Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hZJ14JthiksS"
   },
   "outputs": [],
   "source": [
    "## from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "import numpy as np\n",
    "import cv2\n",
    "import os\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LKBO28SElXt5"
   },
   "outputs": [],
   "source": [
    "class Preprocessor:\n",
    "    def __init__(self, width, height, interpolAlgo=cv2.INTER_AREA):\n",
    "        self.width = width\n",
    "        self.height = height\n",
    "        self.intterpolAlgo = interpolAlgo\n",
    "\n",
    "    def process(self, image):\n",
    "        return cv2.resize(image, (self.width, self.height), interpolation=self.intterpolAlgo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VbfQJ1OOlaV6"
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "class Dataloader:\n",
    "    def __init__(self, preprocessors=None):\n",
    "        self.preprocessors = preprocessors\n",
    "\n",
    "        if self.preprocessors is None:\n",
    "            self.preprocessors = []\n",
    "\n",
    "    def load(self, pathToFolder, verbose=False):\n",
    "        data = []\n",
    "        labels = []\n",
    "\n",
    "        folder = Path(pathToFolder)\n",
    "\n",
    "        for classFolder in folder.iterdir():\n",
    "            if not classFolder.is_dir():\n",
    "                raise ValueError(\n",
    "                    \"Data Loader Error: Inappropriate folder structure. File found at first level\")\n",
    "\n",
    "            # os.path.split splits the path into head and tail, tail being just the folder name.\n",
    "            label = os.path.split(classFolder)[1]\n",
    "            for datapoint in classFolder.iterdir():\n",
    "                if datapoint.is_dir():\n",
    "                    raise ValueError(\n",
    "                        \"Data Loader Error: Inapprpriate folder structure. Folder found at second level\")\n",
    "\n",
    "                image = cv2.imread(str(datapoint))\n",
    "\n",
    "                if image is None:\n",
    "                    raise ValueError(str(datapoint) + \" is corrupted\")\n",
    "                for preprocessor in self.preprocessors:\n",
    "                    image = preprocessor.process(image)\n",
    "\n",
    "                if verbose:\n",
    "                    print(\"[INFO] Loading image: \" + str(datapoint))\n",
    "                data.append(image)\n",
    "                labels.append(label)\n",
    "\n",
    "        return (np.array(data), np.array(labels))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "QGxabzCrlj-m",
    "outputId": "32533f2e-f5b9-4d59-a721-617ac3764427"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import img_to_array\n",
    "\n",
    "\n",
    "class ImageToArrayPreprocessor:\n",
    "    def __init__(self, dataFormat = None):\n",
    "        self.dataFormat = dataFormat\n",
    "\n",
    "    def process(self, image):\n",
    "        return img_to_array(image, data_format=self.dataFormat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "HzBXGzpFlmf4",
    "outputId": "867930c2-7be4-47e0-bdf9-e79f072421aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "yJaM-QFXloQ6",
    "outputId": "0ab21c66-7f42-4284-a733-02377d031cc9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/drive/My Drive\n"
     ]
    }
   ],
   "source": [
    "cd \"drive/My Drive\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iCvbHgnUiksm"
   },
   "outputs": [],
   "source": [
    "sp = Preprocessor(24,24)\n",
    "iap = ImageToArrayPreprocessor()\n",
