[LINEAR]_Fabry_Disease_Code.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "\n",
        "import gspread\n",
        "from google.auth import default\n",
        "creds, _ = default()\n",
        "\n",
        "gc = gspread.authorize(creds)\n",
        "\n",
        "ws = gc.open('Copy of Balanced Dataset Four Classes').sheet1\n",
        "\n",
        "cells = ws.range('B2:I2')\n",
        "cells\n",
        "\n",
        "cells[0].value = \"hello\"\n",
        "cells[1].value = \"hello\"\n",
        "cells[2].value = \"hello\"\n",
        "cells[3].value = \"hello\"\n",
        "cells[4].value = \"hello\"\n",
        "cells[5].value = \"hello\"\n",
        "cells\n",
        "\n",
        "ws.update_cells(cells)"
      ],
      "metadata": {
        "id": "8qbgoEniG4fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "outputId": "7f386cbd-b16c-4fd3-fb69-6a1bd36de879"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SpreadsheetNotFound",
          "evalue": "<Response [200]>",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/gspread/client.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, title, folder_id)\u001b[0m\n\u001b[1;32m    124\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m             properties = finditem(\n\u001b[0m\u001b[1;32m    126\u001b[0m                 \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"name\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/gspread/utils.py\u001b[0m in \u001b[0;36mfinditem\u001b[0;34m(func, seq)\u001b[0m\n\u001b[1;32m    170\u001b[0m     \u001b[0;34m\"\"\"Finds and returns first item in iterable for which func(item) is True.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mseq\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mStopIteration\u001b[0m: ",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mSpreadsheetNotFound\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-ecb99743d972>\u001b[0m in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mgc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgspread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauthorize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcreds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mws\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Copy of Balanced Dataset Four Classes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msheet1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mcells\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mws\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'B2:I2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/gspread/client.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, title, folder_id)\u001b[0m\n\u001b[1;32m    128\u001b[0m             )\n\u001b[1;32m    129\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mSpreadsheetNotFound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m         \u001b[0;31m# Drive uses different terminology\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mSpreadsheetNotFound\u001b[0m: <Response [200]>"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "variable_name = \"\"\n",
        "originalStr = \"ATGCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGCTTCGCTTCCTGGCCCTCGTTTCCTGGGACATCCCTGGGGCTAGAGCACTGGACAATGGATTGGCAAGGACGCCTACCATGGGCTGGCTGCACTGGGAGCGCTTCATGTGCAACCTTGACTGCCAGGAAGAGCCAGATTCCTGCATCAGTGAGAAGCTCTTCATGGAGATGGCAGAGCTCATGGTCTCAGAAGGCTGGAAGGATGCAGGTTATGAGTACCTCTGCATTGATGACTGTTGGATGGCTCCCCAAAGAGATTCAGAAGGCAGACTTCAGGCAGACCCTCAGCGCTTTCCTCATGGGATTCGCCAGCTAGCTAATTATGTTCACAGCAAAGGACTGAAGCTAGGGATTTATGCAGATGTTGGAAATAAAACCTGCGCAGGCTTCCCTGGGAGTTTTGGATACTACGACATTGATGCCCAGACCTTTGCTGACTGGGGAGTAGATCTGCTAAAATTTGATGGTTGTTACTGTGACAGTTTGGAAAATTTGGCAGATGGTTATAAGCACATGTCCTTGGCCCTGAATAGGACTGGCAGAAGCATTGTGTACTCCTGTGAGTGGCCTCTTTATATGTGGCCCTTTCAAAAGCCCAATTATACAGAAATCCGACAGTACTGCAATCACTGGCGAAATTTTGCTGACATTGATGATTCCTGGAAAAGTATAAAGAGTATCTTGGACTGGACATCTTTTAACCAGGAGAGAATTGTTGATGTTGCTGGACCAGGGGGTTGGAATGACCCAGATATGTTAGTGATTGGCAACTTTGGCCTCAGCTGGAATCAGCAAGTAACTCAGATGGCCCTCTGGGCTATCATGGCTGCTCCTTTATTCATGTCTAATGACCTCCGACACATCAGCCCTCAAGCCAAAGCTCTCCTTCAGGATAAGGACGTAATTGCCATCAATCAGGACCCCTTGGGCAAGCAAGGGTACCAGCTTAGACAGGGAGACAACTTTGAAGTGTGGGAACGACCTCTCTCAGGCTTAGCCTGGGCTGTAGCTATGATAAACCGGCAGGAGATTGGTGGACCTCGCTCTTATACCATCGCAGTTGCTTCCCTGGGTAAAGGAGTGGCCTGTAATCCTGCCTGCTTCATCACACAGCTCCTCCCTGTGAAAAGGAAGCTAGGGTTCTATGAATGGACTTCAAGGTTAAGAAGTCACATAAATCCCACAGGCACTGTTTTGCTTCAGCTAGAAAATACAATGCAGATGTCATTAAAAGACTTACTTTAA\"\n",
