03 - Train_CNN_Digital-Readout-Small-v2.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# CNN Training\n",
    "\n",
    "Target of this code is to train a CNN network to classify images of a digital readout to the digits 0 to 9. Additionally a category \"NaN\" is introduced, to mark images that are not amibiguous.\n",
    "\n",
    "### Preparing the training\n",
    "* First all libraries are loaded\n",
    "    * It is assumed, that they are installed during the Python setup\n",
    "* matplotlib is set to print the output inline in the jupyter notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "########### Basic Parameters for Running: ################################\n",
    "    \n",
    "TFlite_Version  = \"1910\"   \n",
    "TFlite_MainType = \"dig-class11\"\n",
    "TFlite_Size     = \"s2\"\n",
    "Training_Percentage = 0.0              # 0.0 = Use all Images for Training\n",
    "Epoch_Anz = 250                        # x2 !!!\n",
    "\n",
    "##########################################################################\n",
    "\n",
    "## 2024-03-30: Code adapted to TF 2.16 ###################################\n",
    "\n",
    "\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import os\n",
    "import numpy as np\n",
    "from sklearn.utils import shuffle\n",
    "from tensorflow.python import keras\n",
    "from tensorflow.python.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense, InputLayer, Conv2D, MaxPool2D, Flatten, BatchNormalization\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.callbacks import History \n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from PIL import Image \n",
    "from pathlib import Path\n",
    "\n",
    "loss_ges = np.array([])\n",
    "val_loss_ges = np.array([])\n",
    "\n",
    "%matplotlib inline\n",
    "np.set_printoptions(precision=4)\n",
    "np.set_printoptions(suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load training data\n",
    "* The data is expected in the \"Input_dir\"\n",
    "* Inside subdirectories are expected from -1, 0, 1, ... 9 in which the pictures are sorted according to their values (=category)\n",
    "* Picture size must be 20x32 with 3 color channels (RGB)\n",
    "* The filename can be arbitrary\n",
    "\n",
    "* The images are stored in the x_data[]\n",
    "* The expected category for each image in the corresponding y_data[]\n",
    "\n",
    "* The last step is a shuffle (from sklearn.utils) and split the data into training and validation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(421, 32, 20, 3)\n",
      "(421, 11)\n"
     ]
    }
   ],
   "source": [
    "Input_dir='03_data_resize_all-use_for_training'\n",
    "\n",
    "files = glob.glob(Input_dir + '/*.jpg')\n",
    "x_data = []\n",
    "y_data = []\n",
    "\n",
    "for aktfile in files:\n",
    "    base = os.path.basename(aktfile)\n",
    "    target = base[0:1]\n",
    "    if target == \"N\":\n",
    "        category = 10                # NaN does not work --> convert to 10\n",
    "    else:\n",
    "        category = int(target)\n",
    "    test_image = Image.open(aktfile)\n",
    "    test_image = np.array(test_image, dtype=\"float32\")\n",
    "    x_data.append(test_image)\n",
    "    y_data.append(np.array([category]))\n",
    "\n",
    "x_data = np.array(x_data)\n",
    "y_data = np.array(y_data)\n",
    "y_data = to_categorical(y_data, 11)\n",
    "print(x_data.shape)\n",
    "print(y_data.shape)\n",
    "\n",
    "x_data, y_data = shuffle(x_data, y_data)\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(x_data, y_data, test_size=Training_Percentage)\n",
    "else:\n",
    "    X_train = x_data\n",
    "    y_train = y_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the model\n",
    "\n",
    "The layout of the network ist a typcial CNN network with alternating **Conv2D** and **MaxPool2D** layers. Finished after **flattening** with additional **Dense** layer.\n",
    "\n",
    "#### Important\n",
    "* Shape of the input layer: (32, 20, 3)\n",
    "* Number of output layers: 11\n",
    "* As loss function \"categorical_crossentropy\" is choosen, as it is a categories task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional\"\u001b[0m\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)      │            <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">65,792</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,827</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m3\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m3\u001b[0m)      │            \u001b[38;5;34m12\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m896\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m9,248\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │         \u001b[38;5;34m9,248\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │        \u001b[38;5;34m65,792\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m)             │         \u001b[38;5;34m2,827\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
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     "output_type": "display_data"
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    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">88,023</span> (343.84 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m88,023\u001b[0m (343.84 KB)\n"
      ]
     },
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">88,017</span> (343.82 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m88,017\u001b[0m (343.82 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6</span> (24.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m6\u001b[0m (24.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inputs = tf.keras.Input(shape=(32, 20, 3))\n",
    "inputs2 = tf.keras.layers.BatchNormalization()(inputs)\n",
    "inputs3 = tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation=\"relu\")(inputs2)\n",
    "inputs4 = tf.keras.layers.MaxPool2D(pool_size=(2,2))(inputs3)\n",
    "inputs5 = tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation=\"relu\")(inputs4)\n",
    "inputs6 = tf.keras.layers.MaxPool2D(pool_size=(2,2))(inputs5)\n",
    "inputs7 = tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation=\"relu\")(inputs6)\n",
    "inputs8 = tf.keras.layers.MaxPool2D(pool_size=(2,2))(inputs7)\n",
    "inputs9 = tf.keras.layers.Flatten()(inputs8)\n",
    "inputs10 = tf.keras.layers.Dense(256,activation=\"relu\")(inputs9)\n",
    "output = tf.keras.layers.Dense(11, activation='softmax')(inputs10)\n",
    "\n",
    "model = tf.keras.Model(inputs=inputs, outputs=output)\n",
    "\n",
    "model.compile(loss=keras.losses.categorical_crossentropy, optimizer=tf.keras.optimizers.Adadelta(learning_rate=1.0, rho=0.95), metrics = [\"accuracy\"])\n",
    "\n",
    "model.summary()\n",
    "\n",
    "#model = Sequential()\n",
    "#model.add(BatchNormalization(input_shape=(32,20,3)))\n",
    "#model.add(Conv2D(32, (3, 3), padding='same', activation=\"relu\"))\n",
    "#model.add(MaxPool2D(pool_size=(2,2)))\n",
    "#model.add(Conv2D(32, (3, 3), padding='same', activation=\"relu\"))\n",
    "#model.add(MaxPool2D(pool_size=(2,2)))\n",
    "#model.add(Conv2D(32, (3, 3), padding='same', activation=\"relu\"))\n",
    "#model.add(MaxPool2D(pool_size=(2,2)))\n",
    "#model.add(Flatten())\n",
    "#model.add(Dense(256,activation=\"relu\"))\n",
    "#model.add(Dense(11, activation = \"softmax\"))\n",
    "\n",
    "#model.compile(loss=keras.losses.categorical_crossentropy, optimizer=tf.keras.optimizers.Adadelta(learning_rate=1.0, rho=0.95), metrics = [\"accuracy\"])\n",
    "\n",
    "#model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training\n",
