example.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "55f330ba",
   "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-21 20:34:49.202842: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-03-21 20:34:49.258623: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n"
     ]
    }
   ],
   "source": [
    "import sys, os \n",
    "import tensorflow as tf \n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow import keras \n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import (Dense, Flatten, Input, Conv2D, Activation, Layer, ReLU, Softmax, BatchNormalization, MaxPool2D, AveragePooling2D, Rescaling, CenterCrop, RandomRotation)\n",
    "import numpy as np \n",
    "import cv2 as cv \n",
    "import pandas as pd\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "# For consistent tests\n",
    "keras.utils.set_random_seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "289bd75c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# func to convert .png to .jpg files (.png files have 4 color channels, so regularize input file type with thsi func)\n",
    "def png_to_jpg(input_dir, output_dir):\n",
    "    for file in os.listdir(input_dir):\n",
    "        if file.endswith('.png'):\n",
    "            filePath = os.path.join(input_dir, file)\n",
    "            image = cv.imread(filePath)\n",
    "            output_file = os.path.splitext(file)[0]+\".jpg\"\n",
    "            output_filepath = os.path.join(output_dir, output_file)\n",
    "            cv.imwrite(output_filepath, image)\n",
    "            os.remove(filePath)\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d02c32b",
   "metadata": {
    "scrolled": true
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   "outputs": [
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      "20230228_174026.jpg\n",
      "20230228_174011.jpg\n",
      "20230228_174015.jpg\n",
      "20221202_185655.jpg\n",
      "IMG_20221202_185053.jpg\n",
      "20230228_174001.jpg\n",
      "20230228_174031.jpg\n",
      "antibottom quark\n",
      "Screen Shot 2023-02-07 at 8.47.01 AM.jpg\n",
      "IMG_20221202_185356.jpg\n",
      "Screen Shot 2023-02-07 at 9.02.48 AM.jpg\n",
      "20230228_173750.jpg\n",
      "20230228_173803.jpg\n",
      "20221202_185629.jpg\n",
      "IMG_20221202_185400.jpg\n",
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      "20230228_173757.jpg\n",
      "20230228_173733.jpg\n",
      "Screen Shot 2023-02-07 at 9.17.52 AM.jpg\n",
      "tachyon\n",
      "20230228_173515.jpg\n",
      "Screen Shot 2023-02-07 at 9.17.01 AM.jpg\n",
      "20221202_185806.jpg\n",
      "20221202_185808.jpg\n",
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      "IMG_20221202_185049.jpg\n",
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      "IMG_20221202_185037.jpg\n",
      "Screen Shot 2023-02-07 at 8.44.35 AM.jpg\n",
      "Screen Shot 2023-02-07 at 9.02.25 AM.jpg\n",
      "20230228_173512.jpg\n",
      "Higgs Boson\n",
      "Screen Shot 2023-02-07 at 9.16.05 AM.jpg\n",
      "20221202_185718.jpg\n",
      "20230228_172006.jpg\n",
      "20221202_185720.jpg\n",
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      "IMG_20221202_185113.jpg\n",
      "20230228_170310.jpg\n",
      "Screen Shot 2023-02-07 at 9.03.04 AM.jpg\n",
      "Screen Shot 2023-02-07 at 8.45.49 AM.jpg\n",
      "20230228_170319.jpg\n",
      "IMG_20221202_185118.jpg\n",
      "20230228_170255.jpg\n",
      "20230228_170322.jpg\n",
      "antimuon\n",
      "20230228_170152.jpg\n",
      "Screen Shot 2023-02-07 at 9.00.30 AM.jpg\n",
      "20230228_170130.jpg\n",
      "20230228_170142.jpg\n",
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      "Screen Shot 2023-02-07 at 8.46.06 AM.jpg\n",
      "20230228_170132.jpg\n",
      "Screen Shot 2023-02-07 at 9.18.04 AM.jpg\n",
      "IMG_20221202_185510.jpg\n",
      "20230228_170133.jpg\n",
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      "20221202_185119.jpg\n",
      "IMG_20221202_185505.jpg\n",
      "20230228_170127.jpg\n",
      "antistrange quark\n",
      "IMG_20221202_185139.jpg\n",
      "20221202_185445.jpg\n",
      "20230228_170506.jpg\n",
      "Screen Shot 2023-02-07 at 8.46.58 AM.jpg\n",
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      "Screen Shot 2023-02-07 at 9.03.19 AM.jpg\n",
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      "IMG_20221202_185134.jpg\n",
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      "20221202_185451.jpg\n",
      "Screen Shot 2023-02-07 at 9.16.55 AM.jpg\n",
      "20221202_185452.jpg\n",
      "antineutron\n",
      "20221202_185529.jpg\n",
      "20230228_173427.jpg\n",
      "Screen Shot 2023-02-07 at 9.03.29 AM.jpg\n",
      "Screen Shot 2023-02-07 at 9.16.15 AM.jpg\n",
      "20230228_173426.jpg\n",
      "IMG_20221202_185206.jpg\n",
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      "IMG_20221202_185200.jpg\n",
      "Screen Shot 2023-02-07 at 8.46.54 AM.jpg\n",
      "gluon\n",
      "20230228_173719.jpg\n",
      "Screen Shot 2023-02-07 at 8.44.49 AM.jpg\n",
      "20230228_173716.jpg\n",
      "20230228_173710.jpg\n",
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      "Screen Shot 2023-02-07 at 9.03.01 AM.jpg\n",
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      "IMG_20221202_185323.jpg\n",
      "Screen Shot 2023-02-07 at 9.16.25 AM.jpg\n",
      "up quark\n",
      "Screen Shot 2023-02-07 at 8.46.20 AM.jpg\n",
      "20221202_185241.jpg\n",
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      "Screen Shot 2023-02-07 at 9.00.22 AM.jpg\n",
      "20230228_172703.jpg\n",
      "20230228_172726.jpg\n",
      "Screen Shot 2023-02-07 at 9.17.58 AM.jpg\n",
      "Z Boson\n",
      "IMG_20221202_185535.jpg\n",
      "Screen Shot 2023-02-07 at 9.00.11 AM.jpg\n",
      "20230228_172628.jpg\n",
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      "IMG_20221202_185539.jpg\n",
      "20221202_185306.jpg\n",
      "tau\n",
      "20230228_172902.jpg\n",
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      "Screen Shot 2023-02-07 at 8.45.56 AM.jpg\n",
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      "20230228_172911.jpg\n"
     ]
    }
   ],
   "source": [
    "## Read in the images, create class labels, and preprocess for input into CNN ##\n",
    "#num_classes = 36\n",
    "\n",
    "path = 'images' # change image path accordingly\n",
    "\n",
    "# list to store particle (class) names\n",
    "particle_types = []\n",
    "\n",
    "# list to store all particle images. This is what will be randomly shuffled and will be train/validation/test split\n",
    "images = []\n",
    "\n",
    "for fileName in os.listdir(path):\n",
    "    \n",
    "    if not fileName.startswith(\".\"):\n",
    "        print(fileName)\n",
    "        sub_dir_path = os.path.join(path, fileName)\n",
    "        png_to_jpg(sub_dir_path, sub_dir_path)\n",
    "        particle_types.append(fileName)\n",
    "        \n",
    "        if os.path.isdir(sub_dir_path):\n",
    "            \n",
    "            # loop over each file in each sub directory and append to a list of images\n",
    "            for file in os.listdir(sub_dir_path):\n",
    "             \n",
    "                # ignore hidden files that your os may generate\n",
    "                if not file.startswith('.') or not file.endswith('.jpg'):\n",
    "                    print(file)\n",
    "\n",
    "                    # create directory path for the image\n",
    "                    directory = os.path.join(sub_dir_path, file)\n",
    "                    \n",
    "                    # read in each image using openCV\n",
    "                    image = cv.imread(directory)\n",
    "                    # resize the images accordingly\n",
    "                    image = cv.resize(image, (75,75))\n",
    "                    # append to the list we are storing images\n",
    "                    images.append(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1fc1bf05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# store images and labels in a dictionary\n",
    "particle_dict = dict.fromkeys(particle_types)\n",
    "\n",
    "for fileName in os.listdir(path):\n",
    "    \n",
    "    if not fileName.startswith(\".\"):\n",
    "        particle_dict[fileName] = []\n",
    "        sub_dir_path = os.path.join(path, fileName)\n",
    "        if os.path.isdir(sub_dir_path):\n",
    "            \n",
    "            # loop over each file in each sub directory and append to a list of images\n",
    "            for file in os.listdir(sub_dir_path):\n",
    "                # ignore hidden files that your os may generate\n",
    "                if not file.startswith('.'):\n",
    "                    directory = os.path.join(sub_dir_path, file)\n",
    "                    image = cv.imread(directory)\n",
    "                    image = cv.resize(image,(75,75))\n",
    "                    particle_dict[fileName].append(image)    \n",
    "\n",
    "#train_data, val_data, test_data = train_val_test_split(particle_dict, 0.6, 0.2, 0.2 )\n",
    "labels = []\n",
    "for key, value in particle_dict.items():\n",
    "    for i in range(len(value)):\n",
    "        labels.append(key)\n",
    "one_hot_labels = pd.get_dummies(labels).values\n",
    "\n",
    "images = np.array(images)\n",
    "        \n",
    "x_train, x_val, y_train, y_val = train_test_split(images, one_hot_labels, random_state = 1, test_size = 0.1, stratify = one_hot_labels)\n",
    "\n",
    "x_train, X_test, y_train, Y_test = train_test_split(x_train, y_train, random_state = 1, test_size = 1/9, stratify = y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b0b1e9c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "528\n",
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      "529\n",
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      "Image shape: (75, 75, 3)\n",
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      "Image shape: (75, 75, 3)\n",
      "531\n",
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      "Image Data Type: <class 'numpy.ndarray'>\n",
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      "533\n",
      "Image Data Type: <class 'numpy.ndarray'>\n",
      "Image shape: (75, 75, 3)\n",
      "534\n",
      "Image Data Type: <class 'numpy.ndarray'>\n",
      "Image shape: (75, 75, 3)\n",
      "535\n",
      "Image Data Type: <class 'numpy.ndarray'>\n",
      "Image shape: (75, 75, 3)\n",
      "536\n",
      "Image Data Type: <class 'numpy.ndarray'>\n",
      "Image shape: (75, 75, 3)\n",
      "537\n",
      "Image Data Type: <class 'numpy.ndarray'>\n",
      "Image shape: (75, 75, 3)\n",
      "538\n",
      "Image Data Type: <class 'numpy.ndarray'>\n",
      "Image shape: (75, 75, 3)\n",
      "539\n",
      "Image Data Type: <class 'numpy.ndarray'>\n",
      "Image shape: (75, 75, 3)\n",
      "540\n",
      "Image Data Type: <class 'numpy.ndarray'>\n",
      "Image shape: (75, 75, 3)\n",
      "541\n",
      "Image Data Type: <class 'numpy.ndarray'>\n",
      "Image shape: (75, 75, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2296db2710>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print out some input data/images and confirm data type (THIS IS UNDERATTED AND OFTEN OVERLLOOKED STEP IN DATA ANALYSIS)\n",
    "print(len(images))\n",
    "for i in range(len(images)):\n",
    "    if type(images[i]) is not None:\n",
    "        print(i)\n",
    "        print('Image Data Type:', type(images[i]))\n",
    "        print('Image shape:', images[i].shape)\n",
    "plt.imshow(images[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e85751a2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-21 20:36:57.702569: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n"
     ]
    }
   ],
   "source": [
    "## Create your classifier NN. In this case I chose a simple CNN ## \n",
    "inputArray = Input((75,75,3))\n",
    "x = Rescaling(1./255)(inputArray)\n",
    "x = RandomRotation(0.1)(x)\n",
    "\n",
    "x = Conv2D(2, 2)(x)\n",
    "x = keras.activations.relu(x)\n",
    "x = Conv2D(3, 3)(x)\n",
    "x = keras.activations.selu(x)\n",
    "x = MaxPool2D(3,3)(x)\n",
    "x = Conv2D(3, 3)(x)\n",
    "x = keras.activations.selu(x)\n",
    "x = AveragePooling2D(3,3)(x)\n",
    "\n",
    "x = Flatten()(x)\n",
    "    \n",
    "x = keras.layers.AlphaDropout(0.1)(x)\n",
    "x = keras.layers.BatchNormalization()(x)\n",
    "\n",
    "x = Dense(5)(x)\n",
    "x = keras.activations.sigmoid(x)\n",
    "x = Dense(36)(x)\n",
    "outputArray = keras.activations.softmax(x)\n",
    "\n",
    "opt = 'adam'\n",
    "\n",
    "## create/declare cnn model \n",
    "convNet = Model(inputs = inputArray, outputs = outputArray)\n",
    "\n",
    "convNet.compile(loss = 'categorical_crossentropy',\n",
    "                metrics = ['accuracy'],\n",
    "               optimizer = opt)\n",
    "convNet.summary()\n",
    "#keras.utils.plot_model(convNet, show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4cb3e2cb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "13/13 [==============================] - 2s 35ms/step - loss: 3.6177 - accuracy: 0.0370 - val_loss: 3.5906 - val_accuracy: 0.0364\n",
      "Epoch 2/1000\n",
      "13/13 [==============================] - 0s 22ms/step - loss: 3.6149 - accuracy: 0.0278 - val_loss: 3.5892 - val_accuracy: 0.0545\n",
      "Epoch 3/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 3.6055 - accuracy: 0.0347 - val_loss: 3.5870 - val_accuracy: 0.0364\n",
      "Epoch 4/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.6008 - accuracy: 0.0301 - val_loss: 3.5853 - val_accuracy: 0.0364\n",
      "Epoch 5/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.6014 - accuracy: 0.0324 - val_loss: 3.5834 - val_accuracy: 0.0364\n",
      "Epoch 6/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 3.5954 - accuracy: 0.0324 - val_loss: 3.5811 - val_accuracy: 0.0364\n",
      "Epoch 7/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.5918 - accuracy: 0.0370 - val_loss: 3.5794 - val_accuracy: 0.0364\n",
      "Epoch 8/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5870 - accuracy: 0.0394 - val_loss: 3.5761 - val_accuracy: 0.0364\n",
      "Epoch 9/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5877 - accuracy: 0.0347 - val_loss: 3.5727 - val_accuracy: 0.0364\n",
      "Epoch 10/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5820 - accuracy: 0.0231 - val_loss: 3.5672 - val_accuracy: 0.0364\n",
      "Epoch 11/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5760 - accuracy: 0.0301 - val_loss: 3.5629 - val_accuracy: 0.0545\n",
      "Epoch 12/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.5832 - accuracy: 0.0347 - val_loss: 3.5630 - val_accuracy: 0.0364\n",
      "Epoch 13/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5808 - accuracy: 0.0301 - val_loss: 3.5638 - val_accuracy: 0.0364\n",
      "Epoch 14/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.5766 - accuracy: 0.0370 - val_loss: 3.5607 - val_accuracy: 0.0545\n",
      "Epoch 15/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5654 - accuracy: 0.0509 - val_loss: 3.5571 - val_accuracy: 0.0545\n",
      "Epoch 16/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5586 - accuracy: 0.0440 - val_loss: 3.5541 - val_accuracy: 0.0545\n",
      "Epoch 17/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5604 - accuracy: 0.0417 - val_loss: 3.5526 - val_accuracy: 0.0545\n",
      "Epoch 18/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.5601 - accuracy: 0.0440 - val_loss: 3.5516 - val_accuracy: 0.0364\n",
      "Epoch 19/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5546 - accuracy: 0.0440 - val_loss: 3.5503 - val_accuracy: 0.0364\n",
      "Epoch 20/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5498 - accuracy: 0.0463 - val_loss: 3.5509 - val_accuracy: 0.0364\n",
      "Epoch 21/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.5471 - accuracy: 0.0347 - val_loss: 3.5480 - val_accuracy: 0.0364\n",
      "Epoch 22/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.5524 - accuracy: 0.0440 - val_loss: 3.5463 - val_accuracy: 0.0364\n",
      "Epoch 23/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 3.5527 - accuracy: 0.0440 - val_loss: 3.5443 - val_accuracy: 0.0364\n",
      "Epoch 24/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5501 - accuracy: 0.0463 - val_loss: 3.5435 - val_accuracy: 0.0364\n",
      "Epoch 25/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5397 - accuracy: 0.0532 - val_loss: 3.5442 - val_accuracy: 0.0545\n",
      "Epoch 26/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5363 - accuracy: 0.0556 - val_loss: 3.5450 - val_accuracy: 0.0364\n",
      "Epoch 27/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 3.5403 - accuracy: 0.0463 - val_loss: 3.5444 - val_accuracy: 0.0545\n",
      "Epoch 28/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5346 - accuracy: 0.0509 - val_loss: 3.5444 - val_accuracy: 0.0545\n",
      "Epoch 29/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5304 - accuracy: 0.0463 - val_loss: 3.5411 - val_accuracy: 0.0727\n",
      "Epoch 30/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5306 - accuracy: 0.0486 - val_loss: 3.5405 - val_accuracy: 0.0545\n",
      "Epoch 31/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.5237 - accuracy: 0.0602 - val_loss: 3.5398 - val_accuracy: 0.0545\n",
      "Epoch 32/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.5198 - accuracy: 0.0625 - val_loss: 3.5383 - val_accuracy: 0.0545\n",
      "Epoch 33/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.5194 - accuracy: 0.0509 - val_loss: 3.5353 - val_accuracy: 0.0545\n",
      "Epoch 34/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5180 - accuracy: 0.0370 - val_loss: 3.5332 - val_accuracy: 0.0727\n",
      "Epoch 35/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5210 - accuracy: 0.0486 - val_loss: 3.5337 - val_accuracy: 0.0545\n",
      "Epoch 36/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5099 - accuracy: 0.0579 - val_loss: 3.5312 - val_accuracy: 0.0545\n",
      "Epoch 37/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.5003 - accuracy: 0.0556 - val_loss: 3.5276 - val_accuracy: 0.0545\n",
      "Epoch 38/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.4881 - accuracy: 0.0532 - val_loss: 3.5282 - val_accuracy: 0.0545\n",
      "Epoch 39/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.4913 - accuracy: 0.0486 - val_loss: 3.5244 - val_accuracy: 0.0545\n",
      "Epoch 40/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.4913 - accuracy: 0.0602 - val_loss: 3.5215 - val_accuracy: 0.0545\n",
      "Epoch 41/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.4982 - accuracy: 0.0602 - val_loss: 3.5195 - val_accuracy: 0.0545\n",
      "Epoch 42/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.4816 - accuracy: 0.0463 - val_loss: 3.5153 - val_accuracy: 0.0545\n",
      "Epoch 43/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.4819 - accuracy: 0.0440 - val_loss: 3.5126 - val_accuracy: 0.0545\n",
      "Epoch 44/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.4785 - accuracy: 0.0556 - val_loss: 3.5104 - val_accuracy: 0.0545\n",
      "Epoch 45/1000\n",
      "13/13 [==============================] - 0s 27ms/step - loss: 3.4753 - accuracy: 0.0579 - val_loss: 3.5060 - val_accuracy: 0.0727\n",
      "Epoch 46/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.4732 - accuracy: 0.0556 - val_loss: 3.5062 - val_accuracy: 0.0727\n",
      "Epoch 47/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.4668 - accuracy: 0.0694 - val_loss: 3.5029 - val_accuracy: 0.0545\n",
