trainSimpleCNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tensorflow 2 version\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from PIL import Image\n",
    "import os\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import glob\n",
    "\n",
    "from tensorflow.keras import __version__\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
    "from tensorflow.keras.layers import (Activation, BatchNormalization, Dense, Dropout,\n",
    "                                     Flatten, Input, MaxPooling2D, Conv2D)\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "import utils.CNN_utils as cu\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "save = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data_from_images(image_path, datasplit):\n",
    "    image_path = image_path\n",
    "    data = {\n",
    "        \"Path\": [\n",
    "                 glob.glob(f\"{image_path}/{datasplit}/asteroids/\" + '*'), \n",
    "                 glob.glob(f\"{image_path}/{datasplit}/other/\" + '*')\n",
    "                ],\n",
    "        \"Label\": [1,0],\n",
    "        \"Set\": datasplit\n",
    "         }\n",
    "    df = pd.DataFrame(data).explode('Path')\n",
    "    df = df.sample(frac=1, random_state=35) #shuffle\n",
    "    x = []\n",
    "    y = []\n",
    "    for i, file in enumerate(df['Path']):\n",
    "        im = Image.open(file)\n",
    "        im = np.asarray(im)\n",
    "        x.append(im)\n",
    "        y.append(df['Label'].iloc[i])\n",
    "    \n",
    "    return df, np.array(x, dtype=int), np.array(y, dtype=float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and process data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_path = \"/data/pg-ds_cit/Projects/Astronomy/AstronomyProject/Images\"\n",
    "\n",
    "trainSet, X_train, Y_train = load_data_from_images(image_path, 'train')\n",
    "validSet, X_valid, Y_valid = load_data_from_images(image_path, 'valid')\n",
    "testSet, X_test, Y_test = load_data_from_images(image_path, 'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Keras version: 2.4.0 with backend: tensorflow 2.3.1\n"
     ]
    }
   ],
   "source": [
    "print('Using Keras version:', __version__, 'with backend:', K.backend(), tf.__version__)\n",
    "\n",
    "# Training hyperparameters\n",
    "subtract_pixel_mean = False\n",
    "epochs = 100 #500\n",
    "early_stop_patience = 20\n",
    "learning_rate = 0.001\n",
    "batch_size = 256\n",
    "# dr = 5 / epochs  # Parameter for Learning rate decay\n",
    "\n",
    "# Make sure data is float32 to have enough decimals after normalization\n",
    "X_train = X_train.astype('float32')\n",
    "X_valid = X_valid.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "\n",
    "# Normalize pixel values between 0 and 1\n",
    "X_train /= 2**8\n",
    "X_valid /= 2**8\n",
    "X_test /= 2**8\n",
    "\n",
    "# If subtract pixel mean is enabled\n",
    "if subtract_pixel_mean:\n",
    "    X_train_mean = np.mean(X_train, axis=0)\n",
    "    X_train -= X_train_mean\n",
    "    X_valid -= X_train_mean\n",
    "    X_test -= X_train_mean\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = X_train.shape[1:3]\n",
    "\n",
    "# Convert to correct Keras format\n",
    "X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)\n",
    "X_valid = X_valid.reshape(X_valid.shape[0], img_rows, img_cols, 1)\n",
    "X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)\n",
    "input_shape = (img_rows, img_cols, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Data loaded: train: 1570 valid: 217 test: 190\n",
      "X_train: (1570, 101, 101, 1)\n",
      "Y_train: (1570,)\n",
      "Model: \"functional_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 101, 101, 1)]     0         \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 101, 101, 1)       4         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 101, 101, 64)      640       \n",
      "_________________________________________________________________\n",
      "activation (Activation)      (None, 101, 101, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 101, 101, 64)      36928     \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 101, 101, 64)      256       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 101, 101, 64)      0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 50, 50, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 50, 50, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 50, 50, 128)       73856     \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 50, 50, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 50, 50, 128)       147584    \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 50, 50, 128)       512       \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 50, 50, 128)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 25, 25, 128)       0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 25, 25, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 25, 25, 192)       221376    \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 25, 25, 192)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 25, 25, 192)       331968    \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 25, 25, 192)       768       \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 25, 25, 192)       0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 25, 25, 192)       0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 120000)            0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               30720256  \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 256)               1024      \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "label (Dense)                (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 31,535,429\n",
      "Trainable params: 31,534,147\n",
      "Non-trainable params: 1,282\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print()\n",
    "print('Data loaded: train:', len(X_train), 'valid:', len(X_valid), 'test:', len(X_test))\n",
    "print('X_train:', X_train.shape)\n",
    "print('Y_train:', Y_train.shape)\n",
    "\n",
    "# number of convolutional filters to use\n",
    "nb_filters = 64\n",
    "# convolution kernel size\n",
    "kernel_size = (3, 3)\n",
    "# size of pooling area for max pooling\n",
    "pool_size = (2, 2)\n",
    "\n",
    "dropoutProb = 0.25\n",
    "\n",
    "input = Input(shape=input_shape)\n",
    "x = BatchNormalization()(input)\n",
    "x = Conv2D(nb_filters, kernel_size,\n",
    "           padding='same',\n",
    "           input_shape=input_shape,\n",
    "           use_bias=True)(x)\n",
    "#x = BatchNormalization()(x)\n",
    "x = Activation(\"relu\")(x)\n",
    "\n",
    "x = Conv2D(nb_filters, kernel_size,\n",
    "           padding='same',\n",
    "           use_bias=True)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Activation(\"relu\")(x)\n",
    "\n",
    "x = MaxPooling2D(pool_size=pool_size)(x)\n",
    "x = Dropout(dropoutProb)(x)\n",
    "\n",
    "\n",
    "x = Conv2D(nb_filters*2, kernel_size,\n",
    "           padding='same',\n",
    "           use_bias=True)(x)\n",
    "#x = BatchNormalization()\n",
    "x = Activation(\"relu\")(x)\n",
    "\n",
    "x = Conv2D(nb_filters*2, kernel_size,\n",
    "           padding='same',\n",
    "           use_bias=True)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Activation(\"relu\")(x)\n",
    "\n",
    "x = MaxPooling2D(pool_size=pool_size)(x)\n",
    "x = Dropout(dropoutProb)(x)\n",
    "\n",
    "\n",
    "x = Conv2D(nb_filters*3, kernel_size,\n",
    "           padding='same',\n",
    "           use_bias=True)(x)\n",
    "#x = BatchNormalization()(x)\n",
    "x = Activation(\"relu\")(x)\n",
    "\n",
    "x = Conv2D(nb_filters*3, kernel_size,\n",
    "           padding='same',\n",
    "           use_bias=True)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Activation(\"relu\")(x)\n",
    "\n",
    "#x = MaxPooling2D(pool_size=pool_size)(x)\n",
    "x = Dropout(dropoutProb)(x)\n",
    "\n",
    "x = Flatten()(x)\n",
    "x = Dense(units=256, use_bias=True)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Activation(\"relu\")(x)\n",
    "x = Dropout(dropoutProb*2)(x)\n",
    "\n",
    "# out1 is the classification unit\n",
    "out1 = Dense(units=1, activation='sigmoid', name='label')(x)\n",
    "\n",
    "optimizer = optimizers.Adam(\n",
    "    lr=learning_rate,\n",
    "    beta_1=0.9,\n",
    "    beta_2=0.999,\n",
    "    epsilon=None,\n",
    "    #decay=dr,\n",
    "    amsgrad=False)\n",
    "\n",
    "metrics = [\n",
    "    tf.keras.metrics.TruePositives(name='tp'),\n",
    "    tf.keras.metrics.FalsePositives(name='fp'),\n",
    "    tf.keras.metrics.TrueNegatives(name='tn'),\n",
    "    tf.keras.metrics.FalseNegatives(name='fn'),\n",
    "    tf.keras.metrics.BinaryAccuracy(name='accuracy'),\n",
    "    tf.keras.metrics.Precision(name='precision'),\n",
    "    tf.keras.metrics.Recall(name='recall'),\n",
    "    tf.keras.metrics.AUC(name='auc'),\n",
    "    cu.f1_metric\n",
    "    ]\n",
    "\n",
    "model = Model(inputs=input, outputs=out1)\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizer,\n",
    "              metrics=[\"MeanAbsoluteError\", metrics])\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Callback to stop training if val_loss hasn't decreased recently.\n",
    "# Patience determines the number of epochs waited before stopping training.\n",
    "earlyStopCB = EarlyStopping(\n",
    "    monitor='val_loss',\n",
    "    patience=early_stop_patience,\n",
    "    verbose=1,\n",
    "    restore_best_weights=True)\n",
    "\n",
    "# Callback to save checkpoints of the best model so far.\n",
    "checkpointCB = ModelCheckpoint(\n",
    "    filepath='/data/p301081/astronomy/Models/checkpoint.hdf5',\n",
    "    verbose=1,\n",
    "    save_best_only=True,\n",
    "    monitor='val_loss',\n",
    "    save_weights_only=False,\n",
    "    save_freq='epoch')\n",
    "\n",
    "# Callback to reduce learning rate if val_loss hasn't improved recently.\n",
    "LRCB = ReduceLROnPlateau(\n",
    "    monitor='val_loss',\n",
    "    verbose=1,\n",
    "    factor=0.2,\n",
    "    patience=5,\n",
