zigzag_cnn_train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "tensorflow version: 2.6.0\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import datetime\n",
    "import os   \n",
    "import cv2\n",
    "import tqdm\n",
    "import zigzag_helper\n",
    "from    tensorflow import keras\n",
    "from    tensorflow.keras import layers,Sequential,optimizers,losses,metrics\n",
    "from    tqdm import tqdm\n",
    "from    sklearn import preprocessing\n",
    "from    sklearn.preprocessing import OneHotEncoder\n",
    "from    pandas import DataFrame\n",
    "\n",
    "print('tensorflow version:',tf.__version__)\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL']='2'\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "pd.set_option('max_columns',200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Zigzag(arrary,size):\n",
    "    #锯齿形编码填充\n",
    "    zigzag=np.zeros([size,size])\n",
    "    row=zigzag.shape[0]\n",
    "    colum=zigzag.shape[1]\n",
    "    i,j,k=0,0,0\n",
    "    while i < row and j < colum and k < len(arrary):\n",
    "        zigzag[i,j]=arrary[k]\n",
    "        k+=1\n",
    "        #i+j为偶数，向右上方向移动\n",
    "        if (i+j)%2==0 :\n",
    "            #如果右边界超出，则向下\n",
    "            if (i-1) in range(row) and (j+1) not in range(colum):\n",
    "                i+=1\n",
    "            #如果上边界超出，则向下\n",
    "            elif (i-1) not in range(row) and (j+1) in range(colum):\n",
    "                j+=1\n",
    "            #如果右上边界都超出，则向下\n",
    "            elif (i-1) not in range(row) and (j+1) not in range(colum):\n",
    "                i+=1\n",
    "            else:\n",
    "                i-=1\n",
    "                j+=1\n",
    "        #i+j为奇数，则向左下移动\n",
    "        elif (i+j)%2==1:\n",
    "            #如果左边界超出，则向下\n",
    "            if (i+1) in range(row) and (j-1) not in range(colum):\n",
    "                i+=1\n",
    "            #如果上边界超出，则向下\n",
    "            elif (i+1) not in range(row) and (j-1) in range(colum):\n",
    "                j+=1\n",
    "            #如果右上边界都超出，则向下\n",
    "            elif (i+1) not in range(row) and (j-1) not in range(colum):\n",
    "                j+=1\n",
    "            else:\n",
    "                i+=1\n",
    "                j-=1\n",
    "    return zigzag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Load_img():\n",
    "    train_file_dir='KDDCUP99_TRAIN_IMGSET(16)'\n",
    "    ds_train=keras.preprocessing.image_dataset_from_directory(directory=train_file_dir,\n",
    "                                                    labels='inferred',\n",
    "                                                    label_mode='categorical',\n",
    "                                                    class_names=None,\n",
    "                                                    color_mode='grayscale',\n",
    "                                                    shuffle=True,\n",
    "                                                    batch_size=256,\n",
    "                                                    image_size=(16,16))\n",
    "    ds_train=ds_train.map(lambda x,y:(x*1/255.,y))\n",
    "    return ds_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def muil_to_bin(x):\n",
    "    if x>0:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train():\n",
    "    print('开始载入数据')\n",
    "    handled_data=pd.read_csv('KDDCUP99_PREPROCESSED_WITHLABELS.csv',header=None)\n",
    "    labels=pd.read_csv('KDDCUP99_AfterNumerical.csv',header=None)\n",
    "    \n",
    "    y=labels.iloc[:,-1]\n",
    "    y=y.map(muil_to_bin)\n",
    "    \n",
    "    temp=DataFrame(handled_data.iloc[:,0:-1])\n",
    "    x=[]\n",
    "    img_size=16\n",
    "    with tqdm(total=temp.shape[0]) as t:\n",
    "        for i in range(temp.shape[0]):\n",
    "            img=Zigzag(np.array(temp.iloc[i]),img_size)\n",
    "            img_idct=cv2.idct(img.flatten())\n",
    "            img_idct=img_idct.reshape([img_size,img_size])\n",
    "            img_idct=tf.reshape(img_idct,[16,16,1])\n",
    "            x.append(img_idct)\n",
    "            t.update(1)\n",
    "\n",
    "    x_train=tf.convert_to_tensor(x,dtype=tf.float32)/255.\n",
    "    y_train=tf.convert_to_tensor(y,dtype=tf.int32)\n",
    "    \n",
    "    y_onehot=tf.one_hot(y_train,depth=2)\n",
    "\n",
    "    ds_train=tf.data.Dataset.from_tensor_slices((x_train,y_onehot)).shuffle(8748454).batch(256)\n",
    "    \n",
    "    print('数据载入，生成datset完成')\n",
    "    return ds_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_val():\n",
    "    input_file='./kddcup99/kddcup.data.corrected'\n",
    "    out_file='KDDCUP99_VALDATA.csv'\n",
    "    zigzag_helper.preprocess(in_file=input_file,out_file=out_file,ifsample=1,sample_num=0,sample_frac=0.02)\n",
    "    val_data=pd.read_csv(out_file,header=None)\n",
    "    x=val_data.iloc[:,0:-1]\n",
    "    y=val_data.iloc[:,-1]\n",
    "\n",
    "    x_val=[]\n",
    "\n",
    "    for i in range(x.shape[0]):\n",
    "        temp=np.array(x.iloc[i])\n",
    "        img=zigzag_helper.zizagcode(temp,16)\n",
    "        img=tf.reshape(img,[16,16,1])\n",
    "        x_val.append(img)\n",
    "        \n",
    "    x_val=tf.convert_to_tensor(x_val,dtype=tf.float32)/255.\n",
    "    y_val=tf.convert_to_tensor(y,dtype=tf.int32)\n",
    "    y_val=tf.one_hot(y_val,depth=2)\n",
    "\n",
    "    ds_val=tf.data.Dataset.from_tensor_slices((x_val,y_val)).shuffle(13124).batch(256)\n",
    "    \n",
    "    return ds_val\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_6 (Conv2D)            (None, 16, 16, 16)        160       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 8, 8, 16)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 8, 8, 32)          4640      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 4, 4, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 2)                 258       \n",
      "=================================================================\n",
      "Total params: 169,282\n",
      "Trainable params: 169,282\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#搭建网络\n",
    "#input=[256,16,16,1]\n",
    "network=Sequential([\n",
    "    layers.Conv2D(16,kernel_size=[3,3],strides=1,padding='same',activation='relu'),\n",
    "    layers.MaxPool2D(pool_size=(2,2),strides=2,padding='same'),\n",
    "    \n",
    "    layers.Conv2D(32,kernel_size=[3,3],strides=1,padding='same',activation='relu'),\n",
    "    layers.MaxPool2D(pool_size=(2,2),strides=2,padding='same'),\n",
    "    \n",
    "    layers.Flatten(),\n",
    "    layers.Dense(256,activation='sigmoid'),\n",
    "    layers.Dropout(0.4),\n",
    "    layers.Dense(128,activation='sigmoid'),\n",
    "    layers.Dropout(0.4),\n",
    "    layers.Dense(2,activation='softmax')\n",
    "])\n",
    "network.build(input_shape=[None,16,16,1])\n",
    "network.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(ds_train,ds_val):\n",
    "    print('开始训练')\n",
    "    current_time = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "    train_log_dir = 'logs/gradient_tape/' + current_time + '/train'\n",
    "    train_summary_writer = tf.summary.create_file_writer(train_log_dir)\n",
    "\n",
    "    tf_callbcak=tf.keras.callbacks.TensorBoard(log_dir=train_log_dir)\n",
    "    \n",
    "    METRICS=[\n",
    "      keras.metrics.TruePositives(name='tp'),\n",
    "      keras.metrics.FalsePositives(name='fp'),\n",
    "      keras.metrics.TrueNegatives(name='tn'),\n",
    "      keras.metrics.FalseNegatives(name='fn'), \n",
    "      keras.metrics.BinaryAccuracy(name='accuracy'),\n",
    "      keras.metrics.Precision(name='precision'),\n",
    "      keras.metrics.Recall(name='recall')\n",
    "    ]\n",
    "    \n",
    "    network.compile(optimizer=optimizers.Adam(learning_rate=6e-4,\n",
    "                                              ),\n",
    "                    loss='binary_crossentropy',\n",
    "                    metrics=METRICS,\n",
    "                    )\n",
    "    network.fit(ds_train,\n",
    "                validation_data=ds_val,\n",
    "                epochs=200,\n",
    "                callbacks=tf_callbcak)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始载入数据\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 494021/494021 [06:35<00:00, 1248.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据载入，生成datset完成\n"
     ]
    }
   ],
   "source": [
    "ds_train=load_train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Anaconda\\envs\\tensorflow\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n",
      "d:\\Anaconda\\envs\\tensorflow\\lib\\site-packages\\sklearn\\base.py:413: UserWarning: X has feature names, but OneHotEncoder was fitted without feature names\n",
      "  warnings.warn(\n",
      "d:\\Anaconda\\envs\\tensorflow\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n",
      "d:\\Anaconda\\envs\\tensorflow\\lib\\site-packages\\sklearn\\base.py:413: UserWarning: X has feature names, but OneHotEncoder was fitted without feature names\n",
      "  warnings.warn(\n",
      "d:\\Anaconda\\envs\\tensorflow\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n",
      "d:\\Anaconda\\envs\\tensorflow\\lib\\site-packages\\sklearn\\base.py:413: UserWarning: X has feature names, but OneHotEncoder was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "afr_oh: (97969, 126)\n",
      "(97969, 123)\n"
     ]
    }
   ],
   "source": [
    "ds_val=load_val()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始训练\n",
      "Epoch 1/200\n",
      "1930/1930 [==============================] - 292s 149ms/step - loss: 0.0527 - tp: 485954.0000 - fp: 8067.0000 - tn: 485954.0000 - fn: 8067.0000 - accuracy: 0.9837 - precision: 0.9837 - recall: 0.9837 - val_loss: 0.0077 - val_tp: 97814.0000 - val_fp: 155.0000 - val_tn: 97814.0000 - val_fn: 155.0000 - val_accuracy: 0.9984 - val_precision: 0.9984 - val_recall: 0.9984\n",
      "Epoch 2/200\n",
