2.Mnist_ANN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyN6ZuG5upOs6Gp2ueOxWgGe",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/singhsourav0/DeepLearningOdyssey/blob/main/Mnist_ANN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1><font color=\"red\">MultiClass Classification on the Mnist DataSet.</font></h1>\n",
        "\n",
        "<font color=\"blue\"> In this notebook we will apply the ANN to demostrate the concept of feedforward Neural Network on the MNIST dataset which contains 70,000 image of hand written digits in the 28×28 pixels.</font>\n"
      ],
      "metadata": {
        "id": "RlGHV3xceIM1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "zev6Ps24d3HP"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ],
      "metadata": {
        "id": "cnrxEJfIgYXq"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten"
      ],
      "metadata": {
        "id": "wcTDpja_h4up"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()"
      ],
      "metadata": {
        "id": "FRX4kqjGhHkV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c5a51bc-1d9b-4211-dc19-7b910056e6ec"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yaCsQWX6hmGg",
        "outputId": "332e353a-d225-4052-e503-ac0a55fef464"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train[0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EioNCZiehqRQ",
        "outputId": "90b405fe-01b2-41b3-e63e-d1828aa218d5"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MwJI_yniiBBU",
        "outputId": "615de2af-9591-4b0e-c98c-c68835a6df78"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(x_train[2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "Q6akaj6SinfI",
        "outputId": "a22dd4e4-1a18-458a-e4c3-6facd5affc88"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7b1a71d97070>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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HD2rGjBlqa2vTK6+8ok2bNulHP/qRJGnDhg36zne+o3379ukHP/hB7CYHACS0a3oPqK2tTZKUmZkpSTp48KC6urpUXFwc2WfixIkaPXq0amtre/wenZ2dCofDURsAYPDrc4C6u7u1YsUKTZ8+XZMmTZIkNTc3KzU1VRkZGVH75uTkqLm5ucfvU1FRoUAgENny8vL6OhIAIIH0OUBlZWU6evSoNm/efE0DlJeXq62tLbI1NTVd0/cDACSGPv0g6vLly7Vjxw7t3btXo0aNijweDAZ18eJFtba2Rl0FtbS0KBgM9vi9/H6//H5/X8YAACQwT1dAzjktX75cW7du1Z49e5Sfnx/1/JQpU5SSkqKqqqrIY3V1dTp+/LiKiopiMzEAYFDwdAVUVlamTZs2afv27UpLS4u8rxMIBDRs2DAFAgE9+OCDWrVqlTIzM5Wenq5HHnlERUVFfAIOABDFU4DWr18vSZo5c2bU4xs2bNCSJUskSb/97W+VlJSkRYsWqbOzUyUlJfrd734Xk2EBAIOHpwA55666z9ChQ1VZWanKyso+DwUg8Vxy3d4XcTOw6xr/+wEAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCiT/8iKgDEwrmp56xHgCGugAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAE9yMFEBMJPv48yy84RUDADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJjgZqQArtC5e6TnNZdu747DJBjMuAICAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEz4nHPOeoi/FQ6HFQgENFPzNMSXYj0OAMCjL1yXqrVdbW1tSk9P73U/roAAACYIEADAhKcAVVRUaOrUqUpLS1N2drbmz5+vurq6qH1mzpwpn88XtT388MMxHRoAkPg8BaimpkZlZWXat2+fdu3apa6uLs2ePVsdHR1R+y1dulSnTp2KbOvWrYvp0ACAxOfpX0TduXNn1NcbN25Udna2Dh48qBkzZkQeHz58uILBYGwmBAAMStf0HlBbW5skKTMzM+rx119/XVlZWZo0aZLKy8t17ty5Xr9HZ2enwuFw1AYAGPw8XQH9re7ubq1YsULTp0/XpEmTIo/fd999GjNmjEKhkI4cOaLHH39cdXV1evvtt3v8PhUVFVq7dm1fxwAAJKg+/xzQsmXL9N577+mDDz7QqFGjet1vz549mjVrlurr6zVu3Lgrnu/s7FRnZ2fk63A4rLy8PH4OCAAS1Df9OaA+XQEtX75cO3bs0N69e782PpJUWFgoSb0GyO/3y+/392UMAEAC8xQg55weeeQRbd26VdXV1crPz7/qmsOHD0uScnNz+zQgAGBw8hSgsrIybdq0Sdu3b1daWpqam5slSYFAQMOGDVNDQ4M2bdqkH//4xxoxYoSOHDmilStXasaMGZo8eXJc/gMAAInJ03tAPp+vx8c3bNigJUuWqKmpST/96U919OhRdXR0KC8vTwsWLNATTzzxtX8P+Le4FxwAJLa4vAd0tVbl5eWppqbGy7cEAFynuBccAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMDEEOsBvso5J0n6Ql2SMx4GAODZF+qS9Nffz3sz4ALU3t4uSfpA7xpPAgC4Fu3t7QoEAr0+73NXS1Q/6+7u1smTJ5WWliafzxf1XDgcVl5enpqampSenm40oT3Ow2Wch8s4D5dxHi4bCOfBOaf29naFQiElJfX+Ts+AuwJKSkrSqFGjvnaf9PT06/oF9iXOw2Wch8s4D5dxHi6zPg9fd+XzJT6EAAAwQYAAACYSKkB+v19r1qyR3++3HsUU5+EyzsNlnIfLOA+XJdJ5GHAfQgAAXB8S6goIADB4ECAAgAkCBAAwQYAAACYSJkCVlZW66aabNHToUBUWFurDDz+0HqnfPfXUU/L5fFHbxIkTrceKu71792ru3LkKhULy+Xzatm1b1PPOOa1evVq5ubkaNmyYiouLdezYMZth4