Neural-Networks-From-Scratch/final_projects/california_housing/california_housing.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rzkG7sVKi_co"
      },
      "source": [
        "#Hands on Neural Network Project: Vizuara AI Labs\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EOUOrFJTi_cs"
      },
      "source": [
        "**Project 1: Fashion Dataset Sorting (Classification)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZuV3AEDi_cs"
      },
      "source": [
        "First let's import TensorFlow and Keras."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mir-OyRGi_cs"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oIjPGmhni_ct"
      },
      "source": [
        "Let's start by loading the fashion MNIST dataset. Keras has a number of functions to load popular datasets in `keras.datasets`. The dataset is already split for you between a training set and a test set, but it can be useful to split the training set further to have a validation set:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nCnAOri7i_ct"
      },
      "outputs": [],
      "source": [
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "(X_train_full, y_train_full), (X_test, y_test) = fashion_mnist.load_data()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ql7Lw-lYi_ct"
      },
      "source": [
        "The training set contains 60,000 grayscale images, each 28x28 pixels:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3y2WX70ri_ct",
        "outputId": "476d3010-bae2-47fe-9924-838ce36b2846",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "X_train_full.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O1jprZEsi_ct"
      },
      "source": [
        "Each pixel intensity is represented as a byte (0 to 255):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DEBkw0Jji_ct",
        "outputId": "ad1e663d-a332-4e08-dca1-e1f6181fb260",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('uint8')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "X_train_full.dtype"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AdjMr2zri_ct"
      },
      "source": [
        "Let's split the full training set into a validation set and a (smaller) training set. We also scale the pixel intensities down to the 0-1 range and convert them to floats, by dividing by 255."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m9oH-AD0i_ct"
      },
      "outputs": [],
      "source": [
        "X_valid, X_train = X_train_full[:5000] / 255., X_train_full[5000:] / 255.\n",
        "y_valid, y_train = y_train_full[:5000], y_train_full[5000:]\n",
        "X_test = X_test / 255."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_valid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ep1sBLxio50e",
        "outputId": "4cea29dd-6733-44f9-d0f3-95524fbc16f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5000"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4WI8Jd93i_ct"
      },
      "source": [
        "You can plot an image using Matplotlib's `imshow()` function, with a `'binary'`\n",
        " color map:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Eh04qqNgi_ct",
        "outputId": "a0ec841a-09cb-40b7-a0c5-48d420e4aca3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.imshow(X_train[0], cmap=\"binary\")\n",
        "plt.axis('off')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lew8UnG4i_cu"
      },
      "source": [
        "The labels are the class IDs (represented as uint8), from 0 to 9:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OUOn9-Smi_cu",
        "outputId": "d4128f41-d809-405b-ebe0-eec848e94189",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 0, 7, ..., 3, 0, 5], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "y_train"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Go7rvcvAGdws",
        "outputId": "3a9775be-2af8-4379-b049-28b532c3e0cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "55000"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G5qntrqOG0R0",
        "outputId": "c895d55a-1220-4fcf-a20d-dd8eaa35e11a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u__pw9U3i_cu"
      },
      "source": [
        "Here are the corresponding class names:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4Gxep_E1i_cu"
      },
      "outputs": [],
      "source": [
        "class_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\",\n",
        "               \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XmcpDBNNi_cu"
      },
      "source": [
        "So the first image in the training set is a coat:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zgBA__xmi_cu",
        "outputId": "a8c86772-dceb-40ec-d18f-416223055d1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Coat'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "class_names[y_train[0]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "At1PsGVCi_cu"
      },
      "source": [
        "The validation set contains 5,000 images, and the test set contains 10,000 images:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9afKaVlxi_cu",
        "outputId": "57b3eaa6-e9a4-415b-d286-c9fc8249e359",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "X_valid.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W7_hr-xqi_cu",
