ANN_Model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SourajitaDewasi/StasticalDataAnalysis/blob/main/191000050_SourajitaDewasi_DL_CourseProject2_ANN_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "9y4MTZG8uXY-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4HcvSYZxwvQY",
        "outputId": "a6e26f28-5d10-4ce8-bca0-fdd4931a4bc3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2021.10.8)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (5.0.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.62.3)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n",
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n",
            "Downloading natural-images.zip to /content\n",
            " 97% 331M/342M [00:01<00:00, 199MB/s]\n",
            "100% 342M/342M [00:01<00:00, 180MB/s]\n",
            "Archive:  natural-images.zip\n",
            "replace data/natural_images/airplane/airplane_0000.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n"
          ]
        }
      ],
      "source": [
        "#Connecting Kaggle Dataset through API\n",
        "!pip install kaggle\n",
        "!mkdir ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "!kaggle datasets download -d prasunroy/natural-images\n",
        "#unzipping the zip files and deleting the zip files\n",
        "!unzip \\*.zip  && rm *.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rd-qi3SN0OAZ"
      },
      "outputs": [],
      "source": [
        "#importing necessary libraries\n",
        "import pandas as pd                                   #reading the data\n",
        "import numpy as np                                    #for linear array, matrices\n",
        "import seaborn as sns                                 #for visualization in form of graphs\n",
        "from matplotlib import pyplot as plt                  #for showing images\n",
        "from sklearn.metrics import accuracy_score            #to calculate accuracy\n",
        "import os                                             #for opening directories\n",
        "import PIL                                            #for opening picture files\n",
        "from PIL import Image\n",
        "from sklearn.model_selection import train_test_split  #for creating testing and training split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "deO2U-rW0VV6"
      },
      "outputs": [],
      "source": [
        "#importing keras for building the CNN model\n",
        "import keras\n",
        "#importing the model and layers \n",
        "from keras import models, layers\n",
        "#all the functions from Keras and Tensorflow\n",
        "from tensorflow.keras.utils import to_categorical      #to convert labels into categorical variable using hot-ecoding\n",
        "# keras imports for the dataset and building our neural network\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Conv2D, MaxPool2D, Flatten\n",
        "from keras.utils import np_utils\n",
        "from tensorflow.keras.utils import plot_model                    #to plot the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0awDAUm70fp4"
      },
      "outputs": [],
      "source": [
        "x_data,x1_data,y_data=[],[],[]\n",
        "#listing the directories\n",
        "dirs = ['/content/natural_images/airplane','/content/natural_images/cat','/content/natural_images/car','/content/natural_images/dog','/content/natural_images/flower','/content/natural_images/fruit','/content/natural_images/motorbike','/content/natural_images/person']\n",
        "\n",
        "#we will switch the y label when we finish with a directory\n",
        "current_y_label = 0\n",
        "for dirname, _, filenames in os.walk('/content/natural_images'):\n",
        "    \n",
        "    #if the directory name is not valid, begin the next iteration\n",
        "    if dirname not in dirs: continue\n",
        "    #for each file (image.png) in the filenames\n",
        "    for filename in filenames: \n",
        "        #combine paths to form complete directory\n",
        "        directory = os.path.join(dirname, filename)\n",
        "        #read image from directory using Image\n",
        "        img = Image.open(directory)\n",
        "        #reshape the image to (128, 128). This maintains the aspect ratio of all the images as same\n",
        "        img = img.resize((128,128), Image.ANTIALIAS)\n",
        "        #we can convert to an array directly using np.array(PIL image)\n",
        "        img = np.array(img)        \n",
        "        #append the y label as the y label\n",
        "        y_data.append(current_y_label)\n",
        "        #append the array to x_data\n",
        "        x_data.append(img)\n",
        "        #append the array after normalizing it by dividing it by 255 to x1_data\n",
        "        x1_data.append(img/255)\n",
        "    #we're finished with the directory. time to change the y label\n",
        "    current_y_label += 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MpH-OnmdutWx"
      },
      "outputs": [],
      "source": [
        "#reading sample images \n",
        "image = plt.imread('/content/natural_images/airplane/airplane_0000.jpg')\n",
        "image1 = plt.imread('/content/natural_images/car/car_0010.jpg')\n",
        "image2 = plt.imread('/content/natural_images/person/person_0050.jpg')\n",
        "image3 = plt.imread('/content/natural_images/fruit/fruit_0250.jpg')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "JmhDUdvlv3ha",
        "outputId": "c6300549-c0d2-4b22-a6b6-fdd807fb6a61"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f51db5205d0>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Plotting all the sample images\n",
        "fx, plots = plt.subplots(1, 4, figsize=(20,10))\n",
        "\n",
        "plots[0].set_title(\"Airplane\")\n",
        "plots[0].imshow(image)\n",
        "\n",
        "plots[1].set_title(\"Car\")\n",
        "plots[1].imshow(image1)\n",
        "\n",
        "plots[2].set_title(\"Person\")\n",
        "plots[2].imshow(image2)\n",
        "\n",
        "plots[3].set_title(\"Fruit\")\n",
        "plots[3].imshow(image3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s_Ld1fa4xy8E"
      },
      "source": [
        "We see that all the images are in different shapes on the plot scale. Printing their shapes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y4Jz-XvEx-MS",
        "outputId": "e15a1bd0-2cdc-4ec5-933e-cdfbde7c5d58"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of Image 1 (104, 300, 3)\n",
            "Shape of Image 2 (100, 100, 3)\n",
            "Shape of Image 3 (256, 256, 3)\n",
            "Shape of Image 4 (100, 100, 3)\n"
          ]
        }
      ],
      "source": [
        "print(\"Shape of Image 1\", image.shape)\n",
        "print(\"Shape of Image 2\", image1.shape)\n",
        "print(\"Shape of Image 3\", image2.shape)\n",
        "print(\"Shape of Image 4\", image3.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2Ysx0K1yNXZ"
      },
      "source": [
        "We need to resize the image to get the same number of pixels to be used as input neuron values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hjTSdGV20-oR",
        "outputId": "7b6473ab-653f-4ffb-d889-ab008676e789"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of predictor variable w/o normalization:  (6899, 128, 128, 3)\n",
            "Shape of predictor variable with normalization:  (6899, 128, 128, 3)\n",
            "Shape of target variable:  (6899, 8)\n"
          ]
        }
      ],
      "source": [
        "#Instead of using PANDAS dataframe, we will be converting x and y into numpy array\n",
        "x=np.array(x_data)\n",
        "x1=np.array(x1_data)\n",
        "y=np.array(y_data)\n",
        "#hot encoding the labels or y array\n",
        "y = to_categorical(y_data)\n",
        "#printing shape of the data (x:predictor variable, y:labels)\n",
        "print(\"Shape of predictor variable w/o normalization: \",x.shape)\n",
        "print(\"Shape of predictor variable with normalization: \",x1.shape)\n",
        "print(\"Shape of target variable: \",y.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-u1yFh9L1HoY"
      },
      "source": [
        "So, we have 6899 images with 128x128 dimension and 8 labels or target classes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zk_URBQg1Ixa"
      },
      "outputs": [],
      "source": [
        "#Shuffling our training dataset to avoid risk of any previous order\n",
        "r = np.arange(x.shape[0])\n",
        "np.random.seed(42)\n",
        "np.random.shuffle(r)\n",
        "x = x[r]\n",
        "y = y[r]\n",
        "x1 = x1[r] #the normalizaton case"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m2d_-c701LfL"
      },
      "outputs": [],
      "source": [
        "#splitting the dataset into training and testing dataset\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size = 0.2)\n",
        "#splitting the dataset into training and testing dataset after normalization\n",
        "X1_train, X1_test, Y1_train, Y1_test = train_test_split(x1, y, test_size = 0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_rJtoVcE1O1L",
        "outputId": "c71d73c5-3c13-4e6c-fed3-29531d53df94"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of training predictor variable:  (5519, 128, 128, 3)\n",
            "Shape of training target variable:  (5519, 8)\n",
            "Shape of testing predictor variable:  (1380, 128, 128, 3)\n",
            "Shape of testing target variable:  (1380, 8)\n"
          ]
        }
      ],
      "source": [
        "#printing the testing and training datasets shape without input normalization\n",
        "print(\"Shape of training predictor variable: \",X_train.shape)\n",
        "print(\"Shape of training target variable: \",Y_train.shape)\n",
        "print(\"Shape of testing predictor variable: \",X_test.shape)\n",
        "print(\"Shape of testing target variable: \",Y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LbP3SrQR1V6K",
        "outputId": "61e6033c-69ab-44fc-d99d-359c6cfa51d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of training predictor variable:  (5519, 128, 128, 3)\n",
            "Shape of training target variable:  (5519, 8)\n",
            "Shape of testing predictor variable:  (1380, 128, 128, 3)\n",
            "Shape of testing target variable:  (1380, 8)\n"
          ]
        }
      ],
      "source": [
        "#For the model where the input is normalized\n",
        "print(\"Shape of training predictor variable: \",X1_train.shape)\n",
        "print(\"Shape of training target variable: \",Y1_train.shape)\n",
        "print(\"Shape of testing predictor variable: \",X1_test.shape)\n",
        "print(\"Shape of testing target variable: \",Y1_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TPCBpGXZ1ZUI",
        "outputId": "df2a2159-87de-4155-86b0-df78c9109088"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten_1 (Flatten)         (None, 49152)             0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 300)               14745900  \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 100)               30100     \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 8)                 808       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14,776,808\n",
            "Trainable params: 14,776,808\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#structuring the MLP model or ANN model\n",
        "model=keras.models.Sequential()\n",
        "model.add(keras.layers.Flatten(input_shape=(128,128,3)))\n",
        "model.add(keras.layers.Dense(300,activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(100,activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(8,activation=\"softmax\"))\n",
        "#seeing the model\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cLqvk8f92ezg",
        "outputId": "9fe7a210-2f64-4f76-fe41-536b430a95a3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-7.0380592e-03, -1.7624293e-03,  3.8225828e-03, ...,\n",
              "        -8.4185107e-03, -9.5208613e-03,  9.7190589e-04],\n",
              "       [ 2.0932248e-03, -5.0357971e-03, -3.6668964e-05, ...,\n",
              "         2.4414239e-03, -9.8368870e-03,  3.9524995e-03],\n",
              "       [-1.0575589e-02, -2.8969841e-03, -2.6366580e-04, ...,\n",
              "         7.2045382e-03,  2.6247995e-03, -8.6962152e-04],\n",
              "       ...,\n",
              "       [-5.3511928e-03, -5.2007549e-03,  9.5349327e-03, ...,\n",
              "         5.6995396e-03,  8.6410865e-03,  3.9839773e-03],\n",
              "       [-5.1719011e-03, -2.7386416e-03, -1.0388479e-02, ...,\n",
              "        -7.8794938e-03,  3.4711557e-03,  1.0761222e-02],\n",
              "       [-2.6309183e-03, -9.2651825e-03, -5.0547812e-04, ...,\n",
              "         4.0625175e-03,  5.6643598e-03,  6.6047236e-03]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "#setting weights and biases for 1st hidden layer in the model\n",
        "weights,biases=model.layers[1].get_weights()\n",
        "weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hqWMpRec3OOH",
        "outputId": "aad05d34-983f-49a2-f12f-77bdb39b8c9a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of Weight (49152, 300)\n",
            "Shape of Bias (300,)\n"
          ]
        }
      ],
      "source": [
        "#printing shape of random weights and bias=0 initially\n",
        "print(\"Shape of Weight\",weights.shape)\n",
        "print(\"Shape of Bias\",biases.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "zXJr0bT_3pjf",
        "outputId": "a5f83b71-d361-43de-c266-28533923fc18"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "#plot the ANN model\n",
        "plot_model(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bXb4dK-E8DdJ"
      },
      "source": [
        "### BEFORE NORMALIZATION"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aSKTy_0Y72lW",
        "outputId": "51fca679-2660-4ccd-bb57-fa9c14138fb4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "138/138 [==============================] - 18s 126ms/step - loss: 940.5753 - accuracy: 0.4668 - val_loss: 278.8138 - val_accuracy: 0.6359\n",
            "Epoch 2/30\n",
            "138/138 [==============================] - 17s 125ms/step - loss: 352.9565 - accuracy: 0.5814 - val_loss: 307.9713 - val_accuracy: 0.6042\n",
            "Epoch 3/30\n",
            "138/138 [==============================] - 17s 124ms/step - loss: 202.1406 - accuracy: 0.6290 - val_loss: 190.1347 - val_accuracy: 0.6386\n",
            "Epoch 4/30\n",
            "138/138 [==============================] - 17s 124ms/step - loss: 111.3469 - accuracy: 0.6840 - val_loss: 182.5141 - val_accuracy: 0.5797\n",
            "Epoch 5/30\n",
            "138/138 [==============================] - 17s 124ms/step - loss: 93.8600 - accuracy: 0.6843 - val_loss: 117.8017 - val_accuracy: 0.6005\n",
            "Epoch 6/30\n",
            "138/138 [==============================] - 17s 124ms/step - loss: 49.6003 - accuracy: 0.6958 - val_loss: 89.1464 - val_accuracy: 0.5697\n",
            "Epoch 7/30\n",
            "138/138 [==============================] - 17s 125ms/step - loss: 37.5630 - accuracy: 0.6648 - val_loss: 53.4611 - val_accuracy: 0.5788\n",
            "Epoch 8/30\n",
            "138/138 [==============================] - 17s 127ms/step - loss: 21.6010 - accuracy: 0.6845 - val_loss: 25.1438 - val_accuracy: 0.5951\n",
            "Epoch 9/30\n",
            "138/138 [==============================] - 17s 126ms/step - loss: 15.2379 - accuracy: 0.6571 - val_loss: 14.7063 - val_accuracy: 0.6178\n",
            "Epoch 10/30\n",
            "138/138 [==============================] - 17s 126ms/step - loss: 7.0697 - accuracy: 0.6206 - val_loss: 7.3975 - val_accuracy: 0.5661\n",
            "Epoch 11/30\n",
            "138/138 [==============================] - 17s 125ms/step - loss: 4.9367 - accuracy: 0.6301 - val_loss: 7.3834 - val_accuracy: 0.6069\n",
            "Epoch 12/30\n",
            "138/138 [==============================] - 17s 125ms/step - loss: 2.4522 - accuracy: 0.6546 - val_loss: 6.5507 - val_accuracy: 0.5199\n",
            "Epoch 13/30\n",
            "138/138 [==============================] - 17s 126ms/step - loss: 2.9149 - accuracy: 0.6217 - val_loss: 9.4134 - val_accuracy: 0.5172\n",
            "Epoch 14/30\n",
            "138/138 [==============================] - 17s 127ms/step - loss: 6.2303 - accuracy: 0.5792 - val_loss: 7.2631 - val_accuracy: 0.5199\n",
            "Epoch 15/30\n",
            "138/138 [==============================] - 17s 125ms/step - loss: 2.7593 - accuracy: 0.5083 - val_loss: 2.6903 - val_accuracy: 0.4511\n",
            "Epoch 16/30\n",
            "138/138 [==============================] - 17s 124ms/step - loss: 1.3882 - accuracy: 0.5010 - val_loss: 2.3321 - val_accuracy: 0.4710\n",
            "Epoch 17/30\n",
