01_pytorch_workflow.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W8XeesyA_lXU"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn   # nn contains all pytorch's building blocks for neural networks\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# creating data set\n",
        "start = 0\n",
        "end = 1\n",
        "step = 0.02\n",
        "weight = 0.7\n",
        "bias = 0.3\n",
        "X = torch.arange(start,end,step).unsqueeze(dim = 1)\n",
        "y = weight*X + bias\n",
        "print(X[:10])\n",
        "print(y[:10])\n",
        "print(len(X))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBQU7g1HARgA",
        "outputId": "320d6fad-4264-4ee1-f918-4f8adbd5bd79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.0000],\n",
            "        [0.0200],\n",
            "        [0.0400],\n",
            "        [0.0600],\n",
            "        [0.0800],\n",
            "        [0.1000],\n",
            "        [0.1200],\n",
            "        [0.1400],\n",
            "        [0.1600],\n",
            "        [0.1800]])\n",
            "tensor([[0.3000],\n",
            "        [0.3140],\n",
            "        [0.3280],\n",
            "        [0.3420],\n",
            "        [0.3560],\n",
            "        [0.3700],\n",
            "        [0.3840],\n",
            "        [0.3980],\n",
            "        [0.4120],\n",
            "        [0.4260]])\n",
            "50\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# test and train split\n",
        "split_value = int(0.8*(len(X)))\n",
        "print(split_value)\n",
        "X_train, y_train = X[:split_value], y[:split_value]\n",
        "X_test, y_test = X[split_value:], y[split_value:]\n",
        "len(X_train), len(y_train), len(X_test), len(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uIMHh1TRBm65",
        "outputId": "9328bcc3-55a1-4626-f1ae-b8bd358e7102"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "40\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40, 40, 10, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = 0.8*len(X)\n",
        "type(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AZHggrsFIZKn",
        "outputId": "2a920bb7-128c-432f-848b-af2d334b4e9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "float"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_predictions(train_data = X_train,\n",
        "                     train_label = y_train,\n",
        "                     test_data = X_test,\n",
        "                     test_label = y_test,\n",
        "                     predictions = None):\n",
        "\n",
        "  plt.figure(figsize=(8,6))\n",
        "\n",
        "  #plot training data in blue\n",
        "  plt.scatter(train_data, train_label, c = \"b\", s=4, label=\"Training data\")\n",
        "\n",
        "  #plot test data in green\n",
        "  plt.scatter(test_data, test_label, c = \"g\", s=4, label =\"Test data\")\n",
        "\n",
        "  #plot predictions\n",
        "  if predictions is not None:\n",
        "    plt.scatter(test_data, predictions, c =\"r\", s=4, label =\"Predictions\")\n",
        "\n",
        "  plt.legend(prop={\"size\": 17});\n"
      ],
      "metadata": {
        "id": "jfaBtqNmC7PZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predictions();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "rMLpTL1yFHGV",
        "outputId": "26d29e4e-03ef-498a-a05f-d10cf84d4bb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Building Models\n",
        "not implemented error - due to indentation errors in functions\n"
      ],
      "metadata": {
        "id": "3MVDQsOjI12i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# building a model for linear regression\n",
        "class linearRegressionModel(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.weight = nn.Parameter(torch.randn(1,\n",
        "                              dtype = torch.float), #torch.float32 by default\n",
        "                              requires_grad= True) #can we update this value with gradient descent\n",
        "    self.bias = nn.Parameter(torch.randn(1,\n",
        "                            dtype = torch.float),\n",
        "                            requires_grad= True)\n",
        "\n",
        "  def forward(self, x: torch.tensor) -> torch.tensor :\n",
        "    return self.weight * x + self.bias\n",
        "\n",
        "#any subclass of nn.module needs a forward method(this defines forward computatuion of a model)"
