diff --git a/notebooks/Generative-adversarial-networks.ipynb b/notebooks/Generative-adversarial-networks.ipynb
new file mode 100755
index 00000000..e0b61759
--- /dev/null
+++ b/notebooks/Generative-adversarial-networks.ipynb
@@ -0,0 +1,2165 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7982ee23-e021-4a2f-ae37-f0d46488530c",
+   "metadata": {},
+   "source": [
+    "Generative adversarial networks can be used to generate more data examples, augmentate existing data, find anomalies etc. Helpful, when large class inbalance or few anomalies. This notebook works on code level, but for better quality results Perceptual loss, WGAN etc. advanced methods would need to be implemented."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e989c4d-f141-4d2e-8b77-682a5738279d",
+   "metadata": {},
+   "source": [
+    "## Data processing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12998767-8cdc-49af-ae63-92ff8951e022",
+   "metadata": {},
+   "source": [
+    "### Loading libraries and defining functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "9ab7494d-a01e-4198-bcb9-15236520c905",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import os\n",
+    "from skimage.io import imread, imsave\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, Dense, Lambda, Flatten, Reshape, Conv2DTranspose, Dropout, BatchNormalization, LeakyReLU, Activation, concatenate, Multiply, Add\n",
+    "from tensorflow.keras.models import Model, Sequential\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "from tensorflow.keras.callbacks import LearningRateScheduler\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from keras.callbacks import EarlyStopping\n",
+    "from sklearn.manifold import TSNE\n",
+    "import keras\n",
+    "from keras.metrics import binary_crossentropy \n",
+    "from keras import backend as K\n",
+    "from skimage.metrics import structural_similarity as ssim\n",
+    "from skimage import exposure\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from keras.regularizers import l1, l2\n",
+    "from tensorflow.keras.losses import MeanSquaredError\n",
+    "from scipy.linalg import sqrtm\n",
+    "from tensorflow.image import resize, grayscale_to_rgb\n",
+    "\n",
+    "\"\"\"Different types of normalizers for variable data shapes, including data with very small (-1e+30) NaN values\"\"\"\n",
+    "\n",
+    "def normalize_tile_z(tile):\n",
+    "    \"\"\"Normalize the tile using its mean and standard deviation.\"\"\"\n",
+    "    mean = np.nanmean(tile)\n",
+    "    std = np.nanstd(tile)\n",
+    "    \n",
+    "    # Return the standardized tile\n",
+    "    return (tile - mean) / (std + 1e-7)\n",
+    "\n",
+    "def normalize_tile_one(tile):\n",
+    "    valid_mask = ~np.isnan(tile)\n",
+    "    \n",
+    "    # Check if there's any valid data in the tile\n",
+    "    if np.any(valid_mask):\n",
+    "        min_val = np.min(tile[valid_mask])\n",
+    "        max_val = np.max(tile[valid_mask])\n",
+    "        \n",
+    "        if max_val != min_val:\n",
+    "            tile[valid_mask] = (tile[valid_mask] - min_val) / (max_val - min_val)\n",
+    "        else:\n",
+    "            tile[valid_mask] = 0.0  # If max equals min, set to 0\n",
+    "    return tile\n",
+    "\n",
+    "def normalize_tile_adaptive(tile, method=\"minmax\"):\n",
+    "    if method == \"standard\":\n",
+    "        mean = np.nanmean(tile)\n",
+    "        std = np.nanstd(tile)\n",
+    "        return (tile - mean) / (std + 1e-7)\n",
+    "    elif method == \"minmax\":\n",
+    "        min_val = np.nanmin(tile)\n",
+    "        max_val = np.nanmax(tile)\n",
+    "        return (tile - min_val) / (max_val - min_val)\n",
+    "\n",
+    "def normalize_tile_clipping(tile):\n",
+    "    \"\"\"Normalize the tile using its mean and standard deviation.\"\"\"\n",
+    "    # Clip the tile values\n",
+    "    lower_threshold = np.nanpercentile(tile, 1)\n",
+    "    upper_threshold = np.nanpercentile(tile, 99)\n",
+    "    tile = np.clip(tile, lower_threshold, upper_threshold)\n",
+    "    \n",
+    "    # Normalize the tile\n",
+    "    mean = np.nanmean(tile)\n",
+    "    std = np.nanstd(tile)\n",
+    "    \n",
+    "    return (tile - mean) / (std + 1e-7)\n",
+    "\n",
+    "def normalize_tile_clipping_255(tile):\n",
+    "    \"\"\"Normalize the tile using its mean and standard deviation and clip it based on the range [mean-2*std, mean+2*std].\"\"\"\n",
+    "\n",
+    "    mean_tile = np.nanmean(tile)\n",
+    "    std_tile = np.nanstd(tile)\n",
+    "\n",
+    "    # Clipping the tile based on the standard deviation and mean\n",
+    "    np.clip(tile, mean_tile - 2 * std_tile, mean_tile + 2 * std_tile, out=tile)\n",
+    "\n",
+    "    # Normalize to 0-255\n",
+    "    tile = ((tile - np.nanmin(tile)) / (np.nanmax(tile) - np.nanmin(tile))) * 255\n",
+    "\n",
+    "    return tile.astype(np.uint8)\n",
+    "\n",
+    "def adaptive_clip_and_scale(tile, factor=2):\n",
+    "    \"\"\"\n",
+    "    Perform adaptive clipping and scaling of the tile.\n",
+    "\n",
+    "    Parameters:\n",
+    "    - tile: The image tile to be processed.\n",
+    "    - factor: Multiplier for std deviation to compute clipping bounds.\n",
+    "\n",
+    "    Returns:\n",
+    "    - Processed tile with values between 0 and 255.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # If the tile consists only of NaNs, return an all-zero tile.\n",
+    "    if np.all(np.isnan(tile)):\n",
+    "        return np.zeros_like(tile, dtype=np.uint8)\n",
+    "\n",
+    "    # Calculate local statistics\n",
+    "    mean = np.nanmean(tile)\n",
+    "    std = np.nanstd(tile)\n",
+    "\n",
+    "    # Check if the standard deviation is 0, if so return a tile filled with mean value scaled to [0,255]\n",
+    "    if std == 0:\n",
+    "        scaled_mean = ((mean - np.nanmin(tile)) / (np.nanmax(tile) - np.nanmin(tile)) * 255).astype(np.uint8)\n",
+    "        return np.full_like(tile, scaled_mean, dtype=np.uint8)\n",
+    "\n",
+    "    # Define bounds for clipping\n",
+    "    lower_bound = mean - factor * std\n",
+    "    upper_bound = mean + factor * std\n",
+    "\n",
+    "    # Clip values\n",
+    "    tile_clipped = np.clip(tile, lower_bound, upper_bound)\n",
+    "\n",
+    "    # Scale values to [0, 255]\n",
+    "    min_val = np.min(tile_clipped)\n",
+    "    max_val = np.max(tile_clipped)\n",
+    "    \n",
+    "    # Check if after clipping, max is less than or equal to min\n",
+    "    if max_val <= min_val:\n",
+    "        return np.full_like(tile, 127, dtype=np.uint8)  # Return a gray tile\n",
+    "\n",
+    "    tile_scaled_0_1 = (tile_clipped - min_val) / (max_val - min_val)\n",
+    "    tile_scaled_0_255 = (tile_scaled_0_1 * 255).astype(np.uint8)\n",
+    "\n",
+    "    return tile_scaled_0_255\n",
+    "\n",
+    "def clip_and_scale(tile, global_mean, global_std, factor=2):\n",
+    "    \"\"\"\n",
+    "    Perform clipping based on global statistics and scale the tile.\n",
+    "\n",
+    "    Parameters:\n",
+    "    - tile: The image tile to be processed.\n",
+    "    - global_mean: Mean of the entire dataset.\n",
+    "    - global_std: Standard deviation of the entire dataset.\n",
+    "    - factor: Multiplier for std deviation to compute clipping bounds.\n",
+    "\n",
+    "    Returns:\n",
+    "    - Processed tile with values between 0 and 255.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Define bounds for clipping based on global statistics\n",
+    "    lower_bound = global_mean - factor * global_std\n",
+    "    upper_bound = global_mean + factor * global_std\n",
+    "\n",
+    "    # Clip values\n",
+    "    tile_clipped = np.clip(tile, lower_bound, upper_bound)\n",
+    "\n",
+    "    # Scale values to [0, 255]\n",
+    "    min_val = np.min(tile_clipped)\n",
+    "    max_val = np.max(tile_clipped)\n",
+    "    \n",
+    "    if max_val <= min_val:\n",
+    "        return np.full_like(tile, 127, dtype=np.uint8)  # Return a gray tile\n",
+    "\n",
+    "    tile_scaled_0_1 = (tile_clipped - min_val) / (max_val - min_val)\n",
+    "    tile_scaled_0_255 = (tile_scaled_0_1 * 255).astype(np.uint8)\n",
+    "\n",
+    "    return tile_scaled_0_255\n",
+    "    \n",
+    "# Loading and Preprocessing Images\n",
+    "\n",
+    "def extract_tiles(img, tile_size):\n",
+    "    images_list = []\n",
+    "\n",
+    "    # Calculate the number of tiles needed in both dimensions\n",
+    "    n_tiles_x = img.shape[0] // tile_size\n",
+    "    n_tiles_y = img.shape[1] // tile_size\n",
+    "\n",
+    "    # Pad the image if necessary to extract tiles\n",
+    "    pad_width_x = tile_size - (img.shape[0] % tile_size) if img.shape[0] % tile_size != 0 else 0\n",
+    "    pad_width_y = tile_size - (img.shape[1] % tile_size) if img.shape[1] % tile_size != 0 else 0\n",
+    "\n",
+    "    img = np.pad(img, ((0, pad_width_x), (0, pad_width_y)), mode='constant')\n",
+    "\n",
+    "    for i in range(0, img.shape[0], tile_size):\n",
+    "        for j in range(0, img.shape[1], tile_size):\n",
+    "            tile = img[i:i+tile_size, j:j+tile_size]\n",
+    "            images_list.append(tile)\n",
+    "\n",
+    "    return np.array(images_list)\n",
+    "\n",
+    "\n",
+    "def load_and_tile_images_from_folder(folder_path, tile_size=64):\n",
+    "    images_list = []\n",
+    "    for filename in sorted(os.listdir(folder_path)):  # Sorting ensures images from different folders align properly\n",
+    "        if filename.endswith('.tif'):\n",
+    "            img = imread(os.path.join(folder_path, filename))\n",
+    "            \n",
+    "            images_list.extend(extract_tiles(img, tile_size))\n",
+    "\n",
+    "            \"\"\"for img in images_list:  # inspect the images\n",
+    "                print(img.shape)\"\"\"\n",
+    "    \n",
+    "    return np.stack(images_list, axis=0) #np.array(images_list)\n",
+    "\n",
+    "# Data Augmentation\n",
+    "\n",
+    "datagen = ImageDataGenerator(\n",
+    "    rotation_range=40,\n",
+    "    width_shift_range=0.2,\n",
+    "    height_shift_range=0.2,\n",
+    "    zoom_range=0.2,\n",
+    "    horizontal_flip=True,\n",
+    "    fill_mode='constant',\n",
+    "    cval=0\n",
+    ")\n",
+    "\n",
+    "datagen_adjuste = ImageDataGenerator(\n",
+    "    rotation_range=40,\n",
+    "    width_shift_range=0.2,\n",
+    "    height_shift_range=0.2,\n",
+    "    zoom_range=0.2,\n",
+    "    horizontal_flip=True,\n",
+    "    fill_mode='constant',\n",
+    "    cval=0,\n",
+    "    brightness_range=[0.5, 1.5],  # adjust brightness\n",
+    "    channel_shift_range=0.2  # shift channel values\n",
+    ")\n",
+    "\n",
+    "def augment_images_together(*image_batches):\n",
+    "    # Assuming all image_batches are of the same length, \n",
+    "    # and images in position i of each batch correspond to each other\n",
+    "    augmented_batches = [[] for _ in range(len(image_batches))]\n",
+    "    \n",
+    "    for i in range(len(image_batches[0])):\n",
+    "        combined_image = np.stack([image_batches[j][i] for j in range(len(image_batches))], axis=-1)  # stack along the channel dimension\n",
+    "        augmentation_iterator = datagen.flow(np.expand_dims(combined_image, axis=0), batch_size=1)\n",
+    "        augmented_image = augmentation_iterator[0].squeeze(axis=0)\n",
+    "        \n",
+    "        for j in range(len(image_batches)):\n",
+    "            augmented_batches[j].append(augmented_image[..., j])  # separate the channels\n",
+    "    \n",
+    "    return [np.array(batch) for batch in augmented_batches]\n",
+    "\n",
+    "def handle_missing_values(images):\n",
+    "    \"\"\"Handle NaN and extremely small values in the dataset.\"\"\"\n",
+    "    \n",
+    "    # Check for NaN values\n",
+    "    has_nan = np.isnan(images).any()\n",
+    "    print(f\"Dataset contains NaN values: {has_nan}\")\n",
+    "    images[np.isnan(images)] = 0.0  # replace NaN with 0\n",
+    "    \n",
+    "    # Handle 'no-data' or extremely small values\n",
+    "    no_data_mask = images < -1e+29\n",
+    "    print(f\"Number of 'no-data' values: {np.sum(no_data_mask)}\")\n",
+    "    #images[no_data_mask] = 0.0\n",
+    "    \n",
+    "    mean_val = np.mean(images[~no_data_mask])\n",
+    "    images[no_data_mask] = mean_val\n",
+    "    \n",
+    "    return images\n",
+    "\n",
+    "def replace_nans(image, value=0):\n",
+    "    \"\"\"Replace NaN values in the image with the specified value.\"\"\"\n",
+    "    nan_mask = np.isnan(image)\n",
+    "    image[nan_mask] = value\n",
+    "    return image\n",
+    "\n",
+    "def replace_nans_with_mean(image):\n",
+    "    \"\"\"Replace NaN values in the image with the mean of the non-NaN values.\"\"\"\n",
+    "    nan_mask = np.isnan(image)\n",
+    "    image[nan_mask] = np.nanmean(image)\n",
+    "    return image\n",
+    "\n",
+    "def handle_nans_and_infs(image):\n",
+    "    \"\"\"Replace NaN and Inf values in an image.\"\"\"\n",
+    "    non_finite_mask = ~np.isfinite(image)  # True for NaN and Inf values\n",
+    "    mean_val = np.mean(image[np.isfinite(image)])  # mean of finite values\n",
+    "    image[non_finite_mask] = mean_val\n",
+    "    return image\n",
+    "\n",
+    "def normalize_images_old(images):\n",
+    "    return images.astype('float32') / 255.0\n",
+    "\n",
+    "def remove_nan(img_np):\n",
+    "    # handle null values\n",
+    "    img_np[img_np < -1e+30] = np.nan \n",
+    "\n",
+    "    return img_np\n",
+    "\n",
+    "# To avoid division by zero error, when max_val == min_val\n",
+    "def normalize_tile(tile):\n",
+    "    min_val = np.min(tile)\n",
+    "    max_val = np.max(tile)\n",
+    "    if max_val != min_val:\n",
+    "        tile = ((tile - min_val) / (max_val - min_val)) * 255\n",
+    "    else:\n",
+    "        tile = np.zeros_like(tile)\n",
+    "\n",
+    "    return tile\n",
+    "\n",
+    "def equalize_images(images):\n",
+    "    return exposure.equalize_hist(images) * 255\n",
+    "\n",
+    "def contrast_stretching(image, new_min=0, new_max=1):\n",
+    "    \"\"\"Perform contrast stretching on the given image.\"\"\"\n",
+    "    return (image - np.min(image)) / (np.max(image) - np.min(image)) * (new_max - new_min) + new_min\n",
+    "\n",
+    "def contrast_stretching_consider_uniform_values(image, new_min=0, new_max=1, epsilon=1e-7):\n",
+    "    \"\"\"Perform contrast stretching on the given image.\"\"\"\n",
+    "    min_val = np.min(image)\n",
+    "    max_val = np.max(image)\n",
+    "\n",
+    "    # Check if the max and min values are very close or identical\n",
+    "    if np.isclose(max_val, min_val, atol=epsilon):\n",
+    "        return np.ones_like(image) * new_min  # or any other default behavior\n",
+    "\n",
+    "    return (image - min_val) / (max_val - min_val + epsilon) * (new_max - new_min) + new_min\n",
+    "\n",
+    "\n",
+    "def apply_preprocessing(images_list):\n",
+    "    \"\"\"Apply preprocessing techniques to a list of images.\"\"\"\n",
+    "    processed_images = []\n",
+    "    \n",
+    "    for image in images_list:\n",
+    "        # Handle NaN values\n",
+    "        image = handle_nans_and_infs(image)\n",
+    "        \n",
+    "        # Apply contrast stretching\n",
+    "        #stretched_image = contrast_stretching(image)\n",
+    "        image = contrast_stretching_consider_uniform_values(image)\n",
+    "        \n",
+    "        # Apply histogram equalization\n",
+    "        equalized_image = exposure.equalize_hist(image) * 255\n",
+    "        \n",
+    "        processed_images.append(equalized_image)\n",
+    "    \n",
+    "    return processed_images\n",
+    "\n",
+    "def normalize_images(images):\n",
+    "    min_val = np.min(images)\n",
+    "    max_val = np.max(images)\n",
+    "    return (images - min_val) / (max_val - min_val)\n",
+    "\n",
+    "# A datagenerator with default values set\n",
+    "def create_datagenerator(rotation_range=20, width_shift_range=0.1, eight_shift_range=0.1, zoom_range=0.1, horizontal_flip=True, fill_mode='constant', cval=0,brightness_range=[0.5, 1.5], channel_shift_range=0.2):\n",
+    "    datagen = ImageDataGenerator(\n",
+    "        rotation_range=rotation_range,\n",
+    "        width_shift_range=width_shift_range,\n",
+    "        height_shift_range=height_shift_range,\n",
+    "        zoom_range=zoom_range,\n",
+    "        horizontal_flip=horizontal_flip,\n",
+    "        fill_mode=fill_mode,\n",
+    "        cval=cval,\n",
+    "        brightness_range=brightness_range,  # adjust brightness\n",
+    "        channel_shift_range=channel_shift_range  # shift channel values\n",
+    "    )\n",
+    "    \n",
+    "    return datagen\n",
+    "\n",
+    "def augment_image_dataset(image_list, augment_times=5):\n",
+    "    # Initialize the ImageDataGenerator with the desired augmentations\n",
+    "    datagen = ImageDataGenerator(\n",
+    "        rotation_range=40,\n",
+    "        width_shift_range=0.2,\n",
+    "        height_shift_range=0.2,\n",
+    "        zoom_range=0.2,\n",
+    "        horizontal_flip=True,\n",
+    "        fill_mode='constant',\n",
+    "        cval=0\n",
+    "    )\n",
+    "\n",
+    "    datagen_adjusted = ImageDataGenerator(\n",
+    "        rotation_range=20,\n",
+    "        #width_shift_range=0.1,\n",
+    "        #height_shift_range=0.1,\n",
+    "        #zoom_range=0.1,\n",
+    "        horizontal_flip=True,\n",
+    "        fill_mode='constant',\n",
+    "        cval=0,\n",
+    "        brightness_range=[0.5, 1.5],  # adjust brightness\n",
+    "        channel_shift_range=0.2  # shift channel values\n",
+    "    )\n",
+    "    \n",
+    "    augmented_images = []\n",
+    "\n",
+    "    for image in image_list:\n",
+    "        #augmentation_iterator = datagen.flow(np.expand_dims(image, axis=0), batch_size=1)\n",
+    "        augmentation_iterator = datagen_adjusted.flow(np.expand_dims(image, axis=0), batch_size=1)\n",
+    "        \n",
+    "        for j in range(augment_times):\n",
+    "            augmented_images.append(augmentation_iterator[0].squeeze(axis=0))\n",
+    "\n",
+    "    return np.array(augmented_images)\n",
+    "\n",
+    "# Clip the dataset length to the shortest dataset (recommended to use data augmentation first to not lose data)\n",
+    "def prepare_multimodal_dataset(augmented_AEM_images, augmented_Gravity_images, augmented_Radiometric_images, augmented_Magnetic_images):\n",
+    "    # Find the smallest length\n",
+    "    min_length = min([len(x) for x in [augmented_AEM_images, augmented_Gravity_images, augmented_Radiometric_images, augmented_Magnetic_images]])\n",
+    "    \n",
+    "    # Trim datasets to match the smallest length\n",
+    "    x1_trimmed = augmented_AEM_images[:min_length]\n",
+    "    x2_trimmed = augmented_Gravity_images[:min_length]\n",
+    "    x3_trimmed = augmented_Radiometric_images[:min_length]\n",
+    "    x4_trimmed = augmented_Magnetic_images[:min_length]\n",
+    "    \n",
+    "    combined_data = np.stack((x1_trimmed, x2_trimmed, x3_trimmed, x4_trimmed), axis=-1)\n",
+    "\n",
+    "    return combined_data\n",
+    "\n",
+    "# Delete failed augmentations or null images, that are extracted from the border of the original image\n",
+    "def delete_null_or_1_value_images(data, min_value=1):\n",
+    "    removed = 0\n",
+    "    indices_to_remove = [i for i in range(len(data)) if len(np.unique(data[i])) <= min_value]\n",
+    "    removed = len(indices_to_remove)\n",
+    "    X = np.delete(data, indices_to_remove, axis=0)\n",
+    "\n",
+    "    print(\"Removed \", removed)    \n",
+    "\n",
+    "    return X"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71ff4da2-402b-49e8-91c1-6ef904251310",
+   "metadata": {},
+   "source": [
+    "### Load your data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df6b83f8-f574-48cc-9d30-847258dfcc52",
+   "metadata": {},
+   "source": [
+    "Here, geophysical images were used (AEM, Gravity, Radiometric, Magnetic)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e4634914-ad15-4b16-976e-b500bcbda611",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(168, 256, 256)\n",
+      "(714, 256, 256)\n",
+      "(168, 256, 256)\n",
+      "(504, 256, 256)\n",
+      "Dataset contains NaN values: False\n",
+      "Number of 'no-data' values: 1171204\n",
+      "Dataset contains NaN values: False\n",
+      "Number of 'no-data' values: 6623948\n",
+      "Dataset contains NaN values: False\n",
+      "Number of 'no-data' values: 0\n",
+      "Dataset contains NaN values: False\n",
+      "Number of 'no-data' values: 391198\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load all images from each category and stack them\n",
+    "\n",
+    "#AEM_folder = ...\n",
+    "#Gravity_folder = ...\n",
+    "#Radiometric_folder = ...\n",
+    "#Magnetic_folder = ...\n",
+    "\n",
+    "AEM_images = load_and_tile_images_from_folder(AEM_folder, 256)\n",
+    "Gravity_images = load_and_tile_images_from_folder(Gravity_folder, 256)\n",
+    "Radiometric_images = load_and_tile_images_from_folder(Radiometric_folder, 256)\n",
+    "Magnetic_images = load_and_tile_images_from_folder(Magnetic_folder, 256)\n",
+    "\n",
+    "augmented_AEM_images, augmented_Gravity_images, augmented_Radiometric_images, augmented_Magnetic_images = AEM_images, Gravity_images, Radiometric_images, Magnetic_images\n",
+    "\n",
+    "print(augmented_AEM_images.shape)\n",
+    "print(augmented_Gravity_images.shape)\n",
+    "print(augmented_Radiometric_images.shape)\n",
+    "print(augmented_Magnetic_images.shape)\n",
+    "\n",
+    "augmented_AEM_images = handle_missing_values(augmented_AEM_images)\n",
+    "augmented_Gravity_images = handle_missing_values(augmented_Gravity_images)\n",
+    "augmented_Radiometric_images = handle_missing_values(augmented_Radiometric_images)\n",
+    "augmented_Magnetic_images = handle_missing_values(augmented_Magnetic_images)\n",
+    "\n",
+    "augmented_AEM_images = apply_preprocessing(augmented_AEM_images)\n",
+    "augmented_Gravity_images = apply_preprocessing(augmented_Gravity_images)\n",
+    "augmented_Radiometric_images = apply_preprocessing(augmented_Radiometric_images)\n",
+    "augmented_Magnetic_images = apply_preprocessing(augmented_Magnetic_images)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "d3f69706-ca46-4f13-9f2d-0f81645f0fa3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removed  96\n",
+      "Removed  0\n",
+      "Range for x_train: 0.0, 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"Check data range\"\"\"\n",
+    "\n",
+    "def process_for_training(input, split_size):\n",
