{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H7gQFbUxOQtb"
   },
   "source": [
    "# Deep Learning with PyTorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-09-05T21:03:48.689121Z",
     "iopub.status.busy": "2024-09-05T21:03:48.688649Z",
     "iopub.status.idle": "2024-09-05T21:04:05.535024Z",
     "shell.execute_reply": "2024-09-05T21:04:05.533997Z"
    },
    "id": "FuhJIaeXO2W9",
    "outputId": "bf494471-115e-45a8-c7cb-15a26f12154a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4.0\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import os\n",
    "\n",
    "## print out the pytorch version used\n",
    "print(torch.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-05T21:04:05.587958Z",
     "iopub.status.busy": "2024-09-05T21:04:05.587256Z",
     "iopub.status.idle": "2024-09-05T21:04:05.592276Z",
     "shell.execute_reply": "2024-09-05T21:04:05.591365Z"
    }
   },
   "outputs": [],
   "source": [
    "## Set up the number of CPU cores to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-05T21:04:05.595454Z",
     "iopub.status.busy": "2024-09-05T21:04:05.594691Z",
     "iopub.status.idle": "2024-09-05T21:04:05.641329Z",
     "shell.execute_reply": "2024-09-05T21:04:05.640879Z"
    }
   },
   "outputs": [],
   "source": [
    "# Tell PyTorch it can use multiple threads.\n",
    "# If you are doing parallel data loading be careful\n",
    "# as the total num of threads+data loaders can't be\n",
    "# greater than n_cores \n",
    "# Get the assigned number of cores \n",
    "n_cores = int(os.environ.get('NSLOTS',1))\n",
    "torch.set_num_threads(n_cores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0a2C_nneO_wp"
   },
   "source": [
    "## Preparation of Neural Network\n",
    "\n",
    "The first step is to process and prepare the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-05T21:04:05.643136Z",
     "iopub.status.busy": "2024-09-05T21:04:05.642938Z",
     "iopub.status.idle": "2024-09-05T21:04:05.649177Z",
     "shell.execute_reply": "2024-09-05T21:04:05.648833Z"
    },
    "id": "JWFtgUX85iwO"
   },
   "outputs": [],
   "source": [
    "x = torch.tensor([[-1.0],  [0.0], [1.0], [2.0], [3.0], [4.0]], dtype=torch.float)\n",
    "y = torch.tensor([[-3.0], [-1.0], [1.0], [3.0], [5.0], [7.0]], dtype=torch.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-09-05T21:04:05.650767Z",
     "iopub.status.busy": "2024-09-05T21:04:05.650482Z",
     "iopub.status.idle": "2024-09-05T21:04:05.655355Z",
     "shell.execute_reply": "2024-09-05T21:04:05.655001Z"
    },
    "id": "NcQUjR_95z5J",
    "outputId": "6db5df38-6f9d-454e-87d6-cee0c29dccb3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([6, 1])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## print size of the input tensor\n",
    "x.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-05T21:04:05.657069Z",
     "iopub.status.busy": "2024-09-05T21:04:05.656844Z",
     "iopub.status.idle": "2024-09-05T21:04:05.728047Z",
     "shell.execute_reply": "2024-09-05T21:04:05.727440Z"
    },
    "id": "N1Ii5JRz3Jud"
   },
   "outputs": [],
   "source": [
    "## Neural network with 1 hidden layer\n",
    "layer1 = nn.Linear(1,1, bias=False)\n",
    "model = nn.Sequential(layer1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-05T21:04:05.730934Z",
     "iopub.status.busy": "2024-09-05T21:04:05.730617Z",
     "iopub.status.idle": "2024-09-05T21:04:17.300723Z",
     "shell.execute_reply": "2024-09-05T21:04:17.299840Z"
    },
    "id": "3hglFpejArxx"
   },
   "outputs": [],
   "source": [
    "## loss function\n",
    "criterion = nn.MSELoss()\n",