      ],
      "metadata": {
        "id": "LbFo5RzqGEna"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "model_0 = linearRegressionModel()\n",
        "list(model_0.parameters())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3csk-6Qd4CH",
        "outputId": "a38743e8-f487-4ffc-b14e-1ddd0287c6e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Parameter containing:\n",
              " tensor([0.3367], requires_grad=True),\n",
              " Parameter containing:\n",
              " tensor([0.1288], requires_grad=True)]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "next(model_0.parameters())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0cRRN9M9D5Ez",
        "outputId": "bf52ca3a-7eb5-4f79-ca80-24071cce16cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Parameter containing:\n",
              "tensor([0.3367], requires_grad=True)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for param in model_0.parameters():\n",
        "    # param is a tensor representing a model parameter\n",
        "    print(param)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sx64RHU1DxTH",
        "outputId": "fccfcf6f-1ead-4af6-e9e3-83698570a6cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Parameter containing:\n",
            "tensor([0.3367], requires_grad=True)\n",
            "Parameter containing:\n",
            "tensor([0.1288], requires_grad=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_0.state_dict()  #another way of seeing parameters as dictionary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KCvX9BY1fCDF",
        "outputId": "e2fce246-ad1b-465b-8717-6a8ed972091a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('weight', tensor([0.3367])), ('bias', tensor([0.1288]))])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.inference_mode():\n",
        "  y_pred = model_0(X_test)"
      ],
      "metadata": {
        "id": "_IX8eOxZfg5h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predictions(predictions = y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "d9KtnBtZgaUE",
        "outputId": "6ec15763-466c-4707-c35f-d73ceddf347c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Training the model\n"
      ],
      "metadata": {
        "id": "MqnYdPJkRWQW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#creating the loss function\n",
        "loss_fn = nn.L1Loss() #MAE loss\n",
        "\n",
        "# choosing optimiser\n",
        "optimiser = torch.optim.SGD(model_0.parameters(), lr = 0.01)"
      ],
      "metadata": {
        "id": "svqmNmSjRZE1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Pass the model through the data for the number of epochs\n",
        "epoch_count = []\n",
        "test_loss_values = []\n",
        "train_loss_values = []\n",
        "epochs = 200\n",
        "for epoch in range(epochs):\n",
        "  #put the model in training mode. This is the default state of model\n",
        "  model_0.train()\n",
        "\n",
        "  #1.forward pass on the training data using forward() method. Basically caculating y_predicted values\n",
        "  y_pred = model_0(X_train)\n",
        "\n",
        "  #2.Calculate the loss of y_predicted w.r.t to the y_true values\n",
        "  loss = loss_fn(y_pred,y_train)\n",
        "\n",
        "  #3.zero the gradients of the optimiser(since after each epoch they accumulate)\n",
        "  optimiser.zero_grad()\n",
        "\n",
        "  #4.perform backpropagation (computes the gradient of every parameter (with requires_grad = true) )\n",
        "  loss.backward()\n",
        "\n",
        "  #5.Step the optimiser with respect to the gradients calculated in loss.backward\n",
        "  # (updates the parameters with the help of gradients and lr)\n",
        "  optimiser.step()\n",
        "\n",
        "  model_0.eval() #turns off different settings in the model not required for evaluation/testing (dropout/Batch norm layers)\n",
        "  with torch.inference_mode(): #turns off  gradient tracking and couple of things behind the scene\n",
        "  #with torch.no_grad(): used in older pytorch codes\n",
        "    test_pred = model_0(X_test)\n",
        "    test_loss = loss_fn(test_pred, y_test)\n",
        "\n",
        "    epoch_count.append(epoch)\n",
        "    train_loss_values.append(loss)\n",
        "    test_loss_values.append(test_loss)\n",
        "  if epoch%10 == 0 :\n",
        "    print(f\"epoch = {epoch} | train loss: {loss} | test loss: {test_loss}\")\n",