        "original = list(originalStr)\n",
        "differences = []\n",
        "conversions = {'A': 10, 'G': 20, 'T': 30, 'C': 40}\n",
        "\n",
        "for i in range (321, 322):\n",
        "  differences.clear()\n",
        "  cells = ws.range('A' + str(i) + ':' + 'H' + str(i))\n",
        "  mutantStr = cells[0].value\n",
        "  mutant = list(mutantStr)\n",
        "\n",
        "  ### SUBSTITUTION = 1 ###\n",
        "\n",
        "  if len(mutant) == len(original):\n",
        "    cells[1].value = 1\n",
        "\n",
        "    for x in range (0, len(mutant)):\n",
        "      if (mutant[x] != original[x]):\n",
        "        differences.append(x)\n",
        "\n",
        "    for x in range (1, len(differences)):\n",
        "      if ((differences[x] - differences[x - 1]) > 2):\n",
        "        cells[1].value = 4\n",
        "\n",
        "    sum = 0\n",
        "    for x in differences:\n",
        "      sum += conversions[original[x]]\n",
        "    sum /= len(differences)\n",
        "    cells[2].value = sum\n",
        "\n",
        "    sum = 0\n",
        "    for x in differences:\n",
        "      sum += conversions[mutant[x]]\n",
        "    sum /= len(differences)\n",
        "    cells[3].value = sum\n",
        "\n",
        "    cells[4].value = differences[0]\n",
        "\n",
        "    transitions = 0\n",
        "    transversions = 0\n",
        "    for x in differences:\n",
        "      if ((original[x] == 'A' and mutant[x] == 'G') or (original[x] == 'G' and mutant[x] == 'A') or (original[x] == 'C' and mutant[x] == 'T') or (original[x] == 'T' and mutant[x] == 'C')):\n",
        "        transitions += 1\n",
        "      if ((original[x] == 'A' and mutant[x] == 'C') or (original[x] == 'C' and mutant[x] == 'A') or (original[x] == 'A' and mutant[x] == 'T') or (original[x] == 'T' and mutant[x] == 'A') or (original[x] == 'G' and mutant[x] == 'C') or (original[x] == 'C' and mutant[x] == 'G') or (original[x] == 'G' and mutant[x] == 'T') or (original[x] == 'T' and mutant[x] == 'G')):\n",
        "        transversions += 1\n",
        "    cells[5].value = transitions\n",
        "    cells[6].value = transversions\n",
        "\n",
        "    cells[7].value = len(differences)\n",
        "\n",
        "  ### DELETION = 2, DELINS = 4 ###\n",
        "\n",
        "  elif len(mutant) < len(original):\n",
        "    cells[1].value = 2\n",
        "\n",
        "    for x in range (0, len(originalStr)):\n",
        "      if (originalStr[:x] != mutantStr[:x]):\n",
        "        startInd = x - 1\n",
        "        break\n",
        "\n",
        "    startInd -= (startInd) % 3\n",
        "    endInd = startInd + len(original) - len(mutant)\n",
        "\n",
        "    if (original[endInd] != mutant[startInd] and original[endInd + 1] != mutant[startInd + 1]):\n",
        "      cells[1].value = 4\n",
        "\n",
        "    for x in range(startInd, endInd):\n",
        "      differences.append(x)\n",
        "\n",
        "    sum = 0\n",
        "    for x in differences:\n",
        "      sum += conversions[original[x]]\n",
        "    sum /= len(differences)\n",
        "    cells[2].value = sum\n",
        "\n",
        "    cells[3].value = 0\n",
        "\n",
        "    cells[4].value = differences[0]\n",
        "\n",
        "    transitions = 0\n",
        "    transversions = 0\n",
        "    for x in differences:\n",
        "      if ((original[x] == 'A' and mutant[x] == 'G') or (original[x] == 'G' and mutant[x] == 'A') or (original[x] == 'C' and mutant[x] == 'T') or (original[x] == 'T' and mutant[x] == 'C')):\n",
        "        transitions += 1\n",
        "      if ((original[x] == 'A' and mutant[x] == 'C') or (original[x] == 'C' and mutant[x] == 'A') or (original[x] == 'A' and mutant[x] == 'T') or (original[x] == 'T' and mutant[x] == 'A') or (original[x] == 'G' and mutant[x] == 'C') or (original[x] == 'C' and mutant[x] == 'G') or (original[x] == 'G' and mutant[x] == 'T') or (original[x] == 'T' and mutant[x] == 'G')):\n",
        "        transversions += 1\n",
        "    cells[5].value = transitions\n",
        "    cells[6].value = transversions\n",
        "\n",
        "    cells[7].value = len(differences)\n",
        "\n",
        "  ### INSERTION = 3 ###\n",