        "outputId": "438e12cb-c509-43ac-b590-a243ed6fa0c0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "X_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GcuKhQwbi_cv"
      },
      "source": [
        "Let's take a look at a sample of the images in the dataset:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vndCgWyOi_cv",
        "outputId": "c20b4a01-491e-4284-de32-7e6c9791c3bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x480 with 40 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "n_rows = 4\n",
        "n_cols = 10\n",
        "plt.figure(figsize=(n_cols * 1.2, n_rows * 1.2))\n",
        "for row in range(n_rows):\n",
        "    for col in range(n_cols):\n",
        "        index = n_cols * row + col\n",
        "        plt.subplot(n_rows, n_cols, index + 1)\n",
        "        plt.imshow(X_train[index], cmap=\"binary\", interpolation=\"nearest\")\n",
        "        plt.axis('off')\n",
        "        plt.title(class_names[y_train[index]], fontsize=12)\n",
        "plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VjgdcQ4ji_cv"
      },
      "outputs": [],
      "source": [
        "model = keras.models.Sequential()\n",
        "model.add(keras.layers.Flatten(input_shape=[28, 28]))\n",
        "model.add(keras.layers.Dense(300, activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(100, activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(10, activation=\"softmax\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nnuxgQD0i_cv"
      },
      "outputs": [],
      "source": [
        "keras.backend.clear_session()\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BTha-LyGi_cv",
        "outputId": "9d3a6124-015b-4904-8718-59d16b606d49",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.src.layers.reshaping.flatten.Flatten at 0x7c3ac8a8e590>,\n",
              " <keras.src.layers.core.dense.Dense at 0x7c3ac8a8e020>,\n",
              " <keras.src.layers.core.dense.Dense at 0x7c3ac8a8c5b0>,\n",
              " <keras.src.layers.core.dense.Dense at 0x7c3ac8a8fa90>]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "model.layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-icQCCAgi_cv",
        "outputId": "5ef6238b-78fd-4805-bfb3-b5e28404ac13",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten_1 (Flatten)         (None, 784)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 300)               235500    \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 100)               30100     \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 10)                1010      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 266610 (1.02 MB)\n",
            "Trainable params: 266610 (1.02 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t8eWgH-Ri_cv",
        "outputId": "2a1d92b2-2613-45f3-eb36-14ed2d262cd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "keras.utils.plot_model(model, \"my_fashion_mnist_model.png\", show_shapes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sjXlrFRai_cw",
        "outputId": "e7804059-ac6d-4c2b-d263-41a604b91942",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'dense'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "hidden1 = model.layers[1]\n",
        "hidden1.name"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CDEwv6Wei_cw",
        "outputId": "f9dd6994-543f-404c-be1f-bfa77230ee15",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "model.get_layer(hidden1.name) is hidden1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k8JyMxvCi_cw"
      },
      "outputs": [],
      "source": [
        "weights, biases = hidden1.get_weights()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "htQEV7Hii_cw",
        "outputId": "8fd2ac47-1195-438f-8823-0cef379d0ba9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.05547573,  0.05219283, -0.04470842, ..., -0.02091905,\n",
              "         0.04866538, -0.0612976 ],\n",
              "       [ 0.04266865, -0.00036068, -0.0105349 , ..., -0.01204414,\n",
              "        -0.03022664,  0.05148147],\n",
              "       [ 0.03384251, -0.0720391 ,  0.05055922, ..., -0.01612951,\n",
              "        -0.00038215, -0.04591045],\n",
              "       ...,\n",
              "       [-0.03103632,  0.0478885 , -0.05481826, ..., -0.07233862,\n",
              "        -0.00248776,  0.07663711],\n",
              "       [ 0.00146075,  0.07297058,  0.01119053, ...,  0.05733179,\n",
              "        -0.01813692,  0.05473914],\n",
              "       [-0.04325661,  0.01617346,  0.00904222, ...,  0.00526305,\n",
              "         0.07360526, -0.03201197]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3An6KHr2i_cw",
        "outputId": "d890c101-4ce3-4030-e486-a0bd41e00ecf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(784, 300)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "weights.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uKcPR_uxi_cw",
        "outputId": "405e66c3-dab9-45e0-a4c1-6031d171d530",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 5.90005070e-02,  4.31531444e-02,  6.22319765e-02,  1.68837234e-01,\n",
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              "        1.00012958e-01,  1.08483747e-01,  7.98385516e-02,  4.00367789e-02,\n",
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              "        3.94843146e-02,  2.38496941e-02,  3.67044583e-02,  9.68351774e-03,\n",
