notebooks/2.1 CVAE for BDG2.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"machine_shape":"hm","gpuType":"T4","authorship_tag":"ABX9TyPStUq7UzdYM4C2Mc1fSq/Z"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","source":["Step 1: Import Necessary Libraries\n","\n"],"metadata":{"id":"8J-LmGCeY-gY"}},{"cell_type":"code","source":["!pip install scipy==1.11.1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"W7zl8DaqGssn","executionInfo":{"status":"ok","timestamp":1711157053149,"user_tz":-480,"elapsed":28096,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}},"outputId":"de884ab2-66e6-4217-a484-80edde47a582"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting scipy==1.11.1\n","  Downloading scipy-1.11.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.3 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m36.3/36.3 MB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: numpy<1.28.0,>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from scipy==1.11.1) (1.25.2)\n","Installing collected packages: scipy\n","  Attempting uninstall: scipy\n","    Found existing installation: scipy 1.11.4\n","    Uninstalling scipy-1.11.4:\n","      Successfully uninstalled scipy-1.11.4\n","Successfully installed scipy-1.11.1\n"]}]},{"cell_type":"code","execution_count":2,"metadata":{"id":"sUEwyUAQY8En","executionInfo":{"status":"ok","timestamp":1711157057819,"user_tz":-480,"elapsed":4677,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}}},"outputs":[],"source":["from keras.layers import Input, Dense, Conv2D, Flatten, Reshape, Conv2DTranspose\n","from keras.models import Model\n","from keras.datasets import mnist\n","from keras.utils import to_categorical\n","from keras import backend as K\n","from keras.callbacks import Callback\n","import tensorflow as tf\n","from tensorflow.keras.callbacks import EarlyStopping\n","\n","from scipy.stats import entropy\n","from scipy.linalg import sqrtm\n","from keras.applications.inception_v3 import InceptionV3\n","\n","import numpy as np\n","import pandas as pd\n","\n","import matplotlib.pyplot as plt\n","\n","from sklearn.preprocessing import LabelEncoder\n","\n","from tensorflow.keras.models import save_model\n","import cv2 # Import OpenCV"]},{"cell_type":"code","source":["import random\n","\n","seed = 42\n","\n","# Python's built-in random library\n","random.seed(seed)\n","\n","# Scikit-learn (only affects some parts of scikit-learn)\n","from sklearn.utils import check_random_state\n","check_random_state(seed)\n","\n","np.random.seed(seed)\n","tf.random.set_seed(seed)\n","tf.keras.utils.set_random_seed(seed)"],"metadata":{"id":"-t4A7lKDiUds","executionInfo":{"status":"ok","timestamp":1711157057820,"user_tz":-480,"elapsed":7,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}}},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":["Step 2: Load and Preprocess the Data\n","\n"],"metadata":{"id":"GY9WXP06ZDEL"}},{"cell_type":"code","source":["img_rows = 168\n","img_cols = 168\n","channels = 1\n","\n","# Input image dimensions\n","img_shape = (img_rows, img_cols, channels)"],"metadata":{"id":"Th7ndwuEICqn","executionInfo":{"status":"ok","timestamp":1711157057820,"user_tz":-480,"elapsed":7,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["# Set path for data source\n","import os\n","from google.colab import drive\n","drive.mount('/content/gdrive')\n","os.chdir(\"/content/gdrive/My Drive/Load Diffusion/dataset/\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EEl4Qrp3HohV","executionInfo":{"status":"ok","timestamp":1711157106778,"user_tz":-480,"elapsed":48965,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}},"outputId":"b13efca9-9b3b-411e-dc77-2537244ef9c2"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n"]}]},{"cell_type":"code","source":["# Set the path to the folder containing saved files\n","load_path = \"./preprocessed_data_168x168_2years\"\n","\n","# Load