+    "    images = np.array(input)\n",
+    "    images = images.astype('float32') / 255.\n",
+    "    x_train, x_test = train_test_split(images, test_size=1-split_size)\n",
+    "\n",
+    "    return x_train, x_test\n",
+    "\n",
+    "data = np.array(augmented_Radiometric_images)\n",
+    "data = data.reshape(-1, 256, 256, 1)\n",
+    "\n",
+    "data = delete_null_or_1_value_images(data, 3)\n",
+    "\n",
+    "data = augment_image_dataset(data, 3)\n",
+    "\n",
+    "data = delete_null_or_1_value_images(data, 3)\n",
+    "\n",
+    "x_train, x_test = process_for_training(data, 0.95)\n",
+    "\n",
+    "datasets = [x_train]#, x_test]\n",
+    "dataset_names = [\"x_train\", \"x_test\"]\n",
+    "\n",
+    "for d, name in zip(datasets, dataset_names):\n",
+    "    min_val = np.min(d)\n",
+    "    max_val = np.max(d)\n",
+    "    print(f\"Range for {name}: {min_val}, {max_val}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd966013-b15e-4961-8abc-ee4f484fb795",
+   "metadata": {},
+   "source": [
+    "### Visualize images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "8a9e1c46-dd71-4d49-b21e-f618e55c0d95",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(205, 256, 256, 1) (11, 256, 256, 1)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(x_train.shape, x_test.shape)\n",
+    "\n",
+    "# Visualize the images side by side\n",
+    "\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "\n",
+    "j = 15\n",
+    "\n",
+    "plt.subplot(1, 3, 1)\n",
+    "plt.imshow(x_train[j], cmap='gray')\n",
+    "plt.title('x1_train')\n",
+    "\n",
+    "plt.subplot(1, 3, 2)\n",
+    "plt.imshow(x_train[j+1, :, :], cmap='gray')\n",
+    "plt.title('x2_train')\n",
+    "\n",
+    "plt.subplot(1, 3, 3)\n",
+    "plt.imshow(x_train[j+2, :, :], cmap='gray')\n",
+    "plt.title('y_train')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5711d51c-b7be-49f4-9794-99b7514e872f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Min: 0.0\n",
+      "Max: 0.9098039\n",
+      "Mean: 0.42763305\n",
+      "Standard Deviation: 0.27902043\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def visualize_image(img_data):\n",
+    "    # Load the image using raster.io\n",
+    "    \"\"\"with rasterio.open(image_path, 'r') as src:\n",
+    "        img_data = src.read(1)\"\"\"\n",
+    "    \n",
+    "    # Print basic statistics\n",
+    "    print(\"Min:\", np.nanmin(img_data))\n",
+    "    print(\"Max:\", np.nanmax(img_data))\n",
+    "    print(\"Mean:\", np.nanmean(img_data))\n",
+    "    print(\"Standard Deviation:\", np.nanstd(img_data))\n",
+    "    \n",
+    "    valid_data = img_data[~np.isnan(img_data)]\n",
+    "    \n",
+    "    # Plot the histogram\n",
+    "    plt.figure(figsize=(12, 8))\n",
+    "    plt.subplot(2, 2, 1)\n",
+    "    plt.hist(valid_data.ravel(), bins=100)\n",
+    "    plt.title(\"Histogram\")\n",
+    "    \n",
+    "    # Plot the CDF\n",
+    "    plt.subplot(2, 2, 2)\n",
+    "    plt.hist(valid_data.ravel(), bins=100, cumulative=True, density=True, histtype='step', color='blue', alpha=0.7)\n",
+    "    plt.title(\"CDF\")\n",
+    "    \n",
+    "    # Plot the image using mean±2 standard deviations as the display range\n",
+    "    plt.subplot(2, 2, 3)\n",
+    "    vmin = max(0, np.nanmean(img_data) - 2*np.nanstd(img_data))\n",
+    "    vmax = min(1, np.nanmean(img_data) + 2*np.nanstd(img_data))\n",
+    "    plt.imshow(img_data, cmap='gray', vmin=vmin, vmax=vmax)\n",
+    "    plt.title(f\"Image (range: {vmin:.3f}-{vmax:.3f})\")\n",
+    "    plt.colorbar()\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "visualize_image(x_train[j])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c46d6e0-bbdf-4634-90aa-30ffda57eab5",
+   "metadata": {},
+   "source": [
+    "## Generative adversarial network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b66b02aa-3696-4fef-a1b6-2932bfa2b05e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def augment_image_dataset(image_list, augment_times=5):\n",
+    "    \"\"\"\n",
+    "    Augment a list of images a specified number of times.\n",
+    "\n",
+    "    Parameters:\n",
+    "    - image_list: A list of images to augment.\n",
+    "    - augment_times: How many augmented copies to produce for each input image.\n",
+    "\n",
+    "    Returns:\n",
+    "    - A numpy array containing the augmented images.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Initialize the ImageDataGenerator\n",
+    "    datagen = ImageDataGenerator(\n",
+    "        rotation_range=40,\n",
+    "        width_shift_range=0.2,\n",
+    "        height_shift_range=0.2,\n",
+    "        zoom_range=0.2,\n",
+    "        horizontal_flip=True,\n",
+    "        fill_mode='constant',\n",
+    "        cval=0\n",
+    "    )\n",
+    "\n",
+    "    augmented_images = []\n",
+    "\n",
+    "    for image in image_list:\n",
+    "        augmentation_iterator = datagen.flow(np.expand_dims(image, axis=0), batch_size=1)\n",
+    "        \n",
+    "        for j in range(augment_times):\n",
+    "            augmented_images.append(augmentation_iterator[0].squeeze(axis=0))\n",
+    "\n",
+    "    return np.array(augmented_images)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "be24eddb-3f0f-45ec-ac57-eb503c2cfe89",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(615, 256, 256, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Visualize the images side by side\n",
+    "\n",
+    "x_train = augment_image_dataset(x_train, 3)\n",
+    "x_test = augment_image_dataset(x_test, 3)\n",
+    "\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "\n",
+    "j = 16\n",
+    "\n",
+    "plt.subplot(1, 3, 1)\n",
+    "plt.imshow(x_train[j, :, :], cmap='gray')\n",
+    "plt.title('x1_train')\n",
+    "\n",
+    "plt.subplot(1, 3, 2)\n",
+    "plt.imshow(x_train[j+1, :, :], cmap='gray')\n",
+    "plt.title('x2_train')\n",
+    "\n",
+    "plt.subplot(1, 3, 3)\n",
+    "plt.imshow(x_train[j+2, :, :], cmap='gray')\n",
+    "plt.title('y_train')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(x_train.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a5cdc820-d392-4f0f-9ebc-c82048f23714",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Range for x_train: 0.0, 1.0\n",
+      "Range for x_test: 0.0, 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"Check data range\"\"\"\n",
+    "\n",
+    "datasets = [x_train, x_test]\n",
+    "dataset_names = [\"x_train\", \"x_test\"]\n",
+    "\n",
+    "for data, name in zip(datasets, dataset_names):\n",
+    "    min_val = np.min(data)\n",
+    "    max_val = np.max(data)\n",
+    "    print(f\"Range for {name}: {min_val}, {max_val}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e8c9fcf9-3cb8-4542-a5ee-f791fcb816a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def save_generated_samples(epoch, generator, noise_dim=100, num_samples=1, figsize=(1)):\n",
+    "    \"\"\"Save generated samples as a grid image.\n",
+    "    \n",
+    "    Args:\n",
+    "    - epoch (int): Current epoch number, for filename.\n",
+    "    - generator (Model): Generator model.\n",
+    "    - noise_dim (int): Dimension of random noise vector.\n",
+    "    - num_samples (int): Number of samples to generate for grid.\n",
+    "    - figsize (tuple): Size of the full grid image.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Generate random noise samples\n",
+    "    noise = np.random.normal(0, 1, (num_samples, noise_dim))\n",
+    "    \n",
+    "    # Generate images\n",
+    "    generated_imgs = generator.predict(noise)\n",
+    "    \n",
+    "    # Rescale images from [-1, 1] to [0, 1]\n",
+    "    #generated_imgs = 0.5 * generated_imgs + 0.5\n",
+    "\n",
+    "    # Plot images in a grid\n",
+    "    fig, axs = plt.subplots(int(np.sqrt(num_samples)), int(np.sqrt(num_samples)), figsize=figsize)\n",
+    "    \n",
+    "    cnt = 0\n",
+    "    for i in range(int(np.sqrt(num_samples))):\n",
+    "        for j in range(int(np.sqrt(num_samples))):\n",
+    "            axs[i, j].imshow(generated_imgs[cnt, :, :, 0], cmap='gray')\n",
+    "            axs[i, j].axis('off')\n",
+    "            cnt += 1\n",
+    "            \n",
+    "    # Save figure\n",
+    "    fig.savefig(f\"gan_256_output_epoch_{epoch}.png\")\n",
+    "    plt.close()\n",
+    "\n",
+    "def add_label_noise(labels, noise_factor=0.05):\n",
+    "    \"\"\"Adds random noise to the labels.\"\"\"\n",
+    "    noise = tf.random.uniform(shape=labels.shape, minval=-noise_factor, maxval=noise_factor)\n",
+    "    return tf.clip_by_value(labels + noise, 0.0, 1.0)\n",
+    "\n",
+    "def save_generated_samples_single(epoch, generator, noise_dim=100):\n",
+    "    \"\"\"Save a single generated sample.\n",
+    "    \n",
+    "    Args:\n",
+    "    - epoch (int): Current epoch number, for filename.\n",
+    "    - generator (Model): Generator model.\n",
+    "    - noise_dim (int): Dimension of random noise vector.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Generate random noise sample\n",
+    "    noise = np.random.normal(0, 1, (1, noise_dim))\n",
+    "    \n",
+    "    # Generate an image\n",
+    "    generated_img = generator.predict(noise)[0]\n",
+    "    \n",
+    "    # Rescale image from [-1, 1] to [0, 1] if needed\n",
+    "    # generated_img = 0.5 * generated_img + 0.5\n",
+    "\n",
+    "    plt.imshow(generated_img[:, :, 0], cmap='gray')\n",
+    "    plt.axis('off')\n",
+    "    \n",
+    "    # Save figure\n",
+    "    plt.savefig(f\"gan_256_output_single_epoch_{epoch}.png\")\n",
+    "    plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "365fc3ed-4d36-4227-9a3c-db5c085fbdee",
+   "metadata": {},
+   "source": [
+    "### Basic GAN with modular size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "fc4f03c0-46b4-40c6-8853-66855640f9f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_generator_modular(img_size=64, num_layers=2, dropout=0, regularization=0):\n",
+    "    noise_dim = 100\n",
+    "    initial_size = img_size // (2 ** num_layers)\n",
+    "\n",
+    "    input_noise = Input(shape=(noise_dim,))\n",
+    "    x = Dense(256 * initial_size * initial_size)(input_noise)\n",
+    "    x = Dropout(dropout)(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = LeakyReLU(alpha=0.2)(x)\n",
+    "    x = Reshape((initial_size, initial_size, 256))(x)\n",
+    "\n",
+    "    for i in range(num_layers):\n",
+    "        x = Conv2DTranspose(256 // (2 ** (i+1)), kernel_size=4, strides=2, padding=\"same\", kernel_regularizer=l1(regularization))(x)\n",
+    "        x = Dropout(dropout)(x)\n",
+    "        x = BatchNormalization()(x)\n",
+    "        x = LeakyReLU(alpha=0.2)(x)\n",
+    "\n",
+    "    x = Conv2DTranspose(1, kernel_size=4, strides=1, padding=\"same\", activation=\"sigmoid\")(x)\n",
+    "    generator = Model(input_noise, x, name=\"generator_modular\")\n",
+    "    \n",
+    "    return generator\n",
+    "\n",
+    "def build_discriminator_modular(img_size=256, num_layers=4, dropout=0, regularization=0):\n",
+    "    input_img = Input(shape=(img_size, img_size, 1))\n",
+    "    x = input_img\n",
+    "\n",
+    "    for i in range(num_layers):\n",
+    "        x = Conv2D(64 * (2 ** i), kernel_size=4, strides=2, padding=\"same\", kernel_regularizer=l1(regularization))(x)\n",
+    "        x = Dropout(dropout)(x)\n",
+    "        x = LeakyReLU(alpha=0.2)(x)\n",
+    "\n",
+    "        # Add BatchNormalization to all layers except the first one\n",
+    "        if i > 0:\n",
+    "            x = BatchNormalization()(x)\n",
+    "\n",
+    "    # Flatten and apply sigmoid activation\n",
+    "    x = Flatten()(x)\n",
+    "    x = Dense(1, activation='sigmoid')(x)\n",
+    "\n",
+    "    discriminator = Model(input_img, x, name=\"discriminator_modular\")\n",
+    "\n",
+    "    return discriminator\n",
+    "\n",
+    "# Compiling \n",
+    "generator = build_generator_modular(img_size=256, num_layers=3)\n",
+    "discriminator = build_discriminator_modular(img_size=256, num_layers=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "017591ed-bdf6-4a40-989a-aeb78e14e05b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(None, 256, 256, 1)\n",
+      "(None, 256, 256, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Only compile the disc\n",
+    "discriminator.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "# Make the discriminator non-trainable when we are training the generator\n",
+    "discriminator.trainable = False\n",
+    "\n",
+    "print(generator.output_shape)\n",
+    "print(discriminator.input_shape)\n",
+    "\n",
+    "# Create the combined model\n",
+    "noise = Input(shape=(100,))\n",
+    "generated_image = generator(noise)\n",
+    "validity = discriminator(generated_image)\n",
+    "\n",
+    "combined = Model(inputs=noise, outputs=validity)\n",
+    "\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "from tensorflow.keras.callbacks import LearningRateScheduler\n",
+    "\n",
+    "def scheduler(epoch, lr):\n",
+    "    if epoch % 10 == 0 and epoch != 0:\n",
+    "        return lr * 0.9\n",
+    "    return lr\n",
+    "\n",
+    "lr_callback = LearningRateScheduler(scheduler, verbose=1)\n",
+    "\n",
+    "# Define the optimizer with a custom learning rate\n",
+    "optimizer = Adam(learning_rate=0.001)\n",
+    "\n",
+    "combined.compile(optimizer=optimizer, loss='binary_crossentropy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "65c15c65-8538-48e7-bee4-9c7c7eacfda4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2/2 [==============================] - 0s 349ms/step\n",
+      "1/1 [==============================] - 0s 116ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 28ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "1/1 [==============================] - 0s 8ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "0/200 [D loss: 3.7718982696533203 | D accuracy: 42.97%] [G loss: 12.081825733184814]\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 24ms/step\n",
+      "2/2 [==============================] - 0s 24ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "1/200 [D loss: 3.8767969608306885 | D accuracy: 46.88%] [G loss: 0.0014727976522408426]\n",
+      "2/2 [==============================] - 0s 24ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 24ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 24ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/200 [D loss: 1.4660842418670654 | D accuracy: 47.66%] [G loss: 4.859987912109176e-09]\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 29ms/step\n",
+      "2/2 [==============================] - 0s 24ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 24ms/step\n",
+      "2/2 [==============================] - 0s 24ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "3/200 [D loss: 0.8065026104450226 | D accuracy: 57.03%] [G loss: 0.11535673588514328]\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 24ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 24ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 27ms/step\n",
+      "4/200 [D loss: 1.1653395891189575 | D accuracy: 50.78%] [G loss: 0.009438836947083473]\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "1/1 [==============================] - 0s 8ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "2/2 [==============================] - 0s 24ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "2/2 [==============================] - 0s 27ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "5/200 [D loss: 0.5855782628059387 | D accuracy: 50.00%] [G loss: 7.766478085646789e-11]\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 24ms/step\n",
+      "2/2 [==============================] - 0s 24ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "6/200 [D loss: 1.5265442728996277 | D accuracy: 49.22%] [G loss: 9.011206150054932]\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "7/200 [D loss: 1.551819622516632 | D accuracy: 50.00%] [G loss: 0.0562129020690918]\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "8/200 [D loss: 1.4615123867988586 | D accuracy: 46.88%] [G loss: 0.2375705987215042]\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "9/200 [D loss: 1.478222131729126 | D accuracy: 48.44%] [G loss: 0.01594029413536191]\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 11ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "10/200 [D loss: 1.501696616411209 | D accuracy: 39.06%] [G loss: 12.66310739517212]\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "11/200 [D loss: 1.0791860222816467 | D accuracy: 56.25%] [G loss: 2.4913408756256104]\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "12/200 [D loss: 1.1035517454147339 | D accuracy: 53.12%] [G loss: 12.558570384979248]\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "13/200 [D loss: 1.0260406732559204 | D accuracy: 50.78%] [G loss: 23.588512420654297]\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "14/200 [D loss: 0.9024885892868042 | D accuracy: 40.62%] [G loss: 2.598273482984505e-17]\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "15/200 [D loss: 0.7602247595787048 | D accuracy: 49.22%] [G loss: 3.7001870323380185e-24]\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "16/200 [D loss: 0.8434847593307495 | D accuracy: 54.69%] [G loss: 3.212867125426146e-22]\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "17/200 [D loss: 1.1667467951774597 | D accuracy: 42.97%] [G loss: 1.45318990365108e-32]\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "18/200 [D loss: 0.831252932548523 | D accuracy: 53.91%] [G loss: 1.0218145169427536e-34]\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "19/200 [D loss: 0.5043897479772568 | D accuracy: 54.69%] [G loss: 0.0]\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "2/2 [==============================] - 0s 29ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "20/200 [D loss: 0.7331758737564087 | D accuracy: 45.31%] [G loss: 0.0]\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 29ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "21/200 [D loss: 0.43452760577201843 | D accuracy: 50.78%] [G loss: 0.0]\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 24ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 26ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 27ms/step\n",
+      "22/200 [D loss: 0.6611649394035339 | D accuracy: 49.22%] [G loss: 0.0]\n",
+      "2/2 [==============================] - 0s 27ms/step\n",
+      "2/2 [==============================] - 0s 29ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "23/200 [D loss: 0.5623246133327484 | D accuracy: 48.44%] [G loss: 0.0]\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "24/200 [D loss: 0.5805435180664062 | D accuracy: 42.19%] [G loss: 0.0]\n",
+      "2/2 [==============================] - 0s 25ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "25/200 [D loss: 1.9461826086044312 | D accuracy: 48.44%] [G loss: 0.0016087504045572132]\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "26/200 [D loss: 0.8542640507221222 | D accuracy: 44.53%] [G loss: 0.0]\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "27/200 [D loss: 1.5385975241661072 | D accuracy: 46.09%] [G loss: 0.0]\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "28/200 [D loss: 2.0323145389556885 | D accuracy: 49.22%] [G loss: 0.0]\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "29/200 [D loss: 1.382832944393158 | D accuracy: 47.66%] [G loss: 0.0]\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 8ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "30/200 [D loss: 0.6776311099529266 | D accuracy: 50.00%] [G loss: 0.0]\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "31/200 [D loss: 0.8234777450561523 | D accuracy: 50.78%] [G loss: 0.0]\n",
+      "2/2 [==============================] - 0s 30ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "32/200 [D loss: 0.982109010219574 | D accuracy: 46.88%] [G loss: 0.0]\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 28ms/step\n",
+      "2/2 [==============================] - 0s 28ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 24ms/step\n",
+      "2/2 [==============================] - 0s 29ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "33/200 [D loss: 2.185569167137146 | D accuracy: 46.88%] [G loss: 0.0]\n",
+      "2/2 [==============================] - 0s 29ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 25ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 29ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "34/200 [D loss: 1.465938925743103 | D accuracy: 49.22%] [G loss: 0.0]\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "2/2 [==============================] - 0s 20ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 30ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 30ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "1/1 [==============================] - 0s 9ms/step\n",
+      "2/2 [==============================] - 0s 30ms/step\n",
+      "1/1 [==============================] - 0s 10ms/step\n",
+      "35/200 [D loss: 0.9403793215751648 | D accuracy: 49.22%] [G loss: 0.0]\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n",
+      "2/2 [==============================] - 0s 21ms/step\n",
+      "2/2 [==============================] - 0s 22ms/step\n",
+      "2/2 [==============================] - 0s 23ms/step\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[25], line 55\u001b[0m\n\u001b[0;32m     51\u001b[0m         \u001b[38;5;66;03m# Print progress\u001b[39;00m\n\u001b[0;32m     52\u001b[0m         \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mepoch\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mepochs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m [D loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00md_loss[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m | D accuracy: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;241m100\u001b[39m\u001b[38;5;241m*\u001b[39md_loss[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m%] [G loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mg_loss\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 55\u001b[0m \u001b[43mtrain_gan\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgenerator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdiscriminator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcombined\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m200\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m64\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_interval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[1;32mIn[25], line 42\u001b[0m, in \u001b[0;36mtrain_gan\u001b[1;34m(generator, discriminator, combined, data, epochs, batch_size, save_interval)\u001b[0m\n\u001b[0;32m     40\u001b[0m     noise \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mnormal(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, (batch_size, \u001b[38;5;241m100\u001b[39m))\n\u001b[0;32m     41\u001b[0m     \u001b[38;5;66;03m# Train the generator (to have the discriminator label samples as valid)\u001b[39;00m\n\u001b[1;32m---> 42\u001b[0m     g_loss \u001b[38;5;241m=\u001b[39m \u001b[43mcombined\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_on_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnoise\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalid\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     43\u001b[0m     g_losses\u001b[38;5;241m.\u001b[39mappend(g_loss)\n\u001b[0;32m     45\u001b[0m g_loss \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmean(g_losses)\n",
