brain_fmri_classification.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "e0498eb2",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-04-28T11:56:25.367940Z",
          "iopub.status.busy": "2022-04-28T11:56:25.367509Z",
          "iopub.status.idle": "2022-04-28T11:56:30.040221Z",
          "shell.execute_reply": "2022-04-28T11:56:30.039376Z"
        },
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          "status": "completed"
        },
        "tags": [],
        "id": "e0498eb2"
      },
      "outputs": [],
      "source": [
        "# Brain Function Activity Analysis on a fMRI Dataset to validate the Brain Mapping/Finger Printing Test\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import layers\n",
        "import matplotlib.image as img\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "f02c18ad",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-04-28T11:56:30.110339Z",
          "iopub.status.busy": "2022-04-28T11:56:30.109637Z",
          "iopub.status.idle": "2022-04-28T11:57:00.522003Z",
          "shell.execute_reply": "2022-04-28T11:57:00.520207Z"
        },
        "papermill": {
          "duration": 30.432432,
          "end_time": "2022-04-28T11:57:00.524566",
          "exception": false,
          "start_time": "2022-04-28T11:56:30.092134",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f02c18ad",
        "outputId": "29f451d4-d3e3-40f8-8f75-e61325b480ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: split-folders in /usr/local/lib/python3.10/dist-packages (0.5.1)\n",
            "Mounted at /content/drive\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Copying files: 6400 files [02:05, 50.89 files/s] \n"
          ]
        }
      ],
      "source": [
        "!pip install split-folders\n",
        "import splitfolders\n",
        "\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "dataset_path = '/content/drive/My Drive/MRI-Preprocessed/Dataset'\n",
        "splitfolders.ratio(dataset_path, output=\"output\", seed=1345, ratio=(.8, 0.1,0.1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "2955fc73",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-04-28T11:57:00.660190Z",
          "iopub.status.busy": "2022-04-28T11:57:00.659911Z",
          "iopub.status.idle": "2022-04-28T11:57:03.847293Z",
          "shell.execute_reply": "2022-04-28T11:57:03.846510Z"
        },
        "papermill": {
          "duration": 3.258095,
          "end_time": "2022-04-28T11:57:03.849517",
          "exception": false,
          "start_time": "2022-04-28T11:57:00.591422",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2955fc73",
        "outputId": "cbbc65d4-b4ea-4c94-d9c4-e69c0da0f567"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 5119 files belonging to 4 classes.\n",
            "Found 642 files belonging to 4 classes.\n",
            "Found 639 files belonging to 4 classes.\n"
          ]
        }
      ],
      "source": [
        "IMG_HEIGHT = 128\n",
        "IMG_WIDTH = 128\n",
        "train_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "\"./output/train\",\n",
        "seed=123,\n",
        "image_size=(IMG_HEIGHT, IMG_WIDTH),\n",
        "batch_size=64\n",
        ")\n",
        "\n",
        "test_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "\"./output/test\",\n",
        "seed=123,\n",
        "image_size=(IMG_HEIGHT, IMG_WIDTH),\n",
        "batch_size=64\n",
        ")\n",
        "\n",
        "val_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "\"./output/val\",\n",
        "seed=123,\n",
        "image_size=(IMG_HEIGHT, IMG_WIDTH),\n",
        "batch_size=64\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "a2f442dd",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-04-28T11:57:04.275469Z",
