DeepLense_Test_III_Learning_Mass_of_DarkMatter_Halo.ipynb

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            "right": null,
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        "cc29970af1ad449bbab2b519cadc079c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
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            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Vamsi995/DeepLense-ML4SCI-EvalTest/blob/main/DeepLense_Test_III_Learning_Mass_of_DarkMatter_Halo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Imports\n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import os\n",
        "import torch\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision import transforms\n",
        "from tqdm.notebook import tqdm\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "from sklearn.model_selection import train_test_split\n",
        "import random\n",
        "from sklearn.metrics import r2_score\n",
        "import time\n",
        "\n",
        "seed = 43\n",
        "torch.manual_seed(seed)"
      ],
      "metadata": {
        "id": "yFUuDk_OSiMp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f203c14e-3c46-4222-94d9-f6aee8851ac2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7fab6de70e30>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Utilities\n"
      ],
      "metadata": {
        "id": "Uu_CckZX1zpS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class LensingData:\n",
        "  \"\"\"\n",
        "    Lensing Data Class is a wrapper class that helps in handling data manipulation.\n",
        "  \"\"\"\n",
        "  def __init__(self, path):\n",
        "    self.images = {}\n",
        "    self.masses = {}\n",
        "    self.path = path\n",
        "    self.initialize_data_dict()\n",
        "\n",
        "  def initialize_data_dict(self):\n",
        "    \"\"\"\n",
        "      Initializes images dict and masses dict.\n",
        "    \"\"\"\n",
        "  \n",
        "    self.images[\"train\"] = []\n",
        "    self.masses[\"train\"] = []\n",
        "\n",
        "    self.images[\"test\"] = []\n",
        "    self.images[\"val\"] = []\n",
        "\n",
        "    self.masses[\"test\"] = []\n",
        "    self.masses[\"val\"] = []\n",
        "\n",
        "\n",
        "  def load(self):\n",
        "    \"\"\" \n",
        "      Loads the lensing data into python dictionaries\n",
        "    \"\"\"\n",
        "    for lens_file in tqdm(os.listdir(self.path), desc=\"Reading lensing data\"):\n",
        "      lens_image, darkmatter_mass = np.load(self.path + lens_file, allow_pickle=True)\n",
        "      self.images[\"train\"].append(lens_image)\n",
        "      self.masses[\"train\"].append(darkmatter_mass)\n",
        "    print(\"Number of data samples: {}\".format(len(self.masses[\"train\"])))\n",
        "\n",
        "  def min_max_filter(self, image):\n",
        "    \"\"\"\n",
        "      Applies Min-Max filter to the given image\n",
        "    \"\"\"\n",
        "    return (image - image.min())/(image.max() - image.min())\n",
        "\n",
        "  def get_random_image_sample(self):\n",
        "    rand_index = random.randrange(len(self.images[\"train\"]))\n",
        "    return self.images[\"train\"][rand_index]\n",
        "\n",
        "  def get_random_mass_sample(self):\n",
        "    rand_index = random.randrange(len(self.masses[\"train\"]))\n",
        "    return self.masses[\"train\"][rand_index]\n",
        "    \n",
        "  def calc_mean_std(self):\n",
        "    \"\"\"\n",
        "      Calculates the mean and std of the entire dataset\n",
        "    \"\"\"\n",
        "    psum = 0\n",
        "    pstd = 0\n",
        "    for image in self.images[\"train\"]:\n",
        "      image = self.min_max_filter(image)\n",
        "      psum += image.sum() \n",
        "      pstd += (image**2).sum()\n",
        "    \n",
        "    image = self.get_random_sample()\n",
        "    height = image.shape[0]\n",
        "    width = image.shape[1]\n",
        "    total_pixels = (len(self.images[\"train\"])*height*width)\n",
        "    mean = psum/total_pixels\n",
        "    std = np.sqrt((pstd/total_pixels) - (mean**2))\n",
        "\n",
        "    print(\"Mean: {}, Std: {}\".format(mean, std))\n",
        "    return mean, std\n",
        "\n",
        "  \n",
        "  def train_test_val_split(self):\n",
        "    \"\"\"\n",
        "      Performs train-test-val split of the training dataset\n",
        "    \"\"\"\n",
        "    trunc_train_images, test_images, trunc_train_masses, test_masses = train_test_split(self.images[\"train\"], self.masses[\"train\"], test_size=0.1, random_state=43)\n",
        "    self.images[\"test\"] = test_images\n",
        "    self.masses[\"test\"] = test_masses\n",
        "    trunc_train_images, val_images, trunc_train_masses, val_masses = train_test_split(trunc_train_images, trunc_train_masses, test_size=0.1, random_state=43)\n",
        "    \n",
        "    self.images[\"val\"] = val_images\n",
        "    self.masses[\"val\"] = val_masses\n",
        "    \n",
        "    self.images[\"train\"] = trunc_train_images\n",
        "    self.masses[\"train\"] = trunc_train_masses\n",
        "\n",
        "    print(\"Number of Train Samples: {}, Number of Test Samples: {}, Number of Validation Samples: {}\".format(len(self.images[\"train\"]), len(self.images[\"test\"]), len(self.images[\"val\"])))\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Mm0RueGK1y4s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Data\n",
        "\n"
      ],
      "metadata": {
        "id": "vUtD2Xi_PYv4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Importing data by copying the dataset to google drive and mounting google drive to the colab notebook. \n",
        "\n",
        "- Please try to access this notebook via colab.\n"
      ],
      "metadata": {
        "id": "siMlRYjGYVhL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yNN3kiwMKTXu",
        "outputId": "9944691a-09fa-4eb6-eb4d-e54233cacf12"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "# Connecting to my google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- The path (/drive/MyDrive/ML4SCI/lens_data_alt.tgz) below to the dataset is the path in my google drive, please change it accordingly and point it to the dataset path in your drive."
      ],
      "metadata": {
        "id": "dLZrmupoSsMn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create images/ directory\n",
        "!mkdir images\n",
        "\n",
        "# Unzipping gzipped files\n",
        "!tar -xvf \"/content/drive/MyDrive/ML4SCI/lens_data_alt.tgz\" -C \"images/\""
      ],
      "metadata": {
        "id": "tlR9ko43QjPd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fad98215-824b-49f7-9b10-7c334bfb4a6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "lens_data/image_126163163385867457264917008655190746502.npy\n",
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            "lens_data/image_65081728922035495044762911482230486665.npy\n",
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            "lens_data/image_141971511886906834854889168876657274621.npy\n",
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            "lens_data/image_226533948987512516320958385489268809302.npy\n",
            "lens_data/image_20960240726533496672014132709042283796.npy\n",
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            "lens_data/image_17331083505413324755817470658668240430.npy\n",
            "lens_data/image_333858133375605641325646032862882158548.npy\n",
            "lens_data/image_221588020872657583613362990231896017786.npy\n",
            "lens_data/image_195965725587113704262645175957720489194.npy\n",
            "lens_data/image_270486029057480846787437683504251502745.npy\n",
            "lens_data/image_38636927165380876570841329224339128974.npy\n",
            "lens_data/image_339404575807876138228355885348985396325.npy\n",
            "lens_data/image_202564934496390169741046997304409620879.npy\n",
            "lens_data/image_47889664830340342502840004286274080870.npy\n",
            "lens_data/image_209896915296436389564916251234364592096.npy\n",
            "lens_data/image_270902918407523122006954495961289834097.npy\n",
            "lens_data/image_173910559283491180234846054814083516012.npy\n",
            "lens_data/image_63460206530625547367169034707319213657.npy\n",
            "lens_data/image_171898905402335285077950188476860858085.npy\n",
            "lens_data/image_316375028461605677807769149674141534121.npy\n",
            "lens_data/image_116881537221697753791403339464218986597.npy\n",
            "lens_data/image_308374860870800250524982436693804980781.npy\n",
            "lens_data/image_162100291069362364640877898093513034083.npy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Path to the extracted data\n",
        "lens_data_path = \"/content/images/lens_data/\""
      ],
      "metadata": {
        "id": "3jaoaST-SWMx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Custom data wrapper class to manage data\n",
        "lensing_data = LensingData(lens_data_path)"
      ],
      "metadata": {
        "id": "8CWptiRiPDon"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load data from the disk\n",
        "lensing_data.load()"
      ],
      "metadata": {
        "id": "ehmWbtRFS3OF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "de47916567c84388a48db643b386a993",
            "e47128c6f40e4ea7a8edec8fadac02f6",
            "dec379d3417e4c06bad47a8f79505e5a",
            "9567df9c5918401a99fdb3f506cae236",
            "60d63910bd724929a8a28e5ba3d4550e",
            "f0337616ec824b238b4f4584ceb765a3",
            "ad3f11121c8048d19abe4a9dbf7999fc",
            "2545e9584df74506b09c3887fc2962d0",
            "7960c805b34d4b07a486470914243368",
            "ee754ff79cb14357adba881908b8ef17",
            "a7b21ecf4358468c94f519d525d957dd"
          ]
        },