        "    print(model_0.state_dict())\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z2nYekFCSs68",
        "outputId": "5aaf17b1-d9e5-4403-e079-c7d208dbef7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch = 0 | train loss: 0.31288138031959534 | test loss: 0.48106518387794495\n",
            "OrderedDict([('weight', tensor([0.3406])), ('bias', tensor([0.1388]))])\n",
            "epoch = 10 | train loss: 0.1976713240146637 | test loss: 0.3463551998138428\n",
            "OrderedDict([('weight', tensor([0.3796])), ('bias', tensor([0.2388]))])\n",
            "epoch = 20 | train loss: 0.08908725529909134 | test loss: 0.21729660034179688\n",
            "OrderedDict([('weight', tensor([0.4184])), ('bias', tensor([0.3333]))])\n",
            "epoch = 30 | train loss: 0.053148526698350906 | test loss: 0.14464017748832703\n",
            "OrderedDict([('weight', tensor([0.4512])), ('bias', tensor([0.3768]))])\n",
            "epoch = 40 | train loss: 0.04543796554207802 | test loss: 0.11360953003168106\n",
            "OrderedDict([('weight', tensor([0.4748])), ('bias', tensor([0.3868]))])\n",
            "epoch = 50 | train loss: 0.04167863354086876 | test loss: 0.09919948130846024\n",
            "OrderedDict([('weight', tensor([0.4938])), ('bias', tensor([0.3843]))])\n",
            "epoch = 60 | train loss: 0.03818932920694351 | test loss: 0.08886633068323135\n",
            "OrderedDict([('weight', tensor([0.5116])), ('bias', tensor([0.3788]))])\n",
            "epoch = 70 | train loss: 0.03476089984178543 | test loss: 0.0805937647819519\n",
            "OrderedDict([('weight', tensor([0.5288])), ('bias', tensor([0.3718]))])\n",
            "epoch = 80 | train loss: 0.03132382780313492 | test loss: 0.07232122868299484\n",
            "OrderedDict([('weight', tensor([0.5459])), ('bias', tensor([0.3648]))])\n",
            "epoch = 90 | train loss: 0.02788739837706089 | test loss: 0.06473556160926819\n",
            "OrderedDict([('weight', tensor([0.5629])), ('bias', tensor([0.3573]))])\n",
            "epoch = 100 | train loss: 0.024458957836031914 | test loss: 0.05646304413676262\n",
            "OrderedDict([('weight', tensor([0.5800])), ('bias', tensor([0.3503]))])\n",
            "epoch = 110 | train loss: 0.021020207554101944 | test loss: 0.04819049686193466\n",
            "OrderedDict([('weight', tensor([0.5972])), ('bias', tensor([0.3433]))])\n",
            "epoch = 120 | train loss: 0.01758546568453312 | test loss: 0.04060482233762741\n",
            "OrderedDict([('weight', tensor([0.6141])), ('bias', tensor([0.3358]))])\n",
            "epoch = 130 | train loss: 0.014155393466353416 | test loss: 0.03233227878808975\n",
            "OrderedDict([('weight', tensor([0.6313])), ('bias', tensor([0.3288]))])\n",
            "epoch = 140 | train loss: 0.010716589167714119 | test loss: 0.024059748277068138\n",
            "OrderedDict([('weight', tensor([0.6485])), ('bias', tensor([0.3218]))])\n",
            "epoch = 150 | train loss: 0.0072835334576666355 | test loss: 0.016474086791276932\n",
            "OrderedDict([('weight', tensor([0.6654])), ('bias', tensor([0.3143]))])\n",
            "epoch = 160 | train loss: 0.0038517764769494534 | test loss: 0.008201557211577892\n",
            "OrderedDict([('weight', tensor([0.6826])), ('bias', tensor([0.3073]))])\n",
            "epoch = 170 | train loss: 0.008932482451200485 | test loss: 0.005023092031478882\n",
            "OrderedDict([('weight', tensor([0.6951])), ('bias', tensor([0.2993]))])\n",
            "epoch = 180 | train loss: 0.008932482451200485 | test loss: 0.005023092031478882\n",
            "OrderedDict([('weight', tensor([0.6951])), ('bias', tensor([0.2993]))])\n",
            "epoch = 190 | train loss: 0.008932482451200485 | test loss: 0.005023092031478882\n",
            "OrderedDict([('weight', tensor([0.6951])), ('bias', tensor([0.2993]))])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predictions(predictions = test_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "JPzc-F_dhb6p",
        "outputId": "aeb7207c-9aa7-4abf-d7e0-e24289d5300d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "(torch.tensor(train_loss_values).numpy())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lx10fsT7WTbw",
        "outputId": "e56dc840-5554-41af-9d09-cdc8c2a029cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.31288138, 0.30136034, 0.28983936, 0.27831835, 0.26679736,\n",
              "       0.25527635, 0.24375534, 0.23223433, 0.22071333, 0.20919232,\n",