+hq52HJkiVXvD7mzJljM2ycVFRUaOrUqUpLS1N2drbmz5+vurq6qH0uXLigsrIyjRgxQjfeeKMWLVqklpYWo4nj45uch5kzZ17xenj44YeNJu5ZQgTozTff1KpVq7RmzRp99NFHKigoUElJiU6fPm09Wr+79dZbderUqcj2wQcfWI8Udx0dHSooKFBlZWWPz69bt04vvviiXn75Ze3fv1833HCDSkpKdOHChX6eNL6udh4kac6cOVGvjzfeeKMfJ4y/mpoalZWVad++fdq1a5e6uro0e/ZsdXR0RPZZuXKl3nnnHW3ZskU1NTU6efKkFi5caDh17H2T8yBJS5cujXo9rFu3zmjiXrgEMG3aNFdWVhb5+tKlSy4UCrmKigrDqfrfmjVrXEFBgfUYpiS5rVu3Rr7u7u52wWDQPffcc5HHWltbnd/vd2+88YbBhP3jq+fBOecWL17s5s2bZzKPldOnTztJrqamxjl3+f99SkqK27JlS2Sfjz/+2ElytbW1VmPG3VfPg3PO/fCHP3Q///nP7Yb6Bgb8FdDFixd18OBBFRcXRx5LSkpScXGxamtrDSezcezYMYVCIY0dO1b333+/jh8/bj2SqcbGRjU3N0e9PgKBgAoLC6/L10d1dbWys7M1YcIELVu2TGfOnLEeKa7a2tokSZmZmZKkgwcPqqurK+r1MHHiRI0ePXpQvx6+eh6+9PrrrysrK0uTJk1SeXm5zp07ZzFerwbczUi/6vPPP9elS5eUk5MT9XhOTo4++eQTo6lsFBYWauPGjZowYYJOnTqltWvX6s4779TRo0eVlpZmPZ6J5uZmSerx9fHlc9eLOXPmaOHChcrPz1dDQ4N+9atfqbS0VLW1tUpOTrYeL+a6u7u1YsUKTZ8+XZMmTZJ0+fWQmpqqjIyMqH0H8+uhp/MgSffdd5/GjBmjUCikI0eO6PHHH1ddXZ3efvttw2mjDfgA4a9KS0sjv548ebIKCws1ZswYvfXWW3rwwQcNJ8NAcM8990R+fdttt2ny5MkaN26cqqurNWvWLMPJ4qOsrExHjx69Lt4H/Tq9nYeHHnoo8uvbbrtNubm5mjVrlhoaGjRu3Lj+HrNHA/6v4LKyspScnHzFp1haWloUDAaNphoYMjIyNH78eNXX11uPYubL1wCvjyuNHTtWWVlZg/L1sXz5cu3YsUPvv/9+1D/fEgwGdfHiRbW2tkbtP1hfD72dh54UFhZK0oB6PQz4AKWmpmrKlCmqqqqKPNbd3a2qqioVFRUZTmbv7NmzamhoUG5urvUoZvLz8xUMBqNeH+FwWPv377/uXx8nTpzQmTNnBtXrwzmn5cuXa+vWrdqzZ4/y8/Ojnp8yZYpSUlKiXg91dXU6fvz4oHo9XO089OTw4cOSNLBeD9afgvgmNm/e7Px+v9u4caP785//7B566CGXkZHhmpubrUfrV7/4xS9cdXW1a2xsdH/4wx9ccXGxy8rKcqdPn7YeLa7a29vdoUOH3KFDh5wk9/zzz7tDhw65Tz/91Dnn3G9+8xuXkZHhtm/f7o4cOeLmzZvn8vPz3fnz540nj62vOw/t7e3u0UcfdbW1ta6xsdHt3r3bfe9733O33HKLu3DhgvXoMbNs2TIXCARcdXW1O3XqVGQ7d+5cZJ+HH37YjR492u3Zs8cdOHDAFRUVuaKiIsOpY+9q56G+vt49/fTT7sCBA66xsdFt377djR071s2YMcN48mgJESDnnHvppZfc6NGjXWpqqps2bZrbt2+f9Uj97u6773a5ubkuNTXVffvb33Z33323q6+vtx4r7t5//30n6Ypt8eLFzrnLH8V+8sknXU5OjvP7/W7WrFmurq7Odug4+LrzcO7cOTd79mw3cuRIl5KS4saMGeOWLl066P6Q1tN/vyS3YcOGyD7nz593P/vZz9y3vvUtN3z4cLdgwQJ36tQpu6Hj4Grn4fjx427GjBkuMzPT+f1+d/PNN7tf/vKXrq2tzXbwr+CfYwAAmBjw7wEBAAYnAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMDE/wB3z3opkp0DGwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = x_train/255\n",
        "x_test = x_test/255"
      ],
      "metadata": {
        "id": "OwOgKfrYiuxc"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sWYzL__HjUYK",
        "outputId": "fb7ed62d-d72f-4bff-92ba-e511b0715e0c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        ],\n",
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              "        0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
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              "        0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.01176471, 0.07058824, 0.07058824,\n",
              "        0.07058824, 0.49411765, 0.53333333, 0.68627451, 0.10196078,\n",
              "        0.65098039, 1.        , 0.96862745, 0.49803922, 0.        ,\n",
              "        0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.11764706, 0.14117647,\n",
              "        0.36862745, 0.60392157, 0.66666667, 0.99215686, 0.99215686,\n",
              "        0.99215686, 0.99215686, 0.99215686, 0.88235294, 0.6745098 ,\n",
              "        0.99215686, 0.94901961, 0.76470588, 0.25098039, 0.        ,\n",
              "        0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
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              "        0.        , 0.        , 0.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()"
      ],
      "metadata": {
        "id": "WiimljK1jzBK"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Flatten(input_shape= (28,28)))\n",
        "model.add(Dense(128,activation='relu'))\n",
        "model.add(Dense(32, activation ='relu'))\n",
        "model.add(Dense(10,activation='softmax'))\n"
      ],
      "metadata": {
        "id": "sz6iHPrNkVz5"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQWHFvKwmFdD",