        "outputId": "7d184c66-6fd6-4f1f-ac70-93905ad18b52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Reading lensing data:   0%|          | 0/20000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de47916567c84388a48db643b386a993"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of data samples: 20000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Analysis"
      ],
      "metadata": {
        "id": "0S5EWYupWpKO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Image Analysis"
      ],
      "metadata": {
        "id": "hHdGsRMecSyk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Image Properties\n",
        "image = lensing_data.get_random_image_sample()\n",
        "print(\"Image Type: {}\".format(type(image)))\n",
        "print(\"Image Shape: {}\".format(image.shape))\n",
        "print(\"Image Data Type: {}\".format(image.dtype))\n",
        "print(\"Image Dimensions: {}\".format(image.ndim))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZbfl_IabwmY",
        "outputId": "31f04b71-17ca-46bb-a5a7-f701b03988d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image Type: <class 'numpy.ndarray'>\n",
            "Image Shape: (150, 150)\n",
            "Image Data Type: int64\n",
            "Image Dimensions: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sample Visualization\n",
        "plt.imshow(image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "McEL5URxd8tJ",
        "outputId": "6620413d-30ea-4c0b-8e54-2beb67badf7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7faaf2a62c70>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pixel Distribution\n",
        "sns.histplot(image.ravel(),bins=50).set(title='Pixel Distribution')"
      ],
      "metadata": {
        "id": "aZI9zqqVc1RD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "cd58ee5d-d8f9-4991-dd92-e712e0e7ff85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Pixel Distribution')]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Image Normalization\n",
        "\n"
      ],
      "metadata": {
        "id": "GYYSQ0udgbZw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that the pixel distribution is heavily skewed, this might affect the gradients during backpropagation. The neuron will become saturated if you multiply your weights by large pixel values. But eventually, in order to offset the impact of the large input, your weights will be updated to really small values. Sadly, this will not occur because the gradients for saturated activations will be too small to allow for any appreciable update. Your weights will eventually become stuck. "
      ],
      "metadata": {
        "id": "wn6rAzj0YDtM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Min-Max\n",
        "\n"
      ],
      "metadata": {
        "id": "vCVF9-mttGAU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Min-max normalization (usually called feature scaling) performs a linear transformation on the original data. This technique gets all the scaled data in the range (0, 1).\n",
        "\n",
        ">>> $\\Large x_{scaled} = \\Large\\frac{x - x_{min}}{x_{max}-x_{min}}$"
      ],
      "metadata": {
        "id": "PQrx0laSYYeL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def min_max_filter(image):\n",
        "  return (image - image.min())/(image.max() - image.min())"
      ],
      "metadata": {
        "id": "3BxbzccGghVu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image = lensing_data.get_random_image_sample().copy()\n",
        "min_max_image = min_max_filter(image)"
      ],
      "metadata": {
        "id": "GK8lACpeuNZF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "histfig, axes = plt.subplots(1,2, figsize=(20,10))\n",
        "axes[0].set_title(\"Image Pixel Distribution\")\n",
        "axes[1].set_title(\"Min Max Image Pixel Distribution\")\n",
        "sns.histplot(image.ravel(),binwidth=20,ax=axes[0])\n",
        "sns.histplot(min_max_image.ravel(), binwidth=0.01,ax=axes[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "DCLfU33E2fLK",
        "outputId": "54bf8c1d-5f45-426f-b51f-b0f164bc43de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Min Max Image Pixel Distribution'}, ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": 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kGYR/y+SKwD+95+4AC+cUOQAAAABGMYMJAAAAgFHWLHcB0/DQhz60jznmmOUuAwCYoksvvfQL3b1uuevgLsZgALC67Wn8tSoDpmOOOSabN0/1KqUAwDKrqquXuwbuzhgMAFa3PY2/nCIHAAAAwCgCJgAAAABGETABAAAAMIqACQAAAIBRBEwAAAAAjCJgAgAAAGAUARMAAAAAowiYAAAAABhFwAQAAADAKAImAAAAAEYRMAEAAAAwioAJAAAAgFEETAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEXABAAAAMAoAiYAAAAARhEwAQAAADCKgAkAYJWpqsOr6sKq+nRVXV5V31lVD66qi6vqiuH7EUPfqqrXVdXWqtpSVY+dsZ9Th/5XVNWpy/eMAIADnYAJAGD1OTPJ33b3Nyf51iSXJ3l5kvd19/FJ3jfcT5KnJDl++Do9yRuTpKoenOSMJI9P8rgkZ+wOpQAAZhMwAQCsIlV1WJLvSnJ2knT317r7piSnJDl36HZukmcMt09J8taeuCTJ4VV1VJInJ7m4u3d2941JLk5y8pI9EQBgRREwAQCsLscm2ZHkj6vqY1X15qp6QJIju/u6oc/1SY4cbq9Pcs2Mx28b2uZrv5uqOr2qNlfV5h07dizyUwEAVoo1y10AAACLak2Sxyb5me7+cFWdmbtOh0uSdHdXVS/Gwbr7rCRnJcnGjRsXZZ/747bbbsvmzZvv0b5x48asXbt2GSoCgIOLgGnK1m94RK7dds3eOyZ5+NEbsv2az025IgBglduWZFt3f3i4f2EmAdPnq+qo7r5uOAXuhmH79iQbZjz+6KFte5LvmdX+wSnWPcrmzZvzkje8K4etP+7Otpu3b82ZL0w2bdq0jJUBwMFBwDRl1267Js9+04cW1Pf8FzxxytUAAKtdd19fVddU1Td192eSnJjksuHr1CSvGb6/e3jIRUleXFXnZbKg981DCPXeJL8xY2Hvk5K8Yimfy746bP1xWXfcCctdBgAclARMAACrz88keXtV3SfJlUmen8namxdU1WlJrk7yrKHve5I8NcnWJF8d+qa7d1bVq5J8ZOj3yu7euXRPAQBYSQRMAACrTHd/PMnGOTadOEffTvKiefZzTpJzFrU4AGBVchU5AAAAAEYRMAEAAAAwioAJAAAAgFEETAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEXABAAAAMAoAiYAAAAARhEwAQAAADCKgAkAAACAUQRMAAAAAIwiYAIAAABgFAETAAAAAKMImAAAAAAYRcAEAAAAwCgCJgAAAABGETABAAAAMIqACQAAAIBRBEwAAAAAjCJgAgAAAGAUARMAAAAAowiYAAAAABhFwAQAAADAKAImAAAAAEYRMAEAAAAwioAJAAAAgFEETAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEXABAAAAMAoAiYAAAAARplqwFRVP1dVn6qqT1bVO6rqvlV1bFV9uKq2VtX5VXWfoe/a4f7WYfsxM/bziqH9M1X15GnWDAAAAMC+mVrAVFXrk/xsko3d/egkhyR5TpLfSvLa7j4uyY1JThseclqSG4f21w79UlWPHB73qCQnJ3lDVR0yrboBAAAA2DfTPkVuTZL7VdWaJPdPcl2SJyW5cNh+bpJnDLdPGe5n2H5iVdXQfl5339bdVyXZmuRxU64bAAAAgAWaWsDU3duT/E6Sz2USLN2c5NIkN3X3rqHbtiTrh9vrk1wzPHbX0P8hM9vneMydqur0qtpcVZt37Nix+E8IAAAAgDlN8xS5IzKZfXRskocneUAmp7hNRXef1d0bu3vjunXrpnUYAAAAAGaZ5ily35vkqu7e0d23J/mLJJuSHD6cMpckRyfZPtzenmRDkgzbD0vyxZntczwGAAAAgGU2zYDpc0meUFX3H9ZSOjHJZUk+kOSZQ59Tk7x7uH3RcD/D9vd3dw/tzxmuMndskuOT/OsU6wYAAABgH6zZe5f9090frqoLk3w0ya4kH0tyVpK/TnJeVf360Hb28JCzk7ytqrYm2ZnJlePS3Z+qqgsyCad2JXlRd399WnUDAAAAsG+mFjAlSXefkeSMWc1XZo6rwHX3rUl+eJ79vDrJqxe9QAAAAABGm+YpcgAAAAAcBARMAAAAAIwiYAIAAABgFAETAAAAAKMImAAAAAAYRcAEAAAAwCgCJgAAAABGETABAAAAMIqACQAAAIBRBEwAAAAAjCJgAgAAAGAUARMAAAAAowiYAAAAABhFwAQAAADAKAImAAAAAEYRMAEAAAAwioAJAAAAgFEETAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEXABAAAAMAoAiYAAAAARhEwAQAAADCKgAkAAACAUQRMAAAAAIwiYAIAAABgFAETAAAAAKMImAAAAAAYRcAEAAAAwCgCJgAAAABGETABAAAAMIqACQAAAIBRBEwAAAAAjCJgAgAAAGAUARMAAAAAowiYAABWmar6bFV9oqo+XlWbh7YHV9XFVXXF8P2Iob2q6nVVtbWqtlTVY2fs59Sh/xVVdepyPR8A4MAnYAIAWJ3+a3d/W3dvHO6/PMn7uvv4JO8b7ifJU5IcP3ydnuSNySSQSnJGkscneVySM3aHUgAAswmYAAAODqckOXe4fW6SZ8xof2tPXJLk8Ko6KsmTk1zc3Tu7+8YkFyc5eYlrBgBWCAETAMDq00n+rqourarTh7Yju/u64fb1SY4cbq9Pcs2Mx24b2uZrv5uqOr2qNlfV5h07dizmcwAAVpA1y10AAACL7r909/aq+g9JLq6qT8/c2N1dVb0YB+rus5KclSQbN25clH0CACuPGUwAAKtMd28fvt+Q5J2ZrKH0+eHUtwzfbxi6b0+yYcbDjx7a5msHALgHARMAwCpSVQ+oqkN3305yUpJPJrkoye4rwZ2a5N3D7YuSPG+4mtwTktw8nEr33iQnVdURw+LeJw1tAAD34BQ5AIDV5cgk76yqZDLW+9Pu/tuq+kiSC6rqtCRXJ3nW0P89SZ6aZGuSryZ5fpJ0986qelWSjwz9XtndO5fuaQAAK4mACQBgFenuK5N86xztX0xy4hztneRF8+zrnCTnLHaNAMDq4xQ5AAAAAEYRMAEAAAAwioAJAAAAgFEETAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEXABAAAAMAoAiYAAAAARhEwAQAAADCKgAkAAACAUQRMAAAAAIwiYAIAAABgFAETAAAAAKMImAAAAAAYRcAEAAAAwCgCJgAAAABGETABAAAAMIqACQAAAIBRBEwAAAAAjCJgAgAAAGAUARMAAAAAowiYAAAAABhFwAQAAADAKAImAAAAAEYRMAEAAAAwioAJAAAAgFEETAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEXABAAAAMAoAiYAAAAARhEwAQAAADCKgAkAAACAUQRMAAAAAIwiYAIAAABgFAETAAAAAKMImAAAAAAYRcAEAAAAwCgCJgAAAABGETABAAAAMIqACQAAAIBRBEwAAAAAjCJgAgAAAGAUARMAAAAAowiYAAAAABhFwAQAAADAKAImAAAAAEYRMAEAAAAwioAJAAAAgFEETAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEXABAAAAMAoAiYAAAAARhEwAQAAADCKgAkAAACAUQRMAAAAAIwiYAIAAABgFAETAAAAAKMImAAAAAAYRcAEAAAAwCgCJgAAAABGETABAAAAMMpUA6aqOryqLqyqT1fV5VX1nVX14Kq6uKquGL4fMfStqnpdVW2tqi1V9dgZ+zl16H9FVZ06zZoBAAAA2DfTnsF0ZpK/7e5vTvKtSS5P8vIk7+vu45O8b7ifJE9JcvzwdXqSNyZJVT04yRlJHp/kcUnO2B1KAQAAALD8phYwVdVhSb4rydlJ0t1f6+6bkpyS5Nyh27lJnjHcPiXJW3vikiSHV9VRSZ6c5OLu3tndNya5OMnJ06obAAAAgH0zzRlMxybZkeSPq+pjVfXmqnpAkiO7+7qhz/VJjhxur09yzYzHbxva5mu/m6o6vao2V9XmHTt2LPJTAQAAAGA+0wyY1iR5bJI3dvdjknwld50OlyTp7k7Si3Gw7j6ruzd298Z169Ytxi4BAAAAWIBpBkzbkmzr7g8P9y/MJHD6/HDqW4bvNwzbtyfZMOPxRw9t87UDAAAAcACYWsDU3dcnuaaqvmloOjHJZUkuSrL7SnCnJnn3cPuiJM8brib3hCQ3D6fSvTfJSVV1xLC490lDGwAAAAAHgDVT3v/PJHl7Vd0nyZVJnp9JqHVBVZ2W5Ookzxr6vifJU5NsTfLVoW+6e2dVvSrJR4Z+r+zunVOuGwAAAIAFmmrA1N0fT7Jxjk0nztG3k7xonv2ck+ScRS0OAAAAgEUxzTWYAAAAADgICJgAAAAAGEXABACwylTVIVX1sar6q+H+sVX14araWlXnD+tjpqrWDve3DtuPmbGPVwztn6mqJy/TUwEAVggBEwDA6vOSJJfPuP9bSV7b3ccluTHJaUP7aUluHNpfO/RLVT0yyXOSPCrJyUneUFWHLFHtAMAKJGACAFhFquroJN+f5M3D/UrypCQXDl3OTfKM4fYpw/0M208c+p+S5Lzuvq27r8rkKr+PW5InAACsSAImAIDV5feT/GKSO4b7D0lyU3fvGu5vS7J+uL0+yTVJMmy/eeh/Z/scj7mbqjq9qjZX1eYdO3Ys4tMAAFYSARMAwCpRVU9LckN3X7pUx+zus7p7Y3dvXLdu3VIdFgA4wKxZ7gIAAFg0m5I8vaqemuS+SR6U5Mwkh1fVmmGW0tFJtg/9tyfZkGRbVa1JcliSL85o323mYwAA7sEMJgCAVaK7X9HdR3f3MZks0v3+7n5ukg8keebQ7dQk7x5uXzTcz7D9/d3dQ/tzhqvMHZvk+CT/ukRPAwBYgcxgAgBY/X4pyXlV9etJPpbk7KH97CRvq6qtSXZmEkqluz9VVRckuSzJriQv6u6vL33ZAMBKIWACAFiFuvuDST443L4yc1wFrrtvTfLD8zz+1UlePb0KAYDVxClyAAAAAIwiYAIAAABgFAETAAAAAKMImAAAAAAYRcAEAAAAwCgCJgAAAABGETABAAAAMIqACQAAAIBRBEwAAAAAjCJgAgAAAGAUARMAAAAAowiYAAAAABhFwLSP1m94RKpqwV8AAAAAq92a5S5gpbl22zV59ps+tOD+57/giVOsBgAAAGD5mcEEAAAAwCgCJgAAAABGETABAAAAMIqACQAAAIBRBEwAAAAAjCJgAgAAAGAUARMAAAAAowiYAAAAABhFwAQAAADAKAImAAAAAEYRMAEAAAAwioAJAAAAgFEETAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEXABAAAAMAoAiYAAAAARlmz3AUAAMA03LHr9mzZsuUe7Rs3bszatWuXoSIAWL0ETAAArEq3XH91Xv/ZW/Owq+6atH/z9q0584XJpk2blrEyAFh9BEwAAKxahx51bNYdd8JylwEAq541mAAAAAAYRcAEAAAAwCgCJgAAAABGETABAAAAMIqACQAAAIBRBEwAAAAAjCJgAgAAAGAUARMAAAAAowiYAAAAABhFwAQAAADAKAImAAAAAEYRMAEAAAAwioAJAAAAgFEETAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEXABAAAAMAoAiYAAAAARhEwAQAAADCKgAkAAACAUQRMAAAAAIyyoICpqjYtpA0AgMVh/AUArCQLncH0vxbYBgDA4jD+AgBWjDV72lhV35nkiUnWVdXPz9j0oCSHTLMwAICDkfEXALAS7TFgSnKfJA8c+h06o/2WJM+cVlEAAAcx4y8AYMXZY8DU3f+Q5B+q6i3dffUS1QQAcNAy/gIAVqK9zWDabW1VnZXkmJmP6e4nTaMoAACMvwCAlWOhAdOfJfnDJG9O8vXplQMAwMD4CwBYMRYaMO3q7jdOtRIAAGYy/gIAVox7LbDfX1bVC6vqqKp68O6vqVYGAHBwM/4CAFaMhc5gOnX4/gsz2jrJf1zccgAAGBh/AQArxoICpu4+dtqFAABwF+MvAGAlWVDAVFXPm6u9u9+6uOUAAJAYfwEAK8tCT5H7jhm375vkxCQfTWKAAwAwHcZfAMCKsdBT5H5m5v2qOjzJedMoCAAA4y8AYGVZ6FXkZvtKEusCAAAsHeMvAOCAtdA1mP4yk6uWJMkhSb4lyQXTKgoA4GBn/AUArCQLXYPpd2bc3pXk6u7eNoV6AACYMP4CAFaMBZ0i193/kOTTSQ5NckSSr02zKACAg53xFwCwkiwoYKqqZyX51yQ/nORZST5cVc+cZmEAAAcz4y8AYCVZ6Clyv5LkO7r7hiSpqnVJ/j7JhdMqDADgIGf8tQe33XZbNm/efOf9LVu2pLuWsSIAOLgtNGC61+7BzeCL2f8r0AEAsHfGX3uwefPmvOQN78ph649Lkmz/+D/l8OM3LnNVAHDwWmjA9LdV9d4k7xjuPzvJe6ZTEgAA2c/xV1XdN8k/JlmbyVjvwu4+o6qOTXJekockuTTJj3X316pqbZK3Jvn2TEKsZ3f3Z4d9vSLJaUm+nuRnu/u9i/j8Rjts/XFZd9wJSZKbtm1d5moA4OC2x4Cpqo5LcmR3/0JV/VCS/zJs+pckb592cQAAB5tFGH/dluRJ3f3lqrp3kn+uqr9J8vNJXtvd51XVH2YSHL1x+H5jdx9XVc9J8ltJnl1Vj0zynCSPSvLwJH9fVd/Y3V9fxKcLAKwSe5tm/ftJbkmS7v6L7v757v75JO8ctgEAsLh+PyPGXz3x5eHuvYevTvKk3LV+07lJnjHcPmW4n2H7iVVVQ/t53X1bd1+VZGuSx416ZgDAqrW3gOnI7v7E7Mah7ZipVAQAcHAbPf6qqkOq6uNJbkhycZJ/S3JTd+8aumxLsn64vT7JNcMxdiW5OZPT6O5sn+MxM491elVtrqrNO3bsWEh5AMAqtLeA6fA9bLvfItYBAMDE4XvYtqDxV3d/vbu/LcnRmcw6+ubxZc17rLO6e2N3b1y3bt20DgMAHOD2FjBtrqqfnN1YVT+RyeKQAAAsrkUbf3X3TUk+kOQ7kxxeVbvX3zw6yfbh9vYkG4ZjrElyWCaLfd/ZPsdjAADuZm9XkXtpkndW1XNz14BmY5L7JPnBKdYFAHCwemlGjL+qal2S27v7pqq6X5Lvy2Th7g8keWYmV5I7Ncm7h4dcNNz/l2H7+7u7q+qiJH9aVb+XySLfxyf510V5hgDAqrPHgKm7P5/kiVX1X5M8emj+6+5+/9QrAwA4CC3C+OuoJOdW1SGZzFa/oLv/qqouS3JeVf16ko8lOXvof3aSt1XV1iQ7M7lyXLr7U1V1QZLLkuxK8iJXkAMA5rO3GUxJku7+QCafegEAsAT2d/zV3VuSPGaO9iszx1XguvvWJD88z75eneTV+1oDAHDw2dsaTAAAAACwRwImAAAAAEYRMAEAAAAwioAJAAAAgFEETAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEXABAAAAMAoAiYAAAAARhEwAQAAADCKgAkAAACAUaYeMFXVIVX1sar6q+H+sVX14araWlXnV9V9hva1w/2tw/ZjZuzjFUP7Z6rqydOuGQAAAICFW4oZTC9JcvmM+7+V5LXdfVySG5OcNrSfluTGof21Q79U1SOTPCfJo5KcnOQNVXXIEtQNAAAAwAJMNWCqqqOTfH+SNw/3K8mTklw4dDk3yTOG26cM9zNsP3Hof0qS87r7tu6+KsnWJI+bZt0AAAAALNy0ZzD9fpJfTHLHcP8hSW7q7l3D/W1J1g+31ye5JkmG7TcP/e9sn+Mxd6qq06tqc1Vt3rFjxyI/DQAAAADmM7WAqaqeluSG7r50WseYqbvP6u6N3b1x3bp1S3FIAAAAAJKsmeK+NyV5elU9Ncl9kzwoyZlJDq+qNcMspaOTbB/6b0+yIcm2qlqT5LAkX5zRvtvMxwAAAACwzKY2g6m7X9HdR3f3MZks0v3+7n5ukg8keebQ7dQk7x5uXzTcz7D9/d3dQ/tzhqvMHZvk+CT/Oq26AQAAANg305zBNJ9fSnJeVf16ko8lOXtoPzvJ26pqa5KdmYRS6e5PVdUFSS5LsivJi7r760tfNgAAAABzWZKAqbs/mOSDw+0rM8dV4Lr71iQ/PM/jX53k1dOrEAAAAID9Ne2ryAEAAACwygmYAAAAABhFwAQAAADAKAImAAAAAEYRMAEAAAAwioAJAAAAgFEETAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEXABAAAAMAoAiYAAAAARhEwAQAAADCKgAkAAACAUQRMAAAAAIwiYAIAAABgFAETAAAAAKMImAAAAAAYRcAEAAAAwCgCJgAAAABGETABAAAAMIqACQAAAIBRBEwAAAAAjCJgAgAAAGAUARMAAAAAowiYAAAAABhFwAQAAADAKAImAAAAAEYRMAEAAAAwioAJAAAAgFEETAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEXABAAAAMAoAiYAAAAARhEwAQAAADCKgAkAAACAUQRMAAAAAIwiYAIAAABgFAETAAAAAKMImAAAAAAYRcAEAAAAwCgCJgAAAABGETABAAAAMIqACQAAAIBRBEwAAAAAjCJgAgAAAGAUARMAAAAAowiYAAAAABhFwAQAAADAKAImAAAAAEYRMAEAAAAwioAJAAAAgFEETAAAAACMImACAAAAYBQBEwAAAACjCJgAAFaRqtpQVR+oqsuq6lNV9ZKh/cFVdXFVXTF8P2Jor6p6XVVtraotVfXYGfs6deh/RVWdulzPCQA48AmYAABWl11JXtbdj0zyhCQvqqpHJnl5kvd19/FJ3jfcT5KnJDl++Do9yRuTSSCV5Iwkj0/yuCRn7A6lAABmEzABAKwi3X1dd390uP2lJJcnWZ/klCTnDt3OTfKM4fYpSd7aE5ckObyqjkry5CQXd/fO7r4xycVJTl66ZwIArCQCJgCAVaqqjknymCQfTnJkd183bLo+yZHD7fVJrpnxsG1D23zts49xelVtrqrNO3bsWNwnAACsGAImAIBVqKoemOTPk7y0u2+Zua27O0kvxnG6+6zu3tjdG9etW7cYuwQAViABEwDAKlNV984kXHp7d//F0Pz54dS3DN9vGNq3J9kw4+FHD23ztQMA3IOACQBgFamqSnJ2ksu7+/dmbLooye4rwZ2a5N0z2p83XE3uCUluHk6le2+Sk6rqiGFx75OGNgCAe1iz3AUAALCoNiX5sSSfqKqPD22/nOQ1SS6oqtOSXJ3kWcO29yR5apKtSb6a5PlJ0t07q+pVST4y9Htld+9ckmcAAKw4AiYAgFWku/85Sc2z+cQ5+neSF82zr3OSnLN41QEAq5VT5AAAAAAYRcAEAAAAwCgCJgAAAABGETABAAAAMIqACQAAAIBRBEwAAAAAjCJgAgAAAGAUARMAAAAAowiYAAAAABhFwAQAAADAKAImAAAAAEYRMAEAAAAwioAJAAAAgFEETAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEXABAAAAMAoAiYAAAAARhEwAQAAADCKgAkAAACAUQRMAAAAAIwiYAIAAABgFAETAAAAAKMImAAAAAAYRcAEAAAAwCgCJgAAAABGETABAAAAMIqACQAAAIBRBEwAAAAAjCJgAgAAAGAUARMAAAAAowiYAAAAABhFwAQAAADAKGuWuwAAAFgqd+y6PVu2bLlb28aNG7N27dplqggAVgcBEwAAB41brr86r//srXnYVZOJ/Ddv35ozX5hs2rRpmSsDgJVNwAQAwEHl0KOOzbrjTljuMgBgVbEGEwAAAACjCJgAAAAAGEXABAAAAMAoAiYAAAAARhEwAQAAADCKgAkAAACAUQRMAAAAAIwytYCpqjZU1Qeq6rKq+lRVvWRof3BVXVxVVwzfjxjaq6peV1Vbq2pLVT12xr5OHfpfUVWnTqtmAAAAAPbdNGcw7Urysu5+ZJInJHlRVT0yycuTvK+7j0/yvuF+kjwlyfHD1+lJ3phMAqkkZyR5fJLHJTljdygFAAAAwPKbWsDU3dd190eH219KcnmS9UlOSXLu0O3cJM8Ybp+S5K09cUmSw6vqqCRPTnJxd+/s7huTXJzk5GnVDQAAAMC+WZI1mKrqmCSPSfLhJEd293XDpuuTHDncXp/kmhkP2za0zdc++xinV9Xmqtq8Y8eOxX0CAAAAAMxr6gFTVT0wyZ8neWl33zJzW3d3kl6M43T3Wd29sbs3rlu3bjF2CQAAAMACTDVgqqp7ZxIuvb27/2Jo/vxw6luG7zcM7duTbJjx8KOHtvnaV597rUlVLehr/YZHLHe1AAAAAEmSNdPacVVVkrOTXN7dvzdj00VJTk3ymuH7u2e0v7iqzstkQe+bu/u6qnpvkt+YsbD3SUleMa26l9Udu/LsN31oQV3Pf8ETp1wMAAAAwMJMLWBKsinJjyX5RFV9fGj75UyCpQuq6rQkVyd51rDtPUmemmRrkq8meX6SdPfOqnpVko8M/V7Z3TunWDcAAAAA+2BqAVN3/3OSmmfziXP07yQvmmdf5yQ5Z/GqAwAAAGCxLMlV5AAAAABYvQRMAAAAAIwiYAIAAABgFAETAAAAAKMImAAAAAAYRcAEAAAAwCgCJgAAAABGETABAAAAMIqACQAAAIBRBEwAAAAAjCJgAgAAAGAUARMAAAAAowiYAAAAABhFwAQAAADAKAImAAAAAEYRMAEAAAAwioAJAAAAgFEETAAAAACMImACAAAAYJQ1y10AAAAslzt23Z4tW7bco33jxo1Zu3btMlQEACuTgAkAgIPWLddfndd/9tY87Kq7JvbfvH1rznxhsmnTpmWsDABWFgETAAAHtUOPOjbrjjthucsAgBXNGkwAAAAAjCJgAgAAAGAUARMAwCpSVedU1Q1V9ckZbQ+uqour6orh+xFDe1XV66pqa1VtqarHznjMqUP/K6rq1OV4LgDAyiFgAgBYXd6S5ORZbS9P8r7uPj7J+4b7SfKUJMcPX6cneWMyCaSSnJHk8Ukel+SM3aEUAMBcBEwAAKtId/9jkp2zmk9Jcu5w+9wkz5jR/taeuCTJ4VV1VJInJ7m4u3d2941JLs49QysAgDsJmAAAVr8ju/u64fb1SY4cbq9Pcs2MftuGtvna76GqTq+qzVW1eceOHYtbNQCwYgiYAAAOIt3dSXoR93dWd2/s7o3r1q1brN0CACuMgAkAYPX7/HDqW4bvNwzt25NsmNHv6KFtvnYAgDkJmAAAVr+Lkuy+EtypSd49o/15w9XknpDk5uFUuvcmOamqjhgW9z5paAMAmNOa5S4AAIDFU1XvSPI9SR5aVdsyuRrca5JcUFWnJbk6ybOG7u9J8tQkW5N8Ncnzk6S7d1bVq5J8ZOj3yu6evXA4AMCdBEwAAKtId//IPJtOnKNvJ3nRPPs5J8k5i1gaALCKOUUOAAAAgFEETAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEXABAAAAMAoAiYAAAAARhEwAQAAADCKgAkAAACAUQRMAAAAAIwiYAIAAABgFAETAAAAAKMImAAAAAAYRcAEAAAAwCgCppXqXmtSVQv6Wr/hEctdLQAAALCKrVnuAthPd+zKs9/0oQV1Pf8FT5xyMQAAAMDBzAwmAAAAAEYRMAEAAAAwioAJAAAAgFEETAAAAACMImACAAAAYBQBEwAAAACjCJgAAAAAGEXABAAAAMAoAiYAAAAARhEwAQAAADDKmuUuAAAADiR37Lo9W7ZsuVvbxo0bs3bt2mWqCAAOfAImAACY4Zbrr87rP3trHnbVZLL/zdu35swXJps2bVrmygDgwCVgAgCAWQ496tisO+6E5S4DAFYMazABAAAAMIqACQAAAIBRBEwAAAAAjCJgAgAAAGAUARMAAAAAo7iKHAAA7MEdu27Pli1b7tG+cePGrF27dhkqAoADj4AJAAD24Jbrr87rP3trHnbVXZP/b96+NWe+MNm0adMyVgYABw4BEwAA7MWhRx2bdcedsNxlAMAByxpMB4N7rUlVLfhr/YZHLHfFAAAAwApiBtPB4I5defabPrTg7ue/4IlTLAYAAABYbcxgAgAAAGAUARMAAAAAowiYAAAAABjFGkwAALCP7th1e7Zs2XKP9o0bN2bt2rXLUBEALC8BEwAA7KNbrr86r//srXnYVXedEHDz9q0584XJpk2blrEyAFgeAiYAANgPhx51bNYdd8JylwEABwRrMAEAAAAwioAJAAAAgFEETNzTvdakqhb0tX7DI5a7WgAAAGCZWYOJe7pjV579pg8tqOv5L3jilIsBAFgZ5rqynKvKAXCwEDABAMAimH1luRs/9+n85HdvyQkn3H0hcKETAKuRgAkAABbJzCvL3bRta15/8WV3Bk5JcvP2rTnzhcmmTZuWq0QAmAoBE+MM6zUtxMOP3pDt13xuygUBABw4ZgZOALCaCZgYx3pNAAAAcNBzFTkAAAAARhEwAQAAADCKgAkAAACAUazBxNKxIDgAcJC7Y9ft2bJly93aNm7cmLVr1y5TRQCwOARMLB0LggMAB7lbrr86r//srXnYVZMTCW7evjVnvjDZtGnTMlcGAOMImDgw7cNsp8SMJwBg5Tj0qGOz7rgTksw9oykxqwmAlUfAxIFpH2Y7JWY8AQAr0+wZTYlZTQCsTAImAABYRjNnNAHASiVgYnWwgDgArFq33XZbNm/efLe2LVu2pHvhp9OvJAs5bW6u12R2HwBYSgImVod9WUD8p79LGAUAK8jmzZvzkje8K4etP+7Otu0f/6ccfvzGZaxqeuY6be7Gz306P/ndW3LCCZOZTlu2bMmb//HKHL7huHn77CZ0AmApCJg4+LiaHQCsOIetP+5up5HdtG3rMlYzfbNPm7tp29a8/uLL7gyddgdse+qTWM8JgKUjYAIAgBVgZug0X8BmPScAlouACQAADhLWbgJgWgRMAACwSs1eMHwhazd97WtfS5Lc5z73udu+hFAA7ImACQAAVqnZC4YvZO2m7R//hxzywIfkYcc9+s4+FhAHYG8ETLAn91rjinMAwIq2r2s33bRta+59xFF7XUB8f2Y+zXWKnhlTAKuDgAn2ZF+uOPfT37XgMCoRSAEAK8ver2y395lPc52iN9fjXP0OYOURMMFi2YcwKknOf8ETp1gMAMD07evMp/lO0Zv9OABWHgETLBen3wEAB4GFnKIHwMonYILlMqXT74RRAMBKN/vqd/u7vtPsPgBMj4AJVgJhFABwELnn1e/2vk7T5s2b85I3vCuHrb9rfSdXvwNYOgImWG2muDD5Ifdem6/fftuC+gqvAIAx9ra+0+xZTlu2bMmDHv6f9vnqd8k9A6eFzIZa6hlTcx1PUAYcSARMcDDbj4XJzaQCAA4E95zlNFlAfLa9Xf1ursBprqvdLeSKeAsJr/bX7BlarrQHHGgETMB0TGkm1b7MokqEVwCwmu3vAuKzHzd7ltN8V7tbyBXx9hZezbWe1FxtcwVTh60/7s7jzZ7BtdB9L2Q9K4D9IWAClt++hFH7MIsqmd5MqvUbHpFrt12z6PsFAJbeXLOc9tZvoX3uGV7dcz2p2W3zzarqvmtMM3sG10L3PVef2cdbaAi2kPBqmoGWhd3hwLJiAqaqOjnJmUkOSfLm7n7NMpcErARTXJNqWvudVtA1zTW0BG6wOhl/wXhzhVez15Oa3banWVWLse+5+tx9dtbeg6q52hb6uP0JtObqs5DTFBdzVtfsQGt/Z6Pt7xpfixnyLVYIt5DXZDGPx4FtRQRMVXVIkj9I8n1JtiX5SFVd1N2XLW9lwKqyjzOpprHfZIpB15TW0NrnOqZ0SqTTJ2FxGX/B8lrorKppHG8hQdVcbfvyuH0NtObus5BTGRdnVldyz0BrzGy0/Vnja7FCvv09dXMhId9CX8v9CcaWembdQkO+aV0kYCXO0FsRAVOSxyXZ2t1XJklVnZfklCQGOMDqM62ga0o17HMdUzolcpqnT04r6NrXUOxAqEMwd1A5oMZfN2+/+3+uv/KF7Tnk1luz4wEPmPP+QtsWq8+BuO8DsaaVuu8DsaaVuu872x74kCyGL1131VT2/dWdn89vvm1rjjjqk3e2feHKT+bwY0/Yw6P2fz+z++3PscbWdMj9HpQjjnrEvG3z9dlbnQs53pjjL9bjvvLF6/KyZ3/f3UK+3z3/4jzgIUfN+7jZj5nvcQvZ9/70mc9yLf5f3b0sB94XVfXMJCd3908M938syeO7+8Uz+pye5PTh7jcl+czIwz40yRdG7oPl4/1b+byHK5v3b+VbCe/hN3T3uuUuYrVayPhraF/sMdh8VsLP5Gri9V56XvOl5fVeWl7vpTXN13ve8ddKmcG0V919VpKzFmt/VbW5u+95nVNWBO/fyuc9XNm8fyuf95CFWuwx2Hz8TC4tr/fS85ovLa/30vJ6L63ler3vtfcuB4TtSTbMuH/00AYAwHQYfwEAC7ZSAqaPJDm+qo6tqvskeU6Si5a5JgCA1cz4CwBYsBVxilx376qqFyd5byaXyT2nuz815cNOfao3U+X9W/m8hyub92/l8x4e5JZp/LUnfiaXltd76XnNl5bXe2l5vZfWsrzeK2KRbwAAAAAOXCvlFDkAAAAADlACJgAAAABGETDNUlUnV9VnqmprVb18uevhnqpqQ1V9oKouq6pPVdVLhvYHV9XFVXXF8P2Iob2q6nXDe7qlqh67vM+A3arqkKr6WFX91XD/2Kr68PBenT8sKpuqWjvc3zpsP2ZZCydVdXhVXVhVn66qy6vqO/0OrixV9XPD39BPVtU7quq+fgc5EOxtLObncXEt4PX++WHMtaWq3ldV37Acda4WC/2/RlX931XVVeWy7iMs5PWuqmfN+H/Fny51javNAv6mPGL4v9zHhr8rT12OOleDqjqnqm6oqk/Os33Jx+ACphmq6pAkf5DkKUkemeRHquqRy1sVc9iV5GXd/cgkT0jyouF9enmS93X38UneN9xPJu/n8cPX6UneuPQlM4+XJLl8xv3fSvLa7j4uyY1JThvaT0ty49D+2qEfy+vMJH/b3d+c5FszeR/9Dq4QVbU+yc8m2djdj85kAefnxO8gy2yBYzE/j4tkga/3xzL5W3FCkguT/PbSVrl6LPT/GlV1aCZjpA8vbYWry0Je76o6Pskrkmzq7kcleelS17maLPBn/P9JckF3PyaTsccblrbKVeUtSU7ew/YlH4MLmO7ucUm2dveV3f21JOclOWWZa2KW7r6uuz863P5SJv+xXZ/Je3Xu0O3cJM8Ybp+S5K09cUmSw6vqqKWtmtmq6ugk35/kzcP9SvKkTAavyT3fw93v7YVJThz6swyq6rAk35Xk7CTp7q91903xO7jSrElyv6pak+T+Sa6L30GW30LGYn4eF89eX+/u/kB3f3W4e0mSo5e4xtVkof/XeFUmwemtS1ncKrSQ1/snk/xBd9+YJN19wxLXuNos5DXvJA8abh+W5NolrG9V6e5/TLJzD12WfAwuYLq79UmumXF/29DGAWqYFv+YTD7hObK7rxs2XZ/kyOG29/XA9PtJfjHJHcP9hyS5qbt3Dfdnvk93vofD9puH/iyPY5PsSPLHw/TmN1fVA+J3cMXo7u1JfifJ5zIJlm5Ocmn8DrL8FvL3ws/j4tnXv8+nJfmbqVa0uu319R5OYdnQ3X+9lIWtUgv5+f7GJN9YVf+7qi6pqj3NBmHvFvKa/1qSH62qbUnek+Rnlqa0g9KSj8EFTKxYVfXAJH+e5KXdfcvMbd3dmaTjHICq6mlJbujuS5e7FvbLmiSPTfLGYXrzV3LX6XBJ/A4e6Ib1sU7JJCx8eJIHZM9TrIGDXFX9aJKNSf7ncteyWlXVvZL8XpKXLXctB5E1mZw+9D1JfiTJH1XV4ctZ0EHgR5K8pbuPTvLUJG8bfvZZBbyRd7c9yYYZ948e2jjAVNW9MwmX3t7dfzE0f373lL/h++4prt7XA8+mJE+vqs9mMnX2SZms6XP4cLpOcvf36c73cNh+WJIvLmXB3M22JNu6e/faEBdmEjj5HVw5vjfJVd29o7tvT/IXmfxe+h1kuS3k74Wfx8WzoL/PVfW9SX4lydO7+7Ylqm012tvrfWiSRyf54DBGekKSiyz0vd8W8vO9LclF3X17d1+V5P9kEjixfxbymp+W5IIk6e5/SXLfJA9dkuoOPks+Bhcw3d1Hkhw/XEXnPpksOnbRMtfELMM6C2cnuby7f2/GpouSnDrcPjXJu2e0P29YRf8JSW6ecRoPy6C7X9HdR3f3MZn8nr2/u5+b5ANJnjl0m/0e7n5vnzn0NztmmXT39UmuqapvGppOTHJZ/A6uJJ9L8oSquv/wN3X3e+h3kOW2kLGYn8fFs9fXu6oek+RNmYRL1qcZZ4+vd3ff3N0P7e5jhjHSJZm87puXp9wVbyF/T96VyeylVNVDMzll7solrHG1Wchr/rlMxh2pqm/JJGDasaRVHjyWfAy+Zu9dDh7dvauqXpzkvZlcUeec7v7UMpfFPW1K8mNJPlFVHx/afjnJa5JcUFWnJbk6ybOGbe/JZPrl1iRfTfL8Ja2WffFLSc6rql/P5Ko1Zw/tZ2cyfXZrJgvZPWeZ6uMuP5Pk7cPg4cpMfq/uFb+DK0J3f7iqLkzy0UyuzPmxJGcl+ev4HWQZzTcWq6pXJtnc3RfFz+OiWeDr/T+TPDDJnw1rqX+uu5++bEWvYAt8vVkkC3y935vkpKq6LMnXk/xCd5sRuZ8W+Jq/LJNTEX8uk+UUftyHBPunqt6RSUD60GFNqzOS3DtJuvsPswxj8PJeAgAAADCGU+QAAAAAGEXABAAAAMAoAiYAAAAARhEwAQAAADCKgAkAAACAUQRMAAAAAIwiYAIAAABglP8f3uHuSRENdIsAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# If