    "\n",
    "## optimizer algorithm\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FKj6jvZTUtGh"
   },
   "source": [
    "## Training the Neural Network Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-09-05T21:04:17.305839Z",
     "iopub.status.busy": "2024-09-05T21:04:17.305307Z",
     "iopub.status.idle": "2024-09-05T21:04:17.486092Z",
     "shell.execute_reply": "2024-09-05T21:04:17.485457Z"
    },
    "id": "JeOr9i-aBzRv",
    "outputId": "299a0b60-a64c-46c4-d031-8aaf1cacbff9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Loss: 4.8011\n",
      "Epoch: 1 | Loss: 3.9708\n",
      "Epoch: 2 | Loss: 3.3032\n",
      "Epoch: 3 | Loss: 2.7664\n",
      "Epoch: 4 | Loss: 2.3349\n",
      "Epoch: 5 | Loss: 1.9879\n",
      "Epoch: 6 | Loss: 1.7089\n",
      "Epoch: 7 | Loss: 1.4846\n",
      "Epoch: 8 | Loss: 1.3043\n",
      "Epoch: 9 | Loss: 1.1593\n",
      "Epoch: 10 | Loss: 1.0427\n",
      "Epoch: 11 | Loss: 0.9490\n",
      "Epoch: 12 | Loss: 0.8737\n",
      "Epoch: 13 | Loss: 0.8131\n",
      "Epoch: 14 | Loss: 0.7644\n",
      "Epoch: 15 | Loss: 0.7252\n",
      "Epoch: 16 | Loss: 0.6937\n",
      "Epoch: 17 | Loss: 0.6684\n",
      "Epoch: 18 | Loss: 0.6480\n",
      "Epoch: 19 | Loss: 0.6317\n",
      "Epoch: 20 | Loss: 0.6185\n",
      "Epoch: 21 | Loss: 0.6079\n",
      "Epoch: 22 | Loss: 0.5994\n",
      "Epoch: 23 | Loss: 0.5926\n",
      "Epoch: 24 | Loss: 0.5871\n",
      "Epoch: 25 | Loss: 0.5827\n",
      "Epoch: 26 | Loss: 0.5791\n",
      "Epoch: 27 | Loss: 0.5762\n",
      "Epoch: 28 | Loss: 0.5739\n",
      "Epoch: 29 | Loss: 0.5721\n",
      "Epoch: 30 | Loss: 0.5706\n",
      "Epoch: 31 | Loss: 0.5694\n",
      "Epoch: 32 | Loss: 0.5685\n",
      "Epoch: 33 | Loss: 0.5677\n",
      "Epoch: 34 | Loss: 0.5671\n",
      "Epoch: 35 | Loss: 0.5666\n",
      "Epoch: 36 | Loss: 0.5662\n",
      "Epoch: 37 | Loss: 0.5658\n",
      "Epoch: 38 | Loss: 0.5656\n",
      "Epoch: 39 | Loss: 0.5654\n",
      "Epoch: 40 | Loss: 0.5652\n",
      "Epoch: 41 | Loss: 0.5651\n",
      "Epoch: 42 | Loss: 0.5650\n",
      "Epoch: 43 | Loss: 0.5649\n",
      "Epoch: 44 | Loss: 0.5648\n",
      "Epoch: 45 | Loss: 0.5647\n",
      "Epoch: 46 | Loss: 0.5647\n",
      "Epoch: 47 | Loss: 0.5647\n",
      "Epoch: 48 | Loss: 0.5646\n",
      "Epoch: 49 | Loss: 0.5646\n",
      "Epoch: 50 | Loss: 0.5646\n",
      "Epoch: 51 | Loss: 0.5646\n",
      "Epoch: 52 | Loss: 0.5646\n",
      "Epoch: 53 | Loss: 0.5646\n",
      "Epoch: 54 | Loss: 0.5645\n",
      "Epoch: 55 | Loss: 0.5645\n",
      "Epoch: 56 | Loss: 0.5645\n",
      "Epoch: 57 | Loss: 0.5645\n",
      "Epoch: 58 | Loss: 0.5645\n",
      "Epoch: 59 | Loss: 0.5645\n",
      "Epoch: 60 | Loss: 0.5645\n",
      "Epoch: 61 | Loss: 0.5645\n",
      "Epoch: 62 | Loss: 0.5645\n",
      "Epoch: 63 | Loss: 0.5645\n",
      "Epoch: 64 | Loss: 0.5645\n",
      "Epoch: 65 | Loss: 0.5645\n",
      "Epoch: 66 | Loss: 0.5645\n",
      "Epoch: 67 | Loss: 0.5645\n",
      "Epoch: 68 | Loss: 0.5645\n",
      "Epoch: 69 | Loss: 0.5645\n",
      "Epoch: 70 | Loss: 0.5645\n",
      "Epoch: 71 | Loss: 0.5645\n",
      "Epoch: 72 | Loss: 0.5645\n",
      "Epoch: 73 | Loss: 0.5645\n",
      "Epoch: 74 | Loss: 0.5645\n",
      "Epoch: 75 | Loss: 0.5645\n",
      "Epoch: 76 | Loss: 0.5645\n",
      "Epoch: 77 | Loss: 0.5645\n",
      "Epoch: 78 | Loss: 0.5645\n",
      "Epoch: 79 | Loss: 0.5645\n",
      "Epoch: 80 | Loss: 0.5645\n",
      "Epoch: 81 | Loss: 0.5645\n",
      "Epoch: 82 | Loss: 0.5645\n",
      "Epoch: 83 | Loss: 0.5645\n",
      "Epoch: 84 | Loss: 0.5645\n",
