cicy3o_nn_transfer-learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning for Complete Intersection Calabi-Yau Manifold\n",
    "\n",
    "We use neural networks previously trained on the configuration matrix to perform **transfer learning** to the feature engineered dataset. We want to explore the possibility to improve the results even further by feeding the network additional information coming from engineered features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Infrastructure\n",
    "\n",
    "We print information about the current OS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current OS:                  Linux (kernel release: 5.6.11-arch1-1, architecture: x86_64)\n",
      "Number of available threads: 8\n",
      "Current CPU frequency:       3100 MHz (max: 3800 MHz)\n",
      "Available RAM memory:        8480 MB (tot: 15758 MB)\n",
      "Mon May 11 16:55:33 2020       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 440.82       Driver Version: 440.82       CUDA Version: 10.2     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  GeForce 940MX       Off  | 00000000:02:00.0 Off |                  N/A |\n",
      "| N/A   64C    P8    N/A /  N/A |      5MiB /  2004MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                       GPU Memory |\n",
      "|  GPU       PID   Type   Process name                             Usage      |\n",
      "|=============================================================================|\n",
      "|    0     15009      G   /usr/lib/Xorg                                  4MiB |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "from mltools.libos import InfoOS\n",
    "\n",
    "print('Current OS:                  {} (kernel release: {}, architecture: {})'.format(InfoOS().os, InfoOS().kernel, InfoOS().arch))\n",
    "print('Number of available threads: {:d}'.format(InfoOS().threads))\n",
    "print('Current CPU frequency:       {:.0f} MHz (max: {:.0f} MHz)'.format(InfoOS().freq, InfoOS().freqm))\n",
    "print('Available RAM memory:        {:d} MB (tot: {:d} MB)'.format(InfoOS().vmav, InfoOS().vmtot))\n",
    "\n",
    "# print info on GPU\n",
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "We import the Python modules we use and print their versions to keep track of changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version: 3.7\n",
      "Matplot version: 3.2.1\n",
      "Numpy version: 1.18.4\n",
      "Pandas version: 1.0.3\n",
      "Scikit-learn version: 0.22.2.post1\n",
      "Tensorflow version: 2.0.0\n",
      "Keras version: 2.2.4-tf (backend: tensorflow)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "import matplotlib as mpl\n",
    "import random     as rnd\n",
    "import sklearn    as skl\n",
    "import numpy      as np\n",
    "import pandas     as pd\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow       import keras\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=UserWarning) # ignore user warnings: nothing that I can really do anything about it...\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "mpl.rc('axes', labelsize=12)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# print the version of the modules\n",
    "print('Python version: {:d}.{:d}'      .format(sys.version_info.major, sys.version_info.minor))\n",
    "print('Matplot version: {}'            .format(mpl.__version__))\n",
    "print('Numpy version: {}'              .format(np.__version__))\n",
    "print('Pandas version: {}'             .format(pd.__version__))\n",
    "print('Scikit-learn version: {}'       .format(skl.__version__))\n",
    "print('Tensorflow version: {}'         .format(tf.__version__))\n",
    "print('Keras version: {} (backend: {})'.format(keras.__version__, K.backend()))\n",
    "\n",
    "# fix random_seed\n",
    "RAND = 42\n",
    "rnd.seed(RAND)\n",
    "np.random.seed(RAND)\n",
    "tf.random.set_seed(RAND)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Session Preparation\n",
    "\n",
    "in order to save the results of the analysis, we define where to store images, log files and models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import path, makedirs\n",
    "\n",
    "ROOT_DIR = '.' #-------------------------------------------------- root directory\n",
    "IMG_DIR  = 'img' #------------------------------------------------ directory of images\n",
    "MOD_DIR  = 'models' #--------------------------------------------- directory of saved models\n",
    "LOG_DIR  = 'log' #------------------------------------------------ directory of logs\n",
    "OUT_DIR  = 'output' #--------------------------------------------- directory for predictions, various outputs, etc.\n",
    "\n",
    "DB_NAME = 'cicy3o' #---------------------------------------------- name of the dataset\n",
    "DB_FILE = DB_NAME + '_analysis.h5' #------------------------------ full name with extension\n",
    "DB_PATH = path.join(ROOT_DIR, DB_FILE) #-------------------------- full path of the dataset\n",
    "DB_DIR  = 'original' if DB_NAME == 'cicy3o' else 'favourable' #--- subdir where to store images, models, logs\n",
    "\n",
    "# define full paths\n",
    "IMG_PATH = path.join(ROOT_DIR, IMG_DIR, DB_DIR)\n",
    "MOD_PATH = path.join(ROOT_DIR, MOD_DIR, DB_DIR)\n",
    "LOG_PATH = path.join(ROOT_DIR, LOG_DIR, DB_DIR)\n",
    "OUT_PATH = path.join(ROOT_DIR, OUT_DIR, DB_DIR)\n",
    "\n",
    "# create directories if non existent\n",
    "if not path.isdir(IMG_PATH):\n",
    "    makedirs(IMG_PATH, exist_ok=True)\n",
    "if not path.isdir(MOD_PATH):\n",
    "    makedirs(MOD_PATH, exist_ok=True)\n",
    "if not path.isdir(LOG_PATH):\n",
    "    makedirs(LOG_PATH, exist_ok=True)\n",
    "if not path.isdir(OUT_PATH):\n",
    "    makedirs(OUT_PATH, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also create a log file to store debug and related information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rotating existing logs...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-11 16:55:36,180: INFO ==> New logging session started. Log is at ./log/original/cicy3o_nn_transfer_learning.log.\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "\n",
    "from mltools.liblog import create_logfile\n",
    "\n",
    "path_to_log = path.join(LOG_PATH,\n",
    "                        DB_NAME + '_nn_transfer_learning.log'\n",
    "                       )\n",
    "log = create_logfile(path_to_log,\n",
    "                     name=DB_NAME + '_nn_transfer_learning',\n",
    "                     level=logging.DEBUG\n",
    "                    )\n",
    "\n",
    "# these lines provide the same setup also for the Jupyter logging\n",
    "logger = logging.getLogger() #------------------------------------------------- get the current logging session\n",
    "\n",
    "fmt = logging.Formatter('%(asctime)s: %(levelname)s ==> %(message)s') #-------- customise the formatting options\n",
    "\n",
    "handler = logging.StreamHandler() #-------------------------------------------- handle the stream to the default (stderr)\n",
    "handler.setLevel(logging.DEBUG) #---------------------------------------------- print everything\n",
    "handler.setFormatter(fmt) #---------------------------------------------------- set the formatting options\n",
    "\n",
    "logger.handlers = [handler] #-------------------------------------------------- override the default stream\n",
    "\n",
    "# we are ready to go!\n",
    "log.info('New logging session started. Log is at {}.'.format(path_to_log))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We finally set the _memory growth_ property of the GPU in order to avoid overflowing its RAM memory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU setup: 1 physical GPUs, 1 logical GPUs.\n"
     ]
    }
   ],
   "source": [
    "gpus = tf.config.experimental.list_physical_devices('GPU') #--------------------------------------- list of physical GPUs\n",
    "\n",
    "if gpus: #----------------------------------------------------------------------------------------- set memory growth only if GPU is active\n",
    "    try:\n",
    "        for gpu in gpus:\n",
    "            tf.config.experimental.set_memory_growth(gpu, True) #---------------------------------- set memory growth\n",
    "            \n",
    "        logical_gpus = tf.config.experimental.list_logical_devices('GPU') #------------------------ list of logical devices\n",
    "        print('GPU setup: {:d} physical GPUs, {:d} logical GPUs.'.format(len(gpus),\n",
    "                                                                         len(logical_gpus)\n",
    "                                                                        )\n",
    "             )\n",
    "    except RuntimeError as e:\n",
    "        print(e)\n",
    "else:\n",
    "    print('No GPUs in the setup!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Dataset\n",
    "\n",
    "We first load the dataset we built during the preanalysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-11 16:55:36,675: DEBUG ==> Database loaded.\n",
      "2020-05-11 16:55:36,675: INFO ==> Shape is 7851 rows x 7 columns.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# load the dataset\n",
    "if path.isfile(DB_PATH):\n",
    "    df = pd.read_hdf(DB_PATH)\n",
    "    log.debug('Database loaded.')\n",
    "    log.info('Shape is {:d} rows x {:d} columns.'.format(df.shape[0], df.shape[1]))\n",
    "else:\n",
    "    log.error('Cannot load database from {}!'.format(DB_PATH))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dense Format Extraction\n",
    "\n",
    "We now extract the matrix from the available features and the labels. Notice that in this case we do not flatten the output as it will be processed by _Conv2D_ layers in _Keras_: we reshape it as if it was a black and white image, that is we take each matrix of shape (width, height) and output a matrix of shape (width, height, 1):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-11 16:55:38,145: DEBUG ==> Matrix shape: (7851, 12, 15, 1)\n"
     ]
    }
   ],
   "source": [
    "from mltools.libtransformer import ExtractTensor\n",
    "\n",
    "# extract the labels\n",
    "h11    = df['h11'].values\n",
    "h21    = df['h21'].values\n",
    "\n",
    "# we extract scalar and vector features\n",
    "num_cp     = np.array(ExtractTensor(flatten=True).fit_transform(df['num_cp'])).reshape(-1,1)\n",
    "dim_cp     = np.array(ExtractTensor(flatten=True).fit_transform(df['dim_cp']))\n",
    "dim_h0_amb = np.array(ExtractTensor(flatten=True).fit_transform(df['dim_h0_amb']))\n",
    "\n",
    "# extract the tensor features\n",
    "matrix = np.array(ExtractTensor(flatten=False).fit_transform(df['matrix'])) #-------- old shape is (batch, width, height)\n",
    "matrix = np.reshape(matrix, (-1, np.shape(matrix)[1], np.shape(matrix)[2], 1)) #----- new shape is (batch, width, height, 1)\n",
    "\n",
    "log.debug('Matrix shape: {}'.format(np.shape(matrix)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and Validation Strategy\n",
    "\n",
    "We now define the validation strategy and split the dataset into training, validation and test sets.\n",
    "\n",
    "We will take out 10% of the dataset to be our **test set**. For the remaining 90% of the dataset, we will use **holdout validation** with $\\frac{1}{9}$ of the training samples as validation (effectively we use 80% of the total set for training and 10% as validation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-11 16:55:38,187: DEBUG ==> Train set size: 6280\n",
      "2020-05-11 16:55:38,188: DEBUG ==> Validation set size: 785\n",
      "2020-05-11 16:55:38,192: DEBUG ==> Test set size: 786\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# divide into training and test sets\n",
    "num_cp_train, num_cp_test, \\\n",
    "dim_cp_train, dim_cp_test, \\\n",
    "dim_h0_amb_train, dim_h0_amb_test, \\\n",
    "matrix_train, matrix_test, \\\n",
    "num_cp_train, num_cp_test, \\\n",
    "h11_train, h11_test, \\\n",
    "h21_train, h21_test = train_test_split(num_cp, dim_cp, dim_h0_amb, matrix, num_cp, h11, h21, test_size=0.1, shuffle=False)\n",
    "\n",
    "# keep a holdout validation set\n",
    "num_cp_train, num_cp_val, \\\n",
    "dim_cp_train, dim_cp_val, \\\n",
    "dim_h0_amb_train, dim_h0_amb_val, \\\n",
    "matrix_train, matrix_val, \\\n",
    "num_cp_train, num_cp_val, \\\n",
    "h11_train, h11_val, \\\n",
    "h21_train, h21_val = train_test_split(num_cp_train, dim_cp_train, dim_h0_amb_train, matrix_train, num_cp_train, h11_train, h21_train, test_size=1.0/9.0, shuffle=False)\n",
    "\n",
    "log.debug('Train set size: {:d}'.format(np.shape(matrix_train)[0]))\n",
    "log.debug('Validation set size: {:d}'.format(np.shape(matrix_val)[0]))\n",
    "log.debug('Test set size: {:d}'.format(np.shape(matrix_test)[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also provide a way to visualise the predictions by comparing the predictions as function of a (scalar) base feature (e.g.: `num_cp`) with respect to the ground truth:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_counts(base_feature, label):\n",
