images belonging to 7 classes.\n", + "Found 7178 images belonging to 7 classes.\n" + ] + } + ], + "source": [ + "# Reducig overfitting is known as regularization\n", + "# Data augmentation\n", + "import tensorflow as tf\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "\n", + "train_datagen = ImageDataGenerator(rescale=1./255,rotation_range=0.2,\n", + " shear_range=0.2,zoom_range=0.2,\n", + " width_shift_range=0.2,\n", + " height_shift_range=0.3,\n", + " horizontal_flip=True,\n", + " fill_mode='nearest') # divide pizel for normalization\n", + "\n", + "test_datagen = ImageDataGenerator(rescale=1./255) # convert to tensors\n", + "\n", + "print('Augmented data')\n", + "train_data = train_datagen.flow_from_directory(train_dir,\n", + " target_size=(48,48),\n", + " batch_size=32,\n", + " color_mode='grayscale',\n", + " class_mode='categorical',\n", + " shuffle=True)\n", + "\n", + "test_data = test_datagen.flow_from_directory(test_dir,\n", + " target_size=(48,48),\n", + " batch_size=32,\n", + " color_mode='grayscale',\n", + " class_mode='categorical',\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lceX8rfrdfSh", + "outputId": "2893d44b-bd55-45e5-a964-11bd0af9f318" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(48, 48, 1)\n" + ] + } + ], + "source": [ + "# Get a sample of the training data batch\n", + "images, labels = train_data.next() # get the 'next' batch of images/labels\n", + "print(images[0].shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Y-1PcJQTdfQY", + "outputId": "5b86b6be-85d7-415b-881c-ace0bcc629a0" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 0., 0., 0., 0., 0., 0.],\n", + " [0., 0., 0., 0., 0., 0., 1.],\n", + " [0., 0., 1., 0., 0., 0., 0.],\n", + " [0., 0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 0., 0., 1., 0., 0.],\n", + " [0., 0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 0., 0., 1., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0.],\n", + " [0., 0., 0., 0., 0., 0., 1.],\n", + " [0., 0., 0., 0., 0., 1., 0.],\n", + " [0., 0., 0., 0., 0., 1., 0.],\n", + " [0., 0., 0., 0., 0., 1., 0.],\n", + " [0., 0., 1., 0., 0., 0., 0.],\n", + " [0., 0., 0., 0., 0., 1., 0.],\n", + " [0., 0., 0., 0., 0., 0., 1.],\n", + " [1., 0., 0., 0., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0., 0.],\n", + " [0., 0., 0., 0., 0., 0., 1.],\n", + " [0., 0., 0., 0., 0., 0., 1.],\n", + " [0., 0., 0., 0., 0., 1., 0.],\n", + " [0., 0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 0., 0., 0., 1., 0.],\n", + " [0., 0., 1., 0., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0., 0.],\n", + " [0., 0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0., 0.],\n", + " [0., 0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 0., 0., 0., 1., 0.]], dtype=float32)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# View the first batch of labels\n", + "labels" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qa6x1a2Zyq7i" + }, + "source": [ + "# Additional Imports\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "QWFki5Bwys8s" + }, + "outputs": [], + "source": [ + "import os\n", + "import random\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import cv2\n", + "import imghdr\n", + "import tensorflow as tf\n", + "from PIL import Image\n", + "import seaborn as sns\n", + "\n", + "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc\n", + "from sklearn.utils.class_weight import compute_class_weight\n", + "\n", + "from tensorflow.keras import layers, models, regularizers, optimizers\n", + "from tensorflow.keras.applications import VGG16, ResNet50V2\n", + "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, TensorBoard, CSVLogger\n", + "from tensorflow.keras.layers import Conv2D, MaxPooling2D, BatchNormalization, Dropout, Flatten, Dense, Activation, GlobalAveragePooling2D\n", + "from tensorflow.keras.models import Sequential, Model\n", + "from tensorflow.keras.optimizers import Adam, Adamax\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array\n", + "from keras.utils import plot_model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-DECMIjMbVLp" + }, + "source": [ + "# Model 1 - CNN Baseline ( Very simple model )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5dSCgQGUdfOJ", + "outputId": "8e11fd83-3832-4052-af34-6463c1357279" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "898/898 [==============================] - 97s 106ms/step - loss: 1.6116 - accuracy: 0.3748 - val_loss: 1.5009 - val_accuracy: 0.4185\n" + ] + } + ], + "source": [ + "# Make the creating of our model a little easier\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Activation\n", + "from tensorflow.keras import Sequential\n", + "\n", + "# Baseline, a 3 layer Convolutional Neural Network)\n", + "model1 = Sequential([\n", + " Conv2D(filters=10,\n", + " kernel_size=3,\n", + " strides=1,\n", + " padding='valid',\n", + " activation='relu',\n", + " input_shape=(48, 48, 3)), # input layer (specify input shape)\n", + " Conv2D(10, 3, activation='relu'),\n", + " Conv2D(10, 3, activation='relu'),\n", + " Flatten(),\n", + " Dense(7, activation='softmax') # output layer (specify output shape)\n", + "])\n", + "\n", + "# Compile the model\n", + "model1.compile(loss='categorical_crossentropy',\n", + " optimizer=Adam(),\n", + " metrics=['accuracy'])\n", + "\n", + "# Fit the model\n", + "history1 = model1.fit(train_data,\n", + " epochs=5,\n", + " steps_per_epoch=len(train_data),\n", + " validation_data=test_data,\n", + " validation_steps=len(test_data))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EtOB76Ejj0XP", + "outputId": "13c38b18-9e91-4fe5-b790-4dbd7176ebd6" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(None, 48, 48, 3)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Input Shape\n", + "model1.input_shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wvQhUwdTY4ad" + }, + "outputs": [], + "source": [ + "#Save Model\n", + "model1.save('emotion.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "x_0NIkYqZege" + }, + "outputs": [], + "source": [ + "model1.save('emotion.keras')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 599 + }, + "id": "1Ci-FZgPdfLv", + "outputId": "98ac8d4f-d7ae-485e-a67c-91825bd99b1b" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the training curves\n", + "import pandas as pd\n", + "pd.DataFrame(history1.history).plot(figsize=(10, 7));" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GSXkuLje2HUP" + }, + "source": [ + "## Plot loss curves function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-L-P_1indfJi" + }, + "outputs": [], + "source": [ + "# Plot the validation and training data separately\n", + "def plot_loss_curves(history):\n", + " \"\"\"\n", + " Returns separate loss curves for training and validation metrics.\n", + " \"\"\"\n", + " loss = history.history['loss']\n", + " val_loss = history.history['val_loss']\n", + "\n", + " accuracy = history.history['accuracy']\n", + " val_accuracy = history.history['val_accuracy']\n", + "\n", + " epochs = range(len(history.history['loss']))\n", + "\n", + " # Plot loss\n", + " plt.plot(epochs, loss, label='training_loss')\n", + " plt.plot(epochs, val_loss, label='val_loss')\n", + " plt.title('Loss')\n", + " plt.xlabel('Epochs')\n", + " plt.legend()\n", + "\n", + " # Plot accuracy\n", + " plt.figure()\n", + " plt.plot(epochs, accuracy, label='training_accuracy')\n", + " plt.plot(epochs, val_accuracy, label='val_accuracy')\n", + " plt.title('Accuracy')\n", + " plt.xlabel('Epochs')\n", + " plt.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 927 + }, + "id": "HKJD00JuiEfu", + "outputId": "38c5d902-967d-4d9b-b75f-18d97f49f686" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Check out the loss curves of model_4\n", + "plot_loss_curves(history1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8Dyc3wg1bXEe" + }, + "source": [ + "# Model 2 - Adding MaxPool2D to baseline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Vh-e5ZdjiEpy", + "outputId": "a2673d40-566a-4803-c513-9e39df3bd54a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "898/898 [==============================] - 43s 46ms/step - loss: 1.8055 - accuracy: 0.2519 - val_loss: 1.7863 - val_accuracy: 0.2534\n", + "Epoch 2/5\n", + "898/898 [==============================] - 46s 51ms/step - loss: 1.7863 - accuracy: 0.2548 - val_loss: 1.7798 - val_accuracy: 0.2579\n", + "Epoch 3/5\n", + "898/898 [==============================] - 43s 47ms/step - loss: 1.7802 - accuracy: 0.2592 - val_loss: 1.7624 - val_accuracy: 0.2690\n", + "Epoch 4/5\n", + "898/898 [==============================] - 42s 46ms/step - loss: 1.7709 - accuracy: 0.2656 - val_loss: 1.7511 - val_accuracy: 0.2856\n", + "Epoch 5/5\n", + "898/898 [==============================] - 43s 48ms/step - loss: 1.7639 - accuracy: 0.2696 - val_loss: 1.7354 - val_accuracy: 0.2933\n" + ] + } + ], + "source": [ + "# Create the model (this can be our baseline, a 3 layer Convolutional Neural Network)\n", + "model2 = Sequential([\n", + " Conv2D(10, 3, activation='relu', input_shape=(48, 48, 3)),\n", + " MaxPool2D(pool_size=2), # reduce number of features by half\n", + " Conv2D(10, 3, activation='relu'),\n", + " MaxPool2D(),\n", + " Conv2D(10, 3, activation='relu'),\n", + " MaxPool2D(),\n", + " Flatten(),\n", + " Dense(7, activation='softmax')\n", + "])\n", + "\n", + "# Compile model (same as model_4)\n", + "model2.compile(loss='categorical_crossentropy',\n", + " optimizer=Adam(),\n", + " metrics=['accuracy'])\n", + "\n", + "# Fit the model\n", + "history2 = model2.fit(train_data,\n", + " epochs=5,\n", + " steps_per_epoch=len(train_data),\n", + " validation_data=test_data,\n", + " validation_steps=len(test_data))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1tcF5B8N5YXx" + }, + "source": [ + "## Alternative" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rfD0vVGY5lxE" + }, + "outputs": [], + "source": [ + "# Build a CNN ( Tiny VGG CNN Explainer website)\n", + "\n", + "model = tf.keras.models.Sequential([\n", + " tf.keras.layers.Conv2D(filters=10,kernel_size=3,\n", + " activation='relu',input_shape=(48,48,3)),\n", + " tf.keras.layers.Conv2D(filters=10,kernel_size=3,\n", + " activation='relu'),\n", + " tf.keras.layers.MaxPool2D(pool_size=2,padding='valid'),\n", + " tf.keras.layers.Conv2D(filters=10,kernel_size=3,\n", + " activation='relu'),\n", + " tf.keras.layers.Conv2D(filters=10,kernel_size=3,\n", + " activation='relu'),\n", + " #tf.keras.layers.Activations(tf.nn.relu)\n", + " tf.keras.layers.MaxPool2D(pool_size=2),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(1,activation='softmax')\n", + "])\n", + "\n", + "model.compile(loss='categorical_crossentropy',\n", + " optimizer=tf.keras.optimizers.Adam(),\n", + " metrics=['accuracy'])\n", + "\n", + "history = model.fit(train_data,epochs=5,steps_per_epoch=len(train_data),\n", + " validation_data=test_data,\n", + " validation_steps=len(test_data))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 927 + }, + "id": "o8NXVBFhiEr-", + "outputId": "59cc1e8f-ae22-4245-905a-8a5d42166ac2" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot loss curves of model_5 results\n", + "plot_loss_curves(history2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3XEHmNQ5bYfK" + }, + "source": [ + "#Advanced Model 3 - Increased complexity + Dropout" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "suk2HsfR6CAI" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Activation, MaxPooling2D,Dropout\n", + "from tensorflow.keras import Sequential" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "f8Y9i_mDx5HZ", + "outputId": "15fdd52f-ce04-441a-951e-f64dd1e1ee4b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_3\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d_4 (Conv2D) (None, 46, 46, 32) 320 \n", + " \n", + " conv2d_5 (Conv2D) (None, 44, 44, 64) 18496 \n", + " \n", + " max_pooling2d_1 (MaxPoolin (None, 22, 22, 64) 0 \n", + " g2D) \n", + " \n", + " dropout (Dropout) (None, 22, 22, 64) 0 \n", + " \n", + " conv2d_6 (Conv2D) (None, 20, 20, 128) 73856 \n", + " \n", + " max_pooling2d_2 (MaxPoolin (None, 10, 10, 128) 0 \n", + " g2D) \n", + " \n", + " dropout_1 (Dropout) (None, 10, 10, 128) 0 \n", + " \n", + " conv2d_7 (Conv2D) (None, 8, 8, 256) 295168 \n", + " \n", + " max_pooling2d_3 (MaxPoolin (None, 4, 4, 256) 0 \n", + " g2D) \n", + " \n", + " dropout_2 (Dropout) (None, 4, 4, 256) 0 \n", + " \n", + " flatten (Flatten) (None, 4096) 0 \n", + " \n", + " dense (Dense) (None, 512) 2097664 \n", + " \n", + " dropout_3 (Dropout) (None, 512) 0 \n", + " \n", + " dense_1 (Dense) (None, 7) 3591 \n", + " \n", + "=================================================================\n", + "Total params: 2489095 (9.50 MB)\n", + "Trainable params: 2489095 (9.50 MB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", + "_________________________________________________________________\n", + "None\n", + "Epoch 1/30\n", + "898/898 [==============================] - 492s 545ms/step - loss: 1.8016 - accuracy: 0.2512 - val_loss: 1.7820 - val_accuracy: 0.2520\n", + "Epoch 2/30\n", + "663/898 [=====================>........] - ETA: 1:58 - loss: 1.7729 - accuracy: 0.2618" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m history_new1 = model.fit(train_data,\n\u001b[0m\u001b[1;32m 29\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 63\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_bound_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m outputs = self._bound_context.call_function(\n\u001b[0m\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 253\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/context.py\u001b[0m in \u001b[0;36mcall_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m 1484\u001b[0m \u001b[0mcancellation_context\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcancellation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1485\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcancellation_context\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1486\u001b[0;31m outputs = execute.execute(\n\u001b[0m\u001b[1;32m 1487\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1488\u001b[0m \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m 54\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 55\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# Create the model\n", + "import tensorflow as tf\n", + "model3 = Sequential()\n", + "\n", + "model3.