      "1930/1930 [==============================] - 269s 139ms/step - loss: 0.0158 - tp: 491627.0000 - fp: 2394.0000 - tn: 491627.0000 - fn: 2394.0000 - accuracy: 0.9952 - precision: 0.9952 - recall: 0.9952 - val_loss: 0.0035 - val_tp: 97868.0000 - val_fp: 101.0000 - val_tn: 97868.0000 - val_fn: 101.0000 - val_accuracy: 0.9990 - val_precision: 0.9990 - val_recall: 0.9990\n",
      "Epoch 3/200\n",
      "1930/1930 [==============================] - 283s 146ms/step - loss: 0.0079 - tp: 492817.0000 - fp: 1204.0000 - tn: 492817.0000 - fn: 1204.0000 - accuracy: 0.9976 - precision: 0.9976 - recall: 0.9976 - val_loss: 0.0052 - val_tp: 97858.0000 - val_fp: 111.0000 - val_tn: 97858.0000 - val_fn: 111.0000 - val_accuracy: 0.9989 - val_precision: 0.9989 - val_recall: 0.9989\n",
      "Epoch 4/200\n",
      "1930/1930 [==============================] - 306s 158ms/step - loss: 0.0052 - tp: 493329.0000 - fp: 692.0000 - tn: 493329.0000 - fn: 692.0000 - accuracy: 0.9986 - precision: 0.9986 - recall: 0.9986 - val_loss: 0.0021 - val_tp: 97916.0000 - val_fp: 53.0000 - val_tn: 97916.0000 - val_fn: 53.0000 - val_accuracy: 0.9995 - val_precision: 0.9995 - val_recall: 0.9995\n",
      "Epoch 5/200\n",
      "1930/1930 [==============================] - 311s 161ms/step - loss: 0.0038 - tp: 493491.0000 - fp: 530.0000 - tn: 493491.0000 - fn: 530.0000 - accuracy: 0.9989 - precision: 0.9989 - recall: 0.9989 - val_loss: 0.0022 - val_tp: 97905.0000 - val_fp: 64.0000 - val_tn: 97905.0000 - val_fn: 64.0000 - val_accuracy: 0.9993 - val_precision: 0.9993 - val_recall: 0.9993\n",
      "Epoch 6/200\n",
      "1930/1930 [==============================] - 315s 163ms/step - loss: 0.0032 - tp: 493579.0000 - fp: 442.0000 - tn: 493579.0000 - fn: 442.0000 - accuracy: 0.9991 - precision: 0.9991 - recall: 0.9991 - val_loss: 0.0018 - val_tp: 97923.0000 - val_fp: 46.0000 - val_tn: 97923.0000 - val_fn: 46.0000 - val_accuracy: 0.9995 - val_precision: 0.9995 - val_recall: 0.9995\n",
      "Epoch 7/200\n",
      "1930/1930 [==============================] - 308s 159ms/step - loss: 0.0029 - tp: 493603.0000 - fp: 418.0000 - tn: 493603.0000 - fn: 418.0000 - accuracy: 0.9992 - precision: 0.9992 - recall: 0.9992 - val_loss: 0.0021 - val_tp: 97914.0000 - val_fp: 55.0000 - val_tn: 97914.0000 - val_fn: 55.0000 - val_accuracy: 0.9994 - val_precision: 0.9994 - val_recall: 0.9994\n",
      "Epoch 8/200\n",
      "1930/1930 [==============================] - 317s 164ms/step - loss: 0.0028 - tp: 493595.0000 - fp: 426.0000 - tn: 493595.0000 - fn: 426.0000 - accuracy: 0.9991 - precision: 0.9991 - recall: 0.9991 - val_loss: 0.0016 - val_tp: 97926.0000 - val_fp: 43.0000 - val_tn: 97926.0000 - val_fn: 43.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 9/200\n",
      "1930/1930 [==============================] - 332s 172ms/step - loss: 0.0026 - tp: 493656.0000 - fp: 365.0000 - tn: 493656.0000 - fn: 365.0000 - accuracy: 0.9993 - precision: 0.9993 - recall: 0.9993 - val_loss: 0.0016 - val_tp: 97922.0000 - val_fp: 47.0000 - val_tn: 97922.0000 - val_fn: 47.0000 - val_accuracy: 0.9995 - val_precision: 0.9995 - val_recall: 0.9995\n",
      "Epoch 10/200\n",
      "1930/1930 [==============================] - 329s 170ms/step - loss: 0.0025 - tp: 493640.0000 - fp: 381.0000 - tn: 493640.0000 - fn: 381.0000 - accuracy: 0.9992 - precision: 0.9992 - recall: 0.9992 - val_loss: 0.0015 - val_tp: 97931.0000 - val_fp: 38.0000 - val_tn: 97931.0000 - val_fn: 38.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 11/200\n",
      "1930/1930 [==============================] - 330s 171ms/step - loss: 0.0024 - tp: 493670.0000 - fp: 351.0000 - tn: 493670.0000 - fn: 351.0000 - accuracy: 0.9993 - precision: 0.9993 - recall: 0.9993 - val_loss: 0.0018 - val_tp: 97923.0000 - val_fp: 46.0000 - val_tn: 97923.0000 - val_fn: 46.0000 - val_accuracy: 0.9995 - val_precision: 0.9995 - val_recall: 0.9995\n",
      "Epoch 12/200\n",
      "1930/1930 [==============================] - 321s 166ms/step - loss: 0.0023 - tp: 493658.0000 - fp: 363.0000 - tn: 493658.0000 - fn: 363.0000 - accuracy: 0.9993 - precision: 0.9993 - recall: 0.9993 - val_loss: 0.0018 - val_tp: 97918.0000 - val_fp: 51.0000 - val_tn: 97918.0000 - val_fn: 51.0000 - val_accuracy: 0.9995 - val_precision: 0.9995 - val_recall: 0.9995\n",
      "Epoch 13/200\n",
      "1930/1930 [==============================] - 312s 161ms/step - loss: 0.0022 - tp: 493694.0000 - fp: 327.0000 - tn: 493694.0000 - fn: 327.0000 - accuracy: 0.9993 - precision: 0.9993 - recall: 0.9993 - val_loss: 0.0022 - val_tp: 97897.0000 - val_fp: 72.0000 - val_tn: 97897.0000 - val_fn: 72.0000 - val_accuracy: 0.9993 - val_precision: 0.9993 - val_recall: 0.9993\n",
      "Epoch 14/200\n",
      "1930/1930 [==============================] - 315s 163ms/step - loss: 0.0023 - tp: 493677.0000 - fp: 344.0000 - tn: 493677.0000 - fn: 344.0000 - accuracy: 0.9993 - precision: 0.9993 - recall: 0.9993 - val_loss: 0.0019 - val_tp: 97915.0000 - val_fp: 54.0000 - val_tn: 97915.0000 - val_fn: 54.0000 - val_accuracy: 0.9994 - val_precision: 0.9994 - val_recall: 0.9994\n",
      "Epoch 15/200\n",
      "1930/1930 [==============================] - 312s 161ms/step - loss: 0.0023 - tp: 493662.0000 - fp: 359.0000 - tn: 493662.0000 - fn: 359.0000 - accuracy: 0.9993 - precision: 0.9993 - recall: 0.9993 - val_loss: 0.0016 - val_tp: 97923.0000 - val_fp: 46.0000 - val_tn: 97923.0000 - val_fn: 46.0000 - val_accuracy: 0.9995 - val_precision: 0.9995 - val_recall: 0.9995\n",
      "Epoch 16/200\n",
      "1930/1930 [==============================] - 312s 161ms/step - loss: 0.0020 - tp: 493698.0000 - fp: 323.0000 - tn: 493698.0000 - fn: 323.0000 - accuracy: 0.9993 - precision: 0.9993 - recall: 0.9993 - val_loss: 0.0015 - val_tp: 97926.0000 - val_fp: 43.0000 - val_tn: 97926.0000 - val_fn: 43.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 17/200\n",
      "1930/1930 [==============================] - 325s 168ms/step - loss: 0.0021 - tp: 493701.0000 - fp: 320.0000 - tn: 493701.0000 - fn: 320.0000 - accuracy: 0.9994 - precision: 0.9994 - recall: 0.9994 - val_loss: 0.0013 - val_tp: 97929.0000 - val_fp: 40.0000 - val_tn: 97929.0000 - val_fn: 40.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 18/200\n",
      "1930/1930 [==============================] - 325s 168ms/step - loss: 0.0019 - tp: 493721.0000 - fp: 300.0000 - tn: 493721.0000 - fn: 300.0000 - accuracy: 0.9994 - precision: 0.9994 - recall: 0.9994 - val_loss: 0.0016 - val_tp: 97926.0000 - val_fp: 43.0000 - val_tn: 97926.0000 - val_fn: 43.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 19/200\n",
      "1930/1930 [==============================] - 323s 167ms/step - loss: 0.0020 - tp: 493710.0000 - fp: 311.0000 - tn: 493710.0000 - fn: 311.0000 - accuracy: 0.9994 - precision: 0.9994 - recall: 0.9994 - val_loss: 0.0012 - val_tp: 97941.0000 - val_fp: 28.0000 - val_tn: 97941.0000 - val_fn: 28.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 20/200\n",
      "1930/1930 [==============================] - 322s 167ms/step - loss: 0.0019 - tp: 493731.0000 - fp: 290.0000 - tn: 493731.0000 - fn: 290.0000 - accuracy: 0.9994 - precision: 0.9994 - recall: 0.9994 - val_loss: 0.0021 - val_tp: 97911.0000 - val_fp: 58.0000 - val_tn: 97911.0000 - val_fn: 58.0000 - val_accuracy: 0.9994 - val_precision: 0.9994 - val_recall: 0.9994\n",
      "Epoch 21/200\n",
      "1930/1930 [==============================] - 319s 165ms/step - loss: 0.0018 - tp: 493743.0000 - fp: 278.0000 - tn: 493743.0000 - fn: 278.0000 - accuracy: 0.9994 - precision: 0.9994 - recall: 0.9994 - val_loss: 0.0022 - val_tp: 97920.0000 - val_fp: 49.0000 - val_tn: 97920.0000 - val_fn: 49.0000 - val_accuracy: 0.9995 - val_precision: 0.9995 - val_recall: 0.9995\n",
      "Epoch 22/200\n",
      "1930/1930 [==============================] - 318s 165ms/step - loss: 0.0019 - tp: 493719.0000 - fp: 302.0000 - tn: 493719.0000 - fn: 302.0000 - accuracy: 0.9994 - precision: 0.9994 - recall: 0.9994 - val_loss: 0.0017 - val_tp: 97926.0000 - val_fp: 43.0000 - val_tn: 97926.0000 - val_fn: 43.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 23/200\n",
      "1930/1930 [==============================] - 322s 166ms/step - loss: 0.0018 - tp: 493738.0000 - fp: 283.0000 - tn: 493738.0000 - fn: 283.0000 - accuracy: 0.9994 - precision: 0.9994 - recall: 0.9994 - val_loss: 0.0013 - val_tp: 97929.0000 - val_fp: 40.0000 - val_tn: 97929.0000 - val_fn: 40.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 24/200\n",
      "1930/1930 [==============================] - 320s 165ms/step - loss: 0.0018 - tp: 493745.0000 - fp: 276.0000 - tn: 493745.0000 - fn: 276.0000 - accuracy: 0.9994 - precision: 0.9994 - recall: 0.9994 - val_loss: 0.0014 - val_tp: 97933.0000 - val_fp: 36.0000 - val_tn: 97933.0000 - val_fn: 36.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 25/200\n",
      "1930/1930 [==============================] - 323s 167ms/step - loss: 0.0017 - tp: 493771.0000 - fp: 250.0000 - tn: 493771.0000 - fn: 250.0000 - accuracy: 0.9995 - precision: 0.9995 - recall: 0.9995 - val_loss: 0.0025 - val_tp: 97894.0000 - val_fp: 75.0000 - val_tn: 97894.0000 - val_fn: 75.0000 - val_accuracy: 0.9992 - val_precision: 0.9992 - val_recall: 0.9992\n",
      "Epoch 26/200\n",
      "1930/1930 [==============================] - 328s 169ms/step - loss: 0.0018 - tp: 493746.0000 - fp: 275.0000 - tn: 493746.0000 - fn: 275.0000 - accuracy: 0.9994 - precision: 0.9994 - recall: 0.9994 - val_loss: 0.0021 - val_tp: 97921.0000 - val_fp: 48.0000 - val_tn: 97921.0000 - val_fn: 48.0000 - val_accuracy: 0.9995 - val_precision: 0.9995 - val_recall: 0.9995\n",
      "Epoch 27/200\n",
      "1930/1930 [==============================] - 324s 167ms/step - loss: 0.0017 - tp: 493757.0000 - fp: 264.0000 - tn: 493757.0000 - fn: 264.0000 - accuracy: 0.9995 - precision: 0.9995 - recall: 0.9995 - val_loss: 0.0013 - val_tp: 97928.0000 - val_fp: 41.0000 - val_tn: 97928.0000 - val_fn: 41.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 28/200\n",