            "138/138 [==============================] - 17s 125ms/step - loss: 1.3611 - accuracy: 0.4732 - val_loss: 2.7828 - val_accuracy: 0.4674\n",
            "Epoch 18/30\n",
            "138/138 [==============================] - 18s 128ms/step - loss: 1.4121 - accuracy: 0.4829 - val_loss: 2.2245 - val_accuracy: 0.4937\n",
            "Epoch 19/30\n",
            "138/138 [==============================] - 18s 129ms/step - loss: 1.5858 - accuracy: 0.4109 - val_loss: 2.4493 - val_accuracy: 0.2953\n",
            "Epoch 20/30\n",
            "138/138 [==============================] - 17s 125ms/step - loss: 1.6284 - accuracy: 0.3601 - val_loss: 2.3035 - val_accuracy: 0.3469\n",
            "Epoch 21/30\n",
            "138/138 [==============================] - 18s 127ms/step - loss: 1.5295 - accuracy: 0.3719 - val_loss: 2.5975 - val_accuracy: 0.3261\n",
            "Epoch 22/30\n",
            "138/138 [==============================] - 18s 128ms/step - loss: 1.5793 - accuracy: 0.3672 - val_loss: 2.3935 - val_accuracy: 0.4303\n",
            "Epoch 23/30\n",
            "138/138 [==============================] - 18s 128ms/step - loss: 1.5856 - accuracy: 0.4014 - val_loss: 3.4251 - val_accuracy: 0.4565\n",
            "Epoch 24/30\n",
            "138/138 [==============================] - 17s 126ms/step - loss: 1.5971 - accuracy: 0.3875 - val_loss: 2.0942 - val_accuracy: 0.3741\n",
            "Epoch 25/30\n",
            "138/138 [==============================] - 18s 129ms/step - loss: 1.5865 - accuracy: 0.4000 - val_loss: 2.2345 - val_accuracy: 0.4348\n",
            "Epoch 26/30\n",
            "138/138 [==============================] - 18s 128ms/step - loss: 1.7595 - accuracy: 0.3812 - val_loss: 2.3590 - val_accuracy: 0.3288\n",
            "Epoch 27/30\n",
            "138/138 [==============================] - 17s 126ms/step - loss: 1.7744 - accuracy: 0.3427 - val_loss: 2.7197 - val_accuracy: 0.3279\n",
            "Epoch 28/30\n",
            "138/138 [==============================] - 17s 123ms/step - loss: 1.9050 - accuracy: 0.2657 - val_loss: 2.1506 - val_accuracy: 0.2600\n",
            "Epoch 29/30\n",
            "138/138 [==============================] - 17s 124ms/step - loss: 1.8849 - accuracy: 0.2453 - val_loss: 2.1474 - val_accuracy: 0.3596\n",
            "Epoch 30/30\n",
            "138/138 [==============================] - 17s 126ms/step - loss: 1.6737 - accuracy: 0.3590 - val_loss: 2.3685 - val_accuracy: 0.3025\n"
          ]
        }
      ],
      "source": [
        "#let's compile the model using sgd optimizer\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "#fitting the model in 30 epochs with validation split at 0.2 before normalization\n",
        "history = model.fit(X_train, Y_train, epochs=30, validation_split=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "CYjCBHcA8ban",
        "outputId": "607364c2-289b-4e58-e0ba-e3dcb965d75d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#ANN model before normalization\n",
        "plt.figure(figsize = (10,10))\n",
        "plt.plot(history.history['accuracy'], label='training accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='val accuracy')\n",
        "plt.plot(history.history['loss'], label='training loss')\n",
        "plt.plot(history.history['val_loss'], label='val loss')\n",
        "plt.title('Loss/accuracy in ANN model before normalization')\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P3JJOLIo9DzC",
        "outputId": "2d492813-fe08-436e-9dfc-11ba6877e6a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "44/44 [==============================] - 2s 35ms/step - loss: 1.9672 - accuracy: 0.2703\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[1.967193841934204, 0.2702898681163788]"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Evaluation of model\n",
        "model.evaluate(X_test, Y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LL0598ax9DzF"
      },
      "outputs": [],
      "source": [
        "predict_x=model.predict(X_test) \n",
        "classes_x=np.argmax(predict_x,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JgWFjCHo9DzF"
      },
      "outputs": [],
      "source": [
        "#converting over Y test to actual labels.\n",
        "Y_test = np.argmax(Y_test, axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WBziTVNb9DzG",
        "outputId": "ba76efaa-23fa-4254-8ef5-cfaace97e7d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.16      1.00      0.28       196\n",
            "           1       1.00      0.03      0.05       186\n",
            "           2       1.00      0.01      0.01       178\n",
            "           3       1.00      0.05      0.10       148\n",
            "           4       0.00      0.00      0.00       146\n",
            "           5       0.92      0.26      0.41       179\n",
            "           6       0.97      0.59      0.74       196\n",
            "           7       0.00      0.00      0.00       151\n",
            "\n",
            "    accuracy                           0.27      1380\n",
            "   macro avg       0.63      0.24      0.20      1380\n",
            "weighted avg       0.65      0.27      0.22      1380\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(Y_test, classes_x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O_zUx9cs9DzH",
        "outputId": "0c38110e-a5fc-492a-debf-844d61f8e85f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[[ 185,  999],\n",
              "        [   0,  196]],\n",
              "\n",
              "       [[1194,    0],\n",
              "        [ 181,    5]],\n",
              "\n",
              "       [[1202,    0],\n",
              "        [ 177,    1]],\n",
              "\n",
              "       [[1232,    0],\n",
              "        [ 140,    8]],\n",
              "\n",
              "       [[1234,    0],\n",
              "        [ 146,    0]],\n",
              "\n",
              "       [[1197,    4],\n",
              "        [ 132,   47]],\n",
              "\n",
              "       [[1181,    3],\n",
              "        [  80,  116]],\n",
              "\n",
              "       [[1228,    1],\n",
              "        [ 151,    0]]])"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import multilabel_confusion_matrix\n",
        "multilabel_confusion_matrix(Y_test, classes_x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AE0V-B5X9U3k"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mf3bIEGF8K4G"
      },
      "source": [
        "### AFTER NORMALIZATION"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T7C08rQQ3vd3",
        "outputId": "540c1a55-e757-457c-ee09-91699f84588f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "138/138 [==============================] - 13s 90ms/step - loss: 4.5018 - accuracy: 0.4834 - val_loss: 1.6889 - val_accuracy: 0.6295\n",
            "Epoch 2/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 1.2132 - accuracy: 0.6682 - val_loss: 0.9476 - val_accuracy: 0.7002\n",
            "Epoch 3/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 1.1209 - accuracy: 0.6849 - val_loss: 1.2545 - val_accuracy: 0.6232\n",
            "Epoch 4/30\n",
            "138/138 [==============================] - 12s 87ms/step - loss: 0.7888 - accuracy: 0.7400 - val_loss: 1.3197 - val_accuracy: 0.6594\n",
            "Epoch 5/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.7739 - accuracy: 0.7452 - val_loss: 1.1789 - val_accuracy: 0.6784\n",
            "Epoch 6/30\n",
            "138/138 [==============================] - 12s 87ms/step - loss: 0.6700 - accuracy: 0.7780 - val_loss: 0.8116 - val_accuracy: 0.7237\n",
            "Epoch 7/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.6567 - accuracy: 0.7715 - val_loss: 1.1325 - val_accuracy: 0.6576\n",
            "Epoch 8/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.5741 - accuracy: 0.7982 - val_loss: 0.9229 - val_accuracy: 0.7183\n",
            "Epoch 9/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.4794 - accuracy: 0.8224 - val_loss: 0.8231 - val_accuracy: 0.7409\n",
            "Epoch 10/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.4869 - accuracy: 0.8199 - val_loss: 0.7916 - val_accuracy: 0.7319\n",
            "Epoch 11/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.4882 - accuracy: 0.8290 - val_loss: 1.0682 - val_accuracy: 0.6884\n",
            "Epoch 12/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.4043 - accuracy: 0.8537 - val_loss: 0.6580 - val_accuracy: 0.7844\n",
            "Epoch 13/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.3691 - accuracy: 0.8614 - val_loss: 0.8391 - val_accuracy: 0.7437\n",
            "Epoch 14/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.3848 - accuracy: 0.8571 - val_loss: 0.6965 - val_accuracy: 0.7844\n",
            "Epoch 15/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.3977 - accuracy: 0.8571 - val_loss: 0.6973 - val_accuracy: 0.7772\n",
            "Epoch 16/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.3194 - accuracy: 0.8763 - val_loss: 1.3794 - val_accuracy: 0.6712\n",
            "Epoch 17/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.4678 - accuracy: 0.8347 - val_loss: 0.7159 - val_accuracy: 0.7772\n",
            "Epoch 18/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.3779 - accuracy: 0.8582 - val_loss: 0.9769 - val_accuracy: 0.7409\n",
            "Epoch 19/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.3112 - accuracy: 0.8790 - val_loss: 0.8150 - val_accuracy: 0.7600\n",
            "Epoch 20/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.2552 - accuracy: 0.8994 - val_loss: 0.7554 - val_accuracy: 0.7736\n",
            "Epoch 21/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.2432 - accuracy: 0.9060 - val_loss: 0.7181 - val_accuracy: 0.7980\n",
            "Epoch 22/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.2543 - accuracy: 0.8992 - val_loss: 1.0123 - val_accuracy: 0.7400\n",
            "Epoch 23/30\n",
            "138/138 [==============================] - 12s 87ms/step - loss: 0.3271 - accuracy: 0.8815 - val_loss: 0.8388 - val_accuracy: 0.7609\n",
            "Epoch 24/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.2770 - accuracy: 0.8951 - val_loss: 0.7406 - val_accuracy: 0.7835\n",
            "Epoch 25/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.2848 - accuracy: 0.8972 - val_loss: 0.9498 - val_accuracy: 0.7446\n",
            "Epoch 26/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.2193 - accuracy: 0.9157 - val_loss: 1.0494 - val_accuracy: 0.7518\n",
            "Epoch 27/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.2439 - accuracy: 0.9058 - val_loss: 1.0105 - val_accuracy: 0.7527\n",
            "Epoch 28/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.3029 - accuracy: 0.8922 - val_loss: 1.3130 - val_accuracy: 0.6793\n",
            "Epoch 29/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.3136 - accuracy: 0.8920 - val_loss: 0.8614 - val_accuracy: 0.7781\n",
            "Epoch 30/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 0.2697 - accuracy: 0.9006 - val_loss: 0.9128 - val_accuracy: 0.7726\n"
          ]
        }
      ],
      "source": [
        "#let's compile the model using sgd optimizer\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "#fitting the model in 30 epochs with validation split at 0.2 after normalization\n",
        "history = model.fit(X1_train, Y1_train, epochs=30, validation_split=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "XGnAdj7R4P5v",
        "outputId": "b2ebaf56-d74c-4e9a-a32a-d354fd86601c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#ANN model after normalization\n",
        "plt.figure(figsize = (10,10))\n",
        "plt.plot(history.history['accuracy'], label='training accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='val accuracy')\n",
        "plt.plot(history.history['loss'], label='training loss')\n",
        "plt.plot(history.history['val_loss'], label='val loss')\n",
        "plt.title('Loss/accuracy in ANN model after normalization')\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z0f6OI174bNB",
        "outputId": "cfa156c2-663e-4521-8cf3-a718301f892f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "44/44 [==============================] - 1s 23ms/step - loss: 0.9585 - accuracy: 0.7457\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9585289359092712, 0.7456521987915039]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "#Evaluation of model\n",
        "model.evaluate(X1_test, Y1_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xDNCgXcf4dpw"
      },
      "outputs": [],
      "source": [
        "predict_x=model.predict(X1_test) \n",
        "classes_x=np.argmax(predict_x,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ICPmzoPLUQNc",
        "outputId": "6b9feb99-fe8f-41a3-a9f2-d73b7e4dfd46"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 6, 0, ..., 2, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "classes_x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ekXmlXRA6m7d"
      },
      "outputs": [],
      "source": [
        "#converting over Y test to actual labels.\n",
        "Y1_test = np.argmax(Y1_test, axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ylGIAQe4fdW",
        "outputId": "d6d56fa0-5eac-487a-ea09-ab9277566773"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.97      0.93       206\n",
            "           1       0.48      0.85      0.62       163\n",
            "           2       0.49      0.57      0.52       175\n",
            "           3       0.98      0.77      0.86       143\n",
            "           4       0.45      0.10      0.17       138\n",
            "           5       0.90      0.72      0.80       196\n",
            "           6       0.88      0.99      0.93       207\n",
            "           7       0.90      0.81      0.85       152\n",
            "\n",
            "    accuracy                           0.75      1380\n",
            "   macro avg       0.75      0.72      0.71      1380\n",
            "weighted avg       0.76      0.75      0.73      1380\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(Y1_test, classes_x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OT1_UuMq4f-4",
        "outputId": "8ebb0c39-72b2-4239-b731-f0c3be2c7c59"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[1151,   23],\n",
              "        [   7,  199]],\n",
              "\n",
              "       [[1070,  147],\n",
              "        [  25,  138]],\n",
              "\n",
              "       [[1101,  104],\n",
              "        [  76,   99]],\n",
              "\n",
              "       [[1235,    2],\n",
              "        [  33,  110]],\n",
              "\n",
              "       [[1225,   17],\n",
              "        [ 124,   14]],\n",
              "\n",
              "       [[1169,   15],\n",
              "        [  55,  141]],\n",
              "\n",
              "       [[1144,   29],\n",
              "        [   2,  205]],\n",
              "\n",
              "       [[1214,   14],\n",
              "        [  29,  123]]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "from sklearn.metrics import multilabel_confusion_matrix\n",
        "multilabel_confusion_matrix(Y1_test, classes_x)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Done for final test"
      ],
      "metadata": {
        "id": "TGvLuD8SPoho"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#trying RMSprop and Adam optimizers with different learning rates\n",
        "from tensorflow.keras.optimizers import Adam,RMSprop\n",
        "opt_a1=Adam(lr=0.01)\n",
        "opt_a2=RMSprop(lr=0.01)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cuQeegCi6Bqr",
        "outputId": "ca09f541-4fef-4cd6-b23a-f43ef5ccb06d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n",
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/rmsprop.py:130: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(RMSprop, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### TRYING OPTIMIZER ADAM WITH LEARNING RATE=0.01\n"
      ],
      "metadata": {
        "id": "I1Kn5vAx6CgB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd6473c4-2ecf-4291-c99c-4e8cc78cca5d",
        "id": "KnCopLIB6mhA"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "138/138 [==============================] - 13s 91ms/step - loss: 28.6827 - accuracy: 0.1411 - val_loss: 2.0713 - val_accuracy: 0.1504\n",
            "Epoch 2/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0726 - accuracy: 0.1436 - val_loss: 2.0696 - val_accuracy: 0.1504\n",
            "Epoch 3/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.3545 - accuracy: 0.1481 - val_loss: 2.0704 - val_accuracy: 0.1377\n",
            "Epoch 4/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0717 - accuracy: 0.1386 - val_loss: 2.0690 - val_accuracy: 0.1377\n",
            "Epoch 5/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0717 - accuracy: 0.1438 - val_loss: 2.0691 - val_accuracy: 0.1504\n",
