Regression_model_from_scratch.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DMFxQiBRUyP6",
        "outputId": "dbd97e22-41fd-4ccc-b6c3-cd5fd9af13bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/boston_housing.npz\n",
            "57026/57026 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.datasets import boston_housing\n",
        "(train_data, train_targets), (test_data, test_targets) = (\n",
        " boston_housing.load_data())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YloLm76TVKGJ",
        "outputId": "efbeac82-6569-4696-d358-042e0c84c4e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(404, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " len(train_targets)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D-rt9obVVNYg",
        "outputId": "05f79116-f9ed-4ae7-c253-0923d22f3233"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "404"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean = train_data.mean(axis=0)\n",
        "train_data -= mean"
      ],
      "metadata": {
        "id": "fHCNy3THWnBe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "std = train_data.std(axis=0)\n",
        "train_data /= std\n",
        "test_data -= mean\n",
        "test_data /= std"
      ],
      "metadata": {
        "id": "hJauWBFpWo-Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "def build_model():\n",
        " model = keras.Sequential([\n",
        " layers.Dense(64, activation=\"relu\"),\n",
        " layers.Dense(64, activation=\"relu\"),\n",
        " layers.Dense(1)\n",
        " ])\n",
        " model.compile(optimizer=\"rmsprop\", loss=\"mse\", metrics=[\"mae\"])\n",
        " return model"
      ],
      "metadata": {
        "id": "5YHhb5xDXDA3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# K-Fold validation"
      ],
      "metadata": {
        "id": "LHKYZJS9ZxnM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "k = 4\n",
        "num_val_samples = len(train_data) // k\n",
        "num_epochs = 100\n",
        "all_scores = []\n",
        "for i in range(k):\n",
        " print(f\"Processing fold #{i}\")\n",
        " val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\n",
        " val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\n",
        " partial_train_data = np.concatenate(\n",
        " [train_data[:i * num_val_samples],\n",
        " train_data[(i + 1) * num_val_samples:]],\n",
        " axis=0)\n",
        " partial_train_targets = np.concatenate(\n",
        " [train_targets[:i * num_val_samples],\n",
        " train_targets[(i + 1) * num_val_samples:]],\n",
        " axis=0)\n",
        " model = build_model()\n",
        " model.fit(partial_train_data, partial_train_targets,\n",
        " epochs=num_epochs, batch_size=16, verbose=0)  # Trains the model (in silent mode, verbose = 0)\n",
        " val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)\n",
        " all_scores.append(val_mae)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vwrfYLi8Zwt_",
        "outputId": "2ba1da14-0c76-4948-a0e7-74e857da8cd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing fold #0\n",
            "Processing fold #1\n",
            "Processing fold #2\n",
            "Processing fold #3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SQEcEKMdadPV",
        "outputId": "fdc16bb4-aa85-4bae-a43c-fb17d8303552"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.8846012353897095, 2.4399166107177734, 2.4548308849334717, 2.375880241394043]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(all_scores)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NOfrO-staifg",
        "outputId": "87ee2f26-f7f2-4d84-d912-d5f8349de1e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.2888072431087494"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 500\n",
        "all_mae_histories = []\n",
        "for i in range(k):\n",
        " print(f\"Processing fold #{i}\")\n",
        " val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\n",
        " val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\n",
        " partial_train_data = np.concatenate(\n",
        " [train_data[:i * num_val_samples],\n",
        " train_data[(i + 1) * num_val_samples:]],\n",
        " axis=0)\n",
        " partial_train_targets = np.concatenate(\n",
        " [train_targets[:i * num_val_samples],\n",
        " train_targets[(i + 1) * num_val_samples:]],\n",
        " axis=0)\n",
        " model = build_model()\n",
        " history = model.fit(partial_train_data, partial_train_targets,\n",
        " validation_data=(val_data, val_targets),\n",
        " epochs=num_epochs, batch_size=16, verbose=0)\n",
        " mae_history = history.history[\"val_mae\"]\n",
        " all_mae_histories.append(mae_history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LCBN9P9Taw1x",
        "outputId": "2538d692-f7ee-4d4a-9a6c-8186151f8f4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing fold #0\n",
            "Processing fold #1\n",
            "Processing fold #2\n",
            "Processing fold #3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "average_mae_history = [\n",
        " np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]"
      ],
      "metadata": {
        "id": "JlU0zLzga1I6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(range(1, len(average_mae_history) + 1), average_mae_history)\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Validation MAE\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "bb3xIURwa3qJ",
        "outputId": "50915875-b775-4bfc-d5a7-7a9592a77d0d"
      },
      "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": "code",
      "source": [
        "truncated_mae_history = average_mae_history[10:]\n",
        "plt.plot(range(1, len(truncated_mae_history) + 1), truncated_mae_history)\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Validation MAE\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "do_sSd_TbF6D",
        "outputId": "75c667c7-85c9-42ef-b3ef-6906b46b93ec"
      },
      "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": "code",
      "source": [
        "model = build_model()\n",
        "model.fit(train_data, train_targets,\n",
        "epochs=130, batch_size=16, verbose=0)\n",
        "test_mse_score, test_mae_score = model.evaluate(test_data, test_targets)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v-W5s-JRbtax",
        "outputId": "e6dd9b34-7699-4332-a1d8-1c775d0392c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 4ms/step - loss: 16.4902 - mae: 2.6365\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_mae_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FkXnjQ6tbwY5",
        "outputId": "e57c6176-9f38-4bc7-8ee3-dbc21396da17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.636528253555298"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(test_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fcCnhn0Xb4kb",
        "outputId": "88772d1b-97e5-4e33-a9d3-f1f088af69a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h4QJC6XKb7M9",
        "outputId": "ac60000b-3ee4-4c55-a6ae-a111c1cc2acd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([8.470745], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first house in the test set is predicted to have a price of about $8,500."
      ],
      "metadata": {
        "id": "g451oc9Ab9U8"
      }
    }
  ]
}