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              "       -1.69979874e-02,  3.78307118e-03,  5.62446266e-02,  8.24216462e-04,\n",
              "       -2.75443681e-02,  9.99851450e-02,  1.01759128e-01,  1.72792688e-01,\n",
              "        2.19599232e-02,  5.98198287e-02,  9.93737299e-03,  5.32951578e-02,\n",
              "       -1.70655139e-02,  1.31718710e-01,  1.26668932e-02,  1.04340248e-01,\n",
              "        5.61607890e-02, -1.37216235e-02, -2.70637423e-02,  3.31151150e-02,\n",
              "        3.92891839e-02,  2.43099295e-02, -1.03538465e-02,  1.84802059e-02,\n",
              "       -1.35525659e-01,  1.54513299e-01,  3.43280509e-02,  9.00344998e-02,\n",
              "        4.17837128e-02, -2.40051243e-02,  8.61214846e-02,  5.73878698e-02,\n",
              "        1.46156847e-02, -5.86994067e-02, -2.24406663e-02,  1.35710597e-01,\n",
              "        6.53171837e-02,  2.28158146e-01,  1.33347943e-01, -7.03518912e-02,\n",
              "       -1.04224809e-01,  8.71689059e-03,  5.60356537e-03,  6.36834800e-02,\n",
              "        2.88057085e-02, -1.58804767e-02,  3.40567827e-02,  5.18423654e-02],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "biases"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vLYR9l0ai_cw",
        "outputId": "f4873686-7733-4b7c-b0ed-c6da6dbb00e2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(300,)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "biases.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NMWVTaVKi_cw"
      },
      "outputs": [],
      "source": [
        "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=\"sgd\",\n",
        "              metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQrn-VPHi_cw"
      },
      "source": [
        "This is equivalent to:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x2H0neoni_cw"
      },
      "source": [
        "```python\n",
        "model.compile(loss=keras.losses.sparse_categorical_crossentropy,\n",
        "              optimizer=keras.optimizers.SGD(),\n",
        "              metrics=[keras.metrics.sparse_categorical_accuracy])\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iD04YGxHi_cw",
        "outputId": "46a09003-29b6-40ac-8261-9b7a07371e48",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "1719/1719 [==============================] - 10s 5ms/step - loss: 0.7158 - accuracy: 0.7671 - val_loss: 0.5137 - val_accuracy: 0.8244\n",
            "Epoch 2/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.4862 - accuracy: 0.8301 - val_loss: 0.4392 - val_accuracy: 0.8522\n",
            "Epoch 3/30\n",
            "1719/1719 [==============================] - 8s 4ms/step - loss: 0.4412 - accuracy: 0.8458 - val_loss: 0.5342 - val_accuracy: 0.7998\n",
            "Epoch 4/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.4148 - accuracy: 0.8559 - val_loss: 0.3981 - val_accuracy: 0.8662\n",
            "Epoch 5/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3964 - accuracy: 0.8621 - val_loss: 0.3791 - val_accuracy: 0.8684\n",
            "Epoch 6/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3783 - accuracy: 0.8667 - val_loss: 0.3778 - val_accuracy: 0.8718\n",
            "Epoch 7/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3664 - accuracy: 0.8714 - val_loss: 0.3674 - val_accuracy: 0.8738\n",
            "Epoch 8/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3545 - accuracy: 0.8753 - val_loss: 0.3917 - val_accuracy: 0.8586\n",
            "Epoch 9/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3446 - accuracy: 0.8789 - val_loss: 0.3608 - val_accuracy: 0.8710\n",
            "Epoch 10/30\n",
            "1719/1719 [==============================] - 6s 4ms/step - loss: 0.3351 - accuracy: 0.8810 - val_loss: 0.3543 - val_accuracy: 0.8716\n",
            "Epoch 11/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3264 - accuracy: 0.8832 - val_loss: 0.3495 - val_accuracy: 0.8766\n",
            "Epoch 12/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3173 - accuracy: 0.8866 - val_loss: 0.3417 - val_accuracy: 0.8774\n",
            "Epoch 13/30\n",
            "1719/1719 [==============================] - 8s 4ms/step - loss: 0.3104 - accuracy: 0.8896 - val_loss: 0.3396 - val_accuracy: 0.8766\n",
            "Epoch 14/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3043 - accuracy: 0.8912 - val_loss: 0.3591 - val_accuracy: 0.8684\n",
            "Epoch 15/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2965 - accuracy: 0.8945 - val_loss: 0.3299 - val_accuracy: 0.8814\n",
            "Epoch 16/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.2914 - accuracy: 0.8953 - val_loss: 0.3166 - val_accuracy: 0.8830\n",
            "Epoch 17/30\n",
            "1719/1719 [==============================] - 6s 4ms/step - loss: 0.2851 - accuracy: 0.8977 - val_loss: 0.3652 - val_accuracy: 0.8718\n",
            "Epoch 18/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2792 - accuracy: 0.8999 - val_loss: 0.3248 - val_accuracy: 0.8860\n",
            "Epoch 19/30\n",
            "1719/1719 [==============================] - 6s 4ms/step - loss: 0.2746 - accuracy: 0.9010 - val_loss: 0.3133 - val_accuracy: 0.8870\n",