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48,48,1)))\n", + "\n", + "model3.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\n", + "model3.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model3.add(Dropout(0.1))\n", + "\n", + "model3.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n", + "model3.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model3.add(Dropout(0.1))\n", + "\n", + "model3.add(Conv2D(256, kernel_size=(3, 3), activation='relu'))\n", + "model3.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model3.add(Dropout(0.1))\n", + "\n", + "model3.add(Flatten())\n", + "model3.add(Dense(512, activation='relu'))\n", + "model3.add(Dropout(0.2))\n", + "\n", + "model3.add(Dense(7, activation='softmax'))\n", + "\n", + "model3.compile(optimizer = 'adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "print(model3.summary())\n", + "\n", + "# Add early stopping callback as well\n", + "history3 = model3.fit(train_data,\n", + " steps_per_epoch=len(train_data),\n", + " epochs=30,\n", + " validation_data=test_data,\n", + " validation_steps=len(test_data),\n", + " callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Oh_8BKim3SJf" + }, + "source": [ + "#Advanced Model 4 - Different variation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "STdvZr_ty6dp" + }, + "outputs": [], + "source": [ + "# create model structure\n", + "model4 = Sequential()\n", + "\n", + "model4.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48, 48, 1)))\n", + "model4.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\n", + "model4.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model4.add(Dropout(0.25))\n", + "\n", + "model4.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n", + "model4.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model4.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n", + "model4.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model4.add(Dropout(0.25))\n", + "\n", + "model4.add(Flatten())\n", + "model4.add(Dense(1024, activation='relu'))\n", + "model4.add(Dropout(0.5))\n", + "model4.add(Dense(7, activation='softmax'))\n", + "\n", + "#cv2.ocl.setUseOpenCL(False)\n", + "\n", + "model4.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.0001, decay=1e-6), metrics=['accuracy'])\n", + "\n", + "# Train the neural network/model\n", + "history4 = model4.fit(\n", + " train_data,\n", + " steps_per_epoch=len(train_data),\n", + " epochs=30,\n", + " validation_data=test_data,\n", + " validation_steps=len(test_data),\n", + " callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)])\n" + ] + }, + { + "cell_type": "code", + "source": [ + "# save model structure\n", + "model_json = emotion_model.to_json()\n", + "with open(\"model4.json\", \"w\") as json_file:\n", + " json_file.write(model_json)\n", + "\n", + "# save trained model weight in .h5 file\n", + "emotion_model.save_weights('model4.h5')" + ], + "metadata": { + "id": "WelpHD9EPLTN" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LbfHEP5ibadV" + }, + "source": [ + "# Advanced Model 5 - Increased complexity + Batch Normalization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cdBHOkr_-Czr" + }, + "outputs": [], + "source": [ + "# Initialising the CNN\n", + "model5 = Sequential()\n", + "\n", + "# Adding convolutional layers with activations on the same line for clarity\n", + "model5.add(Conv2D(32, kernel_size=(3, 3),kernel_initializer=\"glorot_uniform\", padding='same', input_shape=(img_width, img_height, 1)))\n", + "model5.add(Activation('relu'))\n", + "model5.add(Conv2D(64, kernel_size=(3, 3), padding='same'))\n", + "model5.add(Activation('relu'))\n", + "model5.add(BatchNormalization())\n", + "model5.add(MaxPooling2D(2, 2))\n", + "model5.add(Dropout(0.25))\n", + "\n", + "model5.add(Conv2D(128, kernel_size=(3, 3), padding='same', kernel_regularizer=regularizers.l2(0.01)))\n", + "model5.add(Activation('relu'))\n", + "model5.add(Conv2D(256, kernel_size=(3, 3), kernel_regularizer=regularizers.l2(0.01)))\n", + "model5.add(Activation('relu'))\n", + "model5.add(BatchNormalization())\n", + "model5.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model5.add(Dropout(0.25))\n", + "\n", + "model5.add(Conv2D(512, kernel_size=(3, 3), padding='same', kernel_regularizer=regularizers.l2(0.01)))\n", + "model5.add(Activation('relu'))\n", + "model5.add(Conv2D(512, kernel_size=(3, 3), padding='same', kernel_regularizer=regularizers.l2(0.01)))\n", + "model5.add(Activation('relu'))\n", + "model5.add(BatchNormalization())\n", + "model5.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model5.add(Dropout(0.25))\n", + "\n", + "# Flattening and adding dense layers\n", + "model5.add(Flatten())\n", + "model5.add(Dense(1024))\n", + "model5.add(Activation('relu'))\n", + "model5.add(Dropout(0.5))\n", + "\n", + "# Output layer\n", + "model5.add(Dense(7))\n", + "model5.add(Activation('softmax'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "19quaqw5ydPT" + }, + "outputs": [], + "source": [ + "model5.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Summary of the model\n", + "model5.summary()\n", + "\n", + "# Train the model\n", + "history5 = model5.fit(\n", + " train_data,\n", + " epochs=30,\n", + " validation_data=test_data,\n", + " callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)]\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NejZJorbbdoQ" + }, + "source": [ + "# Advanced Model 6 - Function + Variation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lrHzU8duiEuM", + "outputId": "516693bf-e150-47d0-ba2f-ba43c6ab9cea" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_3\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d_9 (Conv2D) (None, 48, 48, 32) 896 \n", + " \n", + " batch_normalization (Batch (None, 48, 48, 32) 128 \n", + " Normalization) \n", + " \n", + " max_pooling2d_3 (MaxPoolin (None, 24, 24, 32) 0 \n", + " g2D) \n", + " \n", + " dropout (Dropout) (None, 24, 24, 32) 0 \n", + " \n", + " conv2d_10 (Conv2D) (None, 24, 24, 64) 18496 \n", + " \n", + " batch_normalization_1 (Bat (None, 24, 24, 64) 256 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_4 (MaxPoolin (None, 12, 12, 64) 0 \n", + " g2D) \n", + " \n", + " dropout_1 (Dropout) (None, 12, 12, 64) 0 \n", + " \n", + " conv2d_11 (Conv2D) (None, 12, 12, 128) 73856 \n", + " \n", + " batch_normalization_2 (Bat (None, 12, 12, 128) 512 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_5 (MaxPoolin (None, 6, 6, 128) 0 \n", + " g2D) \n", + " \n", + " dropout_2 (Dropout) (None, 6, 6, 128) 0 \n", + " \n", + " flatten_3 (Flatten) (None, 4608) 0 \n", + " \n", + " dense_3 (Dense) (None, 512) 2359808 \n", + " \n", + " batch_normalization_3 (Bat (None, 512) 2048 \n", + " chNormalization) \n", + " \n", + " dropout_3 (Dropout) (None, 512) 0 \n", + " \n", + " dense_4 (Dense) (None, 7) 3591 \n", + " \n", + "=================================================================\n", + "Total params: 2459591 (9.38 MB)\n", + "Trainable params: 2458119 (9.38 MB)\n", + "Non-trainable params: 1472 (5.75 KB)\n", + "_________________________________________________________________\n", + "Epoch 1/30\n", + "898/898 [==============================] - 52s 52ms/step - loss: 2.0626 - accuracy: 0.2269 - val_loss: 1.7543 - val_accuracy: 0.2824\n", + "Epoch 2/30\n", + "898/898 [==============================] - 49s 54ms/step - loss: 1.7711 - accuracy: 0.2844 - val_loss: 1.6874 - val_accuracy: 0.3129\n", + "Epoch 3/30\n", + "898/898 [==============================] - 45s 51ms/step - loss: 1.7190 - accuracy: 0.3104 - val_loss: 1.8847 - val_accuracy: 0.3256\n", + "Epoch 4/30\n", + "898/898 [==============================] - 43s 48ms/step - loss: 1.6507 - accuracy: 0.3480 - val_loss: 1.6632 - val_accuracy: 0.3313\n", + "Epoch 5/30\n", + "898/898 [==============================] - 44s 49ms/step - loss: 1.5979 - accuracy: 0.3662 - val_loss: 1.5592 - val_accuracy: 0.3703\n", + "Epoch 6/30\n", + "898/898 [==============================] - 46s 52ms/step - loss: 1.5611 - accuracy: 0.3893 - val_loss: 1.3902 - val_accuracy: 0.4802\n", + "Epoch 7/30\n", + "898/898 [==============================] - 46s 51ms/step - loss: 1.5305 - accuracy: 0.4057 - val_loss: 1.5417 - val_accuracy: 0.4689\n", + "Epoch 8/30\n", + "898/898 [==============================] - 45s 50ms/step - loss: 1.5086 - accuracy: 0.4154 - val_loss: 1.3673 - val_accuracy: 0.4939\n", + "Epoch 9/30\n", + "898/898 [==============================] - 46s 52ms/step - loss: 1.4892 - accuracy: 0.4255 - val_loss: 1.4134 - val_accuracy: 0.4987\n", + "Epoch 10/30\n", + "898/898 [==============================] - 44s 49ms/step - loss: 1.4647 - accuracy: 0.4356 - val_loss: 1.5748 - val_accuracy: 0.3958\n", + "Epoch 11/30\n", + "898/898 [==============================] - 44s 49ms/step - loss: 1.4538 - accuracy: 0.4397 - val_loss: 1.3389 - val_accuracy: 0.4928\n", + "Epoch 12/30\n", + "898/898 [==============================] - 46s 51ms/step - loss: 1.4410 - accuracy: 0.4472 - val_loss: 1.3217 - val_accuracy: 0.4935\n", + "Epoch 13/30\n", + "898/898 [==============================] - 47s 53ms/step - loss: 1.4395 - accuracy: 0.4482 - val_loss: 1.4246 - val_accuracy: 0.4741\n", + "Epoch 14/30\n", + "898/898 [==============================] - 45s 50ms/step - loss: 1.4367 - accuracy: 0.4490 - val_loss: 1.2295 - val_accuracy: 0.5320\n", + "Epoch 15/30\n", + "898/898 [==============================] - 44s 49ms/step - loss: 1.4052 - accuracy: 0.4608 - val_loss: 1.3953 - val_accuracy: 0.4763\n", + "Epoch 16/30\n", + "898/898 [==============================] - 43s 48ms/step - loss: 1.4075 - accuracy: 0.4563 - val_loss: 2.5061 - val_accuracy: 0.4349\n", + "Epoch 17/30\n", + "898/898 [==============================] - 45s 50ms/step - loss: 1.4524 - accuracy: 0.4422 - val_loss: 1.2628 - val_accuracy: 0.5205\n", + "Epoch 18/30\n", + "898/898 [==============================] - 44s 49ms/step - loss: 1.4057 - accuracy: 0.4607 - val_loss: 1.2979 - val_accuracy: 0.5014\n", + "Epoch 19/30\n", + "898/898 [==============================] - 43s 48ms/step - loss: 1.3783 - accuracy: 0.4733 - val_loss: 1.7397 - val_accuracy: 0.5024\n" + ] + } + ], + "source": [ + "# Example CNN model\n", + "def create_cnn_model(input_shape):\n", + " model = tf.keras.Sequential([\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=input_shape),\n", + " tf.keras.layers.BatchNormalization(),\n", + " tf.keras.layers.MaxPooling2D((2, 2)),\n", + " tf.keras.layers.Dropout(0.25),\n", + "\n", + " tf.keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same'),\n", + " tf.keras.layers.BatchNormalization(),\n", + " tf.keras.layers.MaxPooling2D((2, 2)),\n", + " tf.keras.layers.Dropout(0.25),\n", + "\n", + " tf.keras.layers.Conv2D(128, (3, 3), activation='relu', padding='same'),\n", + " tf.keras.layers.BatchNormalization(),\n", + " tf.keras.layers.MaxPooling2D((2, 2)),\n", + " tf.keras.layers.Dropout(0.25),\n", + "\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(512, activation='relu'),\n", + " tf.keras.layers.BatchNormalization(),\n", + " tf.keras.layers.Dropout(0.5),\n", + " tf.keras.layers.Dense(7, activation='softmax') # Assuming 7 classes for facial expressions\n", + " ])\n", + " return model\n", + "\n", + "# Define input shape\n", + "input_shape = (48, 48, 3)\n", + "\n", + "# Create and compile the model\n", + "model6 = create_cnn_model(input_shape)\n", + "model6.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Summary of the model\n", + "model6.summary()\n", + "\n", + "# Train the model\n", + "history6 = model6.fit(\n", + " train_data,\n", + " epochs=30,\n", + " validation_data=test_data,\n", + " callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)]\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 927 + }, + "id": "cYRr4pbwiEwO", + "outputId": "077ef075-bbe3-4439-9317-7a5baefebc16" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot loss curves\n", + "plot_loss_curves(history6)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4QuJONGH4sPK" + }, + "source": [ + "## Predict and plotting function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "M1-X0q1L49Ou" + }, + "outputs": [], + "source": [ + "# Create a function to import an image and resize it to be able to be used with our model\n", + "def load_and_prep_image(filename, img_shape=224):\n", + " \"\"\"\n", + " Reads an image from filename, turns it into a tensor\n", + " and reshapes it to (img_shape, img_shape, colour_channel).\n", + " \"\"\"\n", + " # Read in target file (an image)\n", + " img = tf.io.read_file(filename)\n", + "\n", + " # Decode the read file into a tensor & ensure 3 colour channels\n", + " # (our model is trained on images with 3 colour channels and sometimes images have 4 colour channels)\n", + " img = tf.image.decode_image(img, channels=3)\n", + "\n", + " # Resize the image (to the same size our model was trained on)\n", + " img = tf.image.resize(img, size = [img_shape, img_shape])\n", + "\n", + " # Rescale the image (get all values between 0 and 1)\n", + " img = img/255.\n", + " return img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OIGOWYLTiE03" + }, + "outputs": [], + "source": [ + "\n", + "def pred_and_plot(model, filename, class_names):\n", + " \"\"\"\n", + " Imports an image located at filename, makes a prediction on it with\n", + " a trained model and plots the image with the predicted class as the title.