+      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py:2383\u001b[0m, in \u001b[0;36mModel.train_on_batch\u001b[1;34m(self, x, y, sample_weight, class_weight, reset_metrics, return_dict)\u001b[0m\n\u001b[0;32m   2380\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_function \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmake_train_function()\n\u001b[0;32m   2381\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_function(iterator)\n\u001b[1;32m-> 2383\u001b[0m logs \u001b[38;5;241m=\u001b[39m \u001b[43mtf_utils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync_to_numpy_or_python_type\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlogs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2384\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_dict:\n\u001b[0;32m   2385\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m logs\n",
+      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\keras\\utils\\tf_utils.py:635\u001b[0m, in \u001b[0;36msync_to_numpy_or_python_type\u001b[1;34m(tensors)\u001b[0m\n\u001b[0;32m    632\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m t\n\u001b[0;32m    633\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m t\u001b[38;5;241m.\u001b[39mitem() \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39mndim(t) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m t\n\u001b[1;32m--> 635\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_structure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_to_single_numpy_or_python_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\tensorflow\\python\\util\\nest.py:917\u001b[0m, in \u001b[0;36mmap_structure\u001b[1;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[0;32m    913\u001b[0m flat_structure \u001b[38;5;241m=\u001b[39m (flatten(s, expand_composites) \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m structure)\n\u001b[0;32m    914\u001b[0m entries \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mflat_structure)\n\u001b[0;32m    916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pack_sequence_as(\n\u001b[1;32m--> 917\u001b[0m     structure[\u001b[38;5;241m0\u001b[39m], [func(\u001b[38;5;241m*\u001b[39mx) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m entries],\n\u001b[0;32m    918\u001b[0m     expand_composites\u001b[38;5;241m=\u001b[39mexpand_composites)\n",
+      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\tensorflow\\python\\util\\nest.py:917\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    913\u001b[0m flat_structure \u001b[38;5;241m=\u001b[39m (flatten(s, expand_composites) \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m structure)\n\u001b[0;32m    914\u001b[0m entries \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mflat_structure)\n\u001b[0;32m    916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pack_sequence_as(\n\u001b[1;32m--> 917\u001b[0m     structure[\u001b[38;5;241m0\u001b[39m], [\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m entries],\n\u001b[0;32m    918\u001b[0m     expand_composites\u001b[38;5;241m=\u001b[39mexpand_composites)\n",
+      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\keras\\utils\\tf_utils.py:628\u001b[0m, in \u001b[0;36msync_to_numpy_or_python_type.<locals>._to_single_numpy_or_python_type\u001b[1;34m(t)\u001b[0m\n\u001b[0;32m    625\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_to_single_numpy_or_python_type\u001b[39m(t):\n\u001b[0;32m    626\u001b[0m     \u001b[38;5;66;03m# Don't turn ragged or sparse tensors to NumPy.\u001b[39;00m\n\u001b[0;32m    627\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(t, tf\u001b[38;5;241m.\u001b[39mTensor):\n\u001b[1;32m--> 628\u001b[0m         t \u001b[38;5;241m=\u001b[39m \u001b[43mt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnumpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    629\u001b[0m     \u001b[38;5;66;03m# Strings, ragged and sparse tensors don't have .item(). Return them\u001b[39;00m\n\u001b[0;32m    630\u001b[0m     \u001b[38;5;66;03m# as-is.\u001b[39;00m\n\u001b[0;32m    631\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(t, (np\u001b[38;5;241m.\u001b[39mndarray, np\u001b[38;5;241m.\u001b[39mgeneric)):\n",
+      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py:1157\u001b[0m, in \u001b[0;36m_EagerTensorBase.numpy\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1134\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Copy of the contents of this Tensor into a NumPy array or scalar.\u001b[39;00m\n\u001b[0;32m   1135\u001b[0m \n\u001b[0;32m   1136\u001b[0m \u001b[38;5;124;03mUnlike NumPy arrays, Tensors are immutable, so this method has to copy\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1154\u001b[0m \u001b[38;5;124;03m    NumPy dtype.\u001b[39;00m\n\u001b[0;32m   1155\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1156\u001b[0m \u001b[38;5;66;03m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[39;00m\n\u001b[1;32m-> 1157\u001b[0m maybe_arr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[0;32m   1158\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m maybe_arr\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(maybe_arr, np\u001b[38;5;241m.\u001b[39mndarray) \u001b[38;5;28;01melse\u001b[39;00m maybe_arr\n",
+      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py:1123\u001b[0m, in \u001b[0;36m_EagerTensorBase._numpy\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1121\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_numpy\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m   1122\u001b[0m   \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1123\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_numpy_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1124\u001b[0m   \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[0;32m   1125\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_status_to_exception(e) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "def add_label_noise(labels, noise_factor=0.05):\n",
+    "    \"\"\"Adds random noise to the labels.\"\"\"\n",
+    "    noise = tf.random.uniform(shape=labels.shape, minval=-noise_factor, maxval=noise_factor)\n",
+    "    return tf.clip_by_value(labels + noise, 0.0, 1.0)\n",
+    "\n",
+    "\n",
+    "def train_gan(generator, discriminator, combined, data, epochs, batch_size=128, save_interval=50):\n",
+    "    # Get the number of batches to cover all data\n",
+    "    num_batches = len(data) // batch_size\n",
+    "\n",
+    "    # Adversarial ground truths\n",
+    "    valid = np.ones((batch_size, 1))\n",
+    "    fake = np.zeros((batch_size, 1))\n",
+    "\n",
+    "    for epoch in range(epochs):\n",
+    "\n",
+    "        # Shuffle data at the beginning of each epoch\n",
+    "        np.random.shuffle(data)\n",
+    "\n",
+    "        for batch in range(num_batches):\n",
+    "            # Get the current batch of real images\n",
+    "            imgs = data[batch * batch_size: (batch + 1) * batch_size]\n",
+    "\n",
+    "            # Generate batch of random noise inputs\n",
+    "            noise = np.random.normal(0, 1, (batch_size, 100))\n",
+    "\n",
+    "            # Generate a batch of new images\n",
+    "            gen_imgs = generator.predict(noise)\n",
+    "\n",
+    "            #  Train Discriminator\n",
+    "\n",
+    "            # Train the discriminator\n",
+    "            real_loss = discriminator.train_on_batch(imgs, add_label_noise(valid))\n",
+    "            fake_loss = discriminator.train_on_batch(gen_imgs, add_label_noise(fake))\n",
+    "            d_loss = 0.5 * np.add(real_loss, fake_loss)\n",
+    "\n",
+    "            #  Train Generator (multiple times for every discriminator training step)\n",
+    "            g_losses = []\n",
+    "            for _ in range(2):  # Train generator two times per discriminator step\n",
+    "                noise = np.random.normal(0, 1, (batch_size, 100))\n",
+    "                # Train the generator (to have the discriminator label samples as valid)\n",
+    "                g_loss = combined.train_on_batch(noise, valid)\n",
+    "                g_losses.append(g_loss)\n",
+    "\n",
+    "            g_loss = np.mean(g_losses)\n",
+    "\n",
+    "            # Save generated image samples at save_interval\n",
+    "            if epoch % save_interval == 0:\n",
+    "                save_generated_samples_single(epoch, generator)\n",
+    "\n",
+    "        # Print progress\n",
+    "        print(f\"{epoch}/{epochs} [D loss: {d_loss[0]} | D accuracy: {100*d_loss[1]:.2f}%] [G loss: {g_loss}]\")\n",
+    "\n",
+    "\n",
+    "train_gan(generator, discriminator, combined, x_train, epochs=200, batch_size=64, save_interval=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5137df3-0947-4bb5-b1f1-fed44d53300b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Predict on noise and generate a visualizable sample\n",
+    "\n",
+    "# Generate random noise samples\n",
+    "noise = np.random.normal(0, 1, (1, 100))\n",
+    "\n",
+    "# Generate images\n",
+    "generated_imgs = generator.predict(noise)\n",
+    "\n",
+    "# Rescale images from [-1, 1] to [0, 1]\n",
+    "# generated_imgs = 0.5 * generated_imgs + 0.5\n",
+    "\n",
+    "generated_imgs = np.squeeze(generated_imgs, axis=0)\n",
+    "\n",
+    "plt.imshow(generated_imgs, cmap='gray')\n",
+    "plt.colorbar()\n",
+    "plt.show()\n",
+    "\n",
+    "print(np.max(generated_imgs))\n",
+    "print(np.min(generated_imgs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9300727f-f72b-4bf2-b20a-9c1b69eb53eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.applications.inception_v3 import InceptionV3, preprocess_input\n",
+    "\n",
+    "def compute_fid(model, real_images, generated_images):\n",
+    "    \"\"\"\n",
+    "    Compute FID score between real and generated images.\n",
+    "    \n",
+    "    Parameters:\n",
+    "    - model: The model (e.g., InceptionV3) to extract features\n",
+    "    - real_images: Numpy array of real images\n",
+    "    - generated_images: Numpy array of generated images\n",
+    "    \n",
+    "    Returns:\n",
+    "    - FID score\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Ensure the real and generated images have the same number of samples\n",
+    "    assert real_images.shape[0] == generated_images.shape[0], \"Number of real and generated images should be the same.\"\n",
+    "    \n",
+    "    # Preprocess images for the chosen model\n",
+    "    real_images = preprocess_input(real_images)\n",
+    "    generated_images = preprocess_input(generated_images)\n",
+    "    \n",
+    "    # Compute activations for real and generated images\n",
+    "    real_activations = model.predict(real_images)\n",
+    "    generated_activations = model.predict(generated_images)\n",
+    "    \n",
+    "    # Compute mean and covariance for real images\n",
+    "    mu_real = np.mean(real_activations, axis=0)\n",
+    "    sigma_real = np.cov(real_activations, rowvar=False)\n",
+    "    \n",
+    "    # Compute mean and covariance for generated images\n",
+    "    mu_gen = np.mean(generated_activations, axis=0)\n",
+    "    sigma_gen = np.cov(generated_activations, rowvar=False)\n",
+    "    \n",
+    "    # Compute sum of squared differences between means\n",
+    "    ssdiff = np.sum((mu_real - mu_gen) ** 2.0)\n",
+    "    \n",
+    "    # Compute sqrt of product between covariances\n",
+    "    cov_mean = sqrtm(sigma_real.dot(sigma_gen))\n",
+    "    \n",
+    "    # Check for imaginary numbers in sqrt of product of covariances\n",
+    "    if np.iscomplexobj(cov_mean):\n",
+    "        cov_mean = cov_mean.real\n",
+    "    \n",
+    "    # Compute FID score\n",
+    "    fid = ssdiff + np.trace(sigma_real + sigma_gen - 2.0 * cov_mean)\n",
+    "    return fid\n",
+    "\n",
+    "def grayscale_to_rgb(images):\n",
+    "    #Convert grayscale images to RGB by repeating the grayscale channel.\n",
+    "    return np.repeat(images, 3, axis=-1)\n",
+    "\n",
+    "\n",
+    "def generate_images(generator, num_images, noise_dim=100):\n",
+    "    noise = np.random.normal(0, 1, (num_images, noise_dim))\n",
+    "    generated_images = generator.predict(noise)\n",
+    "    return generated_images\n",
+    "\n",
+    "def preprocess_images_for_inception(images):\n",
+    "    # Convert grayscale to RGB\n",
+    "    images_rgb = grayscale_to_rgb(images)\n",
+    "    # Resize images\n",
+    "    images_resized = resize(images_rgb, (299, 299))\n",
+    "    return images_resized\n",
+    "\n",
+    "# Load the GAN's generator model\n",
+    "generator = generator\n",
+    "\n",
+    "# Load InceptionV3 model without classification layer\n",
+    "model = InceptionV3(include_top=False, pooling='avg', input_shape=(299, 299, 3))\n",
+    "\n",
+    "num_images = 100  # number of images to evaluate with\n",
+    "generated_images = generate_images(generator, num_images)\n",
+    "generated_images = preprocess_images_for_inception(generated_images)\n",
+    "\n",
+    "real_images = x_test[:num_images]\n",
+    "real_images = preprocess_images_for_inception(real_images)\n",
+    "\n",
+    "fid_score = compute_fid(model, real_images, generated_images)\n",
+    "print(f\"FID Score: {fid_score}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9869f937-30d5-4cee-b25b-8bb0bafe8ac0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Saving and loading the models\n",
+    "\n",
+    "#generator.save(location...)\n",
+    "#discriminator.save(location...)\n",
+    "\n",
+    "#loaded_generator = load_model('generator_model.h5', compile=True)\n",
+    "#loaded_discriminator = load_model('discriminator_model.h5', compile=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a9ca4fd-c85e-4193-b6e7-8f13dbc01a7d",
+   "metadata": {},
+   "source": [
+    "### Functions for perceptual loss (not used)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0eb15319-ca50-4ecb-aad9-35fcf73cb1dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract features from autoencoder and CNN\n",
+    "def feature_extractor(model, layer_name):\n",
+    "    return Model(inputs=model.input, outputs=model.get_layer(layer_name).output)\n",
+    "\n",
+    "def perceptual_loss_autoencoder(autoencoder, layer_name):\n",
+    "    \"\"\"\n",
+    "    Return a perceptual loss function based on the output of the layer_name in the autoencoder.\n",
+    "    \"\"\"\n",
+    "    # Create a model that will return the activations of layer_name\n",
+    "    perceptual_model = Model(inputs=autoencoder.input,\n",
+    "                             outputs=autoencoder.get_layer(layer_name).output)\n",
+    "    mse = MeanSquaredError()\n",
+    "    \n",
+    "    def loss(y_true, y_pred):\n",
+    "        return mse(perceptual_model(y_true), perceptual_model(y_pred))\n",
+    "    \n",
+    "    return loss\n",
+    "\n",
+    "# Compute perceptual loss\n",
+    "def perceptual_loss_autoencoder_and_cnn(y_true, y_pred):\n",
+    "    y_true_features_ae = encoder(y_true)\n",
+    "    y_pred_features_ae = encoder(y_pred)\n",
+    "    \n",
+    "    y_true_features_cnn = cnn_features(y_true)\n",
+    "    y_pred_features_cnn = cnn_features(y_pred)\n",
+    "\n",
+    "    loss_ae = K.mean(K.square(y_true_features_ae - y_pred_features_ae))\n",
+    "    loss_cnn = K.mean(K.square(y_true_features_cnn - y_pred_features_cnn))\n",
+    "    \n",
+    "    # Weighted sum of the two losses. Weights can be adjusted if required.\n",
+    "    return 0.5 * loss_ae + 0.5 * loss_cnn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54a93a1e-f85d-47cb-96d2-2608f551b954",
+   "metadata": {},
+   "source": [
+    "### Monolithic GANs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad71ae8f-3512-4c84-844f-9f656f285b86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generator\n",
+    "def build_generator():\n",
+    "    noise_dim = 100  # Size of the noise vector\n",
+    "\n",
+    "    input_noise = Input(shape=(noise_dim,))\n",
+    "    x = Dense(128 * 64 * 64)(input_noise)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = LeakyReLU(alpha=0.2)(x)\n",
+    "    x = Reshape((64, 64, 128))(x)\n",
+    "\n",
+    "    x = Conv2DTranspose(128, kernel_size=4, strides=2, padding=\"same\")(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = LeakyReLU(alpha=0.2)(x)\n",
+    "\n",
+    "    x = Conv2DTranspose(64, kernel_size=4, strides=2, padding=\"same\")(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = LeakyReLU(alpha=0.2)(x)\n",
+    "\n",
+    "    x = Conv2DTranspose(1, kernel_size=4, strides=1, padding=\"same\", activation=\"tanh\")(x)  # 3 channels\n",
+    "\n",
+    "    generator = Model(input_noise, x, name=\"generator\")\n",
+    "    return generator\n",
+    "\n",
+    "# Discriminator\n",
+    "def build_discriminator():\n",
+    "    img_shape = (256, 256, 1)  # Input image shape\n",
+    "\n",
+    "    input_img = Input(shape=img_shape)\n",
+    "    x = Conv2D(64, kernel_size=4, strides=2, padding=\"same\")(input_img)\n",
+    "    x = LeakyReLU(alpha=0.2)(x)\n",
+    "\n",
+    "    x = Conv2D(128, kernel_size=4, strides=2, padding=\"same\")(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = LeakyReLU(alpha=0.2)(x)\n",
+    "\n",
+    "    x = Conv2D(256, kernel_size=4, strides=2, padding=\"same\")(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = LeakyReLU(alpha=0.2)(x)\n",
+    "\n",
+    "    x = Flatten()(x)\n",
+    "    x = Dense(1, activation=\"sigmoid\")(x)\n",
+    "\n",
+    "    discriminator = Model(input_img, x, name=\"discriminator\")\n",
+    "    return discriminator\n",
+    "\n",
+    "# Generator\n",
+    "def build_generator_drop_reg(dropout=0, regularization=0):\n",
+    "    noise_dim = 100  # Size of the noise vector\n",
+    "\n",
+    "    input_noise = Input(shape=(noise_dim,))\n",
+    "    x = Dense(256 * 64 * 64)(input_noise)\n",
+    "    x = Dropout(dropout)(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = LeakyReLU(alpha=0.2)(x)\n",
+    "    x = Reshape((64, 64, 256))(x)\n",
+    "\n",
+    "\n",
+    "    x = Conv2DTranspose(128, kernel_size=4, strides=2, padding=\"same\", kernel_regularizer=l1(regularization))(x)\n",
+    "    x = Dropout(dropout)(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = LeakyReLU(alpha=0.2)(x)\n",
+    "\n",
+    "    x = Conv2DTranspose(64, kernel_size=4, strides=2, padding=\"same\", kernel_regularizer=l1(regularization))(x)\n",
+    "    x = Dropout(dropout)(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = LeakyReLU(alpha=0.2)(x)\n",
+    "\n",
+    "    x = Conv2DTranspose(1, kernel_size=4, strides=1, padding=\"same\", activation=\"sigmoid\")(x)  # 3 channels\n",
+    "\n",
+    "    generator = Model(input_noise, x, name=\"generator\")\n",
+    "    return generator\n",
+    "\n",
+    "# Discriminator\n",
+    "def build_discriminator_drop_reg(dropout=0, regularization=0):\n",
+    "    img_shape = (256, 256, 1)  # Input image shape\n",
+    "\n",
+    "    input_img = Input(shape=img_shape)\n",
+    "    x = Conv2D(64, kernel_size=4, strides=2, padding=\"same\", kernel_regularizer=l1(regularization))(input_img)\n",
+    "    x = Dropout(dropout)(x)\n",
+    "    x = LeakyReLU(alpha=0.2)(x)\n",
+    "\n",
+    "    x = Conv2D(128, kernel_size=4, strides=2, padding=\"same\", kernel_regularizer=l1(regularization))(x)\n",
+    "    x = Dropout(dropout)(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = LeakyReLU(alpha=0.2)(x)\n",
+    "\n",
+    "    x = Conv2D(256, kernel_size=4, strides=2, padding=\"same\", kernel_regularizer=l1(regularization))(x)\n",
+    "    x = Dropout(dropout)(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = LeakyReLU(alpha=0.2)(x)\n",
+    "\n",
+    "    x = Conv2D(512, kernel_size=4, strides=2, padding=\"same\", kernel_regularizer=l1(regularization))(x)\n",
+    "    x = Dropout(dropout)(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = LeakyReLU(alpha=0.2)(x)\n",
+    "\n",
+    "    discriminator = Model(input_img, x, name=\"discriminator\")\n",
+    "    return discriminator          "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad974d10-d8b2-4d88-a56e-5118dc59b1d9",
+   "metadata": {},
+   "source": [
+    "## CNN for GAN's perceptual loss to replace VGG model / Fréchet (or Kernel) inception distance to replace Inception v3 model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9f84b0f3-c4b6-4062-b88b-2c31e6d7d14d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate labels\n",
+    "np_AEM = np.array(augmented_AEM_images)\n",
+    "np_Radio = np.array(augmented_Radiometric_images)\n",
+    "\n",
+    "labels_class1 = np.zeros((np_AEM.shape[0], 1))\n",
+    "labels_class2 = np.ones((np_Radio.shape[0], 1))\n",
+    "\n",
+    "# Concatenate data and labels\n",
+    "#all_images = tf.concat([augmented_AEM_images, augmented_Radiometric_images], axis=0)\n",
+    "#all_labels = tf.concat([labels_class1, labels_class2], axis=0)\n",
+    "\n",
+    "X = np.vstack((np_AEM, np_Radio))\n",
+    "Y = np.vstack((labels_class1, labels_class2))\n",
+    "\n",
+    "# Shuffle and split data into training and testing\n",
+    "\n",
+    "X = np.array(X)\n",
+    "X = X.astype('float32') / 255."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "394380a9-628f-47a8-bd70-6b1c4fec529c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removed  1059\n"
+     ]
+    }
+   ],
+   "source": [
+    "def delete_null_or_1_value_images_and_labels(data, labels):\n",
+    "    #or len(np.unique(combined_data[i,:,:,1])) == 1 or len(np.unique(combined_data[i,:,:,2])) == 1 or len(np.unique(combined_data[i])) == 1:\n",
+    "    removed = 0\n",
+    "    indices_to_remove = [i for i in range(len(data)) if len(np.unique(data[i])) <= 2]\n",
+    "    removed = len(indices_to_remove)\n",
+    "    X = np.delete(data, indices_to_remove, axis=0)\n",