saved NumPy arrays\n","X_train = np.load(os.path.join(load_path, 'X_train.npy'))\n","X_test = np.load(os.path.join(load_path, 'X_test.npy'))\n","y_train = np.load(os.path.join(load_path, 'y_train.npy'))\n","y_test = np.load(os.path.join(load_path, 'y_test.npy'))\n","\n","# Load saved Pandas DataFrames\n","train_data = pd.read_pickle(os.path.join(load_path, 'train_data.pkl'))\n","test_data = pd.read_pickle(os.path.join(load_path, 'test_data.pkl'))\n","metadata = pd.read_pickle(os.path.join(load_path, 'metadata.pkl'))\n","metadata_original = pd.read_pickle(os.path.join(load_path, 'metadata_original.pkl'))"],"metadata":{"id":"pzd2pc9yBqgN","executionInfo":{"status":"ok","timestamp":1711157155158,"user_tz":-480,"elapsed":48387,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["def merge_dummified_columns(df, dummified_columns, new_column_name):\n","    \"\"\"\n","    Merges dummified columns into a single column in a DataFrame.\n","\n","    Parameters:\n","        df (pd.DataFrame): The DataFrame containing the dummified columns.\n","        dummified_columns (list): A list of the names of the dummified columns.\n","        new_column_name (str): The name of the new column to create.\n","\n","    Returns:\n","        pd.DataFrame: The DataFrame with the dummified columns merged into a single new column.\n","    \"\"\"\n","    # Create a new column containing the column name where the value is 1 for each row\n","    df[new_column_name] = df[dummified_columns].idxmax(axis=1)\n","\n","    # Drop the dummified columns\n","    df.drop(dummified_columns, axis=1, inplace=True)\n","\n","    return df\n","\n","test_metadata = metadata_original.loc[test_data.columns]\n","\n","test_metadata = merge_dummified_columns(test_metadata,\n","                     dummified_columns=['chilledwater', 'electricity', 'gas', 'hotwater', 'steam'],\n","                     new_column_name='meter_type')\n","test_metadata = merge_dummified_columns(test_metadata,\n","                     dummified_columns=['Education', 'Entertainment/public assembly', 'Lodging/residential', 'Office', 'Public services'],\n","                     new_column_name='building_type')\n","\n","test_metadata\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"gNhNicI3DZQ4","executionInfo":{"status":"ok","timestamp":1711157155159,"user_tz":-480,"elapsed":16,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}},"outputId":"8a0a66bc-13ad-49f2-c05e-6f6707e74379"},"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                              lat         lng  year  \\\n","electricity_Lamb_office_Caitlin_2016    51.497838   -3.186246  2016   \n","electricity_Rat_public_Kelle_2016       38.903504  -77.005349  2016   \n","electricity_Rat_assembly_Ezequiel_2016  38.903504  -77.005349  2016   \n","electricity_Bear_assembly_Roxy_2016     37.871903 -122.260729  2016   \n","gas_Shrew_office_Rose_2017              51.499840   -0.124663  2017   \n","...                                           ...         ...   ...   \n","steam_Moose_education_Omar_2016         45.421500  -75.697200  2016   \n","electricity_Rat_public_Chrissy_2017     38.903504  -77.005349  2017   \n","electricity_Hog_office_Merilyn_2016     44.978782  -93.255398  2016   \n","steam_Bull_assembly_Amalia_2016         30.267200  -97.743100  2016   \n","electricity_Rat_office_Jeannie_2017     38.903504  -77.005349  2017   \n","\n","                                         meter_type  \\\n","electricity_Lamb_office_Caitlin_2016    electricity   \n","electricity_Rat_public_Kelle_2016       electricity   \n","electricity_Rat_assembly_Ezequiel_2016  electricity   \n","electricity_Bear_assembly_Roxy_2016     electricity   \n","gas_Shrew_office_Rose_2017                      gas   \n","...                                             ...   \n","steam_Moose_education_Omar_2016               steam   \n","electricity_Rat_public_Chrissy_2017     electricity   \n","electricity_Hog_office_Merilyn_2016     electricity   \n","steam_Bull_assembly_Amalia_2016               steam   \n","electricity_Rat_office_Jeannie_2017     electricity   \n","\n","                                                        building_type  \n","electricity_Lamb_office_Caitlin_2016                           Office  \n","electricity_Rat_public_Kelle_2016                     Public services  \n","electricity_Rat_assembly_Ezequiel_2016  Entertainment/public assembly  \n","electricity_Bear_assembly_Roxy_2016     Entertainment/public assembly  \n","gas_Shrew_office_Rose_2017                                     Office  \n","...                                                               ...  \n","steam_Moose_education_Omar_2016                             Education  \n","electricity_Rat_public_Chrissy_2017                   Public services  \n","electricity_Hog_office_Merilyn_2016                            Office  \n","steam_Bull_assembly_Amalia_2016         Entertainment/public assembly  \n","electricity_Rat_office_Jeannie_2017                            Office  \n","\n","[901 rows x 5 columns]"],"text/html":["\n","  <div id=\"df-45922dc6-e9b6-489f-b197-97e3ae548d0c\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>lat</th>\n","      <th>lng</th>\n","      <th>year</th>\n","      <th>meter_type</th>\n","      <th>building_type</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>electricity_Lamb_office_Caitlin_2016</th>\n","      <td>51.497838</td>\n","      <td>-3.186246</td>\n","      <td>2016</td>\n","      <td>electricity</td>\n","      <td>Office</td>\n","    </tr>\n","    <tr>\n","      <th>electricity_Rat_public_Kelle_2016</th>\n","      <td>38.903504</td>\n","      <td>-77.005349</td>\n","      <td>2016</td>\n","      <td>electricity</td>\n","      <td>Public services</td>\n","    </tr>\n","    <tr>\n","      <th>electricity_Rat_assembly_Ezequiel_2016</th>\n","      <td>38.903504</td>\n","      <td>-77.005349</td>\n","      <td>2016</td>\n","      <td>electricity</td>\n","      <td>Entertainment/public assembly</td>\n","    </tr>\n","    <tr>\n","      <th>electricity_Bear_assembly_Roxy_2016</th>\n","      <td>37.871903</td>\n","      <td>-122.260729</td>\n","      <td>2016</td>\n","      <td>electricity</td>\n","      <td>Entertainment/public assembly</td>\n","    </tr>\n","    <tr>\n","      <th>gas_Shrew_office_Rose_2017</th>\n","      <td>51.499840</td>\n","      <td>-0.124663</td>\n","      <td>2017</td>\n","      <td>gas</td>\n","      <td>Office</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>steam_Moose_education_Omar_2016</th>\n","      <td>45.421500</td>\n","      <td>-75.697200</td>\n","      <td>2016</td>\n","      <td>steam</td>\n","      <td>Education</td>\n","    </tr>\n","    <tr>\n","      <th>electricity_Rat_public_Chrissy_2017</th>\n","      <td>38.903504</td>\n","      <td>-77.005349</td>\n","      <td>2017</td>\n","      <td>electricity</td>\n","      <td>Public services</td>\n","    </tr>\n","    <tr>\n","      <th>electricity_Hog_office_Merilyn_2016</th>\n","      <td>44.978782</td>\n","      <td>-93.255398</td>\n","      <td>2016</td>\n","      <td>electricity</td>\n","      <td>Office</td>\n","    </tr>\n","    <tr>\n","      <th>steam_Bull_assembly_Amalia_2016</th>\n","      <td>30.267200</td>\n","      <td>-97.743100</td>\n","      <td>2016</td>\n","      <td>steam</td>\n","      <td>Entertainment/public assembly</td>\n","    </tr>\n","    <tr>\n","      <th>electricity_Rat_office_Jeannie_2017</th>\n","      <td>38.903504</td>\n","      <td>-77.005349</td>\n","      <td>2017</td>\n","      <td>electricity</td>\n","      <td>Office</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>901 rows × 5 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45922dc6-e9b6-489f-b197-97e3ae548d0c')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","     