    "    '''\n",
    "    Returns unique values and counts of the label as a function of the base_feature.\n",
    "    \n",
    "    Required arguments:\n",
    "        base_feature: the feature considered as base for the comparison,\n",
    "        label:        the label we are interested in comparing.\n",
    "    \n",
    "    Yields:\n",
    "        [ unique value of base_feature, unique value of label, count ].\n",
    "    '''\n",
    "    \n",
    "    for n in np.sort(np.unique(base_feature)):\n",
    "        uniques, counts = np.unique(label[np.argwhere(base_feature == n)[:,0]], return_counts=True)\n",
    "        \n",
    "        for u, c in np.c_[uniques, counts]:\n",
    "            yield [n, u, c]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the use of `num_cp` will only be necessary to show in a simple way how well the algorithm can predict $h_{11}$ and $h_{21}$. `num_cp` will be taken as a representative of the matrices in the test set and $h_{11}$ and $h_{21}$ will be shown in a 2D plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Rescaling\n",
    "\n",
    "Before proceeding we rescale the input matrices to have entries in $\\left[ 0, 1 \\right]$: we divide each entry of each matrix by the range of variation of its entries (since they are all positive, we divide by the maximum) in the **training** set. We then apply the **same scaling** to validation and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# compute the scale factor\n",
    "scale_factor = np.max(matrix_train) - np.min(matrix_train)\n",
    "\n",
    "# rescale the sets\n",
    "matrix_train = matrix_train / scale_factor\n",
    "matrix_val   = matrix_val   / scale_factor\n",
    "matrix_test  = matrix_test  / scale_factor\n",
    "\n",
    "# define the shape of the tensors (batch axis excluded) since we will use it later one\n",
    "input_shape  = np.shape(matrix_train)[1:]\n",
    "\n",
    "# then apply the usual rescaling to the other features\n",
    "num_cp_scaler     = StandardScaler()\n",
    "dim_cp_scaler     = StandardScaler()\n",
    "dim_h0_amb_scaler = StandardScaler()\n",
    "\n",
    "num_cp_train     = num_cp_scaler.fit_transform(num_cp_train)\n",
    "num_cp_val       = num_cp_scaler.transform(num_cp_val)\n",
    "num_cp_test      = num_cp_scaler.transform(num_cp_test)\n",
    "dim_cp_train     = dim_cp_scaler.fit_transform(dim_cp_train)\n",
    "dim_cp_val       = dim_cp_scaler.transform(dim_cp_val)\n",
    "dim_cp_test      = dim_cp_scaler.transform(dim_cp_test)\n",
    "dim_h0_amb_train = dim_h0_amb_scaler.fit_transform(dim_h0_amb_train)\n",
    "dim_h0_amb_val   = dim_h0_amb_scaler.transform(dim_h0_amb_val)\n",
    "dim_h0_amb_test  = dim_h0_amb_scaler.transform(dim_h0_amb_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building\n",
    "\n",
    "We the build the architecture of the CNN. In this section we use **transfer learning** from the networks previously trained on the configuration matrix to predict $h_{11}$ and $h_{21}$. We then add other inputs to the matrices in order to improve the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras              import Input\n",
    "from tensorflow.keras.utils        import model_to_dot\n",
    "from tensorflow.keras.models       import Model, load_model\n",
    "from tensorflow.keras.layers       import Conv1D, Conv2D, Dense, Lambda, Flatten, BatchNormalization, Dropout, LeakyReLU, concatenate, Activation\n",
    "from tensorflow.keras.optimizers   import Adam\n",
    "from tensorflow.keras.regularizers import l1_l2\n",
    "from tensorflow.keras.callbacks    import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\n",
    "\n",
    "def cnn_full_model(model_name,\n",
    "                   h11_model_path,\n",
    "                   h21_model_path,\n",
    "                   num_cp_shape,\n",
    "                   dim_cp_shape,\n",
    "                   dim_h0_amb_shape,\n",
    "                   num_cp_neurons_h11=[30],\n",
    "                   num_cp_neurons_h21=[30],\n",
    "                   dim_cp_neurons_h11=[30],\n",
    "                   dim_cp_neurons_h21=[30],\n",
    "                   dim_h0_amb_neurons_h11=[30],\n",
    "                   dim_h0_amb_neurons_h21=[30],\n",
    "                   learning_rate=0.01,\n",
    "                   batch_norm_momentum=0.99,\n",
    "                   dropout_rate=0.3,\n",
    "                   leaky_alpha=0.3,\n",
    "                   l1_reg=0.0,\n",
    "                   l2_reg=0.0,\n",
    "                   last_relu=False,\n",
    "                   last_layer_h11=1,\n",
    "                   last_layer_h21=1\n",
    "                  ):\n",
    "    '''\n",
    "    Build a convolutional neural network with given parameters.\n",
    "    \n",
    "    Required arguments:\n",
    "        model_name:             the name of the model\n",
    "        h11_model_path:         the path of the CNN model to predict h11\n",
    "        h21_model_path:         the path of the CNN model to predict h21\n",
    "        num_cp_shape:           the shape of the tensor for num_cp\n",
    "        dim_cp_shape:           the shape of the tensor for dim_cp\n",
    "        dim_h0_amb_shape:       the shape of the tensor for dim_h0_amb\n",
    "        \n",
    "    Optional arguments:\n",
    "        learning_rate:          the learning rate of SGD (Adam optimizer)\n",
    "        num_cp_neurons_h11:     the list of units in each layer of the network for num_cp (for h11)\n",
    "        num_cp_neurons_h21:     the list of units in each layer of the network for num_cp (for h21)\n",
    "        dim_cp_neurons_h11:     the list of units in each layer of the network for dim_cp (for h11)\n",
    "        dim_cp_neurons_h21:     the list of units in each layer of the network for dim_cp (for h21)\n",
    "        dim_h0_amb_neurons_h11: the list of units in each layer of the network for dim_h0_amb (for h11)\n",
    "        dim_h0_amb_neurons_h21: the list of units in each layer of the network for dim_h0_amb (for h21)\n",
    "        batch_norm_momentum:    the momentum of the batch normalization\n",
    "        dropout_rate:           the rate of the dropout in percentage\n",
    "        leaky_alpha:            the slope of the LeakyReLU activation function\n",
