moores_law.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "5xnR6bSiAA2-"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/lazyprogrammer/machine_learning_examples.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tfT7G4XvPP0I",
        "outputId": "38a6111c-833c-447e-8047-4dc5f33431a8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path 'machine_learning_examples' already exists and is not an empty directory.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/machine_learning_examples/tf2.0/moore.csv\", header=None).values"
      ],
      "metadata": {
        "id": "MxPlQiz-PZ7k"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GMup8cyjP9-8",
        "outputId": "539d0a3d-92c5-4ee0-ca77-19551b5bbce0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[:, 0].reshape(-1, 1)\n",
        "Y = df[:, 1].reshape(-1, 1)"
      ],
      "metadata": {
        "id": "JptqEzSMP-03"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X, Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "IYWhC8IuQ4_m",
        "outputId": "66468c25-a9d9-40fc-a404-a5cee440c8ee"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f4b80f6b350>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mx = X.mean()\n",
        "sx = X.std()\n",
        "my = Y.mean()\n",
        "sy = Y.std()\n",
        "\n",
        "X = (X-mx)/sx\n",
        "\n",
        "Y = (Y-my)/sy"
      ],
      "metadata": {
        "id": "qMPbHB2oQ6j9"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X, Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "O6n40JYkRGzE",
        "outputId": "3edf0c91-0ef9-4c94-a6e0-668a5a078797"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f4b80f43ad0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = X.astype(np.float32)\n",
        "Y = Y.astype(np.float32)"
      ],
      "metadata": {
        "id": "D-9RZt3tRKe3"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = nn.Linear(1, 1)"
      ],
      "metadata": {
        "id": "ls5lD6HrRVCE"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.MSELoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.7)"
      ],
      "metadata": {
        "id": "oWB1gkrxRhk_"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = torch.from_numpy(X)\n",
        "targets = torch.from_numpy(Y)"
      ],
      "metadata": {
        "id": "HgpQ8jOlSjZk"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(inputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v4hbdKBKSsjA",
        "outputId": "d0db554a-8f9f-4d53-eaa7-4316fbcb0a76"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Tensor"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_epochs=100\n",
        "Losses = []\n",
        "for i in range(n_epochs):\n",
        "  optimizer.zero_grad()\n",
        "  #Froward propagation\n",
        "  outputs = model(inputs)\n",
        "\n",
        "  loss=criterion(outputs, targets)\n",
        "  Losses.append(loss.item())\n",
        "  #Back Propagation\n",
        "  loss.backward()\n",
        "  optimizer.step()\n",
        "\n",
        "  print(f\"Epochs {i+1}/{n_epochs}, Loss: {loss.item():.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6-hBIxknStzt",
        "outputId": "36404ff1-d3ad-4522-cb49-2951bc1b4bfd"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs 1/100, Loss: 1.1948\n",
            "Epochs 2/100, Loss: 1.1740\n",
            "Epochs 3/100, Loss: 1.1400\n",
            "Epochs 4/100, Loss: 1.0987\n",
            "Epochs 5/100, Loss: 1.0544\n",
            "Epochs 6/100, Loss: 1.0102\n",
            "Epochs 7/100, Loss: 0.9680\n",
            "Epochs 8/100, Loss: 0.9288\n",
            "Epochs 9/100, Loss: 0.8933\n",
            "Epochs 10/100, Loss: 0.8615\n",
            "Epochs 11/100, Loss: 0.8336\n",
            "Epochs 12/100, Loss: 0.8092\n",
            "Epochs 13/100, Loss: 0.7880\n",
            "Epochs 14/100, Loss: 0.7698\n",
            "Epochs 15/100, Loss: 0.7542\n",
            "Epochs 16/100, Loss: 0.7409\n",
            "Epochs 17/100, Loss: 0.7296\n",
            "Epochs 18/100, Loss: 0.7200\n",
            "Epochs 19/100, Loss: 0.7119\n",
            "Epochs 20/100, Loss: 0.7051\n",
            "Epochs 21/100, Loss: 0.6993\n",
            "Epochs 22/100, Loss: 0.6944\n",
            "Epochs 23/100, Loss: 0.6903\n",
            "Epochs 24/100, Loss: 0.6869\n",
            "Epochs 25/100, Loss: 0.6840\n",
            "Epochs 26/100, Loss: 0.6816\n",
            "Epochs 27/100, Loss: 0.6795\n",
            "Epochs 28/100, Loss: 0.6778\n",
            "Epochs 29/100, Loss: 0.6764\n",
            "Epochs 30/100, Loss: 0.6752\n",
            "Epochs 31/100, Loss: 0.6742\n",
            "Epochs 32/100, Loss: 0.6734\n",
            "Epochs 33/100, Loss: 0.6726\n",