      "Epoch 48/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 3.4682 - accuracy: 0.0602 - val_loss: 3.4984 - val_accuracy: 0.0545\n",
      "Epoch 49/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 0s 32ms/step - loss: 3.4620 - accuracy: 0.0579 - val_loss: 3.4983 - val_accuracy: 0.0545\n",
      "Epoch 50/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.4558 - accuracy: 0.0741 - val_loss: 3.4986 - val_accuracy: 0.0545\n",
      "Epoch 51/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.4496 - accuracy: 0.0579 - val_loss: 3.4964 - val_accuracy: 0.0727\n",
      "Epoch 52/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.4571 - accuracy: 0.0556 - val_loss: 3.4878 - val_accuracy: 0.0727\n",
      "Epoch 53/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.4498 - accuracy: 0.0764 - val_loss: 3.4820 - val_accuracy: 0.0727\n",
      "Epoch 54/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.4365 - accuracy: 0.0602 - val_loss: 3.4785 - val_accuracy: 0.0727\n",
      "Epoch 55/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.4327 - accuracy: 0.0602 - val_loss: 3.4815 - val_accuracy: 0.0727\n",
      "Epoch 56/1000\n",
      "13/13 [==============================] - 0s 26ms/step - loss: 3.4448 - accuracy: 0.0556 - val_loss: 3.4788 - val_accuracy: 0.0909\n",
      "Epoch 57/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.4284 - accuracy: 0.0694 - val_loss: 3.4762 - val_accuracy: 0.0909\n",
      "Epoch 58/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.4249 - accuracy: 0.0741 - val_loss: 3.4682 - val_accuracy: 0.0909\n",
      "Epoch 59/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.4138 - accuracy: 0.0741 - val_loss: 3.4672 - val_accuracy: 0.0727\n",
      "Epoch 60/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.4109 - accuracy: 0.0556 - val_loss: 3.4653 - val_accuracy: 0.0909\n",
      "Epoch 61/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.4144 - accuracy: 0.0741 - val_loss: 3.4645 - val_accuracy: 0.0727\n",
      "Epoch 62/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.4005 - accuracy: 0.0926 - val_loss: 3.4586 - val_accuracy: 0.0727\n",
      "Epoch 63/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 3.4035 - accuracy: 0.0741 - val_loss: 3.4525 - val_accuracy: 0.0727\n",
      "Epoch 64/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 3.4020 - accuracy: 0.0880 - val_loss: 3.4531 - val_accuracy: 0.0545\n",
      "Epoch 65/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 3.3972 - accuracy: 0.0509 - val_loss: 3.4481 - val_accuracy: 0.0545\n",
      "Epoch 66/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 3.3841 - accuracy: 0.0718 - val_loss: 3.4436 - val_accuracy: 0.0545\n",
      "Epoch 67/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 3.3869 - accuracy: 0.0718 - val_loss: 3.4539 - val_accuracy: 0.0727\n",
      "Epoch 68/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 3.3794 - accuracy: 0.0833 - val_loss: 3.4439 - val_accuracy: 0.0545\n",
      "Epoch 69/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 3.3770 - accuracy: 0.0694 - val_loss: 3.4377 - val_accuracy: 0.0545\n",
      "Epoch 70/1000\n",
      "13/13 [==============================] - 1s 39ms/step - loss: 3.3653 - accuracy: 0.0764 - val_loss: 3.4288 - val_accuracy: 0.0727\n",
      "Epoch 71/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 3.3649 - accuracy: 0.0833 - val_loss: 3.4329 - val_accuracy: 0.0545\n",
      "Epoch 72/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.3621 - accuracy: 0.0764 - val_loss: 3.4244 - val_accuracy: 0.0545\n",
      "Epoch 73/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.3543 - accuracy: 0.0833 - val_loss: 3.4221 - val_accuracy: 0.0545\n",
      "Epoch 74/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 3.3432 - accuracy: 0.0856 - val_loss: 3.4191 - val_accuracy: 0.0909\n",
      "Epoch 75/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.3459 - accuracy: 0.0833 - val_loss: 3.4081 - val_accuracy: 0.0909\n",
      "Epoch 76/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.3409 - accuracy: 0.0810 - val_loss: 3.4154 - val_accuracy: 0.0909\n",
      "Epoch 77/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.3366 - accuracy: 0.0856 - val_loss: 3.4178 - val_accuracy: 0.0727\n",
      "Epoch 78/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.3452 - accuracy: 0.0741 - val_loss: 3.4137 - val_accuracy: 0.0727\n",
      "Epoch 79/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.3158 - accuracy: 0.0880 - val_loss: 3.3974 - val_accuracy: 0.0727\n",
      "Epoch 80/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.3176 - accuracy: 0.0903 - val_loss: 3.4036 - val_accuracy: 0.0545\n",
      "Epoch 81/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.3134 - accuracy: 0.0903 - val_loss: 3.4079 - val_accuracy: 0.0545\n",
      "Epoch 82/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.3030 - accuracy: 0.0787 - val_loss: 3.3897 - val_accuracy: 0.0545\n",
      "Epoch 83/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.3054 - accuracy: 0.0949 - val_loss: 3.3924 - val_accuracy: 0.0364\n",
      "Epoch 84/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.3077 - accuracy: 0.0903 - val_loss: 3.4090 - val_accuracy: 0.0182\n",
      "Epoch 85/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.2922 - accuracy: 0.0995 - val_loss: 3.4049 - val_accuracy: 0.0364\n",
      "Epoch 86/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.2800 - accuracy: 0.0995 - val_loss: 3.3930 - val_accuracy: 0.0545\n",
      "Epoch 87/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.2797 - accuracy: 0.0880 - val_loss: 3.3809 - val_accuracy: 0.0545\n",
      "Epoch 88/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.2733 - accuracy: 0.1088 - val_loss: 3.3613 - val_accuracy: 0.0545\n",
      "Epoch 89/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.2761 - accuracy: 0.0995 - val_loss: 3.3976 - val_accuracy: 0.0545\n",
      "Epoch 90/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.2728 - accuracy: 0.0949 - val_loss: 3.3863 - val_accuracy: 0.0182\n",
      "Epoch 91/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 3.2578 - accuracy: 0.1065 - val_loss: 3.3563 - val_accuracy: 0.0364\n",
      "Epoch 92/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 3.2528 - accuracy: 0.1157 - val_loss: 3.3681 - val_accuracy: 0.0545\n",
      "Epoch 93/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 3.2828 - accuracy: 0.1111 - val_loss: 3.3522 - val_accuracy: 0.0545\n",
      "Epoch 94/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 3.2452 - accuracy: 0.0995 - val_loss: 3.3222 - val_accuracy: 0.0727\n",
      "Epoch 95/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 3.2410 - accuracy: 0.1088 - val_loss: 3.3594 - val_accuracy: 0.0545\n",
      "Epoch 96/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 3.2323 - accuracy: 0.1134 - val_loss: 3.3428 - val_accuracy: 0.0364\n",
      "Epoch 97/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 3.2420 - accuracy: 0.0972 - val_loss: 3.3443 - val_accuracy: 0.0364\n",
      "Epoch 98/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.2407 - accuracy: 0.1134 - val_loss: 3.3763 - val_accuracy: 0.0364\n",
      "Epoch 99/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 3.2276 - accuracy: 0.1157 - val_loss: 3.3733 - val_accuracy: 0.0364\n",
      "Epoch 100/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 3.2200 - accuracy: 0.1111 - val_loss: 3.3540 - val_accuracy: 0.0545\n",
      "Epoch 101/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 3.2067 - accuracy: 0.1065 - val_loss: 3.3241 - val_accuracy: 0.0727\n",
      "Epoch 102/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 3.2096 - accuracy: 0.1204 - val_loss: 3.3242 - val_accuracy: 0.0545\n",
      "Epoch 103/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 3.2116 - accuracy: 0.1227 - val_loss: 3.3487 - val_accuracy: 0.0545\n",
      "Epoch 104/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.1965 - accuracy: 0.1319 - val_loss: 3.3676 - val_accuracy: 0.0545\n",
      "Epoch 105/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 3.1941 - accuracy: 0.1366 - val_loss: 3.3605 - val_accuracy: 0.0545\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 106/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 3.2064 - accuracy: 0.1111 - val_loss: 3.3362 - val_accuracy: 0.0545\n",
      "Epoch 107/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 3.2034 - accuracy: 0.1134 - val_loss: 3.3391 - val_accuracy: 0.0545\n",
      "Epoch 108/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 3.1784 - accuracy: 0.1227 - val_loss: 3.3451 - val_accuracy: 0.0364\n",
      "Epoch 109/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 3.1861 - accuracy: 0.1134 - val_loss: 3.2938 - val_accuracy: 0.0545\n",
      "Epoch 110/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 3.1763 - accuracy: 0.1157 - val_loss: 3.3490 - val_accuracy: 0.0364\n",
      "Epoch 111/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.1799 - accuracy: 0.1296 - val_loss: 3.3454 - val_accuracy: 0.0364\n",
      "Epoch 112/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.1878 - accuracy: 0.1134 - val_loss: 3.3013 - val_accuracy: 0.0364\n",
      "Epoch 113/1000\n",
      "13/13 [==============================] - 0s 29ms/step - loss: 3.1722 - accuracy: 0.1250 - val_loss: 3.3270 - val_accuracy: 0.0545\n",
      "Epoch 114/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 3.1662 - accuracy: 0.1227 - val_loss: 3.3306 - val_accuracy: 0.0364\n",
      "Epoch 115/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.1622 - accuracy: 0.1227 - val_loss: 3.3029 - val_accuracy: 0.0364\n",
      "Epoch 116/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.1564 - accuracy: 0.1296 - val_loss: 3.3256 - val_accuracy: 0.0364\n",
      "Epoch 117/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.1610 - accuracy: 0.1273 - val_loss: 3.3133 - val_accuracy: 0.0364\n",
      "Epoch 118/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.1528 - accuracy: 0.1157 - val_loss: 3.2936 - val_accuracy: 0.0364\n",
      "Epoch 119/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.1560 - accuracy: 0.1319 - val_loss: 3.3205 - val_accuracy: 0.0182\n",
      "Epoch 120/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.1483 - accuracy: 0.1343 - val_loss: 3.3271 - val_accuracy: 0.0364\n",
      "Epoch 121/1000\n",
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      "13/13 [==============================] - 0s 31ms/step - loss: 3.1343 - accuracy: 0.1204 - val_loss: 3.3325 - val_accuracy: 0.0182\n",
      "Epoch 123/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.1443 - accuracy: 0.1319 - val_loss: 3.3155 - val_accuracy: 0.0182\n",
      "Epoch 124/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 3.1324 - accuracy: 0.1227 - val_loss: 3.3423 - val_accuracy: 0.0364\n",
      "Epoch 125/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.1208 - accuracy: 0.1296 - val_loss: 3.3232 - val_accuracy: 0.0364\n",
      "Epoch 126/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 3.1189 - accuracy: 0.1227 - val_loss: 3.2984 - val_accuracy: 0.0182\n",
      "Epoch 127/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 3.1121 - accuracy: 0.1250 - val_loss: 3.2720 - val_accuracy: 0.0182\n",
      "Epoch 128/1000\n",
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      "Epoch 130/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.1244 - accuracy: 0.1343 - val_loss: 3.3094 - val_accuracy: 0.0182\n",
      "Epoch 131/1000\n",
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      "Epoch 132/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.1164 - accuracy: 0.1204 - val_loss: 3.3245 - val_accuracy: 0.0182\n",
      "Epoch 133/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.1082 - accuracy: 0.1273 - val_loss: 3.3056 - val_accuracy: 0.0545\n",
      "Epoch 134/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 3.1086 - accuracy: 0.1181 - val_loss: 3.2740 - val_accuracy: 0.0182\n",
      "Epoch 135/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.0962 - accuracy: 0.1389 - val_loss: 3.2791 - val_accuracy: 0.0182\n",
      "Epoch 136/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 3.0826 - accuracy: 0.1319 - val_loss: 3.2637 - val_accuracy: 0.0364\n",
      "Epoch 137/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 3.0958 - accuracy: 0.1250 - val_loss: 3.2558 - val_accuracy: 0.0545\n",
      "Epoch 138/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.0895 - accuracy: 0.1343 - val_loss: 3.3044 - val_accuracy: 0.0364\n",
      "Epoch 139/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.0758 - accuracy: 0.1204 - val_loss: 3.2588 - val_accuracy: 0.0182\n",
      "Epoch 140/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.0677 - accuracy: 0.1227 - val_loss: 3.2539 - val_accuracy: 0.0182\n",
      "Epoch 141/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.0794 - accuracy: 0.1088 - val_loss: 3.2510 - val_accuracy: 0.0182\n",
      "Epoch 142/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.0845 - accuracy: 0.1366 - val_loss: 3.2554 - val_accuracy: 0.0182\n",
      "Epoch 143/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.0710 - accuracy: 0.1157 - val_loss: 3.2615 - val_accuracy: 0.0364\n",
      "Epoch 144/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.0689 - accuracy: 0.1227 - val_loss: 3.2620 - val_accuracy: 0.0182\n",
      "Epoch 145/1000\n",
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      "Epoch 146/1000\n",
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      "Epoch 147/1000\n",
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      "Epoch 148/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.0647 - accuracy: 0.1204 - val_loss: 3.2763 - val_accuracy: 0.0182\n",
      "Epoch 149/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.0589 - accuracy: 0.1343 - val_loss: 3.2613 - val_accuracy: 0.0182\n",
      "Epoch 150/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.0469 - accuracy: 0.1505 - val_loss: 3.2838 - val_accuracy: 0.0182\n",
      "Epoch 151/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.0501 - accuracy: 0.1273 - val_loss: 3.2942 - val_accuracy: 0.0545\n",
      "Epoch 152/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.0439 - accuracy: 0.1366 - val_loss: 3.3014 - val_accuracy: 0.0545\n",
      "Epoch 153/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.0508 - accuracy: 0.1551 - val_loss: 3.2787 - val_accuracy: 0.0545\n",
      "Epoch 154/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 3.0514 - accuracy: 0.1227 - val_loss: 3.2955 - val_accuracy: 0.0182\n",
      "Epoch 155/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.0308 - accuracy: 0.1435 - val_loss: 3.2772 - val_accuracy: 0.0182\n",
      "Epoch 156/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.0476 - accuracy: 0.1227 - val_loss: 3.2891 - val_accuracy: 0.0364\n",
      "Epoch 157/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.0331 - accuracy: 0.1296 - val_loss: 3.2915 - val_accuracy: 0.0364\n",
      "Epoch 158/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.0330 - accuracy: 0.1273 - val_loss: 3.2954 - val_accuracy: 0.0364\n",
      "Epoch 159/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.0369 - accuracy: 0.1157 - val_loss: 3.2856 - val_accuracy: 0.0182\n",
      "Epoch 160/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.0412 - accuracy: 0.1019 - val_loss: 3.2723 - val_accuracy: 0.0182\n",
      "Epoch 161/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.0226 - accuracy: 0.1319 - val_loss: 3.2874 - val_accuracy: 0.0182\n",
      "Epoch 162/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 0s 31ms/step - loss: 3.0087 - accuracy: 0.1481 - val_loss: 3.2773 - val_accuracy: 0.0545\n",
      "Epoch 163/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.0358 - accuracy: 0.1250 - val_loss: 3.2979 - val_accuracy: 0.0182\n",
      "Epoch 164/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 3.0130 - accuracy: 0.1250 - val_loss: 3.2745 - val_accuracy: 0.0182\n",
      "Epoch 165/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 3.0225 - accuracy: 0.1389 - val_loss: 3.2809 - val_accuracy: 0.0364\n",
      "Epoch 166/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 3.0134 - accuracy: 0.1227 - val_loss: 3.3029 - val_accuracy: 0.0545\n",
      "Epoch 167/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 3.0144 - accuracy: 0.1250 - val_loss: 3.2848 - val_accuracy: 0.0182\n",
      "Epoch 168/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 3.0000 - accuracy: 0.1181 - val_loss: 3.2661 - val_accuracy: 0.0182\n",
      "Epoch 169/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 3.0067 - accuracy: 0.1273 - val_loss: 3.2538 - val_accuracy: 0.0364\n",
      "Epoch 170/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.9880 - accuracy: 0.1181 - val_loss: 3.2521 - val_accuracy: 0.0182\n",
      "Epoch 171/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 3.0031 - accuracy: 0.1273 - val_loss: 3.2232 - val_accuracy: 0.0545\n",
      "Epoch 172/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.9843 - accuracy: 0.1435 - val_loss: 3.2371 - val_accuracy: 0.0364\n",
      "Epoch 173/1000\n",
      "13/13 [==============================] - 0s 38ms/step - loss: 2.9925 - accuracy: 0.1343 - val_loss: 3.2165 - val_accuracy: 0.0545\n",
      "Epoch 174/1000\n",
      "13/13 [==============================] - 0s 38ms/step - loss: 2.9866 - accuracy: 0.1389 - val_loss: 3.2405 - val_accuracy: 0.0364\n",
      "Epoch 175/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.9737 - accuracy: 0.1412 - val_loss: 3.2377 - val_accuracy: 0.0545\n",
      "Epoch 176/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.9902 - accuracy: 0.1412 - val_loss: 3.2478 - val_accuracy: 0.0364\n",
      "Epoch 177/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.9968 - accuracy: 0.1273 - val_loss: 3.2439 - val_accuracy: 0.0545\n",
      "Epoch 178/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.9930 - accuracy: 0.1458 - val_loss: 3.2495 - val_accuracy: 0.0182\n",
      "Epoch 179/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.9944 - accuracy: 0.1273 - val_loss: 3.2636 - val_accuracy: 0.0364\n",
      "Epoch 180/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.9981 - accuracy: 0.1481 - val_loss: 3.2747 - val_accuracy: 0.0364\n",
      "Epoch 181/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.9840 - accuracy: 0.1412 - val_loss: 3.2620 - val_accuracy: 0.0182\n",
      "Epoch 182/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.9942 - accuracy: 0.1412 - val_loss: 3.2504 - val_accuracy: 0.0182\n",
      "Epoch 183/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.9862 - accuracy: 0.1319 - val_loss: 3.2210 - val_accuracy: 0.0545\n",
      "Epoch 184/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.9860 - accuracy: 0.1296 - val_loss: 3.2313 - val_accuracy: 0.0364\n",
      "Epoch 185/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.9707 - accuracy: 0.1157 - val_loss: 3.2440 - val_accuracy: 0.0182\n",
      "Epoch 186/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.9906 - accuracy: 0.1412 - val_loss: 3.2372 - val_accuracy: 0.0182\n",
      "Epoch 187/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.9682 - accuracy: 0.1343 - val_loss: 3.2209 - val_accuracy: 0.0182\n",
      "Epoch 188/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.9917 - accuracy: 0.1319 - val_loss: 3.2340 - val_accuracy: 0.0545\n",
      "Epoch 189/1000\n",
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      "Epoch 190/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.9428 - accuracy: 0.1528 - val_loss: 3.1972 - val_accuracy: 0.0545\n",