we observe carefully the min max image has more contrast as compared to the base image.\n",
        "imagefig, axes = plt.subplots(1,2, figsize=(20,20))\n",
        "axes[0].set_title(\"Image\")\n",
        "axes[0].imshow(image)\n",
        "\n",
        "axes[1].set_title(\"Min Max Image\")\n",
        "axes[1].imshow(min_max_image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "jLYLoa_quLa4",
        "outputId": "f6e33fb5-8e41-4e96-db04-2998277ecd1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7faad83ea370>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ],
            "image/png": 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          "metadata": {
            "needs_background": "light"
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Standardization"
      ],
      "metadata": {
        "id": "kdbIk16z32NB"
      }
    },
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      "cell_type": "markdown",
      "source": [
        "These initial inputs will be multiplied (weighted) and added to (biassed) in order to produce activations, which we will then backpropogate with the gradients to train the model. We want each feature in this process to have a comparable range so that our gradients don't get out of hand (and that we only need one global learning rate multiplier). Another way to think about it is that deep learning networks typically share a lot of parameters. If your inputs weren't scaled to produce similarly-ranged feature values (i.e., across the entire dataset by removing the mean), sharing wouldn't be very straightforward because w is too big for some regions of the image and too small for others.\n",
        "\n",
        "             \n",
        ">>> $ Z = \\Large\\frac{x - μ}{σ}$, where $μ$ is the mean and $σ$ is the standard deviation."
      ],
      "metadata": {
        "id": "ZhdoSiD-dfdV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate mean and std of the entire dataset\n",
        "def calc_mean_std(dataset):\n",
        "  psum = 0\n",
        "  pstd = 0\n",
        "  for image in dataset:\n",
        "    image = min_max_filter(image)\n",
        "    psum += image.sum() \n",
        "    pstd += (image**2).sum()\n",
        "  \n",
        "  image = lensing_data.get_random_image_sample()\n",
        "  height = dataset[0].shape[0]\n",
        "  width = dataset[0].shape[1]\n",
        "  count = (len(dataset)*height*width)\n",
        "  mean = psum/count\n",
        "  std = np.sqrt((pstd/count) - (mean**2))\n",
        "  return mean, std\n",
        "\n",
        "\n",
        "mean, std = calc_mean_std(lensing_data.images[\"train\"])\n",
        "print(mean, std)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a6AIeph45pdh",
        "outputId": "cff17419-34bf-4dc2-e020-ed9e01ecbff3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.07407581904556135 0.1178155528000387\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Transform to standardized image\n",
        "standardized_image = (min_max_image - mean)/std\n",
        "sns.histplot(standardized_image.ravel()).set(title='Standardized Pixel Distribution')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "AZsj4HZXV2da",
        "outputId": "d0c67263-3675-496f-b89c-4b1dfc4f3198"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Standardized Pixel Distribution')]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEICAYAAACuxNj9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdd0lEQVR4nO3de5hcVZ3u8e9LunPHdGN6ciCJBDVe0HlEjIjiEZRRLl5ADyI4QmCCAQXEkTMK6gjqYbyM4AU8YAwZAioICIfIZAQkKF6BcBEI0SEi5GKAQFINCQmmO7/zx17V7FS6e1c6XZdOv5/n6adrr7Vr7dW7u+uttdauKkUEZmZm/dml0R0wM7Pm57AwM7NCDgszMyvksDAzs0IOCzMzK+SwMDOzQg4LayhJB0laOYjtnSDp17nt9ZJeOljtpzZ/IemkAdzvEkn/uoPHniYpJLXsQBs73I9cWy9J53hE2h7Quemn/f+SNHOw2rOBc1gYkt4q6beSOiWtlfQbSW9MdVs9+A41ETE+Ih6p1/EknStpc3oALaXz+ubUl1Mi4ss1Pv6jkjZKejZ3/FMk9fyvV9uP1NY/9LdPRCxP57h7EPp+rqQfVLR/WETM39G2bcc5LIY5SS8CbgQuBHYDJgNfBJ5vZL+qsSPPrmvsxxExHugAfg1cJ0l1PP57I2JXYE/gq8BngEsH+yBNfP6tBhwW9gqAiLgyIrojYmNE3BwR90t6NXAJ8ObyM2UASe+WdK+kZyStkHRuubHcNMlMScslPSXpc7n6MZIuk7RO0kPAG/OdkXSWpD+nZ8YPSXp/ru6ENOr5pqSngXMlvVjSgtSXO4GXVbQXkl4uaY/0M5S/npMUuf3+SdLS1K+bJO2Zq3unpD+mkddFQFUP/BGxGZgP/A/gxenn/j+pzc9IuqP8gCvpY5KWSBotaZfceXha0tWSdqvmmBXH74yIBcCHgJmSXpuOle/HREk3plHIWkm/Sse/AngJ8NN0vj6d+93OkrQcWNTHtNjLJN2Zfic3lPuuXqYcy6MXSYcCnwU+lI73h1TfM62V+vV5SY9JelLS5ZImpLp+/+5sxzks7L+BbknzJR0mqb1cERFLgVOA36WphrZUtQE4HmgD3g18TNKRFe2+FXglcDDwhRQ8AOeQPaC/DDgEqJyP/jPwP4EJZCOcH0jaPVf/JuARYBJwHvBdYBOwO/BP6WsbEfHX9DOMT8/6rweuApB0BNkD1QfIRgO/Aq5MdROB64DPAxNT/w7o7RiVJI0CTgBWRMRTFdX/TjZ6+7yk6cC/AR+JiE3A6cCRwIHAHsC69HMOSETcCawkO6+Vzkx1HWTn9LPZXeI4YDnZKGV8RHw9d58DgVeT/f56czzZ72F3oAv4ThV9/BnZOfhxOt7retnthPT1duClwHjgoop9+vq7sx3ksBjmIuIZsn+wAL4PrEnP1Cf1c59fRMQDEbElIu4ne2A9sGK3L6ZRyh+APwDlf/6jgfMiYm1ErKDigSQirkkP7Fsi4sfAw8B+uV3+GhEXRkQX8DfgfwFfiIgNEfEg2TP5fkn6DPAqXgiWU4CvRMTS1O6/Afuk0cXhwJKIuDaNFL4FPF5wiKPTKGwF8Abg/ZU7RMQWsgfVTwALgK9HxL25/nwuIlZGxPPAucBROzjt81eyacZKm8ke1PeMiM0R8asofsO4c9P53thH/RUR8WBEbAD+lex8jBh413v8I3BBRDwSEeuBs4FjKs5LX393toMcFkZ6kDwhIqYAryV7NvutvvaX9CZJt0laI6mT7MFtYsVu+QfU58ieBZLaXpGre6yi7eMl3ZemRUqpP/m28/ftAFr6a6+Xvh8GnAEcmXuw2xP4du6Ya8mmmiZX9jc9kK6gf1dHRFtE/F1EvCMi7u5tp4h4FLgNmMbWI4c9getz/VkKdJM98x+oyWQ/V6V/B5YBN0t6RNJZVbRV9PNX/j5a2fbvYyD2YOvf72Nkv//8eenr7852kMPCthIRfwQuI3uQhmzEUelHZM+Gp0bEBLJ1jWoXcFcDU3PbLynfSM/kvw+cBrw4TXs9WNF2vj9ryKY5em2vkqRXko08jk6jmrIVwMnpAb78NSYiflvZX0mqON6ASXo38GbgVrIH7Xx/Dqvoz+iIWDXA47yRLCy2uaotIp6NiDMj4qXA+4BPSTq4XN1Hk0Ujj8rfx2bgKbLpy7G5fo0gC/xq2/0rWZDm2+4Cnii4nw0Ch8UwJ+lVks6UNCVtTwWOBX6fdnkCmCJpZO5uuwJrI2KTpP2AD2/HIa8GzpbUno55eq5uHNkDxprUlxN5IbS2kS7XvI5soXuspL3Zdg2k/HO+CLiBbHqn8kHzktSn16R9J0j6YKr7T+A1kj6Qpjs+QbZgvUPSWshc4KTU5/dKOjzXn/NSeCKpI62rbO8xXiTpPWRrMz+IiAd62ec9yi4AENBJNoLZkqqfIFsb2F4fkbS3pLHAl4Br0+/qv4HRyi6QaCVbBxqVu98TwDTlLvOtcCXwz5L2kjSeF9Y4ugbQR9tODgt7lmzR+A5JG8hC4kGyhU+ARcAS4HFJ5UXajwNfkvQs8AWyAKjWF8mmD/4C3AxcUa6IiIeA84HfkT1w/D3wm4L2TiObanicbET0H33sty/Zwuc3lbsqKh33euBrwFWSniH7+Q9LdU8BHyS7BPVpYHoVfarGHOCGiFgYEU8Ds4C5kl4MfJts5HZzOse/J/sdVeun6X4rgM8BFwAn9rHvdODnwHqy8/5/I+K2VPcVsgX4kqT/vR3Hv4Lsd/E4MJosYImITrK/nbnAKrKRRv7qqGvS96cl3dNLu/NS27eT/f2ULwawOpA//MjMzIp4ZGFmZoUcFmZmVshhYWZmhRwWZmZWaKd8I7CJEyfGtGnTGt0NM7Mh5e67734qIjp6q9spw2LatGksXry40d0wMxtSJPX5DgiehjIzs0IOCzMzK+SwMDOzQg4LMzMr5LAwM7NCDgszMyvksDAzs0IOCzMzK+SwqEJEsG7dOiJiq9tmZsOFw6IKpVKJY86/gVKptNVtM7PhwmFRpdYx43u9bWY2HDgszMyskMPCzMwKOSzMzKyQw8LMzAo5LMzMrJDDwszMCjkszMyskMPCzMwKOSzMzKyQw8LMzAo5LMzMrJDDwszMCjkszMyskMPCzMwK1SwsJE2VdJukhyQtkXRGKj9X0ipJ96Wvw3P3OVvSMkl/knRIrvzQVLZM0lm16rOZmfWupYZtdwFnRsQ9knYF7pZ0S6r7ZkR8I7+zpL2BY4DXAHsAP5f0ilT9XeCdwErgLkkLIuKhGvbdzMxyahYWEbEaWJ1uPytpKTC5n7scAVwVEc8Df5G0DNgv1S2LiEcAJF2V9nVYmJnVSV3WLCRNA14P3JGKTpN0v6R5ktpT2WRgRe5uK1NZX+WVx5gtabGkxWvWrBnsH8HMbFireVhIGg/8BPhkRDwDXAy8DNiHbORx/mAcJyLmRMSMiJjR0dExGE2amVlSyzULJLWSBcUPI+I6gIh4Ilf/feDGtLkKmJq7+5RURj/lZmZWB7W8GkrApcDSiLggV757brf3Aw+m2wuAYySNkrQXMB24E7gLmC5pL0kjyRbBF9Sq32Zmtq1ajiwOAI4DHpB0Xyr7LHCspH2AAB4FTgaIiCWSriZbuO4CTo2IbgBJpwE3ASOAeRGxpIb9NjOzCrW8GurXgHqpWtjPfc4DzuulfGF/9zMzs9ryK7jNzKyQw8LMzAo5LMzMrJDDwszMCjkszMyskMPCzMwKOSzMzKxQTd/uY2cSEZRKpUZ3w8ysIRwWVeratIGPX3EnWzZvQq2jG90dM7O6clhsh9axu7Ll+RF0dXc3uitmZnXlNQszMyvksDAzs0IOCzMzK+SwMDOzQg4LMzMr5LAwM7NCDgszMyvksDAzs0IOCzMzK+SwMDOzQg4LMzMr5LAwM7NCDgszMyvksDAzs0IOCzMzK+SwMDOzQg4LMzMr5LAwM7NCDgszMytUs7CQNFXSbZIekrRE0hmpfDdJt0h6OH1vT+WS9B1JyyTdL2nfXFsz0/4PS5pZqz6bmVnvajmy6ALOjIi9gf2BUyXtDZwF3BoR04Fb0zbAYcD09DUbuBiycAHOAd4E7AecUw4YMzOrj5qFRUSsjoh70u1ngaXAZOAIYH7abT5wZLp9BHB5ZH4PtEnaHTgEuCUi1kbEOuAW4NBa9dvMzLZVlzULSdOA1wN3AJMiYnWqehyYlG5PBlbk7rYylfVVXnmM2ZIWS1q8Zs2awf0BzMyGuZqHhaTxwE+AT0bEM/m6iAggBuM4ETEnImZExIyOjo7BaNLMzJKahoWkVrKg+GFEXJeKn0jTS6TvT6byVcDU3N2npLK+ys3MrE5qeTWUgEuBpRFxQa5qAVC+omkmcEOu/Ph0VdT+QGearroJeJek9rSw/a5UZmZmddJSw7YPAI4DHpB0Xyr7LPBV4GpJs4DHgKNT3ULgcGAZ8BxwIkBErJX0ZeCutN+XImJtDfttZmYVahYWEfFrQH1UH9zL/gGc2kdb84B5g9c7MzPbHn4Ft5mZFXJYmJlZIYeFmZkVcliYmVkhh4WZmRVyWJiZWSGHhZmZFXJYmJlZIYeFmZkVcliYmVkhh4WZmRVyWJiZWSGHhZmZFXJYmJlZIYeFmZkVcliYmVkhh4WZmRVyWJiZWSGHhZmZFXJYmJlZIYeFmZkVcliYmVkhh4WZmRWqKiwkHVBNmZmZ7ZyqHVlcWGWZmZnthFr6q5T0ZuAtQIekT+WqXgSMqGXHzMysefQbFsBIYHzab9dc+TPAUbXqlJmZNZd+wyIifgn8UtJlEfFYnfrU9CKCUqlEW1sbkhrdHTOzmqt2zWKUpDmSbpa0qPzV3x0kzZP0pKQHc2XnSlol6b70dXiu7mxJyyT9SdIhufJDU9kySWdt909YA12bNvDROYsolUqN7oqZWV0UTUOVXQNcAswFuqu8z2XARcDlFeXfjIhv5Ask7Q0cA7wG2AP4uaRXpOrvAu8EVgJ3SVoQEQ9V2YeaaRkzvtFdMDOrm2rDoisiLt6ehiPidknTqtz9COCqiHge+IukZcB+qW5ZRDwCIOmqtG/Dw8LMbDipdhrqp5I+Lml3SbuVvwZ4zNMk3Z+mqdpT2WRgRW6flamsr/JtSJotabGkxWvWrBlg18zMrDfVhsVM4F+A3wJ3p6/FAzjexcDLgH2A1cD5A2ijVxExJyJmRMSMjo6OwWrWzMyochoqIvYajINFxBPl25K+D9yYNlcBU3O7Tkll9FNeF+Urn8zMhrOqwkLS8b2VR0Tl4nVRO7tHxOq0+X6gfKXUAuBHki4gW+CeDtwJCJguaS+ykDgG+PD2HHNHlUolZl20EI0czah6HtjMrIlUu8D9xtzt0cDBwD1se6VTD0lXAgcBEyWtBM4BDpK0DxDAo8DJABGxRNLVZAvXXcCpEdGd2jkNuInsFePzImJJlX0eNK1jxtHVXe1FYGZmO59qp6FOz29LagOuKrjPsb0UX9rP/ucB5/VSvhBYWE0/zcysNgb6FuUbgEFZxzAzs+ZX7ZrFT8mmjiCbDno1cHWtOjUU5Be+/bYfZrazq3bNIv+K6y7gsYhYWYP+DBldmzbw8SvuZERLC1d87B20t7cX38nMbIiqahoqvaHgH8neebYd+FstOzVUtI7dlZF+2w8zGwaq/aS8o8kuZf0gcDRwhyS/RbmZ2TBR7TTU54A3RsSTAJI6gJ8D19aqY2Zm1jyqvRpql3JQJE9vx33NzGyIq3Zk8TNJNwFXpu0P4dc+mJkNG0Wfwf1yYFJE/IukDwBvTVW/A35Y686ZmVlzKBpZfAs4GyAirgOuA5D096nuvTXsm5mZNYmidYdJEfFAZWEqm1aTHpmZWdMpCou2furGDGI/zMysiRWFxWJJH60slHQS2QcgmZnZMFC0ZvFJ4HpJ/8gL4TADGEn2eRRmZjYM9BsW6ZPt3iLp7cBrU/F/RsSimvfMzMyaRrWfZ3EbcFuN+2JmZk3Kr8I2M7NCDgszMyvksDAzs0IOCzMzK+SwMDOzQg4LMzMr5LAwM7NCDgszMyvksDAzs0IOCzMzK+SwMDOzQg4LMzMr5LAwM7NCNQsLSfMkPSnpwVzZbpJukfRw+t6eyiXpO5KWSbpf0r65+8xM+z8saWat+mtmZn2r5cjiMuDQirKzgFsjYjpwa9oGOAyYnr5mAxdDFi7AOcCbgP2Ac8oBY2Zm9VOzsIiI24G1FcVHAPPT7fnAkbnyyyPze6BN0u7AIcAtEbE2ItYBt7BtAJmZWY3Ve81iUkSsTrcfByal25OBFbn9Vqayvsq3IWm2pMWSFq9Zs2Zwe21mNsw1bIE7IgKIQWxvTkTMiIgZHR0dg9WsmZlR/7B4Ik0vkb4/mcpXAVNz+01JZX2Vm5lZHdU7LBYA5SuaZgI35MqPT1dF7Q90pumqm4B3SWpPC9vvSmVmZlZHLbVqWNKVwEHAREkrya5q+ipwtaRZwGPA0Wn3hcDhwDLgOeBEgIhYK+nLwF1pvy9FROWiuZmZ1VjNwiIiju2j6uBe9g3g1D7amQfMG8SumZnZdvIruHdQRFAqlcjyzsxs5+Sw2EGbN67no3MWUSqVGt0VM7OacVgMgpYx4xvdBTOzmnJYmJlZIYeFmZkVcliYmVkhh4WZmRVyWJiZWSGHhZmZFXJYmJlZIYeFmZkVcliYmVkhh4WZmRVyWJiZWSGHhZmZFXJYmJlZIYeFmZkVcliYmVkhh4WZmRVyWJiZWSGHhZmZFXJYmJlZIYeFmZkVcliYmVkhh4WZmRVyWJiZWSGHhZmZFWppdAd2FhHBunXrAGhra0NSg3tkZjZ4GjKykPSopAck3SdpcSrbTdItkh5O39tTuSR9R9IySfdL2rcRfS7S2dnJcRcv4riLF1EqlRrdHTOzQdXIaai3R8Q+ETEjbZ8F3BoR04Fb0zbAYcD09DUbuLjuPa3SyDHjGTlmfKO7YWY26JppzeIIYH66PR84Mld+eWR+D7RJ2r0B/TMzG7YaFRYB3CzpbkmzU9mkiFidbj8OTEq3JwMrcvddmcq2Imm2pMWSFq9Zs6ZW/e5VRNDZ2VnXY5qZ1VOjwuKtEbEv2RTTqZLelq+MiCALlKpFxJyImBERMzo6Ogaxq8W6Nm3gjPm309XdXdfjmpnVS0PCIiJWpe9PAtcD+wFPlKeX0vcn0+6rgKm5u09JZU2lZfS4RnfBzKxm6h4WksZJ2rV8G3gX8CCwAJiZdpsJ3JBuLwCOT1dF7Q905qarzMysDhrxOotJwPXpdQgtwI8i4meS7gKuljQLeAw4Ou2/EDgcWAY8B5xY/y6bmQ1vdQ+LiHgEeF0v5U8DB/dSHsCpdeiamZn1wa/g7kNEUCqV/AI7MzMcFn0qlUocd/EiNm9c76uczGzYa6YX5TWdkWPG0zqAq5zK7xOVzaCZmQ19DotBFhEsX76cY86/wVNYZrbTcFgMss0b13PG/NvRyDGN7oqZ2aBxWNSAX6BnZjsbL3DXSPlqKvDnW5jZ0OeRRY10bdrAx6+4059vYWY7BY8saqh17K60jBjR6G6Yme0wjyzMzKyQw6IO/LoLMxvqHBZ10NnZ6dddmNmQ5rCok1Z/NreZDWEOizrzlJSZDUUOizorlUqekjKzIcdhUWMRQWdn51ZlnpIys6HGYVFj5feK2tzV1fP5GOVXd3sqysyGCodFHbSMHtfziu5T5t3Opg3P8NE5i3oCw2sYZtbsHBZ11Dp2157Px2hJU1FewzCzocBv99Eg+TcaLK9hlEcZAO3t7X7zQTNrGg6LBilPS23ZvAm1jgayUcZR513JLiNHc82nP0BbWxulUsnvWmtmDedpqF7kn/XXUn5aqqdszLitpqg+9I3/x6OPPuo1DTNrKIdFL0qlErMuWkhXd3ddjlcOp94CSlKfi+FeHDezenFY9KF1TP0+7S5/pVRvAdXXYnh5e926dT1fEdETIg4SMxssXrNoEq1jd2XL8yPoWt+5zTRY5WJ4OQxKpRKtY8bT2dnJ6VfeA8Dlp7x9q+0rPvYO2tvbt2rHayBmtr0cFk0ov/jd1d0N3S9s0zKK5cuX8/mFf2bzxvXQMorOzk5GptFHZ2cnsy5ayPjd92LELrv0jC7a29t71kC+d9JBTJs2DaCnXhKStgqScrhMmDCh51XolUHjADIbHhwWTSo/0shvb1zfyRnzb2fC5JfTGrHV9ohddqGzs7NnCm3zxvWceOGNtIway9yTDwZeWAO55tMfAOCo866ke0s3Y9snscuIEVz04Tew55570tnZSalU4pRLf8klsw7caqRSvkoLsrA49oIFXHXmEb56y2wn5rAYgloqrqAqb5ffWqRl9DhGVdTlRyoto8flprXGoe7uFEbPcdL3buX8Y2a8MHJpHd0zcsmvhXziqnuJCM5798t7XidSHrlcMuvAnsBoa2vrqYNtRyZmNjQ4LHYylUFSlh+pbDPNlSf1OXLZ8vxznHjhjQA922fMv53RbX/XEwaSOPHCG3tGKhceuy9AT7hc9OE39EyB5ae4ygvxbW1tPVNe5bpywFROeXkKzKx+hkxYSDoU+DYwApgbEV9tcJeGtMpprry+Ri591VWGT8vocT0jlcpwOel7t/ZMiZ089xd8/YOv7xnFdG3e3DOqKY9aPn3NvXzvpIOYMGFCz7RYX9vltZfygn5+PQboCaT8dnn/cviUX0FfWQ9sE1T5V9tX1pfv31eYOehsqBkSYSFpBPBd4J3ASuAuSQsi4qHBPla9XpC3s+krfLYZ6Ug9wdK9ZctWo5iurq1HMeUptfL+m9Z3Fm/n1mdmXbSwZz2mXA9stV3evxw+J333vxg1YWKv7Z089xc9U2zlCwnUOqrXeqDPMOurLjs9fQdbfhteGIWVL5fenvvXor3K4C2/NmhH+iap53dTLstvV9OXcll+KrT8O4BtR7CVHPoZDYXr8CW9GTg3Ig5J22cDRMRXett/xowZsXjx4gEda926dbzv83PZZeSofh9kBnu7nsdq9Hatj9U6ehxb/raJ7i3d27X/pg3P0jpmbL/tbd74HGPbO7Zqv6/6cnv59vury/rWUdV2d3c33z7hbXzm2vvYvHHDdt+/Fu21jh7LZWe8t+dCh1Mu/eUO923EiBF87ah9OP37tzDqRbtts11NX+CFcAa4ZNaBAD3bXztqHz51xW+49LTDe/bPK79It7f6/uoapTzSHQhJd0fEjF7rhkhYHAUcGhEnpe3jgDdFxGm5fWYDs9PmK4E/1b2jxSYCTzW6E03M56d/Pj9987npX7XnZ8+I6OitYkhMQ1UjIuYAcxrdj/5IWtxXapvPTxGfn7753PRvMM7PUHm7j1XA1Nz2lFRmZmZ1MFTC4i5guqS9JI0EjgEWNLhPZmbDxpCYhoqILkmnATeRXTo7LyKWNLhbA9HU02RNwOenfz4/ffO56d8On58hscBtZmaNNVSmoczMrIEcFmZmVshhUSeSDpX0J0nLJJ3V6P40C0lTJd0m6SFJSySd0eg+NSNJIyTdK+nGRvel2Uhqk3StpD9KWppexGuJpH9O/1sPSrpS0uiBtOOwqIPc25UcBuwNHCtp78b2qml0AWdGxN7A/sCpPje9OgNY2uhONKlvAz+LiFcBr8PnqYekycAngBkR8VqyC4SOGUhbDov62A9YFhGPRMTfgKuAIxrcp6YQEasj4p50+1myf/TJje1Vc5E0BXg3MLfRfWk2kiYAbwMuBYiIv0VEqaGdaj4twBhJLcBY4K8DacRhUR+TgRW57ZX4AXEbkqYBrwfuaHBXms23gE8DWxrcj2a0F7AG+I80TTdXUu/v0z8MRcQq4BvAcmA10BkRNw+kLYeFNQVJ44GfAJ+MiGca3Z9mIek9wJMRcXej+9KkWoB9gYsj4vXABsBrgomkdrJZjL2APYBxkj4ykLYcFvXhtyvph6RWsqD4YURc1+j+NJkDgPdJepRs+vIdkn7Q2C41lZXAyogoj0avJQsPy/wD8JeIWBMRm4HrgLcMpCGHRX347Ur6oOxDAC4FlkbEBY3uT7OJiLMjYkpETCP7u1kUEQN6ZrgziojHgRWSXpmKDgYG/XNuhrDlwP6Sxqb/tYMZ4AUAQ+LtPoa6nejtSmrhAOA44AFJ96Wyz0bEwsZ1yYaY04EfpidijwAnNrg/TSMi7pB0LXAP2ZWH9zLAt/7w232YmVkhT0OZmVkhh4WZmRVyWJiZWSGHhZmZFXJYmJlZIYeFmZkVcliYmVmh/w8Cxa5dlLIYCQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize Images\n",
        "imagefig, axes = plt.subplots(1,2, figsize=(20,20))\n",
        "axes[0].set_title(\"Image\")\n",
        "axes[0].imshow(image)\n",
        "\n",
        "axes[1].set_title(\"Standardized Image\")\n",
        "axes[1].imshow(standardized_image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "j8fjo-Hq551p",
        "outputId": "666df9d3-de98-492c-9744-8864909a72e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7faad804ba90>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mass Analysis"
      ],
      "metadata": {
        "id": "xkdpukDVcZGf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Mass Properties\n",
        "mass = lensing_data.get_random_mass_sample()\n",
        "print(\"Mass Type: {}\".format(type(mass)))\n",
        "print(\"Mass Shape: {}\".format(mass.shape))\n",
        "print(\"Mass Data Type: {}\".format(mass.dtype))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oFAEvAMGclDC",
        "outputId": "24f4e8e6-d9df-4802-fbcc-f28880a08296"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mass Type: <class 'numpy.float64'>\n",
            "Mass Shape: ()\n",
            "Mass Data Type: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mass Distribution\n",
        "sns.histplot(lensing_data.masses[\"train\"]).set(title=\"Mass Distribution\")\n",
        "# Here its a normal distribution, and it seems to be in scale, so no transformations required here."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "AkUgOnY1aagS",
        "outputId": "4737bbff-e3c3-497e-f92c-798019da4238"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Mass Distribution')]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scaling masses in the range [0,1]\n",
        "lensing_data.masses[\"train\"] = [item * 10 for item in lensing_data.masses[\"train\"]]"
      ],
      "metadata": {
        "id": "R5FdYbAnkd_u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scaled Mass Distribution\n",
        "# This is to avoid lesser weight values in the network\n",
        "# Note that this will also effect the final loss values, since the mass values are scaled.\n",
        "sns.histplot(lensing_data.masses[\"train\"]).set(title=\"Scaled Mass Distribution\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "yt5FRU2Q6JbP",
        "outputId": "aa18d9d5-997e-4107-b8a3-092bd70d84a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Scaled Mass Distribution')]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Pipeline\n",
        "- Train/Test/Val Split\n",
        "- Data Transformations/Augmentations\n",
        "- Custom Pytorch Dataset\n",
        "- Pytorch Dataloader"
      ],
      "metadata": {
        "id": "sJPQvP9OeaO-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train/Test/Val Split"
      ],
      "metadata": {
        "id": "-LLJ_nTBjcTz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Performing train, test, val split \n",
        "# Train: Test = 90:10, Train: Val = 90:10\n",
        "lensing_data.train_test_val_split()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MrWdtn41IU_d",
        "outputId": "5ab85b1a-c279-4df1-f4c1-6b76e7fe6882"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Train Samples: 16200, Number of Test Samples: 2000, Number of Validation Samples: 1800\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(lensing_data.masses[\"train\"]).set(title=\"Training Mass Distribution after split\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "tz1tjYd_IdPz",
        "outputId": "9ba4f8e7-cc49-4dcd-f938-b62311eb750a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Training Mass Distribution after split')]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Similar distribution as of training mass distribution\n",
        "sns.histplot(lensing_data.masses[\"test\"]).set(title=\"Testing Mass Distribution\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "YlgtXCCbIkOd",
        "outputId": "ec28102a-3881-419b-b52e-d50977123553"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Testing Mass Distribution')]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Similar distribution as of training mass distribution\n",
        "sns.histplot(lensing_data.masses[\"val\"]).set(title=\"Validation Mass Distribution\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "-9o8yIBQIlmZ",
        "outputId": "3b249462-1343-4812-955d-720b7be831d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Validation Mass Distribution')]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Transformations"
      ],
      "metadata": {
        "id": "kYnBsBtijCKk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Performing Data Augmentations as per [[3]](https://arxiv.org/pdf/2112.12121.pdf)"
      ],
      "metadata": {
        "id": "NMbYgpExzRYa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Performing Data Transformations/Augmentations\n",
        "\n",
        "# Normalizing data through mean and std of the entire dataset\n",
        "\n",
        "data_transformations = {\n",
        "  \"train\" : transforms.Compose([\n",
        "      transforms.ToTensor(),\n",