      "1930/1930 [==============================] - 326s 169ms/step - loss: 0.0017 - tp: 493770.0000 - fp: 251.0000 - tn: 493770.0000 - fn: 251.0000 - accuracy: 0.9995 - precision: 0.9995 - recall: 0.9995 - val_loss: 0.0015 - val_tp: 97931.0000 - val_fp: 38.0000 - val_tn: 97931.0000 - val_fn: 38.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 29/200\n",
      "1930/1930 [==============================] - 318s 164ms/step - loss: 0.0017 - tp: 493770.0000 - fp: 251.0000 - tn: 493770.0000 - fn: 251.0000 - accuracy: 0.9995 - precision: 0.9995 - recall: 0.9995 - val_loss: 0.0012 - val_tp: 97938.0000 - val_fp: 31.0000 - val_tn: 97938.0000 - val_fn: 31.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 30/200\n",
      "1930/1930 [==============================] - 314s 162ms/step - loss: 0.0016 - tp: 493773.0000 - fp: 248.0000 - tn: 493773.0000 - fn: 248.0000 - accuracy: 0.9995 - precision: 0.9995 - recall: 0.9995 - val_loss: 0.0013 - val_tp: 97935.0000 - val_fp: 34.0000 - val_tn: 97935.0000 - val_fn: 34.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 31/200\n",
      "1930/1930 [==============================] - 318s 164ms/step - loss: 0.0016 - tp: 493787.0000 - fp: 234.0000 - tn: 493787.0000 - fn: 234.0000 - accuracy: 0.9995 - precision: 0.9995 - recall: 0.9995 - val_loss: 0.0014 - val_tp: 97934.0000 - val_fp: 35.0000 - val_tn: 97934.0000 - val_fn: 35.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 32/200\n",
      "1930/1930 [==============================] - 314s 163ms/step - loss: 0.0016 - tp: 493790.0000 - fp: 231.0000 - tn: 493790.0000 - fn: 231.0000 - accuracy: 0.9995 - precision: 0.9995 - recall: 0.9995 - val_loss: 0.0014 - val_tp: 97933.0000 - val_fp: 36.0000 - val_tn: 97933.0000 - val_fn: 36.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 33/200\n",
      "1930/1930 [==============================] - 313s 162ms/step - loss: 0.0016 - tp: 493785.0000 - fp: 236.0000 - tn: 493785.0000 - fn: 236.0000 - accuracy: 0.9995 - precision: 0.9995 - recall: 0.9995 - val_loss: 0.0016 - val_tp: 97932.0000 - val_fp: 37.0000 - val_tn: 97932.0000 - val_fn: 37.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 34/200\n",
      "1930/1930 [==============================] - 327s 169ms/step - loss: 0.0015 - tp: 493787.0000 - fp: 234.0000 - tn: 493787.0000 - fn: 234.0000 - accuracy: 0.9995 - precision: 0.9995 - recall: 0.9995 - val_loss: 0.0013 - val_tp: 97933.0000 - val_fp: 36.0000 - val_tn: 97933.0000 - val_fn: 36.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 35/200\n",
      "1930/1930 [==============================] - 320s 165ms/step - loss: 0.0015 - tp: 493803.0000 - fp: 218.0000 - tn: 493803.0000 - fn: 218.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0015 - val_tp: 97938.0000 - val_fp: 31.0000 - val_tn: 97938.0000 - val_fn: 31.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 36/200\n",
      "1930/1930 [==============================] - 324s 167ms/step - loss: 0.0015 - tp: 493813.0000 - fp: 208.0000 - tn: 493813.0000 - fn: 208.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0016 - val_tp: 97934.0000 - val_fp: 35.0000 - val_tn: 97934.0000 - val_fn: 35.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 37/200\n",
      "1930/1930 [==============================] - 326s 168ms/step - loss: 0.0016 - tp: 493791.0000 - fp: 230.0000 - tn: 493791.0000 - fn: 230.0000 - accuracy: 0.9995 - precision: 0.9995 - recall: 0.9995 - val_loss: 0.0015 - val_tp: 97931.0000 - val_fp: 38.0000 - val_tn: 97931.0000 - val_fn: 38.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 38/200\n",
      "1930/1930 [==============================] - 311s 161ms/step - loss: 0.0015 - tp: 493807.0000 - fp: 214.0000 - tn: 493807.0000 - fn: 214.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0017 - val_tp: 97930.0000 - val_fp: 39.0000 - val_tn: 97930.0000 - val_fn: 39.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 39/200\n",
      "1930/1930 [==============================] - 324s 168ms/step - loss: 0.0015 - tp: 493810.0000 - fp: 211.0000 - tn: 493810.0000 - fn: 211.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0015 - val_tp: 97929.0000 - val_fp: 40.0000 - val_tn: 97929.0000 - val_fn: 40.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 40/200\n",
      "1930/1930 [==============================] - 326s 168ms/step - loss: 0.0014 - tp: 493801.0000 - fp: 220.0000 - tn: 493801.0000 - fn: 220.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0017 - val_tp: 97935.0000 - val_fp: 34.0000 - val_tn: 97935.0000 - val_fn: 34.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 41/200\n",
      "1930/1930 [==============================] - 319s 165ms/step - loss: 0.0014 - tp: 493805.0000 - fp: 216.0000 - tn: 493805.0000 - fn: 216.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0015 - val_tp: 97937.0000 - val_fp: 32.0000 - val_tn: 97937.0000 - val_fn: 32.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 42/200\n",
      "1930/1930 [==============================] - 321s 166ms/step - loss: 0.0014 - tp: 493819.0000 - fp: 202.0000 - tn: 493819.0000 - fn: 202.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0015 - val_tp: 97934.0000 - val_fp: 35.0000 - val_tn: 97934.0000 - val_fn: 35.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 43/200\n",
      "1930/1930 [==============================] - 319s 165ms/step - loss: 0.0014 - tp: 493810.0000 - fp: 211.0000 - tn: 493810.0000 - fn: 211.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0014 - val_tp: 97939.0000 - val_fp: 30.0000 - val_tn: 97939.0000 - val_fn: 30.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 44/200\n",
      "1930/1930 [==============================] - 323s 167ms/step - loss: 0.0014 - tp: 493816.0000 - fp: 205.0000 - tn: 493816.0000 - fn: 205.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0013 - val_tp: 97941.0000 - val_fp: 28.0000 - val_tn: 97941.0000 - val_fn: 28.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 45/200\n",
      "1930/1930 [==============================] - 328s 169ms/step - loss: 0.0014 - tp: 493824.0000 - fp: 197.0000 - tn: 493824.0000 - fn: 197.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0014 - val_tp: 97936.0000 - val_fp: 33.0000 - val_tn: 97936.0000 - val_fn: 33.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 46/200\n",
      "1930/1930 [==============================] - 316s 163ms/step - loss: 0.0014 - tp: 493831.0000 - fp: 190.0000 - tn: 493831.0000 - fn: 190.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0016 - val_tp: 97936.0000 - val_fp: 33.0000 - val_tn: 97936.0000 - val_fn: 33.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 47/200\n",
      "1930/1930 [==============================] - 314s 162ms/step - loss: 0.0014 - tp: 493820.0000 - fp: 201.0000 - tn: 493820.0000 - fn: 201.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0020 - val_tp: 97924.0000 - val_fp: 45.0000 - val_tn: 97924.0000 - val_fn: 45.0000 - val_accuracy: 0.9995 - val_precision: 0.9995 - val_recall: 0.9995\n",
      "Epoch 48/200\n",
      "1930/1930 [==============================] - 325s 168ms/step - loss: 0.0013 - tp: 493828.0000 - fp: 193.0000 - tn: 493828.0000 - fn: 193.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0018 - val_tp: 97926.0000 - val_fp: 43.0000 - val_tn: 97926.0000 - val_fn: 43.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 49/200\n",
      "1930/1930 [==============================] - 317s 164ms/step - loss: 0.0013 - tp: 493823.0000 - fp: 198.0000 - tn: 493823.0000 - fn: 198.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0019 - val_tp: 97934.0000 - val_fp: 35.0000 - val_tn: 97934.0000 - val_fn: 35.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 50/200\n",
      "1930/1930 [==============================] - 323s 167ms/step - loss: 0.0013 - tp: 493817.0000 - fp: 204.0000 - tn: 493817.0000 - fn: 204.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0021 - val_tp: 97924.0000 - val_fp: 45.0000 - val_tn: 97924.0000 - val_fn: 45.0000 - val_accuracy: 0.9995 - val_precision: 0.9995 - val_recall: 0.9995\n",
      "Epoch 51/200\n",
      "1930/1930 [==============================] - 324s 168ms/step - loss: 0.0014 - tp: 493817.0000 - fp: 204.0000 - tn: 493817.0000 - fn: 204.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0014 - val_tp: 97932.0000 - val_fp: 37.0000 - val_tn: 97932.0000 - val_fn: 37.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 52/200\n",
      "1930/1930 [==============================] - 315s 163ms/step - loss: 0.0013 - tp: 493841.0000 - fp: 180.0000 - tn: 493841.0000 - fn: 180.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0013 - val_tp: 97937.0000 - val_fp: 32.0000 - val_tn: 97937.0000 - val_fn: 32.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 53/200\n",
      "1930/1930 [==============================] - 325s 168ms/step - loss: 0.0013 - tp: 493818.0000 - fp: 203.0000 - tn: 493818.0000 - fn: 203.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0016 - val_tp: 97935.0000 - val_fp: 34.0000 - val_tn: 97935.0000 - val_fn: 34.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 54/200\n",
      "1930/1930 [==============================] - 319s 165ms/step - loss: 0.0013 - tp: 493822.0000 - fp: 199.0000 - tn: 493822.0000 - fn: 199.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0014 - val_tp: 97929.0000 - val_fp: 40.0000 - val_tn: 97929.0000 - val_fn: 40.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 55/200\n",
      "1930/1930 [==============================] - 323s 167ms/step - loss: 0.0013 - tp: 493842.0000 - fp: 179.0000 - tn: 493842.0000 - fn: 179.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0013 - val_tp: 97938.0000 - val_fp: 31.0000 - val_tn: 97938.0000 - val_fn: 31.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 56/200\n",
      "1930/1930 [==============================] - 331s 171ms/step - loss: 0.0013 - tp: 493846.0000 - fp: 175.0000 - tn: 493846.0000 - fn: 175.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0014 - val_tp: 97934.0000 - val_fp: 35.0000 - val_tn: 97934.0000 - val_fn: 35.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 57/200\n",
