1_Deep_Network_MNIST.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shilpamathew14/FDP_Adishankara/blob/main/1_Deep_Network_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vHIMRfJrGmP0"
      },
      "source": [
        "# **DEEP NEURAL NETWORKS**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yvc3Trb67hHS"
      },
      "source": [
        "### set path to Google Drive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bkEaHIpjG_ng"
      },
      "source": [
        "- Mount Google Drive\n",
        "- set path to your folder in drive\n",
        "- check the current working directory"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1oK7ci1P3er-"
      },
      "outputs": [],
      "source": [
        "import os\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y1UJVWtf5q52"
      },
      "outputs": [],
      "source": [
        "os.chdir('/content/drive/MyDrive/DL_FDP_Jan 2023')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "961L0OdO59Oh",
        "outputId": "88d3f5a5-ad5d-4c5b-bff8-0ee2be6e5332"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/drive/MyDrive/DL_FDP_Jan 2023'"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pwd()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mUcvfzTa7NkA"
      },
      "source": [
        "# Set up a Deep Neural Network\n",
        "* 1 input layer\n",
        "* 2 hidden layers\n",
        "* 1 output layer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iGo30-9VGVAs"
      },
      "source": [
        "![Deep Network.jpeg](data:image/jpeg;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "48G6apBV7nZX"
      },
      "source": [
        "## Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "jO8tByj-5_qo"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8bm9gpqZ7tp-"
      },
      "source": [
        "## download dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "tpaLEURi7N7_"
      },
      "outputs": [],
      "source": [
        "mnist = tf.keras.datasets.mnist"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "veYOsaQb7XnI",
        "outputId": "a445bf14-3489-4f10-9ebf-61e0a723de7a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((array([[[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         ...,\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8),\n",
              "  array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)),\n",
              " (array([[[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         ...,\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]],\n",
              "  \n",
              "         [[0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          ...,\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0],\n",
              "          [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8),\n",
              "  array([7, 2, 1, ..., 4, 5, 6], dtype=uint8)))"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "mnist.load_data()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJWHAWaA7yNS"
      },
      "source": [
        "## Train test split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Ae6hfcwI7fmQ"
      },
      "outputs": [],
      "source": [
        "(X_train_full, y_train_full), (X_test, y_test) = mnist.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_train_full)\n",
        "len(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OGvFlYQLJed6",
        "outputId": "1dedd762-c5b4-4598-8ee3-cf38bdde1ce1"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L9k_Kjoh79y9",
        "outputId": "4821c8d0-f5cd-4053-8d6d-a53c3604e74c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "          0,   0],\n",
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              "          0,   0],\n",
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              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,  16,  93, 252, 253, 187,   0,   0,   0,   0,   0,   0,\n",
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              "          0,  46, 130, 183, 253, 253, 207,   2,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  39,\n",
              "        148, 229, 253, 253, 253, 250, 182,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  24, 114, 221,\n",
              "        253, 253, 253, 253, 201,  78,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
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              "        253, 253, 198,  81,   2,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
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              "        195,  80,   9,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,  55, 172, 226, 253, 253, 253, 253, 244, 133,\n",
              "         11,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
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              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "X_train_full[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZr6zy8M7-_t"
      },
      "source": [
        "## display images in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "trfFAVNN8nM7",
        "outputId": "54905467-f0c2-4fff-a9e3-c5656f8a7db2"
