{
 "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/"
    },
    "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Set up the number of CPU cores to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": null,
   "metadata": {
    "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": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NcQUjR_95z5J",
    "outputId": "6db5df38-6f9d-454e-87d6-cee0c29dccb3"
   },
   "outputs": [],
   "source": [
    "## print size of the input tensor\n",
    "x.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "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": null,
   "metadata": {
    "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": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JeOr9i-aBzRv",
    "outputId": "299a0b60-a64c-46c4-d031-8aaf1cacbff9"
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "V1odfZpGFoBi",
    "outputId": "a447b232-729e-4ccf-adc2-5aeaf79cc2ea"
   },
   "outputs": [],
   "source": [
    "## test the model\n",
    "sample = torch.tensor([10.0], dtype=torch.float)\n",
    "predicted = model(sample)\n",
    "print(predicted.detach().item())"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}