PneumoniaDetector_MultipleCNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import platform\n",
    "import os\n",
    "import imblearn\n",
    "from sklearn.utils import class_weight\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%aimport utils\n",
    "%autoreload 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### CACHE FILENAMES ###\n",
    "\n",
    "train_normal_cache = \"training_normal.pickle\"\n",
    "train_pneumonia_cache = \"training_pneumonia.pickle\"\n",
    "val_normal_cache = \"validation_normal.pickle\"\n",
    "val_pneumonia_cache = \"validation_pneumonia.pickle\"\n",
    "test_normal_cache = \"testing_normal.pickle\"\n",
    "test_pneumonia_cache = \"testing_pneumonia.pickle\"\n",
    "\n",
    "### DATA SETTINGS ###\n",
    "\n",
    "pdiv = \"/\" if platform.system() == \"Windows\" else \"\\\\\"\n",
    "\n",
    "path_train_normal = \".{}chest_xray{}train{}NORMAL{}\".format(pdiv, pdiv, pdiv, pdiv)\n",
    "path_train_pneumonia = \".{}chest_xray{}train{}PNEUMONIA{}\".format(pdiv, pdiv, pdiv, pdiv)\n",
    "path_val_normal = \".{}chest_xray{}val{}NORMAL{}\".format(pdiv, pdiv, pdiv, pdiv)\n",
    "path_val_pneumonia = \".{}chest_xray{}val{}PNEUMONIA{}\".format(pdiv, pdiv, pdiv, pdiv)\n",
    "path_test_normal = \".{}chest_xray{}test{}NORMAL{}\".format(pdiv, pdiv, pdiv, pdiv)\n",
    "path_test_pneumonia = \".{}chest_xray{}test{}PNEUMONIA{}\".format(pdiv, pdiv, pdiv, pdiv)\n",
    "\n",
    "### MODELS PATH ###\n",
    "\n",
    "path_models = \".{}CNNModels{}\".format(pdiv, pdiv)\n",
    "\n",
    "### LABELS ###\n",
    "\n",
    "normal_label = 0\n",
    "pneumonia_label = 1\n",
    "labels = sorted([normal_label, pneumonia_label])\n",
    "\n",
    "### IMAGE SETTINGS ###\n",
    "\n",
    "dimension = (64, 64, 3)\n",
    "resize_dim = dimension[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cache not found, loading images..\n",
      "Cache not found, loading images..\n"
     ]
    }
   ],
   "source": [
    "### LOAD TRAINING DATA AND LABELS ###\n",
    "\n",
    "data_train_normal, label_train_normal = utils.load_data(path_train_normal, train_normal_cache, normal_label)\n",
    "data_train_pneumonia, label_train_pneumonia = utils.load_data(path_train_pneumonia, train_pneumonia_cache, pneumonia_label)\n",
    "\n",
    "# Caching, disabled for now\n",
    "# utils.cache(data_train_normal, train_normal_cache)\n",
    "# utils.cache(data_train_pneumonia, train_pneumonia_cache)\n",
    "\n",
    "# Combine training images and labels\n",
    "data_train = np.concatenate((data_train_normal, data_train_pneumonia), axis=0)\n",
    "label_train = np.asarray(label_train_normal + label_train_pneumonia)\n",
    "\n",
    "# Shuffle\n",
    "rand_order = np.random.permutation(data_train.shape[0])\n",
    "data_train = data_train[rand_order]\n",
    "label_train = label_train[rand_order]\n",
    "\n",
    "del data_train_normal\n",
    "del data_train_pneumonia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "### IMAGE PROCESSING ###\n",
    "\n",
    "# Image cropping\n",
    "data_train = utils.images_crop_from_centre(data_train, percentage=0.9)\n",
    "\n",
    "# Image resizing\n",
    "data_train = utils.images_resize(data_train, resize_dim) \n",
    "\n",
    "# Image normalisation\n",
    "data_train = utils.images_normalize(data_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### HANDLE IMBALANCED DATA ###\n",
    "from collections import Counter\n",
    "from imblearn.over_sampling import SMOTE, RandomOverSampler, KMeansSMOTE, BorderlineSMOTE\n",
    "\n",
    "# Transform the dataset\n",
    "# oversample = SMOTE(sampling_strategy='auto', k_neighbors=8, random_state=100)\n",
    "# oversample = KMeansSMOTE(sampling_strategy='auto', k_neighbors=8, random_state=100)\n",
    "# oversample = RandomOverSampler(random_state=42)\n",
    "oversample = BorderlineSMOTE(random_state=100)\n",
    "data_train, label_train = oversample.fit_resample(data_train.reshape(data_train.shape[0], -1), label_train)\n",
    "data_train = data_train.reshape(data_train.shape[0], dimension[0], dimension[1], dimension[2])\n",
    "\n",
    "# Shuffle\n",
    "rand_order = np.random.permutation(data_train.shape[0])\n",
    "data_train = data_train[rand_order]\n",
    "label_train = label_train[rand_order]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_weights = class_weight.compute_class_weight('balanced', np.asarray(labels), label_train)\n",
    "class_weights = dict(enumerate(class_weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "### MODEL PARAMETERS ###\n",
    "loss_func = \"mean_squared_error\"\n",
    "epochs_num = 10000\n",
    "model_name = \"mutliCNN_smote\"\n",
    "checkpoint_name = \"_best_overall\"\n",
    "intermediary_name = model_name + checkpoint_name\n",
    "ext = \".hdf5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully loaded ./CNNModels/dense121_smote_71.hdf5\n",
      "Successfully loaded ./CNNModels/dense169_smote_79.hdf5\n",
      "Successfully loaded ./CNNModels/dense201_smote_80.hdf5\n",
      "Successfully loaded ./CNNModels/resnet101v2_smote_79.hdf5\n",
      "Successfully loaded ./CNNModels/resnet152v2_smote_75.hdf5\n",
      "Successfully loaded ./CNNModels/resnet50v2_smote_79.hdf5\n",
      "Successfully loaded ./CNNModels/vgg16_smote_82.hdf5\n",
      "Successfully loaded ./CNNModels/vgg19_smote_82.hdf5\n"
     ]
    }
   ],
   "source": [
    "### LOAD ALL MODELS ###\n",
    "from keras.models import load_model\n",
    "\n",
    "models = []\n",
    "(_, _, model_names) = next(os.walk(path_models))\n",
    "for name in model_names:\n",
    "    models.append(load_model(path_models + name))\n",
    "    print(\"Successfully loaded {}\".format(path_models + name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7750/7750 [==============================] - 136s 18ms/step\n",
      "7750/7750 [==============================] - 176s 23ms/step\n",
      "7750/7750 [==============================] - 236s 30ms/step\n",
      "7750/7750 [==============================] - 258s 33ms/step\n",
      "7750/7750 [==============================] - 368s 48ms/step\n",
      "7750/7750 [==============================] - 134s 17ms/step\n",
      "7750/7750 [==============================] - 253s 33ms/step\n",
      "7750/7750 [==============================] - 284s 37ms/step\n"
     ]
    }
   ],
   "source": [
    "### GET OUTPUT OF EACH MODEL ###\n",
    "compiled_output_train = np.asarray(list(map(lambda model: model.predict(data_train, verbose=1), models))).T\n",
    "compiled_output_train = compiled_output_train.reshape((data_train.shape[0], len(models), 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of layers is 4\n",
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 8, 1)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 8, 8)              16        \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 81\n",
      "Trainable params: 81\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "### MODEL CREATION ###\n",
    "from keras.models import Model, Sequential\n",
    "from keras.layers import Input, Dense, Dropout, Flatten\n",
    "from keras.utils import print_summary\n",
    "from keras.optimizers import SGD\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "\n",
    "model_input = Input(shape=(len(models), 1))\n",
    "# model = Dense(units=12, activation='relu')(model_input)\n",
    "# model = Flatten(model)\n",
    "model = Dense(units=8, activation='relu')(model_input)\n",
    "model = Flatten()(model)\n",
    "preds = Dense(units=1, activation='sigmoid')(model)\n",
    "model = Model(inputs=model_input, outputs=preds)\n",
    "\n",
    "# Set trainable layers\n",
    "for layer in model.layers:\n",
    "    layer.trainable = True\n",
    "\n",
    "# Callbacks\n",
    "# es_val_loss = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=4)\n",
    "# es_val_acc = EarlyStopping(monitor='val_accuracy', mode='max', verbose=1, patience=4)\n",
    "checkpointer = ModelCheckpoint(intermediary_name + ext, verbose=1, save_best_only=True)\n",
    "# callbacks_list = [es_val_loss, es_val_acc, checkpointer]\n",
    "callbacks_list = [checkpointer]\n",
    "\n",
    "# Define optimiser, loss function, and metrics to track\n",
    "opt = SGD(lr=0.01)\n",
    "model.compile(optimizer=opt, loss=loss_func, metrics=['accuracy'])\n",
    "\n",
    "print(\"Total number of layers is {}\".format(len(model.layers)))\n",
    "print_summary(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6975 samples, validate on 775 samples\n",
      "Epoch 1/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.2060 - accuracy: 0.6404 - val_loss: 0.1924 - val_accuracy: 0.6761\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.19235, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 2/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.1762 - accuracy: 0.7368 - val_loss: 0.1669 - val_accuracy: 0.7742\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.19235 to 0.16687, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 3/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.1527 - accuracy: 0.8173 - val_loss: 0.1452 - val_accuracy: 0.8529\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.16687 to 0.14518, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 4/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.1328 - accuracy: 0.8899 - val_loss: 0.1265 - val_accuracy: 0.9213\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.14518 to 0.12651, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 5/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.1158 - accuracy: 0.9351 - val_loss: 0.1106 - val_accuracy: 0.9497\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.12651 to 0.11063, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 6/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.1015 - accuracy: 0.9510 - val_loss: 0.0972 - val_accuracy: 0.9626\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.11063 to 0.09723, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 7/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0895 - accuracy: 0.9623 - val_loss: 0.0860 - val_accuracy: 0.9742\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.09723 to 0.08599, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 8/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0795 - accuracy: 0.9690 - val_loss: 0.0766 - val_accuracy: 0.9794\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.08599 to 0.07659, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 9/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0712 - accuracy: 0.9733 - val_loss: 0.0687 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.07659 to 0.06872, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 10/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0642 - accuracy: 0.9758 - val_loss: 0.0621 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.06872 to 0.06211, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 11/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0583 - accuracy: 0.9768 - val_loss: 0.0565 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.06211 to 0.05653, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 12/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0534 - accuracy: 0.9784 - val_loss: 0.0518 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.05653 to 0.05181, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 13/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0492 - accuracy: 0.9786 - val_loss: 0.0478 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.05181 to 0.04779, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 14/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0457 - accuracy: 0.9789 - val_loss: 0.0444 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.04779 to 0.04435, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 15/1000\n",
      "6975/6975 [==============================] - 0s 22us/step - loss: 0.0426 - accuracy: 0.9795 - val_loss: 0.0414 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.04435 to 0.04139, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 16/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0400 - accuracy: 0.9796 - val_loss: 0.0388 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.04139 to 0.03884, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 17/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0377 - accuracy: 0.9801 - val_loss: 0.0366 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.03884 to 0.03662, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 18/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0358 - accuracy: 0.9804 - val_loss: 0.0347 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.03662 to 0.03467, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 19/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0340 - accuracy: 0.9805 - val_loss: 0.0330 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.03467 to 0.03297, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 20/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0325 - accuracy: 0.9805 - val_loss: 0.0315 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.03297 to 0.03147, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 21/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0312 - accuracy: 0.9805 - val_loss: 0.0301 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00021: val_loss improved from 0.03147 to 0.03014, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 22/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0300 - accuracy: 0.9805 - val_loss: 0.0290 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00022: val_loss improved from 0.03014 to 0.02896, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 23/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0290 - accuracy: 0.9806 - val_loss: 0.0279 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00023: val_loss improved from 0.02896 to 0.02791, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 24/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0281 - accuracy: 0.9808 - val_loss: 0.0270 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00024: val_loss improved from 0.02791 to 0.02697, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 25/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0272 - accuracy: 0.9808 - val_loss: 0.0261 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.02697 to 0.02613, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 26/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0265 - accuracy: 0.9808 - val_loss: 0.0254 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00026: val_loss improved from 0.02613 to 0.02536, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 27/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0258 - accuracy: 0.9806 - val_loss: 0.0247 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00027: val_loss improved from 0.02536 to 0.02468, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 28/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0252 - accuracy: 0.9808 - val_loss: 0.0240 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00028: val_loss improved from 0.02468 to 0.02405, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 29/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0246 - accuracy: 0.9808 - val_loss: 0.0235 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00029: val_loss improved from 0.02405 to 0.02348, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 30/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0241 - accuracy: 0.9808 - val_loss: 0.0230 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00030: val_loss improved from 0.02348 to 0.02296, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 31/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0237 - accuracy: 0.9806 - val_loss: 0.0225 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00031: val_loss improved from 0.02296 to 0.02248, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 32/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0232 - accuracy: 0.9808 - val_loss: 0.0220 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00032: val_loss improved from 0.02248 to 0.02204, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 33/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0229 - accuracy: 0.9806 - val_loss: 0.0216 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00033: val_loss improved from 0.02204 to 0.02164, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 34/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0225 - accuracy: 0.9806 - val_loss: 0.0213 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00034: val_loss improved from 0.02164 to 0.02127, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 35/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0222 - accuracy: 0.9806 - val_loss: 0.0209 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00035: val_loss improved from 0.02127 to 0.02093, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 36/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0219 - accuracy: 0.9811 - val_loss: 0.0206 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00036: val_loss improved from 0.02093 to 0.02061, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 37/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0216 - accuracy: 0.9811 - val_loss: 0.0203 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00037: val_loss improved from 0.02061 to 0.02031, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 38/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0213 - accuracy: 0.9811 - val_loss: 0.0200 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00038: val_loss improved from 0.02031 to 0.02004, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 39/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0211 - accuracy: 0.9808 - val_loss: 0.0198 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00039: val_loss improved from 0.02004 to 0.01978, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 40/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0209 - accuracy: 0.9809 - val_loss: 0.0195 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00040: val_loss improved from 0.01978 to 0.01954, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 41/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0206 - accuracy: 0.9809 - val_loss: 0.0193 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00041: val_loss improved from 0.01954 to 0.01931, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 42/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0205 - accuracy: 0.9809 - val_loss: 0.0191 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00042: val_loss improved from 0.01931 to 0.01910, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 43/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0203 - accuracy: 0.9809 - val_loss: 0.0189 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00043: val_loss improved from 0.01910 to 0.01890, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 44/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0201 - accuracy: 0.9809 - val_loss: 0.0187 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00044: val_loss improved from 0.01890 to 0.01872, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 45/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0199 - accuracy: 0.9809 - val_loss: 0.0185 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00045: val_loss improved from 0.01872 to 0.01854, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 46/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0198 - accuracy: 0.9809 - val_loss: 0.0184 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00046: val_loss improved from 0.01854 to 0.01838, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 47/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0196 - accuracy: 0.9809 - val_loss: 0.0182 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00047: val_loss improved from 0.01838 to 0.01822, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 48/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0195 - accuracy: 0.9809 - val_loss: 0.0181 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00048: val_loss improved from 0.01822 to 0.01807, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 49/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0194 - accuracy: 0.9811 - val_loss: 0.0179 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00049: val_loss improved from 0.01807 to 0.01793, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 50/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0192 - accuracy: 0.9809 - val_loss: 0.0178 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00050: val_loss improved from 0.01793 to 0.01780, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 51/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0191 - accuracy: 0.9808 - val_loss: 0.0177 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00051: val_loss improved from 0.01780 to 0.01767, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 52/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0190 - accuracy: 0.9808 - val_loss: 0.0176 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00052: val_loss improved from 0.01767 to 0.01755, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 53/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0189 - accuracy: 0.9808 - val_loss: 0.0174 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00053: val_loss improved from 0.01755 to 0.01743, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 54/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0188 - accuracy: 0.9808 - val_loss: 0.0173 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00054: val_loss improved from 0.01743 to 0.01732, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 55/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0187 - accuracy: 0.9808 - val_loss: 0.0172 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00055: val_loss improved from 0.01732 to 0.01722, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 56/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0186 - accuracy: 0.9808 - val_loss: 0.0171 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00056: val_loss improved from 0.01722 to 0.01713, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 57/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0185 - accuracy: 0.9808 - val_loss: 0.0170 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00057: val_loss improved from 0.01713 to 0.01703, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 58/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0185 - accuracy: 0.9809 - val_loss: 0.0169 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00058: val_loss improved from 0.01703 to 0.01694, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 59/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0184 - accuracy: 0.9811 - val_loss: 0.0169 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00059: val_loss improved from 0.01694 to 0.01685, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 60/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0183 - accuracy: 0.9811 - val_loss: 0.0168 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00060: val_loss improved from 0.01685 to 0.01677, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 61/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0182 - accuracy: 0.9811 - val_loss: 0.0167 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00061: val_loss improved from 0.01677 to 0.01669, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 62/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0182 - accuracy: 0.9811 - val_loss: 0.0166 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00062: val_loss improved from 0.01669 to 0.01661, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 63/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0181 - accuracy: 0.9811 - val_loss: 0.0165 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00063: val_loss improved from 0.01661 to 0.01654, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 64/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0180 - accuracy: 0.9811 - val_loss: 0.0165 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00064: val_loss improved from 0.01654 to 0.01647, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 65/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0180 - accuracy: 0.9811 - val_loss: 0.0164 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00065: val_loss improved from 0.01647 to 0.01639, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 66/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0179 - accuracy: 0.9811 - val_loss: 0.0163 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00066: val_loss improved from 0.01639 to 0.01633, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 67/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0179 - accuracy: 0.9812 - val_loss: 0.0163 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00067: val_loss improved from 0.01633 to 0.01626, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 68/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0178 - accuracy: 0.9809 - val_loss: 0.0162 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00068: val_loss improved from 0.01626 to 0.01620, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 69/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0177 - accuracy: 0.9812 - val_loss: 0.0161 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00069: val_loss improved from 0.01620 to 0.01614, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 70/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0177 - accuracy: 0.9812 - val_loss: 0.0161 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00070: val_loss improved from 0.01614 to 0.01608, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 71/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0176 - accuracy: 0.9812 - val_loss: 0.0160 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00071: val_loss improved from 0.01608 to 0.01602, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 72/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0176 - accuracy: 0.9812 - val_loss: 0.0160 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00072: val_loss improved from 0.01602 to 0.01597, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 