      },
      "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"
          }
        }
      ],
      "source": [
        "plt.imshow(X_train_full[0], cmap='binary')\n",
        "plt.axis(\"off\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "HNVsHepj80rf",
        "outputId": "111293c0-837b-4714-94b7-be471f0f3bbe"
      },
      "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"
          }
        }
      ],
      "source": [
        "plt.imshow(X_train_full[1], cmap='binary')\n",
        "plt.axis(\"off\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "fqQUWU6u9FtA",
        "outputId": "834c3cc5-eb5e-423f-9f10-e109f4f53ef0"
      },
      "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"
          }
        }
      ],
      "source": [
        "plt.imshow(X_train_full[2], cmap='binary')\n",
        "plt.axis(\"off\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "r8jMHKGo9HgJ",
        "outputId": "69dd32c5-027c-4fb0-9296-ce5588780d60"
      },
      "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"
          }
        }
      ],
      "source": [
        "plt.imshow(X_train_full[3], cmap='binary')\n",
        "plt.axis(\"off\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qrPSn_az9Jdh",
        "outputId": "9fc2e8ab-4841-47e3-ec0a-238aba607b1d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "X_train_full[3].shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-VZB_GqI8GTo"
      },
      "source": [
        "## Pixel wise values for the image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 880
        },
        "id": "VySl0hb_9WP2",
        "outputId": "923b959e-ba0a-4678-e26d-5e25c08504b6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f550825a910>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "sns.heatmap(X_train_full[2], annot=True, cmap='binary')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "alwRoZHw8Qqc"
      },
      "source": [
        "## Scaling data 0-1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "5uv5eBfH9rO0"
      },
      "outputs": [],
      "source": [
        "#Scale to the range 0-1\n",
        "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:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "B4FIngh0C_jt"
      },
      "outputs": [],
      "source": [
        "X_test =X_test/255."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rPuxQm4IDl5q",
        "outputId": "6decd495-5d40-4aa0-9baa-1f250de4ca1f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "X_valid.shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jpTkddxdDstS",
        "outputId": "6571b4ca-0dad-4451-827a-9f25f9a0e345"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(55000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "X_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 884
        },
        "id": "6xFB6vxyDw2a",
        "outputId": "afff1e2f-c97a-4aac-98bd-68241891cf09"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f550539f730>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "sns.heatmap(X_train[2], annot=True, cmap='binary')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dxn5ZszM8lbA"
      },
      "source": [
        "## Create Layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "BJIKIFHREf-U"
      },
      "outputs": [],
      "source": [
        "LAYERS = [tf.keras.layers.Flatten(input_shape=[28,28], name=\"inputlayer\"),\n",
        "          tf.keras.layers.Dense(300,activation='relu', name = \"hiddenlayer1\"),\n",
        "          tf.keras.layers.Dense(100,activation='relu', name = \"hiddenlayer2\"),\n",
        "          tf.keras.layers.Dense(10,activation='softmax', name = \"outputlayer\")\n",
        "          ]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_36Zg_VF8rVZ"
      },
      "source": [
        "## Create Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "MGinh4ReEPPd"
      },
      "outputs": [],
      "source": [
        "model1 = tf.keras.models.Sequential(LAYERS)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KP7PByYFV5A",
        "outputId": "b2f96995-d79a-417d-cb09-4325901b4739"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " inputlayer (Flatten)        (None, 784)               0         \n",
            "                                                                 \n",
            " hiddenlayer1 (Dense)        (None, 300)               235500    \n",
            "                                                                 \n",
            " hiddenlayer2 (Dense)        (None, 100)               30100     \n",
            "                                                                 \n",
            " outputlayer (Dense)         (None, 10)                1010      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 266,610\n",
            "Trainable params: 266,610\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model1.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D-9kR2at8vt3"
      },
      "source": [
        "## Set Hyper parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "_I4DwiiTFr4a"
      },
      "outputs": [],
      "source": [