    "    min_lr=0.00001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.39408, saving model to /data/p301081/astronomy/Models/checkpoint.hdf5\n",
      "7/7 - 4s - loss: 0.4055 - mean_absolute_error: 0.2164 - tp: 597.0000 - fp: 87.0000 - tn: 708.0000 - fn: 178.0000 - accuracy: 0.8312 - precision: 0.8728 - recall: 0.7703 - auc: 0.9098 - f1_metric: 0.8066 - val_loss: 0.3941 - val_mean_absolute_error: 0.3001 - val_tp: 92.0000 - val_fp: 10.0000 - val_tn: 100.0000 - val_fn: 15.0000 - val_accuracy: 0.8848 - val_precision: 0.9020 - val_recall: 0.8598 - val_auc: 0.9482 - val_f1_metric: 0.8804\n",
      "Epoch 2/100\n",
      "\n",
      "Epoch 00002: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.2402 - mean_absolute_error: 0.1074 - tp: 735.0000 - fp: 57.0000 - tn: 738.0000 - fn: 40.0000 - accuracy: 0.9382 - precision: 0.9280 - recall: 0.9484 - auc: 0.9587 - f1_metric: 0.9383 - val_loss: 0.3946 - val_mean_absolute_error: 0.3038 - val_tp: 107.0000 - val_fp: 39.0000 - val_tn: 71.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.8203 - val_precision: 0.7329 - val_recall: 1.0000 - val_auc: 0.9644 - val_f1_metric: 0.8458\n",
      "Epoch 3/100\n",
      "\n",
      "Epoch 00003: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.1892 - mean_absolute_error: 0.0925 - tp: 739.0000 - fp: 57.0000 - tn: 738.0000 - fn: 36.0000 - accuracy: 0.9408 - precision: 0.9284 - recall: 0.9535 - auc: 0.9716 - f1_metric: 0.9411 - val_loss: 0.4847 - val_mean_absolute_error: 0.3649 - val_tp: 107.0000 - val_fp: 48.0000 - val_tn: 62.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.7788 - val_precision: 0.6903 - val_recall: 1.0000 - val_auc: 0.9556 - val_f1_metric: 0.8168\n",
      "Epoch 4/100\n",
      "\n",
      "Epoch 00004: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.1665 - mean_absolute_error: 0.0838 - tp: 748.0000 - fp: 54.0000 - tn: 741.0000 - fn: 27.0000 - accuracy: 0.9484 - precision: 0.9327 - recall: 0.9652 - auc: 0.9762 - f1_metric: 0.9514 - val_loss: 0.5507 - val_mean_absolute_error: 0.4042 - val_tp: 107.0000 - val_fp: 60.0000 - val_tn: 50.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.7235 - val_precision: 0.6407 - val_recall: 1.0000 - val_auc: 0.9342 - val_f1_metric: 0.7810\n",
      "Epoch 5/100\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.1343 - mean_absolute_error: 0.0730 - tp: 756.0000 - fp: 52.0000 - tn: 743.0000 - fn: 19.0000 - accuracy: 0.9548 - precision: 0.9356 - recall: 0.9755 - auc: 0.9848 - f1_metric: 0.9537 - val_loss: 0.6306 - val_mean_absolute_error: 0.4437 - val_tp: 107.0000 - val_fp: 83.0000 - val_tn: 27.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.6175 - val_precision: 0.5632 - val_recall: 1.0000 - val_auc: 0.8653 - val_f1_metric: 0.7205\n",
      "Epoch 6/100\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.1198 - mean_absolute_error: 0.0605 - tp: 765.0000 - fp: 47.0000 - tn: 748.0000 - fn: 10.0000 - accuracy: 0.9637 - precision: 0.9421 - recall: 0.9871 - auc: 0.9882 - f1_metric: 0.9615 - val_loss: 0.6800 - val_mean_absolute_error: 0.4647 - val_tp: 107.0000 - val_fp: 103.0000 - val_tn: 7.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.5253 - val_precision: 0.5095 - val_recall: 1.0000 - val_auc: 0.8377 - val_f1_metric: 0.6751\n",
      "Epoch 7/100\n",
      "\n",
      "Epoch 00007: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.1134 - mean_absolute_error: 0.0542 - tp: 767.0000 - fp: 44.0000 - tn: 751.0000 - fn: 8.0000 - accuracy: 0.9669 - precision: 0.9457 - recall: 0.9897 - auc: 0.9904 - f1_metric: 0.9676 - val_loss: 0.7957 - val_mean_absolute_error: 0.4748 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.8609 - val_f1_metric: 0.6605\n",
      "Epoch 8/100\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0751 - mean_absolute_error: 0.0416 - tp: 772.0000 - fp: 38.0000 - tn: 757.0000 - fn: 3.0000 - accuracy: 0.9739 - precision: 0.9531 - recall: 0.9961 - auc: 0.9965 - f1_metric: 0.9737 - val_loss: 1.0247 - val_mean_absolute_error: 0.4912 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.8719 - val_f1_metric: 0.6605\n",
      "Epoch 9/100\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0778 - mean_absolute_error: 0.0410 - tp: 771.0000 - fp: 38.0000 - tn: 757.0000 - fn: 4.0000 - accuracy: 0.9732 - precision: 0.9530 - recall: 0.9948 - auc: 0.9957 - f1_metric: 0.9732 - val_loss: 1.2214 - val_mean_absolute_error: 0.4874 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.9834 - val_f1_metric: 0.6605\n",
      "Epoch 10/100\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0626 - mean_absolute_error: 0.0386 - tp: 772.0000 - fp: 30.0000 - tn: 765.0000 - fn: 3.0000 - accuracy: 0.9790 - precision: 0.9626 - recall: 0.9961 - auc: 0.9972 - f1_metric: 0.9815 - val_loss: 1.4792 - val_mean_absolute_error: 0.4992 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.8760 - val_f1_metric: 0.6605\n",
      "Epoch 11/100\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0482 - mean_absolute_error: 0.0280 - tp: 771.0000 - fp: 22.0000 - tn: 773.0000 - fn: 4.0000 - accuracy: 0.9834 - precision: 0.9723 - recall: 0.9948 - auc: 0.9981 - f1_metric: 0.9854 - val_loss: 1.6559 - val_mean_absolute_error: 0.4986 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.9452 - val_f1_metric: 0.6605\n",
      "Epoch 12/100\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0307 - mean_absolute_error: 0.0219 - tp: 774.0000 - fp: 13.0000 - tn: 782.0000 - fn: 1.0000 - accuracy: 0.9911 - precision: 0.9835 - recall: 0.9987 - auc: 0.9998 - f1_metric: 0.9920 - val_loss: 1.8766 - val_mean_absolute_error: 0.5032 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.8576 - val_f1_metric: 0.6605\n",
      "Epoch 13/100\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0336 - mean_absolute_error: 0.0188 - tp: 771.0000 - fp: 12.0000 - tn: 783.0000 - fn: 4.0000 - accuracy: 0.9898 - precision: 0.9847 - recall: 0.9948 - auc: 0.9984 - f1_metric: 0.9909 - val_loss: 1.8371 - val_mean_absolute_error: 0.5005 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.9564 - val_f1_metric: 0.6605\n",
      "Epoch 14/100\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0238 - mean_absolute_error: 0.0174 - tp: 774.0000 - fp: 8.0000 - tn: 787.0000 - fn: 1.0000 - accuracy: 0.9943 - precision: 0.9898 - recall: 0.9987 - auc: 0.9997 - f1_metric: 0.9949 - val_loss: 1.9876 - val_mean_absolute_error: 0.5022 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.9469 - val_f1_metric: 0.6605\n",
      "Epoch 15/100\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0158 - mean_absolute_error: 0.0113 - tp: 773.0000 - fp: 4.0000 - tn: 791.0000 - fn: 2.0000 - accuracy: 0.9962 - precision: 0.9949 - recall: 0.9974 - auc: 0.9999 - f1_metric: 0.9966 - val_loss: 2.0720 - val_mean_absolute_error: 0.5029 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.9135 - val_f1_metric: 0.6605\n",
      "Epoch 16/100\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0149 - mean_absolute_error: 0.0094 - tp: 775.0000 - fp: 3.0000 - tn: 792.0000 - fn: 0.0000e+00 - accuracy: 0.9981 - precision: 0.9961 - recall: 1.0000 - auc: 0.9991 - f1_metric: 0.9983 - val_loss: 2.2007 - val_mean_absolute_error: 0.5033 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.9193 - val_f1_metric: 0.6605\n",
      "Epoch 17/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00017: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0085 - mean_absolute_error: 0.0064 - tp: 775.0000 - fp: 3.0000 - tn: 792.0000 - fn: 0.0000e+00 - accuracy: 0.9981 - precision: 0.9961 - recall: 1.0000 - auc: 1.0000 - f1_metric: 0.9983 - val_loss: 2.4079 - val_mean_absolute_error: 0.5048 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.7698 - val_f1_metric: 0.6605\n",
      "Epoch 18/100\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0053 - mean_absolute_error: 0.0050 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.6909 - val_mean_absolute_error: 0.5056 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.7818 - val_f1_metric: 0.6605\n",
      "Epoch 19/100\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0059 - mean_absolute_error: 0.0044 - tp: 775.0000 - fp: 1.0000 - tn: 794.0000 - fn: 0.0000e+00 - accuracy: 0.9994 - precision: 0.9987 - recall: 1.0000 - auc: 1.0000 - f1_metric: 0.9995 - val_loss: 2.7932 - val_mean_absolute_error: 0.5058 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.5909 - val_f1_metric: 0.6605\n",
      "Epoch 20/100\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0049 - mean_absolute_error: 0.0039 - tp: 775.0000 - fp: 1.0000 - tn: 794.0000 - fn: 0.0000e+00 - accuracy: 0.9994 - precision: 0.9987 - recall: 1.0000 - auc: 1.0000 - f1_metric: 0.9995 - val_loss: 2.8080 - val_mean_absolute_error: 0.5057 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.6000 - val_f1_metric: 0.6605\n",
      "Epoch 21/100\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0058 - mean_absolute_error: 0.0046 - tp: 774.0000 - fp: 2.0000 - tn: 793.0000 - fn: 1.0000 - accuracy: 0.9981 - precision: 0.9974 - recall: 0.9987 - auc: 1.0000 - f1_metric: 0.9983 - val_loss: 2.7963 - val_mean_absolute_error: 0.5055 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.6227 - val_f1_metric: 0.6605\n",
      "Epoch 22/100\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0068 - mean_absolute_error: 0.0045 - tp: 774.0000 - fp: 1.0000 - tn: 794.0000 - fn: 1.0000 - accuracy: 0.9987 - precision: 0.9987 - recall: 0.9987 - auc: 1.0000 - f1_metric: 0.9989 - val_loss: 2.6314 - val_mean_absolute_error: 0.5047 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.8500 - val_f1_metric: 0.6605\n",
      "Epoch 23/100\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0033 - mean_absolute_error: 0.0032 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.6103 - val_mean_absolute_error: 0.5045 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.8773 - val_f1_metric: 0.6605\n",
      "Epoch 24/100\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0083 - mean_absolute_error: 0.0052 - tp: 773.0000 - fp: 1.0000 - tn: 794.0000 - fn: 2.0000 - accuracy: 0.9981 - precision: 0.9987 - recall: 0.9974 - auc: 1.0000 - f1_metric: 0.9983 - val_loss: 2.6828 - val_mean_absolute_error: 0.5046 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.7591 - val_f1_metric: 0.6605\n",