      "1930/1930 [==============================] - 329s 170ms/step - loss: 0.0012 - tp: 493832.0000 - fp: 189.0000 - tn: 493832.0000 - fn: 189.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0014 - val_tp: 97940.0000 - val_fp: 29.0000 - val_tn: 97940.0000 - val_fn: 29.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 58/200\n",
      "1930/1930 [==============================] - 321s 166ms/step - loss: 0.0012 - tp: 493844.0000 - fp: 177.0000 - tn: 493844.0000 - fn: 177.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0016 - val_tp: 97936.0000 - val_fp: 33.0000 - val_tn: 97936.0000 - val_fn: 33.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 59/200\n",
      "1930/1930 [==============================] - 306s 158ms/step - loss: 0.0013 - tp: 493844.0000 - fp: 177.0000 - tn: 493844.0000 - fn: 177.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0015 - val_tp: 97934.0000 - val_fp: 35.0000 - val_tn: 97934.0000 - val_fn: 35.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 60/200\n",
      "1930/1930 [==============================] - 311s 161ms/step - loss: 0.0012 - tp: 493827.0000 - fp: 194.0000 - tn: 493827.0000 - fn: 194.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0017 - val_tp: 97938.0000 - val_fp: 31.0000 - val_tn: 97938.0000 - val_fn: 31.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 61/200\n",
      "1930/1930 [==============================] - 317s 164ms/step - loss: 0.0012 - tp: 493846.0000 - fp: 175.0000 - tn: 493846.0000 - fn: 175.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0015 - val_tp: 97942.0000 - val_fp: 27.0000 - val_tn: 97942.0000 - val_fn: 27.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 62/200\n",
      "1930/1930 [==============================] - 321s 166ms/step - loss: 0.0012 - tp: 493842.0000 - fp: 179.0000 - tn: 493842.0000 - fn: 179.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0017 - val_tp: 97929.0000 - val_fp: 40.0000 - val_tn: 97929.0000 - val_fn: 40.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 63/200\n",
      "1930/1930 [==============================] - 320s 165ms/step - loss: 0.0013 - tp: 493837.0000 - fp: 184.0000 - tn: 493837.0000 - fn: 184.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0016 - val_tp: 97935.0000 - val_fp: 34.0000 - val_tn: 97935.0000 - val_fn: 34.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 64/200\n",
      "1930/1930 [==============================] - 320s 165ms/step - loss: 0.0012 - tp: 493837.0000 - fp: 184.0000 - tn: 493837.0000 - fn: 184.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0014 - val_tp: 97948.0000 - val_fp: 21.0000 - val_tn: 97948.0000 - val_fn: 21.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 65/200\n",
      "1930/1930 [==============================] - 319s 165ms/step - loss: 0.0012 - tp: 493838.0000 - fp: 183.0000 - tn: 493838.0000 - fn: 183.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0015 - val_tp: 97944.0000 - val_fp: 25.0000 - val_tn: 97944.0000 - val_fn: 25.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 66/200\n",
      "1930/1930 [==============================] - 325s 168ms/step - loss: 0.0013 - tp: 493836.0000 - fp: 185.0000 - tn: 493836.0000 - fn: 185.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0020 - val_tp: 97925.0000 - val_fp: 44.0000 - val_tn: 97925.0000 - val_fn: 44.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 67/200\n",
      "1930/1930 [==============================] - 326s 168ms/step - loss: 0.0012 - tp: 493838.0000 - fp: 183.0000 - tn: 493838.0000 - fn: 183.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0015 - val_tp: 97935.0000 - val_fp: 34.0000 - val_tn: 97935.0000 - val_fn: 34.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 68/200\n",
      "1930/1930 [==============================] - 325s 168ms/step - loss: 0.0011 - tp: 493851.0000 - fp: 170.0000 - tn: 493851.0000 - fn: 170.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0016 - val_tp: 97935.0000 - val_fp: 34.0000 - val_tn: 97935.0000 - val_fn: 34.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 69/200\n",
      "1930/1930 [==============================] - 333s 172ms/step - loss: 0.0011 - tp: 493861.0000 - fp: 160.0000 - tn: 493861.0000 - fn: 160.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0014 - val_tp: 97941.0000 - val_fp: 28.0000 - val_tn: 97941.0000 - val_fn: 28.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 70/200\n",
      "1930/1930 [==============================] - 317s 164ms/step - loss: 0.0012 - tp: 493859.0000 - fp: 162.0000 - tn: 493859.0000 - fn: 162.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0018 - val_tp: 97939.0000 - val_fp: 30.0000 - val_tn: 97939.0000 - val_fn: 30.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 71/200\n",
      "1930/1930 [==============================] - 316s 163ms/step - loss: 0.0012 - tp: 493837.0000 - fp: 184.0000 - tn: 493837.0000 - fn: 184.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0018 - val_tp: 97938.0000 - val_fp: 31.0000 - val_tn: 97938.0000 - val_fn: 31.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 72/200\n",
      "1930/1930 [==============================] - 326s 168ms/step - loss: 0.0012 - tp: 493854.0000 - fp: 167.0000 - tn: 493854.0000 - fn: 167.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0014 - val_tp: 97945.0000 - val_fp: 24.0000 - val_tn: 97945.0000 - val_fn: 24.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 73/200\n",
      "1930/1930 [==============================] - 321s 166ms/step - loss: 0.0010 - tp: 493857.0000 - fp: 164.0000 - tn: 493857.0000 - fn: 164.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0015 - val_tp: 97946.0000 - val_fp: 23.0000 - val_tn: 97946.0000 - val_fn: 23.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 74/200\n",
      "1930/1930 [==============================] - 325s 168ms/step - loss: 0.0012 - tp: 493846.0000 - fp: 175.0000 - tn: 493846.0000 - fn: 175.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0016 - val_tp: 97941.0000 - val_fp: 28.0000 - val_tn: 97941.0000 - val_fn: 28.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 75/200\n",
      "1930/1930 [==============================] - 324s 168ms/step - loss: 0.0012 - tp: 493851.0000 - fp: 170.0000 - tn: 493851.0000 - fn: 170.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0015 - val_tp: 97944.0000 - val_fp: 25.0000 - val_tn: 97944.0000 - val_fn: 25.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 76/200\n",
      "1930/1930 [==============================] - 334s 173ms/step - loss: 0.0012 - tp: 493840.0000 - fp: 181.0000 - tn: 493840.0000 - fn: 181.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0022 - val_tp: 97913.0000 - val_fp: 56.0000 - val_tn: 97913.0000 - val_fn: 56.0000 - val_accuracy: 0.9994 - val_precision: 0.9994 - val_recall: 0.9994\n",
      "Epoch 77/200\n",
      "1930/1930 [==============================] - 338s 175ms/step - loss: 0.0011 - tp: 493857.0000 - fp: 164.0000 - tn: 493857.0000 - fn: 164.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0019 - val_tp: 97931.0000 - val_fp: 38.0000 - val_tn: 97931.0000 - val_fn: 38.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 78/200\n",
      "1930/1930 [==============================] - 323s 167ms/step - loss: 0.0012 - tp: 493839.0000 - fp: 182.0000 - tn: 493839.0000 - fn: 182.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0018 - val_tp: 97936.0000 - val_fp: 33.0000 - val_tn: 97936.0000 - val_fn: 33.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 79/200\n",
      "1930/1930 [==============================] - 329s 170ms/step - loss: 0.0011 - tp: 493861.0000 - fp: 160.0000 - tn: 493861.0000 - fn: 160.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0016 - val_tp: 97943.0000 - val_fp: 26.0000 - val_tn: 97943.0000 - val_fn: 26.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 80/200\n",
      "1930/1930 [==============================] - 332s 172ms/step - loss: 0.0012 - tp: 493859.0000 - fp: 162.0000 - tn: 493859.0000 - fn: 162.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0016 - val_tp: 97939.0000 - val_fp: 30.0000 - val_tn: 97939.0000 - val_fn: 30.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 81/200\n",
      "1930/1930 [==============================] - 316s 163ms/step - loss: 0.0011 - tp: 493861.0000 - fp: 160.0000 - tn: 493861.0000 - fn: 160.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0017 - val_tp: 97940.0000 - val_fp: 29.0000 - val_tn: 97940.0000 - val_fn: 29.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 82/200\n",
      "1930/1930 [==============================] - 325s 168ms/step - loss: 0.0011 - tp: 493859.0000 - fp: 162.0000 - tn: 493859.0000 - fn: 162.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0018 - val_tp: 97942.0000 - val_fp: 27.0000 - val_tn: 97942.0000 - val_fn: 27.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 83/200\n",
      "1930/1930 [==============================] - 323s 167ms/step - loss: 0.0011 - tp: 493867.0000 - fp: 154.0000 - tn: 493867.0000 - fn: 154.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0017 - val_tp: 97939.0000 - val_fp: 30.0000 - val_tn: 97939.0000 - val_fn: 30.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 84/200\n",
      "1930/1930 [==============================] - 315s 163ms/step - loss: 0.0011 - tp: 493859.0000 - fp: 162.0000 - tn: 493859.0000 - fn: 162.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0024 - val_tp: 97921.0000 - val_fp: 48.0000 - val_tn: 97921.0000 - val_fn: 48.0000 - val_accuracy: 0.9995 - val_precision: 0.9995 - val_recall: 0.9995\n",
      "Epoch 85/200\n",
      "1930/1930 [==============================] - 324s 168ms/step - loss: 0.0012 - tp: 493851.0000 - fp: 170.0000 - tn: 493851.0000 - fn: 170.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0021 - val_tp: 97928.0000 - val_fp: 41.0000 - val_tn: 97928.0000 - val_fn: 41.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 86/200\n",