+    "    Y = np.delete(labels, indices_to_remove, axis=0)\n",
+    "\n",
+    "    print(\"Removed \", removed)    \n",
+    "\n",
+    "    return X, Y\n",
+    "\n",
+    "X, Y = delete_null_or_1_value_images_and_labels(X, Y)\n",
+    "\n",
+    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.05, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "66f33af7-5e29-49c8-bd5a-b682b7e49c32",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3341, 64, 64) (176, 64, 64) (3341, 1) (176, 1)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)\n",
+    "\n",
+    "# 3. Visualize the images side by side\n",
+    "\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "\n",
+    "j = 1\n",
+    "\n",
+    "plt.subplot(1, 3, 1)\n",
+    "plt.imshow(x_train[j], cmap='gray')\n",
+    "plt.title('x1_train')\n",
+    "\n",
+    "plt.subplot(1, 3, 2)\n",
+    "plt.imshow(x_train[j+1, :, :], cmap='gray')\n",
+    "plt.title('x2_train')\n",
+    "\n",
+    "plt.subplot(1, 3, 3)\n",
+    "plt.imshow(x_train[j+2, :, :], cmap='gray')\n",
+    "plt.title('y_train')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d0997547-2a72-41cf-9079-de86fb5fa951",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_classifier():\n",
+    "    model = Sequential()\n",
+    "    \n",
+    "    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 1)))\n",
+    "    model.add(MaxPooling2D((2, 2)))\n",
+    "    \n",
+    "    model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+    "    model.add(MaxPooling2D((2, 2)))\n",
+    "    \n",
+    "    model.add(Conv2D(128, (3, 3), activation='relu'))\n",
+    "    model.add(MaxPooling2D((2, 2)))\n",
+    "    \n",
+    "    model.add(Flatten())\n",
+    "    model.add(Dense(128, activation='relu'))\n",
+    "    model.add(Dropout(0.5))\n",
+    "    model.add(Dense(1, activation='sigmoid'))  # Binary classification\n",
+    "    \n",
+    "    return model\n",
+    "\n",
+    "classifier = build_classifier()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b0cded50-ba16-4dcd-b3ba-0e0abf5e037c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "105/105 [==============================] - 1s 5ms/step - loss: 0.6785 - accuracy: 0.5609 - val_loss: 0.6373 - val_accuracy: 0.6023\n",
+      "Epoch 2/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.6056 - accuracy: 0.6660 - val_loss: 0.5808 - val_accuracy: 0.6591\n",
+      "Epoch 3/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.5324 - accuracy: 0.7240 - val_loss: 0.4743 - val_accuracy: 0.7841\n",
+      "Epoch 4/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.4688 - accuracy: 0.7641 - val_loss: 0.4355 - val_accuracy: 0.8295\n",
+      "Epoch 5/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.4497 - accuracy: 0.7800 - val_loss: 0.4529 - val_accuracy: 0.7670\n",
+      "Epoch 6/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.4167 - accuracy: 0.8022 - val_loss: 0.4674 - val_accuracy: 0.7784\n",
+      "Epoch 7/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.4025 - accuracy: 0.8066 - val_loss: 0.3961 - val_accuracy: 0.8239\n",
+      "Epoch 8/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.3874 - accuracy: 0.8201 - val_loss: 0.3974 - val_accuracy: 0.8295\n",
+      "Epoch 9/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.3655 - accuracy: 0.8366 - val_loss: 0.4039 - val_accuracy: 0.8182\n",
+      "Epoch 10/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.3404 - accuracy: 0.8485 - val_loss: 0.9266 - val_accuracy: 0.6080\n",
+      "Epoch 11/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.3411 - accuracy: 0.8420 - val_loss: 0.3896 - val_accuracy: 0.8239\n",
+      "Epoch 12/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.3347 - accuracy: 0.8441 - val_loss: 0.4884 - val_accuracy: 0.7898\n",
+      "Epoch 13/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.3068 - accuracy: 0.8656 - val_loss: 0.3979 - val_accuracy: 0.8239\n",
+      "Epoch 14/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.2966 - accuracy: 0.8710 - val_loss: 0.3538 - val_accuracy: 0.8466\n",
+      "Epoch 15/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.2671 - accuracy: 0.8893 - val_loss: 0.6185 - val_accuracy: 0.7557\n",
+      "Epoch 16/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.2574 - accuracy: 0.8940 - val_loss: 0.3482 - val_accuracy: 0.8580\n",
+      "Epoch 17/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.2474 - accuracy: 0.8985 - val_loss: 0.5551 - val_accuracy: 0.7784\n",
+      "Epoch 18/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.2362 - accuracy: 0.9021 - val_loss: 0.7041 - val_accuracy: 0.6818\n",
+      "Epoch 19/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.2153 - accuracy: 0.9087 - val_loss: 0.4334 - val_accuracy: 0.8409\n",
+      "Epoch 20/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.1873 - accuracy: 0.9348 - val_loss: 0.3361 - val_accuracy: 0.8636\n",
+      "Epoch 21/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.1815 - accuracy: 0.9297 - val_loss: 0.3726 - val_accuracy: 0.8466\n",
+      "Epoch 22/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.1716 - accuracy: 0.9333 - val_loss: 0.7783 - val_accuracy: 0.6875\n",
+      "Epoch 23/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.1583 - accuracy: 0.9425 - val_loss: 0.6831 - val_accuracy: 0.7386\n",
+      "Epoch 24/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.1526 - accuracy: 0.9431 - val_loss: 0.4286 - val_accuracy: 0.8352\n",
+      "Epoch 25/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.1218 - accuracy: 0.9572 - val_loss: 0.3370 - val_accuracy: 0.8864\n",
+      "Epoch 26/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.1199 - accuracy: 0.9551 - val_loss: 0.4179 - val_accuracy: 0.8523\n",
+      "Epoch 27/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.1143 - accuracy: 0.9599 - val_loss: 0.4163 - val_accuracy: 0.8580\n",
+      "Epoch 28/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0916 - accuracy: 0.9731 - val_loss: 0.8808 - val_accuracy: 0.7102\n",
+      "Epoch 29/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0863 - accuracy: 0.9740 - val_loss: 0.3811 - val_accuracy: 0.9034\n",
+      "Epoch 30/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0788 - accuracy: 0.9749 - val_loss: 0.5671 - val_accuracy: 0.8295\n",
+      "Epoch 31/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0637 - accuracy: 0.9835 - val_loss: 0.4025 - val_accuracy: 0.8920\n",
+      "Epoch 32/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0586 - accuracy: 0.9826 - val_loss: 0.7033 - val_accuracy: 0.7898\n",
+      "Epoch 33/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0485 - accuracy: 0.9865 - val_loss: 0.3979 - val_accuracy: 0.9034\n",
+      "Epoch 34/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0494 - accuracy: 0.9865 - val_loss: 0.4557 - val_accuracy: 0.8920\n",
+      "Epoch 35/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0384 - accuracy: 0.9925 - val_loss: 0.3854 - val_accuracy: 0.8750\n",
+      "Epoch 36/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0345 - accuracy: 0.9931 - val_loss: 0.5093 - val_accuracy: 0.8807\n",
+      "Epoch 37/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0346 - accuracy: 0.9922 - val_loss: 0.6248 - val_accuracy: 0.8523\n",
+      "Epoch 38/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0398 - accuracy: 0.9898 - val_loss: 0.4943 - val_accuracy: 0.8864\n",
+      "Epoch 39/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0251 - accuracy: 0.9955 - val_loss: 0.5710 - val_accuracy: 0.8580\n",
+      "Epoch 40/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0176 - accuracy: 0.9991 - val_loss: 0.7498 - val_accuracy: 0.8352\n",
+      "Epoch 41/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0191 - accuracy: 0.9973 - val_loss: 0.4459 - val_accuracy: 0.9034\n",
+      "Epoch 42/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0200 - accuracy: 0.9961 - val_loss: 0.5351 - val_accuracy: 0.8864\n",
+      "Epoch 43/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0160 - accuracy: 0.9979 - val_loss: 0.9214 - val_accuracy: 0.7955\n",
+      "Epoch 44/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0159 - accuracy: 0.9982 - val_loss: 0.6224 - val_accuracy: 0.8807\n",
+      "Epoch 45/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.1122 - accuracy: 0.9761 - val_loss: 0.5152 - val_accuracy: 0.8920\n",
+      "Epoch 46/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0150 - accuracy: 0.9988 - val_loss: 0.5344 - val_accuracy: 0.9148\n",
+      "Epoch 47/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0109 - accuracy: 0.9994 - val_loss: 0.4989 - val_accuracy: 0.9148\n",
+      "Epoch 48/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0096 - accuracy: 0.9991 - val_loss: 0.5961 - val_accuracy: 0.9034\n",
+      "Epoch 49/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0063 - accuracy: 1.0000 - val_loss: 0.5110 - val_accuracy: 0.9148\n",
+      "Epoch 50/50\n",
+      "105/105 [==============================] - 0s 4ms/step - loss: 0.0071 - accuracy: 0.9991 - val_loss: 0.5639 - val_accuracy: 0.9148\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.optimizers import Adam\n",
+    "\n",
+    "optimizer = Adam(learning_rate=0.0002, beta_1=0.5)\n",
+    "classifier.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
+    "\n",
+    "history = classifier.fit(x_train, y_train, batch_size=32, epochs=50, validation_data=[x_test, y_test])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "e232e756-52aa-472c-9a0f-7b5db9fb0026",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Layer name: conv2d_5\n",
+      "Layer name: max_pooling2d_2\n",
+      "Layer name: conv2d_6\n",
+      "Layer name: max_pooling2d_3\n",
+      "Layer name: conv2d_7\n",
+      "Layer name: max_pooling2d_4\n",
+      "Layer name: flatten\n",
+      "Layer name: dense\n",
+      "Layer name: dropout\n",
+      "Layer name: dense_1\n",
+      "\n",
+      "\n",
+      "Layer name: input_1\n",
+      "Layer name: conv2d\n",
+      "Layer name: max_pooling2d\n",
+      "Layer name: conv2d_1\n",
+      "Layer name: max_pooling2d_1\n",
+      "Layer name: conv2d_2\n",
+      "Layer name: up_sampling2d\n",
+      "Layer name: conv2d_3\n",
+      "Layer name: up_sampling2d_1\n",
+      "Layer name: conv2d_4\n"
+     ]
+    }
+   ],
+   "source": [
+    "# use the last convolutional layer's output as the feature layer (subject to change)\n",
+    "#feature_extractor = Model(inputs=classifier.input, outputs=classifier.layers[4].output)\n",
+    "\n",
+    "# list layer names for selection\n",
+    "for layer in classifier.layers:\n",
+    "    print('Layer name:', layer.name)\n",
+    "\n",
+    "print('\\n')\n",
+    "\n",
+    "for layer in autoencoder.layers: \n",
+    "    print('Layer name:', layer.name)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.17"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/autoencoder_convolutional_geophysical.ipynb b/notebooks/autoencoder_convolutional_geophysical.ipynb
new file mode 100755
index 00000000..f8b37563
--- /dev/null
+++ b/notebooks/autoencoder_convolutional_geophysical.ipynb
@@ -0,0 +1,1691 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0b81422b-ec75-48ad-a51e-22fd2ed4f67f",
+   "metadata": {},
+   "source": [
+    "## Data preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a637bb4-ed80-4be3-9323-3f7412a1af9d",
+   "metadata": {},
+   "source": [
+    "### Loading libraries and defining functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "e7853fb5-58bd-4f75-b639-6ed55a6cfe56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import os\n",
+    "from skimage.io import imread, imsave\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.layers import Input, Layer, Conv2D, MaxPooling2D, UpSampling2D, Dense, Dropout, Lambda, Flatten, Reshape, Conv2DTranspose, Activation, concatenate, Multiply, Add, BatchNormalization\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "from keras.models import Model\n",
+    "from tensorflow.keras.callbacks import LearningRateScheduler\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from keras.callbacks import EarlyStopping\n",
+    "from sklearn.manifold import TSNE\n",
+    "import keras\n",
+    "from keras.metrics import binary_crossentropy \n",
+    "from keras import backend as K\n",
+    "from skimage.metrics import structural_similarity as ssim\n",
+    "from skimage import exposure\n",
+    "from keras.regularizers import l1, l2\n",
+    "from keras.losses import mean_squared_error\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "\"\"\"Different types of normalizers for variable data shapes, including data with very small (-1e+30) NaN values\"\"\"\n",
+    "\n",
+    "def normalize_tile_z(tile):\n",
+    "    \"\"\"Normalize the tile using its mean and standard deviation.\"\"\"\n",
+    "    mean = np.nanmean(tile)\n",
+    "    std = np.nanstd(tile)\n",
+    "    \n",
+    "    # Return the standardized tile\n",
+    "    return (tile - mean) / (std + 1e-7)\n",
+    "\n",
+    "def normalize_tile_one(tile):\n",
+    "    valid_mask = ~np.isnan(tile)\n",
+    "    \n",
+    "    # Check if there's any valid data in the tile\n",
+    "    if np.any(valid_mask):\n",
+    "        min_val = np.min(tile[valid_mask])\n",
+    "        max_val = np.max(tile[valid_mask])\n",
+    "        \n",
+    "        if max_val != min_val:\n",
+    "            tile[valid_mask] = (tile[valid_mask] - min_val) / (max_val - min_val)\n",
+    "        else:\n",
+    "            tile[valid_mask] = 0.0  # If max equals min, set to 0\n",
+    "    return tile\n",
+    "\n",
+    "def normalize_tile_adaptive(tile, method=\"minmax\"):\n",
+    "    if method == \"standard\":\n",
+    "        mean = np.nanmean(tile)\n",
+    "        std = np.nanstd(tile)\n",
+    "        return (tile - mean) / (std + 1e-7)\n",
+    "    elif method == \"minmax\":\n",
+    "        min_val = np.nanmin(tile)\n",
+    "        max_val = np.nanmax(tile)\n",
+    "        return (tile - min_val) / (max_val - min_val)\n",
+    "\n",
+    "def normalize_tile_clipping(tile):\n",
+    "    \"\"\"Normalize the tile using its mean and standard deviation.\"\"\"\n",
+    "    # Clip the tile values\n",
+    "    lower_threshold = np.nanpercentile(tile, 1)\n",
+    "    upper_threshold = np.nanpercentile(tile, 99)\n",
+    "    tile = np.clip(tile, lower_threshold, upper_threshold)\n",
+    "    \n",
+    "    # Normalize the tile\n",
+    "    mean = np.nanmean(tile)\n",
+    "    std = np.nanstd(tile)\n",
+    "    \n",
+    "    return (tile - mean) / (std + 1e-7)\n",
+    "\n",
+    "def normalize_tile_clipping_255(tile):\n",
+    "    \"\"\"Normalize the tile using its mean and standard deviation and clip it based on the range [mean-2*std, mean+2*std].\"\"\"\n",
+    "\n",
+    "    mean_tile = np.nanmean(tile)\n",
+    "    std_tile = np.nanstd(tile)\n",
+    "\n",
+    "    # Clipping the tile based on the standard deviation and mean\n",
+    "    np.clip(tile, mean_tile - 2 * std_tile, mean_tile + 2 * std_tile, out=tile)\n",
+    "\n",
+    "    # Normalize to 0-255\n",
+    "    tile = ((tile - np.nanmin(tile)) / (np.nanmax(tile) - np.nanmin(tile))) * 255\n",
+    "\n",
+    "    return tile.astype(np.uint8)\n",
+    "\n",
+    "def adaptive_clip_and_scale(tile, factor=2):\n",
+    "    \"\"\"\n",
+    "    Perform adaptive clipping and scaling of the tile.\n",
+    "\n",
+    "    Parameters:\n",
+    "    - tile: The image tile to be processed.\n",
+    "    - factor: Multiplier for std deviation to compute clipping bounds.\n",
+    "\n",
+    "    Returns:\n",
+    "    - Processed tile with values between 0 and 255.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # If the tile consists only of NaNs, return an all-zero tile.\n",
+    "    if np.all(np.isnan(tile)):\n",
+    "        return np.zeros_like(tile, dtype=np.uint8)\n",
+    "\n",
+    "    # Calculate local statistics\n",
+    "    mean = np.nanmean(tile)\n",
+    "    std = np.nanstd(tile)\n",
+    "\n",
+    "    # Check if the standard deviation is 0, if so return a tile filled with mean value scaled to [0,255]\n",
+    "    if std == 0:\n",
+    "        scaled_mean = ((mean - np.nanmin(tile)) / (np.nanmax(tile) - np.nanmin(tile)) * 255).astype(np.uint8)\n",
+    "        return np.full_like(tile, scaled_mean, dtype=np.uint8)\n",
+    "\n",
+    "    # Define bounds for clipping\n",
+    "    lower_bound = mean - factor * std\n",
+    "    upper_bound = mean + factor * std\n",
+    "\n",
+    "    # Clip values\n",
+    "    tile_clipped = np.clip(tile, lower_bound, upper_bound)\n",
+    "\n",
+    "    # Scale values to [0, 255]\n",
+    "    min_val = np.min(tile_clipped)\n",
+    "    max_val = np.max(tile_clipped)\n",
+    "    \n",
+    "    # Check if after clipping, max is less than or equal to min\n",
+    "    if max_val <= min_val:\n",
+    "        return np.full_like(tile, 127, dtype=np.uint8)  # Return a gray tile\n",
+    "\n",
+    "    tile_scaled_0_1 = (tile_clipped - min_val) / (max_val - min_val)\n",
+    "    tile_scaled_0_255 = (tile_scaled_0_1 * 255).astype(np.uint8)\n",
+    "\n",
+    "    return tile_scaled_0_255\n",
+    "\n",
+    "def clip_and_scale(tile, global_mean, global_std, factor=2):\n",
+    "    \"\"\"\n",
+    "    Perform clipping based on global statistics and scale the tile.\n",
+    "\n",
+    "    Parameters:\n",
+    "    - tile: The image tile to be processed.\n",
+    "    - global_mean: Mean of the entire dataset.\n",
+    "    - global_std: Standard deviation of the entire dataset.\n",
+    "    - factor: Multiplier for std deviation to compute clipping bounds.\n",
+    "\n",
+    "    Returns:\n",
+    "    - Processed tile with values between 0 and 255.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Define bounds for clipping based on global statistics\n",
+    "    lower_bound = global_mean - factor * global_std\n",
+    "    upper_bound = global_mean + factor * global_std\n",
+    "\n",
+    "    # Clip values\n",
+    "    tile_clipped = np.clip(tile, lower_bound, upper_bound)\n",
+    "\n",
+    "    # Scale values to [0, 255]\n",
+    "    min_val = np.min(tile_clipped)\n",
+    "    max_val = np.max(tile_clipped)\n",
+    "    \n",
+    "    if max_val <= min_val:\n",
+    "        return np.full_like(tile, 127, dtype=np.uint8)  # Return a gray tile\n",
+    "\n",
+    "    tile_scaled_0_1 = (tile_clipped - min_val) / (max_val - min_val)\n",
+    "    tile_scaled_0_255 = (tile_scaled_0_1 * 255).astype(np.uint8)\n",
+    "\n",
+    "    return tile_scaled_0_255\n",
+    "\n",
+    "def extract_tiles(img, tile_size):\n",
+    "    images_list = []\n",
+    "\n",
+    "    # Calculate the number of tiles needed in both dimensions\n",
+    "    n_tiles_x = img.shape[0] // tile_size\n",
+    "    n_tiles_y = img.shape[1] // tile_size\n",
+    "\n",
+    "    # Pad the image if necessary to extract tiles\n",
+    "    pad_width_x = tile_size - (img.shape[0] % tile_size) if img.shape[0] % tile_size != 0 else 0\n",
+    "    pad_width_y = tile_size - (img.shape[1] % tile_size) if img.shape[1] % tile_size != 0 else 0\n",
+    "\n",
+    "    img = np.pad(img, ((0, pad_width_x), (0, pad_width_y)), mode='constant')\n",
+    "\n",
+    "    for i in range(0, img.shape[0], tile_size):\n",
+    "        for j in range(0, img.shape[1], tile_size):\n",
+    "            tile = img[i:i+tile_size, j:j+tile_size]\n",
+    "            images_list.append(tile)\n",
+    "\n",
+    "    return np.array(images_list)\n",
+    "\n",
+    "\n",
+    "def load_and_tile_images_from_folder(folder_path, tile_size=64):\n",
+    "    images_list = []\n",
+    "    for filename in sorted(os.listdir(folder_path)):  # Sorting ensures images from different folders align properly\n",
+    "        if filename.endswith('.tif'):\n",
+    "            img = imread(os.path.join(folder_path, filename))\n",
+    "            \n",
+    "            images_list.extend(extract_tiles(img, tile_size))\n",
+    "\n",
+    "    return np.stack(images_list, axis=0) #np.array(images_list)\n",
+    "\n",
+    "\n",
+    "datagen = ImageDataGenerator(\n",
+    "    rotation_range=40,\n",
+    "    width_shift_range=0.2,\n",
+    "    height_shift_range=0.2,\n",
+    "    zoom_range=0.2,\n",
+    "    horizontal_flip=True,\n",
+    "    fill_mode='constant',\n",
+    "    cval=0\n",
+    ")\n",
+    "\n",
+    "datagen_adjuste = ImageDataGenerator(\n",
+    "    rotation_range=40,\n",
+    "    width_shift_range=0.2,\n",
+    "    height_shift_range=0.2,\n",
+    "    zoom_range=0.2,\n",
+    "    horizontal_flip=True,\n",
+    "    fill_mode='constant',\n",
+    "    cval=0,\n",
+    "    brightness_range=[0.5, 1.5],  # adjust brightness\n",
+    "    channel_shift_range=0.2  # shift channel values\n",
+    ")\n",
+    "\n",
+    "def augment_images_together(*image_batches):\n",
+    "    # Assuming all image_batches are of the same length, \n",
+    "    # and images in position i of each batch correspond to each other\n",
+    "    augmented_batches = [[] for _ in range(len(image_batches))]\n",
+    "    \n",
+    "    for i in range(len(image_batches[0])):\n",
+    "        combined_image = np.stack([image_batches[j][i] for j in range(len(image_batches))], axis=-1)  # stack along the channel dimension\n",
+    "        augmentation_iterator = datagen.flow(np.expand_dims(combined_image, axis=0), batch_size=1)\n",
+    "        augmented_image = augmentation_iterator[0].squeeze(axis=0)\n",
+    "        \n",
+    "        for j in range(len(image_batches)):\n",
+    "            augmented_batches[j].append(augmented_image[..., j])  # separate the channels\n",
+    "    \n",
+    "    return [np.array(batch) for batch in augmented_batches]\n",
+    "\n",
+    "def handle_missing_values(images):\n",
+    "    \"\"\"Handle NaN and extremely small values in the dataset.\"\"\"\n",
+    "    \n",
+    "    # Check for NaN values\n",
+    "    has_nan = np.isnan(images).any()\n",
+    "    print(f\"Dataset contains NaN values: {has_nan}\")\n",
+    "    images[np.isnan(images)] = 0.0  # replace NaN with 0\n",
+    "    \n",
+    "    # Handle 'no-data' or extremely small values\n",
+    "    no_data_mask = images < -1e+29\n",
+    "    print(f\"Number of 'no-data' values: {np.sum(no_data_mask)}\")\n",
+    "    #images[no_data_mask] = 0.0\n",
+    "    \n",
+    "    mean_val = np.mean(images[~no_data_mask])\n",
+    "    images[no_data_mask] = mean_val\n",
+    "    \n",
+    "    return images\n",
+    "\n",
+    "def replace_nans(image, value=0):\n",
+    "    \"\"\"Replace NaN values in the image with the specified value.\"\"\"\n",
+    "    nan_mask = np.isnan(image)\n",