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\"min\": -122.26072860000008,\n        \"max\": 6.2603,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          -111.92814039999979,\n          -0.1345562307692307,\n          -3.186245571244898\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2016,\n        \"max\": 2017,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2017,\n          2016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"meter_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"gas\",\n          \"steam\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"building_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Public services\",\n          \"Lodging/residential\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["X_train = tf.image.resize(X_train, [img_rows, img_cols]).numpy()\n","X_test = tf.image.resize(X_test, [img_rows, img_cols]).numpy()"],"metadata":{"id":"Mp7FvZjQPu6e","executionInfo":{"status":"ok","timestamp":1711157156861,"user_tz":-480,"elapsed":1713,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["X_train.max(),X_train.min()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZlBuRpZAHeQR","executionInfo":{"status":"ok","timestamp":1711157157530,"user_tz":-480,"elapsed":675,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}},"outputId":"e841a144-8be1-4fa2-8aa1-fff03e7ad45b"},"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1.0, -1.0)"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["X_train = (X_train+1)/2\n","X_test = (X_test+1)/2"],"metadata":{"id":"yHIIXHDyB9Av","executionInfo":{"status":"ok","timestamp":1711157157530,"user_tz":-480,"elapsed":21,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["X_train.max(),X_train.min()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Mje8RQpIHhiD","executionInfo":{"status":"ok","timestamp":1711157157531,"user_tz":-480,"elapsed":21,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}},"outputId":"8e25888d-a269-44cf-8ac6-1aa8856e4bb5"},"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1.0, 0.0)"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["X_train.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aaLA7PbqH0Q8","executionInfo":{"status":"ok","timestamp":1711157157531,"user_tz":-480,"elapsed":19,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}},"outputId":"d5a70c02-ab25-43cb-ec56-bca8b5425456"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(2702, 168, 168, 1)"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["X_test.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KRsvAecmH0Tg","executionInfo":{"status":"ok","timestamp":1711157157531,"user_tz":-480,"elapsed":17,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}},"outputId":"a02036ed-681a-44bf-d43a-e5277c217a0f"},"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(901, 168, 168, 1)"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["y_train.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"At8gmD6bJLIl","executionInfo":{"status":"ok","timestamp":1711157157532,"user_tz":-480,"elapsed":16,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}},"outputId":"901fe397-36da-4e01-982e-e4d828c59ecd"},"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(2702, 13)"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["y_test.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nN9qGlkfJLLI","executionInfo":{"status":"ok","timestamp":1711157157532,"user_tz":-480,"elapsed":12,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}},"outputId":"a83cbe89-eeb4-4d71-fdc8-356e203c3185"},"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(901, 13)"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["metadata_test = metadata.loc[test_data.columns]"],"metadata":{"id":"zLIJqNMJP_k4","executionInfo":{"status":"ok","timestamp":1711157157533,"user_tz":-480,"elapsed":10,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}}},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":["Step 