    "The input pictures are randomly scattered for brightness, pixel shift variations and rotation angle. This is implemented with a ImageDataGenerator.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Muell\\anaconda3\\envs\\py311-tf218\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.1394 - loss: 2.4254\n",
      "Epoch 2/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1927 - loss: 2.3354\n",
      "Epoch 3/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.2018 - loss: 2.2584\n",
      "Epoch 4/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.2297 - loss: 2.1460\n",
      "Epoch 5/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3969 - loss: 1.7726\n",
      "Epoch 6/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4915 - loss: 1.5079\n",
      "Epoch 7/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5746 - loss: 1.3188\n",
      "Epoch 8/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6299 - loss: 1.0781\n",
      "Epoch 9/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7082 - loss: 0.9198\n",
      "Epoch 10/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7661 - loss: 0.7997\n",
      "Epoch 11/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7223 - loss: 0.7563\n",
      "Epoch 12/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7914 - loss: 0.7246\n",
      "Epoch 13/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7863 - loss: 0.6582\n",
      "Epoch 14/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8238 - loss: 0.5870\n",
      "Epoch 15/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8116 - loss: 0.6333\n",
      "Epoch 16/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8553 - loss: 0.4813\n",
      "Epoch 17/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8626 - loss: 0.4178\n",
      "Epoch 18/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8339 - loss: 0.4932\n",
      "Epoch 19/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8432 - loss: 0.4387\n",
      "Epoch 20/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8604 - loss: 0.4782\n",
      "Epoch 21/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8587 - loss: 0.4767\n",
      "Epoch 22/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9105 - loss: 0.3043\n",
      "Epoch 23/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8594 - loss: 0.5307\n",
      "Epoch 24/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8395 - loss: 0.5148\n",
      "Epoch 25/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8888 - loss: 0.2737\n",
      "Epoch 26/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8791 - loss: 0.3350\n",
      "Epoch 27/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9019 - loss: 0.2950\n",
      "Epoch 28/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9081 - loss: 0.2935\n",
      "Epoch 29/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8888 - loss: 0.3161\n",
      "Epoch 30/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8791 - loss: 0.3679\n",
      "Epoch 31/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9004 - loss: 0.3119\n",
      "Epoch 32/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9116 - loss: 0.2196\n",
      "Epoch 33/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9095 - loss: 0.2466\n",
      "Epoch 34/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9507 - loss: 0.1800\n",
      "Epoch 35/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8674 - loss: 0.4077\n",
      "Epoch 36/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9004 - loss: 0.3206\n",
      "Epoch 37/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9352 - loss: 0.2128\n",
      "Epoch 38/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8797 - loss: 0.3505\n",
      "Epoch 39/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9281 - loss: 0.2341\n",
      "Epoch 40/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9332 - loss: 0.2000\n",
      "Epoch 41/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9664 - loss: 0.1272\n",
      "Epoch 42/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9477 - loss: 0.1576\n",
      "Epoch 43/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9547 - loss: 0.1532\n",
      "Epoch 44/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9038 - loss: 0.2905\n",
      "Epoch 45/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9411 - loss: 0.1498\n",
      "Epoch 46/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9568 - loss: 0.1630\n",
      "Epoch 47/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9371 - loss: 0.1933\n",
      "Epoch 48/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9600 - loss: 0.1221\n",
      "Epoch 49/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9410 - loss: 0.2143\n",
      "Epoch 50/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9261 - loss: 0.3485\n",
      "Epoch 51/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9516 - loss: 0.1943\n",
      "Epoch 52/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9214 - loss: 0.2616\n",
      "Epoch 53/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9641 - loss: 0.1835\n",
      "Epoch 54/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9431 - loss: 0.2441\n",
      "Epoch 55/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9590 - loss: 0.1715\n",
      "Epoch 56/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9341 - loss: 0.1989\n",
      "Epoch 57/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9475 - loss: 0.1792\n",
      "Epoch 58/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9591 - loss: 0.1448\n",
      "Epoch 59/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9562 - loss: 0.1451\n",
      "Epoch 60/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9542 - loss: 0.1763\n",
      "Epoch 61/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9373 - loss: 0.1957\n",
      "Epoch 62/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9350 - loss: 0.2716\n",
      "Epoch 63/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9603 - loss: 0.1159\n",
      "Epoch 64/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9562 - loss: 0.1110\n",
      "Epoch 65/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9640 - loss: 0.1538\n",
      "Epoch 66/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9621 - loss: 0.2320\n",
      "Epoch 67/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9523 - loss: 0.1542\n",
      "Epoch 68/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9587 - loss: 0.1066\n",
      "Epoch 69/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9509 - loss: 0.1501\n",
      "Epoch 70/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9715 - loss: 0.0960\n",
      "Epoch 71/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9437 - loss: 0.1794\n",
      "Epoch 72/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9608 - loss: 0.1311\n",
      "Epoch 73/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9664 - loss: 0.0899\n",
      "Epoch 74/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9436 - loss: 0.1738\n",
      "Epoch 75/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9424 - loss: 0.1673\n",
      "Epoch 76/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9722 - loss: 0.1032\n",
      "Epoch 77/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9678 - loss: 0.1290\n",
      "Epoch 78/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9849 - loss: 0.0698\n",
      "Epoch 79/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9648 - loss: 0.0973\n",
      "Epoch 80/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9624 - loss: 0.1760\n",
      "Epoch 81/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9681 - loss: 0.1147\n",
      "Epoch 82/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9546 - loss: 0.1243\n",