\n", + " \"\"\"\n", + " # Import the target image and preprocess it\n", + " img = load_and_prep_image(filename)\n", + "\n", + " # Make a prediction\n", + " pred = model.predict(tf.expand_dims(img, axis=0))\n", + "\n", + " # Get the predicted class\n", + " if len(pred[0]) > 1: # check for multi-class\n", + " pred_class = class_names[pred.argmax()] # if more than one output, take the max\n", + " else:\n", + " pred_class = class_names[int(tf.round(pred)[0][0])] # if only one output, round\n", + "\n", + " # Plot the image and predicted class\n", + " plt.imshow(img)\n", + " plt.title(f\"Prediction: {pred_class}\")\n", + " plt.axis(False);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cDgmuYWe7dyt" + }, + "source": [ + "# Model 7 - Advanced Variation + Callbacks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iWNUzYkZ6HqJ" + }, + "outputs": [], + "source": [ + "train_data_dir = 'train'\n", + "test_data_dir = 'test'\n", + "\n", + "# Set some parameters\n", + "img_width, img_height = 48, 48\n", + "batch_size = 64\n", + "epochs = 10\n", + "num_classes = 7 # 7 emotions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DoIA24RL7ssG" + }, + "source": [ + "## New image data generators" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "o7tCKvxhXYjV", + "outputId": "6e6c92e0-a147-449c-db9d-bb733de99008" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 22968 images belonging to 7 classes.\n", + "Found 5741 images belonging to 7 classes.\n", + "Found 7178 images belonging to 7 classes.\n" + ] + } + ], + "source": [ + "\n", + "data_generator = ImageDataGenerator(rescale=1./255,\n", + " validation_split=0.2)\n", + "\n", + "# Automatically retrieve images and their classes for train and validation sets\n", + "train_generator = data_generator.flow_from_directory(\n", + " train_data_dir,\n", + " target_size=(img_width, img_height),\n", + " batch_size=batch_size,\n", + " class_mode='categorical',\n", + " color_mode='grayscale',\n", + " subset='training')\n", + "\n", + "validation_generator = data_generator.flow_from_directory(\n", + " train_data_dir,\n", + " target_size=(img_width, img_height),\n", + " batch_size=batch_size,\n", + " class_mode='categorical',\n", + " color_mode='grayscale',\n", + " subset='validation')\n", + "\n", + "test_generator = data_generator.flow_from_directory(\n", + " test_data_dir,\n", + " target_size=(img_width, img_height),\n", + " batch_size=batch_size,\n", + " class_mode='categorical',\n", + " color_mode='grayscale',)\n", + " # subset='validation')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JtU82VBuXYku" + }, + "outputs": [], + "source": [ + "# Initialising the CNN\n", + "model7 = Sequential()\n", + "\n", + "# Adding convolutional layers with activations on the same line for clarity\n", + "model7.add(Conv2D(32, kernel_size=(3, 3),kernel_initializer=\"glorot_uniform\", padding='same', input_shape=(img_width, img_height, 1)))\n", + "model7.add(Activation('relu'))\n", + "model7.add(Conv2D(64, kernel_size=(3, 3), padding='same'))\n", + "model7.add(Activation('relu'))\n", + "model7.add(BatchNormalization())\n", + "model7.add(MaxPooling2D(2, 2))\n", + "model7.add(Dropout(0.25))\n", + "\n", + "model7.add(Conv2D(128, kernel_size=(3, 3), padding='same', kernel_regularizer=regularizers.l2(0.01)))\n", + "model7.add(Activation('relu'))\n", + "model7.add(Conv2D(256, kernel_size=(3, 3), kernel_regularizer=regularizers.l2(0.01)))\n", + "model7.add(Activation('relu'))\n", + "model7.add(BatchNormalization())\n", + "model7.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model7.add(Dropout(0.25))\n", + "\n", + "model7.add(Conv2D(512, kernel_size=(3, 3), padding='same', kernel_regularizer=regularizers.l2(0.01)))\n", + "model7.add(Activation('relu'))\n", + "model7.add(Conv2D(512, kernel_size=(3, 3), padding='same', kernel_regularizer=regularizers.l2(0.01)))\n", + "model7.add(Activation('relu'))\n", + "model7.add(BatchNormalization())\n", + "model7.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model7.add(Dropout(0.25))\n", + "\n", + "# Flattening and adding dense layers\n", + "model7.add(Flatten())\n", + "model7.add(Dense(1024))\n", + "model7.add(Activation('relu'))\n", + "model7.add(Dropout(0.5))\n", + "\n", + "# Output layer\n", + "model7.add(Dense(num_classes))\n", + "model7.add(Activation('softmax'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XAVJ9a5WXYlZ", + "outputId": "0d58c7c6-0410-4dd0-c71b-6a8fb2d37f4a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d_6 (Conv2D) (None, 48, 48, 32) 320 \n", + " \n", + " activation_8 (Activation) (None, 48, 48, 32) 0 \n", + " \n", + " conv2d_7 (Conv2D) (None, 48, 48, 64) 18496 \n", + " \n", + " activation_9 (Activation) (None, 48, 48, 64) 0 \n", + " \n", + " batch_normalization_3 (Bat (None, 48, 48, 64) 256 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_3 (MaxPoolin (None, 24, 24, 64) 0 \n", + " g2D) \n", + " \n", + " dropout_4 (Dropout) (None, 24, 24, 64) 0 \n", + " \n", + " conv2d_8 (Conv2D) (None, 24, 24, 128) 73856 \n", + " \n", + " activation_10 (Activation) (None, 24, 24, 128) 0 \n", + " \n", + " conv2d_9 (Conv2D) (None, 22, 22, 256) 295168 \n", + " \n", + " activation_11 (Activation) (None, 22, 22, 256) 0 \n", + " \n", + " batch_normalization_4 (Bat (None, 22, 22, 256) 1024 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_4 (MaxPoolin (None, 11, 11, 256) 0 \n", + " g2D) \n", + " \n", + " dropout_5 (Dropout) (None, 11, 11, 256) 0 \n", + " \n", + " conv2d_10 (Conv2D) (None, 11, 11, 512) 1180160 \n", + " \n", + " activation_12 (Activation) (None, 11, 11, 512) 0 \n", + " \n", + " conv2d_11 (Conv2D) (None, 11, 11, 512) 2359808 \n", + " \n", + " activation_13 (Activation) (None, 11, 11, 512) 0 \n", + " \n", + " batch_normalization_5 (Bat (None, 11, 11, 512) 2048 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_5 (MaxPoolin (None, 5, 5, 512) 0 \n", + " g2D) \n", + " \n", + " dropout_6 (Dropout) (None, 5, 5, 512) 0 \n", + " \n", + " flatten_1 (Flatten) (None, 12800) 0 \n", + " \n", + " dense_2 (Dense) (None, 1024) 13108224 \n", + " \n", + " activation_14 (Activation) (None, 1024) 0 \n", + " \n", + " dropout_7 (Dropout) (None, 1024) 0 \n", + " \n", + " dense_3 (Dense) (None, 7) 7175 \n", + " \n", + " activation_15 (Activation) (None, 7) 0 \n", + " \n", + "=================================================================\n", + "Total params: 17046535 (65.03 MB)\n", + "Trainable params: 17044871 (65.02 MB)\n", + "Non-trainable params: 1664 (6.50 KB)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model7.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DyQAm4HBXYkE", + "outputId": "da51139a-e0a8-4d0f-b2eb-62d00e53e5c8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training class labels: {'angry': 0, 'disgust': 1, 'fear': 2, 'happy': 3, 'neutral': 4, 'sad': 5, 'surprise': 6}\n", + "Validation class labels: {'angry': 0, 'disgust': 1, 'fear': 2, 'happy': 3, 'neutral': 4, 'sad': 5, 'surprise': 6}\n", + "Validation class labels: {'angry': 0, 'disgust': 1, 'fear': 2, 'happy': 3, 'neutral': 4, 'sad': 5, 'surprise': 6}\n" + ] + } + ], + "source": [ + "# Class Labels\n", + "\n", + "print(\"Training \", train_generator.class_indices)\n", + "\n", + "print(\"Validation \", validation_generator.class_indices)\n", + "\n", + "print(\"Testing \", test_generator.class_indices)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Al8Ifl4YXYmJ" + }, + "outputs": [], + "source": [ + "# Compiling the model\n", + "model7.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "_XveQmRPXYm0", + "outputId": "231c0f69-7242-494b-d5cc-388e2bfab070" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot_model(model7,\n", + " show_shapes=True, show_layer_names=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EFFRYoB48lHX" + }, + "source": [ + "## Adding callbacks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jGv5BY1gXYsx" + }, + "outputs": [], + "source": [ + "# File path for the model checkpoint\n", + "path1 = 'model_checkpoints1'\n", + "path2 = 'model7.keras'\n", + "chk_path = os.path.join(path1, path2)\n", + "\n", + "# Model checkpoint\n", + "checkpoint = ModelCheckpoint(filepath=chk_path,\n", + " save_best_only=True,\n", + " verbose=1,\n", + " mode='min',\n", + " monitor='val_loss')\n", + "\n", + "# Early stopping\n", + "earlystop = EarlyStopping(monitor='val_loss',\n", + " min_delta=0,\n", + " patience=3,\n", + " verbose=1,\n", + " restore_best_weights=True)\n", + "\n", + "# Learning rate\n", + "reduce_lr = ReduceLROnPlateau(monitor='val_loss',\n", + " factor=0.2,\n", + " patience=6,\n", + " verbose=1,\n", + " min_delta=0.0001)\n", + "\n", + "# Log training data\n", + "csv_logger = CSVLogger(os.path.join(path1,'training.log'))\n", + "\n", + "\n", + "callbacks = [checkpoint, earlystop, reduce_lr, csv_logger] # Adjusted as per your use-case\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JFtp0-Jd9U6w" + }, + "outputs": [], + "source": [ + "train_generator.samples # 22968\n", + "\n", + "train_spe = train_generator.samples // train_generator.batch_size + 1\n", + "validation_spe = validation_generator.samples // validation_generator.batch_size + 1\n", + "test_spe = test_generator.samples // test_generator.batch_size + 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HXD1878gXY7U", + "outputId": "429d4d7b-2721-460a-f5cf-73670b5e7cf3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "359/359 [==============================] - ETA: 0s - loss: 7.6519 - accuracy: 0.3948\n", + "Epoch 1: val_loss improved from 14.19725 to 6.34270, saving model to model_checkpoints/cnn_model10.keras\n", + "359/359 [==============================] - 29s 71ms/step - loss: 7.6519 - accuracy: 0.3948 - val_loss: 6.3427 - val_accuracy: 0.4424 - lr: 1.0000e-04\n", + "Epoch 2/20\n", + "359/359 [==============================] - ETA: 0s - loss: 5.3704 - accuracy: 0.4414\n", + "Epoch 2: val_loss improved from 6.34270 to 4.55842, saving model to model_checkpoints/cnn_model10.keras\n", + "359/359 [==============================] - 26s 71ms/step - loss: 5.3704 - accuracy: 0.4414 - val_loss: 4.5584 - val_accuracy: 0.4708 - lr: 1.0000e-04\n", + "Epoch 3/20\n", + "359/359 [==============================] - ETA: 0s - loss: 3.9491 - accuracy: 0.4833\n", + "Epoch 3: val_loss improved from 4.55842 to 3.45616, saving model to model_checkpoints/cnn_model10.keras\n", + "359/359 [==============================] - 26s 71ms/step - loss: 3.9491 - accuracy: 0.4833 - val_loss: 3.4562 - val_accuracy: 0.4982 - lr: 1.0000e-04\n", + "Epoch 4/20\n", + "359/359 [==============================] - ETA: 0s - loss: 3.0536 - accuracy: 0.5124\n", + "Epoch 4: val_loss improved from 3.45616 to 2.73257, saving model to model_checkpoints/cnn_model10.keras\n", + "359/359 [==============================] - 26s 72ms/step - loss: 3.0536 - accuracy: 0.5124 - val_loss: 2.7326 - val_accuracy: 0.5170 - lr: 1.0000e-04\n", + "Epoch 5/20\n", + "359/359 [==============================] - ETA: 0s - loss: 2.4893 - accuracy: 0.5385\n", + "Epoch 5: val_loss improved from 2.73257 to 2.37609, saving model to model_checkpoints/cnn_model10.keras\n", + "359/359 [==============================] - 28s 79ms/step - loss: 2.4893 - accuracy: 0.5385 - val_loss: 2.3761 - val_accuracy: 0.4952 - lr: 1.0000e-04\n", + "Epoch 6/20\n", + "359/359 [==============================] - ETA: 0s - loss: 2.1109 - accuracy: 0.5596\n", + "Epoch 6: val_loss improved from 2.37609 to 2.11322, saving model to model_checkpoints/cnn_model10.keras\n", + "359/359 [==============================] - 26s 71ms/step - loss: 2.1109 - accuracy: 0.5596 - val_loss: 2.1132 - val_accuracy: 0.5341 - lr: 1.0000e-04\n", + "Epoch 7/20\n", + "359/359 [==============================] - ETA: 0s - loss: 1.8555 - accuracy: 0.5855\n", + "Epoch 7: val_loss improved from 2.11322 to 1.85599, saving model to model_checkpoints/cnn_model10.keras\n", + "359/359 [==============================] - 26s 72ms/step - loss: 1.8555 - accuracy: 0.5855 - val_loss: 1.8560 - val_accuracy: 0.5471 - lr: 1.0000e-04\n", + "Epoch 8/20\n", + "359/359 [==============================] - ETA: 0s - loss: 1.6744 - accuracy: 0.5984\n", + "Epoch 8: val_loss improved from 1.85599 to 1.68801, saving model to model_checkpoints/cnn_model10.keras\n", + "359/359 [==============================] - 26s 73ms/step - loss: 1.6744 - accuracy: 0.5984 - val_loss: 1.6880 - val_accuracy: 0.5818 - lr: 1.0000e-04\n", + "Epoch 9/20\n", + "359/359 [==============================] - ETA: 0s - loss: 1.5318 - accuracy: 0.6209\n", + "Epoch 9: val_loss did not improve from 1.68801\n", + "359/359 [==============================] - 25s 69ms/step - loss: 1.5318 - accuracy: 0.6209 - val_loss: 1.6946 - val_accuracy: 0.5550 - lr: 1.0000e-04\n", + "Epoch 10/20\n", + "359/359 [==============================] - ETA: 0s - loss: 1.4197 - accuracy: 0.6416\n", + "Epoch 10: val_loss improved from 1.68801 to 1.58292, saving model to model_checkpoints/cnn_model10.keras\n", + "359/359 [==============================] - 26s 72ms/step - loss: 1.4197 - accuracy: 0.6416 - val_loss: 1.5829 - val_accuracy: 0.5752 - lr: 1.0000e-04\n", + "Epoch 11/20\n", + "359/359 [==============================] - ETA: 0s - loss: 1.3163 - accuracy: 0.6699\n", + "Epoch 11: val_loss improved from 1.58292 to 1.49736, saving model to model_checkpoints/cnn_model10.keras\n", + "359/359 [==============================] - 26s 72ms/step - loss: 1.3163 - accuracy: 0.6699 - val_loss: 1.4974 - val_accuracy: 0.5983 - lr: 1.0000e-04\n", + "Epoch 12/20\n", + "359/359 [==============================] - ETA: 0s - loss: 1.2307 - accuracy: 0.6928\n", + "Epoch 12: val_loss improved from 1.49736 to 1.47752, saving model to model_checkpoints/cnn_model10.keras\n", + "359/359 [==============================] - 29s 80ms/step - loss: 1.2307 - accuracy: 0.6928 - val_loss: 1.4775 - val_accuracy: 0.6048 - lr: 1.0000e-04\n", + "Epoch 13/20\n", + "359/359 [==============================] - ETA: 0s - loss: 1.1429 - accuracy: 0.7198\n", + "Epoch 13: val_loss did not improve from 1.47752\n", + "359/359 [==============================] - 25s 69ms/step - loss: 1.1429 - accuracy: 0.7198 - val_loss: 1.4848 - val_accuracy: 0.6051 - lr: 1.0000e-04\n", + "Epoch 14/20\n", + "359/359 [==============================] - ETA: 0s - loss: 1.0660 - accuracy: 0.7458\n", + "Epoch 14: val_loss improved from 1.47752 to 1.45133, saving model to model_checkpoints/cnn_model10.keras\n", + "359/359 [==============================] - 26s 72ms/step - loss: 1.0660 - accuracy: 0.7458 - val_loss: 1.4513 - val_accuracy: 0.6096 - lr: 1.0000e-04\n", + "Epoch 15/20\n", + "359/359 [==============================] - ETA: 0s - loss: 0.9977 - accuracy: 0.7655\n", + "Epoch 15: val_loss did not improve from 1.45133\n", + "359/359 [==============================] - 25s 69ms/step - loss: 0.9977 - accuracy: 0.7655 - val_loss: 1.4735 - val_accuracy: 0.6086 - lr: 1.0000e-04\n", + "Epoch 16/20\n", + "359/359 [==============================] - ETA: 0s - loss: 0.9187 - accuracy: 0.7959\n", + "Epoch 16: val_loss did not improve from 1.45133\n", + "359/359 [==============================] - 25s 68ms/step - loss: 0.9187 - accuracy: 0.7959 - val_loss: 1.5004 - val_accuracy: 0.6081 - lr: 1.0000e-04\n", + "Epoch 17/20\n", + "359/359 [==============================] - ETA: 0s - loss: 0.8618 - accuracy: 0.8137\n", + "Epoch 17: val_loss did not improve from 1.45133\n", + "Restoring model weights from the end of the best epoch: 14.