      "1930/1930 [==============================] - 320s 166ms/step - loss: 0.0011 - tp: 493865.0000 - fp: 156.0000 - tn: 493865.0000 - fn: 156.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0015 - val_tp: 97947.0000 - val_fp: 22.0000 - val_tn: 97947.0000 - val_fn: 22.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 87/200\n",
      "1930/1930 [==============================] - 327s 169ms/step - loss: 0.0011 - tp: 493857.0000 - fp: 164.0000 - tn: 493857.0000 - fn: 164.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0017 - val_tp: 97940.0000 - val_fp: 29.0000 - val_tn: 97940.0000 - val_fn: 29.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 88/200\n",
      "1930/1930 [==============================] - 327s 169ms/step - loss: 0.0012 - tp: 493857.0000 - fp: 164.0000 - tn: 493857.0000 - fn: 164.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0013 - val_tp: 97947.0000 - val_fp: 22.0000 - val_tn: 97947.0000 - val_fn: 22.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 89/200\n",
      "1930/1930 [==============================] - 321s 166ms/step - loss: 0.0011 - tp: 493857.0000 - fp: 164.0000 - tn: 493857.0000 - fn: 164.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0015 - val_tp: 97943.0000 - val_fp: 26.0000 - val_tn: 97943.0000 - val_fn: 26.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 90/200\n",
      "1930/1930 [==============================] - 321s 166ms/step - loss: 0.0011 - tp: 493869.0000 - fp: 152.0000 - tn: 493869.0000 - fn: 152.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0020 - val_tp: 97935.0000 - val_fp: 34.0000 - val_tn: 97935.0000 - val_fn: 34.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 91/200\n",
      "1930/1930 [==============================] - 321s 166ms/step - loss: 0.0011 - tp: 493864.0000 - fp: 157.0000 - tn: 493864.0000 - fn: 157.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0016 - val_tp: 97946.0000 - val_fp: 23.0000 - val_tn: 97946.0000 - val_fn: 23.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 92/200\n",
      "1930/1930 [==============================] - 330s 170ms/step - loss: 0.0011 - tp: 493857.0000 - fp: 164.0000 - tn: 493857.0000 - fn: 164.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0019 - val_tp: 97948.0000 - val_fp: 21.0000 - val_tn: 97948.0000 - val_fn: 21.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 93/200\n",
      "1930/1930 [==============================] - 332s 172ms/step - loss: 0.0010 - tp: 493875.0000 - fp: 146.0000 - tn: 493875.0000 - fn: 146.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0015 - val_tp: 97945.0000 - val_fp: 24.0000 - val_tn: 97945.0000 - val_fn: 24.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 94/200\n",
      "1930/1930 [==============================] - 333s 172ms/step - loss: 0.0010 - tp: 493881.0000 - fp: 140.0000 - tn: 493881.0000 - fn: 140.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0017 - val_tp: 97936.0000 - val_fp: 33.0000 - val_tn: 97936.0000 - val_fn: 33.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 95/200\n",
      "1930/1930 [==============================] - 331s 171ms/step - loss: 0.0012 - tp: 493866.0000 - fp: 155.0000 - tn: 493866.0000 - fn: 155.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0020 - val_tp: 97928.0000 - val_fp: 41.0000 - val_tn: 97928.0000 - val_fn: 41.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 96/200\n",
      "1930/1930 [==============================] - 325s 168ms/step - loss: 0.0011 - tp: 493883.0000 - fp: 138.0000 - tn: 493883.0000 - fn: 138.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0016 - val_tp: 97939.0000 - val_fp: 30.0000 - val_tn: 97939.0000 - val_fn: 30.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 97/200\n",
      "1930/1930 [==============================] - 325s 168ms/step - loss: 0.0011 - tp: 493874.0000 - fp: 147.0000 - tn: 493874.0000 - fn: 147.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0018 - val_tp: 97942.0000 - val_fp: 27.0000 - val_tn: 97942.0000 - val_fn: 27.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 98/200\n",
      "1930/1930 [==============================] - 331s 171ms/step - loss: 0.0010 - tp: 493884.0000 - fp: 137.0000 - tn: 493884.0000 - fn: 137.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0019 - val_tp: 97941.0000 - val_fp: 28.0000 - val_tn: 97941.0000 - val_fn: 28.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 99/200\n",
      "1930/1930 [==============================] - 328s 169ms/step - loss: 0.0011 - tp: 493858.0000 - fp: 163.0000 - tn: 493858.0000 - fn: 163.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0018 - val_tp: 97945.0000 - val_fp: 24.0000 - val_tn: 97945.0000 - val_fn: 24.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 100/200\n",
      "1930/1930 [==============================] - 344s 178ms/step - loss: 0.0010 - tp: 493872.0000 - fp: 149.0000 - tn: 493872.0000 - fn: 149.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0021 - val_tp: 97939.0000 - val_fp: 30.0000 - val_tn: 97939.0000 - val_fn: 30.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 101/200\n",
      "1930/1930 [==============================] - 322s 167ms/step - loss: 0.0011 - tp: 493848.0000 - fp: 173.0000 - tn: 493848.0000 - fn: 173.0000 - accuracy: 0.9996 - precision: 0.9996 - recall: 0.9996 - val_loss: 0.0021 - val_tp: 97929.0000 - val_fp: 40.0000 - val_tn: 97929.0000 - val_fn: 40.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 102/200\n",
      "1930/1930 [==============================] - 327s 169ms/step - loss: 0.0010 - tp: 493867.0000 - fp: 154.0000 - tn: 493867.0000 - fn: 154.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0019 - val_tp: 97940.0000 - val_fp: 29.0000 - val_tn: 97940.0000 - val_fn: 29.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 103/200\n",
      "1930/1930 [==============================] - 339s 175ms/step - loss: 0.0012 - tp: 493856.0000 - fp: 165.0000 - tn: 493856.0000 - fn: 165.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0015 - val_tp: 97945.0000 - val_fp: 24.0000 - val_tn: 97945.0000 - val_fn: 24.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 104/200\n",
      "1930/1930 [==============================] - 327s 169ms/step - loss: 0.0011 - tp: 493860.0000 - fp: 161.0000 - tn: 493860.0000 - fn: 161.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0016 - val_tp: 97944.0000 - val_fp: 25.0000 - val_tn: 97944.0000 - val_fn: 25.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 105/200\n",
      "1930/1930 [==============================] - 335s 173ms/step - loss: 0.0011 - tp: 493866.0000 - fp: 155.0000 - tn: 493866.0000 - fn: 155.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0014 - val_tp: 97945.0000 - val_fp: 24.0000 - val_tn: 97945.0000 - val_fn: 24.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 106/200\n",
      "1930/1930 [==============================] - 328s 169ms/step - loss: 0.0010 - tp: 493868.0000 - fp: 153.0000 - tn: 493868.0000 - fn: 153.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0016 - val_tp: 97945.0000 - val_fp: 24.0000 - val_tn: 97945.0000 - val_fn: 24.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 107/200\n",
      "1930/1930 [==============================] - 323s 167ms/step - loss: 0.0011 - tp: 493866.0000 - fp: 155.0000 - tn: 493866.0000 - fn: 155.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0026 - val_tp: 97921.0000 - val_fp: 48.0000 - val_tn: 97921.0000 - val_fn: 48.0000 - val_accuracy: 0.9995 - val_precision: 0.9995 - val_recall: 0.9995\n",
      "Epoch 108/200\n",
      "1930/1930 [==============================] - 332s 172ms/step - loss: 0.0011 - tp: 493865.0000 - fp: 156.0000 - tn: 493865.0000 - fn: 156.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0018 - val_tp: 97937.0000 - val_fp: 32.0000 - val_tn: 97937.0000 - val_fn: 32.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 109/200\n",
      "1930/1930 [==============================] - 337s 174ms/step - loss: 0.0011 - tp: 493872.0000 - fp: 149.0000 - tn: 493872.0000 - fn: 149.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0015 - val_tp: 97944.0000 - val_fp: 25.0000 - val_tn: 97944.0000 - val_fn: 25.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 110/200\n",
      "1930/1930 [==============================] - 336s 173ms/step - loss: 0.0011 - tp: 493870.0000 - fp: 151.0000 - tn: 493870.0000 - fn: 151.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0016 - val_tp: 97947.0000 - val_fp: 22.0000 - val_tn: 97947.0000 - val_fn: 22.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 111/200\n",
      "1930/1930 [==============================] - 338s 175ms/step - loss: 0.0011 - tp: 493866.0000 - fp: 155.0000 - tn: 493866.0000 - fn: 155.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0017 - val_tp: 97943.0000 - val_fp: 26.0000 - val_tn: 97943.0000 - val_fn: 26.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 112/200\n",
      "1930/1930 [==============================] - 323s 167ms/step - loss: 0.0011 - tp: 493875.0000 - fp: 146.0000 - tn: 493875.0000 - fn: 146.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0017 - val_tp: 97934.0000 - val_fp: 35.0000 - val_tn: 97934.0000 - val_fn: 35.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 113/200\n",
      "1930/1930 [==============================] - 317s 164ms/step - loss: 0.0010 - tp: 493874.0000 - fp: 147.0000 - tn: 493874.0000 - fn: 147.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0020 - val_tp: 97943.0000 - val_fp: 26.0000 - val_tn: 97943.0000 - val_fn: 26.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 114/200\n",
      "1930/1930 [==============================] - 325s 168ms/step - loss: 0.0011 - tp: 493854.0000 - fp: 167.0000 - tn: 493854.0000 - fn: 167.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0018 - val_tp: 97942.0000 - val_fp: 27.0000 - val_tn: 97942.0000 - val_fn: 27.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 115/200\n",