      "Epoch 191/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.9567 - accuracy: 0.1319 - val_loss: 3.2204 - val_accuracy: 0.0364\n",
      "Epoch 192/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.9609 - accuracy: 0.1389 - val_loss: 3.2169 - val_accuracy: 0.0545\n",
      "Epoch 193/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.9712 - accuracy: 0.1250 - val_loss: 3.2130 - val_accuracy: 0.0364\n",
      "Epoch 194/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.9715 - accuracy: 0.1412 - val_loss: 3.2459 - val_accuracy: 0.0545\n",
      "Epoch 195/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.9640 - accuracy: 0.1273 - val_loss: 3.2528 - val_accuracy: 0.0545\n",
      "Epoch 196/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.9598 - accuracy: 0.1343 - val_loss: 3.2590 - val_accuracy: 0.0545\n",
      "Epoch 197/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.9608 - accuracy: 0.1250 - val_loss: 3.2594 - val_accuracy: 0.0364\n",
      "Epoch 198/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.9443 - accuracy: 0.1505 - val_loss: 3.2465 - val_accuracy: 0.0182\n",
      "Epoch 199/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.9483 - accuracy: 0.1435 - val_loss: 3.2312 - val_accuracy: 0.0364\n",
      "Epoch 200/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.9337 - accuracy: 0.1319 - val_loss: 3.2082 - val_accuracy: 0.0182\n",
      "Epoch 201/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.9404 - accuracy: 0.1343 - val_loss: 3.2156 - val_accuracy: 0.0182\n",
      "Epoch 202/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.9336 - accuracy: 0.1505 - val_loss: 3.2680 - val_accuracy: 0.0545\n",
      "Epoch 203/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.9295 - accuracy: 0.1597 - val_loss: 3.2459 - val_accuracy: 0.0364\n",
      "Epoch 204/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.9183 - accuracy: 0.1597 - val_loss: 3.2549 - val_accuracy: 0.0364\n",
      "Epoch 205/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.9186 - accuracy: 0.1134 - val_loss: 3.2657 - val_accuracy: 0.0364\n",
      "Epoch 206/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.9506 - accuracy: 0.1481 - val_loss: 3.2734 - val_accuracy: 0.0364\n",
      "Epoch 207/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.9318 - accuracy: 0.1412 - val_loss: 3.2588 - val_accuracy: 0.0182\n",
      "Epoch 208/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.9300 - accuracy: 0.1574 - val_loss: 3.2566 - val_accuracy: 0.0182\n",
      "Epoch 209/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.9181 - accuracy: 0.1435 - val_loss: 3.2591 - val_accuracy: 0.0364\n",
      "Epoch 210/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.9252 - accuracy: 0.1597 - val_loss: 3.2327 - val_accuracy: 0.0182\n",
      "Epoch 211/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.9150 - accuracy: 0.1551 - val_loss: 3.2292 - val_accuracy: 0.0545\n",
      "Epoch 212/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.9329 - accuracy: 0.1366 - val_loss: 3.2421 - val_accuracy: 0.0182\n",
      "Epoch 213/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.9320 - accuracy: 0.1389 - val_loss: 3.2595 - val_accuracy: 0.0545\n",
      "Epoch 214/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.9387 - accuracy: 0.1528 - val_loss: 3.2655 - val_accuracy: 0.0364\n",
      "Epoch 215/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.9266 - accuracy: 0.1481 - val_loss: 3.2643 - val_accuracy: 0.0364\n",
      "Epoch 216/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.9136 - accuracy: 0.1574 - val_loss: 3.2677 - val_accuracy: 0.0182\n",
      "Epoch 217/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.9160 - accuracy: 0.1481 - val_loss: 3.2419 - val_accuracy: 0.0364\n",
      "Epoch 218/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 0s 35ms/step - loss: 2.9173 - accuracy: 0.1620 - val_loss: 3.2486 - val_accuracy: 0.0364\n",
      "Epoch 219/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.9303 - accuracy: 0.1343 - val_loss: 3.2382 - val_accuracy: 0.0182\n",
      "Epoch 220/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.9052 - accuracy: 0.1458 - val_loss: 3.1916 - val_accuracy: 0.0364\n",
      "Epoch 221/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.9015 - accuracy: 0.1528 - val_loss: 3.2136 - val_accuracy: 0.0182\n",
      "Epoch 222/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.9186 - accuracy: 0.1319 - val_loss: 3.2225 - val_accuracy: 0.0364\n",
      "Epoch 223/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.9088 - accuracy: 0.1481 - val_loss: 3.2210 - val_accuracy: 0.0545\n",
      "Epoch 224/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.8741 - accuracy: 0.1435 - val_loss: 3.2040 - val_accuracy: 0.0364\n",
      "Epoch 225/1000\n",
      "13/13 [==============================] - 0s 29ms/step - loss: 2.9225 - accuracy: 0.1458 - val_loss: 3.1751 - val_accuracy: 0.0727\n",
      "Epoch 226/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.8923 - accuracy: 0.1597 - val_loss: 3.2012 - val_accuracy: 0.0364\n",
      "Epoch 227/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.8976 - accuracy: 0.1412 - val_loss: 3.1884 - val_accuracy: 0.0182\n",
      "Epoch 228/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.9102 - accuracy: 0.1551 - val_loss: 3.2060 - val_accuracy: 0.0182\n",
      "Epoch 229/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.8855 - accuracy: 0.1597 - val_loss: 3.1961 - val_accuracy: 0.0545\n",
      "Epoch 230/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8979 - accuracy: 0.1597 - val_loss: 3.2298 - val_accuracy: 0.0182\n",
      "Epoch 231/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8948 - accuracy: 0.1181 - val_loss: 3.2224 - val_accuracy: 0.0182\n",
      "Epoch 232/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8599 - accuracy: 0.1644 - val_loss: 3.2189 - val_accuracy: 0.0727\n",
      "Epoch 233/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8794 - accuracy: 0.1574 - val_loss: 3.2201 - val_accuracy: 0.0182\n",
      "Epoch 234/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.8799 - accuracy: 0.1852 - val_loss: 3.2034 - val_accuracy: 0.0364\n",
      "Epoch 235/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.8756 - accuracy: 0.1505 - val_loss: 3.1832 - val_accuracy: 0.0364\n",
      "Epoch 236/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.9017 - accuracy: 0.1574 - val_loss: 3.2110 - val_accuracy: 0.0182\n",
      "Epoch 237/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.9019 - accuracy: 0.1343 - val_loss: 3.2252 - val_accuracy: 0.0545\n",
      "Epoch 238/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8830 - accuracy: 0.1667 - val_loss: 3.2666 - val_accuracy: 0.0364\n",
      "Epoch 239/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.8818 - accuracy: 0.1505 - val_loss: 3.2786 - val_accuracy: 0.0545\n",
      "Epoch 240/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.8737 - accuracy: 0.1366 - val_loss: 3.2529 - val_accuracy: 0.0545\n",
      "Epoch 241/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8995 - accuracy: 0.1481 - val_loss: 3.2433 - val_accuracy: 0.0364\n",
      "Epoch 242/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.8965 - accuracy: 0.1759 - val_loss: 3.2295 - val_accuracy: 0.0182\n",
      "Epoch 243/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8723 - accuracy: 0.1551 - val_loss: 3.2324 - val_accuracy: 0.0364\n",
      "Epoch 244/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8770 - accuracy: 0.1481 - val_loss: 3.2268 - val_accuracy: 0.0364\n",
      "Epoch 245/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.8752 - accuracy: 0.1713 - val_loss: 3.2500 - val_accuracy: 0.0364\n",
      "Epoch 246/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8506 - accuracy: 0.1713 - val_loss: 3.2789 - val_accuracy: 0.0727\n",
      "Epoch 247/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8707 - accuracy: 0.1574 - val_loss: 3.2573 - val_accuracy: 0.0182\n",
      "Epoch 248/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8809 - accuracy: 0.1620 - val_loss: 3.2255 - val_accuracy: 0.0364\n",
      "Epoch 249/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8554 - accuracy: 0.1250 - val_loss: 3.1843 - val_accuracy: 0.0364\n",
      "Epoch 250/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.8308 - accuracy: 0.1690 - val_loss: 3.1761 - val_accuracy: 0.0545\n",
      "Epoch 251/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8593 - accuracy: 0.1713 - val_loss: 3.1770 - val_accuracy: 0.0545\n",
      "Epoch 252/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.8440 - accuracy: 0.1620 - val_loss: 3.2077 - val_accuracy: 0.0364\n",
      "Epoch 253/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8430 - accuracy: 0.1921 - val_loss: 3.2200 - val_accuracy: 0.0364\n",
      "Epoch 254/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8324 - accuracy: 0.1736 - val_loss: 3.2195 - val_accuracy: 0.0545\n",
      "Epoch 255/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.8438 - accuracy: 0.1574 - val_loss: 3.2203 - val_accuracy: 0.0545\n",
      "Epoch 256/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8403 - accuracy: 0.1597 - val_loss: 3.2144 - val_accuracy: 0.0545\n",
      "Epoch 257/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.8454 - accuracy: 0.1806 - val_loss: 3.1973 - val_accuracy: 0.0727\n",
      "Epoch 258/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8651 - accuracy: 0.1412 - val_loss: 3.1543 - val_accuracy: 0.0364\n",
      "Epoch 259/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.8735 - accuracy: 0.1644 - val_loss: 3.1796 - val_accuracy: 0.0364\n",
      "Epoch 260/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.8594 - accuracy: 0.1620 - val_loss: 3.1909 - val_accuracy: 0.0364\n",
      "Epoch 261/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.8436 - accuracy: 0.1458 - val_loss: 3.1881 - val_accuracy: 0.0364\n",
      "Epoch 262/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8371 - accuracy: 0.1435 - val_loss: 3.1869 - val_accuracy: 0.0364\n",
      "Epoch 263/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.8526 - accuracy: 0.1690 - val_loss: 3.2087 - val_accuracy: 0.0545\n",
      "Epoch 264/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8199 - accuracy: 0.1875 - val_loss: 3.1465 - val_accuracy: 0.0545\n",
      "Epoch 265/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8247 - accuracy: 0.1644 - val_loss: 3.1428 - val_accuracy: 0.0727\n",
      "Epoch 266/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8293 - accuracy: 0.1736 - val_loss: 3.1255 - val_accuracy: 0.0364\n",
      "Epoch 267/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.8170 - accuracy: 0.1690 - val_loss: 3.1521 - val_accuracy: 0.0545\n",
      "Epoch 268/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.8376 - accuracy: 0.1644 - val_loss: 3.1901 - val_accuracy: 0.0545\n",
      "Epoch 269/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.8286 - accuracy: 0.1713 - val_loss: 3.1692 - val_accuracy: 0.0545\n",
      "Epoch 270/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.8468 - accuracy: 0.1574 - val_loss: 3.1391 - val_accuracy: 0.0364\n",
      "Epoch 271/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.8179 - accuracy: 0.1829 - val_loss: 3.1235 - val_accuracy: 0.0727\n",
      "Epoch 272/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.8393 - accuracy: 0.1597 - val_loss: 3.1807 - val_accuracy: 0.0364\n",
      "Epoch 273/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.8365 - accuracy: 0.1597 - val_loss: 3.2106 - val_accuracy: 0.0182\n",
      "Epoch 274/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 0s 32ms/step - loss: 2.8209 - accuracy: 0.1782 - val_loss: 3.2079 - val_accuracy: 0.0364\n",
      "Epoch 275/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.8448 - accuracy: 0.1690 - val_loss: 3.2204 - val_accuracy: 0.0364\n",
      "Epoch 276/1000\n",
      "13/13 [==============================] - 0s 25ms/step - loss: 2.8606 - accuracy: 0.1736 - val_loss: 3.2280 - val_accuracy: 0.0364\n",
      "Epoch 277/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.8349 - accuracy: 0.1875 - val_loss: 3.2660 - val_accuracy: 0.0182\n",
      "Epoch 278/1000\n",
      "13/13 [==============================] - 0s 29ms/step - loss: 2.8143 - accuracy: 0.1574 - val_loss: 3.2519 - val_accuracy: 0.0364\n",
      "Epoch 279/1000\n",
      "13/13 [==============================] - 0s 22ms/step - loss: 2.8146 - accuracy: 0.1875 - val_loss: 3.2254 - val_accuracy: 0.0364\n",
      "Epoch 280/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.8077 - accuracy: 0.1690 - val_loss: 3.1782 - val_accuracy: 0.0364\n",
      "Epoch 281/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.8356 - accuracy: 0.1551 - val_loss: 3.2011 - val_accuracy: 0.0545\n",
      "Epoch 282/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8253 - accuracy: 0.1551 - val_loss: 3.2467 - val_accuracy: 0.0182\n",
      "Epoch 283/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8079 - accuracy: 0.1736 - val_loss: 3.2999 - val_accuracy: 0.0182\n",
      "Epoch 284/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8279 - accuracy: 0.1574 - val_loss: 3.2716 - val_accuracy: 0.0364\n",
      "Epoch 285/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.7849 - accuracy: 0.1875 - val_loss: 3.2276 - val_accuracy: 0.0364\n",
      "Epoch 286/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.8075 - accuracy: 0.1759 - val_loss: 3.1929 - val_accuracy: 0.0364\n",
      "Epoch 287/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8067 - accuracy: 0.1620 - val_loss: 3.1633 - val_accuracy: 0.0364\n",
      "Epoch 288/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.7853 - accuracy: 0.1667 - val_loss: 3.2037 - val_accuracy: 0.0364\n",
      "Epoch 289/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8436 - accuracy: 0.1667 - val_loss: 3.2246 - val_accuracy: 0.0182\n",
      "Epoch 290/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.8376 - accuracy: 0.1551 - val_loss: 3.2095 - val_accuracy: 0.0182\n",
      "Epoch 291/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.7749 - accuracy: 0.1690 - val_loss: 3.1832 - val_accuracy: 0.0545\n",
      "Epoch 292/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.7648 - accuracy: 0.1921 - val_loss: 3.1612 - val_accuracy: 0.0364\n",
      "Epoch 293/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8009 - accuracy: 0.1690 - val_loss: 3.1470 - val_accuracy: 0.0364\n",
      "Epoch 294/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.8074 - accuracy: 0.1620 - val_loss: 3.1691 - val_accuracy: 0.0182\n",
      "Epoch 295/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8100 - accuracy: 0.1898 - val_loss: 3.1834 - val_accuracy: 0.0364\n",
      "Epoch 296/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8077 - accuracy: 0.1528 - val_loss: 3.1667 - val_accuracy: 0.0364\n",
      "Epoch 297/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.7950 - accuracy: 0.1898 - val_loss: 3.1702 - val_accuracy: 0.0545\n",
      "Epoch 298/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.7937 - accuracy: 0.1667 - val_loss: 3.2067 - val_accuracy: 0.0545\n",
      "Epoch 299/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.7777 - accuracy: 0.1644 - val_loss: 3.1840 - val_accuracy: 0.0364\n",
      "Epoch 300/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.7668 - accuracy: 0.1644 - val_loss: 3.1567 - val_accuracy: 0.0364\n",
      "Epoch 301/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8106 - accuracy: 0.1597 - val_loss: 3.1472 - val_accuracy: 0.0364\n",
      "Epoch 302/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.7828 - accuracy: 0.1597 - val_loss: 3.1477 - val_accuracy: 0.0182\n",
      "Epoch 303/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.7852 - accuracy: 0.1690 - val_loss: 3.1590 - val_accuracy: 0.0364\n",
      "Epoch 304/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8164 - accuracy: 0.1505 - val_loss: 3.1343 - val_accuracy: 0.0182\n",
      "Epoch 305/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.7709 - accuracy: 0.1782 - val_loss: 3.1519 - val_accuracy: 0.0364\n",
      "Epoch 306/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.7804 - accuracy: 0.1620 - val_loss: 3.1747 - val_accuracy: 0.0182\n",
      "Epoch 307/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7925 - accuracy: 0.1574 - val_loss: 3.1950 - val_accuracy: 0.0364\n",
      "Epoch 308/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.7750 - accuracy: 0.1597 - val_loss: 3.1827 - val_accuracy: 0.0182\n",
      "Epoch 309/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.7789 - accuracy: 0.1597 - val_loss: 3.1754 - val_accuracy: 0.0364\n",
      "Epoch 310/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.8086 - accuracy: 0.1505 - val_loss: 3.2103 - val_accuracy: 0.0364\n",
      "Epoch 311/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7832 - accuracy: 0.1667 - val_loss: 3.2045 - val_accuracy: 0.0364\n",
      "Epoch 312/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7733 - accuracy: 0.1736 - val_loss: 3.2560 - val_accuracy: 0.0364\n",
      "Epoch 313/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.7794 - accuracy: 0.1829 - val_loss: 3.2717 - val_accuracy: 0.0545\n",
      "Epoch 314/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7859 - accuracy: 0.1620 - val_loss: 3.2207 - val_accuracy: 0.0364\n",
      "Epoch 315/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7727 - accuracy: 0.1736 - val_loss: 3.2319 - val_accuracy: 0.0545\n",
      "Epoch 316/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.7607 - accuracy: 0.1921 - val_loss: 3.2141 - val_accuracy: 0.0364\n",
      "Epoch 317/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7583 - accuracy: 0.1806 - val_loss: 3.2271 - val_accuracy: 0.0364\n",
      "Epoch 318/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.7363 - accuracy: 0.2060 - val_loss: 3.2499 - val_accuracy: 0.0364\n",
      "Epoch 319/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7246 - accuracy: 0.1852 - val_loss: 3.2641 - val_accuracy: 0.0545\n",
      "Epoch 320/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7750 - accuracy: 0.1852 - val_loss: 3.2543 - val_accuracy: 0.0545\n",
      "Epoch 321/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7506 - accuracy: 0.1713 - val_loss: 3.2568 - val_accuracy: 0.0545\n",
      "Epoch 322/1000\n",
      "13/13 [==============================] - 0s 27ms/step - loss: 2.7596 - accuracy: 0.1620 - val_loss: 3.2555 - val_accuracy: 0.0364\n",
      "Epoch 323/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7563 - accuracy: 0.1667 - val_loss: 3.2252 - val_accuracy: 0.0364\n",
      "Epoch 324/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7509 - accuracy: 0.1713 - val_loss: 3.2371 - val_accuracy: 0.0364\n",
      "Epoch 325/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7614 - accuracy: 0.1667 - val_loss: 3.2581 - val_accuracy: 0.0364\n",
      "Epoch 326/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.7588 - accuracy: 0.1597 - val_loss: 3.2442 - val_accuracy: 0.0545\n",
      "Epoch 327/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.7642 - accuracy: 0.1782 - val_loss: 3.2114 - val_accuracy: 0.0545\n",
      "Epoch 328/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7676 - accuracy: 0.1597 - val_loss: 3.2073 - val_accuracy: 0.0545\n",
      "Epoch 329/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7624 - accuracy: 0.1690 - val_loss: 3.1943 - val_accuracy: 0.0545\n",
      "Epoch 330/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7708 - accuracy: 0.1505 - val_loss: 3.2246 - val_accuracy: 0.0364\n",