            "Epoch 20/30\n",
            "1719/1719 [==============================] - 10s 6ms/step - loss: 0.2688 - accuracy: 0.9028 - val_loss: 0.3390 - val_accuracy: 0.8760\n",
            "Epoch 21/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2637 - accuracy: 0.9043 - val_loss: 0.3072 - val_accuracy: 0.8920\n",
            "Epoch 22/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.2587 - accuracy: 0.9056 - val_loss: 0.3017 - val_accuracy: 0.8928\n",
            "Epoch 23/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2546 - accuracy: 0.9081 - val_loss: 0.3042 - val_accuracy: 0.8900\n",
            "Epoch 24/30\n",
            "1719/1719 [==============================] - 8s 4ms/step - loss: 0.2497 - accuracy: 0.9098 - val_loss: 0.3146 - val_accuracy: 0.8888\n",
            "Epoch 25/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2456 - accuracy: 0.9110 - val_loss: 0.3026 - val_accuracy: 0.8922\n",
            "Epoch 26/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2409 - accuracy: 0.9134 - val_loss: 0.3058 - val_accuracy: 0.8878\n",
            "Epoch 27/30\n",
            "1719/1719 [==============================] - 8s 4ms/step - loss: 0.2376 - accuracy: 0.9157 - val_loss: 0.3047 - val_accuracy: 0.8930\n",
            "Epoch 28/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2338 - accuracy: 0.9158 - val_loss: 0.3110 - val_accuracy: 0.8862\n",
            "Epoch 29/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.2302 - accuracy: 0.9168 - val_loss: 0.3087 - val_accuracy: 0.8854\n",
            "Epoch 30/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2260 - accuracy: 0.9189 - val_loss: 0.3145 - val_accuracy: 0.8898\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(X_train, y_train, epochs=30,\n",
        "                    validation_data=(X_valid, y_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WkF6be10i_cx",
        "outputId": "23a4d4ba-ce22-4f7f-a2c4-69cf43c09da4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'verbose': 1, 'epochs': 30, 'steps': 1719}"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "history.params"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kxodEcQ0i_cx",
        "outputId": "436f3964-c976-4956-84bf-cec9e203191f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]\n"
          ]
        }
      ],
      "source": [
        "print(history.epoch)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q9GfVKMoi_cx",
        "outputId": "8dd92818-2be9-4796-f57e-9a63a38026d0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "history.history.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yAx0BN6Wi_cx",
        "outputId": "b606d716-fd94-47ec-abf4-1d2bc1e4fcd0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "pd.DataFrame(history.history).plot(figsize=(8, 5))\n",
        "plt.grid(True)\n",
        "plt.gca().set_ylim(0, 1)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0IjOdALWi_cx",
        "outputId": "7b7fd8e0-89d6-48e7-b157-6254aed1bf07",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3446 - accuracy: 0.8784\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.34455999732017517, 0.8784000277519226]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "model.evaluate(X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##TESTING"
      ],
      "metadata": {
        "id": "6AI0Pog8MdEH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "03s8ED3Ri_cx"
      },
      "outputs": [],
      "source": [
        "X_new = X_test[:3]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**This is how the test data looks like**"
      ],
      "metadata": {
        "id": "yPoK8YvzOE06"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(7.2, 2.4))\n",
        "for index, image in enumerate(X_new):\n",
        "    plt.subplot(1, 3, index + 1)\n",
        "    plt.imshow(image, cmap=\"binary\", interpolation=\"nearest\")\n",
        "plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "MA1a_m6KMq1z",
        "outputId": "a4ce5ee0-9ac2-4038-d0a4-6c7dc4cbebe9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x240 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTibBGnhi_cx"
      },
      "source": [
        "**Warning**: `model.predict_classes(X_new)` is deprecated. It is replaced with `np.argmax(model.predict(X_new), axis=-1)`."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let's see what our neural network predicts**"
      ],
      "metadata": {
        "id": "xGye_UfdOId7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-4cxq5_Ni_cx",
        "outputId": "ce5f468c-eecd-4e2d-da40-db31991bbaa7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 37ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 2, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "#y_pred = model.predict_classes(X_new) # deprecated\n",
        "y_pred = np.argmax(model.predict(X_new), axis=-1)\n",
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r0_naPcOi_cx",
        "outputId": "7792f5ff-a528-48a5-d101-0da20a336aad",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Ankle boot', 'Pullover', 'Trouser'], dtype='<U11')"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "np.array(class_names)[y_pred]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2bEzZMd6i_cx",