    "sdl = Dataloader(preprocessors=[sp, iap])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "eFYjFg31iksx",
    "outputId": "86497c43-cd27-41bd-e3b4-0437a8e91341"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.09 s, sys: 2.05 s, total: 4.14 s\n",
      "Wall time: 18.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "(data, labels) =  sdl.load(\"eyes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TOFtaGfPiks7"
   },
   "outputs": [],
   "source": [
    "labels = labels.reshape(labels.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "ztas61ryiktB",
    "outputId": "7bcbf41b-88c0-42d1-9bd4-3c53be9a1418"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4846, 24, 24, 3)\n",
      "(4846, 1)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)\n",
    "print(labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ox38Q8G0iktL"
   },
   "outputs": [],
   "source": [
    "data = data / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "nPF5J-okiktW",
    "outputId": "a2beb783-02ea-46a9-c169-b8555427496c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.22352941 0.22352941 0.22352941]\n"
     ]
    }
   ],
   "source": [
    "print(data[0][0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kq8kVyGiiktf"
   },
   "outputs": [],
   "source": [
    "lb = LabelBinarizer()\n",
    "labels = lb.fit_transform(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yvDDOx2Ziktk"
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.convolutional import MaxPooling2D\n",
    "from keras.layers.core import Dropout\n",
    "from keras.layers.core import Activation\n",
    "from keras.layers.core import Flatten\n",
    "from keras.layers.core import Dense\n",
    "from keras import backend as keras_backend\n",
    "from keras.optimizers import SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M97t4G7QqIiL"
   },
   "outputs": [],
   "source": [
    "class NoNet:\n",
    "    @staticmethod\n",
    "    def build(width, height, depth, classes):\n",
    "        inputShape = (height, width, depth)\n",
    "        \n",
    "        if keras_backend.image_data_format() == \"channels_first\":\n",
    "            inputShape = (depth, height, width)\n",
    "        model = Sequential([\n",
    "            Conv2D(32, kernel_size=(3, 3), activation='relu',\n",
    "                   input_shape= inputShape),\n",
    "            MaxPooling2D(pool_size=(1, 1)),\n",
    "\n",
    "            Conv2D(64, (3, 3), activation='relu'),\n",
    "            MaxPooling2D(pool_size=(1, 1)),\n",
    "            # maybe consider increasing this. \n",
    "            Dropout(0.5),\n",
    "            Flatten(),\n",
    "            Dense(128, activation='relu'),\n",
    "            Dropout(0.5),\n",
    "            Dense(classes, activation='sigmoid')\n",
    "        ])\n",
    "\n",
    "        return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9yMStZeRiktt"
   },
   "outputs": [],
   "source": [
    "trainX, testX, trainY, testY = train_test_split(data, labels, test_size = 0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cPKrh8Plikty"
   },
   "outputs": [],
   "source": [
    "opt = SGD(lr=0.005) # consider changing to ADAM or RMSprop.\n",
    "model = NoNet.build(24,24, 3, 1)\n",
    "model.compile(loss=\"binary_crossentropy\", optimizer=opt,metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "IjXYeCJftSTn",
    "outputId": "8da545c3-d565-44c3-f5fd-fc0b5e917c7d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3634 samples, validate on 1212 samples\n",
      "Epoch 1/100\n",