            "Epoch 6/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0716 - accuracy: 0.1434 - val_loss: 2.0689 - val_accuracy: 0.1504\n",
            "Epoch 7/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0718 - accuracy: 0.1445 - val_loss: 2.0691 - val_accuracy: 0.1504\n",
            "Epoch 8/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0716 - accuracy: 0.1429 - val_loss: 2.0688 - val_accuracy: 0.1504\n",
            "Epoch 9/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 2.0723 - accuracy: 0.1475 - val_loss: 2.0682 - val_accuracy: 0.1504\n",
            "Epoch 10/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0720 - accuracy: 0.1468 - val_loss: 2.0687 - val_accuracy: 0.1476\n",
            "Epoch 11/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 2.0723 - accuracy: 0.1382 - val_loss: 2.0687 - val_accuracy: 0.1476\n",
            "Epoch 12/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 2.0722 - accuracy: 0.1400 - val_loss: 2.0695 - val_accuracy: 0.1504\n",
            "Epoch 13/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 2.0726 - accuracy: 0.1456 - val_loss: 2.0689 - val_accuracy: 0.1504\n",
            "Epoch 14/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0721 - accuracy: 0.1450 - val_loss: 2.0701 - val_accuracy: 0.1504\n",
            "Epoch 15/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0720 - accuracy: 0.1479 - val_loss: 2.0690 - val_accuracy: 0.1504\n",
            "Epoch 16/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0719 - accuracy: 0.1438 - val_loss: 2.0689 - val_accuracy: 0.1504\n",
            "Epoch 17/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0722 - accuracy: 0.1438 - val_loss: 2.0694 - val_accuracy: 0.1504\n",
            "Epoch 18/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0724 - accuracy: 0.1438 - val_loss: 2.0693 - val_accuracy: 0.1504\n",
            "Epoch 19/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 2.0723 - accuracy: 0.1461 - val_loss: 2.0688 - val_accuracy: 0.1504\n",
            "Epoch 20/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 2.0724 - accuracy: 0.1413 - val_loss: 2.0697 - val_accuracy: 0.1377\n",
            "Epoch 21/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 2.0720 - accuracy: 0.1409 - val_loss: 2.0693 - val_accuracy: 0.1504\n",
            "Epoch 22/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0724 - accuracy: 0.1429 - val_loss: 2.0689 - val_accuracy: 0.1504\n",
            "Epoch 23/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0723 - accuracy: 0.1445 - val_loss: 2.0693 - val_accuracy: 0.1504\n",
            "Epoch 24/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0723 - accuracy: 0.1450 - val_loss: 2.0694 - val_accuracy: 0.1504\n",
            "Epoch 25/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0722 - accuracy: 0.1443 - val_loss: 2.0686 - val_accuracy: 0.1504\n",
            "Epoch 26/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0721 - accuracy: 0.1470 - val_loss: 2.0691 - val_accuracy: 0.1504\n",
            "Epoch 27/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0719 - accuracy: 0.1445 - val_loss: 2.0699 - val_accuracy: 0.1504\n",
            "Epoch 28/30\n",
            "138/138 [==============================] - 12s 88ms/step - loss: 2.0723 - accuracy: 0.1434 - val_loss: 2.0694 - val_accuracy: 0.1476\n",
            "Epoch 29/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0723 - accuracy: 0.1361 - val_loss: 2.0694 - val_accuracy: 0.1476\n",
            "Epoch 30/30\n",
            "138/138 [==============================] - 12s 89ms/step - loss: 2.0720 - accuracy: 0.1425 - val_loss: 2.0690 - val_accuracy: 0.1504\n"
          ]
        }
      ],
      "source": [
        "#let's compile the model using sgd optimizer\n",
        "model.compile(loss='categorical_crossentropy', optimizer=opt_a1, metrics=['accuracy'])\n",
        "#fitting the model in 30 epochs with validation split at 0.2 after normalization\n",
        "history = model.fit(X1_train, Y1_train, epochs=30, validation_split=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "8b820270-c30d-4b85-e202-7adc646c95f0",
        "id": "bCz0quTn6mhD"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#ANN model with Adam optimizer or default 0.01 learning rate\n",
        "plt.figure(figsize = (10,10))\n",
        "plt.plot(history.history['accuracy'], label='training accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='val accuracy')\n",
        "plt.plot(history.history['loss'], label='training loss')\n",
        "plt.plot(history.history['val_loss'], label='val loss')\n",
        "plt.title('Loss/accuracy in ANN model after normalization')\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80b5ee72-cd55-4f78-dcbf-85487f669430",
        "id": "dwqs3Nf46mhF"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "44/44 [==============================] - 1s 23ms/step - loss: 2.0768 - accuracy: 0.1326\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.0767858028411865, 0.13260869681835175]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "#Evaluation of model\n",
        "model.evaluate(X1_test, Y1_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HuaiexF-6mhH"
      },
      "outputs": [],
      "source": [
        "predict_x=model.predict(X1_test) \n",
        "classes_x=np.argmax(predict_x,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7qmiKM9K6mhI"
      },
      "outputs": [],
      "source": [
        "#converting over Y test to actual labels.\n",
        "Y1_test = np.argmax(Y1_test, axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd28568d-47a5-488c-8e59-b0421a0027a8",
        "id": "onZPd0_56mhJ"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00       205\n",
            "           1       0.00      0.00      0.00       165\n",
            "           2       0.00      0.00      0.00       187\n",
            "           3       0.00      0.00      0.00       134\n",
            "           4       0.00      0.00      0.00       158\n",
            "           5       0.00      0.00      0.00       190\n",
            "           6       0.13      1.00      0.23       183\n",
            "           7       0.00      0.00      0.00       158\n",
            "\n",
            "    accuracy                           0.13      1380\n",
            "   macro avg       0.02      0.12      0.03      1380\n",
            "weighted avg       0.02      0.13      0.03      1380\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(Y1_test, classes_x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "912fa4e6-602d-46ed-d29b-842b45c66233",
        "id": "RIMxac-f6mhL"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[1175,    0],\n",
              "        [ 205,    0]],\n",
              "\n",
              "       [[1215,    0],\n",
              "        [ 165,    0]],\n",
              "\n",
              "       [[1193,    0],\n",
              "        [ 187,    0]],\n",
              "\n",
              "       [[1246,    0],\n",
              "        [ 134,    0]],\n",
              "\n",
              "       [[1222,    0],\n",
              "        [ 158,    0]],\n",
              "\n",
              "       [[1190,    0],\n",
              "        [ 190,    0]],\n",
              "\n",
              "       [[   0, 1197],\n",
              "        [   0,  183]],\n",
              "\n",
              "       [[1222,    0],\n",
              "        [ 158,    0]]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "from sklearn.metrics import multilabel_confusion_matrix\n",
        "multilabel_confusion_matrix(Y1_test, classes_x)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Done for final test"
      ],
      "metadata": {
        "id": "WRDichb8S0qm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### TRYING OPTIMIZER RMSProp WITH LEARNING RATE=0.01 and batch size=256"
      ],
      "metadata": {
        "id": "egNRFuFw6MD5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "56fc027d-6895-4dd8-d255-07fc71e57f36",
        "id": "VVoFBbPp60ox"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "18/18 [==============================] - 10s 390ms/step - loss: 688.1940 - accuracy: 0.1866 - val_loss: 23.9815 - val_accuracy: 0.1839\n",
            "Epoch 2/30\n",
            "18/18 [==============================] - 7s 368ms/step - loss: 8.5138 - accuracy: 0.2870 - val_loss: 2.3981 - val_accuracy: 0.2871\n",
            "Epoch 3/30\n",
            "18/18 [==============================] - 7s 369ms/step - loss: 1.7583 - accuracy: 0.3708 - val_loss: 2.0176 - val_accuracy: 0.2400\n",
            "Epoch 4/30\n",
            "18/18 [==============================] - 7s 368ms/step - loss: 1.7981 - accuracy: 0.3126 - val_loss: 13.3996 - val_accuracy: 0.1295\n",
            "Epoch 5/30\n",
            "18/18 [==============================] - 7s 369ms/step - loss: 14.6854 - accuracy: 0.2100 - val_loss: 1.8993 - val_accuracy: 0.2274\n",
            "Epoch 6/30\n",
            "18/18 [==============================] - 7s 369ms/step - loss: 6.1410 - accuracy: 0.2487 - val_loss: 2.3006 - val_accuracy: 0.1268\n",
            "Epoch 7/30\n",
            "18/18 [==============================] - 7s 370ms/step - loss: 1.9257 - accuracy: 0.2301 - val_loss: 2.4034 - val_accuracy: 0.2364\n",
            "Epoch 8/30\n",
            "18/18 [==============================] - 7s 369ms/step - loss: 1.9401 - accuracy: 0.2197 - val_loss: 1.9067 - val_accuracy: 0.2319\n",
            "Epoch 9/30\n",
            "18/18 [==============================] - 7s 370ms/step - loss: 2.0899 - accuracy: 0.1663 - val_loss: 1.9247 - val_accuracy: 0.2283\n",
            "Epoch 10/30\n",
            "18/18 [==============================] - 7s 367ms/step - loss: 2.0102 - accuracy: 0.1841 - val_loss: 2.0397 - val_accuracy: 0.1739\n",
            "Epoch 11/30\n",
            "18/18 [==============================] - 7s 370ms/step - loss: 2.0089 - accuracy: 0.1925 - val_loss: 2.0462 - val_accuracy: 0.1730\n",
            "Epoch 12/30\n",
            "18/18 [==============================] - 7s 369ms/step - loss: 2.0988 - accuracy: 0.1624 - val_loss: 2.0719 - val_accuracy: 0.1576\n",
            "Epoch 13/30\n",
            "18/18 [==============================] - 7s 370ms/step - loss: 2.0642 - accuracy: 0.1407 - val_loss: 2.0664 - val_accuracy: 0.1341\n",
            "Epoch 14/30\n",
            "18/18 [==============================] - 7s 369ms/step - loss: 2.3057 - accuracy: 0.1515 - val_loss: 2.0431 - val_accuracy: 0.1721\n",
            "Epoch 15/30\n",
            "18/18 [==============================] - 7s 369ms/step - loss: 2.0051 - accuracy: 0.1703 - val_loss: 2.0869 - val_accuracy: 0.2373\n",
            "Epoch 16/30\n",
            "18/18 [==============================] - 7s 369ms/step - loss: 1.9220 - accuracy: 0.2260 - val_loss: 1.9414 - val_accuracy: 0.2074\n",
            "Epoch 17/30\n",
            "18/18 [==============================] - 7s 369ms/step - loss: 1.9937 - accuracy: 0.1928 - val_loss: 2.0210 - val_accuracy: 0.2364\n",
            "Epoch 18/30\n",
            "18/18 [==============================] - 7s 368ms/step - loss: 1.9688 - accuracy: 0.2050 - val_loss: 1.9316 - val_accuracy: 0.2292\n",
            "Epoch 19/30\n",
            "18/18 [==============================] - 7s 369ms/step - loss: 1.9788 - accuracy: 0.1952 - val_loss: 2.2564 - val_accuracy: 0.1549\n",
            "Epoch 20/30\n",
            "18/18 [==============================] - 7s 368ms/step - loss: 2.0960 - accuracy: 0.1966 - val_loss: 2.0849 - val_accuracy: 0.1567\n",
            "Epoch 21/30\n",
            "18/18 [==============================] - 7s 369ms/step - loss: 2.0365 - accuracy: 0.1579 - val_loss: 2.0088 - val_accuracy: 0.1676\n",
            "Epoch 22/30\n",
            "18/18 [==============================] - 7s 368ms/step - loss: 1.9162 - accuracy: 0.2245 - val_loss: 1.9220 - val_accuracy: 0.2346\n",
            "Epoch 23/30\n",
            "18/18 [==============================] - 7s 368ms/step - loss: 2.0168 - accuracy: 0.2095 - val_loss: 2.0162 - val_accuracy: 0.1875\n",
            "Epoch 24/30\n",
            "18/18 [==============================] - 7s 368ms/step - loss: 1.8905 - accuracy: 0.2202 - val_loss: 2.1145 - val_accuracy: 0.1322\n",
            "Epoch 25/30\n",
            "18/18 [==============================] - 7s 371ms/step - loss: 1.8838 - accuracy: 0.2362 - val_loss: 1.8892 - val_accuracy: 0.2192\n",
            "Epoch 26/30\n",
            "18/18 [==============================] - 7s 369ms/step - loss: 1.8297 - accuracy: 0.2471 - val_loss: 1.8573 - val_accuracy: 0.2527\n",
            "Epoch 27/30\n",
            "18/18 [==============================] - 7s 370ms/step - loss: 1.9479 - accuracy: 0.2129 - val_loss: 1.8703 - val_accuracy: 0.2572\n",
            "Epoch 28/30\n",
            "18/18 [==============================] - 7s 370ms/step - loss: 1.9387 - accuracy: 0.2322 - val_loss: 1.9623 - val_accuracy: 0.1947\n",
            "Epoch 29/30\n",
            "18/18 [==============================] - 7s 371ms/step - loss: 1.8328 - accuracy: 0.2451 - val_loss: 1.8271 - val_accuracy: 0.2409\n",
            "Epoch 30/30\n",
            "18/18 [==============================] - 7s 369ms/step - loss: 1.9805 - accuracy: 0.2333 - val_loss: 1.9131 - val_accuracy: 0.2101\n"
          ]
        }
      ],
      "source": [
        "#let's compile the model using sgd optimizer\n",
        "model.compile(loss='categorical_crossentropy', optimizer=opt_a2, metrics=['accuracy'])\n",
        "#fitting the model in 30 epochs with validation split at 0.2 after normalization\n",
        "history = model.fit(X1_train, Y1_train, batch_size=256, epochs=30, validation_split=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "39972651-8cd7-45be-941b-422308221da2",
        "id": "5gxp5GvL60o0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#ANN model with SGD optimizer or default 0.01 learning rate\n",
        "plt.figure(figsize = (10,10))\n",
        "plt.plot(history.history['accuracy'], label='training accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='val accuracy')\n",
        "plt.plot(history.history['loss'], label='training loss')\n",
        "plt.plot(history.history['val_loss'], label='val loss')\n",
        "plt.title('Loss/accuracy in ANN model after normalization')\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "782c4a9a-e756-4f10-c454-eea020fad42b",
        "id": "KcJpGtjF60o2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "44/44 [==============================] - 1s 24ms/step - loss: 1.9332 - accuracy: 0.2181\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.9332376718521118, 0.2181159406900406]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "#Evaluation of model\n",
        "model.evaluate(X1_test, Y1_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sc5U_ZdS60o4"
      },
      "outputs": [],
      "source": [
        "predict_x=model.predict(X1_test) \n",
        "classes_x=np.argmax(predict_x,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dvmEw3kE60o4"
      },
      "outputs": [],
      "source": [
        "#converting over Y test to actual labels.\n",
        "Y1_test = np.argmax(Y1_test, axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11c52543-cbba-47b9-f072-05c044bf21e4",
        "id": "v_UTLmYl60o5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00       206\n",
            "           1       0.00      0.00      0.00       163\n",
            "           2       0.00      0.00      0.00       175\n",
            "           3       0.00      0.00      0.00       143\n",
            "           4       0.00      0.00      0.00       138\n",
            "           5       0.15      0.99      0.27       196\n",
            "           6       0.96      0.51      0.67       207\n",
            "           7       0.00      0.00      0.00       152\n",
            "\n",
            "    accuracy                           0.22      1380\n",
            "   macro avg       0.14      0.19      0.12      1380\n",
            "weighted avg       0.17      0.22      0.14      1380\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(Y1_test, classes_x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "197a6677-06eb-4ee4-f68a-aeb7d7b4a81d",
        "id": "n18P7jfN60o7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[1174,    0],\n",
              "        [ 206,    0]],\n",
              "\n",
              "       [[1217,    0],\n",
              "        [ 163,    0]],\n",
              "\n",
              "       [[1199,    6],\n",
              "        [ 175,    0]],\n",
              "\n",
              "       [[1237,    0],\n",
              "        [ 143,    0]],\n",
              "\n",
              "       [[1242,    0],\n",
              "        [ 138,    0]],\n",
              "\n",
              "       [[ 115, 1069],\n",