3: Define the CVAE Architecture\n","\n"],"metadata":{"id":"P9VuPe-kZGtQ"}},{"cell_type":"code","source":["dense_dims = 64\n","vae_dim = 512 #256\n","\n","# Encoder\n","inputs = Input(shape=(img_rows, img_cols, 1))\n","labels = Input(shape=(y_test.shape[1],))\n","x = Conv2D(16, 3, activation='relu')(inputs)\n","x = Conv2D(32, 3, activation='relu')(inputs)\n","x = Conv2D(64, 3, activation='relu')(x)\n","x = Flatten()(x)\n","x = Dense(dense_dims, activation='relu')(x)\n","concat = K.concatenate([x, labels], axis=1)\n","mu = Dense(vae_dim)(concat)\n","log_var = Dense(vae_dim)(concat)\n","encoder = Model([inputs, labels], [mu, log_var])\n","\n","# Sampling function\n","def sampling(args):\n","    mu, log_var = args\n","    epsilon = K.random_normal(shape=K.shape(mu), mean=0., stddev=1.)\n","    return mu + K.exp(log_var / 2) * epsilon\n","\n","z = sampling([mu, log_var])\n","\n","# Decoder\n","decoder_input = Input(shape=(vae_dim + y_test.shape[1],))  # Concatenated z and labels\n","x = Dense(dense_dims, activation='relu')(decoder_input)\n","x = Dense(int(img_rows/8) * int(img_cols/8) * 128, activation='relu')(x)\n","x = Reshape((int(img_rows/8) , int(img_cols/8), 128))(x)\n","x = Conv2DTranspose(64, 3, strides=2, padding='same', activation='relu')(x)\n","x = Conv2DTranspose(32, 3, strides=2, padding='same', activation='relu')(x)\n","x = Conv2DTranspose(16, 3, strides=2, padding='same', activation='relu')(x)\n","decoded = Conv2DTranspose(1, 3, padding='same', activation='sigmoid')(x)\n","decoder = Model(decoder_input, decoded)\n","\n","z_and_labels = K.concatenate([z, labels], axis=1)\n","outputs = decoder(z_and_labels)\n","vae = Model([inputs, labels], outputs)"],"metadata":{"id":"3PRfA1oxZEyZ","executionInfo":{"status":"ok","timestamp":1711157158025,"user_tz":-480,"elapsed":501,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":["def generate_digits(rows):\n","  # Number of columns (fixed at 10 for 10 digits)\n","  cols = 5\n","  rows = 2\n","\n","  # Total number of samples\n","  n_samples = rows * cols\n","\n","  labels_to_condition = y_test[:n_samples]\n","\n","  # Sample random points in the latent space\n","  random_latent_points = np.random.normal(size=(n_samples, vae_dim))\n","\n","  # Concatenate the latent points with the one-hot encoded labels\n","  latent_and_labels = np.concatenate([random_latent_points, labels_to_condition], axis=1)\n","\n","  # Decode the latent and label points to generate images\n","  generated_images = decoder.predict(latent_and_labels)\n","\n","  # Plot the generated images in a grid with specified rows and 10 columns\n","  fig, axes = plt.subplots(rows, cols, figsize=(10, 1.5 * rows))\n","\n","  display(metadata_test.iloc[:n_samples])\n","  for i in range(rows):\n","      for j in range(cols):\n","          # First, squeeze to remove the channel dimension\n","          image = generated_images[i * cols + j, :, :, 0].squeeze()\n","\n","          # Resize image to be square (100x100) using OpenCV\n","          resized_image = cv2.resize(image, (256, 256), interpolation = cv2.INTER_AREA)\n","\n","          axes[i, j].imshow(resized_image, cmap='gray')\n","          axes[i, j].axis('off')\n","\n","  plt.show()\n","  print(generated_images.max(),generated_images.min())\n"],"metadata":{"id":"ThVgth6MiZ6G","executionInfo":{"status":"ok","timestamp":1711157158026,"user_tz":-480,"elapsed":4,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}}},"execution_count":18,"outputs":[]},{"cell_type":"markdown","source":["Step 4: Define the Loss Function and Compile the Model\n","\n"],"metadata":{"id":"LdmgTvx9ZLRo"}},{"cell_type":"code","source":["# Loss function\n","reconstruction_loss = K.sum(K.square(inputs - outputs), axis=[1, 2, 3])  # Using MSE instead of binary cross-entropy\n","#reconstruction_loss = K.sum(K.binary_crossentropy(inputs, outputs), axis=[1, 