          "iopub.status.busy": "2022-04-28T11:57:04.275181Z",
          "iopub.status.idle": "2022-04-28T11:57:05.066213Z",
          "shell.execute_reply": "2022-04-28T11:57:05.064741Z"
        },
        "papermill": {
          "duration": 0.866355,
          "end_time": "2022-04-28T11:57:05.070512",
          "exception": false,
          "start_time": "2022-04-28T11:57:04.204157",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "a2f442dd",
        "outputId": "1f5fee78-4939-4955-8d34-0fadfae27dd5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load an image\n",
        "image = cv2.imread('image_67.jpg')  # Replace 'your_image.jpg' with the image file path\n",
        "\n",
        "# Convert the image from BGR to RGB (OpenCV loads images in BGR format)\n",
        "image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "# Display the image\n",
        "plt.imshow(image_rgb)\n",
        "plt.axis('off')  # Hide axis\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "008e414f",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-04-28T11:57:05.731744Z",
          "iopub.status.busy": "2022-04-28T11:57:05.731470Z",
          "iopub.status.idle": "2022-04-28T11:57:05.847358Z",
          "shell.execute_reply": "2022-04-28T11:57:05.846566Z"
        },
        "papermill": {
          "duration": 0.192404,
          "end_time": "2022-04-28T11:57:05.849849",
          "exception": false,
          "start_time": "2022-04-28T11:57:05.657445",
          "status": "completed"
        },
        "tags": [],
        "id": "008e414f"
      },
      "outputs": [],
      "source": [
        "model = keras.models.Sequential()\n",
        "model.add(keras.layers.experimental.preprocessing.Rescaling(1./255, input_shape=(IMG_HEIGHT,IMG_WIDTH, 3)))\n",
        "model.add(keras.layers.Conv2D(filters=16,kernel_size=(3,3),padding='same',activation='relu',kernel_initializer=\"he_normal\"))\n",
        "model.add(keras.layers.MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "\n",
        "model.add(keras.layers.Conv2D(filters=32,kernel_size=(3,3),padding='same',activation='relu',kernel_initializer=\"he_normal\"))\n",
        "model.add(keras.layers.MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(keras.layers.Dropout(0.20))\n",
        "\n",
        "model.add(keras.layers.Conv2D(filters=64,kernel_size=(3,3),padding='same',activation='relu',kernel_initializer=\"he_normal\"))\n",
        "model.add(keras.layers.MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(keras.layers.Dropout(0.25))\n",
        "model.add(keras.layers.Flatten())\n",
        "model.add(keras.layers.Dense(128,activation=\"relu\",kernel_initializer=\"he_normal\"))\n",
        "model.add(keras.layers.Dense(64,\"relu\"))\n",
        "model.add(keras.layers.Dense(4,\"softmax\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "5640f409",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-04-28T11:57:06.007639Z",
          "iopub.status.busy": "2022-04-28T11:57:06.006973Z",
          "iopub.status.idle": "2022-04-28T11:57:06.020294Z",
          "shell.execute_reply": "2022-04-28T11:57:06.019620Z"
        },
        "papermill": {
          "duration": 0.094017,
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        "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer = \"Adam\",metrics=[\"accuracy\"])"
      ]
