Domestic Audio Classification (Keras).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H:\\SINS dataset\n"
     ]
    }
   ],
   "source": [
    "%cd H:/SINS dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "seed = 1356\n",
    "\n",
    "import numpy as np\n",
    "np.random.seed(seed)\n",
    "from tensorflow import set_random_seed\n",
    "set_random_seed(seed)\n",
    "\n",
    "import sklearn\n",
    "import cv2\n",
    "import random\n",
    "import math\n",
    "import os\n",
    "import datetime\n",
    "\n",
    "from itertools import chain\n",
    "from collections import Counter\n",
    "from sklearn.metrics import f1_score, confusion_matrix\n",
    "\n",
    "# constants\n",
    "img_folder = 'audio'\n",
    "img_name = ['_pressure.png', '_spec1.png', '_spec2.png', '_spec3.png', '.wav']\n",
    "\n",
    "im_size = 64\n",
    "im_size_flat = im_size * im_size\n",
    "n_labels = 9\n",
    "n_channels = 1 # grayscale\n",
    "sd = np.sqrt(2) / np.sqrt(im_size_flat)\n",
    "\n",
    "req_improve = 500\n",
    "update_cnt = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Conv1D, MaxPooling1D, LSTM\n",
    "from keras.layers import Activation, Dropout, Flatten, Dense\n",
    "from keras.utils import np_utils\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras import optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import librosa\n",
    "\n",
    "afs, labels, _ = zip(*[line.rstrip('\\n').split('\\t') for line in open('meta.txt').readlines()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_folds(index, n_folds = 4):\n",
    "    afs, labels, _ = zip(*[line.rstrip('\\n').split('\\t') for line in open('meta.txt').readlines()])\n",
    "    afs_imlinks = [img_folder + af[5:-4] + img_name[index] for af in afs]\n",
    "        \n",
    "    # get smallest no. of classes\n",
    "    mincnt = Counter(labels).most_common()[-1][1]\n",
    "    \n",
    "    afs_label_sep = {}\n",
    "    for (af, label) in zip(afs_imlinks, labels):\n",
    "        if label in afs_label_sep:\n",
    "            afs_label_sep[label].append(af)\n",
    "        else:\n",
    "            afs_label_sep[label] = [af]\n",
    "\n",
    "    afs_label_sep_sampled = {label:random.sample(afs_label_sep[label], mincnt) for label in sorted(afs_label_sep.keys())}\n",
    "    \n",
    "    # partition the graph into n_folds partitions for cross-validation\n",
    "    folds = {fold+1:[] for fold in range(n_folds)}\n",
    "    samples = int(math.ceil(mincnt / n_folds))\n",
    "\n",
    "    for label in sorted(afs_label_sep_sampled.keys()):\n",
    "        random.shuffle(afs_label_sep_sampled[label])\n",
    "        for fold in range(n_folds):\n",
    "            folds[fold+1] += [(af, label) for af in afs_label_sep_sampled[label][fold*samples:(fold+1)*samples]]\n",
    "\n",
    "    return list([folds[key] for key in sorted(folds.keys())])\n",
    "#     return list(folds.values())\n",
    "\n",
    "# 0-indexed get training/testing sets\n",
    "def get_sets(folds, fold = -1):\n",
    "    _, labels, _ = zip(*[line.rstrip('\\n').split('\\t') for line in open('meta.txt').readlines()])\n",
    "    sces = sorted(list(set(labels)))\n",
    "    sce_int_map = {sce:i+1 for i, sce in enumerate(sces)}\n",
    "    int_sce_map = {sce_int_map[i]:i for i in sce_int_map.keys()}\n",
    "        \n",
    "    ex_fold = folds[fold]\n",
    "    ot_fold = folds[0:fold] + folds[fold+1:]\n",
    "    chainfold = list(chain.from_iterable(ot_fold))\n",
    "    random.shuffle(ex_fold)\n",
    "    random.shuffle(chainfold)\n",
    "    trX, trY = zip(*[(af, label) for (af, label) in chainfold])\n",
    "    tX, tY = zip(*[(af, label) for (af, label) in ex_fold])\n",
    "        \n",
    "    trY = np.array([[1 if int_sce_map[c+1] == label else 0 for c in range(len(sces))] for label in trY])\n",
    "    tY = np.array([[1 if int_sce_map[c+1] == label else 0 for c in range(len(sces))] for label in tY])\n",
    "        \n",
    "    return (trX, trY, tX, tY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "mfccs = pickle.load(open(\"mfccs.pickle\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ims(links):\n",
    "    return np.array([(1 - cv2.imread(link, 0)/255).reshape((im_size, im_size, 1)) for link in links])\n",
    "def get_wav(links):\n",
    "    temp = []\n",
    "    for link in links:\n",
    "#         y, sr = librosa.load(link, sr = None, mono = True)\n",
    "#         temp.append(librosa.feature.mfcc(y=y,sr=sr).T)\n",
    "        temp.append(mfccs[link])\n",
    "    return np.array(temp)\n",
    "def get_wav2(links):\n",
    "    temp = []\n",
    "    for link in links:\n",
    "#         y, sr = librosa.load(link, sr = None, mono = True)\n",
    "#         temp.append(librosa.feature.mfcc(y=y,sr=sr).T)\n",
    "        temp.append(mfccs2[link])\n",
    "    return np.array(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_loader(files, batch_size, file_type = 0):\n",
    "    L = len(files)\n",
    "    while True:\n",
    "        batch_start = 0\n",
    "        batch_end = batch_size\n",
    "        \n",
    "        while batch_start < L:\n",
    "            lim = min(L, batch_end)\n",
    "            if file_type == 0:\n",
    "                X = get_ims([f[0] for f in files[batch_start:lim]])\n",
    "                Y = np.array([f[1] for f in files[batch_start:lim]])\n",
    "                yield (X, Y)\n",
    "            elif file_type == 1:\n",
    "                # wav files\n",
    "                X = get_wav([f[0] for f in files[batch_start:lim]])\n",
    "                Y = np.array([f[1] for f in files[batch_start:lim]])\n",
    "                yield (X, Y)\n",
    "            batch_start += batch_size\n",
    "            batch_end += batch_size\n",
    "            \n",
    "def data_loader2(files, batch_size, file_type = 0):\n",
    "    L = len(files)\n",
    "    while True:\n",
    "        batch_start = 0\n",
    "        batch_end = batch_size\n",
    "        \n",
    "        while batch_start < L:\n",
    "            lim = min(L, batch_end)\n",
    "            if file_type == 0:\n",
    "                X = get_ims(files[batch_start:lim])\n",
    "                yield X\n",
    "            elif file_type == 1:\n",
    "                # wav files\n",
    "                X = get_wav2(files[batch_start:lim])\n",
    "                yield X\n",
    "            batch_start += batch_size\n",
    "            batch_end += batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_model1D():\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Conv1D(filters = 128, kernel_size = 100, input_shape=(313, 20), activation = 'relu'))\n",
    "    model.add(Conv1D(filters = 128, kernel_size = 30, activation = 'relu'))\n",
    "    model.add(MaxPooling1D(pool_size=2))\n",
    "    \n",
    "    model.add(Conv1D(filters = 128, kernel_size = 15, activation = 'relu'))\n",
    "    model.add(MaxPooling1D(pool_size=2))\n",
    "    \n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(128, activation = 'relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(n_labels, activation = 'softmax'))\n",
    "\n",
    "    adam = optimizers.Adam(lr=1e-5, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\n",
    "    model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_model2D_S():\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Conv2D(filters = 32, kernel_size = (3, 3), input_shape=(im_size, im_size, 1), activation = 'relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    \n",
    "    model.add(Conv2D(filters = 64, kernel_size = (3, 3), activation = 'relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    \n",
    "    model.add(Conv2D(filters = 64, kernel_size = (3, 3), activation = 'relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "    model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors\n",
    "    model.add(Dense(units = 128, activation = 'relu'))\n",
    "    model.add(Dropout(rate = 0.5))\n",
    "    model.add(Dense(units = n_labels, activation = 'softmax'))\n",
    "\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_model2D():\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Conv2D(filters = 64, kernel_size = (3, 3), input_shape=(im_size, im_size, 1), activation = 'relu'))\n",
    "    model.add(Conv2D(filters = 64, kernel_size = (3, 3), activation = 'relu'))\n",
    "    model.add(Conv2D(filters = 64, kernel_size = (3, 3), activation = 'relu'))\n",
    "    model.add(Conv2D(filters = 64, kernel_size = (3, 3), activation = 'relu'))\n",
    "    \n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "    model.add(Conv2D(filters = 128, kernel_size = (3, 3), activation = 'relu', dilation_rate = (3, 3)))\n",
    "    model.add(Conv2D(filters = 128, kernel_size = (3, 3), activation = 'relu'))\n",
    "    model.add(Conv2D(filters = 128, kernel_size = (3, 3), activation = 'relu'))\n",
    "    \n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "    model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors\n",
    "    model.add(Dense(units = 128, activation = 'relu'))\n",
    "    model.add(Dropout(rate = 0.5))\n",
    "    model.add(Dense(units = n_labels, activation = 'softmax'))\n",
    "\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_modelLSTM():\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(LSTM(units = 64, return_sequences = True, stateful = False, input_shape = (313, 20)))\n",
    "    model.add(LSTM(units = 64, return_sequences = True, stateful = False))\n",
    "    model.add(LSTM(units = 64, stateful = False))\n",
    "    \n",
    "    model.add(Dropout(rate = 0.5))\n",
    "    model.add(Dense(units = n_labels, activation = 'softmax'))\n",
    "\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_modelCom():\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Conv1D(filters = 32, kernel_size = 32, input_shape=(313, 20), activation = 'relu'))\n",
    "    model.add(Conv1D(filters = 32, kernel_size = 32, activation = 'relu'))\n",
    "    model.add(MaxPooling1D(pool_size=2))\n",
    "\n",
    "    model.add(LSTM(units = 64, return_sequences = True, stateful = False))\n",
    "    model.add(LSTM(units = 64, return_sequences = True, stateful = False))\n",
    "    model.add(LSTM(units = 64, stateful = False))\n",
    "\n",
    "    model.add(Dropout(rate = 0.5))\n",
    "    model.add(Dense(units = n_labels, activation = 'softmax'))\n",
    "\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cnn(index, use_model, n_folds = 4, batch_size = 50, verbose = 1, sel_fold = 0, epochs = 100, save_model = False, val_split = 0.1, file_type = 0, name = None):\n",
    "    random.seed(seed)\n",
    "    model = use_model()\n",
    "    \n",
    "    trX, trY, tX, tY = get_sets(get_folds(index, n_folds), sel_fold)\n",
    "    vsplit = int(len(tX) * val_split)\n",
    "    val_X = tX[:vsplit]\n",
    "    val_Y = tY[:vsplit]\n",
    "    testX = tX[vsplit:]\n",
    "    testY = tY[vsplit:]\n",
    "                \n",
    "    batch_per_epoch = int(math.ceil(len(trX) / batch_size))\n",
    "    batch_per_val = int(math.ceil(len(val_X) / batch_size))\n",
    "    batch_per_test = int(math.ceil(len(testX) / batch_size))\n",
    "    \n",
    "    if save_model:\n",
    "        filepath = 'models/model_{index}.hdf5'.format(index = name)\n",
    "        checkpoint = ModelCheckpoint(filepath, monitor = 'val_acc', verbose = 0, save_best_only = True, mode = 'max')\n",
    "        model.fit_generator(data_loader(list(zip(trX, trY)), batch_size, file_type),\n",
    "                            steps_per_epoch = batch_per_epoch,\n",
    "                            epochs = epochs, verbose = verbose,\n",
    "                            callbacks = [checkpoint],\n",
    "                            validation_data = data_loader(list(zip(val_X, val_Y)), batch_size, file_type),\n",
    "                            validation_steps = batch_per_val)\n",
    "    else:\n",
    "        model.fit_generator(data_loader(list(zip(trX, trY)), batch_size, file_type),\n",
    "                            steps_per_epoch = batch_per_epoch,\n",
    "                            epochs = epochs,\n",
    "                            verbose = verbose,\n",
    "                            validation_data = data_loader(list(zip(val_X, val_Y)), batch_size, file_type),\n",
    "                            validation_steps = batch_per_val)\n",
    "    \n",
    "    preds = model.predict_generator(data_loader(list(zip(testX, testY)), batch_size, file_type),\n",
    "                                    steps = batch_per_test,\n",
    "                                    verbose = verbose)\n",
    "    \n",
    "    predsT = np.argmax(preds, axis = 1)\n",
    "    trueT = np.argmax(testY, axis = 1)\n",
    "    print(\"Accuracy: {}. F1: {}\".format(np.mean(np.equal(trueT, predsT)), f1_score(trueT, predsT, average = 'macro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "def test_model(index, model, n_folds = 4, batch_size = 50, verbose = 1, sel_fold = 0, val_split = 0.1, file_type = 0):\n",
    "    random.seed(seed)\n",
    "    \n",
    "    trX, trY, tX, tY = get_sets(get_folds(index, n_folds), sel_fold)\n",
    "    vsplit = int(len(tX) * val_split)\n",
    "    testX = tX[vsplit:]\n",
    "    testY = tY[vsplit:]\n",
    "                \n",
    "    batch_per_test = int(math.ceil(len(testX) / batch_size))\n",
    "        \n",
    "    preds = model.predict_generator(data_loader(list(zip(testX, testY)), batch_size, file_type),\n",
    "                                    steps = batch_per_test,\n",
    "                                    verbose = verbose)\n",
    "    \n",
    "    predsT = np.argmax(preds, axis = 1)\n",
    "    trueT = np.argmax(testY, axis = 1)\n",
    "    print(\"Accuracy: {}. F1: {}\".format(np.mean(np.equal(trueT, predsT)), f1_score(trueT, predsT, average = 'macro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_model_avg(indices, models, weights, file_types, n_folds = 4, batch_size = 50, verbose = 1, sel_fold = 0, val_split = 0.1):\n",
    "    \n",
    "    if not (len(indices) == len(models) == len(weights) == len(file_types)):\n",
    "        print(\"Weights, models, file_types need to be the same length.\")\n",
    "        return\n",
    "    if sum(weights) != 1:\n",
    "        print(\"Sum of weights should equal 1\")\n",
    "        \n",
    "    preds = None\n",
    "    \n",
    "    for index, model, weight, file_type in zip(indices, models, weights, file_types):\n",
    "        \n",
    "        if weight == 0:\n",
    "            continue\n",
    "    \n",
    "        random.seed(seed)\n",
    "\n",
    "        trX, trY, tX, tY = get_sets(get_folds(index, n_folds), sel_fold)\n",
    "        vsplit = int(len(tX) * val_split)\n",
    "        testX = tX[vsplit:]\n",
    "        testY = tY[vsplit:]\n",
    "\n",
    "        batch_per_test = int(math.ceil(len(testX) / batch_size))\n",
    "\n",
    "        if preds is None:\n",
    "            preds = weight * model.predict_generator(data_loader(list(zip(testX, testY)), batch_size, file_type),\n",
    "                                                     steps = batch_per_test,\n",
    "                                                     verbose = verbose)\n",
    "        else:\n",
    "            preds += weight * model.predict_generator(data_loader(list(zip(testX, testY)), batch_size, file_type),\n",
    "                                                      steps = batch_per_test,\n",
    "                                                      verbose = verbose)\n",
    "    \n",
    "    predsT = np.argmax(preds, axis = 1)\n",
    "    trueT = np.argmax(testY, axis = 1)\n",
    "    print(\"Accuracy: {}. F1: {}\".format(np.mean(np.equal(trueT, predsT)), f1_score(trueT, predsT, average = 'macro')))\n",
    "    print(confusion_matrix(trueT, predsT))\n",
    "    print(f1_score(trueT, predsT, average = None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(index, model, n_folds = 4, batch_size = 50, verbose = 1, sel_fold = 0, epochs = 100, save_model = False, val_split = 0.1, file_type = 0, name = None):\n",
    "    random.seed(seed)\n",
    "    \n",
    "    trX, trY, tX, tY = get_sets(get_folds(index, n_folds), sel_fold)\n",
    "    vsplit = int(len(tX) * val_split)\n",
    "    val_X = tX[:vsplit]\n",
    "    val_Y = tY[:vsplit]\n",
    "    testX = tX[vsplit:]\n",
    "    testY = tY[vsplit:]\n",
    "    \n",
    "    batch_per_epoch = int(math.ceil(len(trX) / batch_size))\n",
    "    batch_per_val = int(math.ceil(len(val_X) / batch_size))\n",
    "    batch_per_test = int(math.ceil(len(testX) / batch_size))\n",
    "    \n",
    "    if save_model:\n",
    "        filepath = 'models/model_{index}.hdf5'.format(index = name)\n",
    "        checkpoint = ModelCheckpoint(filepath, monitor = 'val_acc', verbose = 0, save_best_only = True, mode = 'max')\n",
    "        model.fit_generator(data_loader(list(zip(trX, trY)), batch_size, file_type),\n",
    "                            steps_per_epoch = batch_per_epoch,\n",
    "                            epochs = epochs, verbose = verbose,\n",
    "                            callbacks = [checkpoint],\n",
    "                            validation_data = data_loader(list(zip(val_X, val_Y)), batch_size, file_type),\n",
    "                            validation_steps = batch_per_val)\n",
    "    else:\n",
    "        model.fit_generator(data_loader(list(zip(trX, trY)), batch_size, file_type),\n",
    "                            steps_per_epoch = batch_per_epoch,\n",
    "                            epochs = epochs,\n",
    "                            verbose = verbose,\n",
    "                            validation_data = data_loader(list(zip(val_X, val_Y)), batch_size, file_type),\n",
    "                            validation_steps = batch_per_val)\n",
    "    \n",
    "    preds = model.predict_generator(data_loader(list(zip(testX, testY)), batch_size, file_type),\n",
    "                                    steps = batch_per_test,\n",
    "                                    verbose = verbose)\n",
    "    \n",
    "    predsT = np.argmax(preds, axis = 1)\n",
    "    trueT = np.argmax(testY, axis = 1)\n",
    "    print(\"Accuracy: {}. F1: {}\".format(np.mean(np.equal(trueT, predsT)), f1_score(trueT, predsT, average = 'macro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "mfccs2 = pickle.load(open(\"eval/eval/new/mfccs.pickle\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_model_eval():\n",
    "    weights = [0.3, 0.4, 0.3]\n",
    "    \n",
    "    ismap = {0:'absence', 1:'cooking', 2:'dishwashing', 3:'eating', 4:'other', 5:'social activity', 6:'vacuum cleaning', 7:'watching tv', 8:'working'}\n",
    "    \n",
    "    model1 = load_model('models/model_2D_small_3.hdf5')\n",
    "    model2 = load_model('models/model_1D_norm.hdf5')\n",
    "    model3 = load_model('models/model_LSTM_norm.hdf5')\n",
    "        \n",
    "    afs = [line.rstrip('\\n') for line in open('eval/eval/meta.txt').readlines()]\n",
    "    tlbatch = int(math.ceil(len(afs)/50))\n",
    "    \n",
    "    model1_eval_data = ['eval/eval/src/' + af[6:-4] + '_spec3.png' for af in afs]\n",
    "    model23_eval_data = afs\n",
    "    \n",
    "    preds = weights[0] * model1.predict_generator(data_loader2(model1_eval_data, 50, 0),\n",
    "                                                  steps = tlbatch,\n",
    "                                                  verbose = 1)\n",
    "    \n",
    "    preds += weights[1] * model2.predict_generator(data_loader2(model23_eval_data, 50, 1),\n",
    "                                                   steps = tlbatch,\n",
    "                                                   verbose = 1)\n",
    "    \n",
    "    preds += weights[2] * model3.predict_generator(data_loader2(model23_eval_data, 50, 1),\n",
    "                                                   steps = tlbatch,\n",
    "                                                   verbose = 1)\n",
    "    \n",
    "    predsT = np.argmax(preds, axis = 1)\n",
    "    return predsT\n",
    "#     res = {af:ismap[pred] for af,pred in zip(afs,preds)}\n",
    "#     return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1460/1460 [==============================] - ETA:  - 211s 145ms/step\n",
      "1460/1460 [==============================] - 290s 198ms/step\n",
      "1460/1460 [==============================] - 180s 123ms/step\n"
     ]
    }
   ],
   "source": [
    "predsT = test_model_eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "afs = [line.rstrip('\\n') for line in open('eval/eval/meta.txt').readlines()]\n",
    "ismap = {0:'absence', 1:'cooking', 2:'dishwashing', 3:'eating', 4:'other', 5:'social activity', 6:'vacuum cleaning', 7:'watching tv', 8:'working'}\n",
    "res = {af:ismap[pred] for af,pred in zip(afs,predsT)}\n",
    "with open('eval/eval/res2.txt', 'w') as f:\n",
    "    for af in sorted(res.keys()):\n",
    "        f.write(af + ' ' + res[af] + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "132/132 [==============================] - 143s 1s/step - loss: 1.6718 - acc: 0.3581 - val_loss: 1.4093 - val_acc: 0.4083\n",
      "Epoch 2/500\n",
      "132/132 [==============================] - 30s 229ms/step - loss: 1.4294 - acc: 0.4508 - val_loss: 1.2829 - val_acc: 0.4954\n",
      "Epoch 3/500\n",
      "132/132 [==============================] - 35s 264ms/step - loss: 1.3506 - acc: 0.4765 - val_loss: 1.2107 - val_acc: 0.5642\n",
      "Epoch 4/500\n",
      "132/132 [==============================] - 34s 261ms/step - loss: 1.2755 - acc: 0.5158 - val_loss: 1.2250 - val_acc: 0.5183\n",
      "Epoch 5/500\n",
      "132/132 [==============================] - 34s 257ms/step - loss: 1.2331 - acc: 0.5338 - val_loss: 1.1709 - val_acc: 0.5183\n",
      "Epoch 6/500\n",
      "132/132 [==============================] - 34s 257ms/step - loss: 1.2002 - acc: 0.5413 - val_loss: 1.1404 - val_acc: 0.5367\n",
      "Epoch 7/500\n",
      "132/132 [==============================] - 34s 254ms/step - loss: 1.1640 - acc: 0.5554 - val_loss: 1.1906 - val_acc: 0.5367\n",
      "Epoch 8/500\n",
      "132/132 [==============================] - 35s 263ms/step - loss: 1.1296 - acc: 0.5700 - val_loss: 1.1463 - val_acc: 0.5642\n",
      "Epoch 9/500\n",
      "132/132 [==============================] - 36s 273ms/step - loss: 1.1127 - acc: 0.5761 - val_loss: 1.1204 - val_acc: 0.5780\n",
      "Epoch 10/500\n",
      "132/132 [==============================] - 35s 265ms/step - loss: 1.0712 - acc: 0.5921 - val_loss: 1.0879 - val_acc: 0.5780\n",
      "Epoch 11/500\n",
      "132/132 [==============================] - 34s 255ms/step - loss: 1.0451 - acc: 0.6040 - val_loss: 1.1251 - val_acc: 0.5872\n",
      "Epoch 12/500\n",
      "132/132 [==============================] - 34s 259ms/step - loss: 1.0270 - acc: 0.6175 - val_loss: 1.1091 - val_acc: 0.5688\n",
      "Epoch 13/500\n",
      "132/132 [==============================] - 34s 254ms/step - loss: 0.9981 - acc: 0.6209 - val_loss: 1.1264 - val_acc: 0.5917\n",
      "Epoch 14/500\n",
      "132/132 [==============================] - 34s 254ms/step - loss: 0.9676 - acc: 0.6386 - val_loss: 1.0956 - val_acc: 0.5917\n",
      "Epoch 15/500\n",
      "132/132 [==============================] - 33s 246ms/step - loss: 0.9441 - acc: 0.6412 - val_loss: 1.1265 - val_acc: 0.6193\n",
      "Epoch 16/500\n",
      "132/132 [==============================] - 33s 251ms/step - loss: 0.9166 - acc: 0.6607 - val_loss: 1.1231 - val_acc: 0.6239\n",
      "Epoch 17/500\n",
      "132/132 [==============================] - 33s 247ms/step - loss: 0.8851 - acc: 0.6567 - val_loss: 1.1410 - val_acc: 0.5780\n",
      "Epoch 18/500\n",
      "132/132 [==============================] - 33s 246ms/step - loss: 0.8692 - acc: 0.6717 - val_loss: 1.1978 - val_acc: 0.5917\n",
      "Epoch 19/500\n",
      "132/132 [==============================] - 33s 247ms/step - loss: 0.8518 - acc: 0.6729 - val_loss: 1.1938 - val_acc: 0.6055\n",
      "Epoch 20/500\n",
      "132/132 [==============================] - 32s 246ms/step - loss: 0.8183 - acc: 0.6870 - val_loss: 1.2295 - val_acc: 0.6101\n",
      "Epoch 21/500\n",
      "132/132 [==============================] - 33s 247ms/step - loss: 0.8034 - acc: 0.6970 - val_loss: 1.2082 - val_acc: 0.6147\n",
      "Epoch 22/500\n",
      "132/132 [==============================] - 34s 254ms/step - loss: 0.7752 - acc: 0.7057 - val_loss: 1.2347 - val_acc: 0.6284\n",
      "Epoch 23/500\n",
      "132/132 [==============================] - 32s 246ms/step - loss: 0.7587 - acc: 0.7098 - val_loss: 1.2797 - val_acc: 0.6147\n",
      "Epoch 24/500\n",
      "132/132 [==============================] - 33s 247ms/step - loss: 0.7243 - acc: 0.7242 - val_loss: 1.2919 - val_acc: 0.6101\n",
      "Epoch 25/500\n",
      "132/132 [==============================] - 33s 246ms/step - loss: 0.7176 - acc: 0.7278 - val_loss: 1.3502 - val_acc: 0.5688\n",
      "Epoch 26/500\n",
      "132/132 [==============================] - 33s 247ms/step - loss: 0.6879 - acc: 0.7374 - val_loss: 1.3349 - val_acc: 0.6101\n",
      "Epoch 27/500\n",
      "132/132 [==============================] - 32s 246ms/step - loss: 0.6673 - acc: 0.7516 - val_loss: 1.4104 - val_acc: 0.5826\n",
      "Epoch 28/500\n",
      "132/132 [==============================] - 33s 248ms/step - loss: 0.6556 - acc: 0.7563 - val_loss: 1.3941 - val_acc: 0.6055\n",
      "Epoch 29/500\n",
      "132/132 [==============================] - 32s 245ms/step - loss: 0.6511 - acc: 0.7541 - val_loss: 1.3866 - val_acc: 0.6193\n",
      "Epoch 30/500\n",
      "132/132 [==============================] - 33s 247ms/step - loss: 0.6384 - acc: 0.7581 - val_loss: 1.4756 - val_acc: 0.5642\n",
      "Epoch 31/500\n",
      "132/132 [==============================] - 33s 247ms/step - loss: 0.6405 - acc: 0.7595 - val_loss: 1.4851 - val_acc: 0.6055\n",
      "Epoch 32/500\n",
      "132/132 [==============================] - 32s 246ms/step - loss: 0.6181 - acc: 0.7629 - val_loss: 1.5277 - val_acc: 0.5917\n",
      "Epoch 33/500\n",
      "132/132 [==============================] - 33s 248ms/step - loss: 0.5886 - acc: 0.7742 - val_loss: 1.5737 - val_acc: 0.5826\n",
      "Epoch 34/500\n",
      "132/132 [==============================] - 33s 252ms/step - loss: 0.5820 - acc: 0.7781 - val_loss: 1.5385 - val_acc: 0.6055\n",
      "Epoch 35/500\n",
      "132/132 [==============================] - 34s 254ms/step - loss: 0.5610 - acc: 0.7843 - val_loss: 1.5922 - val_acc: 0.5688\n",
      "Epoch 36/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.5587 - acc: 0.7886 - val_loss: 1.5871 - val_acc: 0.5963\n",
      "Epoch 37/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.5611 - acc: 0.7851 - val_loss: 1.7197 - val_acc: 0.5872\n",
      "Epoch 38/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.5630 - acc: 0.7817 - val_loss: 1.6891 - val_acc: 0.5780\n",
      "Epoch 39/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.5587 - acc: 0.7875 - val_loss: 1.7430 - val_acc: 0.5963\n",
      "Epoch 40/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.5343 - acc: 0.7976 - val_loss: 1.7968 - val_acc: 0.5734\n",
      "Epoch 41/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.5289 - acc: 0.8034 - val_loss: 1.7907 - val_acc: 0.6055\n",
      "Epoch 42/500\n",
      "132/132 [==============================] - 31s 233ms/step - loss: 0.5323 - acc: 0.7985 - val_loss: 1.8369 - val_acc: 0.5963\n",
      "Epoch 43/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.4978 - acc: 0.8110 - val_loss: 1.7437 - val_acc: 0.5963\n",
      "Epoch 44/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.4984 - acc: 0.8116 - val_loss: 1.8182 - val_acc: 0.6009\n",
      "Epoch 45/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.4849 - acc: 0.8173 - val_loss: 1.8135 - val_acc: 0.6101\n",
      "Epoch 46/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.4678 - acc: 0.8239 - val_loss: 1.8330 - val_acc: 0.5963\n",
      "Epoch 47/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.4731 - acc: 0.8173 - val_loss: 1.9078 - val_acc: 0.5688\n",
      "Epoch 48/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.4676 - acc: 0.8235 - val_loss: 1.8621 - val_acc: 0.5963\n",
      "Epoch 49/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.4588 - acc: 0.8267 - val_loss: 1.8269 - val_acc: 0.5963\n",
      "Epoch 50/500\n",
      "132/132 [==============================] - 31s 233ms/step - loss: 0.4550 - acc: 0.8283 - val_loss: 1.9002 - val_acc: 0.6055\n",
      "Epoch 51/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.4488 - acc: 0.8320 - val_loss: 1.7824 - val_acc: 0.6193\n",
      "Epoch 52/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.4423 - acc: 0.8361 - val_loss: 1.9905 - val_acc: 0.5780\n",
      "Epoch 53/500\n",
      "132/132 [==============================] - 31s 233ms/step - loss: 0.4502 - acc: 0.8289 - val_loss: 1.8724 - val_acc: 0.6239\n",
      "Epoch 54/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.4433 - acc: 0.8346 - val_loss: 1.9451 - val_acc: 0.6009\n",
      "Epoch 55/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.4318 - acc: 0.8398 - val_loss: 1.8416 - val_acc: 0.6193\n",
      "Epoch 56/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.4296 - acc: 0.8396 - val_loss: 2.0277 - val_acc: 0.5917\n",
      "Epoch 57/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.4229 - acc: 0.8375 - val_loss: 1.9107 - val_acc: 0.6009\n",
      "Epoch 58/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.4177 - acc: 0.8476 - val_loss: 2.1016 - val_acc: 0.5826\n",
      "Epoch 59/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.4221 - acc: 0.8427 - val_loss: 1.9824 - val_acc: 0.5963\n",
      "Epoch 60/500\n",
      "132/132 [==============================] - 31s 233ms/step - loss: 0.4071 - acc: 0.8481 - val_loss: 2.0522 - val_acc: 0.6193\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 61/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.3941 - acc: 0.8534 - val_loss: 2.1242 - val_acc: 0.5826\n",
      "Epoch 62/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.3917 - acc: 0.8536 - val_loss: 2.2786 - val_acc: 0.5917\n",
      "Epoch 63/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.3873 - acc: 0.8558 - val_loss: 2.3598 - val_acc: 0.5459\n",
      "Epoch 64/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.3941 - acc: 0.8537 - val_loss: 2.4919 - val_acc: 0.5550\n",
      "Epoch 65/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.3916 - acc: 0.8552 - val_loss: 2.3296 - val_acc: 0.6009\n",
      "Epoch 66/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.3866 - acc: 0.8563 - val_loss: 2.2588 - val_acc: 0.5826\n",
      "Epoch 67/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.3763 - acc: 0.8578 - val_loss: 2.2652 - val_acc: 0.5872\n",
      "Epoch 68/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.3743 - acc: 0.8614 - val_loss: 2.3258 - val_acc: 0.5872\n",
      "Epoch 69/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.3879 - acc: 0.8557 - val_loss: 2.2374 - val_acc: 0.5734\n",
      "Epoch 70/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.3830 - acc: 0.8573 - val_loss: 2.1057 - val_acc: 0.5963\n",
      "Epoch 71/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.3739 - acc: 0.8601 - val_loss: 2.2482 - val_acc: 0.5780\n",
      "Epoch 72/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.3617 - acc: 0.8629 - val_loss: 2.2746 - val_acc: 0.5780\n",
      "Epoch 73/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.3648 - acc: 0.8670 - val_loss: 2.4250 - val_acc: 0.5917\n",
      "Epoch 74/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.3537 - acc: 0.8686 - val_loss: 2.3297 - val_acc: 0.5780\n",
      "Epoch 75/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.3661 - acc: 0.8619 - val_loss: 2.2730 - val_acc: 0.5963\n",
      "Epoch 76/500\n",
      "132/132 [==============================] - 31s 239ms/step - loss: 0.3575 - acc: 0.8654 - val_loss: 2.2949 - val_acc: 0.6101\n",
      "Epoch 77/500\n",
      "132/132 [==============================] - 32s 242ms/step - loss: 0.3425 - acc: 0.8736 - val_loss: 2.3128 - val_acc: 0.5826\n",
      "Epoch 78/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.3436 - acc: 0.8673 - val_loss: 2.3576 - val_acc: 0.6101\n",
      "Epoch 79/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.3537 - acc: 0.8699 - val_loss: 2.5111 - val_acc: 0.5734\n",
      "Epoch 80/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.3496 - acc: 0.8689 - val_loss: 2.4251 - val_acc: 0.5917\n",
      "Epoch 81/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.3413 - acc: 0.8731 - val_loss: 2.4373 - val_acc: 0.5780\n",
      "Epoch 82/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.3424 - acc: 0.8732 - val_loss: 2.2500 - val_acc: 0.5963\n",
      "Epoch 83/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.3340 - acc: 0.8773 - val_loss: 2.3934 - val_acc: 0.5642\n",
      "Epoch 84/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.3379 - acc: 0.8747 - val_loss: 2.4145 - val_acc: 0.5734\n",
      "Epoch 85/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.3373 - acc: 0.8761 - val_loss: 2.4928 - val_acc: 0.5872\n",
      "Epoch 86/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.3398 - acc: 0.8764 - val_loss: 2.5017 - val_acc: 0.6147\n",
      "Epoch 87/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.3376 - acc: 0.8755 - val_loss: 2.5546 - val_acc: 0.5734\n",
      "Epoch 88/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.3288 - acc: 0.8767 - val_loss: 2.6118 - val_acc: 0.5917\n",
      "Epoch 89/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.3277 - acc: 0.8796 - val_loss: 2.7243 - val_acc: 0.5688\n",
      "Epoch 90/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.3185 - acc: 0.8818 - val_loss: 2.4506 - val_acc: 0.5505\n",
      "Epoch 91/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.3218 - acc: 0.8797 - val_loss: 2.5563 - val_acc: 0.5688\n",
      "Epoch 92/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.3309 - acc: 0.8809 - val_loss: 2.6024 - val_acc: 0.5505\n",
      "Epoch 93/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.3197 - acc: 0.8832 - val_loss: 2.5314 - val_acc: 0.5688\n",
      "Epoch 94/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.3137 - acc: 0.8820 - val_loss: 2.5605 - val_acc: 0.5917\n",
      "Epoch 95/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.3260 - acc: 0.8773 - val_loss: 2.5560 - val_acc: 0.6009\n",
      "Epoch 96/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.3225 - acc: 0.8817 - val_loss: 2.4963 - val_acc: 0.6284\n",
      "Epoch 97/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.3217 - acc: 0.8796 - val_loss: 2.4437 - val_acc: 0.6193\n",
      "Epoch 98/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.3002 - acc: 0.8895 - val_loss: 2.6824 - val_acc: 0.6009\n",
      "Epoch 99/500\n",
      "132/132 [==============================] - 31s 233ms/step - loss: 0.3038 - acc: 0.8869 - val_loss: 2.6532 - val_acc: 0.5872\n",
      "Epoch 100/500\n",
      "132/132 [==============================] - 31s 233ms/step - loss: 0.3201 - acc: 0.8819 - val_loss: 2.4978 - val_acc: 0.6239\n",
      "Epoch 101/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.3184 - acc: 0.8831 - val_loss: 2.4929 - val_acc: 0.5917\n",
      "Epoch 102/500\n",
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      "Epoch 168/500\n",
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      "Epoch 169/500\n",
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      "Epoch 170/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.2417 - acc: 0.9125 - val_loss: 3.3061 - val_acc: 0.5872\n",
      "Epoch 171/500\n",
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      "Epoch 172/500\n",
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      "Epoch 174/500\n",
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      "Epoch 175/500\n",
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      "Epoch 176/500\n",
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      "Epoch 177/500\n",
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      "Epoch 178/500\n",
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      "Epoch 179/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 31s 234ms/step - loss: 0.2321 - acc: 0.9160 - val_loss: 3.1985 - val_acc: 0.6055\n",
      "Epoch 180/500\n",
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      "Epoch 183/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.2342 - acc: 0.9155 - val_loss: 3.3896 - val_acc: 0.5826\n",
      "Epoch 184/500\n",
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      "Epoch 186/500\n",
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      "Epoch 187/500\n",
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      "Epoch 188/500\n",
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      "Epoch 190/500\n",
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      "Epoch 191/500\n",
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      "Epoch 194/500\n",
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      "Epoch 195/500\n",
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      "Epoch 199/500\n",
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      "Epoch 200/500\n",
      "132/132 [==============================] - 31s 233ms/step - loss: 0.2228 - acc: 0.9205 - val_loss: 3.5055 - val_acc: 0.5872\n",
      "Epoch 201/500\n",
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      "Epoch 226/500\n",
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      "Epoch 227/500\n",
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      "Epoch 252/500\n",
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      "Epoch 259/500\n",
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      "Epoch 264/500\n",
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      "Epoch 267/500\n",
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      "Epoch 268/500\n",
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      "Epoch 271/500\n",
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      "Epoch 276/500\n",
      "132/132 [==============================] - 33s 250ms/step - loss: 0.2029 - acc: 0.9284 - val_loss: 3.8409 - val_acc: 0.5642\n",
      "Epoch 277/500\n",
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      "Epoch 278/500\n",
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      "Epoch 279/500\n",
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      "Epoch 282/500\n",
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      "Epoch 283/500\n",
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      "Epoch 284/500\n",
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      "Epoch 285/500\n",
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      "Epoch 286/500\n",
      "132/132 [==============================] - 32s 243ms/step - loss: 0.1920 - acc: 0.9322 - val_loss: 3.6889 - val_acc: 0.5872\n",
      "Epoch 287/500\n",
      "132/132 [==============================] - 34s 260ms/step - loss: 0.1792 - acc: 0.9381 - val_loss: 3.8296 - val_acc: 0.5688\n",
      "Epoch 288/500\n",
      "132/132 [==============================] - 32s 243ms/step - loss: 0.1760 - acc: 0.9348 - val_loss: 3.7708 - val_acc: 0.5963\n",
      "Epoch 289/500\n",
      "132/132 [==============================] - 32s 243ms/step - loss: 0.1782 - acc: 0.9363 - val_loss: 3.6729 - val_acc: 0.6101\n",
      "Epoch 290/500\n",
      "132/132 [==============================] - 34s 257ms/step - loss: 0.1678 - acc: 0.9386 - val_loss: 3.7330 - val_acc: 0.6009\n",
      "Epoch 291/500\n",
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      "Epoch 292/500\n",
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      "Epoch 293/500\n",
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      "Epoch 294/500\n",
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      "Epoch 295/500\n",
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      "Epoch 296/500\n",
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      "Epoch 297/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 31s 237ms/step - loss: 0.1807 - acc: 0.9383 - val_loss: 3.9368 - val_acc: 0.5734\n",
      "Epoch 298/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.1840 - acc: 0.9370 - val_loss: 3.8765 - val_acc: 0.5688\n",
      "Epoch 299/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.1805 - acc: 0.9370 - val_loss: 3.9428 - val_acc: 0.5826\n",
      "Epoch 300/500\n",
      "132/132 [==============================] - 31s 233ms/step - loss: 0.1834 - acc: 0.9348 - val_loss: 3.7680 - val_acc: 0.5963\n",
      "Epoch 301/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.1802 - acc: 0.9358 - val_loss: 4.1115 - val_acc: 0.5734\n",
      "Epoch 302/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1667 - acc: 0.9381 - val_loss: 4.0665 - val_acc: 0.5596\n",
      "Epoch 303/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1621 - acc: 0.9425 - val_loss: 3.9224 - val_acc: 0.5780\n",
      "Epoch 304/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1635 - acc: 0.9399 - val_loss: 3.8437 - val_acc: 0.5963\n",
      "Epoch 305/500\n",
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      "132/132 [==============================] - 31s 235ms/step - loss: 0.1643 - acc: 0.9417 - val_loss: 3.8150 - val_acc: 0.6239\n",
      "Epoch 307/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1722 - acc: 0.9373 - val_loss: 3.8155 - val_acc: 0.6193\n",
      "Epoch 308/500\n",
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      "Epoch 309/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1614 - acc: 0.9411 - val_loss: 3.9953 - val_acc: 0.5734\n",
      "Epoch 310/500\n",
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      "Epoch 311/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1610 - acc: 0.9420 - val_loss: 4.1436 - val_acc: 0.5780\n",
      "Epoch 312/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1532 - acc: 0.9448 - val_loss: 3.9308 - val_acc: 0.5826\n",
      "Epoch 313/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1876 - acc: 0.9348 - val_loss: 3.8929 - val_acc: 0.5505\n",
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      "Epoch 316/500\n",
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      "Epoch 317/500\n",
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      "Epoch 318/500\n",
      "132/132 [==============================] - 31s 233ms/step - loss: 0.1633 - acc: 0.9422 - val_loss: 3.8548 - val_acc: 0.5780\n",
      "Epoch 319/500\n",
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      "Epoch 320/500\n",
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      "Epoch 321/500\n",
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      "Epoch 322/500\n",
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      "Epoch 324/500\n",
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      "Epoch 325/500\n",
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      "Epoch 326/500\n",
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      "Epoch 328/500\n",
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      "Epoch 330/500\n",
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      "Epoch 344/500\n",
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      "Epoch 346/500\n",
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      "Epoch 350/500\n",
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      "Epoch 352/500\n",
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      "Epoch 353/500\n",
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      "Epoch 354/500\n",
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      "Epoch 377/500\n",
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      "Epoch 378/500\n",
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      "Epoch 383/500\n",
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      "Epoch 384/500\n",
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      "Epoch 385/500\n",
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      "Epoch 388/500\n",
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      "Epoch 389/500\n",
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      "Epoch 394/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1518 - acc: 0.9475 - val_loss: 4.0200 - val_acc: 0.5505\n",
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      "Epoch 396/500\n",
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      "Epoch 400/500\n",
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      "Epoch 401/500\n",
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      "Epoch 402/500\n",
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      "Epoch 403/500\n",