2, 3])\n","kl_loss = - 0.5 * K.sum(1 + log_var - K.square(mu) - K.exp(log_var), axis=-1)\n","vae_loss = K.mean(reconstruction_loss + kl_loss)"],"metadata":{"id":"ZPDoXp7RZIkU","executionInfo":{"status":"ok","timestamp":1711157229759,"user_tz":-480,"elapsed":446,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}}},"execution_count":19,"outputs":[]},{"cell_type":"code","source":["# Compile and train\n","vae.add_loss(vae_loss)\n","vae.compile(optimizer='adam')"],"metadata":{"id":"h-x5_iK9ZlaR","executionInfo":{"status":"ok","timestamp":1711157232277,"user_tz":-480,"elapsed":579,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}}},"execution_count":20,"outputs":[]},{"cell_type":"markdown","source":["Step 5: Train the Model\n","\n"],"metadata":{"id":"tu7U1XUfZOVV"}},{"cell_type":"code","source":["#vae.fit([X_train, y_train], epochs=10, batch_size=32, validation_data=([X_test, y_test], None))"],"metadata":{"id":"PkLtmAmLZo3P","executionInfo":{"status":"ok","timestamp":1711157233906,"user_tz":-480,"elapsed":5,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["def calculate_fid(model, images1, images2):\n","    # Calculate activations\n","    act1 = model.predict(images1)\n","    act2 = model.predict(images2)\n","\n","    # Calculate mean and covariance statistics\n","    mu1, sigma1 = act1.mean(axis=0), np.cov(act1, rowvar=False)\n","    mu2, sigma2 = act2.mean(axis=0), np.cov(act2, rowvar=False)\n","\n","    # Compute the sum squared difference between means\n","    ssdiff = np.sum((mu1 - mu2)**2.0)\n","\n","    # Compute the square root of the product between covariances\n","    covmean = sqrtm(sigma1.dot(sigma2))\n","\n","    # Check and correct imaginary numbers from sqrt\n","    if np.iscomplexobj(covmean):\n","        covmean = covmean.real\n","\n","    # Compute the FID\n","    fid = ssdiff + np.trace(sigma1 + sigma2 - 2.0 * covmean)\n","    return fid\n","\n","def preprocess_real_images(images, shape_size):\n","\n","    # Ensure the images are in the shape (batch_size, shape_size[0], shape_size[1], 1) as required\n","    processed_images = tf.image.resize(images, shape_size, method=tf.image.ResizeMethod.BILINEAR)\n","\n","    # Stack the single channel image into three channels\n","    processed_images = np.repeat(processed_images, 3, axis=-1)\n","\n","    return processed_images"],"metadata":{"id":"kkz3_u566o2l","executionInfo":{"status":"ok","timestamp":1711157244103,"user_tz":-480,"elapsed":3,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}}},"execution_count":23,"outputs":[]},{"cell_type":"code","source":["n_samples = X_test.shape[0]\n","\n","class GenerateDigitsCallback(Callback):\n","    def __init__(self, rows=5):\n","        self.rows = rows\n","\n","    def on_epoch_end(self, epoch, logs=None):\n","        generate_digits(self.rows)\n","        print(\"\\n\")  # Add a newline to separate the plots from the training log\n","\n","class ComputeFIDCallback(Callback):\n","    def __init__(self, patience=5):\n","        self.best_fid_score = float('inf')\n","        self.patience = patience\n","        self.epochs_without_improvement = 0\n","\n","    def on_epoch_end(self, epoch, logs=None):\n","        # Generate the images as you've done before\n","        labels_to_condition = np.array([i % 10 for i in range(n_samples)])\n","        labels_one_hot = to_categorical(labels_to_condition, y_test.shape[1])\n","        random_latent_points = np.random.normal(size=(n_samples, vae_dim))\n","        latent_and_labels = np.concatenate([random_latent_points, labels_one_hot], axis=1)\n","        generated_images = decoder.predict(latent_and_labels)\n","\n","        # Preprocess real and generated images\n","        real_images = preprocess_real_images(X_test, (75,75))\n","        generated_images = preprocess_real_images(generated_images, (75,75))\n","\n","        # Compute FID\n","        