Model_03-Adam-k=3-amsgrad-3e-4_10p.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mandatory imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit\n",
    "from os.path import join as opj\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import pylab\n",
    "plt.rcParams['figure.figsize'] = 10, 10\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_json(\"input/train.json\")\n",
    "target_train=train['is_iceberg']\n",
    "test = pd.read_json(\"input/test.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_train=train['is_iceberg']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "train['inc_angle']=pd.to_numeric(train['inc_angle'], errors='coerce')#We have only 133 NAs.\n",
    "train['inc_angle']=train['inc_angle'].fillna(method='pad')\n",
    "X_angle=train['inc_angle']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "X_test_angle=test['inc_angle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate the training data\n",
    "X_band_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_1\"]])\n",
    "X_band_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_2\"]])\n",
    "X_band_3=(X_band_1+X_band_2)/2\n",
    "#X_band_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in train[\"inc_angle\"]])\n",
    "X_train = np.concatenate([X_band_1[:, :, :, np.newaxis]\n",
    "                          , X_band_2[:, :, :, np.newaxis]\n",
    "                         , X_band_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_band_test_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_1\"]])\n",
    "X_band_test_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_2\"]])\n",
    "X_band_test_3=(X_band_test_1+X_band_test_2)/2\n",
    "#X_band_test_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in test[\"inc_angle\"]])\n",
    "X_test = np.concatenate([X_band_test_1[:, :, :, np.newaxis]\n",
    "                          , X_band_test_2[:, :, :, np.newaxis]\n",
    "                         , X_band_test_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Import Keras.\n",
    "from matplotlib import pyplot\n",
    "from keras.optimizers import RMSprop\n",
    "# from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Input, Flatten, Activation\n",
    "from keras.layers import GlobalMaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.merge import Concatenate\n",
    "from keras.models import Model\n",
    "from keras import initializers\n",
    "from keras.optimizers import Adam\n",
    "from keras.optimizers import rmsprop\n",
    "from keras.layers.advanced_activations import LeakyReLU, PReLU\n",
    "from keras.optimizers import SGD\n",
    "from keras.optimizers import Adadelta\n",
    "from keras.callbacks import ModelCheckpoint, Callback, EarlyStopping\n",
    "\n",
    "from keras.datasets import cifar10\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.applications.xception import Xception\n",
    "from keras.applications.mobilenet import MobileNet\n",
    "from keras.applications.vgg19 import VGG19\n",
    "from keras.layers import Concatenate, Dense, LSTM, Input, concatenate\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.vgg16 import preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Aug for multi-input\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "batch_size=64\n",
    "# Define the image transformations here\n",
    "gen = ImageDataGenerator(horizontal_flip = True,\n",
    "                         vertical_flip = True,\n",
    "                         width_shift_range = 0.,\n",
    "                         height_shift_range = 0.,\n",
    "                         channel_shift_range=0,\n",
    "                         zoom_range = 0.2,\n",
    "                         rotation_range = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here is the function that merges our two generators\n",
    "# We use the exact same generator with the same random seed for both the y and angle arrays\n",
    "def gen_flow_for_two_inputs(X1, X2, y):\n",
    "    genX1 = gen.flow(X1,y,  batch_size=batch_size,seed=55)\n",
    "    genX2 = gen.flow(X1,X2, batch_size=batch_size,seed=55)\n",
    "    while True:\n",
    "            X1i = genX1.next()\n",
    "            X2i = genX2.next()\n",
    "            #Assert arrays are equal - this was for peace of mind, but slows down training\n",
    "            #np.testing.assert_array_equal(X1i[0],X2i[0])\n",
    "            yield [X1i[0], X2i[1]], X1i[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finally create generator\n",
    "def get_callbacks(filepath, patience=2):\n",
    "    es = EarlyStopping('val_loss', patience=10, mode=\"min\")\n",
    "    msave = ModelCheckpoint(filepath, save_best_only=True)\n",
    "    return [es, msave]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getVggAngleModel():\n",
    "    input_2 = Input(shape=[1], name=\"angle\")\n",
    "    angle_layer = Dense(1, )(input_2)\n",
    "    base_model = VGG16(weights='imagenet', include_top=False, \n",
    "                 input_shape=X_train.shape[1:], classes=1)\n",
    "    x = base_model.get_layer('block5_pool').output\n",
    "    \n",
    "\n",
    "    x = GlobalMaxPooling2D()(x)\n",
    "    merge_one = concatenate([x, angle_layer])\n",
    "    merge_one = Dense(512, activation='relu', name='fc2')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    merge_one = Dense(512, activation='relu', name='fc3')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    \n",
    "    predictions = Dense(1, activation='sigmoid')(merge_one)\n",
    "    \n",
    "    model = Model(inputs=[base_model.input, input_2], outputs=predictions)\n",
    "    \n",
    "    adam = Adam(lr=3e-4, beta_1=0.9, beta_2=0.999, epsilon=1.0, decay=1e-6, amsgrad=True)\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                    optimizer=adam,\n",
    "                    metrics=['accuracy'])\n",
    "    \n",
    "#     sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "#     model.compile(loss='binary_crossentropy',\n",
    "#                   optimizer=sgd,\n",
    "#                   metrics=['accuracy'])\n",
    "    return model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================FOLD= 0\n",
      "Epoch 1/500\n",
      "24/24 [==============================] - 16s 687ms/step - loss: 1.5454 - acc: 0.4782 - val_loss: 1.0029 - val_acc: 0.4692\n",
      "Epoch 2/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 1.1457 - acc: 0.4892 - val_loss: 0.7119 - val_acc: 0.5327\n",
      "Epoch 3/500\n",
      "24/24 [==============================] - 15s 628ms/step - loss: 0.9399 - acc: 0.5349 - val_loss: 0.6998 - val_acc: 0.5308\n",
      "Epoch 4/500\n",
      "24/24 [==============================] - 15s 636ms/step - loss: 0.9291 - acc: 0.5094 - val_loss: 0.6777 - val_acc: 0.5421\n",
      "Epoch 5/500\n",
      "24/24 [==============================] - 15s 630ms/step - loss: 0.9087 - acc: 0.5163 - val_loss: 0.6573 - val_acc: 0.5701\n",
      "Epoch 6/500\n",
      "24/24 [==============================] - 15s 634ms/step - loss: 0.8604 - acc: 0.5303 - val_loss: 0.6460 - val_acc: 0.5738\n",
      "Epoch 7/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.8599 - acc: 0.5502 - val_loss: 0.6206 - val_acc: 0.6243\n",
      "Epoch 8/500\n",
      "24/24 [==============================] - 15s 630ms/step - loss: 0.8255 - acc: 0.5514 - val_loss: 0.6065 - val_acc: 0.6280\n",
      "Epoch 9/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.7958 - acc: 0.5580 - val_loss: 0.5990 - val_acc: 0.5981\n",
      "Epoch 10/500\n",
      "24/24 [==============================] - 15s 631ms/step - loss: 0.7899 - acc: 0.5760 - val_loss: 0.5755 - val_acc: 0.6598\n",
      "Epoch 11/500\n",
      "24/24 [==============================] - 15s 636ms/step - loss: 0.7402 - acc: 0.6004 - val_loss: 0.5543 - val_acc: 0.6860\n",
      "Epoch 12/500\n",
      "24/24 [==============================] - 16s 663ms/step - loss: 0.7715 - acc: 0.5862 - val_loss: 0.5399 - val_acc: 0.7047\n",
      "Epoch 13/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.7194 - acc: 0.6182 - val_loss: 0.5230 - val_acc: 0.7364\n",
      "Epoch 14/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.6983 - acc: 0.6335 - val_loss: 0.4986 - val_acc: 0.7645\n",
      "Epoch 15/500\n",
      "24/24 [==============================] - 15s 628ms/step - loss: 0.6594 - acc: 0.6425 - val_loss: 0.4779 - val_acc: 0.7738\n",
      "Epoch 16/500\n",
      "24/24 [==============================] - 15s 635ms/step - loss: 0.6394 - acc: 0.6637 - val_loss: 0.4625 - val_acc: 0.7757\n",
      "Epoch 17/500\n",
      "24/24 [==============================] - 15s 634ms/step - loss: 0.6666 - acc: 0.6399 - val_loss: 0.4450 - val_acc: 0.7869\n",
      "Epoch 18/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.6398 - acc: 0.6658 - val_loss: 0.4318 - val_acc: 0.7944\n",
      "Epoch 19/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.5980 - acc: 0.6919 - val_loss: 0.4199 - val_acc: 0.7963\n",
      "Epoch 20/500\n",
      "24/24 [==============================] - 15s 626ms/step - loss: 0.5854 - acc: 0.7081 - val_loss: 0.4060 - val_acc: 0.7907\n",
      "Epoch 21/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.5503 - acc: 0.7162 - val_loss: 0.3993 - val_acc: 0.7981\n",
      "Epoch 22/500\n",
      "24/24 [==============================] - 15s 628ms/step - loss: 0.5697 - acc: 0.7175 - val_loss: 0.3873 - val_acc: 0.8000\n",
      "Epoch 23/500\n",
      "24/24 [==============================] - 15s 634ms/step - loss: 0.5241 - acc: 0.7419 - val_loss: 0.3820 - val_acc: 0.8112\n",
      "Epoch 24/500\n",
      "24/24 [==============================] - 15s 635ms/step - loss: 0.5247 - acc: 0.7339 - val_loss: 0.3707 - val_acc: 0.8056\n",
      "Epoch 25/500\n",
      "24/24 [==============================] - 15s 629ms/step - loss: 0.5430 - acc: 0.7386 - val_loss: 0.3534 - val_acc: 0.8280\n",
      "Epoch 26/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.4744 - acc: 0.7664 - val_loss: 0.3520 - val_acc: 0.8168\n",
      "Epoch 27/500\n",
      "24/24 [==============================] - 15s 630ms/step - loss: 0.4479 - acc: 0.7837 - val_loss: 0.3406 - val_acc: 0.8336\n",
      "Epoch 28/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.4527 - acc: 0.7824 - val_loss: 0.3369 - val_acc: 0.8299\n",
      "Epoch 29/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.4341 - acc: 0.7990 - val_loss: 0.3326 - val_acc: 0.8336\n",
      "Epoch 30/500\n",
      "24/24 [==============================] - 15s 627ms/step - loss: 0.4257 - acc: 0.8177 - val_loss: 0.3175 - val_acc: 0.8486\n",
      "Epoch 31/500\n",
      "24/24 [==============================] - 15s 635ms/step - loss: 0.4268 - acc: 0.7969 - val_loss: 0.3142 - val_acc: 0.8449\n",
      "Epoch 32/500\n",
      "24/24 [==============================] - 15s 627ms/step - loss: 0.4253 - acc: 0.8081 - val_loss: 0.3109 - val_acc: 0.8505\n",
      "Epoch 33/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.4133 - acc: 0.8069 - val_loss: 0.3093 - val_acc: 0.8467\n",
      "Epoch 34/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.4112 - acc: 0.8126 - val_loss: 0.3128 - val_acc: 0.8542\n",
      "Epoch 35/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.3797 - acc: 0.8274 - val_loss: 0.3127 - val_acc: 0.8542\n",
      "Epoch 36/500\n",
      "24/24 [==============================] - 15s 634ms/step - loss: 0.3710 - acc: 0.8358 - val_loss: 0.2997 - val_acc: 0.8579\n",
      "Epoch 37/500\n",
      "24/24 [==============================] - 13s 549ms/step - loss: 0.3841 - acc: 0.8329 - val_loss: 0.3082 - val_acc: 0.8598\n",
      "Epoch 38/500\n",
      "24/24 [==============================] - 15s 634ms/step - loss: 0.3656 - acc: 0.8412 - val_loss: 0.2827 - val_acc: 0.8636\n",
      "Epoch 39/500\n",
      "24/24 [==============================] - 13s 548ms/step - loss: 0.3472 - acc: 0.8472 - val_loss: 0.2923 - val_acc: 0.8692\n",
      "Epoch 40/500\n",
      "24/24 [==============================] - 15s 631ms/step - loss: 0.3490 - acc: 0.8344 - val_loss: 0.2794 - val_acc: 0.8673\n",
      "Epoch 41/500\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.3677 - acc: 0.8355 - val_loss: 0.2918 - val_acc: 0.8673\n",
      "Epoch 42/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.3596 - acc: 0.8413 - val_loss: 0.2870 - val_acc: 0.8710\n",
      "Epoch 43/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.3235 - acc: 0.8504 - val_loss: 0.3044 - val_acc: 0.8654\n",