+    "    image[nan_mask] = value\n",
+    "    return image\n",
+    "\n",
+    "def replace_nans_with_mean(image):\n",
+    "    \"\"\"Replace NaN values in the image with the mean of the non-NaN values.\"\"\"\n",
+    "    nan_mask = np.isnan(image)\n",
+    "    image[nan_mask] = np.nanmean(image)\n",
+    "    return image\n",
+    "\n",
+    "def handle_nans_and_infs(image):\n",
+    "    \"\"\"Replace NaN and Inf values in an image.\"\"\"\n",
+    "    non_finite_mask = ~np.isfinite(image)  # True for NaN and Inf values\n",
+    "    mean_val = np.mean(image[np.isfinite(image)])  # mean of finite values\n",
+    "    image[non_finite_mask] = mean_val\n",
+    "    return image\n",
+    "\n",
+    "def normalize_images_old(images):\n",
+    "    return images.astype('float32') / 255.0\n",
+    "\n",
+    "def remove_nan(img_np):\n",
+    "    # handle null values\n",
+    "    img_np[img_np < -1e+30] = np.nan \n",
+    "\n",
+    "    return img_np\n",
+    "\n",
+    "# To avoid division by zero error, when max_val == min_val\n",
+    "def normalize_tile(tile):\n",
+    "    min_val = np.min(tile)\n",
+    "    max_val = np.max(tile)\n",
+    "    if max_val != min_val:\n",
+    "        tile = ((tile - min_val) / (max_val - min_val)) * 255\n",
+    "    else:\n",
+    "        tile = np.zeros_like(tile)\n",
+    "\n",
+    "    return tile\n",
+    "\n",
+    "def equalize_images(images):\n",
+    "    return exposure.equalize_hist(images) * 255\n",
+    "\n",
+    "def contrast_stretching(image, new_min=0, new_max=1):\n",
+    "    \"\"\"Perform contrast stretching on the given image.\"\"\"\n",
+    "    return (image - np.min(image)) / (np.max(image) - np.min(image)) * (new_max - new_min) + new_min\n",
+    "\n",
+    "def contrast_stretching_consider_uniform_values(image, new_min=0, new_max=1, epsilon=1e-7):\n",
+    "    \"\"\"Perform contrast stretching on the given image.\"\"\"\n",
+    "    min_val = np.min(image)\n",
+    "    max_val = np.max(image)\n",
+    "\n",
+    "    # Check if the max and min values are very close or identical\n",
+    "    if np.isclose(max_val, min_val, atol=epsilon):\n",
+    "        return np.ones_like(image) * new_min  # or any other default behavior\n",
+    "\n",
+    "    return (image - min_val) / (max_val - min_val + epsilon) * (new_max - new_min) + new_min\n",
+    "\n",
+    "\n",
+    "def apply_preprocessing(images_list):\n",
+    "    \"\"\"Apply preprocessing techniques to a list of images.\"\"\"\n",
+    "    processed_images = []\n",
+    "    \n",
+    "    for image in images_list:\n",
+    "        # Handle NaN values\n",
+    "        image = handle_nans_and_infs(image)\n",
+    "        \n",
+    "        # Apply contrast stretching\n",
+    "        image = contrast_stretching_consider_uniform_values(image)\n",
+    "        \n",
+    "        # Apply histogram equalization\n",
+    "        equalized_image = exposure.equalize_hist(image) * 255\n",
+    "        \n",
+    "        processed_images.append(equalized_image)\n",
+    "    \n",
+    "    return processed_images\n",
+    "\n",
+    "def normalize_images(images):\n",
+    "    min_val = np.min(images)\n",
+    "    max_val = np.max(images)\n",
+    "    return (images - min_val) / (max_val - min_val)\n",
+    "\n",
+    "# Create a datagenerator\n",
+    "def create_datagenerator(rotation_range=20, width_shift_range=0.1, eight_shift_range=0.1, zoom_range=0.1, horizontal_flip=True, fill_mode='constant', cval=0,brightness_range=[0.5, 1.5], channel_shift_range=0.2):\n",
+    "    datagen = ImageDataGenerator(\n",
+    "        rotation_range=rotation_range,\n",
+    "        width_shift_range=width_shift_range,\n",
+    "        height_shift_range=height_shift_range,\n",
+    "        zoom_range=zoom_range,\n",
+    "        horizontal_flip=horizontal_flip,\n",
+    "        fill_mode=fill_mode,\n",
+    "        cval=cval,\n",
+    "        brightness_range=brightness_range,  # adjust brightness\n",
+    "        channel_shift_range=channel_shift_range  # shift channel values\n",
+    "    )\n",
+    "    \n",
+    "    return datagen\n",
+    "\n",
+    "def augment_image_dataset(image_list, augment_times=5):\n",
+    "    # Initialize the ImageDataGenerator with the desired augmentations\n",
+    "    datagen = ImageDataGenerator(\n",
+    "        rotation_range=40,\n",
+    "        width_shift_range=0.2,\n",
+    "        height_shift_range=0.2,\n",
+    "        zoom_range=0.2,\n",
+    "        horizontal_flip=True,\n",
+    "        fill_mode='constant',\n",
+    "        cval=0\n",
+    "    )\n",
+    "\n",
+    "    datagen_adjusted = ImageDataGenerator(\n",
+    "        rotation_range=20,\n",
+    "        #width_shift_range=0.1,\n",
+    "        #height_shift_range=0.1,\n",
+    "        #zoom_range=0.1,\n",
+    "        horizontal_flip=True,\n",
+    "        fill_mode='constant',\n",
+    "        cval=0,\n",
+    "        brightness_range=[0.5, 1.5],  # adjust brightness\n",
+    "        channel_shift_range=0.2  # shift channel values\n",
+    "    )\n",
+    "    \n",
+    "    augmented_images = []\n",
+    "\n",
+    "    for image in image_list:\n",
+    "        #augmentation_iterator = datagen.flow(np.expand_dims(image, axis=0), batch_size=1)\n",
+    "        augmentation_iterator = datagen_adjusted.flow(np.expand_dims(image, axis=0), batch_size=1)\n",
+    "        \n",
+    "        for j in range(augment_times):\n",
+    "            augmented_images.append(augmentation_iterator[0].squeeze(axis=0))\n",
+    "\n",
+    "    return np.array(augmented_images)\n",
+    "\n",
+    "# Clip the dataset length to the shortest dataset (recommended to use data augmentation first, to not lose data)\n",
+    "def prepare_multimodal_dataset(augmented_AEM_images, augmented_Gravity_images, augmented_Radiometric_images, augmented_Magnetic_images):\n",
+    "    # Find the smallest length\n",
+    "    min_length = min([len(x) for x in [augmented_AEM_images, augmented_Gravity_images, augmented_Radiometric_images, augmented_Magnetic_images]])\n",
+    "    \n",
+    "    # Trim datasets to match the smallest length\n",
+    "    x1_trimmed = augmented_AEM_images[:min_length]\n",
+    "    x2_trimmed = augmented_Gravity_images[:min_length]\n",
+    "    x3_trimmed = augmented_Radiometric_images[:min_length]\n",
+    "    x4_trimmed = augmented_Magnetic_images[:min_length]\n",
+    "    \n",
+    "    combined_data = np.stack((x1_trimmed, x2_trimmed, x3_trimmed, x4_trimmed), axis=-1)\n",
+    "\n",
+    "    return combined_data\n",
+    "\n",
+    "# Delete failed augmentations or null images, that are extracted from the border of the original image\n",
+    "def delete_null_or_1_value_images(data, min_value=1):\n",
+    "    removed = 0\n",
+    "    indices_to_remove = [i for i in range(len(data)) if len(np.unique(data[i])) <= min_value]\n",
+    "    removed = len(indices_to_remove)\n",
+    "    X = np.delete(data, indices_to_remove, axis=0)\n",
+    "\n",
+    "    print(\"Removed \", removed)    \n",
+    "\n",
+    "    return X"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b684f22-cfc9-436f-b511-7daed9cbb75f",
+   "metadata": {},
+   "source": [
+    "### Load your data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76a3f8c5-bd7e-4ae6-9324-dba0c23e67b5",
+   "metadata": {},
+   "source": [
+    "Here, geophysical images were used (AEM, Gravity, Radiometric, Magnetic)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "c385f84d-b2fc-4eee-b9a5-64803d4df5e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2288, 64, 64)\n",
+      "(9724, 64, 64)\n",
+      "(2288, 64, 64)\n",
+      "(6864, 64, 64)\n",
+      "Dataset contains NaN values: False\n",
+      "Number of 'no-data' values: 1171204\n",
+      "Dataset contains NaN values: False\n",
+      "Number of 'no-data' values: 6623948\n",
+      "Dataset contains NaN values: False\n",
+      "Number of 'no-data' values: 0\n",
+      "Dataset contains NaN values: False\n",
+      "Number of 'no-data' values: 391198\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load all images from each category and stack them\n",
+    "\n",
+    "#AEM_folder = ...\n",
+    "#Gravity_folder = ...\n",
+    "#Radiometric_folder = ... \n",
+    "#Magnetic_folder = ...\n",
+    "\n",
+    "AEM_images = load_and_tile_images_from_folder(AEM_folder, 64)\n",
+    "Gravity_images = load_and_tile_images_from_folder(Gravity_folder, 64)\n",
+    "Radiometric_images = load_and_tile_images_from_folder(Radiometric_folder, 64)\n",
+    "Magnetic_images = load_and_tile_images_from_folder(Magnetic_folder, 64)\n",
+    "\n",
+    "augmented_AEM_images, augmented_Gravity_images, augmented_Radiometric_images, augmented_Magnetic_images = AEM_images, Gravity_images, Radiometric_images, Magnetic_images\n",
+    "\n",
+    "print(augmented_AEM_images.shape)\n",
+    "print(augmented_Gravity_images.shape)\n",
+    "print(augmented_Radiometric_images.shape)\n",
+    "print(augmented_Magnetic_images.shape)\n",
+    "\n",
+    "augmented_AEM_images = handle_missing_values(augmented_AEM_images)\n",
+    "augmented_Gravity_images = handle_missing_values(augmented_Gravity_images)\n",
+    "augmented_Radiometric_images = handle_missing_values(augmented_Radiometric_images)\n",
+    "augmented_Magnetic_images = handle_missing_values(augmented_Magnetic_images)\n",
+    "\n",
+    "augmented_AEM_images = apply_preprocessing(augmented_AEM_images)\n",
+    "augmented_Gravity_images = apply_preprocessing(augmented_Gravity_images)\n",
+    "augmented_Radiometric_images = apply_preprocessing(augmented_Radiometric_images)\n",
+    "augmented_Magnetic_images = apply_preprocessing(augmented_Magnetic_images)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9e3792ee-f2b0-4251-bdee-0d4d64c451e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removed  676\n",
+      "Removed  0\n",
+      "Range for x_train: 0.0, 1.0\n",
+      "Range for x_test: 0.0, 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"Check data range\"\"\"\n",
+    "\n",
+    "# Transform the data to numpy array (if not already, change type to float32, normalize to [0, 1], split the data to train and test sets)\n",
+    "def process_for_training(input, split_size=0.9):\n",
+    "    images = np.array(input)\n",
+    "    images = images.astype('float32') / 255.\n",
+    "    x_train, x_test = train_test_split(images, test_size=1-split_size)\n",
+    "\n",
+    "    return x_train, x_test\n",
+    "\n",
+    "data = np.array(augmented_Radiometric_images)\n",
+    "data = data.reshape(-1, 64, 64, 1)\n",
+    "\n",
+    "data = delete_null_or_1_value_images(data, 3)\n",
+    "\n",
+    "data = augment_image_dataset(data, 3)\n",
+    "\n",
+    "data = delete_null_or_1_value_images(data, 3)\n",
+    "\n",
+    "x_train, x_test = process_for_training(data, 0.95)\n",
+    "\n",
+    "datasets = [x_train, x_test]\n",
+    "dataset_names = [\"x_train\", \"x_test\"]\n",
+    "\n",
+    "for d, name in zip(datasets, dataset_names):\n",
+    "    min_val = np.min(d)\n",
+    "    max_val = np.max(d)\n",
+    "    print(f\"Range for {name}: {min_val}, {max_val}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "048dde14-971a-4d5f-9361-7a0b94fa1b70",
+   "metadata": {},
+   "source": [
+    "### Visualize data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3945e6ef-9da7-4f0e-a578-f937c586ac11",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(4594, 64, 64, 1) (242, 64, 64, 1)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(x_train.shape, x_test.shape)\n",
+    "\n",
+    "# Visualize the images side by side\n",
+    "\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "\n",
+    "j = 66\n",
+    "\n",
+    "plt.subplot(1, 3, 1)\n",
+    "plt.imshow(x_train[j], cmap='gray')\n",
+    "plt.title('x1_train')\n",
+    "\n",
+    "plt.subplot(1, 3, 2)\n",
+    "plt.imshow(x_train[j+1, :, :], cmap='gray')\n",
+    "plt.title('x2_train')\n",
+    "\n",
+    "plt.subplot(1, 3, 3)\n",
+    "plt.imshow(x_train[j+2, :, :], cmap='gray')\n",
+    "plt.title('y_train')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5506bf54-3541-4159-9293-630429fa8540",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Min: 0.0\n",
+      "Max: 0.9882353\n",
+      "Mean: 0.44932503\n",
+      "Standard Deviation: 0.3045543\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def visualize_image(img_data):\n",
+    "    # Load the image using raster.io\n",
+    "    \"\"\"with rasterio.open(image_path, 'r') as src:\n",
+    "        img_data = src.read(1)\"\"\"\n",
+    "    \n",
+    "    # Print basic statistics\n",
+    "    print(\"Min:\", np.nanmin(img_data))\n",
+    "    print(\"Max:\", np.nanmax(img_data))\n",
+    "    print(\"Mean:\", np.nanmean(img_data))\n",
+    "    print(\"Standard Deviation:\", np.nanstd(img_data))\n",
+    "    \n",
+    "    valid_data = img_data[~np.isnan(img_data)]\n",
+    "    \n",
+    "    # Plot the histogram\n",
+    "    plt.figure(figsize=(12, 8))\n",
+    "    plt.subplot(2, 2, 1)\n",
+    "    plt.hist(valid_data.ravel(), bins=100)\n",
+    "    plt.title(\"Histogram\")\n",
+    "    \n",
+    "    # Plot the CDF\n",
+    "    plt.subplot(2, 2, 2)\n",
+    "    plt.hist(valid_data.ravel(), bins=100, cumulative=True, density=True, histtype='step', color='blue', alpha=0.7)\n",
+    "    plt.title(\"CDF\")\n",
+    "    \n",
+    "    # Plot the image using mean±2 standard deviations as the display range\n",
+    "    plt.subplot(2, 2, 3)\n",
+    "    vmin = max(0, np.nanmean(img_data) - 2*np.nanstd(img_data))\n",
+    "    vmax = min(1, np.nanmean(img_data) + 2*np.nanstd(img_data))\n",
+    "    plt.imshow(img_data, cmap='gray', vmin=vmin, vmax=vmax)\n",
+    "    plt.title(f\"Image (range: {vmin:.3f}-{vmax:.3f})\")\n",
+    "    plt.colorbar()\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "visualize_image(x_train[j])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "014cf3d7-3bac-4a70-9d6c-ccb3c7a7ce26",
+   "metadata": {},
+   "source": [
+    "## Training an autoencoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "e6fc9858-2bdb-4f76-a823-802e272a8170",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " input_1 (InputLayer)        [(None, 64, 64, 1)]       0         \n",
+      "                                                                 \n",
+      " conv2d (Conv2D)             (None, 64, 64, 32)        320       \n",
+      "                                                                 \n",
+      " max_pooling2d (MaxPooling2D  (None, 32, 32, 32)       0         \n",
+      " )                                                               \n",
+      "                                                                 \n",
+      " conv2d_1 (Conv2D)           (None, 32, 32, 64)        18496     \n",
+      "                                                                 \n",
+      " max_pooling2d_1 (MaxPooling  (None, 16, 16, 64)       0         \n",
+      " 2D)                                                             \n",
+      "                                                                 \n",
+      " conv2d_2 (Conv2D)           (None, 16, 16, 64)        36928     \n",
+      "                                                                 \n",
+      " up_sampling2d (UpSampling2D  (None, 32, 32, 64)       0         \n",
+      " )                                                               \n",
+      "                                                                 \n",
+      " conv2d_3 (Conv2D)           (None, 32, 32, 32)        18464     \n",
+      "                                                                 \n",
+      " up_sampling2d_1 (UpSampling  (None, 64, 64, 32)       0         \n",
+      " 2D)                                                             \n",
+      "                                                                 \n",
+      " conv2d_4 (Conv2D)           (None, 64, 64, 1)         289       \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 74,497\n",
+      "Trainable params: 74,497\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "def scheduler(epoch, lr):\n",
+    "    if epoch % 10 == 0 and epoch != 0:\n",
+    "        return lr * 0.9\n",
+    "    return lr\n",
+    "\n",
+    "def create_autoencoder(input_shape):\n",
+    "    input_img = Input(shape=input_shape)  # adapt this if using `channels_first` image data format\n",
+    "\n",
+    "    # Encoder\n",
+    "    x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)\n",
+    "    x = MaxPooling2D((2, 2), padding='same')(x)\n",
+    "    x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
+    "    encoded = MaxPooling2D((2, 2), padding='same')(x)\n",
+    "\n",
+    "    # Decoder\n",
+    "    x = Conv2D(64, (3, 3), activation='relu', padding='same')(encoded)\n",
+    "    x = UpSampling2D((2, 2))(x)\n",
+    "    x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
+    "    x = UpSampling2D((2, 2))(x)\n",
+    "    decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n",
+    "\n",
+    "\n",
+    "    # Define the optimizer with a custom learning rate\n",
+    "    optimizer = Adam(learning_rate=0.0005)\n",
+    "    \n",
+    "    autoencoder = Model(input_img, decoded)\n",
+    "    autoencoder.compile(optimizer=optimizer, loss='mean_squared_error')\n",
+    "\n",
+    "    return autoencoder\n",
+    "\n",
+    "def create_adjusted_autoencoder(input_shape):\n",
+    "    input_img = Input(shape=input_shape)\n",
+    "\n",
+    "    # Encoder\n",
+    "    x = Conv2D(64, (3, 3), activation='relu', padding='same')(input_img)\n",
+    "    x = MaxPooling2D((2, 2), padding='same')(x)\n",
+    "    x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n",
+    "    encoded = MaxPooling2D((2, 2), padding='same')(x)\n",
+    "\n",
+    "    # Decoder\n",
+    "    x = Conv2D(128, (3, 3), activation='relu', padding='same')(encoded)\n",
+    "    x = UpSampling2D((2, 2))(x)\n",
+    "    x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
+    "    x = UpSampling2D((2, 2))(x)\n",
+    "    decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n",
+    "\n",
+    "    autoencoder = Model(input_img, decoded)\n",
+    "    autoencoder.compile(optimizer=tf.keras.optimizers.Adam(lr=0.0005), loss='mean_squared_error')\n",
+    "\n",
+    "    return autoencoder\n",
+    "\n",
+    "#autoencoder = create_adjusted_autoencoder((64, 64, 1))\n",
+    "\n",
+    "autoencoder = create_autoencoder((64, 64, 1))\n",
+    "autoencoder.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "7ada6881-2e27-4c89-b377-d21c849582dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "43/43 [==============================] - 7s 25ms/step - loss: 0.0528 - val_loss: 0.0202\n",
+      "Epoch 2/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0170 - val_loss: 0.0141\n",
+      "Epoch 3/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0126 - val_loss: 0.0110\n",
+      "Epoch 4/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0102 - val_loss: 0.0090\n",
+      "Epoch 5/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0086 - val_loss: 0.0078\n",
+      "Epoch 6/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0078 - val_loss: 0.0071\n",
+      "Epoch 7/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0072 - val_loss: 0.0066\n",
+      "Epoch 8/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0065 - val_loss: 0.0061\n",
+      "Epoch 9/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0065 - val_loss: 0.0058\n",
+      "Epoch 10/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0059 - val_loss: 0.0056\n",
+      "Epoch 11/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0056 - val_loss: 0.0054\n",
+      "Epoch 12/50\n",
+      "43/43 [==============================] - 1s 18ms/step - loss: 0.0053 - val_loss: 0.0049\n",
+      "Epoch 13/50\n",
+      "43/43 [==============================] - 1s 18ms/step - loss: 0.0051 - val_loss: 0.0048\n",
+      "Epoch 14/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0049 - val_loss: 0.0045\n",
+      "Epoch 15/50\n",
+      "43/43 [==============================] - 1s 16ms/step - loss: 0.0046 - val_loss: 0.0044\n",
+      "Epoch 16/50\n",
+      "43/43 [==============================] - 1s 16ms/step - loss: 0.0044 - val_loss: 0.0043\n",
+      "Epoch 17/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0043 - val_loss: 0.0040\n",
+      "Epoch 18/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0041 - val_loss: 0.0039\n",
+      "Epoch 19/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0041 - val_loss: 0.0039\n",
+      "Epoch 20/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0039 - val_loss: 0.0038\n",
+      "Epoch 21/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0038 - val_loss: 0.0036\n",
+      "Epoch 22/50\n",
+      "43/43 [==============================] - 1s 16ms/step - loss: 0.0037 - val_loss: 0.0035\n",
+      "Epoch 23/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0036 - val_loss: 0.0034\n",
+      "Epoch 24/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0036 - val_loss: 0.0036\n",
+      "Epoch 25/50\n",
+      "43/43 [==============================] - 1s 16ms/step - loss: 0.0034 - val_loss: 0.0034\n",
+      "Epoch 26/50\n",
+      "43/43 [==============================] - 1s 16ms/step - loss: 0.0033 - val_loss: 0.0031\n",
+      "Epoch 27/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0032 - val_loss: 0.0036\n",
+      "Epoch 28/50\n",
+      "43/43 [==============================] - 1s 16ms/step - loss: 0.0032 - val_loss: 0.0030\n",
+      "Epoch 29/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0030 - val_loss: 0.0029\n",
+      "Epoch 30/50\n",
+      "43/43 [==============================] - 1s 16ms/step - loss: 0.0029 - val_loss: 0.0028\n",
+      "Epoch 31/50\n",
+      "43/43 [==============================] - 1s 19ms/step - loss: 0.0029 - val_loss: 0.0028\n",
+      "Epoch 32/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0028 - val_loss: 0.0028\n",
+      "Epoch 33/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0028 - val_loss: 0.0029\n",
+      "Epoch 34/50\n",
+      "43/43 [==============================] - 1s 18ms/step - loss: 0.0027 - val_loss: 0.0026\n",
+      "Epoch 35/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0027 - val_loss: 0.0025\n",
+      "Epoch 36/50\n",
+      "43/43 [==============================] - 1s 16ms/step - loss: 0.0026 - val_loss: 0.0026\n",
+      "Epoch 37/50\n",
+      "43/43 [==============================] - 1s 16ms/step - loss: 0.0026 - val_loss: 0.0025\n",
+      "Epoch 38/50\n",
+      "43/43 [==============================] - 1s 16ms/step - loss: 0.0025 - val_loss: 0.0024\n",