        "\n",
        "  elif len(mutant) > len(original):\n",
        "    cells[1].value = 3\n",
        "\n",
        "    for x in range (0, len(mutantStr)):\n",
        "      if (originalStr[:x] != mutantStr[:x]):\n",
        "        startInd = x - 1\n",
        "        break\n",
        "\n",
        "    startInd -= (startInd) % 3\n",
        "    endInd = startInd + len(mutant) - len(original)\n",
        "\n",
        "    for x in range(startInd, endInd):\n",
        "      differences.append(x)\n",
        "\n",
        "    for x in differences:\n",
        "      print(mutant[x])\n",
        "\n",
        "    cells[2].value = 0\n",
        "\n",
        "    sum = 0\n",
        "    for x in differences:\n",
        "      sum += conversions[mutant[x]]\n",
        "    sum /= len(differences)\n",
        "    cells[3].value = sum\n",
        "\n",
        "    cells[4].value = differences[0]\n",
        "\n",
        "    transitions = 0\n",
        "    transversions = 0\n",
        "    for x in differences:\n",
        "      if ((original[x] == 'A' and mutant[x] == 'G') or (original[x] == 'G' and mutant[x] == 'A') or (original[x] == 'C' and mutant[x] == 'T') or (original[x] == 'T' and mutant[x] == 'C')):\n",
        "        transitions += 1\n",
        "      if ((original[x] == 'A' and mutant[x] == 'C') or (original[x] == 'C' and mutant[x] == 'A') or (original[x] == 'A' and mutant[x] == 'T') or (original[x] == 'T' and mutant[x] == 'A') or (original[x] == 'G' and mutant[x] == 'C') or (original[x] == 'C' and mutant[x] == 'G') or (original[x] == 'G' and mutant[x] == 'T') or (original[x] == 'T' and mutant[x] == 'G')):\n",
        "        transversions += 1\n",
        "    cells[5].value = transitions\n",
        "    cells[6].value = transversions\n",
        "\n",
        "    cells[7].value = len(differences)\n",
        "\n",
        "  ws.update_cells(cells)"
      ],
      "metadata": {
        "id": "RFaie2ObohJr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "eWck15ImRR-a"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.nn.utils import (\n",
        "  parameters_to_vector as Params2Vec,\n",
        "  vector_to_parameters as Vec2Params\n",
        ")\n",
        "import torch.nn.functional as F\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Model(nn.Module):\n",
        "  def __init__(self, inputFeatures = 7, hiddenLayer1 = 110, hiddenLayer2 = 100, hiddenLayer3 = 90, hiddenLayer4 = 80,\n",
        "               hiddenLayer5 = 70, hiddenLayer6 = 60, hiddenLayer7 = 50, hiddenLayer8 = 40,\n",
        "               hiddenLayer9 = 30, hiddenLayer10 = 20, hiddenLayer11 = 10, hiddenLayer12 = 5, output = 4):\n",
        "    super().__init__()\n",
        "    self.fc1 = nn.Linear(inputFeatures, hiddenLayer1)\n",
        "    self.fc2 = nn.Linear(hiddenLayer1, hiddenLayer2)\n",
        "    self.fc3 = nn.Linear(hiddenLayer2, hiddenLayer3)\n",
        "    self.fc4 = nn.Linear(hiddenLayer3, hiddenLayer4)\n",
        "    self.fc5 = nn.Linear(hiddenLayer4, hiddenLayer5)\n",
        "    self.fc6 = nn.Linear(hiddenLayer5, hiddenLayer6)\n",
        "    self.fc7 = nn.Linear(hiddenLayer6, hiddenLayer7)\n",
        "    self.fc8 = nn.Linear(hiddenLayer7, hiddenLayer8)\n",
        "    self.fc9 = nn.Linear(hiddenLayer8, hiddenLayer9)\n",
        "    self.fc10 = nn.Linear(hiddenLayer9, hiddenLayer10)\n",
        "    self.fc11 = nn.Linear(hiddenLayer10, hiddenLayer11)\n",
        "    self.fc12 = nn.Linear(hiddenLayer11, hiddenLayer12)\n",
        "    self.out = nn.Linear(hiddenLayer10, output)\n",
        "\n",
        "  def forward(self, x):\n",
        "    x = F.relu(self.fc1(x))\n",
        "    x = F.relu(self.fc2(x))\n",
        "    x = F.relu(self.fc3(x))\n",
        "    x = F.relu(self.fc4(x))\n",
        "    x = F.relu(self.fc5(x))\n",
        "    x = F.relu(self.fc6(x))\n",
        "    x = F.relu(self.fc7(x))\n",
        "    x = F.relu(self.fc8(x))\n",
        "    x = F.relu(self.fc9(x))\n",
        "    x = F.relu(self.fc10(x))\n",
        "    x = self.out(x)\n",
        "\n",
        "    return x"
      ],
      "metadata": {
        "id": "arnbmfOHUG-R"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(50)\n",
        "model = Model()\n",
        "\n",
        "url = 'https://docs.google.com/spreadsheets/d/e/2PACX-1vQ8vF7S_kcDPBKm8WbbtPnhX8l-U4B4NvCbby880UuhLY7XAFqwf5zH9kk5rFHhW67wSkGnKLQwiNOR/pub?output=csv'\n",