      "Epoch 44/500\n",
      "24/24 [==============================] - 13s 549ms/step - loss: 0.3575 - acc: 0.8352 - val_loss: 0.3290 - val_acc: 0.8411\n",
      "Epoch 45/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.3266 - acc: 0.8454 - val_loss: 0.2744 - val_acc: 0.8748\n",
      "Epoch 46/500\n",
      "24/24 [==============================] - 15s 635ms/step - loss: 0.3351 - acc: 0.8534 - val_loss: 0.2633 - val_acc: 0.8766\n",
      "Epoch 47/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.3118 - acc: 0.8623 - val_loss: 0.2667 - val_acc: 0.8822\n",
      "Epoch 48/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.3437 - acc: 0.8465 - val_loss: 0.2562 - val_acc: 0.8785\n",
      "Epoch 49/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.3224 - acc: 0.8635 - val_loss: 0.2566 - val_acc: 0.8766\n",
      "Epoch 50/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.3464 - acc: 0.8366 - val_loss: 0.2506 - val_acc: 0.8841\n",
      "Epoch 51/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.3135 - acc: 0.8596 - val_loss: 0.2492 - val_acc: 0.8879\n",
      "Epoch 52/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.3021 - acc: 0.8642 - val_loss: 0.2525 - val_acc: 0.8841\n",
      "Epoch 53/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.3133 - acc: 0.8650 - val_loss: 0.2679 - val_acc: 0.8822\n",
      "Epoch 54/500\n",
      "24/24 [==============================] - 13s 548ms/step - loss: 0.3063 - acc: 0.8626 - val_loss: 0.2518 - val_acc: 0.8897\n",
      "Epoch 55/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.3057 - acc: 0.8641 - val_loss: 0.2636 - val_acc: 0.8841\n",
      "Epoch 56/500\n",
      "24/24 [==============================] - 15s 627ms/step - loss: 0.2918 - acc: 0.8703 - val_loss: 0.2414 - val_acc: 0.8916\n",
      "Epoch 57/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2726 - acc: 0.8886 - val_loss: 0.2501 - val_acc: 0.8879\n",
      "Epoch 58/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.2788 - acc: 0.8825 - val_loss: 0.2377 - val_acc: 0.8897\n",
      "Epoch 59/500\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2649 - acc: 0.8767 - val_loss: 0.2484 - val_acc: 0.8916\n",
      "Epoch 60/500\n",
      "24/24 [==============================] - 15s 630ms/step - loss: 0.3037 - acc: 0.8628 - val_loss: 0.2365 - val_acc: 0.8972\n",
      "Epoch 61/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 15s 629ms/step - loss: 0.2906 - acc: 0.8714 - val_loss: 0.2322 - val_acc: 0.8897\n",
      "Epoch 62/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.2973 - acc: 0.8668 - val_loss: 0.2313 - val_acc: 0.9009\n",
      "Epoch 63/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.2844 - acc: 0.8773 - val_loss: 0.2376 - val_acc: 0.8935\n",
      "Epoch 64/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.2879 - acc: 0.8741 - val_loss: 0.2389 - val_acc: 0.8972\n",
      "Epoch 65/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.2827 - acc: 0.8814 - val_loss: 0.2297 - val_acc: 0.9028\n",
      "Epoch 66/500\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2768 - acc: 0.8702 - val_loss: 0.2350 - val_acc: 0.9009\n",
      "Epoch 67/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.2872 - acc: 0.8810 - val_loss: 0.2418 - val_acc: 0.8972\n",
      "Epoch 68/500\n",
      "24/24 [==============================] - 15s 630ms/step - loss: 0.2977 - acc: 0.8763 - val_loss: 0.2289 - val_acc: 0.8935\n",
      "Epoch 69/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.2753 - acc: 0.8805 - val_loss: 0.2328 - val_acc: 0.9009\n",
      "Epoch 70/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.2610 - acc: 0.8838 - val_loss: 0.2383 - val_acc: 0.8991\n",
      "Epoch 71/500\n",
      "24/24 [==============================] - 15s 626ms/step - loss: 0.2761 - acc: 0.8726 - val_loss: 0.2253 - val_acc: 0.9065\n",
      "Epoch 72/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.2587 - acc: 0.8997 - val_loss: 0.2229 - val_acc: 0.8972\n",
      "Epoch 73/500\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2814 - acc: 0.8816 - val_loss: 0.2359 - val_acc: 0.9047\n",
      "Epoch 74/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.2727 - acc: 0.8840 - val_loss: 0.2310 - val_acc: 0.9028\n",
      "Epoch 75/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.2733 - acc: 0.8804 - val_loss: 0.2208 - val_acc: 0.9047\n",
      "Epoch 76/500\n",
      "24/24 [==============================] - 15s 627ms/step - loss: 0.2664 - acc: 0.8814 - val_loss: 0.2200 - val_acc: 0.8991\n",
      "Epoch 77/500\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.2615 - acc: 0.8819 - val_loss: 0.2237 - val_acc: 0.9047\n",
      "Epoch 78/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.2455 - acc: 0.8990 - val_loss: 0.2555 - val_acc: 0.8972\n",
      "Epoch 79/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2562 - acc: 0.8907 - val_loss: 0.2284 - val_acc: 0.9009\n",
      "Epoch 80/500\n",
      "24/24 [==============================] - 15s 631ms/step - loss: 0.2696 - acc: 0.8869 - val_loss: 0.2177 - val_acc: 0.9065\n",
      "Epoch 81/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2778 - acc: 0.8760 - val_loss: 0.2229 - val_acc: 0.9084\n",
      "Epoch 82/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.2324 - acc: 0.9005 - val_loss: 0.2168 - val_acc: 0.9103\n",
      "Epoch 83/500\n",
      "24/24 [==============================] - 15s 626ms/step - loss: 0.2497 - acc: 0.9020 - val_loss: 0.2164 - val_acc: 0.9084\n",
      "Epoch 84/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2528 - acc: 0.8916 - val_loss: 0.2202 - val_acc: 0.9121\n",
      "Epoch 85/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.2463 - acc: 0.8873 - val_loss: 0.2257 - val_acc: 0.9084\n",
      "Epoch 86/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.2401 - acc: 0.8971 - val_loss: 0.2153 - val_acc: 0.9047\n",
      "Epoch 87/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2458 - acc: 0.8941 - val_loss: 0.2210 - val_acc: 0.9028\n",
      "Epoch 88/500\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2302 - acc: 0.9062 - val_loss: 0.2367 - val_acc: 0.8916\n",
      "Epoch 89/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2524 - acc: 0.8977 - val_loss: 0.2215 - val_acc: 0.9178\n",
      "Epoch 90/500\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2319 - acc: 0.9027 - val_loss: 0.2196 - val_acc: 0.9084\n",
      "Epoch 91/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.2327 - acc: 0.9050 - val_loss: 0.2071 - val_acc: 0.9140\n",
      "Epoch 92/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2409 - acc: 0.8981 - val_loss: 0.2200 - val_acc: 0.9121\n",
      "Epoch 93/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.2308 - acc: 0.8988 - val_loss: 0.2178 - val_acc: 0.9103\n",
      "Epoch 94/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2389 - acc: 0.9084 - val_loss: 0.2172 - val_acc: 0.9103\n",
      "Epoch 95/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2380 - acc: 0.9057 - val_loss: 0.2098 - val_acc: 0.9103\n",
      "Epoch 96/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.2392 - acc: 0.9032 - val_loss: 0.2270 - val_acc: 0.9065\n",
      "Epoch 97/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2257 - acc: 0.9019 - val_loss: 0.2220 - val_acc: 0.9065\n",
      "Epoch 98/500\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2309 - acc: 0.9018 - val_loss: 0.2098 - val_acc: 0.9121\n",
      "Epoch 99/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2306 - acc: 0.8986 - val_loss: 0.2153 - val_acc: 0.9196\n",
      "Epoch 100/500\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2215 - acc: 0.9111 - val_loss: 0.2086 - val_acc: 0.9215\n",
      "Epoch 101/500\n",
      "24/24 [==============================] - 15s 631ms/step - loss: 0.2159 - acc: 0.9147 - val_loss: 0.2053 - val_acc: 0.9178\n",
      "Epoch 102/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.2110 - acc: 0.9031 - val_loss: 0.2026 - val_acc: 0.9178\n",
      "Epoch 103/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2221 - acc: 0.8995 - val_loss: 0.2163 - val_acc: 0.9159\n",
      "Epoch 104/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.2435 - acc: 0.8994 - val_loss: 0.2089 - val_acc: 0.9178\n",
      "Epoch 105/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2167 - acc: 0.9049 - val_loss: 0.2040 - val_acc: 0.9121\n",
      "Epoch 106/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2414 - acc: 0.9019 - val_loss: 0.2143 - val_acc: 0.9234\n",
      "Epoch 107/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2096 - acc: 0.9106 - val_loss: 0.2125 - val_acc: 0.9234\n",
      "Epoch 108/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.2130 - acc: 0.9110 - val_loss: 0.2150 - val_acc: 0.9196\n",
      "Epoch 109/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.2240 - acc: 0.9044 - val_loss: 0.2021 - val_acc: 0.9234\n",
      "Epoch 110/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.1984 - acc: 0.9153 - val_loss: 0.2035 - val_acc: 0.9178\n",
      "Epoch 111/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2216 - acc: 0.9014 - val_loss: 0.2071 - val_acc: 0.9140\n",
      "Epoch 112/500\n",
      "24/24 [==============================] - 15s 627ms/step - loss: 0.2120 - acc: 0.9089 - val_loss: 0.2016 - val_acc: 0.9196\n",
      "Epoch 113/500\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.2056 - acc: 0.9135 - val_loss: 0.2122 - val_acc: 0.9215\n",
      "Epoch 114/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2219 - acc: 0.9041 - val_loss: 0.2023 - val_acc: 0.9121\n",
      "Epoch 115/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2139 - acc: 0.9082 - val_loss: 0.2634 - val_acc: 0.8897\n",
      "Epoch 116/500\n",
      "24/24 [==============================] - 15s 631ms/step - loss: 0.2083 - acc: 0.9127 - val_loss: 0.2007 - val_acc: 0.9178\n",
      "Epoch 117/500\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2120 - acc: 0.9147 - val_loss: 0.2035 - val_acc: 0.9159\n",
      "Epoch 118/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2078 - acc: 0.9070 - val_loss: 0.2052 - val_acc: 0.9178\n",
      "Epoch 119/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2248 - acc: 0.9053 - val_loss: 0.2865 - val_acc: 0.8804\n",
      "Epoch 120/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.2038 - acc: 0.9122 - val_loss: 0.2000 - val_acc: 0.9215\n",
      "Epoch 121/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 13s 550ms/step - loss: 0.2268 - acc: 0.9038 - val_loss: 0.2013 - val_acc: 0.9140\n",
      "Epoch 122/500\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2099 - acc: 0.9123 - val_loss: 0.2041 - val_acc: 0.9121\n",
      "Epoch 123/500\n",
      "24/24 [==============================] - 15s 631ms/step - loss: 0.1979 - acc: 0.9144 - val_loss: 0.1967 - val_acc: 0.9196\n",
      "Epoch 124/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.2046 - acc: 0.9107 - val_loss: 0.2003 - val_acc: 0.9252\n",
      "Epoch 125/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2008 - acc: 0.9153 - val_loss: 0.2020 - val_acc: 0.9215\n",
      "Epoch 126/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.1922 - acc: 0.9251 - val_loss: 0.2012 - val_acc: 0.9196\n",
      "Epoch 127/500\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2116 - acc: 0.9112 - val_loss: 0.2080 - val_acc: 0.9234\n",
      "Epoch 128/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2024 - acc: 0.9236 - val_loss: 0.1969 - val_acc: 0.9196\n",
      "Epoch 129/500\n",
      "24/24 [==============================] - 15s 626ms/step - loss: 0.1901 - acc: 0.9236 - val_loss: 0.1965 - val_acc: 0.9271\n",
      "Epoch 130/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.1954 - acc: 0.9147 - val_loss: 0.1961 - val_acc: 0.9234\n",
      "Epoch 131/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.1850 - acc: 0.9188 - val_loss: 0.1994 - val_acc: 0.9215\n",
      "Epoch 132/500\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.1954 - acc: 0.9133 - val_loss: 0.2286 - val_acc: 0.9159\n",
      "Epoch 133/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2007 - acc: 0.9187 - val_loss: 0.2288 - val_acc: 0.9103\n",
      "Epoch 134/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.1977 - acc: 0.9157 - val_loss: 0.1987 - val_acc: 0.9234\n",
      "Epoch 135/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.1686 - acc: 0.9295 - val_loss: 0.2079 - val_acc: 0.9234\n",