      "3634/3634 [==============================] - 7s 2ms/step - loss: 0.6836 - accuracy: 0.5405 - val_loss: 0.6581 - val_accuracy: 0.6683\n",
      "Epoch 2/100\n",
      "3634/3634 [==============================] - 1s 184us/step - loss: 0.6503 - accuracy: 0.6346 - val_loss: 0.6134 - val_accuracy: 0.6782\n",
      "Epoch 3/100\n",
      "3634/3634 [==============================] - 1s 186us/step - loss: 0.6144 - accuracy: 0.6767 - val_loss: 0.5611 - val_accuracy: 0.7731\n",
      "Epoch 4/100\n",
      "3634/3634 [==============================] - 1s 188us/step - loss: 0.5719 - accuracy: 0.7221 - val_loss: 0.5188 - val_accuracy: 0.7805\n",
      "Epoch 5/100\n",
      "3634/3634 [==============================] - 1s 187us/step - loss: 0.5395 - accuracy: 0.7345 - val_loss: 0.4910 - val_accuracy: 0.7789\n",
      "Epoch 6/100\n",
      "3634/3634 [==============================] - 1s 199us/step - loss: 0.5068 - accuracy: 0.7565 - val_loss: 0.4611 - val_accuracy: 0.7970\n",
      "Epoch 7/100\n",
      "3634/3634 [==============================] - 1s 221us/step - loss: 0.4873 - accuracy: 0.7716 - val_loss: 0.4563 - val_accuracy: 0.7937\n",
      "Epoch 8/100\n",
      "3634/3634 [==============================] - 1s 188us/step - loss: 0.4568 - accuracy: 0.7909 - val_loss: 0.4074 - val_accuracy: 0.8267\n",
      "Epoch 9/100\n",
      "3634/3634 [==============================] - 1s 212us/step - loss: 0.4375 - accuracy: 0.8021 - val_loss: 0.3849 - val_accuracy: 0.8441\n",
      "Epoch 10/100\n",
      "3634/3634 [==============================] - 1s 211us/step - loss: 0.4213 - accuracy: 0.8184 - val_loss: 0.3756 - val_accuracy: 0.8432\n",
      "Epoch 11/100\n",
      "3634/3634 [==============================] - 1s 211us/step - loss: 0.4060 - accuracy: 0.8176 - val_loss: 0.3515 - val_accuracy: 0.8581\n",
      "Epoch 12/100\n",
      "3634/3634 [==============================] - 1s 219us/step - loss: 0.3916 - accuracy: 0.8283 - val_loss: 0.3425 - val_accuracy: 0.8630\n",
      "Epoch 13/100\n",
      "3634/3634 [==============================] - 1s 207us/step - loss: 0.3720 - accuracy: 0.8374 - val_loss: 0.3247 - val_accuracy: 0.8680\n",
      "Epoch 14/100\n",
      "3634/3634 [==============================] - 1s 214us/step - loss: 0.3615 - accuracy: 0.8484 - val_loss: 0.3131 - val_accuracy: 0.8771\n",
      "Epoch 15/100\n",
      "3634/3634 [==============================] - 1s 217us/step - loss: 0.3534 - accuracy: 0.8476 - val_loss: 0.3031 - val_accuracy: 0.8837\n",
      "Epoch 16/100\n",
      "3634/3634 [==============================] - 1s 206us/step - loss: 0.3314 - accuracy: 0.8643 - val_loss: 0.2949 - val_accuracy: 0.8837\n",
      "Epoch 17/100\n",
      "3634/3634 [==============================] - 1s 190us/step - loss: 0.3203 - accuracy: 0.8671 - val_loss: 0.2838 - val_accuracy: 0.8903\n",
      "Epoch 18/100\n",
      "3634/3634 [==============================] - 1s 217us/step - loss: 0.3200 - accuracy: 0.8712 - val_loss: 0.2786 - val_accuracy: 0.8919\n",
      "Epoch 19/100\n",
      "3634/3634 [==============================] - 1s 185us/step - loss: 0.3057 - accuracy: 0.8781 - val_loss: 0.2676 - val_accuracy: 0.8944\n",
      "Epoch 20/100\n",
      "3634/3634 [==============================] - 1s 204us/step - loss: 0.2961 - accuracy: 0.8817 - val_loss: 0.2622 - val_accuracy: 0.9002\n",
      "Epoch 21/100\n",
      "3634/3634 [==============================] - 1s 186us/step - loss: 0.2905 - accuracy: 0.8811 - val_loss: 0.2613 - val_accuracy: 0.8969\n",
      "Epoch 22/100\n",
      "3634/3634 [==============================] - 1s 184us/step - loss: 0.2807 - accuracy: 0.8910 - val_loss: 0.2464 - val_accuracy: 0.9117\n",