        "my_df = pd.read_csv(url)\n",
        "\n",
        "X = my_df.drop('Disease Type', axis = 1)\n",
        "y = my_df['Disease Type']\n",
        "\n",
        "X = X.values\n",
        "y = y.values\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 100)\n",
        "\n",
        "X_train = torch.FloatTensor(X_train)\n",
        "X_test = torch.FloatTensor(X_test)\n",
        "\n",
        "y_train = torch.LongTensor(y_train)\n",
        "y_test = torch.LongTensor(y_test)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr = 0.001)"
      ],
      "metadata": {
        "id": "FZOty14hTSMy"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 250\n",
        "\n",
        "losses = []\n",
        "\n",
        "for i in range(epochs):\n",
        "  y_pred = model.forward(X_train)\n",
        "  loss = criterion(y_pred, y_train)\n",
        "  losses.append(loss.detach().numpy())\n",
        "  if i % 50 == 0:\n",
        "    print(f'Epoch: {i} and loss: {loss}')\n",
        "  optimizer.zero_grad()\n",
        "  loss.backward()\n",
        "  optimizer.step()\n",
        "\n",
        "plt.plot(range(epochs), losses)\n",
        "plt.ylabel(\"Loss/Error\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "\n",
        "correct = 0\n",
        "with torch.no_grad():\n",
        "  for i, data in enumerate(X_test):\n",
        "    y_val = model.forward(data)\n",
        "    if y_val.argmax().item() == y_test[i]:\n",
        "      correct += 1\n",
        "\n",
        "print(correct / len(y_test.numpy()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "U8e03MkJWDfi",
        "outputId": "c33b58fc-040f-47b7-a383-5157a7230291"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 and loss: 0.09113627672195435\n",
            "Epoch: 50 and loss: 0.5357682108879089\n",
            "Epoch: 100 and loss: 0.0950167179107666\n",
            "Epoch: 150 and loss: 0.0752330794930458\n",
            "Epoch: 200 and loss: 0.08856820315122604\n",
            "0.59375\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.interpolate import griddata\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "# Simulate a simple model for demonstration\n",
        "class Model(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Model, self).__init__()\n",
        "        self.fc1 = nn.Linear(7, 10)\n",
        "        self.fc2 = nn.Linear(10, 4)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = torch.relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "# Generate random data for demonstration\n",
        "input_data = np.random.rand(200, 7)  # Assuming 7 input features\n",
        "output_data = np.random.randint(0, 4, size=(200,))  # Assuming 4 output classes\n",
        "\n",
        "# Define the model\n",
        "model = Model()\n",
        "\n",
        "# Define loss function (assuming classification)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Define optimizer\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "# Number of epochs for training\n",
        "epochs = 500\n",
        "\n",
        "# Lists to store losses and parameters\n",
        "losses = []\n",
        "parameters = []\n",
        "\n",
        "# Training loop\n",
        "for epoch in range(epochs):\n",
        "    # Forward pass\n",
        "    outputs = model(torch.Tensor(input_data))\n",
        "    loss = criterion(outputs, torch.LongTensor(output_data))\n",
        "\n",
        "    # Backward pass and optimization\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    # Store loss and parameters\n",
        "    losses.append(loss.item())\n",
        "    parameters.append([param.detach().numpy().flatten() for param in model.parameters()])\n",
        "\n",
        "# Extract weights and biases from parameters\n",
        "weights = []\n",
        "biases = []\n",
        "for param_set in parameters:\n",
        "    w = []\n",
        "    b = []\n",
        "    for i, param in enumerate(param_set):\n",
        "        if i % 2 == 0:  # Weights\n",
        "            w.extend(param)\n",
        "        else:  # Biases\n",
        "            b.extend(param)\n",
        "    weights.append(np.array(w))\n",
        "    biases.append(np.array(b))\n",
        "\n",
        "# Convert to numpy arrays\n",
        "weights = np.array(weights)\n",