      "Epoch 136/500\n",
      "24/24 [==============================] - 13s 549ms/step - loss: 0.1948 - acc: 0.9169 - val_loss: 0.1983 - val_acc: 0.9234\n",
      "Epoch 137/500\n",
      "24/24 [==============================] - 13s 549ms/step - loss: 0.2022 - acc: 0.9169 - val_loss: 0.2063 - val_acc: 0.9196\n",
      "Epoch 138/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2047 - acc: 0.9136 - val_loss: 0.2089 - val_acc: 0.9252\n",
      "Epoch 139/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.2103 - acc: 0.9132 - val_loss: 0.1984 - val_acc: 0.9215\n",
      "Epoch 140/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.1944 - acc: 0.9192 - val_loss: 0.2044 - val_acc: 0.9215\n",
      "Train loss: 0.140152894497\n",
      "Train accuracy: 0.951356407858\n",
      "Test loss: 0.196137472514\n",
      "Test accuracy: 0.923364486093\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/500\n",
      "24/24 [==============================] - 17s 699ms/step - loss: 0.8513 - acc: 0.5127 - val_loss: 0.6988 - val_acc: 0.5159\n",
      "Epoch 2/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.8543 - acc: 0.5083 - val_loss: 0.6845 - val_acc: 0.5364\n",
      "Epoch 3/500\n",
      "24/24 [==============================] - 15s 627ms/step - loss: 0.8364 - acc: 0.5237 - val_loss: 0.6703 - val_acc: 0.5645\n",
      "Epoch 4/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.7991 - acc: 0.5388 - val_loss: 0.6578 - val_acc: 0.5832\n",
      "Epoch 5/500\n",
      "24/24 [==============================] - 15s 630ms/step - loss: 0.7727 - acc: 0.5412 - val_loss: 0.6467 - val_acc: 0.6000\n",
      "Epoch 6/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.7707 - acc: 0.5475 - val_loss: 0.6351 - val_acc: 0.6243\n",
      "Epoch 7/500\n",
      "24/24 [==============================] - 15s 631ms/step - loss: 0.7491 - acc: 0.5433 - val_loss: 0.6238 - val_acc: 0.6617\n",
      "Epoch 8/500\n",
      "24/24 [==============================] - 15s 628ms/step - loss: 0.7196 - acc: 0.5718 - val_loss: 0.6138 - val_acc: 0.6729\n",
      "Epoch 9/500\n",
      "24/24 [==============================] - 15s 636ms/step - loss: 0.7096 - acc: 0.5707 - val_loss: 0.6023 - val_acc: 0.6860\n",
      "Epoch 10/500\n",
      "24/24 [==============================] - 15s 630ms/step - loss: 0.6840 - acc: 0.6038 - val_loss: 0.5925 - val_acc: 0.6916\n",
      "Epoch 11/500\n",
      "24/24 [==============================] - 15s 635ms/step - loss: 0.6972 - acc: 0.5831 - val_loss: 0.5819 - val_acc: 0.7009\n",
      "Epoch 12/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.6794 - acc: 0.5941 - val_loss: 0.5713 - val_acc: 0.7252\n",
      "Epoch 13/500\n",
      "24/24 [==============================] - 15s 630ms/step - loss: 0.6478 - acc: 0.6146 - val_loss: 0.5580 - val_acc: 0.7439\n",
      "Epoch 14/500\n",
      "24/24 [==============================] - 15s 635ms/step - loss: 0.6559 - acc: 0.6163 - val_loss: 0.5458 - val_acc: 0.7308\n",
      "Epoch 15/500\n",
      "24/24 [==============================] - 15s 629ms/step - loss: 0.6303 - acc: 0.6369 - val_loss: 0.5324 - val_acc: 0.7458\n",
      "Epoch 16/500\n",
      "24/24 [==============================] - 15s 636ms/step - loss: 0.6199 - acc: 0.6403 - val_loss: 0.5177 - val_acc: 0.7458\n",
      "Epoch 17/500\n",
      "24/24 [==============================] - 15s 634ms/step - loss: 0.5996 - acc: 0.6670 - val_loss: 0.5050 - val_acc: 0.7607\n",
      "Epoch 18/500\n",
      "24/24 [==============================] - 15s 636ms/step - loss: 0.5920 - acc: 0.6666 - val_loss: 0.4934 - val_acc: 0.7645\n",
      "Epoch 19/500\n",
      "24/24 [==============================] - 15s 636ms/step - loss: 0.5678 - acc: 0.6782 - val_loss: 0.4738 - val_acc: 0.7869\n",
      "Epoch 20/500\n",
      "24/24 [==============================] - 15s 629ms/step - loss: 0.5560 - acc: 0.7048 - val_loss: 0.4584 - val_acc: 0.7850\n",
      "Epoch 21/500\n",
      "24/24 [==============================] - 15s 634ms/step - loss: 0.5363 - acc: 0.7166 - val_loss: 0.4451 - val_acc: 0.7794\n",
      "Epoch 22/500\n",
      "24/24 [==============================] - 15s 631ms/step - loss: 0.5406 - acc: 0.7169 - val_loss: 0.4312 - val_acc: 0.7925\n",
      "Epoch 23/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.5024 - acc: 0.7381 - val_loss: 0.4082 - val_acc: 0.8131\n",
      "Epoch 24/500\n",
      "24/24 [==============================] - 15s 634ms/step - loss: 0.4953 - acc: 0.7392 - val_loss: 0.3981 - val_acc: 0.8150\n",
      "Epoch 25/500\n",
      "24/24 [==============================] - 15s 630ms/step - loss: 0.4772 - acc: 0.7599 - val_loss: 0.3814 - val_acc: 0.8224\n",
      "Epoch 26/500\n",
      "24/24 [==============================] - 15s 634ms/step - loss: 0.4476 - acc: 0.7788 - val_loss: 0.3766 - val_acc: 0.8318\n",
      "Epoch 27/500\n",
      "24/24 [==============================] - 15s 628ms/step - loss: 0.4662 - acc: 0.7635 - val_loss: 0.3666 - val_acc: 0.8393\n",
      "Epoch 28/500\n",
      "24/24 [==============================] - 15s 635ms/step - loss: 0.4389 - acc: 0.7784 - val_loss: 0.3475 - val_acc: 0.8579\n",
      "Epoch 29/500\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.4037 - acc: 0.7920 - val_loss: 0.3490 - val_acc: 0.8393\n",
      "Epoch 30/500\n",
      "24/24 [==============================] - 13s 549ms/step - loss: 0.4152 - acc: 0.7966 - val_loss: 0.3523 - val_acc: 0.8542\n",
      "Epoch 31/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.3951 - acc: 0.8027 - val_loss: 0.3351 - val_acc: 0.8598\n",
      "Epoch 32/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.3900 - acc: 0.8156 - val_loss: 0.3426 - val_acc: 0.8430\n",
      "Epoch 33/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.3823 - acc: 0.8067 - val_loss: 0.3116 - val_acc: 0.8729\n",
      "Epoch 34/500\n",
      "24/24 [==============================] - 13s 553ms/step - loss: 0.3819 - acc: 0.8124 - val_loss: 0.3157 - val_acc: 0.8654\n",
      "Epoch 35/500\n",
      "24/24 [==============================] - 13s 553ms/step - loss: 0.3703 - acc: 0.8280 - val_loss: 0.3136 - val_acc: 0.8636\n",
      "Epoch 36/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.3539 - acc: 0.8268 - val_loss: 0.2990 - val_acc: 0.8673\n",
      "Epoch 37/500\n",
      "24/24 [==============================] - 15s 630ms/step - loss: 0.3599 - acc: 0.8269 - val_loss: 0.2928 - val_acc: 0.8673\n",
      "Epoch 38/500\n",
      "24/24 [==============================] - 15s 623ms/step - loss: 0.3528 - acc: 0.8239 - val_loss: 0.2901 - val_acc: 0.8748\n",
      "Epoch 39/500\n",
      "24/24 [==============================] - 15s 627ms/step - loss: 0.3628 - acc: 0.8291 - val_loss: 0.2881 - val_acc: 0.8748\n",
      "Epoch 40/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 15s 635ms/step - loss: 0.3580 - acc: 0.8363 - val_loss: 0.2741 - val_acc: 0.8841\n",
      "Epoch 41/500\n",
      "24/24 [==============================] - 13s 553ms/step - loss: 0.3289 - acc: 0.8472 - val_loss: 0.2786 - val_acc: 0.8935\n",
      "Epoch 42/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.3261 - acc: 0.8502 - val_loss: 0.2747 - val_acc: 0.8860\n",
      "Epoch 43/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.3265 - acc: 0.8434 - val_loss: 0.2874 - val_acc: 0.8729\n",
      "Epoch 44/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.3331 - acc: 0.8358 - val_loss: 0.2818 - val_acc: 0.8766\n",
      "Epoch 45/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.3220 - acc: 0.8461 - val_loss: 0.2839 - val_acc: 0.8748\n",
      "Epoch 46/500\n",
      "24/24 [==============================] - 15s 634ms/step - loss: 0.3098 - acc: 0.8496 - val_loss: 0.2637 - val_acc: 0.8972\n",
      "Epoch 47/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.3201 - acc: 0.8352 - val_loss: 0.2851 - val_acc: 0.8692\n",
      "Epoch 48/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.3012 - acc: 0.8483 - val_loss: 0.2807 - val_acc: 0.8748\n",
      "Epoch 49/500\n",
      "24/24 [==============================] - 13s 548ms/step - loss: 0.3071 - acc: 0.8609 - val_loss: 0.2677 - val_acc: 0.8879\n",
      "Epoch 50/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.3075 - acc: 0.8625 - val_loss: 0.2636 - val_acc: 0.8916\n",
      "Epoch 51/500\n",
      "24/24 [==============================] - 15s 634ms/step - loss: 0.3274 - acc: 0.8567 - val_loss: 0.2564 - val_acc: 0.8804\n",
      "Epoch 52/500\n",
      "24/24 [==============================] - 15s 621ms/step - loss: 0.3033 - acc: 0.8594 - val_loss: 0.2506 - val_acc: 0.9047\n",
      "Epoch 53/500\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.3094 - acc: 0.8543 - val_loss: 0.2595 - val_acc: 0.8879\n",
      "Epoch 54/500\n",
      "24/24 [==============================] - 15s 627ms/step - loss: 0.2843 - acc: 0.8678 - val_loss: 0.2492 - val_acc: 0.9084\n",
      "Epoch 55/500\n",
      "24/24 [==============================] - 15s 635ms/step - loss: 0.2997 - acc: 0.8616 - val_loss: 0.2462 - val_acc: 0.9103\n",
      "Epoch 56/500\n",
      "24/24 [==============================] - 15s 626ms/step - loss: 0.3002 - acc: 0.8568 - val_loss: 0.2450 - val_acc: 0.9103\n",
      "Epoch 57/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2914 - acc: 0.8670 - val_loss: 0.2614 - val_acc: 0.8953\n",
      "Epoch 58/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2837 - acc: 0.8663 - val_loss: 0.3069 - val_acc: 0.8598\n",
      "Epoch 59/500\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.3192 - acc: 0.8510 - val_loss: 0.2845 - val_acc: 0.8804\n",
      "Epoch 60/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2910 - acc: 0.8615 - val_loss: 0.2545 - val_acc: 0.8972\n",
      "Epoch 61/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.2697 - acc: 0.8801 - val_loss: 0.2532 - val_acc: 0.9065\n",
      "Epoch 62/500\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.2743 - acc: 0.8760 - val_loss: 0.2507 - val_acc: 0.8972\n",
      "Epoch 63/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2659 - acc: 0.8843 - val_loss: 0.2509 - val_acc: 0.9028\n",
      "Epoch 64/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2610 - acc: 0.8768 - val_loss: 0.2548 - val_acc: 0.8953\n",
      "Epoch 65/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.2805 - acc: 0.8717 - val_loss: 0.2354 - val_acc: 0.9178\n",
      "Epoch 66/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.2693 - acc: 0.8790 - val_loss: 0.2766 - val_acc: 0.8729\n",
      "Epoch 67/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2471 - acc: 0.8874 - val_loss: 0.2472 - val_acc: 0.9009\n",
      "Epoch 68/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2623 - acc: 0.8811 - val_loss: 0.2360 - val_acc: 0.9159\n",
      "Epoch 69/500\n",
      "24/24 [==============================] - 13s 553ms/step - loss: 0.2577 - acc: 0.8809 - val_loss: 0.2437 - val_acc: 0.9121\n",
      "Epoch 70/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.2584 - acc: 0.8773 - val_loss: 0.2315 - val_acc: 0.9215\n",
      "Epoch 71/500\n",
      "24/24 [==============================] - 15s 627ms/step - loss: 0.2796 - acc: 0.8778 - val_loss: 0.2303 - val_acc: 0.9178\n",
      "Epoch 72/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2438 - acc: 0.8940 - val_loss: 0.2517 - val_acc: 0.9009\n",
      "Epoch 73/500\n",
      "24/24 [==============================] - 15s 628ms/step - loss: 0.2635 - acc: 0.8785 - val_loss: 0.2222 - val_acc: 0.9178\n",
      "Epoch 74/500\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.2359 - acc: 0.8916 - val_loss: 0.2338 - val_acc: 0.9159\n",
      "Epoch 75/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2537 - acc: 0.8800 - val_loss: 0.2246 - val_acc: 0.9065\n",
      "Epoch 76/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.2416 - acc: 0.8867 - val_loss: 0.2288 - val_acc: 0.9196\n",
      "Epoch 77/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2648 - acc: 0.8774 - val_loss: 0.2286 - val_acc: 0.9159\n",
      "Epoch 78/500\n",
      "24/24 [==============================] - 13s 549ms/step - loss: 0.2461 - acc: 0.8880 - val_loss: 0.2264 - val_acc: 0.9196\n",
      "Epoch 79/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2355 - acc: 0.8891 - val_loss: 0.2271 - val_acc: 0.9215\n",
      "Epoch 80/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2586 - acc: 0.8775 - val_loss: 0.2287 - val_acc: 0.9196\n",