      "Epoch 83/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9555 - loss: 0.1110\n",
      "Epoch 84/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9627 - loss: 0.1550\n",
      "Epoch 85/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9730 - loss: 0.1386\n",
      "Epoch 86/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9838 - loss: 0.0736\n",
      "Epoch 87/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9766 - loss: 0.0625\n",
      "Epoch 88/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9788 - loss: 0.0661\n",
      "Epoch 89/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9452 - loss: 0.1877\n",
      "Epoch 90/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9793 - loss: 0.0643\n",
      "Epoch 91/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9719 - loss: 0.0897\n",
      "Epoch 92/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9809 - loss: 0.0704\n",
      "Epoch 93/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9796 - loss: 0.0692\n",
      "Epoch 94/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9589 - loss: 0.1303\n",
      "Epoch 95/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9662 - loss: 0.1291\n",
      "Epoch 96/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9795 - loss: 0.0758\n",
      "Epoch 97/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9699 - loss: 0.1528\n",
      "Epoch 98/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9852 - loss: 0.0793\n",
      "Epoch 99/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9583 - loss: 0.1141\n",
      "Epoch 100/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9752 - loss: 0.0979\n",
      "Epoch 101/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9753 - loss: 0.0651\n",
      "Epoch 102/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9721 - loss: 0.0622\n",
      "Epoch 103/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9765 - loss: 0.1271\n",
      "Epoch 104/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9570 - loss: 0.0964\n",
      "Epoch 105/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9693 - loss: 0.1029\n",
      "Epoch 106/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9507 - loss: 0.1231\n",
      "Epoch 107/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9746 - loss: 0.1052\n",
      "Epoch 108/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9753 - loss: 0.0607\n",
      "Epoch 109/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9668 - loss: 0.1033\n",
      "Epoch 110/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9759 - loss: 0.0873\n",
      "Epoch 111/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9761 - loss: 0.1415\n",
      "Epoch 112/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9874 - loss: 0.0428\n",
      "Epoch 113/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9642 - loss: 0.0786\n",
      "Epoch 114/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9742 - loss: 0.0841\n",
      "Epoch 115/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9497 - loss: 0.1929\n",
      "Epoch 116/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9742 - loss: 0.0829\n",
      "Epoch 117/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9825 - loss: 0.0557\n",
      "Epoch 118/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9548 - loss: 0.1270\n",
      "Epoch 119/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9782 - loss: 0.0713\n",
      "Epoch 120/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9857 - loss: 0.0694\n",
      "Epoch 121/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9635 - loss: 0.0968\n",
      "Epoch 122/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9810 - loss: 0.0724\n",
      "Epoch 123/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9877 - loss: 0.0382\n",
      "Epoch 124/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9788 - loss: 0.0806\n",
      "Epoch 125/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9843 - loss: 0.0454\n",
      "Epoch 126/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9711 - loss: 0.1298\n",
      "Epoch 127/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9818 - loss: 0.0806\n",
      "Epoch 128/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9538 - loss: 0.1526\n",
      "Epoch 129/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9740 - loss: 0.0695\n",
      "Epoch 130/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9762 - loss: 0.1591\n",
      "Epoch 131/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9773 - loss: 0.0885\n",
      "Epoch 132/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9843 - loss: 0.0360\n",
      "Epoch 133/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9893 - loss: 0.0468\n",
      "Epoch 134/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9804 - loss: 0.2170\n",
      "Epoch 135/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9638 - loss: 0.0956\n",
      "Epoch 136/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9580 - loss: 0.1541\n",
      "Epoch 137/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9745 - loss: 0.0869\n",
      "Epoch 138/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9834 - loss: 0.0580\n",
      "Epoch 139/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9934 - loss: 0.0339\n",
      "Epoch 140/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9822 - loss: 0.0375\n",
      "Epoch 141/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9801 - loss: 0.0478\n",
      "Epoch 142/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9698 - loss: 0.0752\n",
      "Epoch 143/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9722 - loss: 0.0916\n",
      "Epoch 144/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9695 - loss: 0.0907\n",
      "Epoch 145/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9780 - loss: 0.0551\n",
      "Epoch 146/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9799 - loss: 0.0438\n",
      "Epoch 147/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9761 - loss: 0.0865\n",
      "Epoch 148/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9804 - loss: 0.0541\n",
      "Epoch 149/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9809 - loss: 0.0667\n",
      "Epoch 150/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9801 - loss: 0.0972\n",
      "Epoch 151/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9908 - loss: 0.0333\n",
      "Epoch 152/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9727 - loss: 0.1196\n",
      "Epoch 153/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9648 - loss: 0.1638\n",
      "Epoch 154/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9837 - loss: 0.0473\n",
      "Epoch 155/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9590 - loss: 0.1734\n",
      "Epoch 156/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9787 - loss: 0.0656\n",
      "Epoch 157/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9779 - loss: 0.0402\n",
      "Epoch 158/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9646 - loss: 0.0944\n",
      "Epoch 159/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9879 - loss: 0.0322\n",
      "Epoch 160/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9682 - loss: 0.1206\n",
      "Epoch 161/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9704 - loss: 0.0884\n",
      "Epoch 162/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9772 - loss: 0.0453\n",
      "Epoch 163/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9694 - loss: 0.1023\n",
      "Epoch 164/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9774 - loss: 0.1200\n",
      "Epoch 165/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9746 - loss: 0.1580\n",