      "Epoch 25/100\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0039 - mean_absolute_error: 0.0033 - tp: 774.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 1.0000 - accuracy: 0.9994 - precision: 1.0000 - recall: 0.9987 - auc: 1.0000 - f1_metric: 0.9995 - val_loss: 2.6545 - val_mean_absolute_error: 0.5043 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.7955 - val_f1_metric: 0.6605\n",
      "Epoch 26/100\n",
      "\n",
      "Epoch 00026: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0039 - mean_absolute_error: 0.0032 - tp: 775.0000 - fp: 1.0000 - tn: 794.0000 - fn: 0.0000e+00 - accuracy: 0.9994 - precision: 0.9987 - recall: 1.0000 - auc: 1.0000 - f1_metric: 0.9995 - val_loss: 2.5061 - val_mean_absolute_error: 0.5033 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.9136 - val_f1_metric: 0.6605\n",
      "Epoch 27/100\n",
      "\n",
      "Epoch 00027: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0034 - mean_absolute_error: 0.0030 - tp: 773.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 2.0000 - accuracy: 0.9987 - precision: 1.0000 - recall: 0.9974 - auc: 1.0000 - f1_metric: 0.9989 - val_loss: 2.3756 - val_mean_absolute_error: 0.5019 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.9273 - val_f1_metric: 0.6605\n",
      "Epoch 28/100\n",
      "\n",
      "Epoch 00028: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0023 - mean_absolute_error: 0.0022 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.4312 - val_mean_absolute_error: 0.5022 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.9136 - val_f1_metric: 0.6605\n",
      "Epoch 29/100\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0018 - mean_absolute_error: 0.0018 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.6594 - val_mean_absolute_error: 0.5040 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.8182 - val_f1_metric: 0.6605\n",
      "Epoch 30/100\n",
      "\n",
      "Epoch 00030: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0017 - mean_absolute_error: 0.0016 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.8953 - val_mean_absolute_error: 0.5051 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.6136 - val_f1_metric: 0.6605\n",
      "Epoch 31/100\n",
      "\n",
      "Epoch 00031: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0015 - mean_absolute_error: 0.0014 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.9976 - val_mean_absolute_error: 0.5054 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.5636 - val_f1_metric: 0.6605\n",
      "Epoch 32/100\n",
      "\n",
      "Epoch 00032: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0012 - mean_absolute_error: 0.0012 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.9354 - val_mean_absolute_error: 0.5052 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.5909 - val_f1_metric: 0.6605\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 33/100\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 0.0011 - mean_absolute_error: 0.0011 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.9736 - val_mean_absolute_error: 0.5053 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.5727 - val_f1_metric: 0.6605\n",
      "Epoch 34/100\n",
      "\n",
      "Epoch 00034: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 7.9521e-04 - mean_absolute_error: 7.9183e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.9603 - val_mean_absolute_error: 0.5052 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.5909 - val_f1_metric: 0.6605\n",
      "Epoch 35/100\n",
      "\n",
      "Epoch 00035: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 8.7378e-04 - mean_absolute_error: 8.6839e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.9384 - val_mean_absolute_error: 0.5050 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.6045 - val_f1_metric: 0.6605\n",
      "Epoch 36/100\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 7.2450e-04 - mean_absolute_error: 7.2087e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.9539 - val_mean_absolute_error: 0.5051 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.6045 - val_f1_metric: 0.6605\n",
      "Epoch 37/100\n",
      "\n",
      "Epoch 00037: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 7.5695e-04 - mean_absolute_error: 7.4339e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.9715 - val_mean_absolute_error: 0.5051 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.5955 - val_f1_metric: 0.6605\n",
      "Epoch 38/100\n",
      "\n",
      "Epoch 00038: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 6.3879e-04 - mean_absolute_error: 6.3547e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.9732 - val_mean_absolute_error: 0.5051 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.5955 - val_f1_metric: 0.6605\n",
      "Epoch 39/100\n",
      "\n",
      "Epoch 00039: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 6.5790e-04 - mean_absolute_error: 6.5459e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.9257 - val_mean_absolute_error: 0.5049 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.6045 - val_f1_metric: 0.6605\n",
      "Epoch 40/100\n",
      "\n",
      "Epoch 00040: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 5.9773e-04 - mean_absolute_error: 5.9541e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.8061 - val_mean_absolute_error: 0.5043 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.6909 - val_f1_metric: 0.6605\n",
      "Epoch 41/100\n",
      "\n",
      "Epoch 00041: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 6.0328e-04 - mean_absolute_error: 6.0078e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.7388 - val_mean_absolute_error: 0.5039 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.7273 - val_f1_metric: 0.6605\n",
      "Epoch 42/100\n",
      "\n",
      "Epoch 00042: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 8.4547e-04 - mean_absolute_error: 8.2584e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.8049 - val_mean_absolute_error: 0.5042 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.7091 - val_f1_metric: 0.6605\n",
      "Epoch 43/100\n",
      "\n",
      "Epoch 00043: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 5.8587e-04 - mean_absolute_error: 5.8377e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.6697 - val_mean_absolute_error: 0.5032 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.7682 - val_f1_metric: 0.6605\n",
      "Epoch 44/100\n",
      "\n",
      "Epoch 00044: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 4.6617e-04 - mean_absolute_error: 4.6489e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.5060 - val_mean_absolute_error: 0.5016 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.8636 - val_f1_metric: 0.6605\n",
      "Epoch 45/100\n",
      "\n",
      "Epoch 00045: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 6.5532e-04 - mean_absolute_error: 6.5067e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.4017 - val_mean_absolute_error: 0.5001 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.8955 - val_f1_metric: 0.6605\n",
      "Epoch 46/100\n",
      "\n",
      "Epoch 00046: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 4.8682e-04 - mean_absolute_error: 4.8529e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.3700 - val_mean_absolute_error: 0.4995 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.9045 - val_f1_metric: 0.6605\n",
      "Epoch 47/100\n",
      "\n",
      "Epoch 00047: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 5.4107e-04 - mean_absolute_error: 5.3807e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.2638 - val_mean_absolute_error: 0.4975 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.9182 - val_f1_metric: 0.6605\n",
      "Epoch 48/100\n",
      "\n",
      "Epoch 00048: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 4.8790e-04 - mean_absolute_error: 4.8624e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.0994 - val_mean_absolute_error: 0.4935 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.9409 - val_f1_metric: 0.6605\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 49/100\n",
      "\n",
      "Epoch 00049: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 5.0895e-04 - mean_absolute_error: 5.0436e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 2.0172 - val_mean_absolute_error: 0.4906 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.9409 - val_f1_metric: 0.6605\n",
      "Epoch 50/100\n",
      "\n",
      "Epoch 00050: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 4.8491e-04 - mean_absolute_error: 4.8322e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 1.9144 - val_mean_absolute_error: 0.4862 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.9500 - val_f1_metric: 0.6605\n",
      "Epoch 51/100\n",
      "\n",
      "Epoch 00051: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 5.1785e-04 - mean_absolute_error: 5.1350e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 1.8019 - val_mean_absolute_error: 0.4804 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.9591 - val_f1_metric: 0.6605\n",
      "Epoch 52/100\n",
      "\n",
      "Epoch 00052: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 4.9092e-04 - mean_absolute_error: 4.8899e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 1.5134 - val_mean_absolute_error: 0.4596 - val_tp: 107.0000 - val_fp: 110.0000 - val_tn: 0.0000e+00 - val_fn: 0.0000e+00 - val_accuracy: 0.4931 - val_precision: 0.4931 - val_recall: 1.0000 - val_auc: 0.9773 - val_f1_metric: 0.6605\n",
      "Epoch 53/100\n",
      "\n",
      "Epoch 00053: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 4.6467e-04 - mean_absolute_error: 4.6204e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 1.2875 - val_mean_absolute_error: 0.4351 - val_tp: 107.0000 - val_fp: 107.0000 - val_tn: 3.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.5069 - val_precision: 0.5000 - val_recall: 1.0000 - val_auc: 0.9773 - val_f1_metric: 0.6667\n",
      "Epoch 54/100\n",
      "\n",
      "Epoch 00054: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 4.5507e-04 - mean_absolute_error: 4.5317e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 1.2169 - val_mean_absolute_error: 0.4246 - val_tp: 107.0000 - val_fp: 105.0000 - val_tn: 5.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.5161 - val_precision: 0.5047 - val_recall: 1.0000 - val_auc: 0.9773 - val_f1_metric: 0.6708\n",