      "Epoch 331/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.7345 - accuracy: 0.1852 - val_loss: 3.1857 - val_accuracy: 0.0545\n",
      "Epoch 332/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7288 - accuracy: 0.1690 - val_loss: 3.1748 - val_accuracy: 0.0545\n",
      "Epoch 333/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.7552 - accuracy: 0.1713 - val_loss: 3.1752 - val_accuracy: 0.0364\n",
      "Epoch 334/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.7431 - accuracy: 0.1574 - val_loss: 3.2053 - val_accuracy: 0.0545\n",
      "Epoch 335/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.7286 - accuracy: 0.1690 - val_loss: 3.2318 - val_accuracy: 0.0364\n",
      "Epoch 336/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.7588 - accuracy: 0.1667 - val_loss: 3.2418 - val_accuracy: 0.0545\n",
      "Epoch 337/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.7686 - accuracy: 0.1690 - val_loss: 3.2677 - val_accuracy: 0.0364\n",
      "Epoch 338/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.7497 - accuracy: 0.1713 - val_loss: 3.2463 - val_accuracy: 0.0364\n",
      "Epoch 339/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.7521 - accuracy: 0.1875 - val_loss: 3.2599 - val_accuracy: 0.0364\n",
      "Epoch 340/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.7268 - accuracy: 0.1667 - val_loss: 3.2372 - val_accuracy: 0.0364\n",
      "Epoch 341/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.7380 - accuracy: 0.1690 - val_loss: 3.1963 - val_accuracy: 0.0545\n",
      "Epoch 342/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.7266 - accuracy: 0.1690 - val_loss: 3.1886 - val_accuracy: 0.0545\n",
      "Epoch 343/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.7177 - accuracy: 0.1736 - val_loss: 3.1761 - val_accuracy: 0.0909\n",
      "Epoch 344/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.7230 - accuracy: 0.1829 - val_loss: 3.2067 - val_accuracy: 0.0727\n",
      "Epoch 345/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.7358 - accuracy: 0.1829 - val_loss: 3.2465 - val_accuracy: 0.0545\n",
      "Epoch 346/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.7417 - accuracy: 0.1690 - val_loss: 3.2828 - val_accuracy: 0.0727\n",
      "Epoch 347/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.7100 - accuracy: 0.1597 - val_loss: 3.2952 - val_accuracy: 0.0545\n",
      "Epoch 348/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.7407 - accuracy: 0.1944 - val_loss: 3.2984 - val_accuracy: 0.0727\n",
      "Epoch 349/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.7279 - accuracy: 0.1620 - val_loss: 3.2339 - val_accuracy: 0.0727\n",
      "Epoch 350/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.7373 - accuracy: 0.1852 - val_loss: 3.1559 - val_accuracy: 0.0727\n",
      "Epoch 351/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6904 - accuracy: 0.1898 - val_loss: 3.1504 - val_accuracy: 0.0909\n",
      "Epoch 352/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7114 - accuracy: 0.1806 - val_loss: 3.1648 - val_accuracy: 0.1091\n",
      "Epoch 353/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7157 - accuracy: 0.1921 - val_loss: 3.1694 - val_accuracy: 0.0727\n",
      "Epoch 354/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7212 - accuracy: 0.1782 - val_loss: 3.1818 - val_accuracy: 0.0727\n",
      "Epoch 355/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7311 - accuracy: 0.1875 - val_loss: 3.1598 - val_accuracy: 0.0909\n",
      "Epoch 356/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.7462 - accuracy: 0.1736 - val_loss: 3.2007 - val_accuracy: 0.0545\n",
      "Epoch 357/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6895 - accuracy: 0.2083 - val_loss: 3.1975 - val_accuracy: 0.0727\n",
      "Epoch 358/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7093 - accuracy: 0.1713 - val_loss: 3.2407 - val_accuracy: 0.0545\n",
      "Epoch 359/1000\n",
      "13/13 [==============================] - 0s 24ms/step - loss: 2.7422 - accuracy: 0.1806 - val_loss: 3.2441 - val_accuracy: 0.0727\n",
      "Epoch 360/1000\n",
      "13/13 [==============================] - 0s 24ms/step - loss: 2.6910 - accuracy: 0.2106 - val_loss: 3.2047 - val_accuracy: 0.0727\n",
      "Epoch 361/1000\n",
      "13/13 [==============================] - 0s 26ms/step - loss: 2.6883 - accuracy: 0.1991 - val_loss: 3.2117 - val_accuracy: 0.0727\n",
      "Epoch 362/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7814 - accuracy: 0.1574 - val_loss: 3.2122 - val_accuracy: 0.0545\n",
      "Epoch 363/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.6944 - accuracy: 0.1806 - val_loss: 3.1878 - val_accuracy: 0.0545\n",
      "Epoch 364/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.6884 - accuracy: 0.1921 - val_loss: 3.2122 - val_accuracy: 0.0545\n",
      "Epoch 365/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6692 - accuracy: 0.1829 - val_loss: 3.1961 - val_accuracy: 0.0545\n",
      "Epoch 366/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.6902 - accuracy: 0.1921 - val_loss: 3.1973 - val_accuracy: 0.0727\n",
      "Epoch 367/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6960 - accuracy: 0.1736 - val_loss: 3.1646 - val_accuracy: 0.0727\n",
      "Epoch 368/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7052 - accuracy: 0.1829 - val_loss: 3.1534 - val_accuracy: 0.1091\n",
      "Epoch 369/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7052 - accuracy: 0.1875 - val_loss: 3.1812 - val_accuracy: 0.0727\n",
      "Epoch 370/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.7011 - accuracy: 0.1991 - val_loss: 3.1573 - val_accuracy: 0.0909\n",
      "Epoch 371/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6761 - accuracy: 0.2106 - val_loss: 3.1639 - val_accuracy: 0.0545\n",
      "Epoch 372/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.7005 - accuracy: 0.1852 - val_loss: 3.1653 - val_accuracy: 0.0727\n",
      "Epoch 373/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6970 - accuracy: 0.1782 - val_loss: 3.2265 - val_accuracy: 0.0727\n",
      "Epoch 374/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7143 - accuracy: 0.1667 - val_loss: 3.2276 - val_accuracy: 0.0727\n",
      "Epoch 375/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6765 - accuracy: 0.1852 - val_loss: 3.2592 - val_accuracy: 0.0909\n",
      "Epoch 376/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.6750 - accuracy: 0.1944 - val_loss: 3.2139 - val_accuracy: 0.0909\n",
      "Epoch 377/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7018 - accuracy: 0.1782 - val_loss: 3.1857 - val_accuracy: 0.0909\n",
      "Epoch 378/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.7042 - accuracy: 0.1782 - val_loss: 3.1591 - val_accuracy: 0.0727\n",
      "Epoch 379/1000\n",
      "13/13 [==============================] - 0s 26ms/step - loss: 2.7429 - accuracy: 0.1690 - val_loss: 3.1365 - val_accuracy: 0.1091\n",
      "Epoch 380/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6946 - accuracy: 0.1759 - val_loss: 3.1381 - val_accuracy: 0.0909\n",
      "Epoch 381/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.6779 - accuracy: 0.1782 - val_loss: 3.1572 - val_accuracy: 0.0727\n",
      "Epoch 382/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.6971 - accuracy: 0.1944 - val_loss: 3.1866 - val_accuracy: 0.0727\n",
      "Epoch 383/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.7156 - accuracy: 0.1921 - val_loss: 3.2049 - val_accuracy: 0.0545\n",
      "Epoch 384/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6594 - accuracy: 0.1736 - val_loss: 3.2123 - val_accuracy: 0.0545\n",
      "Epoch 385/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6704 - accuracy: 0.2060 - val_loss: 3.2049 - val_accuracy: 0.0545\n",
      "Epoch 386/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6988 - accuracy: 0.1713 - val_loss: 3.1828 - val_accuracy: 0.0727\n",
      "Epoch 387/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6711 - accuracy: 0.2060 - val_loss: 3.1642 - val_accuracy: 0.0727\n",
      "Epoch 388/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6869 - accuracy: 0.1852 - val_loss: 3.1895 - val_accuracy: 0.0909\n",
      "Epoch 389/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6975 - accuracy: 0.1528 - val_loss: 3.1311 - val_accuracy: 0.1091\n",
      "Epoch 390/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6703 - accuracy: 0.1991 - val_loss: 3.1342 - val_accuracy: 0.0909\n",
      "Epoch 391/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.6599 - accuracy: 0.1806 - val_loss: 3.1370 - val_accuracy: 0.0909\n",
      "Epoch 392/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.6843 - accuracy: 0.1921 - val_loss: 3.1387 - val_accuracy: 0.0727\n",
      "Epoch 393/1000\n",
      "13/13 [==============================] - 0s 26ms/step - loss: 2.6440 - accuracy: 0.2176 - val_loss: 3.1435 - val_accuracy: 0.0545\n",
      "Epoch 394/1000\n",
      "13/13 [==============================] - 0s 29ms/step - loss: 2.6495 - accuracy: 0.2199 - val_loss: 3.1327 - val_accuracy: 0.1091\n",
      "Epoch 395/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.7086 - accuracy: 0.1806 - val_loss: 3.1208 - val_accuracy: 0.1091\n",
      "Epoch 396/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.6487 - accuracy: 0.2060 - val_loss: 3.1037 - val_accuracy: 0.0909\n",
      "Epoch 397/1000\n",
      "13/13 [==============================] - 0s 29ms/step - loss: 2.6632 - accuracy: 0.2014 - val_loss: 3.1110 - val_accuracy: 0.1091\n",
      "Epoch 398/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.6729 - accuracy: 0.1829 - val_loss: 3.1504 - val_accuracy: 0.0727\n",
      "Epoch 399/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.6464 - accuracy: 0.2199 - val_loss: 3.1602 - val_accuracy: 0.0727\n",
      "Epoch 400/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.6397 - accuracy: 0.1829 - val_loss: 3.1489 - val_accuracy: 0.0727\n",
      "Epoch 401/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.6599 - accuracy: 0.1944 - val_loss: 3.1331 - val_accuracy: 0.0727\n",
      "Epoch 402/1000\n",
      "13/13 [==============================] - 0s 29ms/step - loss: 2.6883 - accuracy: 0.1875 - val_loss: 3.1305 - val_accuracy: 0.0727\n",
      "Epoch 403/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.6577 - accuracy: 0.1759 - val_loss: 3.1335 - val_accuracy: 0.0909\n",
      "Epoch 404/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.6473 - accuracy: 0.1782 - val_loss: 3.1549 - val_accuracy: 0.0727\n",
      "Epoch 405/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.7156 - accuracy: 0.1620 - val_loss: 3.1252 - val_accuracy: 0.0909\n",
      "Epoch 406/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6406 - accuracy: 0.1875 - val_loss: 3.1145 - val_accuracy: 0.0909\n",
      "Epoch 407/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.6501 - accuracy: 0.1667 - val_loss: 3.1356 - val_accuracy: 0.0727\n",
      "Epoch 408/1000\n",
      "13/13 [==============================] - 1s 41ms/step - loss: 2.6916 - accuracy: 0.1968 - val_loss: 3.1906 - val_accuracy: 0.0727\n",
      "Epoch 409/1000\n",
      "13/13 [==============================] - 0s 29ms/step - loss: 2.6414 - accuracy: 0.1829 - val_loss: 3.2042 - val_accuracy: 0.0727\n",
      "Epoch 410/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.6324 - accuracy: 0.2245 - val_loss: 3.1989 - val_accuracy: 0.0727\n",
      "Epoch 411/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.6322 - accuracy: 0.2060 - val_loss: 3.1859 - val_accuracy: 0.0727\n",
      "Epoch 412/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.6871 - accuracy: 0.1921 - val_loss: 3.1726 - val_accuracy: 0.0727\n",
      "Epoch 413/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.6284 - accuracy: 0.2176 - val_loss: 3.1405 - val_accuracy: 0.0727\n",
      "Epoch 414/1000\n",
      "13/13 [==============================] - 0s 27ms/step - loss: 2.6454 - accuracy: 0.2176 - val_loss: 3.1463 - val_accuracy: 0.0727\n",
      "Epoch 415/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6421 - accuracy: 0.2037 - val_loss: 3.1804 - val_accuracy: 0.0909\n",
      "Epoch 416/1000\n",
      "13/13 [==============================] - 0s 29ms/step - loss: 2.6410 - accuracy: 0.1898 - val_loss: 3.1679 - val_accuracy: 0.0727\n",
      "Epoch 417/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.6175 - accuracy: 0.1991 - val_loss: 3.1644 - val_accuracy: 0.0727\n",
      "Epoch 418/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.6700 - accuracy: 0.1944 - val_loss: 3.1440 - val_accuracy: 0.0727\n",
      "Epoch 419/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.6198 - accuracy: 0.1921 - val_loss: 3.1560 - val_accuracy: 0.0727\n",
      "Epoch 420/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.6255 - accuracy: 0.2083 - val_loss: 3.1344 - val_accuracy: 0.0727\n",
      "Epoch 421/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.6200 - accuracy: 0.2106 - val_loss: 3.1355 - val_accuracy: 0.0727\n",
      "Epoch 422/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6680 - accuracy: 0.1782 - val_loss: 3.1573 - val_accuracy: 0.0727\n",
      "Epoch 423/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.6640 - accuracy: 0.1759 - val_loss: 3.1513 - val_accuracy: 0.0727\n",
      "Epoch 424/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.6137 - accuracy: 0.2176 - val_loss: 3.1806 - val_accuracy: 0.0727\n",
      "Epoch 425/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.6561 - accuracy: 0.1829 - val_loss: 3.1317 - val_accuracy: 0.0727\n",
      "Epoch 426/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.6224 - accuracy: 0.2060 - val_loss: 3.1344 - val_accuracy: 0.0909\n",
      "Epoch 427/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.6318 - accuracy: 0.2037 - val_loss: 3.1623 - val_accuracy: 0.0727\n",
      "Epoch 428/1000\n",
      "13/13 [==============================] - 0s 27ms/step - loss: 2.6318 - accuracy: 0.1898 - val_loss: 3.1425 - val_accuracy: 0.0727\n",
      "Epoch 429/1000\n",
      "13/13 [==============================] - 0s 21ms/step - loss: 2.6400 - accuracy: 0.2060 - val_loss: 3.1188 - val_accuracy: 0.0727\n",
      "Epoch 430/1000\n",
      "13/13 [==============================] - 0s 22ms/step - loss: 2.6539 - accuracy: 0.1806 - val_loss: 3.1189 - val_accuracy: 0.0909\n",
      "Epoch 431/1000\n",
      "13/13 [==============================] - 0s 22ms/step - loss: 2.6172 - accuracy: 0.2106 - val_loss: 3.1381 - val_accuracy: 0.0727\n",
      "Epoch 432/1000\n",
      "13/13 [==============================] - 0s 29ms/step - loss: 2.6278 - accuracy: 0.1991 - val_loss: 3.1215 - val_accuracy: 0.0727\n",
      "Epoch 433/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6194 - accuracy: 0.2037 - val_loss: 3.1577 - val_accuracy: 0.0727\n",
      "Epoch 434/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.5914 - accuracy: 0.2153 - val_loss: 3.1372 - val_accuracy: 0.0727\n",
      "Epoch 435/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.6289 - accuracy: 0.1782 - val_loss: 3.1357 - val_accuracy: 0.0727\n",
      "Epoch 436/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.6266 - accuracy: 0.1875 - val_loss: 3.1955 - val_accuracy: 0.0909\n",
      "Epoch 437/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.6770 - accuracy: 0.2014 - val_loss: 3.2129 - val_accuracy: 0.0909\n",
      "Epoch 438/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5964 - accuracy: 0.1852 - val_loss: 3.1738 - val_accuracy: 0.0909\n",
      "Epoch 439/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.6123 - accuracy: 0.1991 - val_loss: 3.1682 - val_accuracy: 0.0909\n",
      "Epoch 440/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5927 - accuracy: 0.2269 - val_loss: 3.1847 - val_accuracy: 0.0909\n",
      "Epoch 441/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.6228 - accuracy: 0.1875 - val_loss: 3.1889 - val_accuracy: 0.0909\n",
      "Epoch 442/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 0s 33ms/step - loss: 2.6133 - accuracy: 0.1968 - val_loss: 3.2181 - val_accuracy: 0.0909\n",
      "Epoch 443/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.6337 - accuracy: 0.1898 - val_loss: 3.1915 - val_accuracy: 0.0909\n",
      "Epoch 444/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.6258 - accuracy: 0.1968 - val_loss: 3.1783 - val_accuracy: 0.0909\n",
      "Epoch 445/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.6327 - accuracy: 0.1806 - val_loss: 3.1678 - val_accuracy: 0.0909\n",
      "Epoch 446/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6428 - accuracy: 0.2106 - val_loss: 3.1672 - val_accuracy: 0.0727\n",
      "Epoch 447/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6153 - accuracy: 0.2083 - val_loss: 3.1741 - val_accuracy: 0.0909\n",
      "Epoch 448/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.6350 - accuracy: 0.2153 - val_loss: 3.1617 - val_accuracy: 0.0727\n",
      "Epoch 449/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.5815 - accuracy: 0.2245 - val_loss: 3.1533 - val_accuracy: 0.1091\n",
      "Epoch 450/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5622 - accuracy: 0.2106 - val_loss: 3.1324 - val_accuracy: 0.1091\n",
      "Epoch 451/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.5773 - accuracy: 0.1898 - val_loss: 3.1535 - val_accuracy: 0.0545\n",
      "Epoch 452/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.6427 - accuracy: 0.1968 - val_loss: 3.1808 - val_accuracy: 0.0909\n",
      "Epoch 453/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6327 - accuracy: 0.1852 - val_loss: 3.1867 - val_accuracy: 0.0727\n",
      "Epoch 454/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5980 - accuracy: 0.1944 - val_loss: 3.2331 - val_accuracy: 0.0909\n",
      "Epoch 455/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.5857 - accuracy: 0.2199 - val_loss: 3.1906 - val_accuracy: 0.0727\n",
      "Epoch 456/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6380 - accuracy: 0.1852 - val_loss: 3.1512 - val_accuracy: 0.0727\n",
      "Epoch 457/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.5742 - accuracy: 0.2199 - val_loss: 3.1615 - val_accuracy: 0.0545\n",
      "Epoch 458/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6282 - accuracy: 0.1829 - val_loss: 3.1884 - val_accuracy: 0.0727\n",
      "Epoch 459/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.5917 - accuracy: 0.2130 - val_loss: 3.1898 - val_accuracy: 0.0727\n",
      "Epoch 460/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6072 - accuracy: 0.2014 - val_loss: 3.2540 - val_accuracy: 0.0727\n",
      "Epoch 461/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.5887 - accuracy: 0.2060 - val_loss: 3.2262 - val_accuracy: 0.0545\n",
      "Epoch 462/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5856 - accuracy: 0.2245 - val_loss: 3.2093 - val_accuracy: 0.0545\n",
      "Epoch 463/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.6008 - accuracy: 0.1921 - val_loss: 3.1811 - val_accuracy: 0.0545\n",
      "Epoch 464/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.6095 - accuracy: 0.2199 - val_loss: 3.1780 - val_accuracy: 0.0545\n",
      "Epoch 465/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.5683 - accuracy: 0.2176 - val_loss: 3.2025 - val_accuracy: 0.0727\n",
      "Epoch 466/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5920 - accuracy: 0.2060 - val_loss: 3.1921 - val_accuracy: 0.0727\n",
      "Epoch 467/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5509 - accuracy: 0.2292 - val_loss: 3.1719 - val_accuracy: 0.0727\n",
      "Epoch 468/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.5696 - accuracy: 0.2083 - val_loss: 3.1879 - val_accuracy: 0.0364\n",