    "        l1_reg:                 the l1 regularization\n",
    "        l2_reg:                 the l2 regularization\n",
    "        last_relu:              whether to add ReLU activation function on the output layer\n",
    "        last_layer_h11:         the no. of layers to insert between the concatenation of layers and the output node for h11\n",
    "        last_layer_h21:         the no. of layers to insert between the concatenation of layers and the output node for h22\n",
    "        \n",
    "    Returns:\n",
    "        the compiled model\n",
    "    '''\n",
    "    # regularization\n",
    "    regularization = l1_l2(l1=l1_reg, l2=l2_reg)\n",
    "    \n",
    "    # import the pretrained models\n",
    "    if path.isfile(h11_model_path):\n",
    "        h11_model = load_model(h11_model_path)\n",
    "\n",
    "    if path.isfile(h21_model_path):\n",
    "        h21_model = load_model(h21_model_path)\n",
    "        \n",
    "    # freeze the layers in the pre-trained models\n",
    "    h11_model.trainable = False\n",
    "    h21_model.trainable = False\n",
    "    \n",
    "    for n in range(len(h11_model.layers)):\n",
    "        h11_model.layers[n].trainable = False\n",
    "    for n in range(len(h21_model.layers)):\n",
    "        h21_model.layers[n].trainable = False\n",
    "        \n",
    "    # then build the network for num_cp\n",
    "    num_cp_in  = Input(shape=num_cp_shape, name='num_cp_input')\n",
    "    num_cp_h11 = Lambda(lambda z: z, name='lambda_num_cp_h11')(num_cp_in)\n",
    "    num_cp_h21 = Lambda(lambda z: z, name='lambda_num_cp_h21')(num_cp_in)\n",
    "    \n",
    "    for n_layer in range(len(num_cp_neurons_h11)):\n",
    "        num_cp_h11 = Dense(units=num_cp_neurons_h11[n_layer],\n",
    "                           kernel_regularizer=regularization,\n",
    "                           name='num_cp_h11_{:d}'.format(n_layer)\n",
    "                          )(num_cp_h11)\n",
    "        num_cp_h11 = LeakyReLU(alpha=leaky_alpha,\n",
    "                               name='num_cp_h11_activation_{:d}'.format(n_layer)\n",
    "                              )(num_cp_h11)\n",
    "        \n",
    "        if batch_norm_momentum > 0.0:\n",
    "            num_cp_h11 = BatchNormalization(momentum=batch_norm_momentum,\n",
    "                                            name='num_cp_h11_batch_norm_{:d}'.format(n_layer)\n",
    "                                           )(num_cp_h11)\n",
    "    \n",
    "    for n_layer in range(len(num_cp_neurons_h21)):\n",
    "        num_cp_h21 = Dense(units=num_cp_neurons_h21[n_layer],\n",
    "                           kernel_regularizer=regularization,\n",
    "                           name='num_cp_h21_{:d}'.format(n_layer)\n",
    "                          )(num_cp_h21)\n",
    "        num_cp_h21 = LeakyReLU(alpha=leaky_alpha,\n",
    "                               name='num_cp_h21_activation_{:d}'.format(n_layer)\n",
    "                              )(num_cp_h21)\n",
    "        \n",
    "        if batch_norm_momentum > 0.0:\n",
    "            num_cp_h21 = BatchNormalization(momentum=batch_norm_momentum,\n",
    "                                            name='num_cp_h21_batch_norm_{:d}'.format(n_layer)\n",
    "                                           )(num_cp_h21)\n",
    "    \n",
    "    if dropout_rate > 0.0:\n",
    "        num_cp_h11 = Dropout(rate=dropout_rate, name='num_cp_h11_dropout')(num_cp_h11)\n",
    "        num_cp_h21 = Dropout(rate=dropout_rate, name='num_cp_h21_dropout')(num_cp_h21)\n",
    "        \n",
    "    # then build the network for dim_cp\n",
    "    dim_cp_in  = Input(shape=dim_cp_shape, name='dim_cp_input')\n",
    "    dim_cp_h11 = Lambda(lambda z: z, name='lambda_dim_cp_h11')(dim_cp_in)\n",
    "    dim_cp_h21 = Lambda(lambda z: z, name='lambda_dim_cp_h21')(dim_cp_in)\n",
    "    \n",
    "    for n_layer in range(len(dim_cp_neurons_h11)):\n",
    "        dim_cp_h11 = Dense(units=dim_cp_neurons_h11[n_layer],\n",
    "                           kernel_regularizer=regularization,\n",
    "                           name='dim_cp_h11_{:d}'.format(n_layer)\n",
    "                          )(dim_cp_h11)\n",
    "        dim_cp_h11 = LeakyReLU(alpha=leaky_alpha,\n",
    "                               name='dim_cp_h11_activation_{:d}'.format(n_layer)\n",
    "                              )(dim_cp_h11)\n",
    "        \n",
    "        if batch_norm_momentum > 0.0:\n",
    "            dim_cp_h11 = BatchNormalization(momentum=batch_norm_momentum,\n",
    "                                            name='dim_cp_h11_batch_norm_{:d}'.format(n_layer)\n",
    "                                           )(dim_cp_h11)\n",
    "    \n",
    "    for n_layer in range(len(dim_cp_neurons_h21)):\n",
    "        dim_cp_h21 = Dense(units=dim_cp_neurons_h21[n_layer],\n",
    "                           kernel_regularizer=regularization,\n",
    "                           name='dim_cp_h21_{:d}'.format(n_layer)\n",
    "                          )(dim_cp_h21)\n",
    "        dim_cp_h21 = LeakyReLU(alpha=leaky_alpha,\n",
    "                               name='dim_cp_h21_activation_{:d}'.format(n_layer)\n",
    "                              )(dim_cp_h21)\n",
    "        \n",
    "        if batch_norm_momentum > 0.0:\n",
    "            dim_cp_h21 = BatchNormalization(momentum=batch_norm_momentum,\n",
    "                                            name='dim_cp_h21_batch_norm_{:d}'.format(n_layer)\n",
    "                                           )(dim_cp_h21)\n",
    "    \n",
    "    if dropout_rate > 0.0:\n",
    "        dim_cp_h11 = Dropout(rate=dropout_rate, name='dim_cp_h11_dropout')(dim_cp_h11)\n",
    "        dim_cp_h21 = Dropout(rate=dropout_rate, name='dim_cp_h21_dropout')(dim_cp_h21)\n",
    "        \n",
    "    # then build the network for dim_h0_amb\n",
    "    dim_h0_amb_in  = Input(shape=dim_h0_amb_shape, name='dim_h0_amb_input')\n",
    "    dim_h0_amb_h11 = Lambda(lambda z: z, name='lambda_dim_h0_amb_h11')(dim_h0_amb_in)\n",
    "    dim_h0_amb_h21 = Lambda(lambda z: z, name='lambda_dim_h0_amb_h21')(dim_h0_amb_in)\n",
    "    \n",
    "    for n_layer in range(len(dim_h0_amb_neurons_h11)):\n",
    "        dim_h0_amb_h11 = Dense(units=dim_h0_amb_neurons_h11[n_layer],\n",
    "                           kernel_regularizer=regularization,\n",
    "                           name='dim_h0_amb_h11_{:d}'.format(n_layer)\n",