fid_score = calculate_fid(inception_model, real_images, generated_images)\n","\n","        # Print FID Score\n","        print('FID Score at end of epoch {}: {}'.format(epoch, fid_score))\n","\n","        # Check for improvement\n","        if fid_score < self.best_fid_score:\n","            self.best_fid_score = fid_score\n","            self.epochs_without_improvement = 0\n","        else:\n","            self.epochs_without_improvement += 1\n","\n","        # Stop training if the tolerance is exceeded\n","        if self.epochs_without_improvement >= self.patience:\n","            print('Stopping training due to lack of improvement in FID score.')\n","            self.model.stop_training = True\n","\n","        # Store FID score in logs to be recorded in history\n","        logs['fid_score'] = fid_score\n","\n","class ModelCheckpointCallback(Callback):\n","    def __init__(self, decoder, save_path='./weights_168x168'):\n","        self.decoder = decoder\n","        self.save_path = save_path\n","\n","    def on_epoch_end(self, epoch, logs=None):\n","        # Save the model at the end of each epoch\n","        save_model(self.decoder, f\"{self.save_path}/cvae_mse_epoch_{epoch+1}.keras\")\n","        print(f\"Model saved: {self.save_path}/cvae_mse_epoch_{epoch+1}.keras\")\n","\n","# Create instances of all callbacks\n","generate_digits_callback = GenerateDigitsCallback(rows=3)\n","compute_fid_callback = ComputeFIDCallback(patience=10)\n","model_checkpoint_callback = ModelCheckpointCallback(decoder=decoder)\n","\n","# Make sure the directory exists\n","import os\n","if not os.path.exists('./weights_168x168'):\n","    os.makedirs('./weights_168x168')\n","\n","# Train the model with all callbacks\n","history = vae.fit([X_train, y_train],\n","                  epochs=100,\n","                  batch_size=64,\n","                  validation_data=([X_test, y_test], None),\n","                  callbacks=[generate_digits_callback, model_checkpoint_callback])\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"1awK2qmqhKSPfC406PVTG5QitND2RGzjC"},"id":"n51A58L87ob_","outputId":"d1fbbdff-4d10-44a4-96f4-5e8bd1fb8dfa","executionInfo":{"status":"ok","timestamp":1711158191014,"user_tz":-480,"elapsed":886380,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}}},"execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}]},{"cell_type":"code","source":["\n","# Create a figure and axis\n","fig, ax1 = plt.subplots()\n","\n","# Plot training loss\n","ax1.plot(history.history['loss'][1:], label='Train Loss', color='b')\n","\n","# Plot validation loss\n","ax1.plot(history.history['val_loss'][1:], label='Validation Loss', color='g')\n","\n","# Label the first y-axis\n","ax1.set_xlabel('Epochs')\n","ax1.set_ylabel('Loss Value')\n","ax1.legend(loc='upper left')\n","\n","# Create a second y-axis\n","ax2 = ax1.twinx()\n","\n","# Plot FID score on the second y-axis\n","#ax2.plot(history.history['fid_score'], label='FID Score', color='r')\n","\n","# Label the second y-axis\n","#ax2.set_ylabel('FID Score')\n","#ax2.legend(loc='upper right')\n","\n","plt.show()\n"],"metadata":{"id":"Y4kbTYquDpPk","colab":{"base_uri":"https://localhost:8080/","height":455},"executionInfo":{"status":"ok","timestamp":1711158191014,"user_tz":-480,"elapsed":14,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}},"outputId":"e7f587b1-35e1-4ec5-ff45-81f25e6959c6"},"execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["generate_digits(rows=5)"],"metadata":{"id":"_6unaDhqaKkb","colab":{"base_uri":"https://localhost:8080/","height":648},"executionInfo":{"status":"ok","timestamp":1711158191014,"user_tz":-480,"elapsed":5,"user":{"displayName":"Patrick Fu","userId":"17527192923561102849"}},"outputId":"8c3c0e4d-a3b8-4a8d-a617-ee4776ae82e7"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n"]},{"output_type":"display_data","data":{"text/plain":["                                              