      "Epoch 469/1000\n",
      "13/13 [==============================] - 0s 27ms/step - loss: 2.5959 - accuracy: 0.1875 - val_loss: 3.1623 - val_accuracy: 0.0545\n",
      "Epoch 470/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6261 - accuracy: 0.1690 - val_loss: 3.1284 - val_accuracy: 0.0545\n",
      "Epoch 471/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.5735 - accuracy: 0.2176 - val_loss: 3.1364 - val_accuracy: 0.0545\n",
      "Epoch 472/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5629 - accuracy: 0.2292 - val_loss: 3.1004 - val_accuracy: 0.0727\n",
      "Epoch 473/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.5774 - accuracy: 0.1991 - val_loss: 3.1175 - val_accuracy: 0.0545\n",
      "Epoch 474/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.5991 - accuracy: 0.1944 - val_loss: 3.1769 - val_accuracy: 0.0727\n",
      "Epoch 475/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.5353 - accuracy: 0.2037 - val_loss: 3.1696 - val_accuracy: 0.0909\n",
      "Epoch 476/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5663 - accuracy: 0.2269 - val_loss: 3.1736 - val_accuracy: 0.0727\n",
      "Epoch 477/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.5505 - accuracy: 0.2130 - val_loss: 3.1629 - val_accuracy: 0.0727\n",
      "Epoch 478/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6377 - accuracy: 0.2083 - val_loss: 3.1711 - val_accuracy: 0.0727\n",
      "Epoch 479/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.6056 - accuracy: 0.1875 - val_loss: 3.1901 - val_accuracy: 0.0727\n",
      "Epoch 480/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5909 - accuracy: 0.2037 - val_loss: 3.1874 - val_accuracy: 0.0727\n",
      "Epoch 481/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5953 - accuracy: 0.1829 - val_loss: 3.1122 - val_accuracy: 0.0909\n",
      "Epoch 482/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5600 - accuracy: 0.1944 - val_loss: 3.1116 - val_accuracy: 0.0727\n",
      "Epoch 483/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5455 - accuracy: 0.2037 - val_loss: 3.1378 - val_accuracy: 0.0727\n",
      "Epoch 484/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5432 - accuracy: 0.1829 - val_loss: 3.1495 - val_accuracy: 0.0545\n",
      "Epoch 485/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.5543 - accuracy: 0.2292 - val_loss: 3.1525 - val_accuracy: 0.0545\n",
      "Epoch 486/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.6018 - accuracy: 0.2060 - val_loss: 3.1371 - val_accuracy: 0.0545\n",
      "Epoch 487/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.6030 - accuracy: 0.2245 - val_loss: 3.1608 - val_accuracy: 0.0545\n",
      "Epoch 488/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5700 - accuracy: 0.2106 - val_loss: 3.1822 - val_accuracy: 0.0727\n",
      "Epoch 489/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5501 - accuracy: 0.2199 - val_loss: 3.2166 - val_accuracy: 0.0545\n",
      "Epoch 490/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.5420 - accuracy: 0.2083 - val_loss: 3.2359 - val_accuracy: 0.0545\n",
      "Epoch 491/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5535 - accuracy: 0.2153 - val_loss: 3.2219 - val_accuracy: 0.0545\n",
      "Epoch 492/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5889 - accuracy: 0.2060 - val_loss: 3.1936 - val_accuracy: 0.0727\n",
      "Epoch 493/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5341 - accuracy: 0.2222 - val_loss: 3.1700 - val_accuracy: 0.0909\n",
      "Epoch 494/1000\n",
      "13/13 [==============================] - 0s 29ms/step - loss: 2.5722 - accuracy: 0.2153 - val_loss: 3.1391 - val_accuracy: 0.0727\n",
      "Epoch 495/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.5310 - accuracy: 0.2083 - val_loss: 3.1098 - val_accuracy: 0.0545\n",
      "Epoch 496/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.5605 - accuracy: 0.2014 - val_loss: 3.1034 - val_accuracy: 0.0727\n",
      "Epoch 497/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5241 - accuracy: 0.2245 - val_loss: 3.1180 - val_accuracy: 0.0727\n",
      "Epoch 498/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 0s 31ms/step - loss: 2.5720 - accuracy: 0.2431 - val_loss: 3.1489 - val_accuracy: 0.0727\n",
      "Epoch 499/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5857 - accuracy: 0.2222 - val_loss: 3.1810 - val_accuracy: 0.0727\n",
      "Epoch 500/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5477 - accuracy: 0.2176 - val_loss: 3.1869 - val_accuracy: 0.0727\n",
      "Epoch 501/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.5307 - accuracy: 0.1921 - val_loss: 3.2051 - val_accuracy: 0.0545\n",
      "Epoch 502/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.5343 - accuracy: 0.2176 - val_loss: 3.2181 - val_accuracy: 0.0727\n",
      "Epoch 503/1000\n",
      "13/13 [==============================] - 0s 29ms/step - loss: 2.5614 - accuracy: 0.2176 - val_loss: 3.2591 - val_accuracy: 0.0545\n",
      "Epoch 504/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.5500 - accuracy: 0.2014 - val_loss: 3.2880 - val_accuracy: 0.0545\n",
      "Epoch 505/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.5495 - accuracy: 0.2083 - val_loss: 3.2571 - val_accuracy: 0.0545\n",
      "Epoch 506/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5546 - accuracy: 0.2037 - val_loss: 3.2460 - val_accuracy: 0.0727\n",
      "Epoch 507/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.5535 - accuracy: 0.2060 - val_loss: 3.2546 - val_accuracy: 0.0545\n",
      "Epoch 508/1000\n",
      "13/13 [==============================] - 0s 24ms/step - loss: 2.5538 - accuracy: 0.2106 - val_loss: 3.2664 - val_accuracy: 0.0545\n",
      "Epoch 509/1000\n",
      "13/13 [==============================] - 0s 29ms/step - loss: 2.5286 - accuracy: 0.2037 - val_loss: 3.2669 - val_accuracy: 0.0727\n",
      "Epoch 510/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.5272 - accuracy: 0.2407 - val_loss: 3.2415 - val_accuracy: 0.0727\n",
      "Epoch 511/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5429 - accuracy: 0.2245 - val_loss: 3.2243 - val_accuracy: 0.0727\n",
      "Epoch 512/1000\n",
      "13/13 [==============================] - 0s 29ms/step - loss: 2.5294 - accuracy: 0.2315 - val_loss: 3.2319 - val_accuracy: 0.0727\n",
      "Epoch 513/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.5692 - accuracy: 0.1875 - val_loss: 3.2314 - val_accuracy: 0.0727\n",
      "Epoch 514/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5494 - accuracy: 0.1921 - val_loss: 3.1862 - val_accuracy: 0.0909\n",
      "Epoch 515/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.5447 - accuracy: 0.2176 - val_loss: 3.1974 - val_accuracy: 0.0727\n",
      "Epoch 516/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.5117 - accuracy: 0.2060 - val_loss: 3.2108 - val_accuracy: 0.0909\n",
      "Epoch 517/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.5310 - accuracy: 0.2083 - val_loss: 3.1736 - val_accuracy: 0.0727\n",
      "Epoch 518/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4976 - accuracy: 0.2199 - val_loss: 3.1961 - val_accuracy: 0.0909\n",
      "Epoch 519/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5410 - accuracy: 0.2176 - val_loss: 3.2154 - val_accuracy: 0.0727\n",
      "Epoch 520/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5345 - accuracy: 0.2245 - val_loss: 3.1934 - val_accuracy: 0.0545\n",
      "Epoch 521/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5112 - accuracy: 0.2083 - val_loss: 3.2058 - val_accuracy: 0.0545\n",
      "Epoch 522/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5001 - accuracy: 0.2014 - val_loss: 3.2150 - val_accuracy: 0.0545\n",
      "Epoch 523/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5304 - accuracy: 0.1875 - val_loss: 3.2090 - val_accuracy: 0.0545\n",
      "Epoch 524/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4841 - accuracy: 0.2384 - val_loss: 3.2048 - val_accuracy: 0.0727\n",
      "Epoch 525/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5617 - accuracy: 0.1829 - val_loss: 3.2551 - val_accuracy: 0.0727\n",
      "Epoch 526/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5514 - accuracy: 0.2153 - val_loss: 3.2436 - val_accuracy: 0.0727\n",
      "Epoch 527/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.5412 - accuracy: 0.2199 - val_loss: 3.2352 - val_accuracy: 0.0727\n",
      "Epoch 528/1000\n",
      "13/13 [==============================] - 0s 26ms/step - loss: 2.5484 - accuracy: 0.1875 - val_loss: 3.2564 - val_accuracy: 0.0909\n",
      "Epoch 529/1000\n",
      "13/13 [==============================] - 0s 25ms/step - loss: 2.5337 - accuracy: 0.2361 - val_loss: 3.2485 - val_accuracy: 0.0364\n",
      "Epoch 530/1000\n",
      "13/13 [==============================] - 0s 29ms/step - loss: 2.5385 - accuracy: 0.2083 - val_loss: 3.2386 - val_accuracy: 0.0727\n",
      "Epoch 531/1000\n",
      "13/13 [==============================] - 0s 25ms/step - loss: 2.5658 - accuracy: 0.2130 - val_loss: 3.1997 - val_accuracy: 0.0909\n",
      "Epoch 532/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.5413 - accuracy: 0.2014 - val_loss: 3.1916 - val_accuracy: 0.0909\n",
      "Epoch 533/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.5518 - accuracy: 0.1991 - val_loss: 3.2096 - val_accuracy: 0.0909\n",
      "Epoch 534/1000\n",
      "13/13 [==============================] - 0s 29ms/step - loss: 2.5276 - accuracy: 0.2083 - val_loss: 3.2186 - val_accuracy: 0.0909\n",
      "Epoch 535/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4952 - accuracy: 0.2361 - val_loss: 3.2779 - val_accuracy: 0.0545\n",
      "Epoch 536/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.5512 - accuracy: 0.2083 - val_loss: 3.2561 - val_accuracy: 0.0364\n",
      "Epoch 537/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5292 - accuracy: 0.2222 - val_loss: 3.2459 - val_accuracy: 0.0545\n",
      "Epoch 538/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.5099 - accuracy: 0.2083 - val_loss: 3.2533 - val_accuracy: 0.0545\n",
      "Epoch 539/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.5595 - accuracy: 0.2130 - val_loss: 3.2839 - val_accuracy: 0.0727\n",
      "Epoch 540/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.5327 - accuracy: 0.2014 - val_loss: 3.2984 - val_accuracy: 0.0545\n",
      "Epoch 541/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.5030 - accuracy: 0.2407 - val_loss: 3.2826 - val_accuracy: 0.0727\n",
      "Epoch 542/1000\n",
      "13/13 [==============================] - 0s 38ms/step - loss: 2.4982 - accuracy: 0.2523 - val_loss: 3.2876 - val_accuracy: 0.0545\n",
      "Epoch 543/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.4958 - accuracy: 0.2245 - val_loss: 3.2164 - val_accuracy: 0.0545\n",
      "Epoch 544/1000\n",
      "13/13 [==============================] - 1s 40ms/step - loss: 2.4819 - accuracy: 0.2292 - val_loss: 3.1931 - val_accuracy: 0.0909\n",
      "Epoch 545/1000\n",
      "13/13 [==============================] - 0s 38ms/step - loss: 2.5117 - accuracy: 0.2199 - val_loss: 3.2247 - val_accuracy: 0.0364\n",
      "Epoch 546/1000\n",
      "13/13 [==============================] - 0s 39ms/step - loss: 2.5052 - accuracy: 0.2199 - val_loss: 3.2392 - val_accuracy: 0.0727\n",
      "Epoch 547/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.5202 - accuracy: 0.2153 - val_loss: 3.2363 - val_accuracy: 0.0364\n",
      "Epoch 548/1000\n",
      "13/13 [==============================] - 0s 38ms/step - loss: 2.5155 - accuracy: 0.2060 - val_loss: 3.1851 - val_accuracy: 0.0727\n",
      "Epoch 549/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.5026 - accuracy: 0.1968 - val_loss: 3.1612 - val_accuracy: 0.0364\n",
      "Epoch 550/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.5114 - accuracy: 0.2176 - val_loss: 3.1292 - val_accuracy: 0.0364\n",
      "Epoch 551/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.5122 - accuracy: 0.2083 - val_loss: 3.2193 - val_accuracy: 0.0727\n",
      "Epoch 552/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.5238 - accuracy: 0.2083 - val_loss: 3.2567 - val_accuracy: 0.0364\n",
      "Epoch 553/1000\n",
      "13/13 [==============================] - 0s 39ms/step - loss: 2.5535 - accuracy: 0.2037 - val_loss: 3.1948 - val_accuracy: 0.0727\n",
      "Epoch 554/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 0s 38ms/step - loss: 2.5503 - accuracy: 0.2083 - val_loss: 3.2574 - val_accuracy: 0.0364\n",
      "Epoch 555/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.5105 - accuracy: 0.2176 - val_loss: 3.2403 - val_accuracy: 0.0545\n",
      "Epoch 556/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.4927 - accuracy: 0.2431 - val_loss: 3.2811 - val_accuracy: 0.0364\n",
      "Epoch 557/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.5125 - accuracy: 0.1991 - val_loss: 3.2537 - val_accuracy: 0.0545\n",
      "Epoch 558/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.4999 - accuracy: 0.2130 - val_loss: 3.2451 - val_accuracy: 0.0545\n",
      "Epoch 559/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.5380 - accuracy: 0.1968 - val_loss: 3.2086 - val_accuracy: 0.0364\n",
      "Epoch 560/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.5114 - accuracy: 0.2315 - val_loss: 3.1843 - val_accuracy: 0.0364\n",
      "Epoch 561/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.5252 - accuracy: 0.2222 - val_loss: 3.1864 - val_accuracy: 0.0364\n",
      "Epoch 562/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.5084 - accuracy: 0.1991 - val_loss: 3.2240 - val_accuracy: 0.0364\n",
      "Epoch 563/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4702 - accuracy: 0.2222 - val_loss: 3.2098 - val_accuracy: 0.0545\n",
      "Epoch 564/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.5121 - accuracy: 0.2060 - val_loss: 3.1808 - val_accuracy: 0.0727\n",
      "Epoch 565/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.5169 - accuracy: 0.2037 - val_loss: 3.1666 - val_accuracy: 0.0545\n",
      "Epoch 566/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4523 - accuracy: 0.2199 - val_loss: 3.2405 - val_accuracy: 0.0727\n",
      "Epoch 567/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.4875 - accuracy: 0.2060 - val_loss: 3.2594 - val_accuracy: 0.0545\n",
      "Epoch 568/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.4561 - accuracy: 0.2245 - val_loss: 3.2840 - val_accuracy: 0.0545\n",
      "Epoch 569/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.4787 - accuracy: 0.2407 - val_loss: 3.2546 - val_accuracy: 0.0727\n",
      "Epoch 570/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4980 - accuracy: 0.2130 - val_loss: 3.2378 - val_accuracy: 0.0545\n",
      "Epoch 571/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.5074 - accuracy: 0.2245 - val_loss: 3.2307 - val_accuracy: 0.0545\n",
      "Epoch 572/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.5447 - accuracy: 0.1944 - val_loss: 3.2271 - val_accuracy: 0.0727\n",
      "Epoch 573/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.4659 - accuracy: 0.2315 - val_loss: 3.2113 - val_accuracy: 0.0727\n",
      "Epoch 574/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.4939 - accuracy: 0.2083 - val_loss: 3.1834 - val_accuracy: 0.0545\n",
      "Epoch 575/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4883 - accuracy: 0.2222 - val_loss: 3.1384 - val_accuracy: 0.0545\n",
      "Epoch 576/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.5138 - accuracy: 0.2500 - val_loss: 3.1593 - val_accuracy: 0.0727\n",
      "Epoch 577/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.5330 - accuracy: 0.2130 - val_loss: 3.2002 - val_accuracy: 0.0727\n",
      "Epoch 578/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4884 - accuracy: 0.2176 - val_loss: 3.2234 - val_accuracy: 0.0727\n",
      "Epoch 579/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.5316 - accuracy: 0.2292 - val_loss: 3.2258 - val_accuracy: 0.0727\n",
      "Epoch 580/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4981 - accuracy: 0.2060 - val_loss: 3.2192 - val_accuracy: 0.0909\n",
      "Epoch 581/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4612 - accuracy: 0.2269 - val_loss: 3.2364 - val_accuracy: 0.0545\n",
      "Epoch 582/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.4757 - accuracy: 0.2060 - val_loss: 3.2512 - val_accuracy: 0.0545\n",
      "Epoch 583/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.5187 - accuracy: 0.1875 - val_loss: 3.2745 - val_accuracy: 0.0545\n",
      "Epoch 584/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.4485 - accuracy: 0.2431 - val_loss: 3.3028 - val_accuracy: 0.0545\n",
      "Epoch 585/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.5265 - accuracy: 0.2014 - val_loss: 3.2932 - val_accuracy: 0.0545\n",
      "Epoch 586/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4866 - accuracy: 0.2130 - val_loss: 3.2653 - val_accuracy: 0.0545\n",
      "Epoch 587/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.4706 - accuracy: 0.2199 - val_loss: 3.2331 - val_accuracy: 0.0727\n",
      "Epoch 588/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4491 - accuracy: 0.2454 - val_loss: 3.2056 - val_accuracy: 0.0727\n",
      "Epoch 589/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.5043 - accuracy: 0.2338 - val_loss: 3.1718 - val_accuracy: 0.0545\n",
      "Epoch 590/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4807 - accuracy: 0.2431 - val_loss: 3.1908 - val_accuracy: 0.0909\n",
      "Epoch 591/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4744 - accuracy: 0.1921 - val_loss: 3.2538 - val_accuracy: 0.0727\n",
      "Epoch 592/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.5047 - accuracy: 0.2106 - val_loss: 3.3376 - val_accuracy: 0.0364\n",
      "Epoch 593/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.5067 - accuracy: 0.2315 - val_loss: 3.3147 - val_accuracy: 0.0000e+00\n",
      "Epoch 594/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.4642 - accuracy: 0.2338 - val_loss: 3.3248 - val_accuracy: 0.0182\n",
      "Epoch 595/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4682 - accuracy: 0.2106 - val_loss: 3.2770 - val_accuracy: 0.0364\n",
      "Epoch 596/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4898 - accuracy: 0.2106 - val_loss: 3.2480 - val_accuracy: 0.0364\n",
      "Epoch 597/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4758 - accuracy: 0.2037 - val_loss: 3.2223 - val_accuracy: 0.0545\n",
      "Epoch 598/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4815 - accuracy: 0.2037 - val_loss: 3.2324 - val_accuracy: 0.0545\n",
      "Epoch 599/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.5103 - accuracy: 0.2431 - val_loss: 3.2219 - val_accuracy: 0.0545\n",
      "Epoch 600/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.5038 - accuracy: 0.2176 - val_loss: 3.2202 - val_accuracy: 0.0545\n",
      "Epoch 601/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.4935 - accuracy: 0.2245 - val_loss: 3.2131 - val_accuracy: 0.0909\n",
      "Epoch 602/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.4756 - accuracy: 0.2292 - val_loss: 3.2339 - val_accuracy: 0.0545\n",
      "Epoch 603/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.5188 - accuracy: 0.2130 - val_loss: 3.2155 - val_accuracy: 0.0727\n",
      "Epoch 604/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4370 - accuracy: 0.2407 - val_loss: 3.1984 - val_accuracy: 0.0727\n",
      "Epoch 605/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.4861 - accuracy: 0.2338 - val_loss: 3.2350 - val_accuracy: 0.0364\n",