\n", + "359/359 [==============================] - 25s 69ms/step - loss: 0.8618 - accuracy: 0.8137 - val_loss: 1.5044 - val_accuracy: 0.6131 - lr: 1.0000e-04\n", + "Epoch 17: early stopping\n" + ] + } + ], + "source": [ + "history7 = model7.fit(\n", + " train_generator,\n", + " steps_per_epoch=train_spe,\n", + " epochs=20,\n", + " validation_data=validation_generator,\n", + " validation_steps=validation_spe,\n", + " callbacks=callbacks)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XHScgMhC922u" + }, + "source": [ + "## Plot training history" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UYJLHm-6XY9f" + }, + "outputs": [], + "source": [ + "def plot_training_history(history):\n", + " \"\"\"\n", + " Plots the training and validation accuracy and loss.\n", + "\n", + " Parameters:\n", + " - history: A Keras History object. Contains the logs from the training process.\n", + "\n", + " Returns:\n", + " - None. Displays the matplotlib plots for training/validation accuracy and loss.\n", + " \"\"\"\n", + " acc = history.history['accuracy']\n", + " val_acc = history.history['val_accuracy']\n", + " loss = history.history['loss']\n", + " val_loss = history.history['val_loss']\n", + "\n", + " epochs_range = range(len(acc))\n", + "\n", + " plt.figure(figsize=(20, 5))\n", + "\n", + " # Plot training and validation accuracy\n", + " plt.subplot(1, 2, 1)\n", + " plt.plot(epochs_range, acc, label='Training Accuracy')\n", + " plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n", + " plt.legend(loc='lower right')\n", + " plt.title('Training and Validation Accuracy')\n", + "\n", + " # Plot training and validation loss\n", + " plt.subplot(1, 2, 2)\n", + " plt.plot(epochs_range, loss, label='Training Loss')\n", + " plt.plot(epochs_range, val_loss, label='Validation Loss')\n", + " plt.legend(loc='upper right')\n", + " plt.title('Training and Validation Loss')\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 488 + }, + "id": "Wj0QBGP5XY_5", + "outputId": "bf9ab9ea-4b73-4227-eef6-a81d302fe225" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_training_history(history7)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E7Jo13KC-bsM" + }, + "source": [ + "## Evaluate Predictions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FTWmN4wwXZCO", + "outputId": "e1b0c6af-4b1a-4721-d959-3625d20f25ab" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "359/359 [==============================] - 9s 26ms/step - loss: 0.8127 - accuracy: 0.8673\n", + "113/113 [==============================] - 3s 27ms/step - loss: 1.4378 - accuracy: 0.6179\n", + "final train accuracy = 86.73 , validation accuracy = 61.79\n" + ] + } + ], + "source": [ + "train_loss, train_accu = model7.evaluate(train_generator)\n", + "test_loss, test_accu = model7.evaluate(test_generator)\n", + "print(\"final train accuracy = {:.2f} , validation accuracy = {:.2f}\".format(train_accu*100, test_accu*100))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 735 + }, + "id": "jKw3mYfmXZEX", + "outputId": "7acc8489-cf70-49ff-bde0-99b38b186a63" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "113/113 [==============================] - 5s 35ms/step\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Assuming your true_classes and predicted_classes are already defined\n", + "true_classes = test_generator.classes\n", + "predicted_classes = np.argmax(model7.predict(test_generator, steps=np.ceil(test_generator.samples/test_generator.batch_size)), axis=1)\n", + "class_labels = list(test_generator.class_indices.keys())\n", + "\n", + "# Generate the confusion matrix\n", + "cm = confusion_matrix(true_classes, predicted_classes)\n", + "\n", + "# Plotting with seaborn\n", + "plt.figure(figsize=(10, 8))\n", + "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=class_labels, yticklabels=class_labels)\n", + "plt.title('Confusion Matrix')\n", + "plt.ylabel('True label')\n", + "plt.xlabel('Predicted label')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AEtspOLCXZGn", + "outputId": "1d631e34-9973-4ec8-9ec3-63a8d5843bd9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " angry 0.12 0.14 0.13 958\n", + " disgust 0.00 0.00 0.00 111\n", + " fear 0.14 0.09 0.11 1024\n", + " happy 0.25 0.26 0.25 1774\n", + " neutral 0.19 0.19 0.19 1233\n", + " sad 0.17 0.19 0.18 1247\n", + " surprise 0.11 0.11 0.11 831\n", + "\n", + " accuracy 0.17 7178\n", + " macro avg 0.14 0.14 0.14 7178\n", + "weighted avg 0.17 0.17 0.17 7178\n", + "\n" + ] + } + ], + "source": [ + "# Printing the classification report\n", + "report = classification_report(true_classes, predicted_classes, target_names=class_labels)\n", + "print(\"Classification Report:\\n\", report)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 494 + }, + "id": "aH4rqJl1mO21", + "outputId": "65c7d8c0-cc76-4e8c-9a32-d14dfefccb9e" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Emotion classes for the dataset\n", + "Emotion_Classes = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']\n", + "\n", + "# Assuming test_generator and model are already defined\n", + "batch_size = test_generator.batch_size\n", + "\n", + "# Selecting a random batch from the test generator\n", + "Random_batch = np.random.randint(0, len(test_generator) - 1)\n", + "\n", + "# Selecting random image indices from the batch\n", + "Random_Img_Index = np.random.randint(0, batch_size, 10)\n", + "\n", + "# Setting up the plot\n", + "fig, axes = plt.subplots(nrows=2, ncols=5, figsize=(10, 5),\n", + " subplot_kw={'xticks': [], 'yticks': []})\n", + "\n", + "for i, ax in enumerate(axes.flat):\n", + " # Fetching the random image and its label\n", + " Random_Img = test_generator[Random_batch][0][Random_Img_Index[i]]\n", + " Random_Img_Label = np.argmax(test_generator[Random_batch][1][Random_Img_Index[i]], axis=0)\n", + "\n", + " # Making a prediction using the model\n", + " Model_Prediction = np.argmax(model.predict(tf.expand_dims(Random_Img, axis=0), verbose=0), axis=1)[0]\n", + "\n", + " # Displaying the image\n", + " ax.imshow(Random_Img.squeeze(), cmap='gray') # Assuming the images are grayscale\n", + " # Setting the title with true and predicted labels, colored based on correctness\n", + " color = \"green\" if Emotion_Classes[Random_Img_Label] == Emotion_Classes[Model_Prediction] else \"red\"\n", + " ax.set_title(f\"True: {Emotion_Classes[Random_Img_Label]}\\nPredicted: {Emotion_Classes[Model_Prediction]}\", color=color)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5EMBVqwy_Abh" + }, + "source": [ + "# Model 8 - Adding data augmentation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r1IdzefRBgOi" + }, + "source": [ + "## Augmented Data Generator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AmBQGeNJmO5E", + "outputId": "5a655983-db99-46db-c391-af896acffd51" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 22968 images belonging to 7 classes.\n", + "Found 5741 images belonging to 7 classes.\n", + "Found 7178 images belonging to 7 classes.\n" + ] + } + ], + "source": [ + "\n", + "data_generator = ImageDataGenerator(\n", + " rescale=1./255,\n", + " rotation_range=40,\n", + " width_shift_range=0.2,\n", + " height_shift_range=0.2,\n", + " shear_range=0.2,\n", + " zoom_range=0.2,\n", + " horizontal_flip=True,\n", + " fill_mode='nearest',\n", + " validation_split=0.2\n", + ")\n", + "\n", + "test_data_generator = ImageDataGenerator(rescale=1./255)\n", + "\n", + "\n", + "# Automatically retrieve images and their classes for train and validation sets\n", + "train_generator = data_generator.flow_from_directory(\n", + " train_data_dir,\n", + " target_size=(img_width, img_height),\n", + " batch_size=batch_size,\n", + " class_mode='categorical',\n", + " color_mode='grayscale',\n", + " subset='training')\n", + "\n", + "validation_generator = data_generator.flow_from_directory(\n", + " train_data_dir,\n", + " target_size=(img_width, img_height),\n", + " batch_size=batch_size,\n", + " class_mode='categorical',\n", + " color_mode='grayscale',\n", + " subset='validation')\n", + "\n", + "test_generator = test_data_generator.flow_from_directory(\n", + " test_data_dir,\n", + " target_size=(img_width, img_height),\n", + " batch_size=batch_size,\n", + " class_mode='categorical',\n", + " color_mode='grayscale'\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 241 + }, + "id": "x-ax0xXbmO7O", + "outputId": "af4a20c2-c4d7-48b8-a1c0-3ab866254603" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Path to your specific image\n", + "image_path = '/content/train/angry/Training_10118481.jpg'\n", + "\n", + "# Load and convert the image to an array\n", + "img = load_img(image_path, color_mode='grayscale', target_size=(img_width, img_height)) # Adjust color_mode if necessary\n", + "img_array = img_to_array(img) # Converts the image to a numpy array\n", + "img_array = img_array.reshape((1,) + img_array.shape) # Reshapes the image to (1, width, height, channels) for the generator\n", + "\n", + "# Setting up the plot\n", + "fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(10, 4))\n", + "# Plot the original image first. Since img_array is a 3D array after reshaping, we use [:, :, 0]\n", + "axes[0].imshow(img_array[0, :, :, 0], cmap='gray')\n", + "axes[0].set_title('Original Image')\n", + "axes[0].axis('off')\n", + "\n", + "# Generate and plot augmented images\n", + "for i, ax in enumerate(axes.flat[1:]): # Start from the second subplot\n", + " # Generate a batch of augmented images\n", + " aug_iter = data_generator.flow(img_array, batch_size=1)\n", + " aug_img = next(aug_iter)[0] # Get the first augmented image from the batch\n", + "\n", + " # Plot the augmented image. We directly use [:, :, 0] without an initial batch index\n", + " ax.imshow(aug_img[:, :, 0], cmap='gray')\n", + " ax.set_title(f'Augmented Image {i+1}')\n", + " ax.axis('off')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8ZwcaIJqBpOS" + }, + "source": [ + "## Callbacks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7Fasr3xmCTm0" + }, + "outputs": [], + "source": [ + "# File path for the model checkpoint\n", + "path1 = 'model_checkpoints2'\n", + "path2 = 'cnn_model8_augmented.keras'\n", + "chk_path = os.path.join(path1, path2)\n", + "\n", + "# Callback to save the model checkpoint\n", + "checkpoint = ModelCheckpoint(filepath=chk_path,\n", + " save_best_only=True,\n", + " verbose=1,\n", + " mode='min',\n", + " monitor='val_loss')\n", + "\n", + "# Callback to log training data to a CSV file\n", + "csv_logger = CSVLogger(os.path.join(path1,'training.log'))\n", + "\n", + "# Aggregating all callbacks into a list\n", + "callbacks = [checkpoint, earlystop, reduce_lr, csv_logger] # Adjusted as per your use-case" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zrjBej4XmO94", + "outputId": "c1b51e44-ab6a-416f-f6de-1b712e46870b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "359/359 [==============================] - ETA: 0s - loss: 12.5095 - accuracy: 0.2175\n", + "Epoch 1: val_loss improved from inf to 12.68249, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 36s 89ms/step - loss: 12.5095 - accuracy: 0.2175 - val_loss: 12.6825 - val_accuracy: 0.1684 - lr: 1.0000e-04\n", + "Epoch 2/100\n", + "359/359 [==============================] - ETA: 0s - loss: 10.3380 - accuracy: 0.2384\n", + "Epoch 2: val_loss improved from 12.68249 to 9.90142, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 57s 159ms/step - loss: 10.3380 - accuracy: 0.2384 - val_loss: 9.9014 - val_accuracy: 0.2005 - lr: 1.0000e-04\n", + "Epoch 3/100\n", + "359/359 [==============================] - ETA: 0s - loss: 8.2814 - accuracy: 0.2426\n", + "Epoch 3: val_loss improved from 9.90142 to 7.54121, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 45s 126ms/step - loss: 8.2814 - accuracy: 0.2426 - val_loss: 7.5412 - val_accuracy: 0.2550 - lr: 1.0000e-04\n", + "Epoch 4/100\n", + "359/359 [==============================] - ETA: 0s - loss: 6.4847 - accuracy: 0.2511\n", + "Epoch 4: val_loss improved from 7.54121 to 5.91799, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 53s 147ms/step - loss: 6.4847 - accuracy: 0.2511 - val_loss: 5.9180 - val_accuracy: 0.2536 - lr: 1.0000e-04\n", + "Epoch 5/100\n", + "359/359 [==============================] - ETA: 0s - loss: 5.0626 - accuracy: 0.2526\n", + "Epoch 5: val_loss improved from 5.91799 to 4.58342, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 45s 124ms/step - loss: 5.0626 - accuracy: 0.2526 - val_loss: 4.5834 - val_accuracy: 0.2604 - lr: 1.0000e-04\n", + "Epoch 6/100\n", + "359/359 [==============================] - ETA: 0s - loss: 3.9947 - accuracy: 0.2644\n", + "Epoch 6: val_loss improved from 4.58342 to 3.57340, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 39s 109ms/step - loss: 3.9947 - accuracy: 0.2644 - val_loss: 3.5734 - val_accuracy: 0.2707 - lr: 1.0000e-04\n", + "Epoch 7/100\n", + "359/359 [==============================] - ETA: 0s - loss: 3.2322 - accuracy: 0.2752\n", + "Epoch 7: val_loss improved from 3.57340 to 2.93822, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 34s 95ms/step - loss: 3.2322 - accuracy: 0.2752 - val_loss: 2.9382 - val_accuracy: 0.2747 - lr: 1.0000e-04\n", + "Epoch 8/100\n", + "359/359 [==============================] - ETA: 0s - loss: 