      "132/132 [==============================] - 31s 233ms/step - loss: 0.1346 - acc: 0.9531 - val_loss: 4.2211 - val_acc: 0.5642\n",
      "Epoch 404/500\n",
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      "Epoch 405/500\n",
      "132/132 [==============================] - 31s 233ms/step - loss: 0.1272 - acc: 0.9520 - val_loss: 4.1736 - val_acc: 0.5642\n",
      "Epoch 406/500\n",
      "132/132 [==============================] - 31s 233ms/step - loss: 0.1400 - acc: 0.9508 - val_loss: 4.1822 - val_acc: 0.5734\n",
      "Epoch 407/500\n",
      "132/132 [==============================] - 31s 233ms/step - loss: 0.1634 - acc: 0.9460 - val_loss: 4.0162 - val_acc: 0.5596\n",
      "Epoch 408/500\n",
      "132/132 [==============================] - 31s 233ms/step - loss: 0.1383 - acc: 0.9508 - val_loss: 4.1178 - val_acc: 0.5688\n",
      "Epoch 409/500\n",
      "132/132 [==============================] - 31s 232ms/step - loss: 0.1431 - acc: 0.9516 - val_loss: 4.1353 - val_acc: 0.5826\n",
      "Epoch 410/500\n",
      "132/132 [==============================] - 31s 233ms/step - loss: 0.1412 - acc: 0.9525 - val_loss: 4.2795 - val_acc: 0.5642\n",
      "Epoch 411/500\n",
      "132/132 [==============================] - 31s 233ms/step - loss: 0.1301 - acc: 0.9548 - val_loss: 4.2362 - val_acc: 0.5688\n",
      "Epoch 412/500\n",
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      "Epoch 413/500\n",
      "132/132 [==============================] - 31s 233ms/step - loss: 0.1341 - acc: 0.9495 - val_loss: 4.0620 - val_acc: 0.5963\n",
      "Epoch 414/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1295 - acc: 0.9564 - val_loss: 4.0798 - val_acc: 0.6101\n",
      "Epoch 415/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 31s 234ms/step - loss: 0.1335 - acc: 0.9543 - val_loss: 4.0307 - val_acc: 0.5872\n",
      "Epoch 416/500\n",
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      "Epoch 417/500\n",
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      "Epoch 419/500\n",
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      "Epoch 421/500\n",
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      "Epoch 425/500\n",
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      "Epoch 426/500\n",
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      "Epoch 427/500\n",
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      "Epoch 430/500\n",
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      "Epoch 435/500\n",
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      "Epoch 436/500\n",
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      "Epoch 437/500\n",
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      "Epoch 438/500\n",
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      "Epoch 439/500\n",
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      "Epoch 440/500\n",
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      "Epoch 441/500\n",
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      "Epoch 442/500\n",
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      "Epoch 444/500\n",
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      "Epoch 446/500\n",
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      "Epoch 447/500\n",
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      "Epoch 448/500\n",
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      "Epoch 450/500\n",
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      "Epoch 454/500\n",
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      "Epoch 473/500\n",
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      "Epoch 474/500\n",
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      "Epoch 475/500\n",
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      "Epoch 476/500\n",
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      "Epoch 477/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1263 - acc: 0.9580 - val_loss: 4.4224 - val_acc: 0.5642\n",
      "Epoch 478/500\n",
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      "Epoch 479/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.1107 - acc: 0.9614 - val_loss: 4.3239 - val_acc: 0.5780\n",
      "Epoch 480/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1201 - acc: 0.9579 - val_loss: 4.5515 - val_acc: 0.5688\n",
      "Epoch 481/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.1245 - acc: 0.9585 - val_loss: 4.5318 - val_acc: 0.5642\n",
      "Epoch 482/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1284 - acc: 0.9608 - val_loss: 4.3845 - val_acc: 0.5642\n",
      "Epoch 483/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1264 - acc: 0.9576 - val_loss: 4.3571 - val_acc: 0.5917\n",
      "Epoch 484/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.1516 - acc: 0.9506 - val_loss: 4.3145 - val_acc: 0.5826\n",
      "Epoch 485/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1230 - acc: 0.9600 - val_loss: 4.3055 - val_acc: 0.5780\n",
      "Epoch 486/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1282 - acc: 0.9580 - val_loss: 4.3866 - val_acc: 0.5872\n",
      "Epoch 487/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1249 - acc: 0.9573 - val_loss: 4.2973 - val_acc: 0.5963\n",
      "Epoch 488/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.1127 - acc: 0.9620 - val_loss: 4.3914 - val_acc: 0.5826\n",
      "Epoch 489/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.1276 - acc: 0.9592 - val_loss: 4.3275 - val_acc: 0.5780\n",
      "Epoch 490/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1228 - acc: 0.9592 - val_loss: 4.4770 - val_acc: 0.5413\n",
      "Epoch 491/500\n",
      "132/132 [==============================] - 31s 233ms/step - loss: 0.1290 - acc: 0.9568 - val_loss: 4.3410 - val_acc: 0.5872\n",
      "Epoch 492/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.1164 - acc: 0.9590 - val_loss: 4.1288 - val_acc: 0.5963\n",
      "Epoch 493/500\n",
      "132/132 [==============================] - 31s 232ms/step - loss: 0.1151 - acc: 0.9612 - val_loss: 4.4569 - val_acc: 0.5917\n",
      "Epoch 494/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1068 - acc: 0.9637 - val_loss: 4.3189 - val_acc: 0.5872\n",
      "Epoch 495/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1079 - acc: 0.9612 - val_loss: 4.2749 - val_acc: 0.6009\n",
      "Epoch 496/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1202 - acc: 0.9606 - val_loss: 4.5202 - val_acc: 0.5596\n",
      "Epoch 497/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1290 - acc: 0.9570 - val_loss: 4.2441 - val_acc: 0.6009\n",
      "Epoch 498/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1167 - acc: 0.9597 - val_loss: 4.4212 - val_acc: 0.6009\n",
      "Epoch 499/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.1253 - acc: 0.9588 - val_loss: 4.3808 - val_acc: 0.5872\n",
      "Epoch 500/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1051 - acc: 0.9615 - val_loss: 4.2584 - val_acc: 0.5734\n",
      "40/40 [==============================] - 37s 930ms/step\n",
      "Accuracy: 0.5540883697308279. F1: 0.545068878917053\n"
     ]
    }
   ],
   "source": [
    "cnn(0, make_model2D_S, epochs = 500, save_model = True, name = \"2D_small_0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "132/132 [==============================] - 92s 695ms/step - loss: 2.1612 - acc: 0.1454 - val_loss: 1.9334 - val_acc: 0.2569\n",
      "Epoch 2/500\n",
      "132/132 [==============================] - 27s 204ms/step - loss: 1.5063 - acc: 0.4263 - val_loss: 1.2985 - val_acc: 0.4862\n",
      "Epoch 3/500\n",
      "132/132 [==============================] - 31s 232ms/step - loss: 1.1907 - acc: 0.5279 - val_loss: 1.1807 - val_acc: 0.5092\n",
      "Epoch 4/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 1.0952 - acc: 0.5775 - val_loss: 1.0861 - val_acc: 0.5459\n",
      "Epoch 5/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 1.0292 - acc: 0.6064 - val_loss: 1.2076 - val_acc: 0.5550\n",
      "Epoch 6/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.9832 - acc: 0.6279 - val_loss: 0.9657 - val_acc: 0.6330\n",
      "Epoch 7/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.9184 - acc: 0.6621 - val_loss: 0.9102 - val_acc: 0.6560\n",
      "Epoch 8/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.8710 - acc: 0.6770 - val_loss: 0.9005 - val_acc: 0.6330\n",
      "Epoch 9/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.8230 - acc: 0.7010 - val_loss: 0.7943 - val_acc: 0.7202\n",
      "Epoch 10/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.7797 - acc: 0.7231 - val_loss: 0.7727 - val_acc: 0.7248\n",
      "Epoch 11/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.7418 - acc: 0.7254 - val_loss: 0.7392 - val_acc: 0.7248\n",
      "Epoch 12/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.7141 - acc: 0.7407 - val_loss: 0.7202 - val_acc: 0.7339\n",
      "Epoch 13/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.6714 - acc: 0.7566 - val_loss: 0.6961 - val_acc: 0.7156\n",
      "Epoch 14/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.6654 - acc: 0.7575 - val_loss: 0.6512 - val_acc: 0.7569\n",
      "Epoch 15/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.6455 - acc: 0.7678 - val_loss: 0.6714 - val_acc: 0.7339\n",
      "Epoch 16/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.6024 - acc: 0.7786 - val_loss: 0.6384 - val_acc: 0.7615\n",
      "Epoch 17/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.5885 - acc: 0.7879 - val_loss: 0.6411 - val_acc: 0.7431\n",
      "Epoch 18/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.5698 - acc: 0.7901 - val_loss: 0.6291 - val_acc: 0.7523\n",
      "Epoch 19/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.5343 - acc: 0.8097 - val_loss: 0.6221 - val_acc: 0.7523\n",
      "Epoch 20/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.5392 - acc: 0.8032 - val_loss: 0.6486 - val_acc: 0.7477\n",
      "Epoch 21/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.5175 - acc: 0.8122 - val_loss: 0.6426 - val_acc: 0.7615\n",
      "Epoch 22/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.4968 - acc: 0.8150 - val_loss: 0.5974 - val_acc: 0.7752\n",
      "Epoch 23/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.4849 - acc: 0.8201 - val_loss: 0.5778 - val_acc: 0.7661\n",
      "Epoch 24/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.4647 - acc: 0.8323 - val_loss: 0.5829 - val_acc: 0.7798\n",
      "Epoch 25/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.4440 - acc: 0.8377 - val_loss: 0.5612 - val_acc: 0.7798\n",
      "Epoch 26/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.4339 - acc: 0.8401 - val_loss: 0.5767 - val_acc: 0.7752\n",
      "Epoch 27/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.4197 - acc: 0.8458 - val_loss: 0.5677 - val_acc: 0.7844\n",
      "Epoch 28/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.4046 - acc: 0.8522 - val_loss: 0.6004 - val_acc: 0.7615\n",
      "Epoch 29/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.3934 - acc: 0.8594 - val_loss: 0.5564 - val_acc: 0.7706\n",
      "Epoch 30/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.3865 - acc: 0.8551 - val_loss: 0.5830 - val_acc: 0.7844\n",
      "Epoch 31/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.3705 - acc: 0.8660 - val_loss: 0.5578 - val_acc: 0.7844\n",
      "Epoch 32/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.3596 - acc: 0.8674 - val_loss: 0.5374 - val_acc: 0.8073\n",
      "Epoch 33/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.3423 - acc: 0.8795 - val_loss: 0.5763 - val_acc: 0.7844\n",
      "Epoch 34/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.3372 - acc: 0.8786 - val_loss: 0.5465 - val_acc: 0.7982\n",
      "Epoch 35/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.3088 - acc: 0.8842 - val_loss: 0.5604 - val_acc: 0.7936\n",
      "Epoch 36/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.3155 - acc: 0.8832 - val_loss: 0.5977 - val_acc: 0.7890\n",
      "Epoch 37/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.3128 - acc: 0.8875 - val_loss: 0.5506 - val_acc: 0.8211\n",
      "Epoch 38/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.3037 - acc: 0.8883 - val_loss: 0.5752 - val_acc: 0.8028\n",
      "Epoch 39/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.3045 - acc: 0.8870 - val_loss: 0.6096 - val_acc: 0.8073\n",
      "Epoch 40/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.2752 - acc: 0.8971 - val_loss: 0.5810 - val_acc: 0.7936\n",
      "Epoch 41/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.2737 - acc: 0.8970 - val_loss: 0.6378 - val_acc: 0.7982\n",
      "Epoch 42/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.2730 - acc: 0.8977 - val_loss: 0.6374 - val_acc: 0.7890\n",
      "Epoch 43/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.2679 - acc: 0.9008 - val_loss: 0.6438 - val_acc: 0.7706\n",
      "Epoch 44/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.2607 - acc: 0.9011 - val_loss: 0.6324 - val_acc: 0.7844\n",
      "Epoch 45/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.2439 - acc: 0.9106 - val_loss: 0.7420 - val_acc: 0.7615\n",
      "Epoch 46/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.2355 - acc: 0.9121 - val_loss: 0.6119 - val_acc: 0.7890\n",
      "Epoch 47/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.2286 - acc: 0.9145 - val_loss: 0.6477 - val_acc: 0.7890\n",
      "Epoch 48/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.2427 - acc: 0.9100 - val_loss: 0.6842 - val_acc: 0.7752\n",
      "Epoch 49/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.2237 - acc: 0.9161 - val_loss: 0.6477 - val_acc: 0.8073\n",
      "Epoch 50/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.2069 - acc: 0.9234 - val_loss: 0.6499 - val_acc: 0.8165\n",
      "Epoch 51/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.2037 - acc: 0.9258 - val_loss: 0.6863 - val_acc: 0.7752\n",
      "Epoch 52/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.2015 - acc: 0.9270 - val_loss: 0.6292 - val_acc: 0.7936\n",
      "Epoch 53/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.2044 - acc: 0.9249 - val_loss: 0.7519 - val_acc: 0.7752\n",
      "Epoch 54/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.1951 - acc: 0.9282 - val_loss: 0.7176 - val_acc: 0.8073\n",
      "Epoch 55/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1825 - acc: 0.9379 - val_loss: 0.7114 - val_acc: 0.7844\n",
      "Epoch 56/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1787 - acc: 0.9357 - val_loss: 0.7652 - val_acc: 0.7798\n",
      "Epoch 57/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1541 - acc: 0.9429 - val_loss: 0.8025 - val_acc: 0.7890\n",
      "Epoch 58/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.1762 - acc: 0.9348 - val_loss: 0.6868 - val_acc: 0.7844\n",
      "Epoch 59/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1573 - acc: 0.9430 - val_loss: 0.6965 - val_acc: 0.8073\n",
      "Epoch 60/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1587 - acc: 0.9414 - val_loss: 0.8865 - val_acc: 0.7569\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 61/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1535 - acc: 0.9415 - val_loss: 0.8049 - val_acc: 0.7844\n",
      "Epoch 62/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1562 - acc: 0.9442 - val_loss: 0.8549 - val_acc: 0.7844\n",
      "Epoch 63/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.1599 - acc: 0.9436 - val_loss: 0.8668 - val_acc: 0.7844\n",
      "Epoch 64/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1470 - acc: 0.9458 - val_loss: 0.9332 - val_acc: 0.7431\n",
      "Epoch 65/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1381 - acc: 0.9512 - val_loss: 0.7894 - val_acc: 0.8028\n",
      "Epoch 66/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1284 - acc: 0.9518 - val_loss: 0.8904 - val_acc: 0.7890\n",
      "Epoch 67/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1333 - acc: 0.9514 - val_loss: 1.0124 - val_acc: 0.7661\n",
      "Epoch 68/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1404 - acc: 0.9535 - val_loss: 1.1382 - val_acc: 0.7523\n",
      "Epoch 69/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.1260 - acc: 0.9518 - val_loss: 0.9398 - val_acc: 0.7752\n",
      "Epoch 70/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.1094 - acc: 0.9591 - val_loss: 1.1117 - val_acc: 0.7661\n",
      "Epoch 71/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.1200 - acc: 0.9570 - val_loss: 0.9844 - val_acc: 0.7661\n",
      "Epoch 72/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1275 - acc: 0.9533 - val_loss: 1.0262 - val_acc: 0.7798\n",
      "Epoch 73/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.1267 - acc: 0.9551 - val_loss: 0.9854 - val_acc: 0.7569\n",
      "Epoch 74/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.1180 - acc: 0.9559 - val_loss: 0.9601 - val_acc: 0.7936\n",
      "Epoch 75/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.1060 - acc: 0.9635 - val_loss: 1.0178 - val_acc: 0.7844\n",
      "Epoch 76/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1033 - acc: 0.9614 - val_loss: 1.0832 - val_acc: 0.7339\n",
      "Epoch 77/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.1095 - acc: 0.9639 - val_loss: 1.0915 - val_acc: 0.7569\n",
      "Epoch 78/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.1081 - acc: 0.9592 - val_loss: 1.1198 - val_acc: 0.7706\n",
      "Epoch 79/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.1046 - acc: 0.9639 - val_loss: 1.1740 - val_acc: 0.7569\n",
      "Epoch 80/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0962 - acc: 0.9647 - val_loss: 1.0515 - val_acc: 0.7615\n",
      "Epoch 81/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0959 - acc: 0.9661 - val_loss: 1.1223 - val_acc: 0.7615\n",
      "Epoch 82/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0987 - acc: 0.9641 - val_loss: 1.1532 - val_acc: 0.7569\n",
      "Epoch 83/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1011 - acc: 0.9621 - val_loss: 1.1070 - val_acc: 0.7798\n",
      "Epoch 84/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0930 - acc: 0.9673 - val_loss: 0.9888 - val_acc: 0.7477\n",
      "Epoch 85/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.1069 - acc: 0.9615 - val_loss: 1.0100 - val_acc: 0.7752\n",
      "Epoch 86/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0917 - acc: 0.9665 - val_loss: 1.0643 - val_acc: 0.7752\n",
      "Epoch 87/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0883 - acc: 0.9674 - val_loss: 1.1714 - val_acc: 0.7339\n",
      "Epoch 88/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.1013 - acc: 0.9605 - val_loss: 1.0219 - val_acc: 0.7890\n",
      "Epoch 89/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0889 - acc: 0.9693 - val_loss: 1.0895 - val_acc: 0.7706\n",
      "Epoch 90/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.0862 - acc: 0.9695 - val_loss: 1.3182 - val_acc: 0.7248\n",
      "Epoch 91/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0966 - acc: 0.9630 - val_loss: 1.1402 - val_acc: 0.7569\n",
      "Epoch 92/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0967 - acc: 0.9639 - val_loss: 1.1219 - val_acc: 0.7569\n",
      "Epoch 93/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0795 - acc: 0.9711 - val_loss: 1.1646 - val_acc: 0.8073\n",
      "Epoch 94/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.0806 - acc: 0.9697 - val_loss: 1.2709 - val_acc: 0.7615\n",
      "Epoch 95/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0697 - acc: 0.9733 - val_loss: 1.1811 - val_acc: 0.7661\n",
      "Epoch 96/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0841 - acc: 0.9673 - val_loss: 1.0818 - val_acc: 0.7523\n",
      "Epoch 97/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0852 - acc: 0.9691 - val_loss: 1.2824 - val_acc: 0.7294\n",
      "Epoch 98/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0850 - acc: 0.9680 - val_loss: 1.2548 - val_acc: 0.7615\n",
      "Epoch 99/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0971 - acc: 0.9656 - val_loss: 1.1800 - val_acc: 0.7844\n",
      "Epoch 100/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0957 - acc: 0.9682 - val_loss: 1.2631 - val_acc: 0.7569\n",
      "Epoch 101/500\n",
      "132/132 [==============================] - 32s 242ms/step - loss: 0.0814 - acc: 0.9689 - val_loss: 1.1478 - val_acc: 0.7798\n",
      "Epoch 102/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0826 - acc: 0.9714 - val_loss: 1.2999 - val_acc: 0.7477\n",
      "Epoch 103/500\n",
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      "Epoch 170/500\n",
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      "Epoch 171/500\n",
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      "Epoch 172/500\n",
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      "Epoch 178/500\n",
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      "Epoch 179/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 31s 234ms/step - loss: 0.0404 - acc: 0.9859 - val_loss: 1.7109 - val_acc: 0.7615\n",
      "Epoch 180/500\n",
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      "Epoch 189/500\n",
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      "Epoch 190/500\n",
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      "Epoch 194/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0434 - acc: 0.9833 - val_loss: 1.6141 - val_acc: 0.7294\n",
      "Epoch 195/500\n",
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      "Epoch 200/500\n",
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      "Epoch 201/500\n",
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      "Epoch 202/500\n",
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      "Epoch 226/500\n",
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      "Epoch 227/500\n",
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      "Epoch 228/500\n",
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      "Epoch 229/500\n",
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      "Epoch 240/500\n",
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      "Epoch 248/500\n",
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      "Epoch 249/500\n",
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      "Epoch 250/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0267 - acc: 0.9906 - val_loss: 2.3220 - val_acc: 0.7202\n",
      "Epoch 251/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0428 - acc: 0.9847 - val_loss: 2.1748 - val_acc: 0.7248\n",
      "Epoch 252/500\n",
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      "Epoch 253/500\n",
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      "Epoch 254/500\n",
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      "Epoch 255/500\n",
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      "Epoch 256/500\n",
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      "Epoch 257/500\n",
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      "Epoch 258/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0295 - acc: 0.9900 - val_loss: 2.5309 - val_acc: 0.7294\n",
      "Epoch 259/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0381 - acc: 0.9861 - val_loss: 1.9240 - val_acc: 0.7477\n",
      "Epoch 260/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0427 - acc: 0.9858 - val_loss: 2.2211 - val_acc: 0.7385\n",
      "Epoch 261/500\n",
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      "Epoch 262/500\n",
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      "Epoch 263/500\n",
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      "Epoch 264/500\n",
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      "Epoch 265/500\n",
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      "Epoch 266/500\n",
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      "Epoch 267/500\n",
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      "Epoch 268/500\n",
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      "Epoch 269/500\n",
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      "Epoch 270/500\n",
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      "Epoch 271/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0416 - acc: 0.9855 - val_loss: 2.0630 - val_acc: 0.7248\n",
      "Epoch 272/500\n",
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      "Epoch 273/500\n",
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      "Epoch 274/500\n",
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      "Epoch 275/500\n",
      "132/132 [==============================] - 33s 248ms/step - loss: 0.0378 - acc: 0.9871 - val_loss: 2.7007 - val_acc: 0.7064\n",
      "Epoch 276/500\n",
      "132/132 [==============================] - 33s 249ms/step - loss: 0.0355 - acc: 0.9874 - val_loss: 2.0525 - val_acc: 0.7385\n",
      "Epoch 277/500\n",
      "132/132 [==============================] - 33s 249ms/step - loss: 0.0434 - acc: 0.9847 - val_loss: 2.3277 - val_acc: 0.7248\n",
      "Epoch 278/500\n",
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      "Epoch 279/500\n",
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      "Epoch 280/500\n",
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      "Epoch 281/500\n",
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      "Epoch 282/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0373 - acc: 0.9868 - val_loss: 1.9479 - val_acc: 0.7339\n",
      "Epoch 283/500\n",
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      "Epoch 284/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0370 - acc: 0.9891 - val_loss: 1.7624 - val_acc: 0.7569\n",
      "Epoch 285/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0354 - acc: 0.9885 - val_loss: 2.0107 - val_acc: 0.7294\n",
      "Epoch 286/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0345 - acc: 0.9882 - val_loss: 2.3340 - val_acc: 0.7248\n",
      "Epoch 287/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0316 - acc: 0.9895 - val_loss: 1.8717 - val_acc: 0.7569\n",
      "Epoch 288/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0378 - acc: 0.9870 - val_loss: 2.1321 - val_acc: 0.7202\n",
      "Epoch 289/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0283 - acc: 0.9888 - val_loss: 2.0779 - val_acc: 0.7477\n",
      "Epoch 290/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0272 - acc: 0.9894 - val_loss: 2.2171 - val_acc: 0.7477\n",
      "Epoch 291/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0398 - acc: 0.9876 - val_loss: 1.5727 - val_acc: 0.7706\n",
      "Epoch 292/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0422 - acc: 0.9853 - val_loss: 1.9629 - val_acc: 0.7477\n",
      "Epoch 293/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0437 - acc: 0.9861 - val_loss: 2.0494 - val_acc: 0.6835\n",
      "Epoch 294/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0439 - acc: 0.9859 - val_loss: 2.1657 - val_acc: 0.7431\n",
      "Epoch 295/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0316 - acc: 0.9880 - val_loss: 2.1048 - val_acc: 0.7385\n",
      "Epoch 296/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0277 - acc: 0.9905 - val_loss: 2.0242 - val_acc: 0.7431\n",
      "Epoch 297/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0300 - acc: 0.9898 - val_loss: 1.9756 - val_acc: 0.7431\n",
      "Epoch 298/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0389 - acc: 0.9859 - val_loss: 2.0851 - val_acc: 0.7248\n",
      "Epoch 299/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0222 - acc: 0.9920 - val_loss: 2.1150 - val_acc: 0.7385\n",
      "Epoch 300/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0331 - acc: 0.9894 - val_loss: 2.0452 - val_acc: 0.7248\n",
      "Epoch 301/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0242 - acc: 0.9908 - val_loss: 1.9959 - val_acc: 0.7569\n",
      "Epoch 302/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0296 - acc: 0.9905 - val_loss: 2.1234 - val_acc: 0.7431\n",
      "Epoch 303/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0424 - acc: 0.9861 - val_loss: 2.3179 - val_acc: 0.7523\n",
      "Epoch 304/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0362 - acc: 0.9882 - val_loss: 2.4917 - val_acc: 0.7156\n",
      "Epoch 305/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0342 - acc: 0.9883 - val_loss: 2.1547 - val_acc: 0.7523\n",
      "Epoch 306/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0382 - acc: 0.9844 - val_loss: 2.2382 - val_acc: 0.7431\n",
      "Epoch 307/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0331 - acc: 0.9883 - val_loss: 1.8640 - val_acc: 0.7615\n",
      "Epoch 308/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0172 - acc: 0.9933 - val_loss: 2.1068 - val_acc: 0.7431\n",
      "Epoch 309/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0370 - acc: 0.9885 - val_loss: 2.1252 - val_acc: 0.7339\n",
      "Epoch 310/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0366 - acc: 0.9871 - val_loss: 2.0135 - val_acc: 0.7431\n",
      "Epoch 311/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0327 - acc: 0.9885 - val_loss: 1.9755 - val_acc: 0.7477\n",
      "Epoch 312/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0309 - acc: 0.9883 - val_loss: 2.1393 - val_acc: 0.7477\n",
      "Epoch 313/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0428 - acc: 0.9848 - val_loss: 2.1250 - val_acc: 0.7661\n",
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      "Epoch 315/500\n",
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      "Epoch 316/500\n",
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      "Epoch 317/500\n",
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      "Epoch 318/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0234 - acc: 0.9914 - val_loss: 1.9849 - val_acc: 0.7339\n",
      "Epoch 319/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0283 - acc: 0.9895 - val_loss: 2.1402 - val_acc: 0.7385\n",
      "Epoch 320/500\n",
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      "Epoch 321/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0462 - acc: 0.9850 - val_loss: 2.1039 - val_acc: 0.7385\n",
      "Epoch 322/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0338 - acc: 0.9891 - val_loss: 2.0334 - val_acc: 0.7339\n",
      "Epoch 323/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0385 - acc: 0.9864 - val_loss: 1.9096 - val_acc: 0.7431\n",
      "Epoch 324/500\n",
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      "Epoch 325/500\n",
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      "Epoch 326/500\n",
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      "Epoch 327/500\n",
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      "Epoch 328/500\n",
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      "Epoch 329/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0361 - acc: 0.9868 - val_loss: 1.7801 - val_acc: 0.7661\n",
      "Epoch 330/500\n",
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      "Epoch 331/500\n",
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      "Epoch 332/500\n",
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      "Epoch 334/500\n",
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      "Epoch 344/500\n",
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      "Epoch 345/500\n",
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      "Epoch 346/500\n",
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      "Epoch 350/500\n",
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      "Epoch 351/500\n",
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      "Epoch 352/500\n",
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      "Epoch 353/500\n",
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      "Epoch 354/500\n",
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      "Epoch 355/500\n",
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      "132/132 [==============================] - 31s 236ms/step - loss: 0.0527 - acc: 0.9836 - val_loss: 1.6877 - val_acc: 0.7294\n",
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      "Epoch 377/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0212 - acc: 0.9921 - val_loss: 2.0992 - val_acc: 0.7752\n",
      "Epoch 378/500\n",
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      "Epoch 380/500\n",
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      "Epoch 382/500\n",
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      "Epoch 383/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0522 - acc: 0.9845 - val_loss: 1.8958 - val_acc: 0.7339\n",
      "Epoch 384/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0419 - acc: 0.9862 - val_loss: 2.1133 - val_acc: 0.7569\n",
      "Epoch 385/500\n",
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      "Epoch 386/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0175 - acc: 0.9938 - val_loss: 2.1303 - val_acc: 0.7661\n",
      "Epoch 387/500\n",
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      "Epoch 388/500\n",
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      "Epoch 389/500\n",
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      "Epoch 390/500\n",
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      "132/132 [==============================] - 31s 238ms/step - loss: 0.0200 - acc: 0.9921 - val_loss: 2.1377 - val_acc: 0.7294\n",
      "Epoch 394/500\n",
      "132/132 [==============================] - 31s 239ms/step - loss: 0.0279 - acc: 0.9908 - val_loss: 2.2346 - val_acc: 0.7661\n",
      "Epoch 395/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0241 - acc: 0.9924 - val_loss: 2.4024 - val_acc: 0.7431\n",
      "Epoch 396/500\n",
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      "Epoch 397/500\n",
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      "Epoch 398/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0312 - acc: 0.9915 - val_loss: 2.0839 - val_acc: 0.7752\n",
      "Epoch 399/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0346 - acc: 0.9876 - val_loss: 2.2198 - val_acc: 0.7385\n",
      "Epoch 400/500\n",
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      "Epoch 401/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0257 - acc: 0.9912 - val_loss: 1.8864 - val_acc: 0.7706\n",
      "Epoch 402/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0260 - acc: 0.9905 - val_loss: 2.3783 - val_acc: 0.7339\n",
      "Epoch 403/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0246 - acc: 0.9915 - val_loss: 2.2988 - val_acc: 0.7248\n",
      "Epoch 404/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0351 - acc: 0.9874 - val_loss: 2.2196 - val_acc: 0.7523\n",
      "Epoch 405/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0257 - acc: 0.9902 - val_loss: 2.0591 - val_acc: 0.7523\n",
      "Epoch 406/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0246 - acc: 0.9908 - val_loss: 2.0662 - val_acc: 0.7339\n",
      "Epoch 407/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0352 - acc: 0.9876 - val_loss: 1.9495 - val_acc: 0.7569\n",
      "Epoch 408/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0372 - acc: 0.9888 - val_loss: 2.0497 - val_acc: 0.7477\n",
      "Epoch 409/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0244 - acc: 0.9902 - val_loss: 2.2666 - val_acc: 0.7523\n",
      "Epoch 410/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0353 - acc: 0.9879 - val_loss: 2.4305 - val_acc: 0.7248\n",
      "Epoch 411/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0285 - acc: 0.9882 - val_loss: 2.4373 - val_acc: 0.7248\n",
      "Epoch 412/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0228 - acc: 0.9908 - val_loss: 2.5699 - val_acc: 0.7248\n",
      "Epoch 413/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0277 - acc: 0.9917 - val_loss: 2.3835 - val_acc: 0.7385\n",
      "Epoch 414/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0232 - acc: 0.9909 - val_loss: 2.0311 - val_acc: 0.7661\n",
      "Epoch 415/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0300 - acc: 0.9902 - val_loss: 1.9197 - val_acc: 0.7523\n",
      "Epoch 416/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0296 - acc: 0.9898 - val_loss: 2.2117 - val_acc: 0.7569\n",
      "Epoch 417/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0416 - acc: 0.9874 - val_loss: 2.1320 - val_acc: 0.7385\n",
      "Epoch 418/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0298 - acc: 0.9901 - val_loss: 2.2242 - val_acc: 0.7431\n",
      "Epoch 419/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0270 - acc: 0.9908 - val_loss: 2.2912 - val_acc: 0.7202\n",
      "Epoch 420/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0538 - acc: 0.9827 - val_loss: 2.0445 - val_acc: 0.7706\n",
      "Epoch 421/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0242 - acc: 0.9905 - val_loss: 2.1947 - val_acc: 0.7661\n",
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      "132/132 [==============================] - 31s 236ms/step - loss: 0.0269 - acc: 0.9914 - val_loss: 2.3270 - val_acc: 0.7431\n",
      "Epoch 423/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0243 - acc: 0.9927 - val_loss: 2.3757 - val_acc: 0.7661\n",
      "Epoch 424/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0375 - acc: 0.9903 - val_loss: 2.0836 - val_acc: 0.7385\n",
      "Epoch 425/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0190 - acc: 0.9927 - val_loss: 2.2266 - val_acc: 0.7752\n",
      "Epoch 426/500\n",
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      "Epoch 427/500\n",
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      "Epoch 428/500\n",
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      "Epoch 430/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0256 - acc: 0.9918 - val_loss: 2.1343 - val_acc: 0.7339\n",
      "Epoch 431/500\n",
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      "Epoch 434/500\n",
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      "Epoch 435/500\n",
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      "Epoch 436/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0435 - acc: 0.9873 - val_loss: 2.0889 - val_acc: 0.7569\n",
      "Epoch 437/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0415 - acc: 0.9883 - val_loss: 2.0200 - val_acc: 0.7477\n",
      "Epoch 438/500\n",
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      "Epoch 439/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0303 - acc: 0.9889 - val_loss: 1.7516 - val_acc: 0.7752\n",
      "Epoch 440/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0213 - acc: 0.9917 - val_loss: 2.3748 - val_acc: 0.7294\n",
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      "Epoch 442/500\n",
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      "Epoch 444/500\n",
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      "Epoch 445/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0285 - acc: 0.9895 - val_loss: 1.9090 - val_acc: 0.7752\n",
      "Epoch 446/500\n",
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      "Epoch 447/500\n",
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      "Epoch 448/500\n",
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      "Epoch 450/500\n",
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      "Epoch 451/500\n",
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      "Epoch 456/500\n",
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      "Epoch 473/500\n",
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      "Epoch 474/500\n",
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      "Epoch 475/500\n",
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      "Epoch 476/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0256 - acc: 0.9929 - val_loss: 1.8908 - val_acc: 0.8028\n",
      "Epoch 477/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0304 - acc: 0.9908 - val_loss: 2.1360 - val_acc: 0.7752\n",
      "Epoch 478/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0360 - acc: 0.9906 - val_loss: 2.0295 - val_acc: 0.7339\n",
      "Epoch 479/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0218 - acc: 0.9920 - val_loss: 2.0374 - val_acc: 0.7569\n",
      "Epoch 480/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0323 - acc: 0.9885 - val_loss: 1.9252 - val_acc: 0.7661\n",
      "Epoch 481/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0260 - acc: 0.9914 - val_loss: 2.1972 - val_acc: 0.7385\n",
      "Epoch 482/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0290 - acc: 0.9908 - val_loss: 2.1449 - val_acc: 0.7615\n",
      "Epoch 483/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0204 - acc: 0.9926 - val_loss: 2.1922 - val_acc: 0.7661\n",
      "Epoch 484/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0281 - acc: 0.9911 - val_loss: 2.0221 - val_acc: 0.7706\n",
      "Epoch 485/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0242 - acc: 0.9911 - val_loss: 2.1672 - val_acc: 0.7339\n",
      "Epoch 486/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0233 - acc: 0.9927 - val_loss: 1.8082 - val_acc: 0.7615\n",
      "Epoch 487/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0255 - acc: 0.9920 - val_loss: 1.8552 - val_acc: 0.7477\n",
      "Epoch 488/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0291 - acc: 0.9906 - val_loss: 1.9892 - val_acc: 0.7523\n",
      "Epoch 489/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0246 - acc: 0.9923 - val_loss: 2.0256 - val_acc: 0.7569\n",
      "Epoch 490/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0265 - acc: 0.9912 - val_loss: 2.0865 - val_acc: 0.7752\n",
      "Epoch 491/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0384 - acc: 0.9889 - val_loss: 1.8411 - val_acc: 0.7661\n",
      "Epoch 492/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0316 - acc: 0.9894 - val_loss: 1.8232 - val_acc: 0.7706\n",
      "Epoch 493/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0245 - acc: 0.9921 - val_loss: 2.1024 - val_acc: 0.7569\n",
      "Epoch 494/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0309 - acc: 0.9889 - val_loss: 1.7618 - val_acc: 0.7844\n",
      "Epoch 495/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0236 - acc: 0.9932 - val_loss: 1.8037 - val_acc: 0.7844\n",
      "Epoch 496/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0252 - acc: 0.9918 - val_loss: 2.1368 - val_acc: 0.7477\n",
      "Epoch 497/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0219 - acc: 0.9933 - val_loss: 2.2068 - val_acc: 0.7569\n",
      "Epoch 498/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0289 - acc: 0.9902 - val_loss: 2.0211 - val_acc: 0.7431\n",
      "Epoch 499/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0292 - acc: 0.9918 - val_loss: 1.8347 - val_acc: 0.7798\n",
      "Epoch 500/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0265 - acc: 0.9897 - val_loss: 1.7367 - val_acc: 0.7844\n",
      "40/40 [==============================] - 29s 731ms/step\n",
      "Accuracy: 0.7760284408329101. F1: 0.7735467200258921\n"
     ]
    }
   ],
   "source": [
    "cnn(1, make_model2D_S, epochs = 500, save_model = True, name = \"2D_small_1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "132/132 [==============================] - 172s 1s/step - loss: 1.3186 - acc: 0.5022 - val_loss: 1.0449 - val_acc: 0.6055\n",
      "Epoch 2/500\n",
      "132/132 [==============================] - 28s 215ms/step - loss: 0.9194 - acc: 0.6523 - val_loss: 0.9006 - val_acc: 0.6743\n",
      "Epoch 3/500\n",
      "132/132 [==============================] - 29s 216ms/step - loss: 0.8205 - acc: 0.6957 - val_loss: 0.8033 - val_acc: 0.7110\n",
      "Epoch 4/500\n",
      "132/132 [==============================] - 28s 208ms/step - loss: 0.7478 - acc: 0.7182 - val_loss: 0.7884 - val_acc: 0.7202\n",
      "Epoch 5/500\n",
      "132/132 [==============================] - 28s 209ms/step - loss: 0.6895 - acc: 0.7419 - val_loss: 0.7391 - val_acc: 0.7523\n",
      "Epoch 6/500\n",
      "132/132 [==============================] - 27s 206ms/step - loss: 0.6544 - acc: 0.7503 - val_loss: 0.7000 - val_acc: 0.7569\n",
      "Epoch 7/500\n",
      "132/132 [==============================] - 28s 213ms/step - loss: 0.6083 - acc: 0.7774 - val_loss: 0.6733 - val_acc: 0.7523\n",
      "Epoch 8/500\n",
      "132/132 [==============================] - 28s 213ms/step - loss: 0.5806 - acc: 0.7805 - val_loss: 0.6648 - val_acc: 0.7569\n",
      "Epoch 9/500\n",
      "132/132 [==============================] - 28s 208ms/step - loss: 0.5495 - acc: 0.7935 - val_loss: 0.6452 - val_acc: 0.8073\n",
      "Epoch 10/500\n",
      "132/132 [==============================] - 28s 214ms/step - loss: 0.5179 - acc: 0.8048 - val_loss: 0.6552 - val_acc: 0.7936\n",
      "Epoch 11/500\n",
      "132/132 [==============================] - 27s 208ms/step - loss: 0.4955 - acc: 0.8142 - val_loss: 0.6305 - val_acc: 0.7752\n",