73/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0176 - accuracy: 0.9809 - val_loss: 0.0159 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00073: val_loss improved from 0.01597 to 0.01592, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 74/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0175 - accuracy: 0.9812 - val_loss: 0.0159 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00074: val_loss improved from 0.01592 to 0.01586, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 75/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0175 - accuracy: 0.9812 - val_loss: 0.0158 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00075: val_loss improved from 0.01586 to 0.01581, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 76/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0174 - accuracy: 0.9812 - val_loss: 0.0158 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00076: val_loss improved from 0.01581 to 0.01577, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 77/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0174 - accuracy: 0.9812 - val_loss: 0.0157 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00077: val_loss improved from 0.01577 to 0.01572, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 78/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0173 - accuracy: 0.9812 - val_loss: 0.0157 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00078: val_loss improved from 0.01572 to 0.01568, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 79/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0173 - accuracy: 0.9812 - val_loss: 0.0156 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00079: val_loss improved from 0.01568 to 0.01563, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 80/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0173 - accuracy: 0.9812 - val_loss: 0.0156 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00080: val_loss improved from 0.01563 to 0.01559, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 81/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0172 - accuracy: 0.9812 - val_loss: 0.0156 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00081: val_loss improved from 0.01559 to 0.01555, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 82/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0172 - accuracy: 0.9812 - val_loss: 0.0155 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00082: val_loss improved from 0.01555 to 0.01551, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 83/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0172 - accuracy: 0.9814 - val_loss: 0.0155 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00083: val_loss improved from 0.01551 to 0.01547, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 84/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0171 - accuracy: 0.9814 - val_loss: 0.0154 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00084: val_loss improved from 0.01547 to 0.01543, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 85/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0171 - accuracy: 0.9814 - val_loss: 0.0154 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00085: val_loss improved from 0.01543 to 0.01540, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 86/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0171 - accuracy: 0.9814 - val_loss: 0.0154 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00086: val_loss improved from 0.01540 to 0.01536, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 87/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0170 - accuracy: 0.9814 - val_loss: 0.0153 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00087: val_loss improved from 0.01536 to 0.01532, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 88/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0170 - accuracy: 0.9815 - val_loss: 0.0153 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00088: val_loss improved from 0.01532 to 0.01528, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 89/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0170 - accuracy: 0.9815 - val_loss: 0.0152 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00089: val_loss improved from 0.01528 to 0.01525, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 90/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0170 - accuracy: 0.9812 - val_loss: 0.0152 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00090: val_loss improved from 0.01525 to 0.01522, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 91/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0169 - accuracy: 0.9815 - val_loss: 0.0152 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00091: val_loss improved from 0.01522 to 0.01518, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 92/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0169 - accuracy: 0.9815 - val_loss: 0.0152 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00092: val_loss improved from 0.01518 to 0.01515, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 93/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0169 - accuracy: 0.9815 - val_loss: 0.0151 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00093: val_loss improved from 0.01515 to 0.01512, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 94/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0168 - accuracy: 0.9815 - val_loss: 0.0151 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00094: val_loss improved from 0.01512 to 0.01509, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 95/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0168 - accuracy: 0.9814 - val_loss: 0.0151 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00095: val_loss improved from 0.01509 to 0.01506, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 96/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0168 - accuracy: 0.9814 - val_loss: 0.0150 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00096: val_loss improved from 0.01506 to 0.01503, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 97/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0168 - accuracy: 0.9814 - val_loss: 0.0150 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00097: val_loss improved from 0.01503 to 0.01500, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 98/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0168 - accuracy: 0.9812 - val_loss: 0.0150 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00098: val_loss improved from 0.01500 to 0.01497, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 99/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0167 - accuracy: 0.9812 - val_loss: 0.0149 - val_accuracy: 0.9806\n",
      "\n",
      "Epoch 00099: val_loss improved from 0.01497 to 0.01494, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 100/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0167 - accuracy: 0.9812 - val_loss: 0.0149 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00100: val_loss improved from 0.01494 to 0.01492, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 101/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0167 - accuracy: 0.9812 - val_loss: 0.0149 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00101: val_loss improved from 0.01492 to 0.01489, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 102/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0167 - accuracy: 0.9812 - val_loss: 0.0149 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00102: val_loss improved from 0.01489 to 0.01486, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 103/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0166 - accuracy: 0.9812 - val_loss: 0.0148 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00103: val_loss improved from 0.01486 to 0.01484, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 104/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0166 - accuracy: 0.9811 - val_loss: 0.0148 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00104: val_loss improved from 0.01484 to 0.01481, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 105/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0166 - accuracy: 0.9811 - val_loss: 0.0148 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00105: val_loss improved from 0.01481 to 0.01479, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 106/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0166 - accuracy: 0.9811 - val_loss: 0.0148 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00106: val_loss improved from 0.01479 to 0.01476, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 107/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0166 - accuracy: 0.9811 - val_loss: 0.0147 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00107: val_loss improved from 0.01476 to 0.01474, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 108/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0166 - accuracy: 0.9812 - val_loss: 0.0147 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00108: val_loss improved from 0.01474 to 0.01472, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 109/1000\n",
      "6975/6975 [==============================] - 0s 25us/step - loss: 0.0165 - accuracy: 0.9811 - val_loss: 0.0147 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00109: val_loss improved from 0.01472 to 0.01469, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 110/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0165 - accuracy: 0.9811 - val_loss: 0.0147 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00110: val_loss improved from 0.01469 to 0.01467, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 111/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0165 - accuracy: 0.9811 - val_loss: 0.0147 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00111: val_loss improved from 0.01467 to 0.01465, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 112/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0165 - accuracy: 0.9811 - val_loss: 0.0146 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00112: val_loss improved from 0.01465 to 0.01463, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 113/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0165 - accuracy: 0.9811 - val_loss: 0.0146 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00113: val_loss improved from 0.01463 to 0.01461, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 114/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0165 - accuracy: 0.9812 - val_loss: 0.0146 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00114: val_loss improved from 0.01461 to 0.01459, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 115/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0164 - accuracy: 0.9812 - val_loss: 0.0146 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00115: val_loss improved from 0.01459 to 0.01457, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 116/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0164 - accuracy: 0.9812 - val_loss: 0.0145 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00116: val_loss improved from 0.01457 to 0.01454, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 117/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0164 - accuracy: 0.9812 - val_loss: 0.0145 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00117: val_loss improved from 0.01454 to 0.01452, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 118/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0164 - accuracy: 0.9812 - val_loss: 0.0145 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00118: val_loss improved from 0.01452 to 0.01450, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 119/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0164 - accuracy: 0.9814 - val_loss: 0.0145 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00119: val_loss improved from 0.01450 to 0.01448, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 120/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0164 - accuracy: 0.9814 - val_loss: 0.0145 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00120: val_loss improved from 0.01448 to 0.01446, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 121/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0163 - accuracy: 0.9814 - val_loss: 0.0144 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00121: val_loss improved from 0.01446 to 0.01444, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 122/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0163 - accuracy: 0.9814 - val_loss: 0.0144 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00122: val_loss improved from 0.01444 to 0.01443, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 123/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0163 - accuracy: 0.9814 - val_loss: 0.0144 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00123: val_loss improved from 0.01443 to 0.01441, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 124/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0163 - accuracy: 0.9814 - val_loss: 0.0144 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00124: val_loss improved from 0.01441 to 0.01439, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 125/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0163 - accuracy: 0.9814 - val_loss: 0.0144 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00125: val_loss improved from 0.01439 to 0.01437, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 126/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0163 - accuracy: 0.9814 - val_loss: 0.0144 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00126: val_loss improved from 0.01437 to 0.01435, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 127/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0163 - accuracy: 0.9814 - val_loss: 0.0143 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00127: val_loss improved from 0.01435 to 0.01434, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 128/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0162 - accuracy: 0.9814 - val_loss: 0.0143 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00128: val_loss improved from 0.01434 to 0.01432, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 129/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0162 - accuracy: 0.9814 - val_loss: 0.0143 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00129: val_loss improved from 0.01432 to 0.01430, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 130/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0162 - accuracy: 0.9815 - val_loss: 0.0143 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00130: val_loss improved from 0.01430 to 0.01429, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 131/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0162 - accuracy: 0.9815 - val_loss: 0.0143 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00131: val_loss improved from 0.01429 to 0.01427, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 132/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0162 - accuracy: 0.9815 - val_loss: 0.0143 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00132: val_loss improved from 0.01427 to 0.01425, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 133/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0162 - accuracy: 0.9815 - val_loss: 0.0142 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00133: val_loss improved from 0.01425 to 0.01424, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 134/1000\n",
      "6975/6975 [==============================] - 0s 23us/step - loss: 0.0162 - accuracy: 0.9815 - val_loss: 0.0142 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00134: val_loss improved from 0.01424 to 0.01422, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 135/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0162 - accuracy: 0.9814 - val_loss: 0.0142 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00135: val_loss improved from 0.01422 to 0.01420, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 136/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0162 - accuracy: 0.9815 - val_loss: 0.0142 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00136: val_loss improved from 0.01420 to 0.01419, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 137/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0161 - accuracy: 0.9815 - val_loss: 0.0142 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00137: val_loss improved from 0.01419 to 0.01417, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 138/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0161 - accuracy: 0.9815 - val_loss: 0.0142 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00138: val_loss improved from 0.01417 to 0.01416, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 139/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0161 - accuracy: 0.9814 - val_loss: 0.0141 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00139: val_loss improved from 0.01416 to 0.01414, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 140/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0161 - accuracy: 0.9814 - val_loss: 0.0141 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00140: val_loss improved from 0.01414 to 0.01413, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 141/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0161 - accuracy: 0.9814 - val_loss: 0.0141 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00141: val_loss improved from 0.01413 to 0.01411, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 142/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0161 - accuracy: 0.9812 - val_loss: 0.0141 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00142: val_loss improved from 0.01411 to 0.01410, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 143/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0161 - accuracy: 0.9814 - val_loss: 0.0141 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00143: val_loss improved from 0.01410 to 0.01408, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 144/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0161 - accuracy: 0.9814 - val_loss: 0.0141 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00144: val_loss improved from 0.01408 to 0.01407, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 145/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0161 - accuracy: 0.9812 - val_loss: 0.0141 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00145: val_loss improved from 0.01407 to 0.01405, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 146/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0160 - accuracy: 0.9815 - val_loss: 0.0140 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00146: val_loss improved from 0.01405 to 0.01404, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 147/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0160 - accuracy: 0.9815 - val_loss: 0.0140 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00147: val_loss improved from 0.01404 to 0.01402, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 148/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0160 - accuracy: 0.9815 - val_loss: 0.0140 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00148: val_loss improved from 0.01402 to 0.01401, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 149/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0160 - accuracy: 0.9814 - val_loss: 0.0140 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00149: val_loss improved from 0.01401 to 0.01400, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 150/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0160 - accuracy: 0.9814 - val_loss: 0.0140 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00150: val_loss improved from 0.01400 to 0.01398, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 151/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0160 - accuracy: 0.9815 - val_loss: 0.0140 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00151: val_loss improved from 0.01398 to 0.01397, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 152/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0160 - accuracy: 0.9815 - val_loss: 0.0140 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00152: val_loss improved from 0.01397 to 0.01395, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 153/1000\n",
      "6975/6975 [==============================] - 0s 43us/step - loss: 0.0160 - accuracy: 0.9814 - val_loss: 0.0139 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00153: val_loss improved from 0.01395 to 0.01394, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 154/1000\n",
      "6975/6975 [==============================] - 1s 92us/step - loss: 0.0160 - accuracy: 0.9814 - val_loss: 0.0139 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00154: val_loss improved from 0.01394 to 0.01393, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 155/1000\n",
      "6975/6975 [==============================] - 0s 68us/step - loss: 0.0160 - accuracy: 0.9814 - val_loss: 0.0139 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00155: val_loss improved from 0.01393 to 0.01392, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 156/1000\n",
      "6975/6975 [==============================] - 0s 48us/step - loss: 0.0160 - accuracy: 0.9814 - val_loss: 0.0139 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00156: val_loss improved from 0.01392 to 0.01390, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 157/1000\n",
      "6975/6975 [==============================] - 0s 43us/step - loss: 0.0159 - accuracy: 0.9814 - val_loss: 0.0139 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00157: val_loss improved from 0.01390 to 0.01389, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 158/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0159 - accuracy: 0.9814 - val_loss: 0.0139 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00158: val_loss improved from 0.01389 to 0.01388, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 159/1000\n",
      "6975/6975 [==============================] - 0s 39us/step - loss: 0.0159 - accuracy: 0.9814 - val_loss: 0.0139 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00159: val_loss improved from 0.01388 to 0.01387, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 160/1000\n",
      "6975/6975 [==============================] - 0s 35us/step - loss: 0.0159 - accuracy: 0.9814 - val_loss: 0.0139 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00160: val_loss improved from 0.01387 to 0.01385, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 161/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 38us/step - loss: 0.0159 - accuracy: 0.9812 - val_loss: 0.0138 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00161: val_loss improved from 0.01385 to 0.01384, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 162/1000\n",
      "6975/6975 [==============================] - 0s 35us/step - loss: 0.0159 - accuracy: 0.9814 - val_loss: 0.0138 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00162: val_loss improved from 0.01384 to 0.01383, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 163/1000\n",
      "6975/6975 [==============================] - 0s 38us/step - loss: 0.0159 - accuracy: 0.9814 - val_loss: 0.0138 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00163: val_loss improved from 0.01383 to 0.01382, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 164/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0159 - accuracy: 0.9812 - val_loss: 0.0138 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00164: val_loss improved from 0.01382 to 0.01381, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 165/1000\n",
      "6975/6975 [==============================] - 0s 35us/step - loss: 0.0159 - accuracy: 0.9812 - val_loss: 0.0138 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00165: val_loss improved from 0.01381 to 0.01380, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 166/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0159 - accuracy: 0.9812 - val_loss: 0.0138 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00166: val_loss improved from 0.01380 to 0.01378, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 167/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0159 - accuracy: 0.9812 - val_loss: 0.0138 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00167: val_loss improved from 0.01378 to 0.01377, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 168/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0159 - accuracy: 0.9812 - val_loss: 0.0138 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00168: val_loss improved from 0.01377 to 0.01376, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 169/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0159 - accuracy: 0.9814 - val_loss: 0.0138 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00169: val_loss improved from 0.01376 to 0.01375, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 170/1000\n",