2.7156 - accuracy: 0.2912\n", + "Epoch 8: val_loss improved from 2.93822 to 2.59739, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 46s 127ms/step - loss: 2.7156 - accuracy: 0.2912 - val_loss: 2.5974 - val_accuracy: 0.2874 - lr: 1.0000e-04\n", + "Epoch 9/100\n", + "359/359 [==============================] - ETA: 0s - loss: 2.3593 - accuracy: 0.3102\n", + "Epoch 9: val_loss improved from 2.59739 to 2.47407, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 51s 142ms/step - loss: 2.3593 - accuracy: 0.3102 - val_loss: 2.4741 - val_accuracy: 0.3090 - lr: 1.0000e-04\n", + "Epoch 10/100\n", + "359/359 [==============================] - ETA: 0s - loss: 2.1070 - accuracy: 0.3572\n", + "Epoch 10: val_loss did not improve from 2.47407\n", + "359/359 [==============================] - 37s 104ms/step - loss: 2.1070 - accuracy: 0.3572 - val_loss: 2.4875 - val_accuracy: 0.3202 - lr: 1.0000e-04\n", + "Epoch 11/100\n", + "359/359 [==============================] - ETA: 0s - loss: 1.9170 - accuracy: 0.4025\n", + "Epoch 11: val_loss improved from 2.47407 to 1.84979, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 51s 142ms/step - loss: 1.9170 - accuracy: 0.4025 - val_loss: 1.8498 - val_accuracy: 0.4158 - lr: 1.0000e-04\n", + "Epoch 12/100\n", + "359/359 [==============================] - ETA: 0s - loss: 1.7695 - accuracy: 0.4349\n", + "Epoch 12: val_loss improved from 1.84979 to 1.79974, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 35s 97ms/step - loss: 1.7695 - accuracy: 0.4349 - val_loss: 1.7997 - val_accuracy: 0.4175 - lr: 1.0000e-04\n", + "Epoch 13/100\n", + "359/359 [==============================] - ETA: 0s - loss: 1.6683 - accuracy: 0.4647\n", + "Epoch 13: val_loss improved from 1.79974 to 1.64056, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 31s 86ms/step - loss: 1.6683 - accuracy: 0.4647 - val_loss: 1.6406 - val_accuracy: 0.4719 - lr: 1.0000e-04\n", + "Epoch 14/100\n", + "359/359 [==============================] - ETA: 0s - loss: 1.6105 - accuracy: 0.4781\n", + "Epoch 14: val_loss improved from 1.64056 to 1.59788, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 31s 86ms/step - loss: 1.6105 - accuracy: 0.4781 - val_loss: 1.5979 - val_accuracy: 0.4778 - lr: 1.0000e-04\n", + "Epoch 15/100\n", + "359/359 [==============================] - ETA: 0s - loss: 1.5451 - accuracy: 0.4946\n", + "Epoch 15: val_loss improved from 1.59788 to 1.49177, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 32s 90ms/step - loss: 1.5451 - accuracy: 0.4946 - val_loss: 1.4918 - val_accuracy: 0.5065 - lr: 1.0000e-04\n", + "Epoch 16/100\n", + "359/359 [==============================] - ETA: 0s - loss: 1.5023 - accuracy: 0.5040\n", + "Epoch 16: val_loss did not improve from 1.49177\n", + "359/359 [==============================] - 31s 86ms/step - loss: 1.5023 - accuracy: 0.5040 - val_loss: 1.5029 - val_accuracy: 0.5039 - lr: 1.0000e-04\n", + "Epoch 17/100\n", + "359/359 [==============================] - ETA: 0s - loss: 1.4707 - accuracy: 0.5111\n", + "Epoch 17: val_loss improved from 1.49177 to 1.45971, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 31s 86ms/step - loss: 1.4707 - accuracy: 0.5111 - val_loss: 1.4597 - val_accuracy: 0.5238 - lr: 1.0000e-04\n", + "Epoch 18/100\n", + "359/359 [==============================] - ETA: 0s - loss: 1.4408 - accuracy: 0.5240\n", + "Epoch 18: val_loss improved from 1.45971 to 1.44302, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 31s 86ms/step - loss: 1.4408 - accuracy: 0.5240 - val_loss: 1.4430 - val_accuracy: 0.5166 - lr: 1.0000e-04\n", + "Epoch 19/100\n", + "359/359 [==============================] - ETA: 0s - loss: 1.4156 - accuracy: 0.5334\n", + "Epoch 19: val_loss improved from 1.44302 to 1.41037, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 32s 89ms/step - loss: 1.4156 - accuracy: 0.5334 - val_loss: 1.4104 - val_accuracy: 0.5368 - lr: 1.0000e-04\n", + "Epoch 20/100\n", + "359/359 [==============================] - ETA: 0s - loss: 1.4018 - accuracy: 0.5374\n", + "Epoch 20: val_loss did not improve from 1.41037\n", + "359/359 [==============================] - 30s 84ms/step - loss: 1.4018 - accuracy: 0.5374 - val_loss: 1.5097 - val_accuracy: 0.4827 - lr: 1.0000e-04\n", + "Epoch 21/100\n", + "359/359 [==============================] - ETA: 0s - loss: 1.3766 - accuracy: 0.5426\n", + "Epoch 21: val_loss improved from 1.41037 to 1.38889, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 31s 86ms/step - loss: 1.3766 - accuracy: 0.5426 - val_loss: 1.3889 - val_accuracy: 0.5368 - lr: 1.0000e-04\n", + "Epoch 22/100\n", + "359/359 [==============================] - ETA: 0s - loss: 1.3657 - accuracy: 0.5451\n", + "Epoch 22: val_loss improved from 1.38889 to 1.32704, saving model to model_checkpoints2/cnn_model11_augmented.keras\n", + "359/359 [==============================] - 32s 88ms/step - loss: 1.3657 - accuracy: 0.5451 - val_loss: 1.3270 - val_accuracy: 0.5497 - lr: 1.0000e-04\n", + "Epoch 23/100\n", + "359/359 [==============================] - ETA: 0s - loss: 1.3505 - accuracy: 0.5491\n", + "Epoch 23: val_loss did not improve from 1.32704\n", + "359/359 [==============================] - 30s 85ms/step - loss: 1.3505 - accuracy: 0.5491 - val_loss: 1.4059 - val_accuracy: 0.5264 - lr: 1.0000e-04\n", + "Epoch 24/100\n", + "359/359 [==============================] - ETA: 0s - loss: 1.3330 - accuracy: 0.5600\n", + "Epoch 24: val_loss did not improve from 1.32704\n", + "359/359 [==============================] - 30s 84ms/step - loss: 1.3330 - accuracy: 0.5600 - val_loss: 1.3581 - val_accuracy: 0.5407 - lr: 1.0000e-04\n", + "Epoch 25/100\n", + "359/359 [==============================] - ETA: 0s - loss: 1.3353 - accuracy: 0.5565\n", + "Epoch 25: val_loss did not improve from 1.32704\n", + "Restoring model weights from the end of the best epoch: 22.\n", + "359/359 [==============================] - 31s 87ms/step - loss: 1.3353 - accuracy: 0.5565 - val_loss: 1.3440 - val_accuracy: 0.5516 - lr: 1.0000e-04\n", + "Epoch 25: early stopping\n" + ] + } + ], + "source": [ + "# Assuming img_width, img_height, and classes are defined. For example:\n", + "img_width, img_height = 48, 48\n", + "classes = 7\n", + "\n", + "#Initialising the CNN\n", + "model8 = tf.keras.models.Sequential()\n", + "\n", + "# Adding convolutional layers with activations on separate lines\n", + "model8.add(Conv2D(32, kernel_size=(3, 3), padding='same', input_shape=(img_width, img_height, 1)))\n", + "model8.add(tf.keras.layers.Activation('relu'))\n", + "model8.add(Conv2D(64, kernel_size=(3, 3), padding='same'))\n", + "model8.add(tf.keras.layers.Activation('relu'))\n", + "model8.add(BatchNormalization())\n", + "model8.add(MaxPooling2D(2, 2))\n", + "model8.add(Dropout(0.25))\n", + "\n", + "model8.add(Conv2D(128, kernel_size=(3, 3), padding='same', kernel_regularizer=regularizers.l2(0.01)))\n", + "model8.add(tf.keras.layers.Activation('relu'))\n", + "model8.add(Conv2D(256, kernel_size=(3, 3), kernel_regularizer=regularizers.l2(0.01)))\n", + "model8.add(tf.keras.layers.Activation('relu'))\n", + "model8.add(BatchNormalization())\n", + "model8.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model8.add(Dropout(0.25))\n", + "\n", + "model8.add(Conv2D(512, kernel_size=(3, 3), padding='same', kernel_regularizer=regularizers.l2(0.01)))\n", + "model8.add(tf.keras.layers.Activation('relu'))\n", + "model8.add(Conv2D(512, kernel_size=(3, 3), padding='same', kernel_regularizer=regularizers.l2(0.01)))\n", + "model8.add(tf.keras.layers.Activation('relu'))\n", + "model8.add(BatchNormalization())\n", + "model8.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model8.add(Dropout(0.25))\n", + "\n", + "# Flattening and adding dense layers\n", + "model8.add(Flatten())\n", + "model8.add(Dense(1024))\n", + "model8.add(tf.keras.layers.Activation('relu'))\n", + "model8.add(Dropout(0.5))\n", + "\n", + "model8.add(Dense(classes))\n", + "model8.add(tf.keras.layers.Activation('softmax'))\n", + "\n", + "# Compiling the model\n", + "model8.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "\n", + "history8 = model8.fit(\n", + " train_generator,\n", + " steps_per_epoch=train_spe\n", + " epochs=100,\n", + " validation_data=validation_generator,\n", + " validation_steps=validation_spe,\n", + " callbacks=callbacks)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ReTbJMK4mO_n" + }, + "outputs": [], + "source": [ + "plot_training_history(history8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LHKwKuBCmPCB" + }, + "outputs": [], + "source": [ + "train_loss, train_accu = model8.evaluate(train_generator)\n", + "test_loss, test_accu = model8.evaluate(test_generator)\n", + "print(\"final train accuracy = {:.2f} , validation accuracy = {:.2f}\".format(train_accu*100, test_accu*100))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "E65ysDZFmPEG" + }, + "outputs": [], + "source": [ + "# Assuming your true_classes and predicted_classes are already defined\n", + "true_classes = test_generator.classes\n", + "predicted_classes = np.argmax(model8.predict(test_generator, steps=np.ceil(test_generator.samples/test_generator.batch_size)), axis=1)\n", + "class_labels = list(test_generator.class_indices.keys())\n", + "\n", + "# Generate the confusion matrix\n", + "cm = confusion_matrix(true_classes, predicted_classes)\n", + "\n", + "# Plotting with seaborn\n", + "plt.figure(figsize=(10, 8))\n", + "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=class_labels, yticklabels=class_labels)\n", + "plt.title('Confusion Matrix')\n", + "plt.ylabel('True label')\n", + "plt.xlabel('Predicted label')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KzU1FdSWmPGS" + }, + "outputs": [], + "source": [ + "# Printing the classification report\n", + "report = classification_report(true_classes,\n", + " predicted_classes,\n", + " target_names=class_labels,\n", + " zero_division=0)\n", + "print(\"Classification Report:\\n\", report)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bA_vHqJ2up0Y" + }, + "source": [ + "# Transfer Learning Models" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MZUnmePoDPfK" + }, + "source": [ + "## Model 9 - EfficientNetB0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8ZgNjbyriE3I" + }, + "outputs": [], + "source": [ + "# Create ModelCheckpoint callback to save model's progress\n", + "checkpoint_path = \"efficient_net/cp.ckpt\" # saving weights requires \".ckpt\" extension\n", + "model_checkpoint = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,\n", + " monitor=\"val_accuracy\", # save the model weights with best validation accuracy\n", + " save_best_only=True, # only save the best weights\n", + " save_weights_only=True, # only save model weights (not whole model)\n", + " verbose=0) # don't print out whether or not model is being saved" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Gv5_aZCZEORM" + }, + "source": [ + "### Feature extraction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kfisUqDgiE5j" + }, + "outputs": [], + "source": [ + "from tensorflow.keras import layers\n", + "import tensorflow as tf\n", + "\n", + "# Create base model\n", + "input_shape = (48, 48, 3)\n", + "base_model = tf.keras.applications.EfficientNetB0(include_top=False)\n", + "base_model.trainable = False # freeze base model layers\n", + "\n", + "# Create Functional model\n", + "inputs = layers.Input(shape=input_shape, name=\"input_layer\")\n", + "# Note: EfficientNetBX models have rescaling built-in but if your model didn't you could have a layer like below\n", + "# x = layers.Rescaling(1./255)(x)\n", + "x = base_model(inputs, training=False) # set base_model to inference mode only\n", + "x = layers.GlobalAveragePooling2D(name=\"pooling_layer\")(x)\n", + "x = layers.Dense(len(class_names))(x) # want one output neuron per class\n", + "# Separate activation of output layer so we can output float32 activations\n", + "outputs = layers.Activation(\"softmax\", dtype=tf.float32, name=\"softmax_float32\")(x)\n", + "model9 = tf.keras.Model(inputs, outputs)\n", + "\n", + "# Compile the model\n", + "model9.compile(loss=\"categorical_crossentropy\", # Use sparse_categorical_crossentropy when labels are *not* one-hot\n", + " optimizer=tf.keras.optimizers.Adam(),\n", + " metrics=[\"accuracy\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 530 + }, + "id": "TRZuxBYPiE74", + "outputId": "8fcf9b07-0bb5-4982-bead-d165ccd3921a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/30\n", + "898/898 [==============================] - 101s 53ms/step - loss: 1.8279 - accuracy: 0.2405 - val_loss: 1.8421 - val_accuracy: 0.2471\n", + "Epoch 2/30\n", + "898/898 [==============================] - 46s 52ms/step - loss: 1.8250 - accuracy: 0.2393 - val_loss: 1.8284 - val_accuracy: 0.2471\n", + "Epoch 3/30\n", + "898/898 [==============================] - 46s 51ms/step - loss: 1.8268 - accuracy: 0.2395 - val_loss: 1.8279 - val_accuracy: 0.2471\n", + "Epoch 4/30\n", + "898/898 [==============================] - 49s 55ms/step - loss: 1.8256 - accuracy: 0.2413 - val_loss: 1.8401 - val_accuracy: 0.2471\n", + "Epoch 5/30\n", + " 54/898 [>.............................] - ETA: 37s - loss: 1.8179 - accuracy: 0.2355" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# Fit the model with callbacks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m feature_history = fmodel.fit(train_data, \n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;31m#steps_per_epoch=len(train_data),\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m 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function._call_flat( # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m 140\u001b[0m \u001b[0mflat_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 141\u001b[0m )\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[1;32m 1321\u001b[0m and executing_eagerly):\n\u001b[1;32m 1322\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1323\u001b[0;31m \u001b[0;32mreturn\u001b[0m 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\u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m 54\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 55\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# Turn off all warnings except for errors\n", + "tf.get_logger().setLevel('ERROR')\n", + "\n", + "# Fit the model with callbacks\n", + "history9 = model9.fit(train_data,\n", + " epochs=30,\n", + " #steps_per_epoch=len(train_data),\n", + " validation_data=test_data,\n", + " #validation_steps=int(0.15 * len(test_data)),\n", + " callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True),model_checkpoint])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VggbujaIx7oJ", + "outputId": "1eaf2d6c-960c-48c9-c705-0476da4f352b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model_1\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_layer (InputLayer) [(None, 48, 48, 3)] 0 \n", + " \n", + " efficientnetb0 (Functional (None, None, None, 1280 4049571 \n", + " ) ) \n", + " \n", + " pooling_layer (GlobalAvera (None, 1280) 0 \n", + " gePooling2D) \n", + " \n", + " dense_8 (Dense) (None, 7) 8967 \n", + " \n", + " softmax_float32 (Activatio (None, 7) 0 \n", + " n) \n", + " \n", + "=================================================================\n", + "Total params: 4058538 (15.48 MB)\n", + "Trainable params: 8967 (35.03 KB)\n", + "Non-trainable params: 4049571 (15.45 MB)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model9.