      "Epoch: 85 | Loss: 0.5645\n",
      "Epoch: 86 | Loss: 0.5645\n",
      "Epoch: 87 | Loss: 0.5645\n",
      "Epoch: 88 | Loss: 0.5645\n",
      "Epoch: 89 | Loss: 0.5645\n",
      "Epoch: 90 | Loss: 0.5645\n",
      "Epoch: 91 | Loss: 0.5645\n",
      "Epoch: 92 | Loss: 0.5645\n",
      "Epoch: 93 | Loss: 0.5645\n",
      "Epoch: 94 | Loss: 0.5645\n",
      "Epoch: 95 | Loss: 0.5645\n",
      "Epoch: 96 | Loss: 0.5645\n",
      "Epoch: 97 | Loss: 0.5645\n",
      "Epoch: 98 | Loss: 0.5645\n",
      "Epoch: 99 | Loss: 0.5645\n",
      "Epoch: 100 | Loss: 0.5645\n",
      "Epoch: 101 | Loss: 0.5645\n",
      "Epoch: 102 | Loss: 0.5645\n",
      "Epoch: 103 | Loss: 0.5645\n",
      "Epoch: 104 | Loss: 0.5645\n",
      "Epoch: 105 | Loss: 0.5645\n",
      "Epoch: 106 | Loss: 0.5645\n",
      "Epoch: 107 | Loss: 0.5645\n",
      "Epoch: 108 | Loss: 0.5645\n",
      "Epoch: 109 | Loss: 0.5645\n",
      "Epoch: 110 | Loss: 0.5645\n",
      "Epoch: 111 | Loss: 0.5645\n",
      "Epoch: 112 | Loss: 0.5645\n",
      "Epoch: 113 | Loss: 0.5645\n",
      "Epoch: 114 | Loss: 0.5645\n",
      "Epoch: 115 | Loss: 0.5645\n",
      "Epoch: 116 | Loss: 0.5645\n",
      "Epoch: 117 | Loss: 0.5645\n",
      "Epoch: 118 | Loss: 0.5645\n",
      "Epoch: 119 | Loss: 0.5645\n",
      "Epoch: 120 | Loss: 0.5645\n",
      "Epoch: 121 | Loss: 0.5645\n",
      "Epoch: 122 | Loss: 0.5645\n",
      "Epoch: 123 | Loss: 0.5645\n",
      "Epoch: 124 | Loss: 0.5645\n",
      "Epoch: 125 | Loss: 0.5645\n",
      "Epoch: 126 | Loss: 0.5645\n",
      "Epoch: 127 | Loss: 0.5645\n",
      "Epoch: 128 | Loss: 0.5645\n",
      "Epoch: 129 | Loss: 0.5645\n",
      "Epoch: 130 | Loss: 0.5645\n",
      "Epoch: 131 | Loss: 0.5645\n",
      "Epoch: 132 | Loss: 0.5645\n",
      "Epoch: 133 | Loss: 0.5645\n",
      "Epoch: 134 | Loss: 0.5645\n",
      "Epoch: 135 | Loss: 0.5645\n",
      "Epoch: 136 | Loss: 0.5645\n",
      "Epoch: 137 | Loss: 0.5645\n",
      "Epoch: 138 | Loss: 0.5645\n",
      "Epoch: 139 | Loss: 0.5645\n",
      "Epoch: 140 | Loss: 0.5645\n",
      "Epoch: 141 | Loss: 0.5645\n",
      "Epoch: 142 | Loss: 0.5645\n",
      "Epoch: 143 | Loss: 0.5645\n",
      "Epoch: 144 | Loss: 0.5645\n",
      "Epoch: 145 | Loss: 0.5645\n",
      "Epoch: 146 | Loss: 0.5645\n",
      "Epoch: 147 | Loss: 0.5645\n",
      "Epoch: 148 | Loss: 0.5645\n",
      "Epoch: 149 | Loss: 0.5645\n"
     ]
    }
   ],
   "source": [
    "## training\n",
    "for ITER in range(150):\n",
    "    model = model.train()\n",
    "\n",
    "    ## forward\n",
    "    output = model(x)\n",
    "    loss = criterion(output, y)\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    ## backward + update model params\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    model.eval()\n",
    "    print('Epoch: %d | Loss: %.4f' %(ITER, loss.detach().item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Bp50Q7J0Xkiw"
   },
   "source": [
    "## Testing the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-09-05T21:04:17.490296Z",
     "iopub.status.busy": "2024-09-05T21:04:17.490137Z",
     "iopub.status.idle": "2024-09-05T21:04:17.494116Z",
     "shell.execute_reply": "2024-09-05T21:04:17.493571Z"
    },
    "id": "V1odfZpGFoBi",
    "outputId": "a447b232-729e-4ccf-adc2-5aeaf79cc2ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.096769332885742\n"
     ]
    }
   ],
   "source": [
    "## test the model\n",
    "sample = torch.tensor([10.0], dtype=torch.float)\n",
    "predicted = model(sample)\n",
    "print(predicted.detach().item())"
   ]
  }
 ],
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