+      "Epoch 39/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0024 - val_loss: 0.0024\n",
+      "Epoch 40/50\n",
+      "43/43 [==============================] - 1s 16ms/step - loss: 0.0025 - val_loss: 0.0023\n",
+      "Epoch 41/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0024 - val_loss: 0.0023\n",
+      "Epoch 42/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0023 - val_loss: 0.0023\n",
+      "Epoch 43/50\n",
+      "43/43 [==============================] - 1s 19ms/step - loss: 0.0023 - val_loss: 0.0023\n",
+      "Epoch 44/50\n",
+      "43/43 [==============================] - 1s 17ms/step - loss: 0.0022 - val_loss: 0.0022\n",
+      "Epoch 45/50\n",
+      "43/43 [==============================] - 1s 16ms/step - loss: 0.0022 - val_loss: 0.0022\n",
+      "Epoch 46/50\n",
+      "43/43 [==============================] - 1s 16ms/step - loss: 0.0022 - val_loss: 0.0021\n",
+      "Epoch 47/50\n",
+      "43/43 [==============================] - 1s 16ms/step - loss: 0.0022 - val_loss: 0.0021\n",
+      "Epoch 48/50\n",
+      "43/43 [==============================] - 1s 16ms/step - loss: 0.0022 - val_loss: 0.0021\n",
+      "Epoch 49/50\n",
+      "43/43 [==============================] - 1s 16ms/step - loss: 0.0021 - val_loss: 0.0020\n",
+      "Epoch 50/50\n",
+      "43/43 [==============================] - 1s 16ms/step - loss: 0.0021 - val_loss: 0.0020\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Reshaping the channel\n",
+    "x_train = x_train.reshape(-1, 64, 64, 1)\n",
+    "\n",
+    "# EarlyStopping function\n",
+    "early_stopping = EarlyStopping(\n",
+    "    monitor='val_loss',   # Value to be monitored\n",
+    "    patience=5,           # Number of epochs with no improvement after which the training will be stopped\n",
+    "    mode='auto',          # 'auto', 'min' or 'max'. In 'auto', algorithm will detect the direction\n",
+    "    restore_best_weights=True  # Whether to restore model weights from the epoch with the best value result\n",
+    ")\n",
+    "\n",
+    "# Train the autoencoder\n",
+    "history = autoencoder.fit(\n",
+    "    x_train, x_train,\n",
+    "    epochs=50,\n",
+    "    batch_size=128,\n",
+    "    shuffle=True,\n",
+    "    validation_split=0.1,\n",
+    "    callbacks=[early_stopping]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0da63585-614d-4d47-b50e-a845d5cd9430",
+   "metadata": {},
+   "source": [
+    "### Visualize results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "c0d51bb7-0ce1-4f19-94ad-9c01c6430b1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 10ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9AAAAH6CAYAAADvBqSRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAABx3klEQVR4nO3debBfd33f/7dXSdZ6pXt1dbVeLZaNjQO2iRMWYyC0DMUEE5YEiLHBMUtY4o5xS4ayBDIQSJhxJmVLCoHWSVswhjjNSoBASZiEksZ2vGvfpbtot2Rb1rd/MFIR8uf5/vA5upJxno+Z38yvfvt8zzmf81nOxzec12m9Xq8XkiRJkiQJnX6qL0CSJEmSpJ8EbqAlSZIkSargBlqSJEmSpApuoCVJkiRJquAGWpIkSZKkCm6gJUmSJEmq4AZakiRJkqQKbqAlSZIkSargBlqSJEmSpApuoKUKH/jAB+K0005rOvbzn/98nHbaabFu3boTe1E/ZN26dXHaaafF5z//+Qk7hyRJ+sni+4F04rmB1pPa3XffHb/8y78cCxYsiEmTJsX8+fPjda97Xdx9992n+tJOib/927+N0047LW699dZTfSmSpCeoI//h98j/d+aZZ8aCBQvi2muvjc2bN5/qyzvhPvnJT57yDeapvgbfD6R6bqD1pHXbbbfFJZdcEl//+tfjDW94Q3zyk5+M6667Lr75zW/GJZdcEl/5yleqf+s//af/FAcOHGi6jquvvjoOHDgQS5YsaTpekqRT4YMf/GD8t//23+LTn/50vPjFL45bbrklrrjiijh48OCpvrQT6lRvXp8o1yCpzpmn+gKkibB69eq4+uqrY9myZfHtb387BgYGjtZ+7dd+LS6//PK4+uqr484774xly5YVf2f//v0xderUOPPMM+PMM9uGyxlnnBFnnHFG07GSJJ0qL37xi+MZz3hGRET8yq/8SvT398dHP/rRuP322+PVr371Kb66U+PIe4Gkf738C7SelH77t387Hnroofj93//9YzbPERH9/f3xmc98Jvbv3x8f+9jHjv7zI/8753vuuSde+9rXRl9fXzznOc85pvbDDhw4EO985zujv78/pk+fHj//8z8fmzdvjtNOOy0+8IEPHP33Hu9/Az08PBxXXnllfOc734nLLrssJk+eHMuWLYv/+l//6zHnGB8fj3e9611x0UUXxbRp02LGjBnx4he/OO64444T1FL//94eeOCB+OVf/uWYOXNmDAwMxHvf+97o9XqxcePGeNnLXhYzZsyIefPmxcc//vFjjn/kkUfife97X1x66aUxc+bMmDp1alx++eXxzW9+87hzjY2NxdVXXx0zZsyIWbNmxTXXXBN33HHH4/7vs+6777545StfGbNnz47JkyfHM57xjLj99ttP2H1Lkn48l19+eUT84D9S/7Da+XrXrl3x7//9v4/h4eGYNGlSLFy4MF7/+tfH6Ojo0X9nx44dcd1118Xg4GBMnjw5nva0p8UXvvCFY37nyP+u93d+53fi93//92P58uUxadKk+Omf/un43ve+d8y/u23btnjDG94QCxcujEmTJsXQ0FC87GUvO7omDw8Px9133x3f+ta3jv6frD/vec+LiP+/fn/rW9+KX/3VX425c+fGwoULIyLi2muvjeHh4ePusfTNlFtuuSUuu+yyOOecc6Kvry+e+9znxl//9V+n13Ck3W644YZYtGhRTJo0KVasWBEf/ehH4/Dhw8e177XXXhszZ848usbu2rXruGup5fuB9Pj8C7SelP70T/80hoeHjy72P+q5z31uDA8Px5/92Z8dV3vVq14V5557bnz4wx+OXq9XPMe1114bX/ziF+Pqq6+On/3Zn41vfetb8ZKXvKT6GletWhWvfOUr47rrrotrrrkmPve5z8W1114bl156aVx44YUREbFmzZr46le/Gq961ati6dKlsX379vjMZz4TV1xxRdxzzz0xf/786vNlfvEXfzGe8pSnxG/91m/Fn/3Zn8Vv/uZvxuzZs+Mzn/lMvOAFL4iPfvSj8Ud/9Efxrne9K376p386nvvc50ZExJ49e+K//Jf/Eq95zWvi+uuvj71798ZnP/vZeNGLXhT/+I//GE9/+tMjIuLw4cPx0pe+NP7xH/8x3vrWt8b5558ff/InfxLXXHPNcddy9913x7Of/exYsGBBvPvd746pU6fGF7/4xbjqqqviy1/+crz85S8/YfctSapzZNPZ19d39J/Vztf79u2Lyy+/PO6999544xvfGJdcckmMjo7G7bffHps2bYr+/v44cOBAPO95z4tVq1bF29/+9li6dGl86UtfimuvvTZ27doVv/Zrv3bM9fzxH/9x7N27N9785jfHaaedFh/72MfiF37hF2LNmjVx1llnRUTEK17xirj77rvjHe94RwwPD8eOHTvia1/7WmzYsCGGh4fj5ptvjne84x0xbdq0eM973hMREYODg8ec51d/9VdjYGAg3ve+98X+/ft/7Hb7jd/4jfjABz4Qz3rWs+KDH/xgnH322fEP//AP8Y1vfCP+7b/9t3gNDz30UFxxxRWxefPmePOb3xyLFy+Ov//7v49f//Vfj61bt8bNN98cERG9Xi9e9rKXxXe+8514y1veEk95ylPiK1/5yuOusT8u3w+kH9GTnmR27drVi4jey172Mvz3fv7nf74XEb09e/b0er1e7/3vf38vInqvec1rjvt3j9SO+P73v9+LiN4NN9xwzL937bXX9iKi9/73v//oP/vDP/zDXkT01q5de/SfLVmypBcRvW9/+9tH/9mOHTt6kyZN6t14441H/9nBgwd7jz322DHnWLt2bW/SpEm9D37wg8f8s4jo/eEf/iHe8ze/+c1eRPS+9KUvHXdvb3rTm47+s0OHDvUWLlzYO+2003q/9Vu/dfSf79y5szdlypTeNddcc8y/+/DDDx9znp07d/YGBwd7b3zjG4/+sy9/+cu9iOjdfPPNR//ZY4891nvBC15w3LX/3M/9XO+iiy7qHTx48Og/O3z4cO9Zz3pW79xzz8V7lCR1c2Td+pu/+ZveyMhIb+PGjb1bb721NzAw0Js0aVJv48aNR//d2vn6fe97Xy8ierfddttx5zt8+HCv1+v1br755l5E9G655ZajtUceeaT3zGc+szdt2rSj6/WRNW/OnDm98fHxo//un/zJn/Qiovenf/qnvV7vB2tRRPR++7d/G+/3wgsv7F1xxRXFdnjOc57TO3To0DG1a665prdkyZLjjvnR94UHH3ywd/rpp/de/vKXH7eeH7lvuoYPfehDvalTp/YeeOCBY/75u9/97t4ZZ5zR27BhQ6/X6/W++tWv9iKi97GPfezov3Po0KHe5Zdf7vuBdIL5f8KtJ529e/dGRMT06dPx3ztS37NnzzH//C1veUt6jr/8y7+MiB/8V+kf9o53vKP6Oi+44IJj/kI+MDAQ5513XqxZs+boP5s0aVKcfvoPhuljjz0WY2NjMW3atDjvvPPin/7pn6rPVeNXfuVXjv7/n3HGGfGMZzwjer1eXHfddUf/+axZs467xjPOOCPOPvvsiPjBf0UeHx+PQ4cOxTOe8YxjrvEv//Iv46yzzorrr7/+6D87/fTT421ve9sx1zE+Ph7f+MY34tWvfnXs3bs3RkdHY3R0NMbGxuJFL3pRPPjgg0/Kr8BK0hPNC1/4whgYGIhFixbFK1/5ypg6dWrcfvvtR//PmH+c+frLX/5yPO1pT3vcvxAe+T95/vM///OYN29evOY1rzlaO+uss+Kd73xn7Nu3L771rW8dc9wv/uIvHvPX8CNr6pE1asqUKXH22WfH3/7t38bOnTub2+H6669v/pbJV7/61Th8+HC8733vO7qeH1ETj/mlL30pLr/88ujr6zvavqOjo/HCF74wHnvssfj2t78dET9ouzPPPDPe+ta3Hj32jDPO+LHeS0p8P5CO5f8Jt550jmyMj2ykS0ob7aVLl6bnWL9+fZx++unH/bsrVqyovs7Fixcf98/6+vqOWeQPHz4cv/u7vxuf/OQnY+3atfHYY48drc2ZM6f6XC3XM3PmzJg8eXL09/cf98/HxsaO+Wdf+MIX4uMf/3jcd9998eijjx795z/cPuvXr4+hoaE455xzjjn2R9ts1apV0ev14r3vfW+8973vfdxr3bFjRyxYsKD+5iRJP7ZPfOITsXLlyti9e3d87nOfi29/+9sxadKko/UfZ75evXp1vOIVr8DzrV+/Ps4999zjNppPecpTjtZ/2I+uW0c200fW0UmTJsVHP/rRuPHGG2NwcDB+9md/Nq688sp4/etfH/PmzatogR+oeS8oWb16dZx++ulxwQUXNB3/4IMPxp133nnc91yO2LFjR0T8/zV22rRpx9TPO++8pvP+MN8PpGO5gdaTzsyZM2NoaCjuvPNO/PfuvPPOWLBgQcyYMeOYfz5lypSJvLyjSv81u/dD/7vrD3/4w/He97433vjGN8aHPvShmD17dpx++ulxww03HPfxkIm4npprvOWWW+Laa6+Nq666Km666aaYO3dunHHGGfGRj3zkuA/N1DhyX+9617viRS960eP+Oz/Of6iQJLW57LLLjn6F+6qrrornPOc58drXvjbuv//+mDZt2imfr2vWqBtuuCFe+tKXxle/+tX4q7/6q3jve98bH/nIR+Ib3/hGXHzxxVXnebz3gtJfj3/4P3SfCIcPH45/82/+TfyH//AfHre+cuXKE3q+x+P7gXQsN9B6UrryyivjD/7gD+I73/nO0S9p/7D//b//d6xbty7e/OY3N/3+kiVL4vDhw7F27do499xzj/7zVatWNV/z47n11lvj+c9/fnz2s5895p/v2rXruP/ye6rceuutsWzZsrjtttuOeaF4//vff8y/t2TJkvjmN78ZDz300DH/lflH2+xIrNhZZ50VL3zhCyfwyiVJtY5sfJ7//OfHf/7P/zne/e53/1jz9fLly+Nf/uVf8N9ZsmRJ3HnnnXH48OFj/gp93333Ha23WL58edx4441x4403xoMPPhhPf/rT4+Mf/3jccsstEVH3f0r9o/r6+h73C9c/+lfy5cuXx+HDh+Oee+45+tGsx1O6huXLl8e+ffvS9l2yZEl8/etfj3379h3zV+j7778fj5tIvh/oycr/DbSelG666aaYMmVKvPnNbz7u/5xofHw83vKWt8Q555wTN910U9PvH/kvn5/85CeP+ee/93u/13bBBWecccZxXwL/0pe+9IT63/gc+a/QP3yd//AP/xDf/e53j/n3XvSiF8Wjjz4af/AHf3D0nx0+fDg+8YlPHPPvzZ07N573vOfFZz7zmdi6detx5xsZGTmRly9JqvS85z0vLrvssrj55pvj4MGDP9Z8/YpXvCLuuOOO+MpXvnLcv3dk/fh3/+7fxbZt2+J//s//ebR26NCh+L3f+72YNm1aXHHFFT/W9T700ENx8ODBY/7Z8uXLY/r06fHwww8f/WdTp079seOeli9fHrt37z7m/9pt69atx93fVVddFaeffnp88IMfPO7/cuyH183SNbz61a+O7373u/FXf/VXx9V27doVhw4diogftN2hQ4fiU5/61NH6Y489dsLfS34cvh/oycq/QOtJ6dxzz40vfOEL8brXvS4uuuiiuO6662Lp0qWxbt26+OxnPxujo6Px3//7f4/ly5c3/f6ll14ar3jFK+Lmm2+OsbGxozFWDzzwQES0/dfsx3PllVfGBz/4wXjDG94Qz3rWs+Kuu+6KP/qjPzr6X2GfCK688sq47bbb4uUvf3m85CUvibVr18anP/3puOCCC2Lfvn1H/72rrroqLrvssrjxxhtj1apVcf7558ftt98e4+PjEXFsm33iE5+I5zznOXHRRRfF9ddfH8uWLYvt27fHd7/73di0adMJzcGWJNW76aab4lWvelV8/vOfj7e85S3V8/VNN90Ut956a7zqVa+KN77xjXHppZfG+Ph43H777fHpT386nva0p8Wb3vSm+MxnPhPXXnttfP/734/h4eG49dZb4+/+7u/i5ptvTj8O+qMeeOCB+Lmf+7l49atfHRdccEGceeaZ8ZWvfCW2b98ev/RLv3T037v00kvjU5/6VPzmb/5mrFixIubOnRsveMEL8Ld/6Zd+Kf7jf/yP8fKXvzze+c53xkMPPRSf+tSnYuXKlcd8IGvFihXxnve8Jz70oQ/F5ZdfHr/wC78QkyZNiu9973sxf/78+MhHPoLXcNNNN8Xtt98eV1555dGoy/3798ddd90Vt956a6xbty76+/vjpS99aTz72c+Od7/73bFu3bq44IIL4rbbbovdu3f/WG12Ivl+oCetU/Hpb+lkufPOO3uvec1rekNDQ72zzjqrN2/evN5rXvOa3l133XXcv3skrmFkZKRY+2H79+/vve1tb+vNnj27N23atN5VV13Vu//++3sRcUy0QynG6iUveclx57niiiuOibE4ePBg78Ybb+wNDQ31pkyZ0nv2s5/d++53v3vcv3ciYqx+9L6vueaa3tSpUx/3Gi+88MKj/+/Dhw/3PvzhD/eWLFnSmzRpUu/iiy/u/a//9b8eN+JjZGSk99rXvrY3ffr03syZM3vXXntt7+/+7u96EdH7H//jfxzz765evbr3+te/vjdv3rzeWWed1VuwYEHvyiuv7N166614j5Kkbo6sW9/73veOqz322GO95cuX95YvX3402ql2vh4bG+u9/e1v7y1YsKB39tln9xYuXNi75ppreqOjo0f/ne3bt/fe8IY39Pr7+3tnn31276KLLjpubTuy5j1ePFX8UJTk6Oho721ve1vv/PPP702dOrU3c+bM3s/8zM/0vvjFLx5zzLZt23oveclLetOnT+9FxNH1ldqh1+v1/vqv/7r31Kc+tXf22Wf3zjvvvN4tt9zyuO8LvV6v97nPfa538cUX9yZNmtTr6+vrXXHFFb2vfe1r6TX0er3e3r17e7/+67/eW7FiRe/ss8/u9ff39571rGf1fud3fqf3yCOPHNO+V199dW/GjBm9mTNn9q6++ure//2//9f3A+kEO63X+5H/+1BJzf75n/85Lr744rjlllvida973am+nJ8IX/3qV+PlL395fOc734lnP/vZp/pyJEnSE4DvB3qi8n8DLTU6cODAcf/s5ptvjtNPPz2e+9znnoIreuL70TY78r/PmjFjRlxyySWn6KokSdKp5PuBfpL4v4GWGn3sYx+L73//+/H85z8/zjzzzPiLv/iL+Iu/+It405veFIsWLTrVl/eE9I53vCMOHDgQz3zmM+Phhx+O2267Lf7+7/8+PvzhD5+0+DBJkvTE4vuBfpL4f8ItNfra174Wv/EbvxH33HNP7Nu3LxYvXhxXX311vOc974kzz/S/TT2eP/7jP46Pf/zjsWrVqjh48GCsWLEi3vrWt8bb3/72U31pkiTpFPH9QD9J3EBLkiRJklTB/w20JEmSJEkV3EBLkiRJklTBDbQkSZIkSRWqv3R0Kv6n0nTOxx57DI89fPhwsfboo48Wa4cOHWqqZU4/ve2/VWTt3nqfe/bsKdY2bdqE59yyZUuxtnv37mJt5syZ+LsXXnhhsbZy5cpijb7OSG0QEbF27dpi7Rvf+Eax9n/+z/8p1qhtIyIWLFhQrNHXu+fMmVOsnXXWWXhOaod9+/YVa3v37sXfffjhh4s1Gi80frNnRufcv39/sTYyMlKsbd26Fc+5ffv2Ym10dLRYozboMqeedtppxVr2Abu+vr5ijcYSXW/2zB555JGm36V+3TqnRvC8SX0zW3eoHbKxdKLR/JVFwlAfolqXNZvq9LzonF3GWOvv0rVGcB95vGjEI6j/jI+PN5+TxtisWbPwd2fPnl2s0VxCY5faIILX7D//8z8v1v7mb/6m6Xoi+D4vuuiiYu3iiy8u1rL3yda+sGvXLvxdWiMPHjxYrFEfouMyNO5prc/6/OrVq4u1sbGxYo2eSza2aV3usmZPnTq1WJs0aRIeW0JtG5Gv6SU0ls4++2w8lurUfq39NoLbnvpJhH+BliRJkiSpihtoSZIkSZIquIGWJEmSJKmCG2hJkiRJkiq4gZYkSZIkqUL1V7jp63P0dbQuv9tai2j/AjDJvnZH7UBfputyn/SlxoceeqhYo6/innPOOXjOhQsXFmuLFy8u1uhLlhH8dWr6oid9RS/7uiZ9ZXRgYKBYmz9/ftNvRvDXtOlYaoMM9QX60iB9fTqCvwba+tVO6psR7XMRHTd58mQ8J40Jmhda55oM9etp06bhsTNmzCjWaCx1+dJl61da6ZllX57vMi+0XE+X350Iw8PDxRp91TWifS3r8nXqiVizT4Xsy990n9nXeEuyOZPqtK709/fj79IX/WlOpT6UPWtK86Bz0vtFlpxBXwf+qZ/6qWKN2ofW5IiIHTt2FGtdvnpNaza9T9I5s3shNC90+SJ2a5JAl7Sd1q/+Z8edccYZxRq9m7S2bUT7nEtrB91HVqcaXetEJkg9cVZ7SZIkSZKewNxAS5IkSZJUwQ20JEmSJEkV3EBLkiRJklTBDbQkSZIkSRXcQEuSJEmSVMENtCRJkiRJFapDBynnM8u+bM3G7JIp2XosZZhledeUU0ZtQLUsX5VQG9A5KSc2grMNKZMuyx6lPMqJylele6V8R8qizDI5p0+fXqxRDjTl9o2MjOA5KcOR+nXW/yj/kc65f//+Yo2yKDOUDUzjs0v2I80nNAa75BN2OTbLYiyhrMUsM5LqrXNulpnbmqnb+jxr6icTzW1d5lN6Jqfi/umc2Zo9EbJztr4LUK3LOWnOpPzj7NiJeB/KLF26tFibNm1asbZv3z78XbqX2bNnF2vUN3ft2oXnHB8fL9Zojnr44Yfxd2l93b17d7FGaz2t5xHd3uVbfjOC+9GkSZOKtaz9WtH1ZusnvUvRGO0y/7W+J9BxXeaT1ueZ9a8u737+BVqSJEmSpApuoCVJkiRJquAGWpIkSZKkCm6gJUmSJEmq4AZakiRJkqQKbqAlSZIkSapQnfNBnwLPYgdaY0q6fPad6vTZ8i4xVhMR1zVRn+qne8naluJhWj99H8F9jOKU6HqyT9jT9S5atKhYmz9/frGWfRa/tY9R1EYWvbBnz55ijSK5KD4hgvsfxXBQ1Fd2ztbrIdk46zIvTAS63r179+KxNCYmT57cdFyXcUY1msOoFsGRGXS9NH6zftIayTIR6Fmeij47Ubq8J7T+Lsnalq6p9T0rGwu0RtLvZmtZa7xpl/cEei7UDhQRmcXr0L1Q29K1ZjF7NH5bozAjOMaqNW4q6ycTMS9mcaGtEWunYm7M2ocixGg+oWjY7D4nIl5yoiKPSXafXfqmf4GWJEmSJKmCG2hJkiRJkiq4gZYkSZIkqYIbaEmSJEmSKriBliRJkiSpghtoSZIkSZIqVMdYdfk0+RMtMqM1jiX7bH7r71L7ZHEP1Pb0GX+6nixGiOKU6Hfpk/oREdOmTSvWzjnnnGKN7jPrexTbQG3bpU9T+1IfGxsbK9Z27tyJ56S4EXouc+bMwd+l/knPhWJDKL4joj2apkukDT0zar/WyJBMl+geGr/U/+g+6VlH8PNu7SdZDMypiMSYiHO2omvp0vcmaj1v7dM0t1Gty++SLrEpdD0UVdglborGfDaX0Pw2derUYq01YiiC25fuhfpX9qyp7el3KX6I5uGIiN27dxdrdJ/0rhTBsZWt76lZDBj1o9ZYN2rbCH5mtHZ0iazsMq+S1ohXinXL3rNa93xd1sDWPVRrH+rqibPaS5IkSZL0BOYGWpIkSZKkCm6gJUmSJEmq4AZakiRJkqQKbqAlSZIkSargBlqSJEmSpArVMVZdoitaP01On4TProc+wU6/S5++zz6bT7EDe/bsKdboWrNIAoqWod+l+9y/fz+ec9euXcUaxVpk8RQUhdAaKZWdszXOgGJDqH0iOHKK+gm1bRZjQqiNsnFGMQmzZs1qOq6vrw/PSX2X2ohiQ7K4FopdoTFIUS5ZXFyX2C3SGj1GsRfZPJVFZpRQREfW56l9W2MvsnN2GYcnUzauW6NcukSYUL9sjTDpEsNE6yA95y5tS+8mVMv6XWtcF73T1Jy3hNb6LnMJ9SFaN8bHx/GcIyMjxdr27duLNVrrs3gdaiNaP7O4UIqmbI0UnD17Np6zdS6mMZjNNfS8W2PUuqzJExUd2Lo3y9ZkipWl/kdtRPNtBM9FExVj1SXmyr9AS5IkSZJUwQ20JEmSJEkV3EBLkiRJklTBDbQkSZIkSRXcQEuSJEmSVMENtCRJkiRJFdxAS5IkSZJUoToHmmQ5ZF3yz1rPSVpzUKkWwZnCZO/evcValilMWu8zy9ej36U2yHJvx8bGijXKaWzN2I7gzEk6ljLBKRcyImLt2rXF2rZt24o16vMzZszAc1KdsoqzTEk6ltqWjstQnjONF8ospd/MjqXnQm2QzSetud/ZfNs6H9O4zzIlqf898sgjxRplNFLOZwRnSrbm7U7EWjZR6HmdCl3ykVuz6rPnRX2I1mUam1lWLF0T9XfKXm1994jg8Ze9f+zYsaNYozWbzpnNJTNnzizWqC9QpvXGjRvxnOvWrSvW6D6pf2X3OTg4WKwNDAwUa3PnzsXfpbW3dS3L5hp63vQuReOMjotoH780PrvsO0j2u/RO3rpnyebG1vWzNXM+q9PcOFEZ2xn/Ai1JkiRJUgU30JIkSZIkVXADLUmSJElSBTfQkiRJkiRVcAMtSZIkSVIFN9CSJEmSJFWozj6gT313+QQ71eiz5Rn6rD598pyuJ/tUOn2qnyKcKEIni9fZv39/sUbtN23atGJt1qxZeE6q0+9m8U4UO7Bhw4ZijaI0Mq0xQ9TuFFMVEbFq1apijaK8KKpkzpw5eE56ZrNnzy7W+vr68Hfnz59frFHbUiRLNp+0RhtR+2XxChR70VrL4m6oHahvZvMmzWOtcTh0nxE8N5KsjUhrjFPr+hAxcTEn/xpkbVtCYyGLCsqi0EqoP9M6FsFrOt3L9OnTi7UsxrBLbCChCElaB7M2IvRMzz777GJtdHS0WMveTegdg+KUaC6ma43gd8bWc0a0r9kUaUnHRfA4o6g0qmWxqPSORsd22XfQ+km1rC+0/i6tgRSFGcF9jObqLmtga5Rha8xhTZ34F2hJkiRJkiq4gZYkSZIkqYIbaEmSJEmSKriBliRJkiSpghtoSZIkSZIquIGWJEmSJKlCdWZJl0+7t0ZDdfq8eGM8Fn0SniImIjhegSIAunxqnuJjWu8zi/ShtqV7yfoQRQtQJMHOnTuLtexeKD6A7oViGeh6IjgegGJ7aKxQ+0Rw36RzUtxUBI8JaluqZXE2reOFolOysU3onBR3k8XiUSTcwMBAsTZ37lz8XYq02b17d7FGMTAUNxLB44X6Al1rFv3RpY+VZPNxl3XyRKNryfreEy2Oi55Xl0g3mhdbfzeLaKKoQprbaJ7OYqrod2mOz8YYXROtSTRfZNF1dC/0zGhuoziuCJ6/6B2jyxikuDN6LlmkFEVazpw5s1ijSMv+/n48J62D9MzGx8eLtezdpDV6t3VtiOD5hOJCs+hYel+ntqV+nUX4Ub+eqPmE7pOeGR2X3Wf27kz8C7QkSZIkSRXcQEuSJEmSVMENtCRJkiRJFdxAS5IkSZJUwQ20JEmSJEkV3EBLkiRJklShOsaKPpXe6QLg8+P0OfnsU/MUH9AaLZCdkz7H3/p59izSoTVCh86ZxQO0Rn9Q5FYEf06eIq7oM/VZv6VP9WftUELxQxEcM0RxJNQXqH9FcL9ujeiI4DgDimYYGhoq1rL2oxgwOpbuhSJOIjh2pTXGJItEmjNnTrF27rnnFmsrV67E350+fXqxtnbt2mKN2n3Hjh14Thr7NBdR7AW1TwRHsmRxGiXZM8vqJxPNfTRuM11iXkhrdFZrrFEEx0vSXELzRdYHKJqH2oDmErqPCF6zaW3IopZa+xGtZTTPRPBal70vlWTzAbVf6zPL2o7GGa3Z2VpG7UttS9GJ2VzcGs9JkW9ZXCO1A52T3jWz8UBRVQsWLGg6LoLXMoqX3LRpU9NxEe1jifoQRaFF8DxGY5SOy6IMswg74l+gJUmSJEmq4AZakiRJkqQKbqAlSZIkSargBlqSJEmSpApuoCVJkiRJquAGWpIkSZKkCm6gJUmSJEmqUJ0DTZlhWV5ua54uZThmeWykNW+yC7peyimjTLqIiO3btxdrlIM3Y8aMYi3L/qU6ZRtmuXN0vfv27SvWKO80y+SkrDt6LpR1R/nHERGDg4PFGrUtPessy47agTKOs3xpyoakYynDkfISIzjHkvIAKVczmxMoO5narzU/OiKiv7+/WKP2y/JF9+7d23RNNB+fc845eE5C2ZDz5s0r1hYuXNj8u5Qp2SW7O3umJxPNi1nuLY0HGkdd1mUyERnREdynKfuXjuvSR2g9ovVxZGQEz0ntR8+MMquza2rNRO+SM07Pm3K9s3WO3mHpHa31WUfwc6HfPXDgAP4uZUhTv6a1ldbHDM23dJ/ZvoKylSlfmtonm9+pHWjOzfofnbc1tzqbG6l9qS/Qmr1o0SI8J7UftVHr/ioi32MR/wItSZIkSVIFN9CSJEmSJFVwAy1JkiRJUgU30JIkSZIkVXADLUmSJElSBTfQkiRJkiRVqI6xuu+++4q1lStX4rFTp04t1rJPqZdkcRlUb61l6F4oHoA+F5993p6iECjuh+6T4k8i8jiqluuJ4Cgmul76pH4WFUHo2C59iCIJKPqjS7wOfaq/SxtRtBb1E4p3mjNnDp6TxgtFhFGMBMVCRbQ/F5oTsnanqI3NmzcXa1u2bMHf3bp1a7FGY7+13SM4Roee9+LFi4s1isuI4BgTemYUY0K1iHyOO5m6xNPRnNq6ZncxUWs29cvWiCtqu4iImTNnFmsUQURzEM3DNfWW64ngGCvqYzSXZJFlrXFA1G+7RJvS71L85kRFbGaxPHRNrRFO2b1QG9FYGhoaKtYoQjMi4uKLLy7WqP2oL3SJddu4cWOxRut5BD9TqtG9UKxbBM9/FBG5bNmyploEj23qf9u2bSvWsjU7i7ki/gVakiRJkqQKbqAlSZIkSargBlqSJEmSpApuoCVJkiRJquAGWpIkSZKkCm6gJUmSJEmqUB1jRZ9Zz+JE6JPnp5/etofPoisoAoBq9Nn37Jx0LH2qnz7Hn32CndqPYlzoc/wUfxIRsWHDhmKNYlwouiKCP5tP0SDUttkzo0/YUz+hT+rv3bsXz0lRB3QsxRVkcSPUj6gNsvajeYH6NUXbUdtGcOwR9TEaDwsXLsRzUuQD9T/q09l9rl69ulij2BA6LiJiZGSkWKNxRvE7FDcSwe1Hz4zWjv7+/uZz7t+/v1ijsZQ9s2wcnkwU7ZHFWNG6Qsd2iQNqjaPqEnFFdYreoXGSReJRG1G/pFg7etYRvKbT2pC1H8191H40l2Qxmq3RgHSf2bil50LXS5FR2X22xktmx1GMIcXVkuydcfr06cUazf/nnHNOsTZ//nw8J71jtMadUT+I4LWV5lR6J8zOS2OU2iBD6yetvTT/0Xoewe81VKOxlMW6dYlB9C/QkiRJkiRVcAMtSZIkSVIFN9CSJEmSJFVwAy1JkiRJUgU30JIkSZIkVXADLUmSJElSheoYK4pQoFoEf9a8NTaqSzxRl98l9Jl6ir2gNsiin6htKe6Hom66xDBRnMGsWbPwd+nT+DNmzCjWKCqI+kFWp0/jd4kB27RpU7GWtX2r1n6dxXuMjo4Wa/fcc0+xtmfPnmJt7dq1eE6KTFqyZEmxtnjx4mIti1egGA6K2qBIjOyZLF++vFjbvn17sbZlyxb83dZ4NroXqmVo/qNrzcYKRepRLMjOnTuLtR07duA5s7XwZLr//vuLtSzqpjVCh+birI/QeGiNx8rmfzonRd1QDFMWiUfrMsUe0bxI83AEr1c0Fs4++2z8XXrnoXhTatssKo7qNP5aI8Ii+LlQPBZF6GR9k7TGv0bw+wn1MZpPaTxEcOTUggULirWVK1c2n5Pq9F5Nc032nkDRT9TH6J0wgt+XaIxSLdtbUAQWvQ+RLK6LxhIdS22bRQF3iV70L9CSJEmSJFVwAy1JkiRJUgU30JIkSZIkVXADLUmSJElSBTfQkiRJkiRVcAMtSZIkSVIFN9CSJEmSJFWozoGmbEzKJI3gPDbKVaN81SyfkDInW3NHs9zW1qzdyZMnF2vZfVIbUVYg5RqOjIzgOSkDjtovy+2jHGjqJ9RGlOUcwfl6lBtJx1HbRnCe4qFDh4o16rdZll3rsVmfpmw+anvKKqY+HcF5zj/zMz/T9LuUpx7BbdSayZk9M8qOX7ZsWbGW5ZBT/iPN85S7nJ2TxgTlTbbmQkZwziqNQcrM3bZtG54zu6aT6cEHHyzWsgxaytqlOYHWBurPEbxmt46xLlm7lGlNfTZbs6ltac2h+XTjxo14Tmp7aiO6zwjOeh4eHm66HhqbETzX0BxF7U55zRH8XOjYLu+T1Oeplr3z0BxP90LHUa53BL9TUjtQH6LxmdXperus9TRehoaGirUVK1bg79K9zJgxo1jr8m5H76I0Hmh8Zs+M+m6XdwFC+6+Mf4GWJEmSJKmCG2hJkiRJkiq4gZYkSZIkqYIbaEmSJEmSKriBliRJkiSpghtoSZIkSZIqVMdYUexM9mly+jQ+xSDMnz+/WKNPt0fkURIl9Jn6LBKD6vS7FOlAkTNZnT5hT599p4imCH6eFCORxVgtWLCgWBsYGCjWKP5kbGwMz0nRFq19IYsHoAgFig6g380ikeheusTHUF84cOAAHluSxd3QNVEcBF1rl7ibLs+F0LEU+fbUpz4Vf3fatGnFGsXhUFxhtgbQnEKxNTRPZf2LxhL1oS7RM1mcy8lEMVYZigyhfjk4OFiszZo1C89JcSLUv+iZZM+rddzT9WTvHnRN9Lv0rrRw4UI8J0XLUMRc9p61fPnyYo3iBqmNski8LKaphMY1zRUR/J5Ax07UukK1LJKLxjb1BXouXSLq6L2P7oWeSUR7rCzJ3oeoTmNpyZIl+Ls051I8Jz3rbP2k6DFal0dHR4u1bJzR/EdtQOtu1jezdxfiX6AlSZIkSargBlqSJEmSpApuoCVJkiRJquAGWpIkSZKkCm6gJUmSJEmq4AZakiRJkqQKJyTGKvucPH0unT41T1EHFHkRwZ8up0/N071k90kxCfSZ9S5RB/Rpd4q4olio/fv34znpXijqYOrUqfi7FM0zd+7cYo3aPftUP/U/iuSiuIfsedJn8+leuvQTipGgWpcYmNY4oCwqgo6lc5Iu8UOt58yOo8gHqmW/S3MGjdE5c+YUa1kUBM1F27ZtK9Z27txZrFFcRgT3k76+vmKN5qHh4WE8Z7ZGnEw0F1MkWQTPCdQP6LgMxby0xlJ2mb9aI1WyNmh956H4uaGhITxnaxxQtmZTBBH1E2r3LIaJ2m+iYiDpmdKxXWIMaR2k352oNbtLXBe9L9H7JkWWUURTBF8vrfet7Z6hPpTF1U6fPr1Yo/FLbdsl+rT1nNkzo30d1SjKK4tP7MK/QEuSJEmSVMENtCRJkiRJFdxAS5IkSZJUwQ20JEmSJEkV3EBLkiRJklTBDbQkSZIkSRXcQEuSJEmSVKE6/JRy07IMs6xe0iUDjrIEKd+R8uy6ZAVSplprdmEEXy+1H7VBlilJ90lZlRnKwKTcuYceeqhYyzKtKZ+Qfjfrf4TGErVBl4zo1r6ZZQpT/8z6butxreekcZbl97ZmStJzyZ4Z9bHx8fFibdOmTfi7Y2NjxRrNm62Z81md+jydM8sLpzrlai5evLhYW7p0afM5n0iy+YvWDhoL9LvZGKN6a7tmY6x1juqS/ds6R9EcnmWdLlmypOl6snuh/NXWfrJ371485+7du5t+l+4z6180R9F9Zusnac0jzs5JdWqj1ozoCJ5PWt/tsn5CY4Lef0k2HqiN6D67PLPWTPrsXqjPT1RWNqH3LMqrz+bGLmv2T8ZqL0mSJEnSKeYGWpIkSZKkCm6gJUmSJEmq4AZakiRJkqQKbqAlSZIkSargBlqSJEmSpArVMVbz5s0r1pYvX47HLliwoFibOXNmsUafmj9w4ACekz7fPnny5GKNPpWefZ699fPt9Il6+vx/BMfOtMaCUBtEcHQFxUhQJEEER/Ns27atWKNYi5GRETznrl27irWs7Vu1RgB0iRuZKF3iXEqyWAEaLxRLtnPnzmKN+ldExJQpU4q1vr6+Yq01Mi+iPcaKalm9NRIjmzMoEoMirmiupnko0xpjksU/Ze1wMs2dO7dYo1ijCI7+oDWb4kSytjkVc1/rObvE07UeS9eaxUe2RvhlMZA7duxoOueePXuKta1bt+I5KaaPfpeup0tE6USN+da1tTWaLaK9/1H7ZCiOasuWLcVaFk/UuregtSq7z9aIumxdoTaidx46Z/aeRXM5vfPQvdC1RnD70nrfpf8ZYyVJkiRJ0gRzAy1JkiRJUgU30JIkSZIkVXADLUmSJElSBTfQkiRJkiRVcAMtSZIkSVKF6u/v02fLs0iMoaGhYo0+J0+fJs9iByYiKmKiPpXeJYaDImDoeikKiD59H8H3Qm2bfcKeIgsoLoM+8Z/FA9A10b1QdEUWa0Ht1xqd0sVExcC09vms/eiZbt68uVi79957m64ngsfL+eefX6xRlEaXWDxq2yyiiep0n12ihGguoogwimKiiKsInsfoekZHR4u1LIrviRRjtXTp0qZaBEeGUJ+mZ9kpLgSO7RLJ2BoV1CVGqHUep3id6dOnN5+Toh6zSDyab2kc0dikWMqaekmXd7vWtWyi4qZINhe3jqUu7dfa/+65555iLZtPKNqN1kCKx+ryntAlrpbeeWgsdeljFGOVRYiVUMxcBMfyUh+j42h/EJHPncS/QEuSJEmSVMENtCRJkiRJFdxAS5IkSZJUwQ20JEmSJEkV3EBLkiRJklTBDbQkSZIkSRWqMzfoc+jZp9Lp8+30OXmKbaCYjex3u8RRkS6xWyVdImnoudCn77Poj0cffbRYo/vMImAo9mLDhg3F2v79+4u1rP1aI1Co/2UxYFSnz/GTiYq4ylAbtUZVZfFE1K8pVoXiDLK+SXFx/f39xRpFaWTjjOYT6n/Z3EiRGNQO1G+zPk/PjJ439aEsfoLG0tjYWLFGkXkHDhzAc2bzzU+K1qjC1hiXmnoJjZMs6qZLtFZJdh80FlrvJbsPismh55lFwGzcuLFYo/WcZHMJzZtd5ltCz5Teh1rHShdd+jzVuowzageKNlq/fn3zOSluj2KYaP3M+hCNbXre2ftba9vT9Wb7oNb3j2z8ktYYV3qnof1BRP6+SfwLtCRJkiRJFdxAS5IkSZJUwQ20JEmSJEkV3EBLkiRJklTBDbQkSZIkSRXcQEuSJEmSVMENtCRJkiRJFaqD8dasWVOsZRnHCxcuLNYoQ7Wvr69YmzNnDp5z6tSpxRrlm1GtS24rtVFr9l4E54625sNlmXSUz90lY5vOSzVqA8qFzOr0zCh3bvfu3XjOTZs2FWuUXduav53VqZ9kv0vHUj+ZMmVKsZbl8lFfoJxealuaLyIihoeHi7WlS5cWa/Pnzy/Wsral7NYuWYutmfSU0Ui1Lr/bJVeY2oj6EI2zLC+8y/x3om3durVYy/KqaV6kfknrcpZPTmOwdf7P1mw6tnU96pJ3TdfTJYeXcuxJNs/QOkg16l9ZvvuMGTOKNepj+/btK9a2bduG56S8a2qj1jkoQ3Nm1hdoTFD7UR/K5n/qu7Quj4+PF2v0DhHB89+OHTuKNZrDsrale6F5k46L4L7Sun/I3gNa88279Gu6F/pdGmd0H9mxGf8CLUmSJElSBTfQkiRJkiRVcAMtSZIkSVIFN9CSJEmSJFVwAy1JkiRJUgU30JIkSZIkVaiOsXrggQeKNfokfATHWC1ZsqRYW7RoUbFGkQQR/Dl++rQ7Re9kURDZJ+5L6HPy2TnpU/4TFTdFWiMSIji+YnBwsOmcdFwExwxRXAbFDmzYsAHPOXPmzGLt/vvvL9Yo0iH7VD9Fp3SJHaB+RH2X+kIWT5HFxJTQ+MxifSi+aHR0tFjbvHlzsZbdJ8WhURTa2NgY/u6ePXuKNYojoevN4uJ27dpVrO3cubNYo3uh38zQ8x4YGCjWsvjELnFeJ9q9995brG3ZsgWPbY2QpPl07ty5eE7qQ/RMuqxzNEfRmKc5KItFoWuidqf2yeYSesegY2fNmoW/S8+U7pP6UDbGqC9QG+3du7dYy8YDvWPQukJzVPYO2xqVlq3nExVHSCjCqXU9p+cZwesctT2tu9nYpne09evXF2sUkxYRsX///mKNnjdFA2bPmiJBqf2oz1M/yOo0z9PeIYsozerEv0BLkiRJklTBDbQkSZIkSRXcQEuSJEmSVMENtCRJkiRJFdxAS5IkSZJUwQ20JEmSJEkVqmOsRkZGijX67HvExHxOfvbs2XjO1s+aU7wOfUY9gj8LT5/qp+MofiKC4xUoLmnevHnFGsU3RfD1UixI9rsUaUZxGXROiryI4Kg0OifFWGXnpHagGkUd0PiM4LFEcQVZxARFlVC8B/Xr7JzU/2iMTp48uVibqBgrOieN3QiOg6DYlW3btuHvUt9tjYro0n7UN+leaF2J4HieBQsWFGs0J2TrDo2lk40i1LLoSVojKWaI+ixFsWTn3Lp1a7FGEX5Z7Az194cffrhY6zKuKWppaGioWKO+R/FXETyndlk/qX1p/aS5pL+/H89J7y5Z7FZJFv1H7fDggw8WazQGs+ii1gisLCqI0PPMxhKhMUoxTPSekMWA0bpC0Ym0ntNxERHbt28v1u6+++5iLesL1A609tIYzNZsWssoQozaKOubNHfSnEvjPpsTukT6+hdoSZIkSZIquIGWJEmSJKmCG2hJkiRJkiq4gZYkSZIkqYIbaEmSJEmSKriBliRJkiSpQnWMFX1+PPu8PX0mnOJYSBaJQZ88p+uluBX6FH8ER2BRPBHFrWSfmqf6woULm47Lztka10VxGRH8yX36vD39LkV5RXAkBh1LbUD9IIKjSoaHh4u1VatWFWv33nsvnvOBBx4o1igCi6JcIjiqip4Z9ZMsxormIhqjFHuRzWGtsStZXyA0N953333FGsWqRPBzGRwcLNboPrPoHorOotgLitLI+iZdU2sUXzafdInEONEoHiZrOxpH2fgsydqG1h3qPzSus6gbek+giCtaq7IYJpprKOKK2j2Lh6G2p3eeLEaTorVozqT7zCK56JwUsUb9Kzsn9RN63hRPRL8ZEbF27dpirUukFM191EY0n9Jc0+V66D5pTojgGDCKxaP38ew+KXZx06ZNxVoWQ0p9hWpdIkppXqAISapl6w7dC71L0XtoNs669F3/Ai1JkiRJUgU30JIkSZIkVXADLUmSJElSBTfQkiRJkiRVcAMtSZIkSVIFN9CSJEmSJFVwAy1JkiRJUoXqHGjK0srykSmvjfKcKcctyyekbLnR0dGmWpZhNnXq1GJt0aJFxdqyZcuKNcqbjODcQ3pm9Ewoyy6Cs/laM3oj2jNUKbc1y3ijDFrKwaMcS6pFcCZ4axZ2lndKOYN79+7FYwnlRlL/o2edjTPquzRntM5DEfxMKZeUnhnlTUbweKH2o3kogvNtKbuVjsvGbmtuKc3jVIvgDMyxsbFijfptNlZaM5InAo3N7Hm05q9SBiitrRH8PGn+2r17d7FGfSCC+zTl+1IeeJb9Tms2vdfQfVK7R3C/pAxVap8IXntpnaPjusxfdC80HjLz5s0r1mi+oDbI3ofoXYr6dTYX0zijtqV+na3Z9B5Gfb5L9jm9242Pjxdrs2bNKtZoPc/Qc8neBWi8UL+m8ZC9G1Od+hA9syy7m8YEtX3rO01Evpck/gVakiRJkqQKbqAlSZIkSargBlqSJEmSpApuoCVJkiRJquAGWpIkSZKkCm6gJUmSJEmqUP1N9qGhoWJtx44deCx9upxq9Kn+7LPv9Fl9+rx9lxgrqlMkBkUdPOUpT8FzLliwoFijz/zTZ/yzGKssGqQki8Sg+Ap63vSJ/yxWhj7V31rL4nUIHUuf8acojQiOXaHP/FPbRnC0BcW10PVk8QrUP6mNaNxn80lrfAXFSNC4j+A5g6LvsngUijyjOYz6ZhaLRDE7tAZQbFQWPUbXRLFI9LvUbyO6xZycaNS/KKIvgtuA+hf1rSzGip4XHdt6rRE87imejua2iy66CM9JMTk099G8t2nTJjwnjSOKcenr68PfpdhFWhtoXekS6UNRQTQXZ+OW1leqURtk8Tp0LL0rZTFW9B5GfXNgYKBYy+KJaPzSuyjNJ12iE+l6aZ3LIuoo7pLipqgNsmPpPulZZ+/GtEbQGG2Nz43ge6E5jJ5ZFlOVzTfEv0BLkiRJklTBDbQkSZIkSRXcQEuSJEmSVMENtCRJkiRJFdxAS5IkSZJUwQ20JEmSJEkVqjM3LrzwwmIti8SgT9FTJFJrxFUERzhRVAsdl332ner0qXSKorr44ovxnAsXLizWKPZiy5YtxdqqVavwnBQbQp/Np1iQDLUfxRlksQOtEU4Ue5F9qp/GCz0zijrIYoQo9mJwcLBYy/o8xV5Q3xweHi7WslgB6rv3339/sbZ+/fpiLYsxoTE6f/78Ym3x4sXFWjYeqJ5FYJHt27cXaxRJ2DqPZ6jvtsa6RXBsGV1v63ER3SIxTjTql9QHItpjXlqjY7JjKY6Kjssi8ahOa8e5555brK1cuRLPSb+7a9euploWEbZx48ZijdbsrL/TGtk6R1HcVFan9Yre+7K4M3rnoXlxoiLWKEYt6/O0rtCaTfNJdk6KDVy9enWxRmt9FitL73atkakU5xvBbUtxStSHInhNotru3buLNYqWjODIRlp7qUYxXxH8jkvPe2xsrFijeTMij0Mj/gVakiRJkqQKbqAlSZIkSargBlqSJEmSpApuoCVJkiRJquAGWpIkSZKkCm6gJUmSJEmqUB1jRbEz9957Lx5Ln2+nSAyKJMgiAOiz+q2fYKdrjeDP39Pn2ykKqL+/H89JkRhUo7bdu3cvnpM+GU/nPOuss/B3KZqB4jImT55crFFcQXZO+l2KZMkiYtatW9d07NatW4u17FP99LzpmWWf+Kf2o+gnmk/mzZuH56SoCBpnFEGRRezQ79IYpTmBxn1ERF9fX7FGER1ZpAjFqFEcDkX8UVxLBEflUD+hiLUlS5bgOWkstUanZPeZrUsnE43NLMKkdR2k47rEhdCxWewRoX5JY4zGfLbm0DnpXYnGdRYlSutDl+dCbUQxOPQuQPNMBI8xaiNaA7P2o3ceiv6j+CaKrIzg+6T3oSy6k9ZXil2kiCu6noiIRYsWFWs0T61Zs6ZYy+YwaoeBgYFijfot1SLa3yez/kdzHPUTaiOaayL4PYsizaifUORWBEeN0rpM4zPrJ1kcGvEv0JIkSZIkVXADLUmSJElSBTfQkiRJkiRVcAMtSZIkSVIFN9CSJEmSJFVwAy1JkiRJUgU30JIkSZIkVajOgaZMsCzPlHIRKTeyNTs5gnPTqHbo0KFi7cCBA3hOyoijPFjKFt28eTOekzI5KTeNss+yTD/KjaQ2yvLYxsfHizXqQ5T9mGWE0u/SsZQ79+CDD+I5KQeasp53795drGXZ3ZRzTH2IxmBE+zijc3bJW6cxSGM7yx5tzXinsUR5sBHt81R2LzTf0PilPp9lKVJuLuVAU2ZpNrYp65meGa1JlPka0S1T8kSjtqO5tqZeQtmr2ZpNuaT0TOhaaZxERMyYMaNYo7FLa1nWdnQvNMa6jD/KQKZatmbTekXtR3NUNq4p95beTWjeGx0dxXNu2LChWKO8ecq9zTJxs77bitqIxiBdD+WBR/A4o0xhyk6mbPMI7kdz584t1mitz7KTCR3b5Xfp3Y7WcxpHETxfL1++vFijd+psD0W539///veLNVrrs/dJ2j9k/Au0JEmSJEkV3EBLkiRJklTBDbQkSZIkSRXcQEuSJEmSVMENtCRJkiRJFdxAS5IkSZJUoTrGiiJXhoaG8Fj6dDnFXixatKhYmzNnDp6TPl1OcUAUU/LQQw/hOenz7VSjuKmvf/3reE76zD99xp8iHSgKKKI96mbbtm34uxQHcf/99xdrg4ODxVrWN9euXVusUQzHvn37irWNGzfiOSmajKIZKK4gi7ygOsWqUC27JhpL1DezSBG6F4pzoXNSdEVWP+uss4o1igzJ4hPoXqgNsnmKomnoedL1UhtEtEdiLF26tFjL+iatEa0xavQ8I/KYq5Opr6+vWKNYygheI2nNoedM0TERPMZoXqR1JYtNofcauh6Kb/qnf/qn5nPSfdK6kUXSkC4xVnReesegKEeKX4vgPkZtS9czMjKC56R3NIqQbJ3DI/j9g+YhmsMjuI9RX6DYreyZUUwTnZPQ3iGC24+OpbUsi4uj+YaeN/XNCB6H1P+ydwFC0WT0vGndyZ41xZ3Ruky/m42HLtGT/gVakiRJkqQKbqAlSZIkSargBlqSJEmSpApuoCVJkiRJquAGWpIkSZKkCm6gJUmSJEmqUB1jNW3atGJt5cqVeCzFDNHn+Okz6hRlEMFRG/Q5dIpleOSRR/CcVKfP29Mn6u+66y48J33+nj41TxEvFAURwfEAhOITIvhz/PTM1q9fX6xlcS3Uxygmhz6bTxEwWZ3uk6IgKKIpgiOI6Hlmn/invkt9fvXq1cVaFnVAY59iEChKLouxau3z9Dyz+YSeaZfoGZozWiMdsvaj59Lf39/0u1mkFP3uwMBA03FZTFUWm3QyLVy4sFjL+h6tn1SjCMT58+fjOelZ0ziiSMFszaFxRG1EY+hf/uVf8JytkYJ0n7Q2RPDcR22U9efW2Eq6z2wuof5H8US0BmZxP9QO1IdaI5oiuM9T1FJ2Tpo36XlSPOeGDRvwnHQv1Bdo3aU9SQS/49J4oWvN1sfWd7us/9EYpfWe9jPZe37rPE/v1Fl0Jz0XijWmmNvsPum5ZPwLtCRJkiRJFdxAS5IkSZJUwQ20JEmSJEkV3EBLkiRJklTBDbQkSZIkSRXcQEuSJEmSVOGExFitWLECj6XYBqrR5/YpriCCYxvoXuiz+VlUEH2KfuvWrcUaRQBkn82nOl1Pa1xSBH8avzUeIILv5eDBg8UafcY/i50h1P8oRiJD/Yh+l2pdoscoroAiJiK4L1Ckw+joaLFGcWYRHJNA8UQU65bF4lG/prmIIkWo7SI48oEidijuJoLHGV0v9TF6Jlmd5mrq89k8RaiPUd/MIsK6RGKcaBQfmc1fNI6oH1AEURYpSPPQRERcRfA4on4wPj5erFHcTwRH0tA8TfNFFv1E7zwU0URxXRH8jkFtS88si6ej9ZPWK/rdbNzSOel3aY7qsrbS9WT3Qu9SrTWKLItoj/ej+MO5c+fisbR+9vX1FWs0lrI9ALURtUE2T9GcQWsZjUGabyN4LqI1gPp1FmNFv0s16vNZrFuXqDn/Ai1JkiRJUgU30JIkSZIkVXADLUmSJElSBTfQkiRJkiRVcAMtSZIkSVIFN9CSJEmSJFVwAy1JkiRJUoXqHOiZM2c2n4RytiiTlLK9sgyzOXPmFGuUAUd5iVluH2XAUQ4q3UvW7q15upRJl+WiUR4btV+WdUe5wdQXKC+Rnkn2u/S8WzPyItpzl6mW5RO25utlfb41K7s10y+CMycpb3316tXF2tKlS/GclF9IOdCUKZnlQFM7UL+m7MfsvHQsHXfo0CE8J/UTqnXJO6U5jvLh16xZU6xR/4rolil5olHucpYbTOsOjYXWcZLVW9fsDF0vZa+uW7euWMvGNaG1gTKFaT2K4DaidzB6h8iuieYEqmVZsaQ1I3qi1uws05rQXEJ9LHs3bm0jQu9uETyW6F4obz17t6M9APWx1jbIroky06kWwW3UOh/T2I1oX5dJNrYpd572OrQub968Gc9Jv5vxL9CSJEmSJFVwAy1JkiRJUgU30JIkSZIkVXADLUmSJElSBTfQkiRJkiRVcAMtSZIkSVKFExJjlcU20Cfa6XPorZEqEfyJdvrMfxYHQSi2Z3BwsOl66FP8EfwJdqpRpFQWSUOxFxSXkX36vjX+o0ukD90r9SG6lyyuhSLN6JwU25M9M4ojydqIzJgxo1ijOYPGSnYvIyMjxdrY2Bge23rOuXPnFmuLFi0q1rrESLT2vwz1IxrbFDGRRX/Q77bKYqxa43molsW1dBlLJxrNQ1m8DvU9mqepzbM+S3MC3QutOVk8EY1P+l261izKjOZ/up4u8UMU6dblPYvqrTF82TmpfemcdFw2/7dGBZFs/qJrovdU6psRPPYpLo7uc8+ePXhO6mPUN6kvZPe5c+fOpuuh59Jl3aU5tcu7AKF7yfot1el3u7ynUtwZxZfSXoei0CK4/2X8C7QkSZIkSRXcQEuSJEmSVMENtCRJkiRJFdxAS5IkSZJUwQ20JEmSJEkV3EBLkiRJklShOsaKIh2y2AH6HDrFGVDsQHZO+lw6fYp+ypQpxVoWidEa20Cf1M8+m0+ft6foD4rOyiJO6D4pAmbq1Kn4u9TH6BP2FFeQfcK+Nc6LYiSobSO4H1HbUj+h/pXV6Xln/Y+eKT1PaiOKS4rII1taZDFLWTRNi6xtqY9RLftdihWkuZraKItvmqiol1YUgUK17D6zOJcnimwMUZ3WXmqfrA/QWkZxjjR/TcRcEcHz3qxZs/BYmt9ozaF1I1uzaf6i+SJbsynGcMeOHcUardnZGKJ5iPom9a8sEon6ER3bup5nx5JsrWp93nQ9FCOUXRO1Ax2XvSdQnd6H6D67xP9RH+rynk+67KFa4+JItgbQ+zjNGRR/Re87Ed3e7fwLtCRJkiRJFdxAS5IkSZJUwQ20JEmSJEkV3EBLkiRJklTBDbQkSZIkSRXcQEuSJEmSVMENtCRJkiRJFapDEil3NMvQo7wxyvaiTMQMZQm2ZgV2yZSk3Dm61s2bN+PvUs4x3Qtl9FK2YwRn+M6cObNYy/Ixh4aGijXKGaQaZVFGcL40PZfWXMMIzsKjtqWswC65riTLCqR+TfdCfTPLCqQsxtZM66x9qB0OHDhwwo+L4DZqzZyP4HmVfrdLjiqtH/TMWvMvM63jNxvbWZ74yUTjKMu+pPsYGxsr1mguzsYYjQfKge7r68PfJdTfqUbtk+XTUi5pa15z1raU77tkyZJibe7cufi7CxcuLNZa1+UtW7bgOan/Udu3tm0EZ3BTjebabG5rzafN3pup79KxVMvmPVoHW/OGs3PSOKM1kub4bJ1rlfU/WiPpmlrfhyImbu0ltGZRH2rt0xHtmdYR/gVakiRJkqQqbqAlSZIkSargBlqSJEmSpApuoCVJkiRJquAGWpIkSZKkCm6gJUmSJEmqUJ1tQ58Qz6JuKHZg+/btxRpFOGVRNxSvQLEDFPeQffadPoc+MjJSrK1ataqpFsExVuecc06xNjg4WKxR/FBExPTp05tq8+fPx9+lZ7p79+5ijfrQpk2b8Jz9/f3FGrUtXU8WY0L9hPofxRV0iZuiz/xnfZ5+l2JM6HopWiyC25f6UGssVARHW1Bcxr59+5quJ4JjfbrEWLW2H80nWURdl3m1VWuEGD2zLhF1JxvdYxbtQXMfrctbt24t1rK2mT17drFG8YgUyZhFAbWuObQu33333XhOehcgFOVFEZARvKZTu0+ZMgV/l9qP5kXqXxs2bMBzrl+/vlijeCyaD7LxQHM11aj/UftE8FzTJeK1dc2m42jOjODrpT5EtWwupvFL/Y/2K10iNul5d1lXKMKO4iNpPc9MxPOM4PajOaw1MrUr/wItSZIkSVIFN9CSJEmSJFVwAy1JkiRJUgU30JIkSZIkVXADLUmSJElSBTfQkiRJkiRVqI6xok/C7927F4+lmKG1a9cWa6tXry7Wstie7JpafjeLinj44YeLtTVr1hRr9913X9NxERF79uwp1ij6g+IVKC4pq1OkyMDAAP4uHUufzafYoxkzZuA5586dW6xR21JETBZJQPdCNXpm2XigvtkaNxLB8RW7du1qOm7nzp14zomIscrQ71LbUvtlMSat8ShZjBX1azqWIjEyFFVFbUtxXVmUC40Jalua3ygyJGLiIrla0LPM+gjF2VDM0MaNG4u1bI7q6+sr1igGjZ4lRTRFcDtQXNcdd9xRrGXRkzR/0RijeSaLm6J1kGI9s/WTjqXxSc8lOydF5lEEUeu6G8F9tzVSkN6pI/h50xqZjW2q03szHUfXmqE5k8Z2NhfTukL3SeOe5sUI7iddIi0JvQtQLFT2nk/HEhoPWVwotQPNjRSfS3NURD5eyBNntZckSZIk6QnMDbQkSZIkSRXcQEuSJEmSVMENtCRJkiRJFdxAS5IkSZJUwQ20JEmSJEkVqmOsKHoh+xw/1SlShT41n302f2RkpFibPHlysUbxRBS5lR1LUUsUVbVp0yY8J32CnWJBSBYPQJ+Tp4iJLCqCYgco6oDiupYsWYLnpDgNigegz+1nn82n+CLqtzRWskgk6psU/UHRCxE8DluvNxvb1PbUT+i5nHPOOXhOOpbGIMVeZHFn9MxoPu4Sy0DtQGOFxm4Ej306lmo0PiO4n9CcMW/evGIti1h7IqH2of4TwRF0tC536ZfURyhahuJWKP4qgu/lgQceaKrRfBrBfZrGH0VVZRGRNN9Su2exbPQuRWg+zcY1ReH09/cXa/Rek81fVKc1sjXmMatTBBG9a0Zwn299587iieidkZ4L3WcWd0bjhe6F2i+Lx6X1gWpZpBStV/TOPTg4WKxlsZTZPqBFNs4IzRlz5swp1rJ+Qu9ZGf8CLUmSJElSBTfQkiRJkiRVcAMtSZIkSVIFN9CSJEmSJFVwAy1JkiRJUgU30JIkSZIkVXADLUmSJElSheqgL8opy7IAp02bVqxRvhnljma5t5QZRsdu3bq1WMtyjCnzj7LlNm7cWKxlWYGE8jEprzPLp6V2oKzPLFO4NQf60UcfLdayfELq15STR/milD8Ywe1L10NtkN1na3ZrNs6oTjXK+syyW2k+oWc2ffr0Ym1oaAjPSfms9Dyp3SnzO4Jz52meyrIWKSt1eHi4WKM8xaz/Ue4m9RPq89l8THV6nnPnzi3WKNc7gueiJ5Ksj1AGKOWO0lyS5W1Sbndr1nr2vKh+3333FWs0/rK8a1qXW/t7Nv4ItXuX36X7pFqWP0tzPL2L0vxP1xPB6xXN8bt37y7WsuxfGqN79uxpOi6C+yeNUWqD7JnRc6Fj6V2A9gcRnIFM7xD0PLdv347npHmBfjfrf9SP6P0jy0AmrfMUobkm+10aL13ywsfHx7FO/Au0JEmSJEkV3EBLkiRJklTBDbQkSZIkSRXcQEuSJEmSVMENtCRJkiRJFdxAS5IkSZJUoTrGimI/sqgDil9YsWJFsTZnzpxiLYvXoU/u0/VSDEkWT0GfqR8bGyvWKKqKYkEiOK6L2oBiBShaLIIjACiSIIvHoudCtdYYoQiObaC2pfvMYt3ok/ut8VjZfdIYpD6fRWLQsdT/KCIh63/z588v1iiGiX43ixSh+AWKaKL4CWqfCB77FGOSzcfUr+l3af7LxjbdS2u8B7V7BN8LXS/16Sw664mE5iFazyM4yoviY+i4bM2mfkBzH/WDrF9SjBVdT2v/yVB/p/gmGtMRvF5Rn+4SPUb3Qr9Lc2YEx2FSP6Frzdbs1gjJ1sjKiDwaqhVdb7bel9A7YUTEwMBAsUbv+XRcFk9E15SNl5IshonGEtWyeYr6H40HWneztaz1fbxL/6J3DJoX6D6zc7ZGckX4F2hJkiRJkqq4gZYkSZIkqYIbaEmSJEmSKriBliRJkiSpghtoSZIkSZIquIGWJEmSJKlC9Xfy6ZP62ef2+/r6irVZs2YVa/Q59CwSgyJMKDZqfHy8+ZytEVitn76P4KgIaj/6HH8Wr9Aa6ZB9Tp5ifagdWp91BD8Xagdqg+yz+PS7rVEbWSwNjUGKQdi5cyf+bmu8DPVbutaIiEWLFhVrS5YsKdYoEiObw+iZUr+m/kdtl/0uyaJn6Lw0BqmfUExfBPcjiqdo7V8R3PYjIyPF2rZt24q1bDxkUYcnE0W1ZBEwdCzFw9D4y9pm+/btxRr16S5rNvWh1ti2Lms2jb8s9q4VrdnZvbT2dxrztJ5HcF+gNqIYsOydJ6uX0LqRRSm1HtvlXlqjsyh+M4Lf8+fNm1esDQ4OFmu0nkdETJ8+vVijdymK1crQc6F1hdbWDM1xmzZtKtay6DGKC128eHGxRv0rmy+oHWjOoFq2BmSxn8S/QEuSJEmSVMENtCRJkiRJFdxAS5IkSZJUwQ20JEmSJEkV3EBLkiRJklTBDbQkSZIkSRWqv1lP0QvZp+/ps+b0OXk6jiKjIiJGR0eLNYoHoN/N4hUogogiaaiWRVdQDAd9Ep5iXHbs2IHnpMgCaoMs3ol+l+6TPmFPESfZsRRJQDEmWbwORTrQOWmsZNFP9FxI1v9ovLRGP9F9Zui5UFxBFunQ399frM2cObNY279/f7FG8R0RHAl00UUXFWvr1q3D392wYUOxRte7evXqYo3aPYL7AsXLkCwSg+axrVu3NtVovojIx/7JRNF2Wexd61ih+8/ahsY9xYdR1FIWY0jzUGu8ZBb9RGOBxhHFN2X9kt5d6Hlm6watV9S2tC7v3bsXz9kandUlIozGCx1L78YUsxTBaw6tu1kkEh3bGrFGa2AEv9vRuKe4vSxuitqXavR+lq1VFLG5cePGYi1bs+lYet5Uo7U+gvsuzRnLly9vPidFGdJ6TnNGNl8YYyVJkiRJ0gRzAy1JkiRJUgU30JIkSZIkVXADLUmSJElSBTfQkiRJkiRVcAMtSZIkSVIFN9CSJEmSJFWozoGmvL8M5WxR1jNl0mX5hJQ7R9lolPeXtUHrvVD7ZPmqdGxr7mGWI0iZnMuWLSvWsnuh/D06ljIlt23bhuek/Dh63q0ZeRHtWbGUQZhlJ7fmME6ZMgV/l+qzZ88u1qiPZXnhDz30ULFGGb40Z2T3SddEbUt9gY6LiJg/f36xtnjx4mKNsjMjuM9v2rSpWKNxRsdFcE4tjYfR0VH8XUJ515Q3SbmRlG1bUz+ZaD3K1jJaV1pz2rO2ofFAx1I+cpYDTfdCY56ynrN1juq0LtO7SfY+RH2aMqJpDs+uifrQ2NhYsZblQFOWbGv2NLVBBLdDa956lp1M74y0XmXrCuUn07xI6262ZtNcRLnUNLazfF9qe2pbar/h4WE854IFC4q1hQsXFmvZmk3vSzTH0XHUthE8T1HbP/3pTy/Wsn6yZs2aYm3Lli3FGvXNTHZNeGzzkZIkSZIk/SviBlqSJEmSpApuoCVJkiRJquAGWpIkSZKkCm6gJUmSJEmq4AZakiRJkqQK1TFWFMdCn6GP4M+h07FUy6KA6FiK8KBP2E+fPh3PScfSZ/OpfSguI4JjG+hYar/sk/D0aXyKiqCIoQiOdWiNMcmiDrK+W0JtlEWnzJs3r1jr7+8v1oaGhoo1iqaI4LgW+l2KZYjgqCWKINqxY0exRhEnERyJRH2enll2TvpduheKp8hiTFrn3L6+Pvxd6gsUEUNzxsjICJ7zzjvvLNZoPlm1alWxlsUp0e9SFF9rfExEHif0RJFFSlGcEs231H+yuZjWQYoNpHU5G2O0ZlO8SZd3E2pbOrZLdBbFNNH8NTAwgL9LbU/jiMZf9v5BMVetMWC07kbwOrh06dJircv8P3fu3GKt9X0ogp83Rf/RHE/vfRH8vGnOoHN2idal+YT6CR0XwWt267obEXH33XcXazQv0JybjTOaU6hGkZbZeypdE63n9FyysZ3tsYh/gZYkSZIkqYIbaEmSJEmSKriBliRJkiSpghtoSZIkSZIquIGWJEmSJKmCG2hJkiRJkipUx1hRpAPVIiJ6vV6xRp9Dp0+aZ/FD9FlzinmhT9ifddZZeE6KUKCYHIoVyD5vT+1A7U7XSjERERHbt28v1ihCYe3atfi7kyZNKtayti+hNojg9muNF6PoioiICy+8sFj7qZ/6qWKNIkMoiiq7Jhq/FKUREbFs2bJijfru+vXri7U1a9bgOTdv3lysUd+lPpRFj1F0FsVTUPtl8QmzZ88u1uiZUZRGBEePLVy4sFhbvXp1sZbNxzSXr1u3rlijuYbmi4g8cqRFFmWYxXScTBSpRfN/RPvaS2M+OyfVKfKH4myyWDFaryh2kWJ7usRYUY1iXDZu3IjnpEhBGn8U8xXB70v0XLq821H70rxI15Otc9RPqI0osjKbv1pjUbN3HlpXKPKH3mEpuiiC13v6XXoHy6IT6Viap6h9Zs2aheek50K1bM2mfkRrTmusZ3Zs6/yX3Sedk2rUttmaTc8741+gJUmSJEmq4AZakiRJkqQKbqAlSZIkSargBlqSJEmSpApuoCVJkiRJquAGWpIkSZKkCtU5HxQtkMUOtEZVZZ9ZJxQBQJ99p7iflStX4jnpE/cUT0Htk0WxUDwFtR9FHRw+fBjPSVFB9Al7+tR8pjUeINP6XKj99uzZg+ekPrZgwYJijWJMsogYivegeKesbSnGhGoU/ZTF4lGdYo/ouWRzDT1vilegeejgwYN4Tnqm1LZZlNL555+P9RKKv9qwYQMeSzFhFClCtWxupD42ZcqUYo1iLwYGBvCcFAN2stE8nkWoUWwPjRV6XlkUIcUM0TjqMhaoj9B7Dc1Bd999N56zNQKG5oNszaE1m6K+shiw1nW5SwxTa6QqxUbRWp/97uDgYLG2YsWKYi1bW1tjPbus2RQzRGOJfjPT+i6VRbzS+kqxlHRctubQux09zyz6dPny5cUatRH97pYtW/CcNGfQeKFaNk/ROKO2p/GQtS2tARn/Ai1JkiRJUgU30JIkSZIkVXADLUmSJElSBTfQkiRJkiRVcAMtSZIkSVIFN9CSJEmSJFVwAy1JkiRJUoXqHGjKG6bMyAjOnKTMNcoTy7JiKQOzNU+MctwiOPOP8h0pk3T27Nl4TsqcpExcynjrkilM7Z7lhbfmblLuYZZjSX2T7oV+l55nBPcFylilcZZlIlJGeZdMSWqH1n4yNDSE56Q8bHqerZnfETxP0e/Sc+mSPUq5uJRLGsG5m5SBTDnQ99xzD56T5inKo2zNoozgvkmZw9TnZ86ciefM+u7JRGOhS3+neYjmti757q0Z0ZRrG5HPby3nzHJF77333mKN3rNoLsnWVhrzNBdnaO5rXRsydCydk9ooy/fduXNnsUYZ9zSOuuRdU7/N7oWObW3bgYEBPCe1A80ZdE5q9+yc1BdozqBrjeBnSs8ly9FeunRpsdb6XLJ5avXq1cUa5TlTG2XzFN0LtR8dl83xtLZk/Au0JEmSJEkV3EBLkiRJklTBDbQkSZIkSRXcQEuSJEmSVMENtCRJkiRJFdxAS5IkSZJUoTrGiqIr6HPxERx1QFEb9En4LAKAfpfupUs8ANWXL19erNHn5LNPsFPsAMUpUVxG9ql5OifVKKIpgmM66HcphinrJ9R3qQ/RZ/Oz6CxqX4qXoX5LMRsRHL9D7Ue1iPZ2oH6dRTrMmTOnWKM+RPEK2RxGz4z6SWssWVanGJ2s/9F8Q3FnNJbGx8fxnNu2bSvWKBKjtd0juB3oXmi9yuaTrH4yUdtlcSxZvaQ1IjJD8yLdZxZRQtGUFEnW5T2BomU2bdpUrD344IPFWra2UhtRLYu4ysZgCY2TbP5qHdd0L1msG60rFKdEkYvZGKO27fLOQ+g9i/o1rUcRHMdK46w12jSC1yRqe3pm2Tij9whqv+xeaJ6ieEk6Lhu7tC7Tu0mX+aQ1xqq1FpHHzhL/Ai1JkiRJUgU30JIkSZIkVXADLUmSJElSBTfQkiRJkiRVcAMtSZIkSVIFN9CSJEmSJFWojrGiz6Fnn82nT5fT77bG4GQodmD37t3Nv9saBzRt2rRijSIvIvgT9tR+FB3Q5ZP69FyyCICRkZFijfoYnTOL5KLnvXfv3mKNog4oBieCIzMoXoGiAyhmIzuWIiiycUYxMfTMqA2y9qP+R2OpSxRfa1QV1bZu3YrnpPbrEkNHfYzanmItshgY6kfUhyhiIovEaJ2PqZbNjVk02clEfTYbY7R2UAwaRYZkkVJ0vbRmUz/InldrvBO1D8XsRUQsXLiwWKO2pVr2DkbPheaDbFyPjY01XVNr22Z16ic0NicqYo3O2SUSqcv7b2sMGMnaj9blwcHBYo3GbxZ3SfdJ6yc9l40bN+I56Vhay7L5mOY4mjdb32Gza6L+12UstT5v6l8zZszAc/b39+cXVuBfoCVJkiRJquAGWpIkSZKkCm6gJUmSJEmq4AZakiRJkqQKbqAlSZIkSargBlqSJEmSpApuoCVJkiRJqlCdA00ZZpQxGJHndZZQ1liWm0YZZzt37mw6jvISIzjDrDVHm3LcMjNnzizW5s2b13RcBOcGUx5blu9ImZLU9tTu2TmpfR944IFi7Y477ijWsixxyo2kPOcu+eWUe0jjM+sLlE1K+YR0vdnYpuedZbCWZHMUXRNlmtJcs2nTJjznP//zP2O9JMvbpTq1LfVbus8InldbM4epFsH5j319fcXawMBAsUaZpRE8Nz6RZPnItN5TzmeWQU5oLqZ81ZGRkWIty3encU33SetRlu9L70tUozU763fUb7NxROheaU6ld7tszaZnes899xRrtJ5n+e00Z9L72549e4q1bdu24Tmpb9L8leUj0720vnNn6yc9U+rzNA/Ru2ZWHx8fL9boXWnr1q14zqwflXSZj1vzpel9O4LvpXWvk2VE0zw2d+7cYo3mt/POOw/PuWDBAqwT/wItSZIkSVIFN9CSJEmSJFVwAy1JkiRJUgU30JIkSZIkVXADLUmSJElSBTfQkiRJkiRVqI6xav30fUQeS1NCkRhZVATF5NAn7CkSI4tqoeulqAOKh8nuk85Jz4U+J5/F4FC0EX1OPotEoqgbiq6gz+ZPnz4dz0ntSzE49In/devW4TmpfSn2YsuWLcVaFh9DEVizZ88u1ig6IILjNKjP0/PM+h/1XYrwoLiMLAaMIrloXli7dm2xlvUTet4Uo5P1+f7+/mKN+nxrZFkEP+/WGBOKm4qIWLp0abG2aNGiploWnZJFOp5MNP9nazaNXZpvaR7KIl7oWIpcofFH63kE90uK5qF3miyGidqBxjW1O43bCI73o3GURVy1vvPQHJ5FItEaSddDx2WRPoSiJ1etWlWs0XtfRMSsWbOKtaGhoWKN5vcIXu9p7aA+n73j03sWjQd6nln0E83FdL00Z2RrNr1nZWskoXciep5dIuqojaht6Zz0vhjBa/aKFSuKtfnz5xdrw8PDeE56x8j4F2hJkiRJkiq4gZYkSZIkqYIbaEmSJEmSKriBliRJkiSpghtoSZIkSZIquIGWJEmSJKlCdeYGfbY8i22gz81TZAF99j37bD59Gp8+Nb99+/ZiLYvE2Lt3b9P1UHxTFmNFcRD0eXZqA4r2iODnPXXq1KZaVqdP41NcRhYdQLEhFIk0b968Yo1iLSI4moH6wpo1a4o16rcRHIlEEQDZvVD/pMgCitHJzkn9k543PessxorGL8WRUHRKNp9QO1Cfz+L2qI9R3A1FnGSRIq2RehTlsnDhQjwnxV5cdNFFxdp5551XrGWRF1k7nExdogrpPuh3aT7I1hUaRzQ+169fX6zRvBfBY4zeMWj8Ze9DdJ+0NlBUVdbvKPaIatmaTfdK73Z0n1m8E6G+SXFd2XtC671s2rSpWMvmaXqmtGYvX74cf5eigii2kq4nG9t0rxQhRutu9sxoLmqNO8ves+jYLJ6N0DijtqX1nN5vI/h50/s4rZE010REXHDBBcXaJZdc0vS7Waxbl+fyxFntJUmSJEl6AnMDLUmSJElSBTfQkiRJkiRVcAMtSZIkSVIFN9CSJEmSJFVwAy1JkiRJUoXqGCuKgMnQJ9ip1iUShD5T3xq1kX2qn36X4jIo/io7J8XDUFxNlxgr+kw9xc5kETAUQUHXRO2exYBR7AC1EfXNLPqjtY+Njo4Wa9TfI7jtqQ0oriWC+x9F5UyaNKlYy6Iitm7dWqxRbA2NQYqIieD2o+iPDRs2FGtZ22b11uOor4yPjxdrFBuSRWJQ+7VGYkybNg3PSXMRPW+6l2xsU9zjyUZrK821ETy/tUZaZtGTFF9E7x9dIpForGRzaqvW+Z/aL+t3NGcODg42/y49b3qvoVoWY0hzFK3ZtB5lcwm1PUWs0ZqdxedQfceOHcUaRT9F8FjKji2hdo/gyEa6F2r3bM2ZiDkjm8NoPmldz7Nj6XrpeWZRhtS+NF5mzpxZrGWRUsPDw8Xa4sWLm34323cYYyVJkiRJ0gRzAy1JkiRJUgU30JIkSZIkVXADLUmSJElSBTfQkiRJkiRVcAMtSZIkSVIFN9CSJEmSJFWoDq3MsoEJZUq25kZSJmkE57xRNlprDmMEZ6PNnj0bjy3JciwpT5Hy9eg+6TcjInbu3FmsUd5flilJ9dZ8ZGqDCM6cbM2nHRoawnNSPjddL90n1SLaM9WpDSJ4/NIzo2ed3QtlPW/atKlYo3FPGYMRnG1I90lZi1mOJeWLdsmUJDSvUvtRrncE9z8aS9RGWY4l5TvSGKRadp+Ui36y0dqRzQfUdjQnUB/JMmapnq33JdkYmz59erFGbUR9L8uPpvUzm/tazzk2NlasUa5ylxxomr+2b99erD300EN4zuz9pIRy4bNxTXN86/skzTMRPAaplmVaUwY8zRm05mQ50PTMqB1oPs3ex+lYagOa37K2pb4wUWs2jUHqJ9kaQHPnjBkzijXa6/T19eE56ZlS32x9B+vKv0BLkiRJklTBDbQkSZIkSRXcQEuSJEmSVMENtCRJkiRJFdxAS5IkSZJUwQ20JEmSJEkVqmOsukSq0GfW6ViKZsg+m091ii6ic2aRPvSJ9kWLFhVr8+fPL9ayT82vWbOmWHvggQeKtW3bthVr2X3SNVFcAUUHRPBn8+m5PPzww8VaFgNGUQcDAwPFGkViZPEKFFVCbU/9Nov2oDai2BCKjIrg2IZVq1YVaxSP0iUSaXBwsFijuKQu0R80v9H1zJs3D8+5Z8+eYo2eGV1PBLcfjQeKg8jmDBrbU6dOLdbomVFERwSPidYoviymiuYFupeJQPMFzQdZncYutXm2ZlO701ig+Zb6VgRHrtDYpVi7bM2h+1y3bl2xRm2bjXlCbdvlXYDmKIqdySK56JroedN8m52T2ogi1uiZZeekMUjnpMiyCJ7D6D7pWWcxt/Se37rmZBF1dCzNYRRDmt0nPTPqC10iruheaKxk8U6tcwqty1lEHb1v0pxLv7tgwQI8J7UDnTPCv0BLkiRJklTFDbQkSZIkSRXcQEuSJEmSVMENtCRJkiRJFdxAS5IkSZJUwQ20JEmSJEkVqmOs6DPhXT7HT5EPFJFDMSQRHBVEcRp0PVkMyZw5c4q1JUuWFGsXXHBBsZbFcNAn2umcFGO1b98+PCd9cp/ifrJP9VNMwvTp04s1+tT88PAwnpOeKcWSUS0bDxs3bizWKAaHYhuyz+1Tn6dnlkUFbd++vVijOYPOuXjxYjwn9Wt63hRZk8UTUTtQtEV/f3+xNmPGDDwnje0dO3YUa1lEEY3D1nixLKKOxhnVaE6gmKYIjsSgZ0a/S+tKBPc/GtsToUv0JK0BFBtFx2XrCrVtFn9SQutGRMTChQuLtXPPPbdYo4irbC6hqCDqs7Ru0G9GtEfSZJFcNO5pXFN0Z3attA5S9CS9n1EsVER79CRZu3Yt1mm80DydtV9rjCvJ2oDanp4Z1bJ3Y+qb1AZ0LzRfZHV6587isagv0BrZGh8ZwfFONMfRu1K2b6M9H9Xo/XflypV4TnqXz6JG/Qu0JEmSJEkV3EBLkiRJklTBDbQkSZIkSRXcQEuSJEmSVMENtCRJkiRJFdxAS5IkSZJUoTrGij5vv3fvXjyW6mNjY8Xa+Ph48znpM/UUzUARABQdEMGffadP6tPvZp+ap6gNioqgdqcojQiOR6FPzWft1xpVRVFBWXQMnZNie+heshghihag66H7XLZsGZ5zZGSkWKNogSx2gMZo61iicRTBbURxGTQe6JlEcGwDPW+K4Vi0aBGe85JLLinWqN0pLiOC506KKKJ4iizuZiJ+N4s2onZYtWpVsUbxYtkz27p1a7H21Kc+FY890WguzmJTaH2g/k5rQ3ZOmmsoZohiZ7K5hOb4adOmFWu0rmSRPhSnN3fu3GKNYu1ofo/gGBx6t8veP6hO83SXZ0bH0vXQHJ/NXxTJRddDETlZXCNFFVJsWRYXR3MxjdEuazb1eYr+GxoaKtaycUZrNq0dNP9n8X/nn39+0zmzeMQNGzYUazTn0rPO2i+LMC2hNqI41QgehzQe6FnTmhzBfeyZz3wmHutfoCVJkiRJquAGWpIkSZKkCm6gJUmSJEmq4AZakiRJkqQKbqAlSZIkSargBlqSJEmSpApuoCVJkiRJqlCdA03Zj5Q3GcEZZ5TRRTmeWdbd6aeX/9sAZdZR9iNlRkZw7i1lwFG+Gd1HBGfWUXYy5RpSG0Rw5iTlvGUZepSZODg4WKxR9m+WY0nZhvQ8KSMvu0+6XsqqpBzjLF+Psr3Xrl1brGXZ3XSvlH1OGYRZ/6NrohxLyj/OMhGpL1AGIfXbefPm4Tkpw5HGIOWvRvCcS/milMVL7ZMdS3Pj/v37i7UuucKUc0zWrVuH9fvuu69Yu/7665vO2YryfaldIzhnnHJH6bjsPaE1g5bWZRqbEdwvaSzQ2krzXgSvSbSe07ju7+/Hc1J/p3GSvfNQHjZdE7VflilMfYGeN71LUT+I4Pclyjim7G5azyN4zaZc4PXr1+Pv0hilNYfW+qyfUBsNDAwUa4sWLSrWsnc7ul7az1DfpLkvuyaa37J7ofcTevejc2Z7i9b2o7FEa1IE7+toH0n50dl4oP53ww034LH+BVqSJEmSpApuoCVJkiRJquAGWpIkSZKkCm6gJUmSJEmq4AZakiRJkqQKbqAlSZIkSapQHWNFsT3Zp8kppoSiIujz7PSbERwHRJ+pp4ghimzI0OfZKa6rC4rEoNgBihyI4E/qU/tlURH0zOiaKPaoSzwRfcaf7iWLsaL7pAiULN6D0FiieBS61ghue7pe+l3qtxHt0SkUKZJFP1E8CvUFiu7JYn0oaohiJLJ+QrEN1LbUBllcEK0RNDdSv6U4lgiOZKG2pTUpi8TIYq5OptY+G8HPhGLQKGoke08gND7p3YT6QARH223atKlYowiYLB6G1iSKS2qN0utybPa7rTGa9Dyz6ERq3yxOr+U3M62RjFncGbU9jaUsoo/mPpoXqI2yfkLvm/QuQO2XxV3SvEBtT2trFhdK56S27bJmZ+/rJdkzozaiPkRRXxTNFsHzMbU9rdnZ+3j2TIl/gZYkSZIkqYIbaEmSJEmSKriBliRJkiSpghtoSZIkSZIquIGWJEmSJKmCG2hJkiRJkipUx1jRJ+Pps+URHCXxyCOPFGv0qf4snqL1nPQJe/p0e3ZOqnWJ96BP0VMcxNSpU5t+M4JjB1qjFyI46oA+qU/n7BJPQZ+/7xKdQnX63dbryer0XA4ePIi/S3FydCw9l+xeKKqE+jz1rywSg36X7pOiltauXYvnpDgSihnKYmAoqooiMSiWJovhoGdGcV47duwo1jZs2IDnpHZojR7buXMnnjNbI04mWsuyGEjq0xQ1Qvefjeus37agtT6CnyeNPzou6yNPf/rTi7XWSKksEomilmg97xLJRddE80FrFFUEr5Fd1k/SGhGWPTMaDxO1ZmfjpfWcNBfRfNJlD0BzPN0nXU+2ZtPY7/KeT2vv0NBQsUZrfTbfUr+m2KiRkZFiLZtPqB/RO0/rmhTR7bn4F2hJkiRJkiq4gZYkSZIkqYIbaEmSJEmSKriBliRJkiSpghtoSZIkSZIquIGWJEmSJKnCCcmNyD4DTp+wb/1sfoY+YU+f8d+1a1fTb0ZwHAR9np3ap0ukFEVVUQRA9qn51kiuLvFOFPnQGpeR1VujIuhz+xEcmdEa5ZI9M4rwoNieLKJu8+bNxRq1A8UrUL+N4PHS2q+z9qM69ZNNmzYVa3fddRee895772065/DwMP7uBRdcUKzRc1mwYEGxRvNQBI9RGg/z5s0r1rKx0hpHRTEcWfRMlwieE40i1LIYK3omrTFy2RijtqW4pNZaBN8LtUGXOZPeMWbMmFGstcZHRnDbUoRfl/ePLuvykwWtu13agNY5OmeGxihFFWZzcesYpevJ2o/GL9VoTzI6OornpJgrWrMppiqC34kGBweLNVo/s7FN/Yhio7rEhdJ8QnMuXQ/VIvK1kPgXaEmSJEmSKriBliRJkiSpghtoSZIkSZIquIGWJEmSJKmCG2hJkiRJkiq4gZYkSZIkqYIbaEmSJEmSKlSHzm7cuLFY27ZtGx5LuYiUwUVZu5TjFsGZzZTzRtm1XbIWWzP0sqw7ykYbGxsr1ug+qd0jIvbv31+s0XOhNojgrDvqJ3QvWT4ttT3l4FEfyrLN6ZnSOalPUz/I6tR+lCUbwbmI9LuUiUi5htnvUkY0tcGePXvwnISysLds2VKsbdiwAX93zZo1xVqXLHHKep49e3bTcVOmTMFztmbCzpkzp1ijXOoIzn+k+aRL22bj5WTavn17sZblmVLOLM3jNN/Sb2a/S/MijflszaacWbpemuOz+6ScVOqz/f39xVo2/qiN6F6yNZvGEc3x1AZd8mlbj6NrzepUo7al97MIzhRev359sUZrTgTnkNPcR30se88aGRkp1vr6+oq1HTt2FGvUpyP4PWHnzp3F2gMPPFCs3XnnnXhOembUF2hsR3Afo3WQ8qUpfzuC1zqqUZZ9NjfSej937txijfKuab/SlX+BliRJkiSpghtoSZIkSZIquIGWJEmSJKmCG2hJkiRJkiq4gZYkSZIkqYIbaEmSJEmSKpzWa80EkCRJkiTpXxH/Ai1JkiRJUgU30JIkSZIkVXADLUmSJElSBTfQkiRJkiRVcAMtSZIkSVIFN9CSJEmSJFVwAy1JkiRJUgU30JIkSZIkVXADLUmSJElShf8Hq1ILSxE/cj8AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Choose an image for visualization\n",
+    "index = 11\n",
+    "test_image = data2[index]#x_test[index]\n",
+    "test_image_reshaped = test_image.reshape(1, 64, 64, 1)  # reshape for prediction\n",
+    "\n",
+    "# Make a prediction\n",
+    "predicted_image = autoencoder.predict(test_image_reshaped)\n",
+    "\n",
+    "# Reshape predicted image for visualization\n",
+    "predicted_image = predicted_image.reshape(64, 64)\n",
+    "\n",
+    "# Plot original vs reconstructed\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "\n",
+    "# Original image\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.imshow(test_image, cmap='gray')\n",
+    "plt.title(\"Original Image\")\n",
+    "plt.axis(\"off\")\n",
+    "\n",
+    "# Reconstructed image\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.imshow(predicted_image, cmap='gray')\n",
+    "plt.title(\"Reconstructed Image\")\n",
+    "plt.axis(\"off\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5835bb7f-fab1-47b2-93ed-6083bd476fd5",
+   "metadata": {},
+   "source": [
+    "## Evaluation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d231a12-6799-46ea-b7f8-c92c7c60e2bc",
+   "metadata": {},
+   "source": [
+    "Lets evaluate using Mean squared error (MSE, i.e. reconstruction loss) and Structural similarity index (SSIM)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "323a8981-3873-4872-954e-9106ab3a5efc",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10/10 [==============================] - 0s 2ms/step\n",
+      "Average MSE: 0.002289454685524106, Average SSIM: 0.9365273173131262\n"
+     ]
+    }
+   ],
+   "source": [
+    "def compute_mse_ssim(autoencoder, test_images):\n",
+    "    \"\"\"\n",
+    "    Compute the MSE and SSIM for the set of test images using the provided autoencoder.\n",
+    "\n",
+    "    :param autoencoder: Trained autoencoder model\n",
+    "    :param test_images: List or numpy array of test images\n",
+    "    :return: Average MSE and average SSIM for the test set\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Get the autoencoder's predictions\n",
+    "    reconstructed_images = autoencoder.predict(test_images)\n",
+    "\n",
+    "    # Initialize accumulators for MSE and SSIM\n",
+    "    mse_accumulator = 0.0\n",
+    "    ssim_accumulator = 0.0\n",
+    "\n",
+    "    # Compute MSE and SSIM for each image\n",
+    "    for original, reconstructed in zip(test_images, reconstructed_images):\n",
+    "        # MSE\n",
+    "        mse_accumulator += K.mean(K.square(original - reconstructed))\n",
+    "\n",
+    "        # Scale the images to be in the range [0,255] for SSIM computation\n",
+    "        original_for_ssim = (original * 255).astype(np.uint8)\n",
+    "        reconstructed_for_ssim = (reconstructed * 255).astype(np.uint8)\n",
+    "\n",
+    "        # SSIM (used on 2D grayscale images; adapt as necessary for color images)\n",
+    "        ssim_value, _ = ssim(original_for_ssim.squeeze(), reconstructed_for_ssim.squeeze(), full=True)\n",
+    "        ssim_accumulator += ssim_value\n",
+    "\n",
+    "    # Calculate average MSE and SSIM\n",
+    "    avg_mse = mse_accumulator / len(test_images)\n",
+    "    avg_ssim = ssim_accumulator / len(test_images)\n",
+    "\n",
+    "    return avg_mse, avg_ssim\n",
+    "\n",
+    "avg_mse, avg_ssim = compute_mse_ssim(autoencoder, x_test)\n",
+    "print(f\"Average MSE: {avg_mse}, Average SSIM: {avg_ssim}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0868fc9e-ad50-48b8-a94d-60eeddee9fad",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "## Applications"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9b00e3f-7ad9-408d-8791-5bfc6de79e3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Latent space visualization\"\"\"\n",
+    "\n",
+    "print(x_test.shape)\n",
+    "\n",
+    "for layer in autoencoder.layers:\n",
+    "    print('Layer name:', layer.name)\n",