        "LOSS_FUNCTION = \"sparse_categorical_crossentropy\"\n",
        "OPTIMIZER = \"adam\"\n",
        "METRICS = [\"accuracy\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JFD5ynZnWKz1"
      },
      "source": [
        "* Loss Function \n",
        "  - BinaryCrossentropy \n",
        "  - CategoricalCrossentropy\n",
        "  - SparseCategoricalCrossentropy\n",
        "  - Poisson\n",
        "\n",
        "* Optimizers\n",
        "  - Gradient Descent (static learning rate)\n",
        "      - SGD (shallow nw)\n",
        "      - Batch Gradient\n",
        "      - mini batch Geadient\n",
        "      - **SGD with momentum**\n",
        "  - Adaptive (dynamic learning rate) (used for sparse data)\n",
        "      - RMSprop (deepnets)\n",
        "      - **Adam** (deepnets)\n",
        "      - Adamax\n",
        "      - AdamW\n",
        "      - Adadelta\n",
        "      - Adagrad\n",
        "      - Adafactor\n",
        "      - Nadam\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ugBOUMn98067"
      },
      "source": [
        "## Compile model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "V2NiI8UDGUKz"
      },
      "outputs": [],
      "source": [
        "model1.compile(loss=LOSS_FUNCTION, optimizer = OPTIMIZER, metrics=METRICS)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VJIREpdRGm1s",
        "outputId": "d1df33f2-12a3-4c79-cef2-1694460406bb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.layers.reshaping.flatten.Flatten at 0x7f55082da340>,\n",
              " <keras.layers.core.dense.Dense at 0x7f55082daa60>,\n",
              " <keras.layers.core.dense.Dense at 0x7f550539cac0>,\n",
              " <keras.layers.core.dense.Dense at 0x7f550539cbe0>]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "model1.layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9RncECJ-GrnT",
        "outputId": "c4586425-0602-4610-81a9-299c084b4603"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.layers.reshaping.flatten.Flatten at 0x7f55082da340>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "model1.layers[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "bmfYtKOlGxwl",
        "outputId": "df1cea35-ea89-43c0-95d3-345d2bcc174e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'inputlayer'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "model1.layers[0].name"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "41cmY7D8G2ZP"
      },
      "outputs": [],
      "source": [
        "hidden1 = model1.layers[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "FMYIokmsHE91"
      },
      "outputs": [],
      "source": [
        "w, b = hidden1.get_weights()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-9BZyKojHQW9",
        "outputId": "120464d3-f9c9-4c65-aab0-23971bc65047"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.0356709 ,  0.03373279,  0.04444418, ...,  0.02121885,\n",
              "        -0.01318881, -0.04549884],\n",
              "       [-0.00855137,  0.03394105,  0.07105391, ..., -0.06515094,\n",
              "         0.00152588,  0.02987494],\n",
              "       [-0.05059303, -0.00370798,  0.01942027, ...,  0.05470726,\n",
              "        -0.00328686,  0.02122856],\n",
              "       ...,\n",
              "       [ 0.00545136, -0.03887334, -0.02922565, ...,  0.05591626,\n",
              "        -0.05563243, -0.01589755],\n",
              "       [ 0.04327635,  0.02599834, -0.07245454, ...,  0.03074213,\n",
              "         0.01351048,  0.04988734],\n",
              "       [-0.03812042,  0.06095731, -0.01213032, ..., -0.04308211,\n",
              "         0.06152099,  0.03214631]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "w #weight"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NYO0zxqH87nM"
      },
      "source": [
        "## Train the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "n4Q6fAhUHRaB"
      },
      "outputs": [],
      "source": [
        "EPOCHS=5\n",
        "VALIDATION_SET =(X_valid, y_valid)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AzB8eYmFHzNQ",
        "outputId": "0124c7a7-3d47-4b65-fc96-9fb353885f86"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.2130 - accuracy: 0.9368 - val_loss: 0.1107 - val_accuracy: 0.9652\n",
            "Epoch 2/5\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.0870 - accuracy: 0.9733 - val_loss: 0.0870 - val_accuracy: 0.9734\n",
            "Epoch 3/5\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.0583 - accuracy: 0.9809 - val_loss: 0.0625 - val_accuracy: 0.9812\n",
            "Epoch 4/5\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.0449 - accuracy: 0.9855 - val_loss: 0.0875 - val_accuracy: 0.9766\n",
            "Epoch 5/5\n",
            "1719/1719 [==============================] - 8s 4ms/step - loss: 0.0336 - accuracy: 0.9887 - val_loss: 0.0799 - val_accuracy: 0.9776\n"
          ]
        }
      ],
      "source": [
        "history = model1.fit(X_train, y_train, epochs= EPOCHS, validation_data= VALIDATION_SET)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nnXrSzog9CXU"
      },