      "1930/1930 [==============================] - 328s 170ms/step - loss: 0.0010 - tp: 493876.0000 - fp: 145.0000 - tn: 493876.0000 - fn: 145.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0019 - val_tp: 97942.0000 - val_fp: 27.0000 - val_tn: 97942.0000 - val_fn: 27.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 116/200\n",
      "1930/1930 [==============================] - 330s 170ms/step - loss: 0.0011 - tp: 493868.0000 - fp: 153.0000 - tn: 493868.0000 - fn: 153.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0017 - val_tp: 97943.0000 - val_fp: 26.0000 - val_tn: 97943.0000 - val_fn: 26.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 117/200\n",
      "1930/1930 [==============================] - 329s 170ms/step - loss: 0.0010 - tp: 493882.0000 - fp: 139.0000 - tn: 493882.0000 - fn: 139.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0019 - val_tp: 97946.0000 - val_fp: 23.0000 - val_tn: 97946.0000 - val_fn: 23.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 118/200\n",
      "1930/1930 [==============================] - 323s 167ms/step - loss: 0.0011 - tp: 493862.0000 - fp: 159.0000 - tn: 493862.0000 - fn: 159.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0021 - val_tp: 97941.0000 - val_fp: 28.0000 - val_tn: 97941.0000 - val_fn: 28.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 119/200\n",
      "1930/1930 [==============================] - 318s 165ms/step - loss: 0.0011 - tp: 493878.0000 - fp: 143.0000 - tn: 493878.0000 - fn: 143.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0020 - val_tp: 97939.0000 - val_fp: 30.0000 - val_tn: 97939.0000 - val_fn: 30.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 120/200\n",
      "1930/1930 [==============================] - 322s 167ms/step - loss: 0.0010 - tp: 493873.0000 - fp: 148.0000 - tn: 493873.0000 - fn: 148.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0022 - val_tp: 97933.0000 - val_fp: 36.0000 - val_tn: 97933.0000 - val_fn: 36.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 121/200\n",
      "1930/1930 [==============================] - 318s 164ms/step - loss: 0.0011 - tp: 493857.0000 - fp: 164.0000 - tn: 493857.0000 - fn: 164.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0019 - val_tp: 97943.0000 - val_fp: 26.0000 - val_tn: 97943.0000 - val_fn: 26.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 122/200\n",
      "1930/1930 [==============================] - 320s 165ms/step - loss: 0.0011 - tp: 493860.0000 - fp: 161.0000 - tn: 493860.0000 - fn: 161.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0026 - val_tp: 97923.0000 - val_fp: 46.0000 - val_tn: 97923.0000 - val_fn: 46.0000 - val_accuracy: 0.9995 - val_precision: 0.9995 - val_recall: 0.9995\n",
      "Epoch 123/200\n",
      "1930/1930 [==============================] - 318s 164ms/step - loss: 9.6759e-04 - tp: 493880.0000 - fp: 141.0000 - tn: 493880.0000 - fn: 141.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0020 - val_tp: 97942.0000 - val_fp: 27.0000 - val_tn: 97942.0000 - val_fn: 27.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 124/200\n",
      "1930/1930 [==============================] - 327s 169ms/step - loss: 0.0011 - tp: 493867.0000 - fp: 154.0000 - tn: 493867.0000 - fn: 154.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0015 - val_tp: 97945.0000 - val_fp: 24.0000 - val_tn: 97945.0000 - val_fn: 24.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 125/200\n",
      "1930/1930 [==============================] - 319s 165ms/step - loss: 0.0010 - tp: 493882.0000 - fp: 139.0000 - tn: 493882.0000 - fn: 139.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0016 - val_tp: 97948.0000 - val_fp: 21.0000 - val_tn: 97948.0000 - val_fn: 21.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 126/200\n",
      "1930/1930 [==============================] - 313s 162ms/step - loss: 0.0010 - tp: 493884.0000 - fp: 137.0000 - tn: 493884.0000 - fn: 137.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0016 - val_tp: 97945.0000 - val_fp: 24.0000 - val_tn: 97945.0000 - val_fn: 24.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 127/200\n",
      "1930/1930 [==============================] - 322s 166ms/step - loss: 0.0010 - tp: 493872.0000 - fp: 149.0000 - tn: 493872.0000 - fn: 149.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0016 - val_tp: 97943.0000 - val_fp: 26.0000 - val_tn: 97943.0000 - val_fn: 26.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 128/200\n",
      "1930/1930 [==============================] - 316s 163ms/step - loss: 0.0010 - tp: 493878.0000 - fp: 143.0000 - tn: 493878.0000 - fn: 143.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0023 - val_tp: 97936.0000 - val_fp: 33.0000 - val_tn: 97936.0000 - val_fn: 33.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 129/200\n",
      "1930/1930 [==============================] - 313s 162ms/step - loss: 0.0011 - tp: 493867.0000 - fp: 154.0000 - tn: 493867.0000 - fn: 154.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0026 - val_tp: 97939.0000 - val_fp: 30.0000 - val_tn: 97939.0000 - val_fn: 30.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 130/200\n",
      "1930/1930 [==============================] - 320s 165ms/step - loss: 0.0011 - tp: 493872.0000 - fp: 149.0000 - tn: 493872.0000 - fn: 149.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0016 - val_tp: 97942.0000 - val_fp: 27.0000 - val_tn: 97942.0000 - val_fn: 27.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 131/200\n",
      "1930/1930 [==============================] - 322s 167ms/step - loss: 9.2978e-04 - tp: 493885.0000 - fp: 136.0000 - tn: 493885.0000 - fn: 136.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0021 - val_tp: 97941.0000 - val_fp: 28.0000 - val_tn: 97941.0000 - val_fn: 28.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 132/200\n",
      "1930/1930 [==============================] - 315s 163ms/step - loss: 0.0010 - tp: 493874.0000 - fp: 147.0000 - tn: 493874.0000 - fn: 147.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0018 - val_tp: 97945.0000 - val_fp: 24.0000 - val_tn: 97945.0000 - val_fn: 24.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 133/200\n",
      "1930/1930 [==============================] - 314s 162ms/step - loss: 0.0010 - tp: 493872.0000 - fp: 149.0000 - tn: 493872.0000 - fn: 149.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0024 - val_tp: 97930.0000 - val_fp: 39.0000 - val_tn: 97930.0000 - val_fn: 39.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 134/200\n",
      "1930/1930 [==============================] - 313s 162ms/step - loss: 0.0011 - tp: 493873.0000 - fp: 148.0000 - tn: 493873.0000 - fn: 148.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0017 - val_tp: 97941.0000 - val_fp: 28.0000 - val_tn: 97941.0000 - val_fn: 28.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 135/200\n",
      "1930/1930 [==============================] - 320s 165ms/step - loss: 0.0010 - tp: 493876.0000 - fp: 145.0000 - tn: 493876.0000 - fn: 145.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0018 - val_tp: 97940.0000 - val_fp: 29.0000 - val_tn: 97940.0000 - val_fn: 29.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 136/200\n",
      "1930/1930 [==============================] - 331s 171ms/step - loss: 0.0010 - tp: 493853.0000 - fp: 168.0000 - tn: 493853.0000 - fn: 168.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0021 - val_tp: 97939.0000 - val_fp: 30.0000 - val_tn: 97939.0000 - val_fn: 30.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 137/200\n",
      "1930/1930 [==============================] - 324s 167ms/step - loss: 9.9751e-04 - tp: 493884.0000 - fp: 137.0000 - tn: 493884.0000 - fn: 137.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0018 - val_tp: 97940.0000 - val_fp: 29.0000 - val_tn: 97940.0000 - val_fn: 29.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 138/200\n",
      "1930/1930 [==============================] - 328s 170ms/step - loss: 0.0010 - tp: 493868.0000 - fp: 153.0000 - tn: 493868.0000 - fn: 153.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0024 - val_tp: 97936.0000 - val_fp: 33.0000 - val_tn: 97936.0000 - val_fn: 33.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 139/200\n",
      "1930/1930 [==============================] - 325s 168ms/step - loss: 9.7929e-04 - tp: 493882.0000 - fp: 139.0000 - tn: 493882.0000 - fn: 139.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0022 - val_tp: 97934.0000 - val_fp: 35.0000 - val_tn: 97934.0000 - val_fn: 35.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 140/200\n",
      "1930/1930 [==============================] - 342s 177ms/step - loss: 0.0011 - tp: 493877.0000 - fp: 144.0000 - tn: 493877.0000 - fn: 144.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0029 - val_tp: 97920.0000 - val_fp: 49.0000 - val_tn: 97920.0000 - val_fn: 49.0000 - val_accuracy: 0.9995 - val_precision: 0.9995 - val_recall: 0.9995\n",
      "Epoch 141/200\n",
      "1930/1930 [==============================] - 327s 169ms/step - loss: 0.0011 - tp: 493875.0000 - fp: 146.0000 - tn: 493875.0000 - fn: 146.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0018 - val_tp: 97941.0000 - val_fp: 28.0000 - val_tn: 97941.0000 - val_fn: 28.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 142/200\n",
      "1930/1930 [==============================] - 321s 166ms/step - loss: 0.0010 - tp: 493878.0000 - fp: 143.0000 - tn: 493878.0000 - fn: 143.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0018 - val_tp: 97944.0000 - val_fp: 25.0000 - val_tn: 97944.0000 - val_fn: 25.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 143/200\n",
      "1930/1930 [==============================] - 321s 166ms/step - loss: 0.0010 - tp: 493874.0000 - fp: 147.0000 - tn: 493874.0000 - fn: 147.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0018 - val_tp: 97942.0000 - val_fp: 27.0000 - val_tn: 97942.0000 - val_fn: 27.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 144/200\n",