      "Epoch 23/100\n",
      "3634/3634 [==============================] - 1s 205us/step - loss: 0.2769 - accuracy: 0.8897 - val_loss: 0.2447 - val_accuracy: 0.9076\n",
      "Epoch 24/100\n",
      "3634/3634 [==============================] - 1s 182us/step - loss: 0.2656 - accuracy: 0.8982 - val_loss: 0.2357 - val_accuracy: 0.9142\n",
      "Epoch 25/100\n",
      "3634/3634 [==============================] - 1s 192us/step - loss: 0.2650 - accuracy: 0.8976 - val_loss: 0.2563 - val_accuracy: 0.8969\n",
      "Epoch 26/100\n",
      "3634/3634 [==============================] - 1s 186us/step - loss: 0.2555 - accuracy: 0.9042 - val_loss: 0.2295 - val_accuracy: 0.9167\n",
      "Epoch 27/100\n",
      "3634/3634 [==============================] - 1s 208us/step - loss: 0.2535 - accuracy: 0.8993 - val_loss: 0.2248 - val_accuracy: 0.9134\n",
      "Epoch 28/100\n",
      "3634/3634 [==============================] - 1s 190us/step - loss: 0.2465 - accuracy: 0.9048 - val_loss: 0.2382 - val_accuracy: 0.9068\n",
      "Epoch 29/100\n",
      "3634/3634 [==============================] - 1s 213us/step - loss: 0.2464 - accuracy: 0.9031 - val_loss: 0.2162 - val_accuracy: 0.9142\n",
      "Epoch 30/100\n",
      "3634/3634 [==============================] - 1s 185us/step - loss: 0.2400 - accuracy: 0.9051 - val_loss: 0.2127 - val_accuracy: 0.9224\n",
      "Epoch 31/100\n",
      "3634/3634 [==============================] - 1s 205us/step - loss: 0.2337 - accuracy: 0.9075 - val_loss: 0.2066 - val_accuracy: 0.9158\n",
      "Epoch 32/100\n",
      "3634/3634 [==============================] - 1s 213us/step - loss: 0.2307 - accuracy: 0.9139 - val_loss: 0.2218 - val_accuracy: 0.9167\n",
      "Epoch 33/100\n",
      "3634/3634 [==============================] - 1s 186us/step - loss: 0.2203 - accuracy: 0.9177 - val_loss: 0.1996 - val_accuracy: 0.9216\n",
      "Epoch 34/100\n",
      "3634/3634 [==============================] - 1s 195us/step - loss: 0.2214 - accuracy: 0.9174 - val_loss: 0.2015 - val_accuracy: 0.9249\n",
      "Epoch 35/100\n",
      "3634/3634 [==============================] - 1s 214us/step - loss: 0.2174 - accuracy: 0.9136 - val_loss: 0.1930 - val_accuracy: 0.9216\n",
      "Epoch 36/100\n",
      "3634/3634 [==============================] - 1s 201us/step - loss: 0.2151 - accuracy: 0.9194 - val_loss: 0.1939 - val_accuracy: 0.9282\n",
      "Epoch 37/100\n",
      "3634/3634 [==============================] - 1s 195us/step - loss: 0.2103 - accuracy: 0.9227 - val_loss: 0.1888 - val_accuracy: 0.9274\n",
      "Epoch 38/100\n",
      "3634/3634 [==============================] - 1s 199us/step - loss: 0.2090 - accuracy: 0.9185 - val_loss: 0.1867 - val_accuracy: 0.9299\n",
      "Epoch 39/100\n",
      "3634/3634 [==============================] - 1s 195us/step - loss: 0.1989 - accuracy: 0.9232 - val_loss: 0.1981 - val_accuracy: 0.9299\n",
      "Epoch 40/100\n",
      "3634/3634 [==============================] - 1s 219us/step - loss: 0.1985 - accuracy: 0.9194 - val_loss: 0.1818 - val_accuracy: 0.9323\n",
      "Epoch 41/100\n",
      "3634/3634 [==============================] - 1s 217us/step - loss: 0.2004 - accuracy: 0.9235 - val_loss: 0.1872 - val_accuracy: 0.9332\n",
      "Epoch 42/100\n",
      "3634/3634 [==============================] - 1s 257us/step - loss: 0.1894 - accuracy: 0.9298 - val_loss: 0.1767 - val_accuracy: 0.9315\n",
      "Epoch 43/100\n",
      "3634/3634 [==============================] - 1s 226us/step - loss: 0.1916 - accuracy: 0.9276 - val_loss: 0.1719 - val_accuracy: 0.9290\n",