        "biases = np.array(biases)\n",
        "losses = np.array(losses)\n",
        "\n",
        "# Take the first two dimensions for visualization\n",
        "weights_2d = weights[:, :2]\n",
        "biases_2d = biases[:, :2]\n",
        "\n",
        "# Create a grid of weight and bias values\n",
        "grid_x, grid_y = np.mgrid[\n",
        "    weights_2d[:, 0].min():weights_2d[:, 0].max():100j,\n",
        "    biases_2d[:, 0].min():biases_2d[:, 0].max():100j\n",
        "]\n",
        "\n",
        "# Interpolate the loss values on the grid\n",
        "grid_z = griddata((weights_2d[:, 0], biases_2d[:, 0]), losses, (grid_x, grid_y), method='cubic')\n",
        "\n",
        "# Plot the contour plot\n",
        "plt.figure(figsize=(10, 7))\n",
        "contour = plt.contourf(grid_x, grid_y, grid_z, cmap='viridis', levels=20)\n",
        "plt.colorbar(contour, label='Loss')\n",
        "plt.xlabel('Weight (first dimension)')\n",
        "plt.ylabel('Bias (first dimension)')\n",
        "plt.title('Contour plot of Loss with respect to Weight and Bias')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "5zN1exjAGLsv",
        "outputId": "648abeb9-c02a-479a-a693-30fb794bfd71"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "\n",
        "# Generate random data for demonstration\n",
        "input_data = np.random.rand(200, 7)  # Assuming 7 input features\n",
        "output_data = np.random.rand(200, 4)  # Assuming 4 output classes\n",
        "\n",
        "# Define the model\n",
        "model = Model()\n",
        "\n",
        "# Define loss function (assuming classification)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Define optimizer\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr = 0.001)\n",
        "\n",
        "# Number of epochs for training\n",
        "epochs = 500\n",
        "\n",
        "# Lists to store losses and parameters\n",
        "losses = []\n",
        "parameters = []\n",
        "\n",
        "# Training loop\n",
        "for epoch in range(epochs):\n",
        "    # Forward pass\n",
        "    outputs = model(torch.Tensor(input_data))\n",
        "    loss = criterion(outputs, torch.LongTensor(np.argmax(output_data, axis=1)))\n",
        "\n",
        "    # Backward pass and optimization\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    # Store loss and parameters\n",
        "    losses.append(loss.item())\n",
        "    parameters.append(list(model.parameters()))\n",
        "\n",
        "# Extract weights and biases from parameters\n",
        "weights = []\n",
        "biases = []\n",
        "for param_set in parameters:\n",
        "    w = []\n",
        "    b = []\n",
        "    for param in param_set:\n",
        "        if param.requires_grad:\n",
        "            if len(param.data.shape) == 2:  # Weights\n",
        "                w.append(param.data.numpy().ravel())\n",
        "            else:  # Biases\n",
        "                b.append(param.data.numpy())\n",
        "    weights.append(np.concatenate(w))\n",
        "    biases.append(np.concatenate(b))\n",
        "\n",
        "# Plot 3D surface of loss with respect to weights and biases\n",
        "weights = np.array(weights)\n",
        "biases = np.array(biases)\n",
        "losses = np.array(losses)\n",
        "\n",
        "weights = weights.tolist()\n",
        "biases = biases.tolist()\n",
        "\n",
        "for i in range(100, 44900):\n",
        "  weights[0].pop()\n",
        "\n",
        "for i in range(130, 699):\n",
        "  biases[0].pop()\n",
        "\n",
        "for i in range(len(losses)):\n",
        "  losses[i] = round(losses[i], 1)\n",
        "\n",
        "print(len(weights[0]))\n",
        "print(len(biases[0]))\n",
        "print(len(losses))\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111, projection='3d')\n",
        "\n",
        "ax.plot_trisurf(weights[0], biases[0], losses, cmap='viridis', edgecolor='none')\n",
        "ax.set_xlabel('Weight')\n",
        "ax.set_ylabel('Bias')\n",
        "ax.set_zlabel('Loss')\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "GPrjWyNn0QCd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "c6561317-ff44-4777-f213-1401d20e167a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "100\n",
            "100\n",
            "500\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}