      "6975/6975 [==============================] - 0s 37us/step - loss: 0.0158 - accuracy: 0.9814 - val_loss: 0.0137 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00170: val_loss improved from 0.01375 to 0.01374, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 171/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0158 - accuracy: 0.9814 - val_loss: 0.0137 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00171: val_loss improved from 0.01374 to 0.01373, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 172/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0158 - accuracy: 0.9814 - val_loss: 0.0137 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00172: val_loss improved from 0.01373 to 0.01372, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 173/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0158 - accuracy: 0.9814 - val_loss: 0.0137 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00173: val_loss improved from 0.01372 to 0.01371, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 174/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0158 - accuracy: 0.9814 - val_loss: 0.0137 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00174: val_loss improved from 0.01371 to 0.01369, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 175/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0158 - accuracy: 0.9814 - val_loss: 0.0137 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00175: val_loss improved from 0.01369 to 0.01368, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 176/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0158 - accuracy: 0.9814 - val_loss: 0.0137 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00176: val_loss improved from 0.01368 to 0.01367, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 177/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0158 - accuracy: 0.9814 - val_loss: 0.0137 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00177: val_loss improved from 0.01367 to 0.01366, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 178/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0158 - accuracy: 0.9814 - val_loss: 0.0137 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00178: val_loss improved from 0.01366 to 0.01365, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 179/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0158 - accuracy: 0.9814 - val_loss: 0.0136 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00179: val_loss improved from 0.01365 to 0.01364, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 180/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0158 - accuracy: 0.9814 - val_loss: 0.0136 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00180: val_loss improved from 0.01364 to 0.01363, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 181/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0158 - accuracy: 0.9814 - val_loss: 0.0136 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00181: val_loss improved from 0.01363 to 0.01362, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 182/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0158 - accuracy: 0.9814 - val_loss: 0.0136 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00182: val_loss improved from 0.01362 to 0.01361, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 183/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0158 - accuracy: 0.9814 - val_loss: 0.0136 - val_accuracy: 0.9819\n",
      "\n",
      "Epoch 00183: val_loss improved from 0.01361 to 0.01360, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 184/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0158 - accuracy: 0.9814 - val_loss: 0.0136 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00184: val_loss improved from 0.01360 to 0.01359, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 185/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0157 - accuracy: 0.9814 - val_loss: 0.0136 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00185: val_loss improved from 0.01359 to 0.01358, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 186/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0157 - accuracy: 0.9814 - val_loss: 0.0136 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00186: val_loss improved from 0.01358 to 0.01357, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 187/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0157 - accuracy: 0.9814 - val_loss: 0.0136 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00187: val_loss improved from 0.01357 to 0.01356, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 188/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0157 - accuracy: 0.9812 - val_loss: 0.0135 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00188: val_loss improved from 0.01356 to 0.01355, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 189/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0157 - accuracy: 0.9814 - val_loss: 0.0135 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00189: val_loss improved from 0.01355 to 0.01354, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 190/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0157 - accuracy: 0.9811 - val_loss: 0.0135 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00190: val_loss improved from 0.01354 to 0.01353, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 191/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0157 - accuracy: 0.9811 - val_loss: 0.0135 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00191: val_loss improved from 0.01353 to 0.01352, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 192/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0157 - accuracy: 0.9814 - val_loss: 0.0135 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00192: val_loss improved from 0.01352 to 0.01351, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 193/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0157 - accuracy: 0.9811 - val_loss: 0.0135 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00193: val_loss improved from 0.01351 to 0.01350, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 194/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0157 - accuracy: 0.9812 - val_loss: 0.0135 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00194: val_loss improved from 0.01350 to 0.01349, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 195/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0157 - accuracy: 0.9812 - val_loss: 0.0135 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00195: val_loss improved from 0.01349 to 0.01348, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 196/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0157 - accuracy: 0.9811 - val_loss: 0.0135 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00196: val_loss improved from 0.01348 to 0.01347, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 197/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0157 - accuracy: 0.9812 - val_loss: 0.0135 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00197: val_loss improved from 0.01347 to 0.01346, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 198/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0157 - accuracy: 0.9812 - val_loss: 0.0135 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00198: val_loss improved from 0.01346 to 0.01345, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 199/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0157 - accuracy: 0.9812 - val_loss: 0.0134 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00199: val_loss improved from 0.01345 to 0.01344, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 200/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0157 - accuracy: 0.9812 - val_loss: 0.0134 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00200: val_loss improved from 0.01344 to 0.01343, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 201/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0157 - accuracy: 0.9812 - val_loss: 0.0134 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00201: val_loss improved from 0.01343 to 0.01343, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 202/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0156 - accuracy: 0.9812 - val_loss: 0.0134 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00202: val_loss improved from 0.01343 to 0.01342, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 203/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0156 - accuracy: 0.9812 - val_loss: 0.0134 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00203: val_loss improved from 0.01342 to 0.01341, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 204/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0156 - accuracy: 0.9812 - val_loss: 0.0134 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00204: val_loss improved from 0.01341 to 0.01340, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 205/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0156 - accuracy: 0.9812 - val_loss: 0.0134 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00205: val_loss improved from 0.01340 to 0.01339, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 206/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0156 - accuracy: 0.9812 - val_loss: 0.0134 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00206: val_loss improved from 0.01339 to 0.01338, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 207/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0156 - accuracy: 0.9812 - val_loss: 0.0134 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00207: val_loss improved from 0.01338 to 0.01337, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 208/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0156 - accuracy: 0.9812 - val_loss: 0.0134 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00208: val_loss improved from 0.01337 to 0.01336, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 209/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0156 - accuracy: 0.9812 - val_loss: 0.0134 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00209: val_loss improved from 0.01336 to 0.01335, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 210/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0156 - accuracy: 0.9812 - val_loss: 0.0133 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00210: val_loss improved from 0.01335 to 0.01334, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 211/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0156 - accuracy: 0.9812 - val_loss: 0.0133 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00211: val_loss improved from 0.01334 to 0.01334, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 212/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0156 - accuracy: 0.9812 - val_loss: 0.0133 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00212: val_loss improved from 0.01334 to 0.01333, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 213/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0156 - accuracy: 0.9814 - val_loss: 0.0133 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00213: val_loss improved from 0.01333 to 0.01332, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 214/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0156 - accuracy: 0.9814 - val_loss: 0.0133 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00214: val_loss improved from 0.01332 to 0.01331, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 215/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0156 - accuracy: 0.9814 - val_loss: 0.0133 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00215: val_loss improved from 0.01331 to 0.01330, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 216/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0156 - accuracy: 0.9814 - val_loss: 0.0133 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00216: val_loss improved from 0.01330 to 0.01329, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 217/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0156 - accuracy: 0.9814 - val_loss: 0.0133 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00217: val_loss improved from 0.01329 to 0.01329, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 218/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0156 - accuracy: 0.9814 - val_loss: 0.0133 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00218: val_loss improved from 0.01329 to 0.01328, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 219/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0156 - accuracy: 0.9814 - val_loss: 0.0133 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00219: val_loss improved from 0.01328 to 0.01327, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 220/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0156 - accuracy: 0.9814 - val_loss: 0.0133 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00220: val_loss improved from 0.01327 to 0.01326, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 221/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0156 - accuracy: 0.9814 - val_loss: 0.0133 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00221: val_loss improved from 0.01326 to 0.01325, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 222/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0156 - accuracy: 0.9814 - val_loss: 0.0132 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00222: val_loss improved from 0.01325 to 0.01325, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 223/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0155 - accuracy: 0.9814 - val_loss: 0.0132 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00223: val_loss improved from 0.01325 to 0.01324, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 224/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0155 - accuracy: 0.9814 - val_loss: 0.0132 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00224: val_loss improved from 0.01324 to 0.01323, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 225/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0132 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00225: val_loss improved from 0.01323 to 0.01322, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 226/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0132 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00226: val_loss improved from 0.01322 to 0.01321, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 227/1000\n",
      "6975/6975 [==============================] - 0s 35us/step - loss: 0.0155 - accuracy: 0.9814 - val_loss: 0.0132 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00227: val_loss improved from 0.01321 to 0.01321, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 228/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0132 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00228: val_loss improved from 0.01321 to 0.01320, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 229/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0155 - accuracy: 0.9814 - val_loss: 0.0132 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00229: val_loss improved from 0.01320 to 0.01319, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 230/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0132 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00230: val_loss improved from 0.01319 to 0.01318, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 231/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0132 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00231: val_loss improved from 0.01318 to 0.01318, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 232/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0132 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00232: val_loss improved from 0.01318 to 0.01317, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 233/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0132 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00233: val_loss improved from 0.01317 to 0.01316, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 234/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0132 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00234: val_loss improved from 0.01316 to 0.01315, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 235/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0131 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00235: val_loss improved from 0.01315 to 0.01315, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 236/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0131 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00236: val_loss improved from 0.01315 to 0.01314, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 237/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0131 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00237: val_loss improved from 0.01314 to 0.01313, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 238/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0131 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00238: val_loss improved from 0.01313 to 0.01313, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 239/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0131 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00239: val_loss improved from 0.01313 to 0.01312, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 240/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0131 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00240: val_loss improved from 0.01312 to 0.01311, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 241/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0131 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00241: val_loss improved from 0.01311 to 0.01310, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 242/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0131 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00242: val_loss improved from 0.01310 to 0.01310, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 243/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0131 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00243: val_loss improved from 0.01310 to 0.01309, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 244/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0131 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00244: val_loss improved from 0.01309 to 0.01308, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 245/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0131 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00245: val_loss improved from 0.01308 to 0.01308, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 246/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0155 - accuracy: 0.9812 - val_loss: 0.0131 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00246: val_loss improved from 0.01308 to 0.01307, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 247/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0131 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00247: val_loss improved from 0.01307 to 0.01306, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 248/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0131 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00248: val_loss improved from 0.01306 to 0.01306, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 249/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0130 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00249: val_loss improved from 0.01306 to 0.01305, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 250/1000\n",
      "6975/6975 [==============================] - 0s 22us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0130 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00250: val_loss improved from 0.01305 to 0.01304, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 251/1000\n",
      "6975/6975 [==============================] - 0s 25us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0130 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00251: val_loss improved from 0.01304 to 0.01303, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 252/1000\n",
      "6975/6975 [==============================] - 0s 25us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0130 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00252: val_loss improved from 0.01303 to 0.01303, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 253/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0130 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00253: val_loss improved from 0.01303 to 0.01302, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 254/1000\n",
      "6975/6975 [==============================] - 0s 24us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0130 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00254: val_loss improved from 0.01302 to 0.01302, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 255/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0130 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00255: val_loss improved from 0.01302 to 0.01301, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 256/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0130 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00256: val_loss improved from 0.01301 to 0.01300, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 257/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 25us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0130 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00257: val_loss improved from 0.01300 to 0.01300, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 258/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0130 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00258: val_loss improved from 0.01300 to 0.01299, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 259/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0130 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00259: val_loss improved from 0.01299 to 0.01298, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 260/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0130 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00260: val_loss improved from 0.01298 to 0.01298, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 261/1000\n",
      "6975/6975 [==============================] - 0s 25us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0130 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00261: val_loss improved from 0.01298 to 0.01297, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 262/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0130 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00262: val_loss improved from 0.01297 to 0.01296, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 263/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0130 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00263: val_loss improved from 0.01296 to 0.01296, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 264/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0130 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00264: val_loss improved from 0.01296 to 0.01295, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 265/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0129 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00265: val_loss improved from 0.01295 to 0.01295, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 266/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0129 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00266: val_loss improved from 0.01295 to 0.01294, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 267/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0129 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00267: val_loss improved from 0.01294 to 0.01293, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 268/1000\n",
      "6975/6975 [==============================] - 0s 25us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0129 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00268: val_loss improved from 0.01293 to 0.01293, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 269/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0129 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00269: val_loss improved from 0.01293 to 0.01292, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 270/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0129 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00270: val_loss improved from 0.01292 to 0.01292, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 271/1000\n",
      "6975/6975 [==============================] - 0s 25us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0129 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00271: val_loss improved from 0.01292 to 0.01291, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 272/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0129 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00272: val_loss improved from 0.01291 to 0.01290, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 273/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0129 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00273: val_loss improved from 0.01290 to 0.01290, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 274/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0129 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00274: val_loss improved from 0.01290 to 0.01289, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 275/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0154 - accuracy: 0.9812 - val_loss: 0.0129 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00275: val_loss improved from 0.01289 to 0.01289, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 276/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0154 - accuracy: 0.9811 - val_loss: 0.0129 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00276: val_loss improved from 0.01289 to 0.01288, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 277/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0129 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00277: val_loss improved from 0.01288 to 0.01288, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 278/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0129 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00278: val_loss improved from 0.01288 to 0.01287, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 279/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0129 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00279: val_loss improved from 0.01287 to 0.01287, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 280/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0129 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00280: val_loss improved from 0.01287 to 0.01286, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 281/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0129 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00281: val_loss improved from 0.01286 to 0.01285, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 282/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00282: val_loss improved from 0.01285 to 0.01285, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 283/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00283: val_loss improved from 0.01285 to 0.01284, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 284/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00284: val_loss improved from 0.01284 to 0.01284, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 285/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00285: val_loss improved from 0.01284 to 0.01283, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 286/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00286: val_loss improved from 0.01283 to 0.01283, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 287/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0153 - accuracy: 0.9811 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00287: val_loss improved from 0.01283 to 0.01282, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 288/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00288: val_loss improved from 0.01282 to 0.01282, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 289/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0153 - accuracy: 0.9811 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00289: val_loss improved from 0.01282 to 0.01281, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 290/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0153 - accuracy: 0.9811 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00290: val_loss improved from 0.01281 to 0.01281, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 291/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0153 - accuracy: 0.9811 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00291: val_loss improved from 0.01281 to 0.01280, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 292/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0153 - accuracy: 0.9811 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00292: val_loss improved from 0.01280 to 0.01280, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 293/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00293: val_loss improved from 0.01280 to 0.01279, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 294/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0153 - accuracy: 0.9811 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00294: val_loss improved from 0.01279 to 0.01279, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 295/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00295: val_loss improved from 0.01279 to 0.01278, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 296/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00296: val_loss improved from 0.01278 to 0.01278, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 297/1000\n",