      "Epoch 55/100\n",
      "\n",
      "Epoch 00055: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 3.7944e-04 - mean_absolute_error: 3.7853e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 1.1729 - val_mean_absolute_error: 0.4168 - val_tp: 107.0000 - val_fp: 103.0000 - val_tn: 7.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.5253 - val_precision: 0.5095 - val_recall: 1.0000 - val_auc: 0.9773 - val_f1_metric: 0.6751\n",
      "Epoch 56/100\n",
      "\n",
      "Epoch 00056: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 4.0700e-04 - mean_absolute_error: 4.0568e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 1.0373 - val_mean_absolute_error: 0.3921 - val_tp: 107.0000 - val_fp: 95.0000 - val_tn: 15.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.5622 - val_precision: 0.5297 - val_recall: 1.0000 - val_auc: 0.9773 - val_f1_metric: 0.6926\n",
      "Epoch 57/100\n",
      "\n",
      "Epoch 00057: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 4.0390e-04 - mean_absolute_error: 4.0264e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.9390 - val_mean_absolute_error: 0.3716 - val_tp: 107.0000 - val_fp: 93.0000 - val_tn: 17.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.5714 - val_precision: 0.5350 - val_recall: 1.0000 - val_auc: 0.9864 - val_f1_metric: 0.6971\n",
      "Epoch 58/100\n",
      "\n",
      "Epoch 00058: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 5.7159e-04 - mean_absolute_error: 5.4871e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 1.0512 - val_mean_absolute_error: 0.3974 - val_tp: 107.0000 - val_fp: 97.0000 - val_tn: 13.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.5530 - val_precision: 0.5245 - val_recall: 1.0000 - val_auc: 0.9864 - val_f1_metric: 0.6881\n",
      "Epoch 59/100\n",
      "\n",
      "Epoch 00059: val_loss did not improve from 0.39408\n",
      "7/7 - 1s - loss: 6.3666e-04 - mean_absolute_error: 6.1562e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.6215 - val_mean_absolute_error: 0.2705 - val_tp: 107.0000 - val_fp: 61.0000 - val_tn: 49.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.7189 - val_precision: 0.6369 - val_recall: 1.0000 - val_auc: 0.9864 - val_f1_metric: 0.7782\n",
      "Epoch 60/100\n",
      "\n",
      "Epoch 00060: val_loss improved from 0.39408 to 0.36535, saving model to /data/p301081/astronomy/Models/checkpoint.hdf5\n",
      "7/7 - 2s - loss: 5.9204e-04 - mean_absolute_error: 5.8816e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.3654 - val_mean_absolute_error: 0.1670 - val_tp: 107.0000 - val_fp: 26.0000 - val_tn: 84.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.8802 - val_precision: 0.8045 - val_recall: 1.0000 - val_auc: 0.9864 - val_f1_metric: 0.8917\n",
      "Epoch 61/100\n",
      "\n",
      "Epoch 00061: val_loss improved from 0.36535 to 0.17748, saving model to /data/p301081/astronomy/Models/checkpoint.hdf5\n",
      "7/7 - 2s - loss: 0.0016 - mean_absolute_error: 0.0012 - tp: 775.0000 - fp: 1.0000 - tn: 794.0000 - fn: 0.0000e+00 - accuracy: 0.9994 - precision: 0.9987 - recall: 1.0000 - auc: 1.0000 - f1_metric: 0.9995 - val_loss: 0.1775 - val_mean_absolute_error: 0.0824 - val_tp: 107.0000 - val_fp: 12.0000 - val_tn: 98.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9447 - val_precision: 0.8992 - val_recall: 1.0000 - val_auc: 0.9955 - val_f1_metric: 0.9469\n",
      "Epoch 62/100\n",
      "\n",
      "Epoch 00062: val_loss improved from 0.17748 to 0.11485, saving model to /data/p301081/astronomy/Models/checkpoint.hdf5\n",
      "7/7 - 2s - loss: 9.6132e-04 - mean_absolute_error: 9.0705e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.1149 - val_mean_absolute_error: 0.0487 - val_tp: 107.0000 - val_fp: 7.0000 - val_tn: 103.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9677 - val_precision: 0.9386 - val_recall: 1.0000 - val_auc: 0.9953 - val_f1_metric: 0.9683\n",
      "Epoch 63/100\n",
      "\n",
      "Epoch 00063: val_loss improved from 0.11485 to 0.06082, saving model to /data/p301081/astronomy/Models/checkpoint.hdf5\n",
      "7/7 - 2s - loss: 8.3798e-04 - mean_absolute_error: 8.2117e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0608 - val_mean_absolute_error: 0.0210 - val_tp: 107.0000 - val_fp: 4.0000 - val_tn: 106.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9816 - val_precision: 0.9640 - val_recall: 1.0000 - val_auc: 0.9952 - val_f1_metric: 0.9817\n",
      "Epoch 64/100\n",
      "\n",
      "Epoch 00064: val_loss did not improve from 0.06082\n",
      "7/7 - 1s - loss: 5.8745e-04 - mean_absolute_error: 5.8366e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0634 - val_mean_absolute_error: 0.0226 - val_tp: 107.0000 - val_fp: 5.0000 - val_tn: 105.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9770 - val_precision: 0.9554 - val_recall: 1.0000 - val_auc: 0.9953 - val_f1_metric: 0.9772\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 65/100\n",
      "\n",
      "Epoch 00065: val_loss improved from 0.06082 to 0.05562, saving model to /data/p301081/astronomy/Models/checkpoint.hdf5\n",
      "7/7 - 2s - loss: 9.3483e-04 - mean_absolute_error: 8.8589e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0556 - val_mean_absolute_error: 0.0189 - val_tp: 107.0000 - val_fp: 3.0000 - val_tn: 107.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9862 - val_precision: 0.9727 - val_recall: 1.0000 - val_auc: 0.9952 - val_f1_metric: 0.9862\n",
      "Epoch 66/100\n",
      "\n",
      "Epoch 00066: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 0.0015 - mean_absolute_error: 0.0013 - tp: 774.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 1.0000 - accuracy: 0.9994 - precision: 1.0000 - recall: 0.9987 - auc: 1.0000 - f1_metric: 0.9995 - val_loss: 0.1352 - val_mean_absolute_error: 0.0541 - val_tp: 107.0000 - val_fp: 8.0000 - val_tn: 102.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9631 - val_precision: 0.9304 - val_recall: 1.0000 - val_auc: 0.9955 - val_f1_metric: 0.9640\n",
      "Epoch 67/100\n",
      "\n",
      "Epoch 00067: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 0.0012 - mean_absolute_error: 9.2911e-04 - tp: 775.0000 - fp: 1.0000 - tn: 794.0000 - fn: 0.0000e+00 - accuracy: 0.9994 - precision: 0.9987 - recall: 1.0000 - auc: 1.0000 - f1_metric: 0.9995 - val_loss: 0.2338 - val_mean_absolute_error: 0.1156 - val_tp: 107.0000 - val_fp: 17.0000 - val_tn: 93.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9217 - val_precision: 0.8629 - val_recall: 1.0000 - val_auc: 0.9909 - val_f1_metric: 0.9264\n",
      "Epoch 68/100\n",
      "\n",
      "Epoch 00068: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 7.1174e-04 - mean_absolute_error: 7.0389e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 1.2753 - val_mean_absolute_error: 0.4264 - val_tp: 107.0000 - val_fp: 104.0000 - val_tn: 6.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.5207 - val_precision: 0.5071 - val_recall: 1.0000 - val_auc: 0.9773 - val_f1_metric: 0.6730\n",
      "Epoch 69/100\n",
      "\n",
      "Epoch 00069: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 7.6606e-04 - mean_absolute_error: 7.4908e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 1.8471 - val_mean_absolute_error: 0.4748 - val_tp: 107.0000 - val_fp: 109.0000 - val_tn: 1.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.4977 - val_precision: 0.4954 - val_recall: 1.0000 - val_auc: 0.9455 - val_f1_metric: 0.6625\n",
      "Epoch 70/100\n",
      "\n",
      "Epoch 00070: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 5.1975e-04 - mean_absolute_error: 5.1722e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 1.0512 - val_mean_absolute_error: 0.3863 - val_tp: 107.0000 - val_fp: 91.0000 - val_tn: 19.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.5806 - val_precision: 0.5404 - val_recall: 1.0000 - val_auc: 0.9818 - val_f1_metric: 0.7016\n",
      "Epoch 71/100\n",
      "\n",
      "Epoch 00071: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 4.8646e-04 - mean_absolute_error: 4.8295e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.6758 - val_mean_absolute_error: 0.2964 - val_tp: 107.0000 - val_fp: 65.0000 - val_tn: 45.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.7005 - val_precision: 0.6221 - val_recall: 1.0000 - val_auc: 0.9864 - val_f1_metric: 0.7670\n",
      "Epoch 72/100\n",
      "\n",
      "Epoch 00072: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 6.0811e-04 - mean_absolute_error: 5.8155e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.3435 - val_mean_absolute_error: 0.1638 - val_tp: 107.0000 - val_fp: 27.0000 - val_tn: 83.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.8756 - val_precision: 0.7985 - val_recall: 1.0000 - val_auc: 0.9864 - val_f1_metric: 0.8880\n",
      "Epoch 73/100\n",
      "\n",
      "Epoch 00073: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 4.1058e-04 - mean_absolute_error: 4.0852e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.1997 - val_mean_absolute_error: 0.0917 - val_tp: 107.0000 - val_fp: 14.0000 - val_tn: 96.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9355 - val_precision: 0.8843 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9386\n",
      "Epoch 74/100\n",
      "\n",
      "Epoch 00074: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 3.8143e-04 - mean_absolute_error: 3.7977e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.1677 - val_mean_absolute_error: 0.0766 - val_tp: 107.0000 - val_fp: 12.0000 - val_tn: 98.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9447 - val_precision: 0.8992 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9469\n",
      "Epoch 75/100\n",
      "\n",
      "Epoch 00075: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 4.2516e-04 - mean_absolute_error: 4.2256e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.1201 - val_mean_absolute_error: 0.0530 - val_tp: 107.0000 - val_fp: 7.0000 - val_tn: 103.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9677 - val_precision: 0.9386 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9683\n",
      "Epoch 76/100\n",