      "Epoch 81/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.2402 - acc: 0.8870 - val_loss: 0.2801 - val_acc: 0.8729\n",
      "Epoch 82/500\n",
      "24/24 [==============================] - 13s 553ms/step - loss: 0.2461 - acc: 0.8928 - val_loss: 0.2334 - val_acc: 0.9121\n",
      "Epoch 83/500\n",
      "24/24 [==============================] - 15s 629ms/step - loss: 0.2672 - acc: 0.8837 - val_loss: 0.2214 - val_acc: 0.9121\n",
      "Epoch 84/500\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.2483 - acc: 0.8922 - val_loss: 0.2549 - val_acc: 0.8953\n",
      "Epoch 85/500\n",
      "24/24 [==============================] - 15s 631ms/step - loss: 0.2532 - acc: 0.8855 - val_loss: 0.2183 - val_acc: 0.9065\n",
      "Epoch 86/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.2205 - acc: 0.9012 - val_loss: 0.2111 - val_acc: 0.9121\n",
      "Epoch 87/500\n",
      "24/24 [==============================] - 13s 553ms/step - loss: 0.2448 - acc: 0.8849 - val_loss: 0.2184 - val_acc: 0.9140\n",
      "Epoch 88/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2256 - acc: 0.9037 - val_loss: 0.2765 - val_acc: 0.8897\n",
      "Epoch 89/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2378 - acc: 0.8809 - val_loss: 0.2169 - val_acc: 0.9178\n",
      "Epoch 90/500\n",
      "24/24 [==============================] - 13s 548ms/step - loss: 0.2468 - acc: 0.8851 - val_loss: 0.2132 - val_acc: 0.9178\n",
      "Epoch 91/500\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.2456 - acc: 0.8962 - val_loss: 0.2323 - val_acc: 0.8972\n",
      "Epoch 92/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2242 - acc: 0.9029 - val_loss: 0.2362 - val_acc: 0.9121\n",
      "Epoch 93/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2261 - acc: 0.9065 - val_loss: 0.2158 - val_acc: 0.9140\n",
      "Epoch 94/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2156 - acc: 0.9085 - val_loss: 0.2224 - val_acc: 0.9159\n",
      "Epoch 95/500\n",
      "24/24 [==============================] - 13s 548ms/step - loss: 0.2358 - acc: 0.9003 - val_loss: 0.2196 - val_acc: 0.9140\n",
      "Epoch 96/500\n",
      "24/24 [==============================] - 13s 553ms/step - loss: 0.2223 - acc: 0.8964 - val_loss: 0.2215 - val_acc: 0.9159\n",
      "Train loss: 0.188798665582\n",
      "Train accuracy: 0.916744621922\n",
      "Test loss: 0.211066427883\n",
      "Test accuracy: 0.912149534047\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/500\n",
      "24/24 [==============================] - 19s 804ms/step - loss: 0.8317 - acc: 0.5353 - val_loss: 0.6421 - val_acc: 0.5843\n",
      "Epoch 2/500\n",
      "24/24 [==============================] - 15s 629ms/step - loss: 0.7972 - acc: 0.5428 - val_loss: 0.6175 - val_acc: 0.6292\n",
      "Epoch 3/500\n",
      "24/24 [==============================] - 15s 626ms/step - loss: 0.7717 - acc: 0.5421 - val_loss: 0.6044 - val_acc: 0.6742\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.7612 - acc: 0.5536 - val_loss: 0.5970 - val_acc: 0.7022\n",
      "Epoch 5/500\n",
      "24/24 [==============================] - 15s 628ms/step - loss: 0.7334 - acc: 0.5654 - val_loss: 0.5896 - val_acc: 0.7060\n",
      "Epoch 6/500\n",
      "24/24 [==============================] - 15s 631ms/step - loss: 0.7172 - acc: 0.5695 - val_loss: 0.5814 - val_acc: 0.6854\n",
      "Epoch 7/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.7079 - acc: 0.5801 - val_loss: 0.5695 - val_acc: 0.7247\n",
      "Epoch 8/500\n",
      "24/24 [==============================] - 15s 626ms/step - loss: 0.6883 - acc: 0.6018 - val_loss: 0.5586 - val_acc: 0.7547\n",
      "Epoch 9/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.6595 - acc: 0.6120 - val_loss: 0.5471 - val_acc: 0.7659\n",
      "Epoch 10/500\n",
      "24/24 [==============================] - 15s 630ms/step - loss: 0.6658 - acc: 0.6075 - val_loss: 0.5344 - val_acc: 0.7678\n",
      "Epoch 11/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.6271 - acc: 0.6326 - val_loss: 0.5218 - val_acc: 0.7772\n",
      "Epoch 12/500\n",
      "24/24 [==============================] - 15s 635ms/step - loss: 0.6200 - acc: 0.6440 - val_loss: 0.5073 - val_acc: 0.7772\n",
      "Epoch 13/500\n",
      "24/24 [==============================] - 15s 627ms/step - loss: 0.5997 - acc: 0.6620 - val_loss: 0.4939 - val_acc: 0.7790\n",
      "Epoch 14/500\n",
      "24/24 [==============================] - 15s 631ms/step - loss: 0.6004 - acc: 0.6637 - val_loss: 0.4795 - val_acc: 0.7921\n",
      "Epoch 15/500\n",
      "24/24 [==============================] - 15s 627ms/step - loss: 0.5588 - acc: 0.6973 - val_loss: 0.4662 - val_acc: 0.7921\n",
      "Epoch 16/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.5560 - acc: 0.7061 - val_loss: 0.4503 - val_acc: 0.7846\n",
      "Epoch 17/500\n",
      "24/24 [==============================] - 16s 653ms/step - loss: 0.5379 - acc: 0.7212 - val_loss: 0.4394 - val_acc: 0.7959\n",
      "Epoch 18/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.5107 - acc: 0.7299 - val_loss: 0.4250 - val_acc: 0.7921\n",
      "Epoch 19/500\n",
      "24/24 [==============================] - 15s 631ms/step - loss: 0.4883 - acc: 0.7467 - val_loss: 0.4115 - val_acc: 0.7978\n",
      "Epoch 20/500\n",
      "24/24 [==============================] - 15s 627ms/step - loss: 0.4622 - acc: 0.7614 - val_loss: 0.4031 - val_acc: 0.8052\n",
      "Epoch 21/500\n",
      "24/24 [==============================] - 15s 635ms/step - loss: 0.4663 - acc: 0.7686 - val_loss: 0.3915 - val_acc: 0.8109\n",
      "Epoch 22/500\n",
      "24/24 [==============================] - 15s 628ms/step - loss: 0.4494 - acc: 0.7704 - val_loss: 0.3815 - val_acc: 0.8146\n",
      "Epoch 23/500\n",
      "24/24 [==============================] - 15s 634ms/step - loss: 0.4426 - acc: 0.7772 - val_loss: 0.3776 - val_acc: 0.8165\n",
      "Epoch 24/500\n",
      "24/24 [==============================] - 15s 636ms/step - loss: 0.4109 - acc: 0.7972 - val_loss: 0.3708 - val_acc: 0.8184\n",
      "Epoch 25/500\n",
      "24/24 [==============================] - 15s 627ms/step - loss: 0.4228 - acc: 0.7838 - val_loss: 0.3630 - val_acc: 0.8240\n",
      "Epoch 26/500\n",
      "24/24 [==============================] - 15s 631ms/step - loss: 0.3964 - acc: 0.8100 - val_loss: 0.3569 - val_acc: 0.8277\n",
      "Epoch 27/500\n",
      "24/24 [==============================] - 15s 628ms/step - loss: 0.3851 - acc: 0.8115 - val_loss: 0.3516 - val_acc: 0.8240\n",
      "Epoch 28/500\n",
      "24/24 [==============================] - 15s 634ms/step - loss: 0.4070 - acc: 0.8015 - val_loss: 0.3495 - val_acc: 0.8258\n",
      "Epoch 29/500\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.3658 - acc: 0.8220 - val_loss: 0.3515 - val_acc: 0.8333\n",
      "Epoch 30/500\n",
      "24/24 [==============================] - 15s 628ms/step - loss: 0.3649 - acc: 0.8284 - val_loss: 0.3444 - val_acc: 0.8258\n",
      "Epoch 31/500\n",
      "24/24 [==============================] - 13s 553ms/step - loss: 0.3653 - acc: 0.8271 - val_loss: 0.3505 - val_acc: 0.8258\n",
      "Epoch 32/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.3499 - acc: 0.8412 - val_loss: 0.3588 - val_acc: 0.8202\n",
      "Epoch 33/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.3363 - acc: 0.8475 - val_loss: 0.3459 - val_acc: 0.8258\n",
      "Epoch 34/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.3429 - acc: 0.8370 - val_loss: 0.3460 - val_acc: 0.8221\n",
      "Epoch 35/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.3257 - acc: 0.8534 - val_loss: 0.3422 - val_acc: 0.8315\n",
      "Epoch 36/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.3112 - acc: 0.8442 - val_loss: 0.3423 - val_acc: 0.8333\n",
      "Epoch 37/500\n",
      "24/24 [==============================] - 15s 627ms/step - loss: 0.3216 - acc: 0.8532 - val_loss: 0.3278 - val_acc: 0.8483\n",
      "Epoch 38/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.3162 - acc: 0.8563 - val_loss: 0.3251 - val_acc: 0.8464\n",
      "Epoch 39/500\n",
      "24/24 [==============================] - 13s 549ms/step - loss: 0.2900 - acc: 0.8628 - val_loss: 0.3285 - val_acc: 0.8652\n",
      "Epoch 40/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.3033 - acc: 0.8670 - val_loss: 0.3232 - val_acc: 0.8652\n",
      "Epoch 41/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.2911 - acc: 0.8714 - val_loss: 0.3218 - val_acc: 0.8483\n",
      "Epoch 42/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.2848 - acc: 0.8641 - val_loss: 0.3270 - val_acc: 0.8521\n",
      "Epoch 43/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2863 - acc: 0.8763 - val_loss: 0.3236 - val_acc: 0.8371\n",
      "Epoch 44/500\n",
      "24/24 [==============================] - 15s 628ms/step - loss: 0.2829 - acc: 0.8661 - val_loss: 0.3218 - val_acc: 0.8614\n",
      "Epoch 45/500\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.3048 - acc: 0.8587 - val_loss: 0.3280 - val_acc: 0.8258\n",
      "Epoch 46/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2709 - acc: 0.8850 - val_loss: 0.3236 - val_acc: 0.8408\n",
      "Epoch 47/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.2611 - acc: 0.8861 - val_loss: 0.3278 - val_acc: 0.8390\n",
      "Epoch 48/500\n",
      "24/24 [==============================] - 15s 630ms/step - loss: 0.2829 - acc: 0.8710 - val_loss: 0.3170 - val_acc: 0.8464\n",
      "Epoch 49/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2803 - acc: 0.8778 - val_loss: 0.3323 - val_acc: 0.8670\n",
      "Epoch 50/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2725 - acc: 0.8751 - val_loss: 0.3183 - val_acc: 0.8596\n",
      "Epoch 51/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2618 - acc: 0.8895 - val_loss: 0.3207 - val_acc: 0.8427\n",
      "Epoch 52/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2781 - acc: 0.8793 - val_loss: 0.3210 - val_acc: 0.8689\n",
      "Epoch 53/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.2777 - acc: 0.8752 - val_loss: 0.3156 - val_acc: 0.8633\n",
      "Epoch 54/500\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2781 - acc: 0.8763 - val_loss: 0.3226 - val_acc: 0.8427\n",
      "Epoch 55/500\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.2613 - acc: 0.8832 - val_loss: 0.3146 - val_acc: 0.8633\n",
      "Epoch 56/500\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2644 - acc: 0.8800 - val_loss: 0.3146 - val_acc: 0.8483\n",
      "Epoch 57/500\n",
      "24/24 [==============================] - 15s 630ms/step - loss: 0.2416 - acc: 0.8989 - val_loss: 0.3114 - val_acc: 0.8596\n",
      "Epoch 58/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2443 - acc: 0.8951 - val_loss: 0.3222 - val_acc: 0.8502\n",
      "Epoch 59/500\n",
      "24/24 [==============================] - 13s 548ms/step - loss: 0.2455 - acc: 0.8948 - val_loss: 0.3181 - val_acc: 0.8689\n",
      "Epoch 60/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.2539 - acc: 0.8810 - val_loss: 0.3067 - val_acc: 0.8614\n",
      "Epoch 61/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2414 - acc: 0.8938 - val_loss: 0.3110 - val_acc: 0.8745\n",
      "Epoch 62/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.2466 - acc: 0.8977 - val_loss: 0.3053 - val_acc: 0.8689\n",
      "Epoch 63/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2571 - acc: 0.8908 - val_loss: 0.3175 - val_acc: 0.8577\n",
      "Epoch 64/500\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2391 - acc: 0.8928 - val_loss: 0.3091 - val_acc: 0.8539\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 65/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2407 - acc: 0.8957 - val_loss: 0.3092 - val_acc: 0.8708\n",
      "Epoch 66/500\n",
      "24/24 [==============================] - 15s 627ms/step - loss: 0.2328 - acc: 0.8918 - val_loss: 0.3045 - val_acc: 0.8783\n",
      "Epoch 67/500\n",
      "24/24 [==============================] - 15s 631ms/step - loss: 0.2278 - acc: 0.8998 - val_loss: 0.3034 - val_acc: 0.8652\n",
      "Epoch 68/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.2418 - acc: 0.8946 - val_loss: 0.3102 - val_acc: 0.8727\n",
      "Epoch 69/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2302 - acc: 0.8960 - val_loss: 0.3056 - val_acc: 0.8652\n",
      "Epoch 70/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.2410 - acc: 0.8970 - val_loss: 0.3013 - val_acc: 0.8689\n",