      "Epoch 44/100\n",
      "3634/3634 [==============================] - 1s 192us/step - loss: 0.1872 - accuracy: 0.9263 - val_loss: 0.1838 - val_accuracy: 0.9307\n",
      "Epoch 45/100\n",
      "3634/3634 [==============================] - 1s 225us/step - loss: 0.1843 - accuracy: 0.9296 - val_loss: 0.1700 - val_accuracy: 0.9307\n",
      "Epoch 46/100\n",
      "3634/3634 [==============================] - 1s 182us/step - loss: 0.1830 - accuracy: 0.9362 - val_loss: 0.1702 - val_accuracy: 0.9373\n",
      "Epoch 47/100\n",
      "3634/3634 [==============================] - 1s 222us/step - loss: 0.1786 - accuracy: 0.9318 - val_loss: 0.1670 - val_accuracy: 0.9340\n",
      "Epoch 48/100\n",
      "3634/3634 [==============================] - 1s 219us/step - loss: 0.1782 - accuracy: 0.9298 - val_loss: 0.1730 - val_accuracy: 0.9266\n",
      "Epoch 49/100\n",
      "3634/3634 [==============================] - 1s 207us/step - loss: 0.1763 - accuracy: 0.9373 - val_loss: 0.1686 - val_accuracy: 0.9307\n",
      "Epoch 50/100\n",
      "3634/3634 [==============================] - 1s 211us/step - loss: 0.1713 - accuracy: 0.9364 - val_loss: 0.1828 - val_accuracy: 0.9323\n",
      "Epoch 51/100\n",
      "3634/3634 [==============================] - 1s 194us/step - loss: 0.1729 - accuracy: 0.9342 - val_loss: 0.1583 - val_accuracy: 0.9398\n",
      "Epoch 52/100\n",
      "3634/3634 [==============================] - 1s 189us/step - loss: 0.1679 - accuracy: 0.9370 - val_loss: 0.1571 - val_accuracy: 0.9414\n",
      "Epoch 53/100\n",
      "3634/3634 [==============================] - 1s 218us/step - loss: 0.1657 - accuracy: 0.9370 - val_loss: 0.1548 - val_accuracy: 0.9414\n",
      "Epoch 54/100\n",
      "3634/3634 [==============================] - 1s 185us/step - loss: 0.1683 - accuracy: 0.9351 - val_loss: 0.1569 - val_accuracy: 0.9431\n",
      "Epoch 55/100\n",
      "3634/3634 [==============================] - 1s 198us/step - loss: 0.1666 - accuracy: 0.9329 - val_loss: 0.1601 - val_accuracy: 0.9381\n",
      "Epoch 56/100\n",
      "3634/3634 [==============================] - 1s 207us/step - loss: 0.1618 - accuracy: 0.9375 - val_loss: 0.1527 - val_accuracy: 0.9431\n",
      "Epoch 57/100\n",
      "3634/3634 [==============================] - 1s 187us/step - loss: 0.1568 - accuracy: 0.9422 - val_loss: 0.1517 - val_accuracy: 0.9422\n",
      "Epoch 58/100\n",
      "3634/3634 [==============================] - 1s 210us/step - loss: 0.1569 - accuracy: 0.9411 - val_loss: 0.1492 - val_accuracy: 0.9464\n",
      "Epoch 59/100\n",
      "3634/3634 [==============================] - 1s 185us/step - loss: 0.1582 - accuracy: 0.9386 - val_loss: 0.1481 - val_accuracy: 0.9464\n",
      "Epoch 60/100\n",
      "3634/3634 [==============================] - 1s 220us/step - loss: 0.1575 - accuracy: 0.9408 - val_loss: 0.1519 - val_accuracy: 0.9464\n",
      "Epoch 61/100\n",
      "3634/3634 [==============================] - 1s 185us/step - loss: 0.1522 - accuracy: 0.9428 - val_loss: 0.1458 - val_accuracy: 0.9439\n",
      "Epoch 62/100\n",
      "3634/3634 [==============================] - 1s 208us/step - loss: 0.1481 - accuracy: 0.9428 - val_loss: 0.1476 - val_accuracy: 0.9464\n",
      "Epoch 63/100\n",
      "3634/3634 [==============================] - 1s 183us/step - loss: 0.1467 - accuracy: 0.9430 - val_loss: 0.1511 - val_accuracy: 0.9439\n",
      "Epoch 64/100\n",
      "3634/3634 [==============================] - 1s 193us/step - loss: 0.1478 - accuracy: 0.9425 - val_loss: 0.1587 - val_accuracy: 0.9381\n",