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            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " rescaling (Rescaling)       (None, 128, 128, 3)       0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 128, 128, 16)      448       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2  (None, 64, 64, 16)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 64, 64, 32)        4640      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 32, 32, 32)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 32, 32, 32)        0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 32, 32, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPoolin  (None, 16, 16, 64)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 16, 16, 64)        0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 16384)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               2097280   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 4)                 260       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2129380 (8.12 MB)\n",
            "Trainable params: 2129380 (8.12 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
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        "model.summary()"
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          "text": [
            "Epoch 1/100\n",
            "80/80 [==============================] - 64s 768ms/step - loss: 1.4547 - accuracy: 0.4921 - val_loss: 1.0139 - val_accuracy: 0.5415\n",
            "Epoch 2/100\n",
            "80/80 [==============================] - 53s 658ms/step - loss: 0.9566 - accuracy: 0.5312 - val_loss: 0.9235 - val_accuracy: 0.6103\n",
            "Epoch 3/100\n",
            "80/80 [==============================] - 53s 657ms/step - loss: 0.9103 - accuracy: 0.5694 - val_loss: 0.8318 - val_accuracy: 0.6385\n",
            "Epoch 4/100\n",
            "80/80 [==============================] - 53s 656ms/step - loss: 0.8419 - accuracy: 0.6118 - val_loss: 0.8150 - val_accuracy: 0.6072\n",
            "Epoch 5/100\n",
            "80/80 [==============================] - 51s 643ms/step - loss: 0.7834 - accuracy: 0.6476 - val_loss: 0.7189 - val_accuracy: 0.6823\n",
            "Epoch 6/100\n",
            "80/80 [==============================] - 53s 658ms/step - loss: 0.7211 - accuracy: 0.6878 - val_loss: 0.6258 - val_accuracy: 0.7621\n",
            "Epoch 7/100\n",
            "80/80 [==============================] - 53s 662ms/step - loss: 0.6146 - accuracy: 0.7339 - val_loss: 0.5552 - val_accuracy: 0.7543\n",
            "Epoch 8/100\n",
            "80/80 [==============================] - 53s 660ms/step - loss: 0.5475 - accuracy: 0.7636 - val_loss: 0.4927 - val_accuracy: 0.7997\n",
            "Epoch 9/100\n",
            "80/80 [==============================] - 51s 639ms/step - loss: 0.4781 - accuracy: 0.8013 - val_loss: 0.3677 - val_accuracy: 0.8592\n",
            "Epoch 10/100\n",
            "80/80 [==============================] - 53s 658ms/step - loss: 0.4143 - accuracy: 0.8308 - val_loss: 0.3056 - val_accuracy: 0.8889\n",
            "Epoch 11/100\n",
            "80/80 [==============================] - 53s 660ms/step - loss: 0.3680 - accuracy: 0.8535 - val_loss: 0.3234 - val_accuracy: 0.8592\n",
            "Epoch 12/100\n",
            "80/80 [==============================] - 53s 657ms/step - loss: 0.3029 - accuracy: 0.8785 - val_loss: 0.1926 - val_accuracy: 0.9390\n",
            "Epoch 13/100\n",
            "80/80 [==============================] - 51s 637ms/step - loss: 0.2428 - accuracy: 0.9035 - val_loss: 0.2119 - val_accuracy: 0.9296\n",
            "Epoch 14/100\n",
            "80/80 [==============================] - 50s 623ms/step - loss: 0.2154 - accuracy: 0.9228 - val_loss: 0.1888 - val_accuracy: 0.9264\n",
            "Epoch 15/100\n",
            "80/80 [==============================] - 52s 645ms/step - loss: 0.1979 - accuracy: 0.9246 - val_loss: 0.1488 - val_accuracy: 0.9484\n",
            "Epoch 16/100\n",
            "80/80 [==============================] - 52s 644ms/step - loss: 0.1592 - accuracy: 0.9418 - val_loss: 0.1077 - val_accuracy: 0.9703\n",