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "U2Z60eNbyP4O", + "outputId": "0dd9e5e7-734e-4554-dd93-1a8c0155ce94" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "input_layer True float32 \n", + "efficientnetb0 True float32 \n", + "pooling_layer True float32 \n", + "dense_8 True float32 \n", + "softmax_float32 True float32 \n" + ] + } + ], + "source": [ + "# Are any of the layers in our model frozen?\n", + "for layer in fmodel.layers:\n", + " layer.trainable = True # set all layers to trainable\n", + " print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy) # make sure loaded model is using mixed precision dtype_policy (\"mixed_float16\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UstZJOwIyYo2", + "outputId": "49ef09a8-da7b-46dd-9a71-2a59ffa5ea82" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "block6d_project_conv True float32 \n", + "block6d_project_bn True float32 \n", + "block6d_drop True float32 \n", + "block6d_add True float32 \n", + "block7a_expand_conv True float32 \n", + "block7a_expand_bn True float32 \n", + "block7a_expand_activation True float32 \n", + "block7a_dwconv True float32 \n", + "block7a_bn True float32 \n", + "block7a_activation True float32 \n", + "block7a_se_squeeze True float32 \n", + "block7a_se_reshape True float32 \n", + "block7a_se_reduce True float32 \n", + "block7a_se_expand True float32 \n", + "block7a_se_excite True float32 \n", + "block7a_project_conv True float32 \n", + "block7a_project_bn True float32 \n", + "top_conv True float32 \n", + "top_bn True float32 \n", + "top_activation True float32 \n" + ] + } + ], + "source": [ + "# Check the layers in the base model and see what dtype policy they're using\n", + "for layer in fmodel.layers[1].layers[-20:]:\n", + " layer.trainable = True\n", + " print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iXqlFi6mxnEN" + }, + "outputs": [], + "source": [ + "# ## Saving model to Google Drive (optional)\n", + "\n", + "# # Create save path to drive\n", + "# save_dir = \"drive/MyDrive/tensorflow_course/food_vision/07_efficientnetb0_feature_extract_model_mixed_precision/\"\n", + "# # os.makedirs(save_dir) # Make directory if it doesn't exist\n", + "\n", + "# # Save model\n", + "# model.save(save_dir)\n", + "\n", + "# Creating learning rate reduction callback\n", + "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\",\n", + " factor=0.2, # multiply the learning rate by 0.2 (reduce by 5x)\n", + " patience=2,\n", + " verbose=1, # print out when learning rate goes down\n", + " min_lr=1e-7)\n", + "\n", + "# Setup EarlyStopping callback to stop training if model's val_loss doesn't improve for 3 epochs\n", + "early_stopping = tf.keras.callbacks.EarlyStopping(monitor=\"val_loss\", # watch the val loss metric\n", + " patience=3) # if val loss decreases for 3 epochs in a row, stop training\n", + "\n", + "# Create ModelCheckpoint callback to save best model during fine-tuning\n", + "checkpoint_path = \"fine_tune_checkpoints/\"\n", + "model_checkpoint = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,\n", + " save_best_only=True,\n", + " monitor=\"val_loss\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dha20t9by8Pm" + }, + "outputs": [], + "source": [ + "# Compile the model\n", + "model9.compile(loss=\"categorical_crossentropy\", # sparse_categorical_crossentropy for labels that are *not* one-hot\n", + " optimizer=tf.keras.optimizers.Adam(0.0001), # 10x lower learning rate than the default\n", + " metrics=[\"accuracy\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 564 + }, + "id": "AT5SNN6jy8NF", + "outputId": "f18913dd-1092-4709-ef73-d27b2ded27d0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "898/898 [==============================] - 160s 119ms/step - loss: 1.8159 - accuracy: 0.2481 - val_loss: 1.8163 - val_accuracy: 0.2471 - lr: 1.0000e-04\n", + "Epoch 2/100\n", + "898/898 [==============================] - 107s 119ms/step - loss: 1.8133 - accuracy: 0.2513 - val_loss: 1.8147 - val_accuracy: 0.2471 - lr: 1.0000e-04\n", + "Epoch 3/100\n", + "898/898 [==============================] - 76s 85ms/step - loss: 1.8126 - accuracy: 0.2513 - val_loss: 1.8149 - val_accuracy: 0.2471 - lr: 1.0000e-04\n", + "Epoch 4/100\n", + "898/898 [==============================] - 108s 121ms/step - loss: 1.8122 - accuracy: 0.2513 - val_loss: 1.8141 - val_accuracy: 0.2471 - lr: 1.0000e-04\n", + "Epoch 5/100\n", + "898/898 [==============================] - 79s 88ms/step - loss: 1.8121 - accuracy: 0.2513 - val_loss: 1.8151 - val_accuracy: 0.2471 - lr: 1.0000e-04\n", + "Epoch 6/100\n", + "201/898 [=====>........................] - ETA: 57s - loss: 1.8086 - accuracy: 0.2558" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Start to fine-tune (all layers)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m finetune_history = fmodel.fit(train_data,\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# fine-tune for a maximum of 100 epochs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m 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the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 867\u001b[0m \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 868\u001b[0;31m return tracing_compilation.call_function(\n\u001b[0m\u001b[1;32m 869\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_no_variable_creation_config\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 870\u001b[0m )\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py\u001b[0m in \u001b[0;36mcall_function\u001b[0;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0mbound_args\u001b[0m \u001b[0;34m=\u001b[0m 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forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m 1325\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py\u001b[0m in \u001b[0;36mcall_preflattened\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcall_preflattened\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[0;34m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m \u001b[0mflat_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 217\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpack_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflat_outputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py\u001b[0m in \u001b[0;36mcall_flat\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0;32mwith\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/context.py\u001b[0m in \u001b[0;36mcall_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m 1484\u001b[0m \u001b[0mcancellation_context\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcancellation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1485\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcancellation_context\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1486\u001b[0;31m outputs = execute.execute(\n\u001b[0m\u001b[1;32m 1487\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1488\u001b[0m \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m 54\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 55\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# Start to fine-tune (all layers)\n", + "history9 = model9.fit(train_data,\n", + " epochs=100, # fine-tune for a maximum of 100 epochs\n", + " steps_per_epoch=len(train_data),\n", + " validation_data=test_data,\n", + " validation_steps=int(0.15 * len(test_data)), # validation during training on 15% of test data\n", + " callbacks=[\n", + " model_checkpoint, # save only the best model during training\n", + " early_stopping, # stop model after X epochs of no improvements\n", + " reduce_lr]) # reduce the learning rate after X epochs of no improvements" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LTfzgR9FFJ6D" + }, + "source": [ + "## Model 10 - VGGFace TensorflowAddons" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NbwKRyhPy8F6", + "outputId": "bb0b14bf-8ba8-44b1-d896-c53760796586" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting tensorflow_addons\n", + " Downloading tensorflow_addons-0.23.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (611 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m611.8/611.8 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow_addons) (24.0)\n", + "Collecting typeguard<3.0.0,>=2.7 (from tensorflow_addons)\n", + " Downloading typeguard-2.13.3-py3-none-any.whl (17 kB)\n", + "Installing collected packages: typeguard, tensorflow_addons\n", + "Successfully installed tensorflow_addons-0.23.0 typeguard-2.13.3\n" + ] + } + ], + "source": [ + "!pip install tensorflow_addons" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 367 + }, + "collapsed": true, + "id": "VAjnaZcOiE-L", + "outputId": "763919f8-acb1-4e8b-83ba-e580a7623a9f" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n", + "\n", + "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", + "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", + "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", + "\n", + "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", + "\n", + " warnings.warn(\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "module 'tensorflow_addons' has no attribute 'networks'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# Load VGGFace model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mvggface_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtfa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnetworks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvggface\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mVGGFace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minclude_top\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m48\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m48\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpooling\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'avg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# Freeze the pre-trained layers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'tensorflow_addons' has no attribute 'networks'" + ] + } + ], + "source": [ + "import tensorflow_addons as tfa\n", + "\n", + "# Load VGGFace model\n", + "vggface_model = tfa.networks.vggface.VGGFace(include_top=False, input_shape=(48, 48, 3), pooling='avg')\n", + "\n", + "# Freeze the pre-trained layers\n", + "vggface_model.trainable = False\n", + "\n", + "# Add additional layers for fine-tuning or classification\n", + "model = tf.keras.Sequential([\n", + " vggface_model,\n", + " tf.keras.layers.Dense(256, activation='relu'),\n", + " tf.keras.layers.Dropout(0.5),\n", + " tf.keras.layers.Dense(7, activation='softmax') # Assuming num_classes is the number of emotions\n", + "])\n", + "\n", + "# Compile the model\n", + "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Train the model\n", + "history = model.fit(train_dataset, epochs=10, validation_data=val_dataset)\n", + "\n", + "# Errors !" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0nLmgS37Fw1z" + }, + "source": [ + "## Model 11 - Mobile Net" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 384 + }, + "collapsed": true, + "id": "rrbJaHq9iFAf", + "outputId": "e0518c93-335f-46bd-ab91-4f769aa0fae8" + }, + "outputs": [ + { + "ename": "ImportError", + "evalue": "cannot import name 'Adam' from 'tensorflow.keras.layers' (/usr/local/lib/python3.10/dist-packages/keras/api/_v2/keras/layers/__init__.py)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapplications\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMobileNetV2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDense\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGlobalAveragePooling2D\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDropout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mImportError\u001b[0m: cannot import name 'Adam' from 'tensorflow.keras.layers' (/usr/local/lib/python3.10/dist-packages/keras/api/_v2/keras/layers/__init__.py)", + "", + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.applications import MobileNetV2\n", + "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout,Adam\n", + "from tensorflow.keras.models import Model\n", + "\n", + "# Load MobileNetV2 model without the top (classification) layers\n", + "base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(48, 48, 3))\n", + "\n", + "# Freeze the pre-trained layers\n", + "for layer in base_model.layers:\n", + " layer.trainable = False\n", + "\n", + "# Add your own classification layers\n", + "x = base_model.output\n", + "x = GlobalAveragePooling2D()(x)\n", + "x = Dense(256, activation='relu')(x)\n", + "x = Dropout(0.5)(x)\n", + "predictions = Dense(7 ,activation='softmax')(x) # Assuming num_classes is the number of emotions\n", + "\n", + "# Combine the base model with your custom layers\n", + "model = Model(inputs=base_model.input, outputs=predictions)\n", + "\n", + "# Compile the model\n", + "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Train the model\n", + "history = model.fit(train_data, epochs=10, validation_data=test_data)\n", + "\n", + "# Errors !" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CnFIeS82nu_m" + }, + "source": [ + "## Model 12 - VGGNET" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dR7Wgz9jmPIc" + }, + "outputs": [], + "source": [ + "# Define paths to the train and validation directories\n", + "train_data_dir = '/content/train'\n", + "test_data_dir = '/content/test'\n", + "\n", + "# validation_data_dir = '/content/emotion_detection_project/datasets/raw/test'\n", + "\n", + "# Set some parameters\n", + "img_width, img_height = 224, 224 # Size of images\n", + "batch_size = 64\n", + "epochs = 10\n", + "num_classes = 7 # Update this based on the number of your classes\n", + "\n", + "# Rescale the pixel values (0-255) to the [0, 1] interval\n", + "data_generator = ImageDataGenerator(\n", + " rescale = 1 / 255.,\n", + " rotation_range=10,\n", + " zoom_range=0.2,\n", + " width_shift_range=0.1,\n", + " height_shift_range=0.1,\n", + " horizontal_flip=True,\n", + " fill_mode='nearest',\n", + " # validation_split=0.2\n", + " )\n", + "\n", + "test_preprocessor = ImageDataGenerator(\n", + " rescale = 1 / 255.,\n", + ")\n", + "\n", + "# Automatically retrieve images and their classes for train and validation sets\n", + "train_generator = data_generator.flow_from_directory(\n", + " train_data_dir,\n", + " target_size=(img_width, img_height),\n", + " batch_size=batch_size,\n", + " class_mode='categorical',\n", + " color_mode='rgb',\n", + " subset='training',\n", + " shuffle = True)\n", + "\n", + "test_generator = test_preprocessor.flow_from_directory(\n", + " test_data_dir,\n", + " target_size=(img_width, img_height),\n", + " batch_size=batch_size,\n", + " class_mode='categorical',\n", + " color_mode='rgb',)\n", + " # subset='validation')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wQuLPDIXmPKn" + }, + "outputs": [], + "source": [ + "# Extract class labels for all instances in the training dataset\n", + "classes = np.array(train_generator.classes)\n", + "\n", + "# Calculate class weights to handle imbalances in the training data\n", + "# 'balanced' mode automatically adjusts weights inversely proportional to class frequencies\n", + "class_weights = compute_class_weight(\n", + " class_weight='balanced', # Strategy to balance classes\n", + " classes=np.unique(classes), # Unique class labels\n", + " y=classes # Class labels for each instance in the training dataset\n", + ")\n", + "\n", + "# Create a dictionary mapping class indices to their calculated weights\n", + "class_weights_dict = dict(enumerate(class_weights))\n", + "\n", + "# Output the class weights dictionary\n", + "print(\"Class Weights Dictionary:\", class_weights_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pqrXZ_fdmPOQ" + }, + "outputs": [], + "source": [ + "classes = 7\n", + "\n", + "# Clear the previous TensorFlow sessionx\n", + "tf.keras.backend.clear_session()\n", + "\n", + "# Load the VGG16 base model, excluding its top (fully connected) layers\n", + "vgg = VGG16(input_shape=(224, 224, 3), include_top=False, weights='imagenet')\n", + "vgg.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6ZDkVC7EmPP-" + }, + "outputs": [], + "source": [ + "# Make the specified layers non-trainable\n", + "for layer in vgg.layers[:-3]:\n", + " layer.trainable = False\n", + "\n", + "vgg.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SXvEb2BwmPRu" + }, + "outputs": [], + "source": [ + "classes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "a6r0OjWxmPTL" + }, + "outputs": [], + "source": [ + "# Flattening the layer and adding custom Dense layers\n", + "x = Flatten()(vgg.output)\n", + "\n", + "# Adding a fully connected layer with ReLU activation and He normal initializer\n", + "x = Dense(1024, activation='relu', kernel_initializer='he_normal')(x)\n", + "x = Dropout(0.5)(x) # Adding dropout for regularization\n", + "\n", + "x = Dense(512, activation='relu', kernel_initializer='he_normal')(x)\n", + "x = Dropout(0.5)(x) # Adding dropout for regularization\n", + "\n", + "# Adding the output layer with softmax activation\n", + "# Note: Adjust the number of units to match the number of classes you have\n", + "output = Dense(7, activation='softmax', kernel_initializer='he_normal')(x)\n", + "\n", + "# Creating the model\n", + "model12 = Model(inputs=vgg.input, outputs=output)\n", + "\n", + "# Compile the model\n", + "model12.compile(loss='categorical_crossentropy',\n", + " optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001, beta_1=0.9, beta_2=0.999, amsgrad=False),\n", + " metrics=['accuracy'])\n", + "\n", + "# Model summary to see all layers\n", + "model12.summary()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8ANrGmoKmPVZ" + }, + "outputs": [], + "source": [ + "# File path for the model checkpoint\n", + "cnn_path = 'model_checkpoints3'\n", + "name = 'model12_vgg16.keras'\n", + "chk_path = os.path.join(cnn_path, name)\n", + "\n", + "# Callback to save the model checkpoint\n", + "checkpoint = ModelCheckpoint(filepath=chk_path,\n", + " save_best_only=True,\n", + " verbose=1,\n", + " mode='min',\n", + " monitor='val_loss')\n", + "\n", + "# Callback for early stopping\n", + "earlystop = EarlyStopping(monitor='val_loss',\n", + " min_delta=0,\n", + " patience=3,\n", + " verbose=1,\n", + " restore_best_weights=True)\n", + "\n", + "# Callback to reduce learning rate\n", + "reduce_lr = ReduceLROnPlateau(monitor='val_loss',\n", + " factor=0.2,\n", + " patience=6,\n", + " verbose=1,\n", + " min_delta=0.0001)\n", + "\n", + "# Callback to log training data to a CSV file\n", + "csv_logger = CSVLogger(os.path.join(cnn_path,'training.log'))\n", + "\n", + "# Aggregating all callbacks into a list\n", + "callbacks = [checkpoint, earlystop, reduce_lr, csv_logger] # Adjusted as per your use-case\n", + "\n", + "train_steps_per_epoch = train_generator.samples // train_generator.batch_size + 1\n", + "# validation_steps_epoch = validation_generator.samples // validation_generator.batch_size + 1\n", + "test_steps_epoch = test_generator.samples // test_generator.batch_size + 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pacSqwR9mPYA" + }, + "outputs": [], + "source": [ + "plot_training_history(history)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1yY0aYxPmPdK" + }, + "outputs": [], + "source": [ + "train_loss, train_accu = model12.evaluate(train_generator)\n", + "test_loss, test_accu = model12.evaluate(test_generator)\n", + "print(\"final train accuracy = {:.2f} , validation accuracy = {:.2f}\".format(train_accu*100, test_accu*100))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0-_3SxLDmPfF" + }, + "outputs": [], + "source": [ + "# Assuming your true_classes and predicted_classes are already defined\n", + "true_classes = test_generator.classes\n", + "predicted_classes = np.argmax(model12.predict(test_generator, steps=np.ceil(test_generator.samples/test_generator.batch_size)), axis=1)\n", + "class_labels = list(test_generator.class_indices.keys())\n", + "\n", + "# Generate the confusion matrix\n", + "cm = confusion_matrix(true_classes, predicted_classes)\n", + "\n", + "# Plotting with seaborn\n", + "plt.figure(figsize=(10, 8))\n", + "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=class_labels, yticklabels=class_labels)\n", + "plt.title('Confusion Matrix')\n", + "plt.ylabel('True label')\n", + "plt.xlabel('Predicted label')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "y5taSRp4mPh1" + }, + "outputs": [], + "source": [ + "# Printing the classification report\n", + "report = classification_report(true_classes,\n", + " predicted_classes,\n", + " target_names=class_labels,\n", + " zero_division=0)\n", + "print(\"Classification Report:\\n\", report)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7sbLfRfuogaD" + }, + "source": [ + "## Model 13 - ResNet50 (Best)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oHkr8vDwmPjc", + "outputId": "1c0a18ff-03fc-4dad-ff60-aaf20fef7116" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found 28709 images belonging to 7 classes.\n", + "Found 7178 images belonging to 7 classes.\n" + ] + } + ], + "source": [ + "# Directory paths for training and testing data\n", + "train_dir = 'train'\n", + "test_dir = 'test'\n", + "\n", + "# Batch size for data generators\n", + "batch_size = 64\n", + "\n", + "# Data augmentation configuration for training data\n", + "train_datagen = ImageDataGenerator(\n", + " rescale=1 / 255.,\n", + " rotation_range=10,\n", + " zoom_range=0.2,\n", + " width_shift_range=0.1,\n", + " height_shift_range=0.1,\n", + " horizontal_flip=True,\n", + " fill_mode='nearest'\n", + " )\n", + "\n", + "test_datagen = ImageDataGenerator(\n", + " rescale=1 / 255.\n", + " )\n", + "\n", + "# Data generators for training and testing data\n", + "train_generator = train_datagen.flow_from_directory(\n", + " train_dir,\n", + " class_mode=\"categorical\",\n", + " target_size=(224, 224),\n", + " color_mode='rgb',\n", + " shuffle=True,\n", + " batch_size=batch_size,\n", + " subset='training'\n", + " )\n", + "\n", + "test_generator = test_datagen.flow_from_directory(\n", + " test_dir,\n", + " class_mode=\"categorical\",\n", + " target_size=(224, 224),\n", + " color_mode=\"rgb\",\n", + " shuffle=False,\n", + " batch_size=batch_size\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YI8kyOico3oW" + }, + "source": [ + "### Class weights for imbalanced data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "f2lnbb05mPk3", + "outputId": "6ead1414-0c38-4ecc-9219-a0ede9ab5eef" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Class Weights Dictionary: {0: 1.0266046844269623, 1: 9.406618610747051, 2: 1.0010460615781582, 3: 0.5684387684387684, 4: 0.8260394187886635, 5: 0.8491274770777877, 6: 1.293372978330405}\n" + ] + } + ], + "source": [ + "# Extract class labels for all instances in the training dataset\n", + "classes = np.array(train_generator.classes)\n", + "\n", + "# Calculate class weights to handle imbalances in the training data\n", + "# 'balanced' mode automatically adjusts weights inversely proportional to class frequencies\n", + "class_weights = compute_class_weight(\n", + " class_weight='balanced', # Strategy to balance classes\n", + " classes=np.unique(classes), # Unique class labels\n", + " y=classes # Class labels for each instance in the training dataset\n", + ")\n", + "\n", + "# Create a dictionary mapping class indices to their calculated weights\n", + "class_weights_dict = dict(enumerate(class_weights))\n", + "\n", + "# Output the class weights dictionary\n", + "print(\"Class Weights Dictionary:\", class_weights_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SCqXOQfgmPmb", + "outputId": "6e0de8b5-2c84-4133-9e38-6d3dedbd78a2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "94668760/94668760 [==============================] - 4s 0us/step\n" + ] + } + ], + "source": [ + "ResNet50V2 = tf.keras.applications.ResNet50V2(input_shape=(224, 224, 3),\n", + " include_top= False,\n", + " weights='imagenet'\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "7OgKWF9PmPn6" + }, + "outputs": [], + "source": [ + "# Freezing all layers except last 50\n", + "\n", + "ResNet50V2.trainable = True\n", + "\n", + "for layer in ResNet50V2.layers[:-50]:\n", + " layer.trainable = False" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "6y_CGoY8mPq4" + }, + "outputs": [], + "source": [ + "def Create_ResNet50V2_Model():\n", + "\n", + " model = Sequential([\n", + " ResNet50V2,\n", + " Dropout(0.25),\n", + " BatchNormalization(),\n", + " Flatten(),\n", + " Dense(64, activation='relu'),\n", + " BatchNormalization(),\n", + " Dropout(0.5),\n", + " Dense(7,activation='softmax')\n", + " ])\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wU3rruFMmPsg", + "outputId": "1fc222b3-ec58-4105-ec14-60fe933c1ca4" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " resnet50v2 (Functional) (None, 7, 7, 2048) 23564800 \n", + " \n", + " dropout (Dropout) (None, 7, 7, 2048) 0 \n", + " \n", + " batch_normalization (Batch (None, 7, 7, 2048) 8192 \n", + " Normalization) \n", + " \n", + " flatten (Flatten) (None, 100352) 0 \n", + " \n", + " dense (Dense) (None, 64) 6422592 \n", + " \n", + " batch_normalization_1 (Bat (None, 64) 256 \n", + " chNormalization) \n", + " \n", + " dropout_1 (Dropout) (None, 64) 0 \n", + " \n", + " dense_1 (Dense) (None, 7) 455 \n", + " \n", + "=================================================================\n", + "Total params: 29996295 (114.43 MB)\n", + "Trainable params: 22779527 (86.90 MB)\n", + "Non-trainable params: 7216768 (27.53 MB)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model13 = Create_ResNet50V2_Model()\n", + "model13.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "wy01hfaimPuu" + }, + "outputs": [], + "source": [ + "model13.