      "\n",
      "Epoch 00076: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 3.5788e-04 - mean_absolute_error: 3.5710e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0952 - val_mean_absolute_error: 0.0393 - val_tp: 107.0000 - val_fp: 5.0000 - val_tn: 105.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9770 - val_precision: 0.9554 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9772\n",
      "Epoch 77/100\n",
      "\n",
      "Epoch 00077: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 3.4595e-04 - mean_absolute_error: 3.4484e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.1159 - val_mean_absolute_error: 0.0487 - val_tp: 107.0000 - val_fp: 6.0000 - val_tn: 104.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9724 - val_precision: 0.9469 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9727\n",
      "Epoch 78/100\n",
      "\n",
      "Epoch 00078: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 2.9869e-04 - mean_absolute_error: 2.9814e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.1202 - val_mean_absolute_error: 0.0500 - val_tp: 107.0000 - val_fp: 6.0000 - val_tn: 104.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9724 - val_precision: 0.9469 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9727\n",
      "Epoch 79/100\n",
      "\n",
      "Epoch 00079: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 3.3116e-04 - mean_absolute_error: 3.2980e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.1031 - val_mean_absolute_error: 0.0412 - val_tp: 107.0000 - val_fp: 6.0000 - val_tn: 104.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9724 - val_precision: 0.9469 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9727\n",
      "Epoch 80/100\n",
      "\n",
      "Epoch 00080: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 2.8041e-04 - mean_absolute_error: 2.7973e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.1024 - val_mean_absolute_error: 0.0416 - val_tp: 107.0000 - val_fp: 6.0000 - val_tn: 104.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9724 - val_precision: 0.9469 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9727\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 81/100\n",
      "\n",
      "Epoch 00081: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 2.7116e-04 - mean_absolute_error: 2.7039e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.1080 - val_mean_absolute_error: 0.0449 - val_tp: 107.0000 - val_fp: 6.0000 - val_tn: 104.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9724 - val_precision: 0.9469 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9727\n",
      "Epoch 82/100\n",
      "\n",
      "Epoch 00082: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 2.3347e-04 - mean_absolute_error: 2.3317e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.1076 - val_mean_absolute_error: 0.0444 - val_tp: 107.0000 - val_fp: 6.0000 - val_tn: 104.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9724 - val_precision: 0.9469 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9727\n",
      "Epoch 83/100\n",
      "\n",
      "Epoch 00083: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 2.4858e-04 - mean_absolute_error: 2.4822e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0974 - val_mean_absolute_error: 0.0390 - val_tp: 107.0000 - val_fp: 5.0000 - val_tn: 105.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9770 - val_precision: 0.9554 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9772\n",
      "Epoch 84/100\n",
      "\n",
      "Epoch 00084: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 2.3780e-04 - mean_absolute_error: 2.3746e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0884 - val_mean_absolute_error: 0.0337 - val_tp: 107.0000 - val_fp: 5.0000 - val_tn: 105.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9770 - val_precision: 0.9554 - val_recall: 1.0000 - val_auc: 0.9999 - val_f1_metric: 0.9772\n",
      "Epoch 85/100\n",
      "\n",
      "Epoch 00085: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 2.8820e-04 - mean_absolute_error: 2.8618e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.1003 - val_mean_absolute_error: 0.0396 - val_tp: 107.0000 - val_fp: 5.0000 - val_tn: 105.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9770 - val_precision: 0.9554 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9772\n",
      "Epoch 86/100\n",
      "\n",
      "Epoch 00086: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 2.4106e-04 - mean_absolute_error: 2.4067e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0965 - val_mean_absolute_error: 0.0380 - val_tp: 107.0000 - val_fp: 5.0000 - val_tn: 105.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9770 - val_precision: 0.9554 - val_recall: 1.0000 - val_auc: 0.9999 - val_f1_metric: 0.9772\n",
      "Epoch 87/100\n",
      "\n",
      "Epoch 00087: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 2.1096e-04 - mean_absolute_error: 2.1077e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0839 - val_mean_absolute_error: 0.0320 - val_tp: 107.0000 - val_fp: 4.0000 - val_tn: 106.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9816 - val_precision: 0.9640 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9817\n",
      "Epoch 88/100\n",
      "\n",
      "Epoch 00088: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 2.3104e-04 - mean_absolute_error: 2.3069e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0732 - val_mean_absolute_error: 0.0272 - val_tp: 107.0000 - val_fp: 4.0000 - val_tn: 106.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9816 - val_precision: 0.9640 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9817\n",
      "Epoch 89/100\n",
      "\n",
      "Epoch 00089: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 2.1422e-04 - mean_absolute_error: 2.1398e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0673 - val_mean_absolute_error: 0.0253 - val_tp: 107.0000 - val_fp: 4.0000 - val_tn: 106.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9816 - val_precision: 0.9640 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9817\n",
      "Epoch 90/100\n",
      "\n",
      "Epoch 00090: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 1.9600e-04 - mean_absolute_error: 1.9577e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0618 - val_mean_absolute_error: 0.0232 - val_tp: 107.0000 - val_fp: 4.0000 - val_tn: 106.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9816 - val_precision: 0.9640 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9817\n",
      "Epoch 91/100\n",
      "\n",
      "Epoch 00091: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 1.9757e-04 - mean_absolute_error: 1.9735e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0585 - val_mean_absolute_error: 0.0216 - val_tp: 107.0000 - val_fp: 4.0000 - val_tn: 106.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9816 - val_precision: 0.9640 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9817\n",
      "Epoch 92/100\n",
      "\n",
      "Epoch 00092: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 2.0135e-04 - mean_absolute_error: 2.0097e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0601 - val_mean_absolute_error: 0.0219 - val_tp: 107.0000 - val_fp: 4.0000 - val_tn: 106.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9816 - val_precision: 0.9640 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9817\n",
      "Epoch 93/100\n",
      "\n",
      "Epoch 00093: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 2.0380e-04 - mean_absolute_error: 2.0352e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0600 - val_mean_absolute_error: 0.0215 - val_tp: 107.0000 - val_fp: 4.0000 - val_tn: 106.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9816 - val_precision: 0.9640 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9817\n",
      "Epoch 94/100\n",
      "\n",
      "Epoch 00094: val_loss did not improve from 0.05562\n",
      "7/7 - 1s - loss: 1.8633e-04 - mean_absolute_error: 1.8614e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0565 - val_mean_absolute_error: 0.0199 - val_tp: 107.0000 - val_fp: 4.0000 - val_tn: 106.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9816 - val_precision: 0.9640 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9817\n",
      "Epoch 95/100\n",
      "\n",
      "Epoch 00095: val_loss improved from 0.05562 to 0.05444, saving model to /data/p301081/astronomy/Models/checkpoint.hdf5\n",
      "7/7 - 2s - loss: 1.7425e-04 - mean_absolute_error: 1.7407e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0544 - val_mean_absolute_error: 0.0193 - val_tp: 107.0000 - val_fp: 4.0000 - val_tn: 106.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9816 - val_precision: 0.9640 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9817\n",
      "Epoch 96/100\n",
      "\n",
      "Epoch 00096: val_loss improved from 0.05444 to 0.05020, saving model to /data/p301081/astronomy/Models/checkpoint.hdf5\n",
      "7/7 - 2s - loss: 1.9365e-04 - mean_absolute_error: 1.9332e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0502 - val_mean_absolute_error: 0.0180 - val_tp: 107.0000 - val_fp: 4.0000 - val_tn: 106.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9816 - val_precision: 0.9640 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9817\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 97/100\n",
      "\n",
      "Epoch 00097: val_loss improved from 0.05020 to 0.04718, saving model to /data/p301081/astronomy/Models/checkpoint.hdf5\n",
      "7/7 - 2s - loss: 1.8828e-04 - mean_absolute_error: 1.8794e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0472 - val_mean_absolute_error: 0.0171 - val_tp: 107.0000 - val_fp: 3.0000 - val_tn: 107.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9862 - val_precision: 0.9727 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9862\n",
      "Epoch 98/100\n",
      "\n",
      "Epoch 00098: val_loss improved from 0.04718 to 0.04648, saving model to /data/p301081/astronomy/Models/checkpoint.hdf5\n",
      "7/7 - 2s - loss: 2.1254e-04 - mean_absolute_error: 2.1180e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0465 - val_mean_absolute_error: 0.0172 - val_tp: 107.0000 - val_fp: 3.0000 - val_tn: 107.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9862 - val_precision: 0.9727 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9862\n",