      "Epoch 65/100\n",
      "3634/3634 [==============================] - 1s 205us/step - loss: 0.1464 - accuracy: 0.9433 - val_loss: 0.1406 - val_accuracy: 0.9488\n",
      "Epoch 66/100\n",
      "3634/3634 [==============================] - 1s 212us/step - loss: 0.1425 - accuracy: 0.9436 - val_loss: 0.1394 - val_accuracy: 0.9480\n",
      "Epoch 67/100\n",
      "3634/3634 [==============================] - 1s 206us/step - loss: 0.1413 - accuracy: 0.9433 - val_loss: 0.1432 - val_accuracy: 0.9488\n",
      "Epoch 68/100\n",
      "3634/3634 [==============================] - 1s 210us/step - loss: 0.1370 - accuracy: 0.9499 - val_loss: 0.1457 - val_accuracy: 0.9373\n",
      "Epoch 69/100\n",
      "3634/3634 [==============================] - 1s 208us/step - loss: 0.1383 - accuracy: 0.9463 - val_loss: 0.1399 - val_accuracy: 0.9431\n",
      "Epoch 70/100\n",
      "3634/3634 [==============================] - 1s 190us/step - loss: 0.1374 - accuracy: 0.9494 - val_loss: 0.1427 - val_accuracy: 0.9455\n",
      "Epoch 71/100\n",
      "3634/3634 [==============================] - 1s 191us/step - loss: 0.1304 - accuracy: 0.9513 - val_loss: 0.1409 - val_accuracy: 0.9505\n",
      "Epoch 72/100\n",
      "3634/3634 [==============================] - 1s 192us/step - loss: 0.1327 - accuracy: 0.9496 - val_loss: 0.1412 - val_accuracy: 0.9488\n",
      "Epoch 73/100\n",
      "3634/3634 [==============================] - 1s 216us/step - loss: 0.1311 - accuracy: 0.9524 - val_loss: 0.1383 - val_accuracy: 0.9497\n",
      "Epoch 74/100\n",
      "3634/3634 [==============================] - 1s 192us/step - loss: 0.1293 - accuracy: 0.9469 - val_loss: 0.1371 - val_accuracy: 0.9455\n",
      "Epoch 75/100\n",
      "3634/3634 [==============================] - 1s 186us/step - loss: 0.1259 - accuracy: 0.9505 - val_loss: 0.1383 - val_accuracy: 0.9488\n",
      "Epoch 76/100\n",
      "3634/3634 [==============================] - 1s 196us/step - loss: 0.1368 - accuracy: 0.9455 - val_loss: 0.1351 - val_accuracy: 0.9488\n",
      "Epoch 77/100\n",
      "3634/3634 [==============================] - 1s 189us/step - loss: 0.1310 - accuracy: 0.9494 - val_loss: 0.1331 - val_accuracy: 0.9513\n",
      "Epoch 78/100\n",
      "3634/3634 [==============================] - 1s 201us/step - loss: 0.1305 - accuracy: 0.9472 - val_loss: 0.1364 - val_accuracy: 0.9480\n",
      "Epoch 79/100\n",
      "3634/3634 [==============================] - 1s 197us/step - loss: 0.1230 - accuracy: 0.9551 - val_loss: 0.1371 - val_accuracy: 0.9513\n",
      "Epoch 80/100\n",
      "3634/3634 [==============================] - 1s 209us/step - loss: 0.1290 - accuracy: 0.9488 - val_loss: 0.1328 - val_accuracy: 0.9513\n",
      "Epoch 81/100\n",
      "3634/3634 [==============================] - 1s 195us/step - loss: 0.1267 - accuracy: 0.9513 - val_loss: 0.1385 - val_accuracy: 0.9488\n",
      "Epoch 82/100\n",
      "3634/3634 [==============================] - 1s 196us/step - loss: 0.1230 - accuracy: 0.9518 - val_loss: 0.1307 - val_accuracy: 0.9513\n",
      "Epoch 83/100\n",
      "3634/3634 [==============================] - 1s 186us/step - loss: 0.1259 - accuracy: 0.9527 - val_loss: 0.1351 - val_accuracy: 0.9488\n",
      "Epoch 84/100\n",
      "3634/3634 [==============================] - 1s 197us/step - loss: 0.1181 - accuracy: 0.9557 - val_loss: 0.1349 - val_accuracy: 0.9521\n",
      "Epoch 85/100\n",
      "3634/3634 [==============================] - 1s 188us/step - loss: 0.1183 - accuracy: 0.9560 - val_loss: 0.1642 - val_accuracy: 0.9406\n",
      "Epoch 86/100\n",
      "3634/3634 [==============================] - 1s 192us/step - loss: 0.1248 - accuracy: 0.9483 - val_loss: 0.1294 - val_accuracy: 0.9546\n",
      "Epoch 87/100\n",
      "3634/3634 [==============================] - 1s 192us/step - loss: 0.1183 - accuracy: 0.9505 - val_loss: 0.1358 - val_accuracy: 0.9513\n",
      "Epoch 88/100\n",
      "3634/3634 [==============================] - 1s 187us/step - loss: 0.1120 - accuracy: 0.9590 - val_loss: 0.1335 - val_accuracy: 0.9472\n",
      "Epoch 89/100\n",
      "3634/3634 [==============================] - 1s 193us/step - loss: 0.1153 - accuracy: 0.9532 - val_loss: 0.1303 - val_accuracy: 0.9538\n",
      "Epoch 90/100\n",
      "3634/3634 [==============================] - 1s 188us/step - loss: 0.1114 - accuracy: 0.9557 - val_loss: 0.1274 - val_accuracy: 0.9521\n",
      "Epoch 91/100\n",
      "3634/3634 [==============================] - 1s 233us/step - loss: 0.1118 - accuracy: 0.9562 - val_loss: 0.1244 - val_accuracy: 0.9521\n",
      "Epoch 92/100\n",
      "3634/3634 [==============================] - 1s 195us/step - loss: 0.1102 - accuracy: 0.9576 - val_loss: 0.1306 - val_accuracy: 0.9513\n",
      "Epoch 93/100\n",
      "3634/3634 [==============================] - 1s 189us/step - loss: 0.1087 - accuracy: 0.9576 - val_loss: 0.1345 - val_accuracy: 0.9530\n",
      "Epoch 94/100\n",
      "3634/3634 [==============================] - 1s 214us/step - loss: 0.1088 - accuracy: 0.9582 - val_loss: 0.1289 - val_accuracy: 0.9521\n",
      "Epoch 95/100\n",
      "3634/3634 [==============================] - 1s 182us/step - loss: 0.1114 - accuracy: 0.9551 - val_loss: 0.1299 - val_accuracy: 0.9530\n",
      "Epoch 96/100\n",
      "3634/3634 [==============================] - 1s 191us/step - loss: 0.1071 - accuracy: 0.9584 - val_loss: 0.1490 - val_accuracy: 0.9389\n",
      "Epoch 97/100\n",
      "3634/3634 [==============================] - 1s 214us/step - loss: 0.1134 - accuracy: 0.9568 - val_loss: 0.1322 - val_accuracy: 0.9505\n",
      "Epoch 98/100\n",
      "3634/3634 [==============================] - 1s 231us/step - loss: 0.1052 - accuracy: 0.9601 - val_loss: 0.1344 - val_accuracy: 0.9497\n",
      "Epoch 99/100\n",
      "3634/3634 [==============================] - 1s 196us/step - loss: 0.1061 - accuracy: 0.9582 - val_loss: 0.1236 - val_accuracy: 0.9521\n",
      "Epoch 100/100\n",
      "3634/3634 [==============================] - 1s 182us/step - loss: 0.1035 - accuracy: 0.9601 - val_loss: 0.1310 - val_accuracy: 0.9538\n"
     ]
    }
   ],
   "source": [
    "H = model.fit(trainX, trainY, validation_data=(testX, testY), batch_size=32, epochs=100, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "colab_type": "code",
    "id": "hB4A9GLvikt8",
    "outputId": "49b8bcc2-f360-4823-976d-ca4a55012016"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"ggplot\")\n",
    "plt.figure()\n",
    "plt.plot(np.arange(0, 100), H.history[\"loss\"], label=\"train_loss\")\n",
    "plt.plot(np.arange(0, 100), H.history[\"val_loss\"], label=\"val_loss\")\n",
    "plt.plot(np.arange(0, 100), H.history[\"accuracy\"], label=\"train_acc\")\n",
    "plt.plot(np.arange(0, 100), H.history[\"val_accuracy\"], label=\"val_acc\")\n",
    "plt.title(\"Training Loss and Accuracy\")\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Loss/Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rhCaBt2RikuB"
   },
   "outputs": [],
   "source": [
    "model.save(\"nonet.hdf5\")"
   ]
  }
 ],
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   "name": "DDD-Shallow.ipynb",
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