            "Epoch 17/100\n",
            "80/80 [==============================] - 50s 621ms/step - loss: 0.1579 - accuracy: 0.9396 - val_loss: 0.1035 - val_accuracy: 0.9687\n",
            "Epoch 18/100\n",
            "80/80 [==============================] - 51s 643ms/step - loss: 0.1403 - accuracy: 0.9461 - val_loss: 0.1147 - val_accuracy: 0.9640\n",
            "Epoch 19/100\n",
            "80/80 [==============================] - 51s 628ms/step - loss: 0.1437 - accuracy: 0.9443 - val_loss: 0.0643 - val_accuracy: 0.9875\n",
            "Epoch 20/100\n",
            "80/80 [==============================] - 51s 643ms/step - loss: 0.1158 - accuracy: 0.9590 - val_loss: 0.0824 - val_accuracy: 0.9797\n",
            "Epoch 21/100\n",
            "80/80 [==============================] - 52s 645ms/step - loss: 0.1155 - accuracy: 0.9582 - val_loss: 0.0420 - val_accuracy: 0.9922\n",
            "Epoch 22/100\n",
            "80/80 [==============================] - 51s 642ms/step - loss: 0.0919 - accuracy: 0.9674 - val_loss: 0.0449 - val_accuracy: 0.9890\n",
            "Epoch 23/100\n",
            "80/80 [==============================] - 51s 639ms/step - loss: 0.0885 - accuracy: 0.9666 - val_loss: 0.0418 - val_accuracy: 0.9906\n",
            "Epoch 24/100\n",
            "80/80 [==============================] - 53s 669ms/step - loss: 0.0726 - accuracy: 0.9728 - val_loss: 0.0795 - val_accuracy: 0.9797\n",
            "Epoch 25/100\n",
            "80/80 [==============================] - 53s 658ms/step - loss: 0.0739 - accuracy: 0.9738 - val_loss: 0.0518 - val_accuracy: 0.9828\n",
            "Epoch 26/100\n",
            "80/80 [==============================] - 52s 652ms/step - loss: 0.0611 - accuracy: 0.9775 - val_loss: 0.0451 - val_accuracy: 0.9828\n",
            "Epoch 27/100\n",
            "80/80 [==============================] - 51s 635ms/step - loss: 0.0615 - accuracy: 0.9783 - val_loss: 0.0563 - val_accuracy: 0.9812\n",
            "Epoch 28/100\n",
            "80/80 [==============================] - 51s 642ms/step - loss: 0.0481 - accuracy: 0.9850 - val_loss: 0.0332 - val_accuracy: 0.9875\n",
            "Epoch 29/100\n",
            "80/80 [==============================] - 54s 672ms/step - loss: 0.0422 - accuracy: 0.9822 - val_loss: 0.0406 - val_accuracy: 0.9875\n",
            "Epoch 30/100\n",
            "80/80 [==============================] - 52s 645ms/step - loss: 0.0651 - accuracy: 0.9771 - val_loss: 0.0354 - val_accuracy: 0.9890\n",
            "Epoch 31/100\n",
            "80/80 [==============================] - 51s 639ms/step - loss: 0.0510 - accuracy: 0.9814 - val_loss: 0.0337 - val_accuracy: 0.9937\n",
            "Epoch 32/100\n",
            "80/80 [==============================] - 51s 627ms/step - loss: 0.0503 - accuracy: 0.9809 - val_loss: 0.0328 - val_accuracy: 0.9937\n",
            "Epoch 33/100\n",
            "80/80 [==============================] - 52s 646ms/step - loss: 0.0361 - accuracy: 0.9863 - val_loss: 0.0335 - val_accuracy: 0.9875\n",
            "Epoch 34/100\n",
            "80/80 [==============================] - 52s 644ms/step - loss: 0.0370 - accuracy: 0.9881 - val_loss: 0.0195 - val_accuracy: 0.9937\n",
            "Epoch 35/100\n",
            "80/80 [==============================] - 51s 640ms/step - loss: 0.0542 - accuracy: 0.9814 - val_loss: 0.0325 - val_accuracy: 0.9906\n",
            "Epoch 36/100\n",
            "80/80 [==============================] - 51s 636ms/step - loss: 0.0365 - accuracy: 0.9879 - val_loss: 0.0168 - val_accuracy: 0.9937\n",
            "Epoch 37/100\n",
            "80/80 [==============================] - 51s 643ms/step - loss: 0.0367 - accuracy: 0.9895 - val_loss: 0.0297 - val_accuracy: 0.9875\n",
            "Epoch 38/100\n",