      "Epoch 606/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4484 - accuracy: 0.2130 - val_loss: 3.2495 - val_accuracy: 0.0364\n",
      "Epoch 607/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.4193 - accuracy: 0.2500 - val_loss: 3.2517 - val_accuracy: 0.0545\n",
      "Epoch 608/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4765 - accuracy: 0.2176 - val_loss: 3.3089 - val_accuracy: 0.0364\n",
      "Epoch 609/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.4813 - accuracy: 0.2199 - val_loss: 3.3010 - val_accuracy: 0.0364\n",
      "Epoch 610/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4915 - accuracy: 0.2083 - val_loss: 3.2796 - val_accuracy: 0.0364\n",
      "Epoch 611/1000\n",
      "13/13 [==============================] - 0s 27ms/step - loss: 2.4937 - accuracy: 0.2014 - val_loss: 3.2445 - val_accuracy: 0.0182\n",
      "Epoch 612/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.5322 - accuracy: 0.1991 - val_loss: 3.2236 - val_accuracy: 0.0727\n",
      "Epoch 613/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4726 - accuracy: 0.2130 - val_loss: 3.2019 - val_accuracy: 0.0364\n",
      "Epoch 614/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4757 - accuracy: 0.2222 - val_loss: 3.2132 - val_accuracy: 0.0727\n",
      "Epoch 615/1000\n",
      "13/13 [==============================] - 0s 27ms/step - loss: 2.4453 - accuracy: 0.2338 - val_loss: 3.1521 - val_accuracy: 0.0545\n",
      "Epoch 616/1000\n",
      "13/13 [==============================] - 0s 21ms/step - loss: 2.4943 - accuracy: 0.2060 - val_loss: 3.2053 - val_accuracy: 0.0545\n",
      "Epoch 617/1000\n",
      "13/13 [==============================] - 0s 18ms/step - loss: 2.4646 - accuracy: 0.2269 - val_loss: 3.2083 - val_accuracy: 0.0364\n",
      "Epoch 618/1000\n",
      "13/13 [==============================] - 0s 18ms/step - loss: 2.4541 - accuracy: 0.2153 - val_loss: 3.2002 - val_accuracy: 0.0727\n",
      "Epoch 619/1000\n",
      "13/13 [==============================] - 0s 22ms/step - loss: 2.5058 - accuracy: 0.1968 - val_loss: 3.2027 - val_accuracy: 0.0364\n",
      "Epoch 620/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4761 - accuracy: 0.2338 - val_loss: 3.2121 - val_accuracy: 0.0182\n",
      "Epoch 621/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4304 - accuracy: 0.2245 - val_loss: 3.1815 - val_accuracy: 0.0545\n",
      "Epoch 622/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.4932 - accuracy: 0.2153 - val_loss: 3.2779 - val_accuracy: 0.0545\n",
      "Epoch 623/1000\n",
      "13/13 [==============================] - 0s 29ms/step - loss: 2.4443 - accuracy: 0.2199 - val_loss: 3.3859 - val_accuracy: 0.0364\n",
      "Epoch 624/1000\n",
      "13/13 [==============================] - 0s 26ms/step - loss: 2.4500 - accuracy: 0.2222 - val_loss: 3.3957 - val_accuracy: 0.0545\n",
      "Epoch 625/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4712 - accuracy: 0.2130 - val_loss: 3.3005 - val_accuracy: 0.0364\n",
      "Epoch 626/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.4649 - accuracy: 0.2523 - val_loss: 3.2816 - val_accuracy: 0.0182\n",
      "Epoch 627/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4114 - accuracy: 0.2569 - val_loss: 3.2973 - val_accuracy: 0.0182\n",
      "Epoch 628/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.4529 - accuracy: 0.2269 - val_loss: 3.2586 - val_accuracy: 0.0545\n",
      "Epoch 629/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4541 - accuracy: 0.2176 - val_loss: 3.2369 - val_accuracy: 0.0364\n",
      "Epoch 630/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4644 - accuracy: 0.2222 - val_loss: 3.2205 - val_accuracy: 0.0545\n",
      "Epoch 631/1000\n",
      "13/13 [==============================] - 0s 38ms/step - loss: 2.4699 - accuracy: 0.2176 - val_loss: 3.2606 - val_accuracy: 0.0182\n",
      "Epoch 632/1000\n",
      "13/13 [==============================] - 0s 27ms/step - loss: 2.4966 - accuracy: 0.2176 - val_loss: 3.2632 - val_accuracy: 0.0364\n",
      "Epoch 633/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4516 - accuracy: 0.2222 - val_loss: 3.2640 - val_accuracy: 0.0364\n",
      "Epoch 634/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4168 - accuracy: 0.2315 - val_loss: 3.2891 - val_accuracy: 0.0182\n",
      "Epoch 635/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4246 - accuracy: 0.2245 - val_loss: 3.2957 - val_accuracy: 0.0000e+00\n",
      "Epoch 636/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.4378 - accuracy: 0.2431 - val_loss: 3.2925 - val_accuracy: 0.0364\n",
      "Epoch 637/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4691 - accuracy: 0.1968 - val_loss: 3.3383 - val_accuracy: 0.0182\n",
      "Epoch 638/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4343 - accuracy: 0.2222 - val_loss: 3.2961 - val_accuracy: 0.0364\n",
      "Epoch 639/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4554 - accuracy: 0.2384 - val_loss: 3.2314 - val_accuracy: 0.0545\n",
      "Epoch 640/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.5121 - accuracy: 0.2199 - val_loss: 3.1977 - val_accuracy: 0.0182\n",
      "Epoch 641/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4638 - accuracy: 0.2269 - val_loss: 3.2091 - val_accuracy: 0.0727\n",
      "Epoch 642/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4697 - accuracy: 0.2431 - val_loss: 3.2397 - val_accuracy: 0.0000e+00\n",
      "Epoch 643/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.4009 - accuracy: 0.2546 - val_loss: 3.2541 - val_accuracy: 0.0182\n",
      "Epoch 644/1000\n",
      "13/13 [==============================] - 0s 25ms/step - loss: 2.4841 - accuracy: 0.2292 - val_loss: 3.2902 - val_accuracy: 0.0000e+00\n",
      "Epoch 645/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4241 - accuracy: 0.2222 - val_loss: 3.3428 - val_accuracy: 0.0364\n",
      "Epoch 646/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.4753 - accuracy: 0.2292 - val_loss: 3.2962 - val_accuracy: 0.0727\n",
      "Epoch 647/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.4277 - accuracy: 0.2384 - val_loss: 3.2581 - val_accuracy: 0.0000e+00\n",
      "Epoch 648/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.4597 - accuracy: 0.2546 - val_loss: 3.2678 - val_accuracy: 0.0182\n",
      "Epoch 649/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3890 - accuracy: 0.2407 - val_loss: 3.2495 - val_accuracy: 0.0000e+00\n",
      "Epoch 650/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.4006 - accuracy: 0.2338 - val_loss: 3.2608 - val_accuracy: 0.0000e+00\n",
      "Epoch 651/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.4417 - accuracy: 0.2546 - val_loss: 3.3166 - val_accuracy: 0.0000e+00\n",
      "Epoch 652/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.4446 - accuracy: 0.2315 - val_loss: 3.3719 - val_accuracy: 0.0000e+00\n",
      "Epoch 653/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.4776 - accuracy: 0.2176 - val_loss: 3.3578 - val_accuracy: 0.0000e+00\n",
      "Epoch 654/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.4536 - accuracy: 0.2292 - val_loss: 3.3430 - val_accuracy: 0.0182\n",
      "Epoch 655/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.4274 - accuracy: 0.2500 - val_loss: 3.3178 - val_accuracy: 0.0000e+00\n",
      "Epoch 656/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4589 - accuracy: 0.2153 - val_loss: 3.3095 - val_accuracy: 0.0000e+00\n",
      "Epoch 657/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4222 - accuracy: 0.2269 - val_loss: 3.2303 - val_accuracy: 0.0182\n",
      "Epoch 658/1000\n",
      "13/13 [==============================] - 0s 26ms/step - loss: 2.3962 - accuracy: 0.2315 - val_loss: 3.2001 - val_accuracy: 0.0182\n",
      "Epoch 659/1000\n",
      "13/13 [==============================] - 0s 27ms/step - loss: 2.4100 - accuracy: 0.2361 - val_loss: 3.2172 - val_accuracy: 0.0364\n",
      "Epoch 660/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4533 - accuracy: 0.2477 - val_loss: 3.1863 - val_accuracy: 0.0909\n",
      "Epoch 661/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4202 - accuracy: 0.2384 - val_loss: 3.1867 - val_accuracy: 0.0909\n",
      "Epoch 662/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.4478 - accuracy: 0.2083 - val_loss: 3.2023 - val_accuracy: 0.0909\n",
      "Epoch 663/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.4367 - accuracy: 0.2361 - val_loss: 3.2307 - val_accuracy: 0.0909\n",
      "Epoch 664/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.3688 - accuracy: 0.2315 - val_loss: 3.2630 - val_accuracy: 0.0364\n",
      "Epoch 665/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4359 - accuracy: 0.2361 - val_loss: 3.2776 - val_accuracy: 0.0364\n",
      "Epoch 666/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3969 - accuracy: 0.2176 - val_loss: 3.3105 - val_accuracy: 0.0545\n",
      "Epoch 667/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4787 - accuracy: 0.1944 - val_loss: 3.3305 - val_accuracy: 0.0364\n",
      "Epoch 668/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4346 - accuracy: 0.2454 - val_loss: 3.3444 - val_accuracy: 0.0364\n",
      "Epoch 669/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4177 - accuracy: 0.2454 - val_loss: 3.3379 - val_accuracy: 0.0364\n",
      "Epoch 670/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4475 - accuracy: 0.2106 - val_loss: 3.2863 - val_accuracy: 0.0727\n",
      "Epoch 671/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3992 - accuracy: 0.2407 - val_loss: 3.3180 - val_accuracy: 0.0182\n",
      "Epoch 672/1000\n",
      "13/13 [==============================] - 0s 23ms/step - loss: 2.3968 - accuracy: 0.2361 - val_loss: 3.3090 - val_accuracy: 0.0364\n",
      "Epoch 673/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4555 - accuracy: 0.2060 - val_loss: 3.2991 - val_accuracy: 0.0364\n",
      "Epoch 674/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3874 - accuracy: 0.2685 - val_loss: 3.2560 - val_accuracy: 0.0545\n",
      "Epoch 675/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4402 - accuracy: 0.2292 - val_loss: 3.2922 - val_accuracy: 0.0727\n",
      "Epoch 676/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.3854 - accuracy: 0.2593 - val_loss: 3.3507 - val_accuracy: 0.0545\n",
      "Epoch 677/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4301 - accuracy: 0.2338 - val_loss: 3.3287 - val_accuracy: 0.0364\n",
      "Epoch 678/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.4147 - accuracy: 0.2083 - val_loss: 3.4007 - val_accuracy: 0.0545\n",
      "Epoch 679/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4579 - accuracy: 0.2269 - val_loss: 3.3805 - val_accuracy: 0.0364\n",
      "Epoch 680/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4381 - accuracy: 0.2037 - val_loss: 3.4397 - val_accuracy: 0.0182\n",
      "Epoch 681/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.3962 - accuracy: 0.2338 - val_loss: 3.4299 - val_accuracy: 0.0182\n",
      "Epoch 682/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4605 - accuracy: 0.2315 - val_loss: 3.4296 - val_accuracy: 0.0182\n",
      "Epoch 683/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4136 - accuracy: 0.2153 - val_loss: 3.3727 - val_accuracy: 0.0182\n",
      "Epoch 684/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4326 - accuracy: 0.2060 - val_loss: 3.3272 - val_accuracy: 0.0182\n",
      "Epoch 685/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3920 - accuracy: 0.2338 - val_loss: 3.3056 - val_accuracy: 0.0364\n",
      "Epoch 686/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4484 - accuracy: 0.2106 - val_loss: 3.2923 - val_accuracy: 0.0364\n",
      "Epoch 687/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3880 - accuracy: 0.2153 - val_loss: 3.3568 - val_accuracy: 0.0545\n",
      "Epoch 688/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3908 - accuracy: 0.2523 - val_loss: 3.3783 - val_accuracy: 0.0364\n",
      "Epoch 689/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4096 - accuracy: 0.2384 - val_loss: 3.3540 - val_accuracy: 0.0727\n",
      "Epoch 690/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3917 - accuracy: 0.2315 - val_loss: 3.3243 - val_accuracy: 0.0364\n",
      "Epoch 691/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4376 - accuracy: 0.2153 - val_loss: 3.3104 - val_accuracy: 0.0364\n",
      "Epoch 692/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4225 - accuracy: 0.2269 - val_loss: 3.2885 - val_accuracy: 0.0727\n",
      "Epoch 693/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4339 - accuracy: 0.2384 - val_loss: 3.3169 - val_accuracy: 0.0364\n",
      "Epoch 694/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.4142 - accuracy: 0.2454 - val_loss: 3.3566 - val_accuracy: 0.0727\n",
      "Epoch 695/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3883 - accuracy: 0.2523 - val_loss: 3.3869 - val_accuracy: 0.0364\n",
      "Epoch 696/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4041 - accuracy: 0.2269 - val_loss: 3.3274 - val_accuracy: 0.0364\n",
      "Epoch 697/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4463 - accuracy: 0.2338 - val_loss: 3.3447 - val_accuracy: 0.0727\n",
      "Epoch 698/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.4280 - accuracy: 0.2384 - val_loss: 3.3156 - val_accuracy: 0.0364\n",
      "Epoch 699/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4318 - accuracy: 0.2384 - val_loss: 3.2888 - val_accuracy: 0.0364\n",
      "Epoch 700/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4474 - accuracy: 0.2292 - val_loss: 3.3491 - val_accuracy: 0.0727\n",
      "Epoch 701/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3983 - accuracy: 0.2315 - val_loss: 3.3026 - val_accuracy: 0.0727\n",
      "Epoch 702/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.3841 - accuracy: 0.2245 - val_loss: 3.3313 - val_accuracy: 0.0364\n",
      "Epoch 703/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4180 - accuracy: 0.2014 - val_loss: 3.2922 - val_accuracy: 0.0364\n",
      "Epoch 704/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.3936 - accuracy: 0.2269 - val_loss: 3.3408 - val_accuracy: 0.0545\n",
      "Epoch 705/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4154 - accuracy: 0.2245 - val_loss: 3.2950 - val_accuracy: 0.0545\n",
      "Epoch 706/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4256 - accuracy: 0.2222 - val_loss: 3.2998 - val_accuracy: 0.0364\n",
      "Epoch 707/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4316 - accuracy: 0.2292 - val_loss: 3.3227 - val_accuracy: 0.0545\n",
      "Epoch 708/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.3784 - accuracy: 0.2361 - val_loss: 3.3429 - val_accuracy: 0.0364\n",
      "Epoch 709/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4054 - accuracy: 0.2477 - val_loss: 3.3304 - val_accuracy: 0.0364\n",
      "Epoch 710/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4141 - accuracy: 0.2269 - val_loss: 3.2194 - val_accuracy: 0.0364\n",
      "Epoch 711/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4453 - accuracy: 0.2384 - val_loss: 3.2465 - val_accuracy: 0.0364\n",
      "Epoch 712/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.3866 - accuracy: 0.2500 - val_loss: 3.1725 - val_accuracy: 0.0364\n",
      "Epoch 713/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4400 - accuracy: 0.2407 - val_loss: 3.2283 - val_accuracy: 0.0364\n",
      "Epoch 714/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4287 - accuracy: 0.2338 - val_loss: 3.2071 - val_accuracy: 0.0364\n",
      "Epoch 715/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3936 - accuracy: 0.2407 - val_loss: 3.1984 - val_accuracy: 0.0364\n",
      "Epoch 716/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4181 - accuracy: 0.2431 - val_loss: 3.2348 - val_accuracy: 0.0545\n",
      "Epoch 717/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4089 - accuracy: 0.2199 - val_loss: 3.2272 - val_accuracy: 0.0364\n",
      "Epoch 718/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.3918 - accuracy: 0.2616 - val_loss: 3.2480 - val_accuracy: 0.0545\n",
      "Epoch 719/1000\n",
      "13/13 [==============================] - 0s 29ms/step - loss: 2.3805 - accuracy: 0.2593 - val_loss: 3.2124 - val_accuracy: 0.0364\n",
      "Epoch 720/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3890 - accuracy: 0.2431 - val_loss: 3.3306 - val_accuracy: 0.0364\n",
      "Epoch 721/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3863 - accuracy: 0.2639 - val_loss: 3.3014 - val_accuracy: 0.0545\n",
      "Epoch 722/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 0s 31ms/step - loss: 2.3988 - accuracy: 0.2199 - val_loss: 3.2932 - val_accuracy: 0.0364\n",
      "Epoch 723/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3592 - accuracy: 0.2477 - val_loss: 3.3208 - val_accuracy: 0.0545\n",
      "Epoch 724/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3770 - accuracy: 0.2593 - val_loss: 3.3244 - val_accuracy: 0.0364\n",
      "Epoch 725/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3959 - accuracy: 0.2338 - val_loss: 3.3533 - val_accuracy: 0.0364\n",
      "Epoch 726/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.3730 - accuracy: 0.2477 - val_loss: 3.3953 - val_accuracy: 0.0545\n",
      "Epoch 727/1000\n",
      "13/13 [==============================] - 0s 30ms/step - loss: 2.4044 - accuracy: 0.2315 - val_loss: 3.3562 - val_accuracy: 0.0545\n",
      "Epoch 728/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3961 - accuracy: 0.2500 - val_loss: 3.3490 - val_accuracy: 0.0182\n",
      "Epoch 729/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.4129 - accuracy: 0.2315 - val_loss: 3.3739 - val_accuracy: 0.0545\n",
      "Epoch 730/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.3785 - accuracy: 0.2361 - val_loss: 3.3418 - val_accuracy: 0.0182\n",
      "Epoch 731/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.3814 - accuracy: 0.2407 - val_loss: 3.3201 - val_accuracy: 0.0545\n",
      "Epoch 732/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4025 - accuracy: 0.2176 - val_loss: 3.2936 - val_accuracy: 0.0545\n",
      "Epoch 733/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.4228 - accuracy: 0.2477 - val_loss: 3.2834 - val_accuracy: 0.0364\n",
      "Epoch 734/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3678 - accuracy: 0.2546 - val_loss: 3.3447 - val_accuracy: 0.0182\n",
      "Epoch 735/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3758 - accuracy: 0.2454 - val_loss: 3.3657 - val_accuracy: 0.0545\n",
      "Epoch 736/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4326 - accuracy: 0.2245 - val_loss: 3.3170 - val_accuracy: 0.0364\n",
      "Epoch 737/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3931 - accuracy: 0.2616 - val_loss: 3.3357 - val_accuracy: 0.0727\n",
      "Epoch 738/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3558 - accuracy: 0.2593 - val_loss: 3.3506 - val_accuracy: 0.0545\n",
      "Epoch 739/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3949 - accuracy: 0.2407 - val_loss: 3.3619 - val_accuracy: 0.0364\n",
      "Epoch 740/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.4069 - accuracy: 0.2593 - val_loss: 3.3984 - val_accuracy: 0.0545\n",
      "Epoch 741/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.4087 - accuracy: 0.2106 - val_loss: 3.3866 - val_accuracy: 0.0545\n",
      "Epoch 742/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.4355 - accuracy: 0.2222 - val_loss: 3.3540 - val_accuracy: 0.0545\n",
      "Epoch 743/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3588 - accuracy: 0.2569 - val_loss: 3.3185 - val_accuracy: 0.0545\n",