      "Epoch 166/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9634 - loss: 0.1754\n",
      "Epoch 167/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9737 - loss: 0.1328\n",
      "Epoch 168/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9787 - loss: 0.0395\n",
      "Epoch 169/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9847 - loss: 0.0488\n",
      "Epoch 170/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9719 - loss: 0.1112\n",
      "Epoch 171/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9950 - loss: 0.0203\n",
      "Epoch 172/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9863 - loss: 0.0407\n",
      "Epoch 173/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9708 - loss: 0.0521\n",
      "Epoch 174/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9790 - loss: 0.0780\n",
      "Epoch 175/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9883 - loss: 0.0447\n",
      "Epoch 176/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9791 - loss: 0.0640\n",
      "Epoch 177/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9792 - loss: 0.0616\n",
      "Epoch 178/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9748 - loss: 0.1162\n",
      "Epoch 179/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9826 - loss: 0.0785\n",
      "Epoch 180/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9907 - loss: 0.0344\n",
      "Epoch 181/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9671 - loss: 0.0769\n",
      "Epoch 182/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9934 - loss: 0.0439\n",
      "Epoch 183/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9750 - loss: 0.0628\n",
      "Epoch 184/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9960 - loss: 0.0170\n",
      "Epoch 185/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9703 - loss: 0.1734\n",
      "Epoch 186/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9743 - loss: 0.1034\n",
      "Epoch 187/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9839 - loss: 0.0334\n",
      "Epoch 188/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9773 - loss: 0.1229\n",
      "Epoch 189/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9850 - loss: 0.0897\n",
      "Epoch 190/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9785 - loss: 0.1079\n",
      "Epoch 191/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9790 - loss: 0.0997\n",
      "Epoch 192/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9796 - loss: 0.0637\n",
      "Epoch 193/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9793 - loss: 0.0998\n",
      "Epoch 194/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9728 - loss: 0.0538\n",
      "Epoch 195/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9923 - loss: 0.0177\n",
      "Epoch 196/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9922 - loss: 0.0302\n",
      "Epoch 197/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9885 - loss: 0.0933\n",
      "Epoch 198/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9879 - loss: 0.0727\n",
      "Epoch 199/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9918 - loss: 0.0383\n",
      "Epoch 200/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9777 - loss: 0.0542\n",
      "Epoch 201/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9868 - loss: 0.0527\n",
      "Epoch 202/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9915 - loss: 0.0448\n",
      "Epoch 203/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9740 - loss: 0.0673\n",
      "Epoch 204/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9752 - loss: 0.0587\n",
      "Epoch 205/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9929 - loss: 0.0211\n",
      "Epoch 206/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9860 - loss: 0.0931\n",
      "Epoch 207/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9859 - loss: 0.0701\n",
      "Epoch 208/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9814 - loss: 0.0355\n",
      "Epoch 209/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9675 - loss: 0.1340\n",
      "Epoch 210/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9919 - loss: 0.0248\n",
      "Epoch 211/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9912 - loss: 0.0312\n",
      "Epoch 212/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9929 - loss: 0.0175\n",
      "Epoch 213/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9989 - loss: 0.0073\n",
      "Epoch 214/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9832 - loss: 0.0372\n",
      "Epoch 215/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9847 - loss: 0.0707\n",
      "Epoch 216/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9708 - loss: 0.0538\n",
      "Epoch 217/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9954 - loss: 0.0387\n",
      "Epoch 218/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9791 - loss: 0.1166\n",
      "Epoch 219/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.1131\n",
      "Epoch 220/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9899 - loss: 0.0224\n",
      "Epoch 221/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9777 - loss: 0.1392\n",
      "Epoch 222/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9790 - loss: 0.0696\n",
      "Epoch 223/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9928 - loss: 0.0577\n",
      "Epoch 224/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9733 - loss: 0.0897\n",
      "Epoch 225/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9921 - loss: 0.0659\n",
      "Epoch 226/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9721 - loss: 0.1227\n",
      "Epoch 227/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9858 - loss: 0.0474\n",
      "Epoch 228/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9877 - loss: 0.0199\n",
      "Epoch 229/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9867 - loss: 0.0412\n",
      "Epoch 230/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9785 - loss: 0.1141\n",
      "Epoch 231/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9875 - loss: 0.0534\n",
      "Epoch 232/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9780 - loss: 0.0626\n",
      "Epoch 233/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9882 - loss: 0.0301\n",
      "Epoch 234/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9926 - loss: 0.0145\n",
      "Epoch 235/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9930 - loss: 0.0323\n",
      "Epoch 236/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9855 - loss: 0.0593\n",
      "Epoch 237/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9919 - loss: 0.0506\n",
      "Epoch 238/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9846 - loss: 0.0603\n",
      "Epoch 239/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9627 - loss: 0.0921\n",
      "Epoch 240/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9890 - loss: 0.0332\n",
      "Epoch 241/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9917 - loss: 0.0505\n",
      "Epoch 242/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9970 - loss: 0.0118\n",
      "Epoch 243/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9929 - loss: 0.0225\n",
      "Epoch 244/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9820 - loss: 0.0455\n",
      "Epoch 245/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9724 - loss: 0.1254\n",
      "Epoch 246/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9907 - loss: 0.0313\n",
      "Epoch 247/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9918 - loss: 0.0381\n",