+    "\n",
+    "#encoder_model = Model(inputs=autoencoder.input, outputs=autoencoder.get_layer('encoder').output)\n",
+    "encoder_model = Model(inputs=autoencoder.input, outputs=autoencoder.get_layer('max_pooling2d_1').output)\n",
+    "\n",
+    "encoded_images = encoder_model.predict(x_test)\n",
+    "\n",
+    "print(encoded_images.shape)\n",
+    "\n",
+    "reshaped_data = encoded_images.reshape(encoded_images.shape[0], -1)\n",
+    "\n",
+    "# Reduce dimensionality to 2D for visualization\n",
+    "encoded_2D = TSNE(n_components=2, perplexity=20).fit_transform(reshaped_data)\n",
+    "encoded_3D = TSNE(n_components=3, perplexity=20).fit_transform(reshaped_data)\n",
+    "\n",
+    "plt.scatter(encoded_2D[:, 0], encoded_2D[:, 1])\n",
+    "plt.xlabel('Dimension 1')\n",
+    "plt.ylabel('Dimension 2')\n",
+    "plt.title('2D TSNE of Encoded Images')\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "\"\"\"Dimensionality reduction\"\"\"\n",
+    "\n",
+    "# Extract the encoder\n",
+    "#encoder_model = Model(inputs=autoencoder.input, outputs=autoencoder.get_layer('encoder').output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b88b6580-c77f-4fe6-a0a8-facd8029c5d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 3D representation\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 8))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "ax.scatter(encoded_3D[:, 0], encoded_3D[:, 1], encoded_3D[:, 2], marker='o')\n",
+    "\n",
+    "ax.set_xlabel('X Label')\n",
+    "ax.set_ylabel('Y Label')\n",
+    "ax.set_zlabel('Z Label')\n",
+    "plt.title(\"3D t-SNE Visualization\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "013cde28-57d1-4cd2-8f53-1f11148c5f4d",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "## Find an optimal number of computing units (Convolutional filters & layers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c56a9c56-ddab-44b3-95bc-dc5265156c05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_autoencoder_looping(input_shape, num_layers, num_filters):\n",
+    "    input_img = Input(shape=input_shape)\n",
+    "    x = input_img\n",
+    "    \n",
+    "    # Encoder\n",
+    "    for _ in range(num_layers):\n",
+    "        x = Conv2D(num_filters, (3, 3), activation='relu', padding='same')(x)\n",
+    "        x = MaxPooling2D((2, 2), padding='same')(x)\n",
+    "    \n",
+    "    # Decoder\n",
+    "    for _ in range(num_layers):\n",
+    "        x = Conv2D(num_filters, (3, 3), activation='relu', padding='same')(x)\n",
+    "        x = UpSampling2D((2, 2))(x)\n",
+    "    decoded = Conv2D(input_shape[-1], (3, 3), activation='sigmoid', padding='same')(x)\n",
+    "    \n",
+    "    return Model(input_img, decoded)\n",
+    "\n",
+    "def create_adjusted_autoencoder_looping(input_shape, num_layers, num_filters):\n",
+    "    input_img = Input(shape=input_shape)\n",
+    "    x = input_img\n",
+    "    num_filters = initial_num_filters\n",
+    "    \n",
+    "    # Encoder\n",
+    "    for _ in range(num_layers):\n",
+    "        x = Conv2D(num_filters, (3, 3), activation='relu', padding='same')(x)\n",
+    "        x = MaxPooling2D((2, 2), padding='same')(x)\n",
+    "        num_filters *= 2  # Double the number of filters for the next layer\n",
+    "    \n",
+    "    # Decoder\n",
+    "    # Start with half of the final number of filters from the encoder\n",
+    "    num_filters //= 2\n",
+    "    for _ in range(num_layers):\n",
+    "        x = Conv2D(num_filters, (3, 3), activation='relu', padding='same')(x)\n",
+    "        x = UpSampling2D((2, 2))(x)\n",
+    "        num_filters //= 2  # Halve the number of filters for the next layer\n",
+    "\n",
+    "    decoded = Conv2D(input_shape[-1], (3, 3), activation='sigmoid', padding='same')(x)\n",
+    "    \n",
+    "    return Model(input_img, decoded)\n",
+    "\n",
+    "def create_autoencoder_symmetrical_looping(input_shape, num_layers, initial_num_filters):\n",
+    "    input_img = Input(shape=input_shape)\n",
+    "    x = input_img\n",
+    "    filters = []\n",
+    "    num_filters = initial_num_filters\n",
+    "    \n",
+    "    # Encoder\n",
+    "    for _ in range(num_layers):\n",
+    "        x = Conv2D(num_filters, (3, 3), activation='relu', padding='same')(x)\n",
+    "        x = MaxPooling2D((2, 2), padding='same')(x)\n",
+    "        filters.append(num_filters)\n",
+    "        num_filters *= 2  # Double the number of filters for the next layer\n",
+    "    \n",
+    "    # Decoder\n",
+    "    for f in reversed(filters): # take the stored list of filters and apply it reversely (from largest to smallest) to achieve symmetry\n",
+    "        x = Conv2D(f, (3, 3), activation='relu', padding='same')(x)\n",
+    "        x = UpSampling2D((2, 2))(x)\n",
+    "\n",
+    "    decoded = Conv2D(input_shape[-1], (3, 3), activation='sigmoid', padding='same')(x)\n",
+    "    \n",
+    "    return Model(input_img, decoded)\n",
+    "\n",
+    "results = []\n",
+    "\n",
+    "x_train = x_train.reshape(-1, 64, 64, 1)\n",
+    "\n",
+    "# Loop over different architectures to find the best one\n",
+    "for num_layers in [1, 2, 3, 4, 5]:  # list of layer counts to test\n",
+    "    for num_filters in [8, 16, 32, 64, 128]:  # list of filter counts counts to test\n",
+    "        model = create_autoencoder_symmetrical_looping(x_train.shape[1:], num_layers, num_filters)\n",
+    "        model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.0005), loss='mean_squared_error')\n",
+    "        \n",
+    "        model.fit(x_train, x_train, epochs=10, batch_size=128, validation_data=(x_test, x_test))\n",
+    "        \n",
+    "        loss = model.evaluate(x_test, x_test)\n",
+    "        \n",
+    "        results.append({\n",
+    "            'num_layers': num_layers,\n",
+    "            'num_filters': num_filters,\n",
+    "            'loss': loss\n",
+    "        })\n",
+    "\n",
+    "# Sort results by loss to find the best configuration\n",
+    "sorted_results = sorted(results, key=lambda x: x['loss'])\n",
+    "print(sorted_results[0])  # print the architecture with the lowest loss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b075679e-d8b9-4c57-924b-95d0214fabff",
+   "metadata": {},
+   "source": [
+    "Find the smallest model (as per layers, then convolutional filters) that exceeds some baseline, here MSI loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f0d1328-dea4-4a79-af6e-b816503246f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Flatten the loops into a list of configurations\n",
+    "configs = [(layers, filters) for layers in [1, 2, 3, 4, 5] for filters in [8, 16, 32, 64, 128]]\n",
+    "\n",
+    "# Sort configurations by complexity\n",
+    "configs.sort(key=lambda x: (x[0], x[1]))\n",
+    "\n",
+    "min_acceptable_performance = 0.1  # 90% accuracy in terms of mean squared error\n",
+    "best_config = None\n",
+    "\n",
+    "for num_layers, initial_num_filters in configs:\n",
+    "    model_minimum = create_autoencoder_symmetrical_looping(x_train.shape[1:], num_layers, initial_num_filters)\n",
+    "    model_minimum.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "    model_minimum.fit(x_train, x_train, epochs=50, batch_size=256, shuffle=True, validation_data=(x_test, x_test), verbose=1)\n",
+    "    \n",
+    "    # Evaluate the model\n",
+    "    predictions = model_minimum.predict(x_test)\n",
+    "    mse = mean_squared_error(x_test.flatten(), predictions.flatten())\n",
+    "    \n",
+    "    if mse <= min_acceptable_performance:\n",
+    "        best_config = (num_layers, initial_num_filters)\n",
+    "        break\n",
+    "\n",
+    "print(f\"The smallest configuration that exceeds the performance criteria is {best_config}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0fff2d16-d5ed-48da-97c2-6666ad00abd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Choose an image for visualization\n",
+    "index = 1 \n",
+    "test_image = x_test[index]\n",
+    "test_image_reshaped = test_image.reshape(1, 64, 64, 1)  # reshape for prediction\n",
+    "\n",
+    "# Make a prediction\n",
+    "predictions = autoencoder.predict(test_image_reshaped)\n",
+    "\n",
+    "# Reshape predicted image for visualization\n",
+    "predictions = predictions.reshape(64, 64)\n",
+    "\n",
+    "print(x_test[0].shape, predictions[0].shape)\n",
+    "\n",
+    "# Plot original vs reconstructed\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "\n",
+    "# Original image\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.imshow(x_test[0], cmap='gray')\n",
+    "plt.title(\"Original Image\")\n",
+    "plt.axis(\"off\")\n",
+    "\n",
+    "# Reconstructed image\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.imshow(predictions[0], cmap='gray')\n",
+    "plt.title(\"Reconstructed Image\")\n",
+    "plt.axis(\"off\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4506a6b4-3de9-4c54-b5bf-9341f1192a5e",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "## Autoencoder with skip-connections and attention block"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "2156273e-837f-4a7c-818a-126a237eac8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A layer to scale the multimodal inputs\n",
+    "class ScalingLayer(Layer):\n",
+    "    def __init__(self, multipliers, **kwargs):\n",
+    "        super(ScalingLayer, self).__init__(**kwargs)\n",
+    "        self.multipliers = tf.constant(multipliers, dtype=tf.float32)\n",
+    "\n",
+    "    def call(self, inputs):\n",
+    "        # Rescale each tensor based on its corresponding multiplier\n",
+    "        return [inputs[i] * self.multipliers[i] for i in range(len(inputs))]\n",
+    "\n",
+    "    def get_config(self):\n",
+    "        config = super(ScalingLayer, self).get_config()\n",
+    "        config.update({'multipliers': self.multipliers.numpy().tolist()})\n",
+    "        return config\n",
+    "\n",
+    "# Attention blocks\n",
+    "\n",
+    "def attention_block(x, shortcut):\n",
+    "    g = Conv2D(filters=shortcut.shape[-1], kernel_size=1, strides=1, padding='same')(x)\n",
+    "    x = Conv2D(filters=shortcut.shape[-1], kernel_size=1, strides=1, padding='same')(shortcut)\n",
+    "    f = Activation('relu')(g + x)\n",
+    "    psi = Conv2D(filters=1, kernel_size=1, strides=1, padding='same')(f)\n",
+    "    rate = Activation('sigmoid')(psi)\n",
+    "    att_x = Multiply()([shortcut, rate])\n",
+    "    return att_x\n",
+    "\n",
+    "def attention_block_skip(x, g, inter_channel):\n",
+    "    theta_x = Conv2D(inter_channel, [1, 1], strides=[1, 1])(x)\n",
+    "    phi_g = Conv2D(inter_channel, [1, 1], strides=[1, 1])(g)\n",
+    "    f = Activation('relu')(Add()([theta_x, phi_g]))\n",
+    "    psi_f = Conv2D(1, [1, 1], strides=[1, 1])(f)\n",
+    "    rate = Activation('sigmoid')(psi_f)\n",
+    "    att_x = Multiply()([x, rate])\n",
+    "    return att_x\n",
+    "\n",
+    "def build_autoencoder_multichannel_skip_attention_modular(resolution, modality, dropout=0.2, regularization=0, \n",
+    "                                                  modality_multipliers=None, number_of_layers=3, filter_size_start=8):\n",
+    "    \n",
+    "    # List to hold all input layers\n",
+    "    input_imgs = [Input(shape=(resolution, resolution, 1)) for _ in range(modality)]\n",
+    "    skip_connections = [] \n",
+    "    encoded_imgs = []\n",
+    "    \n",
+    "    if modality_multipliers is None:\n",
+    "        multipliers = [1 for _ in range(modality)]\n",
+    "    else:\n",
+    "        multipliers = modality_multipliers\n",
+    "\n",
+    "    # Scaling\n",
+    "    scaling_layer = ScalingLayer(multipliers=multipliers)\n",
+    "    scaled_imgs = scaling_layer(input_imgs)\n",
+    "\n",
+    "    # Encoder\n",
+    "    for idx, input_img in enumerate(scaled_imgs):\n",
+    "        x = input_img\n",
+    "        current_filter_size = filter_size_start\n",
+    "        for i in range(number_of_layers):\n",
+    "            x = Conv2D(current_filter_size, (3, 3), padding='same', kernel_regularizer=l1(regularization))(x)\n",
+    "            x = Dropout(dropout)(x)\n",
+    "            x = BatchNormalization()(x)\n",
+    "            x = Activation('relu')(x)\n",
+    "            \n",
+    "            # Store the encoder output for skip connections\n",
+    "            skip_connections.append(x)\n",
+    "            \n",
+    "            x = MaxPooling2D((2, 2), padding='same')(x)\n",
+    "            current_filter_size *= 2  # Double the filter size for the next layer\n",
+    "        encoded_imgs.append(x)\n",
+    "\n",
+    "    x = concatenate(encoded_imgs, axis=-1)\n",
+    "\n",
+    "    # Decoder\n",
+    "    current_filter_size = filter_size_start * number_of_layers\n",
+    "    for i in range(number_of_layers):\n",
+    "        x = Conv2D(current_filter_size, (3, 3), padding='same', kernel_regularizer=l1(regularization))(x)\n",
+    "        x = Dropout(dropout)(x)\n",
+    "        x = BatchNormalization()(x)\n",
+    "        x = Activation('relu')(x)\n",
+    "        x = UpSampling2D((2, 2))(x)\n",
+    "        \n",
+    "        # Get the corresponding encoder output for skip connection\n",
+    "        skip = skip_connections[-(i+1)]\n",
+    "        \n",
+    "        # Apply the attention block to skip connection\n",
+    "        x = attention_block_skip(x, skip, current_filter_size)\n",
+    "        \n",
+    "        current_filter_size //= 2  # Halve the filter size for the next layer\n",
+    "\n",
+    "    # Output layer\n",
+    "    decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n",
+    "\n",
+    "    autoencoder_multi_channel = Model(input_imgs, decoded)\n",
+    "    return autoencoder_multi_channel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "57d685d1-5dcd-4506-a9cb-a7bf3edbe0cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Usage\n",
+    "autoencoder_unet = build_autoencoder_multichannel_skip_attention_modular(256, 3, 0.15, 0, [0.8, 1, 0.15], 3, 8)\n",
+    "autoencoder_unet.compile(optimizer=tf.keras.optimizers.Adam(lr=0.0005), loss='mean_squared_error')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e961a64-eda3-46d3-ba3c-3d0520de296d",
+   "metadata": {},
+   "source": [
+    "## Use pre-trained VGG models for the encoder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fd7472e-f789-4bf9-b77b-3e17416777d4",
+   "metadata": {},
+   "source": [
+    "Option to load a pre-trained VGG model and use it for the encoder, while freezing it's weights (i.e. using it as-is). Note that VGG and most other models as well are RGB, so we need to create a 3 -> 1 band layer (which is also done here)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "23d6ce3a-2522-4b80-8b3f-812d58bdca1a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.applications.vgg16 import preprocess_input\n",
+    "from tensorflow.keras.applications.vgg19 import preprocess_input\n",
+    "from tensorflow.keras.applications import VGG16, VGG19\n",
+    "\n",
+    "def build_vgg19_autoencoder(resolution):\n",
+    "    \n",
+    "    # Load VGG without its top layers, and with default input shape\n",
+    "    vgg19 = keras.applications.VGG19(include_top=False, weights='imagenet')\n",
+    "    \n",
+    "    # Create a new input layer for single channel input\n",
+    "    input_layer = keras.layers.Input(shape=(resolution, resolution, 1))\n",
+    "    \n",
+    "    # The new first layer won't have pretrained weights\n",
+    "    x = keras.layers.Conv2D(64, (3, 3), padding='same', activation='relu')(input_layer)\n",
+    "    \n",
+    "    x = Conv2D(3, (1, 1), activation='relu')(x)  # Convert 64 channels to 3 channels\n",
+    "    \n",
+    "    # Apply the VGG layers (skip the original input layer of VGG)\n",
+    "    for layer in vgg19.layers[1:]:\n",
+    "        x = layer(x)\n",
+    "    \n",
+    "    # New VGG model with modified input\n",
+    "    modified_vgg19 = keras.models.Model(inputs=input_layer, outputs=x)\n",
+    "    \n",
+    "    #Freeze VGG19 layers\n",
+    "    for layer in modified_vgg19.layers:\n",
+    "        layer.trainable = False\n",
+    "        \n",
+    "    #Summary of the pre-trained encoder model\n",
+    "    print(modified_vgg19.summary())\n",
+    "    \n",
+    "    input_img = Input(shape=(resolution, resolution, 1))\n",
+    "    encoded = modified_vgg19(input_img)\n",
+    "    \n",
+    "    # decoder to match the pre-trained encoder and upscale\n",
+    "    x = Conv2D(256, (3, 3), padding='same')(encoded)\n",
+    "    x = Dropout(0.5)(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = Activation('relu')(x)\n",
+    "    x = UpSampling2D((2, 2))(x)\n",
+    "    \n",
+    "    x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n",
+    "    x = Dropout(0.5)(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = Activation('relu')(x)\n",
+    "    x = UpSampling2D((2, 2))(x)\n",
+    "    \n",
+    "    x = Conv2D(64, (3, 3), padding='same')(x)\n",
+    "    x = Dropout(0.5)(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = Activation('relu')(x)\n",
+    "    x = UpSampling2D((2, 2))(x)\n",
+    "    \n",
+    "    x = Conv2D(32, (3, 3), padding='same')(x)\n",
+    "    x = Dropout(0.5)(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = Activation('relu')(x)\n",
+    "    x = UpSampling2D((2, 2))(x)\n",
+    "    \n",
+    "    x = Conv2D(16, (3, 3), padding='same')(x)\n",
+    "    x = Dropout(0.5)(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = Activation('relu')(x)\n",
+    "    x = UpSampling2D((2, 2))(x)\n",
+    "    \n",
+    "    decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n",
+    "    \n",
+    "    # Create the Autoencoder model\n",
+    "    autoencoder_vgg19 = Model(input_img, decoded)\n",
+    "    \n",
+    "    print(autoencoder_vgg19.summary())\n",
+    "\n",
+    "    return autoencoder_vgg19"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be51147b-fdf6-4623-b48c-b45e007ce53d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def preprocess_grayscale_for_vgg(image):\n",
+    "    # Convert 1-channel grayscale to 3-channel grayscale\n",
+    "    image_rgb = tf.concat([image, image, image], axis=-1)\n",
+    "    return preprocess_input(image_rgb * 255.0)  # VGG's 'preprocess_input' function expects pixel values in [0, 255]\n",
+    "\n",
+    "autoencoder_vgg19.compile(optimizer=tf.keras.optimizers.Adadelta(lr=0.003), loss='mean_squared_error')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04275882-08e0-475d-98dd-412aee080790",
+   "metadata": {},
+   "source": [
+    "## Variational autoencoder (Not completed, experimental)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46af1808-f401-411d-a96e-1c4df10d6982",
+   "metadata": {},
+   "source": [
+    "Variational autoencoder can be used to generate more data, augmentate it or find some patterns. Like autoencoder, can capture the strucurality of the data and compress it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b3d76e28-e985-4226-9f1f-a12bf85a7c4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "original_dim = 64 * 64\n",
+    "intermediate_dim = 256\n",
+    "latent_dim = 2\n",
+    "batch_size = 128\n",
+    "epsilon_std = 1.0\n",
+    "\n",
+    "def sampling(args):\n",
+    "    z_mean, z_log_var = args\n",
+    "    batch = K.shape(z_mean)[0]\n",
+    "    dim = K.int_shape(z_mean)[1]\n",
+    "    epsilon = K.random_normal(shape=(batch, dim))\n",
+    "    return z_mean + K.exp(0.5 * z_log_var) * epsilon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02970f19-2068-480a-a332-58494da00f51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_vae(input_shape=(64, 64, 1)):\n",
+    "    input_img = Input(shape=input_shape)\n",
+    "    \n",
+    "    # Encoder\n",
+    "    x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)\n",
+    "    x = MaxPooling2D((2, 2), padding='same')(x)\n",
+    "    x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
+    "    x = MaxPooling2D((2, 2), padding='same')(x)\n",
+    "    shape_before_flattening = K.int_shape(x)\n",
+    "    x = Flatten()(x)\n",
+    "    x = Dense(32, activation='relu')(x)\n",
+    "    \n",
+    "    # outputs: latent mean, and log variance\n",
+    "    z_mean = Dense(latent_dim)(x)\n",
+    "    z_log_var = Dense(latent_dim)(x)\n",
+    "    \n",
+    "    # Use reparameterization to push the sampling as input\n",
+    "    z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])\n",
+    "    \n",
+    "    # Initialize the encoder\n",
+    "    encoder = Model(input_img, [z_mean, z_log_var, z])\n",
+    "    \n",
+    "    # Decoder\n",
+    "    decoder_input = Input(K.int_shape(z)[1:])\n",
+    "    x = Dense(np.prod(shape_before_flattening[1:]), activation='relu')(decoder_input)\n",
+    "    x = Reshape(shape_before_flattening[1:])(x)\n",
+    "    x = Conv2DTranspose(64, (3, 3), activation='relu', padding='same')(x)\n",
+    "    x = UpSampling2D((2, 2))(x)\n",
+    "    x = Conv2DTranspose(32, (3, 3), activation='relu', padding='same')(x)\n",
+    "    x = UpSampling2D((2, 2))(x)\n",
+    "    decoded = Conv2DTranspose(1, (3, 3), activation='sigmoid', padding='same')(x)\n",
+    "    \n",
+    "    # Initialize the decoder\n",
+    "    decoder = Model(decoder_input, decoded)\n",
+    "    \n",
+    "    # Apply the decoder to the sample from the latent distribution\n",
+    "    z_decoded = decoder(z)\n",
+    "    \n",
+    "    # Initialize the VAE\n",
+    "    vae = Model(input_img, z_decoded)\n",
+    "\n",
+    "    return vae\n",
+    "\n",
+    "vae = create_vae()\n",
+    "\n",
+    "# Compute VAE loss\n",
+    "xent_loss = 64 * 64 * binary_crossentropy(K.flatten(x_train[0]), K.flatten(z_decoded))\n",
+    "kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)\n",
+    "vae_loss = K.mean(xent_loss + kl_loss)\n",
+    "beta = 1\n",
+    "\n",
+    "reconstruction_loss = binary_crossentropy(K.flatten(input_img), K.flatten(z_decoded))\n",
+    "reconstruction_loss *= 64 * 64\n",
+    "\n",
+    "vae_loss_reconstruction = K.mean(reconstruction_loss + beta * kl_loss) # beta can be adjusted\n",
+    "\n",
+    "#vae.add_loss(vae_loss)\n",
+    "\n",
+    "vae.add_loss(vae_loss_reconstruction)\n",
+    "#vae.compile(optimizer='rmsprop')\n",
+    "vae.compile(optimizer=tf.keras.optimizers.Adam(lr=0.002))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "249957ff-62de-4bfa-aff0-4c085c78536c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "vae.fit(x_train, None, epochs=1000, batch_size=batch_size, validation_data=(x_test, None))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.17"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}