            "Epochs 34/100, Loss: 0.6721\n",
            "Epochs 35/100, Loss: 0.6716\n",
            "Epochs 36/100, Loss: 0.6711\n",
            "Epochs 37/100, Loss: 0.6708\n",
            "Epochs 38/100, Loss: 0.6705\n",
            "Epochs 39/100, Loss: 0.6703\n",
            "Epochs 40/100, Loss: 0.6701\n",
            "Epochs 41/100, Loss: 0.6699\n",
            "Epochs 42/100, Loss: 0.6697\n",
            "Epochs 43/100, Loss: 0.6696\n",
            "Epochs 44/100, Loss: 0.6695\n",
            "Epochs 45/100, Loss: 0.6694\n",
            "Epochs 46/100, Loss: 0.6694\n",
            "Epochs 47/100, Loss: 0.6693\n",
            "Epochs 48/100, Loss: 0.6693\n",
            "Epochs 49/100, Loss: 0.6692\n",
            "Epochs 50/100, Loss: 0.6692\n",
            "Epochs 51/100, Loss: 0.6692\n",
            "Epochs 52/100, Loss: 0.6691\n",
            "Epochs 53/100, Loss: 0.6691\n",
            "Epochs 54/100, Loss: 0.6691\n",
            "Epochs 55/100, Loss: 0.6691\n",
            "Epochs 56/100, Loss: 0.6691\n",
            "Epochs 57/100, Loss: 0.6691\n",
            "Epochs 58/100, Loss: 0.6691\n",
            "Epochs 59/100, Loss: 0.6691\n",
            "Epochs 60/100, Loss: 0.6690\n",
            "Epochs 61/100, Loss: 0.6690\n",
            "Epochs 62/100, Loss: 0.6690\n",
            "Epochs 63/100, Loss: 0.6690\n",
            "Epochs 64/100, Loss: 0.6690\n",
            "Epochs 65/100, Loss: 0.6690\n",
            "Epochs 66/100, Loss: 0.6690\n",
            "Epochs 67/100, Loss: 0.6690\n",
            "Epochs 68/100, Loss: 0.6690\n",
            "Epochs 69/100, Loss: 0.6690\n",
            "Epochs 70/100, Loss: 0.6690\n",
            "Epochs 71/100, Loss: 0.6690\n",
            "Epochs 72/100, Loss: 0.6690\n",
            "Epochs 73/100, Loss: 0.6690\n",
            "Epochs 74/100, Loss: 0.6690\n",
            "Epochs 75/100, Loss: 0.6690\n",
            "Epochs 76/100, Loss: 0.6690\n",
            "Epochs 77/100, Loss: 0.6690\n",
            "Epochs 78/100, Loss: 0.6690\n",
            "Epochs 79/100, Loss: 0.6690\n",
            "Epochs 80/100, Loss: 0.6690\n",
            "Epochs 81/100, Loss: 0.6690\n",
            "Epochs 82/100, Loss: 0.6690\n",
            "Epochs 83/100, Loss: 0.6690\n",
            "Epochs 84/100, Loss: 0.6690\n",
            "Epochs 85/100, Loss: 0.6690\n",
            "Epochs 86/100, Loss: 0.6690\n",
            "Epochs 87/100, Loss: 0.6690\n",
            "Epochs 88/100, Loss: 0.6690\n",
            "Epochs 89/100, Loss: 0.6690\n",
            "Epochs 90/100, Loss: 0.6690\n",
            "Epochs 91/100, Loss: 0.6690\n",
            "Epochs 92/100, Loss: 0.6690\n",
            "Epochs 93/100, Loss: 0.6690\n",
            "Epochs 94/100, Loss: 0.6690\n",
            "Epochs 95/100, Loss: 0.6690\n",
            "Epochs 96/100, Loss: 0.6690\n",
            "Epochs 97/100, Loss: 0.6690\n",
            "Epochs 98/100, Loss: 0.6690\n",
            "Epochs 99/100, Loss: 0.6690\n",
            "Epochs 100/100, Loss: 0.6690\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(Losses)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "EaM3FjlbTiqC",
        "outputId": "4f9813c6-e69e-4d9b-9e51-b8b4381ee04a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4b809e1350>]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MPZG1lrtU1qL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predicted = model(inputs).detach().numpy()\n",
        "\n",
        "plt.plot(X, Y, \"ro\", label=\"Target Label\")\n",
        "plt.plot(X, predicted, label = \"Predicted Output\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "nED6bI7HTcSi",
        "outputId": "c8e8de5d-7fc0-4c26-d76c-943a098c8961"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weights = model.weight.data.numpy()"
      ],
      "metadata": {
        "id": "pbHYHR8zVP3M"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weights"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xCS2pEGsXI-x",
        "outputId": "d9ffd848-0c7c-474d-e745-cc7b8275678c"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.57525444]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(weights[0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ffXNo8c9XLX-",
        "outputId": "1070bd45-b858-4265-a525-f43a227fcdd7"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.57525444]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = weights[0] * (sy/sx)"
      ],
      "metadata": {
        "id": "jlq78m7uXPyr"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bjpKt1quXTP0",
        "outputId": "7c45e50c-9e54-405a-8745-b4cbf2620e5d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.5145688e+08]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hGfJG9rMXUDt"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}