              "       0.19767132, 0.18615034, 0.17462933, 0.16310832, 0.15158734,\n",
              "       0.14006636, 0.12854536, 0.11702438, 0.10609122, 0.09681284,\n",
              "       0.08908726, 0.08227583, 0.07638873, 0.07160007, 0.06747635,\n",
              "       0.06395438, 0.06097005, 0.05845819, 0.05635259, 0.05458578,\n",
              "       0.05314853, 0.05181945, 0.05069301, 0.04982285, 0.04895269,\n",
              "       0.04819351, 0.04753182, 0.04692793, 0.04642332, 0.04591871,\n",
              "       0.04543797, 0.04503796, 0.04463796, 0.04423796, 0.04383796,\n",
              "       0.04343796, 0.04307463, 0.04272563, 0.04237663, 0.04202763,\n",
              "       0.04167863, 0.04132963, 0.04098063, 0.04063163, 0.04028264,\n",
              "       0.03993364, 0.03958464, 0.03923564, 0.03888664, 0.03853764,\n",
              "       0.03818933, 0.03785244, 0.03750344, 0.03716412, 0.03681822,\n",
              "       0.03647511, 0.03613304, 0.0357861 , 0.03544784, 0.03509884,\n",
              "       0.0347609 , 0.03441364, 0.03407188, 0.03372844, 0.03338287,\n",
              "       0.03304323, 0.03269423, 0.03235766, 0.03200904, 0.03166865,\n",
              "       0.03132383, 0.03097964, 0.03063862, 0.03029063, 0.02995343,\n",
              "       0.02960443, 0.02926542, 0.02891922, 0.02857641, 0.02823403,\n",
              "       0.0278874 , 0.02754883, 0.02719982, 0.02686219, 0.02651462,\n",
              "       0.02617318, 0.02582942, 0.02548417, 0.02514421, 0.02479522,\n",
              "       0.02445896, 0.02411001, 0.02376995, 0.02342481, 0.02308094,\n",
              "       0.02273961, 0.02239193, 0.02205441, 0.02170541, 0.02136672,\n",
              "       0.02102021, 0.02067771, 0.020335  , 0.0199887 , 0.0196498 ,\n",
              "       0.01930081, 0.01896349, 0.0186156 , 0.01827448, 0.0179304 ,\n",
              "       0.01758547, 0.0172452 , 0.01689645, 0.01656   , 0.016211  ,\n",
              "       0.01587125, 0.0155258 , 0.01518224, 0.0148406 , 0.01449323,\n",
              "       0.01415539, 0.0138064 , 0.01346802, 0.01312119, 0.01277901,\n",
              "       0.01243599, 0.01209   , 0.0117508 , 0.01140179, 0.01106479,\n",
              "       0.01071659, 0.01037578, 0.01003139, 0.00968676, 0.00934619,\n",
              "       0.00899776, 0.00866098, 0.00831199, 0.00797254, 0.00762679,\n",
              "       0.00728353, 0.00694158, 0.00659453, 0.00625638, 0.00590738,\n",
              "       0.00556932, 0.00522218, 0.00488031, 0.00453698, 0.0041913 ,\n",
              "       0.00385178, 0.00350277, 0.00316608, 0.00281757, 0.00247707,\n",
              "       0.00213237, 0.00178806, 0.00145183, 0.00118878, 0.00258851,\n",
              "       0.00893248, 0.00258851, 0.00893248, 0.00258851, 0.00893248,\n",
              "       0.00258851, 0.00893248, 0.00258851, 0.00893248, 0.00258851,\n",
              "       0.00893248, 0.00258851, 0.00893248, 0.00258851, 0.00893248,\n",
              "       0.00258851, 0.00893248, 0.00258851, 0.00893248, 0.00258851,\n",
              "       0.00893248, 0.00258851, 0.00893248, 0.00258851, 0.00893248,\n",
              "       0.00258851, 0.00893248, 0.00258851, 0.00893248, 0.00258851],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_loss_values = torch.tensor(train_loss_values).numpy()\n",
        "test_loss_values = torch.tensor(test_loss_values).numpy()\n",
        "\n",
        "plt.plot( epoch_count,train_loss_values, label= \"Train loss\")\n",
        "plt.plot( epoch_count,test_loss_values, label= \"Test loss\")\n",
        "plt.title(\"Loss curve\")\n",
        "plt.legend() ;"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "ui8KJ9feiLED",
        "outputId": "465fb4a5-5ddb-4b9b-a6fc-823dc775eacc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Saving a model in pytorch\n",
        "1. torch.save() : Allows you to save a Pytorch object in Python's pickle format\n",
        "2. torch.load() : Allows you load a saved pytorch object\n",
        "3. torch.nn.Module.load_state_dict()- This allows to load a model's saved state dictionary"
      ],
      "metadata": {
        "id": "SYQkOs8iAI-C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pathlib"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K5Undm1qhDeH",
        "outputId": "b63197ae-4e64-44ee-fe76-ac456a233d16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pathlib in /usr/local/lib/python3.10/dist-packages (1.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Saving our PyTorch model\n",