        "      # Image Standardization\n",
        "      transforms.Normalize([0.07408],[0.11782]),\n",
        "      # Data Augmentations\n",
        "      transforms.RandomHorizontalFlip(),\n",
        "      transforms.RandomVerticalFlip(),\n",
        "      transforms.RandomRotation(degrees=(0,90))\n",
        "  ]),\n",
        "\n",
        "  \"test\": transforms.Compose([\n",
        "      transforms.ToTensor(),\n",
        "      transforms.Normalize([0.07408],[0.11782])\n",
        "  ]),\n",
        "\n",
        "  \"val\": transforms.Compose([\n",
        "      transforms.ToTensor(),\n",
        "      transforms.Normalize([0.07408],[0.11782])\n",
        "  ])\n",
        "}\n"
      ],
      "metadata": {
        "id": "CjMfgIPqrmKy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Custom Dataset"
      ],
      "metadata": {
        "id": "0P4ugFoChhPd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class LensingDataset(Dataset):\n",
        "  \"\"\"\n",
        "    Custom Pytorch Dataset\n",
        "  \"\"\"\n",
        "  def __init__(self, img_data, mass_data, transforms):\n",
        "      self.imgs = img_data\n",
        "      self.mass = mass_data\n",
        "      self.transform = transforms\n",
        "\n",
        "  def __len__(self):\n",
        "      return len(self.mass)\n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "      image, mass = self.imgs[idx], self.mass[idx]\n",
        "      # Min max normalization\n",
        "      image = min_max_filter(image)\n",
        "\n",
        "      if self.transform is not None:\n",
        "          image = self.transform(image)\n",
        "      else:\n",
        "          image = torch.from_numpy(image)\n",
        "      \n",
        "      # Converting tensors to appropriate type for GPU Training according to https://pytorch.org/docs/stable/tensors.html\n",
        "      return image.type(torch.float32), torch.tensor(mass, dtype=torch.float32)\n",
        "  \n",
        "  def min_max_filter(image):\n",
        "      return (image - image.min())/(image.max() - image.min())"
      ],
      "metadata": {
        "id": "KnkndNTRhH_K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initializing Datasets\n",
        "dataset_dict = {\n",
        "    \"train\": LensingDataset(lensing_data.images[\"train\"], lensing_data.masses[\"train\"], data_transformations[\"train\"]),\n",
        "    \"val\": LensingDataset(lensing_data.images[\"val\"], lensing_data.masses[\"val\"], data_transformations[\"val\"]),\n",
        "    \"test\": LensingDataset(lensing_data.images[\"test\"], lensing_data.masses[\"test\"], data_transformations[\"test\"])\n",
        "}"
      ],
      "metadata": {
        "id": "rUu9dmKbjbjM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DataLoader"
      ],
      "metadata": {
        "id": "PNjBnAwrsOkQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Upon iterating over several batch sizes, the batch size of 64 seems to be giving an optimal trade off between training time and regression metrics."
      ],
      "metadata": {
        "id": "AwBA-0LxBf7m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initializing DataLoaders\n",
        "train_dataloader = DataLoader(dataset_dict[\"train\"], batch_size=64, num_workers=2, shuffle=True)\n",
        "val_dataloader = DataLoader(dataset_dict[\"val\"], batch_size=64, num_workers=2, shuffle=False)\n",
        "test_dataloader = DataLoader(dataset_dict[\"test\"], batch_size=64, num_workers=2, shuffle=False)"
      ],
      "metadata": {
        "id": "J-MGFQwCjHeM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model\n",
        "- Model Definition\n",
        "- Loss function\n",
        "- Optimizer\n",
        "- Training"
      ],
      "metadata": {
        "id": "jfo7ci9dsoFC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Definition"
      ],
      "metadata": {
        "id": "8U7H0BIGuGqk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[Resnet18](https://pytorch.org/vision/main/models/generated/torchvision.models.resnet18.html) is used as the backbone model. Resnet and resnet like architectures have shown significant performance when it comes to image classification, and hence I am using this as a feature extractor into the dense layer. I chose resnet18 over other deeper networks because of lesser training time and limited GPU resource. The fully connected layer of resnet is modified by adding a dense layer for regression with a single neuron output. The input layer of the resnet is modified to take in single channel images.\n",
        "\n",
        "One clever optimization of the model would be to freeze the initial base layers upto a depth of 5 or less, and then fine tune the model to this task. This way we can reduce the training time and also leverage the resnets early layers that capture basic information like curves/edges in an image."
      ],
      "metadata": {
        "id": "nWJdV7scSaye"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "\n",
        "class MassRegressor(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(MassRegressor, self).__init__()\n",
        "        self.resnet = torchvision.models.resnet18(weights=None)\n",
        "        # Modifying first layer to accpet single channel input\n",
        "        self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
        "        layers = []\n",
        "        # Dense MLP layers\n",
        "        layers += [nn.Linear(512, 256, bias=True), nn.ReLU(inplace=True)]\n",
        "        layers += [nn.Linear(256, 16, bias=True), nn.ReLU(inplace=True)]\n",
        "        layers += [nn.Linear(16,1)]\n",
        "        self.resnet.fc = nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.resnet(x)\n",
        "\n",
        "\n",
        "model = MassRegressor()\n",
        "model.to(device=device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7vzv0bMMuB40",
        "outputId": "98eacae5-855d-4da8-8f07-1762d52ae1fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MassRegressor(\n",
              "  (resnet): ResNet(\n",
              "    (conv1): Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (relu): ReLU(inplace=True)\n",
              "    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "    (layer1): Sequential(\n",
              "      (0): BasicBlock(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicBlock(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (layer2): Sequential(\n",
              "      (0): BasicBlock(\n",
              "        (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (downsample): Sequential(\n",
              "          (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        )\n",
              "      )\n",
              "      (1): BasicBlock(\n",
              "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (layer3): Sequential(\n",
              "      (0): BasicBlock(\n",
              "        (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (downsample): Sequential(\n",
              "          (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        )\n",
              "      )\n",
              "      (1): BasicBlock(\n",
              "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (layer4): Sequential(\n",
              "      (0): BasicBlock(\n",
              "        (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (downsample): Sequential(\n",
              "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        )\n",
              "      )\n",
              "      (1): BasicBlock(\n",
              "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "    (fc): Sequential(\n",
              "      (0): Linear(in_features=512, out_features=256, bias=True)\n",
              "      (1): ReLU(inplace=True)\n",
              "      (2): Linear(in_features=256, out_features=16, bias=True)\n",
              "      (3): ReLU(inplace=True)\n",
              "      (4): Linear(in_features=16, out_features=1, bias=True)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loss Function"
      ],
      "metadata": {
        "id": "2Vp_562KtoYX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we are using [MSE loss](https://pytorch.org/docs/stable/generated/torch.nn.MSELoss.html) function according to the evaluation test. But a smoother loss function for regression would be the [Huber Loss](https://pytorch.org/docs/stable/generated/torch.nn.HuberLoss.html) as it is less sensitive to the outliers in the data. "
      ],
      "metadata": {
        "id": "aKv_xwfEXoSd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        ">>> $MSE(y_i, y_j) = \\sum_{i=1}^{D}(y_i-y_j)^2$"
      ],
      "metadata": {
        "id": "6pLVgk3km1rs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Mean Squared Error Loss\n",
        "loss_fn = nn.MSELoss()"
      ],
      "metadata": {
        "id": "uAbJ95e6snfY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Optimizer"
      ],
      "metadata": {
        "id": "YjeRaNFct1S7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The choice of optimizer is a tricky selection since each optimizer is designed to perform its best for a specific loss topography. There is no one generic optimizer that performs best in every scenario. Its best to understand how the loss curve is from repetitive training and seeing how the loss is decreasing. \n",
        "\n",
        "> One interesting experiment that I couldnt not do, was applying different learning rates to different parts of the network, and see how a difference in learning rate affect the networks ability to learn.\n",
        "\n",
        "I chose the Adam optimizer according to the papers [[2,3]]() where the resnet acheived a remarkable training accuracy of ~97%. I initialized them according to the values in the paper. I also used a one cycle lr scheduler [[4]](https://arxiv.org/abs/1803.09820v2) in place of a cyclic scheduler as mentioned in the paper [[3]]() as it performed relatively better."