        "outputId": "1868dd78-72d4-46df-e326-393a7f2d9314"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten (Flatten)           (None, 784)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               100480    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 10)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               1408      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 32)                4128      \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 10)                330       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 107636 (420.45 KB)\n",
            "Trainable params: 107636 (420.45 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: Unknown loss function: 'sparse_categorical_crossentrophy'. Please ensure you are using a `keras.utils.custom_object_scope` and that this object is included in the scope. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.\n",
        "\n",
        "model.compile(loss=keras.losses.SparseCategoricalCrossentropy(), optimizer = 'adam', metrics = ['accuracy'])\n",
        "history =model.fit(x_train,y_train, epochs =10, validation_split =0.2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sx7Uiv-8tPpo",
        "outputId": "2fa4f2cc-983a-46c3-9f7d-ea7bdf75d482"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1500/1500 [==============================] - 7s 4ms/step - loss: 0.1488 - accuracy: 0.9826 - val_loss: 0.1685 - val_accuracy: 0.9712\n",
            "Epoch 2/10\n",
            "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0274 - accuracy: 0.9945 - val_loss: 0.2001 - val_accuracy: 0.9668\n",
            "Epoch 3/10\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0298 - accuracy: 0.9938 - val_loss: 0.1606 - val_accuracy: 0.9728\n",
            "Epoch 4/10\n",
            "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0260 - accuracy: 0.9950 - val_loss: 0.1584 - val_accuracy: 0.9726\n",
            "Epoch 5/10\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0283 - accuracy: 0.9943 - val_loss: 0.1730 - val_accuracy: 0.9711\n",
            "Epoch 6/10\n",
            "1500/1500 [==============================] - 9s 6ms/step - loss: 0.0243 - accuracy: 0.9949 - val_loss: 0.1720 - val_accuracy: 0.9707\n",
            "Epoch 7/10\n",
            "1500/1500 [==============================] - 14s 9ms/step - loss: 0.0256 - accuracy: 0.9948 - val_loss: 0.1561 - val_accuracy: 0.9745\n",
            "Epoch 8/10\n",
            "1500/1500 [==============================] - 11s 7ms/step - loss: 0.0221 - accuracy: 0.9958 - val_loss: 0.1648 - val_accuracy: 0.9735\n",
            "Epoch 9/10\n",
            "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0246 - accuracy: 0.9950 - val_loss: 0.1456 - val_accuracy: 0.9758\n",
            "Epoch 10/10\n",
            "1500/1500 [==============================] - 7s 5ms/step - loss: 0.0216 - accuracy: 0.9956 - val_loss: 0.1597 - val_accuracy: 0.9742\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prob = model.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MPNxPR0jtmvw",
        "outputId": "31797c43-baec-4ce2-be0d-c159f0a34601"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred= y_prob.argmax(axis =1)"
      ],
      "metadata": {
        "id": "_6oINr07tvef"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F2w-xZvxvU8g",
        "outputId": "9367206f-9f79-4ad1-a2f3-b4f63be90853"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9759"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot (history.history['loss'])\n",
        "plt.plot (history.history['val_loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "pYCwIqlpwAYg",
        "outputId": "16319d26-c9e2-4503-da8e-defce0c90cf9"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7b1a42fed810>]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "bQklFBg-xNcD",
        "outputId": "81e13a55-b8af-4595-9dad-20f233d358c4"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7b1a50e391b0>]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1fRhmAh3yKXb"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}