      "Epoch 12/500\n",
      "132/132 [==============================] - 27s 204ms/step - loss: 0.4680 - acc: 0.8230 - val_loss: 0.6303 - val_acc: 0.7890\n",
      "Epoch 13/500\n",
      "132/132 [==============================] - 27s 203ms/step - loss: 0.4489 - acc: 0.8304 - val_loss: 0.6284 - val_acc: 0.7844\n",
      "Epoch 14/500\n",
      "132/132 [==============================] - 27s 203ms/step - loss: 0.4201 - acc: 0.8370 - val_loss: 0.6561 - val_acc: 0.7752\n",
      "Epoch 15/500\n",
      "132/132 [==============================] - 27s 204ms/step - loss: 0.3972 - acc: 0.8516 - val_loss: 0.6440 - val_acc: 0.7844\n",
      "Epoch 16/500\n",
      "132/132 [==============================] - 27s 205ms/step - loss: 0.3873 - acc: 0.8492 - val_loss: 0.6417 - val_acc: 0.7890\n",
      "Epoch 17/500\n",
      "132/132 [==============================] - 27s 204ms/step - loss: 0.3590 - acc: 0.8670 - val_loss: 0.6803 - val_acc: 0.7798\n",
      "Epoch 18/500\n",
      "132/132 [==============================] - 27s 205ms/step - loss: 0.3507 - acc: 0.8637 - val_loss: 0.6504 - val_acc: 0.7752\n",
      "Epoch 19/500\n",
      "132/132 [==============================] - 27s 206ms/step - loss: 0.3399 - acc: 0.8711 - val_loss: 0.6420 - val_acc: 0.7982\n",
      "Epoch 20/500\n",
      "132/132 [==============================] - 27s 205ms/step - loss: 0.3256 - acc: 0.8767 - val_loss: 0.6650 - val_acc: 0.7798\n",
      "Epoch 21/500\n",
      "132/132 [==============================] - 27s 204ms/step - loss: 0.3084 - acc: 0.8793 - val_loss: 0.6685 - val_acc: 0.7798\n",
      "Epoch 22/500\n",
      "132/132 [==============================] - 27s 204ms/step - loss: 0.2923 - acc: 0.8867 - val_loss: 0.6734 - val_acc: 0.7982\n",
      "Epoch 23/500\n",
      "132/132 [==============================] - 27s 204ms/step - loss: 0.2810 - acc: 0.8906 - val_loss: 0.7088 - val_acc: 0.7752\n",
      "Epoch 24/500\n",
      "132/132 [==============================] - 27s 205ms/step - loss: 0.2684 - acc: 0.8958 - val_loss: 0.7319 - val_acc: 0.7798\n",
      "Epoch 25/500\n",
      "132/132 [==============================] - 27s 207ms/step - loss: 0.2761 - acc: 0.8942 - val_loss: 0.6921 - val_acc: 0.8028\n",
      "Epoch 26/500\n",
      "132/132 [==============================] - 28s 210ms/step - loss: 0.2630 - acc: 0.9007 - val_loss: 0.7013 - val_acc: 0.8211s - loss: 0.266\n",
      "Epoch 27/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.2458 - acc: 0.9044 - val_loss: 0.6657 - val_acc: 0.8165\n",
      "Epoch 28/500\n",
      "132/132 [==============================] - 33s 251ms/step - loss: 0.2284 - acc: 0.9162 - val_loss: 0.6593 - val_acc: 0.8257\n",
      "Epoch 29/500\n",
      "132/132 [==============================] - 32s 245ms/step - loss: 0.2291 - acc: 0.9133 - val_loss: 0.7523 - val_acc: 0.8165\n",
      "Epoch 30/500\n",
      "132/132 [==============================] - 32s 246ms/step - loss: 0.2144 - acc: 0.9156 - val_loss: 0.7214 - val_acc: 0.8165\n",
      "Epoch 31/500\n",
      "132/132 [==============================] - 32s 245ms/step - loss: 0.2100 - acc: 0.9178 - val_loss: 0.7312 - val_acc: 0.8303\n",
      "Epoch 32/500\n",
      "132/132 [==============================] - 32s 244ms/step - loss: 0.2089 - acc: 0.9171 - val_loss: 0.7225 - val_acc: 0.7982\n",
      "Epoch 33/500\n",
      "132/132 [==============================] - 32s 246ms/step - loss: 0.2020 - acc: 0.9234 - val_loss: 0.7840 - val_acc: 0.7936\n",
      "Epoch 34/500\n",
      "132/132 [==============================] - 33s 248ms/step - loss: 0.1889 - acc: 0.9297 - val_loss: 0.8093 - val_acc: 0.8028\n",
      "Epoch 35/500\n",
      "132/132 [==============================] - 33s 247ms/step - loss: 0.1902 - acc: 0.9279 - val_loss: 0.8067 - val_acc: 0.8028\n",
      "Epoch 36/500\n",
      "132/132 [==============================] - 33s 248ms/step - loss: 0.1877 - acc: 0.9295 - val_loss: 0.7947 - val_acc: 0.8211\n",
      "Epoch 37/500\n",
      "132/132 [==============================] - 33s 249ms/step - loss: 0.1749 - acc: 0.9317 - val_loss: 0.8526 - val_acc: 0.8119\n",
      "Epoch 38/500\n",
      "132/132 [==============================] - 33s 251ms/step - loss: 0.1732 - acc: 0.9359 - val_loss: 0.8144 - val_acc: 0.8028\n",
      "Epoch 39/500\n",
      "132/132 [==============================] - 33s 248ms/step - loss: 0.1679 - acc: 0.9373 - val_loss: 0.8546 - val_acc: 0.7982\n",
      "Epoch 40/500\n",
      "132/132 [==============================] - 34s 254ms/step - loss: 0.1621 - acc: 0.9423 - val_loss: 0.8447 - val_acc: 0.7798\n",
      "Epoch 41/500\n",
      "132/132 [==============================] - 39s 297ms/step - loss: 0.1608 - acc: 0.9385 - val_loss: 0.8267 - val_acc: 0.8028\n",
      "Epoch 42/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.1430 - acc: 0.9451 - val_loss: 0.8215 - val_acc: 0.8028\n",
      "Epoch 43/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.1530 - acc: 0.9397 - val_loss: 0.8465 - val_acc: 0.8028\n",
      "Epoch 44/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.1362 - acc: 0.9512 - val_loss: 0.9194 - val_acc: 0.8119\n",
      "Epoch 45/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.1433 - acc: 0.9476 - val_loss: 0.9556 - val_acc: 0.8073\n",
      "Epoch 46/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.1423 - acc: 0.9451 - val_loss: 0.9159 - val_acc: 0.7798\n",
      "Epoch 47/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.1266 - acc: 0.9520 - val_loss: 0.9697 - val_acc: 0.8119\n",
      "Epoch 48/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.1393 - acc: 0.9465 - val_loss: 0.8269 - val_acc: 0.8349\n",
      "Epoch 49/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.1259 - acc: 0.9536 - val_loss: 0.8866 - val_acc: 0.8257\n",
      "Epoch 50/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.1291 - acc: 0.9526 - val_loss: 0.8923 - val_acc: 0.7844\n",
      "Epoch 51/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.1129 - acc: 0.9574 - val_loss: 0.9911 - val_acc: 0.8119\n",
      "Epoch 52/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.1178 - acc: 0.9588 - val_loss: 0.9778 - val_acc: 0.8073\n",
      "Epoch 53/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.1098 - acc: 0.9595 - val_loss: 1.0788 - val_acc: 0.7982\n",
      "Epoch 54/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.1101 - acc: 0.9586 - val_loss: 0.9609 - val_acc: 0.7982\n",
      "Epoch 55/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.1076 - acc: 0.9589 - val_loss: 1.0069 - val_acc: 0.7936\n",
      "Epoch 56/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.1338 - acc: 0.9515 - val_loss: 0.9623 - val_acc: 0.8073\n",
      "Epoch 57/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.1093 - acc: 0.9603 - val_loss: 0.9877 - val_acc: 0.7936\n",
      "Epoch 58/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.1032 - acc: 0.9614 - val_loss: 1.0643 - val_acc: 0.7844\n",
      "Epoch 59/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.1024 - acc: 0.9612 - val_loss: 1.0508 - val_acc: 0.7936\n",
      "Epoch 60/500\n",
      "132/132 [==============================] - 31s 239ms/step - loss: 0.1087 - acc: 0.9598 - val_loss: 1.1217 - val_acc: 0.7936\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 61/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.1061 - acc: 0.9615 - val_loss: 1.0096 - val_acc: 0.7982\n",
      "Epoch 62/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.1043 - acc: 0.9620 - val_loss: 1.0908 - val_acc: 0.7844\n",
      "Epoch 63/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0969 - acc: 0.9639 - val_loss: 1.0079 - val_acc: 0.8211\n",
      "Epoch 64/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1095 - acc: 0.9620 - val_loss: 0.9788 - val_acc: 0.8165\n",
      "Epoch 65/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0935 - acc: 0.9670 - val_loss: 0.9999 - val_acc: 0.8165\n",
      "Epoch 66/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0901 - acc: 0.9662 - val_loss: 1.1859 - val_acc: 0.8073\n",
      "Epoch 67/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0876 - acc: 0.9688 - val_loss: 1.0028 - val_acc: 0.8257\n",
      "Epoch 68/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0791 - acc: 0.9718 - val_loss: 1.1370 - val_acc: 0.7890\n",
      "Epoch 69/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0865 - acc: 0.9694 - val_loss: 1.1212 - val_acc: 0.8119\n",
      "Epoch 70/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0828 - acc: 0.9703 - val_loss: 1.1576 - val_acc: 0.8119\n",
      "Epoch 71/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0771 - acc: 0.9724 - val_loss: 1.1612 - val_acc: 0.7844\n",
      "Epoch 72/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0876 - acc: 0.9689 - val_loss: 1.1607 - val_acc: 0.8028\n",
      "Epoch 73/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0912 - acc: 0.9671 - val_loss: 1.1174 - val_acc: 0.7844\n",
      "Epoch 74/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0821 - acc: 0.9698 - val_loss: 1.1121 - val_acc: 0.7936\n",
      "Epoch 75/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0810 - acc: 0.9702 - val_loss: 0.9841 - val_acc: 0.8073\n",
      "Epoch 76/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0887 - acc: 0.9692 - val_loss: 1.0571 - val_acc: 0.7982\n",
      "Epoch 77/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0772 - acc: 0.9714 - val_loss: 1.0506 - val_acc: 0.8073\n",
      "Epoch 78/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0819 - acc: 0.9705 - val_loss: 1.1036 - val_acc: 0.7706\n",
      "Epoch 79/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0729 - acc: 0.9726 - val_loss: 1.1196 - val_acc: 0.7706\n",
      "Epoch 80/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0844 - acc: 0.9692 - val_loss: 1.1677 - val_acc: 0.8073\n",
      "Epoch 81/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0754 - acc: 0.9738 - val_loss: 1.2385 - val_acc: 0.7936\n",
      "Epoch 82/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0671 - acc: 0.9765 - val_loss: 1.1887 - val_acc: 0.8028\n",
      "Epoch 83/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0667 - acc: 0.9765 - val_loss: 1.2695 - val_acc: 0.8028\n",
      "Epoch 84/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0648 - acc: 0.9759 - val_loss: 1.2781 - val_acc: 0.7982\n",
      "Epoch 85/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0819 - acc: 0.9694 - val_loss: 1.4506 - val_acc: 0.7569\n",
      "Epoch 86/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0754 - acc: 0.9732 - val_loss: 1.2400 - val_acc: 0.7798\n",
      "Epoch 87/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0734 - acc: 0.9773 - val_loss: 1.2795 - val_acc: 0.8028\n",
      "Epoch 88/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0740 - acc: 0.9730 - val_loss: 1.2163 - val_acc: 0.7844\n",
      "Epoch 89/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0743 - acc: 0.9738 - val_loss: 1.2421 - val_acc: 0.7982\n",
      "Epoch 90/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0740 - acc: 0.9764 - val_loss: 1.1901 - val_acc: 0.7936\n",
      "Epoch 91/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0713 - acc: 0.9752 - val_loss: 1.2478 - val_acc: 0.7936\n",
      "Epoch 92/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0850 - acc: 0.9720 - val_loss: 1.1356 - val_acc: 0.8028\n",
      "Epoch 93/500\n",
      "132/132 [==============================] - 31s 239ms/step - loss: 0.0649 - acc: 0.9783 - val_loss: 1.2921 - val_acc: 0.7844\n",
      "Epoch 94/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0682 - acc: 0.9739 - val_loss: 1.2941 - val_acc: 0.7982\n",
      "Epoch 95/500\n",
      "132/132 [==============================] - 31s 239ms/step - loss: 0.0651 - acc: 0.9779 - val_loss: 1.2635 - val_acc: 0.7798\n",
      "Epoch 96/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0735 - acc: 0.9757 - val_loss: 1.3484 - val_acc: 0.7982\n",
      "Epoch 97/500\n",
      "132/132 [==============================] - 31s 239ms/step - loss: 0.0672 - acc: 0.9759 - val_loss: 1.3266 - val_acc: 0.7844\n",
      "Epoch 98/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0719 - acc: 0.9742 - val_loss: 1.4227 - val_acc: 0.7890\n",
      "Epoch 99/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0742 - acc: 0.9765 - val_loss: 1.2257 - val_acc: 0.7890\n",
      "Epoch 100/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0640 - acc: 0.9777 - val_loss: 1.3340 - val_acc: 0.8073\n",
      "Epoch 101/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0680 - acc: 0.9761 - val_loss: 1.2415 - val_acc: 0.8073\n",
      "Epoch 102/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0651 - acc: 0.9777 - val_loss: 1.2869 - val_acc: 0.8119\n",
      "Epoch 103/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0602 - acc: 0.9792 - val_loss: 1.4259 - val_acc: 0.8165\n",
      "Epoch 104/500\n",
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      "Epoch 134/500\n",
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      "Epoch 165/500\n",
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      "Epoch 166/500\n",
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      "Epoch 167/500\n",
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      "Epoch 168/500\n",
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      "Epoch 169/500\n",
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      "Epoch 170/500\n",
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      "Epoch 171/500\n",
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      "Epoch 172/500\n",
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      "Epoch 174/500\n",
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      "Epoch 175/500\n",
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      "Epoch 176/500\n",
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      "Epoch 177/500\n",
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      "Epoch 178/500\n",
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      "Epoch 179/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0579 - acc: 0.9824 - val_loss: 1.4679 - val_acc: 0.7890\n",
      "Epoch 180/500\n",
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      "Epoch 181/500\n",
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      "Epoch 182/500\n",
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      "Epoch 183/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0362 - acc: 0.9874 - val_loss: 1.6865 - val_acc: 0.8028\n",
      "Epoch 184/500\n",
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      "Epoch 185/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0399 - acc: 0.9879 - val_loss: 1.4458 - val_acc: 0.8303\n",
      "Epoch 186/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0386 - acc: 0.9874 - val_loss: 1.4980 - val_acc: 0.8028\n",
      "Epoch 187/500\n",
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      "Epoch 188/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0389 - acc: 0.9864 - val_loss: 1.6284 - val_acc: 0.8028\n",
      "Epoch 189/500\n",
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      "Epoch 190/500\n",
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      "Epoch 191/500\n",
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      "Epoch 192/500\n",
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      "Epoch 193/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0532 - acc: 0.9832 - val_loss: 1.7151 - val_acc: 0.7798\n",
      "Epoch 194/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0503 - acc: 0.9835 - val_loss: 1.5137 - val_acc: 0.7890\n",
      "Epoch 195/500\n",
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      "Epoch 198/500\n",
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      "Epoch 199/500\n",
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      "Epoch 200/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0277 - acc: 0.9912 - val_loss: 1.7725 - val_acc: 0.7615\n",
      "Epoch 201/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0422 - acc: 0.9871 - val_loss: 1.9924 - val_acc: 0.7706\n",
      "Epoch 202/500\n",
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      "Epoch 203/500\n",
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      "Epoch 205/500\n",
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      "Epoch 206/500\n",
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      "Epoch 208/500\n",
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      "Epoch 209/500\n",
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      "Epoch 210/500\n",
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      "Epoch 211/500\n",
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      "Epoch 212/500\n",
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      "Epoch 226/500\n",
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      "Epoch 227/500\n",
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      "Epoch 228/500\n",
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      "Epoch 229/500\n",
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      "Epoch 230/500\n",
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      "Epoch 238/500\n",
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      "Epoch 239/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0291 - acc: 0.9895 - val_loss: 1.5970 - val_acc: 0.8211\n",
      "Epoch 240/500\n",
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      "Epoch 241/500\n",
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      "Epoch 242/500\n",
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      "Epoch 243/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0296 - acc: 0.9895 - val_loss: 1.7639 - val_acc: 0.7798\n",
      "Epoch 244/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0369 - acc: 0.9886 - val_loss: 1.8526 - val_acc: 0.8028\n",
      "Epoch 245/500\n",
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      "Epoch 247/500\n",
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      "Epoch 248/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0343 - acc: 0.9902 - val_loss: 1.6818 - val_acc: 0.8028\n",
      "Epoch 249/500\n",
      "132/132 [==============================] - 31s 239ms/step - loss: 0.0463 - acc: 0.9871 - val_loss: 1.6924 - val_acc: 0.8073\n",
      "Epoch 250/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0268 - acc: 0.9905 - val_loss: 1.8002 - val_acc: 0.7982\n",
      "Epoch 251/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.0283 - acc: 0.9906 - val_loss: 1.7981 - val_acc: 0.7844\n",
      "Epoch 252/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0235 - acc: 0.9921 - val_loss: 1.9219 - val_acc: 0.7936\n",
      "Epoch 253/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0365 - acc: 0.9882 - val_loss: 1.9385 - val_acc: 0.7752\n",
      "Epoch 254/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0366 - acc: 0.9877 - val_loss: 1.6838 - val_acc: 0.7890\n",
      "Epoch 255/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0414 - acc: 0.9877 - val_loss: 1.7102 - val_acc: 0.8073\n",
      "Epoch 256/500\n",
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      "Epoch 257/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0444 - acc: 0.9873 - val_loss: 1.8256 - val_acc: 0.7661\n",
      "Epoch 258/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0354 - acc: 0.9897 - val_loss: 1.7905 - val_acc: 0.7982\n",
      "Epoch 259/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0278 - acc: 0.9914 - val_loss: 1.8904 - val_acc: 0.7982\n",
      "Epoch 260/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0373 - acc: 0.9885 - val_loss: 1.9200 - val_acc: 0.8028\n",
      "Epoch 261/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0290 - acc: 0.9903 - val_loss: 1.8180 - val_acc: 0.7844\n",
      "Epoch 262/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0289 - acc: 0.9906 - val_loss: 1.8682 - val_acc: 0.8028\n",
      "Epoch 263/500\n",
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      "Epoch 264/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0385 - acc: 0.9868 - val_loss: 1.8706 - val_acc: 0.7982\n",
      "Epoch 265/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0222 - acc: 0.9923 - val_loss: 1.9107 - val_acc: 0.8165\n",
      "Epoch 266/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0293 - acc: 0.9908 - val_loss: 1.9943 - val_acc: 0.7752\n",
      "Epoch 267/500\n",
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      "Epoch 268/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0389 - acc: 0.9870 - val_loss: 1.9736 - val_acc: 0.7661\n",
      "Epoch 269/500\n",
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      "Epoch 270/500\n",
      "132/132 [==============================] - 32s 242ms/step - loss: 0.0274 - acc: 0.9902 - val_loss: 1.7589 - val_acc: 0.7890\n",
      "Epoch 271/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0261 - acc: 0.9908 - val_loss: 1.7884 - val_acc: 0.8028\n",
      "Epoch 272/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0411 - acc: 0.9880 - val_loss: 1.9622 - val_acc: 0.7523\n",
      "Epoch 273/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0387 - acc: 0.9892 - val_loss: 2.0317 - val_acc: 0.7798\n",
      "Epoch 274/500\n",
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      "Epoch 275/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0277 - acc: 0.9897 - val_loss: 1.9898 - val_acc: 0.7936\n",
      "Epoch 276/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0281 - acc: 0.9909 - val_loss: 1.9048 - val_acc: 0.7982\n",
      "Epoch 277/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0316 - acc: 0.9891 - val_loss: 1.9482 - val_acc: 0.7982\n",
      "Epoch 278/500\n",
      "132/132 [==============================] - 31s 239ms/step - loss: 0.0317 - acc: 0.9895 - val_loss: 2.0043 - val_acc: 0.7890\n",
      "Epoch 279/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0215 - acc: 0.9927 - val_loss: 2.1780 - val_acc: 0.7798\n",
      "Epoch 280/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.0336 - acc: 0.9903 - val_loss: 1.9176 - val_acc: 0.7890\n",
      "Epoch 281/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0341 - acc: 0.9906 - val_loss: 1.9799 - val_acc: 0.7844\n",
      "Epoch 282/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0231 - acc: 0.9921 - val_loss: 2.0648 - val_acc: 0.7752\n",
      "Epoch 283/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0286 - acc: 0.9914 - val_loss: 1.8402 - val_acc: 0.8211\n",
      "Epoch 284/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0300 - acc: 0.9918 - val_loss: 1.8570 - val_acc: 0.7798\n",
      "Epoch 285/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0299 - acc: 0.9903 - val_loss: 1.8746 - val_acc: 0.7890\n",
      "Epoch 286/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0308 - acc: 0.9908 - val_loss: 2.0104 - val_acc: 0.7615\n",
      "Epoch 287/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0367 - acc: 0.9888 - val_loss: 1.9848 - val_acc: 0.7890\n",
      "Epoch 288/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0320 - acc: 0.9894 - val_loss: 2.0013 - val_acc: 0.7752\n",
      "Epoch 289/500\n",
      "132/132 [==============================] - 32s 242ms/step - loss: 0.0305 - acc: 0.9911 - val_loss: 2.0480 - val_acc: 0.7844\n",
      "Epoch 290/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0450 - acc: 0.9859 - val_loss: 2.0200 - val_acc: 0.7890\n",
      "Epoch 291/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0370 - acc: 0.9882 - val_loss: 1.8775 - val_acc: 0.7982\n",
      "Epoch 292/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0339 - acc: 0.9900 - val_loss: 1.9009 - val_acc: 0.8028\n",
      "Epoch 293/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0317 - acc: 0.9900 - val_loss: 1.9676 - val_acc: 0.7982\n",
      "Epoch 294/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0270 - acc: 0.9918 - val_loss: 1.7762 - val_acc: 0.8073\n",
      "Epoch 295/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0344 - acc: 0.9891 - val_loss: 1.7934 - val_acc: 0.7982\n",
      "Epoch 296/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0316 - acc: 0.9894 - val_loss: 1.7605 - val_acc: 0.8119\n",
      "Epoch 297/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0311 - acc: 0.9898 - val_loss: 1.7152 - val_acc: 0.8119\n",
      "Epoch 298/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0297 - acc: 0.9905 - val_loss: 1.6479 - val_acc: 0.8073\n",
      "Epoch 299/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0353 - acc: 0.9903 - val_loss: 1.5942 - val_acc: 0.8303\n",
      "Epoch 300/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0289 - acc: 0.9908 - val_loss: 1.9137 - val_acc: 0.7936\n",
      "Epoch 301/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0238 - acc: 0.9918 - val_loss: 1.9934 - val_acc: 0.7936\n",
      "Epoch 302/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0275 - acc: 0.9900 - val_loss: 1.9117 - val_acc: 0.7936\n",
      "Epoch 303/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0248 - acc: 0.9920 - val_loss: 1.8414 - val_acc: 0.7936\n",
      "Epoch 304/500\n",
      "132/132 [==============================] - 33s 251ms/step - loss: 0.0307 - acc: 0.9905 - val_loss: 2.0834 - val_acc: 0.7706\n",
      "Epoch 305/500\n",
      "132/132 [==============================] - 33s 250ms/step - loss: 0.0273 - acc: 0.9917 - val_loss: 1.7901 - val_acc: 0.7569\n",
      "Epoch 306/500\n",
      "132/132 [==============================] - 33s 250ms/step - loss: 0.0260 - acc: 0.9906 - val_loss: 1.7949 - val_acc: 0.7798\n",
      "Epoch 307/500\n",
      "132/132 [==============================] - 33s 249ms/step - loss: 0.0301 - acc: 0.9892 - val_loss: 1.8882 - val_acc: 0.7982\n",
      "Epoch 308/500\n",
      "132/132 [==============================] - 33s 251ms/step - loss: 0.0303 - acc: 0.9900 - val_loss: 1.7591 - val_acc: 0.7982\n",
      "Epoch 309/500\n",
      "132/132 [==============================] - 33s 249ms/step - loss: 0.0370 - acc: 0.9892 - val_loss: 1.9461 - val_acc: 0.7890\n",
      "Epoch 310/500\n",
      "132/132 [==============================] - 33s 252ms/step - loss: 0.0336 - acc: 0.9888 - val_loss: 1.9002 - val_acc: 0.7661\n",
      "Epoch 311/500\n",
      "132/132 [==============================] - 33s 250ms/step - loss: 0.0231 - acc: 0.9914 - val_loss: 1.8744 - val_acc: 0.7936\n",
      "Epoch 312/500\n",
      "132/132 [==============================] - 32s 244ms/step - loss: 0.0259 - acc: 0.9911 - val_loss: 1.7545 - val_acc: 0.7844\n",
      "Epoch 313/500\n",
      "132/132 [==============================] - 32s 244ms/step - loss: 0.0351 - acc: 0.9891 - val_loss: 1.8069 - val_acc: 0.7890\n",
      "Epoch 314/500\n",
      "132/132 [==============================] - 32s 243ms/step - loss: 0.0259 - acc: 0.9920 - val_loss: 2.0386 - val_acc: 0.7706\n",
      "Epoch 315/500\n",
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      "Epoch 316/500\n",
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      "Epoch 317/500\n",
      "132/132 [==============================] - 32s 244ms/step - loss: 0.0386 - acc: 0.9882 - val_loss: 1.8476 - val_acc: 0.7844\n",
      "Epoch 318/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0324 - acc: 0.9915 - val_loss: 1.9168 - val_acc: 0.7982\n",
      "Epoch 319/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0300 - acc: 0.9908 - val_loss: 1.9742 - val_acc: 0.7936\n",
      "Epoch 320/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0336 - acc: 0.9885 - val_loss: 2.0094 - val_acc: 0.7982\n",
      "Epoch 321/500\n",
      "132/132 [==============================] - 31s 239ms/step - loss: 0.0343 - acc: 0.9898 - val_loss: 1.8792 - val_acc: 0.8028\n",
      "Epoch 322/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0307 - acc: 0.9898 - val_loss: 1.8868 - val_acc: 0.7936\n",
      "Epoch 323/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.0287 - acc: 0.9914 - val_loss: 1.7176 - val_acc: 0.7982\n",
      "Epoch 324/500\n",
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      "Epoch 325/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0257 - acc: 0.9908 - val_loss: 1.7514 - val_acc: 0.7936\n",
      "Epoch 326/500\n",
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      "Epoch 327/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0301 - acc: 0.9897 - val_loss: 1.7878 - val_acc: 0.8211\n",
      "Epoch 328/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.0285 - acc: 0.9920 - val_loss: 1.7810 - val_acc: 0.8257\n",
      "Epoch 329/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0236 - acc: 0.9912 - val_loss: 1.8024 - val_acc: 0.7982\n",
      "Epoch 330/500\n",
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      "Epoch 331/500\n",
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      "Epoch 332/500\n",
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      "Epoch 333/500\n",
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      "Epoch 334/500\n",
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      "Epoch 335/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0257 - acc: 0.9921 - val_loss: 1.9434 - val_acc: 0.7982\n",
      "Epoch 336/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0354 - acc: 0.9894 - val_loss: 1.8367 - val_acc: 0.8028\n",
      "Epoch 337/500\n",
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      "Epoch 338/500\n",
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      "Epoch 339/500\n",
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      "Epoch 341/500\n",
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      "Epoch 342/500\n",
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      "Epoch 343/500\n",
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      "Epoch 344/500\n",
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      "Epoch 345/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0277 - acc: 0.9909 - val_loss: 1.8842 - val_acc: 0.8028\n",
      "Epoch 346/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0257 - acc: 0.9911 - val_loss: 1.8796 - val_acc: 0.7936\n",
      "Epoch 347/500\n",
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      "Epoch 348/500\n",
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      "Epoch 350/500\n",
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      "Epoch 351/500\n",
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      "Epoch 352/500\n",
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      "Epoch 353/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0188 - acc: 0.9950 - val_loss: 1.9920 - val_acc: 0.8165\n",
      "Epoch 354/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0235 - acc: 0.9930 - val_loss: 1.9438 - val_acc: 0.7936\n",
      "Epoch 355/500\n",
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      "Epoch 356/500\n",
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      "Epoch 377/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0235 - acc: 0.9921 - val_loss: 1.8764 - val_acc: 0.8028\n",
      "Epoch 378/500\n",
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      "Epoch 382/500\n",
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      "Epoch 383/500\n",
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      "Epoch 384/500\n",
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      "Epoch 385/500\n",
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      "Epoch 386/500\n",
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      "Epoch 387/500\n",
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      "Epoch 388/500\n",
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      "Epoch 389/500\n",
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      "Epoch 390/500\n",
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      "Epoch 393/500\n",
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      "Epoch 394/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0228 - acc: 0.9921 - val_loss: 2.0826 - val_acc: 0.7706\n",
      "Epoch 395/500\n",
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      "Epoch 396/500\n",
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      "Epoch 397/500\n",
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      "Epoch 398/500\n",
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      "Epoch 399/500\n",
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      "Epoch 400/500\n",
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      "Epoch 401/500\n",
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      "Epoch 402/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0282 - acc: 0.9915 - val_loss: 1.9945 - val_acc: 0.7890\n",
      "Epoch 403/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0186 - acc: 0.9926 - val_loss: 1.9330 - val_acc: 0.7890\n",
      "Epoch 404/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0198 - acc: 0.9936 - val_loss: 1.9964 - val_acc: 0.7798\n",
      "Epoch 405/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0212 - acc: 0.9936 - val_loss: 1.8544 - val_acc: 0.8119\n",
      "Epoch 406/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0235 - acc: 0.9924 - val_loss: 1.8943 - val_acc: 0.7890\n",
      "Epoch 407/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0405 - acc: 0.9903 - val_loss: 1.9654 - val_acc: 0.8028\n",
      "Epoch 408/500\n",
      "132/132 [==============================] - 32s 242ms/step - loss: 0.0261 - acc: 0.9917 - val_loss: 1.8261 - val_acc: 0.8028\n",
      "Epoch 409/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0267 - acc: 0.9913 - val_loss: 1.8078 - val_acc: 0.8073\n",
      "Epoch 410/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0269 - acc: 0.9912 - val_loss: 1.6931 - val_acc: 0.8119\n",
      "Epoch 411/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0234 - acc: 0.9921 - val_loss: 1.9310 - val_acc: 0.7798\n",
      "Epoch 412/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0308 - acc: 0.9905 - val_loss: 1.9649 - val_acc: 0.7982\n",
      "Epoch 413/500\n",
      "132/132 [==============================] - 32s 242ms/step - loss: 0.0219 - acc: 0.9924 - val_loss: 2.0649 - val_acc: 0.7936\n",
      "Epoch 414/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0212 - acc: 0.9921 - val_loss: 1.8657 - val_acc: 0.7890\n",
      "Epoch 415/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0229 - acc: 0.9927 - val_loss: 1.7211 - val_acc: 0.7890\n",
      "Epoch 416/500\n",
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      "Epoch 419/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0210 - acc: 0.9927 - val_loss: 1.7911 - val_acc: 0.8028\n",
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      "Epoch 421/500\n",
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      "132/132 [==============================] - 32s 239ms/step - loss: 0.0404 - acc: 0.9886 - val_loss: 1.9734 - val_acc: 0.7982\n",
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      "Epoch 426/500\n",
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      "Epoch 427/500\n",
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      "Epoch 430/500\n",
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      "Epoch 431/500\n",
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      "Epoch 437/500\n",
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      "Epoch 438/500\n",
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      "Epoch 442/500\n",
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      "Epoch 446/500\n",
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      "Epoch 447/500\n",
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      "Epoch 448/500\n",
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      "Epoch 450/500\n",
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      "Epoch 472/500\n",
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      "Epoch 473/500\n",
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      "Epoch 474/500\n",
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      "Epoch 475/500\n",
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      "Epoch 476/500\n",
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      "Epoch 477/500\n",
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      "Epoch 478/500\n",
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      "Epoch 479/500\n",
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      "Epoch 480/500\n",
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      "Epoch 481/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0264 - acc: 0.9918 - val_loss: 1.9352 - val_acc: 0.8028\n",
      "Epoch 482/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0262 - acc: 0.9920 - val_loss: 1.8488 - val_acc: 0.8073\n",
      "Epoch 483/500\n",
      "132/132 [==============================] - 31s 239ms/step - loss: 0.0239 - acc: 0.9938 - val_loss: 1.8700 - val_acc: 0.7982\n",
      "Epoch 484/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0152 - acc: 0.9952 - val_loss: 1.9171 - val_acc: 0.8028\n",
      "Epoch 485/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0184 - acc: 0.9948 - val_loss: 1.8533 - val_acc: 0.7936\n",
      "Epoch 486/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0167 - acc: 0.9945 - val_loss: 1.9940 - val_acc: 0.8073\n",
      "Epoch 487/500\n",
      "132/132 [==============================] - 32s 242ms/step - loss: 0.0191 - acc: 0.9938 - val_loss: 1.9391 - val_acc: 0.8165\n",
      "Epoch 488/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0185 - acc: 0.9932 - val_loss: 1.9806 - val_acc: 0.8119\n",
      "Epoch 489/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0208 - acc: 0.9936 - val_loss: 1.9697 - val_acc: 0.7982\n",
      "Epoch 490/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0192 - acc: 0.9939 - val_loss: 1.9989 - val_acc: 0.8028\n",
      "Epoch 491/500\n",
      "132/132 [==============================] - 31s 239ms/step - loss: 0.0189 - acc: 0.9932 - val_loss: 2.0591 - val_acc: 0.8028\n",
      "Epoch 492/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0199 - acc: 0.9938 - val_loss: 2.0642 - val_acc: 0.8028\n",
      "Epoch 493/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0281 - acc: 0.9914 - val_loss: 2.0395 - val_acc: 0.7890\n",
      "Epoch 494/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0297 - acc: 0.9911 - val_loss: 2.1060 - val_acc: 0.7844\n",
      "Epoch 495/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0189 - acc: 0.9936 - val_loss: 2.3261 - val_acc: 0.7706\n",
      "Epoch 496/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0244 - acc: 0.9912 - val_loss: 2.1769 - val_acc: 0.7890\n",
      "Epoch 497/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0357 - acc: 0.9905 - val_loss: 2.1489 - val_acc: 0.7890\n",
      "Epoch 498/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0268 - acc: 0.9908 - val_loss: 1.8625 - val_acc: 0.8073\n",
      "Epoch 499/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0194 - acc: 0.9939 - val_loss: 2.1564 - val_acc: 0.7890\n",
      "Epoch 500/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0238 - acc: 0.9926 - val_loss: 2.2253 - val_acc: 0.7844\n",
      "40/40 [==============================] - 50s 1s/step\n",
      "Accuracy: 0.8166582021330625. F1: 0.812294948495575\n"
     ]
    }
   ],
   "source": [
    "cnn(2, make_model2D_S, epochs = 500, save_model = True, name = \"2D_small_2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "132/132 [==============================] - 83s 628ms/step - loss: 1.6677 - acc: 0.3698 - val_loss: 1.1795 - val_acc: 0.5596\n",
      "Epoch 2/500\n",
      "132/132 [==============================] - 30s 228ms/step - loss: 1.0659 - acc: 0.5942 - val_loss: 0.9469 - val_acc: 0.6606\n",
      "Epoch 3/500\n",
      "132/132 [==============================] - 32s 242ms/step - loss: 0.9186 - acc: 0.6553 - val_loss: 0.8398 - val_acc: 0.6972\n",
      "Epoch 4/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.8267 - acc: 0.6936 - val_loss: 0.7937 - val_acc: 0.7018\n",
      "Epoch 5/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.7654 - acc: 0.7234 - val_loss: 0.7509 - val_acc: 0.7294\n",
      "Epoch 6/500\n",
      "132/132 [==============================] - 32s 244ms/step - loss: 0.7031 - acc: 0.7441 - val_loss: 0.6537 - val_acc: 0.7385\n",
      "Epoch 7/500\n",
      "132/132 [==============================] - 33s 248ms/step - loss: 0.6657 - acc: 0.7612 - val_loss: 0.5802 - val_acc: 0.7890\n",
      "Epoch 8/500\n",
      "132/132 [==============================] - 33s 248ms/step - loss: 0.6510 - acc: 0.7686 - val_loss: 0.5583 - val_acc: 0.7523\n",
      "Epoch 9/500\n",
      "132/132 [==============================] - 33s 252ms/step - loss: 0.6092 - acc: 0.7819 - val_loss: 0.5453 - val_acc: 0.7890\n",
      "Epoch 10/500\n",
      "132/132 [==============================] - 34s 261ms/step - loss: 0.5962 - acc: 0.7771 - val_loss: 0.5113 - val_acc: 0.7982\n",
      "Epoch 11/500\n",
      "132/132 [==============================] - 35s 268ms/step - loss: 0.5685 - acc: 0.7942 - val_loss: 0.5280 - val_acc: 0.7798\n",
      "Epoch 12/500\n",
      "132/132 [==============================] - 33s 252ms/step - loss: 0.5560 - acc: 0.8024 - val_loss: 0.5037 - val_acc: 0.7890\n",
      "Epoch 13/500\n",
      "132/132 [==============================] - 35s 267ms/step - loss: 0.5295 - acc: 0.8080 - val_loss: 0.4781 - val_acc: 0.8073\n",
      "Epoch 14/500\n",
      "132/132 [==============================] - 35s 264ms/step - loss: 0.4992 - acc: 0.8171 - val_loss: 0.5202 - val_acc: 0.8073\n",
      "Epoch 15/500\n",
      "132/132 [==============================] - 35s 264ms/step - loss: 0.4770 - acc: 0.8302 - val_loss: 0.4893 - val_acc: 0.8028\n",
      "Epoch 16/500\n",
      "132/132 [==============================] - 34s 257ms/step - loss: 0.4605 - acc: 0.8327 - val_loss: 0.4721 - val_acc: 0.8257\n",
      "Epoch 17/500\n",
      "132/132 [==============================] - 33s 247ms/step - loss: 0.4445 - acc: 0.8345 - val_loss: 0.4639 - val_acc: 0.8119\n",
      "Epoch 18/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.4345 - acc: 0.8380 - val_loss: 0.4896 - val_acc: 0.8211\n",
      "Epoch 19/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.4213 - acc: 0.8432 - val_loss: 0.4687 - val_acc: 0.8119\n",
      "Epoch 20/500\n",
      "132/132 [==============================] - 34s 254ms/step - loss: 0.3939 - acc: 0.8517 - val_loss: 0.4105 - val_acc: 0.8440\n",
      "Epoch 21/500\n",
      "132/132 [==============================] - 33s 254ms/step - loss: 0.3896 - acc: 0.8563 - val_loss: 0.4469 - val_acc: 0.8165\n",
      "Epoch 22/500\n",
      "132/132 [==============================] - 33s 249ms/step - loss: 0.3847 - acc: 0.8541 - val_loss: 0.4627 - val_acc: 0.8119\n",
      "Epoch 23/500\n",
      "132/132 [==============================] - 34s 256ms/step - loss: 0.3839 - acc: 0.8586 - val_loss: 0.4236 - val_acc: 0.8394\n",
      "Epoch 24/500\n",
      "132/132 [==============================] - 33s 252ms/step - loss: 0.3583 - acc: 0.8711 - val_loss: 0.4224 - val_acc: 0.8394\n",
      "Epoch 25/500\n",
      "132/132 [==============================] - 34s 259ms/step - loss: 0.3439 - acc: 0.8710 - val_loss: 0.3908 - val_acc: 0.8578\n",
      "Epoch 26/500\n",
      "132/132 [==============================] - 33s 250ms/step - loss: 0.3302 - acc: 0.8823 - val_loss: 0.3874 - val_acc: 0.8486\n",
      "Epoch 27/500\n",
      "132/132 [==============================] - 32s 245ms/step - loss: 0.3216 - acc: 0.8801 - val_loss: 0.4344 - val_acc: 0.8257\n",
      "Epoch 28/500\n",
      "132/132 [==============================] - 33s 252ms/step - loss: 0.3096 - acc: 0.8824 - val_loss: 0.3900 - val_acc: 0.8578\n",
      "Epoch 29/500\n",
      "132/132 [==============================] - 34s 259ms/step - loss: 0.2919 - acc: 0.8941 - val_loss: 0.4114 - val_acc: 0.8578\n",
      "Epoch 30/500\n",
      "132/132 [==============================] - 34s 258ms/step - loss: 0.2911 - acc: 0.8901 - val_loss: 0.4233 - val_acc: 0.8670\n",
      "Epoch 31/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.2997 - acc: 0.8883 - val_loss: 0.4367 - val_acc: 0.8303\n",
      "Epoch 32/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.2885 - acc: 0.8922 - val_loss: 0.4040 - val_acc: 0.8532\n",
      "Epoch 33/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.2716 - acc: 0.8958 - val_loss: 0.4369 - val_acc: 0.8303\n",
      "Epoch 34/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.2751 - acc: 0.8956 - val_loss: 0.4103 - val_acc: 0.8532\n",
      "Epoch 35/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.2627 - acc: 0.9003 - val_loss: 0.3942 - val_acc: 0.8670\n",
      "Epoch 36/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.2496 - acc: 0.9052 - val_loss: 0.4128 - val_acc: 0.8532\n",