            "80/80 [==============================] - 52s 648ms/step - loss: 0.0335 - accuracy: 0.9875 - val_loss: 0.0305 - val_accuracy: 0.9922\n",
            "Epoch 39/100\n",
            "80/80 [==============================] - 51s 642ms/step - loss: 0.0310 - accuracy: 0.9879 - val_loss: 0.0267 - val_accuracy: 0.9906\n",
            "Epoch 40/100\n",
            "80/80 [==============================] - 52s 652ms/step - loss: 0.0273 - accuracy: 0.9916 - val_loss: 0.0309 - val_accuracy: 0.9890\n",
            "Epoch 41/100\n",
            "80/80 [==============================] - 52s 647ms/step - loss: 0.0476 - accuracy: 0.9842 - val_loss: 0.0443 - val_accuracy: 0.9828\n",
            "Epoch 42/100\n",
            "80/80 [==============================] - 52s 647ms/step - loss: 0.0288 - accuracy: 0.9895 - val_loss: 0.0196 - val_accuracy: 0.9922\n",
            "Epoch 43/100\n",
            "80/80 [==============================] - 52s 644ms/step - loss: 0.0306 - accuracy: 0.9896 - val_loss: 0.0168 - val_accuracy: 0.9969\n",
            "Epoch 44/100\n",
            "80/80 [==============================] - 51s 636ms/step - loss: 0.0303 - accuracy: 0.9896 - val_loss: 0.0340 - val_accuracy: 0.9906\n",
            "Epoch 45/100\n",
            "80/80 [==============================] - 52s 645ms/step - loss: 0.0440 - accuracy: 0.9861 - val_loss: 0.0362 - val_accuracy: 0.9859\n",
            "Epoch 46/100\n",
            "80/80 [==============================] - 52s 645ms/step - loss: 0.0401 - accuracy: 0.9838 - val_loss: 0.0229 - val_accuracy: 0.9906\n",
            "Epoch 47/100\n",
            "80/80 [==============================] - 51s 642ms/step - loss: 0.0188 - accuracy: 0.9947 - val_loss: 0.0169 - val_accuracy: 0.9969\n",
            "Epoch 48/100\n",
            "80/80 [==============================] - 51s 643ms/step - loss: 0.0429 - accuracy: 0.9863 - val_loss: 0.0250 - val_accuracy: 0.9937\n",
            "Epoch 49/100\n",
            "80/80 [==============================] - 51s 633ms/step - loss: 0.0213 - accuracy: 0.9926 - val_loss: 0.0148 - val_accuracy: 0.9953\n",
            "Epoch 50/100\n",
            "80/80 [==============================] - 52s 646ms/step - loss: 0.0209 - accuracy: 0.9924 - val_loss: 0.0318 - val_accuracy: 0.9890\n",
            "Epoch 51/100\n",
            "80/80 [==============================] - 51s 636ms/step - loss: 0.0186 - accuracy: 0.9936 - val_loss: 0.0207 - val_accuracy: 0.9906\n",
            "Epoch 52/100\n",
            "80/80 [==============================] - 52s 646ms/step - loss: 0.0292 - accuracy: 0.9904 - val_loss: 0.0222 - val_accuracy: 0.9953\n",
            "Epoch 53/100\n",
            "80/80 [==============================] - 51s 635ms/step - loss: 0.0225 - accuracy: 0.9930 - val_loss: 0.0210 - val_accuracy: 0.9890\n",
            "Epoch 54/100\n",
            "80/80 [==============================] - 51s 634ms/step - loss: 0.0223 - accuracy: 0.9924 - val_loss: 0.0311 - val_accuracy: 0.9859\n",
            "Epoch 55/100\n",
            "80/80 [==============================] - 52s 649ms/step - loss: 0.0186 - accuracy: 0.9928 - val_loss: 0.0247 - val_accuracy: 0.9906\n",
            "Epoch 56/100\n",
            "80/80 [==============================] - 52s 644ms/step - loss: 0.0281 - accuracy: 0.9906 - val_loss: 0.0116 - val_accuracy: 0.9969\n",
            "Epoch 57/100\n",
            "80/80 [==============================] - 52s 646ms/step - loss: 0.0266 - accuracy: 0.9910 - val_loss: 0.0337 - val_accuracy: 0.9875\n",
            "Epoch 58/100\n",
            "80/80 [==============================] - 52s 646ms/step - loss: 0.0331 - accuracy: 0.9904 - val_loss: 0.0290 - val_accuracy: 0.9906\n",
            "Epoch 59/100\n",