    "                          )(dim_h0_amb_h11)\n",
    "        dim_h0_amb_h11 = LeakyReLU(alpha=leaky_alpha,\n",
    "                               name='dim_h0_amb_h11_activation_{:d}'.format(n_layer)\n",
    "                              )(dim_h0_amb_h11)\n",
    "        \n",
    "        if batch_norm_momentum > 0.0:\n",
    "            dim_h0_amb_h11 = BatchNormalization(momentum=batch_norm_momentum,\n",
    "                                            name='dim_h0_amb_h11_batch_norm_{:d}'.format(n_layer)\n",
    "                                           )(dim_h0_amb_h11)\n",
    "    \n",
    "    for n_layer in range(len(dim_h0_amb_neurons_h21)):\n",
    "        dim_h0_amb_h21 = Dense(units=dim_h0_amb_neurons_h21[n_layer],\n",
    "                           kernel_regularizer=regularization,\n",
    "                           name='dim_h0_amb_h21_{:d}'.format(n_layer)\n",
    "                          )(dim_h0_amb_h21)\n",
    "        dim_h0_amb_h21 = LeakyReLU(alpha=leaky_alpha,\n",
    "                               name='dim_h0_amb_h21_activation_{:d}'.format(n_layer)\n",
    "                              )(dim_h0_amb_h21)\n",
    "        \n",
    "        if batch_norm_momentum > 0.0:\n",
    "            dim_h0_amb_h21 = BatchNormalization(momentum=batch_norm_momentum,\n",
    "                                            name='dim_h0_amb_h21_batch_norm_{:d}'.format(n_layer)\n",
    "                                           )(dim_h0_amb_h21)\n",
    "    \n",
    "    if dropout_rate > 0.0:\n",
    "        dim_h0_amb_h11 = Dropout(rate=dropout_rate, name='dim_h0_amb_h11_dropout')(dim_h0_amb_h11)\n",
    "        dim_h0_amb_h21 = Dropout(rate=dropout_rate, name='dim_h0_amb_h21_dropout')(dim_h0_amb_h21)\n",
    "      \n",
    "    # concatenate the outputs\n",
    "    h11_transfer_learning = concatenate([h11_model.output,\n",
    "                                         num_cp_h11,\n",
    "                                         dim_cp_h11,\n",
    "                                         dim_h0_amb_h11],\n",
    "                                        name='h11_concatenate'\n",
    "                                       )\n",
    "    for n in range(last_layer_h11):\n",
    "        h11_transfer_learning = Dense(units=h11_transfer_learning.shape[1],\n",
    "                                      name='h11_concatenate_dense_{:d}'.format(n)\n",
    "                                     )(h11_transfer_learning)\n",
    "        h11_transfer_learning = LeakyReLU(alpha=leaky_alpha,\n",
    "                                          name='h11_concatenate_dense_activation_{:d}'.format(n)\n",
    "                                         )(h11_transfer_learning)\n",
    "    \n",
    "    # improve the result of the matrix with the processed output\n",
    "    h11_transfer_learning = Dense(units=1, name='h11_improvements')(h11_transfer_learning)\n",
    "    h11_transfer_learning = LeakyReLU(alpha=leaky_alpha, name='h11_improvements_activation')(h11_transfer_learning)\n",
    "    h11_transfer_learning = concatenate([h11_transfer_learning, h11_model.output])\n",
    "    h11_transfer_learning = Dense(units=1,\n",
    "                                  activation='relu',\n",
    "                                  name='h11'\n",
    "                                 )(h11_transfer_learning) if last_relu \\\n",
    "                                                          else Dense(units=1,\n",
    "                                                                     name='h11'\n",
    "                                                                    )(h11_transfer_learning)\n",
    "    \n",
    "    h21_transfer_learning = concatenate([h21_model.output,\n",
    "                                         h11_model.output,\n",
    "                                         num_cp_h21,\n",
    "                                         dim_cp_h21,\n",
    "                                         dim_h0_amb_h21],\n",
    "                                        name='h21_concatenate'\n",
    "                                       )\n",
    "    for n in range(last_layer_h21):\n",
    "        h21_transfer_learning = Dense(units=h21_transfer_learning.shape[1],\n",
    "                                      name='h21_concatenate_dense_{:d}'.format(n)\n",
    "                                     )(h21_transfer_learning)\n",
    "        h21_transfer_learning = LeakyReLU(alpha=leaky_alpha,\n",
    "                                          name='h21_concatenate_dense_activation_{:d}'.format(n)\n",
    "                                         )(h21_transfer_learning)\n",
    "        \n",
    "    # improve the result of the matrix with the processed output\n",
    "    h21_transfer_learning = Dense(units=1, activation='relu', name='h21_improvements')(h21_transfer_learning)\n",
    "    h21_transfer_learning = LeakyReLU(alpha=leaky_alpha, name='h21_improvements_activation')(h21_transfer_learning)\n",
    "    h21_transfer_learning = concatenate([h21_transfer_learning, h21_model.output])\n",
    "    h21_transfer_learning = Dense(units=1,\n",
    "                                  activation='relu',\n",
    "                                  name='h21'\n",
    "                                 )(h21_transfer_learning) if last_relu \\\n",
    "                                                          else Dense(units=1,\n",
    "                                                                     name='h21'\n",
    "                                                                    )(h21_transfer_learning)\n",
    "    # compile the model\n",
    "    model = Model(inputs=[h11_model.input, h21_model.input, num_cp_in, dim_cp_in, dim_h0_amb_in],\n",
    "                  outputs=[h11_transfer_learning, h21_transfer_learning],\n",
    "                  name=model_name\n",
    "                 )\n",