              "        [   1,  195]],\n",
              "\n",
              "       [[1169,    4],\n",
              "        [ 101,  106]],\n",
              "\n",
              "       [[1228,    0],\n",
              "        [ 152,    0]]])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "from sklearn.metrics import multilabel_confusion_matrix\n",
        "multilabel_confusion_matrix(Y1_test, classes_x)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Done for the final test"
      ],
      "metadata": {
        "id": "tsUT_WF5VQz3"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nc2E3WCSWPdd"
      },
      "source": [
        "### AFTER CHANGING THE LEARNING RATE AT 0.01 WITH SGD OPTIMIZER AND BATCH SIZE=32"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8k7ZbI6lWUEU",
        "outputId": "eb598f46-966f-4f53-bd4e-528e3f91c9e2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "138/138 [==============================] - 14s 98ms/step - loss: 1.6752 - accuracy: 0.4272 - val_loss: 1.1646 - val_accuracy: 0.6069\n",
            "Epoch 2/30\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 1.0499 - accuracy: 0.6285 - val_loss: 1.0310 - val_accuracy: 0.6277\n",
            "Epoch 3/30\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.8602 - accuracy: 0.6849 - val_loss: 0.8749 - val_accuracy: 0.6857\n",
            "Epoch 4/30\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.7622 - accuracy: 0.7273 - val_loss: 0.8562 - val_accuracy: 0.6966\n",
            "Epoch 5/30\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.6976 - accuracy: 0.7420 - val_loss: 0.7416 - val_accuracy: 0.7337\n",
            "Epoch 6/30\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.6332 - accuracy: 0.7805 - val_loss: 0.8719 - val_accuracy: 0.7111\n",
            "Epoch 7/30\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.5752 - accuracy: 0.7939 - val_loss: 0.6679 - val_accuracy: 0.7636\n",
            "Epoch 8/30\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.5383 - accuracy: 0.8025 - val_loss: 0.7612 - val_accuracy: 0.7283\n",
            "Epoch 9/30\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.5059 - accuracy: 0.8195 - val_loss: 0.7096 - val_accuracy: 0.7373\n",
            "Epoch 10/30\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.4808 - accuracy: 0.8294 - val_loss: 0.7563 - val_accuracy: 0.7274\n",
            "Epoch 11/30\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.4367 - accuracy: 0.8437 - val_loss: 0.6466 - val_accuracy: 0.7754\n",
            "Epoch 12/30\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.4118 - accuracy: 0.8446 - val_loss: 0.5942 - val_accuracy: 0.7880\n",
            "Epoch 13/30\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.3941 - accuracy: 0.8571 - val_loss: 0.7897 - val_accuracy: 0.7219\n",
            "Epoch 14/30\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.3756 - accuracy: 0.8639 - val_loss: 0.5847 - val_accuracy: 0.7908\n",
            "Epoch 15/30\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.3430 - accuracy: 0.8757 - val_loss: 0.6233 - val_accuracy: 0.7844\n",
            "Epoch 16/30\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.3072 - accuracy: 0.8877 - val_loss: 0.6592 - val_accuracy: 0.7717\n",
            "Epoch 17/30\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.2940 - accuracy: 0.8908 - val_loss: 0.5646 - val_accuracy: 0.8053\n",
            "Epoch 18/30\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.2721 - accuracy: 0.9024 - val_loss: 0.5508 - val_accuracy: 0.7998\n",
            "Epoch 19/30\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.2840 - accuracy: 0.8999 - val_loss: 0.5513 - val_accuracy: 0.8143\n",
            "Epoch 20/30\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.2393 - accuracy: 0.9139 - val_loss: 0.5698 - val_accuracy: 0.8062\n",
            "Epoch 21/30\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.2692 - accuracy: 0.9069 - val_loss: 0.7244 - val_accuracy: 0.7600\n",
            "Epoch 22/30\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.2266 - accuracy: 0.9182 - val_loss: 0.5924 - val_accuracy: 0.8053\n",
            "Epoch 23/30\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.2294 - accuracy: 0.9225 - val_loss: 0.5670 - val_accuracy: 0.8071\n",
            "Epoch 24/30\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.1987 - accuracy: 0.9348 - val_loss: 0.6513 - val_accuracy: 0.7871\n",
            "Epoch 25/30\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.1819 - accuracy: 0.9382 - val_loss: 0.6129 - val_accuracy: 0.8089\n",
            "Epoch 26/30\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.2491 - accuracy: 0.9243 - val_loss: 0.6188 - val_accuracy: 0.7980\n",
            "Epoch 27/30\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.1764 - accuracy: 0.9420 - val_loss: 0.7435 - val_accuracy: 0.7736\n",
            "Epoch 28/30\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.1635 - accuracy: 0.9422 - val_loss: 0.5755 - val_accuracy: 0.8179\n",
            "Epoch 29/30\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.1542 - accuracy: 0.9515 - val_loss: 0.5965 - val_accuracy: 0.8034\n",
            "Epoch 30/30\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.2674 - accuracy: 0.9162 - val_loss: 0.7256 - val_accuracy: 0.7808\n"
          ]
        }
      ],
      "source": [
        "#let's compile the model using sgd optimizer\n",
        "model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n",
        "#fitting the model in 30 epochs with validation split at 0.2 after normalization\n",
        "history = model.fit(X1_train, Y1_train, epochs=30, batch_size=32, validation_split=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "B3ffIvf0XNyV",
        "outputId": "c9e1e1e7-3b9a-4094-a45b-0f614de2aac3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#ANN model with SGD optimizer or default 0.01 learning rate\n",
        "plt.figure(figsize = (10,10))\n",
        "plt.plot(history.history['accuracy'], label='training accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='val accuracy')\n",
        "plt.plot(history.history['loss'], label='training loss')\n",
        "plt.plot(history.history['val_loss'], label='val loss')\n",
        "plt.title('Loss/accuracy in ANN model after normalization')\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W8JN6ElgXW9r",
        "outputId": "347878c0-a071-4002-db93-5cf1bd9c43dd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "44/44 [==============================] - 1s 33ms/step - loss: 0.6751 - accuracy: 0.7884\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[0.6750873327255249, 0.7884057760238647]"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Evaluation of model\n",
        "model.evaluate(X1_test, Y1_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_o7uBKBiXW9t"
      },
      "outputs": [],
      "source": [
        "predict_x=model.predict(X1_test) \n",
        "classes_x=np.argmax(predict_x,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vfr8mXFKXW9u"
      },
      "outputs": [],
      "source": [
        "#converting over Y test to actual labels.\n",
        "Y1_test = np.argmax(Y1_test, axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6uFf4sQSXW9u",
        "outputId": "308b7bd3-77db-4c68-df51-439a261c4514"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.99      0.92       206\n",
            "           1       0.92      0.59      0.72       163\n",
            "           2       0.49      0.68      0.57       175\n",
            "           3       0.98      0.84      0.90       143\n",
            "           4       0.58      0.35      0.43       138\n",
            "           5       0.88      0.85      0.87       196\n",
            "           6       0.81      1.00      0.90       207\n",
            "           7       0.87      0.84      0.86       152\n",
            "\n",
            "    accuracy                           0.79      1380\n",
            "   macro avg       0.80      0.77      0.77      1380\n",
            "weighted avg       0.80      0.79      0.78      1380\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(Y1_test, classes_x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_DYnslWmXW9v",
        "outputId": "d06fd826-0fab-49a2-fdc0-9cca4e4d71c6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[[1141,   33],\n",
              "        [   3,  203]],\n",
              "\n",
              "       [[1209,    8],\n",
              "        [  67,   96]],\n",
              "\n",
              "       [[1082,  123],\n",
              "        [  56,  119]],\n",
              "\n",
              "       [[1234,    3],\n",
              "        [  23,  120]],\n",
              "\n",
              "       [[1207,   35],\n",
              "        [  90,   48]],\n",
              "\n",
              "       [[1161,   23],\n",
              "        [  29,  167]],\n",
              "\n",
              "       [[1125,   48],\n",
              "        [   0,  207]],\n",
              "\n",
              "       [[1209,   19],\n",
              "        [  24,  128]]])"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import multilabel_confusion_matrix\n",
        "multilabel_confusion_matrix(Y1_test, classes_x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3y_YGzhjbRPE"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gcH-WQN3dSkL"
      },
      "source": [
        "### AFTER CHANGING THE LEARNING RATE AT 0.001 WITH NADAM OPTIMIZER FOR A FASTER CONVERGENCE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CqqjRwSXdSkN",
        "outputId": "0d4ac687-5d42-406f-f235-83bf4864570b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "138/138 [==============================] - 29s 200ms/step - loss: 6.0667 - accuracy: 0.4220 - val_loss: 1.7742 - val_accuracy: 0.4855\n",
            "Epoch 2/30\n",
            "138/138 [==============================] - 28s 199ms/step - loss: 1.1912 - accuracy: 0.6328 - val_loss: 0.9728 - val_accuracy: 0.6775\n",
            "Epoch 3/30\n",
            "138/138 [==============================] - 27s 199ms/step - loss: 0.9179 - accuracy: 0.6917 - val_loss: 1.0358 - val_accuracy: 0.6621\n",
            "Epoch 4/30\n",
            "138/138 [==============================] - 27s 199ms/step - loss: 0.7576 - accuracy: 0.7395 - val_loss: 1.0449 - val_accuracy: 0.6594\n",
            "Epoch 5/30\n",
            "138/138 [==============================] - 27s 199ms/step - loss: 0.6999 - accuracy: 0.7558 - val_loss: 0.7305 - val_accuracy: 0.7518\n",
            "Epoch 6/30\n",
            "138/138 [==============================] - 27s 199ms/step - loss: 0.6276 - accuracy: 0.7721 - val_loss: 0.7308 - val_accuracy: 0.7482\n",
            "Epoch 7/30\n",
            "138/138 [==============================] - 27s 198ms/step - loss: 0.5723 - accuracy: 0.7912 - val_loss: 0.7746 - val_accuracy: 0.7310\n",
            "Epoch 8/30\n",
            "138/138 [==============================] - 27s 198ms/step - loss: 0.5195 - accuracy: 0.8086 - val_loss: 0.8025 - val_accuracy: 0.7391\n",
            "Epoch 9/30\n",
            "138/138 [==============================] - 27s 197ms/step - loss: 0.5272 - accuracy: 0.8116 - val_loss: 0.8270 - val_accuracy: 0.7255\n",
            "Epoch 10/30\n",
            "138/138 [==============================] - 27s 198ms/step - loss: 0.4317 - accuracy: 0.8419 - val_loss: 1.0582 - val_accuracy: 0.6911\n",
            "Epoch 11/30\n",
            "138/138 [==============================] - 27s 199ms/step - loss: 0.4348 - accuracy: 0.8390 - val_loss: 0.7620 - val_accuracy: 0.7301\n",
            "Epoch 12/30\n",
            "138/138 [==============================] - 28s 200ms/step - loss: 0.3713 - accuracy: 0.8591 - val_loss: 0.7553 - val_accuracy: 0.7518\n",
            "Epoch 13/30\n",
            "138/138 [==============================] - 27s 199ms/step - loss: 0.3764 - accuracy: 0.8612 - val_loss: 0.7713 - val_accuracy: 0.7627\n",
            "Epoch 14/30\n",
            "138/138 [==============================] - 27s 199ms/step - loss: 0.5231 - accuracy: 0.8419 - val_loss: 0.8750 - val_accuracy: 0.7147\n",
            "Epoch 15/30\n",
            "138/138 [==============================] - 27s 199ms/step - loss: 0.4107 - accuracy: 0.8451 - val_loss: 0.7101 - val_accuracy: 0.7808\n",
            "Epoch 16/30\n",
            "138/138 [==============================] - 27s 199ms/step - loss: 0.3549 - accuracy: 0.8684 - val_loss: 0.7627 - val_accuracy: 0.7717\n",
            "Epoch 17/30\n",
            "138/138 [==============================] - 28s 200ms/step - loss: 0.2740 - accuracy: 0.8985 - val_loss: 0.6485 - val_accuracy: 0.7790\n",
            "Epoch 18/30\n",
            "138/138 [==============================] - 28s 200ms/step - loss: 0.2843 - accuracy: 0.8956 - val_loss: 0.7163 - val_accuracy: 0.7917\n",
            "Epoch 19/30\n",
            "138/138 [==============================] - 28s 199ms/step - loss: 0.5056 - accuracy: 0.8385 - val_loss: 1.1352 - val_accuracy: 0.6766\n",
            "Epoch 20/30\n",
            "138/138 [==============================] - 27s 199ms/step - loss: 0.3072 - accuracy: 0.8867 - val_loss: 0.7890 - val_accuracy: 0.7681\n",
            "Epoch 21/30\n",
            "138/138 [==============================] - 27s 199ms/step - loss: 0.2563 - accuracy: 0.9015 - val_loss: 0.7538 - val_accuracy: 0.7790\n",
            "Epoch 22/30\n",
            "138/138 [==============================] - 27s 199ms/step - loss: 0.2324 - accuracy: 0.9144 - val_loss: 0.7245 - val_accuracy: 0.7790\n",
            "Epoch 23/30\n",
            "138/138 [==============================] - 27s 198ms/step - loss: 0.3404 - accuracy: 0.8793 - val_loss: 0.6999 - val_accuracy: 0.7880\n",
            "Epoch 24/30\n",
            "138/138 [==============================] - 27s 199ms/step - loss: 0.2180 - accuracy: 0.9153 - val_loss: 0.7275 - val_accuracy: 0.7926\n",
            "Epoch 25/30\n",
            "138/138 [==============================] - 27s 199ms/step - loss: 0.1962 - accuracy: 0.9255 - val_loss: 1.0585 - val_accuracy: 0.7545\n",
            "Epoch 26/30\n",
            "138/138 [==============================] - 27s 199ms/step - loss: 0.1821 - accuracy: 0.9309 - val_loss: 0.8241 - val_accuracy: 0.7817\n",
            "Epoch 27/30\n",
            "138/138 [==============================] - 27s 199ms/step - loss: 0.2345 - accuracy: 0.9137 - val_loss: 0.9506 - val_accuracy: 0.7681\n",
            "Epoch 28/30\n",
            "138/138 [==============================] - 28s 202ms/step - loss: 0.1888 - accuracy: 0.9275 - val_loss: 1.0460 - val_accuracy: 0.7582\n",
            "Epoch 29/30\n",
            "138/138 [==============================] - 27s 199ms/step - loss: 0.1294 - accuracy: 0.9520 - val_loss: 0.8647 - val_accuracy: 0.7853\n",
            "Epoch 30/30\n",
            "138/138 [==============================] - 27s 199ms/step - loss: 0.3688 - accuracy: 0.8804 - val_loss: 0.8139 - val_accuracy: 0.7826\n"
          ]
        }
      ],
      "source": [
        "#let's compile the model using sgd optimizer\n",
        "model.compile(loss='categorical_crossentropy', optimizer='nadam', metrics=['accuracy'])\n",
        "#fitting the model in 30 epochs with validation split at 0.2 after normalization\n",
        "history = model.fit(X1_train, Y1_train, epochs=30, batch_size=32,validation_split=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "7dNKKcV-dSkP",
        "outputId": "b7ac592f-d062-486b-d19d-ada9b1e91f82"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#ANN model with SGD optimizer or default 0.01 learning rate\n",
        "plt.figure(figsize = (10,10))\n",
        "plt.plot(history.history['accuracy'], label='training accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='val accuracy')\n",
        "plt.plot(history.history['loss'], label='training loss')\n",
        "plt.plot(history.history['val_loss'], label='val loss')\n",
        "plt.title('Loss/accuracy in ANN model after normalization')\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cZJgBxm8dSkP",
        "outputId": "7295f349-fe97-437a-b85d-2127a2b4bd95"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "44/44 [==============================] - 1s 23ms/step - loss: 0.8196 - accuracy: 0.7899\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8196171522140503, 0.7898550629615784]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "#Evaluation of model\n",
        "model.evaluate(X1_test, Y1_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lYRlynC4dSkQ"
      },
      "outputs": [],
      "source": [
        "predict_x=model.predict(X1_test) \n",
        "classes_x=np.argmax(predict_x,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gMP4iJTQdSkR"
      },
      "outputs": [],
      "source": [
        "#converting over Y test to actual labels.\n",
        "Y1_test = np.argmax(Y1_test, axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fhWV0XtgdSkR",