      "Epoch 37/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.2614 - acc: 0.8995 - val_loss: 0.4083 - val_acc: 0.8440\n",
      "Epoch 38/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.2416 - acc: 0.9088 - val_loss: 0.4295 - val_acc: 0.8440\n",
      "Epoch 39/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.2328 - acc: 0.9122 - val_loss: 0.4251 - val_acc: 0.8532\n",
      "Epoch 40/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.2359 - acc: 0.9114 - val_loss: 0.4082 - val_acc: 0.8349\n",
      "Epoch 41/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.2269 - acc: 0.9150 - val_loss: 0.4002 - val_acc: 0.8624\n",
      "Epoch 42/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.2064 - acc: 0.9229 - val_loss: 0.4258 - val_acc: 0.8578\n",
      "Epoch 43/500\n",
      "132/132 [==============================] - 32s 244ms/step - loss: 0.2053 - acc: 0.9220 - val_loss: 0.4535 - val_acc: 0.8349\n",
      "Epoch 44/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.2087 - acc: 0.9217 - val_loss: 0.4191 - val_acc: 0.8486\n",
      "Epoch 45/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.2045 - acc: 0.9217 - val_loss: 0.4468 - val_acc: 0.8670\n",
      "Epoch 46/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.1978 - acc: 0.9245 - val_loss: 0.4443 - val_acc: 0.8624\n",
      "Epoch 47/500\n",
      "132/132 [==============================] - 35s 264ms/step - loss: 0.1941 - acc: 0.9267 - val_loss: 0.4397 - val_acc: 0.8578\n",
      "Epoch 48/500\n",
      "132/132 [==============================] - 32s 245ms/step - loss: 0.1862 - acc: 0.9264 - val_loss: 0.4411 - val_acc: 0.8486\n",
      "Epoch 49/500\n",
      "132/132 [==============================] - 34s 260ms/step - loss: 0.1872 - acc: 0.9288 - val_loss: 0.4824 - val_acc: 0.8670\n",
      "Epoch 50/500\n",
      "132/132 [==============================] - 35s 262ms/step - loss: 0.1613 - acc: 0.9359 - val_loss: 0.5081 - val_acc: 0.8440\n",
      "Epoch 51/500\n",
      "132/132 [==============================] - 33s 249ms/step - loss: 0.1769 - acc: 0.9300 - val_loss: 0.5150 - val_acc: 0.8624\n",
      "Epoch 52/500\n",
      "132/132 [==============================] - 33s 247ms/step - loss: 0.1617 - acc: 0.9376 - val_loss: 0.4704 - val_acc: 0.8578\n",
      "Epoch 53/500\n",
      "132/132 [==============================] - 32s 243ms/step - loss: 0.1614 - acc: 0.9412 - val_loss: 0.5435 - val_acc: 0.8211\n",
      "Epoch 54/500\n",
      "132/132 [==============================] - 34s 257ms/step - loss: 0.1687 - acc: 0.9361 - val_loss: 0.6075 - val_acc: 0.8303\n",
      "Epoch 55/500\n",
      "132/132 [==============================] - 32s 245ms/step - loss: 0.1524 - acc: 0.9411 - val_loss: 0.5612 - val_acc: 0.8257\n",
      "Epoch 56/500\n",
      "132/132 [==============================] - 33s 250ms/step - loss: 0.1391 - acc: 0.9449 - val_loss: 0.4625 - val_acc: 0.8532\n",
      "Epoch 57/500\n",
      "132/132 [==============================] - 35s 263ms/step - loss: 0.1505 - acc: 0.9423 - val_loss: 0.6258 - val_acc: 0.8440\n",
      "Epoch 58/500\n",
      "132/132 [==============================] - 34s 256ms/step - loss: 0.1566 - acc: 0.9401 - val_loss: 0.5103 - val_acc: 0.8578\n",
      "Epoch 59/500\n",
      "132/132 [==============================] - 34s 261ms/step - loss: 0.1323 - acc: 0.9485 - val_loss: 0.5505 - val_acc: 0.8578\n",
      "Epoch 60/500\n",
      "132/132 [==============================] - 32s 242ms/step - loss: 0.1379 - acc: 0.9466 - val_loss: 0.4878 - val_acc: 0.8716\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 61/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.1396 - acc: 0.9458 - val_loss: 0.5611 - val_acc: 0.8624\n",
      "Epoch 62/500\n",
      "132/132 [==============================] - 33s 251ms/step - loss: 0.1402 - acc: 0.9486 - val_loss: 0.4917 - val_acc: 0.8578\n",
      "Epoch 63/500\n",
      "132/132 [==============================] - 32s 243ms/step - loss: 0.1412 - acc: 0.9494 - val_loss: 0.6134 - val_acc: 0.8165\n",
      "Epoch 64/500\n",
      "132/132 [==============================] - 34s 261ms/step - loss: 0.1238 - acc: 0.9547 - val_loss: 0.5525 - val_acc: 0.8624\n",
      "Epoch 65/500\n",
      "132/132 [==============================] - 32s 245ms/step - loss: 0.1228 - acc: 0.9515 - val_loss: 0.5772 - val_acc: 0.8624\n",
      "Epoch 66/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.1086 - acc: 0.9559 - val_loss: 0.6473 - val_acc: 0.8716\n",
      "Epoch 67/500\n",
      "132/132 [==============================] - 33s 250ms/step - loss: 0.1133 - acc: 0.9565 - val_loss: 0.5563 - val_acc: 0.8578\n",
      "Epoch 68/500\n",
      "132/132 [==============================] - 35s 264ms/step - loss: 0.1127 - acc: 0.9583 - val_loss: 0.5683 - val_acc: 0.8670\n",
      "Epoch 69/500\n",
      "132/132 [==============================] - 32s 243ms/step - loss: 0.1055 - acc: 0.9586 - val_loss: 0.5733 - val_acc: 0.8624\n",
      "Epoch 70/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.0988 - acc: 0.9621 - val_loss: 0.5832 - val_acc: 0.8486\n",
      "Epoch 71/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0998 - acc: 0.9636 - val_loss: 0.5855 - val_acc: 0.8394\n",
      "Epoch 72/500\n",
      "132/132 [==============================] - 33s 250ms/step - loss: 0.1140 - acc: 0.9568 - val_loss: 0.5254 - val_acc: 0.8624\n",
      "Epoch 73/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0994 - acc: 0.9626 - val_loss: 0.5056 - val_acc: 0.8624\n",
      "Epoch 74/500\n",
      "132/132 [==============================] - 33s 249ms/step - loss: 0.0900 - acc: 0.9661 - val_loss: 0.5527 - val_acc: 0.8853\n",
      "Epoch 75/500\n",
      "132/132 [==============================] - 33s 250ms/step - loss: 0.0999 - acc: 0.9626 - val_loss: 0.5523 - val_acc: 0.8761\n",
      "Epoch 76/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0999 - acc: 0.9613 - val_loss: 0.5663 - val_acc: 0.8670\n",
      "Epoch 77/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.1385 - acc: 0.9488 - val_loss: 0.5874 - val_acc: 0.8624\n",
      "Epoch 78/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0891 - acc: 0.9653 - val_loss: 0.5255 - val_acc: 0.8899\n",
      "Epoch 79/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0934 - acc: 0.9665 - val_loss: 0.5911 - val_acc: 0.8486\n",
      "Epoch 80/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0954 - acc: 0.9626 - val_loss: 0.5732 - val_acc: 0.8624\n",
      "Epoch 81/500\n",
      "132/132 [==============================] - 32s 244ms/step - loss: 0.0901 - acc: 0.9667 - val_loss: 0.5846 - val_acc: 0.8578\n",
      "Epoch 82/500\n",
      "132/132 [==============================] - 32s 242ms/step - loss: 0.0842 - acc: 0.9680 - val_loss: 0.6011 - val_acc: 0.8486\n",
      "Epoch 83/500\n",
      "132/132 [==============================] - 33s 249ms/step - loss: 0.0958 - acc: 0.9650 - val_loss: 0.5349 - val_acc: 0.8761\n",
      "Epoch 84/500\n",
      "132/132 [==============================] - 32s 244ms/step - loss: 0.0801 - acc: 0.9698 - val_loss: 0.7330 - val_acc: 0.8440\n",
      "Epoch 85/500\n",
      "132/132 [==============================] - 33s 248ms/step - loss: 0.0800 - acc: 0.9694 - val_loss: 0.6742 - val_acc: 0.8486\n",
      "Epoch 86/500\n",
      "132/132 [==============================] - 32s 245ms/step - loss: 0.0840 - acc: 0.9708 - val_loss: 0.6269 - val_acc: 0.8624\n",
      "Epoch 87/500\n",
      "132/132 [==============================] - 33s 250ms/step - loss: 0.0789 - acc: 0.9692 - val_loss: 0.5917 - val_acc: 0.8624\n",
      "Epoch 88/500\n",
      "132/132 [==============================] - 33s 254ms/step - loss: 0.0751 - acc: 0.9714 - val_loss: 0.5783 - val_acc: 0.8578\n",
      "Epoch 89/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0771 - acc: 0.9695 - val_loss: 0.6123 - val_acc: 0.8670\n",
      "Epoch 90/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0839 - acc: 0.9700 - val_loss: 0.7275 - val_acc: 0.8670\n",
      "Epoch 91/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0914 - acc: 0.9679 - val_loss: 0.6232 - val_acc: 0.8578\n",
      "Epoch 92/500\n",
      "132/132 [==============================] - 32s 246ms/step - loss: 0.0813 - acc: 0.9698 - val_loss: 0.6272 - val_acc: 0.8624\n",
      "Epoch 93/500\n",
      "132/132 [==============================] - 34s 259ms/step - loss: 0.0669 - acc: 0.9770 - val_loss: 0.7024 - val_acc: 0.8486\n",
      "Epoch 94/500\n",
      "132/132 [==============================] - 32s 246ms/step - loss: 0.0746 - acc: 0.9723 - val_loss: 0.5374 - val_acc: 0.8578\n",
      "Epoch 95/500\n",
      "132/132 [==============================] - 33s 253ms/step - loss: 0.0690 - acc: 0.9750 - val_loss: 0.6271 - val_acc: 0.8670\n",
      "Epoch 96/500\n",
      "132/132 [==============================] - 34s 258ms/step - loss: 0.0855 - acc: 0.9698 - val_loss: 0.7590 - val_acc: 0.8532\n",
      "Epoch 97/500\n",
      "132/132 [==============================] - 35s 267ms/step - loss: 0.0798 - acc: 0.9688 - val_loss: 0.6380 - val_acc: 0.8716\n",
      "Epoch 98/500\n",
      "132/132 [==============================] - 33s 247ms/step - loss: 0.0764 - acc: 0.9725 - val_loss: 0.6218 - val_acc: 0.8578\n",
      "Epoch 99/500\n",
      "132/132 [==============================] - 33s 248ms/step - loss: 0.0656 - acc: 0.9756 - val_loss: 0.6322 - val_acc: 0.8716\n",
      "Epoch 100/500\n",
      "132/132 [==============================] - 33s 247ms/step - loss: 0.0621 - acc: 0.9780 - val_loss: 0.7663 - val_acc: 0.8440\n",
      "Epoch 101/500\n",
      "132/132 [==============================] - 33s 246ms/step - loss: 0.0845 - acc: 0.9676 - val_loss: 0.7384 - val_acc: 0.8257\n",
      "Epoch 102/500\n",
      "132/132 [==============================] - 33s 249ms/step - loss: 0.0811 - acc: 0.9706 - val_loss: 0.6613 - val_acc: 0.8532\n",
      "Epoch 103/500\n",
      "132/132 [==============================] - 33s 248ms/step - loss: 0.0618 - acc: 0.9794 - val_loss: 0.7658 - val_acc: 0.8486\n",
      "Epoch 104/500\n",
      "132/132 [==============================] - 33s 249ms/step - loss: 0.0563 - acc: 0.9761 - val_loss: 0.7534 - val_acc: 0.8532\n",
      "Epoch 105/500\n",
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      "Epoch 168/500\n",
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      "Epoch 169/500\n",
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      "Epoch 170/500\n",
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      "Epoch 171/500\n",
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      "Epoch 172/500\n",
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      "Epoch 174/500\n",
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      "Epoch 175/500\n",
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      "Epoch 176/500\n",
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      "Epoch 177/500\n",
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      "Epoch 178/500\n",
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      "Epoch 179/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 33s 247ms/step - loss: 0.0309 - acc: 0.9880 - val_loss: 1.0346 - val_acc: 0.8486\n",
      "Epoch 180/500\n",
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      "Epoch 183/500\n",
      "132/132 [==============================] - 33s 247ms/step - loss: 0.0374 - acc: 0.9865 - val_loss: 0.8674 - val_acc: 0.8716\n",
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      "132/132 [==============================] - 33s 247ms/step - loss: 0.0306 - acc: 0.9906 - val_loss: 1.0643 - val_acc: 0.8624\n",
      "Epoch 185/500\n",
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      "Epoch 187/500\n",
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      "Epoch 188/500\n",
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      "Epoch 189/500\n",
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      "Epoch 190/500\n",
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      "Epoch 191/500\n",
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      "Epoch 192/500\n",
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      "Epoch 194/500\n",
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      "Epoch 195/500\n",
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      "Epoch 200/500\n",
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      "Epoch 201/500\n",
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      "Epoch 202/500\n",
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      "Epoch 226/500\n",
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      "Epoch 228/500\n",
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      "Epoch 231/500\n",
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      "Epoch 238/500\n",
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      "Epoch 239/500\n",
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      "Epoch 240/500\n",
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      "Epoch 241/500\n",
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      "Epoch 242/500\n",
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      "Epoch 243/500\n",
      "132/132 [==============================] - 32s 246ms/step - loss: 0.0274 - acc: 0.9897 - val_loss: 0.8758 - val_acc: 0.8899\n",
      "Epoch 244/500\n",
      "132/132 [==============================] - 29s 219ms/step - loss: 0.0191 - acc: 0.9917 - val_loss: 1.0095 - val_acc: 0.8899\n",
      "Epoch 245/500\n",
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      "Epoch 248/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0300 - acc: 0.9894 - val_loss: 0.9974 - val_acc: 0.8761\n",
      "Epoch 249/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0382 - acc: 0.9880 - val_loss: 0.9304 - val_acc: 0.8670\n",
      "Epoch 250/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.0288 - acc: 0.9909 - val_loss: 1.1046 - val_acc: 0.8624\n",
      "Epoch 251/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.0319 - acc: 0.9877 - val_loss: 0.7928 - val_acc: 0.8991\n",
      "Epoch 252/500\n",
      "132/132 [==============================] - 32s 244ms/step - loss: 0.0235 - acc: 0.9915 - val_loss: 1.1124 - val_acc: 0.8440\n",
      "Epoch 253/500\n",
      "132/132 [==============================] - 32s 242ms/step - loss: 0.0337 - acc: 0.9885 - val_loss: 0.8684 - val_acc: 0.8670\n",
      "Epoch 254/500\n",
      "132/132 [==============================] - 31s 239ms/step - loss: 0.0317 - acc: 0.9892 - val_loss: 1.0258 - val_acc: 0.8761\n",
      "Epoch 255/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.0441 - acc: 0.9844 - val_loss: 0.9258 - val_acc: 0.8761\n",
      "Epoch 256/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0223 - acc: 0.9915 - val_loss: 0.9499 - val_acc: 0.8624\n",
      "Epoch 257/500\n",
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      "Epoch 258/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0157 - acc: 0.9935 - val_loss: 0.9777 - val_acc: 0.8761\n",
      "Epoch 259/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0202 - acc: 0.9930 - val_loss: 1.1069 - val_acc: 0.8761\n",
      "Epoch 260/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.0248 - acc: 0.9902 - val_loss: 1.0700 - val_acc: 0.8853\n",
      "Epoch 261/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0248 - acc: 0.9911 - val_loss: 0.9342 - val_acc: 0.8807\n",
      "Epoch 262/500\n",
      "132/132 [==============================] - 33s 249ms/step - loss: 0.0297 - acc: 0.9900 - val_loss: 1.0245 - val_acc: 0.8349\n",
      "Epoch 263/500\n",
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      "Epoch 264/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0231 - acc: 0.9912 - val_loss: 1.0122 - val_acc: 0.8807\n",
      "Epoch 265/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0255 - acc: 0.9903 - val_loss: 1.0115 - val_acc: 0.8670\n",
      "Epoch 266/500\n",
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      "Epoch 267/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0372 - acc: 0.9885 - val_loss: 0.9936 - val_acc: 0.8899\n",
      "Epoch 268/500\n",
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      "Epoch 269/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0222 - acc: 0.9936 - val_loss: 1.1648 - val_acc: 0.8670\n",
      "Epoch 270/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0351 - acc: 0.9874 - val_loss: 0.8661 - val_acc: 0.8991\n",
      "Epoch 271/500\n",
      "132/132 [==============================] - 32s 245ms/step - loss: 0.0322 - acc: 0.9882 - val_loss: 1.0256 - val_acc: 0.8761\n",
      "Epoch 272/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0308 - acc: 0.9882 - val_loss: 1.0875 - val_acc: 0.8807\n",
      "Epoch 273/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0288 - acc: 0.9902 - val_loss: 0.9883 - val_acc: 0.8807\n",
      "Epoch 274/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0335 - acc: 0.9891 - val_loss: 1.0665 - val_acc: 0.8440\n",
      "Epoch 275/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0275 - acc: 0.9915 - val_loss: 1.1629 - val_acc: 0.8807\n",
      "Epoch 276/500\n",
      "132/132 [==============================] - 32s 242ms/step - loss: 0.0267 - acc: 0.9914 - val_loss: 1.0351 - val_acc: 0.8761\n",
      "Epoch 277/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0261 - acc: 0.9909 - val_loss: 0.9689 - val_acc: 0.8670\n",
      "Epoch 278/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0317 - acc: 0.9883 - val_loss: 0.8575 - val_acc: 0.8716\n",
      "Epoch 279/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0241 - acc: 0.9914 - val_loss: 1.0340 - val_acc: 0.8716\n",
      "Epoch 280/500\n",
      "132/132 [==============================] - 32s 242ms/step - loss: 0.0259 - acc: 0.9905 - val_loss: 1.0142 - val_acc: 0.8578\n",
      "Epoch 281/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.0256 - acc: 0.9915 - val_loss: 1.0707 - val_acc: 0.8670\n",
      "Epoch 282/500\n",
      "132/132 [==============================] - 32s 245ms/step - loss: 0.0350 - acc: 0.9874 - val_loss: 0.9133 - val_acc: 0.8853\n",
      "Epoch 283/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0301 - acc: 0.9892 - val_loss: 0.8837 - val_acc: 0.8624\n",
      "Epoch 284/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0245 - acc: 0.9920 - val_loss: 0.9450 - val_acc: 0.8716\n",
      "Epoch 285/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0188 - acc: 0.9932 - val_loss: 1.1255 - val_acc: 0.8670\n",
      "Epoch 286/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0157 - acc: 0.9944 - val_loss: 1.1089 - val_acc: 0.8761\n",
      "Epoch 287/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0302 - acc: 0.9898 - val_loss: 0.9962 - val_acc: 0.8578\n",
      "Epoch 288/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0224 - acc: 0.9920 - val_loss: 1.0378 - val_acc: 0.8761\n",
      "Epoch 289/500\n",
      "132/132 [==============================] - 31s 238ms/step - loss: 0.0193 - acc: 0.9935 - val_loss: 0.9975 - val_acc: 0.8670\n",
      "Epoch 290/500\n",
      "132/132 [==============================] - 32s 244ms/step - loss: 0.0199 - acc: 0.9923 - val_loss: 0.9330 - val_acc: 0.8899\n",
      "Epoch 291/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0261 - acc: 0.9909 - val_loss: 0.9373 - val_acc: 0.8761\n",
      "Epoch 292/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0298 - acc: 0.9894 - val_loss: 0.9084 - val_acc: 0.8899\n",
      "Epoch 293/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0284 - acc: 0.9908 - val_loss: 1.0262 - val_acc: 0.8761\n",
      "Epoch 294/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0264 - acc: 0.9917 - val_loss: 1.0266 - val_acc: 0.8761\n",
      "Epoch 295/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0218 - acc: 0.9920 - val_loss: 0.8664 - val_acc: 0.8761\n",
      "Epoch 296/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0235 - acc: 0.9908 - val_loss: 0.9950 - val_acc: 0.8945\n",
      "Epoch 297/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0353 - acc: 0.9897 - val_loss: 0.8590 - val_acc: 0.8853\n",
      "Epoch 298/500\n",
      "132/132 [==============================] - 31s 239ms/step - loss: 0.0350 - acc: 0.9908 - val_loss: 0.9297 - val_acc: 0.8624\n",
      "Epoch 299/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.0263 - acc: 0.9912 - val_loss: 0.8815 - val_acc: 0.8945\n",
      "Epoch 300/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.0283 - acc: 0.9911 - val_loss: 0.9500 - val_acc: 0.8761\n",
      "Epoch 301/500\n",
      "132/132 [==============================] - 32s 241ms/step - loss: 0.0300 - acc: 0.9900 - val_loss: 0.9754 - val_acc: 0.8899\n",
      "Epoch 302/500\n",
      "132/132 [==============================] - 32s 242ms/step - loss: 0.0319 - acc: 0.9875 - val_loss: 1.2514 - val_acc: 0.8578\n",
      "Epoch 303/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0218 - acc: 0.9912 - val_loss: 1.0164 - val_acc: 0.8899\n",
      "Epoch 304/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0333 - acc: 0.9888 - val_loss: 0.8426 - val_acc: 0.8853\n",
      "Epoch 305/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0278 - acc: 0.9909 - val_loss: 1.2031 - val_acc: 0.8807\n",
      "Epoch 306/500\n",
      "132/132 [==============================] - 33s 250ms/step - loss: 0.0357 - acc: 0.9912 - val_loss: 0.7954 - val_acc: 0.8899\n",
      "Epoch 307/500\n",
      "132/132 [==============================] - 34s 256ms/step - loss: 0.0213 - acc: 0.9923 - val_loss: 0.8547 - val_acc: 0.8899\n",
      "Epoch 308/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.0205 - acc: 0.9926 - val_loss: 1.0941 - val_acc: 0.8807\n",
      "Epoch 309/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0179 - acc: 0.9932 - val_loss: 0.9141 - val_acc: 0.8761\n",
      "Epoch 310/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0180 - acc: 0.9941 - val_loss: 1.0561 - val_acc: 0.8670\n",
      "Epoch 311/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0190 - acc: 0.9924 - val_loss: 0.8290 - val_acc: 0.8899\n",
      "Epoch 312/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0419 - acc: 0.9885 - val_loss: 1.0086 - val_acc: 0.8578\n",
      "Epoch 313/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0228 - acc: 0.9917 - val_loss: 1.0089 - val_acc: 0.8670\n",
      "Epoch 314/500\n",
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      "Epoch 315/500\n",
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      "Epoch 316/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0549 - acc: 0.9836 - val_loss: 1.0491 - val_acc: 0.8807\n",
      "Epoch 317/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0308 - acc: 0.9892 - val_loss: 0.8465 - val_acc: 0.8945\n",
      "Epoch 318/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0153 - acc: 0.9947 - val_loss: 0.9692 - val_acc: 0.8991\n",
      "Epoch 319/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0194 - acc: 0.9936 - val_loss: 0.9146 - val_acc: 0.8761\n",
      "Epoch 320/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0333 - acc: 0.9902 - val_loss: 0.9694 - val_acc: 0.8945\n",
      "Epoch 321/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0280 - acc: 0.9891 - val_loss: 0.9155 - val_acc: 0.8624\n",
      "Epoch 322/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0273 - acc: 0.9905 - val_loss: 0.9646 - val_acc: 0.8670\n",
      "Epoch 323/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0179 - acc: 0.9929 - val_loss: 1.0179 - val_acc: 0.8761\n",
      "Epoch 324/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0177 - acc: 0.9919 - val_loss: 1.0455 - val_acc: 0.8991\n",
      "Epoch 325/500\n",
      "132/132 [==============================] - 27s 203ms/step - loss: 0.0183 - acc: 0.9945 - val_loss: 0.9941 - val_acc: 0.8991\n",
      "Epoch 326/500\n",
      "132/132 [==============================] - 30s 228ms/step - loss: 0.0198 - acc: 0.9918 - val_loss: 1.1271 - val_acc: 0.8991\n",
      "Epoch 327/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0196 - acc: 0.9921 - val_loss: 1.0975 - val_acc: 0.8761\n",
      "Epoch 328/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0234 - acc: 0.9923 - val_loss: 1.0200 - val_acc: 0.8716\n",
      "Epoch 329/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0252 - acc: 0.9914 - val_loss: 1.0603 - val_acc: 0.8624\n",
      "Epoch 330/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0380 - acc: 0.9895 - val_loss: 1.1811 - val_acc: 0.8624\n",
      "Epoch 331/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0194 - acc: 0.9924 - val_loss: 0.9750 - val_acc: 0.8807\n",
      "Epoch 332/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0347 - acc: 0.9898 - val_loss: 1.0690 - val_acc: 0.8761\n",
      "Epoch 333/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.0310 - acc: 0.9911 - val_loss: 1.0873 - val_acc: 0.8807\n",
      "Epoch 334/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0252 - acc: 0.9914 - val_loss: 0.7814 - val_acc: 0.8945\n",
      "Epoch 335/500\n",
      "132/132 [==============================] - 31s 234ms/step - loss: 0.0196 - acc: 0.9930 - val_loss: 1.0076 - val_acc: 0.8807\n",
      "Epoch 336/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0150 - acc: 0.9961 - val_loss: 1.0105 - val_acc: 0.8761\n",
      "Epoch 337/500\n",
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      "Epoch 338/500\n",
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      "Epoch 339/500\n",
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      "Epoch 340/500\n",
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      "Epoch 341/500\n",
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      "Epoch 342/500\n",
      "132/132 [==============================] - 31s 237ms/step - loss: 0.0256 - acc: 0.9911 - val_loss: 1.1189 - val_acc: 0.8716\n",
      "Epoch 343/500\n",
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      "Epoch 344/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0248 - acc: 0.9914 - val_loss: 1.0786 - val_acc: 0.8761\n",
      "Epoch 345/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0222 - acc: 0.9912 - val_loss: 0.9893 - val_acc: 0.8807\n",
      "Epoch 346/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0261 - acc: 0.9921 - val_loss: 0.9373 - val_acc: 0.8899\n",
      "Epoch 347/500\n",
      "132/132 [==============================] - 31s 236ms/step - loss: 0.0216 - acc: 0.9926 - val_loss: 1.0944 - val_acc: 0.8578\n",
      "Epoch 348/500\n",
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      "Epoch 349/500\n",
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      "Epoch 350/500\n",
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      "Epoch 351/500\n",
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      "Epoch 352/500\n",
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      "Epoch 353/500\n",
      "132/132 [==============================] - 34s 257ms/step - loss: 0.0183 - acc: 0.9939 - val_loss: 1.1187 - val_acc: 0.8578\n",
      "Epoch 354/500\n",
      "132/132 [==============================] - 33s 246ms/step - loss: 0.0205 - acc: 0.9941 - val_loss: 1.0180 - val_acc: 0.8624\n",
      "Epoch 355/500\n",
      "132/132 [==============================] - 32s 244ms/step - loss: 0.0338 - acc: 0.9895 - val_loss: 0.9698 - val_acc: 0.8716\n",
      "Epoch 356/500\n",
      "132/132 [==============================] - 31s 235ms/step - loss: 0.0200 - acc: 0.9922 - val_loss: 0.9339 - val_acc: 0.8853\n",
      "Epoch 357/500\n",
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      "Epoch 389/500\n",
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      "Epoch 394/500\n",
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      "Epoch 400/500\n",
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      "Epoch 402/500\n",
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      "Epoch 403/500\n",
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      "Epoch 404/500\n",
      "132/132 [==============================] - 32s 243ms/step - loss: 0.0147 - acc: 0.9939 - val_loss: 0.8984 - val_acc: 0.8807\n",
      "Epoch 405/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0139 - acc: 0.9956 - val_loss: 0.9012 - val_acc: 0.8899\n",
      "Epoch 406/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0179 - acc: 0.9941 - val_loss: 1.0435 - val_acc: 0.8991\n",
      "Epoch 407/500\n",
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      "Epoch 408/500\n",
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      "Epoch 409/500\n",
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      "Epoch 410/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0243 - acc: 0.9930 - val_loss: 1.0728 - val_acc: 0.8532\n",
      "Epoch 411/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0118 - acc: 0.9967 - val_loss: 1.0987 - val_acc: 0.8807\n",
      "Epoch 412/500\n",
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      "Epoch 413/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0276 - acc: 0.9926 - val_loss: 1.3105 - val_acc: 0.8670\n",
      "Epoch 414/500\n",
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      "Epoch 415/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 33s 249ms/step - loss: 0.0160 - acc: 0.9939 - val_loss: 0.9284 - val_acc: 0.8991\n",
      "Epoch 416/500\n",
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      "Epoch 418/500\n",
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      "Epoch 419/500\n",
      "132/132 [==============================] - 32s 242ms/step - loss: 0.0290 - acc: 0.9898 - val_loss: 0.8581 - val_acc: 0.8899\n",
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      "132/132 [==============================] - 32s 241ms/step - loss: 0.0196 - acc: 0.9942 - val_loss: 0.8478 - val_acc: 0.8991\n",
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      "Epoch 474/500\n",
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      "Epoch 475/500\n",
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      "Epoch 476/500\n",
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      "Epoch 480/500\n",
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      "132/132 [==============================] - 32s 240ms/step - loss: 0.0188 - acc: 0.9935 - val_loss: 1.1515 - val_acc: 0.8899\n",
      "Epoch 483/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0125 - acc: 0.9947 - val_loss: 1.1955 - val_acc: 0.8807\n",
      "Epoch 484/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0247 - acc: 0.9932 - val_loss: 1.2016 - val_acc: 0.8578\n",
      "Epoch 485/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0252 - acc: 0.9935 - val_loss: 1.2017 - val_acc: 0.8716\n",
      "Epoch 486/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0149 - acc: 0.9944 - val_loss: 1.1620 - val_acc: 0.8761\n",
      "Epoch 487/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0108 - acc: 0.9956 - val_loss: 1.0615 - val_acc: 0.8716\n",
      "Epoch 488/500\n",
      "132/132 [==============================] - 31s 239ms/step - loss: 0.0176 - acc: 0.9935 - val_loss: 1.0743 - val_acc: 0.8945\n",
      "Epoch 489/500\n",
      "132/132 [==============================] - 32s 243ms/step - loss: 0.0316 - acc: 0.9908 - val_loss: 1.0359 - val_acc: 0.8716\n",
      "Epoch 490/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0303 - acc: 0.9926 - val_loss: 1.1538 - val_acc: 0.8624\n",
      "Epoch 491/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0225 - acc: 0.9927 - val_loss: 1.0616 - val_acc: 0.8761\n",
      "Epoch 492/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0100 - acc: 0.9953 - val_loss: 1.0632 - val_acc: 0.8807\n",
      "Epoch 493/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0111 - acc: 0.9958 - val_loss: 1.1383 - val_acc: 0.8807\n",
      "Epoch 494/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0150 - acc: 0.9950 - val_loss: 1.0115 - val_acc: 0.8761\n",
      "Epoch 495/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0124 - acc: 0.9962 - val_loss: 1.0834 - val_acc: 0.8853\n",
      "Epoch 496/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0081 - acc: 0.9967 - val_loss: 1.0289 - val_acc: 0.8761\n",
      "Epoch 497/500\n",
      "132/132 [==============================] - 32s 239ms/step - loss: 0.0157 - acc: 0.9939 - val_loss: 1.2737 - val_acc: 0.8624\n",
      "Epoch 498/500\n",
      "132/132 [==============================] - 32s 243ms/step - loss: 0.0163 - acc: 0.9950 - val_loss: 1.2649 - val_acc: 0.8670\n",
      "Epoch 499/500\n",
      "132/132 [==============================] - 32s 242ms/step - loss: 0.0194 - acc: 0.9947 - val_loss: 1.3581 - val_acc: 0.8578\n",
      "Epoch 500/500\n",
      "132/132 [==============================] - 32s 240ms/step - loss: 0.0200 - acc: 0.9932 - val_loss: 0.9841 - val_acc: 0.8624\n",
      "40/40 [==============================] - 25s 617ms/step\n",
      "Accuracy: 0.8699847638395124. F1: 0.8686719230910561\n"
     ]
    }
   ],
   "source": [
    "cnn(3, make_model2D_S, epochs = 500, save_model = True, name = \"2D_small_3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "132/132 [==============================] - 78s 588ms/step - loss: 6.0036 - acc: 0.2390 - val_loss: 1.4845 - val_acc: 0.4174\n",
      "Epoch 2/500\n",
      "132/132 [==============================] - 81s 615ms/step - loss: 1.6086 - acc: 0.3918 - val_loss: 1.3040 - val_acc: 0.4358\n",
      "Epoch 3/500\n",
      "132/132 [==============================] - 81s 613ms/step - loss: 1.4592 - acc: 0.4322 - val_loss: 1.2189 - val_acc: 0.4862\n",
      "Epoch 4/500\n",
      "132/132 [==============================] - 77s 586ms/step - loss: 1.3583 - acc: 0.4678 - val_loss: 1.1723 - val_acc: 0.5505\n",
      "Epoch 5/500\n",
      "132/132 [==============================] - 77s 587ms/step - loss: 1.3060 - acc: 0.5016 - val_loss: 1.1102 - val_acc: 0.5321\n",
      "Epoch 6/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 1.2664 - acc: 0.5153 - val_loss: 1.0963 - val_acc: 0.5550\n",
      "Epoch 7/500\n",
      "132/132 [==============================] - 77s 585ms/step - loss: 1.2230 - acc: 0.5340 - val_loss: 1.0858 - val_acc: 0.5642\n",
      "Epoch 8/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 1.1956 - acc: 0.5438 - val_loss: 1.0254 - val_acc: 0.5917\n",
      "Epoch 9/500\n",
      "132/132 [==============================] - 77s 586ms/step - loss: 1.1570 - acc: 0.5583 - val_loss: 0.9900 - val_acc: 0.5826\n",
      "Epoch 10/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 1.1200 - acc: 0.5718 - val_loss: 1.0314 - val_acc: 0.5780\n",
      "Epoch 11/500\n",
      "132/132 [==============================] - 77s 587ms/step - loss: 1.0909 - acc: 0.5800 - val_loss: 0.9421 - val_acc: 0.6193\n",
      "Epoch 12/500\n",
      "132/132 [==============================] - 77s 585ms/step - loss: 1.0663 - acc: 0.5897 - val_loss: 0.9287 - val_acc: 0.6147\n",
      "Epoch 13/500\n",
      "132/132 [==============================] - 77s 586ms/step - loss: 1.0600 - acc: 0.5934 - val_loss: 0.9378 - val_acc: 0.6055\n",
      "Epoch 14/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 1.0259 - acc: 0.6031 - val_loss: 0.9125 - val_acc: 0.6009\n",
      "Epoch 15/500\n",
      "132/132 [==============================] - 78s 588ms/step - loss: 1.0014 - acc: 0.6108 - val_loss: 0.8788 - val_acc: 0.6193\n",
      "Epoch 16/500\n",
      "132/132 [==============================] - 78s 588ms/step - loss: 0.9867 - acc: 0.6213 - val_loss: 0.8769 - val_acc: 0.6284\n",
      "Epoch 17/500\n",
      "132/132 [==============================] - 77s 586ms/step - loss: 0.9460 - acc: 0.6291 - val_loss: 0.8581 - val_acc: 0.6422\n",
      "Epoch 18/500\n",
      "132/132 [==============================] - 77s 586ms/step - loss: 0.9339 - acc: 0.6306 - val_loss: 0.8733 - val_acc: 0.6651\n",
      "Epoch 19/500\n",
      "132/132 [==============================] - 78s 587ms/step - loss: 0.9235 - acc: 0.6402 - val_loss: 0.8364 - val_acc: 0.6330\n",
      "Epoch 20/500\n",
      "132/132 [==============================] - 77s 584ms/step - loss: 0.8993 - acc: 0.6533 - val_loss: 0.8456 - val_acc: 0.6422\n",
      "Epoch 21/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.8910 - acc: 0.6572 - val_loss: 0.8342 - val_acc: 0.6376\n",
      "Epoch 22/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.8487 - acc: 0.6692 - val_loss: 0.7923 - val_acc: 0.6835\n",
      "Epoch 23/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.8559 - acc: 0.6644 - val_loss: 0.7832 - val_acc: 0.7064\n",
      "Epoch 24/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.8214 - acc: 0.6803 - val_loss: 0.7269 - val_acc: 0.7110\n",
      "Epoch 25/500\n",
      "132/132 [==============================] - 77s 586ms/step - loss: 0.8231 - acc: 0.6883 - val_loss: 0.8098 - val_acc: 0.6697\n",
      "Epoch 26/500\n",
      "132/132 [==============================] - 79s 602ms/step - loss: 0.8132 - acc: 0.6851 - val_loss: 0.7685 - val_acc: 0.7156\n",
      "Epoch 27/500\n",
      "132/132 [==============================] - 78s 587ms/step - loss: 0.7811 - acc: 0.6966 - val_loss: 0.7503 - val_acc: 0.6972\n",
      "Epoch 28/500\n",
      "132/132 [==============================] - 81s 616ms/step - loss: 0.7656 - acc: 0.7033 - val_loss: 0.7821 - val_acc: 0.6835\n",
      "Epoch 29/500\n",
      "132/132 [==============================] - 84s 637ms/step - loss: 0.7549 - acc: 0.7116 - val_loss: 0.7446 - val_acc: 0.7156\n",
      "Epoch 30/500\n",
      "132/132 [==============================] - 86s 651ms/step - loss: 0.7594 - acc: 0.7082 - val_loss: 0.7459 - val_acc: 0.7064\n",
      "Epoch 31/500\n",
      "132/132 [==============================] - 79s 596ms/step - loss: 0.7431 - acc: 0.7137 - val_loss: 0.7422 - val_acc: 0.7064\n",
      "Epoch 32/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.7310 - acc: 0.7233 - val_loss: 0.7370 - val_acc: 0.7294\n",
      "Epoch 33/500\n",
      "132/132 [==============================] - 79s 598ms/step - loss: 0.7085 - acc: 0.7310 - val_loss: 0.7276 - val_acc: 0.7202\n",
      "Epoch 34/500\n",
      "132/132 [==============================] - 77s 586ms/step - loss: 0.6985 - acc: 0.7274 - val_loss: 0.7085 - val_acc: 0.7294\n",
      "Epoch 35/500\n",
      "132/132 [==============================] - 80s 610ms/step - loss: 0.6949 - acc: 0.7325 - val_loss: 0.7032 - val_acc: 0.7248\n",
      "Epoch 36/500\n",
      "132/132 [==============================] - 77s 585ms/step - loss: 0.6744 - acc: 0.7379 - val_loss: 0.6937 - val_acc: 0.7523\n",
      "Epoch 37/500\n",
      "132/132 [==============================] - 77s 583ms/step - loss: 0.6691 - acc: 0.7382 - val_loss: 0.6926 - val_acc: 0.7202\n",
      "Epoch 38/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.6466 - acc: 0.7499 - val_loss: 0.7123 - val_acc: 0.7202\n",
      "Epoch 39/500\n",
      "132/132 [==============================] - 78s 588ms/step - loss: 0.6359 - acc: 0.7488 - val_loss: 0.6796 - val_acc: 0.7431\n",
      "Epoch 40/500\n",
      "132/132 [==============================] - 79s 598ms/step - loss: 0.6499 - acc: 0.7536 - val_loss: 0.6606 - val_acc: 0.7477\n",
      "Epoch 41/500\n",
      "132/132 [==============================] - 80s 602ms/step - loss: 0.6095 - acc: 0.7604 - val_loss: 0.6829 - val_acc: 0.7294\n",
      "Epoch 42/500\n",
      "132/132 [==============================] - 88s 670ms/step - loss: 0.6140 - acc: 0.7619 - val_loss: 0.6523 - val_acc: 0.7615\n",
      "Epoch 43/500\n",
      "132/132 [==============================] - 86s 650ms/step - loss: 0.6059 - acc: 0.7697 - val_loss: 0.6886 - val_acc: 0.7110\n",
      "Epoch 44/500\n",
      "132/132 [==============================] - 90s 680ms/step - loss: 0.5905 - acc: 0.7721 - val_loss: 0.6324 - val_acc: 0.7477\n",
      "Epoch 45/500\n",
      "132/132 [==============================] - 86s 651ms/step - loss: 0.5745 - acc: 0.7751 - val_loss: 0.6524 - val_acc: 0.7385\n",
      "Epoch 46/500\n",
      "132/132 [==============================] - 82s 618ms/step - loss: 0.5767 - acc: 0.7765 - val_loss: 0.6372 - val_acc: 0.7615\n",
      "Epoch 47/500\n",
      "132/132 [==============================] - 83s 632ms/step - loss: 0.5613 - acc: 0.7803 - val_loss: 0.6246 - val_acc: 0.7615\n",
      "Epoch 48/500\n",
      "132/132 [==============================] - 86s 648ms/step - loss: 0.5555 - acc: 0.7859 - val_loss: 0.6321 - val_acc: 0.7477\n",
      "Epoch 49/500\n",
      "132/132 [==============================] - 84s 636ms/step - loss: 0.5420 - acc: 0.7838 - val_loss: 0.5992 - val_acc: 0.7569\n",
      "Epoch 50/500\n",
      "132/132 [==============================] - 84s 637ms/step - loss: 0.5334 - acc: 0.7879 - val_loss: 0.5895 - val_acc: 0.7706\n",
      "Epoch 51/500\n",
      "132/132 [==============================] - 84s 638ms/step - loss: 0.5320 - acc: 0.7945 - val_loss: 0.6125 - val_acc: 0.7431\n",
      "Epoch 52/500\n",
      "132/132 [==============================] - 79s 598ms/step - loss: 0.5442 - acc: 0.7846 - val_loss: 0.5849 - val_acc: 0.7844\n",
      "Epoch 53/500\n",
      "132/132 [==============================] - 80s 607ms/step - loss: 0.5135 - acc: 0.8038 - val_loss: 0.5953 - val_acc: 0.7706\n",
      "Epoch 54/500\n",
      "132/132 [==============================] - 81s 612ms/step - loss: 0.5126 - acc: 0.7983 - val_loss: 0.6243 - val_acc: 0.7706\n",
      "Epoch 55/500\n",
      "132/132 [==============================] - 81s 614ms/step - loss: 0.5065 - acc: 0.8010 - val_loss: 0.5983 - val_acc: 0.7706\n",
      "Epoch 56/500\n",
      "132/132 [==============================] - 80s 608ms/step - loss: 0.4947 - acc: 0.8066 - val_loss: 0.5824 - val_acc: 0.7798\n",
      "Epoch 57/500\n",
      "132/132 [==============================] - 78s 587ms/step - loss: 0.4878 - acc: 0.8106 - val_loss: 0.6322 - val_acc: 0.7615\n",
      "Epoch 58/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.4750 - acc: 0.8145 - val_loss: 0.6227 - val_acc: 0.7431\n",
      "Epoch 59/500\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.4760 - acc: 0.8134 - val_loss: 0.6122 - val_acc: 0.7706\n",
      "Epoch 60/500\n",
      "132/132 [==============================] - 80s 605ms/step - loss: 0.4733 - acc: 0.8142 - val_loss: 0.6077 - val_acc: 0.7752\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 61/500\n",
      "132/132 [==============================] - 79s 598ms/step - loss: 0.4682 - acc: 0.8164 - val_loss: 0.5651 - val_acc: 0.7936\n",
      "Epoch 62/500\n",
      "132/132 [==============================] - 84s 638ms/step - loss: 0.4574 - acc: 0.8252 - val_loss: 0.5637 - val_acc: 0.7890\n",
      "Epoch 63/500\n",
      "132/132 [==============================] - 83s 631ms/step - loss: 0.4519 - acc: 0.8218 - val_loss: 0.5568 - val_acc: 0.8119\n",
      "Epoch 64/500\n",
      "132/132 [==============================] - 85s 642ms/step - loss: 0.4478 - acc: 0.8270 - val_loss: 0.5784 - val_acc: 0.7752\n",
      "Epoch 65/500\n",
      "132/132 [==============================] - 83s 629ms/step - loss: 0.4409 - acc: 0.8288 - val_loss: 0.5776 - val_acc: 0.8028\n",
      "Epoch 66/500\n",
      "132/132 [==============================] - 83s 633ms/step - loss: 0.4220 - acc: 0.8357 - val_loss: 0.5574 - val_acc: 0.8119\n",
      "Epoch 67/500\n",
      "132/132 [==============================] - 81s 612ms/step - loss: 0.4231 - acc: 0.8355 - val_loss: 0.5582 - val_acc: 0.7890\n",
      "Epoch 68/500\n",
      "132/132 [==============================] - 82s 624ms/step - loss: 0.4127 - acc: 0.8453 - val_loss: 0.6107 - val_acc: 0.8028\n",
      "Epoch 69/500\n",
      "132/132 [==============================] - 80s 605ms/step - loss: 0.3964 - acc: 0.8436 - val_loss: 0.5481 - val_acc: 0.7890\n",
      "Epoch 70/500\n",
      "132/132 [==============================] - 80s 605ms/step - loss: 0.3878 - acc: 0.8476 - val_loss: 0.5521 - val_acc: 0.8211\n",
      "Epoch 71/500\n",
      "132/132 [==============================] - 78s 588ms/step - loss: 0.3990 - acc: 0.8485 - val_loss: 0.5274 - val_acc: 0.7844\n",
      "Epoch 72/500\n",
      "132/132 [==============================] - 83s 628ms/step - loss: 0.3788 - acc: 0.8521 - val_loss: 0.5415 - val_acc: 0.8119\n",
      "Epoch 73/500\n",
      "132/132 [==============================] - 80s 605ms/step - loss: 0.3794 - acc: 0.8550 - val_loss: 0.5446 - val_acc: 0.7936\n",
      "Epoch 74/500\n",
      "132/132 [==============================] - 85s 645ms/step - loss: 0.3797 - acc: 0.8544 - val_loss: 0.6159 - val_acc: 0.7706\n",
      "Epoch 75/500\n",
      "132/132 [==============================] - 79s 601ms/step - loss: 0.3708 - acc: 0.8563 - val_loss: 0.5525 - val_acc: 0.7936\n",
      "Epoch 76/500\n",
      "132/132 [==============================] - 80s 610ms/step - loss: 0.3703 - acc: 0.8561 - val_loss: 0.5214 - val_acc: 0.8119\n",
      "Epoch 77/500\n",
      "132/132 [==============================] - 79s 596ms/step - loss: 0.3642 - acc: 0.8571 - val_loss: 0.5120 - val_acc: 0.8119\n",