      "6975/6975 [==============================] - 0s 25us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00297: val_loss improved from 0.01278 to 0.01277, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 298/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00298: val_loss improved from 0.01277 to 0.01277, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 299/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00299: val_loss improved from 0.01277 to 0.01276, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 300/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0153 - accuracy: 0.9811 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00300: val_loss improved from 0.01276 to 0.01276, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 301/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0128 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00301: val_loss improved from 0.01276 to 0.01275, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 302/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00302: val_loss improved from 0.01275 to 0.01275, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 303/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00303: val_loss improved from 0.01275 to 0.01274, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 304/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00304: val_loss improved from 0.01274 to 0.01274, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 305/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00305: val_loss improved from 0.01274 to 0.01273, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 306/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0153 - accuracy: 0.9811 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00306: val_loss improved from 0.01273 to 0.01273, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 307/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00307: val_loss improved from 0.01273 to 0.01272, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 308/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00308: val_loss improved from 0.01272 to 0.01272, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 309/1000\n",
      "6975/6975 [==============================] - 0s 25us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00309: val_loss improved from 0.01272 to 0.01271, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 310/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00310: val_loss improved from 0.01271 to 0.01271, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 311/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00311: val_loss improved from 0.01271 to 0.01270, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 312/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0153 - accuracy: 0.9814 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00312: val_loss improved from 0.01270 to 0.01270, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 313/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0153 - accuracy: 0.9812 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00313: val_loss improved from 0.01270 to 0.01269, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 314/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0152 - accuracy: 0.9814 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00314: val_loss improved from 0.01269 to 0.01269, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 315/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0152 - accuracy: 0.9812 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00315: val_loss improved from 0.01269 to 0.01268, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 316/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0152 - accuracy: 0.9814 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00316: val_loss improved from 0.01268 to 0.01268, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 317/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0152 - accuracy: 0.9814 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00317: val_loss improved from 0.01268 to 0.01267, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 318/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0152 - accuracy: 0.9812 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00318: val_loss improved from 0.01267 to 0.01267, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 319/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0152 - accuracy: 0.9812 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00319: val_loss improved from 0.01267 to 0.01266, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 320/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0152 - accuracy: 0.9814 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00320: val_loss improved from 0.01266 to 0.01266, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 321/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0152 - accuracy: 0.9814 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00321: val_loss improved from 0.01266 to 0.01266, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 322/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0152 - accuracy: 0.9814 - val_loss: 0.0127 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00322: val_loss improved from 0.01266 to 0.01265, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 323/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0152 - accuracy: 0.9812 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00323: val_loss improved from 0.01265 to 0.01265, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 324/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0152 - accuracy: 0.9814 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00324: val_loss improved from 0.01265 to 0.01264, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 325/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0152 - accuracy: 0.9814 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00325: val_loss improved from 0.01264 to 0.01264, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 326/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0152 - accuracy: 0.9814 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00326: val_loss improved from 0.01264 to 0.01263, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 327/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0152 - accuracy: 0.9814 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00327: val_loss improved from 0.01263 to 0.01263, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 328/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0152 - accuracy: 0.9814 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00328: val_loss improved from 0.01263 to 0.01263, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 329/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0152 - accuracy: 0.9814 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00329: val_loss improved from 0.01263 to 0.01262, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 330/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0152 - accuracy: 0.9814 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00330: val_loss improved from 0.01262 to 0.01262, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 331/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00331: val_loss improved from 0.01262 to 0.01261, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 332/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0152 - accuracy: 0.9814 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00332: val_loss improved from 0.01261 to 0.01261, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 333/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0152 - accuracy: 0.9814 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00333: val_loss improved from 0.01261 to 0.01260, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 334/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0152 - accuracy: 0.9814 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00334: val_loss improved from 0.01260 to 0.01260, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 335/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00335: val_loss improved from 0.01260 to 0.01260, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 336/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0152 - accuracy: 0.9814 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00336: val_loss improved from 0.01260 to 0.01259, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 337/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0152 - accuracy: 0.9814 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00337: val_loss improved from 0.01259 to 0.01259, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 338/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00338: val_loss improved from 0.01259 to 0.01258, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 339/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00339: val_loss improved from 0.01258 to 0.01258, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 340/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00340: val_loss improved from 0.01258 to 0.01258, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 341/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00341: val_loss improved from 0.01258 to 0.01257, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 342/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00342: val_loss improved from 0.01257 to 0.01257, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 343/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0126 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00343: val_loss improved from 0.01257 to 0.01256, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 344/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0126 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00344: val_loss improved from 0.01256 to 0.01256, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 345/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0126 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00345: val_loss improved from 0.01256 to 0.01256, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 346/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0126 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00346: val_loss improved from 0.01256 to 0.01255, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 347/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00347: val_loss improved from 0.01255 to 0.01255, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 348/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00348: val_loss improved from 0.01255 to 0.01254, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 349/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00349: val_loss improved from 0.01254 to 0.01254, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 350/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00350: val_loss improved from 0.01254 to 0.01254, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 351/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00351: val_loss improved from 0.01254 to 0.01253, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 352/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00352: val_loss improved from 0.01253 to 0.01253, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 353/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00353: val_loss improved from 0.01253 to 0.01252, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 354/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00354: val_loss improved from 0.01252 to 0.01252, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 355/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00355: val_loss improved from 0.01252 to 0.01252, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 356/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00356: val_loss improved from 0.01252 to 0.01251, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 357/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00357: val_loss improved from 0.01251 to 0.01251, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 358/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00358: val_loss improved from 0.01251 to 0.01250, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 359/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00359: val_loss improved from 0.01250 to 0.01250, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 360/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0152 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00360: val_loss improved from 0.01250 to 0.01250, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 361/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0151 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00361: val_loss improved from 0.01250 to 0.01249, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 362/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0151 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00362: val_loss improved from 0.01249 to 0.01249, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 363/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0151 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00363: val_loss improved from 0.01249 to 0.01249, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 364/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0151 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00364: val_loss improved from 0.01249 to 0.01248, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 365/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0151 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00365: val_loss improved from 0.01248 to 0.01248, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 366/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0151 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00366: val_loss improved from 0.01248 to 0.01248, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 367/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00367: val_loss improved from 0.01248 to 0.01247, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 368/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00368: val_loss improved from 0.01247 to 0.01247, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 369/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0151 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00369: val_loss improved from 0.01247 to 0.01246, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 370/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0151 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00370: val_loss improved from 0.01246 to 0.01246, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 371/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0151 - accuracy: 0.9815 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00371: val_loss improved from 0.01246 to 0.01246, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 372/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0125 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00372: val_loss improved from 0.01246 to 0.01245, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 373/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00373: val_loss improved from 0.01245 to 0.01245, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 374/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0151 - accuracy: 0.9815 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00374: val_loss improved from 0.01245 to 0.01245, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 375/1000\n",
      "6975/6975 [==============================] - 0s 31us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00375: val_loss improved from 0.01245 to 0.01244, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 376/1000\n",
      "6975/6975 [==============================] - 0s 34us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00376: val_loss improved from 0.01244 to 0.01244, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 377/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00377: val_loss improved from 0.01244 to 0.01243, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 378/1000\n",
      "6975/6975 [==============================] - 0s 30us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00378: val_loss improved from 0.01243 to 0.01243, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 379/1000\n",
      "6975/6975 [==============================] - 0s 32us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00379: val_loss improved from 0.01243 to 0.01243, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 380/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00380: val_loss improved from 0.01243 to 0.01242, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 381/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00381: val_loss improved from 0.01242 to 0.01242, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 382/1000\n",
      "6975/6975 [==============================] - 0s 33us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00382: val_loss improved from 0.01242 to 0.01242, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 383/1000\n",
      "6975/6975 [==============================] - 0s 25us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00383: val_loss improved from 0.01242 to 0.01241, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 384/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00384: val_loss improved from 0.01241 to 0.01241, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 385/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 24us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00385: val_loss improved from 0.01241 to 0.01241, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 386/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00386: val_loss improved from 0.01241 to 0.01240, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 387/1000\n",
      "6975/6975 [==============================] - 0s 24us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00387: val_loss improved from 0.01240 to 0.01240, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 388/1000\n",
      "6975/6975 [==============================] - 0s 24us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00388: val_loss improved from 0.01240 to 0.01240, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 389/1000\n",
      "6975/6975 [==============================] - 0s 22us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00389: val_loss improved from 0.01240 to 0.01239, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 390/1000\n",
      "6975/6975 [==============================] - 0s 22us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00390: val_loss improved from 0.01239 to 0.01239, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 391/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00391: val_loss improved from 0.01239 to 0.01239, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 392/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00392: val_loss improved from 0.01239 to 0.01238, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 393/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00393: val_loss improved from 0.01238 to 0.01238, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 394/1000\n",
      "6975/6975 [==============================] - 0s 23us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00394: val_loss improved from 0.01238 to 0.01238, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 395/1000\n",
      "6975/6975 [==============================] - 0s 24us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00395: val_loss improved from 0.01238 to 0.01237, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 396/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00396: val_loss improved from 0.01237 to 0.01237, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 397/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00397: val_loss improved from 0.01237 to 0.01237, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 398/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00398: val_loss improved from 0.01237 to 0.01236, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 399/1000\n",
      "6975/6975 [==============================] - 0s 23us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00399: val_loss improved from 0.01236 to 0.01236, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 400/1000\n",
      "6975/6975 [==============================] - 0s 23us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00400: val_loss improved from 0.01236 to 0.01236, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 401/1000\n",
      "6975/6975 [==============================] - 0s 23us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00401: val_loss improved from 0.01236 to 0.01235, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 402/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0124 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00402: val_loss improved from 0.01235 to 0.01235, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 403/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00403: val_loss improved from 0.01235 to 0.01235, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 404/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00404: val_loss improved from 0.01235 to 0.01234, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 405/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00405: val_loss improved from 0.01234 to 0.01234, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 406/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00406: val_loss improved from 0.01234 to 0.01234, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 407/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00407: val_loss improved from 0.01234 to 0.01233, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 408/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00408: val_loss improved from 0.01233 to 0.01233, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 409/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00409: val_loss improved from 0.01233 to 0.01233, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 410/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00410: val_loss improved from 0.01233 to 0.01232, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 411/1000\n",
      "6975/6975 [==============================] - 0s 23us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00411: val_loss improved from 0.01232 to 0.01232, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 412/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00412: val_loss improved from 0.01232 to 0.01232, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 413/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00413: val_loss improved from 0.01232 to 0.01231, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 414/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00414: val_loss improved from 0.01231 to 0.01231, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 415/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00415: val_loss improved from 0.01231 to 0.01231, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 416/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00416: val_loss improved from 0.01231 to 0.01230, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 417/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00417: val_loss improved from 0.01230 to 0.01230, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 418/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00418: val_loss improved from 0.01230 to 0.01230, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 419/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00419: val_loss improved from 0.01230 to 0.01230, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 420/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0151 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00420: val_loss improved from 0.01230 to 0.01229, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 421/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00421: val_loss improved from 0.01229 to 0.01229, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 422/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00422: val_loss improved from 0.01229 to 0.01229, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 423/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00423: val_loss improved from 0.01229 to 0.01228, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 424/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00424: val_loss improved from 0.01228 to 0.01228, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 425/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00425: val_loss improved from 0.01228 to 0.01228, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 426/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00426: val_loss improved from 0.01228 to 0.01227, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 427/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00427: val_loss improved from 0.01227 to 0.01227, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 428/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00428: val_loss improved from 0.01227 to 0.01227, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 429/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00429: val_loss improved from 0.01227 to 0.01227, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 430/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00430: val_loss improved from 0.01227 to 0.01226, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 431/1000\n",
