01_pytorch_workflow.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNJnpqqdKpRGg5JHu7F5i66",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jsgovea/PyTorch-learning/blob/main/01_pytorch_workflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nn4JSFHXP6cd",
        "outputId": "7fed00f7-d870-4170-df01-83f2d7c9495c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{1: 'data (prepare and load)',\n",
              " 2: 'build model',\n",
              " 3: 'fitting the model to data (training)',\n",
              " 4: 'making predictions and evaluating a model (interference)',\n",
              " 5: 'Saving and loading a model',\n",
              " 6: 'putting it all together'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "what_we_are_covering = {1: \"data (prepare and load)\",\n",
        "                        2: \"build model\",\n",
        "                        3: \"fitting the model to data (training)\",\n",
        "                        4: \"making predictions and evaluating a model (interference)\",\n",
        "                        5: \"Saving and loading a model\",\n",
        "                        6: \"putting it all together\"}\n",
        "what_we_are_covering"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# General imports\n",
        "import torch\n",
        "# nn contains all of PyTorch building block for neuronal networks\n",
        "from torch import nn\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Check PyTorch version\n",
        "torch.__version__\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "28_TzTCdTlbS",
        "outputId": "185b746d-bffc-4c90-947a-8857d7aef44b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.1.0+cu118'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data (prearing and loading)\n",
        "\n",
        "Data can be almost anything... in machine leaning.\n",
        "\n",
        "* Excel speadsheet\n",
        "* Images of any kind\n",
        "* Videos (Youtube has lots of data)\n",
        "* Audio like songs or podcasts\n",
        "* DNA\n",
        "* Text\n",
        "\n",
        "Machine learning is a game of two parts:\n",
        "1. Get data into numerical representation.\n",
        "2. Build a model to learn patterns in that numerical representation.\n",
        "\n",
        "We'll use a linear regression to make a straight line with know **parameters**\n"
      ],
      "metadata": {
        "id": "9qWu0upxTlJ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a known parameters\n",
        "weight = 0.7\n",
        "bias = 0.3\n",
        "\n",
        "# Create\n",
        "start = 0\n",
        "end = 1\n",
        "step = 0.02\n",
        "X = torch.arange(start, end, step).unsqueeze(dim=1)\n",
        "y = weight * X + bias\n",
        "\n",
        "X[:10], y[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kn8ZYtDxXUDf",
        "outputId": "7bc74cde-59c8-4a76-b9e7-d3c8278a3d77"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(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]]))"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X), len(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0PjlOw2tXU_V",
        "outputId": "c5de82d7-699e-4df0-e16f-5bf943f49de5"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50, 50)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Splitting data into training and test sets (one of the most important concepts in machine learning in general)\n",
        "\n",
        "E.G.\n",
        "* Course materials **(training set)**\n",
        "* Practice exam **(validation set)**\n",
        "* Final exam **(test set)**"
      ],
      "metadata": {
        "id": "h4_bPrt1XU9R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a train/test split\n",
        "train_split = int(0.8 * len(X))\n",
        "X_train, y_train = X[:train_split], y[:train_split]\n",
        "X_test, y_test = X[train_split:], y[train_split:]\n",
        "\n",
        "len(X_train), len(y_train), len(X_test), len(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vf_2SFHxrj2g",
        "outputId": "0c096959-1905-4e3d-f831-e91bf96bc7dd"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40, 40, 10, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_predictions(train_data=X_train,\n",
        "                     train_labels=y_train,\n",
        "                     test_data=X_test,\n",
        "                     test_labels=y_test,\n",
        "                     predictions=None):\n",
        "  \"\"\"\n",
        "  Plots training data, test data and compares predictions.\n",
        "  \"\"\"\n",
        "\n",
        "  plt.figure(figsize=(10, 7))\n",
        "\n",
        "  # Plot training data in blue\n",
        "  plt.scatter(train_data, train_labels, c=\"b\", s=4, label=\"Training data\")\n",
        "\n",
        "  # Plot test data in green\n",
        "  plt.scatter(test_data, test_labels, c=\"g\", s=4, label=\"Testing data\")\n",
        "\n",
        "  # Are there predictions?\n",
        "  if predictions is not None:\n",
        "    # Plot the predictions if they exist\n",
        "    plt.scatter(test_data, predictions, c=\"r\", s=4, label=\"Predictions\")\n",
        "\n",
        "  # Show the legend\n",
        "  plt.legend(prop={\"size\": 14})\n",
        "\n",
        "plot_predictions()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "LtjpWuxhrpNq",
        "outputId": "3e4a4b07-2d3c-49a4-9633-b40342fb1213"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dPEsnY-G1RGJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}