            "80/80 [==============================] - 51s 638ms/step - loss: 0.0163 - accuracy: 0.9955 - val_loss: 0.0071 - val_accuracy: 0.9969\n",
            "Epoch 60/100\n",
            "80/80 [==============================] - 52s 653ms/step - loss: 0.0148 - accuracy: 0.9947 - val_loss: 0.0122 - val_accuracy: 0.9937\n",
            "Epoch 61/100\n",
            "80/80 [==============================] - 54s 675ms/step - loss: 0.0225 - accuracy: 0.9920 - val_loss: 0.0465 - val_accuracy: 0.9859\n",
            "Epoch 62/100\n",
            "80/80 [==============================] - 52s 647ms/step - loss: 0.0245 - accuracy: 0.9902 - val_loss: 0.0210 - val_accuracy: 0.9922\n",
            "Epoch 63/100\n",
            "80/80 [==============================] - 51s 634ms/step - loss: 0.0192 - accuracy: 0.9943 - val_loss: 0.0123 - val_accuracy: 0.9953\n",
            "Epoch 64/100\n",
            "80/80 [==============================] - 51s 635ms/step - loss: 0.0183 - accuracy: 0.9934 - val_loss: 0.0219 - val_accuracy: 0.9906\n",
            "Epoch 65/100\n",
            "80/80 [==============================] - 51s 639ms/step - loss: 0.0200 - accuracy: 0.9920 - val_loss: 0.0396 - val_accuracy: 0.9875\n",
            "Epoch 66/100\n",
            "80/80 [==============================] - 50s 627ms/step - loss: 0.0353 - accuracy: 0.9873 - val_loss: 0.0561 - val_accuracy: 0.9844\n",
            "Epoch 67/100\n",
            "80/80 [==============================] - 51s 636ms/step - loss: 0.0195 - accuracy: 0.9930 - val_loss: 0.0116 - val_accuracy: 0.9969\n",
            "Epoch 68/100\n",
            "80/80 [==============================] - 51s 630ms/step - loss: 0.0122 - accuracy: 0.9959 - val_loss: 0.0194 - val_accuracy: 0.9906\n",
            "Epoch 69/100\n",
            "80/80 [==============================] - 52s 646ms/step - loss: 0.0192 - accuracy: 0.9936 - val_loss: 0.0198 - val_accuracy: 0.9922\n",
            "Epoch 70/100\n",
            "80/80 [==============================] - 51s 639ms/step - loss: 0.0252 - accuracy: 0.9908 - val_loss: 0.0093 - val_accuracy: 0.9969\n",
            "Epoch 71/100\n",
            "80/80 [==============================] - 51s 641ms/step - loss: 0.0192 - accuracy: 0.9930 - val_loss: 0.0050 - val_accuracy: 1.0000\n",
            "Epoch 72/100\n",
            "80/80 [==============================] - 51s 633ms/step - loss: 0.0243 - accuracy: 0.9916 - val_loss: 0.0279 - val_accuracy: 0.9906\n",
            "Epoch 73/100\n",
            "80/80 [==============================] - 51s 640ms/step - loss: 0.0213 - accuracy: 0.9930 - val_loss: 0.0295 - val_accuracy: 0.9875\n",
            "Epoch 74/100\n",
            "80/80 [==============================] - 51s 637ms/step - loss: 0.0136 - accuracy: 0.9949 - val_loss: 0.0137 - val_accuracy: 0.9922\n",
            "Epoch 75/100\n",
            "80/80 [==============================] - 50s 619ms/step - loss: 0.0190 - accuracy: 0.9936 - val_loss: 0.0155 - val_accuracy: 0.9937\n",
            "Epoch 76/100\n",
            "80/80 [==============================] - 51s 638ms/step - loss: 0.0138 - accuracy: 0.9943 - val_loss: 0.0159 - val_accuracy: 0.9937\n",
            "Epoch 77/100\n",
            "80/80 [==============================] - 51s 643ms/step - loss: 0.0205 - accuracy: 0.9939 - val_loss: 0.0111 - val_accuracy: 0.9953\n",
            "Epoch 78/100\n",
            "80/80 [==============================] - 51s 637ms/step - loss: 0.0085 - accuracy: 0.9973 - val_loss: 0.0143 - val_accuracy: 0.9953\n",
            "Epoch 79/100\n",
            "80/80 [==============================] - 51s 635ms/step - loss: 0.0068 - accuracy: 0.9979 - val_loss: 0.0161 - val_accuracy: 0.9937\n",
            "Epoch 80/100\n",