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "Vp846gnWmPw9" + }, + "outputs": [], + "source": [ + "# File path for the model checkpoint\n", + "path1 = 'resnet_checkpoints'\n", + "path2 = 'model13_resnet50.keras'\n", + "chk_path = os.path.join(path1,path2)\n", + "\n", + "# Callback to save the model checkpoint\n", + "checkpoint = ModelCheckpoint(filepath=chk_path,\n", + " save_best_only=True,\n", + " verbose=1,\n", + " mode='min',\n", + " monitor='val_loss')\n", + "\n", + "# Callback for early stopping\n", + "earlystop = EarlyStopping(monitor = 'val_accuracy',\n", + " patience = 7,\n", + " restore_best_weights = True,\n", + " verbose=1)\n", + "\n", + "# Callback to reduce learning rate\n", + "reduce_lr = ReduceLROnPlateau(monitor='val_loss',\n", + " factor=0.2,\n", + " patience=2,\n", + "# min_lr=0.00005,\n", + " verbose=1)\n", + "\n", + "# Callback to log training data to a CSV file\n", + "csv_logger = CSVLogger(os.path.join(path1,'training.log'))\n", + "\n", + "# Aggregating all callbacks into a list\n", + "callbacks = [checkpoint, earlystop, csv_logger] # Adjusted as per your use-case\n", + "\n", + "train_spe = train_generator.samples // train_generator.batch_size + 1\n", + "# validation_steps_epoch = validation_generator.samples // validation_generator.batch_size + 1\n", + "test_spe = test_generator.samples // test_generator.batch_size + 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "v90EMS_RmPzl", + "outputId": "304a505d-6c8d-44d8-e63f-5f70ac49ed40" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/30\n", + "449/449 [==============================] - ETA: 0s - loss: 1.7012 - accuracy: 0.3910\n", + "Epoch 1: val_loss improved from inf to 92.50460, saving model to resnet_checkpoints/model13_resnet50.keras\n", + "449/449 [==============================] - 480s 1s/step - loss: 1.7012 - accuracy: 0.3910 - val_loss: 92.5046 - val_accuracy: 0.3037\n", + "Epoch 2/30\n", + "449/449 [==============================] - ETA: 0s - loss: 1.3914 - accuracy: 0.4832\n", + "Epoch 2: val_loss improved from 92.50460 to 1.54560, saving model to resnet_checkpoints/model13_resnet50.keras\n", + "449/449 [==============================] - 417s 926ms/step - loss: 1.3914 - accuracy: 0.4832 - val_loss: 1.5456 - val_accuracy: 0.4092\n", + "Epoch 3/30\n", + "449/449 [==============================] - ETA: 0s - loss: 1.2980 - accuracy: 0.5218\n", + "Epoch 3: val_loss improved from 1.54560 to 1.26243, saving model to resnet_checkpoints/model13_resnet50.keras\n", + "449/449 [==============================] - 403s 898ms/step - loss: 1.2980 - accuracy: 0.5218 - val_loss: 1.2624 - val_accuracy: 0.5263\n", + "Epoch 4/30\n", + "449/449 [==============================] - ETA: 0s - loss: 1.1978 - accuracy: 0.5540\n", + "Epoch 4: val_loss improved from 1.26243 to 1.15233, saving model to resnet_checkpoints/model13_resnet50.keras\n", + "449/449 [==============================] - 402s 895ms/step - loss: 1.1978 - accuracy: 0.5540 - val_loss: 1.1523 - val_accuracy: 0.5731\n", + "Epoch 5/30\n", + "449/449 [==============================] - ETA: 0s - loss: 1.1583 - accuracy: 0.5684\n", + "Epoch 5: val_loss did not improve from 1.15233\n", + "449/449 [==============================] - 398s 885ms/step - loss: 1.1583 - accuracy: 0.5684 - val_loss: 1.1631 - val_accuracy: 0.5705\n", + "Epoch 6/30\n", + "449/449 [==============================] - ETA: 0s - loss: 1.1202 - accuracy: 0.5806\n", + "Epoch 6: val_loss did not improve from 1.15233\n", + "449/449 [==============================] - 394s 878ms/step - loss: 1.1202 - accuracy: 0.5806 - val_loss: 1.4234 - val_accuracy: 0.5639\n", + "Epoch 7/30\n", + "449/449 [==============================] - ETA: 0s - loss: 1.1104 - accuracy: 0.5851\n", + "Epoch 7: val_loss improved from 1.15233 to 1.06141, saving model to resnet_checkpoints/model13_resnet50.keras\n", + "449/449 [==============================] - 404s 899ms/step - loss: 1.1104 - accuracy: 0.5851 - val_loss: 1.0614 - val_accuracy: 0.5995\n", + "Epoch 8/30\n", + "449/449 [==============================] - ETA: 0s - loss: 1.0787 - accuracy: 0.6010\n", + "Epoch 8: val_loss did not improve from 1.06141\n", + "449/449 [==============================] - 387s 863ms/step - loss: 1.0787 - accuracy: 0.6010 - val_loss: 63.3778 - val_accuracy: 0.3771\n", + "Epoch 9/30\n", + "449/449 [==============================] - ETA: 0s - loss: 1.0600 - accuracy: 0.5951\n", + "Epoch 9: val_loss did not improve from 1.06141\n", + "449/449 [==============================] - 391s 871ms/step - loss: 1.0600 - accuracy: 0.5951 - val_loss: 1.0751 - val_accuracy: 0.6045\n", + "Epoch 10/30\n", + "226/449 [==============>...............] - ETA: 3:03 - loss: 1.0241 - accuracy: 0.6144" + ] + } + ], + "source": [ + "history13 = model13.fit(\n", + " train_generator,\n", + " steps_per_epoch=train_spe,\n", + " epochs=30,\n", + " validation_data=test_generator,\n", + " validation_steps=test_spe,\n", + " class_weight=class_weights_dict,\n", + " callbacks = callbacks\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Zgl49f7SmP1q" + }, + "outputs": [], + "source": [ + "plot_training_history(history13)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JYT0Ba5ZmP8u" + }, + "outputs": [], + "source": [ + "train_loss, train_accu = model13.evaluate(train_generator)\n", + "test_loss, test_accu = model13.evaluate(test_generator)\n", + "print(\"final train accuracy = {:.2f} , validation accuracy = {:.2f}\".format(train_accu*100, test_accu*100))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DHvrNdUYmP_E" + }, + "outputs": [], + "source": [ + "# Assuming your true_classes and predicted_classes are already defined\n", + "true_classes = test_generator.classes\n", + "predicted_classes = np.argmax(model13.predict(test_generator, steps=np.ceil(test_generator.samples/test_generator.batch_size)), axis=1)\n", + "class_labels = list(test_generator.class_indices.keys())\n", + "\n", + "# Generate the confusion matrix\n", + "cm = confusion_matrix(true_classes, predicted_classes)\n", + "\n", + "# Plotting with seaborn\n", + "plt.figure(figsize=(10, 8))\n", + "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=class_labels, yticklabels=class_labels)\n", + "plt.title('Confusion Matrix')\n", + "plt.ylabel('True label')\n", + "plt.xlabel('Predicted label')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4wthy2GLmQBR" + }, + "outputs": [], + "source": [ + "# Printing the classification report\n", + "report = classification_report(true_classes,\n", + " predicted_classes,\n", + " target_names=class_labels,\n", + " zero_division=0)\n", + "print(\"Classification Report:\\n\", report)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "AQsQ1A42mQHm" + }, + "outputs": [], + "source": [ + "model13.save(\"resnet_model.keras\")" + ] + }, + { + "cell_type": "code", + "source": [ + "model13.save('resnet_model.h5')" + ], + "metadata": { + "id": "rd2YopN-JL7k" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import files\n", + "files.download('resnet_model.h5')" + ], + "metadata": { + "id": "oWigBXz0JWSM" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import tensorflow as tf\n", + "try:\n", + " best_model = tf.keras.models.load_model(chk_path)\n", + "except:\n", + " pass\n", + "else:\n", + " from keras.models import load_model\n", + " best_model = load_model(chk_path)\n", + "\n", + "best_model.save('resnet_best.h5')\n", + "\n", + "from google.colab import files\n", + "files.download('resnet_best.h5')" + ], + "metadata": { + "id": "wYWo15cqJuVm" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lLFwqHefLGH2" + }, + "source": [ + "### Image Prep & Prediction Functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HuVE2oHdmQNX" + }, + "outputs": [], + "source": [ + "def prepare_image(img_pil):\n", + " \"\"\"Preprocess the PIL image to fit your model's input requirements.\"\"\"\n", + " # Convert the PIL image to a numpy array with the target size\n", + " img = img_pil.resize((224, 224))\n", + " img_array = img_to_array(img)\n", + " img_array = np.expand_dims(img_array, axis=0) # Convert single image to a batch.\n", + " img_array /= 255.0 # Rescale pixel values to [0,1], as done during training\n", + " return img_array\n", + "\n", + "\n", + "\n", + "# Define the Gradio interface\n", + "def predict_emotion(image):\n", + " # Preprocess the image\n", + " processed_image = prepare_image(image)\n", + " # Make prediction using the model\n", + " prediction = model.predict(processed_image)\n", + " # Get the emotion label with the highest probability\n", + " predicted_class = np.argmax(prediction, axis=1)\n", + " predicted_emotion = index_to_emotion.get(predicted_class[0], \"Unknown Emotion\")\n", + " return predicted_emotion" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [], + "authorship_tag": "ABX9TyOlHW4ODGfqcZCCFnb1RCvS", + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/model/emotion.h5 b/model/emotion.h5 new file mode 100644 index 000000000..472d85996 Binary files /dev/null and b/model/emotion.h5 differ diff --git a/model/emotion2.h5 b/model/emotion2.h5 new file mode 100644 index 000000000..7539c52d3 Binary files /dev/null and b/model/emotion2.h5 differ diff --git a/model/model.json b/model/model.json new file mode 100644 index 000000000..01689cb00 --- /dev/null +++ b/model/model.json @@ -0,0 +1 @@ +{"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 48, 48, 1], "dtype": "float32", "sparse": false, "ragged": false, "name": "conv2d_input"}}, {"class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "batch_input_shape": [null, 48, 48, 1], "dtype": "float32", "filters": 32, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_1", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Dropout", "config": {"name": "dropout", "trainable": true, "dtype": "float32", "rate": 0.25, "noise_shape": null, "seed": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_2", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Conv2D", "config": {"name": "conv2d_3", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_2", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "dtype": "float32", "rate": 0.25, "noise_shape": null, "seed": null}}, {"class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": "float32", "data_format": "channels_last"}}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 1024, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_2", "trainable": true, "dtype": "float32", "rate": 0.5, "noise_shape": null, "seed": null}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 7, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.4.0", "backend": "tensorflow"} \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 000000000..d4a98f7bc --- /dev/null +++ b/requirements.txt @@ -0,0 +1,6 @@ +tensorflow +keras +streamlit +opencv-python +numpy +tempfile diff --git a/streamlit.py b/streamlit.py new file mode 100644 index 000000000..11846a565 --- /dev/null +++ b/streamlit.py @@ -0,0 +1,128 @@ +import cv2 +import numpy as np +from keras.models import model_from_json, load_model +import streamlit as st +import tempfile +import tensorflow as tf + +# Ensure TensorFlow uses the GPU if available +physical_devices = tf.config.list_physical_devices('GPU') +if physical_devices: + tf.config.experimental.set_memory_growth(physical_devices[0], True) + +st.title('Facial Emotion Detection - Vedanth Aggarwal ') + +emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"} +#emotion_model = load_model('Emotion_detection_with_CNN-main/emotion.h5') +# load json and create model +json_file = open('model/model.json', 'r') +loaded_model_json = json_file.read() +json_file.close() +emotion_model = model_from_json(loaded_model_json) + +# load weights into new model +emotion_model.load_weights("model/emotion2.h5") +print("Loaded model from disk") + +# start the webcam feed +#cap = cv2.VideoCapture(0) + +# pass here your video path +# you may download one from here : https://www.pexels.com/video/three-girls-laughing-5273028/ +#cap = cv2.VideoCapture(0)#"Emotion_detection_with_CNN-main/emotion_video.mp4") + +uploaded_file = st.file_uploader("Upload a video file", type=["mp4", "avi", "mov"]) +previous = [''] * 10 +if uploaded_file is not None: + # Save the uploaded video to a temporary file + tfile = tempfile.NamedTemporaryFile(delete=False) + tfile.write(uploaded_file.read()) + video_path = tfile.name + + # Create a VideoCapture object + cap = cv2.VideoCapture(video_path) + + # Check if video opened successfully + if not cap.isOpened(): + st.write("Error: Could not open video.") + else: + # Process the video + stframe = st.empty() + count = 0 + while cap.isOpened(): + # Find haar cascade to draw bounding box around face + ret, frame = cap.read() + frame = cv2.resize(frame, (1280, 720)) + if not ret: + break + face_detector = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml') + gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + + # detect faces available on camera + num_faces = face_detector.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5) + if count%10 == 0: + #previous = [''] * len(num_faces) + # take each face available on the camera and Preprocess it + for i,(x, y, w, h) in enumerate(num_faces): + + cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (0, 255, 0), 4) + + roi_gray_frame = gray_frame[y:y + h, x:x + w] + + # Convert ROI to RGB + #roi_rgb_frame = cv2.cvtColor(roi_gray_frame, cv2.COLOR_GRAY2RGB) + + # Resize the ROI to (48, 48) + #resized_img = cv2.resize(roi_gray_frame, (48, 48)) + + # Normalize the image + #normalized_img = resized_img / 255.0 + + # Expand dimensions to match the input shape of the model + #cropped_img = np.expand_dims(normalized_img, axis=0) + + cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)),axis=-1),axis=0) + #np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)), -1), 0) + + # predict the emotions + + emotion_prediction = emotion_model.predict(cropped_img) + maxindex = int(np.argmax(emotion_prediction)) + cv2.putText(frame, emotion_dict[maxindex], (x+5, y-20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA) + previous[i] = emotion_dict[maxindex] + else: + for i,(x, y, w, h) in enumerate(num_faces): + + cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (0, 255, 0), 4) + + #roi_gray_frame = gray_frame[y:y + h, x:x + w] + + # Convert ROI to RGB + #roi_rgb_frame = cv2.cvtColor(roi_gray_frame, cv2.COLOR_GRAY2RGB) + + # Resize the ROI to (48, 48) + #resized_img = cv2.resize(roi_rgb_frame, (48, 48)) + + # Normalize the image + #normalized_img = resized_img / 255.0 + + # Expand dimensions to match the input shape of the model + #cropped_img = np.expand_dims(normalized_img, axis=0) + + #cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)),axis=0),axis=0) + #np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)), -1), 0) + + # predict the emotions + + #emotion_prediction = emotion_model.predict(cropped_img) + #maxindex = int(np.argmax(emotion_prediction)) + #print(previous[i]) + cv2.putText(frame, previous[i], (x+5, y-20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA) + #previous[i] = emotion_dict[maxindex] + + + stframe.image(frame, channels="BGR") + count+=1 + + cap.release() + cv2.destroyAllWindows()