    "    model.compile(optimizers=Adam(learning_rate=learning_rate), loss='mean_squared_error', metrics=['mean_squared_error'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then visualise the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-11 16:55:38,374: DEBUG ==> Building the full convolutional model...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"full_model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "h11_inception_same_conv_no_fc_i [(None, 12, 15, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_i [(None, 12, 15, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 12, 15, 32)   416         h11_inception_same_conv_no_fc_inp\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 12, 15, 32)   512         h11_inception_same_conv_no_fc_inp\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 12, 15, 128)  1664        h21_inception_same_conv_no_fc_inp\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 12, 15, 128)  2048        h21_inception_same_conv_no_fc_inp\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 12, 15, 32)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 12, 15, 32)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 12, 15, 128)  0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 12, 15, 128)  0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 12, 15, 64)   0           h11_inception_same_conv_no_fc_con\n",
      "                                                                 h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 12, 15, 256)  0           h21_inception_same_conv_no_fc_con\n",
      "                                                                 h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 12, 15, 64)   256         h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 12, 15, 256)  1024        h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 12, 15, 64)   49216       h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 12, 15, 64)   61504       h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 12, 15, 128)  393344      h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 12, 15, 128)  491648      h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 12, 15, 64)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 12, 15, 64)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 12, 15, 128)  0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 12, 15, 128)  0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 12, 15, 128)  0           h11_inception_same_conv_no_fc_con\n",
      "                                                                 h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 12, 15, 256)  0           h21_inception_same_conv_no_fc_con\n",
      "                                                                 h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 12, 15, 128)  512         h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 12, 15, 256)  1024        h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 12, 15, 32)   49184       h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 12, 15, 32)   61472       h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 12, 15, 32)   98336       h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 12, 15, 32)   122912      h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 12, 15, 32)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 12, 15, 32)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "num_cp_input (InputLayer)       [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dim_cp_input (InputLayer)       [(None, 12)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dim_h0_amb_input (InputLayer)   [(None, 15)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 12, 15, 32)   0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 12, 15, 32)   0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 12, 15, 64)   0           h11_inception_same_conv_no_fc_con\n",
      "                                                                 h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 12, 15, 64)   0           h21_inception_same_conv_no_fc_con\n",
      "                                                                 h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "lambda_num_cp_h21 (Lambda)      (None, 1)            0           num_cp_input[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "lambda_dim_cp_h21 (Lambda)      (None, 12)           0           dim_cp_input[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "lambda_dim_h0_amb_h21 (Lambda)  (None, 15)           0           dim_h0_amb_input[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 12, 15, 64)   256         h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 12, 15, 64)   256         h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "num_cp_h21_0 (Dense)            (None, 10)           20          lambda_num_cp_h21[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dim_cp_h21_0 (Dense)            (None, 10)           130         lambda_dim_cp_h21[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dim_h0_amb_h21_0 (Dense)        (None, 10)           160         lambda_dim_h0_amb_h21[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_d (None, 12, 15, 64)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_d (None, 12, 15, 64)   0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "num_cp_h21_activation_0 (LeakyR (None, 10)           0           num_cp_h21_0[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dim_cp_h21_activation_0 (LeakyR (None, 10)           0           dim_cp_h21_0[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dim_h0_amb_h21_activation_0 (Le (None, 10)           0           dim_h0_amb_h21_0[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_f (None, 11520)        0           h11_inception_same_conv_no_fc_dro\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_f (None, 11520)        0           h21_inception_same_conv_no_fc_dro\n",
      "__________________________________________________________________________________________________\n",
      "num_cp_h21_batch_norm_0 (BatchN (None, 10)           40          num_cp_h21_activation_0[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "dim_cp_h21_batch_norm_0 (BatchN (None, 10)           40          dim_cp_h21_activation_0[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "dim_h0_amb_h21_batch_norm_0 (Ba (None, 10)           40          dim_h0_amb_h21_activation_0[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_o (None, 1)            11521       h11_inception_same_conv_no_fc_fla\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_o (None, 1)            11521       h21_inception_same_conv_no_fc_fla\n",
      "__________________________________________________________________________________________________\n",