      "Epoch 248/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9933 - loss: 0.0177\n",
      "Epoch 249/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0260\n",
      "Epoch 250/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9716 - loss: 0.0455\n"
     ]
    }
   ],
   "source": [
    "Batch_Size = 4\n",
    "Shift_Range = 1\n",
    "Brightness_Range = 0.2\n",
    "Rotation_Angle = 5\n",
    "ZoomRange = 0.3\n",
    "\n",
    "datagen = ImageDataGenerator(width_shift_range=[-Shift_Range,Shift_Range], \n",
    "                             height_shift_range=[-Shift_Range,Shift_Range],\n",
    "                             brightness_range=[1-Brightness_Range,1+Brightness_Range],\n",
    "                             zoom_range=[1-ZoomRange, 1+ZoomRange],\n",
    "                             rotation_range=Rotation_Angle)\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n",
    "    validation_iterator = datagen.flow(X_test, y_test, batch_size=Batch_Size)\n",
    "    history = model.fit(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n",
    "else:\n",
    "    train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n",
    "    history = model.fit(train_iterator, epochs = Epoch_Anz)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9933 - loss: 0.0302\n",
      "Epoch 2/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9877 - loss: 0.0483\n",
      "Epoch 3/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9975 - loss: 0.0097\n",
      "Epoch 4/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9922 - loss: 0.0192\n",
      "Epoch 5/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9933 - loss: 0.0314\n",
      "Epoch 6/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9910 - loss: 0.0207\n",
      "Epoch 7/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9856 - loss: 0.0337\n",
      "Epoch 8/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9769 - loss: 0.0843\n",
      "Epoch 9/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9864 - loss: 0.0505\n",
      "Epoch 10/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9845 - loss: 0.0554\n",
      "Epoch 11/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9792 - loss: 0.0676\n",
      "Epoch 12/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9703 - loss: 0.1855\n",
      "Epoch 13/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9818 - loss: 0.0359\n",
      "Epoch 14/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9841 - loss: 0.0320\n",
      "Epoch 15/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9605 - loss: 0.1692\n",
      "Epoch 16/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9916 - loss: 0.0184\n",
      "Epoch 17/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9874 - loss: 0.0485\n",
      "Epoch 18/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9810 - loss: 0.0537\n",
      "Epoch 19/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9850 - loss: 0.0241\n",
      "Epoch 20/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9884 - loss: 0.0503\n",
      "Epoch 21/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9809 - loss: 0.1155\n",
      "Epoch 22/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9917 - loss: 0.0169\n",
      "Epoch 23/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9918 - loss: 0.0294\n",
      "Epoch 24/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9938 - loss: 0.0213\n",
      "Epoch 25/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9940 - loss: 0.0300\n",
      "Epoch 26/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9914 - loss: 0.0347\n",
      "Epoch 27/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.0592\n",
      "Epoch 28/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9950 - loss: 0.0373\n",
      "Epoch 29/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 1.0000 - loss: 0.0069\n",
      "Epoch 30/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9923 - loss: 0.0307\n",
      "Epoch 31/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9971 - loss: 0.0101\n",
      "Epoch 32/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9792 - loss: 0.0440\n",
      "Epoch 33/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9860 - loss: 0.0516\n",
      "Epoch 34/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9858 - loss: 0.0299\n",
      "Epoch 35/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9946 - loss: 0.0536\n",
      "Epoch 36/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9771 - loss: 0.0530\n",
      "Epoch 37/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9771 - loss: 0.0596\n",
      "Epoch 38/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9926 - loss: 0.0190\n",
      "Epoch 39/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9849 - loss: 0.1073\n",
      "Epoch 40/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9961 - loss: 0.0308\n",
      "Epoch 41/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9821 - loss: 0.0753\n",
      "Epoch 42/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9939 - loss: 0.0350\n",
      "Epoch 43/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9834 - loss: 0.0272\n",
      "Epoch 44/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9933 - loss: 0.0163\n",
      "Epoch 45/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9902 - loss: 0.0278\n",
      "Epoch 46/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9978 - loss: 0.0039  \n",
      "Epoch 47/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9976 - loss: 0.0170\n",
      "Epoch 48/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9759 - loss: 0.0733\n",
      "Epoch 49/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9862 - loss: 0.0565\n",
      "Epoch 50/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9920 - loss: 0.0368\n",
      "Epoch 51/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9848 - loss: 0.0648\n",
      "Epoch 52/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9992 - loss: 0.0062\n",
      "Epoch 53/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9779 - loss: 0.0948\n",
      "Epoch 54/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9942 - loss: 0.0169\n",
      "Epoch 55/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9936 - loss: 0.0114\n",
      "Epoch 56/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9916 - loss: 0.0384\n",
      "Epoch 57/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9867 - loss: 0.0289\n",
      "Epoch 58/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.1027\n",
      "Epoch 59/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9760 - loss: 0.0827\n",
      "Epoch 60/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9980 - loss: 0.0150\n",
      "Epoch 61/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9919 - loss: 0.0150\n",
      "Epoch 62/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9844 - loss: 0.0256\n",
      "Epoch 63/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9868 - loss: 0.0240\n",
      "Epoch 64/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9759 - loss: 0.0530\n",
      "Epoch 65/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9765 - loss: 0.0588\n",
      "Epoch 66/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9877 - loss: 0.0275\n",
      "Epoch 67/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9986 - loss: 0.0116\n",
      "Epoch 68/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9921 - loss: 0.0249\n",
      "Epoch 69/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9879 - loss: 0.0317\n",
      "Epoch 70/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9912 - loss: 0.0403\n",
      "Epoch 71/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9921 - loss: 0.0343\n",