      "source": [
        "## Save model to file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BbGw75s2IE0C"
      },
      "outputs": [],
      "source": [
        "import time\n",
        "import os\n",
        "\n",
        "def saveModel_path(model_dir=\"/SAVED_MODELS/\"):\n",
        "    os.makedirs(model_dir, exist_ok=True)\n",
        "    fileName = time.strftime(\"Model_%Y_%m_%d_%H_%M_%S_.h5\")    \n",
        "    model_path = os.path.join(model_dir, fileName)\n",
        "    print(f\"your model will be saved at the following location\\n{model_path}\")\n",
        "    return model_path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4_SYuptnKUbl",
        "outputId": "666b3455-02e6-4909-f765-76cd76829716"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "your model will be saved at the following location\n",
            "/SAVED_MODELS/Model_2023_01_26_13_54_23_.h5\n"
          ]
        }
      ],
      "source": [
        "model1.save(saveModel_path())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model1.save('/SAVED_MODELS/dnn_model.h5')"
      ],
      "metadata": {
        "id": "xr9JyKckLvHx"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WY1kpC79Gqz"
      },
      "source": [
        "## Prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iCeCjGw1Kayo",
        "outputId": "24a73422-23ef-4780-8e26-1d4bec112504"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 16ms/step\n"
          ]
        }
      ],
      "source": [
        "prediction = model1.predict(X_test[2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UzZm_BxsLAnI",
        "outputId": "68823eb0-fa14-4831-b08b-801ec7e1b842"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.75574737e-08, 9.99490857e-01, 1.02210888e-05, 1.70603478e-08,\n",
              "        3.77063057e-04, 1.30409148e-06, 7.20685819e-07, 1.07897504e-04,\n",
              "        1.16722822e-05, 6.62243167e-08]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "prediction\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0SueDvIIM3B6",
        "outputId": "a364567c-bfa9-477d-e9e8-d422badfad0b"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "BZ3hbPWIP25U"
      },
      "outputs": [],
      "source": [
        "y_prob=prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-1ZCdGz75wCB",
        "outputId": "15bd4515-08e0-4377-8ab5-1fd60be94d20"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "np.argmax(y_prob.round(5))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "noa2h0Gy522m"
      },
      "outputs": [],
      "source": [
        "y_prob=prediction[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oxvYiM8_53Vx",
        "outputId": "5f50ceae-015a-4e05-a314-2269f1c51ce9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.argmax(y_prob)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ogbxNJR-55S5",
        "outputId": "b5721bc0-9590-494c-f603-ca781001334a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6.87468482e-09, 1.50459396e-08, 1.42854717e-07, ...,\n",
              "        9.99890983e-01, 1.76013826e-09, 1.93377279e-08],\n",
              "       [3.62132685e-11, 1.37389984e-08, 9.99999940e-01, ...,\n",
              "        3.48151174e-12, 5.88141341e-11, 1.46899915e-17],\n",
              "       [1.75574719e-08, 9.99490798e-01, 1.02210888e-05, ...,\n",
              "        1.07897496e-04, 1.16722822e-05, 6.62243167e-08],\n",
              "       ...,\n",
              "       [4.01551469e-15, 1.25127708e-12, 2.00830123e-14, ...,\n",
              "        3.21169239e-07, 4.26498364e-10, 1.36013216e-06],\n",
              "       [1.09273875e-10, 7.80805867e-13, 9.32685004e-15, ...,\n",
              "        2.51045070e-11, 1.30743570e-06, 1.90512432e-11],\n",
              "       [8.16268941e-10, 7.36143514e-13, 6.90322474e-13, ...,\n",
              "        2.54545852e-15, 4.21399998e-10, 1.34069311e-10]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "model1.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "Z7YCPkmM5-pW",
        "outputId": "cd4f7a41-7458-45a3-bef8-39fd566f0d81"
      },
      "outputs": [
        {
          "ename": "OSError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-43-ec141403d365>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/SAVED_MODELS/Model_2023_01_22_10_59_59_.h5\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/saving/save.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile, options)\u001b[0m\n\u001b[1;32m    204\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m           \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'No file or directory found at {filepath_str}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m           \u001b[0;32mif\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mOSError\u001b[0m: No file or directory found at /content/SAVED_MODELS/Model_2023_01_22_10_59_59_.h5"
          ]
        }
      ],
      "source": [
        "# from tensorflow.keras.models import load_model\n",
        "# model=load_model(\"/content/SAVED_MODELS/Model_2023_01_22_10_59_59_.h5\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_accuracy = model1.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HkJDAJpKNTLN",