      ],
      "metadata": {
        "id": "qd6jL0NKYXzs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
        "scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=0.01, steps_per_epoch=len(train_dataloader), epochs=100)"
      ],
      "metadata": {
        "id": "U8EGI7tSt4kN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training"
      ],
      "metadata": {
        "id": "XU9_wSY9uKR7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Strategy\n",
        "\n",
        "\n",
        "- Early Stopping: The training loop is set in a way that if the val loss does not improve for the next 15 epochs, then we stop and save the best model with minimum val loss according to the paper [[3]](https://arxiv.org/pdf/2112.12121.pdf).\n",
        "\n",
        "- Model Checkpoints: The model checkpointing starts after 25 epochs where the model is saved if the validation loss reduces below the min validation loss.\n",
        "\n",
        "- Goodness of Fit: We use R-squared as a measure of goodness of fit.\n"
      ],
      "metadata": {
        "id": "cH7S3GbWB0Ff"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class TrainWrapper:\n",
        "  \"\"\"\n",
        "    Train Wrapper for training\n",
        "  \"\"\"\n",
        "  def __init__(self, epochs):\n",
        "    self.epochs = epochs\n",
        "    self.threshold_epoch = 15\n",
        "  \n",
        "  def run_epoch(self, data_loader, train):\n",
        "\n",
        "    running_loss = 0.\n",
        "\n",
        "    if train:\n",
        "      model.train()\n",
        "    else:\n",
        "      model.eval()\n",
        "\n",
        "\n",
        "    for i, data in enumerate(data_loader):\n",
        "\n",
        "        inputs, labels = data\n",
        "        inputs, labels = inputs.to(device=device), labels.to(device=device)\n",
        "        outputs = model(inputs)\n",
        "        loss = loss_fn(outputs.flatten(), labels.flatten())\n",
        "\n",
        "        if train:\n",
        "          optimizer.zero_grad()\n",
        "          loss.backward()\n",
        "          optimizer.step()\n",
        "\n",
        "        running_loss += loss.item() * inputs.size(0)\n",
        "    \n",
        "\n",
        "    return running_loss/len(data_loader)\n",
        "\n",
        "  def train(self):\n",
        "    # validation loss per epoch\n",
        "    self.val_loss_per_epoch = []\n",
        "    \n",
        "    # train loss per epoch\n",
        "    self.train_loss_per_epoch = []\n",
        "\n",
        "    start_time = time.time()\n",
        "    self.min_val_loss = float(\"inf\")\n",
        "    self.stop_counter = 0\n",
        "\n",
        "    for ep in tqdm(range(self.epochs), desc='Training loop'):\n",
        "      # Train one epoch\n",
        "      # Validate one epoch\n",
        "      # LR Scheduler step\n",
        "      train_loss = self.run_epoch(train_dataloader, train=True)\n",
        "      val_loss = self.run_epoch(val_dataloader, train=False)\n",
        "      r2_score = self.get_r2_score_test(val_dataloader)\n",
        "      scheduler.step()\n",
        "\n",
        "      self.train_loss_per_epoch.append(train_loss)\n",
        "      self.val_loss_per_epoch.append(val_loss)\n",
        "\n",
        "      self.checkpoint(ep+1, val_loss)\n",
        "\n",
        "      print(\"Epoch: {} - Train loss: {}, Val Loss: {}, R2 Score: {}, Time Elapsed: {}\".format(ep+1, train_loss, val_loss, r2_score, time.time() - start_time))\n",
        "\n",
        "      if self.stop_early():\n",
        "        print(\"Stopping Training since Val Loss has not improved for {} epochs\".format(self.threshold_epoch))\n",
        "        break\n",
        "\n",
        "  def checkpoint(self, epoch_num, val_loss):\n",
        "    if epoch_num >= 25:\n",
        "      if self.min_val_loss > val_loss:\n",
        "        self.save()\n",
        "        print(\"--- Saving model ---\")\n",
        "        self.min_val_loss = val_loss\n",
        "        self.stop_counter = 0\n",
        "      else:\n",
        "        self.stop_counter += 1\n",
        "\n",
        "\n",
        "  def stop_early(self):\n",
        "    return self.stop_counter == self.threshold_epoch\n",
        "\n",
        "  def get_r2_score_test(self, data_loader):\n",
        "    y_true, y_pred = self.get_test_preds_labels(data_loader)\n",
        "    return r2_score(y_true, y_pred)\n",
        "\n",
        "  def get_test_preds_labels(self, data_loader):\n",
        "    y_true = None\n",
        "    y_pred = None\n",
        "    with torch.no_grad():\n",
        "      for i, data in enumerate(data_loader):\n",
        "        inputs, labels = data[0], data[1]\n",
        "        inputs = inputs.to(device=device)\n",
        "        logits = model(inputs)\n",
        "\n",
        "        if y_true is None:\n",
        "          y_true = labels.flatten()\n",
        "        else:\n",
        "          y_true = torch.cat([y_true.flatten(), labels.flatten()])\n",
        "        \n",
        "        if y_pred is None:\n",
        "          y_pred = logits.flatten()\n",
        "        else:\n",
        "          y_pred = torch.cat([y_pred.flatten(), logits.flatten()])\n",
        "        \n",
        "        del inputs\n",
        "    return y_true.numpy(), y_pred.cpu().detach().numpy()\n",
        "\n",
        "\n",
        "  def test(self):\n",
        "    model.eval()\n",
        "    test_loss = self.run_epoch(test_dataloader, train=False)\n",
        "    print(\"Test MSE Loss: {}\".format(test_loss))\n",
        "\n",
        "  \n",
        "  def save(self):\n",
        "    regression_model_name = \"LensingMassRegression\"\n",
        "    model_save_path = F\"/content/drive/MyDrive/2023 ML/Model Weights/{regression_model_name}\"\n",
        "    torch.save(model.state_dict(), model_save_path)\n",
        "\n",
        "  def load(self):\n",
        "    regression_model_name = \"LensingMassRegression\"\n",
        "    model_save_path = F\"/content/drive/MyDrive/2023 ML/Model Weights/{regression_model_name}\"\n",
        "    if torch.cuda.is_available():\n",
        "      model.load_state_dict(torch.load(model_save_path))\n",
        "    else:\n",
        "      model.load_state_dict(torch.load(model_save_path, map_location=torch.device('cpu')))\n",
        "\n",
        "  def plot_loss_curves(self):\n",
        "    plt.plot(np.arange(len(self.train_loss_per_epoch)+1)[1:], self.train_loss_per_epoch, label=\"train\")\n",
        "    plt.plot(np.arange(len(self.val_loss_per_epoch)+1)[1:], self.val_loss_per_epoch, label=\"val\")\n",
        "    plt.legend()"
      ],
      "metadata": {
        "id": "7t5CeY_MuOkD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize TrainWrapper\n",
        "trainer = TrainWrapper(100)"
      ],
      "metadata": {
        "id": "8rQRk3GeyB_k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Start Training\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "a7e8f99ded0748bcaeea8d57c3fce442",
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            "1d4df380a3ce4f9d836d64d669870216",
            "3f71635b78184d43b93478100ad2a50d",
            "3727df25c89d420dbffe225610fb9ed5",
            "a377f3a1f2c24f149f47b40f902d0713",
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            "cc29970af1ad449bbab2b519cadc079c"
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        },
        "id": "9gIezwVbA-eF",
        "outputId": "34fb775a-edef-48c2-cd64-2fdbbf5951e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training loop:   0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a7e8f99ded0748bcaeea8d57c3fce442"
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1 - Train loss: 1.524380087793812, Val Loss: 1.4552103044657871, R2 Score: -0.07454134744671514, Time Elapsed: 41.01627445220947\n",
            "Epoch: 2 - Train loss: 1.4033182223481455, Val Loss: 1.4041557712801571, R2 Score: -0.036842177516224206, Time Elapsed: 77.19069409370422\n",
            "Epoch: 3 - Train loss: 1.3755257123802591, Val Loss: 1.3814923619401867, R2 Score: -0.0201073017736777, Time Elapsed: 111.50543856620789\n",
            "Epoch: 4 - Train loss: 1.386266354236781, Val Loss: 1.4757066096725135, R2 Score: -0.08967600251594421, Time Elapsed: 147.37154722213745\n",
            "Epoch: 5 - Train loss: 1.3424452916961016, Val Loss: 1.9039974037943215, R2 Score: -0.40593007597554265, Time Elapsed: 182.04128050804138\n",
            "Epoch: 6 - Train loss: 1.3260874037198194, Val Loss: 1.583631328467665, R2 Score: -0.16936866186045574, Time Elapsed: 217.54292559623718\n",
            "Epoch: 7 - Train loss: 1.2901179914868723, Val Loss: 2.483069426026838, R2 Score: -0.8335224709746372, Time Elapsed: 252.79400658607483\n",
            "Epoch: 8 - Train loss: 1.225226110184756, Val Loss: 2.6377796068273742, R2 Score: -0.9477619315028154, Time Elapsed: 287.8293836116791\n",
            "Epoch: 9 - Train loss: 1.1936419074694942, Val Loss: 1.1183422279768978, R2 Score: 0.17420530180498006, Time Elapsed: 323.2565281391144\n",
            "Epoch: 10 - Train loss: 1.130923432100007, Val Loss: 3.0751662994253226, R2 Score: -1.2707324581668353, Time Elapsed: 358.00925064086914\n",
            "Epoch: 11 - Train loss: 1.1007330867307863, Val Loss: 2.2842278686063042, R2 Score: -0.6866959200905316, Time Elapsed: 393.8813621997833\n",
            "Epoch: 12 - Train loss: 1.061192661318488, Val Loss: 1.0904830118705486, R2 Score: 0.19477681146621073, Time Elapsed: 428.54230213165283\n",
            "Epoch: 13 - Train loss: 1.0059612652976213, Val Loss: 1.5130579995697941, R2 Score: -0.11725663704402556, Time Elapsed: 464.1947422027588\n",
            "Epoch: 14 - Train loss: 0.961020200153974, Val Loss: 0.973373866286771, R2 Score: 0.2812513422677645, Time Elapsed: 498.57384181022644\n",
            "Epoch: 15 - Train loss: 0.9532519825917529, Val Loss: 1.5672317044488315, R2 Score: -0.1572590253465702, Time Elapsed: 534.0935146808624\n",
            "Epoch: 16 - Train loss: 0.9342788320592069, Val Loss: 2.7966327913876237, R2 Score: -1.0650606458969416, Time Elapsed: 568.3532843589783\n",
            "Epoch: 17 - Train loss: 0.9240931816340432, Val Loss: 1.099544744039404, R2 Score: 0.18808555094971502, Time Elapsed: 604.043666601181\n",
            "Epoch: 18 - Train loss: 0.9070411818468664, Val Loss: 0.9863175092072323, R2 Score: 0.2716936199275555, Time Elapsed: 638.6067132949829\n",
            "Epoch: 19 - Train loss: 0.8966580252713106, Val Loss: 2.671504467725754, R2 Score: -0.972664712816163, Time Elapsed: 673.7927241325378\n",
            "Epoch: 20 - Train loss: 0.8685902068464775, Val Loss: 1.2034118864043006, R2 Score: 0.1113890435087399, Time Elapsed: 708.3742468357086\n",
            "Epoch: 21 - Train loss: 0.8646131783841163, Val Loss: 0.8286031381837253, R2 Score: 0.3881514484614288, Time Elapsed: 743.5091547966003\n",
            "Epoch: 22 - Train loss: 0.8522529196668798, Val Loss: 1.1971058393346852, R2 Score: 0.11604551440141919, Time Elapsed: 778.7666754722595\n",
            "Epoch: 23 - Train loss: 0.834087302189643, Val Loss: 1.8571610676831212, R2 Score: -0.37134566337520414, Time Elapsed: 813.998649597168\n",
            "Epoch: 24 - Train loss: 0.8237651730557596, Val Loss: 1.2914944553169712, R2 Score: 0.04634802107210578, Time Elapsed: 848.9941945075989\n",
            "--- Saving model ---\n",
            "Epoch: 25 - Train loss: 0.8064671827876192, Val Loss: 0.7558989858832853, R2 Score: 0.44183689624783284, Time Elapsed: 883.8827133178711\n",
            "Epoch: 26 - Train loss: 0.7833213171533008, Val Loss: 2.1397014476101974, R2 Score: -0.5799762076763288, Time Elapsed: 919.8186004161835\n",
            "Epoch: 27 - Train loss: 0.7693912282468766, Val Loss: 1.5242132297877609, R2 Score: -0.12549375665363005, Time Elapsed: 954.464569568634\n",
            "Epoch: 28 - Train loss: 0.7382210579620102, Val Loss: 0.7788776236361471, R2 Score: 0.4248692429302303, Time Elapsed: 990.3869621753693\n",
            "Epoch: 29 - Train loss: 0.7267733760706083, Val Loss: 1.5216918711004586, R2 Score: -0.12363197171468232, Time Elapsed: 1025.0857062339783\n",
            "Epoch: 30 - Train loss: 0.7106544878773802, Val Loss: 0.8429768368087965, R2 Score: 0.37753776587722965, Time Elapsed: 1060.6900436878204\n",
            "Epoch: 31 - Train loss: 0.6904003939584015, Val Loss: 0.9117613609494835, R2 Score: 0.3267466085337005, Time Elapsed: 1095.4853279590607\n",
            "Epoch: 32 - Train loss: 0.6762517939402363, Val Loss: 0.7728537249154058, R2 Score: 0.42931734723893045, Time Elapsed: 1130.9943647384644\n",
            "--- Saving model ---\n",
            "Epoch: 33 - Train loss: 0.670149649662061, Val Loss: 0.6176180366812081, R2 Score: 0.543944878890842, Time Elapsed: 1165.7251126766205\n",
            "Epoch: 34 - Train loss: 0.6337919438800473, Val Loss: 2.501225535211892, R2 Score: -0.846929117774569, Time Elapsed: 1201.090179681778\n",
            "Epoch: 35 - Train loss: 0.6212970857163818, Val Loss: 0.8601742895512745, R2 Score: 0.36483899638638917, Time Elapsed: 1235.6253793239594\n",
            "Epoch: 36 - Train loss: 0.6230658101665927, Val Loss: 0.6468143817679636, R2 Score: 0.5223860121394974, Time Elapsed: 1270.9936168193817\n",
            "--- Saving model ---\n",
            "Epoch: 37 - Train loss: 0.6096865486089639, Val Loss: 0.5930584427611582, R2 Score: 0.562079910645529, Time Elapsed: 1305.665411233902\n",
            "Epoch: 38 - Train loss: 0.5874571690233205, Val Loss: 0.6994320904386455, R2 Score: 0.4835325886833627, Time Elapsed: 1340.4390363693237\n",
            "Epoch: 39 - Train loss: 0.5768972211700725, Val Loss: 0.7404999846014483, R2 Score: 0.4532076575387983, Time Elapsed: 1375.3916652202606\n",
            "Epoch: 40 - Train loss: 0.5693098355935314, Val Loss: 0.7734284362402456, R2 Score: 0.4288929744048856, Time Elapsed: 1410.1950199604034\n",