            "80/80 [==============================] - 51s 632ms/step - loss: 0.0132 - accuracy: 0.9965 - val_loss: 0.0466 - val_accuracy: 0.9844\n",
            "Epoch 81/100\n",
            "80/80 [==============================] - 51s 644ms/step - loss: 0.0194 - accuracy: 0.9932 - val_loss: 0.0111 - val_accuracy: 0.9984\n",
            "Epoch 82/100\n",
            "80/80 [==============================] - 51s 640ms/step - loss: 0.0134 - accuracy: 0.9961 - val_loss: 0.0349 - val_accuracy: 0.9906\n",
            "Epoch 83/100\n",
            "80/80 [==============================] - 50s 617ms/step - loss: 0.0163 - accuracy: 0.9945 - val_loss: 0.0145 - val_accuracy: 0.9922\n",
            "Epoch 84/100\n",
            "80/80 [==============================] - 51s 634ms/step - loss: 0.0186 - accuracy: 0.9939 - val_loss: 0.0099 - val_accuracy: 0.9969\n",
            "Epoch 85/100\n",
            "80/80 [==============================] - 50s 629ms/step - loss: 0.0108 - accuracy: 0.9961 - val_loss: 0.0045 - val_accuracy: 0.9984\n",
            "Epoch 86/100\n",
            "80/80 [==============================] - 51s 637ms/step - loss: 0.0212 - accuracy: 0.9928 - val_loss: 0.0189 - val_accuracy: 0.9937\n",
            "Epoch 87/100\n",
            "80/80 [==============================] - 51s 631ms/step - loss: 0.0168 - accuracy: 0.9949 - val_loss: 0.0349 - val_accuracy: 0.9875\n",
            "Epoch 88/100\n",
            "80/80 [==============================] - 51s 639ms/step - loss: 0.0172 - accuracy: 0.9937 - val_loss: 0.0190 - val_accuracy: 0.9906\n",
            "Epoch 89/100\n",
            "80/80 [==============================] - 51s 636ms/step - loss: 0.0112 - accuracy: 0.9963 - val_loss: 0.0248 - val_accuracy: 0.9875\n",
            "Epoch 90/100\n",
            "80/80 [==============================] - 51s 634ms/step - loss: 0.0145 - accuracy: 0.9951 - val_loss: 0.0075 - val_accuracy: 0.9984\n",
            "Epoch 91/100\n",
            "80/80 [==============================] - 50s 630ms/step - loss: 0.0161 - accuracy: 0.9941 - val_loss: 0.0076 - val_accuracy: 0.9969\n",
            "Epoch 92/100\n",
            "80/80 [==============================] - 51s 633ms/step - loss: 0.0115 - accuracy: 0.9967 - val_loss: 0.0101 - val_accuracy: 0.9969\n",
            "Epoch 93/100\n",
            "80/80 [==============================] - 51s 633ms/step - loss: 0.0101 - accuracy: 0.9963 - val_loss: 0.0127 - val_accuracy: 0.9953\n",
            "Epoch 94/100\n",
            "80/80 [==============================] - 50s 617ms/step - loss: 0.0178 - accuracy: 0.9930 - val_loss: 0.0172 - val_accuracy: 0.9953\n",
            "Epoch 95/100\n",
            "80/80 [==============================] - 50s 624ms/step - loss: 0.0186 - accuracy: 0.9939 - val_loss: 0.0040 - val_accuracy: 0.9984\n",
            "Epoch 96/100\n",
            "80/80 [==============================] - 50s 618ms/step - loss: 0.0224 - accuracy: 0.9924 - val_loss: 0.0076 - val_accuracy: 0.9969\n",
            "Epoch 97/100\n",
            "80/80 [==============================] - 52s 646ms/step - loss: 0.0108 - accuracy: 0.9965 - val_loss: 0.0148 - val_accuracy: 0.9969\n",
            "Epoch 98/100\n",
            "80/80 [==============================] - 51s 632ms/step - loss: 0.0161 - accuracy: 0.9951 - val_loss: 0.0206 - val_accuracy: 0.9937\n",
            "Epoch 99/100\n",
            "80/80 [==============================] - 51s 635ms/step - loss: 0.0109 - accuracy: 0.9971 - val_loss: 0.0049 - val_accuracy: 0.9984\n",
            "Epoch 100/100\n",
            "80/80 [==============================] - 51s 632ms/step - loss: 0.0210 - accuracy: 0.9930 - val_loss: 0.0185 - val_accuracy: 0.9937\n"
          ]
        }
      ],
      "source": [