      "Epoch 99/100\n",
      "\n",
      "Epoch 00099: val_loss improved from 0.04648 to 0.04246, saving model to /data/p301081/astronomy/Models/checkpoint.hdf5\n",
      "7/7 - 2s - loss: 1.8637e-04 - mean_absolute_error: 1.8613e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0425 - val_mean_absolute_error: 0.0162 - val_tp: 107.0000 - val_fp: 3.0000 - val_tn: 107.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9862 - val_precision: 0.9727 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9862\n",
      "Epoch 100/100\n",
      "\n",
      "Epoch 00100: val_loss improved from 0.04246 to 0.04068, saving model to /data/p301081/astronomy/Models/checkpoint.hdf5\n",
      "7/7 - 2s - loss: 1.6749e-04 - mean_absolute_error: 1.6731e-04 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 1.0000 - val_loss: 0.0407 - val_mean_absolute_error: 0.0158 - val_tp: 107.0000 - val_fp: 3.0000 - val_tn: 107.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.9862 - val_precision: 0.9727 - val_recall: 1.0000 - val_auc: 1.0000 - val_f1_metric: 0.9862\n"
     ]
    }
   ],
   "source": [
    "# training\n",
    "history = model.fit(X_train,\n",
    "                    Y_train,\n",
    "                    epochs=epochs,\n",
    "                    batch_size=batch_size,\n",
    "                    verbose=2,\n",
    "                    validation_data=(X_valid, Y_valid),\n",
    "                    callbacks=[checkpointCB],  # Write desired callbacks between the brackets\n",
    "                    shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results and plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50/50 - 1s - loss: 0.0011 - mean_absolute_error: 0.0011 - tp: 775.0000 - fp: 0.0000e+00 - tn: 795.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - f1_metric: 0.9800\n",
      "7/7 - 0s - loss: 0.0407 - mean_absolute_error: 0.0158 - tp: 107.0000 - fp: 3.0000 - tn: 107.0000 - fn: 0.0000e+00 - accuracy: 0.9862 - precision: 0.9727 - recall: 1.0000 - auc: 1.0000 - f1_metric: 0.9837\n",
      "6/6 - 0s - loss: 0.0797 - mean_absolute_error: 0.0193 - tp: 94.0000 - fp: 2.0000 - tn: 93.0000 - fn: 1.0000 - accuracy: 0.9842 - precision: 0.9792 - recall: 0.9895 - auc: 0.9944 - f1_metric: 0.9831\n",
      "\n",
      "Training set:\n",
      "\n",
      "Classification accuracy, precision, recall: 100.00 100.00 100.00\n",
      "TP, TN, FP, FN: 775 795 0 0\n",
      "\n",
      "Validation set:\n",
      "\n",
      "Classification accuracy, precision, recall: 98.62 97.27 100.00\n",
      "TP, TN, FP, FN: 107 107 3 0\n",
      "\n",
      "Test set:\n",
      "\n",
      "Classification accuracy, precision, recall: 98.42 97.92 98.95\n",
      "TP, TN, FP, FN: 94 93 2 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training loss and validation loss history.\n",
    "plt.figure(figsize=(5, 3))\n",
    "plt.plot(history.epoch, history.history['loss'], label=\"loss\")\n",
    "plt.plot(history.epoch, history.history['val_loss'], label=\"val loss\")\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Binary crossentropy')\n",
    "plt.legend()\n",
    "plt.title(f'Loss')\n",
    "plt.tight_layout()\n",
    "if save:\n",
    "    plt.savefig(f\"/data/p301081/astronomy/results/plots_kids/loss.png\")\n",
    "\n",
    "scoresTrain = model.evaluate(X_train, Y_train, verbose=2)\n",
    "scoresValid = model.evaluate(X_valid, Y_valid, verbose=2)\n",
    "scoresTest = model.evaluate(X_test, Y_test, verbose=2)\n",
    "#print(scoresTrain, scoresTest)\n",
    "\n",
    "predictionsTrain = model.predict(X_train)\n",
    "predictionsValid = model.predict(X_valid)\n",
    "predictionsTest = model.predict(X_test)\n",
    "\n",
    "predictionsTrain = [round(pred[0]) for pred in predictionsTrain]\n",
    "predictionsValid = [round(pred[0]) for pred in predictionsValid]\n",
    "predictionsTest = [round(pred[0]) for pred in predictionsTest]\n",
    "\n",
    "print(\"\\nTraining set:\")\n",
    "train_set_metrics = cu.analyze_5unit_errors(predictionsTrain, Y_train)\n",
    "print(\"\\nValidation set:\")\n",
    "train_set_metrics = cu.analyze_5unit_errors(predictionsValid, Y_valid)\n",
    "print(\"\\nTest set:\")\n",
    "test_set_metrics = cu.analyze_5unit_errors(predictionsTest, Y_test)\n",
    "\n",
    "if save:\n",
    "    modelName = f\"cnn_asteroids.h5\"\n",
    "    print(\"\\nSaving model to\", modelName)\n",
    "    model.save(f\"../Models/{modelName}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+6623AY898jBjx+bGHf0wbhxffdXwYPcDDzqEf1x37fTtDTfamNtuHUp1da4i8emnnzBx4kTWWHMtHnv0Eerq6hj77be8/OKL8/CJJf1aKSXGjZ/IMkt0JiLYeYue07974oX3ObjfJtO3V1tusYKv++yrH9J/u3UAWH6priy80Px89NkYllysEx9//i2XD32Sh55+m1V7FH5NaQZRpE9GrDyqLOy+x5784egjePThh1hn3fVo06btLMc8/NAD3Df8Xlo0b06nhRbioEMOY4EFF+SwI4/mkN/vT12qo3nzFpw0+BQWXXTOv9R7bbIpHTp2nL6962/78tVXX9K/766klOjQoQMXXXoFv9lqG154/jl2670j3ZdcklVXW432881XlOeX9MsMvvge7r3sUD7/+nve/fjr6dXHo88exiUn9WOfndenefMq/v3SCI45e1hB17zin//mssH9eWnYSVTX1HLA/91AdU0t/bZbm923XZvqmlpGjfmB0y7/dUuISZUqfu0yB+Vicg2N40FUViZOmEDbdu0YN+579urflyE3DmWhzp1LHZYK1GGdw0sdgqQyM+m1y0reM17iiHuLkrN8dunOmTyblUepAUccdjDjf/yR6upqBh50qImjJKnJM3mUGuAbaCRJ81qlT5gxeZQkScpQpSePzrZWo3HK4EFs1msDdu29Y6lDkVRih+2xGS/fdhKv3H4yh++52QzfHb3Plkx67TI6Ldhu9idLalDZJY8R0arUMagy9e6zK1dede3cD5TUqK20zCLst+uG9NrnXNbtdxbbbbIKyyyRG6/creuCbLH+Cnw26rsSR6mmLCKK8slK2SSPEbFuRLwJjMhv94yIS0sclirIWmuvw/wLLFDqMCSV2ApLLcyLb37KpMnV1NbW8fQrH9J789wakOf8cTdOvvhuGstKI1IplE3yCFwC7AiMBUgp/RfYvKQRSZIqztsffcXGay5LxwXa0aZ1C7bdeGW6LdyBHTZdla++GcebHzT8IgGp6Cp8kfBySh6rUkojZ9rX4HulImJgRLwcES///ZqrixiaJKlSvP/JaM7/x6Pcd+Xh3Hv5YbzxwZfU1NRywgHbcMaV95c6PKnildNs688jYl0gRUQz4Ajgg4ZOSCldDVwNLhIuSfqfIXc/x5C7nwPg9MN34pux4+m3/Tq8eOsgABbrsiDP3XICvfY5l9Fjx5cyVDVBlT7bupySx0PIta6XAEYDj+X3SZL0s3Tu0J4x3//E4gt3oPcWPdlswPlcPvTJ6d+/d//pbLTXOYwdN6F0QarJMnmcR1JK3wD9Sx2HKtcJfzyWl196kXHjvmerLTbhkMOOYNfd+pY6LEklMPS8A+m4YDuqa2o5+uxhjBs/qdQhSY1G2bzbOiKugVlbzymlgYWcb9ta0sx8t7WkmZXDu62X/eODRclZPjxvuyb3buvH6v3cGtgF+LxEsUiSJGk2yiZ5TCndWn87Im4EHi1ROJIkSUXhmMfiWQroXuogVDrPPv0Ufz37TOpq69hlt74c8PsZRzC89OILHH3EoSy2WDcAtvjNVhx86OFMmTKF/fbdi+qpU6mprWWrrbfh0MOPBODC88/l2WeeYvkVVuTMs84BYPi9d/PjDz+w1z4Dsn1ASQWrqgqevfl4vvrmB3Y76m+sutxiXHpyf9q1acXIr8ay38lDGD9hckHnAtx49n70WLIrAAvO14Zx4yexfv+z2aDn0lx8Uj+mVtew76Dr+fjzb1mgfRtu/Ov+7HzY5Zk9rxq3Cs8dyyd5jIjv+d+YxyrgO+DE0kWkUqqtreUvZ57BVddcT9euXdmz32/ZbPMtWGbZZWc4bo211uayK66aYV/Lli259rohtG3Xjurqan63z55s3GsTllp6Gf77+mvcftdwBh3/B0Z88D6LL9Gde+++iyt8raFU1g7fc3Pe/2Q087VrDcCVp+zJiRfexTOvfMi+vdfnmAFbcsYVs1/DceZzAfY58frpP5997C788FNuQs1R+2zBHsddS/dFOjGwby9OvOAuBg3clnOue7iITydVlrJYJDxy9dueQOf8p0NKaemU0rDSRqZSeevNN1h88e50W3xxWrRsybbb78CTTzxe0LkRQdt27QCoqamhpqYGIqiqCqqrq0kpMXnKFJo3b84/rruWPffehxYtWhTzcST9Cot1WZBtN16Z6+/6z/R9Pbp34ZlXPgTgX8+/R58tVy/43JntttWaDHvoFQCqa2pp06oFbdu0oLqmlqW6LcSiXRacfi9pXvDd1vNAyk35viulVJv/OHO6iftm9GgWXmTh6dtdunZl9OjRsxz3xuuv03eXnTn0oAP58MMR0/fX1tay+6692bzXhqy/wYastlpP2rVrz2+22pp+u/VhscW60X6++Xj7rbfYfIvfZPJMkn6Zc4/LvY+6ru5//2p456NR7LjZqgDsutWadOvaoeBz69tozWUY/d14PvpsTO746x7h8sF7cPiem/O3fz7F6YfvxOlX3DePn0iqbGWRPOa9GBFrljoIlYc0m5WXZv6vqhVXWpmHHv0Xt911L3vstQ/HHHHY9O+aNWvGsDvv4ZF//Zu33nyDESNyLyva74DfM+zOe/jj8Sdy+aUXc+gRR3Ln7bdx3LFHcfXfrijuQ0n62bbrtQrffDee196dcfGNg067mYN234Rnbz6e9m1bMbV61rfZzunc+nbfdm1ue+jl6dtvfPAlmw44n20HXsKS3ToxaswPBMGNZ+/HdX/ely4d55t3D6cmK6I4n6yUPHmMiGnjLjcml0C+HxGvRsRrEfFqKWNT6XTtujBfj/p6+vY3o0fTpUuXGY5p37799PZ0r002paamhu+//26GY+aff37WWXc9/vPM0zPsf/fddwDo3n1Jht97N+decDEffjiCkSM/LcLTSPqlNlh9aXbcdFXeu/90bjh7PzZbZzmu+/O+fPDpaHY69HI22uschj30Cp98Mabgc6dp1qyK3lv05PaHZ/+vmhMP3Jazrn6Qkw/ajj/97QGGPvASh+6xWbEeVU1IVVUU5ZOVcpgw8yKwJtCn1IGofKy8yqp89tmnfPHF53Tt0pWHHrifs849f4Zjvh0zhk4LLURE8OYbb1BXV8eCC3bgu+++o3nz5sw///xMnjyZ55/7D/sd8PsZzr380os55bQzqKmpoa42V7GoiiomT5r9bE1JpXHKpfdyyqX3AtBrrR4cve+W7D/4humvH4wITvz9Nlxz+zMFnzvNFustzwefjubLb8bNcu7eO63HQ0+/zbjxk2jbuiV1dYm6ukTb1o6PlsoheQyAlNJHpQ5E5aN58+YMOvkUDhl4IHV1tfTZZTeWXbYHw24dCsDu/fbg0UceZtitQ2nerBmtWrfmr+ddQETw7ZhvGHzSidTV1VJXl9h6m23ZdLPNp1/7X48/xiqrrEqXLrllOlZbfQ1267MTyy23HMuvsEJJnlfSz7P7tmtzUL9NALjnX69zwz3PA7BI5wW44pQ92eWIK+d6jb7brDV9okx9bVq3YO+d1mPHQy8D4JKb/sXQ8w5kanUNAwb9Y949hJqsSl+qp+SvJ4yIL4AL5vR9SmmO39Xn6wklzczXE0qaWTm8nnDlkx8pSs7y9plbN5nXEzYD2pOvQEqSJDVmvmHm1xuVUjqj1EFIkiRlocJzx9LPtsaKoyRJUsUoh8rjlqUOQJIkKSuV3rYueeUxpfTd3I+SJElSOSiHyqMkSVKTYeVRkiRJTYaVR0mSpAxVeOHR5FGSJClLtq0lSZLUZFh5lCRJylCFFx6tPEqSJKlwVh4lSZIyVOljHk0eJUmSMlThuaNta0mSJBXOyqMkSVKGKr1tbeVRkiRJBbPyKEmSlKEKLzyaPEqSJGXJtrUkSZKaDCuPkiRJGarwwqOVR0mSJBXOyqMkSVKGKn3Mo8mjJElShio8d7RtLUmSpMJZeZQkScpQpbetrTxKkiSpYFYeJUmSMlThhUcrj5IkSSqclUdJkqQMVfqYR5NHSZKkDFV68mjbWpIkqYmIiAUj4vaIeC8i3o2IDSKiY0Q8GhEj8n92aOgaJo+SJEkZiijOp0AXAw+llFYAegLvAicCj6eUegCP57fnyORRkiSpCYiI+YFNgL8DpJSmppTGAb2BIfnDhgB9GrqOyaMkSVKGIqJYn4ER8XK9z8CZbr00MAa4PiJei4hrI6Id0DWlNAog/2eXhuJ3wowkSVKGijVfJqV0NXB1A4c0B9YEjkgpvRARFzOXFvXsWHmUJElqGr4AvkgpvZDfvp1cMjk6IhYByP/5TUMXMXmUJEnKULHa1nOTUvoa+Dwils/v2hJ4B7gXGJDfNwC4p6Hr2LaWJElqOo4Abo6IlsDHwH7kionDIuIA4DOgb0MXMHmUJEnKUCnXCE8pvQ6sPZuvtiz0GiaPkiRJGaryDTOSJElqKqw8SpIkZajCC49WHiVJklQ4K4+SJEkZKmRZnXJm8ihJkpShqsrOHW1bS5IkqXBWHiVJkjJU6W1rK4+SJEkqmJVHSZKkDFV44dHKoyRJkgpn5VGSJClDQWWXHk0eJUmSMuRSPZIkSWoyrDxKkiRlyKV6JEmS1GRYeZQkScpQhRceTR4lSZKyVFXh2aNta0mSJBXMyqMkSVKGKrzwOPfKY0QcFRHzR87fI+LViNg6i+AkSZJUXgppW++fUvoR2BroDOwHnF3UqCRJkhqpiCjKJyuFtK2nRbM9cH1K6b9R6QsUSZIklUilZ1GFVB5fiYhHyCWPD0fEfEBdccOSJElSOSqk8ngAsDrwcUppYkR0Ite6liRJ0s/UFJbqScBKwJH57XZA66JFJEmSpLJVSPJ4BbABsEd+ezxwedEikiRJasSiSJ+sFNK2Xi+ltGZEvAaQUvo+IloWOS5JkiSVoUKSx+qIaEaufU1EdMYJM5IkSb9IpS9aU0jyeAlwF9AlIs4EfgsMLmpUkiRJjVRVZeeOc08eU0o3R8QrwJbkWup9UkrvFj0ySZIklZ25Jo8RsQQwERhef19K6bNiBiZJktQYNYW29f3kxjsGuSV6lgLeB1YuYlySJEkqQ4W0rVetvx0RawIHFS0iSZKkRqzCC48FVR5nkFJ6NSLWKUYwkiRJjV2jb1tHxLH1NquANYExRYtIkiRJZauQyuN89X6uITcG8o7ihCNJktS4NYWlek7PIhBJkiSVvzkmjxExnPxbZWYnpbRzUSKSJElqxBrzmMfzMotCkiSpiajs1LGB5DGl9O8sA5EkSVL5K2S2dQ/gLGAlcouEA5BSWrqIcUmSJDVKVRXetq4q4JjrgSvJzbTeHLgBuLGYQUmSJKk8FZI8tkkpPQ5ESmlkSuk0YIvihiVJktQ4RRTnk5VC1nmcHBFVwIiIOBz4EuhS3LAkSZIap0qfbV1I5fFooC1wJLAWsDcwoJhBSZIkqTw1tM7jb4H7Ukov5Xf9BOyXSVSSJEmNVIUXHhusPO4FfBYRN0TEdhHRLKugJEmSVJ7mmDymlHYBlgUeJ9ey/jwiroyITbIKTpIkqbGpiijKJ7P4G/oypfRjSmlISmk7YFXgdeDSiPg8k+gkSZJUVgqZbU1EdAB2BfoBHYE7ihmUJElSY1XpYx4bmjAzH9AH2ANYE7gX+DPwREopZROeJElS