      "6975/6975 [==============================] - 0s 22us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00431: val_loss improved from 0.01226 to 0.01226, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 432/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00432: val_loss improved from 0.01226 to 0.01226, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 433/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00433: val_loss improved from 0.01226 to 0.01225, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 434/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0123 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00434: val_loss improved from 0.01225 to 0.01225, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 435/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00435: val_loss improved from 0.01225 to 0.01225, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 436/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00436: val_loss improved from 0.01225 to 0.01225, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 437/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00437: val_loss improved from 0.01225 to 0.01224, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 438/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00438: val_loss improved from 0.01224 to 0.01224, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 439/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00439: val_loss improved from 0.01224 to 0.01224, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 440/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00440: val_loss improved from 0.01224 to 0.01223, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 441/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00441: val_loss improved from 0.01223 to 0.01223, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 442/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00442: val_loss improved from 0.01223 to 0.01223, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 443/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00443: val_loss improved from 0.01223 to 0.01223, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 444/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00444: val_loss improved from 0.01223 to 0.01222, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 445/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00445: val_loss improved from 0.01222 to 0.01222, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 446/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00446: val_loss improved from 0.01222 to 0.01222, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 447/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00447: val_loss improved from 0.01222 to 0.01222, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 448/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00448: val_loss improved from 0.01222 to 0.01221, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 449/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 22us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00449: val_loss improved from 0.01221 to 0.01221, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 450/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00450: val_loss improved from 0.01221 to 0.01221, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 451/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00451: val_loss improved from 0.01221 to 0.01220, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 452/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00452: val_loss improved from 0.01220 to 0.01220, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 453/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00453: val_loss improved from 0.01220 to 0.01220, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 454/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00454: val_loss improved from 0.01220 to 0.01220, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 455/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00455: val_loss improved from 0.01220 to 0.01219, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 456/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00456: val_loss improved from 0.01219 to 0.01219, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 457/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00457: val_loss improved from 0.01219 to 0.01219, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 458/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00458: val_loss improved from 0.01219 to 0.01219, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 459/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00459: val_loss improved from 0.01219 to 0.01218, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 460/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00460: val_loss improved from 0.01218 to 0.01218, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 461/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00461: val_loss improved from 0.01218 to 0.01218, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 462/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00462: val_loss improved from 0.01218 to 0.01218, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 463/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00463: val_loss improved from 0.01218 to 0.01217, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 464/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00464: val_loss improved from 0.01217 to 0.01217, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 465/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00465: val_loss improved from 0.01217 to 0.01217, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 466/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00466: val_loss improved from 0.01217 to 0.01217, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 467/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00467: val_loss improved from 0.01217 to 0.01216, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 468/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00468: val_loss improved from 0.01216 to 0.01216, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 469/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00469: val_loss improved from 0.01216 to 0.01216, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 470/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0150 - accuracy: 0.9814 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00470: val_loss improved from 0.01216 to 0.01216, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 471/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00471: val_loss improved from 0.01216 to 0.01215, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 472/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0122 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00472: val_loss improved from 0.01215 to 0.01215, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 473/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00473: val_loss improved from 0.01215 to 0.01215, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 474/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00474: val_loss improved from 0.01215 to 0.01214, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 475/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00475: val_loss improved from 0.01214 to 0.01214, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 476/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00476: val_loss improved from 0.01214 to 0.01214, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 477/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00477: val_loss improved from 0.01214 to 0.01214, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 478/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00478: val_loss improved from 0.01214 to 0.01214, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 479/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00479: val_loss improved from 0.01214 to 0.01213, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 480/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00480: val_loss improved from 0.01213 to 0.01213, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 481/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00481: val_loss improved from 0.01213 to 0.01213, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 482/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00482: val_loss improved from 0.01213 to 0.01213, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 483/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00483: val_loss improved from 0.01213 to 0.01212, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 484/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00484: val_loss improved from 0.01212 to 0.01212, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 485/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00485: val_loss improved from 0.01212 to 0.01212, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 486/1000\n",
      "6975/6975 [==============================] - 0s 24us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00486: val_loss improved from 0.01212 to 0.01212, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 487/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00487: val_loss improved from 0.01212 to 0.01212, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 488/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00488: val_loss improved from 0.01212 to 0.01211, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 489/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00489: val_loss improved from 0.01211 to 0.01211, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 490/1000\n",
      "6975/6975 [==============================] - 0s 22us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00490: val_loss improved from 0.01211 to 0.01211, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 491/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00491: val_loss improved from 0.01211 to 0.01210, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 492/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00492: val_loss improved from 0.01210 to 0.01210, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 493/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00493: val_loss improved from 0.01210 to 0.01210, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 494/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00494: val_loss improved from 0.01210 to 0.01210, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 495/1000\n",
      "6975/6975 [==============================] - 0s 23us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00495: val_loss improved from 0.01210 to 0.01210, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 496/1000\n",
      "6975/6975 [==============================] - 0s 24us/step - loss: 0.0150 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00496: val_loss improved from 0.01210 to 0.01209, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 497/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00497: val_loss improved from 0.01209 to 0.01209, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 498/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00498: val_loss improved from 0.01209 to 0.01209, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 499/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00499: val_loss improved from 0.01209 to 0.01209, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 500/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00500: val_loss improved from 0.01209 to 0.01208, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 501/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00501: val_loss improved from 0.01208 to 0.01208, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 502/1000\n",
      "6975/6975 [==============================] - 0s 29us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00502: val_loss improved from 0.01208 to 0.01208, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 503/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00503: val_loss improved from 0.01208 to 0.01208, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 504/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00504: val_loss improved from 0.01208 to 0.01208, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 505/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00505: val_loss improved from 0.01208 to 0.01207, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 506/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00506: val_loss improved from 0.01207 to 0.01207, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 507/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00507: val_loss improved from 0.01207 to 0.01207, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 508/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00508: val_loss improved from 0.01207 to 0.01207, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 509/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00509: val_loss improved from 0.01207 to 0.01206, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 510/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00510: val_loss improved from 0.01206 to 0.01206, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 511/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00511: val_loss improved from 0.01206 to 0.01206, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 512/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00512: val_loss improved from 0.01206 to 0.01206, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 513/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00513: val_loss improved from 0.01206 to 0.01205, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 514/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0121 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00514: val_loss improved from 0.01205 to 0.01205, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 515/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00515: val_loss improved from 0.01205 to 0.01205, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 516/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00516: val_loss improved from 0.01205 to 0.01205, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 517/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00517: val_loss improved from 0.01205 to 0.01205, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 518/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00518: val_loss improved from 0.01205 to 0.01204, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 519/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00519: val_loss improved from 0.01204 to 0.01204, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 520/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00520: val_loss improved from 0.01204 to 0.01204, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 521/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00521: val_loss improved from 0.01204 to 0.01204, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 522/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00522: val_loss improved from 0.01204 to 0.01203, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 523/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00523: val_loss improved from 0.01203 to 0.01203, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 524/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00524: val_loss improved from 0.01203 to 0.01203, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 525/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00525: val_loss improved from 0.01203 to 0.01203, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 526/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00526: val_loss improved from 0.01203 to 0.01202, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 527/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00527: val_loss improved from 0.01202 to 0.01202, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 528/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00528: val_loss improved from 0.01202 to 0.01202, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 529/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00529: val_loss improved from 0.01202 to 0.01202, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 530/1000\n",
      "6975/6975 [==============================] - 0s 23us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00530: val_loss improved from 0.01202 to 0.01201, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 531/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00531: val_loss improved from 0.01201 to 0.01201, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 532/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00532: val_loss improved from 0.01201 to 0.01201, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 533/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0149 - accuracy: 0.9814 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00533: val_loss improved from 0.01201 to 0.01201, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 534/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00534: val_loss improved from 0.01201 to 0.01201, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 535/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00535: val_loss improved from 0.01201 to 0.01200, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 536/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00536: val_loss improved from 0.01200 to 0.01200, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 537/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00537: val_loss improved from 0.01200 to 0.01200, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 538/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0149 - accuracy: 0.9814 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00538: val_loss improved from 0.01200 to 0.01200, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 539/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00539: val_loss improved from 0.01200 to 0.01199, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 540/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00540: val_loss improved from 0.01199 to 0.01199, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 541/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00541: val_loss improved from 0.01199 to 0.01199, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 542/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00542: val_loss improved from 0.01199 to 0.01199, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 543/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00543: val_loss improved from 0.01199 to 0.01199, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 544/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0149 - accuracy: 0.9814 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00544: val_loss improved from 0.01199 to 0.01198, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 545/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9816 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00545: val_loss improved from 0.01198 to 0.01198, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 546/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00546: val_loss improved from 0.01198 to 0.01198, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 547/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00547: val_loss improved from 0.01198 to 0.01198, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 548/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00548: val_loss improved from 0.01198 to 0.01198, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 549/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9814 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00549: val_loss improved from 0.01198 to 0.01197, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 550/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9814 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00550: val_loss improved from 0.01197 to 0.01197, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 551/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00551: val_loss improved from 0.01197 to 0.01197, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 552/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00552: val_loss improved from 0.01197 to 0.01197, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 553/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00553: val_loss improved from 0.01197 to 0.01196, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 554/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00554: val_loss improved from 0.01196 to 0.01196, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 555/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00555: val_loss improved from 0.01196 to 0.01196, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 556/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00556: val_loss improved from 0.01196 to 0.01196, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 557/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9816 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00557: val_loss improved from 0.01196 to 0.01196, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 558/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00558: val_loss improved from 0.01196 to 0.01195, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 559/1000\n",