      "Epoch 744/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.3942 - accuracy: 0.2361 - val_loss: 3.3066 - val_accuracy: 0.0364\n",
      "Epoch 745/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4157 - accuracy: 0.2361 - val_loss: 3.2498 - val_accuracy: 0.0364\n",
      "Epoch 746/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3820 - accuracy: 0.2500 - val_loss: 3.2480 - val_accuracy: 0.0364\n",
      "Epoch 747/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3683 - accuracy: 0.2407 - val_loss: 3.2891 - val_accuracy: 0.0727\n",
      "Epoch 748/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3659 - accuracy: 0.2523 - val_loss: 3.3147 - val_accuracy: 0.0545\n",
      "Epoch 749/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4014 - accuracy: 0.2546 - val_loss: 3.2877 - val_accuracy: 0.0545\n",
      "Epoch 750/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3591 - accuracy: 0.2500 - val_loss: 3.2208 - val_accuracy: 0.0364\n",
      "Epoch 751/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3944 - accuracy: 0.2361 - val_loss: 3.2255 - val_accuracy: 0.0545\n",
      "Epoch 752/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3730 - accuracy: 0.2662 - val_loss: 3.1237 - val_accuracy: 0.0364\n",
      "Epoch 753/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3628 - accuracy: 0.2639 - val_loss: 3.1384 - val_accuracy: 0.0364\n",
      "Epoch 754/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3851 - accuracy: 0.2315 - val_loss: 3.2241 - val_accuracy: 0.0545\n",
      "Epoch 755/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3743 - accuracy: 0.2407 - val_loss: 3.1996 - val_accuracy: 0.0364\n",
      "Epoch 756/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.4107 - accuracy: 0.2315 - val_loss: 3.3082 - val_accuracy: 0.0364\n",
      "Epoch 757/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3308 - accuracy: 0.2454 - val_loss: 3.3276 - val_accuracy: 0.0364\n",
      "Epoch 758/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.4213 - accuracy: 0.2176 - val_loss: 3.3077 - val_accuracy: 0.0364\n",
      "Epoch 759/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3574 - accuracy: 0.2500 - val_loss: 3.2889 - val_accuracy: 0.0364\n",
      "Epoch 760/1000\n",
      "13/13 [==============================] - 0s 31ms/step - loss: 2.3976 - accuracy: 0.2500 - val_loss: 3.3010 - val_accuracy: 0.0364\n",
      "Epoch 761/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3799 - accuracy: 0.2523 - val_loss: 3.2499 - val_accuracy: 0.0364\n",
      "Epoch 762/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3765 - accuracy: 0.2454 - val_loss: 3.2364 - val_accuracy: 0.0364\n",
      "Epoch 763/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4380 - accuracy: 0.2130 - val_loss: 3.1275 - val_accuracy: 0.0545\n",
      "Epoch 764/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3919 - accuracy: 0.2292 - val_loss: 3.1813 - val_accuracy: 0.0727\n",
      "Epoch 765/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3677 - accuracy: 0.2292 - val_loss: 3.2306 - val_accuracy: 0.0545\n",
      "Epoch 766/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3987 - accuracy: 0.2245 - val_loss: 3.1789 - val_accuracy: 0.0545\n",
      "Epoch 767/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3982 - accuracy: 0.2454 - val_loss: 3.1691 - val_accuracy: 0.0727\n",
      "Epoch 768/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3814 - accuracy: 0.2569 - val_loss: 3.1828 - val_accuracy: 0.0545\n",
      "Epoch 769/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3818 - accuracy: 0.2269 - val_loss: 3.2700 - val_accuracy: 0.0545\n",
      "Epoch 770/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.4265 - accuracy: 0.2083 - val_loss: 3.2931 - val_accuracy: 0.0364\n",
      "Epoch 771/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3508 - accuracy: 0.2477 - val_loss: 3.3362 - val_accuracy: 0.0545\n",
      "Epoch 772/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3623 - accuracy: 0.2338 - val_loss: 3.3329 - val_accuracy: 0.0545\n",
      "Epoch 773/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3547 - accuracy: 0.2870 - val_loss: 3.3199 - val_accuracy: 0.0545\n",
      "Epoch 774/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3536 - accuracy: 0.2569 - val_loss: 3.2717 - val_accuracy: 0.0364\n",
      "Epoch 775/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3527 - accuracy: 0.2801 - val_loss: 3.2285 - val_accuracy: 0.0364\n",
      "Epoch 776/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3644 - accuracy: 0.2616 - val_loss: 3.2168 - val_accuracy: 0.0364\n",
      "Epoch 777/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3874 - accuracy: 0.2315 - val_loss: 3.2067 - val_accuracy: 0.0364\n",
      "Epoch 778/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3901 - accuracy: 0.2361 - val_loss: 3.1445 - val_accuracy: 0.0545\n",
      "Epoch 779/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3831 - accuracy: 0.2477 - val_loss: 3.2008 - val_accuracy: 0.0545\n",
      "Epoch 780/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.4243 - accuracy: 0.2292 - val_loss: 3.2287 - val_accuracy: 0.0909\n",
      "Epoch 781/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3601 - accuracy: 0.2569 - val_loss: 3.1919 - val_accuracy: 0.0364\n",
      "Epoch 782/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3432 - accuracy: 0.2523 - val_loss: 3.1734 - val_accuracy: 0.0545\n",
      "Epoch 783/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3611 - accuracy: 0.2569 - val_loss: 3.1457 - val_accuracy: 0.0727\n",
      "Epoch 784/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4189 - accuracy: 0.2431 - val_loss: 3.1444 - val_accuracy: 0.0727\n",
      "Epoch 785/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.4210 - accuracy: 0.2407 - val_loss: 3.1924 - val_accuracy: 0.0545\n",
      "Epoch 786/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3419 - accuracy: 0.2593 - val_loss: 3.1192 - val_accuracy: 0.0909\n",
      "Epoch 787/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3676 - accuracy: 0.2500 - val_loss: 3.1001 - val_accuracy: 0.0545\n",
      "Epoch 788/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3827 - accuracy: 0.2431 - val_loss: 3.1450 - val_accuracy: 0.0545\n",
      "Epoch 789/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4039 - accuracy: 0.2361 - val_loss: 3.1617 - val_accuracy: 0.0545\n",
      "Epoch 790/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3345 - accuracy: 0.2593 - val_loss: 3.1565 - val_accuracy: 0.0545\n",
      "Epoch 791/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3477 - accuracy: 0.2639 - val_loss: 3.1346 - val_accuracy: 0.0545\n",
      "Epoch 792/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3418 - accuracy: 0.2500 - val_loss: 3.1661 - val_accuracy: 0.0364\n",
      "Epoch 793/1000\n",
      "13/13 [==============================] - 0s 19ms/step - loss: 2.3520 - accuracy: 0.2662 - val_loss: 3.2071 - val_accuracy: 0.0364\n",
      "Epoch 794/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3767 - accuracy: 0.2500 - val_loss: 3.2314 - val_accuracy: 0.0364\n",
      "Epoch 795/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3805 - accuracy: 0.2454 - val_loss: 3.2572 - val_accuracy: 0.0545\n",
      "Epoch 796/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3470 - accuracy: 0.2593 - val_loss: 3.3023 - val_accuracy: 0.0364\n",
      "Epoch 797/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3306 - accuracy: 0.2500 - val_loss: 3.3214 - val_accuracy: 0.0545\n",
      "Epoch 798/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3521 - accuracy: 0.2361 - val_loss: 3.3954 - val_accuracy: 0.0364\n",
      "Epoch 799/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3935 - accuracy: 0.2523 - val_loss: 3.3990 - val_accuracy: 0.0545\n",
      "Epoch 800/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3404 - accuracy: 0.2523 - val_loss: 3.3480 - val_accuracy: 0.0364\n",
      "Epoch 801/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3005 - accuracy: 0.2639 - val_loss: 3.3129 - val_accuracy: 0.0364\n",
      "Epoch 802/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3463 - accuracy: 0.2639 - val_loss: 3.2997 - val_accuracy: 0.0364\n",
      "Epoch 803/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3854 - accuracy: 0.2454 - val_loss: 3.2574 - val_accuracy: 0.0727\n",
      "Epoch 804/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.4141 - accuracy: 0.2500 - val_loss: 3.1756 - val_accuracy: 0.0364\n",
      "Epoch 805/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3616 - accuracy: 0.2338 - val_loss: 3.1189 - val_accuracy: 0.0727\n",
      "Epoch 806/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.3646 - accuracy: 0.2523 - val_loss: 3.0738 - val_accuracy: 0.0727\n",
      "Epoch 807/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3506 - accuracy: 0.2454 - val_loss: 3.1345 - val_accuracy: 0.0545\n",
      "Epoch 808/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3419 - accuracy: 0.2708 - val_loss: 3.2138 - val_accuracy: 0.0545\n",
      "Epoch 809/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3746 - accuracy: 0.2593 - val_loss: 3.2296 - val_accuracy: 0.0364\n",
      "Epoch 810/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4708 - accuracy: 0.2176 - val_loss: 3.2810 - val_accuracy: 0.0727\n",
      "Epoch 811/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3812 - accuracy: 0.2546 - val_loss: 3.2584 - val_accuracy: 0.0545\n",
      "Epoch 812/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3455 - accuracy: 0.2616 - val_loss: 3.2174 - val_accuracy: 0.0545\n",
      "Epoch 813/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3141 - accuracy: 0.2616 - val_loss: 3.2652 - val_accuracy: 0.1091\n",
      "Epoch 814/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.4121 - accuracy: 0.2176 - val_loss: 3.2479 - val_accuracy: 0.0364\n",
      "Epoch 815/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3487 - accuracy: 0.2384 - val_loss: 3.2637 - val_accuracy: 0.0364\n",
      "Epoch 816/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3482 - accuracy: 0.2523 - val_loss: 3.2436 - val_accuracy: 0.0364\n",
      "Epoch 817/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3593 - accuracy: 0.2454 - val_loss: 3.2949 - val_accuracy: 0.0909\n",
      "Epoch 818/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3676 - accuracy: 0.2245 - val_loss: 3.3103 - val_accuracy: 0.0727\n",
      "Epoch 819/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3748 - accuracy: 0.2477 - val_loss: 3.3138 - val_accuracy: 0.0727\n",
      "Epoch 820/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3504 - accuracy: 0.2338 - val_loss: 3.2096 - val_accuracy: 0.0364\n",
      "Epoch 821/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3168 - accuracy: 0.2593 - val_loss: 3.1902 - val_accuracy: 0.0545\n",
      "Epoch 822/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3531 - accuracy: 0.2685 - val_loss: 3.1780 - val_accuracy: 0.0909\n",
      "Epoch 823/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.3736 - accuracy: 0.2384 - val_loss: 3.1673 - val_accuracy: 0.0545\n",
      "Epoch 824/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3205 - accuracy: 0.2731 - val_loss: 3.1360 - val_accuracy: 0.0364\n",
      "Epoch 825/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3333 - accuracy: 0.2639 - val_loss: 3.1238 - val_accuracy: 0.0727\n",
      "Epoch 826/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.4005 - accuracy: 0.2338 - val_loss: 3.1119 - val_accuracy: 0.0727\n",
      "Epoch 827/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3707 - accuracy: 0.2384 - val_loss: 3.2099 - val_accuracy: 0.0727\n",
      "Epoch 828/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3242 - accuracy: 0.2454 - val_loss: 3.2497 - val_accuracy: 0.0545\n",
      "Epoch 829/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3724 - accuracy: 0.2500 - val_loss: 3.2678 - val_accuracy: 0.0727\n",
      "Epoch 830/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3516 - accuracy: 0.2407 - val_loss: 3.2689 - val_accuracy: 0.0545\n",
      "Epoch 831/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3686 - accuracy: 0.2384 - val_loss: 3.2547 - val_accuracy: 0.0545\n",
      "Epoch 832/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3290 - accuracy: 0.2755 - val_loss: 3.2877 - val_accuracy: 0.0727\n",
      "Epoch 833/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3819 - accuracy: 0.2454 - val_loss: 3.3874 - val_accuracy: 0.0545\n",
      "Epoch 834/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3767 - accuracy: 0.2407 - val_loss: 3.3953 - val_accuracy: 0.0545\n",
      "Epoch 835/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3283 - accuracy: 0.2662 - val_loss: 3.4504 - val_accuracy: 0.0364\n",
      "Epoch 836/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3265 - accuracy: 0.2685 - val_loss: 3.4208 - val_accuracy: 0.0364\n",
      "Epoch 837/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3495 - accuracy: 0.2292 - val_loss: 3.4445 - val_accuracy: 0.0727\n",
      "Epoch 838/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3412 - accuracy: 0.2454 - val_loss: 3.3762 - val_accuracy: 0.0909\n",
      "Epoch 839/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2894 - accuracy: 0.2569 - val_loss: 3.3441 - val_accuracy: 0.0545\n",
      "Epoch 840/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3471 - accuracy: 0.2500 - val_loss: 3.2945 - val_accuracy: 0.0364\n",
      "Epoch 841/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3539 - accuracy: 0.2685 - val_loss: 3.3533 - val_accuracy: 0.0545\n",
      "Epoch 842/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3074 - accuracy: 0.2685 - val_loss: 3.3821 - val_accuracy: 0.0545\n",
      "Epoch 843/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3769 - accuracy: 0.2546 - val_loss: 3.4123 - val_accuracy: 0.0545\n",
      "Epoch 844/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3214 - accuracy: 0.2500 - val_loss: 3.4215 - val_accuracy: 0.0545\n",
      "Epoch 845/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3455 - accuracy: 0.2384 - val_loss: 3.4605 - val_accuracy: 0.0545\n",
      "Epoch 846/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3282 - accuracy: 0.2477 - val_loss: 3.4715 - val_accuracy: 0.0727\n",
      "Epoch 847/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.2960 - accuracy: 0.2801 - val_loss: 3.4784 - val_accuracy: 0.0545\n",
      "Epoch 848/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2953 - accuracy: 0.2685 - val_loss: 3.4325 - val_accuracy: 0.0727\n",
      "Epoch 849/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3074 - accuracy: 0.2662 - val_loss: 3.3917 - val_accuracy: 0.0545\n",
      "Epoch 850/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3345 - accuracy: 0.2616 - val_loss: 3.3052 - val_accuracy: 0.1091\n",
      "Epoch 851/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4049 - accuracy: 0.2431 - val_loss: 3.2897 - val_accuracy: 0.0727\n",
      "Epoch 852/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3272 - accuracy: 0.2407 - val_loss: 3.3130 - val_accuracy: 0.0727\n",
      "Epoch 853/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2888 - accuracy: 0.2685 - val_loss: 3.3182 - val_accuracy: 0.0545\n",
      "Epoch 854/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3894 - accuracy: 0.2431 - val_loss: 3.2648 - val_accuracy: 0.0545\n",
      "Epoch 855/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.2789 - accuracy: 0.2500 - val_loss: 3.2407 - val_accuracy: 0.0727\n",
      "Epoch 856/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.2421 - accuracy: 0.2894 - val_loss: 3.2723 - val_accuracy: 0.0727\n",
      "Epoch 857/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3392 - accuracy: 0.2500 - val_loss: 3.3173 - val_accuracy: 0.1091\n",
      "Epoch 858/1000\n",
      "13/13 [==============================] - 0s 28ms/step - loss: 2.3230 - accuracy: 0.2546 - val_loss: 3.2785 - val_accuracy: 0.0545\n",
      "Epoch 859/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3215 - accuracy: 0.2431 - val_loss: 3.2388 - val_accuracy: 0.0727\n",
      "Epoch 860/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3417 - accuracy: 0.2662 - val_loss: 3.2415 - val_accuracy: 0.0909\n",
      "Epoch 861/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3368 - accuracy: 0.2292 - val_loss: 3.2089 - val_accuracy: 0.0727\n",
      "Epoch 862/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3057 - accuracy: 0.2778 - val_loss: 3.2446 - val_accuracy: 0.1091\n",
      "Epoch 863/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3696 - accuracy: 0.2639 - val_loss: 3.2655 - val_accuracy: 0.0545\n",
      "Epoch 864/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3096 - accuracy: 0.2361 - val_loss: 3.2368 - val_accuracy: 0.0545\n",
      "Epoch 865/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3393 - accuracy: 0.2431 - val_loss: 3.3274 - val_accuracy: 0.0727\n",
      "Epoch 866/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3222 - accuracy: 0.2569 - val_loss: 3.2978 - val_accuracy: 0.0364\n",
      "Epoch 867/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.2904 - accuracy: 0.2847 - val_loss: 3.3412 - val_accuracy: 0.1091\n",
      "Epoch 868/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3248 - accuracy: 0.2431 - val_loss: 3.3302 - val_accuracy: 0.0727\n",
      "Epoch 869/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3474 - accuracy: 0.2407 - val_loss: 3.3400 - val_accuracy: 0.1091\n",
      "Epoch 870/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3386 - accuracy: 0.2569 - val_loss: 3.3761 - val_accuracy: 0.0545\n",
      "Epoch 871/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3523 - accuracy: 0.2407 - val_loss: 3.3827 - val_accuracy: 0.0545\n",
      "Epoch 872/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3436 - accuracy: 0.2338 - val_loss: 3.3841 - val_accuracy: 0.0545\n",
      "Epoch 873/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3430 - accuracy: 0.2616 - val_loss: 3.4150 - val_accuracy: 0.0727\n",
      "Epoch 874/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.2969 - accuracy: 0.2708 - val_loss: 3.4075 - val_accuracy: 0.0727\n",
      "Epoch 875/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3058 - accuracy: 0.2731 - val_loss: 3.3511 - val_accuracy: 0.0545\n",
      "Epoch 876/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3273 - accuracy: 0.2431 - val_loss: 3.3044 - val_accuracy: 0.0727\n",
      "Epoch 877/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3499 - accuracy: 0.2338 - val_loss: 3.3560 - val_accuracy: 0.0727\n",
      "Epoch 878/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.3073 - accuracy: 0.2523 - val_loss: 3.3641 - val_accuracy: 0.0909\n",
      "Epoch 879/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3659 - accuracy: 0.2477 - val_loss: 3.2920 - val_accuracy: 0.0545\n",
      "Epoch 880/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3351 - accuracy: 0.2407 - val_loss: 3.3072 - val_accuracy: 0.0727\n",
      "Epoch 881/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.4202 - accuracy: 0.2199 - val_loss: 3.2907 - val_accuracy: 0.0545\n",
      "Epoch 882/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2999 - accuracy: 0.2847 - val_loss: 3.2281 - val_accuracy: 0.0364\n",
      "Epoch 883/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3315 - accuracy: 0.2662 - val_loss: 3.1818 - val_accuracy: 0.0545\n",
      "Epoch 884/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3533 - accuracy: 0.2454 - val_loss: 3.2561 - val_accuracy: 0.1091\n",