      "Epoch 72/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9776 - loss: 0.1714\n",
      "Epoch 73/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9843 - loss: 0.0806\n",
      "Epoch 74/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9774 - loss: 0.0756\n",
      "Epoch 75/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9737 - loss: 0.1255\n",
      "Epoch 76/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9924 - loss: 0.0350\n",
      "Epoch 77/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9663 - loss: 0.0790\n",
      "Epoch 78/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9788 - loss: 0.1026\n",
      "Epoch 79/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9830 - loss: 0.0463\n",
      "Epoch 80/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9974 - loss: 0.0084\n",
      "Epoch 81/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9982 - loss: 0.0093\n",
      "Epoch 82/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9741 - loss: 0.0822\n",
      "Epoch 83/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9963 - loss: 0.0197\n",
      "Epoch 84/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9891 - loss: 0.0228\n",
      "Epoch 85/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9986 - loss: 0.0130\n",
      "Epoch 86/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9935 - loss: 0.0219\n",
      "Epoch 87/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9901 - loss: 0.0208\n",
      "Epoch 88/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9901 - loss: 0.0398\n",
      "Epoch 89/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9710 - loss: 0.1683\n",
      "Epoch 90/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9917 - loss: 0.0433\n",
      "Epoch 91/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9609 - loss: 0.1515\n",
      "Epoch 92/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9880 - loss: 0.0347\n",
      "Epoch 93/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9761 - loss: 0.1951\n",
      "Epoch 94/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9729 - loss: 0.1336\n",
      "Epoch 95/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9849 - loss: 0.0279\n",
      "Epoch 96/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9834 - loss: 0.0697\n",
      "Epoch 97/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9951 - loss: 0.0213\n",
      "Epoch 98/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9788 - loss: 0.0872\n",
      "Epoch 99/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9921 - loss: 0.0134\n",
      "Epoch 100/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9982 - loss: 0.0577\n",
      "Epoch 101/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9847 - loss: 0.0373\n",
      "Epoch 102/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9919 - loss: 0.0176\n",
      "Epoch 103/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9987 - loss: 0.0072\n",
      "Epoch 104/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9845 - loss: 0.0720\n",
      "Epoch 105/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0136\n",
      "Epoch 106/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9913 - loss: 0.0392\n",
      "Epoch 107/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9935 - loss: 0.0282\n",
      "Epoch 108/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9727 - loss: 0.0542\n",
      "Epoch 109/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9945 - loss: 0.0203\n",
      "Epoch 110/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9903 - loss: 0.0458\n",
      "Epoch 111/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9780 - loss: 0.0909\n",
      "Epoch 112/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9782 - loss: 0.0385\n",
      "Epoch 113/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9907 - loss: 0.0397\n",
      "Epoch 114/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9870 - loss: 0.0488\n",
      "Epoch 115/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 1.0000 - loss: 0.0027\n",
      "Epoch 116/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9891 - loss: 0.0414\n",
      "Epoch 117/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9886 - loss: 0.0801\n",
      "Epoch 118/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9903 - loss: 0.0404\n",
      "Epoch 119/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9820 - loss: 0.0646\n",
      "Epoch 120/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9946 - loss: 0.0193\n",
      "Epoch 121/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9977 - loss: 0.0129\n",
      "Epoch 122/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9925 - loss: 0.0188\n",
      "Epoch 123/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9977 - loss: 0.0066\n",
      "Epoch 124/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9774 - loss: 0.0395\n",
      "Epoch 125/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9768 - loss: 0.1595\n",
      "Epoch 126/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9871 - loss: 0.0523\n",
      "Epoch 127/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9857 - loss: 0.0639\n",
      "Epoch 128/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9947 - loss: 0.0147\n",
      "Epoch 129/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9827 - loss: 0.0543\n",
      "Epoch 130/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9752 - loss: 0.0786\n",
      "Epoch 131/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9988 - loss: 0.0065\n",
      "Epoch 132/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9956 - loss: 0.0163\n",
      "Epoch 133/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9830 - loss: 0.0407\n",
      "Epoch 134/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9934 - loss: 0.0293\n",
      "Epoch 135/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9919 - loss: 0.0338\n",
      "Epoch 136/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9854 - loss: 0.1312\n",
      "Epoch 137/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9914 - loss: 0.0351\n",
      "Epoch 138/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9908 - loss: 0.0397\n",
      "Epoch 139/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9858 - loss: 0.0282\n",
      "Epoch 140/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9947 - loss: 0.0115\n",
      "Epoch 141/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9802 - loss: 0.0505\n",
      "Epoch 142/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9847 - loss: 0.0312\n",
      "Epoch 143/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9887 - loss: 0.1150\n",
      "Epoch 144/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9720 - loss: 0.1047\n",
      "Epoch 145/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9860 - loss: 0.0214\n",
      "Epoch 146/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9908 - loss: 0.0248\n",
      "Epoch 147/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9980 - loss: 0.0329\n",
      "Epoch 148/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9969 - loss: 0.0238\n",
      "Epoch 149/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9571 - loss: 0.0990\n",
      "Epoch 150/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9885 - loss: 0.0480\n",
      "Epoch 151/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9945 - loss: 0.0245\n",
      "Epoch 152/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9883 - loss: 0.0543\n",
      "Epoch 153/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9928 - loss: 0.0326\n",
      "Epoch 154/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9995 - loss: 0.0033\n",