      "6975/6975 [==============================] - 0s 22us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0120 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00559: val_loss improved from 0.01195 to 0.01195, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 560/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00560: val_loss improved from 0.01195 to 0.01195, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 561/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00561: val_loss improved from 0.01195 to 0.01195, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 562/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00562: val_loss improved from 0.01195 to 0.01195, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 563/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00563: val_loss improved from 0.01195 to 0.01194, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 564/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00564: val_loss improved from 0.01194 to 0.01194, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 565/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9816 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00565: val_loss improved from 0.01194 to 0.01194, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 566/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00566: val_loss improved from 0.01194 to 0.01194, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 567/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00567: val_loss improved from 0.01194 to 0.01194, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 568/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00568: val_loss improved from 0.01194 to 0.01193, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 569/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9816 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00569: val_loss improved from 0.01193 to 0.01193, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 570/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00570: val_loss improved from 0.01193 to 0.01193, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 571/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00571: val_loss improved from 0.01193 to 0.01193, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 572/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00572: val_loss improved from 0.01193 to 0.01193, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 573/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00573: val_loss improved from 0.01193 to 0.01192, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 574/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00574: val_loss improved from 0.01192 to 0.01192, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 575/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00575: val_loss improved from 0.01192 to 0.01192, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 576/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00576: val_loss improved from 0.01192 to 0.01192, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 577/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00577: val_loss improved from 0.01192 to 0.01192, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 578/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00578: val_loss improved from 0.01192 to 0.01191, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 579/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00579: val_loss improved from 0.01191 to 0.01191, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 580/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00580: val_loss improved from 0.01191 to 0.01191, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 581/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00581: val_loss improved from 0.01191 to 0.01191, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 582/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00582: val_loss improved from 0.01191 to 0.01191, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 583/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00583: val_loss improved from 0.01191 to 0.01190, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 584/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00584: val_loss improved from 0.01190 to 0.01190, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 585/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00585: val_loss improved from 0.01190 to 0.01190, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 586/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00586: val_loss improved from 0.01190 to 0.01190, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 587/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00587: val_loss improved from 0.01190 to 0.01190, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 588/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00588: val_loss improved from 0.01190 to 0.01189, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 589/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00589: val_loss improved from 0.01189 to 0.01189, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 590/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9816 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00590: val_loss improved from 0.01189 to 0.01189, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 591/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00591: val_loss improved from 0.01189 to 0.01189, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 592/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00592: val_loss improved from 0.01189 to 0.01189, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 593/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0149 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00593: val_loss improved from 0.01189 to 0.01189, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 594/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00594: val_loss improved from 0.01189 to 0.01188, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 595/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00595: val_loss improved from 0.01188 to 0.01188, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 596/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00596: val_loss improved from 0.01188 to 0.01188, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 597/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9818 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00597: val_loss improved from 0.01188 to 0.01188, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 598/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00598: val_loss improved from 0.01188 to 0.01188, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 599/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00599: val_loss improved from 0.01188 to 0.01187, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 600/1000\n",
      "6975/6975 [==============================] - 0s 25us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00600: val_loss improved from 0.01187 to 0.01187, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 601/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00601: val_loss improved from 0.01187 to 0.01187, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 602/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00602: val_loss improved from 0.01187 to 0.01187, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 603/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00603: val_loss improved from 0.01187 to 0.01187, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 604/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00604: val_loss improved from 0.01187 to 0.01187, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 605/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00605: val_loss improved from 0.01187 to 0.01186, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 606/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00606: val_loss improved from 0.01186 to 0.01186, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 607/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00607: val_loss improved from 0.01186 to 0.01186, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 608/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00608: val_loss improved from 0.01186 to 0.01186, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 609/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00609: val_loss improved from 0.01186 to 0.01186, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 610/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00610: val_loss improved from 0.01186 to 0.01186, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 611/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00611: val_loss improved from 0.01186 to 0.01185, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 612/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00612: val_loss improved from 0.01185 to 0.01185, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 613/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0119 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00613: val_loss improved from 0.01185 to 0.01185, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 614/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9818 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00614: val_loss improved from 0.01185 to 0.01185, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 615/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00615: val_loss improved from 0.01185 to 0.01185, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 616/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00616: val_loss improved from 0.01185 to 0.01184, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 617/1000\n",
      "6975/6975 [==============================] - 0s 23us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00617: val_loss improved from 0.01184 to 0.01184, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 618/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00618: val_loss improved from 0.01184 to 0.01184, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 619/1000\n",
      "6975/6975 [==============================] - 0s 22us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00619: val_loss improved from 0.01184 to 0.01184, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 620/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00620: val_loss improved from 0.01184 to 0.01184, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 621/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00621: val_loss improved from 0.01184 to 0.01184, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 622/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00622: val_loss improved from 0.01184 to 0.01183, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 623/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00623: val_loss improved from 0.01183 to 0.01183, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 624/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00624: val_loss improved from 0.01183 to 0.01183, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 625/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00625: val_loss improved from 0.01183 to 0.01183, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 626/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00626: val_loss improved from 0.01183 to 0.01183, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 627/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00627: val_loss improved from 0.01183 to 0.01183, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 628/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00628: val_loss improved from 0.01183 to 0.01182, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 629/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00629: val_loss improved from 0.01182 to 0.01182, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 630/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00630: val_loss improved from 0.01182 to 0.01182, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 631/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00631: val_loss improved from 0.01182 to 0.01182, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 632/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00632: val_loss improved from 0.01182 to 0.01182, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 633/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00633: val_loss improved from 0.01182 to 0.01182, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 634/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00634: val_loss improved from 0.01182 to 0.01182, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 635/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00635: val_loss improved from 0.01182 to 0.01181, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 636/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00636: val_loss improved from 0.01181 to 0.01181, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 637/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00637: val_loss improved from 0.01181 to 0.01181, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 638/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00638: val_loss improved from 0.01181 to 0.01181, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 639/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00639: val_loss improved from 0.01181 to 0.01181, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 640/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00640: val_loss improved from 0.01181 to 0.01181, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 641/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00641: val_loss improved from 0.01181 to 0.01180, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 642/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00642: val_loss improved from 0.01180 to 0.01180, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 643/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00643: val_loss improved from 0.01180 to 0.01180, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 644/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00644: val_loss improved from 0.01180 to 0.01180, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 645/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00645: val_loss improved from 0.01180 to 0.01180, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 646/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00646: val_loss improved from 0.01180 to 0.01180, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 647/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00647: val_loss improved from 0.01180 to 0.01179, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 648/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00648: val_loss improved from 0.01179 to 0.01179, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 649/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00649: val_loss improved from 0.01179 to 0.01179, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 650/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00650: val_loss improved from 0.01179 to 0.01179, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 651/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00651: val_loss improved from 0.01179 to 0.01179, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 652/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00652: val_loss improved from 0.01179 to 0.01179, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 653/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00653: val_loss improved from 0.01179 to 0.01179, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 654/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00654: val_loss improved from 0.01179 to 0.01178, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 655/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00655: val_loss improved from 0.01178 to 0.01178, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 656/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00656: val_loss improved from 0.01178 to 0.01178, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 657/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00657: val_loss improved from 0.01178 to 0.01178, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 658/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00658: val_loss improved from 0.01178 to 0.01178, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 659/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00659: val_loss improved from 0.01178 to 0.01178, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 660/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00660: val_loss improved from 0.01178 to 0.01178, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 661/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00661: val_loss improved from 0.01178 to 0.01177, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 662/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00662: val_loss improved from 0.01177 to 0.01177, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 663/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00663: val_loss improved from 0.01177 to 0.01177, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 664/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00664: val_loss improved from 0.01177 to 0.01177, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 665/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00665: val_loss improved from 0.01177 to 0.01177, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 666/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00666: val_loss improved from 0.01177 to 0.01177, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 667/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00667: val_loss improved from 0.01177 to 0.01177, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 668/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00668: val_loss improved from 0.01177 to 0.01176, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 669/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00669: val_loss improved from 0.01176 to 0.01176, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 670/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00670: val_loss improved from 0.01176 to 0.01176, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 671/1000\n",
      "6975/6975 [==============================] - 0s 24us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00671: val_loss improved from 0.01176 to 0.01176, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 672/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00672: val_loss improved from 0.01176 to 0.01176, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 673/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0148 - accuracy: 0.9815 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00673: val_loss improved from 0.01176 to 0.01176, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 674/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0148 - accuracy: 0.9818 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00674: val_loss improved from 0.01176 to 0.01176, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 675/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00675: val_loss improved from 0.01176 to 0.01175, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 676/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00676: val_loss improved from 0.01175 to 0.01175, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 677/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00677: val_loss improved from 0.01175 to 0.01175, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 678/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0118 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00678: val_loss improved from 0.01175 to 0.01175, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 679/1000\n",
      "6975/6975 [==============================] - 0s 22us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00679: val_loss improved from 0.01175 to 0.01175, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 680/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00680: val_loss improved from 0.01175 to 0.01175, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 681/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00681: val_loss improved from 0.01175 to 0.01175, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 682/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00682: val_loss improved from 0.01175 to 0.01175, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 683/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9818 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00683: val_loss improved from 0.01175 to 0.01174, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 684/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9818 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00684: val_loss improved from 0.01174 to 0.01174, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 685/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00685: val_loss improved from 0.01174 to 0.01174, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 686/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9818 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00686: val_loss improved from 0.01174 to 0.01174, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 687/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0148 - accuracy: 0.9818 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00687: val_loss improved from 0.01174 to 0.01174, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 688/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9819 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00688: val_loss improved from 0.01174 to 0.01174, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 689/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00689: val_loss improved from 0.01174 to 0.01174, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 690/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00690: val_loss improved from 0.01174 to 0.01173, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 691/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00691: val_loss improved from 0.01173 to 0.01173, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 692/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9819 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00692: val_loss improved from 0.01173 to 0.01173, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 693/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9818 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00693: val_loss improved from 0.01173 to 0.01173, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 694/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00694: val_loss improved from 0.01173 to 0.01173, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 695/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9819 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00695: val_loss improved from 0.01173 to 0.01173, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 696/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9818 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00696: val_loss improved from 0.01173 to 0.01173, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 697/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9818 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00697: val_loss improved from 0.01173 to 0.01173, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 698/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9818 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00698: val_loss improved from 0.01173 to 0.01172, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 699/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9818 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00699: val_loss improved from 0.01172 to 0.01172, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 700/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9818 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00700: val_loss improved from 0.01172 to 0.01172, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 701/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00701: val_loss improved from 0.01172 to 0.01172, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 702/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0148 - accuracy: 0.9819 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00702: val_loss improved from 0.01172 to 0.01172, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 703/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9819 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00703: val_loss improved from 0.01172 to 0.01172, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 704/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00704: val_loss improved from 0.01172 to 0.01172, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 705/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00705: val_loss improved from 0.01172 to 0.01172, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 706/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9821 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00706: val_loss improved from 0.01172 to 0.01171, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 707/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00707: val_loss improved from 0.01171 to 0.01171, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 708/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00708: val_loss improved from 0.01171 to 0.01171, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 709/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9818 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00709: val_loss improved from 0.01171 to 0.01171, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 710/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9818 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00710: val_loss improved from 0.01171 to 0.01171, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 711/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9818 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00711: val_loss improved from 0.01171 to 0.01171, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 712/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0148 - accuracy: 0.9819 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00712: val_loss improved from 0.01171 to 0.01171, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 713/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9818 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00713: val_loss improved from 0.01171 to 0.01171, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 714/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9816 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00714: val_loss improved from 0.01171 to 0.01171, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 715/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0148 - accuracy: 0.9819 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00715: val_loss improved from 0.01171 to 0.01170, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 716/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00716: val_loss improved from 0.01170 to 0.01170, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 717/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9819 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00717: val_loss improved from 0.01170 to 0.01170, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 718/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0148 - accuracy: 0.9819 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00718: val_loss improved from 0.01170 to 0.01170, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 719/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9821 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00719: val_loss improved from 0.01170 to 0.01170, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 720/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00720: val_loss improved from 0.01170 to 0.01170, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 721/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9819 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00721: val_loss improved from 0.01170 to 0.01170, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 722/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9821 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00722: val_loss improved from 0.01170 to 0.01170, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 723/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00723: val_loss improved from 0.01170 to 0.01170, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 724/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9821 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00724: val_loss improved from 0.01170 to 0.01170, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 725/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9821 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00725: val_loss improved from 0.01170 to 0.01170, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 726/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00726: val_loss improved from 0.01170 to 0.01169, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 727/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00727: val_loss improved from 0.01169 to 0.01169, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 728/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00728: val_loss improved from 0.01169 to 0.01169, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 729/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9821 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00729: val_loss improved from 0.01169 to 0.01169, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 730/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9821 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00730: val_loss improved from 0.01169 to 0.01169, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 731/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0148 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00731: val_loss improved from 0.01169 to 0.01169, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 732/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0148 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00732: val_loss improved from 0.01169 to 0.01169, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 733/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0148 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00733: val_loss improved from 0.01169 to 0.01169, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 734/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0148 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00734: val_loss improved from 0.01169 to 0.01169, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 735/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9821 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00735: val_loss improved from 0.01169 to 0.01169, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 736/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9821 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00736: val_loss improved from 0.01169 to 0.01168, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 737/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00737: val_loss improved from 0.01168 to 0.01168, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 738/1000\n",