41LpS/U0VHn8BHiY3NtlHkopVWcTkiRJkspVQ8njEimliZlF8it1WO+oUocgqcyMffHSUocgSbMoZJHtctbQbOuKSRwlSZKUjYImzEiSJGneaMxjHiVJkjSPVVV27tjgbOvhwBxnVaeUdi5KRJIkSSpbDVUez8ssCkmSpCai0VYeU0r/zjIQSZIklb+5jnmMiB7AWcBKQOtp+1NKSxcxLkmSpEapKUyYuR44FbgQ2BzYD6jsp5YkSSqRSm9bF7JOZZuU0uNApJRGppROA7YobliSJEkqR4VUHidHRBUwIiIOB74EuhQ3LEmSpMapwrvWBVUejwbaAkcCawH7AAOKGZQkSZLK01wrjymll/I//kRuvKMkSZJ+oaoKLz0WMtv6CWazWHhKyXGPkiRJP1Mhbd9yVsiYxz/W+7k1sBtQU5xwJEmSVM4KaVu/MtOuZyPCBcQlSZJ+gQrvWhfUtu5Yb7OK3KSZhYsWkSRJkspWIW3rV8iNeQxy7XOMgHEAACAASURBVOpPgAOKGZQkSVJj1egnzAArppQm198REa2KFI8kSZLKWCETfv4zm33PzetAJEmSmoKI4nyyMsfKY0QsDCwGtImINfjf+6znJ7douCRJkn6mSn+3dUNt622A3wHdgPP5X/L4I3BSccOSJElSOZpj8phSGgIMiYjdUkp3ZBiTJElSo1XpE2YKGfO4VkQsOG0jIjpExJ+LGJMkSZLKVCHJ43YppXHTNlJK3wPbFy8kSZKkxqvRTpipp1lEtEopTQGIiDaAS/VIkiT9Ao15wsw0NwGPR8T15BYL3x+4oahRSZIkqSwV8m7rcyLiDeA35GZc/yml9HDRI5MkSWqEgsouPRZSeSSl9BDwEEBEbBQRl6eUDitqZJIkSSo7BSWPEbE6sAfQj9y7re8sZlCSJEmNVaMd8xgRywH9ySWNY4FbgUgpbZ5RbJIkSY1OpSePDS3V8x6wJbBTSmnjlNKlQG02YUmSJGlei4hmEfFaRNyX3+4YEY9GxIj8nx3mdo2GksfdgK+BJyLimojYEip8hKckSVKJRURRPgU6Cni33vaJwOMppR7A4/ntBs0xeUwp3ZVS6gesADwJHAN0jYgrI2LrQiOUJElS6UVEN2AH4Np6u3sDQ/I/DwH6zO06c33DTEppQkrp5pTSjkA34HUKyEolSZI0q6ooziciBkbEy/U+A2e69UXA8UBdvX1dU0qjAPJ/dplb/AXNtp4mpfQdcFX+I0mSpJ+pWK8STCldDVw9+3vGjsA3KaVXImKzX3Ofn5U8SpIkqSJtBOwcEdsDrYH5I+ImYHRELJJSGhURiwDfzO1Cc21bS5Ikad6piijKpyEppUEppW4ppSXJLcX4r5TS3sC9wID8YQOAe+Ya/697fEmSJFWws4GtImIEsFV+u0G2rSVJkjJU6kXCU0pPkltJh5TSWHLrehfMyqMkSZIKZuVRkiQpQ8WabZ0Vk0dJkqQMVVX4C/tsW0uSJKlgVh4lSZIyVOltayuPkiRJKpiVR0mSpAyVeqmeX8vkUZIkKUNzextMubNtLUmSpIJZeZQkScpQhRcerTxKkiSpcFYeJUmSMlTpYx5NHiVJkjJU4bmjbWtJkiQVzsqjJElShiq9clfp8UuSJClDVh4lSZIyFBU+6NHkUZIkKUOVnTratpYkSdLPYOVRkiQpQ5W+zqOVR0mSJBXMyqMkSVKGKrvuaOVRkiRJP4OVR0mSpAxV+JBHk0dJkqQsVfo6j7atJUmSVDArj5IkSRmq9MpdpccvSZKkDFl5lCRJylClj3k0eZQkScpQZaeOtq0lSZL0M1h5lCRJylClt62tPEqSJKlgVh4lSZIyVOmVO5NHSZKkDNm2liRJUpNh5VGSJClDlV13tPIoSZKkn8HKoyRJUoYqfMijyaMkSVKWqiq8cW3bWpIkSQWz8ihJkpShSm9bW3mUJElSwaw8SpIkZSgc8yhJkqSmwsqjJElShip9zKPJoyRJUoZcqkeSJElNhpVHSZKkDFV629rKoyRJkgpm5VGSJClDlV55NHmUJEnKUKWv82jyqJLouEBbHrjyMAC6dpqfuro6xnz/EwC99r2A6praeXKfzdddjgeuPIzeR/yNR/7zLgD3XHowf/37I/zn9Y/nyT0kzTtrrbYSy/ZYbvr2hZdcxqKLdZvtsV99+QVHHnYIt989/Ffd88Df7cO3346hZctWtG3bltP+dCZLLrX0r7qm1JiZPKokvvthIuvveS4AJw/clgmTpnDRjU/MclxEkFL6Vff6/OvvOfGAracnj5LKV6tWrbn1jrszv++ZZ5/Lyqusyh233cqF55/LxZddmXkMajqqKrvwaPKo8rJ0t4UYdv6B/Of1j1lnle70+8O1vPDP41lks0EA9N16DTZfb3kO/dM/6dJxPi4Z1JduC3cg1SX+cO4dvPjWyFmu+fp7n9O+bWs2WXtZnnr5wxm+W2ulJTjr6N60a9uKMd+NZ+Bpt/DNd+NZd5XuXDa4PxMmTuG5/37MFustPz3ZlZStr778gsGDTmDSpEkAnHDSYFZfY80ZjvnowxGcOvgkqqurSXV1nHvRJXTvviT3D7+XoTffSHV1NauuthqDBp9Ks2bN5nivNddah5tvvAGAF55/jgvPO4fa2hpWXnlVTjrlNFq2bMnFF57Pv5/4F82bNWP9DTfi2ONOKN7DS2XI5FFlZ8Wlu3LQ6bdw5FnDaNZszgsCnH/crlww5HFefGskSyzSkTsvGsja/c6e7bF/ve4RTjxgmxmSx5YtmnHeH3flt8dew9hxE+i/3Vqccsj2HH7mrVx12p78/tSbefntzzjr6N7z/Bklzd6UKZPpt1sfABZbrBsXXHIZHTp24sprrqNVq1aMHPkpg477A7cMu2OG824b9k/23Htftt9xJ6qrp1JbW8fHH33EIw89wPU33kKLFi34y59O54H7hrNT7z5zvP9TTz5Bjx7LMWXKFE49eRBX/f16ui+5FIMHncBttw5lx51788Tjj3LX8AeJCMb/+GNR/z7UODnmUZrHPv5iLK+889lcj9t83eXp0b3L9O0O87ehdasWTJ5SPcux/35pBKcevD3rrbrk9H0rLLUwKy6zMPdfcSgAzZpV8eXocXRasB0tWzTn5bdzMdz60Ctsvu5ys1xT0rw3u7Z1TU0Nfz3zT7z//rtUVTXjs5GfznJez56rc+3VVzF69Nds8Zut6N59SV584Tneeedt9u7fF8glph07dpztfU8+8ThatWrNoostxgmDBvPpJ5+waLdudF9yKQB26t2HYUNvod8ee9GyZStOP2UwvTbZlE0222yePr+aBmdbS/PYhElTpv9cV5eIev8ra9WqxfSfI37e5Jq/Xvcoxx+w9QznvzXiK35z4CUzHLfQgu1+aeiSiuDmG/5Bx06duPWOe6irq2P9tXrOcsx2O+zEKqv25OmnnuSwgw7klNP/TEqJnXbuw5HH/GGu95g25nGaH34YN9vjmjdvzk3/vI0Xnn+Ohx98gFuH3szV1w355Q8nVSAXCVdZSykx7seJLLN4ZyKCnTdbbfp3T7z4AQfv3mv69mrLLdbgtR5+9h26dpqPlZZeGIB3P/6aRTsvwNorLwFAi+bNWHHphfl23ASqa2pZc8XFAei79ZpzvKak4vvpp59YqHNnqqqquH/4PdTWzvofjF98/jndFl+cPffel00324IRH7zPuutvwGOPPsJ3Y8cCuYTwq6++LOieSy61NKO+/JLPPsuNo75/+D2stfY6TJw4gZ/Gj6fXJpty3ImDeP+99+bdg6rJiCL9kxUrjyp7gy8Zzr2XHcznX3/Pux9/TauWuf+3Pfrs27hk0O7ss/N6NG9Wxb9fHsExf729wWude92j/PO8AwCYWl3Lnidcz/nH7Ub7tq1o3qyKi296gnc//pqDT7+Fq07bk58mTOGZ1z7ix58mF/05Jc1e3/57cNzRR/LYIw+z9jrr0qZN21mOefihB3jgvuE0b96chRZaiIGHHMoCCyzIYUccxSEDDyDV1dG8RXNOPPkUFl204f/QBGjVqhWn/fkvHH/s0dMnzPy2X39++GEcxxxxGFOnTCGR+MMJJxbjkaWyFr92GZRy0WatoxrHg6gstGvTkgmTpgJw/P5b0XGBdpx4YfbLh+jXGfv8RaUOQVKZadui9CMOn/rgu6LkLJss1zGTZ7PyKM3GjpuuyjH7bkHzZs0YOeo7Bp52c6lDkiQ1Es62lhqhWx96hVsfeqXUYUiSVHZMHlXRDttjU/brswERcP1dz3HZ0H9zyiHbs+Omq05/5eHAU29m1LeuxSY1RV+PGsX/nXQCY7/9lqiqYrff7s6e++xb6rDUxJW+cf7rmDyqYq20zCLs12cDeg04n6nVtdx76cE8+Mw7XHjD45xx5QMAHNp/Ewb9fluOPGtYiaOVVArNmjfj2ONOYMWVVmbChJ/Yc/fdWG/DDVlmmWVLHZpUsVyqRxVrhaW68uJbnzJpcjW1tXU8/eqH9N58VcZP+N86kW3btCThXCqpqercuQsrrrQyAO3atWeppZdhzOjRJY5KTV0U6ZOVskoeI2fviDglv71ERKxb6rhUnt7+cBQbr7EMHRdoS5vWLdh2o5Xo1rUDAKcdugMj7j+N/tuuzZ/yVUhJTdtXX37B++++yyqrzbrIuKTClVXyCFwBbADskd8eD1xeunBUzt7/dDTnD3mc+644lHsvPZg3PviKmto6AE674n567HAa/3zoZQ7ut0mJI5VUahMnTuCPxxzJH08YRPv27Usdjpq4qoiifDKLP7M7FWa9lNJhwGSAlNL3QMs5HRwRAyPi5Yh4uebbt7KKUWVkyD3Ps+Fe57HV7y/l+x8n8uHnY2b4ftiDr9BnC6sMUlNWXV3NH48+ku122Iktt9p67idIRWbbet6qjohmkBukFhGdgbo5HZxSujqltHZKae3mC62SVYwqI5075CoIiy/cgd5brMawh15hmcU7T/9+h01X4YNPHd8kNVUpJU4/ZTBLLb0M+wzYr9ThSI1Cuc22vgS4C+gSEWcCvwUGlzYklbOh5+5PxwXaUV1Ty9Fn38648ZO48v/2oEf3LtSlxGejvuPIvzjTWmqqXn/tVe4ffg89eixHv936AHD4UcfQa5NNSxyZmrQKX6qn7F5PGBErAFuS+6t9PKX0biHn+XpCSTPz9YSSZlYOryd8/qNxRclZ1l9mwUyeraza1hGxDPBJSuly4C1gq4hYsMRhSZIkzTNRpH+yUlbJI3AHUBsRywLXAksBt5Q2JEmSpHknojifrJRb8liXUqoBdgUuTikdAyxS4pgkSZKUV24TZqojYg9gX2Cn/L4WJYxHGauqCp698Y98NeYHdjv6alZbbjEuPWl3WrVsTk1tHUeffRsvv/3ZLOfN7h3XACcP3Jb9d9mAMd//BMCpl9/Pw8++wwY9l+LiQbszdWoN+540hI+/+JYF2rfhxrMHsPPhf8v0mSUV7rTBJ/HUU0/SsWMnbr97+Czfp5Q456wzefbpp2jdujWnn3nW9DfMbL/1FrRr146qqmY0a9aMW4bdAcDFF5zHs08/xXIrrMifz/orAPfdew8//vCD78FWUZR80OWvVG6Vx/3ILRJ+Zkrpk4hYCripxDEpQ4fvsSnv11ta58yjdubMqx9i/T3P5U9/e5Azj9x5lnPqv+N63T3OYbteK8+wXM+ltzzJ+nuey/p7nsvDz74DwFF7b84ex13HKZffx8C+GwMw6PfbcM51jxb3ASX9Kjv12YXL/3bNHL9/5umn+OyzkdzzwMMMPu0M/vKn02f4/urrbuDWO+6enjiOHz+e/77+GsPuupe6ulpGfPA+kydPZvg9d9G3/x6zu4XU5JVV8phSeieldGRKaWh++5OU0tmljkvZWKzLAmy78cpcf/dz0/ellJi/XWsAFmjfmlHf/jjLeXN6x3VDqmtqadOqBW1bt6C6ppalunVi0c4L8MyrH83bh5I0T6219jossMACc/z+3088zo479yYiWK3n6owf/yNjxnwzx+OrqoLq6mpSSkyZPIXmzVsw5Pq/03+vfWjRwsaXiqTCVwkvi7Z1RLxJfmHw2UkprZZhOCqRc/+wKydffA/t88kiwHHn3cXwyw/hrKN7U1UVbL7frEuvvP3hKE47dAc6LtCWSVOq2XajlXj1nc+nf3/w7r3Yc4d1efWdzzjxwrsZN34S517/GJcP7sekKdUc8H83ctbRfTjdd2BLFe+b0aNZeOH/DZXv2nVhvhk9ms6duxARHDrwACJgt7792K1vP9q1a8+WW21N/9/uwrrrr0/7+drzzltvctAhh5XwKdTYZTkzuhjKInkEdix1ACqt7XqtzDff/8Rr731Br7WWnb5/YN+NOP78u7j7X/9lt61W58pT9mCHQ6+Y4dz677ieMHHKDO+4vub2Zznr2odJCU49ZHvOPqYPB58xlDc++JJNf3chAButsQyjxvxABNx41gCqa+o48cK7+ea78dn9BUiaJ2a3dHHkp6Fef+MtdOnSle/GjuXg3+/PkkstzVprr8Pv9j+Q3+1/IACnnzKYQw4/kjtvv43nn3uWHsstz+8POiTLR5DKXlm0rVNKIxv6lDo+Fd8GPZdix01W4b3hp3DDXwaw2To9uO5P+7DXjuty97/+C8Adj77O2it3n+35c3rH9TffjaeuLpFS4rq7npvt+SceuDVnXfswJw/clj9d9SBDH3iJQ/tvUryHlVQ0XRfuytdfj5q+PXr013Tu0gWALl26AtCxUye22PI3vP3mGzOc+967uTHR3bsvyX3D7+Gc8y/iwxEjGDny02yCV5PhUj3zUESsHxEvRcRPETE1ImojYtZBbmp0TrnsPpbd/lRW2OkM9j1pCE++NIL9/+9GRo35YXolcrN1lpueFM5sdu+4Blh4ofmnH9N789V456NRM5y3907r8tAz7zBu/CTatm5JXV2iLiXatm5ZjMeUVGSbbrYF9917Dykl3vjv67RvPx+dO3dh0sSJTJiQW3Vh0sSJPPefZ1mmx3IznHvFpRdzyOFHUFNTQ11tLZAbEzl50uTMn0MqZ+XStp7mMqA/cBuwNrkle5Zt8Aw1aof9+VbO/eOuNG9WxZSp1Rz+538CsMhC83PF/+3BLkddBcz+HdcAZx65M6stvxgpwcivxnJEvfdct2ndgr13XJcdD8u1wS+56QmGnrs/U6trGXDSkIyfVFIhTjzuWF556SXGjfuebbbclIMPzSV7AH379WfjTTblmaefYufttqZ1m9ac9qe/ADB27FiOPepwAGpra9lu+x3ZaONe06/7xOOPsfIqq06vTq7Wc3X67rITPZZbnuVXWCHjp1RjV9kjHsvs3dYR8XJKae2IeGPaJJmI+E9KacO5neu7rSXNzHdbS5pZObzb+tWRPxYlZ1mz+/xN793WwMSIaAm8HhHnRMQxQLtSByVJklTpImLxiHgiIt6NiLcj4qj8/o4R8WhEjMj/2aGh65Rb8rgPuZgOByYAiwO7lTQiSZKkeSiK9E8BaoA/pJRWBNYHDouIlYATgcdTSj2Ax/Pbc1QWYx4jYomU0mf1ZlZPBk5v6BxJkiQVLqU0ChiV/3l8RLwLLAb0BjbLHzYEeBI4YU7XKZfK493TfoiIO0oZiCRJUjGVw1I9EbEksAbwAtA1n1hOSzC7NHRuuSSP9R956ZJFIUmSVKEiYmBEvFzvM3AOx7UH7gCOTin97CURy6JtzYyvJnTWtCRJarSKNSU6pXQ1cHWD945oQS5xvDmldGd+9+iIWCSlNCoiFgHm/EJ4yqfy2DMifoyI8cBq+Z9/jIjxLhIuSZIalSjSZ263zb2r8+/AuymlC+p9dS8wIP/zAOCehq5TFpXHlFKzUscgSZLUyG1EbmWbNyPi9fy+k4CzgWERcQDwGdC3oYuURfIoSZLUVBS4rM48l1J6hjnXKLcs9Drl0raWJElSBbDyKEmSlKHSvyDx1zF5lCRJylCF5462rSVJklQ4K4+SJElZqvDSo5VHSZIkFczKoyRJUoZKtVTPvGLyKEmSlKFKn21t21qSJEkFs/IoSZKUoQovPFp5lCRJUuGsPEqSJGWpwkuPVh4lSZJUMCuPkiRJGXKpHkmSJBXMpXokSZLUZFh5lCRJylCFFx6tPEqSJKlwVh4lSZKyVOGlR5NHSZKkDFX6bGvb1pIkSSqYlUdJkqQMuVSPJEmSmgwrj5IkSRmq8MKjyaMkSVKmKjx7tG0tSZKkgll5lCRJypBL9UiSJKnJsPIoSZKUoUpfqsfkUZIkKUMVnjvatpYkSVLhrDxKkiRlqcJLj1YeJUmSVDArj5IkSRlyqR5JkiQ1GVYeJUmSMuRSPZIkSSpYheeOtq0lSZJUOCuPkiRJGar0trWVR0mSJBXMyqMkSVKmKrv0aPIoSZKUIdvWkiRJajKsPEqSJGWowguPVh4lSZJUOCuPkiRJGar0MY8mj5IkSRmKCm9c27aWJElSwaw8SpIkZamyC49WHiVJklQ4K4+SJEkZqvDCo8mjJElSlip9trVta0mSJBXMyqMkSVKGXKpHkiRJTYaVR0mSpCxVduHRyqMkSZIKZ+VRkiQpQxVeeDR5lCRJypJL9UiSJKnJsPIoSZKUIZfqkSRJUpNh5VGSJClDjnmUJElSk2HyKEmSpILZtpYkScqQbWtJkiQ1GVYeJUmSMlTpS/WYPEqSJGXItrUkSZKaDCuPkiRJGarwwqOVR0mSJBXOyqMkSVKWKrz0aPIoSZKUoUqfbW3bWpIkSQWz8ihJkpQhl+qRJElSk2HlUZIkKUMVXni08ihJkqTCWXmUJEnKUoWXHk0eJUmSMuRSPZIkSWoyrDxKkiRlqNKX6omUUqljkOapiBiYUrq61HFIKh/+XpDmHdvWaowGljoASWXH3wvSPGLyKEmSpIKZPEqSJKlgJo9qjBzXJGlm/l6Q5hEnzEiSJKlgVh4lSZJUMJNHSZIkFcxFwlX2IqIWeLPerj4ppU/ncOySwH0ppVWKH5mkUoqITsDj+c2FgVpgTH573ZTS1JIEJjVyJo+qBJNSSquXOghJ5SWlNBZYHSAiTgN+SimdV/+YiAhy4/vrso9QapxsW6siRcSSEfF0RLya/2w4m2NWjogXI+L1iHgjInrk9+9db/9VEdEs+yeQVCwRsWxEvBURfwNeBRaPiHH1vu8fEdfmf+4aEXdGxMv53wvrlypuqVKYPKoStMkneq9HxF35fd8AW6WU1gT6AZfM5ryDgYvzVcu1gS8iYsX88Rvl99cCexX/ESRlbCXg7ymlNYAvGzjuEuCclNLawO7AtVkEJ1Uy29aqBLNrW7cALouIaQngcrM57zng5IjoBtyZUhoREVsCawEv5bpZtCGXiEpqXD5KKb1UwHG/AZbP/z4A6BARbVJKk4oXmlTZTB5VqY4BRgM9yVXQJ898QErploh4AdgBeDgiDgQCGJJSGpRlsJIyN6Hez3Xk/rc/Tet6PwdOrpF+FtvWqlQLAKPyg+D3AWYZtxgRSwMfp5QuAe4FViM3M/O3EdElf0zHiOieXdiSspb/PfF9RPSIiCpgl3pfPwYcNm0j382Q1ACTR1WqK4ABEfE8uZb1hNkc0w94KyJeB1YAbkgpvQMMBh6JiDeAR4FFMopZUumcADxE7j8gv6i3/zBgo/ykuneA35ciOKmS+HpCSZIkFczKoyRJkgpm8ihJkqSCmTxKkiSpYCaPkiRJKpjJoyRJkv6/vTsPl6Mo9zj+/V0SiBAgbLIGo6wxuSGCCKKBKPumQRGMLLIIohc3NnMRBAQBl+sKXEFQhCAiCoJ4MYjIapAoBmRXICEsgZgQSFgEwnv/eGugM8zM6XNOMOTk93meec5MV011dZ+emberqrtqc/BoZmZmZrU5eDQzMzOz2hw8mpmZmVltDh7NzMzMrDYHj2ZmZmZWm4NHMzMzM6vNwaOZmZmZ1ebg0czMzMxqc/BoZmZmZrU5eDQzMzOz2hw8mpmZmVltDh7NrEckzZM0WdIdki6WtHQvyhot6Yry/AOSxnXIO0jSp3uwjuMlHdFivROblvWT9Lik1btTlpnZ4sLBo5n11HMRMTIihgMvAIdUE5W6/R0TEZdHxKkdsgwCuh08tnE9sJakIZVl2wB3RMRjC2gdZmZ9ioNHM1sQbgDWlTRE0t2SzgBuBQZL2k7SREm3lhbKgQCSdpB0j6QbgQ81CpK0n6TTyvNVJV0q6bby2AI4FVintHp+o+Q7UtIkSbdLOqFS1pck3SvpamCD5kpHxMvAxcCelcUfBS4s7z+olHubpF+2al2VdK