        "hist = model.fit(train_ds,validation_data=val_ds,epochs=100, batch_size=64, verbose=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "e0f60ae5",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-04-28T12:01:19.232900Z",
          "iopub.status.busy": "2022-04-28T12:01:19.232618Z",
          "iopub.status.idle": "2022-04-28T12:01:19.236901Z",
          "shell.execute_reply": "2022-04-28T12:01:19.236189Z"
        },
        "papermill": {
          "duration": 1.012826,
          "end_time": "2022-04-28T12:01:19.238533",
          "exception": false,
          "start_time": "2022-04-28T12:01:18.225707",
          "status": "completed"
        },
        "tags": [],
        "id": "e0f60ae5"
      },
      "outputs": [],
      "source": [
        "get_ac = hist.history['accuracy']\n",
        "get_los = hist.history['loss']\n",
        "val_acc = hist.history['val_accuracy']\n",
        "val_loss = hist.history['val_loss']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "44efaa46",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-04-28T12:01:21.317541Z",
          "iopub.status.busy": "2022-04-28T12:01:21.317270Z",
          "iopub.status.idle": "2022-04-28T12:01:21.884472Z",
          "shell.execute_reply": "2022-04-28T12:01:21.883783Z"
        },
        "papermill": {
          "duration": 1.589104,
          "end_time": "2022-04-28T12:01:21.886440",
          "exception": false,
          "start_time": "2022-04-28T12:01:20.297336",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "44efaa46",
        "outputId": "64745dcf-634d-460a-ad5c-d70e5fd60d8c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "epochs = range(len(get_ac))\n",
        "plt.plot(epochs, get_ac, 'g', label='Accuracy of Training data')\n",
        "plt.plot(epochs, get_los, 'r', label='Loss of Training data')\n",
        "plt.title('Training data accuracy and loss')\n",
        "plt.legend(loc=0)\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, get_ac, 'g', label='Accuracy of Training Data')\n",
        "plt.plot(epochs, val_acc, 'r', label='Accuracy of Validation Data')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.legend(loc=0)\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, get_los, 'g', label='Loss of Training Data')\n",
        "plt.plot(epochs, val_loss, 'r', label='Loss of Validation Data')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.legend(loc=0)\n",
        "plt.figure()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "28b93e8d",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-04-28T12:01:25.952810Z",
          "iopub.status.busy": "2022-04-28T12:01:25.952516Z",
          "iopub.status.idle": "2022-04-28T12:01:26.231574Z",
          "shell.execute_reply": "2022-04-28T12:01:26.230819Z"
        },
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          "end_time": "2022-04-28T12:01:26.235601",
          "exception": false,
          "start_time": "2022-04-28T12:01:24.924231",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "28b93e8d",
        "outputId": "6d60f0ee-74fe-4b18-84af-077534d04084"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "11/11 [==============================] - 2s 151ms/step - loss: 0.0180 - accuracy: 0.9953\n"
          ]
        }
      ],
      "source": [
        "loss, accuracy = model.evaluate(test_ds)"
      ]
    }
  ],
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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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      "end_time": "2022-04-28T12:01:36.046693",
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      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2022-04-28T11:56:17.221379",
      "version": "2.3.4"
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}