Pratical_Keras_Subclassing_Model.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Model Loading"
      ],
      "metadata": {
        "id": "8afHp_wMCO5m"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "3qqCjl1m7j4t"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import fetch_california_housing\n",
        "housing = fetch_california_housing()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    housing.data, housing.target, random_state = 42\n",
        ")"
      ],
      "metadata": {
        "id": "ANd56EJ38pUW"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Building"
      ],
      "metadata": {
        "id": "IET2qSiXCSnN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf \n",
        "from tensorflow import keras"
      ],
      "metadata": {
        "id": "oD80o9p08-oO"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class My_Model(tf.keras.Model):\n",
        "  def __init__(self, units=30, activation=\"relu\"):\n",
        "    super().__init__()\n",
        "    self.norm_layer_wide = tf.keras.layers.Normalization()\n",
        "    self.norm_layer_deep = tf.keras.layers.Normalization()\n",
        "    self.hidden1 = tf.keras.layers.Dense(units, activation=activation)\n",
        "    self.hidden2 = tf.keras.layers.Dense(units, activation=activation)\n",
        "    self.main_output = tf.keras.layers.Dense(1)\n",
        "  def call(self, inputs):\n",
        "    input_wide, input_deep = inputs\n",
        "    norm_wide = self.norm_layer_wide(input_wide)\n",
        "    norm_deep = self.norm_layer_deep(input_deep)\n",
        "    hidden1 = self.hidden1(norm_deep)\n",
        "    hidden2 = self.hidden2(hidden1)\n",
        "    concat = tf.keras.layers.concatenate([norm_wide, hidden2])\n",
        "    output = self.main_output(concat)\n",
        "    return output"
      ],
      "metadata": {
        "id": "-TgjbrUO9JmB"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(42)\n",
        "model = My_Model(30, activation=\"relu\")"
      ],
      "metadata": {
        "id": "AHPCq9wq_PNB"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3)\n",
        "model.compile(\n",
        "    loss = \"mse\",\n",
        "    optimizer=optimizer,\n",
        "    metrics = [\"RootMeanSquaredError\"]\n",
        ")"
      ],
      "metadata": {
        "id": "byOe-W0R_eiw"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_wide, X_train_deep = X_train[:,:5], X_train[:,2:]\n",
        "X_test_wide, X_test_deep = X_test[:, :5], X_test[:,2:]"
      ],
      "metadata": {
        "id": "_Ui52WZG_25h"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.norm_layer_wide.adapt(X_train_wide)\n",
        "model.norm_layer_deep.adapt(X_train_deep)"
      ],
      "metadata": {
        "id": "luujWfAKANjI"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Training"
      ],
      "metadata": {
        "id": "_P4bY54-CVwp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit((X_train_wide, X_train_deep),\n",
        "                    y_train,\n",
        "                    validation_split=0.1,\n",
        "                    epochs = 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QoxB0BJaAa1X",
        "outputId": "466eb181-c109-4df3-85e2-c657be749ee4"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "436/436 [==============================] - 3s 3ms/step - loss: 1.6177 - root_mean_squared_error: 1.2719 - val_loss: 0.7565 - val_root_mean_squared_error: 0.8698\n",
            "Epoch 2/10\n",
            "436/436 [==============================] - 1s 3ms/step - loss: 0.5975 - root_mean_squared_error: 0.7730 - val_loss: 0.5613 - val_root_mean_squared_error: 0.7492\n",
            "Epoch 3/10\n",
            "436/436 [==============================] - 2s 4ms/step - loss: 0.4757 - root_mean_squared_error: 0.6897 - val_loss: 0.4721 - val_root_mean_squared_error: 0.6871\n",
            "Epoch 4/10\n",
            "436/436 [==============================] - 1s 3ms/step - loss: 0.4390 - root_mean_squared_error: 0.6626 - val_loss: 0.4627 - val_root_mean_squared_error: 0.6802\n",
            "Epoch 5/10\n",
            "436/436 [==============================] - 1s 2ms/step - loss: 0.4025 - root_mean_squared_error: 0.6345 - val_loss: 0.4151 - val_root_mean_squared_error: 0.6443\n",
            "Epoch 6/10\n",
            "436/436 [==============================] - 1s 3ms/step - loss: 0.3789 - root_mean_squared_error: 0.6155 - val_loss: 0.3955 - val_root_mean_squared_error: 0.6289\n",
            "Epoch 7/10\n",
            "436/436 [==============================] - 1s 2ms/step - loss: 0.3657 - root_mean_squared_error: 0.6048 - val_loss: 0.3862 - val_root_mean_squared_error: 0.6214\n",
            "Epoch 8/10\n",
            "436/436 [==============================] - 1s 2ms/step - loss: 0.3578 - root_mean_squared_error: 0.5981 - val_loss: 0.3848 - val_root_mean_squared_error: 0.6203\n",
            "Epoch 9/10\n",
            "436/436 [==============================] - 1s 2ms/step - loss: 0.3519 - root_mean_squared_error: 0.5932 - val_loss: 0.3707 - val_root_mean_squared_error: 0.6088\n",
            "Epoch 10/10\n",
            "436/436 [==============================] - 1s 3ms/step - loss: 0.3471 - root_mean_squared_error: 0.5891 - val_loss: 0.3691 - val_root_mean_squared_error: 0.6076\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Evaluation"
      ],
      "metadata": {
        "id": "7CFzA8-HCY0E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eval_results = model.evaluate((X_test_wide, X_test_deep), y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ESjbhqRAvA3",
        "outputId": "6bc541a5-83f3-46aa-dc5a-a60f81fdb322"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "162/162 [==============================] - 0s 3ms/step - loss: 0.3496 - root_mean_squared_error: 0.5913\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eval_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PF3Mn-hoBMTX",
        "outputId": "1ff36572-5309-44c9-f52e-472a9a56bc33"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.34964966773986816, 0.5913118124008179]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prediction"
      ],
      "metadata": {
        "id": "o4G39jS_CcBj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_new_wide, X_new_deep = X_test_wide[:3], X_test_deep[:3]"
      ],
      "metadata": {
        "id": "WKzixXt4BPuY"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict((X_new_wide, X_new_deep))\n",
        "y_pred "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "07cc-u27Bt8A",
        "outputId": "4adfcd4a-52d2-42d0-8e30-801ce350c046"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 192ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.36844897],\n",
              "       [1.4869882 ],\n",
              "       [3.3377788 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_j8ml8VMB4fo",
        "outputId": "4c330d23-eb15-4e28-9fd9-b384442b66b6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.477  , 0.458  , 5.00001])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's connect [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [Instagram](https://www.instagram.com/tirendazacademy) | [GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"
      ],
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}