      "6975/6975 [==============================] - 0s 24us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00738: val_loss improved from 0.01168 to 0.01168, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 739/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00739: val_loss improved from 0.01168 to 0.01168, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 740/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0147 - accuracy: 0.9821 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00740: val_loss improved from 0.01168 to 0.01168, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 741/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00741: val_loss improved from 0.01168 to 0.01168, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 742/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00742: val_loss improved from 0.01168 to 0.01168, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 743/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9821 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00743: val_loss improved from 0.01168 to 0.01168, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 744/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00744: val_loss improved from 0.01168 to 0.01168, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 745/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00745: val_loss improved from 0.01168 to 0.01167, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 746/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00746: val_loss improved from 0.01167 to 0.01167, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 747/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00747: val_loss improved from 0.01167 to 0.01167, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 748/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00748: val_loss improved from 0.01167 to 0.01167, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 749/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00749: val_loss improved from 0.01167 to 0.01167, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 750/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00750: val_loss improved from 0.01167 to 0.01167, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 751/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00751: val_loss improved from 0.01167 to 0.01167, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 752/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00752: val_loss improved from 0.01167 to 0.01167, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 753/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0147 - accuracy: 0.9821 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00753: val_loss improved from 0.01167 to 0.01166, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 754/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00754: val_loss improved from 0.01166 to 0.01166, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 755/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00755: val_loss improved from 0.01166 to 0.01166, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 756/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9821 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00756: val_loss improved from 0.01166 to 0.01166, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 757/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00757: val_loss improved from 0.01166 to 0.01166, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 758/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00758: val_loss improved from 0.01166 to 0.01166, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 759/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00759: val_loss improved from 0.01166 to 0.01166, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 760/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00760: val_loss improved from 0.01166 to 0.01165, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 761/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00761: val_loss improved from 0.01165 to 0.01165, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 762/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00762: val_loss improved from 0.01165 to 0.01165, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 763/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0117 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00763: val_loss improved from 0.01165 to 0.01165, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 764/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00764: val_loss improved from 0.01165 to 0.01165, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 765/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00765: val_loss improved from 0.01165 to 0.01165, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 766/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00766: val_loss improved from 0.01165 to 0.01165, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 767/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00767: val_loss improved from 0.01165 to 0.01164, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 768/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00768: val_loss improved from 0.01164 to 0.01164, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 769/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00769: val_loss improved from 0.01164 to 0.01164, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 770/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00770: val_loss improved from 0.01164 to 0.01164, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 771/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00771: val_loss improved from 0.01164 to 0.01164, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 772/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00772: val_loss improved from 0.01164 to 0.01164, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 773/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00773: val_loss improved from 0.01164 to 0.01164, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 774/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00774: val_loss improved from 0.01164 to 0.01164, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 775/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00775: val_loss improved from 0.01164 to 0.01163, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 776/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00776: val_loss improved from 0.01163 to 0.01163, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 777/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00777: val_loss improved from 0.01163 to 0.01163, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 778/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00778: val_loss improved from 0.01163 to 0.01163, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 779/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00779: val_loss improved from 0.01163 to 0.01163, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 780/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00780: val_loss improved from 0.01163 to 0.01163, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 781/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00781: val_loss improved from 0.01163 to 0.01163, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 782/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00782: val_loss improved from 0.01163 to 0.01162, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 783/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00783: val_loss improved from 0.01162 to 0.01162, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 784/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00784: val_loss improved from 0.01162 to 0.01162, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 785/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00785: val_loss improved from 0.01162 to 0.01162, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 786/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00786: val_loss improved from 0.01162 to 0.01162, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 787/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00787: val_loss improved from 0.01162 to 0.01161, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 788/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00788: val_loss improved from 0.01161 to 0.01161, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 789/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00789: val_loss improved from 0.01161 to 0.01161, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 790/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00790: val_loss improved from 0.01161 to 0.01161, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 791/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00791: val_loss improved from 0.01161 to 0.01161, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 792/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00792: val_loss improved from 0.01161 to 0.01161, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 793/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00793: val_loss improved from 0.01161 to 0.01160, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 794/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9822 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00794: val_loss improved from 0.01160 to 0.01160, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 795/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00795: val_loss improved from 0.01160 to 0.01160, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 796/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00796: val_loss improved from 0.01160 to 0.01160, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 797/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00797: val_loss improved from 0.01160 to 0.01160, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 798/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00798: val_loss improved from 0.01160 to 0.01159, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 799/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00799: val_loss improved from 0.01159 to 0.01159, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 800/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00800: val_loss improved from 0.01159 to 0.01159, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 801/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00801: val_loss improved from 0.01159 to 0.01159, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 802/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00802: val_loss improved from 0.01159 to 0.01158, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 803/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00803: val_loss improved from 0.01158 to 0.01158, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 804/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00804: val_loss improved from 0.01158 to 0.01158, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 805/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00805: val_loss improved from 0.01158 to 0.01158, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 806/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00806: val_loss improved from 0.01158 to 0.01158, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 807/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00807: val_loss improved from 0.01158 to 0.01157, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 808/1000\n",
      "6975/6975 [==============================] - 0s 22us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00808: val_loss improved from 0.01157 to 0.01157, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 809/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00809: val_loss improved from 0.01157 to 0.01157, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 810/1000\n",
      "6975/6975 [==============================] - 0s 25us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00810: val_loss improved from 0.01157 to 0.01157, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 811/1000\n",
      "6975/6975 [==============================] - 0s 24us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00811: val_loss improved from 0.01157 to 0.01157, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 812/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00812: val_loss improved from 0.01157 to 0.01156, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 813/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00813: val_loss improved from 0.01156 to 0.01156, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 814/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00814: val_loss improved from 0.01156 to 0.01156, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 815/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00815: val_loss improved from 0.01156 to 0.01156, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 816/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00816: val_loss improved from 0.01156 to 0.01155, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 817/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00817: val_loss improved from 0.01155 to 0.01155, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 818/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0116 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00818: val_loss improved from 0.01155 to 0.01155, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 819/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00819: val_loss improved from 0.01155 to 0.01155, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 820/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00820: val_loss improved from 0.01155 to 0.01155, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 821/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00821: val_loss improved from 0.01155 to 0.01154, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 822/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00822: val_loss improved from 0.01154 to 0.01154, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 823/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00823: val_loss improved from 0.01154 to 0.01154, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 824/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00824: val_loss improved from 0.01154 to 0.01154, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 825/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00825: val_loss improved from 0.01154 to 0.01154, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 826/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00826: val_loss improved from 0.01154 to 0.01153, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 827/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00827: val_loss improved from 0.01153 to 0.01153, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 828/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00828: val_loss improved from 0.01153 to 0.01153, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 829/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00829: val_loss improved from 0.01153 to 0.01153, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 830/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00830: val_loss improved from 0.01153 to 0.01153, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 831/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00831: val_loss improved from 0.01153 to 0.01152, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 832/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00832: val_loss improved from 0.01152 to 0.01152, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 833/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00833: val_loss improved from 0.01152 to 0.01152, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 834/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00834: val_loss improved from 0.01152 to 0.01152, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 835/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00835: val_loss improved from 0.01152 to 0.01152, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 836/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00836: val_loss improved from 0.01152 to 0.01152, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 837/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00837: val_loss improved from 0.01152 to 0.01152, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 838/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00838: val_loss improved from 0.01152 to 0.01151, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 839/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00839: val_loss improved from 0.01151 to 0.01151, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 840/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00840: val_loss improved from 0.01151 to 0.01151, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 841/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00841: val_loss improved from 0.01151 to 0.01151, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 842/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00842: val_loss improved from 0.01151 to 0.01151, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 843/1000\n",
      "6975/6975 [==============================] - 0s 22us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00843: val_loss improved from 0.01151 to 0.01151, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 844/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00844: val_loss improved from 0.01151 to 0.01150, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 845/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00845: val_loss improved from 0.01150 to 0.01150, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 846/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00846: val_loss improved from 0.01150 to 0.01150, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 847/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00847: val_loss improved from 0.01150 to 0.01150, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 848/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00848: val_loss improved from 0.01150 to 0.01150, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 849/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00849: val_loss improved from 0.01150 to 0.01150, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 850/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00850: val_loss improved from 0.01150 to 0.01150, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 851/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00851: val_loss improved from 0.01150 to 0.01149, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 852/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00852: val_loss improved from 0.01149 to 0.01149, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 853/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00853: val_loss improved from 0.01149 to 0.01149, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 854/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00854: val_loss improved from 0.01149 to 0.01149, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 855/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00855: val_loss improved from 0.01149 to 0.01149, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 856/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00856: val_loss improved from 0.01149 to 0.01149, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 857/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00857: val_loss improved from 0.01149 to 0.01149, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 858/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0147 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00858: val_loss improved from 0.01149 to 0.01149, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 859/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00859: val_loss improved from 0.01149 to 0.01148, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 860/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00860: val_loss improved from 0.01148 to 0.01148, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 861/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00861: val_loss improved from 0.01148 to 0.01148, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 862/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00862: val_loss improved from 0.01148 to 0.01148, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 863/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00863: val_loss improved from 0.01148 to 0.01148, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 864/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00864: val_loss improved from 0.01148 to 0.01148, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 865/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00865: val_loss improved from 0.01148 to 0.01147, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 866/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00866: val_loss improved from 0.01147 to 0.01147, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 867/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00867: val_loss improved from 0.01147 to 0.01147, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 868/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00868: val_loss improved from 0.01147 to 0.01147, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 869/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00869: val_loss improved from 0.01147 to 0.01147, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 870/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00870: val_loss improved from 0.01147 to 0.01147, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 871/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00871: val_loss improved from 0.01147 to 0.01146, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 872/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00872: val_loss improved from 0.01146 to 0.01146, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 873/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00873: val_loss improved from 0.01146 to 0.01146, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 874/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00874: val_loss improved from 0.01146 to 0.01146, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 875/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00875: val_loss improved from 0.01146 to 0.01146, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 876/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00876: val_loss improved from 0.01146 to 0.01146, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 877/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00877: val_loss improved from 0.01146 to 0.01145, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 878/1000\n",
      "6975/6975 [==============================] - 0s 22us/step - loss: 0.0146 - accuracy: 0.9822 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00878: val_loss improved from 0.01145 to 0.01145, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 879/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00879: val_loss improved from 0.01145 to 0.01145, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 880/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0115 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00880: val_loss improved from 0.01145 to 0.01145, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 881/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00881: val_loss improved from 0.01145 to 0.01145, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 882/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00882: val_loss improved from 0.01145 to 0.01145, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 883/1000\n",