      "Epoch 78/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.3649 - acc: 0.8633 - val_loss: 0.5500 - val_acc: 0.7706\n",
      "Epoch 79/500\n",
      "132/132 [==============================] - 82s 621ms/step - loss: 0.3569 - acc: 0.8624 - val_loss: 0.5304 - val_acc: 0.7936\n",
      "Epoch 80/500\n",
      "132/132 [==============================] - 79s 599ms/step - loss: 0.3503 - acc: 0.8678 - val_loss: 0.5389 - val_acc: 0.8165\n",
      "Epoch 81/500\n",
      "132/132 [==============================] - 82s 623ms/step - loss: 0.3415 - acc: 0.8712 - val_loss: 0.5261 - val_acc: 0.8028\n",
      "Epoch 82/500\n",
      "132/132 [==============================] - 77s 585ms/step - loss: 0.3370 - acc: 0.8731 - val_loss: 0.5226 - val_acc: 0.8119\n",
      "Epoch 83/500\n",
      "132/132 [==============================] - 81s 612ms/step - loss: 0.3420 - acc: 0.8692 - val_loss: 0.5198 - val_acc: 0.8119\n",
      "Epoch 84/500\n",
      "132/132 [==============================] - 81s 616ms/step - loss: 0.3363 - acc: 0.8727 - val_loss: 0.5882 - val_acc: 0.7752\n",
      "Epoch 85/500\n",
      "132/132 [==============================] - 83s 626ms/step - loss: 0.3249 - acc: 0.8745 - val_loss: 0.5191 - val_acc: 0.7890\n",
      "Epoch 86/500\n",
      "132/132 [==============================] - 85s 641ms/step - loss: 0.3252 - acc: 0.8782 - val_loss: 0.4667 - val_acc: 0.8394\n",
      "Epoch 87/500\n",
      "132/132 [==============================] - 86s 648ms/step - loss: 0.3110 - acc: 0.8821 - val_loss: 0.5298 - val_acc: 0.8073\n",
      "Epoch 88/500\n",
      "132/132 [==============================] - 84s 639ms/step - loss: 0.3242 - acc: 0.8745 - val_loss: 0.5433 - val_acc: 0.7982\n",
      "Epoch 89/500\n",
      "132/132 [==============================] - 86s 652ms/step - loss: 0.3098 - acc: 0.8808 - val_loss: 0.4955 - val_acc: 0.8257\n",
      "Epoch 90/500\n",
      "132/132 [==============================] - 84s 639ms/step - loss: 0.3282 - acc: 0.8701 - val_loss: 0.5021 - val_acc: 0.8028\n",
      "Epoch 91/500\n",
      "132/132 [==============================] - 82s 622ms/step - loss: 0.3072 - acc: 0.8824 - val_loss: 0.5289 - val_acc: 0.8073\n",
      "Epoch 92/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.3053 - acc: 0.8842 - val_loss: 0.5059 - val_acc: 0.8119\n",
      "Epoch 93/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.2935 - acc: 0.8865 - val_loss: 0.5210 - val_acc: 0.8211\n",
      "Epoch 94/500\n",
      "132/132 [==============================] - 79s 600ms/step - loss: 0.2919 - acc: 0.8861 - val_loss: 0.5127 - val_acc: 0.8486\n",
      "Epoch 95/500\n",
      "132/132 [==============================] - 85s 645ms/step - loss: 0.2958 - acc: 0.8850 - val_loss: 0.5444 - val_acc: 0.7890\n",
      "Epoch 96/500\n",
      "132/132 [==============================] - 82s 624ms/step - loss: 0.2872 - acc: 0.8889 - val_loss: 0.4957 - val_acc: 0.8257\n",
      "Epoch 97/500\n",
      "132/132 [==============================] - 82s 618ms/step - loss: 0.2819 - acc: 0.8926 - val_loss: 0.4980 - val_acc: 0.8211\n",
      "Epoch 98/500\n",
      "132/132 [==============================] - 79s 600ms/step - loss: 0.2807 - acc: 0.8893 - val_loss: 0.4935 - val_acc: 0.8486\n",
      "Epoch 99/500\n",
      "132/132 [==============================] - 79s 600ms/step - loss: 0.2795 - acc: 0.8915 - val_loss: 0.4927 - val_acc: 0.8119\n",
      "Epoch 100/500\n",
      "132/132 [==============================] - 78s 587ms/step - loss: 0.2646 - acc: 0.8976 - val_loss: 0.5159 - val_acc: 0.8394\n",
      "Epoch 101/500\n",
      "132/132 [==============================] - 79s 597ms/step - loss: 0.2777 - acc: 0.8971 - val_loss: 0.5327 - val_acc: 0.8165\n",
      "Epoch 102/500\n",
      "132/132 [==============================] - 79s 599ms/step - loss: 0.2636 - acc: 0.9011 - val_loss: 0.5059 - val_acc: 0.8394\n",
      "Epoch 103/500\n",
      "132/132 [==============================] - 82s 624ms/step - loss: 0.2725 - acc: 0.8998 - val_loss: 0.5304 - val_acc: 0.8440\n",
      "Epoch 104/500\n",
      "132/132 [==============================] - 81s 617ms/step - loss: 0.2548 - acc: 0.9048 - val_loss: 0.5337 - val_acc: 0.8303\n",
      "Epoch 105/500\n",
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      "Epoch 106/500\n",
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      "Epoch 110/500\n",
      "132/132 [==============================] - 82s 622ms/step - loss: 0.2377 - acc: 0.9088 - val_loss: 0.5436 - val_acc: 0.8165\n",
      "Epoch 111/500\n",
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      "Epoch 112/500\n",
      "132/132 [==============================] - 80s 605ms/step - loss: 0.2391 - acc: 0.9068 - val_loss: 0.4938 - val_acc: 0.8394\n",
      "Epoch 113/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.2348 - acc: 0.9082 - val_loss: 0.5184 - val_acc: 0.8440\n",
      "Epoch 114/500\n",
      "132/132 [==============================] - 82s 622ms/step - loss: 0.2278 - acc: 0.9133 - val_loss: 0.5188 - val_acc: 0.8257\n",
      "Epoch 115/500\n",
      "132/132 [==============================] - 79s 596ms/step - loss: 0.2329 - acc: 0.9106 - val_loss: 0.5229 - val_acc: 0.8119\n",
      "Epoch 116/500\n",
      "132/132 [==============================] - 79s 595ms/step - loss: 0.2315 - acc: 0.9121 - val_loss: 0.5026 - val_acc: 0.8349\n",
      "Epoch 117/500\n",
      "132/132 [==============================] - 79s 597ms/step - loss: 0.2299 - acc: 0.9171 - val_loss: 0.5353 - val_acc: 0.8119\n",
      "Epoch 118/500\n",
      "132/132 [==============================] - 85s 646ms/step - loss: 0.2283 - acc: 0.9135 - val_loss: 0.5711 - val_acc: 0.7890\n",
      "Epoch 119/500\n",
      "132/132 [==============================] - 83s 625ms/step - loss: 0.2337 - acc: 0.9115 - val_loss: 0.5033 - val_acc: 0.8349\n",
      "Epoch 120/500\n",
      "132/132 [==============================] - 84s 635ms/step - loss: 0.2096 - acc: 0.9189 - val_loss: 0.5194 - val_acc: 0.8211\n",
      "Epoch 121/500\n",
      "132/132 [==============================] - 83s 631ms/step - loss: 0.2161 - acc: 0.9175 - val_loss: 0.5080 - val_acc: 0.8257\n",
      "Epoch 122/500\n",
      "132/132 [==============================] - 84s 634ms/step - loss: 0.2165 - acc: 0.9161 - val_loss: 0.5291 - val_acc: 0.7936\n",
      "Epoch 123/500\n",
      "132/132 [==============================] - 90s 679ms/step - loss: 0.2179 - acc: 0.9170 - val_loss: 0.4927 - val_acc: 0.8394\n",
      "Epoch 124/500\n",
      "132/132 [==============================] - 85s 646ms/step - loss: 0.2047 - acc: 0.9208 - val_loss: 0.5225 - val_acc: 0.8119\n",
      "Epoch 125/500\n",
      "132/132 [==============================] - 86s 655ms/step - loss: 0.2131 - acc: 0.9201 - val_loss: 0.4964 - val_acc: 0.8165\n",
      "Epoch 126/500\n",
      "132/132 [==============================] - 90s 680ms/step - loss: 0.2072 - acc: 0.9201 - val_loss: 0.5043 - val_acc: 0.8257\n",
      "Epoch 127/500\n",
      "132/132 [==============================] - 94s 709ms/step - loss: 0.2034 - acc: 0.9265 - val_loss: 0.4547 - val_acc: 0.8257\n",
      "Epoch 128/500\n",
      "132/132 [==============================] - 86s 652ms/step - loss: 0.2039 - acc: 0.9242 - val_loss: 0.5198 - val_acc: 0.8119\n",
      "Epoch 129/500\n",
      "132/132 [==============================] - 86s 654ms/step - loss: 0.1986 - acc: 0.9270 - val_loss: 0.5317 - val_acc: 0.8303\n",
      "Epoch 130/500\n",
      "132/132 [==============================] - 81s 611ms/step - loss: 0.1999 - acc: 0.9246 - val_loss: 0.4843 - val_acc: 0.8394\n",
      "Epoch 131/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.1944 - acc: 0.9271 - val_loss: 0.4808 - val_acc: 0.8532\n",
      "Epoch 132/500\n",
      "132/132 [==============================] - 77s 586ms/step - loss: 0.2073 - acc: 0.9200 - val_loss: 0.5290 - val_acc: 0.8303\n",
      "Epoch 133/500\n",
      "132/132 [==============================] - 79s 595ms/step - loss: 0.1898 - acc: 0.9261 - val_loss: 0.5085 - val_acc: 0.8211\n",
      "Epoch 134/500\n",
      "132/132 [==============================] - 79s 595ms/step - loss: 0.1941 - acc: 0.9280 - val_loss: 0.4551 - val_acc: 0.8440\n",
      "Epoch 135/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.1868 - acc: 0.9285 - val_loss: 0.4616 - val_acc: 0.8394\n",
      "Epoch 136/500\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.1958 - acc: 0.9277 - val_loss: 0.4635 - val_acc: 0.8578\n",
      "Epoch 137/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.1835 - acc: 0.9323 - val_loss: 0.5176 - val_acc: 0.8165\n",
      "Epoch 138/500\n",
      "132/132 [==============================] - 78s 588ms/step - loss: 0.1836 - acc: 0.9341 - val_loss: 0.4821 - val_acc: 0.8303\n",
      "Epoch 139/500\n",
      "132/132 [==============================] - 78s 587ms/step - loss: 0.1846 - acc: 0.9312 - val_loss: 0.4970 - val_acc: 0.8394\n",
      "Epoch 140/500\n",
      "132/132 [==============================] - 78s 587ms/step - loss: 0.1761 - acc: 0.9355 - val_loss: 0.4992 - val_acc: 0.8257\n",
      "Epoch 141/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.1792 - acc: 0.9342 - val_loss: 0.4748 - val_acc: 0.8303\n",
      "Epoch 142/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.1791 - acc: 0.9335 - val_loss: 0.4867 - val_acc: 0.8303\n",
      "Epoch 143/500\n",
      "132/132 [==============================] - 79s 596ms/step - loss: 0.1813 - acc: 0.9317 - val_loss: 0.5802 - val_acc: 0.8119\n",
      "Epoch 144/500\n",
      "132/132 [==============================] - 78s 588ms/step - loss: 0.1796 - acc: 0.9331 - val_loss: 0.5244 - val_acc: 0.8165\n",
      "Epoch 145/500\n",
      "132/132 [==============================] - 79s 597ms/step - loss: 0.1669 - acc: 0.9367 - val_loss: 0.5349 - val_acc: 0.8211\n",
      "Epoch 146/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.1645 - acc: 0.9401 - val_loss: 0.4999 - val_acc: 0.8394\n",
      "Epoch 147/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.1697 - acc: 0.9373 - val_loss: 0.4979 - val_acc: 0.8349\n",
      "Epoch 148/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.1690 - acc: 0.9395 - val_loss: 0.5618 - val_acc: 0.8211\n",
      "Epoch 149/500\n",
      "132/132 [==============================] - 79s 597ms/step - loss: 0.1866 - acc: 0.9329 - val_loss: 0.5349 - val_acc: 0.8211\n",
      "Epoch 150/500\n",
      "132/132 [==============================] - 79s 596ms/step - loss: 0.1773 - acc: 0.9344 - val_loss: 0.4944 - val_acc: 0.8303\n",
      "Epoch 151/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.1600 - acc: 0.9417 - val_loss: 0.5123 - val_acc: 0.8440\n",
      "Epoch 152/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.1658 - acc: 0.9353 - val_loss: 0.4770 - val_acc: 0.8394\n",
      "Epoch 153/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.1613 - acc: 0.9424 - val_loss: 0.5189 - val_acc: 0.8165\n",
      "Epoch 154/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.1683 - acc: 0.9356 - val_loss: 0.5008 - val_acc: 0.8257\n",
      "Epoch 155/500\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.1556 - acc: 0.9458 - val_loss: 0.5073 - val_acc: 0.8486\n",
      "Epoch 156/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.1542 - acc: 0.9426 - val_loss: 0.5431 - val_acc: 0.8349\n",
      "Epoch 157/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.1424 - acc: 0.9464 - val_loss: 0.5017 - val_acc: 0.8486\n",
      "Epoch 158/500\n",
      "132/132 [==============================] - 79s 595ms/step - loss: 0.1631 - acc: 0.9398 - val_loss: 0.5131 - val_acc: 0.8303\n",
      "Epoch 159/500\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.1480 - acc: 0.9479 - val_loss: 0.5090 - val_acc: 0.8303\n",
      "Epoch 160/500\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.1461 - acc: 0.9464 - val_loss: 0.5082 - val_acc: 0.8486\n",
      "Epoch 161/500\n",
      "132/132 [==============================] - 79s 597ms/step - loss: 0.1624 - acc: 0.9377 - val_loss: 0.5180 - val_acc: 0.8578\n",
      "Epoch 162/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.1507 - acc: 0.9453 - val_loss: 0.4904 - val_acc: 0.8349\n",
      "Epoch 163/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.1369 - acc: 0.9544 - val_loss: 0.5303 - val_acc: 0.8440\n",
      "Epoch 164/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.1443 - acc: 0.9476 - val_loss: 0.5356 - val_acc: 0.8486\n",
      "Epoch 165/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.1361 - acc: 0.9506 - val_loss: 0.5823 - val_acc: 0.8119\n",
      "Epoch 166/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.1402 - acc: 0.9489 - val_loss: 0.5487 - val_acc: 0.8440\n",
      "Epoch 167/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.1596 - acc: 0.9409 - val_loss: 0.5081 - val_acc: 0.8394\n",
      "Epoch 168/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.1516 - acc: 0.9445 - val_loss: 0.5500 - val_acc: 0.8486\n",
      "Epoch 169/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.1343 - acc: 0.9532 - val_loss: 0.5240 - val_acc: 0.8532\n",
      "Epoch 170/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.1354 - acc: 0.9535 - val_loss: 0.5260 - val_acc: 0.8394\n",
      "Epoch 171/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.1367 - acc: 0.9497 - val_loss: 0.4999 - val_acc: 0.8486\n",
      "Epoch 172/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.1299 - acc: 0.9521 - val_loss: 0.4885 - val_acc: 0.8532\n",
      "Epoch 173/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.1431 - acc: 0.9471 - val_loss: 0.5418 - val_acc: 0.8394\n",
      "Epoch 174/500\n",
      "132/132 [==============================] - 79s 596ms/step - loss: 0.1473 - acc: 0.9461 - val_loss: 0.5411 - val_acc: 0.8486\n",
      "Epoch 175/500\n",
      "132/132 [==============================] - 80s 602ms/step - loss: 0.1263 - acc: 0.9526 - val_loss: 0.5644 - val_acc: 0.8486\n",
      "Epoch 176/500\n",
      "132/132 [==============================] - 79s 602ms/step - loss: 0.1269 - acc: 0.9542 - val_loss: 0.5137 - val_acc: 0.8532\n",
      "Epoch 177/500\n",
      "132/132 [==============================] - 79s 600ms/step - loss: 0.1303 - acc: 0.9547 - val_loss: 0.5678 - val_acc: 0.8624\n",
      "Epoch 178/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.1342 - acc: 0.9494 - val_loss: 0.5914 - val_acc: 0.8532\n",
      "Epoch 179/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 79s 599ms/step - loss: 0.1231 - acc: 0.9571 - val_loss: 0.5077 - val_acc: 0.8853\n",
      "Epoch 180/500\n",
      "132/132 [==============================] - 79s 600ms/step - loss: 0.1222 - acc: 0.9588 - val_loss: 0.5724 - val_acc: 0.8578\n",
      "Epoch 181/500\n",
      "132/132 [==============================] - 79s 598ms/step - loss: 0.1192 - acc: 0.9585 - val_loss: 0.5471 - val_acc: 0.8440\n",
      "Epoch 182/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.1132 - acc: 0.9616 - val_loss: 0.5525 - val_acc: 0.8257\n",
      "Epoch 183/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.1197 - acc: 0.9570 - val_loss: 0.5516 - val_acc: 0.8532\n",
      "Epoch 184/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.1085 - acc: 0.9618 - val_loss: 0.5758 - val_acc: 0.8532\n",
      "Epoch 185/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.1101 - acc: 0.9617 - val_loss: 0.5719 - val_acc: 0.8716\n",
      "Epoch 186/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.1178 - acc: 0.9568 - val_loss: 0.5736 - val_acc: 0.8486\n",
      "Epoch 187/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.1161 - acc: 0.9606 - val_loss: 0.5343 - val_acc: 0.8578\n",
      "Epoch 188/500\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.1112 - acc: 0.9600 - val_loss: 0.6016 - val_acc: 0.8073\n",
      "Epoch 189/500\n",
      "132/132 [==============================] - 79s 597ms/step - loss: 0.1094 - acc: 0.9639 - val_loss: 0.5277 - val_acc: 0.8716\n",
      "Epoch 190/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.1035 - acc: 0.9642 - val_loss: 0.5825 - val_acc: 0.8624\n",
      "Epoch 191/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.1127 - acc: 0.9601 - val_loss: 0.5454 - val_acc: 0.8624\n",
      "Epoch 192/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.1030 - acc: 0.9648 - val_loss: 0.5598 - val_acc: 0.8578\n",
      "Epoch 193/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.1058 - acc: 0.9639 - val_loss: 0.5674 - val_acc: 0.8716\n",
      "Epoch 194/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.1011 - acc: 0.9650 - val_loss: 0.5027 - val_acc: 0.8624\n",
      "Epoch 195/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.1074 - acc: 0.9621 - val_loss: 0.4915 - val_acc: 0.8670\n",
      "Epoch 196/500\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.1031 - acc: 0.9632 - val_loss: 0.5720 - val_acc: 0.8761\n",
      "Epoch 197/500\n",
      "132/132 [==============================] - 79s 597ms/step - loss: 0.0995 - acc: 0.9642 - val_loss: 0.5683 - val_acc: 0.8624\n",
      "Epoch 198/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.1011 - acc: 0.9645 - val_loss: 0.5751 - val_acc: 0.8853\n",
      "Epoch 199/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0975 - acc: 0.9665 - val_loss: 0.5645 - val_acc: 0.8670\n",
      "Epoch 200/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0916 - acc: 0.9709 - val_loss: 0.5437 - val_acc: 0.8440\n",
      "Epoch 201/500\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.0906 - acc: 0.9682 - val_loss: 0.5678 - val_acc: 0.8578\n",
      "Epoch 202/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0921 - acc: 0.9679 - val_loss: 0.5569 - val_acc: 0.8761\n",
      "Epoch 203/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0948 - acc: 0.9651 - val_loss: 0.5042 - val_acc: 0.8670\n",
      "Epoch 204/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0874 - acc: 0.9744 - val_loss: 0.5274 - val_acc: 0.8578\n",
      "Epoch 205/500\n",
      "132/132 [==============================] - 79s 596ms/step - loss: 0.0943 - acc: 0.9673 - val_loss: 0.5606 - val_acc: 0.8716\n",
      "Epoch 206/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0934 - acc: 0.9686 - val_loss: 0.5718 - val_acc: 0.8945\n",
      "Epoch 207/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0978 - acc: 0.9668 - val_loss: 0.5492 - val_acc: 0.8578\n",
      "Epoch 208/500\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.0900 - acc: 0.9684 - val_loss: 0.5879 - val_acc: 0.8761\n",
      "Epoch 209/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0929 - acc: 0.9676 - val_loss: 0.5547 - val_acc: 0.8670\n",
      "Epoch 210/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0831 - acc: 0.9708 - val_loss: 0.5874 - val_acc: 0.8716\n",
      "Epoch 211/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0824 - acc: 0.9741 - val_loss: 0.6227 - val_acc: 0.8853\n",
      "Epoch 212/500\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.0826 - acc: 0.9723 - val_loss: 0.6065 - val_acc: 0.8716\n",
      "Epoch 213/500\n",
      "132/132 [==============================] - 79s 599ms/step - loss: 0.0938 - acc: 0.9655 - val_loss: 0.4996 - val_acc: 0.8670\n",
      "Epoch 214/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0852 - acc: 0.9741 - val_loss: 0.6019 - val_acc: 0.8761\n",
      "Epoch 215/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0978 - acc: 0.9680 - val_loss: 0.5266 - val_acc: 0.8578\n",
      "Epoch 216/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0852 - acc: 0.9729 - val_loss: 0.6121 - val_acc: 0.8303\n",
      "Epoch 217/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0725 - acc: 0.9776 - val_loss: 0.6001 - val_acc: 0.8670\n",
      "Epoch 218/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0763 - acc: 0.9733 - val_loss: 0.5631 - val_acc: 0.8761\n",
      "Epoch 219/500\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.0738 - acc: 0.9756 - val_loss: 0.5289 - val_acc: 0.8761\n",
      "Epoch 220/500\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.0760 - acc: 0.9753 - val_loss: 0.5379 - val_acc: 0.8532\n",
      "Epoch 221/500\n",
      "132/132 [==============================] - 79s 599ms/step - loss: 0.0792 - acc: 0.9745 - val_loss: 0.5650 - val_acc: 0.8716\n",
      "Epoch 222/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0732 - acc: 0.9773 - val_loss: 0.5967 - val_acc: 0.9037\n",
      "Epoch 223/500\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.0791 - acc: 0.9717 - val_loss: 0.5953 - val_acc: 0.8761\n",
      "Epoch 224/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0702 - acc: 0.9765 - val_loss: 0.5803 - val_acc: 0.8670\n",
      "Epoch 225/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0741 - acc: 0.9735 - val_loss: 0.5646 - val_acc: 0.8670\n",
      "Epoch 226/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0757 - acc: 0.9767 - val_loss: 0.5664 - val_acc: 0.8716\n",
      "Epoch 227/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0640 - acc: 0.9782 - val_loss: 0.6186 - val_acc: 0.8532\n",
      "Epoch 228/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0778 - acc: 0.9744 - val_loss: 0.5558 - val_acc: 0.8670\n",
      "Epoch 229/500\n",
      "132/132 [==============================] - 79s 599ms/step - loss: 0.0735 - acc: 0.9786 - val_loss: 0.6307 - val_acc: 0.8807\n",
      "Epoch 230/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0741 - acc: 0.9779 - val_loss: 0.5326 - val_acc: 0.8716\n",
      "Epoch 231/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0697 - acc: 0.9756 - val_loss: 0.4980 - val_acc: 0.8761\n",
      "Epoch 232/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0700 - acc: 0.9758 - val_loss: 0.5959 - val_acc: 0.8716\n",
      "Epoch 233/500\n",
      "132/132 [==============================] - 79s 595ms/step - loss: 0.0638 - acc: 0.9792 - val_loss: 0.5446 - val_acc: 0.8578\n",
      "Epoch 234/500\n",
      "132/132 [==============================] - 79s 596ms/step - loss: 0.0638 - acc: 0.9787 - val_loss: 0.5125 - val_acc: 0.8624\n",
      "Epoch 235/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0718 - acc: 0.9759 - val_loss: 0.5742 - val_acc: 0.8807\n",
      "Epoch 236/500\n",
      "132/132 [==============================] - 79s 598ms/step - loss: 0.0671 - acc: 0.9788 - val_loss: 0.6913 - val_acc: 0.8532\n",
      "Epoch 237/500\n",
      "132/132 [==============================] - 79s 601ms/step - loss: 0.0691 - acc: 0.9782 - val_loss: 0.5902 - val_acc: 0.8670\n",
      "Epoch 238/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0623 - acc: 0.9808 - val_loss: 0.5996 - val_acc: 0.8440\n",
      "Epoch 239/500\n",
      "132/132 [==============================] - 79s 600ms/step - loss: 0.0590 - acc: 0.9800 - val_loss: 0.6090 - val_acc: 0.8716\n",
      "Epoch 240/500\n",
      "132/132 [==============================] - 80s 604ms/step - loss: 0.0703 - acc: 0.9783 - val_loss: 0.5552 - val_acc: 0.8532\n",
      "Epoch 241/500\n",
      "132/132 [==============================] - 79s 602ms/step - loss: 0.0579 - acc: 0.9812 - val_loss: 0.7433 - val_acc: 0.8670\n",
      "Epoch 242/500\n",
      "132/132 [==============================] - 79s 602ms/step - loss: 0.0620 - acc: 0.9806 - val_loss: 0.5367 - val_acc: 0.8807\n",
      "Epoch 243/500\n",
      "132/132 [==============================] - 79s 598ms/step - loss: 0.0587 - acc: 0.9829 - val_loss: 0.5882 - val_acc: 0.8670\n",
      "Epoch 244/500\n",
      "132/132 [==============================] - 80s 603ms/step - loss: 0.0571 - acc: 0.9826 - val_loss: 0.5972 - val_acc: 0.8624\n",
      "Epoch 245/500\n",
      "132/132 [==============================] - 80s 606ms/step - loss: 0.0604 - acc: 0.9806 - val_loss: 0.5442 - val_acc: 0.8761\n",
      "Epoch 246/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0544 - acc: 0.9830 - val_loss: 0.6204 - val_acc: 0.8578\n",
      "Epoch 247/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0495 - acc: 0.9845 - val_loss: 0.5949 - val_acc: 0.8670\n",
      "Epoch 248/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0573 - acc: 0.9812 - val_loss: 0.6172 - val_acc: 0.8624\n",
      "Epoch 249/500\n",
      "132/132 [==============================] - 79s 599ms/step - loss: 0.0500 - acc: 0.9848 - val_loss: 0.5814 - val_acc: 0.8670\n",
      "Epoch 250/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0638 - acc: 0.9786 - val_loss: 0.6404 - val_acc: 0.8394\n",
      "Epoch 251/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0600 - acc: 0.9812 - val_loss: 0.6238 - val_acc: 0.8807\n",
      "Epoch 252/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0493 - acc: 0.9844 - val_loss: 0.5720 - val_acc: 0.8624\n",
      "Epoch 253/500\n",
      "132/132 [==============================] - 79s 601ms/step - loss: 0.0494 - acc: 0.9861 - val_loss: 0.5655 - val_acc: 0.8624\n",
      "Epoch 254/500\n",
      "132/132 [==============================] - 79s 597ms/step - loss: 0.0501 - acc: 0.9820 - val_loss: 0.5726 - val_acc: 0.8578\n",
      "Epoch 255/500\n",
      "132/132 [==============================] - 79s 595ms/step - loss: 0.0522 - acc: 0.9830 - val_loss: 0.5941 - val_acc: 0.8670\n",
      "Epoch 256/500\n",
      "132/132 [==============================] - 79s 595ms/step - loss: 0.0513 - acc: 0.9855 - val_loss: 0.5510 - val_acc: 0.8624\n",
      "Epoch 257/500\n",
      "132/132 [==============================] - 79s 599ms/step - loss: 0.0508 - acc: 0.9827 - val_loss: 0.6830 - val_acc: 0.8853\n",
      "Epoch 258/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0452 - acc: 0.9865 - val_loss: 0.5819 - val_acc: 0.8670\n",
      "Epoch 259/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0459 - acc: 0.9879 - val_loss: 0.6080 - val_acc: 0.8624\n",
      "Epoch 260/500\n",
      "132/132 [==============================] - 79s 598ms/step - loss: 0.0515 - acc: 0.9842 - val_loss: 0.6005 - val_acc: 0.8624\n",
      "Epoch 261/500\n",
      "132/132 [==============================] - 80s 603ms/step - loss: 0.0508 - acc: 0.9844 - val_loss: 0.6102 - val_acc: 0.8670\n",
      "Epoch 262/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0472 - acc: 0.9848 - val_loss: 0.6390 - val_acc: 0.8853\n",
      "Epoch 263/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0423 - acc: 0.9880 - val_loss: 0.6762 - val_acc: 0.8761\n",
      "Epoch 264/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0526 - acc: 0.9829 - val_loss: 0.5131 - val_acc: 0.8899\n",
      "Epoch 265/500\n",
      "132/132 [==============================] - 79s 595ms/step - loss: 0.0515 - acc: 0.9852 - val_loss: 0.5857 - val_acc: 0.8440\n",
      "Epoch 266/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0443 - acc: 0.9864 - val_loss: 0.5834 - val_acc: 0.8899\n",
      "Epoch 267/500\n",
      "132/132 [==============================] - 79s 596ms/step - loss: 0.0422 - acc: 0.9871 - val_loss: 0.5524 - val_acc: 0.8899\n",
      "Epoch 268/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0431 - acc: 0.9865 - val_loss: 0.5104 - val_acc: 0.8624\n",
      "Epoch 269/500\n",
      "132/132 [==============================] - 79s 600ms/step - loss: 0.0430 - acc: 0.9880 - val_loss: 0.5631 - val_acc: 0.8991\n",
      "Epoch 270/500\n",
      "132/132 [==============================] - 78s 595ms/step - loss: 0.0458 - acc: 0.9856 - val_loss: 0.5582 - val_acc: 0.8578\n",
      "Epoch 271/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0411 - acc: 0.9879 - val_loss: 0.5406 - val_acc: 0.8899\n",
      "Epoch 272/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0418 - acc: 0.9885 - val_loss: 0.5453 - val_acc: 0.8807\n",
      "Epoch 273/500\n",
      "132/132 [==============================] - 78s 595ms/step - loss: 0.0454 - acc: 0.9874 - val_loss: 0.5770 - val_acc: 0.8624\n",
      "Epoch 274/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0391 - acc: 0.9888 - val_loss: 0.6036 - val_acc: 0.8716\n",
      "Epoch 275/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0426 - acc: 0.9870 - val_loss: 0.5756 - val_acc: 0.8761\n",
      "Epoch 276/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0367 - acc: 0.9909 - val_loss: 0.5853 - val_acc: 0.8853\n",
      "Epoch 277/500\n",
      "132/132 [==============================] - 80s 602ms/step - loss: 0.0353 - acc: 0.9892 - val_loss: 0.6264 - val_acc: 0.8945\n",
      "Epoch 278/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0437 - acc: 0.9885 - val_loss: 0.5871 - val_acc: 0.8807\n",
      "Epoch 279/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0461 - acc: 0.9867 - val_loss: 0.5707 - val_acc: 0.8853\n",
      "Epoch 280/500\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.0377 - acc: 0.9886 - val_loss: 0.6260 - val_acc: 0.8670\n",
      "Epoch 281/500\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.0429 - acc: 0.9870 - val_loss: 0.6402 - val_acc: 0.8670\n",
      "Epoch 282/500\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.0401 - acc: 0.9870 - val_loss: 0.6215 - val_acc: 0.8624\n",
      "Epoch 283/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0411 - acc: 0.9868 - val_loss: 0.6722 - val_acc: 0.8807\n",
      "Epoch 284/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0397 - acc: 0.9867 - val_loss: 0.6893 - val_acc: 0.8578\n",
      "Epoch 285/500\n",
      "132/132 [==============================] - 79s 599ms/step - loss: 0.0384 - acc: 0.9874 - val_loss: 0.5248 - val_acc: 0.8807\n",
      "Epoch 286/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0375 - acc: 0.9891 - val_loss: 0.6613 - val_acc: 0.8624\n",
      "Epoch 287/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0308 - acc: 0.9917 - val_loss: 0.5543 - val_acc: 0.8624\n",
      "Epoch 288/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0418 - acc: 0.9879 - val_loss: 0.5734 - val_acc: 0.8578\n",
      "Epoch 289/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0327 - acc: 0.9892 - val_loss: 0.5766 - val_acc: 0.8716\n",
      "Epoch 290/500\n",
      "132/132 [==============================] - 79s 595ms/step - loss: 0.0379 - acc: 0.9876 - val_loss: 0.5594 - val_acc: 0.8624\n",
      "Epoch 291/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0443 - acc: 0.9858 - val_loss: 0.5175 - val_acc: 0.8807\n",
      "Epoch 292/500\n",
      "132/132 [==============================] - 79s 595ms/step - loss: 0.0392 - acc: 0.9874 - val_loss: 0.5255 - val_acc: 0.8716\n",
      "Epoch 293/500\n",
      "132/132 [==============================] - 79s 602ms/step - loss: 0.0385 - acc: 0.9882 - val_loss: 0.5746 - val_acc: 0.8670\n",
      "Epoch 294/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0429 - acc: 0.9862 - val_loss: 0.6072 - val_acc: 0.8807\n",
      "Epoch 295/500\n",
      "132/132 [==============================] - 79s 595ms/step - loss: 0.0336 - acc: 0.9911 - val_loss: 0.5982 - val_acc: 0.8761\n",
      "Epoch 296/500\n",
      "132/132 [==============================] - 79s 595ms/step - loss: 0.0362 - acc: 0.9903 - val_loss: 0.7062 - val_acc: 0.8853\n",
      "Epoch 297/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0347 - acc: 0.9906 - val_loss: 0.5426 - val_acc: 0.8899\n",
      "Epoch 298/500\n",
      "132/132 [==============================] - 79s 597ms/step - loss: 0.0312 - acc: 0.9921 - val_loss: 0.5573 - val_acc: 0.8532\n",
      "Epoch 299/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0351 - acc: 0.9886 - val_loss: 0.4975 - val_acc: 0.8853\n",
      "Epoch 300/500\n",
      "132/132 [==============================] - 79s 596ms/step - loss: 0.0302 - acc: 0.9923 - val_loss: 0.6258 - val_acc: 0.9128\n",
      "Epoch 301/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0350 - acc: 0.9900 - val_loss: 0.5320 - val_acc: 0.8716\n",
      "Epoch 302/500\n",
      "132/132 [==============================] - 78s 595ms/step - loss: 0.0346 - acc: 0.9892 - val_loss: 0.5615 - val_acc: 0.8991\n",
      "Epoch 303/500\n",
      "132/132 [==============================] - 79s 597ms/step - loss: 0.0316 - acc: 0.9897 - val_loss: 0.6170 - val_acc: 0.8578\n",
      "Epoch 304/500\n",
      "132/132 [==============================] - 79s 597ms/step - loss: 0.0331 - acc: 0.9897 - val_loss: 0.6553 - val_acc: 0.8761\n",
      "Epoch 305/500\n",
      "132/132 [==============================] - 79s 597ms/step - loss: 0.0288 - acc: 0.9912 - val_loss: 0.5801 - val_acc: 0.8761\n",
      "Epoch 306/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0306 - acc: 0.9921 - val_loss: 0.6598 - val_acc: 0.8486\n",
      "Epoch 307/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0396 - acc: 0.9876 - val_loss: 0.5232 - val_acc: 0.8807\n",
      "Epoch 308/500\n",
      "132/132 [==============================] - 79s 599ms/step - loss: 0.0295 - acc: 0.9926 - val_loss: 0.5706 - val_acc: 0.8761\n",
      "Epoch 309/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0291 - acc: 0.9911 - val_loss: 0.6290 - val_acc: 0.8716\n",
      "Epoch 310/500\n",
      "132/132 [==============================] - 79s 596ms/step - loss: 0.0251 - acc: 0.9932 - val_loss: 0.5833 - val_acc: 0.8853\n",
      "Epoch 311/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0361 - acc: 0.9889 - val_loss: 0.6359 - val_acc: 0.8624\n",
      "Epoch 312/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0361 - acc: 0.9883 - val_loss: 0.5807 - val_acc: 0.8716\n",
      "Epoch 313/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0279 - acc: 0.9932 - val_loss: 0.5255 - val_acc: 0.8807\n",
      "Epoch 314/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0300 - acc: 0.9911 - val_loss: 0.5133 - val_acc: 0.8578\n",
      "Epoch 315/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0300 - acc: 0.9914 - val_loss: 0.6051 - val_acc: 0.8899\n",
      "Epoch 316/500\n",
      "132/132 [==============================] - 79s 599ms/step - loss: 0.0284 - acc: 0.9918 - val_loss: 0.5949 - val_acc: 0.8716\n",
      "Epoch 317/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0292 - acc: 0.9908 - val_loss: 0.5719 - val_acc: 0.8761\n",
      "Epoch 318/500\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.0236 - acc: 0.9935 - val_loss: 0.6146 - val_acc: 0.8945\n",
      "Epoch 319/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0239 - acc: 0.9942 - val_loss: 0.5850 - val_acc: 0.8624\n",
      "Epoch 320/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0349 - acc: 0.9894 - val_loss: 0.5864 - val_acc: 0.8899\n",
      "Epoch 321/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0260 - acc: 0.9927 - val_loss: 0.6070 - val_acc: 0.8899\n",
      "Epoch 322/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0227 - acc: 0.9938 - val_loss: 0.6603 - val_acc: 0.8670\n",
      "Epoch 323/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0252 - acc: 0.9932 - val_loss: 0.6144 - val_acc: 0.8670\n",
      "Epoch 324/500\n",
      "132/132 [==============================] - 79s 601ms/step - loss: 0.0306 - acc: 0.9906 - val_loss: 0.7452 - val_acc: 0.8761\n",
      "Epoch 325/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0273 - acc: 0.9918 - val_loss: 0.5550 - val_acc: 0.8991\n",
      "Epoch 326/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0234 - acc: 0.9930 - val_loss: 0.6818 - val_acc: 0.8716\n",
      "Epoch 327/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0257 - acc: 0.9927 - val_loss: 0.5642 - val_acc: 0.8945\n",
      "Epoch 328/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0377 - acc: 0.9880 - val_loss: 0.6507 - val_acc: 0.8945\n",
      "Epoch 329/500\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.0314 - acc: 0.9909 - val_loss: 0.5586 - val_acc: 0.8807\n",
      "Epoch 330/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0292 - acc: 0.9918 - val_loss: 0.5741 - val_acc: 0.8761\n",
      "Epoch 331/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0313 - acc: 0.9902 - val_loss: 0.6457 - val_acc: 0.8807\n",
      "Epoch 332/500\n",
      "132/132 [==============================] - 79s 602ms/step - loss: 0.0345 - acc: 0.9889 - val_loss: 0.6648 - val_acc: 0.8899\n",
      "Epoch 333/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0245 - acc: 0.9929 - val_loss: 0.5378 - val_acc: 0.8899\n",
      "Epoch 334/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0211 - acc: 0.9950 - val_loss: 0.6183 - val_acc: 0.8716\n",
      "Epoch 335/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0244 - acc: 0.9936 - val_loss: 0.6664 - val_acc: 0.8945\n",
      "Epoch 336/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0233 - acc: 0.9935 - val_loss: 0.7040 - val_acc: 0.8670\n",
      "Epoch 337/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0251 - acc: 0.9930 - val_loss: 0.6759 - val_acc: 0.8991\n",
      "Epoch 338/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0206 - acc: 0.9938 - val_loss: 0.5803 - val_acc: 0.8899\n",
      "Epoch 339/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0235 - acc: 0.9929 - val_loss: 0.6043 - val_acc: 0.8899\n",
      "Epoch 340/500\n",
      "132/132 [==============================] - 79s 600ms/step - loss: 0.0311 - acc: 0.9906 - val_loss: 0.6814 - val_acc: 0.8761\n",
      "Epoch 341/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0251 - acc: 0.9930 - val_loss: 0.6652 - val_acc: 0.8807\n",
      "Epoch 342/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0258 - acc: 0.9941 - val_loss: 0.6195 - val_acc: 0.8853\n",
      "Epoch 343/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0236 - acc: 0.9935 - val_loss: 0.6531 - val_acc: 0.8853\n",
      "Epoch 344/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0244 - acc: 0.9930 - val_loss: 0.6381 - val_acc: 0.8853\n",
      "Epoch 345/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0242 - acc: 0.9935 - val_loss: 0.6100 - val_acc: 0.8853\n",
      "Epoch 346/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0371 - acc: 0.9868 - val_loss: 0.7375 - val_acc: 0.8624\n",
      "Epoch 347/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0248 - acc: 0.9933 - val_loss: 0.6622 - val_acc: 0.8853\n",
      "Epoch 348/500\n",
      "132/132 [==============================] - 79s 602ms/step - loss: 0.0211 - acc: 0.9947 - val_loss: 0.6939 - val_acc: 0.8853\n",
      "Epoch 349/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0218 - acc: 0.9939 - val_loss: 0.7256 - val_acc: 0.8716\n",
      "Epoch 350/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0194 - acc: 0.9942 - val_loss: 0.6598 - val_acc: 0.8761\n",
      "Epoch 351/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0230 - acc: 0.9932 - val_loss: 0.7143 - val_acc: 0.8670\n",
      "Epoch 352/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0202 - acc: 0.9941 - val_loss: 0.6338 - val_acc: 0.8807\n",
      "Epoch 353/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0187 - acc: 0.9947 - val_loss: 0.6316 - val_acc: 0.8716\n",
      "Epoch 354/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0183 - acc: 0.9945 - val_loss: 0.6661 - val_acc: 0.8807\n",
      "Epoch 355/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0231 - acc: 0.9939 - val_loss: 0.6963 - val_acc: 0.8716\n",
      "Epoch 356/500\n",
      "132/132 [==============================] - 79s 599ms/step - loss: 0.0239 - acc: 0.9926 - val_loss: 0.6957 - val_acc: 0.8853\n",
      "Epoch 357/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0188 - acc: 0.9962 - val_loss: 0.6806 - val_acc: 0.8899\n",
      "Epoch 358/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0201 - acc: 0.9944 - val_loss: 0.6548 - val_acc: 0.8853\n",
      "Epoch 359/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0228 - acc: 0.9935 - val_loss: 0.5859 - val_acc: 0.8716\n",
      "Epoch 360/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0184 - acc: 0.9947 - val_loss: 0.6832 - val_acc: 0.8761\n",
      "Epoch 361/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0177 - acc: 0.9953 - val_loss: 0.6689 - val_acc: 0.8670\n",
      "Epoch 362/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0170 - acc: 0.9958 - val_loss: 0.7842 - val_acc: 0.8624\n",
      "Epoch 363/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0239 - acc: 0.9932 - val_loss: 0.6317 - val_acc: 0.8761\n",
      "Epoch 364/500\n",
      "132/132 [==============================] - 79s 599ms/step - loss: 0.0309 - acc: 0.9911 - val_loss: 0.8896 - val_acc: 0.8440\n",
      "Epoch 365/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0257 - acc: 0.9915 - val_loss: 0.7989 - val_acc: 0.8761\n",
      "Epoch 366/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0213 - acc: 0.9938 - val_loss: 0.6768 - val_acc: 0.8761\n",
      "Epoch 367/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0188 - acc: 0.9964 - val_loss: 0.6211 - val_acc: 0.8670\n",
      "Epoch 368/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0261 - acc: 0.9923 - val_loss: 0.6709 - val_acc: 0.8899\n",
      "Epoch 369/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0248 - acc: 0.9927 - val_loss: 0.7706 - val_acc: 0.8761\n",
      "Epoch 370/500\n",
      "132/132 [==============================] - 79s 596ms/step - loss: 0.0154 - acc: 0.9961 - val_loss: 0.6477 - val_acc: 0.8670\n",
      "Epoch 371/500\n",
      "132/132 [==============================] - 79s 595ms/step - loss: 0.0185 - acc: 0.9938 - val_loss: 0.8390 - val_acc: 0.8532\n",
      "Epoch 372/500\n",
      "132/132 [==============================] - 80s 603ms/step - loss: 0.0213 - acc: 0.9932 - val_loss: 0.6290 - val_acc: 0.8807\n",
      "Epoch 373/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0244 - acc: 0.9929 - val_loss: 0.6316 - val_acc: 0.8899\n",
      "Epoch 374/500\n",
      "132/132 [==============================] - 79s 600ms/step - loss: 0.0295 - acc: 0.9903 - val_loss: 0.7818 - val_acc: 0.8807\n",
      "Epoch 375/500\n",
      "132/132 [==============================] - 79s 595ms/step - loss: 0.0246 - acc: 0.9920 - val_loss: 0.6684 - val_acc: 0.8670\n",
      "Epoch 376/500\n",
      "132/132 [==============================] - 79s 596ms/step - loss: 0.0172 - acc: 0.9948 - val_loss: 0.6995 - val_acc: 0.8670\n",
      "Epoch 377/500\n",
      "132/132 [==============================] - 79s 599ms/step - loss: 0.0161 - acc: 0.9948 - val_loss: 0.7610 - val_acc: 0.8761\n",
      "Epoch 378/500\n",
      "132/132 [==============================] - 79s 597ms/step - loss: 0.0247 - acc: 0.9918 - val_loss: 0.8199 - val_acc: 0.8624\n",
      "Epoch 379/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0232 - acc: 0.9938 - val_loss: 0.7131 - val_acc: 0.8761\n",
      "Epoch 380/500\n",
      "132/132 [==============================] - 80s 606ms/step - loss: 0.0223 - acc: 0.9945 - val_loss: 0.6437 - val_acc: 0.8670\n",
      "Epoch 381/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0215 - acc: 0.9945 - val_loss: 0.7001 - val_acc: 0.8716\n",
      "Epoch 382/500\n",
      "132/132 [==============================] - 79s 597ms/step - loss: 0.0208 - acc: 0.9950 - val_loss: 0.6277 - val_acc: 0.8624\n",
      "Epoch 383/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0252 - acc: 0.9924 - val_loss: 0.6039 - val_acc: 0.8716\n",
      "Epoch 384/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0200 - acc: 0.9936 - val_loss: 0.6959 - val_acc: 0.8807\n",
      "Epoch 385/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0138 - acc: 0.9965 - val_loss: 0.7150 - val_acc: 0.8716\n",
      "Epoch 386/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0190 - acc: 0.9939 - val_loss: 0.6460 - val_acc: 0.8853\n",
      "Epoch 387/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0230 - acc: 0.9944 - val_loss: 0.5767 - val_acc: 0.8945\n",
      "Epoch 388/500\n",
      "132/132 [==============================] - 79s 601ms/step - loss: 0.0143 - acc: 0.9964 - val_loss: 0.6533 - val_acc: 0.8853\n",
      "Epoch 389/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0167 - acc: 0.9953 - val_loss: 0.6225 - val_acc: 0.8853\n",
      "Epoch 390/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0185 - acc: 0.9950 - val_loss: 0.6410 - val_acc: 0.8761\n",
      "Epoch 391/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0156 - acc: 0.9959 - val_loss: 0.7466 - val_acc: 0.8670\n",
      "Epoch 392/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0291 - acc: 0.9920 - val_loss: 0.5990 - val_acc: 0.8716\n",
      "Epoch 393/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0188 - acc: 0.9953 - val_loss: 0.5956 - val_acc: 0.8716\n",
      "Epoch 394/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0133 - acc: 0.9974 - val_loss: 0.6270 - val_acc: 0.8624\n",
      "Epoch 395/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0113 - acc: 0.9973 - val_loss: 0.7331 - val_acc: 0.8761\n",
      "Epoch 396/500\n",
      "132/132 [==============================] - 79s 598ms/step - loss: 0.0168 - acc: 0.9953 - val_loss: 0.6694 - val_acc: 0.8761\n",
      "Epoch 397/500\n",
      "132/132 [==============================] - 79s 596ms/step - loss: 0.0245 - acc: 0.9929 - val_loss: 0.6209 - val_acc: 0.8578\n",
      "Epoch 398/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0230 - acc: 0.9935 - val_loss: 0.6091 - val_acc: 0.8257\n",