        "from pathlib import Path\n",
        "\n",
        "#1. Creating model directory\n",
        "MODEL_PATH = Path(\"models\")\n",
        "MODEL_PATH.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "#2. Create model save path\n",
        "MODEL_NAME = \"01_pytorch_model_0.pth\"\n",
        "MODEL_SAVE_PATH = MODEL_PATH / MODEL_NAME\n",
        "\n",
        "MODEL_SAVE_PATH\n",
        "#3. Save the model state dict\n",
        "torch.save(model_0.state_dict(),MODEL_SAVE_PATH)"
      ],
      "metadata": {
        "id": "7li9tDizYi3e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls -l models"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "crR7yj9Ilm9o",
        "outputId": "addfa413-c02d-4436-880f-a82de495a2a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 4\n",
            "-rw-r--r-- 1 root root 1107 Oct 12 09:07 01_pytorch_model_0.pth\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# To load in a saved state_dict we have to instantiate a new instance of our model class\n",
        "loaded_model_0 = linearRegressionModel()\n",
        "#Load the saved state_dict of model_0 (this will update the new instance with updated parameters)\n",
        "loaded_model_0.load_state_dict(torch.load(f=MODEL_SAVE_PATH))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d3srH2tDmEG9",
        "outputId": "a517fa73-b763-4340-e438-109702ad5483"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model_0.state_dict()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y9kDUX5IRxCh",
        "outputId": "c465f935-9f0e-41c7-ac0a-1c2228db7f1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('weight', tensor([0.6990])), ('bias', tensor([0.3093]))])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model_0.eval()\n",
        "with torch.inference_mode():\n",
        "  loaded_model_preds = loaded_model_0(X_test)\n",
        "loaded_model_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2E-27JlKIh8f",
        "outputId": "86406aba-6403-4f84-d6e1-a946fa449818"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.8685],\n",
              "        [0.8825],\n",
              "        [0.8965],\n",
              "        [0.9105],\n",
              "        [0.9245],\n",
              "        [0.9384],\n",
              "        [0.9524],\n",
              "        [0.9664],\n",
              "        [0.9804],\n",
              "        [0.9944]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model_0(X_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O_maOMgYJN0B",
        "outputId": "c7bb0a7b-386b-4f68-f94e-fa1b9d0d1dc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.8685],\n",
              "        [0.8825],\n",
              "        [0.8965],\n",
              "        [0.9105],\n",
              "        [0.9245],\n",
              "        [0.9384],\n",
              "        [0.9524],\n",
              "        [0.9664],\n",
              "        [0.9804],\n",
              "        [0.9944]], grad_fn=<AddBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred == loaded_model_preds\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ro9a_LArJh2j",
        "outputId": "ff2885e4-f204-4f0e-b187-63e88b5e0731"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[True],\n",
              "        [True],\n",
              "        [True],\n",
              "        [True],\n",
              "        [True],\n",
              "        [True],\n",
              "        [True],\n",
              "        [True],\n",
              "        [True],\n",
              "        [True]])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class LinearRegressionModelV2(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    #using nn.layers to create parameters\n",
        "    self.linear_layer = nn.Linear(in_features = 1, out_features = 1)\n",
        "\n",
        "  def forward(self,x: torch.tensor) -> torch.tensor :\n",
        "    return self.linear_layer(x)\n",
        "\n",
        "torch.manual_seed(42)\n",
        "model_1 = LinearRegressionModelV2()\n",
        "model_1.state_dict()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6H3q4RKTKEq",
        "outputId": "4882021e-7528-43bd-cf0b-1a73714f9d5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('linear_layer.weight', tensor([[0.7645]])),\n",
              "             ('linear_layer.bias', tensor([0.8300]))])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    }
  ]
}