      "6975/6975 [==============================] - 0s 23us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00883: val_loss improved from 0.01145 to 0.01145, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 884/1000\n",
      "6975/6975 [==============================] - 0s 22us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00884: val_loss improved from 0.01145 to 0.01144, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 885/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00885: val_loss improved from 0.01144 to 0.01144, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 886/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00886: val_loss improved from 0.01144 to 0.01144, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 887/1000\n",
      "6975/6975 [==============================] - 0s 28us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00887: val_loss improved from 0.01144 to 0.01144, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 888/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00888: val_loss improved from 0.01144 to 0.01144, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 889/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00889: val_loss improved from 0.01144 to 0.01144, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 890/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0146 - accuracy: 0.9824 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00890: val_loss improved from 0.01144 to 0.01143, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 891/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00891: val_loss improved from 0.01143 to 0.01143, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 892/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00892: val_loss improved from 0.01143 to 0.01143, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 893/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00893: val_loss improved from 0.01143 to 0.01143, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 894/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00894: val_loss improved from 0.01143 to 0.01143, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 895/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00895: val_loss improved from 0.01143 to 0.01143, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 896/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00896: val_loss improved from 0.01143 to 0.01143, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 897/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00897: val_loss improved from 0.01143 to 0.01143, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 898/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00898: val_loss improved from 0.01143 to 0.01142, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 899/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00899: val_loss improved from 0.01142 to 0.01142, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 900/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00900: val_loss improved from 0.01142 to 0.01142, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 901/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00901: val_loss improved from 0.01142 to 0.01142, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 902/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00902: val_loss improved from 0.01142 to 0.01142, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 903/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00903: val_loss improved from 0.01142 to 0.01142, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 904/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00904: val_loss improved from 0.01142 to 0.01142, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 905/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00905: val_loss improved from 0.01142 to 0.01142, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 906/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00906: val_loss improved from 0.01142 to 0.01142, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 907/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00907: val_loss improved from 0.01142 to 0.01142, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 908/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00908: val_loss improved from 0.01142 to 0.01142, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 909/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00909: val_loss improved from 0.01142 to 0.01142, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 910/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00910: val_loss did not improve from 0.01142\n",
      "Epoch 911/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00911: val_loss improved from 0.01142 to 0.01142, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 912/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00912: val_loss improved from 0.01142 to 0.01142, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 913/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00913: val_loss did not improve from 0.01142\n",
      "Epoch 914/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00914: val_loss did not improve from 0.01142\n",
      "Epoch 915/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00915: val_loss improved from 0.01142 to 0.01141, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 916/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00916: val_loss improved from 0.01141 to 0.01141, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 917/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00917: val_loss improved from 0.01141 to 0.01141, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 918/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00918: val_loss improved from 0.01141 to 0.01141, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 919/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00919: val_loss improved from 0.01141 to 0.01141, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 920/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00920: val_loss improved from 0.01141 to 0.01141, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 921/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00921: val_loss improved from 0.01141 to 0.01141, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 922/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00922: val_loss improved from 0.01141 to 0.01141, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 923/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00923: val_loss improved from 0.01141 to 0.01141, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 924/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00924: val_loss improved from 0.01141 to 0.01140, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 925/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00925: val_loss improved from 0.01140 to 0.01140, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 926/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00926: val_loss improved from 0.01140 to 0.01140, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 927/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00927: val_loss improved from 0.01140 to 0.01140, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 928/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00928: val_loss improved from 0.01140 to 0.01140, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 929/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00929: val_loss improved from 0.01140 to 0.01140, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 930/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00930: val_loss improved from 0.01140 to 0.01140, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 931/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00931: val_loss improved from 0.01140 to 0.01140, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 932/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00932: val_loss improved from 0.01140 to 0.01140, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 933/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00933: val_loss improved from 0.01140 to 0.01140, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 934/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00934: val_loss improved from 0.01140 to 0.01140, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 935/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00935: val_loss improved from 0.01140 to 0.01139, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 936/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00936: val_loss improved from 0.01139 to 0.01139, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 937/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00937: val_loss improved from 0.01139 to 0.01139, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 938/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00938: val_loss improved from 0.01139 to 0.01139, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 939/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00939: val_loss improved from 0.01139 to 0.01139, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 940/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00940: val_loss improved from 0.01139 to 0.01139, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 941/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00941: val_loss improved from 0.01139 to 0.01139, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 942/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00942: val_loss improved from 0.01139 to 0.01139, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 943/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00943: val_loss improved from 0.01139 to 0.01139, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 944/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00944: val_loss improved from 0.01139 to 0.01139, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 945/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00945: val_loss improved from 0.01139 to 0.01139, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 946/1000\n",
      "6975/6975 [==============================] - 0s 26us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00946: val_loss improved from 0.01139 to 0.01139, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 947/1000\n",
      "6975/6975 [==============================] - 0s 23us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00947: val_loss improved from 0.01139 to 0.01139, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 948/1000\n",
      "6975/6975 [==============================] - 0s 22us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00948: val_loss improved from 0.01139 to 0.01139, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 949/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00949: val_loss improved from 0.01139 to 0.01139, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 950/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00950: val_loss improved from 0.01139 to 0.01138, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 951/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00951: val_loss improved from 0.01138 to 0.01138, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 952/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00952: val_loss improved from 0.01138 to 0.01138, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 953/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00953: val_loss improved from 0.01138 to 0.01138, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 954/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00954: val_loss improved from 0.01138 to 0.01138, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 955/1000\n",
      "6975/6975 [==============================] - 0s 22us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00955: val_loss improved from 0.01138 to 0.01138, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 956/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00956: val_loss improved from 0.01138 to 0.01138, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 957/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00957: val_loss improved from 0.01138 to 0.01138, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 958/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00958: val_loss improved from 0.01138 to 0.01138, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 959/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00959: val_loss improved from 0.01138 to 0.01138, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 960/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00960: val_loss improved from 0.01138 to 0.01138, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 961/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00961: val_loss improved from 0.01138 to 0.01138, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 962/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00962: val_loss improved from 0.01138 to 0.01138, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 963/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00963: val_loss improved from 0.01138 to 0.01138, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 964/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00964: val_loss improved from 0.01138 to 0.01137, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 965/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00965: val_loss improved from 0.01137 to 0.01137, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 966/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00966: val_loss improved from 0.01137 to 0.01137, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 967/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00967: val_loss improved from 0.01137 to 0.01137, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 968/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00968: val_loss improved from 0.01137 to 0.01137, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 969/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00969: val_loss improved from 0.01137 to 0.01137, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 970/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00970: val_loss improved from 0.01137 to 0.01137, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 971/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00971: val_loss improved from 0.01137 to 0.01137, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 972/1000\n",
      "6975/6975 [==============================] - 0s 17us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00972: val_loss improved from 0.01137 to 0.01137, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 973/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00973: val_loss improved from 0.01137 to 0.01137, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 974/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00974: val_loss improved from 0.01137 to 0.01137, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 975/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00975: val_loss improved from 0.01137 to 0.01137, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 976/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0146 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00976: val_loss improved from 0.01137 to 0.01137, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 977/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00977: val_loss improved from 0.01137 to 0.01137, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 978/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00978: val_loss improved from 0.01137 to 0.01136, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 979/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00979: val_loss improved from 0.01136 to 0.01136, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 980/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00980: val_loss improved from 0.01136 to 0.01136, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 981/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00981: val_loss improved from 0.01136 to 0.01136, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 982/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00982: val_loss improved from 0.01136 to 0.01136, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 983/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00983: val_loss did not improve from 0.01136\n",
      "Epoch 984/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00984: val_loss did not improve from 0.01136\n",
      "Epoch 985/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00985: val_loss improved from 0.01136 to 0.01136, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 986/1000\n",
      "6975/6975 [==============================] - ETA: 0s - loss: 0.0145 - accuracy: 0.98 - 0s 19us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00986: val_loss improved from 0.01136 to 0.01136, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 987/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0146 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00987: val_loss improved from 0.01136 to 0.01136, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 988/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0145 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00988: val_loss improved from 0.01136 to 0.01136, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 989/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0145 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00989: val_loss improved from 0.01136 to 0.01136, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 990/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0145 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00990: val_loss improved from 0.01136 to 0.01136, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 991/1000\n",
      "6975/6975 [==============================] - 0s 20us/step - loss: 0.0145 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00991: val_loss improved from 0.01136 to 0.01136, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 992/1000\n",
      "6975/6975 [==============================] - 0s 21us/step - loss: 0.0145 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00992: val_loss improved from 0.01136 to 0.01136, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 993/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0145 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00993: val_loss improved from 0.01136 to 0.01136, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 994/1000\n",
      "6975/6975 [==============================] - 0s 24us/step - loss: 0.0145 - accuracy: 0.9828 - val_loss: 0.0114 - val_accuracy: 0.9845\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00994: val_loss improved from 0.01136 to 0.01135, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 995/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0145 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00995: val_loss improved from 0.01135 to 0.01135, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 996/1000\n",
      "6975/6975 [==============================] - 0s 27us/step - loss: 0.0145 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00996: val_loss improved from 0.01135 to 0.01135, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 997/1000\n",
      "6975/6975 [==============================] - 0s 25us/step - loss: 0.0145 - accuracy: 0.9827 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00997: val_loss improved from 0.01135 to 0.01135, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 998/1000\n",
      "6975/6975 [==============================] - 0s 23us/step - loss: 0.0145 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00998: val_loss improved from 0.01135 to 0.01135, saving model to mutliCNN_smote_best_overall.hdf5\n",
      "Epoch 999/1000\n",
      "6975/6975 [==============================] - 0s 19us/step - loss: 0.0145 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 00999: val_loss did not improve from 0.01135\n",
      "Epoch 1000/1000\n",
      "6975/6975 [==============================] - 0s 18us/step - loss: 0.0145 - accuracy: 0.9825 - val_loss: 0.0114 - val_accuracy: 0.9845\n",
      "\n",
      "Epoch 01000: val_loss improved from 0.01135 to 0.01135, saving model to mutliCNN_smote_best_overall.hdf5\n"
     ]
    }
   ],
   "source": [
    "# Model training\n",
    "history = model.fit(compiled_output_train, label_train, epochs=1000, validation_split=0.1, callbacks=callbacks_list, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "del data_train\n",
    "del compiled_output_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cache not found, loading images..\n",
      "Cache not found, loading images..\n"
     ]
    }
   ],
   "source": [
    "### LOAD VALIDATION DATA AND LABELS ###\n",
    "\n",
    "data_val_normal, label_val_normal = utils.load_data(path_val_normal, val_normal_cache, normal_label)\n",
    "data_val_pneumonia, label_val_pneumonia = utils.load_data(path_val_pneumonia, val_pneumonia_cache, pneumonia_label)\n",
    "\n",
    "# Caching, disabled for now\n",
    "# utils.cache(data_val_normal, val_normal_cache)\n",
    "# utils.cache(data_val_pneumonia, val_pneumonia_cache)\n",
    "\n",
    "# Combine validation images and labels\n",
    "data_val = np.concatenate((data_val_normal, data_val_pneumonia), axis=0)\n",
    "label_val = np.asarray(label_val_normal + label_val_pneumonia)\n",
    "\n",
    "# Shuffle\n",
    "rand_order = np.random.permutation(data_val.shape[0])\n",
    "data_val = data_val[rand_order]\n",
    "label_val = label_val[rand_order]\n",
    "\n",
    "del data_val_normal\n",
    "del data_val_pneumonia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cache not found, loading images..\n",
      "Cache not found, loading images..\n"
     ]
    }
   ],
   "source": [
    "### LOAD TESTING DATA AND LABELS ###\n",
    "\n",
    "data_test_normal, label_test_normal = utils.load_data(path_test_normal, test_normal_cache, normal_label)\n",
    "data_test_pneumonia, label_test_pneumonia = utils.load_data(path_test_pneumonia, test_pneumonia_cache, pneumonia_label)\n",
    "\n",
    "# Caching, disabled for now\n",
    "# utils.cache(data_test_normal, test_normal_cache)\n",
    "# utils.cache(data_test_pneumonia, test_pneumonia_cache)\n",
    "\n",
    "# Combine training images and labels\n",
    "data_test = np.concatenate((data_test_normal, data_test_pneumonia), axis=0)\n",
    "label_test = np.asarray(label_test_normal + label_test_pneumonia)\n",
    "\n",
    "# Shuffle\n",
    "rand_order = np.random.permutation(data_test.shape[0])\n",
    "data_test = data_test[rand_order]\n",
    "label_test = label_test[rand_order]\n",
    "\n",
    "del data_test_normal\n",
    "del data_test_pneumonia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Image processing for both\n",
    "data_val = utils.images_crop_from_centre(data_val, percentage=0.9)\n",
    "data_val = utils.images_resize(data_val, resize_dim) \n",
    "data_val = utils.images_normalize(data_val)\n",
    "\n",
    "data_test = utils.images_crop_from_centre(data_test, percentage=0.9)\n",
    "data_test = utils.images_resize(data_test, resize_dim) \n",
    "data_test = utils.images_normalize(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete existing model\n",
    "del model\n",
    "\n",
    "# Load best version of model\n",
    "model = load_model(intermediary_name + ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16/16 [==============================] - 0s 23ms/step\n",
      "16/16 [==============================] - 1s 32ms/step\n",
      "16/16 [==============================] - 1s 44ms/step\n",
      "16/16 [==============================] - 1s 37ms/step\n",
      "16/16 [==============================] - 1s 57ms/step\n",
      "16/16 [==============================] - 0s 19ms/step\n",
      "16/16 [==============================] - 0s 30ms/step\n",
      "16/16 [==============================] - 1s 38ms/step\n",
      "624/624 [==============================] - 14s 22ms/step\n",
      "624/624 [==============================] - 18s 29ms/step\n",
      "624/624 [==============================] - 23s 37ms/step\n",
      "624/624 [==============================] - 19s 30ms/step\n",
      "624/624 [==============================] - 28s 45ms/step\n",
      "624/624 [==============================] - 10s 16ms/step\n",
      "624/624 [==============================] - 18s 29ms/step\n",
      "624/624 [==============================] - 23s 36ms/step\n",
      "16/16 [==============================] - 0s 1ms/step\n",
      "624/624 [==============================] - 0s 18us/step\n",
      "0.08477805554866791 0.875\n",
      "0.15312387339942618 0.8333333134651184\n"
     ]
    }
   ],
   "source": [
    "# Validation and testing\n",
    "compiled_output_val = np.asarray(list(map(lambda model: model.predict(data_val, verbose=1), models))).T\n",
    "compiled_output_val = compiled_output_val.reshape((data_val.shape[0], len(models), 1))\n",
    "\n",
    "compiled_output_test = np.asarray(list(map(lambda model: model.predict(data_test, verbose=1), models))).T\n",
    "compiled_output_test = compiled_output_test.reshape((data_test.shape[0], len(models), 1))\n",
    "\n",
    "val_loss, val_accuracy = model.evaluate(compiled_output_val, label_val)\n",
    "test_loss, test_accuracy = model.evaluate(compiled_output_test, label_test)\n",
    "\n",
    "print(val_loss, val_accuracy)\n",
    "print(test_loss, test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "del data_val\n",
    "del data_test\n",
    "del compiled_output_val\n",
    "del compiled_output_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model for usage\n",
    "model.save(model_name + \"_\" + str(int(test_accuracy * 100)) + ext)\n",
    "\n",
    "# Delete intermediary model\n",
    "if os.path.exists(intermediary_name + ext):\n",
    "    os.remove(intermediary_name + ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### VISUALISATION ###\n",
    "\n",
    "# Plot training & validation accuracy values\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}