      "Epoch 155/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9825 - loss: 0.0507\n",
      "Epoch 156/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9976 - loss: 0.0053\n",
      "Epoch 157/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9938 - loss: 0.0205\n",
      "Epoch 158/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9820 - loss: 0.0566\n",
      "Epoch 159/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9857 - loss: 0.0409\n",
      "Epoch 160/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9781 - loss: 0.0619\n",
      "Epoch 161/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9977 - loss: 0.0048\n",
      "Epoch 162/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9928 - loss: 0.0487\n",
      "Epoch 163/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9833 - loss: 0.0570\n",
      "Epoch 164/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9959 - loss: 0.0160\n",
      "Epoch 165/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9822 - loss: 0.0848\n",
      "Epoch 166/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9832 - loss: 0.0351\n",
      "Epoch 167/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9933 - loss: 0.0241\n",
      "Epoch 168/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9978 - loss: 0.0067\n",
      "Epoch 169/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9907 - loss: 0.0677\n",
      "Epoch 170/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9984 - loss: 0.0075\n",
      "Epoch 171/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9979 - loss: 0.0085\n",
      "Epoch 172/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9942 - loss: 0.0208\n",
      "Epoch 173/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9965 - loss: 0.0125\n",
      "Epoch 174/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9906 - loss: 0.0254\n",
      "Epoch 175/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9863 - loss: 0.0788\n",
      "Epoch 176/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9953 - loss: 0.0239\n",
      "Epoch 177/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9985 - loss: 0.0120\n",
      "Epoch 178/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9960 - loss: 0.0187\n",
      "Epoch 179/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9758 - loss: 0.0702\n",
      "Epoch 180/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9776 - loss: 0.0974\n",
      "Epoch 181/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9847 - loss: 0.0543\n",
      "Epoch 182/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9938 - loss: 0.0754\n",
      "Epoch 183/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9977 - loss: 0.0130\n",
      "Epoch 184/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9990 - loss: 0.0026\n",
      "Epoch 185/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9899 - loss: 0.0738\n",
      "Epoch 186/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9825 - loss: 0.0647\n",
      "Epoch 187/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9918 - loss: 0.0296\n",
      "Epoch 188/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9786 - loss: 0.0404\n",
      "Epoch 189/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9856 - loss: 0.0387\n",
      "Epoch 190/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9858 - loss: 0.0479\n",
      "Epoch 191/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9820 - loss: 0.0724\n",
      "Epoch 192/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9844 - loss: 0.0235\n",
      "Epoch 193/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9973 - loss: 0.0056\n",
      "Epoch 194/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9908 - loss: 0.0283\n",
      "Epoch 195/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9952 - loss: 0.0176\n",
      "Epoch 196/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9851 - loss: 0.0753\n",
      "Epoch 197/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9911 - loss: 0.0246\n",
      "Epoch 198/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9902 - loss: 0.0237\n",
      "Epoch 199/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9869 - loss: 0.0681\n",
      "Epoch 200/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9961 - loss: 0.0137\n",
      "Epoch 201/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.0208\n",
      "Epoch 202/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9846 - loss: 0.0706\n",
      "Epoch 203/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9872 - loss: 0.1086\n",
      "Epoch 204/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9844 - loss: 0.0470\n",
      "Epoch 205/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9982 - loss: 0.0094  \n",
      "Epoch 206/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9833 - loss: 0.1405\n",
      "Epoch 207/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9975 - loss: 0.0063\n",
      "Epoch 208/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9969 - loss: 0.0084\n",
      "Epoch 209/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9913 - loss: 0.0173\n",
      "Epoch 210/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9828 - loss: 0.0746\n",
      "Epoch 211/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9962 - loss: 0.0344\n",
      "Epoch 212/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9857 - loss: 0.0325\n",
      "Epoch 213/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9840 - loss: 0.0456\n",
      "Epoch 214/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9874 - loss: 0.0243\n",
      "Epoch 215/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9899 - loss: 0.0625\n",
      "Epoch 216/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9884 - loss: 0.0492\n",
      "Epoch 217/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 1.0000 - loss: 0.0054\n",
      "Epoch 218/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9958 - loss: 0.0461\n",
      "Epoch 219/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9883 - loss: 0.0224\n",
      "Epoch 220/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9947 - loss: 0.0091\n",
      "Epoch 221/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9855 - loss: 0.0474\n",
      "Epoch 222/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9723 - loss: 0.1073\n",
      "Epoch 223/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9901 - loss: 0.0341\n",
      "Epoch 224/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9876 - loss: 0.1143\n",
      "Epoch 225/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9894 - loss: 0.1094\n",
      "Epoch 226/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9944 - loss: 0.0255\n",
      "Epoch 227/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9926 - loss: 0.0205\n",
      "Epoch 228/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9935 - loss: 0.0240\n",
      "Epoch 229/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9891 - loss: 0.0464\n",
      "Epoch 230/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9949 - loss: 0.0428\n",
      "Epoch 231/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9985 - loss: 0.0178\n",
      "Epoch 232/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9829 - loss: 0.0672\n",
      "Epoch 233/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9949 - loss: 0.0251\n",
      "Epoch 234/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9823 - loss: 0.0787\n",
      "Epoch 235/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9962 - loss: 0.0181\n",
      "Epoch 236/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9956 - loss: 0.0213\n",
      "Epoch 237/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 1.0000 - loss: 0.0057\n",