      "Epoch 399/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0162 - acc: 0.9953 - val_loss: 0.7675 - val_acc: 0.8899\n",
      "Epoch 400/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0200 - acc: 0.9945 - val_loss: 0.6487 - val_acc: 0.8624\n",
      "Epoch 401/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0188 - acc: 0.9939 - val_loss: 0.6565 - val_acc: 0.8807\n",
      "Epoch 402/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0158 - acc: 0.9962 - val_loss: 0.6747 - val_acc: 0.8899\n",
      "Epoch 403/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0136 - acc: 0.9973 - val_loss: 0.7645 - val_acc: 0.8578\n",
      "Epoch 404/500\n",
      "132/132 [==============================] - 79s 602ms/step - loss: 0.0151 - acc: 0.9964 - val_loss: 0.7649 - val_acc: 0.8532\n",
      "Epoch 405/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0136 - acc: 0.9962 - val_loss: 0.6781 - val_acc: 0.8532\n",
      "Epoch 406/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0172 - acc: 0.9953 - val_loss: 0.6342 - val_acc: 0.8670\n",
      "Epoch 407/500\n",
      "132/132 [==============================] - 80s 605ms/step - loss: 0.0487 - acc: 0.9856 - val_loss: 0.7176 - val_acc: 0.8578\n",
      "Epoch 408/500\n",
      "132/132 [==============================] - 80s 603ms/step - loss: 0.0174 - acc: 0.9952 - val_loss: 0.6226 - val_acc: 0.8761\n",
      "Epoch 409/500\n",
      "132/132 [==============================] - 81s 613ms/step - loss: 0.0204 - acc: 0.9947 - val_loss: 0.6274 - val_acc: 0.8807\n",
      "Epoch 410/500\n",
      "132/132 [==============================] - 81s 613ms/step - loss: 0.0176 - acc: 0.9956 - val_loss: 0.6225 - val_acc: 0.8624\n",
      "Epoch 411/500\n",
      "132/132 [==============================] - 79s 597ms/step - loss: 0.0140 - acc: 0.9961 - val_loss: 0.6759 - val_acc: 0.8624\n",
      "Epoch 412/500\n",
      "132/132 [==============================] - 80s 606ms/step - loss: 0.0142 - acc: 0.9964 - val_loss: 0.5903 - val_acc: 0.8899\n",
      "Epoch 413/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0212 - acc: 0.9938 - val_loss: 0.6293 - val_acc: 0.8440\n",
      "Epoch 414/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0165 - acc: 0.9953 - val_loss: 0.6146 - val_acc: 0.8807\n",
      "Epoch 415/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0165 - acc: 0.9959 - val_loss: 0.6114 - val_acc: 0.8761\n",
      "Epoch 416/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0116 - acc: 0.9970 - val_loss: 0.6662 - val_acc: 0.8624\n",
      "Epoch 417/500\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0173 - acc: 0.9947 - val_loss: 0.5744 - val_acc: 0.8807\n",
      "Epoch 418/500\n",
      "132/132 [==============================] - 79s 596ms/step - loss: 0.0149 - acc: 0.9952 - val_loss: 0.7394 - val_acc: 0.8853\n",
      "Epoch 419/500\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0186 - acc: 0.9945 - val_loss: 0.6595 - val_acc: 0.8807\n",
      "Epoch 420/500\n",
      "132/132 [==============================] - 80s 604ms/step - loss: 0.0142 - acc: 0.9959 - val_loss: 0.6941 - val_acc: 0.9037\n",
      "Epoch 421/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0187 - acc: 0.9944 - val_loss: 0.7151 - val_acc: 0.8532\n",
      "Epoch 422/500\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0185 - acc: 0.9945 - val_loss: 0.6625 - val_acc: 0.8670\n",
      "Epoch 423/500\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0148 - acc: 0.9950 - val_loss: 0.6843 - val_acc: 0.8761\n",
      "Epoch 424/500\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0271 - acc: 0.9906 - val_loss: 0.6741 - val_acc: 0.8578\n",
      "Epoch 425/500\n",
      "132/132 [==============================] - 78s 595ms/step - loss: 0.0108 - acc: 0.9977 - val_loss: 0.6369 - val_acc: 0.8716\n",
      "Epoch 426/500\n",
      "132/132 [==============================] - 79s 598ms/step - loss: 0.0110 - acc: 0.9971 - val_loss: 0.7618 - val_acc: 0.8761\n",
      "Epoch 427/500\n",
      " 25/132 [====>.........................] - ETA: 1:03 - loss: 0.0122 - acc: 0.9960"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
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      "\u001b[1;32m<ipython-input-14-efb27c30c8b8>\u001b[0m in \u001b[0;36mcnn\u001b[1;34m(index, use_model, n_folds, batch_size, verbose, sel_fold, epochs, save_model, val_split, file_type, name)\u001b[0m\n\u001b[0;32m     22\u001b[0m                             \u001b[0mcallbacks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m                             \u001b[0mvalidation_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata_loader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mval_X\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_Y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m                             validation_steps = batch_per_val)\n\u001b[0m\u001b[0;32m     25\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m         model.fit_generator(data_loader(list(zip(trX, trY)), batch_size, file_type),\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\legacy\\interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name +\n\u001b[0;32m     90\u001b[0m                               '` call to the Keras 2 API: ' + signature, stacklevel=2)\n\u001b[1;32m---> 91\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     93\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[1;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[0;32m   1424\u001b[0m             \u001b[0muse_multiprocessing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1425\u001b[0m             \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1426\u001b[1;33m             initial_epoch=initial_epoch)\n\u001b[0m\u001b[0;32m   1427\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1428\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\engine\\training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[1;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[0;32m    189\u001b[0m                 outs = model.train_on_batch(x, y,\n\u001b[0;32m    190\u001b[0m                                             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 191\u001b[1;33m                                             class_weight=class_weight)\n\u001b[0m\u001b[0;32m    192\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    193\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[1;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[0;32m   1218\u001b[0m             \u001b[0mins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1219\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1220\u001b[1;33m         \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1221\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1222\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   2665\u001b[0m                     \u001b[1;34m'In order to feed symbolic tensors to a Keras model '\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2666\u001b[0m                     'in TensorFlow, you need tensorflow 1.8 or higher.')\n\u001b[1;32m-> 2667\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2668\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2669\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m_legacy_call\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   2647\u001b[0m         \u001b[0msession\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2648\u001b[0m         updated = session.run(fetches=fetches, feed_dict=feed_dict,\n\u001b[1;32m-> 2649\u001b[1;33m                               **self.session_kwargs)\n\u001b[0m\u001b[0;32m   2650\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2651\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    903\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    904\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 905\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    906\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    907\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1138\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1139\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1140\u001b[1;33m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m   1141\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1142\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1319\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1320\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[1;32m-> 1321\u001b[1;33m                            run_metadata)\n\u001b[0m\u001b[0;32m   1322\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1323\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1325\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1326\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1327\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1328\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1329\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1310\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1311\u001b[0m       return self._call_tf_sessionrun(\n\u001b[1;32m-> 1312\u001b[1;33m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[0;32m   1313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1314\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m   1418\u001b[0m         return tf_session.TF_Run(\n\u001b[0;32m   1419\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1420\u001b[1;33m             status, run_metadata)\n\u001b[0m\u001b[0;32m   1421\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1422\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "cnn(4, make_model1D, epochs = 500, save_model = True, name = \"1D_norm\", file_type = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py:497: calling conv1d (from tensorflow.python.ops.nn_ops) with data_format=NHWC is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "`NHWC` for data_format is deprecated, use `NWC` instead\n",
      "Epoch 1/100\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0358 - acc: 0.9879 - val_loss: 0.5196 - val_acc: 0.8991\n",
      "Epoch 2/100\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0368 - acc: 0.9889 - val_loss: 0.5279 - val_acc: 0.8670\n",
      "Epoch 3/100\n",
      "132/132 [==============================] - 77s 583ms/step - loss: 0.0329 - acc: 0.9905 - val_loss: 0.5390 - val_acc: 0.8991\n",
      "Epoch 4/100\n",
      "132/132 [==============================] - 77s 584ms/step - loss: 0.0324 - acc: 0.9889 - val_loss: 0.6341 - val_acc: 0.8670\n",
      "Epoch 5/100\n",
      "132/132 [==============================] - 77s 584ms/step - loss: 0.0332 - acc: 0.9908 - val_loss: 0.6258 - val_acc: 0.8807\n",
      "Epoch 6/100\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0402 - acc: 0.9882 - val_loss: 0.5604 - val_acc: 0.8670\n",
      "Epoch 7/100\n",
      "132/132 [==============================] - 79s 598ms/step - loss: 0.0250 - acc: 0.9948 - val_loss: 0.5984 - val_acc: 0.8899\n",
      "Epoch 8/100\n",
      "132/132 [==============================] - 79s 596ms/step - loss: 0.0316 - acc: 0.9900 - val_loss: 0.5923 - val_acc: 0.8578\n",
      "Epoch 9/100\n",
      "132/132 [==============================] - 79s 597ms/step - loss: 0.0368 - acc: 0.9902 - val_loss: 0.6497 - val_acc: 0.8716\n",
      "Epoch 10/100\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0297 - acc: 0.9918 - val_loss: 0.5934 - val_acc: 0.8624\n",
      "Epoch 11/100\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0341 - acc: 0.9907 - val_loss: 0.5626 - val_acc: 0.8945\n",
      "Epoch 12/100\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0417 - acc: 0.9868 - val_loss: 0.6556 - val_acc: 0.8624\n",
      "Epoch 13/100\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0361 - acc: 0.9892 - val_loss: 0.6176 - val_acc: 0.8670\n",
      "Epoch 14/100\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0351 - acc: 0.9894 - val_loss: 0.5738 - val_acc: 0.8945\n",
      "Epoch 15/100\n",
      "132/132 [==============================] - 79s 597ms/step - loss: 0.0265 - acc: 0.9924 - val_loss: 0.5922 - val_acc: 0.8716\n",
      "Epoch 16/100\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0270 - acc: 0.9921 - val_loss: 0.5998 - val_acc: 0.8899\n",
      "Epoch 17/100\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0370 - acc: 0.9879 - val_loss: 0.5618 - val_acc: 0.8807\n",
      "Epoch 18/100\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0267 - acc: 0.9920 - val_loss: 0.6622 - val_acc: 0.8716\n",
      "Epoch 19/100\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0233 - acc: 0.9941 - val_loss: 0.6618 - val_acc: 0.8761\n",
      "Epoch 20/100\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.0322 - acc: 0.9900 - val_loss: 0.7249 - val_acc: 0.8716\n",
      "Epoch 21/100\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0260 - acc: 0.9927 - val_loss: 0.6183 - val_acc: 0.8945\n",
      "Epoch 22/100\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0305 - acc: 0.9915 - val_loss: 0.6146 - val_acc: 0.8624\n",
      "Epoch 23/100\n",
      "132/132 [==============================] - 79s 596ms/step - loss: 0.0291 - acc: 0.9918 - val_loss: 0.6191 - val_acc: 0.8624\n",
      "Epoch 24/100\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0269 - acc: 0.9941 - val_loss: 0.5978 - val_acc: 0.8761\n",
      "Epoch 25/100\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0225 - acc: 0.9938 - val_loss: 0.7897 - val_acc: 0.8716\n",
      "Epoch 26/100\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0261 - acc: 0.9924 - val_loss: 0.5597 - val_acc: 0.8853\n",
      "Epoch 27/100\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0227 - acc: 0.9942 - val_loss: 0.7143 - val_acc: 0.8532\n",
      "Epoch 28/100\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0257 - acc: 0.9932 - val_loss: 0.6000 - val_acc: 0.8945\n",
      "Epoch 29/100\n",
      "132/132 [==============================] - 78s 587ms/step - loss: 0.0266 - acc: 0.9914 - val_loss: 0.6118 - val_acc: 0.8670\n",
      "Epoch 30/100\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0333 - acc: 0.9906 - val_loss: 0.5971 - val_acc: 0.8761\n",
      "Epoch 31/100\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0248 - acc: 0.9935 - val_loss: 0.5546 - val_acc: 0.8716\n",
      "Epoch 32/100\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0280 - acc: 0.9920 - val_loss: 0.7028 - val_acc: 0.8578\n",
      "Epoch 33/100\n",
      "132/132 [==============================] - 79s 596ms/step - loss: 0.0299 - acc: 0.9909 - val_loss: 0.5270 - val_acc: 0.8624\n",
      "Epoch 34/100\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0250 - acc: 0.9920 - val_loss: 0.6029 - val_acc: 0.8807\n",
      "Epoch 35/100\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0242 - acc: 0.9926 - val_loss: 0.7862 - val_acc: 0.8761\n",
      "Epoch 36/100\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0223 - acc: 0.9938 - val_loss: 0.7077 - val_acc: 0.8807\n",
      "Epoch 37/100\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0215 - acc: 0.9950 - val_loss: 0.6493 - val_acc: 0.8670\n",
      "Epoch 38/100\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0273 - acc: 0.9917 - val_loss: 0.6820 - val_acc: 0.8807\n",
      "Epoch 39/100\n",
      "132/132 [==============================] - 79s 595ms/step - loss: 0.0225 - acc: 0.9948 - val_loss: 0.6664 - val_acc: 0.8945\n",
      "Epoch 40/100\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0249 - acc: 0.9930 - val_loss: 0.5996 - val_acc: 0.8761\n",
      "Epoch 41/100\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0253 - acc: 0.9920 - val_loss: 0.7107 - val_acc: 0.8624\n",
      "Epoch 42/100\n",
      "132/132 [==============================] - 77s 587ms/step - loss: 0.0323 - acc: 0.9902 - val_loss: 0.7366 - val_acc: 0.8670\n",
      "Epoch 43/100\n",
      "132/132 [==============================] - 77s 587ms/step - loss: 0.0232 - acc: 0.9936 - val_loss: 0.5950 - val_acc: 0.8716\n",
      "Epoch 44/100\n",
      "132/132 [==============================] - 79s 595ms/step - loss: 0.0242 - acc: 0.9929 - val_loss: 0.6480 - val_acc: 0.8716\n",
      "Epoch 45/100\n",
      "132/132 [==============================] - 77s 587ms/step - loss: 0.0236 - acc: 0.9921 - val_loss: 0.5832 - val_acc: 0.8945\n",
      "Epoch 46/100\n",
      "132/132 [==============================] - 77s 587ms/step - loss: 0.0225 - acc: 0.9920 - val_loss: 0.6868 - val_acc: 0.8578\n",
      "Epoch 47/100\n",
      "132/132 [==============================] - 77s 581ms/step - loss: 0.0206 - acc: 0.9944 - val_loss: 0.7327 - val_acc: 0.8624\n",
      "Epoch 48/100\n",
      "132/132 [==============================] - 77s 583ms/step - loss: 0.0196 - acc: 0.9942 - val_loss: 0.6338 - val_acc: 0.8899\n",
      "Epoch 49/100\n",
      "132/132 [==============================] - 77s 583ms/step - loss: 0.0200 - acc: 0.9941 - val_loss: 0.6406 - val_acc: 0.8761\n",
      "Epoch 50/100\n",
      "132/132 [==============================] - 77s 584ms/step - loss: 0.0237 - acc: 0.9917 - val_loss: 0.5943 - val_acc: 0.8853\n",
      "Epoch 51/100\n",
      "132/132 [==============================] - 77s 587ms/step - loss: 0.0213 - acc: 0.9945 - val_loss: 0.6790 - val_acc: 0.8853\n",
      "Epoch 52/100\n",
      "132/132 [==============================] - 77s 584ms/step - loss: 0.0253 - acc: 0.9923 - val_loss: 0.6493 - val_acc: 0.8670\n",
      "Epoch 53/100\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0234 - acc: 0.9927 - val_loss: 0.6139 - val_acc: 0.8716\n",
      "Epoch 54/100\n",
      "132/132 [==============================] - 77s 587ms/step - loss: 0.0294 - acc: 0.9918 - val_loss: 0.7456 - val_acc: 0.8670\n",
      "Epoch 55/100\n",
      "132/132 [==============================] - 77s 585ms/step - loss: 0.0207 - acc: 0.9948 - val_loss: 0.6135 - val_acc: 0.8945\n",
      "Epoch 56/100\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.0224 - acc: 0.9927 - val_loss: 0.5020 - val_acc: 0.8853\n",
      "Epoch 57/100\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0191 - acc: 0.9947 - val_loss: 0.5357 - val_acc: 0.8807\n",
      "Epoch 58/100\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0195 - acc: 0.9952 - val_loss: 0.6167 - val_acc: 0.8761\n",
      "Epoch 59/100\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0265 - acc: 0.9900 - val_loss: 0.6206 - val_acc: 0.8853\n",
      "Epoch 60/100\n",
      "132/132 [==============================] - 77s 586ms/step - loss: 0.0241 - acc: 0.9935 - val_loss: 0.5309 - val_acc: 0.8761\n",
      "Epoch 61/100\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0250 - acc: 0.9908 - val_loss: 0.7685 - val_acc: 0.8486\n",
      "Epoch 62/100\n",
      "132/132 [==============================] - 78s 594ms/step - loss: 0.0411 - acc: 0.9858 - val_loss: 0.7001 - val_acc: 0.8624\n",
      "Epoch 63/100\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0231 - acc: 0.9926 - val_loss: 0.5914 - val_acc: 0.8761\n",
      "Epoch 64/100\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0204 - acc: 0.9942 - val_loss: 0.5970 - val_acc: 0.8670\n",
      "Epoch 65/100\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0207 - acc: 0.9947 - val_loss: 0.5698 - val_acc: 0.8899\n",
      "Epoch 66/100\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0208 - acc: 0.9926 - val_loss: 0.6255 - val_acc: 0.8945\n",
      "Epoch 67/100\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0242 - acc: 0.9930 - val_loss: 0.6580 - val_acc: 0.8899\n",
      "Epoch 68/100\n",
      "132/132 [==============================] - 77s 583ms/step - loss: 0.0266 - acc: 0.9926 - val_loss: 0.7111 - val_acc: 0.8991\n",
      "Epoch 69/100\n",
      "132/132 [==============================] - 77s 586ms/step - loss: 0.0179 - acc: 0.9952 - val_loss: 0.6328 - val_acc: 0.8945\n",
      "Epoch 70/100\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0192 - acc: 0.9945 - val_loss: 0.6533 - val_acc: 0.8716\n",
      "Epoch 71/100\n",
      "132/132 [==============================] - 77s 584ms/step - loss: 0.0187 - acc: 0.9947 - val_loss: 0.6593 - val_acc: 0.8716\n",
      "Epoch 72/100\n",
      "132/132 [==============================] - 77s 584ms/step - loss: 0.0313 - acc: 0.9898 - val_loss: 0.6003 - val_acc: 0.8578\n",
      "Epoch 73/100\n",
      "132/132 [==============================] - 77s 586ms/step - loss: 0.0193 - acc: 0.9947 - val_loss: 0.6097 - val_acc: 0.8807\n",
      "Epoch 74/100\n",
      "132/132 [==============================] - 77s 587ms/step - loss: 0.0150 - acc: 0.9962 - val_loss: 0.6034 - val_acc: 0.8761\n",
      "Epoch 75/100\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0223 - acc: 0.9940 - val_loss: 0.6202 - val_acc: 0.8991\n",
      "Epoch 76/100\n",
      "132/132 [==============================] - 77s 585ms/step - loss: 0.0238 - acc: 0.9929 - val_loss: 0.6197 - val_acc: 0.8945\n",
      "Epoch 77/100\n",
      "132/132 [==============================] - 78s 588ms/step - loss: 0.0229 - acc: 0.9933 - val_loss: 0.6850 - val_acc: 0.8716\n",
      "Epoch 78/100\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0300 - acc: 0.9920 - val_loss: 0.6726 - val_acc: 0.8716\n",
      "Epoch 79/100\n",
      "132/132 [==============================] - 78s 588ms/step - loss: 0.0176 - acc: 0.9942 - val_loss: 0.6480 - val_acc: 0.8670\n",
      "Epoch 80/100\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.0200 - acc: 0.9947 - val_loss: 0.5865 - val_acc: 0.8716\n",
      "Epoch 81/100\n",
      "132/132 [==============================] - 77s 584ms/step - loss: 0.0208 - acc: 0.9942 - val_loss: 0.6508 - val_acc: 0.8670\n",
      "Epoch 82/100\n",
      "132/132 [==============================] - 77s 585ms/step - loss: 0.0246 - acc: 0.9923 - val_loss: 0.5814 - val_acc: 0.8716\n",
      "Epoch 83/100\n",
      "132/132 [==============================] - 77s 586ms/step - loss: 0.0174 - acc: 0.9952 - val_loss: 0.6696 - val_acc: 0.8807\n",
      "Epoch 84/100\n",
      "132/132 [==============================] - 77s 586ms/step - loss: 0.0213 - acc: 0.9938 - val_loss: 0.6872 - val_acc: 0.8853\n",
      "Epoch 85/100\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0129 - acc: 0.9965 - val_loss: 0.7105 - val_acc: 0.8761\n",
      "Epoch 86/100\n",
      "132/132 [==============================] - 79s 595ms/step - loss: 0.0148 - acc: 0.9959 - val_loss: 0.6290 - val_acc: 0.9037\n",
      "Epoch 87/100\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0262 - acc: 0.9941 - val_loss: 0.6625 - val_acc: 0.8945\n",
      "Epoch 88/100\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0209 - acc: 0.9933 - val_loss: 0.6209 - val_acc: 0.8716\n",
      "Epoch 89/100\n",
      "132/132 [==============================] - 78s 591ms/step - loss: 0.0212 - acc: 0.9941 - val_loss: 0.7767 - val_acc: 0.8761\n",
      "Epoch 90/100\n",
      "132/132 [==============================] - 78s 589ms/step - loss: 0.0261 - acc: 0.9915 - val_loss: 0.6874 - val_acc: 0.8624\n",
      "Epoch 91/100\n",
      "132/132 [==============================] - 78s 587ms/step - loss: 0.0219 - acc: 0.9921 - val_loss: 0.5792 - val_acc: 0.8945\n",
      "Epoch 92/100\n",
      "132/132 [==============================] - 77s 586ms/step - loss: 0.0153 - acc: 0.9974 - val_loss: 0.6037 - val_acc: 0.8716\n",
      "Epoch 93/100\n",
      "132/132 [==============================] - 78s 590ms/step - loss: 0.0163 - acc: 0.9961 - val_loss: 0.6518 - val_acc: 0.8716\n",
      "Epoch 94/100\n",
      "132/132 [==============================] - 78s 595ms/step - loss: 0.0258 - acc: 0.9920 - val_loss: 0.6390 - val_acc: 0.8716\n",
      "Epoch 95/100\n",
      "132/132 [==============================] - 77s 585ms/step - loss: 0.0163 - acc: 0.9947 - val_loss: 0.7046 - val_acc: 0.8807\n",
      "Epoch 96/100\n",
      "132/132 [==============================] - 77s 584ms/step - loss: 0.0147 - acc: 0.9964 - val_loss: 0.6996 - val_acc: 0.8716\n",
      "Epoch 97/100\n",
      "132/132 [==============================] - 77s 583ms/step - loss: 0.0135 - acc: 0.9962 - val_loss: 0.6882 - val_acc: 0.8578\n",
      "Epoch 98/100\n",
      "132/132 [==============================] - 77s 586ms/step - loss: 0.0185 - acc: 0.9939 - val_loss: 0.7157 - val_acc: 0.8670\n",
      "Epoch 99/100\n",
      "132/132 [==============================] - 78s 593ms/step - loss: 0.0226 - acc: 0.9939 - val_loss: 0.5425 - val_acc: 0.8716\n",
      "Epoch 100/100\n",
      "132/132 [==============================] - 78s 592ms/step - loss: 0.0117 - acc: 0.9973 - val_loss: 0.5857 - val_acc: 0.8761\n",
      "40/40 [==============================] - 7s 186ms/step\n",
      "Accuracy: 0.8902996444895886. F1: 0.8890374104248979\n"
     ]
    }
   ],
   "source": [
    "model = load_model(\"models/model_1D_norm.hdf5\")\n",
    "train_model(4, model, file_type = 1, epochs = 100, save_model = True, name = \"1D_norm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "132/132 [==============================] - 70s 529ms/step - loss: 1.4877 - acc: 0.4409 - val_loss: 1.0417 - val_acc: 0.5963\n",
      "Epoch 2/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.9838 - acc: 0.6325 - val_loss: 0.8893 - val_acc: 0.6239\n",
      "Epoch 3/500\n",
      "132/132 [==============================] - 68s 515ms/step - loss: 0.9222 - acc: 0.6501 - val_loss: 0.9605 - val_acc: 0.6376\n",
      "Epoch 4/500\n",
      "132/132 [==============================] - 68s 514ms/step - loss: 0.8451 - acc: 0.6859 - val_loss: 0.8228 - val_acc: 0.7018\n",
      "Epoch 5/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.7673 - acc: 0.7232 - val_loss: 0.8217 - val_acc: 0.6835\n",
      "Epoch 6/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.8932 - acc: 0.6740 - val_loss: 0.8068 - val_acc: 0.6835\n",
      "Epoch 7/500\n",
      "132/132 [==============================] - 68s 515ms/step - loss: 0.7783 - acc: 0.7223 - val_loss: 0.7522 - val_acc: 0.7156\n",
      "Epoch 8/500\n",
      "132/132 [==============================] - 69s 521ms/step - loss: 0.7292 - acc: 0.7457 - val_loss: 0.8725 - val_acc: 0.6789\n",
      "Epoch 9/500\n",
      "132/132 [==============================] - 69s 526ms/step - loss: 0.7931 - acc: 0.7276 - val_loss: 0.9985 - val_acc: 0.6193\n",
      "Epoch 10/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.7908 - acc: 0.7200 - val_loss: 0.7298 - val_acc: 0.7202\n",
      "Epoch 11/500\n",
      "132/132 [==============================] - 69s 519ms/step - loss: 0.7287 - acc: 0.7540 - val_loss: 0.7604 - val_acc: 0.7156\n",
      "Epoch 12/500\n",
      "132/132 [==============================] - 68s 515ms/step - loss: 0.7084 - acc: 0.7575 - val_loss: 0.7620 - val_acc: 0.7156\n",
      "Epoch 13/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.8303 - acc: 0.7065 - val_loss: 0.9063 - val_acc: 0.6835\n",
      "Epoch 14/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.7085 - acc: 0.7523 - val_loss: 0.7390 - val_acc: 0.7477\n",
      "Epoch 15/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.8988 - acc: 0.7030 - val_loss: 0.9524 - val_acc: 0.6239\n",
      "Epoch 16/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.8282 - acc: 0.7088 - val_loss: 0.7657 - val_acc: 0.7202\n",
      "Epoch 17/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.6973 - acc: 0.7607 - val_loss: 0.7638 - val_acc: 0.7248\n",
      "Epoch 18/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.6832 - acc: 0.7576 - val_loss: 0.9498 - val_acc: 0.6697\n",
      "Epoch 19/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.6552 - acc: 0.7748 - val_loss: 0.7554 - val_acc: 0.7339\n",
      "Epoch 20/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.6247 - acc: 0.7838 - val_loss: 0.6874 - val_acc: 0.7339\n",
      "Epoch 21/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.6146 - acc: 0.7817 - val_loss: 0.7337 - val_acc: 0.6972\n",
      "Epoch 22/500\n",
      "132/132 [==============================] - 68s 515ms/step - loss: 0.6472 - acc: 0.7756 - val_loss: 0.7721 - val_acc: 0.7202\n",
      "Epoch 23/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.6271 - acc: 0.7790 - val_loss: 0.7251 - val_acc: 0.7385\n",
      "Epoch 24/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.6119 - acc: 0.7790 - val_loss: 0.7027 - val_acc: 0.7523\n",
      "Epoch 25/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.5932 - acc: 0.7968 - val_loss: 0.6826 - val_acc: 0.7569\n",
      "Epoch 26/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.6026 - acc: 0.7891 - val_loss: 0.8069 - val_acc: 0.7202\n",
      "Epoch 27/500\n",
      "132/132 [==============================] - 70s 527ms/step - loss: 0.5787 - acc: 0.7981 - val_loss: 0.7249 - val_acc: 0.7385\n",
      "Epoch 28/500\n",
      "132/132 [==============================] - 69s 526ms/step - loss: 0.6103 - acc: 0.7877 - val_loss: 0.7283 - val_acc: 0.7615\n",
      "Epoch 29/500\n",
      "132/132 [==============================] - 70s 529ms/step - loss: 0.5779 - acc: 0.7972 - val_loss: 0.6651 - val_acc: 0.7706\n",
      "Epoch 30/500\n",
      "132/132 [==============================] - 70s 532ms/step - loss: 0.5380 - acc: 0.8126 - val_loss: 0.7005 - val_acc: 0.7752\n",
      "Epoch 31/500\n",
      "132/132 [==============================] - 69s 527ms/step - loss: 0.5821 - acc: 0.8011 - val_loss: 0.6916 - val_acc: 0.7569\n",
      "Epoch 32/500\n",
      "132/132 [==============================] - 70s 530ms/step - loss: 0.5504 - acc: 0.8068 - val_loss: 0.6620 - val_acc: 0.7752\n",
      "Epoch 33/500\n",
      "132/132 [==============================] - 69s 521ms/step - loss: 0.5295 - acc: 0.8164 - val_loss: 0.7690 - val_acc: 0.7339\n",
      "Epoch 34/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.6172 - acc: 0.7635 - val_loss: 0.7196 - val_acc: 0.7202\n",
      "Epoch 35/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.5694 - acc: 0.7874 - val_loss: 0.7051 - val_acc: 0.7156\n",
      "Epoch 36/500\n",
      "132/132 [==============================] - 69s 521ms/step - loss: 0.5659 - acc: 0.7902 - val_loss: 0.7012 - val_acc: 0.7294\n",
      "Epoch 37/500\n",
      "132/132 [==============================] - 69s 521ms/step - loss: 0.5619 - acc: 0.7968 - val_loss: 0.6722 - val_acc: 0.7385\n",
      "Epoch 38/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.5389 - acc: 0.8081 - val_loss: 0.6816 - val_acc: 0.7431\n",
      "Epoch 39/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.5165 - acc: 0.8231 - val_loss: 0.6051 - val_acc: 0.8073\n",
      "Epoch 40/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.5178 - acc: 0.8249 - val_loss: 0.6336 - val_acc: 0.7936\n",
      "Epoch 41/500\n",
      "132/132 [==============================] - 69s 519ms/step - loss: 0.4872 - acc: 0.8293 - val_loss: 0.5941 - val_acc: 0.7844\n",
      "Epoch 42/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.4909 - acc: 0.8290 - val_loss: 0.5461 - val_acc: 0.7890\n",
      "Epoch 43/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.5315 - acc: 0.8185 - val_loss: 0.6411 - val_acc: 0.7844\n",
      "Epoch 44/500\n",
      "132/132 [==============================] - 68s 519ms/step - loss: 0.4993 - acc: 0.8290 - val_loss: 0.6280 - val_acc: 0.7890\n",
      "Epoch 45/500\n",
      "132/132 [==============================] - 69s 522ms/step - loss: 0.5578 - acc: 0.8137 - val_loss: 0.6668 - val_acc: 0.7844\n",
      "Epoch 46/500\n",
      "132/132 [==============================] - 70s 527ms/step - loss: 0.5252 - acc: 0.8199 - val_loss: 0.7361 - val_acc: 0.7569\n",
      "Epoch 47/500\n",
      "132/132 [==============================] - 75s 565ms/step - loss: 0.4918 - acc: 0.8305 - val_loss: 0.6588 - val_acc: 0.7844\n",
      "Epoch 48/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.4774 - acc: 0.8369 - val_loss: 0.8217 - val_acc: 0.7431\n",
      "Epoch 49/500\n",
      "132/132 [==============================] - 68s 513ms/step - loss: 0.4947 - acc: 0.8306 - val_loss: 0.5946 - val_acc: 0.7844\n",
      "Epoch 50/500\n",
      "132/132 [==============================] - 68s 515ms/step - loss: 0.4644 - acc: 0.8428 - val_loss: 0.6764 - val_acc: 0.7569\n",
      "Epoch 51/500\n",
      "132/132 [==============================] - 68s 514ms/step - loss: 0.4582 - acc: 0.8446 - val_loss: 0.5887 - val_acc: 0.7752\n",
      "Epoch 52/500\n",
      "132/132 [==============================] - 68s 513ms/step - loss: 0.4480 - acc: 0.8448 - val_loss: 0.6343 - val_acc: 0.7752\n",
      "Epoch 53/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.4549 - acc: 0.8401 - val_loss: 0.5694 - val_acc: 0.8028\n",
      "Epoch 54/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.4265 - acc: 0.8520 - val_loss: 0.6285 - val_acc: 0.7844\n",
      "Epoch 55/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.4266 - acc: 0.8542 - val_loss: 0.5906 - val_acc: 0.7982\n",
      "Epoch 56/500\n",
      "132/132 [==============================] - 68s 515ms/step - loss: 0.4573 - acc: 0.8401 - val_loss: 0.6070 - val_acc: 0.7523\n",
      "Epoch 57/500\n",
      "132/132 [==============================] - 68s 515ms/step - loss: 0.4482 - acc: 0.8469 - val_loss: 0.7028 - val_acc: 0.7431\n",
      "Epoch 58/500\n",
      "132/132 [==============================] - 68s 515ms/step - loss: 0.4346 - acc: 0.8523 - val_loss: 0.5513 - val_acc: 0.8119\n",
      "Epoch 59/500\n",
      "132/132 [==============================] - 68s 515ms/step - loss: 0.4031 - acc: 0.8590 - val_loss: 0.5259 - val_acc: 0.8303\n",
      "Epoch 60/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.4572 - acc: 0.8429 - val_loss: 0.6416 - val_acc: 0.7982\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 61/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.4760 - acc: 0.8359 - val_loss: 0.6226 - val_acc: 0.7752\n",
      "Epoch 62/500\n",
      "132/132 [==============================] - 69s 522ms/step - loss: 0.4610 - acc: 0.8434 - val_loss: 0.5040 - val_acc: 0.8211\n",
      "Epoch 63/500\n",
      "132/132 [==============================] - 70s 530ms/step - loss: 0.4029 - acc: 0.8592 - val_loss: 0.5011 - val_acc: 0.8028\n",
      "Epoch 64/500\n",
      "132/132 [==============================] - 68s 519ms/step - loss: 0.4232 - acc: 0.8565 - val_loss: 0.5381 - val_acc: 0.8165\n",
      "Epoch 65/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.4261 - acc: 0.8551 - val_loss: 0.5612 - val_acc: 0.8165\n",
      "Epoch 66/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.4422 - acc: 0.8494 - val_loss: 0.5171 - val_acc: 0.8165\n",
      "Epoch 67/500\n",
      "132/132 [==============================] - 69s 521ms/step - loss: 0.4290 - acc: 0.8598 - val_loss: 0.5535 - val_acc: 0.8073\n",
      "Epoch 68/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.4261 - acc: 0.8560 - val_loss: 0.5493 - val_acc: 0.8119\n",
      "Epoch 69/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.4299 - acc: 0.8532 - val_loss: 0.6492 - val_acc: 0.7798\n",
      "Epoch 70/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.4529 - acc: 0.8466 - val_loss: 0.5992 - val_acc: 0.8028\n",
      "Epoch 71/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.4310 - acc: 0.8496 - val_loss: 0.5539 - val_acc: 0.8165\n",
      "Epoch 72/500\n",
      "132/132 [==============================] - 69s 522ms/step - loss: 0.4488 - acc: 0.8473 - val_loss: 0.6259 - val_acc: 0.7890\n",
      "Epoch 73/500\n",
      "132/132 [==============================] - 68s 519ms/step - loss: 0.4266 - acc: 0.8510 - val_loss: 0.6011 - val_acc: 0.8028\n",
      "Epoch 74/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.3973 - acc: 0.8642 - val_loss: 0.5526 - val_acc: 0.8349\n",
      "Epoch 75/500\n",
      "132/132 [==============================] - 68s 514ms/step - loss: 0.3750 - acc: 0.8679 - val_loss: 0.5596 - val_acc: 0.8303\n",
      "Epoch 76/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3717 - acc: 0.8720 - val_loss: 0.5196 - val_acc: 0.8349\n",
      "Epoch 77/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.3792 - acc: 0.8708 - val_loss: 0.5977 - val_acc: 0.8119\n",
      "Epoch 78/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3794 - acc: 0.8673 - val_loss: 0.5721 - val_acc: 0.8394\n",
      "Epoch 79/500\n",
      "132/132 [==============================] - 68s 519ms/step - loss: 0.3545 - acc: 0.8799 - val_loss: 0.4861 - val_acc: 0.8349\n",
      "Epoch 80/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3936 - acc: 0.8670 - val_loss: 0.5117 - val_acc: 0.8211\n",
      "Epoch 81/500\n",
      "132/132 [==============================] - 69s 521ms/step - loss: 0.3826 - acc: 0.8681 - val_loss: 0.4941 - val_acc: 0.8486\n",
      "Epoch 82/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.4516 - acc: 0.8483 - val_loss: 0.4740 - val_acc: 0.8394\n",
      "Epoch 83/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.4070 - acc: 0.8611 - val_loss: 0.6432 - val_acc: 0.8119\n",
      "Epoch 84/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.3876 - acc: 0.8666 - val_loss: 0.5155 - val_acc: 0.8349\n",
      "Epoch 85/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3677 - acc: 0.8732 - val_loss: 0.5209 - val_acc: 0.8303\n",
      "Epoch 86/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.4127 - acc: 0.8625 - val_loss: 0.5236 - val_acc: 0.8119\n",
      "Epoch 87/500\n",
      "132/132 [==============================] - 71s 535ms/step - loss: 0.4388 - acc: 0.8521 - val_loss: 0.5613 - val_acc: 0.8349\n",
      "Epoch 88/500\n",
      "132/132 [==============================] - 73s 551ms/step - loss: 0.4174 - acc: 0.8555 - val_loss: 0.5471 - val_acc: 0.8073\n",
      "Epoch 89/500\n",
      "132/132 [==============================] - 69s 525ms/step - loss: 0.3685 - acc: 0.8706 - val_loss: 0.5315 - val_acc: 0.8394\n",
      "Epoch 90/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.3598 - acc: 0.8770 - val_loss: 0.5221 - val_acc: 0.8211\n",
      "Epoch 91/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.3589 - acc: 0.8751 - val_loss: 0.5524 - val_acc: 0.8257\n",
      "Epoch 92/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.3719 - acc: 0.8717 - val_loss: 0.5018 - val_acc: 0.8257\n",
      "Epoch 93/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.3511 - acc: 0.8789 - val_loss: 0.4650 - val_acc: 0.8532\n",
      "Epoch 94/500\n",
      "132/132 [==============================] - 68s 519ms/step - loss: 0.3805 - acc: 0.8690 - val_loss: 0.5016 - val_acc: 0.8486\n",
      "Epoch 95/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.3605 - acc: 0.8764 - val_loss: 0.5222 - val_acc: 0.8303\n",
      "Epoch 96/500\n",
      "132/132 [==============================] - 68s 519ms/step - loss: 0.3506 - acc: 0.8788 - val_loss: 0.4935 - val_acc: 0.8624\n",
      "Epoch 97/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3498 - acc: 0.8817 - val_loss: 0.5108 - val_acc: 0.8394\n",
      "Epoch 98/500\n",
      "132/132 [==============================] - 69s 522ms/step - loss: 0.3364 - acc: 0.8853 - val_loss: 0.4910 - val_acc: 0.8532\n",
      "Epoch 99/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3347 - acc: 0.8838 - val_loss: 0.4889 - val_acc: 0.8624\n",
      "Epoch 100/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.3285 - acc: 0.8854 - val_loss: 0.4422 - val_acc: 0.8486\n",
      "Epoch 101/500\n",
      "132/132 [==============================] - 69s 519ms/step - loss: 0.4026 - acc: 0.8693 - val_loss: 0.6550 - val_acc: 0.7982\n",
      "Epoch 102/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.4205 - acc: 0.8541 - val_loss: 0.4987 - val_acc: 0.8532\n",
      "Epoch 103/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3737 - acc: 0.8697 - val_loss: 0.5745 - val_acc: 0.8211\n",
      "Epoch 104/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.3653 - acc: 0.8751 - val_loss: 0.5370 - val_acc: 0.8486\n",
      "Epoch 105/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3411 - acc: 0.8794 - val_loss: 0.4977 - val_acc: 0.8394\n",
      "Epoch 106/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.3345 - acc: 0.8855 - val_loss: 0.4030 - val_acc: 0.8761\n",
      "Epoch 107/500\n",
      "132/132 [==============================] - 69s 521ms/step - loss: 0.3557 - acc: 0.8795 - val_loss: 0.5181 - val_acc: 0.8349\n",
      "Epoch 108/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.3308 - acc: 0.8847 - val_loss: 0.5085 - val_acc: 0.8440\n",
      "Epoch 109/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.3067 - acc: 0.8919 - val_loss: 0.4455 - val_acc: 0.8578\n",
      "Epoch 110/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.2957 - acc: 0.8962 - val_loss: 0.4685 - val_acc: 0.8624\n",
      "Epoch 111/500\n",
      "132/132 [==============================] - 68s 519ms/step - loss: 0.3075 - acc: 0.8961 - val_loss: 0.4599 - val_acc: 0.8807\n",
      "Epoch 112/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.2998 - acc: 0.8938 - val_loss: 0.4586 - val_acc: 0.8807\n",
      "Epoch 113/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3078 - acc: 0.8939 - val_loss: 0.4751 - val_acc: 0.8624\n",
      "Epoch 114/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.3321 - acc: 0.8875 - val_loss: 0.5124 - val_acc: 0.8486\n",
      "Epoch 115/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.3961 - acc: 0.8633 - val_loss: 0.4728 - val_acc: 0.8394\n",
      "Epoch 116/500\n",
      "132/132 [==============================] - 69s 521ms/step - loss: 0.3320 - acc: 0.8876 - val_loss: 0.4532 - val_acc: 0.8761\n",
      "Epoch 117/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3081 - acc: 0.8931 - val_loss: 0.4398 - val_acc: 0.8486\n",
      "Epoch 118/500\n",
      "132/132 [==============================] - 68s 519ms/step - loss: 0.5813 - acc: 0.8066 - val_loss: 0.8248 - val_acc: 0.7248\n",
      "Epoch 119/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.5208 - acc: 0.8279 - val_loss: 0.5673 - val_acc: 0.8165\n",
      "Epoch 120/500\n",
      "132/132 [==============================] - 69s 519ms/step - loss: 0.4102 - acc: 0.8615 - val_loss: 0.5296 - val_acc: 0.8211\n",
      "Epoch 121/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.4091 - acc: 0.8611 - val_loss: 0.5347 - val_acc: 0.8165\n",
      "Epoch 122/500\n",
      "132/132 [==============================] - 68s 519ms/step - loss: 0.3586 - acc: 0.8740 - val_loss: 0.4458 - val_acc: 0.8532\n",
      "Epoch 123/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3543 - acc: 0.8813 - val_loss: 0.4962 - val_acc: 0.8440\n",
      "Epoch 124/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3149 - acc: 0.8925 - val_loss: 0.4749 - val_acc: 0.8440\n",
      "Epoch 125/500\n",
      "132/132 [==============================] - 69s 522ms/step - loss: 0.3306 - acc: 0.8840 - val_loss: 0.5136 - val_acc: 0.8486\n",
      "Epoch 126/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.2942 - acc: 0.8981 - val_loss: 0.4751 - val_acc: 0.8486\n",
      "Epoch 127/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.3016 - acc: 0.8973 - val_loss: 0.5224 - val_acc: 0.8578\n",
      "Epoch 128/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3620 - acc: 0.8749 - val_loss: 0.5606 - val_acc: 0.8073\n",
      "Epoch 129/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3272 - acc: 0.8826 - val_loss: 0.5363 - val_acc: 0.8486\n",
      "Epoch 130/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3134 - acc: 0.8914 - val_loss: 0.5047 - val_acc: 0.8394\n",
      "Epoch 131/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3527 - acc: 0.8802 - val_loss: 0.5644 - val_acc: 0.8394\n",
      "Epoch 132/500\n",
      "132/132 [==============================] - 69s 519ms/step - loss: 0.3252 - acc: 0.8863 - val_loss: 0.4875 - val_acc: 0.8670\n",
      "Epoch 133/500\n",
      "132/132 [==============================] - 69s 519ms/step - loss: 0.3303 - acc: 0.8878 - val_loss: 0.5171 - val_acc: 0.8486\n",
      "Epoch 134/500\n",
      "132/132 [==============================] - 69s 523ms/step - loss: 0.2949 - acc: 0.8970 - val_loss: 0.4680 - val_acc: 0.8624\n",
      "Epoch 135/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.2845 - acc: 0.9019 - val_loss: 0.5713 - val_acc: 0.8257\n",
      "Epoch 136/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.2719 - acc: 0.9055 - val_loss: 0.4481 - val_acc: 0.8761\n",
      "Epoch 137/500\n",
      "132/132 [==============================] - 69s 521ms/step - loss: 0.3042 - acc: 0.8934 - val_loss: 0.5554 - val_acc: 0.8394\n",
      "Epoch 138/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.2892 - acc: 0.8987 - val_loss: 0.4455 - val_acc: 0.8899\n",
      "Epoch 139/500\n",
      "132/132 [==============================] - 68s 514ms/step - loss: 0.2944 - acc: 0.8957 - val_loss: 0.5819 - val_acc: 0.8349\n",
      "Epoch 140/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.4805 - acc: 0.8376 - val_loss: 0.5353 - val_acc: 0.8119\n",
      "Epoch 141/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3952 - acc: 0.8678 - val_loss: 0.4881 - val_acc: 0.8394\n",
      "Epoch 142/500\n",
      "132/132 [==============================] - 69s 522ms/step - loss: 0.3427 - acc: 0.8843 - val_loss: 0.4871 - val_acc: 0.8349\n",