      "Epoch 885/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3936 - accuracy: 0.2222 - val_loss: 3.2563 - val_accuracy: 0.0909\n",
      "Epoch 886/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3200 - accuracy: 0.2616 - val_loss: 3.1835 - val_accuracy: 0.0909\n",
      "Epoch 887/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.3425 - accuracy: 0.2523 - val_loss: 3.1917 - val_accuracy: 0.0364\n",
      "Epoch 888/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.3548 - accuracy: 0.2292 - val_loss: 3.2050 - val_accuracy: 0.0727\n",
      "Epoch 889/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3255 - accuracy: 0.2431 - val_loss: 3.1825 - val_accuracy: 0.1091\n",
      "Epoch 890/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3014 - accuracy: 0.2616 - val_loss: 3.1399 - val_accuracy: 0.0545\n",
      "Epoch 891/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3726 - accuracy: 0.2662 - val_loss: 3.1875 - val_accuracy: 0.0727\n",
      "Epoch 892/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.3282 - accuracy: 0.2569 - val_loss: 3.0832 - val_accuracy: 0.0909\n",
      "Epoch 893/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3693 - accuracy: 0.2361 - val_loss: 3.1226 - val_accuracy: 0.0909\n",
      "Epoch 894/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.3532 - accuracy: 0.2245 - val_loss: 3.1771 - val_accuracy: 0.1273\n",
      "Epoch 895/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2923 - accuracy: 0.2523 - val_loss: 3.1780 - val_accuracy: 0.0727\n",
      "Epoch 896/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3353 - accuracy: 0.2477 - val_loss: 3.2094 - val_accuracy: 0.0727\n",
      "Epoch 897/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3610 - accuracy: 0.2407 - val_loss: 3.1945 - val_accuracy: 0.0727\n",
      "Epoch 898/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3401 - accuracy: 0.2384 - val_loss: 3.1800 - val_accuracy: 0.1091\n",
      "Epoch 899/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3666 - accuracy: 0.2454 - val_loss: 3.2133 - val_accuracy: 0.0727\n",
      "Epoch 900/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3334 - accuracy: 0.2546 - val_loss: 3.1563 - val_accuracy: 0.1091\n",
      "Epoch 901/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.3376 - accuracy: 0.2431 - val_loss: 3.1410 - val_accuracy: 0.1455\n",
      "Epoch 902/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3576 - accuracy: 0.2454 - val_loss: 3.1818 - val_accuracy: 0.1091\n",
      "Epoch 903/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3025 - accuracy: 0.2685 - val_loss: 3.2064 - val_accuracy: 0.0909\n",
      "Epoch 904/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3172 - accuracy: 0.2500 - val_loss: 3.3082 - val_accuracy: 0.0909\n",
      "Epoch 905/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3204 - accuracy: 0.2546 - val_loss: 3.3267 - val_accuracy: 0.1091\n",
      "Epoch 906/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2905 - accuracy: 0.2523 - val_loss: 3.2832 - val_accuracy: 0.0909\n",
      "Epoch 907/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2913 - accuracy: 0.2454 - val_loss: 3.3179 - val_accuracy: 0.0909\n",
      "Epoch 908/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3269 - accuracy: 0.2315 - val_loss: 3.2682 - val_accuracy: 0.0545\n",
      "Epoch 909/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3827 - accuracy: 0.2454 - val_loss: 3.2437 - val_accuracy: 0.0909\n",
      "Epoch 910/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.2872 - accuracy: 0.3056 - val_loss: 3.2042 - val_accuracy: 0.0909\n",
      "Epoch 911/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3587 - accuracy: 0.2500 - val_loss: 3.1791 - val_accuracy: 0.1273\n",
      "Epoch 912/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2982 - accuracy: 0.2593 - val_loss: 3.1354 - val_accuracy: 0.0909\n",
      "Epoch 913/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3234 - accuracy: 0.2569 - val_loss: 3.1929 - val_accuracy: 0.0909\n",
      "Epoch 914/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3455 - accuracy: 0.2593 - val_loss: 3.1488 - val_accuracy: 0.0909\n",
      "Epoch 915/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2613 - accuracy: 0.2894 - val_loss: 3.0862 - val_accuracy: 0.0909\n",
      "Epoch 916/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3556 - accuracy: 0.2222 - val_loss: 3.1339 - val_accuracy: 0.1273\n",
      "Epoch 917/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3218 - accuracy: 0.2500 - val_loss: 3.1010 - val_accuracy: 0.1273\n",
      "Epoch 918/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3264 - accuracy: 0.2407 - val_loss: 3.1350 - val_accuracy: 0.1455\n",
      "Epoch 919/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3604 - accuracy: 0.2477 - val_loss: 3.2079 - val_accuracy: 0.1091\n",
      "Epoch 920/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2798 - accuracy: 0.2755 - val_loss: 3.2270 - val_accuracy: 0.0909\n",
      "Epoch 921/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3227 - accuracy: 0.2523 - val_loss: 3.1853 - val_accuracy: 0.0909\n",
      "Epoch 922/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3207 - accuracy: 0.2407 - val_loss: 3.1752 - val_accuracy: 0.0909\n",
      "Epoch 923/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3013 - accuracy: 0.2500 - val_loss: 3.2969 - val_accuracy: 0.0545\n",
      "Epoch 924/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3010 - accuracy: 0.2477 - val_loss: 3.2660 - val_accuracy: 0.0545\n",
      "Epoch 925/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.2662 - accuracy: 0.2500 - val_loss: 3.2807 - val_accuracy: 0.0727\n",
      "Epoch 926/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3494 - accuracy: 0.2384 - val_loss: 3.2066 - val_accuracy: 0.1091\n",
      "Epoch 927/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2405 - accuracy: 0.2593 - val_loss: 3.2197 - val_accuracy: 0.1091\n",
      "Epoch 928/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.4077 - accuracy: 0.2176 - val_loss: 3.2543 - val_accuracy: 0.0909\n",
      "Epoch 929/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3404 - accuracy: 0.2315 - val_loss: 3.2119 - val_accuracy: 0.0909\n",
      "Epoch 930/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2916 - accuracy: 0.2338 - val_loss: 3.2615 - val_accuracy: 0.1273\n",
      "Epoch 931/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3001 - accuracy: 0.2477 - val_loss: 3.2895 - val_accuracy: 0.0727\n",
      "Epoch 932/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3654 - accuracy: 0.2361 - val_loss: 3.3434 - val_accuracy: 0.0545\n",
      "Epoch 933/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2786 - accuracy: 0.2616 - val_loss: 3.3533 - val_accuracy: 0.0909\n",
      "Epoch 934/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.2953 - accuracy: 0.2523 - val_loss: 3.3590 - val_accuracy: 0.0909\n",
      "Epoch 935/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3220 - accuracy: 0.2708 - val_loss: 3.3158 - val_accuracy: 0.0727\n",
      "Epoch 936/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3438 - accuracy: 0.2315 - val_loss: 3.2491 - val_accuracy: 0.0545\n",
      "Epoch 937/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.2925 - accuracy: 0.2361 - val_loss: 3.3195 - val_accuracy: 0.1091\n",
      "Epoch 938/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3322 - accuracy: 0.2454 - val_loss: 3.4066 - val_accuracy: 0.0727\n",
      "Epoch 939/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.2855 - accuracy: 0.2523 - val_loss: 3.3468 - val_accuracy: 0.1091\n",
      "Epoch 940/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.3253 - accuracy: 0.2384 - val_loss: 3.3385 - val_accuracy: 0.0909\n",
      "Epoch 941/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3012 - accuracy: 0.2662 - val_loss: 3.2313 - val_accuracy: 0.1091\n",
      "Epoch 942/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3133 - accuracy: 0.2616 - val_loss: 3.1455 - val_accuracy: 0.0727\n",
      "Epoch 943/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.2495 - accuracy: 0.2731 - val_loss: 3.1684 - val_accuracy: 0.0727\n",
      "Epoch 944/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.2973 - accuracy: 0.2731 - val_loss: 3.2968 - val_accuracy: 0.0909\n",
      "Epoch 945/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3291 - accuracy: 0.2639 - val_loss: 3.2747 - val_accuracy: 0.0909\n",
      "Epoch 946/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3149 - accuracy: 0.2338 - val_loss: 3.2771 - val_accuracy: 0.1091\n",
      "Epoch 947/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2383 - accuracy: 0.3009 - val_loss: 3.3070 - val_accuracy: 0.0909\n",
      "Epoch 948/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.2569 - accuracy: 0.2616 - val_loss: 3.3130 - val_accuracy: 0.0909\n",
      "Epoch 949/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2724 - accuracy: 0.2870 - val_loss: 3.2626 - val_accuracy: 0.0909\n",
      "Epoch 950/1000\n",
      "13/13 [==============================] - 0s 33ms/step - loss: 2.3214 - accuracy: 0.2454 - val_loss: 3.2343 - val_accuracy: 0.0909\n",
      "Epoch 951/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2525 - accuracy: 0.2708 - val_loss: 3.2474 - val_accuracy: 0.0909\n",
      "Epoch 952/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2708 - accuracy: 0.2731 - val_loss: 3.1976 - val_accuracy: 0.0909\n",
      "Epoch 953/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2980 - accuracy: 0.2593 - val_loss: 3.1231 - val_accuracy: 0.0909\n",
      "Epoch 954/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3070 - accuracy: 0.2616 - val_loss: 3.0950 - val_accuracy: 0.1091\n",
      "Epoch 955/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2313 - accuracy: 0.2801 - val_loss: 3.1178 - val_accuracy: 0.1091\n",
      "Epoch 956/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2999 - accuracy: 0.2338 - val_loss: 3.1043 - val_accuracy: 0.0909\n",
      "Epoch 957/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2914 - accuracy: 0.2500 - val_loss: 3.1900 - val_accuracy: 0.0364\n",
      "Epoch 958/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3643 - accuracy: 0.2431 - val_loss: 3.1486 - val_accuracy: 0.0545\n",
      "Epoch 959/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3146 - accuracy: 0.2523 - val_loss: 3.1528 - val_accuracy: 0.0545\n",
      "Epoch 960/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3095 - accuracy: 0.2639 - val_loss: 3.2187 - val_accuracy: 0.0545\n",
      "Epoch 961/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3185 - accuracy: 0.2639 - val_loss: 3.2093 - val_accuracy: 0.0727\n",
      "Epoch 962/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.2611 - accuracy: 0.2778 - val_loss: 3.3356 - val_accuracy: 0.0545\n",
      "Epoch 963/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2924 - accuracy: 0.2361 - val_loss: 3.3146 - val_accuracy: 0.0545\n",
      "Epoch 964/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2339 - accuracy: 0.2940 - val_loss: 3.3403 - val_accuracy: 0.0545\n",
      "Epoch 965/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2739 - accuracy: 0.2523 - val_loss: 3.2775 - val_accuracy: 0.0909\n",
      "Epoch 966/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3119 - accuracy: 0.2593 - val_loss: 3.2725 - val_accuracy: 0.0909\n",
      "Epoch 967/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.2930 - accuracy: 0.2593 - val_loss: 3.1835 - val_accuracy: 0.1091\n",
      "Epoch 968/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3029 - accuracy: 0.2546 - val_loss: 3.1449 - val_accuracy: 0.0909\n",
      "Epoch 969/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2957 - accuracy: 0.2454 - val_loss: 3.1664 - val_accuracy: 0.0727\n",
      "Epoch 970/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2557 - accuracy: 0.3079 - val_loss: 3.2444 - val_accuracy: 0.0545\n",
      "Epoch 971/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2797 - accuracy: 0.2616 - val_loss: 3.2476 - val_accuracy: 0.1091\n",
      "Epoch 972/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2926 - accuracy: 0.2639 - val_loss: 3.2518 - val_accuracy: 0.0909\n",
      "Epoch 973/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3220 - accuracy: 0.2662 - val_loss: 3.2904 - val_accuracy: 0.0909\n",
      "Epoch 974/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.2499 - accuracy: 0.2407 - val_loss: 3.3357 - val_accuracy: 0.1091\n",
      "Epoch 975/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2490 - accuracy: 0.2477 - val_loss: 3.3081 - val_accuracy: 0.0909\n",
      "Epoch 976/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.2935 - accuracy: 0.2477 - val_loss: 3.3643 - val_accuracy: 0.0909\n",
      "Epoch 977/1000\n",
      "13/13 [==============================] - 0s 36ms/step - loss: 2.3202 - accuracy: 0.2500 - val_loss: 3.3184 - val_accuracy: 0.0727\n",
      "Epoch 978/1000\n",
      "13/13 [==============================] - 0s 32ms/step - loss: 2.2693 - accuracy: 0.2616 - val_loss: 3.1396 - val_accuracy: 0.0727\n",
      "Epoch 979/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.2661 - accuracy: 0.2523 - val_loss: 3.1497 - val_accuracy: 0.1455\n",
      "Epoch 980/1000\n",
      "13/13 [==============================] - 0s 25ms/step - loss: 2.3511 - accuracy: 0.2338 - val_loss: 3.1450 - val_accuracy: 0.1091\n",
      "Epoch 981/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.3123 - accuracy: 0.2616 - val_loss: 3.2215 - val_accuracy: 0.0909\n",
      "Epoch 982/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2880 - accuracy: 0.2407 - val_loss: 3.0860 - val_accuracy: 0.0909\n",
      "Epoch 983/1000\n",
      "13/13 [==============================] - 1s 41ms/step - loss: 2.3215 - accuracy: 0.2176 - val_loss: 3.1099 - val_accuracy: 0.1273\n",
      "Epoch 984/1000\n",
      "13/13 [==============================] - 0s 38ms/step - loss: 2.3051 - accuracy: 0.2361 - val_loss: 3.1836 - val_accuracy: 0.1273\n",
      "Epoch 985/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.3331 - accuracy: 0.2639 - val_loss: 3.1811 - val_accuracy: 0.1091\n",
      "Epoch 986/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2927 - accuracy: 0.2593 - val_loss: 3.2009 - val_accuracy: 0.1091\n",
      "Epoch 987/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.2963 - accuracy: 0.2685 - val_loss: 3.3105 - val_accuracy: 0.0909\n",
      "Epoch 988/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2485 - accuracy: 0.2801 - val_loss: 3.2478 - val_accuracy: 0.0545\n",
      "Epoch 989/1000\n",
      "13/13 [==============================] - 0s 38ms/step - loss: 2.2886 - accuracy: 0.2523 - val_loss: 3.3300 - val_accuracy: 0.0545\n",
      "Epoch 990/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3214 - accuracy: 0.2662 - val_loss: 3.3278 - val_accuracy: 0.0909\n",
      "Epoch 991/1000\n",
      "13/13 [==============================] - 0s 38ms/step - loss: 2.3495 - accuracy: 0.2407 - val_loss: 3.3189 - val_accuracy: 0.0909\n",
      "Epoch 992/1000\n",
      "13/13 [==============================] - 0s 37ms/step - loss: 2.3239 - accuracy: 0.2477 - val_loss: 3.2563 - val_accuracy: 0.0909\n",
      "Epoch 993/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2998 - accuracy: 0.2917 - val_loss: 3.1660 - val_accuracy: 0.0909\n",
      "Epoch 994/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3253 - accuracy: 0.2222 - val_loss: 3.1467 - val_accuracy: 0.0909\n",
      "Epoch 995/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2597 - accuracy: 0.2685 - val_loss: 3.0189 - val_accuracy: 0.0909\n",
      "Epoch 996/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.2933 - accuracy: 0.2639 - val_loss: 2.9859 - val_accuracy: 0.0909\n",
      "Epoch 997/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3106 - accuracy: 0.2662 - val_loss: 3.1096 - val_accuracy: 0.1273\n",
      "Epoch 998/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.2568 - accuracy: 0.2269 - val_loss: 3.0945 - val_accuracy: 0.1091\n",
      "Epoch 999/1000\n",
      "13/13 [==============================] - 0s 34ms/step - loss: 2.2673 - accuracy: 0.2685 - val_loss: 3.2044 - val_accuracy: 0.0727\n",
      "Epoch 1000/1000\n",
      "13/13 [==============================] - 0s 35ms/step - loss: 2.3127 - accuracy: 0.2685 - val_loss: 3.1997 - val_accuracy: 0.1091\n",
      "2/2 [==============================] - 0s 7ms/step\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 2.8539 - accuracy: 0.1455\n"
     ]
    }
   ],
   "source": [
    "history = convNet.fit(x_train, y_train, epochs = 1000, batch_size = 35, validation_data = (x_val, y_val))\n",
    "\n",
    "y_score = convNet.predict(X_test)\n",
    "y_test_score = convNet.evaluate(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "23615cb8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "426a6662",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting ROC for labels ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC Curve for each class\n",
    "df = pd.DataFrame()\n",
    "fpr  = {}\n",
    "tpr  = {}\n",
    "auc1 = {}\n",
    "labels=['%i'%nr for nr in range (0,36)]\n",
    "print('Plotting ROC for labels {}'.format(labels))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "for i, label in enumerate(labels):\n",
    "    df[label] = Y_test[:,int(label)]\n",
    "    df[label + '_pred'] = y_score[:, int(label)]\n",
    "    fpr[label], tpr[label], threshold = metrics.roc_curve(df[label],df[label+'_pred'])\n",
    "    auc1[label] = metrics.auc(fpr[label], tpr[label])\n",
    "    \n",
    "    plt.plot(fpr[label],tpr[label], label=r'{}, AUC = {:.1f}%'.format(particle_types[int(label)],auc1[label]*100))\n",
    "\n",
    "plt.plot([0,1], [0,1], \"k--\", label=\"Chance, AUC = 50%\")\n",
    "    \n",
    "#plt.semilogx()\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.xlim(-0.1,1.5)\n",
    "plt.ylim(0,1.1)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b75f4ab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "# Macro Average One vs Rest\n",
    "macro_fpr = {}\n",
    "macro_tpr = {}\n",
    "macro_auc = {}\n",
    "n_classes = 36\n",
    "for i in range(n_classes):\n",
    "    macro_fpr[i], macro_tpr[i], _ = metrics.roc_curve(Y_test[:, i], y_score[:, i])\n",
    "    macro_auc[i] = metrics.auc(macro_fpr[i], macro_tpr[i])\n",
    "\n",
    "fpr_grid = np.linspace(0.0, 1.0, 1000)\n",
    "\n",
    "# Interpolate all ROC curves at these points\n",
    "mean_tpr = np.zeros_like(fpr_grid)\n",
    "\n",
    "for i in range(n_classes):\n",
    "    mean_tpr += np.interp(fpr_grid, macro_fpr[i], macro_tpr[i])  # linear interpolation\n",
    "\n",
    "# Average it and compute AUC\n",
    "mean_tpr /= n_classes\n",
    "\n",
    "macro_auc = metrics.auc(fpr_grid, mean_tpr)\n",
    "\n",
    "plt.plot(\n",
    "    fpr_grid,\n",
    "    mean_tpr,\n",
    "    label=f\"Macro-average ROC curve, AUC = {macro_auc:.2f}\",\n",
    ")\n",
    "\n",
    "# Micro Average One vs Rest\n",
    "test_ravel = Y_test.ravel()\n",
    "pred_ravel = y_score.ravel()\n",
    "micro_fpr, micro_tpr, threshold = metrics.roc_curve(test_ravel, pred_ravel)\n",
    "auc = metrics.auc(micro_fpr, micro_tpr)\n",
    "    \n",
    "plt.plot(micro_fpr,micro_tpr, label=f\"Micro-average ROC curve, AUC = {auc:.2f}\")\n",
    "\n",
    "plt.plot([0,1], [0,1], \"k--\", label=\"Chance, AUC = 50%\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.xlim(-0.1,1.1)\n",
    "plt.ylim(0,1.1)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea067079",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}