      "Epoch 238/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9841 - loss: 0.0616\n",
      "Epoch 239/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 1.0000 - loss: 0.0027\n",
      "Epoch 240/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.0127\n",
      "Epoch 241/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9924 - loss: 0.0257\n",
      "Epoch 242/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9940 - loss: 0.0234\n",
      "Epoch 243/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9976 - loss: 0.0130\n",
      "Epoch 244/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9978 - loss: 0.0127\n",
      "Epoch 245/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9903 - loss: 0.0263\n",
      "Epoch 246/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9986 - loss: 0.0066\n",
      "Epoch 247/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9967 - loss: 0.0093\n",
      "Epoch 248/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9948 - loss: 0.0375\n",
      "Epoch 249/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9983 - loss: 0.0078\n",
      "Epoch 250/250\n",
      "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9893 - loss: 0.0721\n"
     ]
    }
   ],
   "source": [
    "loss_ges = np.append(loss_ges, history.history['loss'])\n",
    "plt.semilogy(history.history['loss'])\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    val_loss_ges = np.append(val_loss_ges, history.history['val_loss'])\n",
    "    plt.semilogy(history.history['val_loss'])\n",
    "\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','eval'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    history = model.fit(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n",
    "else:\n",
    "    history = model.fit(train_iterator, epochs = Epoch_Anz)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learing result\n",
    " \n",
    "* Visualization of the training and validation results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_ges = np.append(loss_ges, history.history['loss'])\n",
    "plt.semilogy(history.history['loss'])\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    val_loss_ges = np.append(val_loss_ges, history.history['val_loss'])\n",
    "    plt.semilogy(history.history['val_loss'])\n",
    "\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','eval'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the model by hand\n",
    "\n",
    "* The following code uses the trained model to check the deviation for each picture.\n",
    "* x-axis walks through each pixel, y-axis shows the result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check each image for expected and deviation\n",
    "* setting the switch \"only_deviation = true\" will only print the images for which the classification and the CNN-result deviates\n",
    "\n",
    "The output contains the following information:\n",
    "\n",
    "| Filename      | Expected Category           | Predicted Category        |\n",
    "|------------- |:-----------------------------:|--------------|\n",
    "| ziffer_sortiert_resize_NaN/5\\Ziffer_4_0034.jpg | 4  | -1 |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[8], line 38\u001b[0m\n\u001b[0;32m     32\u001b[0m         \u001b[38;5;28mprint\u001b[39m(aktfile \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m aktsubdir \u001b[38;5;241m+\u001b[39m  \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(classes))\n\u001b[0;32m     35\u001b[0m res \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(res)\n\u001b[1;32m---> 38\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(res[:,\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m     39\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(res[:,\u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m     40\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mResult\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[1;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed"
     ]
    }
   ],
   "source": [
    "Input_dir='ziffer_sortiert_resize'\n",
    "res = []\n",
    "only_deviation = True\n",
    "show_wrong_image = True\n",
    "\n",
    "files = glob.glob(Input_dir + '/*.jpg')\n",
    "\n",
    "for aktfile in files:\n",
    "    base = os.path.basename(aktfile)\n",
    "    target = base[0:1]\n",
    "    if target == \"N\":\n",
    "        zw1 = -1\n",
    "    else:\n",
    "        zw1 = int(target)\n",
    "    expected_class = zw1\n",
    "    image_in = Image.open(aktfile)\n",
    "    test_image = np.array(image_in, dtype=\"float32\")\n",
    "    img = np.reshape(test_image,[1,32,20,3])\n",
    "    classes = np.argmax(model.predict(img, verbose=0), axis=-1)\n",
    "    classes = classes[0]\n",
    "    if classes == 10: \n",
    "        classes = -1\n",
    "    zw2 = classes\n",
    "    zw3 = zw2 - zw1\n",
    "    res.append(np.array([zw1, zw2, zw3]))\n",
    "    if only_deviation == True:\n",
    "        if str(classes) != str(expected_class):\n",
    "            print(aktfile + \" \" + str(expected_class) +  \" \" + str(classes))\n",
    "            if show_wrong_image == True:\n",
    "                display(image_in)\n",
    "    else:\n",
    "        print(aktfile + \" \" + aktsubdir +  \" \" + str(classes))\n",
    "        \n",
    "\n",
    "res = np.asarray(res)\n",
    "\n",
    "\n",
    "plt.plot(res[:,0])\n",
    "plt.plot(res[:,1])\n",
    "plt.title('Result')\n",
    "plt.ylabel('Digital Value')\n",
    "plt.xlabel('#Picture')\n",
    "plt.legend(['real','model'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the model\n",
    "\n",
    "* Save the model to the file with the \"h5\" file format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.export(\"test\", \"tf_saved_model\")  # replace tf.saved_model.save with this line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dig-class11_1910_s2.tflite\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "356636"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FileName = TFlite_MainType + \"_\" + TFlite_Version + \"_\" + TFlite_Size + \".tflite\"\n",
    "\n",
    "converter = tf.lite.TFLiteConverter.from_saved_model(\"test\")\n",
    "tflite_model = converter.convert()\n",
    "open(FileName, \"wb\").write(tflite_model)\n",
    "print(FileName)\n",
    "Path(FileName).stat().st_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dig-class11_1910_s2_q.tflite\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "97192"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FileName = TFlite_MainType + \"_\" + TFlite_Version + \"_\" + TFlite_Size + \"_q.tflite\"\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "def representative_dataset():\n",
    "    for n in range(x_data[0].size):\n",
    "      data = np.expand_dims(x_data[5], axis=0)\n",
    "      yield [data.astype(np.float32)]\n",
    "        \n",
    "converter2 = tf.lite.TFLiteConverter.from_saved_model(\"test\")\n",
    "converter2.representative_dataset = representative_dataset\n",
    "converter2.optimizations = [tf.lite.Optimize.DEFAULT]\n",
    "converter2.representative_dataset = representative_dataset\n",
    "converter2._experimental_disable_per_channel_quantization_for_dense_layers = True\n",
    "\n",
    "tflite_quant_model = converter2.convert()\n",
    "\n",
    "open(FileName, \"wb\").write(tflite_quant_model)\n",
    "print(FileName)\n",
    "Path(FileName).stat().st_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the images shows, that this are border line images, which can be interpreted as a good digit or a faulty one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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