        "outputId": "724b471f-e374-4403-f7a3-9dc39679376c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.93      0.94       206\n",
            "           1       0.85      0.58      0.69       163\n",
            "           2       0.54      0.62      0.58       175\n",
            "           3       0.93      0.87      0.90       143\n",
            "           4       0.43      0.54      0.48       138\n",
            "           5       0.89      0.83      0.86       196\n",
            "           6       0.91      0.99      0.95       207\n",
            "           7       0.86      0.84      0.85       152\n",
            "\n",
            "    accuracy                           0.79      1380\n",
            "   macro avg       0.79      0.78      0.78      1380\n",
            "weighted avg       0.81      0.79      0.79      1380\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(Y1_test, classes_x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M3g8pHpCdSkS",
        "outputId": "f4b8c654-de1d-4a06-a904-26ef0e4d3c7d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[1165,    9],\n",
              "        [  14,  192]],\n",
              "\n",
              "       [[1200,   17],\n",
              "        [  68,   95]],\n",
              "\n",
              "       [[1112,   93],\n",
              "        [  66,  109]],\n",
              "\n",
              "       [[1227,   10],\n",
              "        [  19,  124]],\n",
              "\n",
              "       [[1143,   99],\n",
              "        [  64,   74]],\n",
              "\n",
              "       [[1164,   20],\n",
              "        [  33,  163]],\n",
              "\n",
              "       [[1152,   21],\n",
              "        [   2,  205]],\n",
              "\n",
              "       [[1207,   21],\n",
              "        [  24,  128]]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "from sklearn.metrics import multilabel_confusion_matrix\n",
        "multilabel_confusion_matrix(Y1_test, classes_x)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Done for the final test"
      ],
      "metadata": {
        "id": "xilwCw3jb38B"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yy6ITorIdQIT"
      },
      "source": [
        "### AFTER CHANGING THE LEARNING RATE AT 0.001 WITH ADAM OPTIMIZER AND EPOCHS TO 50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qmXnc_NKdQIV",
        "outputId": "80ee40dc-41e3-43ac-a011-e293dbc8d530"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "138/138 [==============================] - 18s 121ms/step - loss: 0.3526 - accuracy: 0.8759 - val_loss: 0.9216 - val_accuracy: 0.7600\n",
            "Epoch 2/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.2278 - accuracy: 0.9187 - val_loss: 1.0278 - val_accuracy: 0.7355\n",
            "Epoch 3/50\n",
            "138/138 [==============================] - 17s 120ms/step - loss: 0.2498 - accuracy: 0.9092 - val_loss: 1.1265 - val_accuracy: 0.7391\n",
            "Epoch 4/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.3655 - accuracy: 0.8840 - val_loss: 1.1126 - val_accuracy: 0.7500\n",
            "Epoch 5/50\n",
            "138/138 [==============================] - 16s 118ms/step - loss: 0.2054 - accuracy: 0.9210 - val_loss: 0.8130 - val_accuracy: 0.7917\n",
            "Epoch 6/50\n",
            "138/138 [==============================] - 16s 118ms/step - loss: 0.1713 - accuracy: 0.9357 - val_loss: 1.1769 - val_accuracy: 0.7609\n",
            "Epoch 7/50\n",
            "138/138 [==============================] - 16s 118ms/step - loss: 0.2185 - accuracy: 0.9196 - val_loss: 0.9764 - val_accuracy: 0.7663\n",
            "Epoch 8/50\n",
            "138/138 [==============================] - 16s 118ms/step - loss: 0.1598 - accuracy: 0.9395 - val_loss: 0.9940 - val_accuracy: 0.7817\n",
            "Epoch 9/50\n",
            "138/138 [==============================] - 16s 118ms/step - loss: 0.2075 - accuracy: 0.9273 - val_loss: 1.0878 - val_accuracy: 0.7446\n",
            "Epoch 10/50\n",
            "138/138 [==============================] - 17s 122ms/step - loss: 0.2310 - accuracy: 0.9176 - val_loss: 0.9136 - val_accuracy: 0.7917\n",
            "Epoch 11/50\n",
            "138/138 [==============================] - 16s 118ms/step - loss: 0.1232 - accuracy: 0.9538 - val_loss: 1.1489 - val_accuracy: 0.7554\n",
            "Epoch 12/50\n",
            "138/138 [==============================] - 16s 118ms/step - loss: 0.2691 - accuracy: 0.9105 - val_loss: 1.4259 - val_accuracy: 0.7274\n",
            "Epoch 13/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.3121 - accuracy: 0.9012 - val_loss: 1.0244 - val_accuracy: 0.7418\n",
            "Epoch 14/50\n",
            "138/138 [==============================] - 16s 118ms/step - loss: 0.1804 - accuracy: 0.9341 - val_loss: 1.1514 - val_accuracy: 0.7446\n",
            "Epoch 15/50\n",
            "138/138 [==============================] - 16s 118ms/step - loss: 0.1391 - accuracy: 0.9472 - val_loss: 1.0554 - val_accuracy: 0.7636\n",
            "Epoch 16/50\n",
            "138/138 [==============================] - 16s 118ms/step - loss: 0.2319 - accuracy: 0.9187 - val_loss: 1.0517 - val_accuracy: 0.7726\n",
            "Epoch 17/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.1793 - accuracy: 0.9359 - val_loss: 1.3917 - val_accuracy: 0.7391\n",
            "Epoch 18/50\n",
            "138/138 [==============================] - 16s 118ms/step - loss: 0.2728 - accuracy: 0.9094 - val_loss: 0.9062 - val_accuracy: 0.7763\n",
            "Epoch 19/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.1290 - accuracy: 0.9508 - val_loss: 1.0278 - val_accuracy: 0.7808\n",
            "Epoch 20/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.1313 - accuracy: 0.9574 - val_loss: 1.2585 - val_accuracy: 0.7536\n",
            "Epoch 21/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.2074 - accuracy: 0.9248 - val_loss: 1.0725 - val_accuracy: 0.7699\n",
            "Epoch 22/50\n",
            "138/138 [==============================] - 17s 120ms/step - loss: 0.0900 - accuracy: 0.9690 - val_loss: 1.0626 - val_accuracy: 0.8025\n",
            "Epoch 23/50\n",
            "138/138 [==============================] - 17s 120ms/step - loss: 0.0594 - accuracy: 0.9780 - val_loss: 1.1224 - val_accuracy: 0.7808\n",
            "Epoch 24/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.1510 - accuracy: 0.9515 - val_loss: 1.2484 - val_accuracy: 0.7591\n",
            "Epoch 25/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.1700 - accuracy: 0.9373 - val_loss: 1.1664 - val_accuracy: 0.7808\n",
            "Epoch 26/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.1091 - accuracy: 0.9606 - val_loss: 1.0322 - val_accuracy: 0.7944\n",
            "Epoch 27/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.0984 - accuracy: 0.9631 - val_loss: 1.4036 - val_accuracy: 0.7582\n",
            "Epoch 28/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.1580 - accuracy: 0.9436 - val_loss: 1.2095 - val_accuracy: 0.7754\n",
            "Epoch 29/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.1582 - accuracy: 0.9445 - val_loss: 1.1355 - val_accuracy: 0.7908\n",
            "Epoch 30/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.0682 - accuracy: 0.9751 - val_loss: 1.2136 - val_accuracy: 0.7880\n",
            "Epoch 31/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.1205 - accuracy: 0.9570 - val_loss: 1.2778 - val_accuracy: 0.7717\n",
            "Epoch 32/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.2804 - accuracy: 0.9146 - val_loss: 1.4041 - val_accuracy: 0.7455\n",
            "Epoch 33/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.1915 - accuracy: 0.9379 - val_loss: 1.2074 - val_accuracy: 0.7808\n",
            "Epoch 34/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.0568 - accuracy: 0.9803 - val_loss: 1.0429 - val_accuracy: 0.7998\n",
            "Epoch 35/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.0509 - accuracy: 0.9807 - val_loss: 1.1531 - val_accuracy: 0.7971\n",
            "Epoch 36/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.0273 - accuracy: 0.9943 - val_loss: 1.1447 - val_accuracy: 0.8007\n",
            "Epoch 37/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.0444 - accuracy: 0.9851 - val_loss: 1.2568 - val_accuracy: 0.7853\n",
            "Epoch 38/50\n",
            "138/138 [==============================] - 16s 118ms/step - loss: 0.0733 - accuracy: 0.9737 - val_loss: 1.2054 - val_accuracy: 0.7835\n",
            "Epoch 39/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.1027 - accuracy: 0.9597 - val_loss: 1.1831 - val_accuracy: 0.7889\n",
            "Epoch 40/50\n",
            "138/138 [==============================] - 17s 120ms/step - loss: 0.1371 - accuracy: 0.9545 - val_loss: 1.5292 - val_accuracy: 0.7618\n",
            "Epoch 41/50\n",
            "138/138 [==============================] - 17s 120ms/step - loss: 0.2165 - accuracy: 0.9368 - val_loss: 1.2734 - val_accuracy: 0.7672\n",
            "Epoch 42/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.1613 - accuracy: 0.9459 - val_loss: 1.3921 - val_accuracy: 0.7754\n",
            "Epoch 43/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.1153 - accuracy: 0.9590 - val_loss: 1.1480 - val_accuracy: 0.7908\n",
            "Epoch 44/50\n",
            "138/138 [==============================] - 16s 118ms/step - loss: 0.0641 - accuracy: 0.9751 - val_loss: 1.2159 - val_accuracy: 0.7853\n",
            "Epoch 45/50\n",
            "138/138 [==============================] - 16s 118ms/step - loss: 0.0221 - accuracy: 0.9937 - val_loss: 1.2992 - val_accuracy: 0.7926\n",
            "Epoch 46/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.0119 - accuracy: 0.9982 - val_loss: 1.2278 - val_accuracy: 0.7980\n",
            "Epoch 47/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.0254 - accuracy: 0.9934 - val_loss: 1.3138 - val_accuracy: 0.7998\n",
            "Epoch 48/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.4345 - accuracy: 0.8965 - val_loss: 1.3476 - val_accuracy: 0.7654\n",
            "Epoch 49/50\n",
            "138/138 [==============================] - 16s 119ms/step - loss: 0.0702 - accuracy: 0.9744 - val_loss: 1.2087 - val_accuracy: 0.7853\n",
            "Epoch 50/50\n",
            "138/138 [==============================] - 17s 120ms/step - loss: 0.0390 - accuracy: 0.9900 - val_loss: 1.2797 - val_accuracy: 0.7944\n"
          ]
        }
      ],
      "source": [
        "#let's compile the model using sgd optimizer\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "#fitting the model in 30 epochs with validation split at 0.2 after normalization\n",
        "history = model.fit(X1_train, Y1_train, epochs=50, validation_split=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "H5wUzQE_dQIX",
        "outputId": "c8e779ce-b720-4c82-feab-6339e9ed422c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#ANN model with Adam optimizer or default 0.01 learning rate\n",
        "plt.figure(figsize = (10,10))\n",
        "plt.plot(history.history['accuracy'], label='training accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='val accuracy')\n",
        "plt.plot(history.history['loss'], label='training loss')\n",
        "plt.plot(history.history['val_loss'], label='val loss')\n",
        "plt.title('Loss/accuracy in ANN model after normalization')\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3hQmGs0GmWSk"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ena0X84Acytc"
      },
      "source": [
        "### AFTER CHANGING THE LEARNING RATE AT 0.01 WITH SGD OPTIMIZER AND EPOCHS TO 50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sbs9rqcEcyte",
        "outputId": "eb85af5d-ea93-4f45-cb48-c30122f05a5c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "138/138 [==============================] - 13s 88ms/step - loss: 0.0122 - accuracy: 0.9984 - val_loss: 1.2946 - val_accuracy: 0.7953\n",
            "Epoch 2/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0072 - accuracy: 0.9993 - val_loss: 1.3298 - val_accuracy: 0.8016\n",
            "Epoch 3/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0059 - accuracy: 0.9998 - val_loss: 1.3742 - val_accuracy: 0.7980\n",
            "Epoch 4/50\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.0057 - accuracy: 0.9995 - val_loss: 1.3956 - val_accuracy: 0.8007\n",
            "Epoch 5/50\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.0048 - accuracy: 0.9995 - val_loss: 1.4131 - val_accuracy: 0.8016\n",
            "Epoch 6/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0051 - accuracy: 0.9989 - val_loss: 1.4379 - val_accuracy: 0.8007\n",
            "Epoch 7/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0037 - accuracy: 0.9995 - val_loss: 1.4582 - val_accuracy: 0.8034\n",
            "Epoch 8/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0033 - accuracy: 0.9998 - val_loss: 1.4658 - val_accuracy: 0.8007\n",
            "Epoch 9/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0030 - accuracy: 0.9998 - val_loss: 1.4887 - val_accuracy: 0.7998\n",
            "Epoch 10/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0037 - accuracy: 0.9995 - val_loss: 1.5079 - val_accuracy: 0.8016\n",
            "Epoch 11/50\n",
            "138/138 [==============================] - 12s 84ms/step - loss: 0.0026 - accuracy: 0.9998 - val_loss: 1.5246 - val_accuracy: 0.7953\n",
            "Epoch 12/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0025 - accuracy: 0.9998 - val_loss: 1.5377 - val_accuracy: 0.8034\n",
            "Epoch 13/50\n",
            "138/138 [==============================] - 12s 84ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.5465 - val_accuracy: 0.8025\n",
            "Epoch 14/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0024 - accuracy: 0.9998 - val_loss: 1.5498 - val_accuracy: 0.8016\n",
            "Epoch 15/50\n",
            "138/138 [==============================] - 12s 84ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 1.5677 - val_accuracy: 0.8043\n",
            "Epoch 16/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0021 - accuracy: 0.9998 - val_loss: 1.5761 - val_accuracy: 0.8025\n",
            "Epoch 17/50\n",
            "138/138 [==============================] - 12s 84ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 1.5922 - val_accuracy: 0.8016\n",
            "Epoch 18/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 1.6076 - val_accuracy: 0.7989\n",
            "Epoch 19/50\n",
            "138/138 [==============================] - 12s 84ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 1.6157 - val_accuracy: 0.8016\n",
            "Epoch 20/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 1.6165 - val_accuracy: 0.8025\n",
            "Epoch 21/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 1.6303 - val_accuracy: 0.7989\n",
            "Epoch 22/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 1.6462 - val_accuracy: 0.8025\n",
            "Epoch 23/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 1.6418 - val_accuracy: 0.8007\n",
            "Epoch 24/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 1.6555 - val_accuracy: 0.7980\n",
            "Epoch 25/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 1.6587 - val_accuracy: 0.7998\n",
            "Epoch 26/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 1.6671 - val_accuracy: 0.7980\n",
            "Epoch 27/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 1.6716 - val_accuracy: 0.8007\n",
            "Epoch 28/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 1.6799 - val_accuracy: 0.8043\n",
            "Epoch 29/50\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 1.6883 - val_accuracy: 0.7962\n",
            "Epoch 30/50\n",
            "138/138 [==============================] - 12s 87ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 1.6913 - val_accuracy: 0.7998\n",
            "Epoch 31/50\n",
            "138/138 [==============================] - 12s 87ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 1.7014 - val_accuracy: 0.7989\n",
            "Epoch 32/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 9.9341e-04 - accuracy: 1.0000 - val_loss: 1.7075 - val_accuracy: 0.7962\n",
            "Epoch 33/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 9.1899e-04 - accuracy: 1.0000 - val_loss: 1.7108 - val_accuracy: 0.7971\n",
            "Epoch 34/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 9.3328e-04 - accuracy: 1.0000 - val_loss: 1.7185 - val_accuracy: 0.7980\n",
            "Epoch 35/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 9.0891e-04 - accuracy: 1.0000 - val_loss: 1.7241 - val_accuracy: 0.8007\n",
            "Epoch 36/50\n",
            "138/138 [==============================] - 12s 87ms/step - loss: 8.8633e-04 - accuracy: 1.0000 - val_loss: 1.7308 - val_accuracy: 0.7998\n",