      "Epoch 71/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2154 - acc: 0.9014 - val_loss: 0.3047 - val_acc: 0.8689\n",
      "Epoch 72/500\n",
      "24/24 [==============================] - 13s 553ms/step - loss: 0.2135 - acc: 0.9123 - val_loss: 0.3185 - val_acc: 0.8521\n",
      "Epoch 73/500\n",
      "24/24 [==============================] - 15s 627ms/step - loss: 0.2442 - acc: 0.8971 - val_loss: 0.2987 - val_acc: 0.8764\n",
      "Epoch 74/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2219 - acc: 0.9091 - val_loss: 0.3071 - val_acc: 0.8652\n",
      "Epoch 75/500\n",
      "24/24 [==============================] - 13s 553ms/step - loss: 0.2232 - acc: 0.9039 - val_loss: 0.3008 - val_acc: 0.8745\n",
      "Epoch 76/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.2366 - acc: 0.8936 - val_loss: 0.3018 - val_acc: 0.8764\n",
      "Epoch 77/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2266 - acc: 0.9030 - val_loss: 0.3068 - val_acc: 0.8708\n",
      "Epoch 78/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2417 - acc: 0.8927 - val_loss: 0.2987 - val_acc: 0.8745\n",
      "Epoch 79/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2183 - acc: 0.9098 - val_loss: 0.3016 - val_acc: 0.8727\n",
      "Epoch 80/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.2212 - acc: 0.9085 - val_loss: 0.3009 - val_acc: 0.8689\n",
      "Epoch 81/500\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2230 - acc: 0.9088 - val_loss: 0.3030 - val_acc: 0.8670\n",
      "Epoch 82/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.2038 - acc: 0.9129 - val_loss: 0.3051 - val_acc: 0.8652\n",
      "Epoch 83/500\n",
      "24/24 [==============================] - 15s 627ms/step - loss: 0.2380 - acc: 0.9027 - val_loss: 0.2918 - val_acc: 0.8708\n",
      "Epoch 84/500\n",
      "24/24 [==============================] - 13s 553ms/step - loss: 0.2268 - acc: 0.9076 - val_loss: 0.2960 - val_acc: 0.8764\n",
      "Epoch 85/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.2078 - acc: 0.9123 - val_loss: 0.2946 - val_acc: 0.8764\n",
      "Epoch 86/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2257 - acc: 0.8983 - val_loss: 0.2929 - val_acc: 0.8727\n",
      "Epoch 87/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2228 - acc: 0.9036 - val_loss: 0.2945 - val_acc: 0.8820\n",
      "Epoch 88/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2088 - acc: 0.9086 - val_loss: 0.2959 - val_acc: 0.8745\n",
      "Epoch 89/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.2169 - acc: 0.9095 - val_loss: 0.2895 - val_acc: 0.8708\n",
      "Epoch 90/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2111 - acc: 0.9112 - val_loss: 0.3172 - val_acc: 0.8708\n",
      "Epoch 91/500\n",
      "24/24 [==============================] - 13s 553ms/step - loss: 0.2127 - acc: 0.9129 - val_loss: 0.2973 - val_acc: 0.8783\n",
      "Epoch 92/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2074 - acc: 0.9144 - val_loss: 0.3069 - val_acc: 0.8558\n",
      "Epoch 93/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2043 - acc: 0.9132 - val_loss: 0.2977 - val_acc: 0.8745\n",
      "Epoch 94/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.2229 - acc: 0.9104 - val_loss: 0.2930 - val_acc: 0.8708\n",
      "Epoch 95/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2066 - acc: 0.9120 - val_loss: 0.2897 - val_acc: 0.8764\n",
      "Epoch 96/500\n",
      "24/24 [==============================] - 15s 631ms/step - loss: 0.2051 - acc: 0.9124 - val_loss: 0.2874 - val_acc: 0.8670\n",
      "Epoch 97/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2013 - acc: 0.9194 - val_loss: 0.2952 - val_acc: 0.8820\n",
      "Epoch 98/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.2077 - acc: 0.9170 - val_loss: 0.2876 - val_acc: 0.8783\n",
      "Epoch 99/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2114 - acc: 0.9130 - val_loss: 0.2933 - val_acc: 0.8689\n",
      "Epoch 100/500\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.1994 - acc: 0.9201 - val_loss: 0.2879 - val_acc: 0.8801\n",
      "Epoch 101/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2058 - acc: 0.9161 - val_loss: 0.2959 - val_acc: 0.8670\n",
      "Epoch 102/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.1903 - acc: 0.9239 - val_loss: 0.3028 - val_acc: 0.8727\n",
      "Epoch 103/500\n",
      "24/24 [==============================] - 15s 629ms/step - loss: 0.2025 - acc: 0.9266 - val_loss: 0.2805 - val_acc: 0.8745\n",
      "Epoch 104/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.2025 - acc: 0.9203 - val_loss: 0.2838 - val_acc: 0.8764\n",
      "Epoch 105/500\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.1930 - acc: 0.9148 - val_loss: 0.2855 - val_acc: 0.8801\n",
      "Epoch 106/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.1947 - acc: 0.9193 - val_loss: 0.2899 - val_acc: 0.8783\n",
      "Epoch 107/500\n",
      "24/24 [==============================] - 15s 627ms/step - loss: 0.2098 - acc: 0.9145 - val_loss: 0.2793 - val_acc: 0.8783\n",
      "Epoch 108/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.1990 - acc: 0.9143 - val_loss: 0.2864 - val_acc: 0.8764\n",
      "Epoch 109/500\n",
      "24/24 [==============================] - 13s 553ms/step - loss: 0.1983 - acc: 0.9154 - val_loss: 0.2882 - val_acc: 0.8708\n",
      "Epoch 110/500\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.1965 - acc: 0.9132 - val_loss: 0.2886 - val_acc: 0.8801\n",
      "Epoch 111/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.1970 - acc: 0.9189 - val_loss: 0.2864 - val_acc: 0.8764\n",
      "Epoch 112/500\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.1884 - acc: 0.9239 - val_loss: 0.2868 - val_acc: 0.8820\n",
      "Epoch 113/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.1856 - acc: 0.9242 - val_loss: 0.2838 - val_acc: 0.8783\n",
      "Epoch 114/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.1948 - acc: 0.9222 - val_loss: 0.2897 - val_acc: 0.8801\n",
      "Epoch 115/500\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.1915 - acc: 0.9247 - val_loss: 0.2836 - val_acc: 0.8820\n",
      "Epoch 116/500\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.2001 - acc: 0.9161 - val_loss: 0.2792 - val_acc: 0.8820\n",
      "Epoch 117/500\n",
      "24/24 [==============================] - 13s 548ms/step - loss: 0.1920 - acc: 0.9316 - val_loss: 0.2855 - val_acc: 0.8801\n",
      "Epoch 118/500\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.1906 - acc: 0.9150 - val_loss: 0.2858 - val_acc: 0.8745\n",
      "Epoch 119/500\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.1841 - acc: 0.9247 - val_loss: 0.2900 - val_acc: 0.8839\n",
      "Epoch 120/500\n",
      "24/24 [==============================] - 13s 549ms/step - loss: 0.1842 - acc: 0.9238 - val_loss: 0.2874 - val_acc: 0.8764\n",
      "Epoch 121/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.1773 - acc: 0.9270 - val_loss: 0.2888 - val_acc: 0.8839\n",
      "Epoch 122/500\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.1800 - acc: 0.9268 - val_loss: 0.2855 - val_acc: 0.8839\n",
      "Epoch 123/500\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.1782 - acc: 0.9302 - val_loss: 0.2828 - val_acc: 0.8745\n",
      "Epoch 124/500\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.1800 - acc: 0.9279 - val_loss: 0.2919 - val_acc: 0.8727\n",
      "Epoch 125/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 13s 551ms/step - loss: 0.1801 - acc: 0.9312 - val_loss: 0.2889 - val_acc: 0.8801\n",
      "Epoch 126/500\n",
      "24/24 [==============================] - 13s 549ms/step - loss: 0.1968 - acc: 0.9248 - val_loss: 0.2913 - val_acc: 0.8858\n",
      "Train loss: 0.154338312149\n",
      "Train accuracy: 0.939252336449\n",
      "Test loss: 0.279221677267\n",
      "Test accuracy: 0.882022473026\n",
      "\n",
      " Train Log Loss Validation=  0.168934903729\n",
      " Test Log Loss Validation=  0.228777095004\n"
     ]
    }
   ],
   "source": [
    "#def myAngleCV(X_train, X_angle, X_test):\n",
    "K=3\n",
    "folds = list(StratifiedKFold(n_splits=K, shuffle=True, random_state=16).split(X_train, target_train))\n",
    "y_test_pred_log = 0\n",
    "y_train_pred_log=0\n",
    "y_valid_pred_log = 0.0*target_train\n",
    "for j, (train_idx, test_idx) in enumerate(folds):\n",
    "    print('\\n===================FOLD=',j)\n",
    "    X_train_cv = X_train[train_idx]\n",
    "    y_train_cv = target_train[train_idx]\n",
    "    X_holdout = X_train[test_idx]\n",
    "    Y_holdout= target_train[test_idx]\n",
    "    \n",
    "    #Angle\n",
    "    X_angle_cv=X_angle[train_idx]\n",
    "    X_angle_hold=X_angle[test_idx]\n",
    "    #define file path and get callbacks\n",
    "    file_path = \"%s_aug_model_weights.hdf5\"%j\n",
    "    callbacks = get_callbacks(filepath=file_path, patience=5)\n",
    "    gen_flow = gen_flow_for_two_inputs(X_train_cv, X_angle_cv, y_train_cv)\n",
    "    galaxyModel= getVggAngleModel()\n",
    "    histr = galaxyModel.fit_generator(\n",
    "            gen_flow,\n",
    "            steps_per_epoch=24,\n",
    "            epochs=500,\n",
    "            shuffle=True,\n",
    "            verbose=1,\n",
    "            validation_data=([X_holdout,X_angle_hold], Y_holdout),\n",
    "            callbacks=callbacks)\n",
    "     #Getting the Best Model\n",
    "    galaxyModel.load_weights(filepath=file_path)\n",
    "    #Getting Training Score\n",
    "    score = galaxyModel.evaluate([X_train_cv,X_angle_cv], y_train_cv, verbose=0)\n",
    "    print('Train loss:', score[0])\n",
    "    print('Train accuracy:', score[1])\n",
    "    #Getting Test Score\n",
    "    score = galaxyModel.evaluate([X_holdout,X_angle_hold], Y_holdout, verbose=0)\n",
    "    print('Test loss:', score[0])\n",
    "    print('Test accuracy:', score[1])\n",
    "\n",
    "    #Getting validation Score.\n",
    "    pred_valid=galaxyModel.predict([X_holdout,X_angle_hold])\n",
    "    y_valid_pred_log[test_idx] = pred_valid.reshape(pred_valid.shape[0])\n",
    "\n",
    "    #Getting Test Scores\n",
    "    temp_test=galaxyModel.predict([X_test, X_test_angle])\n",
    "    y_test_pred_log+=temp_test.reshape(temp_test.shape[0])\n",
    "\n",
    "    #Getting Train Scores\n",
    "    temp_train=galaxyModel.predict([X_train, X_angle])\n",
    "    y_train_pred_log+=temp_train.reshape(temp_train.shape[0])\n",
    "\n",
    "y_test_pred_log=y_test_pred_log/K\n",
    "y_train_pred_log=y_train_pred_log/K\n",
    "\n",
    "print('\\n Train Log Loss Validation= ',log_loss(target_train, y_train_pred_log))\n",
    "print(' Test Log Loss Validation= ',log_loss(target_train, y_valid_pred_log))\n",
    "#return y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preds=myAngleCV(X_train, X_angle, X_test)\n",
    "preds = y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Submission to kaggle\n",
    "submission = pd.DataFrame()\n",
    "submission['id']=test['id']\n",
    "submission['is_iceberg']=preds\n",
    "submission.to_csv('sub_Adam_3folds_amsgrad_3e-4_10p.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_acc', 'loss', 'acc', 'val_loss'])\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+z7tz04AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1e956cc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1e4d4fb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list all data in history\n",
    "print(histr.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(histr.history['acc'])\n",
    "plt.plot(histr.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(histr.history['loss'])\n",
    "plt.plot(histr.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg\n",
    "\n",
    "min1 = np.min(X_holdout)\n",
    "max1 = np.max(X_holdout)\n",
    "diff1 = max1 - min1\n",
    "my_sum = lambda x: (x - min1)/diff1\n",
    "\n",
    "X_holdout2 = my_sum(X_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9       0\n",
       "13      1\n",
       "14      0\n",
       "19      1\n",
       "26      1\n",
       "27      0\n",
       "28      1\n",
       "36      1\n",
       "38      1\n",
       "42      0\n",
       "43      0\n",
       "44      0\n",
       "52      0\n",
       "63      0\n",
       "73      1\n",
       "76      0\n",
       "77      1\n",
       "79      0\n",
       "80      0\n",
       "81      1\n",
       "83      1\n",
       "86      1\n",
       "89      0\n",
       "92      0\n",
       "93      0\n",