        "outputId": "3077444a-79c4-4417-f53d-a4991951bd17",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 2, 1], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "y_new = y_test[:3]\n",
        "y_new"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZWjPABo3i_cz",
        "outputId": "e3995e94-4443-4ae4-f8d3-dde76c0c0033",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving figure fashion_mnist_images_plot\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x240 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(7.2, 2.4))\n",
        "for index, image in enumerate(X_new):\n",
        "    plt.subplot(1, 3, index + 1)\n",
        "    plt.imshow(image, cmap=\"binary\", interpolation=\"nearest\")\n",
        "    plt.axis('off')\n",
        "    plt.title(class_names[y_test[index]], fontsize=12)\n",
        "plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Awesome! We obtained perfect results :)**\n"
      ],
      "metadata": {
        "id": "vOnS2tXsOSnu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Hands on Neural Network Project: Vizuara AI Labs\n"
      ],
      "metadata": {
        "id": "mfepDTY33BDJ"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xOqObuz4i_cz"
      },
      "source": [
        "##Project : California housing price predictor (Regression)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*The 8 input features are the following:*\n",
        "\n",
        "**MedInc:** median income in block group\n",
        "\n",
        "**HouseAge:** median house age in block group\n",
        "\n",
        "**AveRooms:** average number of rooms per household\n",
        "\n",
        "**AveBedrms:** average number of bedrooms per household\n",
        "\n",
        "**Population:** block group population\n",
        "\n",
        "**AveOccup:** average number of household members\n",
        "\n",
        "**Latitude:** block group latitude\n",
        "\n",
        "**Longitude:** block group longitude\n",
        "\n",
        "*The target variable is a scalar: the median house value for California districts, in dollars.*\n"
      ],
      "metadata": {
        "id": "PSHTN6Q8eqoC"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-o690m7i_cz"
      },
      "source": [
        "Let's load, split and scale the California housing dataset:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "pReVeHCndRAu"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "73FCBO34i_cz"
      },
      "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",
        "\n",
        "housing = fetch_california_housing()\n",
        "\n",
        "X_train_full, X_test, y_train_full, y_test = train_test_split(housing.data, housing.target, random_state=42)\n",
        "X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_train_full)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mYz9FVSiceUK",
        "outputId": "a9b3c20a-3496-4ae2-8012-0eb596e31c8b"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15480"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oXfAqIgX5oBC",
        "outputId": "27df02b8-945e-4619-c373-56fb22e6339a"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5160"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_valid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MDj2w-bK5tme",
        "outputId": "1feccda3-5fe8-4042-bb54-59cfe74a946f"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3870"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9JX160Bs5yI8",
        "outputId": "2ace916a-0f4f-462f-c8e2-1d353e0d8e95"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11610"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert X_train into a DataFrame with attribute names\n",
        "df_train = pd.DataFrame(X_train, columns=housing.feature_names)\n",
        "\n",
        "# Print the DataFrame\n",
        "print(df_train.head())  # Print the first few rows of the DataFrame\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YCWa8jPxfEbP",
        "outputId": "ad48b0c3-d1c6-472e-f151-418f31c77530"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
            "0  3.5214      15.0  3.049945   1.106548      1447.0  1.605993     37.63   \n",
            "1  5.3275       5.0  6.490060   0.991054      3464.0  3.443340     33.69   \n",
            "2  3.1000      29.0  7.542373   1.591525      1328.0  2.250847     38.44   \n",
            "3  7.1736      12.0  6.289003   0.997442      1054.0  2.695652     33.55   \n",
            "4  2.0549      13.0  5.312457   1.085092      3297.0  2.244384     33.93   \n",
            "\n",
            "   Longitude  \n",
            "0    -122.43  \n",
            "1    -117.39  \n",
            "2    -122.98  \n",
            "3    -117.70  \n",
            "4    -116.93  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us standardize the data"
      ],
      "metadata": {
        "id": "5MezZ9hEhgRw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_valid = scaler.transform(X_valid)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "CKri5ezPgraO"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F_YsjUFGc0No",
        "outputId": "e63d5f1c-4cdf-48b8-e068-c59b6906ac72"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11610"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_valid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JJRzc8Wbc3F5",