lat       lng  chilledwater  \\\n","electricity_Lamb_office_Caitlin_2016     0.925421  0.926498           0.0   \n","electricity_Rat_public_Kelle_2016        0.418241  0.352124           0.0   \n","electricity_Rat_assembly_Ezequiel_2016   0.418241  0.352124           0.0   \n","electricity_Bear_assembly_Roxy_2016      0.376698  0.000000           0.0   \n","gas_Shrew_office_Rose_2017               0.925501  0.950320           0.0   \n","electricity_Lamb_assembly_Cherie_2017    0.925421  0.926498           0.0   \n","electricity_Rat_education_Nellie_2017    0.418241  0.352124           0.0   \n","chilledwater_Bull_education_Miquel_2017  0.070454  0.190767           1.0   \n","hotwater_Fox_lodging_Stephen_2016        0.197596  0.080396           0.0   \n","electricity_Rat_assembly_Damaris_2017    0.418241  0.352124           0.0   \n","\n","                                         electricity  gas  hotwater  steam  \\\n","electricity_Lamb_office_Caitlin_2016             1.0  0.0       0.0    0.0   \n","electricity_Rat_public_Kelle_2016                1.0  0.0       0.0    0.0   \n","electricity_Rat_assembly_Ezequiel_2016           1.0  0.0       0.0    0.0   \n","electricity_Bear_assembly_Roxy_2016              1.0  0.0       0.0    0.0   \n","gas_Shrew_office_Rose_2017                       0.0  1.0       0.0    0.0   \n","electricity_Lamb_assembly_Cherie_2017            1.0  0.0       0.0    0.0   \n","electricity_Rat_education_Nellie_2017            1.0  0.0       0.0    0.0   \n","chilledwater_Bull_education_Miquel_2017          0.0  0.0       0.0    0.0   \n","hotwater_Fox_lodging_Stephen_2016                0.0  0.0       1.0    0.0   \n","electricity_Rat_assembly_Damaris_2017            1.0  0.0       0.0    0.0   \n","\n","                                         Education  \\\n","electricity_Lamb_office_Caitlin_2016           0.0   \n","electricity_Rat_public_Kelle_2016              0.0   \n","electricity_Rat_assembly_Ezequiel_2016         0.0   \n","electricity_Bear_assembly_Roxy_2016            0.0   \n","gas_Shrew_office_Rose_2017                     0.0   \n","electricity_Lamb_assembly_Cherie_2017          0.0   \n","electricity_Rat_education_Nellie_2017          1.0   \n","chilledwater_Bull_education_Miquel_2017        1.0   \n","hotwater_Fox_lodging_Stephen_2016              0.0   \n","electricity_Rat_assembly_Damaris_2017          0.0   \n","\n","                                         Entertainment/public assembly  \\\n","electricity_Lamb_office_Caitlin_2016                               0.0   \n","electricity_Rat_public_Kelle_2016                                  0.0   \n","electricity_Rat_assembly_Ezequiel_2016                             1.0   \n","electricity_Bear_assembly_Roxy_2016                                1.0   \n","gas_Shrew_office_Rose_2017                                         0.0   \n","electricity_Lamb_assembly_Cherie_2017                              1.0   \n","electricity_Rat_education_Nellie_2017                              0.0   \n","chilledwater_Bull_education_Miquel_2017                            0.0   \n","hotwater_Fox_lodging_Stephen_2016                                  0.0   \n","electricity_Rat_assembly_Damaris_2017                              1.0   \n","\n","                                         Lodging/residential  Office  \\\n","electricity_Lamb_office_Caitlin_2016                     0.0     1.0   \n","electricity_Rat_public_Kelle_2016                        0.0     0.0   \n","electricity_Rat_assembly_Ezequiel_2016                   0.0     0.0   \n","electricity_Bear_assembly_Roxy_2016                      0.0  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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["0.7629664 0.0069649527\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"ydrCBDd1A0VF"},"execution_count":null,"outputs":[]}]}