            "Epoch 37/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 8.3144e-04 - accuracy: 1.0000 - val_loss: 1.7411 - val_accuracy: 0.7998\n",
            "Epoch 38/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 8.2795e-04 - accuracy: 1.0000 - val_loss: 1.7430 - val_accuracy: 0.8016\n",
            "Epoch 39/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 8.7463e-04 - accuracy: 1.0000 - val_loss: 1.7481 - val_accuracy: 0.7980\n",
            "Epoch 40/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 7.9003e-04 - accuracy: 1.0000 - val_loss: 1.7485 - val_accuracy: 0.7980\n",
            "Epoch 41/50\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 7.7072e-04 - accuracy: 1.0000 - val_loss: 1.7567 - val_accuracy: 0.7980\n",
            "Epoch 42/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 7.9553e-04 - accuracy: 1.0000 - val_loss: 1.7619 - val_accuracy: 0.7962\n",
            "Epoch 43/50\n",
            "138/138 [==============================] - 12s 85ms/step - loss: 7.3878e-04 - accuracy: 1.0000 - val_loss: 1.7652 - val_accuracy: 0.7989\n",
            "Epoch 44/50\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 7.5244e-04 - accuracy: 1.0000 - val_loss: 1.7703 - val_accuracy: 0.7980\n",
            "Epoch 45/50\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 7.0712e-04 - accuracy: 1.0000 - val_loss: 1.7764 - val_accuracy: 0.7998\n",
            "Epoch 46/50\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 6.9401e-04 - accuracy: 1.0000 - val_loss: 1.7775 - val_accuracy: 0.7989\n",
            "Epoch 47/50\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 6.9058e-04 - accuracy: 1.0000 - val_loss: 1.7829 - val_accuracy: 0.7980\n",
            "Epoch 48/50\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 6.6277e-04 - accuracy: 1.0000 - val_loss: 1.7889 - val_accuracy: 0.8007\n",
            "Epoch 49/50\n",
            "138/138 [==============================] - 12s 86ms/step - loss: 6.2821e-04 - accuracy: 1.0000 - val_loss: 1.7856 - val_accuracy: 0.7980\n",
            "Epoch 50/50\n",
            "138/138 [==============================] - 12s 87ms/step - loss: 6.3954e-04 - accuracy: 1.0000 - val_loss: 1.7956 - val_accuracy: 0.8007\n"
          ]
        }
      ],
      "source": [
        "#let's compile the model using sgd optimizer\n",
        "model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n",
        "#fitting the model in 30 epochs with validation split at 0.2 after normalization\n",
        "history = model.fit(X1_train, Y1_train, epochs=50, validation_split=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "8IZlHCm6cytg",
        "outputId": "6c45dfaf-3c06-40cf-fe1e-a937df449f1d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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aVX3ZhxkzZpCcnEx5eTlDhgzhvPPOIyUlZY/prFmzhtmzZ/PUU08xbtw4Xn/9dS666KJ95peamsqyZcuYNm0aDz/8ME8//TT33HMPJ510ErfddhvvvfcezzzzTK21/vGPfyQ5OZlAIMDJJ5/MypUrOfzwwxk/fjyvvPIKQ4YMYceOHcTGxjJ9+nQ2bNjA8uXL8Xg8FBYW7nddfffdd3z66afExsZSVlbG/PnziYmJYc2aNVxwwQUsWbKEd999lzfffJMvvviCuLg4CgsLSU5OpnXr1ixfvpzMzExmzpzJpZdeut/5iYiINEUtKpxFiiOPPHKP63E99thjzJ07F4Ds7GzWrFmzTzjr3r07mZmZAAwePJgNGzbUOu1zzz23epg33ngDgE8//bR6+qNGjSIpKanWcefMmcP06dPx+/1s2bKF7777DmMMHTt2ZMiQIQC0atUKgAULFnDllVfi8TgfoeTk5P0u95lnnklsbCzgXBz42muvZfny5bjdbn788cfq6V566aXExcXtMd1JkyYxc+ZMHnnkEV555RW+/PLL/c5PRESkKWpR4ay+Fq5DKT4+vvr1Rx99xIIFC1i8eDFxcXEMHz681ut1RUdHV792u93VuzXrGs7tdv+iY7vWr1/Pww8/zFdffUVSUhITJ078VdcN83g8BINBgH3Gr7ncjz76KO3bt2fFihUEg0FiYmLqne55551X3QI4ePDgfcKriIhIc6FjzhpZYmIipaWldfYvKSkhKSmJuLg4Vq9ezeeff97gNQwbNow5c+YA8P7771NUVLTPMDt27CA+Pp7WrVuzbds23n33XQB69+7Nli1b+OqrrwAoLS3F7/czYsQInnzyyeoAuHu3Znp6OkuXLgXg9ddfr7OmkpISOnbsiMvl4oUXXiAQCAAwYsQIZs6cSVlZ2R7TjYmJ4dRTT+Wqq67SLk0REWnWFM4aWUpKCsOGDaNfv37cfPPN+/QfNWoUfr+fPn36MGXKFIYOHdrgNUydOpX333+ffv368eqrr9KhQwcSExP3GCYjI4OBAwdy+OGHc+GFFzJs2DAAoqKieOWVV7juuuvIyMhgxIgRVFRUMGnSJLp27cqAAQPIyMhg1qxZ1fO6/vrrycrKwu1211nT1VdfzXPPPUdGRgarV6+ublUbNWoUZ555JllZWWRmZvLwww9XjzNhwgRcLhcjR45s6FUkIiISMYy1Ntw1NJisrCy7ZMmSPbp9//339OnTJ0wVRYbKykrcbjcej4fFixdz1VVXVZ+g0JQ8/PDDlJSUcN9999XaX9taRESaEmPMUmtt1t7dW9QxZy3Vpk2bGDduHMFgkKioKJ566qlwl/SLnXPOOaxdu5YPPvgg3KWIiIg0KoWzFuCwww7j66+/DncZB2X32aYiIiLNnY45ExEREYkgCmciIiIiEUThTERERKSGcJ8sqWPOREREpEWz1lK1YQO7Fi+mbPFiqnI20/2N1zHGhKUehbMIlJCQwM6dO8NdhoiISLPlz89n1+dfsGvRInYtXox/yxYAvJ06ET/sGGxlJWY/d69pLApnsg+/3199z0wREZFIECwro3LdeqrWr6Ny7Vqq1q6jct06fJs340pMwJOcgiclGXdKKp7kZNwpoffJyXhSU3EnJVG1bh27Fi1m1+LFVP7wAwCu1q2JP+oo4v/nCuKPPhpv165hazHbTb/AjWzKlCmkpaVxzTXXAHD33XeTkJDAlVdeyVlnnUVRURE+n4/777+fs846q95pnX322WRnZ1NRUcH111/PFVdcAcB7773H7bffTiAQIDU1lf/+97/s3LmT6667jiVLlmCMYerUqZx33nl7tMq99tprvP322zz77LNMnDiRmJgYvv76a4YNG8b555/P9ddfT0VFBbGxscycOZPevXsTCAS49dZbee+993C5XEyePJm+ffvy2GOP8a9//QuA+fPnM23aNF3+QkREfhFrLf7t26nasJGq9buD2Doq163Fn7vl5wHdbqLS0ojq2ZOEY48lWLYLf0Eh/oJ8qjZl4y8sxIZuA7g34/USO3gwbW+8kfijjybmiD6Yeu5oEw4tK5y9OwW2ftOw0+zQH057oM7e48eP54YbbqgOZ3PmzGHevHnExMQwd+5cWrVqRX5+PkOHDuXMM8+sN63PmDGD5ORkysvLGTJkCOeddx7BYJDJkyezcOFCunfvXn0vyvvuu4/WrVvzzTfO8tZ2P8295eTksGjRItxuNzt27OCTTz7B4/GwYMECbr/9dl5//XWmT5/Ohg0bWL58OR6Ph8LCQpKSkrj66qvJy8ujbdu2zJw5k8suu+yXrEUREWkhrLUE8vOp2rjReWzY+PPrTZuw5eXVw5rYWKK7dyducBbR43oQ1b0H0T17ENW1KyYqqt75BMvK8BcWESjIx19QSKCwAE+HjsQNHoQrNraxF/OgtKxwFgYDBw5k+/bt5ObmkpeXR1JSEmlpafh8Pm6//XYWLlyIy+Vi8+bNbNu2jQ4dOtQ5rccee6y6NSo7O5s1a9aQl5fH8ccfT/fu3QFITk4GYMGCBbz88svV4yYlJe231rFjx1bfD7OkpIRLLrmENWvWYIzB5/NVT/fKK6+s3u25e34XX3wxL774IpdeeimLFy/m+eef/6WrSkREmhB/URFVa9dSuXYdvpxsguUV2KoqbGUl1ldFsLLq5/dVVQSrKrGVVfi3bCFYs1XL6yWqSxeiunUjfuhQotK7EdXNeXg6dsS4ft2FJVxxcUTFxUGXzg20xIdOywpn9bRwNaaxY8fy2muvsXXrVsaPHw/ASy+9RF5eHkuXLsXr9ZKenk5FRUWd0/joo49YsGABixcvJi4ujuHDh9c7fF1qtsztPf7um48D3HnnnZx44onMnTuXDRs2MHz48Hqne+mll3LGGWcQExPD2LFjdcyaiEgzYK3Fv20blT+tpWqdE8Sq1q6lct06AqE9NQB4vbji4jBRXlxR0ZioKOcRHe10i4vD3aYNJiqK+GOOqQ5fUend8HbsiNFvxh60Ng6B8ePHM3nyZPLz8/n4448Bp2WqXbt2eL1ePvzwQzZu3FjvNEpKSkhKSiIuLo7Vq1fz+eefAzB06FCuvvpq1q9fX71bMzk5mREjRvCPf/yDv/71r4CzWzMpKYn27dvz/fff07t3b+bOnUtiYmKd8+vc2flv49lnn63uPmLECJ588klOPPHE6t2aycnJdOrUiU6dOnH//fezYMGCg11lIiJyiNhAAP+2bVRtyqYqexO+TdlUZWfj27SJqo0bCe7aVT2sq3Vronv0IPHkk4jq0dPZxdijJ95Ov76FS/alcHYI9O3bl9LSUjp37kzHjh0BmDBhAmeccQb9+/cnKyuLww8/vN5pjBo1iieeeII+ffrQu3dvhg4dCkDbtm2ZPn065557LsFgkHbt2jF//nzuuOMOrrnmGvr164fb7Wbq1Kmce+65PPDAA4wZM4a2bduSlZVV5yU7brnlFi655BLuv/9+Ro8eXd190qRJ/PjjjwwYMACv18vkyZO59tprq5cpLy+PPn36NMRqExGRg2T9fgJFRfgLC/Hn5xMoLMSfX4Bv8+bqIObLycGGDl0BwOPB27kTUWldaT1wIFE9exDd8zdE9+yBOyUl7GcytgQm3FfBbUhZWVl2yZIle3T7/vvvFRYOkWuvvZaBAwdy+eWXh2X+2tYi0pIEy8udFq7sbKo2ZePbvBl/QT6BgkL8BQUECgoIFBfXOq4rPh5v165EdemCt2saUWldieqahrdrV7wdOmg34yFijFlqrc3au3ujrX1jzAxgDLDdWtuvlv43AxNq1NEHaGutLTTGbABKgQDgr61wiSyDBw8mPj6e//u//wt3KSIizUZg5y6q1q11wlf2JmfX46ZN+DZtwp+Xt8ewroQE53peKSlE9+yJ+8gheJJTcKckO9cAS03BHboWmKtVK7WARbDGjMbPAo8DtZ62Z619CHgIwBhzBvB7a22Nows50Vqb34j1SQNaunRpuEsQEWnSbDBI1YYNlH+9nPIVKyhfvpzKn36CYLB6GE+7dni7phF/7LHVLV1RXbsSlZaGu02bMFYvDanRwpm1dqExJv0AB78AmN1YtYiIiESaQEkJ5Su/qQ5i5StXEtyxAwBXq1bEDhhA4ogRxBzRh6iuXfF26RLx1+eShhH2ncrGmDhgFHBtjc4WeN8YY4EnrbXT6xn/CuAKgK5duzZmqSIiIoDTyuXfvr36eK+qnGx82Tn4t23DBgIQCGCtDT0HIRCEYAAbDHWrqsKXm+tMzBiiDzuMVqNGEZuRQWxmBlHdu+vsxxYs7OEMOAP4bK9dmsdaazcbY9oB840xq621C2sbORTcpoNzQkDjlysiIs2NtRZbUUFw166fH2Vl1a/9eXlUZec4Zzhm5zhnOFZV/TwBtxtvx454OrR3ru/lcoHLBW4XxuUGl6u6m3G7wO2hzbixxGZmEtOvP+6E+LqLkxYnEsLZ+ey1S9Nauzn0vN0YMxc4Eqg1nImIiByoYFkZZV99xc7PPqPsy68IFBVVB7Gax3bVxpWQgLdrGtG/+Q0JJw4nKq0r3rQuRKWlORdS9XoP0VJIcxfWcGaMaQ2cAFxUo1s84LLWloZejwTuDVOJB624uJhZs2Zx9dVX/+JxTz/9dGbNmkWbeg7yvOuuuzj++OM55ZRTDqZMANLT01myZAmpqakHPS0RkUhgg0EqV69m52efseuzRZQvXYr1+TDR0cQNHkxM3yNwxcfjiotznuPjcYee9+iWkuJc4V5nOMoh0JiX0pgNDAdSjTE5wFTAC2CtfSI02DnA+9baXTVGbQ/MDX0BPMAsa+17jVVnYysuLmbatGm1hjO/31/vbY7eeeed/U7/3nubbG4VEWkU/ry86jC2a9EiAgUFAET36kXSxRcTP+wY4gYPxhUTE+ZKRWrXmGdrXnAAwzyLc8mNmt3WARmNU9WhN2XKFNauXUtmZiYjRoxg9OjR3HnnnSQlJbF69Wp+/PFHzj77bLKzs6moqOD666/niiuuAH5uydq5cyennXYaxx57LIsWLaJz5868+eabxMbGMnHiRMaMGcNvf/tb0tPTueSSS/j3v/+Nz+fj1Vdf5fDDDycvL48LL7yQ3Nxcjj76aObPn8/SpUvrbSF75JFHmDFjBuDcFeCGG25g165djBs3jpycHAKBAHfeeSfjx49nypQpvPXWW3g8HkaOHMnDDz98SNatiLRs1u+natMmKn/4gYoffqDyhx+p/OGH6gPt3cnJxB9zDPHHDiP+6GPwtm8X5opFDkwkHHN2yDz45YOsLlzdoNM8PPlwbj3y1jr7P/DAA6xatYrly5cDzg3Mly1bxqpVq+jevTsAM2bMIDk5mfLycoYMGcJ5551HSkrKHtNZs2YNs2fP5qmnnmLcuHG8/vrrXHTRRfvMLzU1lWXLljFt2jQefvhhnn76ae655x5OOukkbrvtNt577z2eeeaZepdp6dKlzJw5ky+++AJrLUcddRQnnHAC69ato1OnTvznP/8BnPtvFhQUMHfuXFavXo0xhuI6rkYtIvJrWWsJ5OdT+dNPe4Swyp9++vmgfLebqO7pxGZmknThBcQdfTQxffrojEdpklpUOIsURx55ZHUwA3jssceYO3cuANnZ2axZs2afcNa9e3cyMzMB52r8GzZsqHXa5557bvUwb7zxBgCffvpp9fRHjRpFUlJSvfV9+umnnHPOOcTHx1dP85NPPmHUqFH84Q9/4NZbb2XMmDEcd9xx+P1+YmJiuPzyyxkzZgxjxoz5hWtDRMQR2LGDqg0bQo+NP7/e6+bb7tRUYnr1ImnCBKJ79yKmVy+ievbEFR0dxupFGk6LCmf1tXAdSrtDDzgtaQsWLGDx4sXExcUxfPhwKioq9hknusYfHbfbTXl5ea3T3j2c2+3G7/c3aN29evVi2bJlvPPOO9xxxx2cfPLJ3HXXXXz55Zf897//5bXXXuPxxx/ngw8+aND5ikjzEqyspPKHHyj/5hsqvv2OqvXrqdq4kUBhjSsqGYO3c2ei0tOdm2+npxPdswfRvXrh0UlL0sy1qHAWDomJiZSWltbZv6SkhKSkJOLi4li9ejWff/55g9cwbNgw5syZw6233sr7779PUVFRvdP3ONcAACAASURBVMMfd9xxTJw4kSlTpmCtZe7cubzwwgvk5uaSnJzMRRddRJs2bXj66afZuXMnZWVlnH766QwbNowePXo0eP0i0nRZv5/KtWup+OYbyr9ZRcU331CxZg34fIBzXFh0z54knnwyUenpRKV3Iyo9HW9aGq6oqDBXLxIeCmeNLCUlhWHDhtGvXz9OO+00Ro8evUf/UaNG8cQTT9CnTx969+7N0KFDG7yGqVOncsEFF/DCCy9w9NFH06FDBxITE+scftCgQUycOJEjjzwScE4IGDhwIPPmzePmm2/G5XLh9Xr55z//SWlpKWeddRYVFRVYa3nkkUcavH4RiVw2GCRQUkKgoAB/QSGBgnz8BYVUZW+iYtW3VHz3HTa0N8CVmEhMv76kTJxITP9+xPbrh6djR12eQmQvxtrmc1H9rKwsu2TJkj26ff/99/Tp0ydMFUWGyspK3G43Ho+HxYsXc9VVV1WfoNCcaFuLNLzgrl1UrltP5dqfqFq7Dt+2rQQKCvEXFDiBrKgIajmEwsTEENOnjxPC+vcnpl8/orp10wH6IjUYY5Zaa7P27q6WsxZg06ZNjBs3jmAwSFRUFE899VS4SxKRCBMoKaFy7TonhP20lsp1zmt/7pafB/J68bZrhzs1BW/HjsT064snJRVPSjLulBQ8KSm4k5PxpKbibt0a43aHb4FEmjCFsxbgsMMO4+uvvw53GSISQXy5uexavJhdny2i7Kuv8OflVfcz0dFE9exB3KDBRI/rSVTPnkT37ElUWppuUSRyCCiciYi0AIHSUsq++IJdixaxa9FiqkKX43G3TSX+qKHE9Dm8OoR5O3VSq5dIGCmciYg0M7sP0q/66afq1rHyb76BYBATF0fckCzanD+e+GOOIfqww3RAvkiEUTgTEWkiguXlVHz3Hb4tWwkUFuIvKiRQVESgsIhAUZHzvrCIQHExBIPOSC4Xsf37k3rl/xB/9NHEZmRgdIkKkYimcCYiEoGstfhzcyn7ejnly51HxerVe54ZaQzuNm2cg/CTkoju3gP34GTcyUl4kpLwdulCXFYW7latwrcgIvKLKZxFoISEBHbu3HnA3UWk6QtWVlLx7XdOEPv6a8qXL68+SN/ExhLbvz8pl11GbGYmUd264k5Oxt2qlY4NE2mGFM5ERA4xay2+zbmUr1hO+YoVlK9YQeV332NDV833pqURN3QosZkZxA0cSHSvXhiP/lyLtBT6tjeyKVOmkJaWxjXXXAPA3XffTUJCAldeeSVnnXUWRUVF+Hw+7r//fs4666wDmqa1lltuuYV3330XYwx33HEH48ePZ8uWLYwfP54dO3bg9/v55z//yTHHHMPll1/OkiVLMMZw2WWX8fvf/74xF1lE9hIsK6N81arqIFa+YgWBvHwg1CrWrx/Jl/yO2IEDic3I0L0jRVq4FhXOtv7pT1R+v7pBpxnd53A63H57nf3Hjx/PDTfcUB3O5syZw7x584iJiWHu3Lm0atWK/Px8hg4dyplnnnlAZ0298cYbLF++nBUrVpCfn8+QIUM4/vjjmTVrFqeeeir/7//9PwKBAGVlZSxfvpzNmzezatUqAIqLixtmwUVkD8HycnxbtuDbnIsvNxffFue5cs1PVP74IwQCAESlp5NwzDBiMzOIzchQq5iI7EN/ERrZwIED2b59O7m5ueTl5ZGUlERaWho+n4/bb7+dhQsX4nK52Lx5M9u2baNDhw77neann37KBRdcgNvtpn379pxwwgl89dVXDBkyhMsuuwyfz8fZZ59NZmYmPXr0YN26dVx33XWMHj2akSNHHoKlFmmerM9HxeofKF+5At+mTfhytzhBLDeXQFHRngO73XjbtycqPZ3E/7mC2IwMYgYMwJOUFJ7iRaTJaFHhrL4WrsY0duxYXnvtNbZu3cr48eMBeOmll8jLy2Pp0qV4vV7S09OpCN0c+Nc6/vjjWbhwIf/5z3+YOHEiN954I7/73e9YsWIF8+bN44knnmDOnDnMmDGjIRZLpNnz5+VRtnw5FStWOM+rvq2+ibeJjcXbqRPeTp2I6dcPb8eOeDs7770dO+Jp104tYiLyq+gvxyEwfvx4Jk+eTH5+Ph9//DEAJSUltGvXDq/Xy4cffsjGjRsPeHrHHXccTz75JJdccgmFhYUsXLiQhx56iI0bN9KlSxcmT55MZWUly5Yt4/TTTycqKorzzjuP3r17c9FFFzXWYoo0WTYQIFBcjC83l/LlK6ovXeHbvNkZwOsl5og+JI0fR2xmpnNcWMeOuniriDQKhbNDoG/fvpSWltK5c2c6duwIwIQJEzjjjDPo378/WVlZHH744Qc8vXPOOYfFixeTkZGBMYa//OUvdOjQgeeee46HHnoIr9dLQkICzz//PJs3b+bSSy8lGLog5Z///OdGWUaRSOXbvJmK77/HX1BIoLCg9ueiIrC2ehxPu3bEZmaSNGECsZmZxPQ9Ald0dBiXQkRaEmNr/EFq6rKysuySJUv26Pb999/Tp0+fMFUkh5K2tewW2LGDHfPmUfLmm5QvWbpHP1diIp7kZNwpKXhSknEn13hu15bYfv3UKiYih4QxZqm1Nmvv7mo5E5Fmwfp87PzkU0reeoudH3yAraoiqnt32t5wPfHDjsXTNhV3cjIu3bpIRCKcwpmIRITgrl2U/Pvf7PriCzwpqc4B9p064unQAW+nTnhSU/e5Gr61lopVqyh58y12vPMOgcJC3ElJtBk7ltZnn0VMv35qARORJqdFhDNrrf5AN3PNafd8S1O5bj1Fs2dTMncuwZ078XToQHDHDoJlZXsO6PHgbdcOT6eOeDt2wp3Uhl2ffkbVunWYqCgSTjqJ1meeScJxx2K83vAsjIhIA2j24SwmJoaCggJSUlIU0Jopay0FBQXExMSEuxQ5QDYQYOfHCyl66SV2ffYZeL20OvVUkiZcSGxmJgDB0lJ8W7bi25KLf+tW55piW7bg37KF8mXL8OflETOgPx0uvZdWp56qm3uLSLPR7MNZly5dyMnJIS90A2FpnmJiYujSpUu4y5D98BcVUfLGGxTNmo1v82Y87dvT9vr/pc3YsfvcssjdqhXuVq2I6d0rTNWKiIRHsw9nXq+X7t27h7sMkRZr93FhRS+/zI63/4OtrCRuyBDa3XwziSefpF2QIiJ7afbhTETCw7dlCyX/fpuSN9+kau1aTGwsrc85m6QLLySml1rDRETqonAmIg0muGsXO+bPp+TNNyn7/AuwltjBg+lw7z20Ou003ImJ4S5RRCTiKZyJyEGxgQBlX3xByZtvsuP9+djycrxpaaRecw2tzzyDqK5dw12iiEiTonAmItVsMEjV+vVUbdiArarC+v3YKp/z7PNh/T7n2ecDv59AyQ5KFyzAv20brsREWp9xBq3PPovYgQN1drSIyK+kcCbSgvkLCylfsYLylSupWLGS8m++IVhaesDjm+ho4ocOpfVtU0g48UTdf1JEpAEonIm0EMGqKiq/+47ylSspX7GS8hUr8OXkOD1dLqJ79aLVaacRmzGA6F69cMXEgMeD8UZhvB6M14vx/PyMx6PWMRGRRqBwJtLMVa5fT/HLr1D8r38RLCkBwNO+PbEZGSRdcD6xAwYQ07cvrri4MFcqIiKgcCbSLFmfj9IPPqTo5dmULf4cPB4SR5xCq1GnEZuZgbd9+3CXKCIidVA4E2lGfFu3UjznVYpfew3/9u14OnWk7Q3X0+a88/C0bRvu8kRE5AAonIk0cTYYZNfixRTNns3ODz+CYJD4446lw913k3DC8Ri3O9wliojIL6BwJtJEBUpLq+9TWbVxI+6kJFIuu5Q248YRlZYW7vJERORXUjgTaWIq166l6KWXKP7Xm9iyMmIHDqTTtdeSeOpIXFFR4S5PREQOksKZSBNgAwF2fryQohdfYNeixZioKFqNHk3SRROI7ds33OWJiEgDUjgTiWCBkhKKX3+Dolmz8OXk4OnQgbY33ECbcWPxJCeHuzwREWkECmciESawcyflK1ZQ+v58St56C1teTmzWYNrddBOJp5zsXABWRESaLf2VFwkjay1VGzZQ/vVyypc7j8o1a8BaTHQ0rc4YQ/KECcT06RPuUkVE5BBROBM5hILl5c6tk5Yvp/zrrylfsYJAcTEArsREYjMySBw5ktjMTGIzM3AnJIS5YhEROdQUzkQOAX9BAYXPPU/RrFkEd+4EIKpHDxJOPonYzEziMjOJ6tkT43KFuVIREQk3hTORRuTLzaVgxkyKX30VW1VF4qmn0uacs4nNyMDdpk24yxMRkQikcCbSCCrXrafg6acpeestAFqfeSYpkyYR3aN7mCsTEZFIp3Am0oAqvvuO/OlPUTpvHiY6mqQLLiDl0ol4O3UKd2kiItJEKJyJHAQbDBIoKaHyhx8oeGYGuz75BFdCAilXXEHy7y7Gk5IS7hJFRKSJUTgTqYW1Fn9uLpU//YS/oBB/QT6BgkL8BQUECgrwFxTgLywgUFgEgQAA7uRk2t54I0kXnI87MTHMSyAiIk2VwpkIzu2RKn/8kbKlyyhftpSyZV/j37p1j2FMXBye5GQ8KSl4O3cmdsAA3CnJeFJS8bRrR8Lxx+GKjQ3TEoiISHOhcCYtUrCsjPKV31C2bCnlS5dRvnw5wV27APB06EDcoEHEDhpEzBFH4GnXFk9yMq64uDBXLSIiLYHCmbQY/vx8drz7HjveeYfylSud3ZHGEN2rF63OPIO4QYOJGzxIB++LiEhYKZxJsxYoLaV0/gJ2vP02uz7/HIJBog8/nJRJk4gbPIjYzEzcrVqFu0wREZFqCmfS7AQrK9n50cfsePttdn78MbaqCm9aGin/cwWtR48m+je/CXeJIiIidVI4k2bBBoPsWryYHW//h9L58wnu3Ik7NZU254+n9ejRxAwYgDEm3GWKiIjsl8KZNGnBXbso/te/KHr+Bao2bsSVmEjiqSNpPXo0cUcdhXG7w12iiIjIL6JwJk2Sb8sWil56iaI5rxLcsYOYjAF0+t+HSTzlFFzR0eEuT0RE5FdTOJMmpXzlSgqffY4d8+aBtSSOHEnyJb8jbuDAcJcmIiLSIBTOJOJZv5/SBf+l8LnnKP/6a1wJCSRffDFJF11EVJfO4S5PRESkQTVaODPGzADGANuttf1q6T8ceBNYH+r0hrX23lC/UcDfADfwtLX2gcaqUyJXsKqK4ldfpXDGTHybN+Pt0oX2t99G63PPw50QH+7yREREGkVjtpw9CzwOPF/PMJ9Ya8fU7GCMcQP/AEYAOcBXxpi3rLXfNVahElmsz0fxG3PJf+IJ/Fu2EDtoEO2m3EriSSfpAH8REWn2Gi2cWWsXGmPSf8WoRwI/WWvXARhjXgbOAhTOmjnr91Py1r/JnzYNX04OsRkZdPrj/cQdfbQugyEiIi1GuI85O9oYswLIBW6y1n4LdAayawyTAxxV1wSMMVcAVwB07dq1EUuVxmIDAXa88w75j/+Dqo0bienblw533kH88ccrlImISIsTznC2DOhmrd1pjDkd+Bdw2C+diLV2OjAdICsryzZsidKYbDBI6fvzyXv871T9tJboXr3o8vjfSTj5ZIUyERFpscIWzqy1O2q8fscYM80YkwpsBtJqDNol1E2aCWstOz/6iLy/PUbl6tVE9exJ50cfIfHUUzEuV7jLExERCauwhTNjTAdgm7XWGmOOBFxAAVAMHGaM6Y4Tys4HLgxXndKwqrKz2Xr//ez6eCHebl3p9JcHaTV6tA70FxERCWnMS2nMBoYDqcaYHGAq4AWw1j4B/Ba4yhjjB8qB8621FvAbY64F5uFcSmNG6Fg0acKCVVUUPP00BU9Ox7jdtJtyK8kTJmC83nCXJiIiElGMk4eah6ysLLtkyZJwlyF72fnZZ2y79z6qNm4k8bRRtJ8yBW/79uEuS0REJKyMMUuttVl7dw/32ZrSjPm2bWPbAw9Q+u57RHXrRtozT5MwbFi4yxIREYloCmfS4KzfT+GLL5L/2N+xgQBtr/9fki+/HFdUVLhLExERiXgKZ9KgypYtY+s991L5ww/En3A8He64g6i0tP2PKCIiIoDCmRyEwI4dVHz7LRXffkv5qm+pWLUKX04Ono4d6fz3x0g85RRdr0xEROQXUjiTAxLYuZOK776jIhTCyr9dhW/jpur+3i5diOnXj6SLJpA0diyueN2YXERE5NdQOJN6la9cyfZHH6Xs8y8gdGavp1NHYvv2o8255xHTry8xRxyBJykpzJWKiIg0DwpnUqvK9evJ++vfKJ03D3dyMqlXXUXswExi+vbFk5wc7vJERESaLYUz2YNv+3byp02j+NXXMNHRpF57LckTJ+JO0G5KERGRQ0HhTADnmLKCZ56h8NnnsD4fSeefT+pVV+JJTQ13aSIiIi2KwlkLF6yqonj2bPL/+QSB4mJanX46bW+4nqiuXcNdmoiISIukcNZCWWvZ8fbb5D36V3y5ucQfczRtb/wDsf36hrs0ERGRFk3hrAXy5eay5c672PXZZ8QccQQd7rtXt1USERGJEApnLYgNBimeM4ftf3kIC7S/606Szj8f43KFuzQREREJUThrIaqys9lyx52UffEF8cccTYd77yOqS+dwlyUiIiJ7UThr5mwwSNGLL7H90Ucxbjcd7ruXNr/9rW6rJCIiEqEUzpqxyvXr2XLHnZQvXUr88cfR8Z578HbsGO6yREREpB4KZ82QDQQofPY58h57DBMdTcc//5nWZ5+l1jIREZEmQOGsmSn7+mu2PfAAFStWknDSSXSYOhVv+3bhLktEREQOkMJZM2CtZefHH1Pw9NOUL1mKOymJTg8/TKvRp6u1TEREpIlROGvCrM/HjnffpeDpZ6j88Uc8HTvS/vbbaPPb3+KKiwt3eSIiIvIrKJw1QcHycopfe53CmTPx5eYSfdhv6PjAn2k9ejTG6w13eSIiInIQFM6aEH9REUWzZlH04ksEioqIHTiQ9nfcQcLwE3QhWRERkWZC4awJCJSWUvDkkxTOmo0tKyNh+HBSJk8ibvDgcJcmIiIiDUzhLIJZv5/i114j77G/EygqotWYMaRMnkRMr17hLk1EREQaicJZhNr52Wdsf+BBKtesIS4ri3a3TSG2b99wlyUiIiKNTOEswlSuW8/2v/yFnR99hLdLFzr/7W8kjhyhS2KIiIi0EApnESJQXEzetGkUzZqNKzqadjffRNLFF+OKigp3aSIiInIIKZyFmfX5KHr5FfIef5xgaSltxo6l7f9ehyclJdyliYiISBgonIWRb/t2si+/nMo1PxF/zNG0u3UKMb11sL+IiEhLpnAWJsHycnKuvoaqzbl0mfYPEk48UceViYiIiMJZONhgkNwpt1Hx7bd0+cc/SDzpxHCXJCIiIhFCl5UPg7y/PUbpvHm0u+UWBTMRERHZg8LZIVY8918UPPkkbcaOJXniJeEuR0RERCKMwtkhVLZkCVvuuou4oUPpcNedOsZMRERE9qFwdohUbdxIzrXXEdWlC13+9leM1xvukkRERCQCKZwdAoGSErKvvAqsJe2Jf+Ju3TrcJYmIiEiE0tmajcz6fOTccANVOTl0feZporp1C3dJIiIiEsEUzhqRtZat991P2eLP6finPxF/5JHhLklEREQinHZrNqLC556jeM4cUiZPps2554S7HBEREWkCFM4aSekHH7L9wb+QOGIEbX9/Q7jLERERkSZC4awRlH/7LZtvuomYI46g04MPYFxazSIiInJglBoaWNnSpWy6ZCLuNq3pMm0arri4cJckIiIiTYjCWQPa+cknbLp8Ep7UVNJffBFv+3bhLklERESaGIWzBrLj3XfJvvoaorp3p9tLL+Lt1CncJYmIiEgTpHDWAIrmzGHzjX8gdsAAuj33LJ6UlHCXJCIiIk2UrnN2kAqeeYbtDz1M/HHH0eWxv+GKjQ13SSIiItKEKZz9StZa8h79KwXTp5N42ig6P/ggJioq3GWJiIhIE6dw9ivYYJCt991H8eyXaTN2LB3unopxu8NdloiIiDQDCme/kPX5yL3tdna8/TYpky6n7R/+gDEm3GWJiIhIM6Fw9gsEKyrYfMPv2fnRR7S98UZSr5gc7pJERESkmVE4O0DW7yd78hWULVlCh6l3kXTBBeEuSURERJohhbMDZDweEk44njbjx9N6zOhwlyMiIiLNlMLZL5AyaVK4SxAREZFmThehFREREYkgCmciIiIiEUThTERERCSCKJyJiIiIRBCFMxEREZEIonAmIiIiEkEUzkREREQiSKOFM2PMDGPMdmPMqjr6TzDGrDTGfGOMWWSMyajRb0Oo+3JjzJLGqlFEREQk0jRmy9mzwKh6+q8HTrDW9gfuA6bv1f9Ea22mtTarkeoTERERiTiNdocAa+1CY0x6Pf0X1Xj7OdClsWoRERERaSoi5Zizy4F3a7y3wPvGmKXGmCvqG9EYc4UxZokxZkleXl6jFikiIiLS2MJ+b01jzIk44ezYGp2PtdZuNsa0A+YbY1ZbaxfWNr61djqhXaJZWVm20QsWERERaURhbTkzxgwAngbOstYW7O5urd0cet4OzAWODE+FIiIiIodW2MKZMaYr8AZwsbX2xxrd440xibtfAyOBWs/4FBEREWluGm23pjFmNjAcSDXG5ABTAS+AtfYJ4C4gBZhmjAHwh87MbA/MDXXzALOste81Vp0iIiIikaQxz9a8YD/9JwGTaum+DsjYdwwRERGR5i9SztYUERERERTORERERCKKwpmIiIhIBFE4ExEREYkgCmciIiIiEUThTERERCSCKJyJiIiIRBCFMxEREZEIonAmIiIiEkEUzkREREQiiMKZiIiISARROBMRERGJIApnIiIiIhFE4UxEREQkgiiciYiIiEQQhTMRERGRCKJwJiIiIhJBFM5EREREIojCmYiIiEgEUTgTERERiSAKZyIiIiIRROFMREREJIIonImIiIhEEIUzERERkQiicCYiIiISQRTORERERCKIwpmIiIhIBFE4ExEREYkgCmciIiIiEUThTERERCSCKJyJiIiIRBCFMxEREZEIonAmIiIiEkEUzkREREQiiMKZiIiISARROBMRERGJIApnIiIiIhFE4UxEREQkgiiciYiIiEQQhTMRERGRCKJwJiIiIhJBFM5EREREIojCmYiIiEgEUTgTERERiSAKZyIiIiIRROFMREREJIIonImIiIhEEIUzERERkQiicCYiIiISQRTORERERCKIwpmIiIhIBFE4ExEREYkgCmciIiIiEUThTERERCSCKJyJiIiIRBCFMxEREZEIonAmIiIiEkEUzkREREQiiMKZiIiISARROBMRERGJII0azowxM4wx240xq+rob4wxjxljfjLGrDTGDKrR7xJjzJrQ45LGrFNEREQkUjR2y9mzwKh6+p8GHBZ6XAH8E8AYkwxMBY4CjgSmGmOSGrVSERERkQjgacyJW2sXGmPS6xnkLOB5a60FPjfGtDHGdASGA/OttYUAxpj5OCFvdmPWuz///X4bBbuq8AWC+PxBfAFLVSDovA8EqarRzR8I4g9agkFLwOI8By0Bu7ub8z5oLQaDMWCMwQCu0GuXAXCeXcYZ5kC5XQav24XHZfC4DR6XK/Rs8LhdeF0Gt8sVqiOIP2DxBy3+QBBf0BIIWHyh7oHgz7UGLYDzbEPvLc5ra8Fif9E6dZar7uU2BgyGoLXV8znYeVtL9fBBC1hCy+ZMwem2e1nZZ9vs3hY/d3Pe79u99mEtTr21rcugdWYatLuHsfvUW3MdhEoVEZEGFOVx8fpVx4Rt/o0azg5AZyC7xvucULe6uu/DGHMFTqsbXbt2bZwqQ/7v/R/5bsuOWvt5QmHI6zZEedx4XAZ3jYfLEHreu7uTuH7+Ed4dEH7+Abeh4HCgrIVA0AlXTsiy1WHRCWFOiKxZ++4w5zw7Ya76tdtVI2j8HJpcocBhaoaTX7A+9w5btS337lDS0PPeIzC5DF6X2SMkuwzV09sdgGoGp2Dw57C0O7jWrL/m8jjD/rwd95mPqT2k7hH8XOAyrjqHFRGRhhPlDu8h+eEOZwfNWjsdmA6QlZXVqG0IT12SRTBoifK4iHK78HqcMOZ1uXC5mt4PZCBo9eMuIiISYcIdzjYDaTXedwl124yza7Nm948OWVV16NwmNtwlNCh3EwyUIiIizV24L6XxFvC70FmbQ4ESa+0WYB4w0hiTFDoRYGSom/z/9u4/SO76vu/467V7e3eSTugHCLAlgbGNg3FjYLjQpNit4xRCHQ+4DTE4sYe4dJjpmIbESVvcZCYpaWZIk6mTmTANjM0Ep46xi0ujZmgwBsdNZ4LRCfAPhElkYpeTQZItfuh00t3t7rt/fL+7+929PWkl3Vf74e75kHa+3+9nv/v9fr4/9vt9fT/73VsAALCildpyZvtzylrAzrI9rewbmDVJiog/lvSQpPdJ2iNpVtJH8+cO2v5tSTvzSd3R+nIAAADASlb2tzU/dJznQ9LHlnjuXkn3llEvAACAVA37Y00AAAAUEM4AAAASQjgDAABICOEMAAAgIYQzAACAhBDOAAAAEkI4AwAASAjhDAAAICGEMwAAgIQQzgAAABJCOAMAAEgI4QwAACAhhDMAAICEEM4AAAASQjgDAABICOEMAAAgIQOFM9u32T7DmU/bftL21WVXDgAAYLUZtOXsX0bEa5KulrRJ0kck3VlarQAAAFapQcOZ8+77JP1pRDxTKAMAAMAyGTSc7bL9JWXh7GHb6yU1y6sWAADA6jQy4Hg3S7pU0vMRMWt7s6SPllctAACA1WnQlrOfkPRcRLxi+8OSfkPSq+VVCwAAYHUaNJz9V0mzti+R9KuSviPpM6XVCgAAYJUaNJzVIyIkXSfpjyLiLknry6sWAADA6jToPWeHbH9C2Z/QeLftiqRaedUCAABYnQZtObtB0pyyv3f2kqRtkn6vtFoBAACsUgOF0P4ajgAAG6JJREFUszyQfVbSBtvvl3Q0IrjnDAAAYJkN+vNNH5T0hKSfk/RBSV+zfX2ZFQMAAFiNBr3n7Ncl/VhE7Jck21skfVnSA2VVDAAAYDUa9J6zSiuY5X54Aq8FAADAgAZtOftL2w9L+lw+fIOkh8qpEgAAwOo1UDiLiH9r+2clXZkX3RMRD5ZXLQAAgNVp0JYzRcQXJX2xxLoAAACsescMZ7YPSYp+T0mKiDijlFoBAACsUscMZxHBTzQBAACcRnzjEgAAICGEMwAAgIQQzgAAABJCOAMAAEgI4QwAACAhhDMAAICEEM4AAAASQjgDAABICOEMAAAgIYQzAACAhBDOAAAAEkI4AwAASMgxf/gcJ+nIK9LMPmndFmnNJsle3ulHSM26VJ+TGvNSYyHv5v3NulQdlaq1vJv3j4xl/ZVqZ1rNpjT3qjR7MKv3kYPSkZfz4Zc7w9HMlmXNJmnN5qy7dnP38JqN3dM+WY26ND8jzR/OuiPj0obtUuUUriUaC9K+b0nTU9LeJ6WFw93rpt3fp2ykX/lY9ziVinT0tT7r7+XFw7Y0ui5/TOSPvH9sovNcbW13nZaqx8ioNL4h2wajEye/v0VIc69ldZ17rbNf1ecW72ONfN9rNrL5TpwtTZwrrT9HGt84eB0isnUysy97HNonHX1Vqq3J10Vh3RTXUW3t8r+vyhKRrdOZlzrL2F7el6SZ/dlzc4eksy+Wtv1Y9th6ubTuzGHXfvVqLGTHn7n8WFStSevPzfZDoGSEs1Nx9DXpwLel/c92dw+92BmnOipNnNN98prIH+vPzQLcwpHspN4biGZf7h6eP9w5QZ4KV/JAUctCSjSXHnd8YydgHnk5C3CKpccfO6MTAnvDYVegqWUn/fnD+eNQp79+dPF0a2ulLT8ibXm7dPZFne6G7YtP0hHSq9PS9E5p766s++LXO9Ndd3YWLBvzUn2+J3TMS82FE16lfdXWdgfXsy/qBJe5Qvic/YH0yvc6w3MzUjRObp6VWv/gvDYP1tFcvF8Vg+PJzreoOtbZ59ef29nfpSyEFMPJzL6T3J/zgDu+sbNsS100rN2crZf5me7QX9wGrfL60Wy79Q3MxbC4Nn/f9lzI9Avlsz/sv0+NTnSOC+f+aDbfl74h/fXvd96Tmy7Iw9pk9jjnR7MwXjR3qLBOW+s3D33zM0tchLTej4ULt9Z7vbj+xjdI1VM8TbQuJlvvs973XP2odPSV/hc1veu32ehzwdlnuSojg4X3iOwY2Dr2zM109oel9svRic7xu99xfe2Z2TF2OVVr3ftg7z6wlIjsYqffeaU13epoz3rsuQCNGOy9s3AkW+/H3T59Lm6XGrd1YbNUo0FreO6QNLZ+6fd/cbi5cOxptfqjIf3a3y7vdjwBjjjGifZ1ZnJyMqampsqbwTcfkL7/VCeEvba381xtrXTW27Ir37Mvkta/MTvpFq+MWwfQ2R8cez4j44WTeuHEOrq+0HrSr7UnL69U86DRr7Wjp2x0YukdenzD4pawZrNzIO138Dz66hItLfOLH9WxY7eOtPrnZ6T935YOPJt1Z17q1Gd0ohPaznijtH93FsZm9nXW5Rsu6bREbJvsH+iKIjrrq56HteJy9Fu+Zl0aP6N7u9XGT2z/Ks6/PictzPZfh31Pbq8ev9WufiSbfm1dvr03Ln0AGz8jW3fHa1WsjGTzPfRST2tQoSVs5qXOyWDN5j4ntXMLQe7crF4Ls4UTZiG4F0P83KG8tbfPAfZEQmat1Yq5LlvmhdnusDao6mif9bkpO1kXQ2qrf2yi/3TmD0vffzrbj6d3Zq29rX2+Oia94Z3Zem8dWxYOL55GpZbNY/yMPvtQYT8+1oVWS6tVtrVslZH+x5JjtbCeqNrawjw35sejjdk67p12cblanyY06yc2r9F1fY5FPS3Z9flO6O3dx+cPnfgynopKrVDfQh1HxrJjQTvQvLI8F1xLaa270YmstbsdwvvsG8tSD2f7Y/F4tXZz5zzRG7iO15ggZftz1/u20H/VHaW30NveFRGTi8oJZyfgnvdI+3ZLW96Wt9zkjy0XSRvPH/xjt8ZCHtj2SYcPZDt1cYeorSlvGVaC2YPSgefysPZsJywfPiBtfkveypCHsXP+weBXmSvdwpHsin5k7PTPuz6XdU/HvIsfzxZbXPqF/9raY38U36hn4adfS8Gi921JH7VGZBeC01NZWPv+U5LcJ9gWwt+gt1M0G9m26Wq9OkbLarH1qm9ry4ncGlB4rNnYHQBP9sJmWOYPd4LakYPZNltOjfnFFyh9W37nFq/LpW5BcWWwiz9pcRBs3VpwIrexNBuFUF1oSW3ML10PV7rr36/B4HjzbLUctvbjak8YO5VbQZYB4Ww5HP5BfuW4DPdVYfnV54YTPAAAOAlLhTPuOTsR684adg1wLAQzAMAKwJ/SAAAASAjhDAAAICGEMwAAgIQQzgAAABJCOAMAAEgI4QwAACAhhDMAAICElBrObF9j+znbe2zf3uf5T9p+On/8re1XCs81Cs/tKLOeAAAAqSjtj9Darkq6S9JVkqYl7bS9IyJ2t8aJiF8pjP9vJF1WmMSRiLi0rPoBAACkqMyWsysk7YmI5yNiXtL9kq47xvgfkvS5EusDAACQvDLD2VZJLxSGp/OyRWyfL+kCSY8VisdtT9l+3PYHlpqJ7Vvy8aYOHDiwHPUGAAAYmlS+EHCjpAciolEoOz//MdCfl/QHtt/S74URcU9ETEbE5JYtW05HXQEAAEpTZjjbK2l7YXhbXtbPjer5SDMi9ubd5yX9lbrvRwMAAFiRygxnOyVdaPsC26PKAtiib13avkjSJkl/UyjbZHss7z9L0pWSdve+FgAAYKUp7duaEVG3faukhyVVJd0bEc/YvkPSVES0gtqNku6PiCi8/O2S7rbdVBYg7yx+yxMAAGClcncmen2bnJyMqampYVcDAADguGzvyu+v75LKFwIAAAAgwhkAAEBSCGcAAAAJIZwBAAAkhHAGAACQEMIZAABAQghnAAAACSGcAQAAJIRwBgAAkBDCGQAAQEIIZwAAAAkhnAEAACSEcAYAAJAQwhkAAEBCCGcAAAAJIZwBAAAkhHAGAACQEMIZAABAQghnAAAACSGcAQAAJIRwBgAAkBDCGQAAQEIIZwAAAAkhnAEAACSEcAYAAJAQwhkAAEBCCGcAAAAJIZwBAAAkhHAGAACQEMIZAABAQghnAAAACSGcAQAAJIRwBgAAkBDCGQAAQEIIZwAAAAkhnAEAACSEcAYAAJAQwhkAAEBCCGcAAAAJIZwBAAAkhHAGAACQEMIZAABAQghnAAAACSGcAQAAJIRwBgAAkBDCGQAAQEIIZwAAAAkhnAEAACSEcAYAAJAQwhkAAEBCCGcAAAAJIZwBAAAkhHAGAACQEMIZAABAQghnAAAACSGcAQAAJIRwBgAAkBDCGQAAQEIIZwAAAAkhnAEAACSk1HBm+xrbz9neY/v2Ps//ou0Dtp/OH/+q8NxNtv8uf9xUZj0BAABSMVLWhG1XJd0l6SpJ05J22t4REbt7Rv18RNza89rNkn5T0qSkkLQrf+3LZdUXAAAgBWW2nF0haU9EPB8R85Lul3TdgK/9aUmPRMTBPJA9IumakuoJAACQjDLD2VZJLxSGp/OyXj9r+xu2H7C9/QRfK9u32J6yPXXgwIHlqDcAAMDQDPsLAf9L0psi4p3KWsfuO9EJRMQ9ETEZEZNbtmxZ9goCAACcTmWGs72StheGt+VlbRHxw4iYywc/JenyQV8LAACwEpUZznZKutD2BbZHJd0oaUdxBNtvKAxeK+nZvP9hSVfb3mR7k6Sr8zIAAIAVrbRva0ZE3fatykJVVdK9EfGM7TskTUXEDkm/ZPtaSXVJByX9Yv7ag7Z/W1nAk6Q7IuJgWXUFAABIhSNi2HVYNpOTkzE1NTXsagAAAByX7V0RMdlbPuwvBAAAAKCAcAYAAJAQwhkAAEBCCGcAAAAJIZwBAAAkhHAGAACQEMIZAABAQghnAAAACSGcAQAAJIRwBgAAkBDCGQAAQEIIZwAAAAkhnAEAACSEcAYAAJAQwhkAAEBCCGcAAAAJIZwBAAAkhHAGAACQEMIZAABAQghnAAAACSGcAQAAJIRwBgAAkBDCGQAAQEIIZwAAAAkhnAEAACSEcAYAAJAQwhkAAEBCCGcAAAAJIZwBAAAkhHAGAACQEMIZAABAQghnAAAACSGcAQAAJIRwBgAAkBDCGQAAQEIIZwAAAAkhnAEAACSEcAYAAJAQwhkAAEBCCGcAAAAJIZwBAAAkhHAGAACQEMIZAABAQghnAAAACSGcAQAAJIRwBgAAkBDCGQAAQEIIZwAAAAkhnAEAACSEcAYAAJAQwhkAAEBCCGcAAAAJIZwBAAAkhHAGAACQEMIZAABAQghnAAAACSGcAQAAJIRwBgAAkBDCGQAAQEJKDWe2r7H9nO09tm/v8/zHbe+2/Q3bj9o+v/Bcw/bT+WNHmfUEAABIxUhZE7ZdlXSXpKskTUvaaXtHROwujPaUpMmImLX9ryX9Z0k35M8diYhLy6ofAABAispsObtC0p6IeD4i5iXdL+m64ggR8ZWImM0HH5e0rcT6AAAAJK/McLZV0guF4em8bCk3S/rfheFx21O2H7f9gaVeZPuWfLypAwcOnFqNAQAAhqy0jzVPhO0PS5qU9E8KxedHxF7bb5b0mO1vRsR3el8bEfdIukeSJicn47RUGAAAoCRltpztlbS9MLwtL+ti+59K+nVJ10bEXKs8Ivbm3ecl/ZWky0qsKwAAQBLKDGc7JV1o+wLbo5JulNT1rUvbl0m6W1kw218o32R7LO8/S9KVkopfJAAAAFiRSvtYMyLqtm+V9LCkqqR7I+IZ23dImoqIHZJ+T9KEpP9uW5L+X0RcK+ntku623VQWIO/s+ZYnAADAiuSIlXOb1uTkZExNTQ27GgAAAMdle1dETPaW8wsBAAAACSGcAQAAJIRwBgAAkBDCGQAAQEIIZwAAAAkhnAEAACSEcAYAAJAQwhkAAEBCCGcAAAAJIZwBAAAkhHAGAACQEMIZAABAQghnAAAACSGcAQAAJIRwBgAAkBDCGQAAQEIIZwAAAAkhnAEAACSEcAYAAJAQwhkAAEBCCGcAAAAJIZwBAAAkhHAGAACQEMIZAABAQghnAAAACSGcAQAAJIRwBgAAkBDCGQAAQEIIZwAAAAkhnAEAACSEcAYAAJAQwhkAAEBCCGcAAAAJIZwBAAAkhHAGAACQEMIZAABAQghnAAAACSGcAQAAJIRwBgAAkBDCGQAAQEIIZwAAAAkhnAEAACSEcAYAAJAQwhkAAEBCCGcAAAAJIZwBAAAkhHAGAACQEMIZAABAQghnAAAACSGcAQAAJIRwBgAAkBDCGQAAQEIIZwAAAAkhnAEAACSEcAYAAJAQwhkAAEBCCGcAAAAJIZwBAAAkhHAGAACQkJFhV+D15LbHbtPBowe1bf227DGRdbev366z1pylism6AADg1JQazmxfI+kPJVUlfSoi7ux5fkzSZyRdLumHkm6IiO/mz31C0s2SGpJ+KSIeLrOugzh/w/maWZjRk/ue1EN//5Ca0Ww/N1Yd09aJre3Qdu66czU+Mq7x6rjGqmPZYyTrjlfHNVod1fhI9lzFFVmWbUlq97f/5eUjlRGNVkY1UhlplwEAgJWltHBmuyrpLklXSZqWtNP2jojYXRjtZkkvR8Rbbd8o6Xcl3WD7Ykk3SnqHpDdK+rLtt0VEo6z6DuLjl3+83b/QWNCLh1/UC4de0PShaU3PTGv60LReOPSCpl6a0mx9ttS6tIJarVpTrZI9RqujqlVqqrqqUKgZTUWEmsq70czK1OmvVWqdEDky1gmTI2NaM7KmHSZr1Wy6FVdUdVXVSrV72FVVKlm/5SxwuhMuK8qH87KKK2pEQ41mQ41oqN6sq96st8vqzbrqUVejmW3y0epo51EZ1Vh1TLVqrd0/Ws3WhdUJuJK6Am9Rq97F5ektq1Sy0NxaV4seaqrZzLvRlOVs+Z0tXyt0t9ZFxZX2ehhUa921pteeljr9AICVpcyWsysk7YmI5yXJ9v2SrpNUDGfXSfqtvP8BSX/k7GxznaT7I2JO0t/b3pNP729KrO8JqVVrOu+M83TeGectei4iNFuf1dH6Uc015tqPpYZbLXARofa/vL9V3goEC80FLTQXNN+Y79tfb9a10FzoOnkXQ0ExNNjWQnNBR+tHdbRxVHP1Ob02/1q7v13emNN8Y75dH6SlHdxUUbVS7QqJvV1J7X2pEQ01m1k3FGo0G+3yUPTdb9rzK5QXW3fbw7Ky/93hvNexQnRxf4uIvuVd8yxcDLRCcWvarekuNc3jKda93wVAsd5dLeCF+bbe18XlaA0X61Vchtb02hc3+Xo9Wb0XKf3qfazxu57rt06WuBhaLktOt09x73aS+td5oKpG7+Bg+1HXvtHueNH6OhnH23/7LqtOcLn7TbfftPrMc9BpnFQdTsN8atWa7nz3nccfsSRlhrOtkl4oDE9L+odLjRMRdduvSjozL3+857Vb+83E9i2SbpGk885bHJSGwbbW1dZpXW3dsKuyrCIiO6FHU/VmvXOCz7utk3ux1a7dXwicreFWi1u1UtWIRzRSGWm3yI1U8uG8FXChkYXQucac5pvzWmgstPvnG52H1Dlwtg+g7U6nvCugRLPdgtdb1qpnK+AUW7C6WrPUHXx6WyhbLZjFj8IHWd/FaSw1zVY9G9Fob6Pitio+WieLdgth3mJYUaXd8tlantY2a7UStvqL5Y28MbvfhUVvt7hc/bZTsbzfyavfSaE1z2Y0pehs29Y0i/vhsabZe7BfKgz2rXth/youS3Gc4i0KXaHB3SGiPY3oDnS93ZM5qfeezLuWq9+FV9+ixWGyuE4i4rihYbn1q3u/0Ns1XvQpO45FJ3of4zkt3jda9eq3Do9nqW2+VPg4Xng82QvtY10kDTrNMvaPQZexeBw4nrHq2CnX61S87r8QEBH3SLpHkiYnJ2naKZFtjTjbZUaro6d13rVKTWtra0/rPAEAGIYyv164V9L2wvC2vKzvOLZHJG1Q9sWAQV4LAACw4pQZznZKutD2BbZHld3gv6NnnB2Sbsr7r5f0WGTtkzsk3Wh7zPYFki6U9ESJdQUAAEhCaR9r5veQ3SrpYWV/SuPeiHjG9h2SpiJih6RPS/rT/Ib/g8oCnPLxvqDsywN1SR8b9jc1AQAATgef7ps3yzQ5ORlTU1PDrgYAAMBx2d4VEZO95fxJewAAgIQQzgAAABJCOAMAAEgI4QwAACAhhDMAAICEEM4AAAASQjgDAABICOEMAAAgIYQzAACAhBDOAAAAEkI4AwAASAjhDAAAICGEMwAAgIQQzgAAABJCOAMAAEgI4QwAACAhhDMAAICEEM4AAAASQjgDAABIiCNi2HVYNrYPSPpeybM5S9IPSp4HTg7bJm1sn7SxfdLFtknbqWyf8yNiS2/higpnp4PtqYiYHHY9sBjbJm1sn7SxfdLFtklbGduHjzUBAAASQjgDAABICOHsxN0z7ApgSWybtLF90sb2SRfbJm3Lvn245wwAACAhtJwBAAAkhHAGAACQEMLZgGxfY/s523ts3z7s+qx2tu+1vd/2twplm20/Yvvv8u6mYdZxtbK93fZXbO+2/Yzt2/Jytk8CbI/bfsL21/Pt8x/z8gtsfy0/xn3e9uiw67pa2a7afsr2X+TDbJtE2P6u7W/aftr2VF627Mc2wtkAbFcl3SXpn0m6WNKHbF883Fqten8i6ZqestslPRoRF0p6NB/G6VeX9KsRcbGkH5f0sfz9wvZJw5yk90bEJZIulXSN7R+X9LuSPhkRb5X0sqSbh1jH1e42Sc8Whtk2afnJiLi08LfNlv3YRjgbzBWS9kTE8xExL+l+SdcNuU6rWkT8H0kHe4qvk3Rf3n+fpA+c1kpBkhQRL0bEk3n/IWUnma1i+yQhMjP5YC1/hKT3SnogL2f7DIntbZJ+RtKn8mGLbZO6ZT+2Ec4Gs1XSC4Xh6bwMaTknIl7M+1+SdM4wKwPJ9pskXSbpa2L7JCP/2OxpSfslPSLpO5JeiYh6PgrHuOH5A0n/TlIzHz5TbJuUhKQv2d5l+5a8bNmPbSOnOgEgRRERtvk7MUNke0LSFyX9ckS8ljUAZNg+wxURDUmX2t4o6UFJFw25SpBk+/2S9kfELtvvGXZ90Ne7ImKv7bMlPWL728Unl+vYRsvZYPZK2l4Y3paXIS37bL9BkvLu/iHXZ9WyXVMWzD4bEf8jL2b7JCYiXpH0FUk/IWmj7dYFO8e44bhS0rW2v6vs9pn3SvpDsW2SERF78+5+ZRc2V6iEYxvhbDA7JV2Yf2NmVNKNknYMuU5YbIekm/L+myT9+RDrsmrl98h8WtKzEfFfCk+xfRJge0veYibbayRdpey+wK9Iuj4fje0zBBHxiYjYFhFvUnaeeSwifkFsmyTYXmd7fatf0tWSvqUSjm38QsCAbL9P2b0AVUn3RsTvDLlKq5rtz0l6j6SzJO2T9JuS/qekL0g6T9L3JH0wInq/NICS2X6XpL+W9E117pv5D8ruO2P7DJntdyq7abmq7AL9CxFxh+03K2ut2SzpKUkfjoi54dV0dcs/1vy1iHg/2yYN+XZ4MB8ckfRnEfE7ts/UMh/bCGcAAAAJ4WNNAACAhBDOAAAAEkI4AwAASAjhDAAAICGEMwAAgIQQzgDgJNh+j+2/GHY9AKw8hDMAAICEEM4ArGi2P2z7CdtP2747/9HvGduftP2M7Udtb8nHvdT247a/YftB25vy8rfa/rLtr9t+0vZb8slP2H7A9rdtfzb/dQTZvtP27nw6vz+kRQfwOkU4A7Bi2X67pBskXRkRl0pqSPoFSeskTUXEOyR9VdkvTEjSZyT9+4h4p7JfOGiVf1bSXRFxiaR/JOnFvPwySb8s6WJJb5Z0Zf7Xwv+5pHfk0/lP5S4lgJWGcAZgJfspSZdL2mn76Xz4zcp+Vurz+Tj/TdK7bG+QtDEivpqX3yfpH+e/pbc1Ih6UpIg4GhGz+ThPRMR0RDQlPS3pTZJelXRU0qdt/wtJrXEBYCCEMwArmSXdFxGX5o8fiYjf6jPeyf6OXfH3DRuSRiKiLukKSQ9Ier+kvzzJaQNYpQhnAFayRyVdb/tsSbK92fb5yo591+fj/Lyk/xsRr0p62fa78/KPSPpqRBySNG37A/k0xmyvXWqGtickbYiIhyT9iqRLylgwACvXyLArAABliYjdtn9D0pdsVyQtSPqYpMOSrsif26/svjRJuknSH+fh63lJH83LPyLpbtt35NP4uWPMdr2kP7c9rqzl7uPLvFgAVjhHnGxrPgC8PtmeiYiJYdcDAPrhY00AAICE0HIGAACQEFrOAAAAEkI4AwAASAjhDAAAICGEMwAAgIQQzgAAABLy/wFclFPzsxxhVQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#ANN model with SGD optimizer or default 0.01 learning rate\n",
        "plt.figure(figsize = (10,10))\n",
        "plt.plot(history.history['accuracy'], label='training accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='val accuracy')\n",
        "plt.plot(history.history['loss'], label='training loss')\n",
        "plt.plot(history.history['val_loss'], label='val loss')\n",
        "plt.title('Loss/accuracy in ANN model after normalization')\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JW7cuanGcytk"
      },
      "outputs": [],
      "source": [
        "predict_x=model.predict(X1_test) \n",
        "classes_x=np.argmax(predict_x,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9IPuKwyscytl",
        "outputId": "f8b1e40f-7c77-4cc4-fa53-2aedafd58af3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.96      0.96       206\n",
            "           1       0.84      0.69      0.75       163\n",
            "           2       0.53      0.63      0.58       175\n",
            "           3       0.93      0.81      0.87       143\n",
            "           4       0.49      0.50      0.49       138\n",
            "           5       0.84      0.85      0.84       196\n",
            "           6       0.93      1.00      0.96       207\n",
            "           7       0.87      0.84      0.85       152\n",
            "\n",
            "    accuracy                           0.80      1380\n",
            "   macro avg       0.80      0.78      0.79      1380\n",
            "weighted avg       0.81      0.80      0.80      1380\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(Y1_test, classes_x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QvN59laocytm",
        "outputId": "891a36aa-9e86-46d2-e56e-326d53c1847f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[[1166,    8],\n",
              "        [   9,  197]],\n",
              "\n",
              "       [[1195,   22],\n",
              "        [  51,  112]],\n",
              "\n",
              "       [[1106,   99],\n",
              "        [  64,  111]],\n",
              "\n",
              "       [[1228,    9],\n",
              "        [  27,  116]],\n",
              "\n",
              "       [[1170,   72],\n",
              "        [  69,   69]],\n",
              "\n",
              "       [[1153,   31],\n",
              "        [  30,  166]],\n",
              "\n",
              "       [[1157,   16],\n",
              "        [   1,  206]],\n",
              "\n",
              "       [[1209,   19],\n",
              "        [  25,  127]]])"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import multilabel_confusion_matrix\n",
        "multilabel_confusion_matrix(Y1_test, classes_x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rKun2GzXemQ7"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5O2944N_enUx"
      },
      "source": [
        "### AFTER AUGMENTING THE IMAGE:\n",
        "      No OF EPOCHS: 50\n",
        "      Optimizer: Adam/SGD both almost works the same. SGD works a little better than Adam at higher epochs.\n",
        "      Loss: Categorical_Crossentropy\n",
        "      Learning Rate: 0.01\n",
        "      {We are using more epochs for SGD and hence mentioned the SGD default learning rate aver Adam)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8M9iYN_utMeY"
      },
      "outputs": [],
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "data_generator = ImageDataGenerator(\n",
        "                        featurewise_center=False,\n",
        "                        featurewise_std_normalization=False,\n",
        "                        rotation_range=10,\n",
        "                        width_shift_range=0.1,\n",
        "                        height_shift_range=0.1,\n",
        "                        zoom_range=.25,\n",
        "                        horizontal_flip=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### SGD AT LEARNING RATE =0.001, EPOCHS=25, AUGMENTED IMAGE"
      ],
      "metadata": {
        "id": "TBZpNsYmuOC7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ny0u996Ctm-B",
        "outputId": "a5587f72-3ffd-4f07-a329-e36e8cd37233"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  after removing the cwd from sys.path.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "173/173 [==============================] - 29s 162ms/step - loss: 1.6754 - accuracy: 0.3696\n",
            "Epoch 2/50\n",
            "173/173 [==============================] - 28s 162ms/step - loss: 1.3149 - accuracy: 0.5244\n",
            "Epoch 3/50\n",
            "173/173 [==============================] - 28s 162ms/step - loss: 1.2085 - accuracy: 0.5583\n",
            "Epoch 4/50\n",