        "outputId": "4551eb96-7d0e-4be7-8b8f-1e0ec49d0738"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3870"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "bUtB0N33i_c0"
      },
      "outputs": [],
      "source": [
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us now define the model architecture"
      ],
      "metadata": {
        "id": "t5bpT4SuhmyD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "4_3_d2hqi_c0",
        "outputId": "1c3898ee-4e61-489a-a574-b067a3d86ca1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 2.3995 - val_loss: 1.9867\n",
            "Epoch 2/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.6861 - val_loss: 0.6335\n",
            "Epoch 3/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.6031 - val_loss: 0.5723\n",
            "Epoch 4/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.5756 - val_loss: 0.5269\n",
            "Epoch 5/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.5526 - val_loss: 0.5069\n",
            "Epoch 6/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.5315 - val_loss: 0.4916\n",
            "Epoch 7/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.5138 - val_loss: 0.4820\n",
            "Epoch 8/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.4988 - val_loss: 0.4607\n",
            "Epoch 9/20\n",
            "363/363 [==============================] - 1s 3ms/step - loss: 0.4851 - val_loss: 0.4473\n",
            "Epoch 10/20\n",
            "363/363 [==============================] - 1s 3ms/step - loss: 0.4741 - val_loss: 0.4447\n",
            "Epoch 11/20\n",
            "363/363 [==============================] - 1s 3ms/step - loss: 0.4655 - val_loss: 0.4447\n",
            "Epoch 12/20\n",
            "363/363 [==============================] - 1s 3ms/step - loss: 0.4574 - val_loss: 0.4250\n",
            "Epoch 13/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.4512 - val_loss: 0.4158\n",
            "Epoch 14/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.4454 - val_loss: 0.4117\n",
            "Epoch 15/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.4402 - val_loss: 0.4104\n",
            "Epoch 16/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.4358 - val_loss: 0.4024\n",
            "Epoch 17/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.4321 - val_loss: 0.4019\n",
            "Epoch 18/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.4285 - val_loss: 0.4081\n",
            "Epoch 19/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.4251 - val_loss: 0.3985\n",
            "Epoch 20/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.4225 - val_loss: 0.4056\n"
          ]
        }
      ],
      "source": [
        "model = keras.models.Sequential([\n",
        "    keras.layers.Dense(30, activation=\"relu\", input_shape=X_train.shape[1:]),\n",
        "    keras.layers.Dense(1)\n",
        "])\n",
        "model.compile(loss=\"mean_squared_error\", optimizer=keras.optimizers.SGD(learning_rate=1e-3))\n",
        "history = model.fit(X_train, y_train, epochs=20, validation_data=(X_valid, y_valid))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the loss\n",
        "plt.plot(history.history['loss'], label='Training loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation loss')\n",
        "plt.legend()\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "405uxpNUmRy2",
        "outputId": "7afbb1d5-8321-4000-a109-163811a2dd80"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us evaluate the model testing performance"
      ],
      "metadata": {
        "id": "963l10oShryj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on the test set\n",
        "mse_test = model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQcRFozmmSCQ",
        "outputId": "7cb41310-06bf-4da4-8480-d7bf80d61fa3"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "162/162 [==============================] - 0s 2ms/step - loss: 0.4192\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Make predictions on new data\n",
        "X_new = X_test[:3]\n",
        "y_pred = model.predict(X_new)\n",
        "\n",
        "# Compare predictions with actual values\n",
        "for i in range(len(X_new)):\n",
        "    print(f\"Sample {i+1}:\")\n",
        "    print(f\"Predicted: {y_pred[i][0]}\")\n",
        "    print(f\"Actual: {y_test[i]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uAFis-W8dJRP",
        "outputId": "66879e94-3023-4e21-8acd-ada4e84982fb"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 61ms/step\n",
            "Sample 1:\n",
            "Predicted: 0.7479265332221985\n",
            "Actual: 0.477\n",
            "Sample 2:\n",
            "Predicted: 1.6871938705444336\n",
            "Actual: 0.458\n",
            "Sample 3:\n",
            "Predicted: 3.6486470699310303\n",
            "Actual: 5.00001\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.10"
    },
    "nav_menu": {
      "height": "264px",
      "width": "369px"
    },
    "toc": {
      "navigate_menu": true,
      "number_sections": true,
      "sideBar": true,
      "threshold": 6,
      "toc_cell": false,
      "toc_section_display": "block",
      "toc_window_display": false
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}