        "outputId": "5f2c3122-c51c-4c85-fa1c-d6445af6e303"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0764 - accuracy: 0.9782\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yq4OU85J6SZc"
      },
      "source": [
        "# Tensor Board"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "kIJJv4Y9YfU9"
      },
      "outputs": [],
      "source": [
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "V1rU6BCNYhNv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "3f731c04-4365-46e0-dbee-1b48783a9553"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-44-2418e2b8c0ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/AdiSankara_Feb_2023'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/drive/MyDrive/AdiSankara_Feb_2023'"
          ]
        }
      ],
      "source": [
        "os.chdir('/content/drive/MyDrive/AdiSankara_Feb_2023')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "E_SGtlx7Yl1K",
        "outputId": "6f1950a3-695e-41c9-8c82-5b55b42f7e83"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "pwd()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yi9WosUW6RjG",
        "outputId": "337d752f-3594-42d7-fb43-7f7878c4cd50"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "saving logs at:\n",
            "/content/drive/MyDrive/AdiSankara_Feb_2023/logs/fit/log_2023_01_26_14_04_40\n"
          ]
        }
      ],
      "source": [
        "#tensorboard\n",
        "import time\n",
        "\n",
        "def get_log_path(log_dir=\"/content/drive/MyDrive/AdiSankara_Feb_2023/logs/fit\"):\n",
        "    fileName = time.strftime(\"log_%Y_%m_%d_%H_%M_%S\")    \n",
        "    log_path = os.path.join(log_dir, fileName)\n",
        "    print(f\"saving logs at:\\n{log_path}\")\n",
        "    return log_path\n",
        "\n",
        "log_dir = get_log_path()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "SumYfiOx6zJ0"
      },
      "outputs": [],
      "source": [
        "#tensorboard_callback =tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
        "tensorboard_callback =tf.keras.callbacks.TensorBoard(log_dir='/content/logs', histogram_freq=1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UZoKFz0cNv7s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "avbUp7je62Yb",
        "outputId": "acdf33da-5dc0-4e9a-b607-79b0f43b8dbe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1719/1719 [==============================] - 11s 6ms/step - loss: 0.0293 - accuracy: 0.9902 - val_loss: 0.0791 - val_accuracy: 0.9798\n",
            "Epoch 2/5\n",
            "1719/1719 [==============================] - 10s 6ms/step - loss: 0.0217 - accuracy: 0.9925 - val_loss: 0.0900 - val_accuracy: 0.9778\n",
            "Epoch 3/5\n",
            "1719/1719 [==============================] - 9s 5ms/step - loss: 0.0213 - accuracy: 0.9929 - val_loss: 0.0855 - val_accuracy: 0.9796\n",
            "Epoch 4/5\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.0193 - accuracy: 0.9936 - val_loss: 0.0839 - val_accuracy: 0.9806\n",
            "Epoch 5/5\n",
            "1719/1719 [==============================] - 8s 4ms/step - loss: 0.0158 - accuracy: 0.9945 - val_loss: 0.0886 - val_accuracy: 0.9788\n"
          ]
        }
      ],
      "source": [
        "EPOCHS=5\n",
        "VALIDATION_SET=(X_valid,y_valid)\n",
        "history=model1.fit(X_train,y_train,epochs=EPOCHS,validation_data=VALIDATION_SET,callbacks=[tensorboard_callback])\n",
        "     "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "e9HN9HdN65QL"
      },
      "outputs": [],
      "source": [
        "%load_ext tensorboard\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "5yYIY2z47DnQ"
      },
      "outputs": [],
      "source": [
        "%reload_ext tensorboard"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9p0Ywej-cN0J"
      },
      "outputs": [],
      "source": [
        "#!kill 3482"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YDUWtaEUcRii",
        "outputId": "ee46f8a6-1224-48e9-bac0-9edd1a7b98f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-02-01 08:21:28.882573: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
            "\n",
            "NOTE: Using experimental fast data loading logic. To disable, pass\n",
            "    \"--load_fast=false\" and report issues on GitHub. More details:\n",
            "    https://github.com/tensorflow/tensorboard/issues/4784\n",
            "\n",
            "Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all\n",
            "TensorBoard 2.9.1 at http://localhost:6007/ (Press CTRL+C to quit)\n"
          ]
        }
      ],
      "source": [
        "!tensorboard --logdir logs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "jBI30k7r7DeA",
        "outputId": "2a76f25c-5cff-42fc-f077-16359574d7ce"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Launching TensorBoard..."
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "%tensorboard --logdir logs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qy3m1zWGZGfH"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}