regressionmlp.ipynb

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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Abhinav00077/neural_network/blob/main/regressionmlp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "I25HFC8bQfLj"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import fetch_california_housing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "housing = fetch_california_housing()\n",
        "X_train_full, X_test, y_train_full, y_test = train_test_split(\n",
        " housing.data, housing.target)\n",
        "X_train, X_valid, y_train, y_valid = train_test_split(\n",
        " X_train_full, y_train_full)\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_valid_scaled = scaler.transform(X_valid)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model= keras.models.Sequential([\n",
        "    keras.layers.Dense(30,activation=\"relu\",input_shape=X_train.shape[1:]),\n",
        "    keras.layers.Dense(1)\n",
        "])"
      ],
      "metadata": {
        "id": "eYQBApAbQxqx"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss=\"mean_squared_error\", optimizer=\"sgd\")\n",
        "history= model.fit(X_train,y_train,epochs=20,\n",
        "                   validation_data=(X_valid,y_valid))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "vRChrLyJTAst",
        "outputId": "1911fe1f-6590-4453-cd23-6a2960d92e22"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "363/363 [==============================] - 3s 6ms/step - loss: nan - val_loss: nan\n",
            "Epoch 2/20\n",
            "363/363 [==============================] - 2s 6ms/step - loss: nan - val_loss: nan\n",
            "Epoch 3/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: nan - val_loss: nan\n",
            "Epoch 4/20\n",
            "363/363 [==============================] - 1s 3ms/step - loss: nan - val_loss: nan\n",
            "Epoch 5/20\n",
            "363/363 [==============================] - 1s 3ms/step - loss: nan - val_loss: nan\n",
            "Epoch 6/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: nan - val_loss: nan\n",
            "Epoch 7/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: nan - val_loss: nan\n",
            "Epoch 8/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: nan - val_loss: nan\n",
            "Epoch 9/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: nan - val_loss: nan\n",
            "Epoch 10/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: nan - val_loss: nan\n",
            "Epoch 11/20\n",
            "363/363 [==============================] - 1s 3ms/step - loss: nan - val_loss: nan\n",
            "Epoch 12/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: nan - val_loss: nan\n",
            "Epoch 13/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: nan - val_loss: nan\n",
            "Epoch 14/20\n",
            "363/363 [==============================] - 1s 3ms/step - loss: nan - val_loss: nan\n",
            "Epoch 15/20\n",
            "363/363 [==============================] - 1s 3ms/step - loss: nan - val_loss: nan\n",
            "Epoch 16/20\n",
            "363/363 [==============================] - 1s 4ms/step - loss: nan - val_loss: nan\n",
            "Epoch 17/20\n",
            "363/363 [==============================] - 2s 7ms/step - loss: nan - val_loss: nan\n",
            "Epoch 18/20\n",
            "363/363 [==============================] - 1s 4ms/step - loss: nan - val_loss: nan\n",
            "Epoch 19/20\n",
            "363/363 [==============================] - 1s 3ms/step - loss: nan - val_loss: nan\n",
            "Epoch 20/20\n",
            "363/363 [==============================] - 2s 5ms/step - loss: nan - val_loss: nan\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mse_test=model.evaluate(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rVNDB37vVDoT",
        "outputId": "682669d7-09d4-40a4-fd6d-7f2454cce208"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "162/162 [==============================] - 0s 3ms/step - loss: nan\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_new=X_test[:3]\n",
        "y_pred=model.predict(x_new)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZy29UzGW6xv",
        "outputId": "7525a288-72a0-4da1-b157-a1d782882b75"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 298ms/step\n"
          ]
        }
      ]
    }
  ]
}