2kd5bnK0uaUp4vIekblXp9sixfXdL1lVbbUT3d6WZmC4ODRzPrFUn9gB2Bv5VFGwDnRcQ7gGeAY4BtImJj4M/AYZIGAD8EdgVGAau1Kf57wHURsRGwMXAnMA64v7R6HilpO2A94F3ASGATSVtK2oQMBN9BBqebtlnHhSUfkpYCdgJ+WdIuiYhNy/rvBg7sxq45EHgqIjYt6z5I0luBjwETImIksBEwuRtlmpktdP0WdgXMbJH1JkmNwOcG4BxgDWBqRNxclm8OvB24SRLAksBEYEPgwYj4O4Ck8cDBLdbxfmBfgIiYBzwlaYWmPNuVx1/L64FkMLkscGlEPFvWcXmrjYiISZIGStoAGArcHBFPluThkk4iu8oHAhO63Cvz12uEpN3L6+VLvSYBP5LUH/hVRDh4NLNFioNHM+up50rr2StKgPhMdRHwu4gY25RvJBALqB4CTomIM5vW8flurONnZOvjUEqXdXEuMCYibpO0HzC6xXtf4tVenAFN9fpMRLwm4JS0JbAzcL6kb0TEeTXraWa20Lnb2sxeTzcD75G0LoCkpSWtD9wDvFXSOiXf2Dbv/z3wqfLeJSQtB8whWxUbJgAHVMZSrinpzeTFMLtJepOkZcku8nYuBPYmWzqrLZTLAo+VVsK92rx3CrBJeb57ZfkE4FPlvUhaX9Iykt4CPBERPyRbazfuUC8zszcctzya2esmImaUFrsLy3hCgGMi4j5JBwO/kfRP4EZgeIsiPgecJelAYB7wqYiYKOkmSXcAV5Zxj0OBiaXlcy6wd0TcKukickzhVLJrvV0975L0LPCXiKi2nB4L/Km8/2/MH7Q2fBP4uaR9gGsqy88GhgC3Kis2AxhDtl4eKenFUtd929XLzOyNSBELqufIzMzMzPo6d1ubmZmZWW0OHs3MzMysNgePZmZmZlabg0czMzMzq83Bo5mZmZnV5uDRzMzMzGpz8GhmZmZmtTl4NDMzM7PaHDyamZmZWW0OHs3MzMysNgePZmZmZlabg0czMzMzq83Bo5mZmZnV5uDRzMzMzGpz8GhmZmZmtTl4NDMzM7PaHDyamZmZWW0OHs3MzMysNgePZmZmZlabg0czMzMzq83Bo5mZmZnV5uDRzMzMzGpbrIJHScdLGv86ln+npNHluST9WNKTkm6RNErSva/DKzFe4AAAEd5JREFUOteWNFfSEgu6bHt9SfqBpGNr5HvluOoLJJ0r6aTyfLSkhxd2nczMrL4+FzxK+pikP5eA6jFJV0p6779j3RExLCKuLS/fC2wLrBUR74qIGyJig96uQ9IUSdtU1vlQRAyMiHm9Ldv+vSLikIg4sUa+6nG1WJO0RTkZmyPp9ubPtqTPSHpQ0tPle6DLz76krSRFI6Aty3aWdKOk2ZKmS/qhpGU7lDFU0jWSnpL0D0m7VdL2Kt9HjcezZX2b9KCszSX9TtIsSTMkXSxp9Q71WlPSZSX/w5IOaUp/v6Rby/56QNLBvSgrJD1T2c6z25VlZou2PhU8SjoM+A5wMrAqsDZwBvDBhVCdtwBTIuKZhbDuRZ6kfgu7DnUsKvXsrjfidklaEbgc+AYwCPg68GtJK5T0zYBTgd2B5YFzgEs7tcpL6g98F/hTU9LywEnAGsBQYK2y3lZl9AMuA64AVgQOBsZLWh8gIi4oJ3gDI2Ig8GngAeDW7pYFrACcBQwhv2PmAD9ut33AeOBB8vtwZ+BkSe+rbPulwJlle/cEviVpo+6WVbFRZVs/0aFeZrYoi4g+8SC//OYCH+mQ53hgfOX1xcB04CngemBYJW0n4C7yy/kR4IiyfGXyi302MAu4AfiPkjYF2AY4EHgemFfqdAIwGni4Uv5g4BJgBjATOK0sXwe4piz7J3ABMKiknQ+8DDxXyj2K/BEJoF/Jswb5AzsL+AdwUNP2/xw4r2zXncA7O+yv7wLTgKeBvwCjKmlLAEcD95ey/gIMLmnDgN+VOjwOHF2WnwucVCmjeZ9MAb4I3A78C+gHjKus4y5gt6Y6HgTcXUnfGDgS+GVTvu8D36lxHDX258HAo8BjwOFN+/AX5A/p08AnyJOwRj1nln28YuU97wX+WI6ZacB+zfuDGsdVeb4UeYL0aHl8B1iquj+Bw4EnSt3378ZnKID/Av4OPFiWbVj5X94L7FHJ/ybgf4Cp5GfoRuBNNT5b1e2e7xjoon67AHc2LbsPOLA83xO4pZK2TNmm1TuUOY4MQl+pU5t8HwL+1iZtOPl5VGXZVcCJbfL/AThuAZW1MTCnTdrAsv2rVJadBZxfnq9a0peupE8Cxna3rMrxs27d480PP/xYdB99qeXx3cAA8ky6riuB9YA3k60AF1TSzgE+GRHLkl/o15Tlh5M/0KuQX75Hk1+ar4iIc4BDgImRZ+DHVdNLS8gV5I/uEGBN4GeNZOAUXm3xGEwGLETEPsBDwK6l3K+32KYLS/3WIFtgTpa0dSX9A2Vdg8gg87QO+2cSMJJsAfkpcLGkASXtMGAsGWQvBxwAPFu69q4GflvqsC7w+w7raDaWbNUYFBEvkQHZKPLk4ASyFWZ1AEkfIffNvqUOHyCDt/HADpIGlXz9yMDi/G7U433ksbEdMK46VIBsyf4FuQ8vAD4LjAG2Ktv8JHB6Wffa5HH2ffKYGQlMbrG+Lo+r4kvA5qWcjYB3AcdU0lcj99Wa5EnM6Y2WuZrGAJsBb5e0DBk4/pT8jIwFzpA0rOT9JrAJsAV5jBxFntxA589WW5LOkHRGu+TyaF42vLLOJSRtVj5jB5D7enqbdb2l5PlKjaptSZ5statXq2XDX7Mw17kleQLXq7K6US81LRsOEBGPk98X+0taQtK7ydbMG7tbVsX1pZv/EklD2tTLzBZ1Czt6XVAPYC9gehd5jqfS8tiUNoj8sV6+vH4I+CSwXFO+r5DdSq85w2b+FqL9gBsraaMpLSxkoDuD0lrYRZ3HAH9ttY7yekipdz8y0JwHLFtJPwU4t7L9V1fS3g481419/CTZLQXZCvXBFnnGVuvblHYuXbc8HtBFHSY31gtMAD7XJt+VlFZXssXqrprb2NifG1aWfR04p7IPr296z93A1pXXqwMvlv/JfwOXdrU/unFc3Q/sVEnbnhwe0difz1WPK7IFcvOa2x7A+yuv9wRuaMpzJnAc2dr6XON46KLc5s9WdbvnOwa6KGclsmV2LNAf+DgZrJ5Z0kUG3S8CL5Et95t2KO8yYM9Wx2ZTvm3Lsb9+m/T+ZDf0UeX5dsALwIQWeY8Fru1Qp+6UNYJsER7VobwbyROXAWQr5Szg3kr6rmTvwEvlcVAvytoSWLL8v08D7qDGd5wffvix6D36UsvjTGDlumO1ypn2qZLul/Q0+QMN2X0I8GGyVW2qpOvKWTnkuKd/AFeVAebjelDXwcDUyJa15nq9WdLPJD1S6jW+UqeurAHMiog5lWVTyVaohmorzLPAgHb7TNLhku4uA/dnky1ajboMJgOZVtvWanld05rqsK+kyeXChdlkS0dXdQD4CbB3eb433Wt1bK7HVHLftqwj2VpzaaWOd5NB/Kpd1LGq7nG1RqlPu7rNbDquniW7HOuqbttbgM0a21W2bS+ydXNlMoh4zbbV+Gz1SETMJFt9DyMDnh3IVu7G1dqfIFsSh5FBzN7AFZLWaC5L0q7kSdZFndYpaXOy5XX3iLivTb1eJE/ydiY/X4eTQxdaXUW+L3lsttvGWmVJWpc8QfpcRNzQYRP2At5K/l//l2wBfriUsSFwUanTkuR+O0rSzt0tq9T9+oh4ISJmA58reYd2qJuZLaL6UvA4kRxnOKZm/o+RP0TbkEHRkLJcABExKSI+SHa7/Yr8Aici5kTE4RHxNvKs/bCmbuE6pgFrtwnaTiFbaUZExHLkD2C1q6hVV2bDo8CKmv+q0LXJMZvdImkUOf5wD2CFiBhEjl9r1GUaOT6zWbvlAM8AS1der9YizyvbV7r4fggcCqxU6nBHjTpA/s9GSBpOtjzW6jatGFx5vja5b19Tx0o9doyIQZXHgIh4pIs6vlpg/ePqUTKoa1e33qpu2zTguqbtGhgRnyJb9Z6n9bZ1/Gz1qnIR10XEphGxIrAPsAFwS0neCPh1RNwXES9HxG/JcZ9btChqa+CdpYt1OtnK+nlJlzUySHoHObTjgIjoOPQiIm6PiK0iYqWI2B54W6VejfLeQwb6v+hNWeVzcTU5DrLjSVFETI2IXSJilYjYjGy9bZQ1nGw5nFD2173Ab4Ade1BWy7ewAP7nZvbG02eCx4h4CvgyOcZrjKSlJfWXtKOkVmMDlyUvyphJBjQnNxIkLam8vcbypSXgabIlCUm7SFpXkirLu3ubnFvIH7VTJS0jaUD5YWnUay4wW9Ka5MUfVY+TPyat9sE08sKMU0qZI8hxb90NnBr1eInSvS7py+S4woazgRMlrac0QtJK5FjO1SR9XtJSkpYtV8FCdjnvJGlFSasBn++iDo0LHmYASNqf+cdYnQ0cIWmTUod1yw8rEfE8+SP9U/Iiiocab1Le7/PaLtZ9bDmGhgH7ky007fwA+Gpj3ZJWkdS4wv8CYBtJe0jqJ2klSSObC+jGcXUhcExZx8rkMV/r3qWS9pM0pU7e4gpgfUn7lM9Sf0mbShoaES8DPyKvzl2jMWZO0lJ0+Gz1lqR3lHosR465fDgiJpTkScDOkt5WjodtgfXJE45mx5a0keVxOXmisn9Zz3By3O5nIuLXNeo1onzmlpZ0BDl04dymbB8nL+Sa85oCapZVvhOuAU6PiB/UqNfQ8hlcUtLeZDf4t0ryX4H1lLfrkaR1yBOt27pblqRhkkaW42AgeSHVI2QrvJn1MX0meASIiG+RXVrHkAHHNLLV6lctsp9Hdvk9Ql6le3NT+j7AlNLtdgivdoGuR571zyVbO8+Ibt6DL/KejLuSF5M8RHb97FmSTyDHEz1FtgJc0vT2U8jgYXb5YWk2lmzpeZS8eOi4iPhdd+pXTCC7xe4j99PzzN+l+S2yNfYqMtg5h7zSdg45RmxXstvt7+TFJ5Bdx7eR3ZhX0TkgIyLuIn+EJpJB838CN1XSLwa+SgaIc8j/84qVIn5S3tPcOjO4Wk4b15HdyL8HvhkRV3XI+10y+LhK0hzyWNqs1PEhcvjD4eQYsclkC1mzusfVScCfySvS/0ZejHJSi3yt1NnuV5T/5XbAR8njaTrwNfKKb4AjSh0mkdv2NfI7pavPVlvKG6d3CoqOIls9p5FB1W6VtPPIi8GuJY/J75EXvd3TXHZp6Z3eeJDjN5+JiFmlrMPJi5fO0av3LWx3YQrk98Vj5BjTrYFtI+Jfle0aQLbiv6bLWtLRkq6sWdYnyJPH4yr1mtuhXtuTYyifJL/HdoiIGWUf3E9283+v7K/rgF+Sn+XG/SnvrFMWOUTjolLOA+R30C7l5NvM+hhFdOoFNVt0Ka90vgdYLSKeriyfTF7gMrPFe4aQ97Lr32pM6qJM0lXkGDm3BpmZWY85eLQ+SdJ/kK2jy0XEAd143xD6aPBoZma2ILzhZpEw6y3l/QkfJ7tOd1jI1TEzM+tT3PJoZmZmZrX1qQtmzMzMzOz15eDRzMzMzGrrM8Fj9bYVkl6W9Fzl9V7l3n4vltezJf1Rr84a06ncOyvlzJP0fOX10eXeeSHpyKb3PSxpdM26T2mq79xy77whpezGsimqOaNNqde8pjJPK2nnlnLfVcm/rqTaYxjKPd+uUc4+8w9Ju7XJd1xZ1zat0kueNSVdJmlW2W+HVNJWlnSTpJnl/zZRr94Ts1VZR0q6Q9IcSQ82/19a5D9L0r3lmNmvxqabmZkt1vpM8FhmvhgYEQPJeyfuWlnWuEn2RSV9FXKe1kskdZwBISKGVcq9ATi0Um7j5sezgC8qb1zcU9X6DoyI6qwhg8r6xwJfllT3IpCJTWUeWkmbRf37A85HOTPOZeRNpFcEDgbGS1q/Kd86wO7kPes6GU9e4bwqOS3byZIa94acS96LbhVgBfJegr9W+2koRU63tgJ5scyhkj7aYd23AZ8m75doZmZmXegzwWN3lBvX/oScHm+lBVDk3eSNnb+wAMpqKyImAncy/ywrPfUTcvq+rXrw3g3Jada+HRHzIuIa8ubT+zTlO42c4vCFdgWV2ShGA1+NiBcj4jZyZpgDIGeKiYh7y4wmImddWYH5bwb+ioj4ekTcGhEvlenWLgPatlRGxOll6rnna2y3mZnZYm+xDB6VU6jtR05t9s8FVOyxwBcktQxqekvpPcAwclqx3nqWnDbuq23WN07SFe2q02bZK0GtpI8AL0TE/3VRDzX9fU1ZpbzbyQDvcuDsiHiii3IprcqjyIDbzMzMFoDFLXjcQ9JscmqzTYAxC6rgiJhMTrn3xR4W8asypm+2pObpFP9JdjOfDYwrLWV1bF4pc7akzZvSzwTWlrRj8xsj4tSI2KVNufeQU6cdqZxneDtgK3Ie40Zr4sl0PXd1Ywq8m8i5pAdI2hj4cKOsSr4R5NzaHyOHHNRxPHmM/7hmfjMzM+vC4naT8J9HxN5dZ+uxLwO3SPp2D947JiKubpO2cg9nO7k5It7bLjEi/iXpROBEcjxlLRHxoqQxwPfJYPnP5DzXjfl3TwDOj4gHaxa5F3A6GdQ/AFwAvL3Fep8HLpR0t6TJpYu7JUmHkmMfR1XnGDYzM7PeWdxaHl9XEXEPcAlw9MKuSzf8GFgeaHm1dDsRcXtEbBURK0XE9sDbgFtK8tbAZyVNlzQdGAz8XFLLVtmImBoRu0TEKhGxGTkO9ZZWeYv+ZX0tSToAGEfOX/1wd7bLzMzMOlvcWh5b0qvzGb81Iqb0srgTgNupjOErt+z5Q0R0vLK7uyRdC1wbEcf3tIyIeEnS8cD3urnuEcB95AnIp4HVgXNL8tZkgNcwCTgMuLJNWUOBh8mWyz2A7YChJW1z8ji9BVgC+Cx5Vfaf2pS1F9ll/r6IeKDGdixZtkFAf0kDyLGaL3f1XjMzs8WRWx7TYHIe5Ed6W1Dpqj0fWKap/Im9LbuFweR4wd66kKbb6ZR7WLYM9op9ynueIIPFbRvdwxExMyKmNx7kFdJPRsTcNmVtT3ZXPwkcAuwQETNK2lJkl/ZM8v+zE7Bz062Mqk4iWy4nVe5v+YMO23EV8BywBXBWeb5lh/xmZmaLNc9tDUg6BpgREWe+TuWfDVwcERMWYJlrlTK7vNG5mZmZ2YLi4NHMzMzManO3tZmZmZnV5uDRzMzMzGpz8GhmZmZmtTl4NDMzM7PaHDyamZmZWW0OHs3MzMysNgePZmZmZlabg0czMzMzq+3/ARfGBPxrVRg8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "cf_matrix = confusion_matrix(Y_test, predictionsTest)\n",
    "cf_matrix\n",
    "group_names = ['True Neg','False Pos','False Neg','True Pos']\n",
    "\n",
    "group_counts = [\"{0:0.0f}\".format(value) for value in\n",
    "                cf_matrix.flatten()]\n",
    "\n",
    "group_percentages = [\"{0:.2%}\".format(value) for value in\n",
    "                     cf_matrix.flatten() / np.sum(cf_matrix)]\n",
    "\n",
    "labels = [f\"{v1}\\n{v2}\\n{v3}\" for v1, v2, v3 in\n",
    "          zip(group_names,group_counts,group_percentages)]\n",
    "\n",
    "labels = np.asarray(labels).reshape(2,2)\n",
    "ax = sns.heatmap(cf_matrix, annot=labels, fmt='', cmap='Blues')\n",
    "\n",
    "ax.set_title(f'Confusion Matrix\\n');\n",
    "ax.set_xlabel('\\nPredicted Values')\n",
    "ax.set_ylabel('Actual Values\\n');\n",
    "\n",
    "## Ticket labels - List must be in alphabetical order\n",
    "ax.xaxis.set_ticklabels(['False','True'])\n",
    "ax.yaxis.set_ticklabels(['False','True'])\n",
    "ax.set_ylim([0,2])\n",
    "\n",
    "ax.text(0, -0.2, test_set_metrics, wrap=True, horizontalalignment='left', fontsize=12, transform = ax.transAxes)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "if save:\n",
    "    plt.savefig(f\"/data/p301081/astronomy/results/plots_kids/confusionMatrix.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1008 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rows = int(group_counts[1]) + int(group_counts[2])\n",
    "fig = plt.figure(figsize=(10,14))\n",
    "plt.axis('off')\n",
    "fig.suptitle(f'', y=.98, fontsize = 16)\n",
    "plt.tight_layout() \n",
    "s = 1\n",
    "for idx, (first, second) in enumerate(zip(predictionsTest, Y_test)):\n",
    "    if first == 1 and first != second:\n",
    "        fig.add_subplot(rows, 1, s)\n",
    "        plt.imshow(X_test[idx,...,0], cmap='gray')\n",
    "        plt_txt = f\"FP index: {idx}\\nLabel: {int(Y_test[idx])}\\nImage path: {testSet['Path'].iloc[idx]}\"\n",
    "        plt.text(0, -10, plt_txt, wrap=True, horizontalalignment='left', fontsize=14)\n",
    "        plt.axis('off')\n",
    "        plt.tight_layout()\n",
    "        s+=1\n",
    "    if first == 0 and first != second:\n",
    "        fig.add_subplot(rows, 1, s)\n",
    "        plt.imshow(X_test[idx,...,0], cmap='gray')\n",
    "        plt_txt = f\"FN index: {idx}\\nLabel: {int(Y_test[idx])}\\nImage path: {testSet['Path'].iloc[idx]}\"\n",
    "        plt.text(0, -10, plt_txt, wrap=True, horizontalalignment='left', fontsize=14)\n",
    "        plt.axis('off')\n",
    "        plt.tight_layout()\n",
    "        s+=1\n",
    "plt.tight_layout()       \n",
    "if save:\n",
    "    plt.savefig(f\"/data/p301081/astronomy/results/plots_kids/fp_fn.png\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "astro",
   "language": "python",
   "name": "astro"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}