      "1930/1930 [==============================] - 317s 164ms/step - loss: 0.0011 - tp: 493858.0000 - fp: 163.0000 - tn: 493858.0000 - fn: 163.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0018 - val_tp: 97931.0000 - val_fp: 38.0000 - val_tn: 97931.0000 - val_fn: 38.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 145/200\n",
      "1930/1930 [==============================] - 321s 166ms/step - loss: 9.8967e-04 - tp: 493880.0000 - fp: 141.0000 - tn: 493880.0000 - fn: 141.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0023 - val_tp: 97935.0000 - val_fp: 34.0000 - val_tn: 97935.0000 - val_fn: 34.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 146/200\n",
      "1930/1930 [==============================] - 319s 165ms/step - loss: 9.9881e-04 - tp: 493876.0000 - fp: 145.0000 - tn: 493876.0000 - fn: 145.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0019 - val_tp: 97942.0000 - val_fp: 27.0000 - val_tn: 97942.0000 - val_fn: 27.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 147/200\n",
      "1930/1930 [==============================] - 320s 165ms/step - loss: 9.9879e-04 - tp: 493883.0000 - fp: 138.0000 - tn: 493883.0000 - fn: 138.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0020 - val_tp: 97946.0000 - val_fp: 23.0000 - val_tn: 97946.0000 - val_fn: 23.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 148/200\n",
      "1930/1930 [==============================] - 336s 174ms/step - loss: 0.0011 - tp: 493878.0000 - fp: 143.0000 - tn: 493878.0000 - fn: 143.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0017 - val_tp: 97944.0000 - val_fp: 25.0000 - val_tn: 97944.0000 - val_fn: 25.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 149/200\n",
      "1930/1930 [==============================] - 329s 170ms/step - loss: 0.0010 - tp: 493879.0000 - fp: 142.0000 - tn: 493879.0000 - fn: 142.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0023 - val_tp: 97924.0000 - val_fp: 45.0000 - val_tn: 97924.0000 - val_fn: 45.0000 - val_accuracy: 0.9995 - val_precision: 0.9995 - val_recall: 0.9995\n",
      "Epoch 150/200\n",
      "1930/1930 [==============================] - 331s 171ms/step - loss: 0.0010 - tp: 493874.0000 - fp: 147.0000 - tn: 493874.0000 - fn: 147.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0022 - val_tp: 97936.0000 - val_fp: 33.0000 - val_tn: 97936.0000 - val_fn: 33.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 151/200\n",
      "1930/1930 [==============================] - 332s 172ms/step - loss: 0.0010 - tp: 493881.0000 - fp: 140.0000 - tn: 493881.0000 - fn: 140.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0027 - val_tp: 97923.0000 - val_fp: 46.0000 - val_tn: 97923.0000 - val_fn: 46.0000 - val_accuracy: 0.9995 - val_precision: 0.9995 - val_recall: 0.9995\n",
      "Epoch 152/200\n",
      "1930/1930 [==============================] - 329s 170ms/step - loss: 0.0011 - tp: 493875.0000 - fp: 146.0000 - tn: 493875.0000 - fn: 146.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0027 - val_tp: 97922.0000 - val_fp: 47.0000 - val_tn: 97922.0000 - val_fn: 47.0000 - val_accuracy: 0.9995 - val_precision: 0.9995 - val_recall: 0.9995\n",
      "Epoch 153/200\n",
      "1930/1930 [==============================] - 325s 168ms/step - loss: 0.0010 - tp: 493867.0000 - fp: 154.0000 - tn: 493867.0000 - fn: 154.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0037 - val_tp: 97898.0000 - val_fp: 71.0000 - val_tn: 97898.0000 - val_fn: 71.0000 - val_accuracy: 0.9993 - val_precision: 0.9993 - val_recall: 0.9993\n",
      "Epoch 154/200\n",
      "1930/1930 [==============================] - 323s 167ms/step - loss: 0.0011 - tp: 493858.0000 - fp: 163.0000 - tn: 493858.0000 - fn: 163.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0017 - val_tp: 97944.0000 - val_fp: 25.0000 - val_tn: 97944.0000 - val_fn: 25.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 155/200\n",
      "1930/1930 [==============================] - 336s 174ms/step - loss: 0.0010 - tp: 493874.0000 - fp: 147.0000 - tn: 493874.0000 - fn: 147.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0020 - val_tp: 97933.0000 - val_fp: 36.0000 - val_tn: 97933.0000 - val_fn: 36.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 156/200\n",
      "1930/1930 [==============================] - 331s 171ms/step - loss: 9.8059e-04 - tp: 493877.0000 - fp: 144.0000 - tn: 493877.0000 - fn: 144.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0020 - val_tp: 97941.0000 - val_fp: 28.0000 - val_tn: 97941.0000 - val_fn: 28.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 157/200\n",
      "1930/1930 [==============================] - 338s 175ms/step - loss: 0.0010 - tp: 493871.0000 - fp: 150.0000 - tn: 493871.0000 - fn: 150.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0020 - val_tp: 97943.0000 - val_fp: 26.0000 - val_tn: 97943.0000 - val_fn: 26.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 158/200\n",
      "1930/1930 [==============================] - 314s 162ms/step - loss: 0.0010 - tp: 493867.0000 - fp: 154.0000 - tn: 493867.0000 - fn: 154.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0017 - val_tp: 97941.0000 - val_fp: 28.0000 - val_tn: 97941.0000 - val_fn: 28.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 159/200\n",
      "1930/1930 [==============================] - 267s 138ms/step - loss: 0.0011 - tp: 493871.0000 - fp: 150.0000 - tn: 493871.0000 - fn: 150.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0017 - val_tp: 97942.0000 - val_fp: 27.0000 - val_tn: 97942.0000 - val_fn: 27.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 160/200\n",
      "1930/1930 [==============================] - 267s 138ms/step - loss: 9.6569e-04 - tp: 493893.0000 - fp: 128.0000 - tn: 493893.0000 - fn: 128.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0018 - val_tp: 97941.0000 - val_fp: 28.0000 - val_tn: 97941.0000 - val_fn: 28.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 161/200\n",
      "1930/1930 [==============================] - 263s 136ms/step - loss: 0.0011 - tp: 493881.0000 - fp: 140.0000 - tn: 493881.0000 - fn: 140.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0022 - val_tp: 97934.0000 - val_fp: 35.0000 - val_tn: 97934.0000 - val_fn: 35.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 162/200\n",
      "1930/1930 [==============================] - 288s 149ms/step - loss: 0.0011 - tp: 493884.0000 - fp: 137.0000 - tn: 493884.0000 - fn: 137.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0016 - val_tp: 97941.0000 - val_fp: 28.0000 - val_tn: 97941.0000 - val_fn: 28.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 163/200\n",
      "1930/1930 [==============================] - 282s 146ms/step - loss: 9.0566e-04 - tp: 493892.0000 - fp: 129.0000 - tn: 493892.0000 - fn: 129.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0017 - val_tp: 97944.0000 - val_fp: 25.0000 - val_tn: 97944.0000 - val_fn: 25.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 164/200\n",
      "1930/1930 [==============================] - 266s 138ms/step - loss: 0.0010 - tp: 493881.0000 - fp: 140.0000 - tn: 493881.0000 - fn: 140.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0017 - val_tp: 97944.0000 - val_fp: 25.0000 - val_tn: 97944.0000 - val_fn: 25.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 165/200\n",
      "1930/1930 [==============================] - 269s 139ms/step - loss: 0.0010 - tp: 493880.0000 - fp: 141.0000 - tn: 493880.0000 - fn: 141.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0019 - val_tp: 97943.0000 - val_fp: 26.0000 - val_tn: 97943.0000 - val_fn: 26.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 166/200\n",
      "1930/1930 [==============================] - 261s 135ms/step - loss: 9.9721e-04 - tp: 493879.0000 - fp: 142.0000 - tn: 493879.0000 - fn: 142.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0018 - val_tp: 97944.0000 - val_fp: 25.0000 - val_tn: 97944.0000 - val_fn: 25.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 167/200\n",
      "1930/1930 [==============================] - 251s 130ms/step - loss: 0.0011 - tp: 493863.0000 - fp: 158.0000 - tn: 493863.0000 - fn: 158.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0019 - val_tp: 97944.0000 - val_fp: 25.0000 - val_tn: 97944.0000 - val_fn: 25.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 168/200\n",
      "1930/1930 [==============================] - 270s 139ms/step - loss: 0.0011 - tp: 493872.0000 - fp: 149.0000 - tn: 493872.0000 - fn: 149.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0017 - val_tp: 97946.0000 - val_fp: 23.0000 - val_tn: 97946.0000 - val_fn: 23.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 169/200\n",
      "1930/1930 [==============================] - 275s 142ms/step - loss: 0.0010 - tp: 493888.0000 - fp: 133.0000 - tn: 493888.0000 - fn: 133.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0020 - val_tp: 97943.0000 - val_fp: 26.0000 - val_tn: 97943.0000 - val_fn: 26.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 170/200\n",
      "1930/1930 [==============================] - 266s 137ms/step - loss: 0.0010 - tp: 493878.0000 - fp: 143.0000 - tn: 493878.0000 - fn: 143.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0019 - val_tp: 97943.0000 - val_fp: 26.0000 - val_tn: 97943.0000 - val_fn: 26.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 171/200\n",
      "1930/1930 [==============================] - 292s 151ms/step - loss: 9.8361e-04 - tp: 493885.0000 - fp: 136.0000 - tn: 493885.0000 - fn: 136.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0017 - val_tp: 97947.0000 - val_fp: 22.0000 - val_tn: 97947.0000 - val_fn: 22.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 172/200\n",
      "1930/1930 [==============================] - 300s 155ms/step - loss: 0.0010 - tp: 493874.0000 - fp: 147.0000 - tn: 493874.0000 - fn: 147.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0016 - val_tp: 97944.0000 - val_fp: 25.0000 - val_tn: 97944.0000 - val_fn: 25.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 173/200\n",