      "Epoch 143/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.3287 - acc: 0.8843 - val_loss: 0.5005 - val_acc: 0.8303\n",
      "Epoch 144/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.3796 - acc: 0.8755 - val_loss: 0.5843 - val_acc: 0.8028\n",
      "Epoch 145/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3345 - acc: 0.8825 - val_loss: 0.5164 - val_acc: 0.8578\n",
      "Epoch 146/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3002 - acc: 0.8945 - val_loss: 0.4542 - val_acc: 0.8578\n",
      "Epoch 147/500\n",
      "132/132 [==============================] - 69s 521ms/step - loss: 0.3109 - acc: 0.8900 - val_loss: 0.4655 - val_acc: 0.8761\n",
      "Epoch 148/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.2981 - acc: 0.8963 - val_loss: 0.4801 - val_acc: 0.8486\n",
      "Epoch 149/500\n",
      "132/132 [==============================] - 69s 521ms/step - loss: 0.2901 - acc: 0.8997 - val_loss: 0.4485 - val_acc: 0.8670\n",
      "Epoch 150/500\n",
      "132/132 [==============================] - 69s 521ms/step - loss: 0.2731 - acc: 0.9026 - val_loss: 0.4507 - val_acc: 0.8486\n",
      "Epoch 151/500\n",
      "132/132 [==============================] - 69s 523ms/step - loss: 0.3018 - acc: 0.8965 - val_loss: 0.4871 - val_acc: 0.8624\n",
      "Epoch 152/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.2857 - acc: 0.8995 - val_loss: 0.4715 - val_acc: 0.8624\n",
      "Epoch 153/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3633 - acc: 0.8776 - val_loss: 0.4737 - val_acc: 0.8257\n",
      "Epoch 154/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3004 - acc: 0.8968 - val_loss: 0.5322 - val_acc: 0.8165\n",
      "Epoch 155/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.3228 - acc: 0.8851 - val_loss: 0.4026 - val_acc: 0.8578\n",
      "Epoch 156/500\n",
      "132/132 [==============================] - 69s 519ms/step - loss: 0.2783 - acc: 0.9030 - val_loss: 0.3921 - val_acc: 0.8761\n",
      "Epoch 157/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.2592 - acc: 0.9083 - val_loss: 0.3625 - val_acc: 0.8899\n",
      "Epoch 158/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.2718 - acc: 0.9063 - val_loss: 0.4592 - val_acc: 0.8349\n",
      "Epoch 159/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.2730 - acc: 0.9054 - val_loss: 0.3830 - val_acc: 0.8853\n",
      "Epoch 160/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.2502 - acc: 0.9136 - val_loss: 0.3935 - val_acc: 0.8716\n",
      "Epoch 161/500\n",
      "132/132 [==============================] - 69s 523ms/step - loss: 0.2601 - acc: 0.9083 - val_loss: 0.3973 - val_acc: 0.8624\n",
      "Epoch 162/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.2818 - acc: 0.9010 - val_loss: 0.3865 - val_acc: 0.8716\n",
      "Epoch 163/500\n",
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      "Epoch 166/500\n",
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      "Epoch 167/500\n",
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      "Epoch 168/500\n",
      "132/132 [==============================] - 69s 523ms/step - loss: 0.2603 - acc: 0.9073 - val_loss: 0.3981 - val_acc: 0.8716\n",
      "Epoch 169/500\n",
      "132/132 [==============================] - 69s 521ms/step - loss: 0.2277 - acc: 0.9185 - val_loss: 0.3989 - val_acc: 0.8853\n",
      "Epoch 170/500\n",
      "132/132 [==============================] - 69s 519ms/step - loss: 0.2381 - acc: 0.9175 - val_loss: 0.3739 - val_acc: 0.8761\n",
      "Epoch 171/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.2178 - acc: 0.9227 - val_loss: 0.4656 - val_acc: 0.8716\n",
      "Epoch 172/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.3089 - acc: 0.8994 - val_loss: 0.4980 - val_acc: 0.8486\n",
      "Epoch 173/500\n",
      "132/132 [==============================] - 69s 519ms/step - loss: 0.2451 - acc: 0.9133 - val_loss: 0.4528 - val_acc: 0.8578\n",
      "Epoch 174/500\n",
      "132/132 [==============================] - 68s 519ms/step - loss: 0.2350 - acc: 0.9161 - val_loss: 0.4948 - val_acc: 0.8670\n",
      "Epoch 175/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.2286 - acc: 0.9206 - val_loss: 0.4855 - val_acc: 0.8670\n",
      "Epoch 176/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.2131 - acc: 0.9236 - val_loss: 0.4164 - val_acc: 0.8899\n",
      "Epoch 177/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.2372 - acc: 0.9169 - val_loss: 0.4967 - val_acc: 0.8303\n",
      "Epoch 178/500\n",
      "132/132 [==============================] - 69s 522ms/step - loss: 0.2474 - acc: 0.9141 - val_loss: 0.4467 - val_acc: 0.8624\n",
      "Epoch 179/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 68s 519ms/step - loss: 0.2634 - acc: 0.9094 - val_loss: 0.5041 - val_acc: 0.8394\n",
      "Epoch 180/500\n",
      "132/132 [==============================] - 68s 519ms/step - loss: 0.2759 - acc: 0.9041 - val_loss: 0.3754 - val_acc: 0.8853\n",
      "Epoch 181/500\n",
      "132/132 [==============================] - 69s 522ms/step - loss: 0.2632 - acc: 0.9073 - val_loss: 0.4349 - val_acc: 0.8624\n",
      "Epoch 182/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.2417 - acc: 0.9136 - val_loss: 0.5491 - val_acc: 0.8578\n",
      "Epoch 183/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.2602 - acc: 0.9082 - val_loss: 0.4700 - val_acc: 0.8761\n",
      "Epoch 184/500\n",
      "132/132 [==============================] - 69s 519ms/step - loss: 0.2167 - acc: 0.9227 - val_loss: 0.3934 - val_acc: 0.8945\n",
      "Epoch 185/500\n",
      "132/132 [==============================] - 67s 511ms/step - loss: 0.2021 - acc: 0.9242 - val_loss: 0.3818 - val_acc: 0.8853\n",
      "Epoch 186/500\n",
      "132/132 [==============================] - 68s 519ms/step - loss: 0.2468 - acc: 0.9125 - val_loss: 0.4971 - val_acc: 0.7982\n",
      "Epoch 187/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.2682 - acc: 0.9045 - val_loss: 0.4535 - val_acc: 0.8578\n",
      "Epoch 188/500\n",
      "132/132 [==============================] - 68s 519ms/step - loss: 0.3053 - acc: 0.8997 - val_loss: 0.4248 - val_acc: 0.8670\n",
      "Epoch 189/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.2626 - acc: 0.9097 - val_loss: 0.4048 - val_acc: 0.8716\n",
      "Epoch 190/500\n",
      "132/132 [==============================] - 69s 522ms/step - loss: 0.2325 - acc: 0.9182 - val_loss: 0.4587 - val_acc: 0.8670\n",
      "Epoch 191/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.2019 - acc: 0.9247 - val_loss: 0.4226 - val_acc: 0.8624\n",
      "Epoch 192/500\n",
      "132/132 [==============================] - 69s 519ms/step - loss: 0.2015 - acc: 0.9312 - val_loss: 0.4248 - val_acc: 0.8716\n",
      "Epoch 193/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.1941 - acc: 0.9301 - val_loss: 0.3855 - val_acc: 0.8807\n",
      "Epoch 194/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.2149 - acc: 0.9253 - val_loss: 0.4564 - val_acc: 0.8761\n",
      "Epoch 195/500\n",
      "132/132 [==============================] - 70s 532ms/step - loss: 0.2114 - acc: 0.9280 - val_loss: 0.5301 - val_acc: 0.8578\n",
      "Epoch 196/500\n",
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      "Epoch 197/500\n",
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      "Epoch 198/500\n",
      "132/132 [==============================] - 69s 519ms/step - loss: 0.1985 - acc: 0.9303 - val_loss: 0.4686 - val_acc: 0.8807\n",
      "Epoch 199/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.2013 - acc: 0.9294 - val_loss: 0.4586 - val_acc: 0.8807\n",
      "Epoch 200/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.1981 - acc: 0.9275 - val_loss: 0.6364 - val_acc: 0.8394\n",
      "Epoch 201/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.2354 - acc: 0.9198 - val_loss: 0.4933 - val_acc: 0.8578\n",
      "Epoch 202/500\n",
      "132/132 [==============================] - 68s 516ms/step - loss: 0.2699 - acc: 0.9089 - val_loss: 0.4577 - val_acc: 0.8624\n",
      "Epoch 203/500\n",
      "132/132 [==============================] - 69s 519ms/step - loss: 0.2181 - acc: 0.9221 - val_loss: 0.4899 - val_acc: 0.8486\n",
      "Epoch 204/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.2286 - acc: 0.9198 - val_loss: 0.4880 - val_acc: 0.8532\n",
      "Epoch 205/500\n",
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      "Epoch 206/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.2379 - acc: 0.9167 - val_loss: 0.4676 - val_acc: 0.8532\n",
      "Epoch 207/500\n",
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      "Epoch 208/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.1786 - acc: 0.9330 - val_loss: 0.4674 - val_acc: 0.8716\n",
      "Epoch 209/500\n",
      "132/132 [==============================] - 69s 521ms/step - loss: 0.1823 - acc: 0.9341 - val_loss: 0.4737 - val_acc: 0.8624\n",
      "Epoch 210/500\n",
      "132/132 [==============================] - 69s 519ms/step - loss: 0.2032 - acc: 0.9268 - val_loss: 0.7722 - val_acc: 0.8165\n",
      "Epoch 211/500\n",
      "132/132 [==============================] - 68s 515ms/step - loss: 0.2927 - acc: 0.8992 - val_loss: 0.4734 - val_acc: 0.8532\n",
      "Epoch 212/500\n",
      "132/132 [==============================] - 68s 514ms/step - loss: 0.2302 - acc: 0.9165 - val_loss: 0.6355 - val_acc: 0.8211\n",
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      "Epoch 214/500\n",
      "132/132 [==============================] - 67s 510ms/step - loss: 0.2099 - acc: 0.9258 - val_loss: 0.4612 - val_acc: 0.8761\n",
      "Epoch 215/500\n",
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      "Epoch 216/500\n",
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      "Epoch 217/500\n",
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      "Epoch 218/500\n",
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      "Epoch 219/500\n",
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      "Epoch 220/500\n",
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      "Epoch 221/500\n",
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      "Epoch 222/500\n",
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      "Epoch 224/500\n",
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      "Epoch 225/500\n",
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      "Epoch 226/500\n",
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      "Epoch 228/500\n",
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      "Epoch 229/500\n",
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      "Epoch 234/500\n",
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      "Epoch 235/500\n",
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      "Epoch 237/500\n",
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      "Epoch 238/500\n",
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      "Epoch 239/500\n",
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      "Epoch 240/500\n",
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      "Epoch 241/500\n",
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      "Epoch 242/500\n",
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      "Epoch 243/500\n",
      "132/132 [==============================] - 68s 513ms/step - loss: 0.1710 - acc: 0.9397 - val_loss: 0.3934 - val_acc: 0.8761\n",
      "Epoch 244/500\n",
      "132/132 [==============================] - 67s 511ms/step - loss: 0.1617 - acc: 0.9435 - val_loss: 0.5318 - val_acc: 0.8624\n",
      "Epoch 245/500\n",
      "132/132 [==============================] - 68s 512ms/step - loss: 0.1758 - acc: 0.9397 - val_loss: 0.3966 - val_acc: 0.8761\n",
      "Epoch 246/500\n",
      "132/132 [==============================] - 68s 512ms/step - loss: 0.1433 - acc: 0.9464 - val_loss: 0.5042 - val_acc: 0.8670\n",
      "Epoch 247/500\n",
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      "Epoch 248/500\n",
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      "Epoch 249/500\n",
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      "Epoch 250/500\n",
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      "Epoch 252/500\n",
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      "Epoch 253/500\n",
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      "Epoch 254/500\n",
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      "Epoch 255/500\n",
      "132/132 [==============================] - 67s 510ms/step - loss: 0.1531 - acc: 0.9453 - val_loss: 0.4352 - val_acc: 0.8853\n",
      "Epoch 256/500\n",
      "132/132 [==============================] - 68s 514ms/step - loss: 0.1445 - acc: 0.9471 - val_loss: 0.5280 - val_acc: 0.8624\n",
      "Epoch 257/500\n",
      "132/132 [==============================] - 68s 511ms/step - loss: 0.1608 - acc: 0.9415 - val_loss: 0.5165 - val_acc: 0.8807\n",
      "Epoch 258/500\n",
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      "Epoch 259/500\n",
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      "Epoch 260/500\n",
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      "Epoch 261/500\n",
      "132/132 [==============================] - 67s 510ms/step - loss: 0.1610 - acc: 0.9424 - val_loss: 0.4065 - val_acc: 0.8991\n",
      "Epoch 262/500\n",
      "132/132 [==============================] - 68s 512ms/step - loss: 0.2384 - acc: 0.9214 - val_loss: 0.4738 - val_acc: 0.8807\n",
      "Epoch 263/500\n",
      "132/132 [==============================] - 67s 511ms/step - loss: 0.1809 - acc: 0.9347 - val_loss: 0.4627 - val_acc: 0.8807\n",
      "Epoch 264/500\n",
      "132/132 [==============================] - 68s 512ms/step - loss: 0.1490 - acc: 0.9480 - val_loss: 0.5462 - val_acc: 0.8624\n",
      "Epoch 265/500\n",
      "132/132 [==============================] - 67s 511ms/step - loss: 0.1777 - acc: 0.9359 - val_loss: 0.5646 - val_acc: 0.8532\n",
      "Epoch 266/500\n",
      "132/132 [==============================] - 67s 509ms/step - loss: 0.1608 - acc: 0.9438 - val_loss: 0.4595 - val_acc: 0.8807\n",
      "Epoch 267/500\n",
      "132/132 [==============================] - 68s 515ms/step - loss: 0.1448 - acc: 0.9501 - val_loss: 0.4624 - val_acc: 0.8761\n",
      "Epoch 268/500\n",
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      "Epoch 269/500\n",
      "132/132 [==============================] - 68s 515ms/step - loss: 0.2179 - acc: 0.9267 - val_loss: 0.4977 - val_acc: 0.8807\n",
      "Epoch 270/500\n",
      "132/132 [==============================] - 67s 510ms/step - loss: 0.3250 - acc: 0.9006 - val_loss: 0.5128 - val_acc: 0.8578\n",
      "Epoch 271/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.2166 - acc: 0.9279 - val_loss: 0.5429 - val_acc: 0.8578\n",
      "Epoch 272/500\n",
      "132/132 [==============================] - 67s 510ms/step - loss: 0.2061 - acc: 0.9289 - val_loss: 0.4805 - val_acc: 0.8670\n",
      "Epoch 273/500\n",
      "132/132 [==============================] - 68s 514ms/step - loss: 0.1636 - acc: 0.9412 - val_loss: 0.4954 - val_acc: 0.8486\n",
      "Epoch 274/500\n",
      "132/132 [==============================] - 67s 511ms/step - loss: 0.1436 - acc: 0.9514 - val_loss: 0.5052 - val_acc: 0.8578\n",
      "Epoch 275/500\n",
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      "Epoch 276/500\n",
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      "Epoch 277/500\n",
      "132/132 [==============================] - 67s 510ms/step - loss: 0.1540 - acc: 0.9476 - val_loss: 0.5752 - val_acc: 0.8624\n",
      "Epoch 278/500\n",
      "132/132 [==============================] - 68s 513ms/step - loss: 0.1421 - acc: 0.9498 - val_loss: 0.5209 - val_acc: 0.8486\n",
      "Epoch 279/500\n",
      "132/132 [==============================] - 67s 509ms/step - loss: 0.1949 - acc: 0.9371 - val_loss: 0.5554 - val_acc: 0.8394\n",
      "Epoch 280/500\n",
      "132/132 [==============================] - 68s 512ms/step - loss: 0.1518 - acc: 0.9455 - val_loss: 0.5304 - val_acc: 0.8578\n",
      "Epoch 281/500\n",
      "132/132 [==============================] - 67s 509ms/step - loss: 0.1373 - acc: 0.9524 - val_loss: 0.5183 - val_acc: 0.8899\n",
      "Epoch 282/500\n",
      "132/132 [==============================] - 67s 511ms/step - loss: 0.1326 - acc: 0.9515 - val_loss: 0.5888 - val_acc: 0.8578\n",
      "Epoch 283/500\n",
      "132/132 [==============================] - 68s 517ms/step - loss: 0.1508 - acc: 0.9464 - val_loss: 0.6534 - val_acc: 0.8394\n",
      "Epoch 284/500\n",
      "132/132 [==============================] - 67s 509ms/step - loss: 0.1290 - acc: 0.9553 - val_loss: 0.6307 - val_acc: 0.8440\n",
      "Epoch 285/500\n",
      "132/132 [==============================] - 68s 513ms/step - loss: 0.1232 - acc: 0.9523 - val_loss: 0.6294 - val_acc: 0.8578\n",
      "Epoch 286/500\n",
      "132/132 [==============================] - 67s 510ms/step - loss: 0.1723 - acc: 0.9438 - val_loss: 0.5287 - val_acc: 0.8624\n",
      "Epoch 287/500\n",
      "132/132 [==============================] - 67s 510ms/step - loss: 0.1871 - acc: 0.9361 - val_loss: 0.5480 - val_acc: 0.8670\n",
      "Epoch 288/500\n",
      "132/132 [==============================] - 67s 511ms/step - loss: 0.1429 - acc: 0.9479 - val_loss: 0.5592 - val_acc: 0.8578\n",
      "Epoch 289/500\n",
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      "Epoch 290/500\n",
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      "Epoch 292/500\n",
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      "Epoch 293/500\n",
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      "Epoch 294/500\n",
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      "Epoch 295/500\n",
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      "Epoch 296/500\n",
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      "Epoch 297/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 67s 507ms/step - loss: 0.1585 - acc: 0.9455 - val_loss: 0.6150 - val_acc: 0.8578\n",
      "Epoch 298/500\n",
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      "Epoch 309/500\n",
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      "Epoch 312/500\n",
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      "Epoch 313/500\n",
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      "Epoch 319/500\n",
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      "Epoch 383/500\n",
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      "Epoch 405/500\n",
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      "Epoch 411/500\n",
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      "Epoch 413/500\n",
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      "Epoch 415/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0389 - acc: 0.9882 - val_loss: 0.6367 - val_acc: 0.8991\n",
      "Epoch 416/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0274 - acc: 0.9917 - val_loss: 0.6392 - val_acc: 0.8761\n",
      "Epoch 417/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0566 - acc: 0.9800 - val_loss: 0.5896 - val_acc: 0.8945\n",
      "Epoch 418/500\n",
      "132/132 [==============================] - 66s 504ms/step - loss: 0.0305 - acc: 0.9906 - val_loss: 0.6935 - val_acc: 0.8991\n",
      "Epoch 419/500\n",
      "132/132 [==============================] - 67s 505ms/step - loss: 0.1253 - acc: 0.9670 - val_loss: 0.6264 - val_acc: 0.8670\n",
      "Epoch 420/500\n",
      "132/132 [==============================] - 67s 508ms/step - loss: 0.0621 - acc: 0.9786 - val_loss: 0.5776 - val_acc: 0.8578\n",
      "Epoch 421/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0985 - acc: 0.9703 - val_loss: 0.4829 - val_acc: 0.8761\n",
      "Epoch 422/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0458 - acc: 0.9853 - val_loss: 0.6459 - val_acc: 0.8761\n",
      "Epoch 423/500\n",
      "132/132 [==============================] - 67s 505ms/step - loss: 0.1768 - acc: 0.9515 - val_loss: 0.6643 - val_acc: 0.8349\n",
      "Epoch 424/500\n",
      "132/132 [==============================] - 67s 505ms/step - loss: 0.1927 - acc: 0.9380 - val_loss: 0.3521 - val_acc: 0.9174\n",
      "Epoch 425/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0776 - acc: 0.9736 - val_loss: 0.4792 - val_acc: 0.8670\n",
      "Epoch 426/500\n",
      "132/132 [==============================] - 67s 508ms/step - loss: 0.0559 - acc: 0.9848 - val_loss: 0.5944 - val_acc: 0.8624\n",
      "Epoch 427/500\n",
      "132/132 [==============================] - 67s 504ms/step - loss: 0.0455 - acc: 0.9847 - val_loss: 0.5983 - val_acc: 0.8807\n",
      "Epoch 428/500\n",
      "132/132 [==============================] - 67s 505ms/step - loss: 0.0839 - acc: 0.9771 - val_loss: 0.9861 - val_acc: 0.8119\n",
      "Epoch 429/500\n",
      "132/132 [==============================] - 68s 512ms/step - loss: 0.1941 - acc: 0.9422 - val_loss: 0.5229 - val_acc: 0.8624\n",
      "Epoch 430/500\n",
      "132/132 [==============================] - 67s 508ms/step - loss: 0.1264 - acc: 0.9600 - val_loss: 0.6222 - val_acc: 0.8532\n",
      "Epoch 431/500\n",
      "132/132 [==============================] - 67s 505ms/step - loss: 0.0675 - acc: 0.9817 - val_loss: 0.5962 - val_acc: 0.8624\n",
      "Epoch 432/500\n",
      "132/132 [==============================] - 67s 507ms/step - loss: 0.0437 - acc: 0.9862 - val_loss: 0.6632 - val_acc: 0.8578\n",
      "Epoch 433/500\n",
      "132/132 [==============================] - 67s 507ms/step - loss: 0.0459 - acc: 0.9861 - val_loss: 0.6575 - val_acc: 0.8440\n",
      "Epoch 434/500\n",
      "132/132 [==============================] - 67s 505ms/step - loss: 0.0774 - acc: 0.9774 - val_loss: 0.7489 - val_acc: 0.8578\n",
      "Epoch 435/500\n",
      "132/132 [==============================] - 67s 510ms/step - loss: 0.3087 - acc: 0.9183 - val_loss: 0.3580 - val_acc: 0.8761\n",
      "Epoch 436/500\n",
      "132/132 [==============================] - 67s 505ms/step - loss: 0.1724 - acc: 0.9420 - val_loss: 0.4055 - val_acc: 0.8807\n",
      "Epoch 437/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0679 - acc: 0.9798 - val_loss: 0.4247 - val_acc: 0.8807\n",
      "Epoch 438/500\n",
      "132/132 [==============================] - 67s 508ms/step - loss: 0.0434 - acc: 0.9870 - val_loss: 0.5208 - val_acc: 0.8853\n",
      "Epoch 439/500\n",
      "132/132 [==============================] - 67s 507ms/step - loss: 0.0396 - acc: 0.9879 - val_loss: 0.5800 - val_acc: 0.8807\n",
      "Epoch 440/500\n",
      "132/132 [==============================] - 67s 505ms/step - loss: 0.0435 - acc: 0.9864 - val_loss: 0.5120 - val_acc: 0.8807\n",
      "Epoch 441/500\n",
      "132/132 [==============================] - 67s 508ms/step - loss: 0.0250 - acc: 0.9932 - val_loss: 0.4867 - val_acc: 0.8945\n",
      "Epoch 442/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0304 - acc: 0.9911 - val_loss: 0.5623 - val_acc: 0.8807\n",
      "Epoch 443/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0566 - acc: 0.9827 - val_loss: 0.4762 - val_acc: 0.8807\n",
      "Epoch 444/500\n",
      "132/132 [==============================] - 67s 510ms/step - loss: 0.0290 - acc: 0.9908 - val_loss: 0.6075 - val_acc: 0.8761\n",
      "Epoch 445/500\n",
      "132/132 [==============================] - 67s 505ms/step - loss: 0.0180 - acc: 0.9948 - val_loss: 0.6302 - val_acc: 0.9037\n",
      "Epoch 446/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0327 - acc: 0.9909 - val_loss: 0.5205 - val_acc: 0.8899\n",
      "Epoch 447/500\n",
      "132/132 [==============================] - 67s 509ms/step - loss: 0.0663 - acc: 0.9783 - val_loss: 0.5821 - val_acc: 0.8716\n",
      "Epoch 448/500\n",
      "132/132 [==============================] - 67s 507ms/step - loss: 0.0878 - acc: 0.9738 - val_loss: 0.6375 - val_acc: 0.8624\n",
      "Epoch 449/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0659 - acc: 0.9797 - val_loss: 0.5897 - val_acc: 0.8578\n",
      "Epoch 450/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0516 - acc: 0.9838 - val_loss: 0.5681 - val_acc: 0.8899\n",
      "Epoch 451/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0335 - acc: 0.9891 - val_loss: 0.6164 - val_acc: 0.8716\n",
      "Epoch 452/500\n",
      "132/132 [==============================] - 67s 505ms/step - loss: 0.0446 - acc: 0.9864 - val_loss: 0.6678 - val_acc: 0.8807\n",
      "Epoch 453/500\n",
      "132/132 [==============================] - 67s 510ms/step - loss: 0.0307 - acc: 0.9917 - val_loss: 0.5479 - val_acc: 0.9083\n",
      "Epoch 454/500\n",
      "132/132 [==============================] - 67s 505ms/step - loss: 0.0344 - acc: 0.9880 - val_loss: 0.5567 - val_acc: 0.8899\n",
      "Epoch 455/500\n",
      "132/132 [==============================] - 67s 508ms/step - loss: 0.0433 - acc: 0.9855 - val_loss: 0.5459 - val_acc: 0.8899\n",
      "Epoch 456/500\n",
      "132/132 [==============================] - 67s 510ms/step - loss: 0.0527 - acc: 0.9823 - val_loss: 0.5377 - val_acc: 0.8807\n",
      "Epoch 457/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0482 - acc: 0.9845 - val_loss: 0.6158 - val_acc: 0.8761\n",
      "Epoch 458/500\n",
      "132/132 [==============================] - 67s 510ms/step - loss: 0.0471 - acc: 0.9873 - val_loss: 0.7190 - val_acc: 0.8532\n",
      "Epoch 459/500\n",
      "132/132 [==============================] - 67s 509ms/step - loss: 0.0342 - acc: 0.9897 - val_loss: 0.5602 - val_acc: 0.8807\n",
      "Epoch 460/500\n",
      "132/132 [==============================] - 67s 505ms/step - loss: 0.0612 - acc: 0.9832 - val_loss: 0.6586 - val_acc: 0.8624\n",
      "Epoch 461/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0647 - acc: 0.9814 - val_loss: 0.5801 - val_acc: 0.8807\n",
      "Epoch 462/500\n",
      "132/132 [==============================] - 67s 510ms/step - loss: 0.0580 - acc: 0.9808 - val_loss: 0.5118 - val_acc: 0.8853\n",
      "Epoch 463/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0749 - acc: 0.9767 - val_loss: 0.4627 - val_acc: 0.8899\n",
      "Epoch 464/500\n",
      "132/132 [==============================] - 67s 508ms/step - loss: 0.0297 - acc: 0.9912 - val_loss: 0.6158 - val_acc: 0.8716\n",
      "Epoch 465/500\n",
      "132/132 [==============================] - 67s 509ms/step - loss: 0.0288 - acc: 0.9917 - val_loss: 0.6872 - val_acc: 0.8761\n",
      "Epoch 466/500\n",
      "132/132 [==============================] - 67s 509ms/step - loss: 0.0547 - acc: 0.9835 - val_loss: 0.6386 - val_acc: 0.8670\n",
      "Epoch 467/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0451 - acc: 0.9859 - val_loss: 0.6357 - val_acc: 0.8578\n",
      "Epoch 468/500\n",
      "132/132 [==============================] - 67s 508ms/step - loss: 0.0340 - acc: 0.9909 - val_loss: 0.6344 - val_acc: 0.8945\n",
      "Epoch 469/500\n",
      "132/132 [==============================] - 67s 508ms/step - loss: 0.0690 - acc: 0.9786 - val_loss: 0.5350 - val_acc: 0.8716\n",
      "Epoch 470/500\n",
      "132/132 [==============================] - 67s 507ms/step - loss: 0.0224 - acc: 0.9942 - val_loss: 0.5556 - val_acc: 0.8991\n",
      "Epoch 471/500\n",
      "132/132 [==============================] - 67s 510ms/step - loss: 0.0156 - acc: 0.9965 - val_loss: 0.6378 - val_acc: 0.8899\n",
      "Epoch 472/500\n",
      "132/132 [==============================] - 67s 507ms/step - loss: 0.0657 - acc: 0.9806 - val_loss: 0.4963 - val_acc: 0.8991\n",
      "Epoch 473/500\n",
      "132/132 [==============================] - 67s 507ms/step - loss: 0.0517 - acc: 0.9852 - val_loss: 0.6223 - val_acc: 0.8578\n",
      "Epoch 474/500\n",
      "132/132 [==============================] - 68s 512ms/step - loss: 0.0741 - acc: 0.9789 - val_loss: 0.7855 - val_acc: 0.8761\n",
      "Epoch 475/500\n",
      "132/132 [==============================] - 67s 509ms/step - loss: 0.0728 - acc: 0.9789 - val_loss: 0.5745 - val_acc: 0.8945\n",
      "Epoch 476/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0332 - acc: 0.9898 - val_loss: 0.5438 - val_acc: 0.9037\n",
      "Epoch 477/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0344 - acc: 0.9876 - val_loss: 0.5808 - val_acc: 0.8945\n",
      "Epoch 478/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.0337 - acc: 0.9895 - val_loss: 0.5876 - val_acc: 0.9037\n",
      "Epoch 479/500\n",
      "132/132 [==============================] - 67s 505ms/step - loss: 0.0369 - acc: 0.9883 - val_loss: 0.5842 - val_acc: 0.8945\n",
      "Epoch 480/500\n",
      "132/132 [==============================] - 67s 507ms/step - loss: 0.0814 - acc: 0.9776 - val_loss: 0.5729 - val_acc: 0.8761\n",
      "Epoch 481/500\n",
      "132/132 [==============================] - 67s 505ms/step - loss: 0.0865 - acc: 0.9770 - val_loss: 0.6622 - val_acc: 0.8578\n",
      "Epoch 482/500\n",
      "132/132 [==============================] - 67s 506ms/step - loss: 0.1073 - acc: 0.9645 - val_loss: 0.5356 - val_acc: 0.8578\n",
      "Epoch 483/500\n",
      "132/132 [==============================] - 68s 514ms/step - loss: 0.0358 - acc: 0.9898 - val_loss: 0.6729 - val_acc: 0.8440\n",
      "Epoch 484/500\n",
      "132/132 [==============================] - 69s 521ms/step - loss: 0.0254 - acc: 0.9924 - val_loss: 0.5878 - val_acc: 0.8761\n",
      "Epoch 485/500\n",
      "132/132 [==============================] - 70s 529ms/step - loss: 0.0367 - acc: 0.9900 - val_loss: 0.6533 - val_acc: 0.8670\n",
      "Epoch 486/500\n",
      "132/132 [==============================] - 75s 567ms/step - loss: 0.0893 - acc: 0.9761 - val_loss: 0.5674 - val_acc: 0.8670\n",
      "Epoch 487/500\n",
      "132/132 [==============================] - 69s 525ms/step - loss: 0.0587 - acc: 0.9842 - val_loss: 0.5797 - val_acc: 0.8624\n",
      "Epoch 488/500\n",
      "132/132 [==============================] - 71s 536ms/step - loss: 0.0307 - acc: 0.9926 - val_loss: 0.5698 - val_acc: 0.8807\n",
      "Epoch 489/500\n",
      "132/132 [==============================] - 72s 546ms/step - loss: 0.0413 - acc: 0.9885 - val_loss: 0.6265 - val_acc: 0.8670\n",
      "Epoch 490/500\n",
      "132/132 [==============================] - 67s 510ms/step - loss: 0.0690 - acc: 0.9783 - val_loss: 0.4475 - val_acc: 0.8807\n",
      "Epoch 491/500\n",
      "132/132 [==============================] - 68s 519ms/step - loss: 0.0284 - acc: 0.9918 - val_loss: 0.5024 - val_acc: 0.8761\n",
      "Epoch 492/500\n",
      "132/132 [==============================] - 68s 514ms/step - loss: 0.0336 - acc: 0.9911 - val_loss: 0.5363 - val_acc: 0.8945\n",
      "Epoch 493/500\n",
      "132/132 [==============================] - 67s 511ms/step - loss: 0.0952 - acc: 0.9732 - val_loss: 0.6371 - val_acc: 0.8670\n",
      "Epoch 494/500\n",
      "132/132 [==============================] - 68s 518ms/step - loss: 0.0892 - acc: 0.9709 - val_loss: 0.4709 - val_acc: 0.8761\n",
      "Epoch 495/500\n",
      "132/132 [==============================] - 67s 510ms/step - loss: 0.0312 - acc: 0.9902 - val_loss: 0.4795 - val_acc: 0.8945\n",
      "Epoch 496/500\n",
      "132/132 [==============================] - 67s 507ms/step - loss: 0.0134 - acc: 0.9964 - val_loss: 0.5932 - val_acc: 0.8761\n",
      "Epoch 497/500\n",
      "132/132 [==============================] - 69s 520ms/step - loss: 0.0086 - acc: 0.9985 - val_loss: 0.6569 - val_acc: 0.8853\n",
      "Epoch 498/500\n",
      "132/132 [==============================] - 69s 526ms/step - loss: 0.0062 - acc: 0.9991 - val_loss: 0.6731 - val_acc: 0.8807\n",
      "Epoch 499/500\n",
      "132/132 [==============================] - 67s 508ms/step - loss: 0.0780 - acc: 0.9756 - val_loss: 0.6632 - val_acc: 0.8853\n",
      "Epoch 500/500\n",
      "132/132 [==============================] - 70s 532ms/step - loss: 0.0938 - acc: 0.9703 - val_loss: 0.6344 - val_acc: 0.8578\n",
      "40/40 [==============================] - 6s 144ms/step\n",
      "Accuracy: 0.8806500761808025. F1: 0.8790933479912493\n"
     ]
    }
   ],
   "source": [
    "cnn(4, make_modelLSTM, epochs = 500, save_model = True, name = \"LSTM_norm\", file_type = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "132/132 [==============================] - 40s 306ms/step - loss: 1.6890 - acc: 0.3368 - val_loss: 1.4291 - val_acc: 0.4037\n",
      "Epoch 2/500\n",
      "132/132 [==============================] - 38s 289ms/step - loss: 1.3679 - acc: 0.4458 - val_loss: 1.3360 - val_acc: 0.4037\n",
      "Epoch 3/500\n",
      "132/132 [==============================] - 45s 342ms/step - loss: 1.4274 - acc: 0.4179 - val_loss: 1.1894 - val_acc: 0.4725\n",
      "Epoch 4/500\n",
      "132/132 [==============================] - 45s 340ms/step - loss: 1.3364 - acc: 0.4626 - val_loss: 1.2203 - val_acc: 0.4541\n",
      "Epoch 5/500\n",
      "132/132 [==============================] - 45s 338ms/step - loss: 1.3266 - acc: 0.4432 - val_loss: 1.1925 - val_acc: 0.4587\n",
      "Epoch 6/500\n",
      "132/132 [==============================] - 45s 340ms/step - loss: 1.2993 - acc: 0.4669 - val_loss: 1.2508 - val_acc: 0.4495\n",
      "Epoch 7/500\n",
      "132/132 [==============================] - 45s 341ms/step - loss: 1.2630 - acc: 0.4908 - val_loss: 1.4301 - val_acc: 0.3853\n",
      "Epoch 8/500\n",
      "132/132 [==============================] - 45s 340ms/step - loss: 1.3976 - acc: 0.4494 - val_loss: 1.2087 - val_acc: 0.4679\n",
      "Epoch 9/500\n",
      "132/132 [==============================] - 45s 338ms/step - loss: 1.1921 - acc: 0.5157 - val_loss: 1.1401 - val_acc: 0.5138\n",
      "Epoch 10/500\n",
      "132/132 [==============================] - 45s 342ms/step - loss: 1.2446 - acc: 0.5141 - val_loss: 1.4212 - val_acc: 0.4037\n",
      "Epoch 11/500\n",
      "132/132 [==============================] - 45s 344ms/step - loss: 1.1915 - acc: 0.5256 - val_loss: 1.1230 - val_acc: 0.4954\n",
      "Epoch 12/500\n",
      "132/132 [==============================] - 46s 346ms/step - loss: 1.1638 - acc: 0.5418 - val_loss: 1.1132 - val_acc: 0.5092\n",
      "Epoch 13/500\n",
      "132/132 [==============================] - 46s 350ms/step - loss: 1.0827 - acc: 0.5735 - val_loss: 1.0360 - val_acc: 0.5321\n",
      "Epoch 14/500\n",
      "132/132 [==============================] - 46s 351ms/step - loss: 1.0130 - acc: 0.5994 - val_loss: 1.0286 - val_acc: 0.5505\n",
      "Epoch 15/500\n",
      "132/132 [==============================] - 46s 350ms/step - loss: 1.0078 - acc: 0.6004 - val_loss: 1.0545 - val_acc: 0.5229\n",
      "Epoch 16/500\n",
      "132/132 [==============================] - 46s 350ms/step - loss: 1.0422 - acc: 0.5888 - val_loss: 0.9681 - val_acc: 0.5459\n",
      "Epoch 17/500\n",
      "132/132 [==============================] - 46s 350ms/step - loss: 0.9660 - acc: 0.6180 - val_loss: 1.0530 - val_acc: 0.5229\n",
      "Epoch 18/500\n",
      "132/132 [==============================] - 46s 349ms/step - loss: 1.0802 - acc: 0.5735 - val_loss: 1.1057 - val_acc: 0.5183\n",
      "Epoch 19/500\n",
      "132/132 [==============================] - 46s 350ms/step - loss: 0.9488 - acc: 0.6275 - val_loss: 0.9378 - val_acc: 0.6009\n",
      "Epoch 20/500\n",
      "132/132 [==============================] - 45s 342ms/step - loss: 0.9636 - acc: 0.6250 - val_loss: 1.1366 - val_acc: 0.5459\n",
      "Epoch 21/500\n",
      "132/132 [==============================] - 46s 347ms/step - loss: 0.9854 - acc: 0.6100 - val_loss: 0.9666 - val_acc: 0.6101\n",
      "Epoch 22/500\n",
      "132/132 [==============================] - 52s 394ms/step - loss: 1.0061 - acc: 0.6090 - val_loss: 1.0071 - val_acc: 0.5459\n",
      "Epoch 23/500\n",
      "132/132 [==============================] - 47s 356ms/step - loss: 0.9425 - acc: 0.6294 - val_loss: 0.9093 - val_acc: 0.5780\n",
      "Epoch 24/500\n",
      "132/132 [==============================] - 47s 355ms/step - loss: 0.9703 - acc: 0.6268 - val_loss: 0.9615 - val_acc: 0.5688\n",
      "Epoch 25/500\n",
      "132/132 [==============================] - 49s 372ms/step - loss: 0.9816 - acc: 0.6250 - val_loss: 1.0056 - val_acc: 0.5550\n",
      "Epoch 26/500\n",
      "132/132 [==============================] - 47s 358ms/step - loss: 0.9360 - acc: 0.6297 - val_loss: 1.0102 - val_acc: 0.5550\n",
      "Epoch 27/500\n",
      "132/132 [==============================] - 54s 413ms/step - loss: 0.8907 - acc: 0.6465 - val_loss: 0.9029 - val_acc: 0.6101\n",
      "Epoch 28/500\n",
      "132/132 [==============================] - 53s 400ms/step - loss: 0.9167 - acc: 0.6472 - val_loss: 0.9631 - val_acc: 0.5872\n",
      "Epoch 29/500\n",
      "132/132 [==============================] - 55s 415ms/step - loss: 0.8861 - acc: 0.6517 - val_loss: 0.9492 - val_acc: 0.5963\n",
      "Epoch 30/500\n",
      "132/132 [==============================] - 57s 433ms/step - loss: 0.8546 - acc: 0.6600 - val_loss: 0.9152 - val_acc: 0.6055\n",
      "Epoch 31/500\n",
      "132/132 [==============================] - 59s 450ms/step - loss: 0.8694 - acc: 0.6679 - val_loss: 0.9575 - val_acc: 0.5963\n",
      "Epoch 32/500\n",
      "132/132 [==============================] - 62s 469ms/step - loss: 0.8293 - acc: 0.6865 - val_loss: 0.8925 - val_acc: 0.6239\n",
      "Epoch 33/500\n",
      "132/132 [==============================] - 60s 458ms/step - loss: 0.8012 - acc: 0.6969 - val_loss: 0.8260 - val_acc: 0.6514\n",
      "Epoch 34/500\n",
      "132/132 [==============================] - 58s 439ms/step - loss: 0.8737 - acc: 0.6621 - val_loss: 0.9118 - val_acc: 0.5642\n",
      "Epoch 35/500\n",
      "132/132 [==============================] - 61s 462ms/step - loss: 0.8369 - acc: 0.6738 - val_loss: 0.8906 - val_acc: 0.6101\n",
      "Epoch 36/500\n",
      "132/132 [==============================] - 63s 479ms/step - loss: 0.7961 - acc: 0.6926 - val_loss: 0.8980 - val_acc: 0.6147\n",
      "Epoch 37/500\n",
      "132/132 [==============================] - 59s 446ms/step - loss: 1.0565 - acc: 0.6039 - val_loss: 1.1557 - val_acc: 0.5183\n",
      "Epoch 38/500\n",
      "132/132 [==============================] - 59s 446ms/step - loss: 0.9814 - acc: 0.6103 - val_loss: 1.0050 - val_acc: 0.5642\n",
      "Epoch 39/500\n",
      "132/132 [==============================] - 60s 457ms/step - loss: 1.2674 - acc: 0.5192 - val_loss: 1.3249 - val_acc: 0.4771\n",
      "Epoch 40/500\n",
      "132/132 [==============================] - 57s 434ms/step - loss: 1.2678 - acc: 0.5062 - val_loss: 1.2206 - val_acc: 0.4633\n",
      "Epoch 41/500\n",
      "132/132 [==============================] - 57s 434ms/step - loss: 1.0384 - acc: 0.5850 - val_loss: 1.0403 - val_acc: 0.5550\n",
      "Epoch 42/500\n",
      "132/132 [==============================] - 62s 473ms/step - loss: 1.0380 - acc: 0.5913 - val_loss: 1.0242 - val_acc: 0.5642\n",
      "Epoch 43/500\n",
      "132/132 [==============================] - 61s 460ms/step - loss: 1.0319 - acc: 0.5976 - val_loss: 1.0550 - val_acc: 0.5413\n",
      "Epoch 44/500\n",
      "132/132 [==============================] - 60s 451ms/step - loss: 1.0777 - acc: 0.5813 - val_loss: 1.1054 - val_acc: 0.5138\n",
      "Epoch 45/500\n",
      "132/132 [==============================] - 59s 450ms/step - loss: 0.9569 - acc: 0.6254 - val_loss: 0.9899 - val_acc: 0.6101\n",
      "Epoch 46/500\n",
      "132/132 [==============================] - 59s 444ms/step - loss: 1.1457 - acc: 0.5381 - val_loss: 1.1402 - val_acc: 0.5000\n",
      "Epoch 47/500\n",
      "132/132 [==============================] - 60s 455ms/step - loss: 1.0581 - acc: 0.5794 - val_loss: 1.0129 - val_acc: 0.5826\n",
      "Epoch 48/500\n",
      "132/132 [==============================] - 57s 432ms/step - loss: 0.9432 - acc: 0.6352 - val_loss: 0.9826 - val_acc: 0.5872\n",
      "Epoch 49/500\n",
      "132/132 [==============================] - 60s 452ms/step - loss: 0.9000 - acc: 0.6407 - val_loss: 0.9866 - val_acc: 0.5642\n",
      "Epoch 50/500\n",
      "132/132 [==============================] - 58s 441ms/step - loss: 0.9749 - acc: 0.6146 - val_loss: 0.9945 - val_acc: 0.5917\n",
      "Epoch 51/500\n",
      "132/132 [==============================] - 60s 452ms/step - loss: 0.8889 - acc: 0.6507 - val_loss: 0.9578 - val_acc: 0.5688\n",
      "Epoch 52/500\n",
      "132/132 [==============================] - 60s 454ms/step - loss: 0.9180 - acc: 0.6275 - val_loss: 0.9214 - val_acc: 0.6009\n",
      "Epoch 53/500\n",
      "132/132 [==============================] - 58s 438ms/step - loss: 0.8439 - acc: 0.6686 - val_loss: 0.8923 - val_acc: 0.6193\n",
      "Epoch 54/500\n",
      "132/132 [==============================] - 54s 410ms/step - loss: 0.8128 - acc: 0.6746 - val_loss: 0.9119 - val_acc: 0.6009\n",
      "Epoch 55/500\n",
      "132/132 [==============================] - 57s 428ms/step - loss: 0.8279 - acc: 0.6810 - val_loss: 0.9161 - val_acc: 0.6147\n",
      "Epoch 56/500\n",
      "132/132 [==============================] - 58s 437ms/step - loss: 0.8419 - acc: 0.6749 - val_loss: 0.9187 - val_acc: 0.5963\n",
      "Epoch 57/500\n",
      "132/132 [==============================] - 59s 445ms/step - loss: 0.9696 - acc: 0.6305 - val_loss: 1.0481 - val_acc: 0.5734\n",
      "Epoch 58/500\n",
      "132/132 [==============================] - 58s 436ms/step - loss: 0.8810 - acc: 0.6587 - val_loss: 0.9416 - val_acc: 0.5917\n",
      "Epoch 59/500\n",
      "132/132 [==============================] - 57s 429ms/step - loss: 0.8559 - acc: 0.6649 - val_loss: 0.9616 - val_acc: 0.6284\n",
      "Epoch 60/500\n",
      "132/132 [==============================] - 59s 445ms/step - loss: 0.8246 - acc: 0.6804 - val_loss: 1.0232 - val_acc: 0.5826\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 61/500\n",
      "132/132 [==============================] - 56s 427ms/step - loss: 0.8375 - acc: 0.6758 - val_loss: 0.9195 - val_acc: 0.5963\n",
      "Epoch 62/500\n",
      "132/132 [==============================] - 58s 441ms/step - loss: 0.7694 - acc: 0.7097 - val_loss: 0.9009 - val_acc: 0.6193\n",
      "Epoch 63/500\n",
      "132/132 [==============================] - 60s 451ms/step - loss: 0.8416 - acc: 0.6769 - val_loss: 0.9258 - val_acc: 0.5963\n",
      "Epoch 64/500\n",
      "132/132 [==============================] - 58s 440ms/step - loss: 0.8206 - acc: 0.6791 - val_loss: 0.8955 - val_acc: 0.6193\n",
      "Epoch 65/500\n",
      "132/132 [==============================] - 57s 430ms/step - loss: 0.7847 - acc: 0.6961 - val_loss: 0.9065 - val_acc: 0.6193\n",
      "Epoch 66/500\n",
      "132/132 [==============================] - 58s 436ms/step - loss: 0.8397 - acc: 0.6748 - val_loss: 1.1287 - val_acc: 0.5596\n",
      "Epoch 67/500\n",
      "132/132 [==============================] - 58s 439ms/step - loss: 0.8644 - acc: 0.6656 - val_loss: 0.9116 - val_acc: 0.6101\n",
      "Epoch 68/500\n",
      "132/132 [==============================] - 56s 422ms/step - loss: 0.8910 - acc: 0.6547 - val_loss: 0.9408 - val_acc: 0.5917\n",
      "Epoch 69/500\n",
      "132/132 [==============================] - 46s 351ms/step - loss: 0.8363 - acc: 0.6673 - val_loss: 1.0255 - val_acc: 0.5872\n",
      "Epoch 70/500\n",
      "132/132 [==============================] - 46s 350ms/step - loss: 0.8468 - acc: 0.6633 - val_loss: 0.8990 - val_acc: 0.6193\n",
      "Epoch 71/500\n",
      "132/132 [==============================] - 46s 350ms/step - loss: 0.8059 - acc: 0.6939 - val_loss: 0.8546 - val_acc: 0.6560\n",
      "Epoch 72/500\n",
      "132/132 [==============================] - 46s 350ms/step - loss: 0.7680 - acc: 0.7166 - val_loss: 0.8891 - val_acc: 0.6422\n",
      "Epoch 73/500\n",
      "132/132 [==============================] - 45s 341ms/step - loss: 0.7721 - acc: 0.7052 - val_loss: 0.8781 - val_acc: 0.6193\n",
      "Epoch 74/500\n",
      "132/132 [==============================] - 44s 337ms/step - loss: 0.7613 - acc: 0.7047 - val_loss: 0.8386 - val_acc: 0.6468\n",
      "Epoch 75/500\n",
      "132/132 [==============================] - 44s 335ms/step - loss: 0.7337 - acc: 0.7246 - val_loss: 0.8566 - val_acc: 0.6468\n",
      "Epoch 76/500\n",
      "132/132 [==============================] - 44s 335ms/step - loss: 0.7250 - acc: 0.7283 - val_loss: 0.8014 - val_acc: 0.6697\n",
      "Epoch 77/500\n",
      "132/132 [==============================] - 50s 377ms/step - loss: 0.7236 - acc: 0.7330 - val_loss: 0.8599 - val_acc: 0.6468\n",
      "Epoch 78/500\n",
      "132/132 [==============================] - 48s 360ms/step - loss: 0.7374 - acc: 0.7210 - val_loss: 0.8665 - val_acc: 0.6651\n",
      "Epoch 79/500\n",
      "132/132 [==============================] - 46s 351ms/step - loss: 0.7316 - acc: 0.7172 - val_loss: 0.8375 - val_acc: 0.6422\n",
      "Epoch 80/500\n",
      "132/132 [==============================] - 46s 350ms/step - loss: 0.7215 - acc: 0.7379 - val_loss: 0.8761 - val_acc: 0.6514\n",
      "Epoch 81/500\n",
      "132/132 [==============================] - 46s 349ms/step - loss: 0.7311 - acc: 0.7232 - val_loss: 0.8637 - val_acc: 0.6789\n",
      "Epoch 82/500\n",