            "--- Saving model ---\n",
            "Epoch: 41 - Train loss: 0.5489276746713271, Val Loss: 0.4990042730652053, R2 Score: 0.6315304275197305, Time Elapsed: 1445.8692150115967\n",
            "Epoch: 42 - Train loss: 0.5539182499637754, Val Loss: 0.511797960719158, R2 Score: 0.6220834372200637, Time Elapsed: 1480.3401594161987\n",
            "Epoch: 43 - Train loss: 0.5473297199190367, Val Loss: 0.7527228876434523, R2 Score: 0.4441821512059756, Time Elapsed: 1516.007873058319\n",
            "Epoch: 44 - Train loss: 0.5289696530094297, Val Loss: 0.6185818735895485, R2 Score: 0.5432331684297027, Time Elapsed: 1550.293926000595\n",
            "Epoch: 45 - Train loss: 0.5229727938976578, Val Loss: 0.514794010668993, R2 Score: 0.6198711295350636, Time Elapsed: 1585.7433037757874\n",
            "--- Saving model ---\n",
            "Epoch: 46 - Train loss: 0.5281585294782646, Val Loss: 0.49065190605048475, R2 Score: 0.6376978939953695, Time Elapsed: 1620.1937856674194\n",
            "Epoch: 47 - Train loss: 0.5351234980690198, Val Loss: 0.7519553632571779, R2 Score: 0.44474889446805765, Time Elapsed: 1655.903272151947\n",
            "--- Saving model ---\n",
            "Epoch: 48 - Train loss: 0.5140747193276413, Val Loss: 0.467007078230381, R2 Score: 0.6551574669153166, Time Elapsed: 1690.4394173622131\n",
            "Epoch: 49 - Train loss: 0.5094868067622654, Val Loss: 0.7315735092450832, R2 Score: 0.45979905683362654, Time Elapsed: 1725.3257865905762\n",
            "Epoch: 50 - Train loss: 0.5206630014645772, Val Loss: 0.47329767921875265, R2 Score: 0.6505124244825138, Time Elapsed: 1760.0678555965424\n",
            "Epoch: 51 - Train loss: 0.5040782904032413, Val Loss: 0.48442250978330087, R2 Score: 0.6422977454040693, Time Elapsed: 1794.4620532989502\n",
            "--- Saving model ---\n",
            "Epoch: 52 - Train loss: 0.49101661840879074, Val Loss: 0.45089451737444974, R2 Score: 0.6670551339881815, Time Elapsed: 1830.072197675705\n",
            "--- Saving model ---\n",
            "Epoch: 53 - Train loss: 0.48814547798178326, Val Loss: 0.4376898143312027, R2 Score: 0.6768056161373264, Time Elapsed: 1864.7275187969208\n",
            "Epoch: 54 - Train loss: 0.48665659314417464, Val Loss: 1.0429137277192082, R2 Score: 0.22990242896669655, Time Elapsed: 1900.411295413971\n",
            "Epoch: 55 - Train loss: 0.4817595124450021, Val Loss: 0.5216349191192923, R2 Score: 0.6148197391083391, Time Elapsed: 1934.7705721855164\n",
            "Epoch: 56 - Train loss: 0.4838601306931475, Val Loss: 0.5731357798494142, R2 Score: 0.5767910081187846, Time Elapsed: 1970.352944135666\n",
            "--- Saving model ---\n",
            "Epoch: 57 - Train loss: 0.4805102834082025, Val Loss: 0.4246144949876029, R2 Score: 0.6864605585143282, Time Elapsed: 2004.662347793579\n",
            "Epoch: 58 - Train loss: 0.4708155619757851, Val Loss: 0.4461937885859917, R2 Score: 0.6705261978926182, Time Elapsed: 2040.216560125351\n",
            "Epoch: 59 - Train loss: 0.46484998818926926, Val Loss: 0.6053641789175314, R2 Score: 0.5529932429289002, Time Elapsed: 2074.6363759040833\n",
            "Epoch: 60 - Train loss: 0.4742420586779362, Val Loss: 0.4464366803395337, R2 Score: 0.6703468430904846, Time Elapsed: 2109.7321214675903\n",
            "--- Saving model ---\n",
            "Epoch: 61 - Train loss: 0.4573678765005953, Val Loss: 0.4187367540495149, R2 Score: 0.6908007381943311, Time Elapsed: 2144.6727735996246\n",
            "Epoch: 62 - Train loss: 0.4672922235803576, Val Loss: 0.6542591424337749, R2 Score: 0.5168887261876822, Time Elapsed: 2179.7374103069305\n",
            "Epoch: 63 - Train loss: 0.4604314633857782, Val Loss: 0.45193486429494, R2 Score: 0.6662869358576995, Time Elapsed: 2214.9855523109436\n",
            "Epoch: 64 - Train loss: 0.4509363355423052, Val Loss: 0.4244552692719575, R2 Score: 0.6865781310596368, Time Elapsed: 2249.9727404117584\n",
            "Epoch: 65 - Train loss: 0.456671830941373, Val Loss: 0.5617330213045252, R2 Score: 0.5852109139264439, Time Elapsed: 2285.142724752426\n",
            "Epoch: 66 - Train loss: 0.46026094942756995, Val Loss: 0.43848645982557327, R2 Score: 0.676217361137828, Time Elapsed: 2319.475395679474\n",
            "--- Saving model ---\n",
            "Epoch: 67 - Train loss: 0.44619314701069057, Val Loss: 0.40213174771132143, R2 Score: 0.7030620343287955, Time Elapsed: 2355.2401463985443\n",
            "Epoch: 68 - Train loss: 0.45924187520533566, Val Loss: 0.4048404206746611, R2 Score: 0.7010619205020571, Time Elapsed: 2389.9892127513885\n",
            "Epoch: 69 - Train loss: 0.4498140367965295, Val Loss: 0.4069089270357428, R2 Score: 0.6995345215284623, Time Elapsed: 2425.263837814331\n",
            "Epoch: 70 - Train loss: 0.44333703554873394, Val Loss: 0.4033147479439604, R2 Score: 0.7021884927437059, Time Elapsed: 2459.778336763382\n",
            "Epoch: 71 - Train loss: 0.4371481218529264, Val Loss: 0.8665358393356718, R2 Score: 0.3601415741063225, Time Elapsed: 2494.8767762184143\n",
            "Epoch: 72 - Train loss: 0.4280969496728398, Val Loss: 0.45789221956812104, R2 Score: 0.661887962550056, Time Elapsed: 2529.7717344760895\n",
            "Epoch: 73 - Train loss: 0.43285712280024696, Val Loss: 0.4069564273131305, R2 Score: 0.6994994431575001, Time Elapsed: 2565.245136976242\n",
            "Epoch: 74 - Train loss: 0.4334167592286125, Val Loss: 0.45655874484058084, R2 Score: 0.6628726139763503, Time Elapsed: 2599.709635257721\n",
            "Epoch: 75 - Train loss: 0.425273509520128, Val Loss: 0.40245328702289485, R2 Score: 0.7028246112600043, Time Elapsed: 2634.827028274536\n",
            "Epoch: 76 - Train loss: 0.4274836247126887, Val Loss: 0.5276541083023466, R2 Score: 0.6103751126314498, Time Elapsed: 2670.066571712494\n",
            "Epoch: 77 - Train loss: 0.42249602048591833, Val Loss: 0.4321687609471124, R2 Score: 0.6808824178157173, Time Elapsed: 2705.4513783454895\n",
            "Epoch: 78 - Train loss: 0.4242231345728157, Val Loss: 0.4663809784527483, R2 Score: 0.6556197809569397, Time Elapsed: 2740.6683292388916\n",
            "--- Saving model ---\n",
            "Epoch: 79 - Train loss: 0.4214515502263946, Val Loss: 0.3798830101973024, R2 Score: 0.7194907207513992, Time Elapsed: 2775.4341218471527\n",
            "Epoch: 80 - Train loss: 0.4146415110545482, Val Loss: 0.40376391904107456, R2 Score: 0.701856823072971, Time Elapsed: 2810.993026971817\n",
            "Epoch: 81 - Train loss: 0.4198803106308218, Val Loss: 0.4164010124473736, R2 Score: 0.6925254701698461, Time Elapsed: 2845.327164888382\n",
            "Epoch: 82 - Train loss: 0.4242068940230946, Val Loss: 0.3933788327307537, R2 Score: 0.7095252743669785, Time Elapsed: 2881.1925666332245\n",
            "Epoch: 83 - Train loss: 0.42485769577031063, Val Loss: 0.38859871094082965, R2 Score: 0.7130549616244609, Time Elapsed: 2915.7899222373962\n",
            "Epoch: 84 - Train loss: 0.4109942057998631, Val Loss: 0.46998231652481803, R2 Score: 0.6529605202416142, Time Elapsed: 2951.089841365814\n",
            "Epoch: 85 - Train loss: 0.4255864074615043, Val Loss: 0.39584856775814087, R2 Score: 0.7077015960454474, Time Elapsed: 2985.5694494247437\n",
            "Epoch: 86 - Train loss: 0.4145269210589683, Val Loss: 0.392992762013756, R2 Score: 0.7098103489587628, Time Elapsed: 3021.0980846881866\n",
            "Epoch: 87 - Train loss: 0.40568749804607057, Val Loss: 0.3877852965017845, R2 Score: 0.7136555922725427, Time Elapsed: 3055.5947873592377\n",
            "--- Saving model ---\n",
            "Epoch: 88 - Train loss: 0.4082020675308826, Val Loss: 0.3646645885089348, R2 Score: 0.730728147171965, Time Elapsed: 3091.1197650432587\n",
            "Epoch: 89 - Train loss: 0.4055406079722906, Val Loss: 0.4786776705034848, R2 Score: 0.6465397852879273, Time Elapsed: 3125.8759531974792\n",
            "Epoch: 90 - Train loss: 0.41250784821280345, Val Loss: 0.3846150627680894, R2 Score: 0.7159965197043814, Time Elapsed: 3161.0754737854004\n",
            "Epoch: 91 - Train loss: 0.40712391670910625, Val Loss: 0.3798297862682877, R2 Score: 0.7195300167242387, Time Elapsed: 3195.844827413559\n",
            "Epoch: 92 - Train loss: 0.3976481338214921, Val Loss: 0.38152917602966574, R2 Score: 0.7182751715039465, Time Elapsed: 3230.462653875351\n",
            "Epoch: 93 - Train loss: 0.39812234932864743, Val Loss: 0.3742858588952443, R2 Score: 0.7236237076189469, Time Elapsed: 3265.493852853775\n",
            "Epoch: 94 - Train loss: 0.398426693959499, Val Loss: 0.3766701617117586, R2 Score: 0.7218631121772054, Time Elapsed: 3299.6479527950287\n",
            "Epoch: 95 - Train loss: 0.3996120332850246, Val Loss: 0.3681663639586547, R2 Score: 0.7281423989516663, Time Elapsed: 3335.472735643387\n",
            "Epoch: 96 - Train loss: 0.3953257723616099, Val Loss: 0.6766494867102854, R2 Score: 0.5003554820035727, Time Elapsed: 3370.08589887619\n",
            "--- Saving model ---\n",
            "Epoch: 97 - Train loss: 0.40097074855969644, Val Loss: 0.3510507992629347, R2 Score: 0.7407806995424953, Time Elapsed: 3405.880362510681\n",
            "Epoch: 98 - Train loss: 0.3886670066029068, Val Loss: 0.4242489769540984, R2 Score: 0.6867304591290053, Time Elapsed: 3440.301961183548\n",
            "Epoch: 99 - Train loss: 0.39146723369444447, Val Loss: 0.3949558799122942, R2 Score: 0.7083607646907182, Time Elapsed: 3475.2261004447937\n",
            "Epoch: 100 - Train loss: 0.39162281451849484, Val Loss: 0.37708714257540377, R2 Score: 0.721555212683938, Time Elapsed: 3509.809154033661\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot loss curves\n",
        "trainer.plot_loss_curves()"
      ],
      "metadata": {
        "id": "rDfxuMV-_K5R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "f0640df0-e12f-4f34-fe77-603a81734e52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here is a link to my [model](https://drive.google.com/file/d/1qKadIyVaxJ8OfD3SLvqmkxHbMVuBB-gf/view?usp=share_link), you can just call the load function here as the path to my model in drive is defined inside the load method."
      ],
      "metadata": {
        "id": "5dHxDXddhtsJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the best model\n",
        "trainer.load()"
      ],
      "metadata": {
        "id": "hgowg12xTx02"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation on test dataset\n",
        "trainer.test()"
      ],
      "metadata": {
        "id": "ULanVtn2SOJe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da726e1a-e7d5-43ee-fe10-31f1982cfa90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test MSE Loss: 0.36514284601435065\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# R2 score on test\n",
        "trainer.get_r2_score_test(test_dataloader)"
      ],
      "metadata": {
        "id": "x7csn2t5wB2o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "591036b7-32f0-4505-c769-2a95b20e776b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7399495992714197"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusion\n"
      ],
      "metadata": {
        "id": "Sjnqsb8V6KyF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- We can see that resnet has performed relatively well, and has acheived a decent r2 score.\n",
        "\n",
        "- One problem we can see is the spikes in validation loss, which can be mitigated if we apply proper regularization which includes, \n",
        " - Ridge/Lasso Regression (L2/L1 Regularization)\n",
        "\n",
        " - Adding drop out layers in the model\n",
        "\n",
        " - Better data augmentation\n",
        "\n",
        "- Things that I missed/couldn't include because of time constraint:\n",
        "\n",
        " - Initializing weights using Kaiming Initialization for better convergence. [Weight Initializaiton](https://adityassrana.github.io/blog/theory/2020/08/26/Weight-Init.html#Solution:-Let's-Compare-Differrent-Initialization-Strategies)\n",
        "\n",
        " - Cross validation for getting the most optimal data split into the network. [Cross Validation](https://www.cs.cmu.edu/~schneide/tut5/node42.html)\n",
        " - Adding Proxy Normalizatin Layers instead of Batch Normalization [Proxy-Normalizing Activations to Match Batch Normalization while Removing Batch Dependence\n",
        "](https://arxiv.org/pdf/2106.03743.pdf)\n",
        " - Adapting to [Pytorch Lightning](https://lightning.ai/docs/pytorch/stable/)"
      ],
      "metadata": {
        "id": "_9dF0XO_4x8u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# References\n",
        "\n",
        "1. [Decoding Dark Matter Substructure without Supervision](https://arxiv.org/pdf/2008.12731.pdf)\n",
        "\n",
        "2. [Deep Learning the Morphology of Dark Matter Substructure](https://arxiv.org/pdf/1909.07346.pdf)\n",
        "\n",
        "3. [Domain Adaptation for Simulation-Based Dark Matter Searches\n",
        "Using Strong Gravitational Lensing](https://arxiv.org/pdf/2112.12121.pdf)\n",
        "\n",
        "4. [A DISCIPLINED APPROACH TO NEURAL NETWORK\n",
        "HYPER-PARAMETERS: PART 1 – LEARNING RATE,\n",
        "BATCH SIZE, MOMENTUM, AND WEIGHT DECAY](https://arxiv.org/abs/1803.09820v2)\n",
        "\n",
        "5. [A Visual Guide to Learning Rate Schedulers in PyTorch](https://towardsdatascience.com/a-visual-guide-to-learning-rate-schedulers-in-pytorch-24bbb262c863)\n",
        "\n",
        "6. [A Comprehensive Analysis of Deep Regression](https://arxiv.org/pdf/1803.08450.pdf)\n",
        "\n",
        "7. [FineTuning TorchVision Models](https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html)\n",
        "\n",
        "8. [The Effect of the Normalization Method Used in Different Sample Sizes on\n",
        "the Success of Artificial Neural Network Model](https://files.eric.ed.gov/fulltext/EJ1246486.pdf)"
      ],
      "metadata": {
        "id": "khQ-Xp--6OeJ"
      }
    }
  ]
}