      "1930/1930 [==============================] - 292s 150ms/step - loss: 0.0010 - tp: 493882.0000 - fp: 139.0000 - tn: 493882.0000 - fn: 139.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0023 - val_tp: 97931.0000 - val_fp: 38.0000 - val_tn: 97931.0000 - val_fn: 38.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 174/200\n",
      "1930/1930 [==============================] - 284s 147ms/step - loss: 0.0011 - tp: 493866.0000 - fp: 155.0000 - tn: 493866.0000 - fn: 155.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0017 - val_tp: 97947.0000 - val_fp: 22.0000 - val_tn: 97947.0000 - val_fn: 22.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 175/200\n",
      "1930/1930 [==============================] - 320s 166ms/step - loss: 0.0011 - tp: 493877.0000 - fp: 144.0000 - tn: 493877.0000 - fn: 144.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0021 - val_tp: 97940.0000 - val_fp: 29.0000 - val_tn: 97940.0000 - val_fn: 29.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 176/200\n",
      "1930/1930 [==============================] - 334s 173ms/step - loss: 0.0010 - tp: 493882.0000 - fp: 139.0000 - tn: 493882.0000 - fn: 139.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0021 - val_tp: 97939.0000 - val_fp: 30.0000 - val_tn: 97939.0000 - val_fn: 30.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 177/200\n",
      "1930/1930 [==============================] - 284s 147ms/step - loss: 9.9914e-04 - tp: 493888.0000 - fp: 133.0000 - tn: 493888.0000 - fn: 133.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0022 - val_tp: 97941.0000 - val_fp: 28.0000 - val_tn: 97941.0000 - val_fn: 28.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 178/200\n",
      "1930/1930 [==============================] - 271s 140ms/step - loss: 0.0011 - tp: 493874.0000 - fp: 147.0000 - tn: 493874.0000 - fn: 147.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0019 - val_tp: 97942.0000 - val_fp: 27.0000 - val_tn: 97942.0000 - val_fn: 27.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 179/200\n",
      "1930/1930 [==============================] - 265s 137ms/step - loss: 0.0011 - tp: 493867.0000 - fp: 154.0000 - tn: 493867.0000 - fn: 154.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0016 - val_tp: 97948.0000 - val_fp: 21.0000 - val_tn: 97948.0000 - val_fn: 21.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 180/200\n",
      "1930/1930 [==============================] - 276s 143ms/step - loss: 9.4045e-04 - tp: 493890.0000 - fp: 131.0000 - tn: 493890.0000 - fn: 131.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0023 - val_tp: 97946.0000 - val_fp: 23.0000 - val_tn: 97946.0000 - val_fn: 23.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 181/200\n",
      "1930/1930 [==============================] - 281s 145ms/step - loss: 0.0010 - tp: 493886.0000 - fp: 135.0000 - tn: 493886.0000 - fn: 135.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0021 - val_tp: 97944.0000 - val_fp: 25.0000 - val_tn: 97944.0000 - val_fn: 25.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 182/200\n",
      "1930/1930 [==============================] - 304s 157ms/step - loss: 0.0011 - tp: 493871.0000 - fp: 150.0000 - tn: 493871.0000 - fn: 150.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0024 - val_tp: 97941.0000 - val_fp: 28.0000 - val_tn: 97941.0000 - val_fn: 28.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 183/200\n",
      "1930/1930 [==============================] - 398s 206ms/step - loss: 9.2359e-04 - tp: 493883.0000 - fp: 138.0000 - tn: 493883.0000 - fn: 138.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0024 - val_tp: 97935.0000 - val_fp: 34.0000 - val_tn: 97935.0000 - val_fn: 34.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 184/200\n",
      "1930/1930 [==============================] - 395s 204ms/step - loss: 0.0010 - tp: 493888.0000 - fp: 133.0000 - tn: 493888.0000 - fn: 133.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0021 - val_tp: 97943.0000 - val_fp: 26.0000 - val_tn: 97943.0000 - val_fn: 26.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 185/200\n",
      "1930/1930 [==============================] - 403s 208ms/step - loss: 0.0011 - tp: 493865.0000 - fp: 156.0000 - tn: 493865.0000 - fn: 156.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0021 - val_tp: 97942.0000 - val_fp: 27.0000 - val_tn: 97942.0000 - val_fn: 27.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 186/200\n",
      "1930/1930 [==============================] - 402s 208ms/step - loss: 9.9532e-04 - tp: 493875.0000 - fp: 146.0000 - tn: 493875.0000 - fn: 146.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0021 - val_tp: 97942.0000 - val_fp: 27.0000 - val_tn: 97942.0000 - val_fn: 27.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 187/200\n",
      "1930/1930 [==============================] - 394s 203ms/step - loss: 9.8625e-04 - tp: 493888.0000 - fp: 133.0000 - tn: 493888.0000 - fn: 133.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0022 - val_tp: 97941.0000 - val_fp: 28.0000 - val_tn: 97941.0000 - val_fn: 28.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 188/200\n",
      "1930/1930 [==============================] - 390s 202ms/step - loss: 0.0010 - tp: 493874.0000 - fp: 147.0000 - tn: 493874.0000 - fn: 147.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0019 - val_tp: 97944.0000 - val_fp: 25.0000 - val_tn: 97944.0000 - val_fn: 25.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 189/200\n",
      "1930/1930 [==============================] - 405s 209ms/step - loss: 9.5969e-04 - tp: 493889.0000 - fp: 132.0000 - tn: 493889.0000 - fn: 132.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0018 - val_tp: 97945.0000 - val_fp: 24.0000 - val_tn: 97945.0000 - val_fn: 24.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 190/200\n",
      "1930/1930 [==============================] - 396s 204ms/step - loss: 9.9957e-04 - tp: 493879.0000 - fp: 142.0000 - tn: 493879.0000 - fn: 142.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0019 - val_tp: 97941.0000 - val_fp: 28.0000 - val_tn: 97941.0000 - val_fn: 28.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 191/200\n",
      "1930/1930 [==============================] - 400s 207ms/step - loss: 9.9978e-04 - tp: 493876.0000 - fp: 145.0000 - tn: 493876.0000 - fn: 145.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0022 - val_tp: 97943.0000 - val_fp: 26.0000 - val_tn: 97943.0000 - val_fn: 26.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 192/200\n",
      "1930/1930 [==============================] - 415s 215ms/step - loss: 9.0917e-04 - tp: 493886.0000 - fp: 135.0000 - tn: 493886.0000 - fn: 135.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0022 - val_tp: 97945.0000 - val_fp: 24.0000 - val_tn: 97945.0000 - val_fn: 24.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 193/200\n",
      "1930/1930 [==============================] - 388s 201ms/step - loss: 0.0010 - tp: 493877.0000 - fp: 144.0000 - tn: 493877.0000 - fn: 144.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0026 - val_tp: 97933.0000 - val_fp: 36.0000 - val_tn: 97933.0000 - val_fn: 36.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 194/200\n",
      "1930/1930 [==============================] - 399s 206ms/step - loss: 0.0011 - tp: 493872.0000 - fp: 149.0000 - tn: 493872.0000 - fn: 149.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0024 - val_tp: 97942.0000 - val_fp: 27.0000 - val_tn: 97942.0000 - val_fn: 27.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 195/200\n",
      "1930/1930 [==============================] - 400s 207ms/step - loss: 0.0010 - tp: 493877.0000 - fp: 144.0000 - tn: 493877.0000 - fn: 144.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0022 - val_tp: 97934.0000 - val_fp: 35.0000 - val_tn: 97934.0000 - val_fn: 35.0000 - val_accuracy: 0.9996 - val_precision: 0.9996 - val_recall: 0.9996\n",
      "Epoch 196/200\n",
      "1930/1930 [==============================] - 402s 208ms/step - loss: 0.0010 - tp: 493879.0000 - fp: 142.0000 - tn: 493879.0000 - fn: 142.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0017 - val_tp: 97947.0000 - val_fp: 22.0000 - val_tn: 97947.0000 - val_fn: 22.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n",
      "Epoch 197/200\n",
      "1930/1930 [==============================] - 393s 203ms/step - loss: 0.0011 - tp: 493881.0000 - fp: 140.0000 - tn: 493881.0000 - fn: 140.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0022 - val_tp: 97939.0000 - val_fp: 30.0000 - val_tn: 97939.0000 - val_fn: 30.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 198/200\n",
      "1930/1930 [==============================] - 385s 199ms/step - loss: 9.7593e-04 - tp: 493890.0000 - fp: 131.0000 - tn: 493890.0000 - fn: 131.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0021 - val_tp: 97943.0000 - val_fp: 26.0000 - val_tn: 97943.0000 - val_fn: 26.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 199/200\n",
      "1930/1930 [==============================] - 402s 208ms/step - loss: 0.0010 - tp: 493882.0000 - fp: 139.0000 - tn: 493882.0000 - fn: 139.0000 - accuracy: 0.9997 - precision: 0.9997 - recall: 0.9997 - val_loss: 0.0022 - val_tp: 97943.0000 - val_fp: 26.0000 - val_tn: 97943.0000 - val_fn: 26.0000 - val_accuracy: 0.9997 - val_precision: 0.9997 - val_recall: 0.9997\n",
      "Epoch 200/200\n",
      "1930/1930 [==============================] - 404s 209ms/step - loss: 9.5827e-04 - tp: 493898.0000 - fp: 123.0000 - tn: 493898.0000 - fn: 123.0000 - accuracy: 0.9998 - precision: 0.9998 - recall: 0.9998 - val_loss: 0.0021 - val_tp: 97946.0000 - val_fp: 23.0000 - val_tn: 97946.0000 - val_fn: 23.0000 - val_accuracy: 0.9998 - val_precision: 0.9998 - val_recall: 0.9998\n"
     ]
    }
   ],
   "source": [
    "train(ds_train,ds_val)\n",
    "network.save('model_zigzag.h5')"
   ]
  }
 ],
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