      "132/132 [==============================] - 46s 349ms/step - loss: 0.6989 - acc: 0.7431 - val_loss: 0.8424 - val_acc: 0.6468\n",
      "Epoch 83/500\n",
      "132/132 [==============================] - 46s 351ms/step - loss: 0.7402 - acc: 0.7078 - val_loss: 0.8266 - val_acc: 0.6606\n",
      "Epoch 84/500\n",
      "132/132 [==============================] - 47s 354ms/step - loss: 0.6998 - acc: 0.7414 - val_loss: 0.8526 - val_acc: 0.6881\n",
      "Epoch 85/500\n",
      "132/132 [==============================] - 47s 353ms/step - loss: 0.6926 - acc: 0.7435 - val_loss: 0.8060 - val_acc: 0.6743\n",
      "Epoch 86/500\n",
      "132/132 [==============================] - 46s 351ms/step - loss: 0.6998 - acc: 0.7407 - val_loss: 0.8283 - val_acc: 0.6606\n",
      "Epoch 87/500\n",
      "132/132 [==============================] - 47s 356ms/step - loss: 0.6885 - acc: 0.7529 - val_loss: 0.8010 - val_acc: 0.6835\n",
      "Epoch 88/500\n",
      "132/132 [==============================] - 46s 350ms/step - loss: 0.7623 - acc: 0.7162 - val_loss: 0.8532 - val_acc: 0.6651\n",
      "Epoch 89/500\n",
      "132/132 [==============================] - 47s 357ms/step - loss: 0.7208 - acc: 0.7349 - val_loss: 0.8232 - val_acc: 0.6835\n",
      "Epoch 90/500\n",
      "132/132 [==============================] - 47s 354ms/step - loss: 0.7631 - acc: 0.7206 - val_loss: 0.7851 - val_acc: 0.6743\n",
      "Epoch 91/500\n",
      "132/132 [==============================] - 46s 350ms/step - loss: 0.6665 - acc: 0.7557 - val_loss: 0.8022 - val_acc: 0.6789\n",
      "Epoch 92/500\n",
      "132/132 [==============================] - 46s 351ms/step - loss: 0.6680 - acc: 0.7573 - val_loss: 0.7526 - val_acc: 0.7110\n",
      "Epoch 93/500\n",
      "132/132 [==============================] - 47s 355ms/step - loss: 0.6731 - acc: 0.7563 - val_loss: 0.7493 - val_acc: 0.7064\n",
      "Epoch 94/500\n",
      "132/132 [==============================] - 46s 350ms/step - loss: 0.6680 - acc: 0.7589 - val_loss: 0.7430 - val_acc: 0.6972\n",
      "Epoch 95/500\n",
      "132/132 [==============================] - 47s 353ms/step - loss: 0.6961 - acc: 0.7432 - val_loss: 1.0366 - val_acc: 0.5917\n",
      "Epoch 96/500\n",
      "132/132 [==============================] - 46s 352ms/step - loss: 0.8166 - acc: 0.6879 - val_loss: 0.8976 - val_acc: 0.6284\n",
      "Epoch 97/500\n",
      "132/132 [==============================] - 47s 358ms/step - loss: 0.6975 - acc: 0.7506 - val_loss: 0.8269 - val_acc: 0.7064\n",
      "Epoch 98/500\n",
      "132/132 [==============================] - 48s 361ms/step - loss: 0.7486 - acc: 0.7263 - val_loss: 0.8273 - val_acc: 0.6697\n",
      "Epoch 99/500\n",
      "132/132 [==============================] - 48s 360ms/step - loss: 0.6707 - acc: 0.7632 - val_loss: 0.7715 - val_acc: 0.7248\n",
      "Epoch 100/500\n",
      "132/132 [==============================] - 47s 357ms/step - loss: 0.6502 - acc: 0.7673 - val_loss: 0.8033 - val_acc: 0.7064\n",
      "Epoch 101/500\n",
      "132/132 [==============================] - 47s 359ms/step - loss: 0.6695 - acc: 0.7582 - val_loss: 0.8290 - val_acc: 0.6514\n",
      "Epoch 102/500\n",
      "132/132 [==============================] - 47s 359ms/step - loss: 0.6609 - acc: 0.7564 - val_loss: 0.7747 - val_acc: 0.6927\n",
      "Epoch 103/500\n",
      "132/132 [==============================] - 48s 365ms/step - loss: 0.6549 - acc: 0.7576 - val_loss: 0.7565 - val_acc: 0.6835\n",
      "Epoch 104/500\n",
      "132/132 [==============================] - 48s 361ms/step - loss: 0.6314 - acc: 0.7726 - val_loss: 0.7981 - val_acc: 0.7018\n",
      "Epoch 105/500\n",
      "132/132 [==============================] - 48s 360ms/step - loss: 0.6236 - acc: 0.7738 - val_loss: 0.7958 - val_acc: 0.7064\n",
      "Epoch 106/500\n",
      "132/132 [==============================] - 48s 365ms/step - loss: 0.6074 - acc: 0.7833 - val_loss: 0.7638 - val_acc: 0.6927\n",
      "Epoch 107/500\n",
      "132/132 [==============================] - 48s 360ms/step - loss: 0.6248 - acc: 0.7767 - val_loss: 0.7690 - val_acc: 0.7156\n",
      "Epoch 108/500\n",
      "132/132 [==============================] - 47s 358ms/step - loss: 0.6687 - acc: 0.7575 - val_loss: 0.7893 - val_acc: 0.7018\n",
      "Epoch 109/500\n",
      "132/132 [==============================] - 48s 361ms/step - loss: 0.7579 - acc: 0.7215 - val_loss: 0.9135 - val_acc: 0.6239\n",
      "Epoch 110/500\n",
      "132/132 [==============================] - 49s 372ms/step - loss: 0.6828 - acc: 0.7510 - val_loss: 0.8233 - val_acc: 0.6697\n",
      "Epoch 111/500\n",
      "132/132 [==============================] - 50s 375ms/step - loss: 0.6475 - acc: 0.7663 - val_loss: 0.7602 - val_acc: 0.7018\n",
      "Epoch 112/500\n",
      "132/132 [==============================] - 48s 362ms/step - loss: 0.6698 - acc: 0.7567 - val_loss: 0.8057 - val_acc: 0.7018\n",
      "Epoch 113/500\n",
      "132/132 [==============================] - 50s 375ms/step - loss: 0.6483 - acc: 0.7670 - val_loss: 0.7675 - val_acc: 0.7248\n",
      "Epoch 114/500\n",
      "132/132 [==============================] - 49s 369ms/step - loss: 0.6106 - acc: 0.7857 - val_loss: 0.7612 - val_acc: 0.7064\n",
      "Epoch 115/500\n",
      "132/132 [==============================] - 51s 386ms/step - loss: 0.6919 - acc: 0.7456 - val_loss: 0.7316 - val_acc: 0.6972\n",
      "Epoch 116/500\n",
      "132/132 [==============================] - 48s 363ms/step - loss: 0.6323 - acc: 0.7684 - val_loss: 0.7469 - val_acc: 0.6972\n",
      "Epoch 117/500\n",
      "132/132 [==============================] - 46s 350ms/step - loss: 0.6023 - acc: 0.7827 - val_loss: 0.7363 - val_acc: 0.7248\n",
      "Epoch 118/500\n",
      "132/132 [==============================] - 46s 351ms/step - loss: 0.6186 - acc: 0.7795 - val_loss: 0.8048 - val_acc: 0.7110\n",
      "Epoch 119/500\n",
      "132/132 [==============================] - 47s 354ms/step - loss: 0.6169 - acc: 0.7757 - val_loss: 0.7376 - val_acc: 0.7385\n",
      "Epoch 120/500\n",
      "132/132 [==============================] - 46s 350ms/step - loss: 0.6273 - acc: 0.7692 - val_loss: 0.7785 - val_acc: 0.6927\n",
      "Epoch 121/500\n",
      "132/132 [==============================] - 47s 355ms/step - loss: 0.6746 - acc: 0.7539 - val_loss: 0.7871 - val_acc: 0.6789\n",
      "Epoch 122/500\n",
      "132/132 [==============================] - 46s 351ms/step - loss: 0.6471 - acc: 0.7707 - val_loss: 0.7496 - val_acc: 0.7248\n",
      "Epoch 123/500\n",
      "132/132 [==============================] - 46s 351ms/step - loss: 0.6125 - acc: 0.7818 - val_loss: 0.7448 - val_acc: 0.7294\n",
      "Epoch 124/500\n",
      "132/132 [==============================] - 47s 353ms/step - loss: 0.6059 - acc: 0.7842 - val_loss: 0.7526 - val_acc: 0.7431\n",
      "Epoch 125/500\n",
      "132/132 [==============================] - 46s 352ms/step - loss: 0.6004 - acc: 0.7902 - val_loss: 0.7449 - val_acc: 0.7156\n",
      "Epoch 126/500\n",
      "132/132 [==============================] - 46s 351ms/step - loss: 0.6097 - acc: 0.7799 - val_loss: 0.7469 - val_acc: 0.7385\n",
      "Epoch 127/500\n",
      "132/132 [==============================] - 47s 358ms/step - loss: 0.5832 - acc: 0.7900 - val_loss: 0.7742 - val_acc: 0.7156\n",
      "Epoch 128/500\n",
      "132/132 [==============================] - 46s 351ms/step - loss: 0.5975 - acc: 0.7895 - val_loss: 0.7328 - val_acc: 0.7064\n",
      "Epoch 129/500\n",
      "132/132 [==============================] - 47s 353ms/step - loss: 0.6346 - acc: 0.7804 - val_loss: 0.7585 - val_acc: 0.7018\n",
      "Epoch 130/500\n",
      "132/132 [==============================] - 46s 349ms/step - loss: 0.5887 - acc: 0.7860 - val_loss: 0.7345 - val_acc: 0.7248\n",
      "Epoch 131/500\n",
      "132/132 [==============================] - 47s 353ms/step - loss: 0.5699 - acc: 0.7961 - val_loss: 0.7267 - val_acc: 0.7248\n",
      "Epoch 132/500\n",
      "132/132 [==============================] - 49s 372ms/step - loss: 0.5850 - acc: 0.7895 - val_loss: 0.7139 - val_acc: 0.7339\n",
      "Epoch 133/500\n",
      "132/132 [==============================] - 49s 368ms/step - loss: 0.5729 - acc: 0.7966 - val_loss: 0.6986 - val_acc: 0.7615\n",
      "Epoch 134/500\n",
      "132/132 [==============================] - 45s 339ms/step - loss: 0.5807 - acc: 0.7916 - val_loss: 0.7131 - val_acc: 0.7202\n",
      "Epoch 135/500\n",
      "132/132 [==============================] - 44s 333ms/step - loss: 0.6138 - acc: 0.7863 - val_loss: 0.9487 - val_acc: 0.6514\n",
      "Epoch 136/500\n",
      "132/132 [==============================] - 44s 332ms/step - loss: 0.6074 - acc: 0.7901 - val_loss: 0.7294 - val_acc: 0.7431\n",
      "Epoch 137/500\n",
      "132/132 [==============================] - 44s 332ms/step - loss: 0.5565 - acc: 0.8061 - val_loss: 0.6638 - val_acc: 0.7615\n",
      "Epoch 138/500\n",
      "132/132 [==============================] - 44s 335ms/step - loss: 0.5575 - acc: 0.8051 - val_loss: 0.7402 - val_acc: 0.7248\n",
      "Epoch 139/500\n",
      "132/132 [==============================] - 44s 334ms/step - loss: 0.5643 - acc: 0.8037 - val_loss: 0.6504 - val_acc: 0.7661\n",
      "Epoch 140/500\n",
      "132/132 [==============================] - 44s 333ms/step - loss: 0.6037 - acc: 0.7885 - val_loss: 0.6980 - val_acc: 0.7385\n",
      "Epoch 141/500\n",
      "132/132 [==============================] - 44s 332ms/step - loss: 0.5756 - acc: 0.7969 - val_loss: 0.6959 - val_acc: 0.7752\n",
      "Epoch 142/500\n",
      "132/132 [==============================] - 44s 335ms/step - loss: 0.5773 - acc: 0.7965 - val_loss: 0.6523 - val_acc: 0.7569\n",
      "Epoch 143/500\n",
      "132/132 [==============================] - 44s 333ms/step - loss: 0.5458 - acc: 0.8096 - val_loss: 0.6598 - val_acc: 0.7661\n",
      "Epoch 144/500\n",
      "132/132 [==============================] - 44s 330ms/step - loss: 0.5549 - acc: 0.8052 - val_loss: 0.7100 - val_acc: 0.7523\n",
      "Epoch 145/500\n",
      "132/132 [==============================] - 44s 331ms/step - loss: 0.5644 - acc: 0.8051 - val_loss: 0.6559 - val_acc: 0.7339\n",
      "Epoch 146/500\n",
      "132/132 [==============================] - 44s 330ms/step - loss: 0.6029 - acc: 0.7864 - val_loss: 0.7332 - val_acc: 0.7110\n",
      "Epoch 147/500\n",
      "132/132 [==============================] - 44s 335ms/step - loss: 0.5751 - acc: 0.7998 - val_loss: 0.6879 - val_acc: 0.7477\n",
      "Epoch 148/500\n",
      "132/132 [==============================] - 44s 333ms/step - loss: 0.5456 - acc: 0.8101 - val_loss: 0.7053 - val_acc: 0.7477\n",
      "Epoch 149/500\n",
      "132/132 [==============================] - 44s 331ms/step - loss: 0.8309 - acc: 0.7013 - val_loss: 0.9836 - val_acc: 0.6284\n",
      "Epoch 150/500\n",
      "132/132 [==============================] - 44s 330ms/step - loss: 0.7248 - acc: 0.7346 - val_loss: 0.7522 - val_acc: 0.6835\n",
      "Epoch 151/500\n",
      "132/132 [==============================] - 44s 331ms/step - loss: 0.6872 - acc: 0.7528 - val_loss: 0.7450 - val_acc: 0.6972\n",
      "Epoch 152/500\n",
      "132/132 [==============================] - 44s 330ms/step - loss: 0.7948 - acc: 0.7083 - val_loss: 1.0157 - val_acc: 0.6101\n",
      "Epoch 153/500\n",
      "132/132 [==============================] - 44s 330ms/step - loss: 0.8632 - acc: 0.6764 - val_loss: 0.8884 - val_acc: 0.6651\n",
      "Epoch 154/500\n",
      "132/132 [==============================] - 44s 331ms/step - loss: 0.7294 - acc: 0.7403 - val_loss: 0.7586 - val_acc: 0.7018\n",
      "Epoch 155/500\n",
      "132/132 [==============================] - 44s 331ms/step - loss: 0.6301 - acc: 0.7773 - val_loss: 0.7110 - val_acc: 0.7477\n",
      "Epoch 156/500\n",
      "132/132 [==============================] - 44s 335ms/step - loss: 0.5980 - acc: 0.7891 - val_loss: 0.7443 - val_acc: 0.7431\n",
      "Epoch 157/500\n",
      "132/132 [==============================] - 44s 332ms/step - loss: 0.5945 - acc: 0.7923 - val_loss: 0.7214 - val_acc: 0.7569\n",
      "Epoch 158/500\n",
      "132/132 [==============================] - 44s 333ms/step - loss: 0.5927 - acc: 0.7910 - val_loss: 0.6712 - val_acc: 0.7431\n",
      "Epoch 159/500\n",
      "132/132 [==============================] - 45s 343ms/step - loss: 0.5751 - acc: 0.7982 - val_loss: 0.7027 - val_acc: 0.7477\n",
      "Epoch 160/500\n",
      "132/132 [==============================] - 48s 364ms/step - loss: 0.5626 - acc: 0.8026 - val_loss: 0.7012 - val_acc: 0.7477\n",
      "Epoch 161/500\n",
      "132/132 [==============================] - 46s 348ms/step - loss: 0.5456 - acc: 0.8119 - val_loss: 0.7212 - val_acc: 0.7569\n",
      "Epoch 162/500\n",
      "132/132 [==============================] - 46s 348ms/step - loss: 0.5309 - acc: 0.8152 - val_loss: 0.6639 - val_acc: 0.7431\n",
      "Epoch 163/500\n",
      "132/132 [==============================] - 46s 348ms/step - loss: 0.5350 - acc: 0.8103 - val_loss: 0.6486 - val_acc: 0.7385\n",
      "Epoch 164/500\n",
      "132/132 [==============================] - 46s 349ms/step - loss: 0.5610 - acc: 0.8032 - val_loss: 0.7189 - val_acc: 0.7385\n",
      "Epoch 165/500\n",
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      "Epoch 168/500\n",
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      "Epoch 169/500\n",
      "132/132 [==============================] - 46s 347ms/step - loss: 0.5339 - acc: 0.8142 - val_loss: 0.6519 - val_acc: 0.7752\n",
      "Epoch 170/500\n",
      "132/132 [==============================] - 46s 351ms/step - loss: 0.5342 - acc: 0.8139 - val_loss: 0.6183 - val_acc: 0.7523\n",
      "Epoch 171/500\n",
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      "Epoch 172/500\n",
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      "Epoch 174/500\n",
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      "Epoch 175/500\n",
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      "Epoch 176/500\n",
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      "Epoch 177/500\n",
      "132/132 [==============================] - 47s 353ms/step - loss: 0.5160 - acc: 0.8243 - val_loss: 0.7101 - val_acc: 0.7431\n",
      "Epoch 178/500\n",
      "132/132 [==============================] - 46s 350ms/step - loss: 0.5055 - acc: 0.8242 - val_loss: 0.6697 - val_acc: 0.7615\n",
      "Epoch 179/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 46s 348ms/step - loss: 0.5281 - acc: 0.8151 - val_loss: 0.6524 - val_acc: 0.7477\n",
      "Epoch 180/500\n",
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      "Epoch 181/500\n",
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      "Epoch 182/500\n",
      "132/132 [==============================] - 46s 347ms/step - loss: 0.5505 - acc: 0.8050 - val_loss: 0.6917 - val_acc: 0.7431\n",
      "Epoch 183/500\n",
      "132/132 [==============================] - 46s 352ms/step - loss: 0.5924 - acc: 0.7869 - val_loss: 0.6994 - val_acc: 0.7477\n",
      "Epoch 184/500\n",
      "132/132 [==============================] - 46s 348ms/step - loss: 0.5305 - acc: 0.8139 - val_loss: 0.6785 - val_acc: 0.7477\n",
      "Epoch 185/500\n",
      "132/132 [==============================] - 46s 350ms/step - loss: 0.5468 - acc: 0.8087 - val_loss: 0.6820 - val_acc: 0.7431\n",
      "Epoch 186/500\n",
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      "Epoch 187/500\n",
      "132/132 [==============================] - 46s 347ms/step - loss: 0.5813 - acc: 0.7916 - val_loss: 0.7292 - val_acc: 0.7385\n",
      "Epoch 188/500\n",
      "132/132 [==============================] - 46s 348ms/step - loss: 0.5830 - acc: 0.7885 - val_loss: 0.6489 - val_acc: 0.7294\n",
      "Epoch 189/500\n",
      "132/132 [==============================] - 46s 348ms/step - loss: 0.5315 - acc: 0.8193 - val_loss: 0.7176 - val_acc: 0.7615\n",
      "Epoch 190/500\n",
      "132/132 [==============================] - 46s 349ms/step - loss: 0.5918 - acc: 0.7921 - val_loss: 0.7220 - val_acc: 0.7294\n",
      "Epoch 191/500\n",
      "132/132 [==============================] - 46s 350ms/step - loss: 0.6037 - acc: 0.7794 - val_loss: 0.6882 - val_acc: 0.7339\n",
      "Epoch 192/500\n",
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      "Epoch 193/500\n",
      "132/132 [==============================] - 46s 348ms/step - loss: 0.5175 - acc: 0.8176 - val_loss: 0.6285 - val_acc: 0.7615\n",
      "Epoch 194/500\n",
      "132/132 [==============================] - 46s 349ms/step - loss: 0.5321 - acc: 0.8149 - val_loss: 0.6604 - val_acc: 0.7523\n",
      "Epoch 195/500\n",
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      "Epoch 196/500\n",
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      "Epoch 199/500\n",
      "132/132 [==============================] - 48s 365ms/step - loss: 0.5493 - acc: 0.8046 - val_loss: 0.6950 - val_acc: 0.7248\n",
      "Epoch 200/500\n",
      "132/132 [==============================] - 44s 332ms/step - loss: 0.5045 - acc: 0.8185 - val_loss: 0.7646 - val_acc: 0.7385\n",
      "Epoch 201/500\n",
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      "Epoch 202/500\n",
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      "Epoch 203/500\n",
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      "Epoch 206/500\n",
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      "Epoch 207/500\n",
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      "Epoch 210/500\n",
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      "Epoch 212/500\n",
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      "Epoch 228/500\n",
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      "Epoch 255/500\n",
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      "Epoch 256/500\n",
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      "Epoch 257/500\n",
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      "Epoch 258/500\n",
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      "Epoch 259/500\n",
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      "Epoch 260/500\n",
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      "Epoch 261/500\n",
      "132/132 [==============================] - 45s 340ms/step - loss: 0.6479 - acc: 0.7619 - val_loss: 0.7734 - val_acc: 0.6972\n",
      "Epoch 262/500\n",
      "132/132 [==============================] - 45s 343ms/step - loss: 0.6086 - acc: 0.7749 - val_loss: 0.7274 - val_acc: 0.7202\n",
      "Epoch 263/500\n",
      "132/132 [==============================] - 45s 344ms/step - loss: 0.6070 - acc: 0.7849 - val_loss: 0.7256 - val_acc: 0.7339\n",
      "Epoch 264/500\n",
      "132/132 [==============================] - 46s 346ms/step - loss: 0.6524 - acc: 0.7573 - val_loss: 0.9079 - val_acc: 0.6330\n",
      "Epoch 265/500\n",
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      "Epoch 266/500\n",
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      "Epoch 267/500\n",
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      "Epoch 268/500\n",
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      "Epoch 269/500\n",
      "132/132 [==============================] - 45s 340ms/step - loss: 0.5382 - acc: 0.8053 - val_loss: 0.7201 - val_acc: 0.7248\n",
      "Epoch 270/500\n",
      "132/132 [==============================] - 45s 340ms/step - loss: 0.5434 - acc: 0.7988 - val_loss: 0.7175 - val_acc: 0.7294\n",
      "Epoch 271/500\n",
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      "Epoch 272/500\n",
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      "Epoch 273/500\n",
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      "Epoch 274/500\n",
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      "Epoch 275/500\n",
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      "Epoch 276/500\n",
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      "Epoch 277/500\n",
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      "Epoch 278/500\n",
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      "Epoch 279/500\n",
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      "Epoch 280/500\n",
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      "Epoch 281/500\n",
      "132/132 [==============================] - 47s 353ms/step - loss: 0.4822 - acc: 0.8257 - val_loss: 0.6790 - val_acc: 0.7477\n",
      "Epoch 282/500\n",
      "132/132 [==============================] - 48s 360ms/step - loss: 0.5010 - acc: 0.8200 - val_loss: 0.7221 - val_acc: 0.7248\n",
      "Epoch 283/500\n",
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      "Epoch 284/500\n",
      "132/132 [==============================] - 46s 352ms/step - loss: 0.4717 - acc: 0.8266 - val_loss: 0.7202 - val_acc: 0.7661\n",
      "Epoch 285/500\n",
      "132/132 [==============================] - 47s 355ms/step - loss: 0.5104 - acc: 0.8208 - val_loss: 0.6358 - val_acc: 0.7798\n",
      "Epoch 286/500\n",
      "132/132 [==============================] - 46s 350ms/step - loss: 0.4719 - acc: 0.8295 - val_loss: 0.5878 - val_acc: 0.7890\n",
      "Epoch 287/500\n",
      "132/132 [==============================] - 51s 387ms/step - loss: 0.4379 - acc: 0.8473 - val_loss: 0.5851 - val_acc: 0.7706\n",
      "Epoch 288/500\n",
      "132/132 [==============================] - 45s 339ms/step - loss: 0.4342 - acc: 0.8454 - val_loss: 0.6647 - val_acc: 0.7569\n",
      "Epoch 289/500\n",
      "132/132 [==============================] - 45s 339ms/step - loss: 0.4544 - acc: 0.8386 - val_loss: 0.6493 - val_acc: 0.7706\n",
      "Epoch 290/500\n",
      "132/132 [==============================] - 45s 342ms/step - loss: 0.4787 - acc: 0.8280 - val_loss: 0.6459 - val_acc: 0.7752\n",
      "Epoch 291/500\n",
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      "Epoch 292/500\n",
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      "Epoch 293/500\n",
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      "Epoch 295/500\n",
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      "Epoch 296/500\n",
      "132/132 [==============================] - 45s 338ms/step - loss: 0.4595 - acc: 0.8372 - val_loss: 0.7627 - val_acc: 0.7248\n",
      "Epoch 297/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 45s 340ms/step - loss: 0.4754 - acc: 0.8356 - val_loss: 0.6961 - val_acc: 0.7477\n",
      "Epoch 298/500\n",
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      "Epoch 301/500\n",
      "132/132 [==============================] - 45s 339ms/step - loss: 0.4813 - acc: 0.8305 - val_loss: 0.6697 - val_acc: 0.7385\n",
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      "Epoch 303/500\n",
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      "Epoch 304/500\n",
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      "Epoch 307/500\n",
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      "Epoch 309/500\n",
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      "Epoch 312/500\n",
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      "Epoch 313/500\n",
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      "Epoch 314/500\n",
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      "Epoch 315/500\n",
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      "Epoch 316/500\n",
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      "Epoch 318/500\n",
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      "Epoch 319/500\n",
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      "Epoch 320/500\n",
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      "Epoch 321/500\n",
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      "Epoch 322/500\n",
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      "Epoch 325/500\n",
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      "Epoch 382/500\n",
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      "Epoch 383/500\n",
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      "Epoch 384/500\n",
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      "Epoch 387/500\n",
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      "Epoch 388/500\n",
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      "Epoch 391/500\n",
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      "Epoch 393/500\n",
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      "Epoch 394/500\n",
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      "Epoch 398/500\n",
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      "Epoch 399/500\n",
      "132/132 [==============================] - 44s 330ms/step - loss: 0.5728 - acc: 0.7870 - val_loss: 0.7314 - val_acc: 0.7248\n",
      "Epoch 400/500\n",
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      "Epoch 401/500\n",
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      "Epoch 402/500\n",
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      "Epoch 403/500\n",
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      "Epoch 404/500\n",
      "132/132 [==============================] - 43s 329ms/step - loss: 0.6012 - acc: 0.7801 - val_loss: 0.7526 - val_acc: 0.7248\n",
      "Epoch 405/500\n",
      "132/132 [==============================] - 43s 329ms/step - loss: 0.6793 - acc: 0.7490 - val_loss: 0.7683 - val_acc: 0.7064\n",
      "Epoch 406/500\n",
      "132/132 [==============================] - 44s 335ms/step - loss: 0.6825 - acc: 0.7451 - val_loss: 0.7993 - val_acc: 0.6972\n",
      "Epoch 407/500\n",
      "132/132 [==============================] - 43s 328ms/step - loss: 0.6293 - acc: 0.7676 - val_loss: 0.6917 - val_acc: 0.7477\n",
      "Epoch 408/500\n",
      "132/132 [==============================] - 43s 329ms/step - loss: 0.6086 - acc: 0.7714 - val_loss: 0.6744 - val_acc: 0.7523\n",
      "Epoch 409/500\n",
      "132/132 [==============================] - 43s 329ms/step - loss: 0.6338 - acc: 0.7635 - val_loss: 0.7736 - val_acc: 0.6972\n",
      "Epoch 410/500\n",
      "132/132 [==============================] - 43s 328ms/step - loss: 0.6675 - acc: 0.7617 - val_loss: 0.7432 - val_acc: 0.7202\n",
      "Epoch 411/500\n",
      "132/132 [==============================] - 43s 329ms/step - loss: 0.6130 - acc: 0.7769 - val_loss: 0.7125 - val_acc: 0.6927\n",
      "Epoch 412/500\n",
      "132/132 [==============================] - 43s 328ms/step - loss: 0.6263 - acc: 0.7773 - val_loss: 0.6587 - val_acc: 0.7569\n",
      "Epoch 413/500\n",
      "132/132 [==============================] - 44s 331ms/step - loss: 0.5557 - acc: 0.7965 - val_loss: 0.6914 - val_acc: 0.7523\n",
      "Epoch 414/500\n",
      "132/132 [==============================] - 43s 328ms/step - loss: 0.5593 - acc: 0.7951 - val_loss: 0.7099 - val_acc: 0.7706\n",
      "Epoch 415/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132/132 [==============================] - 43s 329ms/step - loss: 0.5380 - acc: 0.8039 - val_loss: 0.6668 - val_acc: 0.7752\n",
      "Epoch 416/500\n",
      "132/132 [==============================] - 43s 329ms/step - loss: 0.5497 - acc: 0.8008 - val_loss: 0.6681 - val_acc: 0.7523\n",
      "Epoch 417/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 0.5513 - acc: 0.7935 - val_loss: 0.6763 - val_acc: 0.7523\n",
      "Epoch 418/500\n",
      "132/132 [==============================] - 43s 329ms/step - loss: 0.6049 - acc: 0.7739 - val_loss: 0.7467 - val_acc: 0.7339\n",
      "Epoch 419/500\n",
      "132/132 [==============================] - 43s 329ms/step - loss: 0.5940 - acc: 0.7824 - val_loss: 0.8109 - val_acc: 0.7018\n",
      "Epoch 420/500\n",
      "132/132 [==============================] - 44s 335ms/step - loss: 0.6068 - acc: 0.7731 - val_loss: 0.6997 - val_acc: 0.7385\n",
      "Epoch 421/500\n",
      "132/132 [==============================] - 44s 334ms/step - loss: 0.6169 - acc: 0.7724 - val_loss: 0.7508 - val_acc: 0.7202\n",
      "Epoch 422/500\n",
      "132/132 [==============================] - 43s 329ms/step - loss: 0.6242 - acc: 0.7611 - val_loss: 0.7066 - val_acc: 0.7339\n",
      "Epoch 423/500\n",
      "132/132 [==============================] - 44s 332ms/step - loss: 0.5995 - acc: 0.7795 - val_loss: 0.7261 - val_acc: 0.7294\n",
      "Epoch 424/500\n",
      "132/132 [==============================] - 44s 330ms/step - loss: 0.5882 - acc: 0.7807 - val_loss: 0.7644 - val_acc: 0.7110\n",
      "Epoch 425/500\n",
      "132/132 [==============================] - 44s 331ms/step - loss: 0.6175 - acc: 0.7685 - val_loss: 0.6769 - val_acc: 0.7294\n",
      "Epoch 426/500\n",
      "132/132 [==============================] - 43s 328ms/step - loss: 0.5670 - acc: 0.7954 - val_loss: 0.6329 - val_acc: 0.7661\n",
      "Epoch 427/500\n",
      "132/132 [==============================] - 44s 334ms/step - loss: 0.6296 - acc: 0.7610 - val_loss: 0.7653 - val_acc: 0.7018\n",
      "Epoch 428/500\n",
      "132/132 [==============================] - 43s 328ms/step - loss: 0.6435 - acc: 0.7604 - val_loss: 0.7240 - val_acc: 0.7248\n",
      "Epoch 429/500\n",
      "132/132 [==============================] - 43s 329ms/step - loss: 0.6451 - acc: 0.7617 - val_loss: 0.7146 - val_acc: 0.7339\n",
      "Epoch 430/500\n",
      "132/132 [==============================] - 44s 332ms/step - loss: 0.6064 - acc: 0.7707 - val_loss: 0.6980 - val_acc: 0.7248\n",
      "Epoch 431/500\n",
      "132/132 [==============================] - 44s 331ms/step - loss: 0.6099 - acc: 0.7682 - val_loss: 0.7212 - val_acc: 0.7294\n",
      "Epoch 432/500\n",
      "132/132 [==============================] - 44s 333ms/step - loss: 0.5531 - acc: 0.7982 - val_loss: 0.6261 - val_acc: 0.7615\n",
      "Epoch 433/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 0.5606 - acc: 0.7958 - val_loss: 0.6753 - val_acc: 0.7523\n",
      "Epoch 434/500\n",
      "132/132 [==============================] - 44s 335ms/step - loss: 0.5548 - acc: 0.7935 - val_loss: 0.6418 - val_acc: 0.7706\n",
      "Epoch 435/500\n",
      "132/132 [==============================] - 44s 331ms/step - loss: 0.5389 - acc: 0.8020 - val_loss: 0.7433 - val_acc: 0.7339\n",
      "Epoch 436/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 0.5562 - acc: 0.7942 - val_loss: 0.7026 - val_acc: 0.7661\n",
      "Epoch 437/500\n",
      "132/132 [==============================] - 43s 329ms/step - loss: 0.5405 - acc: 0.8023 - val_loss: 0.7782 - val_acc: 0.7156\n",
      "Epoch 438/500\n",
      "132/132 [==============================] - 43s 328ms/step - loss: 0.5583 - acc: 0.7882 - val_loss: 0.7133 - val_acc: 0.7615\n",
      "Epoch 439/500\n",
      "132/132 [==============================] - 43s 328ms/step - loss: 0.6092 - acc: 0.7772 - val_loss: 0.7699 - val_acc: 0.7431\n",
      "Epoch 440/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 0.6073 - acc: 0.7809 - val_loss: 0.8975 - val_acc: 0.6927\n",
      "Epoch 441/500\n",
      "132/132 [==============================] - 44s 330ms/step - loss: 0.6035 - acc: 0.7754 - val_loss: 0.6718 - val_acc: 0.7385\n",
      "Epoch 442/500\n",
      "132/132 [==============================] - 43s 329ms/step - loss: 0.5526 - acc: 0.8001 - val_loss: 0.7230 - val_acc: 0.7385\n",
      "Epoch 443/500\n",
      "132/132 [==============================] - 43s 328ms/step - loss: 0.5625 - acc: 0.7945 - val_loss: 0.8586 - val_acc: 0.7156\n",
      "Epoch 444/500\n",
      "132/132 [==============================] - 43s 328ms/step - loss: 0.6238 - acc: 0.7675 - val_loss: 0.7429 - val_acc: 0.7339\n",
      "Epoch 445/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 0.5976 - acc: 0.7754 - val_loss: 0.7710 - val_acc: 0.7248\n",
      "Epoch 446/500\n",
      "132/132 [==============================] - 44s 331ms/step - loss: 0.5830 - acc: 0.7836 - val_loss: 0.6846 - val_acc: 0.7385\n",
      "Epoch 447/500\n",
      "132/132 [==============================] - 43s 329ms/step - loss: 0.6111 - acc: 0.7748 - val_loss: 0.6865 - val_acc: 0.7477\n",
      "Epoch 448/500\n",
      "132/132 [==============================] - 44s 335ms/step - loss: 0.6421 - acc: 0.7635 - val_loss: 0.7339 - val_acc: 0.7248\n",
      "Epoch 449/500\n",
      "132/132 [==============================] - 44s 330ms/step - loss: 0.6110 - acc: 0.7801 - val_loss: 0.7211 - val_acc: 0.7202\n",
      "Epoch 450/500\n",
      "132/132 [==============================] - 43s 328ms/step - loss: 0.5807 - acc: 0.7862 - val_loss: 0.6949 - val_acc: 0.7477\n",
      "Epoch 451/500\n",
      "132/132 [==============================] - 43s 328ms/step - loss: 0.6044 - acc: 0.7760 - val_loss: 0.7101 - val_acc: 0.7202\n",
      "Epoch 452/500\n",
      "132/132 [==============================] - 44s 331ms/step - loss: 0.6093 - acc: 0.7782 - val_loss: 0.7396 - val_acc: 0.7385\n",
      "Epoch 453/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 0.5743 - acc: 0.7903 - val_loss: 0.7426 - val_acc: 0.7569\n",
      "Epoch 454/500\n",
      "132/132 [==============================] - 43s 328ms/step - loss: 0.5917 - acc: 0.7803 - val_loss: 0.7869 - val_acc: 0.7339\n",
      "Epoch 455/500\n",
      "132/132 [==============================] - 43s 329ms/step - loss: 0.5853 - acc: 0.7883 - val_loss: 0.7838 - val_acc: 0.7431\n",
      "Epoch 456/500\n",
      "132/132 [==============================] - 43s 328ms/step - loss: 0.6230 - acc: 0.7738 - val_loss: 0.7464 - val_acc: 0.7202\n",
      "Epoch 457/500\n",
      "132/132 [==============================] - 43s 328ms/step - loss: 0.5781 - acc: 0.7849 - val_loss: 0.6273 - val_acc: 0.7569\n",
      "Epoch 458/500\n",
      "132/132 [==============================] - 43s 328ms/step - loss: 0.5596 - acc: 0.7925 - val_loss: 0.6067 - val_acc: 0.7798\n",
      "Epoch 459/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 0.5546 - acc: 0.7967 - val_loss: 0.6808 - val_acc: 0.7661\n",
      "Epoch 460/500\n",
      "132/132 [==============================] - 43s 328ms/step - loss: 0.5791 - acc: 0.7779 - val_loss: 0.6970 - val_acc: 0.7294\n",
      "Epoch 461/500\n",
      "132/132 [==============================] - 43s 328ms/step - loss: 0.5857 - acc: 0.7836 - val_loss: 0.7297 - val_acc: 0.7248\n",
      "Epoch 462/500\n",
      "132/132 [==============================] - 44s 336ms/step - loss: 0.5674 - acc: 0.7908 - val_loss: 0.7160 - val_acc: 0.7385\n",
      "Epoch 463/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 0.5416 - acc: 0.7966 - val_loss: 0.6955 - val_acc: 0.7523\n",
      "Epoch 464/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 0.5758 - acc: 0.7854 - val_loss: 0.6905 - val_acc: 0.7339\n",
      "Epoch 465/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 1.2264 - acc: 0.5328 - val_loss: 1.1161 - val_acc: 0.5321\n",
      "Epoch 466/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 1.1298 - acc: 0.5512 - val_loss: 1.1306 - val_acc: 0.5734\n",
      "Epoch 467/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 1.1191 - acc: 0.5482 - val_loss: 1.1462 - val_acc: 0.5092\n",
      "Epoch 468/500\n",
      "132/132 [==============================] - 43s 329ms/step - loss: 1.0753 - acc: 0.5816 - val_loss: 1.0545 - val_acc: 0.5688\n",
      "Epoch 469/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 0.9828 - acc: 0.6301 - val_loss: 0.9804 - val_acc: 0.6239\n",
      "Epoch 470/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 0.9555 - acc: 0.6288 - val_loss: 0.9911 - val_acc: 0.6147\n",
      "Epoch 471/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 0.9168 - acc: 0.6393 - val_loss: 0.9136 - val_acc: 0.6560\n",
      "Epoch 472/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 0.9481 - acc: 0.6296 - val_loss: 1.1124 - val_acc: 0.5413\n",
      "Epoch 473/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 1.0992 - acc: 0.5646 - val_loss: 1.1174 - val_acc: 0.5642\n",
      "Epoch 474/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 0.9936 - acc: 0.6241 - val_loss: 1.0212 - val_acc: 0.5826\n",
      "Epoch 475/500\n",
      "132/132 [==============================] - 43s 327ms/step - loss: 0.9524 - acc: 0.6311 - val_loss: 1.2036 - val_acc: 0.5963\n",
      "Epoch 476/500\n",
      "132/132 [==============================] - 44s 332ms/step - loss: 0.9502 - acc: 0.6397 - val_loss: 0.9984 - val_acc: 0.6147\n",
      "Epoch 477/500\n",
      "132/132 [==============================] - 44s 331ms/step - loss: 0.8701 - acc: 0.6673 - val_loss: 0.9643 - val_acc: 0.6376\n",
      "Epoch 478/500\n",
      "132/132 [==============================] - 44s 330ms/step - loss: 0.8278 - acc: 0.6733 - val_loss: 0.8738 - val_acc: 0.6284\n",
      "Epoch 479/500\n",
      "132/132 [==============================] - 43s 329ms/step - loss: 0.8177 - acc: 0.6800 - val_loss: 0.8950 - val_acc: 0.6376\n",
      "Epoch 480/500\n",
      "132/132 [==============================] - 44s 330ms/step - loss: 0.9256 - acc: 0.6494 - val_loss: 1.1360 - val_acc: 0.5183\n",
      "Epoch 481/500\n",
      "132/132 [==============================] - 44s 331ms/step - loss: 1.0523 - acc: 0.5892 - val_loss: 1.1415 - val_acc: 0.5826\n",
      "Epoch 482/500\n",
      "132/132 [==============================] - 44s 330ms/step - loss: 1.0149 - acc: 0.6117 - val_loss: 1.0320 - val_acc: 0.5596\n",
      "Epoch 483/500\n",
      "132/132 [==============================] - 44s 336ms/step - loss: 0.8769 - acc: 0.6544 - val_loss: 0.9545 - val_acc: 0.6101\n",
      "Epoch 484/500\n",
      "132/132 [==============================] - 46s 347ms/step - loss: 0.8978 - acc: 0.6541 - val_loss: 1.0379 - val_acc: 0.5596\n",
      "Epoch 485/500\n",
      "132/132 [==============================] - 46s 346ms/step - loss: 0.8819 - acc: 0.6585 - val_loss: 0.9615 - val_acc: 0.6193\n",
      "Epoch 486/500\n",
      "132/132 [==============================] - 45s 338ms/step - loss: 0.8318 - acc: 0.6802 - val_loss: 0.8219 - val_acc: 0.6651\n",
      "Epoch 487/500\n",
      "132/132 [==============================] - 44s 336ms/step - loss: 0.7765 - acc: 0.6960 - val_loss: 0.8912 - val_acc: 0.6835\n",
      "Epoch 488/500\n",
      "132/132 [==============================] - 44s 336ms/step - loss: 0.7568 - acc: 0.7023 - val_loss: 0.8427 - val_acc: 0.6651\n",
      "Epoch 489/500\n",
      "132/132 [==============================] - 44s 337ms/step - loss: 0.7544 - acc: 0.7079 - val_loss: 0.7962 - val_acc: 0.6881\n",
      "Epoch 490/500\n",
      "132/132 [==============================] - 45s 340ms/step - loss: 0.7508 - acc: 0.7032 - val_loss: 0.8604 - val_acc: 0.6514\n",
      "Epoch 491/500\n",
      "132/132 [==============================] - 44s 335ms/step - loss: 0.7237 - acc: 0.7124 - val_loss: 0.7528 - val_acc: 0.7064\n",
      "Epoch 492/500\n",
      "132/132 [==============================] - 44s 335ms/step - loss: 0.7540 - acc: 0.7021 - val_loss: 0.8571 - val_acc: 0.6743\n",
      "Epoch 493/500\n",
      "132/132 [==============================] - 44s 335ms/step - loss: 0.7508 - acc: 0.7118 - val_loss: 0.8592 - val_acc: 0.6330\n",
      "Epoch 494/500\n",
      "132/132 [==============================] - 45s 337ms/step - loss: 0.7409 - acc: 0.7135 - val_loss: 0.8600 - val_acc: 0.6697\n",
      "Epoch 495/500\n",
      "132/132 [==============================] - 44s 337ms/step - loss: 0.7426 - acc: 0.7128 - val_loss: 0.8541 - val_acc: 0.6697\n",
      "Epoch 496/500\n",
      "132/132 [==============================] - 45s 339ms/step - loss: 0.7542 - acc: 0.7122 - val_loss: 0.8150 - val_acc: 0.6560\n",
      "Epoch 497/500\n",
      "132/132 [==============================] - 45s 340ms/step - loss: 0.7240 - acc: 0.7155 - val_loss: 0.7585 - val_acc: 0.6835\n",
      "Epoch 498/500\n",
      "132/132 [==============================] - 45s 338ms/step - loss: 0.7421 - acc: 0.7162 - val_loss: 0.7433 - val_acc: 0.7110\n",
      "Epoch 499/500\n",
      "132/132 [==============================] - 45s 338ms/step - loss: 0.7011 - acc: 0.7310 - val_loss: 0.7754 - val_acc: 0.6651\n",
      "Epoch 500/500\n",
      "132/132 [==============================] - 45s 339ms/step - loss: 0.6899 - acc: 0.7232 - val_loss: 0.7346 - val_acc: 0.6972\n",
      "40/40 [==============================] - 5s 113ms/step\n",
      "Accuracy: 0.7277805992889792. F1: 0.7229566172510475\n"
     ]
    }
   ],
   "source": [
    "cnn(4, make_modelCom, epochs = 500, save_model = True, name = \"Com_norm\", file_type = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [load_model('models/model_2D_small_0.hdf5'), \n",
    "          load_model('models/model_2D_small_1.hdf5'), \n",
    "          load_model('models/model_2D_small_2.hdf5'), \n",
    "          load_model('models/model_2D_small_3.hdf5'), \n",
    "          load_model('models/model_1D_norm.hdf5'), \n",
    "          load_model('models/model_LSTM_norm.hdf5'), \n",
    "          load_model('models/model_Com_norm.hdf5')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40/40 [==============================] - 3s 71ms/step\n",
      "Accuracy: 0.5642458100558659. F1: 0.5559214378924485\n",
      "40/40 [==============================] - 3s 70ms/step\n",
      "Accuracy: 0.7953275774504824. F1: 0.7944290220418879\n",
      "40/40 [==============================] - 21s 517ms/step\n",
      "Accuracy: 0.8110716099542915. F1: 0.8099749109721136\n",
      "40/40 [==============================] - 22s 548ms/step\n",
      "Accuracy: 0.8689690198070086. F1: 0.865455499361151\n",
      "40/40 [==============================] - 7s 167ms/step\n",
      "Accuracy: 0.8969019807008634. F1: 0.8958705030987514\n",
      "40/40 [==============================] - 5s 128ms/step\n",
      "Accuracy: 0.8806500761808025. F1: 0.8800810128115094\n",
      "40/40 [==============================] - 4s 108ms/step\n",
      "Accuracy: 0.8364652107668867. F1: 0.8318890576416703\n"
     ]
    }
   ],
   "source": [
    "test_model(0, models[0])\n",
    "test_model(1, models[1])\n",
    "test_model(2, models[2])\n",
    "test_model(3, models[3])\n",
    "test_model(4, models[4], file_type = 1)\n",
    "test_model(4, models[5], file_type = 1)\n",
    "test_model(4, models[6], file_type = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40/40 [==============================] - 145s 4s/stepA \n",
      "40/40 [==============================] - 5s 115ms/step\n",
      "Accuracy: 0.9116302691721686. F1: 0.9107363152807513\n",
      "[[192   0   0   0  10   1   0   0  18]\n",
      " [  0 214   7   0   5   0   0   0   0]\n",
      " [  0   0 211   1   4   2   0   0   2]\n",
      " [  2   0   4 197   7   1   0   1   4]\n",
      " [ 29   3   2   2 159   5   0   0  12]\n",
      " [  0   0   2   0   4 220   0   0   0]\n",
      " [  0   0   0   0   0   0 222   0   0]\n",
      " [  0   0   0   1   0   0   0 213   0]\n",
      " [ 20   0   1   3  21   0   0   0 167]]\n",
      "[0.82758621 0.96613995 0.94407159 0.93809524 0.7535545  0.96703297\n",
      " 1.         0.9953271  0.80481928]\n"
     ]
    }
   ],
   "source": [
    "indices = [0, 1, 2, 3, 4, 4, 4]\n",
    "avg_models = models\n",
    "weights = [0, 0, 0, 0, .5, .5, 0]\n",
    "file_types = [0, 0, 0, 0, 1, 1, 1]\n",
    "test_model_avg(indices, avg_models, weights, file_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40/40 [==============================] - 3s 81ms/step\n",
      "40/40 [==============================] - 6s 162ms/step\n",
      "40/40 [==============================] - 4s 98ms/step\n",
      "Accuracy: 0.9233113255459624. F1: 0.9219258532903954\n"
     ]
    }
   ],
   "source": [
    "indices = [0, 1, 2, 3, 4, 4, 4]\n",
    "avg_models = models\n",
    "weights = [0, 0, 0, .3, .4, .3, 0]\n",
    "file_types = [0, 0, 0, 0, 1, 1, 1]\n",
    "test_model_avg(indices, avg_models, weights, file_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40/40 [==============================] - 49s 1s/step\n",
      "40/40 [==============================] - 8s 201ms/step\n",
      "40/40 [==============================] - 5s 125ms/step\n",
      "Accuracy: 0.9233113255459624. F1: 0.9219258532903954\n"
     ]
    }
   ],
   "source": [
    "indices = [0, 1, 2, 3, 4, 4, 4]\n",
    "avg_models = models\n",
    "weights = [0, 0, 0, .3, .4, .3, 0]\n",
    "file_types = [0, 0, 0, 0, 1, 1, 1]\n",
    "cm = test_model_avg(indices, avg_models, weights, file_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.84453782 0.97091723 0.95982143 0.93023256 0.7761194  0.97995546\n",
      " 1.         1.         0.83574879]\n"
     ]
    }
   ],
   "source": [
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[201   0   0   0   7   1   0   0  12]\n",
      " [  0 217   7   0   1   0   0   0   1]\n",
      " [  0   0 215   3   0   1   0   0   1]\n",
      " [  2   0   2 200   8   0   0   0   4]\n",
      " [ 35   3   0   6 156   1   0   0  11]\n",
      " [  0   0   2   1   3 220   0   0   0]\n",
      " [  0   0   0   0   0   0 222   0   0]\n",
      " [  0   0   0   0   0   0   0 214   0]\n",
      " [ 17   1   2   4  15   0   0   0 173]]\n"
     ]
    }
   ],
   "source": [
    "print(cm)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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