            "173/173 [==============================] - 28s 162ms/step - loss: 1.1512 - accuracy: 0.5793\n",
            "Epoch 5/50\n",
            "173/173 [==============================] - 28s 161ms/step - loss: 1.1065 - accuracy: 0.6019\n",
            "Epoch 6/50\n",
            "173/173 [==============================] - 28s 161ms/step - loss: 1.0699 - accuracy: 0.6115\n",
            "Epoch 7/50\n",
            "173/173 [==============================] - 28s 161ms/step - loss: 1.0336 - accuracy: 0.6251\n",
            "Epoch 8/50\n",
            "173/173 [==============================] - 28s 161ms/step - loss: 0.9869 - accuracy: 0.6405\n",
            "Epoch 9/50\n",
            "173/173 [==============================] - 28s 162ms/step - loss: 0.9850 - accuracy: 0.6431\n",
            "Epoch 10/50\n",
            "173/173 [==============================] - 28s 161ms/step - loss: 0.9526 - accuracy: 0.6652\n",
            "Epoch 11/50\n",
            "173/173 [==============================] - 28s 161ms/step - loss: 0.8945 - accuracy: 0.6748\n",
            "Epoch 12/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.8968 - accuracy: 0.6684\n",
            "Epoch 13/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.8599 - accuracy: 0.6951\n",
            "Epoch 14/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.8367 - accuracy: 0.6976\n",
            "Epoch 15/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.8387 - accuracy: 0.6978\n",
            "Epoch 16/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.8073 - accuracy: 0.7134\n",
            "Epoch 17/50\n",
            "173/173 [==============================] - 28s 162ms/step - loss: 0.8035 - accuracy: 0.7095\n",
            "Epoch 18/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.7958 - accuracy: 0.7164\n",
            "Epoch 19/50\n",
            "173/173 [==============================] - 28s 161ms/step - loss: 0.7882 - accuracy: 0.7192\n",
            "Epoch 20/50\n",
            "173/173 [==============================] - 28s 161ms/step - loss: 0.7728 - accuracy: 0.7233\n",
            "Epoch 21/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.7459 - accuracy: 0.7313\n",
            "Epoch 22/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.7589 - accuracy: 0.7260\n",
            "Epoch 23/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.7420 - accuracy: 0.7302\n",
            "Epoch 24/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.7477 - accuracy: 0.7298\n",
            "Epoch 25/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.7112 - accuracy: 0.7434\n",
            "Epoch 26/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.7159 - accuracy: 0.7409\n",
            "Epoch 27/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.7021 - accuracy: 0.7467\n",
            "Epoch 28/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.7054 - accuracy: 0.7489\n",
            "Epoch 29/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.6931 - accuracy: 0.7438\n",
            "Epoch 30/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.6781 - accuracy: 0.7547\n",
            "Epoch 31/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.6904 - accuracy: 0.7509\n",
            "Epoch 32/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.6723 - accuracy: 0.7550\n",
            "Epoch 33/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.6891 - accuracy: 0.7514\n",
            "Epoch 34/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.6595 - accuracy: 0.7632\n",
            "Epoch 35/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.6760 - accuracy: 0.7534\n",
            "Epoch 36/50\n",
            "173/173 [==============================] - 28s 159ms/step - loss: 0.6731 - accuracy: 0.7594\n",
            "Epoch 37/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.6444 - accuracy: 0.7654\n",
            "Epoch 38/50\n",
            "173/173 [==============================] - 28s 162ms/step - loss: 0.6541 - accuracy: 0.7623\n",
            "Epoch 39/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.6533 - accuracy: 0.7596\n",
            "Epoch 40/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.6393 - accuracy: 0.7610\n",
            "Epoch 41/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.6514 - accuracy: 0.7690\n",
            "Epoch 42/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.6422 - accuracy: 0.7641\n",
            "Epoch 43/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.6317 - accuracy: 0.7670\n",
            "Epoch 44/50\n",
            "173/173 [==============================] - 28s 161ms/step - loss: 0.6311 - accuracy: 0.7683\n",
            "Epoch 45/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.6110 - accuracy: 0.7755\n",
            "Epoch 46/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.6364 - accuracy: 0.7666\n",
            "Epoch 47/50\n",
            "173/173 [==============================] - 28s 161ms/step - loss: 0.6225 - accuracy: 0.7728\n",
            "Epoch 48/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.6139 - accuracy: 0.7768\n",
            "Epoch 49/50\n",
            "173/173 [==============================] - 28s 160ms/step - loss: 0.6110 - accuracy: 0.7788\n",
            "Epoch 50/50\n",
            "173/173 [==============================] - 28s 161ms/step - loss: 0.6166 - accuracy: 0.7750\n"
          ]
        }
      ],
      "source": [
        "#let's compile the model using sgd optimizer\n",
        "model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n",
        "#fitting the model in 30 epochs with validation split at 0.2 after normalization\n",
        "history1 = model.fit_generator(data_generator.flow(X1_train, Y1_train), epochs=50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "SdSF-njEuKaa",
        "outputId": "f929ee5a-27ae-4844-e058-dc3469c3f4cd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#ANN model with SGD optimizer or default 0.01 learning rate\n",
        "plt.figure(figsize = (10,10))\n",
        "plt.plot(history1.history['accuracy'], label='training accuracy')\n",
        "plt.plot(history1.history['loss'], label='training loss')\n",
        "plt.title('Loss/accuracy in ANN model after normalization')\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_kaAj2Qd3yCk",
        "outputId": "d74b06f2-daa0-441d-f190-95ccfc86ea4e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "44/44 [==============================] - 1s 23ms/step - loss: 0.5455 - accuracy: 0.8014\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.5455224514007568, 0.8014492988586426]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "#Evaluation of model\n",
        "model.evaluate(X1_test, Y1_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zZrQBWAguKac"
      },
      "outputs": [],
      "source": [
        "predict_x=model.predict(X1_test) \n",
        "classes_x=np.argmax(predict_x,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#converting over Y test to actual labels.\n",
        "Y1_test = np.argmax(Y1_test, axis = 1)"
      ],
      "metadata": {
        "id": "Op_heoMAg3T1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vl9Swi9ruKac",
        "outputId": "3c199433-1667-4058-f27e-30364226615f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.95      0.92       206\n",
            "           1       0.85      0.67      0.75       163\n",
            "           2       0.58      0.73      0.64       175\n",
            "           3       0.74      0.83      0.78       143\n",
            "           4       0.62      0.26      0.37       138\n",
            "           5       0.86      0.90      0.88       196\n",
            "           6       0.94      1.00      0.97       207\n",
            "           7       0.82      0.90      0.86       152\n",
            "\n",
            "    accuracy                           0.80      1380\n",
            "   macro avg       0.79      0.78      0.77      1380\n",
            "weighted avg       0.80      0.80      0.79      1380\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(Y1_test, classes_x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MzCa_5QeuKad",
        "outputId": "afca9cd6-6952-47d0-b11d-dc0757504c5f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[1149,   25],\n",
              "        [  11,  195]],\n",
              "\n",
              "       [[1197,   20],\n",
              "        [  53,  110]],\n",
              "\n",
              "       [[1112,   93],\n",
              "        [  48,  127]],\n",
              "\n",
              "       [[1195,   42],\n",
              "        [  25,  118]],\n",
              "\n",
              "       [[1220,   22],\n",
              "        [ 102,   36]],\n",
              "\n",
              "       [[1155,   29],\n",
              "        [  19,  177]],\n",
              "\n",
              "       [[1161,   12],\n",
              "        [   1,  206]],\n",
              "\n",
              "       [[1197,   31],\n",
              "        [  15,  137]]])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "from sklearn.metrics import multilabel_confusion_matrix\n",
        "multilabel_confusion_matrix(Y1_test, classes_x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A5ChMNDf08Bb"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Adam AT LEARNING RATE =0.001, EPOCHS=25, AUGMENTED IMAGE"
      ],
      "metadata": {
        "id": "7T1o4tcUt-fk"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9EAMpwkO08Xl",
        "outputId": "196555be-2941-428a-913c-500cf48a6cb4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  after removing the cwd from sys.path.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "173/173 [==============================] - 33s 179ms/step - loss: 4.1398 - accuracy: 0.3807\n",
            "Epoch 2/25\n",
            "173/173 [==============================] - 30s 175ms/step - loss: 1.9504 - accuracy: 0.4523\n",
            "Epoch 3/25\n",
            "173/173 [==============================] - 30s 175ms/step - loss: 1.5551 - accuracy: 0.5048\n",
            "Epoch 4/25\n",
            "173/173 [==============================] - 30s 175ms/step - loss: 1.4643 - accuracy: 0.5169\n",
            "Epoch 5/25\n",
            "173/173 [==============================] - 30s 174ms/step - loss: 1.2800 - accuracy: 0.5606\n",
            "Epoch 6/25\n",
            "173/173 [==============================] - 30s 175ms/step - loss: 1.1995 - accuracy: 0.5862\n",
            "Epoch 7/25\n",
            "173/173 [==============================] - 30s 174ms/step - loss: 1.2117 - accuracy: 0.5689\n",
            "Epoch 8/25\n",
            "173/173 [==============================] - 30s 174ms/step - loss: 1.1227 - accuracy: 0.6106\n",
            "Epoch 9/25\n",
            "173/173 [==============================] - 30s 174ms/step - loss: 1.1234 - accuracy: 0.6007\n",
            "Epoch 10/25\n",
            "173/173 [==============================] - 30s 175ms/step - loss: 1.0565 - accuracy: 0.6238\n",
            "Epoch 11/25\n",
            "173/173 [==============================] - 30s 176ms/step - loss: 1.0684 - accuracy: 0.6215\n",
            "Epoch 12/25\n",
            "173/173 [==============================] - 30s 175ms/step - loss: 1.0284 - accuracy: 0.6345\n",
            "Epoch 13/25\n",
            "173/173 [==============================] - 30s 175ms/step - loss: 1.0281 - accuracy: 0.6318\n",
            "Epoch 14/25\n",
            "173/173 [==============================] - 30s 175ms/step - loss: 1.0095 - accuracy: 0.6322\n",
            "Epoch 15/25\n",
            "173/173 [==============================] - 30s 175ms/step - loss: 0.9992 - accuracy: 0.6496\n",
            "Epoch 16/25\n",
            "173/173 [==============================] - 30s 175ms/step - loss: 0.9557 - accuracy: 0.6554\n",
            "Epoch 17/25\n",
            "173/173 [==============================] - 30s 175ms/step - loss: 0.9417 - accuracy: 0.6633\n",
            "Epoch 18/25\n",
            "173/173 [==============================] - 30s 175ms/step - loss: 0.9234 - accuracy: 0.6704\n",
            "Epoch 19/25\n",
            "173/173 [==============================] - 30s 175ms/step - loss: 0.9596 - accuracy: 0.6499\n",
            "Epoch 20/25\n",
            "173/173 [==============================] - 30s 174ms/step - loss: 0.8961 - accuracy: 0.6757\n",
            "Epoch 21/25\n",
            "173/173 [==============================] - 31s 176ms/step - loss: 0.9163 - accuracy: 0.6653\n",
            "Epoch 22/25\n",
            "173/173 [==============================] - 30s 176ms/step - loss: 0.8995 - accuracy: 0.6778\n",
            "Epoch 23/25\n",
            "173/173 [==============================] - 30s 175ms/step - loss: 0.9035 - accuracy: 0.6711\n",
            "Epoch 24/25\n",
            "173/173 [==============================] - 30s 175ms/step - loss: 0.8860 - accuracy: 0.6831\n",
            "Epoch 25/25\n",
            "173/173 [==============================] - 30s 174ms/step - loss: 0.8685 - accuracy: 0.6871\n"
          ]
        }
      ],
      "source": [
        "#let's compile the model using sgd optimizer\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "#fitting the model in 30 epochs with validation split at 0.2 after normalization\n",
        "history1 = model.fit_generator(data_generator.flow(X1_train, Y1_train), epochs=25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OQtzCEBG08Xp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "edb954da-ac59-4cc8-a110-5d740334a6f9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#ANN model with SGD optimizer or default 0.01 learning rate\n",
        "plt.figure(figsize = (10,10))\n",
        "plt.plot(history1.history['accuracy'], label='training accuracy')\n",
        "plt.plot(history1.history['loss'], label='training loss')\n",
        "plt.title('Loss/accuracy in ANN model after normalization')\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "czlyjCdK08Xq"
      },
      "outputs": [],
      "source": [
        "predict_x=model.predict(X1_test) \n",
        "classes_x=np.argmax(predict_x,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#converting over Y test to actual labels.\n",
        "Y1_test = np.argmax(Y1_test, axis = 1)"
      ],
      "metadata": {
        "id": "e6lKoL-Dtz82"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KFLHPIsz08Xr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c34a22f-eff2-496e-b819-c7ac0484b843"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.93      0.87       206\n",
            "           1       0.68      0.72      0.70       163\n",
            "           2       0.66      0.35      0.46       175\n",
            "           3       0.82      0.66      0.73       143\n",
            "           4       0.44      0.35      0.39       138\n",
            "           5       0.58      0.93      0.71       196\n",
            "           6       0.86      0.99      0.92       207\n",
            "           7       0.93      0.63      0.75       152\n",
            "\n",
            "    accuracy                           0.72      1380\n",
            "   macro avg       0.72      0.70      0.69      1380\n",
            "weighted avg       0.73      0.72      0.71      1380\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(Y1_test, classes_x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3SarIF4008Xr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0fe9055-0a6e-4d10-abb6-e0c0183d8b5d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[1132,   42],\n",
              "        [  15,  191]],\n",
              "\n",
              "       [[1161,   56],\n",
              "        [  46,  117]],\n",
              "\n",
              "       [[1174,   31],\n",
              "        [ 114,   61]],\n",
              "\n",
              "       [[1216,   21],\n",
              "        [  48,   95]],\n",
              "\n",
              "       [[1182,   60],\n",
              "        [  90,   48]],\n",
              "\n",
              "       [[1050,  134],\n",
              "        [  13,  183]],\n",
              "\n",
              "       [[1140,   33],\n",
              "        [   2,  205]],\n",
              "\n",
              "       [[1221,    7],\n",
              "        [  56,   96]]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "from sklearn.metrics import multilabel_confusion_matrix\n",
        "multilabel_confusion_matrix(Y1_test, classes_x)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "tihG0c0DCNuK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "QI9O9J4tCNm9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "wDsZz-ugCNdp"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "191000050_SourajitaDewasi_DL_CourseProject2_ANN_Model.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNirIMIYkHpaIs1t/nfZejk",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}