       "95      0\n",
       "96      1\n",
       "97      1\n",
       "100     0\n",
       "101     0\n",
       "       ..\n",
       "1533    0\n",
       "1537    0\n",
       "1539    0\n",
       "1541    0\n",
       "1542    0\n",
       "1543    0\n",
       "1545    0\n",
       "1548    0\n",
       "1549    0\n",
       "1552    0\n",
       "1553    0\n",
       "1559    0\n",
       "1561    0\n",
       "1566    0\n",
       "1567    0\n",
       "1569    0\n",
       "1576    0\n",
       "1577    0\n",
       "1579    0\n",
       "1585    0\n",
       "1587    0\n",
       "1588    0\n",
       "1590    0\n",
       "1591    0\n",
       "1596    0\n",
       "1597    0\n",
       "1599    0\n",
       "1601    0\n",
       "1602    0\n",
       "1603    0\n",
       "Name: is_iceberg, Length: 534, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_holdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb1e4c3bba8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nt118r10WKSbUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1e95594a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[0], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[9])+ \",\"+\" iceberg_probability : \"+str(pred_valid[0])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb1e458ed68>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAAEICAYAAAAncI3RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXu0ZetZ1vm887Lu+1Z1Ljm5QIICabA7ihAbUQgGEdA2\naZsRQRpPNHhUWuViN4m0KEp0BBWRMRDiaUAODRgiiqFpRNKBAGEAEi5eQsCQkJCEnFPnUrX3Xntd\n55xf/zG/td/f2rV2VZ3KIbVr5XvGqFFrzzUv3/zmXHN+z/c+7/NaCEEJCQkJCVJ2pxuQkJCQcFGQ\nHogJCQkJEemBmJCQkBCRHogJCQkJEemBmJCQkBCRHogJCQkJEXfNA9HMXmJmH7jB98HMfu8t7Of5\ncd3iNtpw29t+uDCzd5jZSz7MfXy3mb32GWrShcCHe01udN+Y2ZeY2Y9vWtfMXm9mX3d7rX7abXyr\nmc3M7Kc/EsfbNpjZ55jZ2MwaM/ucG617yw/EeFGumln3Fte/Yw+Pi4rYJz9pZhMz+/WbXRwihPDJ\nIYS3/i42L+EMQgjfF0L43HO++yshhG+Qbv6yfobw10IIn3nel0/n3jKzrpl9l5kdmdmjZvbVZ75/\nadzHJO7zY/Hdd5vZIj5gVv/y+N2XnFk+ic+APxi//yoze0887u+Y2Tdvej6Y2WfF7V6LZa80s/rM\n/l9yZruvMLPfMrMTM3unmX2CJIUQ/r8QwkjSb9+sk2/pgWhmz5f0RyUFSX/6VrZJ2Ih/JelXJF2W\n9H9K+kEzu/fONun2sPoRfASOk16ot4anc299vaSPl/Sxkj5b0teY2edJkpndI+nfSvo6SZckvV3S\nD5zZ/h+FEEb4V0unL5DT5ZK+XNJ7JP1y3O6HJX1KCGFX0u+T9CJJf4M7NrNS0rdI+oUN7f65M8d9\nK7b7MkmvkvQnJY0k/SlJT5zbW+fgVkeIf17Sz0v6bkkPnjmBvpl9k5m9z8wOzextZtaXtBreX4tP\n8083s683s+/FtmujSDP7C/HJfhzfJH/56Z5Q3M+fNLNfiW+i95vZ129Y7S/Gt9SHzOx/x7aZmb3G\nzN5tZk+a2RvN7NLttONMmz5B0qdI+rshhGkI4d9I+i+S/pdb3P69q7e+meVm9rWxjcdm9ktm9rz4\n3QvN7M1m9pSZ/YaZveLMru6J3x+b2U+defufu20cGXy7mf2omZ1I+mwzu2xm/0/s5180s9ea2dtu\n4VyCmf2NeI2fMLN/bGZZ/O6VZvazcfTwpKSvj9fkb8d77IqZfY+Z7Z3Z7XnX88Vm9nNmdi1+961m\n1jmz7RfcoC0bzyf2x2vNbCjp30t6NkYuz7Z2dHQZ63+KmT0ef/DPKG7j3npQ0jeEEK6GEN4p6f+S\n9Mr43Z+R9I4Qwr8OIczUPjxfZGYvvI2mPSjpe0JMhwshvDuEcG3VbEmNpLPTFX9T0o9L+vVbPUi8\nXn9X0leFEH4ttHh3COGpp9vgp/NA/L7470+Y2f347p9I+oOS/rDaN8rXqD3R1fB+Pz7Nf+4WjnNF\n7ZN9V9JfkPTNZvYpt9hG4iS2eV/tG+OvmtnLz6zz2Wrfkp8r6dXmFOOvS3q5pM+S9GxJVyX981s5\nqJl9m5l92zlff7Kk94QQjrHsP8XlTxdfLemLJX2B2r76i5Im8cf5ZknfL+k+SV8k6dvM7JOw7ZdI\n+gZJ90j6VbXXVLe47Z+T9A8k7Uh6m9p+OZH0LLU3/9rL8ib4nyV9qtof8sviOazwh9SOLO6Px3tl\n/PfZkj5O7QjgW8/s77zrWUv6qni+ny7ppWpHLrfalhsihHAi6fMl/Q5GLr8j6a2S+DL6UklvCCEs\nz+7DzP6ImV07u/xp4JbvLTM7kPRA/H7Tup/M7+L5vfvMvr48vjR/ycw2PnTji/YzJX3PmeV/zsyO\n1I7eXiTpX5zZ5i9K+vvnnOcfiC+t/2ZmX2fOHp4b//2+OAD6LTP7e6sX29NCCOGG/yT9EUlLSffE\nv39d7ZNYah+oU0kv2rDd89VS7ALLvl7S995onTP7+HeSviJ+fomkD9ygnUHS7z3nu38m6ZvPHPOF\n+P4fSfrO+Pmdkl6K7x6I51/crL036ccvlfTzZ5b9A0nffYvbv1fS58TPvyHpZRvW+bOSfubMsn+h\nduQgtSP8N+C7kdoHxvNucdvvwXd57JdPxLLXSnrbLZxLkPR5+PvLJb0lfn6lpN8+s/5bJH05/v7E\nDddk4/XccOyvlPRDT6Mtbzuz7u9Ff7z2vHsz9ufPoq8elfTip3vfxO3fKunLnol7K17rIKmHZX9c\n0nvj5++U9Loz2/yspFfGz5+ilpYXal/Ix5I+Y8Nxvk7SW2/Q5o9X+2J+Fpa9SdKfPdu/8e+Pk/QC\ntc+c/17Sr0n6W/G7PxzP6f9VOwh6vqT/JukvnfcbOu/frTxBH5T04yGEFR//fvlI4B5JPbVvkA8b\nZvb5Zvbz8e1zTW2H33Mb+/lD1k4GP25mh5L+yob9vB+f36d2NCi18yo/FCnWNbUPyFrtaOXDwVjt\naI7YVXtDPV08T5v7/GMl/aFV22P7v0TtCG6F0/MOIYwlPaX23J/WtpLuVfujeP85398M5/X/pv08\nO67D9QutX5ON+zOzTzCzH7E2eHAk6R/q1u+FDwdvkvRJZvYCtQ+cwxDCf3wG9rsJT+feGuP7Teve\ncF8hhF8OITwZQqhCCD+qlmH8mQ3H+fOSHjmvwSGEd0l6h6RvkyQz+58k7YQQzs5XrtZ/Twjht0II\nTQjhv6gdRX5h/Hoa//9HIYRrIYT3qn2Zf8F5xz8PN3wgWjsX+ApJnxVvqEfV0o8XmdmL1A57Z5J+\nz6Zz2LDsRNIAf5/+2KyNXv8btRT8/hDCvqQfVTvX8HTx/WoncJ8XQtiT9PoN+3kePn+MpN+Jn98v\n6fNDCPv41wshfPA22kG8Q9LHmdkOlr0oLn+6eL829/n7Jf3UmbaPQgh/FeucnreZjdROc/zOLW7L\na/q4pEotVblu37eA8/r/7HEUv/tY/P0x8diP3cL+vl0tq/n40E7mf61u/V64VVx3r4d2/u2Nkv5X\ntSO4//tp7vPp4JbvrRDCVUkfit9vWvcd/C5OpfyeTfta7VJn+tPMPkPtS+UHb9LuQn4fv1TSp+I5\n82clfaWZvekWjvsbkhZavw63Z+N1k6H6F6sdQXyM2ofX6t9PS/qmuM4/V0tpnq2WGny6pK7aB18t\n6RPODM2fiPvbU/sWDbFjduL6nxVP9PMlTXQDWnKmraQzVyQ9GD+/OP79vfHv58d1vy+28ZPj958b\nv/8qtRTlY+Pf9yrSU30YlDlu//NqH/g9tfNW1yTdi/MLN9j2vXLK/H9I+s9qaYdJ+h/U0pgdtSOc\nL5VUxn+fJum/Aw05UjsN0pH0zXJadyvbvvZMm35A7ctnIOmFamUNt0qZ3yLpQO3D6NclPRQ20NS4\n7MskvUstZRqp/aHd6vX8j5L+TuynF6r98ZylwbfUFp1PmV+odpSyd6bdn6F2JH+8up9u8755q25A\nmW92b21Y93WSfiqe8wvVPiA/D/f7odqATE/SNwp0XO2obKR2MPW58dxecmb/DwvTK2eu433x8yep\nfcj+U9x/fMb8gNr781L8/vPVDpRW/f1fFadz4rLvkfQjcT/PjdfxVef9hs7tx5t08o8pPvjOLH+F\n2jmRQlJf7RzdB2NH/rSkflzv76sdSVyT9D/GZf88/v2bkv6S8ICR9L+pfetfU/tGfYNu74H4hWp/\n3Mexk75V1/+AHlI7EnhU0tdgP5naoMVvxO3fLekfntn2vDnP10t6/Q3a+Hy1N/c07v9z8N2XKj6c\nztn29GKqffH8bUm/Fdv4i5KeG7/7RLVzKY9LelLST0j6/fgRv15t8GQcr9ULcIybbXv2gXhvXP8o\ntuEbFeffbnJfBbVyi/fE43yTpHzTQwjX5O+oHcU+Lul7JR3c4vX8TLU/jrGkn1F7T559yN1SW3TO\nAzH+/V1x+2uSno3l71I78r5Rf/xRSeMbfP9W3fyBeKN760vURo5Xf3dje4/U/t6++sy+Pif22TTu\n8/n47mfU/s6P1AZfvujMtr3YBy/d0MZ/GY93ovZ+/sfCXOaZdc/27z/Btu+J17HE97tqnxfH8T75\nO5LsvN/Qef8srphwh2Fm3yHpX4cQ/sOdbsvtwsy+Ue0k+YM3WS+opbC/+ZFp2Z2Dmf2EpO8PIXzH\nh7GPH1fLvN4eQvjsZ6xxHyUws5eqnY7rSvqCEMJPnrtueiAm3C6iNq2jVvP2aWrnfL8shPDvbrLd\nR8UD0cw+Te1o/HlhXRKTcEGRsgASPhzsqM2QeLZaOvNNkt5kZn9UrVj5OoQ2g2HrYWaPqNWzfkV6\nGN49SCPEhISEhIi7ye3m86xNJ/tNM3vNnW5PQkLC9uGuGCFaayTw39TKdj6gNqL5xSGEXztvm27R\nCf1ur/0D52gGyVTunxtsm0d5U4NVm+Br1Phchs3vFEO3rh0TaCCVCljFsvYPXpm1/XEnWB4a/JHh\n3Jp6cxvRLv+MvfPeOG/fWJ39UuCEMhynWdv2+nsv53bn9OH6LYtri7aHtdMIm1ZXwPFXfRfWe90/\nod08Dvtl7TJjN3W2+Tdm4fr7gsc33JR57l4a5wlzeT+tNbHyHa2dx6rBa+fGHfr+1vadtff8dDrR\nfLG4HZ3whcXdMof4Ykm/GUJ4jySZ2RvU5pye+0Dsd3t6ySd9miSpqarT5WWJvP5d/zwzf2gMQpt/\nP+v4TTCfzU4/P1WfnH5+YEGduaOs/D4pO57Pz4fWLPN2VXiuloPWYW2JX0SBJw8fGvnc11nOF36c\ngV/ak+OxNqEovF3dTtsXGdI/q9rbZxP/zH1PSz/+8Wxy+vlg6fse9Pqnn2d9b/u0uP5BvVv5dj38\n1oquX6swR1saP36Woe3w4qmxTlP5MZe45k3c57zxVGPDQ6jE8Tu5n7+Nvc8zrJ/hGh12fJ81HsL9\npt1PhddxQPuKha+7u+vJI3mz+Rk0l/eLSu+L6VM+hVn23L3P4jrW9z4v5efQzLzdU/N9F/H+fOvP\n/szGdtzNuFso83O0nl71gbhsDWb2kJm93czevqgWZ79OSEhIuCHulhHiLSGE8LBalbz2R7uhWb3s\n8EZfBn8D53gDhtzfxnV8Yddz/75ZwqQEFrnHI99uL/ORUP/dPioLB/7WnfR8/atHRxvPoxcT0sqP\nO3WPUrX0N3QGK8IGdLApfORQYVS8O/KMruP59PRz0fNRT1O078bF2uSBvy/r2o+zU/vyAiPKQ9Cq\nCiOhvPZ2rUZFZ/dfFO3yzsyvTwAHnSx8hF4PaMWIc278nC8v/SLNQFlntb8oewXYQuzHMJ5769Cf\nRe3tyvp+/BwjR46ohNHiXuPrLDF9sRrFDoL3yVibpzcajIqzXb/PGt4Xme8nK/z43a73RY2R46pX\nyOgXSwwkaoy+O7hWWbvvc2aC7mrcLSPED2o93/S5cVlCQkLCM4a75YH4i5I+3sxeEM09v0iteUNC\nQkLCM4a7gjKHECoz+2uS/oPaPN7vCiHc2CWmCbJpSyesxGR310+5WWIye+JU6bhz/XuiKZ0f9Brf\n3xRzlVnHl48ecJq6FthGUGE07fkXaGN1qT3+7Emn1CVospNeqR75hPhJ5efQA30qQHEZLT+qnYaO\nyjY4RMJWzp1LlYj6VCP/PEFgKMcxxwhI5RMPtlxeeHCgzLzteZzMr9DP4QnfzoZ+/qS1R6Uf/4HG\nNd8MmNTL6zxZJa1TxWYvXotjP2Yz9PMJA8yTVJimwLU9MT/OsAR97eAzVAnlpL135kPfR4n5GEMf\nZph2qK56UE/3eFAvw3RDtvDr3Nnxdaa4R5p4nzeYRgpdRJxBr9ci+Iu4PmdXtgR3xQNRkkLrvfaj\nd7odCQkJ24u7hTInJCQk/K7jrhkhPl1YnilfReN6fpr5oVMGatgWKP0TYnS5C50etYQNom924pQN\nbEPjAfjYktoyfwf1oM8z0Pd5jBBOhr4dGNCa0Hm29PPpIpre6TsdP25IGT3i2UNAcRLaP+oFNHPB\naWpvAHoLrRx2p1nmy/vg9b0afQfK3pkjWh6jqKzAVI3RblBmAx3d7XgDDhFB7vpsgLqg/lmP0Wdv\n71OztqTJaBc6yD60jxSd476o0ReG6Zh58Puin+P8oZWs435yXM8ake3+EfSTC/T5AjrMEz9nTncY\nKHaz4+2qC0yfRE1sF9MxFZQVAcfp5t4XZZxesnr7OHMaISYkJCREpAdiQkJCQsTWUmbJ81lniP6V\nBz70n1918fSihyhajL7lSJ1aonpkr3DaVUzxTrnq9HV52Zd3EVmsu4jcda4XybbHj/uGYHc29313\nULu9i8/ZOSldHQiQDdRrwYjjrKVH/Z5T7R4EvUSG92jnmkc87yt82wJRyXnux+yAEs4RCV6lOs5B\n/Opne3S0AjUnTeVcQgkx9OHs8PTzrnm7dvC58NOXxU5f4jgUww86VASAdiK9sospAKbRjSfeR1QC\nDIbt+XURwe8P/DjjkdPhPSzPZn7M6cL3VwwxHYDmHlV+fMOUUeegjcoHTOnUU6YieruYFlrvt8cJ\n+fYps9MIMSEhISEiPRATEhISIraWMjeh0bReRcM8ynecefgx7DJyCeH1KroKwaxOkOOKqGC34zRl\nPvd956ByM9BxktBxD5ZiIM2D6PJSIjrZhUXTFNFRCmbpVFLTEQbHXJKSQllbRBrWg7h8ifxd5kln\nyMftTvB5z6PmOUXNsJ9i7nMFejaLNHRewpILlKxARHyGqY7RngvgxxCA7+/v+zqZ88cG1zFHvm+5\naD+PEDafYX/zysPm/V2YflebI60lpjLommSIePfG7bZd5EMzUtzHlMl5Nn0GWitEkANy2YUc636J\nvOZJu3xOp6UCEWlE1o/7OM/YF/x+W5BGiAkJCQkRWztClJmy+Lbju7WDUddy4iO6LkYG3aZ9T3Tg\naXgNmrgu3qJhCLcTjAqoPZzNfHQxH/n6+ZwuptCcRQ0hp6whK1OG1LG6g1FUQTElRmXXMPEOad81\npODlceTGkUi2xHHm8BFEYKjyQaEs91HJAt6Ie0MfxTGoANmeFk9e7/zT2YXX5A50cKPedetKUm/P\nh/QDw1gcN0ABbWEFHWo3Xq8FPCADGljicwWt5nzq9xB9HwXdZsbAC/o8W+lGoY3MMbI3eGdWSC2d\nFL68Ax1ms2AapR9ngGBXH/foSk+7QPuqIbarfQTd4f2Zrc5r+8ZT23dGCQkJCbeJ9EBMSEhIiNha\nymyN1Jm2w3xb42aYtAdNPjpyildG6lMOfPL8MrbjZPKSdUH4egHFJA0pYTnfoF1r6XiRyncaTJKD\ngq/Ra9YrgauL9XHOCJQI2sfeidO9RTeafuIUagRVSuoKsc4Mhrc9BCwEKv/YHJrAArQS+xwNW7qb\ngdLWoK+cM6gRvGnGMEidIthEI1imSCIIkSE4ddxv+87owo/yDHO0hYa3XZzy0Qhpb/PNju2jDG42\nkdbSDWnR9/bNQc0zBJtYQmKBa75Aiianexj4Uw/OT/H0etDJLmAE3GDdAcKBqzTC7NzqLncv0ggx\nISEhISI9EBMSEhIitpYyh1yaRp3h4ClQSWi1Kjio5AOPaI7qNipXQmOX7zl9rmagvUjpy5Dq1hRO\nt0ZwPjnpIx3sBNE91AkZrBxxEPEdI83tsHZ6f2/jhqt8vfWmSDsEDYJUUc2Bt3e54opkvYh+lqDv\n9VWPCBcsKwo62p0zpQ3pipn3XdGD0DNv12fNG5r5slpdwPkwpa8HhxtWLOwjcjtDzZBDiBv7Wbuj\nAP2ooUOzCSgwzHxtB2mcT3qKHCPYBWj44l5MWUTjWE5NVDPoJH0zBfRtQAQ5pwsOsASV5rXLe+yw\n9vgZpnfmqKI4gMxwduTazyrWlFkrTbolSCPEhISEhIj0QExISEiI2F7KLKmJqWnVZVBZCpxhysq0\nt5XLSQfds3jUI6UneyjJiIgjgpaaMQkLeukOjlOSBuLdVO23GzSgo92pb3cZomNDHY0RXHiWSIs7\n7oKGspAIPpbxPE4gri7JtOHIMihd6DtBsfkSxq01Is6rspXSegqgUQQe0+iQZakZ6JtwrQpQ4wVc\neRcQPYeJU8acZWgR/R5TIRDb2yGlhCLAQFPFKQj8gnqYdgldON885vdOdwpKvhKp70NoTgrMlMd9\n3zcdi+ag28WcqXSgyXABarC+ovCerj590Ovj3GkyBeOrkw5NoswJCQkJW4sL9UA0s+8ysytm9l+x\n7JKZvdnM3hX/P7iTbUxISNheXDTK/N2SvlXS92DZayS9JYTwOjN7Tfz71TfbUWambtnSnwnMOheg\nsh3kYnbANpr4nphNPQd5hhoprK/BHOM+BK5dBCWPEFntN97lfXN+GPoM/7b/TRqqhP3joEaNmF2n\nW4dHHuXMke+8ixznJahcbwH6GmngU/Pj02UVIugNxM0Foqw0W6ngsNKHGLgm22Q1T4iHm1N6uHk6\nIsdUwxwXy3A+axcDmEJtXYA+s4zmZNRuO0He+d7Q+7ZG9HuN6oM25shTD6D4w2dtfoevpjtmiCyz\nDGsDKtvD5wYifYrHte9R+07JGkBwLZr6NV3q+gg16XiB+7yPWkDWbfedhNm/ywgh/LSkp84sfpmk\nR+LnRyS9/CPaqISEhI8aXLQR4ibcH0L4UPz8qKT7z1vRzB6S9JC0boWfkJCQcCu4Gx6IpwghBDM7\nN7QVQnhY0sOStLu7G1Z5nrMaEU9Yd2WIFjJXt4hRucPC6VOZkdJutsiagzIi+KrdBayjdpGri9KO\nfdiLVZHiHFCMi5BwDUE5BciCFVkQ6C6MVnu45AUivnXVrt8bOjVmzRPsTiiRot4YtmCI5k8zb9cs\nA/WDpdS0Q/F4GzoeLVHiE+3LYVtV1szl5bVAVBY2b4xy04h3z66vGbNE3vfVE4+yauB9O8zQz4g+\nNxD9Z4gQZ1PQ3QGMY4fteZAC5zNMk6D07dVHn/B9XPKX/bOQb1+tCfCRk4x91shPLiftOj3MafBe\nEcToaznmsc/P/yXevbhQlPkcPGZmD0hS/P/KHW5PQkLCluJueCD+sKQH4+cHJb3pDrYlISFhi3Gh\nKLOZ/StJL5F0j5l9QNLflfQ6SW80s1dJep+kV9zSvtRagEnrYuilGKF0SjJH5HAVod3rDjduR9So\nu2GsF3IZZSMRuWNpRyJDtDiPNUsChMGMlGoCV2VsR8unOagPc1XDxKcBxrmfUydGoieVH7NEuUsD\nZeqSYu14H3FqIoDuDirQdFBM1knpRkX2GPsY5n5uC+SDD6YoGQuhc8PypBBYzzCtYCxbCkq6YuEl\n25QhtI8pgwUSvnsDWJFRJA2RupATT5a5svEqUGI0H/i5nYxdNTD8mHtOP/ehMugc4d6ij9hsc44z\nnbdtJdjGqpx2mGJqpsuSpLFrA5OttwQX6oEYQvjic7566Ue0IQkJCR+VuBsoc0JCQsJHBBdqhPhM\nI4+R44PKo4lPDFD8CTS421z/blgTnpb4HrRiOUIEGb2J8khagHqUJ6C7EL7OkGOb9VqqliOyyYge\nqWHnGBFEUJgc9H3cA99DFDVf+jGXi5Yr7gU/5kkPbtSIrB8fsK8Q5UWO8Wzmf+zQbRkiZGh9VVrb\nrmXlbf3Q3CnjzsCpeYMo82FwjsuiVHsQLJcVXKIzFFlifm5x/fXvXYVtGJX7UAoczn3KhFKvtekL\nRNNrnP+q4NWaSznuiXLX6TiLfzW4bxdIOK9YLGrNVR3XCKJ6xfzxBd3Y+TWmSaaIWp8qLiwJsxMS\nEhK2FumBmJCQkBCxtZQ5BHdf7qEoz31HsPSC8HgJh+tlzH2ewAmKeZ8l8mezpb9TGGVl7eYT+IIN\nkKsKFqSMlC3SU9YIZg3pARKbS2zXzGF55XvTQcepXAX/s0nw9q7Ezmx3p/QOuLL0HOcuIrVdUMMK\njtGjnu/HDC7ZyCWezP2cVoW9DpDs3EGUu8EcxDEsqDsFLhJQs8gW6wcjQt9gGmBFVTNYrlXPcZo+\nxTnsHvp2/S7qPIOmTzucjqClF44fdxng4r0mIu9tDuMyoeDRYoLlvs4A0wSs3T3GFEMer919pXcu\np4lmNe4n5EZXsThZqDcrL+5mpBFiQkJCQkR6ICYkJCREbC1lthCURXFqDVFpB0WUqpHTinHpX/Qj\nJawXjKyxUBKidqDjoxmE2SPkzC4g8MUr6HDmubK7iEuXkYpYD67HEJHPalqY+XJDBLmZoUYwqOFs\nQSsyFLbqXk89mfd7qe/FrKy6fnpBkjo7HhVd7CBn/Kr37RRCdhaIyqKQuYHrdQ/9PF1uVgT0kONc\nLHE7F4jK4pxJpRfso2iL1b9vz7+HGL5bI5d532k9o7m4yhpXPh1AytrwProSpxh2fN9zJMH3UcN6\ngXMoYCF3CeJ1IVpMZ3JBlTCi5Vxs+xwCfYa8DX8sIegO3fYcwhYOp7bwlBISEhJuD+mBmJCQkBCx\ntZQ5z3Lt9ltrJA7tT/pOH45KCI/pyBypUg5qOIVj8r1yy6XCKMCFLdUCgumwmcqMek6Tc9bwiZHO\nANumDt5dc0S8aR2V0fIJu5stIQZmZBsU2+J50E5sQUoLKrWEezPZVtXntIJvMN1HtHSM6DOilLNV\nxH+AaQfQ+CHqTK+JyzFlUaOwVHYIAbxvuaYsWKDxg5jLXY2d6i67mDIxP+YCnThHH00q1FSeoGNw\nL5QDWI49q+0L5trP5r6PJdqiYrMIuodpjyUj2FifBdR6yPFe0foKwu0SP5bJ1Kc3hh2n71nsK0vC\n7ISEhITtxdaOEEMIqqJBLAMSwqikxOcl4x7RHaYLk9FdlOE0TDCveYpAN1bgTTtHrZPuvo8uG6zf\nUDYXgzlMLaOTysj8Lb9oYM+ClLaAfWcQPJZHGEUhaLIaADFFcHmCEQpGZXSsuZa7e04X2rc+RlQ1\n0hVr6DBXqWOSNIsuOw2W5TgO0x9rNJKGql2mV0J7yfQ2Gvf2BNPTWTsaMlyIHaYi7vvnGuPvmhpD\nHH6BFMS9CVgE9JS9bttGg6vRE+jPg46vPAoInqGsaI5r3kBDOqdBL5yCuhgtWtSwdqCl5fXf6SFd\nEucWTmISdSFDAAAgAElEQVR7aUi7JUgjxISEhISI9EBMSEhIiNheylw3Ckct51mS7ixQawQlJ/MD\nnzSum5ZuUCdXMnjCWiTgGAOYyiDTS52dndPPk0OkWpnTugb7rzpR5wXaXYwR7NjBBD9oYom0PNbx\nqKkbhD5wMHJKxnowpzjykwjO+jVFjGR/5LQKpkJaoKzqoPG+PTpBmdM97+AipoYNQVOn666sp1hL\naFurY4KgUp8T/tCNdvz8a0yZdGJf0CA1jBEk2fe+qqDr24X6cAqK3y/RGQeYspgzwNYeK4eZ7HMq\nv1fW6rWQmuM6T0HfK0gS67W8UNy7MADOY4ClWKsT69tNaqyLKZBhXD+VIU1ISEjYYqQHYkJCQkLE\n1lJmmVRH6lnDNebJwjlZZ824FfQg/l/AbUV9p0Y5Sl8uEc2boNzmEHqvHoxYjRTzyClZ1fV302DF\n5BFxFRx2JkhpWzOOFXVzeNfhKtOAlNFSi1Ry8dih7wOGqxMIJbsTmJJmaw3YiAomuhlKf86v+LHC\nKPI9ULMSTjrdjm/XbagZhXEtysaSete70OEhEksT3SpOrxhS+xj5J33OEMHntMrlubdxicjuEaZm\nukgd3TQaGcAUmMatU6buoV4OtbIF7st81xvfw3QDpwwUM0cL0PGrHawM5EeYUun2N66zDUgjxISE\nhISIC/VANLPnmdlPmtmvmdk7zOwr4vJLZvZmM3tX/P/gTrc1ISFh+3DRKHMl6W+GEH7ZzHYk/ZKZ\nvVnSKyW9JYTwOjN7jaTXSHr1jXYUJC2ibLpBxC1D5K4c+tA/h+mpRXecNZccCLOXpVOMHDHPowKG\nq3CY6YIy90A35yMYt8Ik1HZb6lXD8PWk48c0RFMrrNPpO2WjwwrjsjXOM8NnxfV7lzzKydSsDGax\nHzp+7PTznrk7zKDw4z9eO00c+Me1VLv+iJVnWkwXcFXB7VlAmNzg3Fg2dIh1BgGUsUBJWIiaFxCe\nryK9dK9ZE6NPkSIJ2svpmAXNZaFmoKHrDOmC3VF7/+UoU1pDgL9AKmCG5ILlOTVQWDoHHrprYnei\njNfCEDUfjlBWdW3Gxs+t88EoYq/OmSO5i3GhRoghhA+FEH45fj6W9E5Jz5H0MkmPxNUekfTyO9PC\nhISEbcZFGyGewsyeL+kPSPoFSfeHED4Uv3pU0v3nbPOQpIckqYdJ+ISEhIRbwYV8IJrZSNK/kfSV\nIYQjUrcQQjCzjUmUIYSHJT0sSXvDndN1EAjWCDyAecgZosLLmAPNzpnloKzMAcY6+6gHQmoeIJ5e\nDpBLim2rASLXcf8sWVmBJhnqldS7TqUWU+dMcwiT6TxzeYayqRBjV9HodZjjRYJeHqBm6G7XI+Wi\nYBg08T7082TinLkaIsoLgfHqiuY9uKqMwQFhHMtoOoXEA4SFmQdtoKTjHNMaIEj1rOXeOa5KDmea\nCoHVBvcTc8xPmPtN4P5dq5nSrIyAcU+gP/tdp/pPLt1MmMkAdLXZHV4/BSFJfVrX8nqt2gVKnZ14\nn9dQYYxB9fO4PGRJmP27DjMr1T4Mvy+E8G/j4sfM7IH4/QOSrtyp9iUkJGwvLtQD0dqh4HdKemcI\n4Z/iqx+W9GD8/KCkN32k25aQkLD9uGiU+TMkfamk/2JmvxqXfa2k10l6o5m9StL7JL3iZjuyPFN3\nv6V2OWqjwP9UT0BI3UPu74o2XWmcplwKzplI2A3iXkYIafg6R7QuW3I5aAiimNNJTBwGlWEOMtHF\nO61gXu9yg/mqpAkitJ0TiHqzSKtwGJa1PKw9B7tApDhHvvV0BkNRRHbzIaLfmKjIun6wVcnYnJMh\nu75dgWtYdJwCZujDJTbm+nOUcGXdkzltwWL0uQYF7XEKAhFf1q7p5ZhqIWPFtavnm8XOIZ4zo9Ac\nonCq6NKItV6gFMC2NIjtsNYM9pOjDGwVpx6qfUzBQFA+0eZ2VzttL24jZb5QD8QQwtukczPGX/qR\nbEtCQsJHHy4UZU5ISEi4k7hQI8RnGitmwXKKdI/en8JhGHVHVtTjeSi9yfob08apVH4MlS6QD5xi\nd0gDKRIHTZ8undat6nqEA6edowWir4xyIpc4IPzZK2gBjig9XoGsqbLCMTh904DeYp0lasSc55p8\nMvfIckMn7wHKgMJVeoUpzidH3jFrfWQTp3Lj4Psgxbt35MlMFWhijdKjXexzJd6v4VI+mVOHAECw\nXyOa3xROn/ug0jO4hLMezupUF4iCdyGAz5jXPIWInLVMQPG7h8xN930ukW9dzCEeL2OUG87hOa50\n9whthUVZc5q0nhyzExISErYW6YGYkJCQELG1lDk0QdVJS2fpBV2wiBFcqJeVU5IQ85CPzG2iSRML\nCnZpIYbc6O4R8o0hWF704HbcYS6o05bqoIztA31FvimLAi3oDI1zM9C6Lp2XQQnpNr2M5T/zk+uj\n7ZKLiKX1fOzlJZQVvYooZw/5w7BOGz/pll9dUMIstn3IAkoBNlu4iiw4VSCZd2fhbblWecR73xD+\nhSrA5ojWxlxdHHItl1cQw5ek4CjJ2hdE7Yj4DlDwqsF9tipPO0O50xyUvoP+Uc/7sMa+14TUmCWp\nUE1rCPo+2b8+x3069zbRBZv3+Qy2cMVBm+++fYQ5jRATEhISTpEeiAkJCQkRW0uZc5kGUYZrpXOf\nw8IjkcsSlAnC62wW6QYF2F2Ii1GcaM1EAoLlOQr+BFgnMa81IMpbgvp1Ig0qM4ibTxjxRFQSDtMU\nGk/NqRStoOjqXYD67UX1eAVKOUPkvQuh7/g+b9cQucdFAMUjcP4rsby03i8r9+4Ocn0bRMfZ50vQ\nygK0t0HO+LAPazVMWbAQl7D+irILhZUaRIe7iJTTciuDAHwycSF/H/W3j+dO30WH60hPu0vsEIdZ\n1BSG++fq2CluH1MTBR3Acf/BiU4FIvcr6zi6ng86iDjzlqMt2GqaIGwfaU4jxISEhISI9EBMSEhI\niNhayhyaoLBoh/YVIsQdFNk5Ck6P+qCE80HLW5hXC5NsDeEMTSsoQcg7K5Ani+TnAkLm/YWHBUk+\nJvGvBdodaH8FqicUx6oQTe5WiISikV1yMliKnUS3b1L9PsTl1x59yttS06YZ9axxN80oJF5eT40l\nSaCbq0j0AmLwJfqNAd+MdlUQoDcLirQRrcU5d8zXr5DXnU3ae4X3R9HdPAXQILJcd30fE+SML65e\n8w0wTdJhe1e2Xz1cW0wN5Fch1u95X9Sg8izERXQwHRKgJqDD+nzaiud3YDOWoUYzi2wxyr7KwW+2\nMJc5jRATEhISIrZ3hJhJ8/iCr+EOksMgc4RRIQdOq/oVU0NqHWpn7B9yhIA0LtbjyPmuYb4cNHTn\n1KRYucBUDQJASBcsoXdjqdAeaqQsMRJj2luOIAS1imXZjhL20CcLnHP33nu8gXDSWSKNjoGPk+AB\nmWHm7e2h7gljMM2T7WgloNzqAAGrWQejHIy+J9DedY9824DRb4X+PzRvVwfeN1kMSPWZTQiNJwMW\ni4X3ywkcfmgQfKWDQjIEdJZZLBs6RfCEqYU7O17fJsNIdIlyo3PoSrsYfZbHNHpFJ4GhhEG8zxGk\nK1jKFo+HgPKsy5P2nEMKqiQkJCRsL9IDMSEhISFiaymzyU4nlhegBDUoQQ95WjVo0KTbUj9SzYr7\n2AE1yRC8mDpNWsKSZgznkXsWMH2lbhD0rBMdSTLoJ3fnfpzhEtpHULwaOkiWNeVkf4O0tweuwlw2\npoPV0C92L3nApKLJKai+XcM5XwaVh56N9WB4yzGoEfba9decfCjPA01/bOoBi52+Txk0e378HIy1\nQXrbfoE0PpySxfaOGw9k9CqmCHq7u7hvDuYIkuDX9KzM6e613A+Us1RovF5lx/t5grZOrx37dgzw\nDJF+iGkPOtIEXP/8CCVuGeBZTSnh3p8hje8EVLuPsq4rfW92rnXp3Ys0QkxISEiISA/EhISEhIjt\npczBU9ZggqIGUWGBkgpOLYNIt1i7YwRNXjFzqns89XQt0mfqFrt0RwFNbtCUCSLKK23dEDRZoLoV\nHGmWfd/JsTyCOl/452LfaSJLUp5c8rZ0pyvtI1L7pr6PGqLMgKh9CSebEhR0QIebpbsGXUEbd5Eu\nWfSu1/wtqZ9D/tlOgzKoWD6APpJTI4zln2BKIKODTEzZY4peOWcU1fuFSw00tY8LmoE/MwXwauFc\nfhhT9ugq1L/spsRrB4Lmr6m8o1nWlGVlhaiwoY9oIpzN2vUPS99uhBKzyyNvK82EV6mwtn2MOY0Q\nExISEla4UA9EM+uZ2X80s/9kZu8ws78Xl7/AzH7BzH7TzH7AzM5xEUhISEi4fVw0yjyX9MdCCONY\nsP5tZvbvJX21pG8OIbzBzF4v6VWSvv1GOwp1o/qwHfJXBxjuwzUm2wOtOYDbTYyiZqAjOSsyQgxL\nWtUUTkco2F6OPXLXyfxZHhD9fV7t6VPjWNekZP0LCHMPR6DslVP2Dpxvij4irohsNnBNmeJ1eKXT\ntnFkEFEjmD0H8RyAmdWokZLPGE1GWhzoc6jAA5kaGfuRBrpzRN4zRDn3MQfSQMjM1ElOh6CbVUxp\nugsT33isiRBlnThl3N/fP/28VlOG2n7Q5wr91cy8w3YgNrdlbAuuSbmD3EmYAk8RNW6Q6ifcf8UM\ndX9wj7BsqrFsbEyNpAC7A3VAse/bzTBl0J3G42yfLvtijRBDi9UvvIz/gqQ/JukH4/JHJL38DjQv\nISFhy3GhHoiSZGZ5LFJ/RdKbJb1b0rUQTmf7PyDpOeds+5CZvd3M3j6vl5tWSUhISDgXF40yK4RQ\nS/r9ZrYv6YckvfBpbPuwpIclaX9nL1T3tjQ4R7TUhk4JriGvddGgNkbM5zXkD49g4hmQv9uDe8pT\nEPWO4dqyh6TdxcyPmYNiWQ9i36bd53yInFkosK+Ovb5Fj5ewD8NZRLYL5L7SHzWMkW+8orX4vgG9\nzOAkM9t3WtcBZZvAiXa69qr1nc7QL5fh1JLFHNvOIRx+WLIUwnhDNLdCBPewhKgcDjt7uV+jo0tO\nK/ss1TprrymvSY7o7FWD8wwi7p0ppklAk1nOs8R+MoikOyvTYUSEazj8CM47g2vetxVK4kIUIPbz\nBLVbegWi+Yxox2mIHPvIcB9SEZGd4Jgr8XizfZz5wo0QVwghXJP0k5I+XdK+2anrwHMlffCONSwh\nIWFrcaEeiGZ2bxwZysz6kv64pHeqfTB+YVztQUlvujMtTEhI2GZcNMr8gKRHzCxX+7B+YwjhR8zs\n1yS9wcxeK+lXJH3nzXbUWNBRv6VZI4pkYSPVIzsBlyyiOHcBajKbOAUZgIJNEYktEEF+Do45hhh5\nzazznN5fMc8MEcElBMM1y13CzqlEJJYi8QlzabE+o7+rWhssHVLAwHVQIt8WxqU1BMMz0LQF6B5z\nf5/f7HkbWb8lasdz3JJ7PjOgBSKljPKOIMamTpimuELONFigDFHpUbQ/Y12aOSLOHdQi2UGS9YlQ\nL+UI1mIF1AQ09IWauYhWYGS9C1rVoX0BUw3lMYyIEeq1Xa/jsod+zGCvy34J0Waunvl5CmJsYToi\nFIj+x5K14UINp54ZXKgHYgjhP0v6AxuWv0fSiz/yLUpISPhowhY+4xMSEhJuDxdqhPhMIgvSYBmf\n90OcZtjsUl3XqIcRa2Z0c6emU6TPBtCUAmLoDHTYQJmHiNDVyDclrYVDmJoYraxRHrKDyOoAdUwy\naoSRY21LEjEIdncRiTZ+btvSedIpIOt/ZKDaexlKUqJGR9HzTroKt/EOncnR3hoRzZPeMrbUj3PP\nGFHWju97cui2WEuGWZlci1c97a36uPwVROArW6zz8nNt7Oe5pgiQ94Wh7k2BKYNlw8R2/ziP96JN\naE+HyH4JQTucxHNM71SQl/XGiMTDmfsYYeTskFZg8ZiMbE+RJ9047S/hhj6NefVhC5OZ0wgxISEh\nISI9EBMSEhIitpYyB3MRMq24crhKL8agD3CEXh603TKWR9zunUHECwpa7UJUi2gmHJrU7Tv1YALN\n1eD776D846qcZ4DwdTmiYBi2ZROnQxQVL/d8nUXl0c8ecrmbkbc3j1HUcOBWYYwgZ0egpjkKHsGW\nbIFphQGE0YMFSluCnhmct1dWWMewouqXzi8Peh5B5XlWJx4hnZ94fz5aHflyFBbr7Pj57SBv12KB\nrh4drTsuQGdEWiiutCgYtvZ15iz9imsbQD2LeF8s4Sjeh2/JEtMeLPiVT9FHyFmvcF06WGeA++8J\n5DjnUUXQLemVwmN6ZzyJ6lv3dFuLsiyVIU1ISEjYXqQHYkJCQkLE1lJmBa/Ny/KxGQXDI48Q5iOn\nHp1ou1QjagzXprV85Dndi8eIxLFebt8LDi1g9cTX0RPBXaV3i5Z7ZmCpAfRyirzrAXZyZeBtmdb+\n+R4UpTKIsbsQ7J42CbneC1R8anA+CGCqgc1WAZo4z6h6R/QdAusan6soDn5hft/psr3nuuXWWhtR\n2OkE57BEBP/y0q/tElHWGhZZC7hnr5g83auHE5woRO80OZuhj1ggq4J4PEOUnRH3JlJ53nvzOfLr\nT/waUhFgI+SSU+XA6SA4qXPUs4O85umqyhYuVY59N0sapPjnZcxrT3WZExISErYY6YGYkJCQELG1\nlDlIqiIPKhDFC8egu5fLs5tJ8pzY0RiUAPSZ5tkVhMlC9LPww+hDciFxQCR6APH2bu5UpThuKV4F\nO6/FkQumG+xjBjF2htzjXVpBdWBXNsO2iJZ3L7W0/gj5uyM4U9cQMefnWCVPYZe2BGUeQFS8FmXG\nfrKiXWeJaCoLKBW7TvUWRx5BDrgYSGtWjgJhDezPDG0JiJLOY966waW6QM1jxlNZjzifgyfDLi2H\npVe3AK1HrnIR6X4D6mnnsFD2J1KMVaEPK0S5Od0xmPryJQLKWTxuOdhckWOBvuo86cvzlRi+TpQ5\nISEhYWuRHogJCQkJEVtLmZu61jTmvHZqj+JlCJHunIB77Po6Tcx9bQb+vpggItlDIaYG1lZP1U5r\n74EVUw4xbo7jH0L4XSASugoElrCT6u1AsAvH5kUGazFEeZmxPZwhckxleNcv/5Pja+2xYXMFA+w1\nl2gWiupiaqDA/ljTukAe9hyWalqCPkYrsClcr5fIL+eN2t11BThp8tVD9wu7hn4xFKvqQmXAOtad\nSaTMGSzXYAVGet3MIOIvIS7vwpkawmjayM2hECgi3e0h172LaZdw2dUJWbWZnlZwg39i4QXHclir\nZT0XozMyXMcc62sLVzgcoyLZ/tg7dzCAYH+VG55ymRMSEhK2F1s7QrTMTjV3U7wtdwqM7qC5m2My\nv4y1PFhnZYY38QyGn10ENfo1AxYYffQ8YEIN2z6cUhYLH3WsXuJNF0EHBG/yCqlgu34O3QXe2BiV\nPnnNR0450rgaBG12V2VQN5sBrQUPsozvUTj/MHhxTuXsDhx+WCeliG6j9+1eOl12mfpNjnhQL2ba\n8X4eDX3kODv2Uc8TI9QjQYChhlZwEUdgO4VfqwxBFcaRJgPv51GAbs9XUQd1dBhsQhafO8h0cR9C\npxrAPpYIkhRjPx/Wxbk/Q9uhJ7xCE1tEbYbx518jtXEHY6QcQa3qGGmmK+1n0iEmJCQkbC/SAzEh\nISEhYospc6bOoKUNhsn7BqUtj+vZddtJUrNaH9S4l0HXhjQ60qpi4hTiuHAKPMLyBjq3IejZftfp\nXnPUtsu6my8Pa04PKqRuoS1LpO71D5x60h2GFG85aPcDsxlNYRbKUpkGk90hOOCSzi8INtD5RodO\n8TglsOhF+nbC+jN+/EHp/XM0djp8+Pjjp59Pjj2oMAWtHTZ+7QzUk25CIabMzRA864CyIitOBkeY\n5QxBCPPrWcGdKMN+SvRdE++vaePnOQswiPXTVNHdrJnto45PAZo8x3TEMPi2JfbTv9qeh8FVaOqz\nOFqCvhc7cP5ZxVSS201CQkLC9uJCPhDNLDezXzGzH4l/v8DMfsHMftPMfsDsvCn7hISEhNvHRaXM\nX6G2HvNu/PsbJX1zCOENZvZ6Sa+S9O0320ken/cZHFnmh+AhXZSnRGnRLNI3piZlqHtR9W7+Hpkt\nnPoN91zDpSOmhiE1DmUeOwctPaxAr9aMUKGrC6DPLM85RN2VHBH0DiKxh3AwmSza/c9B2TrQ7Am1\nS3Yq76sZ+jAgWplNUc4ThrY14+zQMC6j5vMICwdII7x/7pSNtB5Ziafpf5J0OUOEeur7qZD22Ed7\n80FLGw8rj6Zeazw6u1f6NWQtnLAL2ugZmuov/R5h6dPjApQ5GsDmSLnsj5hbByNeaB+FWisFHINo\nfpzD1YieRhnGQIN7WzehApckW3hfHfM+Q0T5JE411eekcN7NuHAjRDN7rqQ/Kek74t8m6Y9J+sG4\nyiOSXn5nWpeQkLDNuHAPREn/TNLXyOf8L0u6FsLpEOUDkp6zaUMze8jM3m5mb195tiUkJCTcKi4U\nZTazPyXpSgjhl8zsJU93+xDCw5IelqS93d2woswsG0q6xbKhdAppYjnHEhSExqYF3UuY0oaUvns6\nSJdCCtrBaG9j2+nsctTMr9tuiJS6EhR4AqEtI+EnqKNCUW8FSjyYIhK62gduicORb1eCsVV0ssE7\ntQJ9ZjRdqF3CcO0Cfd6PkegKlLIPQfuV6TXdDEyLy89515cQz3cg0p9Xs7iMagJMk+CXkiG9juVr\nA2jqAvccy6DS4WdVx6ZkmdwFj0lKuvke1tCPOcUg4Bifu0idDJiyqA5iO5BOmoPq51AZzNGW01K6\nWyjMvlAPREmfIelPm9kXSOqpnUP8Fkn7ZlbEUeJzJX3wDrYxISFhS3GhKHMI4W+FEJ4bQni+pC+S\n9BMhhC+R9JOSvjCu9qCkN92hJiYkJGwxLtoI8Ty8WtIbzOy1kn5F0nfebINaQcdZO+QfLmC0iloT\npC/F7PrhfwaaukCU9bDySFxAbuyw75HdagKOiUDkDGLjHup0NDPfZx7pSYloYoOaLntwr+mj2Mts\niKj5xPd3VHtkvTsDle35/nc7bfR5tnSq/aw5pgbAeneQp0vHGsweaApqVoN6TRBwL0CfVyVcTyYe\n2f3g1Su+bzR7DAH2eO7rHyFCPIANTjcwzuow5BCvHHnYn4e592GGaYoKwvzexD8/NUCOMY4z63jj\nGS33c/LtaBY7XsKsF+LqhqIzloqFKmA+9pB3jnPKMDWzuNb23QLHrOA+y+mgbPvY8UZc2AdiCOGt\nkt4aP79H0ovvZHsSEhK2HxeKMickJCTcSVzYEeKHizyYdiPlm1MZCxuro8yp3H7tXdFbUaySwlTf\nxbOCi5tzCKoP505xSrxrlsgarhG5bBbM/YWQNkaxF6i/MUByzqyDKHPh0cQZDDunoOYlIqcag+Iv\nYKkVV+nnKFlabz4+ukqGfOCjhdNX+LzqauNUtjwGld3b9ZUW7bEoLj588qq3te9cuyEDRqnOIWzJ\nDNFXBILX6PMSZTZX98gY9HVtqgNTANkM0WzkEo+e8vWbPdiyoTzqvMdpjXb5mmkr7rl75PcZEZDj\n3pwT6d0fuEGx4USWjZ/HNJoS8/sCMu68yxxwX2cn2rxlWzie2r4zSkhISLhNpAdiQkJCQsTWUmYz\nUxFpKE9yAbqpyulWt+dUMY+OxHS3zkAfAqKTGsMiCbnJBtF3D/UwOhui2ZJkc4iUY7uhkdUcbaWd\nGcXbM/Nz68KWazBFJBLh4lntVHYRI4q71WabqWKJKOu+06oZLLxqulrDOq0TIAyeQ6R81acYVvZW\nAVR30WXEE4nPoMx0r+7ASbwLkfoyg3j6HGH2ctrS/QPsfIrIfgbbsk4X0wqI2i8vwzuLwPTBHGqF\nYifuB7VzusipF6LGmBnRCXLWWaKGdVdKiq3xmfneK1SwXBsjb6CACqFnzPVv+3DdOX07sH1nlJCQ\nkHCbSA/EhISEhIitpcxqgrIYuZx0nFfQCuqBhVOfEtHiVcSX+ahLRHxP5i56fRZyk8FY1cBJuQdH\n4jACfQbFyycQ9Ub6HNacuXFuiPj1aa01QzQbYuim8vV7iGaXuyhytaJhcF0WHLifWHoEuUY+dA4K\nulaUCP3Vhat3gygnhcd5zGEeCY7OgcJ43994jvKtPZ8CYAGrDFMTOagdizxRSL6y/6KcoIPpiMkO\nzmEKizRSfNxnzHdfoMxoB/Mg4bDt084O1OpAKJlrjwJedGnHCQVEszmVEmhjh8JiK6s168DRHaVs\nDUXD5oID+ypnewuHU1t4SgkJCQm3h/RATEhISIjYXsocpBBtl3IWw4F79nKKyOng+kgcHavLOWy2\naJG1hE0yUJwgRn0vRMVwbK5BN23slGTFpEmZDPSZIlkKc2tQpkHXqW9nsDlyvBZQjjnZJxlExyxs\nhCgnqVQBcflaBDOQm6KOcY5iRTvXv4/nM0STa9Yz9uN0d9Au9MtaTWO0JUO+eQe0fpL7+ou4bYZI\nOQtLdRFl1YBKAdBX7LsHaszo+3zidH8Zo8wZrhsju7O590V+TsR5iRx4qhymcGxfm0rAPXcUHcl3\nOrhXjmhHjvPc93VWdaab5JidkJCQsL1ID8SEhISEiK2lzI0ajaPztDxAulZ8Z7Lr74MG9XgHMSrX\nhQC3gv+UwdW4i0gxxdi61yO4Y3CcCu7JlxrYhV12SrISWDMfuO5uPo6NnTLu11ASg7FPCz9mDYF1\nibZrtU8squG6nMMlujl22pcfI7KKiC/rX9NGzaBdDsi9tlhcqaqd6pWgndkEfQhxNzFrIJKHYLmA\nYNlQIIzKgrCMkX3Yti2Rj8zpg9BzamxjTFOg/rauuuXacMRItF+jSZwSWdJd+8hvVttFnWXQ5xNM\nH4xwwR5/6glv772eyzxAfvoEcx+X1L/u+D0kKDDvfon87dPLtn2MOY0QExISElZID8SEhISEiK2l\nzFkwjZbt6YX7PMo7RS5pjihyThfklSAY+cUlaQ2Ow2BqDdGxgT41U6eYLGx1vIuoLI6fRzumJShl\nMZpILOMAAB/QSURBVOZRHZ19WEQh33aGCOHy2pEfH/Znxa7TqkWsixwgzJ2haFMftwoj2AtMNdTH\niBAzQgoBeAfbNnNQ3FnbF3sjWl75OS8ap7IUV9MirQdX6RJ1oZfMH+6hvjKP1G/Xp9N5dQgxOuol\nlz3vtxxCdjvZ7Jg9HTt9Ptrza3q5aWtH51x74MdZi5r3vf/7yNOeLv3e6h3geqKPWIu6C7fzWeyX\nvkHcj6mhEyRKz3EPn67bbL4n72akEWJCQkJCxNaOEJVnCgft25spcJ0+3pCoH1LSGDMK0DK4hEwx\nQnkyc+1XP8fIAXqvHC44wx4iCTsI1Ix9P70F9Imx7guqRypAS4iBo6rfdhPVObPu5O0a4LMQd+lg\nZDCLgaIFRoXz2s95gePvYH913xtTYfS5W/v6XQyjG6QUGlLKTvtu4qOOOXSIhCHN0Qab3+lMtRxD\nb1jPfLTW9Px6zbrt+tk93kFF5teNusIlHG4y7KNC4EsoLdowIAad5yKOgAuk4gWkfAboLTlCLycM\nZG122ClzjpC9vQXGQMOYUjlGXdMC5Xb7wftiUTCQ017/PLveOeduRxohJiQkJERcuBGimb1X0rHa\nCaQqhPCpZnZJ0g9Ier6k90p6RQjh6nn7SEhISLgdXLgHYsRnhxCewN+vkfSWEMLrzOw18e9X32gH\nITSn2q01dxBozyqUlhw0mJCPVHJJt5PgVG8H9KkLykRdXbULTR7TuyYMVNAM1D+uzGqvBp/Un2OC\nu4f57b17MMEPfeQcAYnu0Lk0J/trOJ+s3FE4kc+aIgEOL5MZjHUPPAgyQkCkgykAQfuZndMX48fa\ngiT1gbc1A00/BgWu0c9dOPmcIMCzg9S9nvk+r/S87Qc1gw3t5wqUsQv9aoO6OP0K5T6nPu0yAR3u\nXvYLShpmpO/xOtpaAMT3vcSUQYX7o2BdFqR82oHT5/7M15mBPi9QM8Zi2iW6RCcsWbvn1/MAdXDz\nuF0Gree24G6hzC+T9Ej8/Iikl9/BtiQkJGwpLuIDMUj6cTP7JTN7KC67P4Twofj5UUn3b9rQzB4y\ns7eb2dsXy80T8gkJCQnn4SJS5j8SQvigmd0n6c1m9uv8MoQQzGxj0lAI4WFJD0vS7s5OaCI9bkDx\npnBxHSwY3fOI7/z+lp/UiBqPtHP6+eiK15tkrZGM5T5BX3PoGYUSknPkl+UdlM2M7CQDpd6nsSjS\n22akQKBSxRzms3g3NEg7E6KlvcjbAtK8log453C16bJ2zJO+c4MjyuQe53h0B6rh1EN3nGFMDQxI\nkVvaZicdphT2IUrMqBTA+iUMYi+v1bSBAW908Knh1Wq4P1CKRguYpWaI1NPVZ446Lg1mD6htXVV5\nrUBpV9pZSephHiWDye7CUEq0Sx0mOSyu/7Ffx+U+Von3S4100i7KqpZIS6xolhzveWpWtwUXboQY\nQvhg/P+KpB+S9GJJj5nZA5IU/79y51qYkJCwrbhQD0QzG5rZzuqzpM+V9F8l/bCkB+NqD0p6051p\nYUJCwjbjolHm+yX9kLVRxELS94cQfszMflHSG83sVZLeJ+kVN9uRmZ3S0LX6JhBSV33U3QB9rCct\nJSWNrUCvhpd3fTu8U7rHTGUCfYNTDGuDoByKpo1T9p1FGy3sYXcZKMsJxNDzJ92gdtiHES3MZZsa\nKViI1hoEw9eiMxDLhB6UHmVc+CnLIMitUcKShrMZjlmDPpaIfjag4eXKWQjn3KCvGE3OYGJqoMPl\nrjdgDkeYPjIKC6baQbAfynb9PbSpAZUdB78+bGOX0XQoC3pzLMf0yTHT4WJkv9fx6DCNcAsI3Wtc\nl9mQ/YZ7FFMMC7S9QTnXXuFtXETlRA5T2gappRUo82KOGj1RdN7knAvYDlyoB2II4T2SXrRh+ZOS\nXvqRb1FCQsJHEy4UZU5ISEi4k7hQI8RnFHU4rVMyGDiVJMUippdhVhqpgEGkPMyQgwwqx+hjAVPU\nEpHYGtG4OSK3E0S8O1h/FnOIuwbRNyhTQHSa1JiRVQQr14TULM/ZIFd2J/LdDBRwihxX1lQpQZUa\nuLD0ckSIQfHpPDMM3kaKx8eLVqU8QYnTAorlDMLwkaGsK47PWiMN8neXuOaBEX8oz7uxi7IlphRQ\nYnS0YPQbVL7rN0BxhHK3JUT9iFwXY/+8jG1Hl+gE9xx8dTWkkw/uhapBjRbW3YGTUQBNb7D/wZrf\nz2qHaEvXr9sM/VxH95xmC4dTW3hKCQkJCbeH9EBMSEhIiNheypyZFPNSAyhe6Pg7gPUlSKSKSKsC\n6khMwS7mS6egndopUxiCGyGyyShjASq5g9KWLO1Zd9t25xT9IvpooIOX9l1py2h6CTXwcqOMXeqt\n1UNp28Jc195ve14r64joWd7uS7DIKgPzcFnUxD/moGk7C9DnOH1Asfps7HRw2IfJLCKl0zESjpfI\n34bN2+MsqtOFYJtp23W7PMP3y5LnAGqO/jQsz9YMh1GPBjdXzUh0tDpjNBmXVjns56hyyJCbX8Is\nNsP0SRj7DdjFVMYUUzyr3wVLyc5g+RagQuAUhG3xOGp7zywhISHhaSI9EBMSEhIitpYyB5PmUWyb\n0UkaEd/h0inJVbhg701Xy51qHCF/eG/oec0FRNc5oqmsF0KL686S9B2CbdD38bgVW09BpfZ3904/\nZ4ia1hDjWgnBNM6zu+sC6woC27VinjFCWV6CGHvH+dVk7vS1fwT/Mb5SRxAYI1pM+W6FkpzGIHak\nm13onzPQ5EK0KoMD+g6OWW0WbO9nqDUCvlshEnwcrcMwo6I+TjOg3R20kXMtVUBkHe3lShVUBnX0\nzxo1fh/QcivjNAqs6kr0aI1pgnB4fd0TSWpwzll+fS49qo2q4PwKIu5hSHf12JZwzlzMXYw0QkxI\nSEiISA/EhISEhIitpswhRt3oMM0IYQ1WswvhbSc6OS9QwKjTQTQZHHAtHxbLaflUouBUQK7oGIWr\naAuWxcghavxoMneeNkXYMkBofc/AafUAom4Yf+tx7LOPfNuyavdz7cSVw9cynya4t+u0U/CarBFZ\ntRNvY0Be8ZpFGT4zcl3H6YZDCI0HR96+q4h+lozs7vh8SA4qWdL+Cp8DHcFx/cuYn92tN48R7ATn\nTItr3k8smwpVQIMLAJaucTynUc/PoSy931ietFog4o1IONUJcwjJpw0E3n3cxxSpxygzxf2LCuVe\naW0Hx+x+2f4Wsi0cT23fGSUkJCTcJtIDMSEhISFiaylzHkyjWAyoQI4vrbBqiE35ZlhEijGHzRZp\nWg2RbF0hggcLsXkA3UD9YUO+c46CV0dLp4rd6AtGYe6iC3E3aFcPke156XQnnyKajeMMShTCQlR2\nHkXs/SmiuRBAT0ClHhjSC8w/VlW9abF2Bh45ZfS7bqhYvt59+XDgy0bm+2gmTuWZvzxCexv0+Yzl\nJHBdCrh3hxitZX71Ts+PuQTVnoC+D+EwzemDBmJ8pMGrAn3eqdt7oeQUzATzO7y3GLSGMFtQKhRw\nEu/C7b2B+qDImL/c7n/NKg1KAcPxaS22OoPtM/9KI8SEhISEU6QHYkJCQkLE1lJmheACXuYE03k6\nR94uOEl3FX4EBakgri5AZRlZJt2iG/bJs8CZgAJUprf0dTrRHbphkSWyyxkl1b6Pzu+gjvM+7MqQ\nyz0+8iiygfQMdlp6SDuzQJEu6OUMBbn6EE/PYDMFtqsSubTV3Nt47cRzpatoKTXs+v565/hLzYe+\nfICiWBRDG22+QB8XmAZZE4FHKjlFNP+k8M8NLLdGPW/jkmJwCJk51CiR126oUX3SifcX7sO6j/PB\nZe7AJZzTJzVsuQSLsAb59sQUCQDFape4nynuz0ClhfNcxGmHoCTMTkhISNhabO0IMZgbWC6RrsYS\nmgfH3ALavjgaygcwBUXqFN+oHMRkQ0ywT31Y0Kl9VNKHI8wE5SS5fBF1hiy2yhQ9urrUeEs3exiJ\nIpBSoJG7SDucGAIFMdVumvmyBiPBvdx1iP0OjoNgyN4AWkWMKJniVWIElEMrV8egRY05/wZ1VAxa\nzgLGtoaA2eIEWkX0eVhQB+r7L6H/U9QZZghSLJGimGE0HXjden4+c6PGEUEIBJImun7ktlYXZq1m\nKa7tENpLrEGXpgraz+7C15+SFWAEmMcgFEfKjaF2Co+DAOR80o7yw4ZA2N2ONEJMSEhIiLhwD0Qz\n2zezHzSzXzezd5rZp5vZJTN7s5m9K/5/cKfbmZCQsH24iJT5WyT9WAjhC82sI2kg6WslvSWE8Doz\ne42k10h69Y12Yo2UR7eYhpO/9FxFScoFJqF7MVBgmFSuoTEsuhtqUUhrNBHlKFQE1AaBtmswRk0R\nTJQfR9pS4erUqIsyYKwDurHqnHoxOahkGDBFEAagkeJdDtAMIs1whuPXO75ODk1cjiBMCeqZUVqH\nIEBe+RTD8ZWW1h0/AM1mjRo1yLmsF94WUkDEgzRDWmTVhyksAkyzuU9rHHba9WlaW42g5YPG0nt8\nPaUv76AeTYdONegX1Kw5NcOFse8M9VpGFY+/mZ7OIWIs8HMukbqpnLTez+Mk9tGlHDWHEBg6Ab2f\nB97/safPud/uZlyoMzKzPUmfKek7JSmEsAghXJP0MkmPxNUekfTyO9PChISEbcaFeiBKeoGkxyX9\nSzP7FTP7DjMbSro/hPChuM6jagvaXwcze8jM3m5mb18wOyEhISHhFnDRKHMh6VMk/fUQwi+Y2beo\npcenCCEEM9sogAohPCzpYUna290NVbelEzMWqkD0NyD6GRi5LVpKXCGYOh/A5HWBtDzQhgXoTtnf\n7DYzpdsMDGpzpH0V0flm8ZSHwTvUISLiebL0EGHecyrfY9S6t7k2Bs1q9xfXTwMsOz6l0OA4rCkz\nyH2dGdMVxagoypY+6uc0nnn0s763JaIH5sS3BB1eILWxmCNdceH7yAZOZhvoSnPsJyDiuwR9XvXX\nVcEIl0awcJiZQXGwYK0ZAteLpU/7TyDKfE+sYwN6vaBk9Rgmu5w+QdS8GMMdB/ffssC1gPqBdH9W\nt/uZYOqgh+tGlyDOR3Si289FG009E7ho5/QBSR8IIfxC/PsH1T4gHzOzByQp/n/lDrUvISFhi3Gh\nHoghhEclvd/MPjEueqmkX5P0w5IejMselPSmO9C8hISELcdFo8yS9NclfV+MML9H0l9Q++B+o5m9\nStL7JL3iZjtpQtC4aanASRcOKxlrYyBy1wGtmra0aQLhKaOzxxAvdyB6JgUvmNM3BZWBw8sCxp1z\nzAJ0Yii8w2IweHVNlk5xkC2nAmJkrp8h7bABZWStl0VMzVpkKKWJaHq38LaAmWmOGiGkW+wLitcr\n0ndQ9jKm13XADdmHVjqXnApGtBDJMxJKXThrrdApZoJpgN5T7TW9fK+Ly8Go18T910ao6cLpAEyl\n9N6Pa3TJiWr+PC8bO5zGfYJFBwizp33MDB0h/w8JAzXrqOAeHhQeOQ7Hvu0C0xqrKRY649Q4B4r7\nKcB3Mfz2+d1cuAdiCOFXJX3qhq9e+pFuS0JCwkcXLhRlTkhISLiTuHAjxGcKmUzDmBhbTpwGPLEP\nWoPI4S7ymo+H14tgS9TRKK8hN5YroWxndYJoNsqJsnbKAiJZGpDmsWZFeeAC6MPKqVEvc4PWYgET\nT1DGq4jgEjWEvDuMEMemV3B16UPFXvb83EjT+0sIsxHZpVnqaupCko7G13xjMK5+rN/RgPbzc0YX\nFpR+Zc64QYDN3GdepRnUAjuFU9n5/e35odlrwvg5OHhxgrz3QEsaTLvswqy2y/ouQLwX5jDfHWJq\noEC7l5d9+YwmsoggjyFFCJhWWUJAsNOBkH/crlPBbYdutYZkhHKESHWMyG/jaGobzykhISHhtpAe\niAkJCQkRW0uZGwXNYvGRDIzg0pGTlqn5F2NEkcvQrlMg4leDAjfI8TWWh2yQh9thHipsrnY22zgN\nUeZ0WrXHgoOTAuhLBWpWIGrabZwbHcKVNEc0/R45TQ6gW7aK1sL8NExRowU2Wxkj6MzxhgC5g5zd\nALuu5pKXSq1RG6Vc5Xujb0mNbeJ9OALxvOotUWdMwTymJmAz1kMNlIwR6hj9X0BRzSmAOSLVy9Kv\nM/slIN+4oY3ZsRvhLi77couR+DnEBF2U/qQw2nB8Kh6mEInv1b6jawWmWLq+fAyD2P1he60XM0w7\nQFBufe+rDhyP81U+/vb5w6YRYkJCQsIK6YGYkJCQELG1lFkhqI70w06Yp+yrdFDfopxw/N9+JqUK\nBeODiBpOUEcFtU4msPOyApZOWL9kqUjsfhbtonpXXDh8AMut6sipLC8g85R39339GcqT2tjPszeH\nqDxGQmc52gca30HQtoe+Yi4z810bJHD31xyZkYfNtj/ZujBP73fK3kHp1wwi8XoCqzaDe3aJ9zsU\nBNajMZhjSpFypMdPoc7LQfDIbh/XivZvOXLZJ3PfdgKaPhq5SHqK4jijmLdNO7djFFIZLjGNg/4v\noIzv4/wFhcA+bvTFhPnu3hdh5cxOe7IuplHQLgrtV7ZhzRZy5jRCTEhISIhID8SEhISEiK2lzJlM\no6qlEwaKw8hlgEXX0dLLYxZlu34wUmNEKuGAnR940aY5BLPdwGI+6GY4ZheI6MGoWQNFQXl3c66o\njZzKHVUeIVyzIoMfZHZOzmmF6PdsRdWwjxzn2SCXtYZIeobc54Dodw4bryXE04JIfj6GeDweqkZ0\nuoPypR2Ukg3Ihw6IOE/RXyWjpejzBVTlLKM5jnnNpKB0r+6WsFbDfZEhym45RdW+Tgd0Ozvx8y+i\njVrFSPGuT3WUKLK1PHI6HnZxneXbdij7ZsEt5rJjhmPlnt2w3/xr5YgyzzAFkMfrYrZ9ucxphJiQ\nkJAQkR6ICQkJCRFbS5mtkYqYQ1yidq51QIlQebY/cqqyiqg1cNHuFIgsLjZH1zpDOEyDYpagWyVy\nYsPC9991xi6LEdKwC5pILTSi1s0ctArFlzqsaQxqUyGv9qhAtDrmJO/KI6IEc5NRS0o1CiSFCnR7\nz6PFxRDUF9MXncK3farbfl72UMMa0xQNLajRFywyFVAULIOoHgFSFbBiy3F9bdjeIywUdQxKX9OK\nDOqEGix1CbusjCF3uo+h8b2Ysz4GZZ4/6ccscE8SLGw1GLld2RwKAXvAp3Jgnq1mjnB1vUpcQD48\nEgcK3Ks9kOlJ3U4NhRRlTkhISNhepAdiQkJCQsTWUmZiiUjoDMWKshLCYwRCV7RqSsPqY9gao/hP\ncy9yf3HMALtl1vRdgjJVcxfMZjuonRvFuzXcrUkNCebPMrLZgPotEEXMpxAjgz1l8VjTmUczK7hE\nVzj8fT3PR84G3odd9GeN+r4dRKsrCLYHA4jNm/a6PAn76BOw952KOdt4j+M4zSEs13Zh7QV7LQ4B\nePOvClcV6OedPaedLEI2nzE3HQoGFL8S6H6G6ZZe5td5EYX0+dHmCpFNF/nLoOkLTPX0WC8aU0Nl\njekLpNsfLryPiihGt12/0XMkI/TrzffcSkFwnnrhbkYaISYkJCREpAdiQkJCQsTWUubGgqZR+WwQ\nA6/VCAYlrUF3VhZI5Qwu2V3QEUT/5ocQdOekGHA7RpjPQPGsywRmWle1207hNF1QuY2ooDqgyaDP\nhsju5drbfoy5gRLR8mWM4tJR2/qYMzhGPiwsz7qcKICqtwuRMCPRJ8jVvVbD+TniAMWwSwraYa2G\ndOC13OjFJbhUX/N9r+0HfUSrsU6MIjeYUmgQtSWyayhyhciygbIXE0zT7PnnjMrnmENc7cFpHTWX\np0ggZ155L/f7r4dodjaFdRlsxBrUjh7u+zRAE6/LEiLu3oFPh+x0/DisbX21bl3PkzD7dxlm9olm\n9qv4d2RmX2lml8zszWb2rvj/wZ1ua0JCwvbhQo0QQwi/Ien3S5KZ5ZI+KOmHJL1G0ltCCK8zs9fE\nv199o31Zkau41L7hsnOe++XY34xLuMjO4mQ366iwDGOXqVv7rgOrMNk9g/gsR02V0IPDDet5Aptc\nRLo0XMXbeo5jdgcoFXqOROzSmKanDA6127IkZYFRc42aGmW1+bapjnxU1sAgl6OLEiauu/3rnVVm\n1H5igJZhuzmFdQC1koZgR/EEy3BiFAUHo3nUZ3aQ2pkjeGMw4g0jBCGuoQYNRleNVxuVYBDLNiqO\n0KqC7fYRcsC1JbNZhs2azHqIUrkwgu2i7WtjulHso0OwE4yguxhBs8d7O220K8s2B13uZlyoEeIZ\nvFTSu0MI75P0MkmPxOWPSHr5HWtVQkLC1uJCjRDP4Isk/av4+f4Qwofi50cl3b9pAzN7SNJDktTr\n9TatkpCQkHAuLuQD0cw6kv60pL919rsQQjDbTAhDCA9LeliS9vb2wqpOCGkC64vMMYG9QPpUNWy3\nI+2dIXgxgqxtELwL57aZMj/ZRUodaFiOHLiSyyNV2Z+g3CYpIKhejePMpz7Zz3TBGSjWsgFlBA3u\nHsUAFERrE+g391DLcuESvzVTVtbjqEH3agaMEHjqwPllpbNskC7WwMC1zhFUwT6oFTU44jSYDjm6\n1/txKG/8AhT/tDYNY0SYPqiazQEW0u6GNxr20+tAY7rENM20vf+696BDgRpUt2atFQSYjvqo9QM2\nfk1+bv2pN2x36EGVZdHuZ4igE4NarBdUIRhUxn7ewpjKhaXMny/pl0MIj8W/HzOzByQp/n/ljrUs\nISFha3FRH4hfLKfLkvTDkh6Mnx+U9KaPeIsSEhK2HheOMpvZUNIfl/SXsfh1kt5oZq+S9D5Jr7iV\nfa3qejBFbQ7q1YzgvDIjlW2pQjVAXQ5QuQJ0dAGa/HjmmkQ64uzBODRHFJNpch248KzY8/EQJp+g\nj6ieqgKOOTO0dwhR4MnSqXTvstMzRg7zqIlcILVuCtobgrflYOF9NSSthCmtXfVjLu5DGcz/v72z\nCY2riuL4/z9fzSRpExsjQlJtxaJkoxURRRGpG9HiyoWiG3HZRQVLQfcu3PixEEFaXQkq0ZULN9qF\nq+BHF2KjUIrQiGJsjM1HM5OZd1y8O3P/sSqNJpl5t+e3mvdm0rnnnubknXu+mpJ7qNFV6dTTRcaa\nQtx7zf3TqKhJDqWNxj1f18RFKdGsyznz5Wa+3qZEVtu1v/cJa+Iba0ljJuM8KxKJpkTrM3F922P5\nGgfW4vuNujRrlZK/altkkxOjth4IiZt+QxbrHhfWl7qvVxfj605DX0pnpMUhGVM6HP+vlFev1M8/\nJEkUmr4ziGa2AmDsL/cuIo86O47jbBv96jI7juPsOH33hLhlrGfgfHBhxX3RErChknS7ETdsLYxZ\nbInbu1yP/sGCRPC08cqahFmHxAWubZwx2n05WI8uSbtypXtWljovyRvHEqP7MiyyDUrzV3WDRiox\neVwbmCxLZ5n1sPaqzFTRsjAtyyuXZD91PGlFSudG4xc1pblrtS5RTNkLC5/RBPSWDEZZEfdVE9rb\nLXFBJXk+k7LIiiTSr8uRSUv0VQ76Ws6kA5F22Lkcf26pHL9Tx6BWRedNnSMje94ckn0JaxmgRME1\nC0K2XzsflSS825aGr5kkfbdkv8rakUjCxR3PuyrlisvXicscv35DQUGn80967WH9CdFxHKeLG0TH\ncZxAsi5zmxn+KOUu4S6JCo5XYmJqdUUilBIhtdDlpCkzTTSyeFE6xuxpxi3cI/5ooyrum9R86l+g\nkiS+ZtqpJLg4w9XomjZk3KhmxLb0H5ROOpAk5baWZMuAEXXSO0m4A/LhirhJA+XodpVFHopEq5J0\n3JDQ/i49mtA1SuS0e/YgEVSd/5LJrJHV3dqFSKLPsp8ladAKqatuaQGT7Etnr/fGwD8aa1GeTPat\nNh910RoXd1xqtuuaJC5NXHeLy9q5K6Ng0JLouFYKXxqI3znSkF/bQUnAz2RGjtaB6wwcKUCohAyB\n8kSswR6pxeh0M5MGtSLDjZYfwVQSfJ5KTyLHcZz/iBtEx3GcAM1SjBUBJOcBrAD4rddr2QGuh8uZ\nEkWR82YzG+/1IraSZA0iAJD8yszu7vU6thuXMy2uFTn7EXeZHcdxAm4QHcdxAqkbxLd7vYAdwuVM\ni2tFzr4j6TNEx3GczZD6E6LjOM5V4wbRcRwnkKxBJPkIyR9IngujSwsPyX0kT5M8S/I7ksfC/STn\nVpMskzxD8pNwfYDkTNDpB2H2TqEhOUpymuT3JGdJ3peqPotAkgYxzHR+E/lslikAT5Gc6u2qtoQW\ngBfMbArAvQCOBrk6c6sPAvgsXKfAMQCzcv0KgNfM7FYAvwN4rier2lreAPCpmd0O4A7k8qaqz74n\nSYMI4B4A58zsvOW9799HPtu50JjZz2b2TXi9hPyXZwIJzq0mOQngMQAnwzUBHAYwHT5SeDlJjgB4\nEMApADCzppktIkF9FoVUDeIEgAtyPRfuJQPJ/QAOAZjBVc6tLhivAzgBdAe7jAFYNOsOl0lBpwcA\nzAN4NxwNnAwzhVLUZyFI1SAmDclhAB8BeN7MLul7ludRFTqXiuQRAL+a2de9Xss2UwFwF4C3zOwQ\n8tr7De5xCvosEqkaxJ8A7JPryXCv8JCsIjeG75nZx+F2anOr7wfwOMkfkR93HEZ+1jZKdvvtp6DT\nOQBzZjYTrqeRG8jU9FkYUjWIXwI4GKKSNQBPIp/tXGjCOdopALNm9qq8ldTcajN70cwmzWw/ct19\nbmZPAzgN4InwsRTk/AXABZK3hVsPAziLxPRZJJKtVCH5KPJzqDKAd8zs5R4v6X9D8gEAXwD4FvFs\n7SXk54gfArgJYW61mS30ZJFbDMmHABw3syMkb0H+xLgXwBkAz5hZ499+vt8heSfywFENwHkAzyJ/\nUElSn/1OsgbRcRxns6TqMjuO42waN4iO4zgBN4iO4zgBN4iO4zgBN4iO4zgBN4iO4zgBN4iO4ziB\nPwEftoemZc5QZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1e45e6390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[2], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[14])+ \",\"+\" iceberg_probability : \"+str(pred_valid[2])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb1e45712b0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1e45b9400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[1], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[13])+ \",\"+\" iceberg_probability : \"+str(pred_valid[1])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb1e44cf748>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+BOp0YjnCups9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1e4555860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[7], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[36])+ \",\"+\" iceberg_probability : \"+str(pred_valid[7])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb1e44b25f8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3ndN0eVyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1e4487908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[3], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[19])+ \",\"+\" iceberg_probability : \"+str(pred_valid[3])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb1e4413e48>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAT0AAAEICAYAAAAtLCODAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkqRVpNNLkqRV/A6lSgqsd7JBeAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1e446c710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[4], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[26])+ \",\"+\" iceberg_probability : \"+str(pred_valid[4])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb1e43f8748>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1e43d5320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[5], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[27])+ \",\"+\" iceberg_probability : \"+str(pred_valid[5])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb1e435e0b8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+BOp0YjnCups9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1e43b4518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[7], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[36])+ \",\"+\" iceberg_probability : \"+str(pred_valid[7])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}