      "num_cp_h21_dropout (Dropout)    (None, 10)           0           num_cp_h21_batch_norm_0[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "dim_cp_h21_dropout (Dropout)    (None, 10)           0           dim_cp_h21_batch_norm_0[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "dim_h0_amb_h21_dropout (Dropout (None, 10)           0           dim_h0_amb_h21_batch_norm_0[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "lambda_num_cp_h11 (Lambda)      (None, 1)            0           num_cp_input[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "lambda_dim_cp_h11 (Lambda)      (None, 12)           0           dim_cp_input[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "lambda_dim_h0_amb_h11 (Lambda)  (None, 15)           0           dim_h0_amb_input[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "h21_concatenate (Concatenate)   (None, 32)           0           h21_inception_same_conv_no_fc_out\n",
      "                                                                 h11_inception_same_conv_no_fc_out\n",
      "                                                                 num_cp_h21_dropout[0][0]         \n",
      "                                                                 dim_cp_h21_dropout[0][0]         \n",
      "                                                                 dim_h0_amb_h21_dropout[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "num_cp_h11_dropout (Dropout)    (None, 1)            0           lambda_num_cp_h11[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dim_cp_h11_dropout (Dropout)    (None, 12)           0           lambda_dim_cp_h11[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dim_h0_amb_h11_dropout (Dropout (None, 15)           0           lambda_dim_h0_amb_h11[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "h21_concatenate_dense_0 (Dense) (None, 32)           1056        h21_concatenate[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "h11_concatenate (Concatenate)   (None, 29)           0           h11_inception_same_conv_no_fc_out\n",
      "                                                                 num_cp_h11_dropout[0][0]         \n",
      "                                                                 dim_cp_h11_dropout[0][0]         \n",
      "                                                                 dim_h0_amb_h11_dropout[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "h21_concatenate_dense_activatio (None, 32)           0           h21_concatenate_dense_0[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "h11_concatenate_dense_0 (Dense) (None, 29)           870         h11_concatenate[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "h21_concatenate_dense_1 (Dense) (None, 32)           1056        h21_concatenate_dense_activation_\n",
      "__________________________________________________________________________________________________\n",
      "h11_concatenate_dense_activatio (None, 29)           0           h11_concatenate_dense_0[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "h21_concatenate_dense_activatio (None, 32)           0           h21_concatenate_dense_1[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "h11_improvements (Dense)        (None, 1)            30          h11_concatenate_dense_activation_\n",
      "__________________________________________________________________________________________________\n",
      "h21_improvements (Dense)        (None, 1)            33          h21_concatenate_dense_activation_\n",
      "__________________________________________________________________________________________________\n",
      "h11_improvements_activation (Le (None, 1)            0           h11_improvements[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "h21_improvements_activation (Le (None, 1)            0           h21_improvements[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 2)            0           h11_improvements_activation[0][0]\n",
      "                                                                 h11_inception_same_conv_no_fc_out\n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 2)            0           h21_improvements_activation[0][0]\n",
      "                                                                 h21_inception_same_conv_no_fc_out\n",
      "__________________________________________________________________________________________________\n",
      "h11 (Dense)                     (None, 1)            3           concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "h21 (Dense)                     (None, 1)            3           concatenate_1[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 1,362,107\n",
      "Trainable params: 3,421\n",
      "Non-trainable params: 1,358,686\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
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4xS/OO++8888/f8qUKWlHBHTP2NhYeWrf6OjoQw89NDEx8ZrXvGbRokWleX0jIyOHH3747rvvnnakAADJcF8+AACA/3P33Xf/8Ic//J//+Z+0AwEAyI3p06dv2bIl7SgAAAAAgPzZtGnTRRdd9OUvf/lNb3rTTTfdNH/+/LQjArpt9uzZs2fPPuGEE0oPt2zZ8vDDD5fm9Y2Ojv7rv/7rtm3bBgcHDzjggNK8vpGRkSVLlsyaNSvdsAEAYnNfPgAAgP9z/PHHb9q06a677ko7EACA3Dj88MOPO+64Sy65JO1AAAAAAIDcKBaL11133T/8wz8MDQ195jOfef/73592REAW7dq164knnihP7Vu3bt2GDRuCINhrr73KU/uGh4cXLVo0efLktIMFAIjEffkAAAB+a/Xq1d/73ve+853vpB0IAECeTJs2zX35AAAAAIDo1qxZs2LFip/+9Kenn376xRdf/JrXvCbtiICMmjRp0ty5c+fOnVu+cd/mzZsfeOCB0ry+0dHRr371q6+88srQ0NDBBx9cntp32GGHzZw5M93IAQAacV8+AACA33r3u9+9YcOGe+65p1AopB0LAEBuvP3tb99vv/2+/vWvpx0IAAAAAJB1L7/88mc/+9nPfOYzixYt+spXvrJkyZK0IwLybefOnQ8//HBpat/atWt/+tOfPvfcc0EQzJo1qzSvr3T7vkMOOWRgYCDtYAEAgsB9+QAAAEoefPDBm2+++cYbbzSRDwCgJe7LBwAAAABEceutt5555plbtmz57Gc/e9ZZZ5lXA7RvaGhoeHh4eHi4/MzY2FhpXt/o6Oitt976uc99bmJi4jWvec2iRYvKU/sOP/zw3XffPcWwAYB+5r58AAAAQRAE73vf+0ZHRx944AFnjAAAWvI3f/M3Tz/99He/+920AwEAAAAAMmrDhg1nnXXWd7/73dNOO+3zn//8vvvum3ZEQL/YsmXLww8/XJraNzo6unr16m3btk2aNOnAAw8szesr3b5v7ty5aUcKAPQLc/kAAACCDRs2zJ8//5//+Z9PO+20tGMBAMiZc84552c/+9mdd96ZdiAAAAAAQObs3LnzqquuuuCCC2bPnr1q1apjjz027YiAfjc2Njb6qnXr1j322GPFYnGvvfaqnNp36KGHTpkyJe1IAYDeNJh2AAAAAOm79NJLDzjggPe+971pBwIAkD/Tpk3bsmVL2lEAAAAAAJnzv//7vytWrHj88cfPPffcj370o5MnT047IoBg9uzZs2fPPuGEE0oPN2/e/MADD5Tm9Y2Ojn71q1995ZVXhoaGDj744NK8voULFx599NGvfe1r0w0bAOgZ5vIBAAD9buPGjf/yL//ypS99aXDQVyQAgJZNnz5969ataUcBAAAAAGTIs88+e+65515//fVvf/vbv/Od78yZMyftiADqmzFjxrJly5YtW1Z6OD4+vn79+nXr1q1du3Z0dPSKK6549tlngyCYNWtWeWrfyMjIIYccMjAwkGrgAEBeFYrFYtoxAAAApOnss8/+1re+9eijj06ZMiXtWAAA8ueqq6666KKLnnvuubQDAQAAAADSNzExcf3113/4wx+eOnXqlVde+Z73vCftiADaMjY2Vp7aNzo6un79+l27dk2fPn3evHmleX0jIyOLFy/eY4890o4UAMgHN50AAAD62vPPP3/NNddccsklJvIBAMQzffr0LVu2pB0FAAAAAJC+1atXr1ixYnR0dMWKFZdccsm0adPSjgigXbNnz549e/axxx5berhjx45HHnlk9FXf/va3t27dGrx6476S4eHhuXPnpho1AJBd7ssHAAD0tfPPP/+aa655/PHHXSANACCem2+++Z3vfOfOnTsHB108DgAAAAD61IsvvviP//iPq1atWrp06VVXXXXooYemHRFAl4yNjZXm9ZVu3/fggw8Wi8UZM2YMDw+X5vUtXLhwyZIlU6dOTTtSACATzOUDAAD614svvjhnzpyVK1d+5CMfSTsWAIC8uu2229761rdu2rRpxowZaccCAAAAAKTg1ltvPeOMM7Zt2/aZz3zmAx/4QKFQSDsigNS8+OKL999/f3lq3z333LN9+/ahoaGDDz64PLXvqKOO2nfffdOOFABIh8skAwAA/WLnzp2FQqHydjFf+MIXisXiBz/4wRSjAgDIu+nTpwdBsGXLFnP5AAAAAKDfPProo2eeeeYPfvCD00477fLLL585c2baEQGkbM8991y2bNmyZctKD8fHx9evX1+a1zc6OnrllVf+8pe/DIJg1qxZpXl9IyMjIyMjhxxyyMDAQKqBAwBd4r58AABAvxgdHX3Pe96zcuXKv/qrv5oyZcq2bdvmzJlz+umnf+ITn0g7NACAvNqxY8fo6OjSpUu//e1v77///lu3bt28efO2bdv+/M//fPLkyWlHBwAAAAB0yiuvvHLppZdedtll8+fPv/rqq4855pi0IwLIh02bNpXm9ZWsX79+165d06ZNmz9/fnlq3+LFi/fYY4+0IwUAOsJcPgAAoF/ccsstJ5100sDAwD777HP++edv377905/+9OOPP+7akAAALZmYmFi6dOnatWtfeeWV8fHx2gXmzp37i1/8ovuBAQAAAADdcccdd5x++ulPPfXUhRde+Pd///eDg4NpRwSQVzt27HjkkUdK8/rWrVt37733vvDCC0EQzJo1qzSvr3T7voULFxYKhbSDBQAS4OsTAADQL5588smhoaGdO3f+6le/Ou+88yZPnrxs2bJJkyalHRcAQM4MDAy8+c1v/ulPf1r3UnGTJ0/+sz/7s+5HBQAAAAAkZdu2bY3uB/XMM8+sXLnyuuuue8c73vH973//gAMO6HJsAD1m8uTJw8PDw8PD73//+0vPjI2Nlaf23XjjjZ/85CeLxeKMGTOGh4fLU/uWLFkyderUdCMHAOJxXz4AAKBfnHfeeV/4whd27NhRfmZwcHBoaOgDH/jARz7ykVmzZqUYGwBAvjz22GNveMMbGpWXb7/99j/5kz/pckgAAAAAQCLuuOOOlStX3nXXXVXXRZ2YmLjmmmvOPffcmTNnfvnLXz7uuOPSihCgr7z00ktr1qxZt27d2rVrR0dH77nnnu3btw8ODs6bN680r29kZOTII4/cb7/90o4UAIjEXD4AAKBfnHrqqd/4xjcmJiaqnh8aGpo0adLKlSsvuuiiNOICAMilt73tbXfcccf4+HjV87vvvvumTZsmT56cSlQAAAAAQDsee+yxww8/fPPmzVdeeeXf/d3flZ+/5557PvjBD65evXrFihWf/vSnG921D4BOGx8ff/LJJ0vz+kqeeeaZIAhmzZpVnto3MjKyYMGCqinZAEBGmMsHAAD0i6OPPvonP/lJ7fOTJk2aOnXq7bffftRRR3U/KgCAnLr55pvf+c53Vj05adKkk0466aabbkolJAAAAACgHVu3bj3yyCMfffTRnTt37r777o888sjs2bM3b9788Y9/fNWqVcuWLbv66qsPOeSQtMME4Hds2rSpPLVv3bp1999//44dOyZPnnzQQQeNvOqwww6bNm1a2pECAEFgLh8AANA/Zs+eXboUWaXSRL7bbrvt6KOPTiUqAICc2rVr1+tf//qq/GrSpEnXXnvtX/7lX6YVFQAAAAAQT7FYPPnkk2+55Zbx8fEgCIaGhk488cTly5efeeaZAwMDl1122fve975CoZB2mAA0sXPnzocffrg8tW/16tXPP/98EASzZs0qzesr3b5v4cKFCX6qT0xMDAwMJNUaAPQ2c/kAAIC+MDExMWXKlNJppzIT+QAA2nHRRRddcskllSlWoVB45pln9ttvvxSjAgAAAABiuOiiiy6++OKJiYnKJwcGBk4//fRPfepTe+65Z1qBAdCmsbGx0ry+0u37HnrooYmJiT333PPQQw8tzesrzfHbbbfdYndx6623XnPNNZ///OcPPvjgBCMHgJ5kLh8AANAXxsbGfu/3fq/yGRP5AADa9Mwzz7z+9a/ftWtX6WGhUFi8ePHo6Gi6UQEAAAAArbr55pvf9a53Vf2gdGBgYL/99tuwYcPUqVPTCgyAxL300ktr1qwpT+279957X3755cHBwXnz5pWn9h1xxBH7779/9DY/9alPXXjhhYODgx/60IcuvPDCGTNmdC5+AMg7c/kAAIC+8JOf/KRyzp6JfAAAiTjxxBO/973v7dy5MwiCyZMnX3DBBRdeeGHaQQEAAAAALXjwwQeXLFmyffv22h+UDg4OfuxjH7vooovSiAuAbti1a9cTTzxRmtdXun3fhg0bgiDYa6+9yrfsGxkZWbBgwaRJkxo18q53veuWW26ZmJgYGhrabbfdPvnJT55xxhmDg4Nd3A4AyA1z+QAAgL7wrW996z3veU/p70mTJk2ZMuX22283kQ8AoE3f+973jjvuuPLDe+65Z/HixSnGAwAAAAC05Ne//vXixYvHxsbGx8frLjA0NHT//ffPnz+/y4EBkJZNmzZVTu174IEHfvOb30yePPmggw4qzesbHh5evHjxPvvsU17lwAMPfPLJJ8sPBwYGfv/3f/+LX/zi8ccfn8YWAECmmcsHAAD0hSuvvHLlypU7duwwkQ8AIEHFYnHOnDmls7MzZ8587rnnCoVC2kEBAAAAAJGMj48fe+yxP/7xj3fu3Bmy2Fve8pbbbruta1EBkCmvvPLK2rVrV69efd999913331r1qzZvHlzoVCYO3fuYYcd9od/+Ifz589/73vfWzUrYdKkSbt27Xrzm9/85S9/eeHChWkFDwAZ5Ma1AABAX3jqqacCd+QDAEhaoVA4/fTTL7jggkKh8M53vtNEPgAAAADIkQ9/+MN33nnnrl27qp4fGhoaHx8vFot77bXX0qVLly5dunXr1mnTpqUSJADpmjp1aul2fOVnxsbGSrfsW7t27Te+8Y2HHnqo9vZCpX8ud9555x/8wR+cccYZn/jEJ2bMmNHVuAEgq9yXDwAA6AunnHLKjTfeuMcee9x2220m8gEAJOj555+fPXv2zp07b7nllhNPPDHtcAAAAACASL7+9a//9V//denvSZMmFQqF8fHxoaGhRYsW/b//9/+OOuqoo48+es6cOanGCEAOXH755StXrhwfH2+0wNDQ0G677fbJT37yjDPOGBx0LyIA+p25fABAEATB5Zdfftddd6UdBUAH/fd///eLL774x3/8x3vvvXfasQAEQRDceOON7TciiwMy4ic/+cnTTz994oknOv8KZEoiGRcAAMRw1113XX755WlHARDmhRde+OEPfzgxMREEwe677z5z5sy999577733njFjxsDAQNrRQX/p1SqWjKh/3HPPPY8//njpf0q46dOnL168eN999+1CVAB0Tq9mL13jGxcAEARBcNddd919991pRwHQQePj4ybyEcXdd9/d2/8Tb7rppo0bN6YdRb/buHHjTTfdlEhTsjggI+bOnbvvvvuayEdEMi66IMGMCwAAYnjqqadkpECW7dy5c8OGDfPnz3/jG994wgknHH/88UceeeRBBx209957m8hXpopFF/R2FUtG1D9+/etf153IVygUCoVC6e+BgYHp06fPmDHj+eef3759e3cDhH4he6ELejt76Ro/rQAAfuvoo492mQSgh/385z9fsmRJ2lGQAyeffHLQ05cOKhQK55xzzimnnJJ2IH3thhtuWL58eVKtyeKALCgWi7fffvuxxx6bdiDkg4yLLkg24wIAgHh6+IsPQD9QxaIL+qGK1cMHESUTExN77LFHoVAYHBzctWtXaVLfa1/72gULFixatGjBggULFiyYN2/eAQccUJ7XB3SI7IUu6IfspQvM5QMAAPqCiXwAAJ1TKBRM5AMAAAAAAOg3jz/++Bve8IbSnL1DDjlk/vz58+fPnz59etpxAUB2mcsHAAAAAAAAAAAAAAC0Zu7cuQ888EDaUQBAngykHQAAAAAAAAAAAAAAAAAA9Dhz+QAAAAAAAAAAAAAAAACgs8zlAwAAAAAAAAAAAAAAAIDOGkw7AAAAAMi3QqEQBEGxWEw7kORVbVrpYUn07W00Pgm2VtlUSw22Glv48z35HgCAjOjh/7YyrkDGBQAAAPSKHq5gqGIFqlgA9KIe/s8lewlkLxnmvnwAAABAHbUlreKrgnqFpJBGmj7fZmtVWippNX2+/LA8ILUj0NKYAACUybiqHsq4AAAAADJIFavqoSoWAGSc7KXqoewla8zlAwAAgLaUyxwd1eWKSfiFoBIpG7U6blFai95mm7HVvqqwBQAdJeMKbySkcRkXAAAAQNeoYoU3EtK4KhYApEL2Et5ISOOyF9phLh8AAABkXdZqJdGvFNWoNlR+PnphK9nqYbKxAQA9QMYV3loMMi4AAACAxKlihbcWgyoWAHSU7CW8tRhkLz1gMO0AAAAAIMeqLtpU92G5OFL5aqO/Q9qsbK3uFaSSVdV4shWlpJoqS2pAmrZQKBRKu6zuRaqyVn8EgN4g42q/5UTIuAAAAABCqGK133IiVLEAICLZS/stJ0L20ofclw8AAADiCyn9VFWjgt8tb9X9O7zNZC/RFCK8NNOFglpLKoelUCh0rq5UW52sS2ELABIn40qdjAsAAACgKVWs1KliAUBLZC+pk730LXP5AAAAIHmlWlWj0k/p+XYKQ12rcHVagleWSnxAsla/AwCqyLgiknEBAAAApEgVKyJVLADICNlLRLIXYjOXDwAAAJLXq6WQXBR6OnSNqKptdyUqAEhdxnOS2GRcMi4AAACgl2S8zhObKpYqFgC9KuP/32OTvchessNcPgAAAKAFCZa0Eq+RdS62yocKWwBAp8m4ZFwAAAAA2aeKpYoFAPkie5G9ZIS5fAAAAEAKsnyxq6axZTNsAIAqMi4AAAAAsk8VCwDIF9kLbTKXDwAAAIgqqWpO3es/Jd5sIo24DBUA0GUyLgAAAACyTxULAMgX2QvZYS4fAAAAdFupUFJbgql8PnytQqGQSrWltt/wSMovVS5T+WTV6vFaazRuCcZW2k1Vq7hOFQBkmYyrcl0ZFwAAAEA2qWJVrquKBQDZJ3upXFf2Qjzm8gEAAEB8VWWOuoWS2ofFYrGqLFJ5PaTaayPVfbVzkr0MVaOHybZWHs+IwceIrVzYCi9pKXUBQOJkXCFkXAAAAAAZoYoVQhULADJI9hJC9kJHDaYdAAAAAORYVQmjtqIRvfZR+UzTdrpQOqlbQav7TKOSUIy6T5Zba/pqm2U7AKARGVeWcyQZFwAAAECJKlaW606qWABQS/aS5XxD9tLb3JcPAAAAiC/ZUk6WWwMASEuWcyQZFwAAAAAlWa47qWIBALWynG/IXnqbuXwAAABAtdKlmJpWhUoLJHW5rCy3lrXuAIAeIOPKeHcAAAAABKpYme8OAKgie8l4dwRBMJh2AABAbpTz2uj3Wa5dsqWELzvZYXYiIRER38zZEeWwqvtSgl3na6xy/SW205sQ/naK3VpLTTUqlERppGn8jRbIyJu5HEahUMj+YVUsFguFQnioyW5FllsLl/FUQRaXhUhIREY+zKNLMYvrKBlXxPZLZFzdJ+Nq2mNmWwvn3zoAAL0n+teokHxY4Y4syEhNIDqnXyOKXeaqXbHThZq6obZTIusCVaymPWa2tXD+x7VKRpSFSEhEvv7LBz16KlP20lGyl6Y9Zra1cP4fpcJ9+QCAqFr9wV/Th5CK3H3xaHocdXRbcjRQgQ+ZaMr7NJGdG6+RyhhKSg+j7MGm8TdaoPQw9bd0sUK6kUSUo1DTlfGBksXRG2RxGeEDIQoZV4yAEyTj6lUGCgCA3tPSz81bfQm6RuGuJb00UI3WKv1guqz0TGdirB9b7dyDGCWyLlDF6lUGqlUyInqDjCgLZC+dJnvpVQYqFebyAQCJKX8jrZvVxfg6kWKCWBWtVLXHZOQHplGEH1ZU6k7NohMfDj5wgpwcj9DDZHHkhSwudTKuXOvDTQYAAOgByf5sXeGODlG46z3xBqruNIZOD3VLoZYXy8gP4oGIZETkgowoXbIXIF/M5QMAklT+dlH7NSNH3/187elhedy5IYcVlbrwIdOJ908e35NAT5LFkXF53Lk9mcXJuAAAAKDLQr6MK9yRBXncuT1ZuOuEBAeq00Ntn0LPkxGRcXncuT3531P2AuTIYNoBAAB5VXtJkmS/V1TdN7zuw6oeK78VV75U+3xla406KndRewfzRn1FCTJ8e0vd1TYYvZeWRqZpPLWrtBpS+OBHCSDGHm/aZt1Bi95OyCqJj0/EUWpJS8HXXTHkqAyivTOb7t+6R0TiQjY5ZNfU/XBouivD3xud+MBp6SOoSniPIc3WfU+2dOw3DQNonyyuti9ZnCwu41lc7Ts/ZBCi52wyLhlXxB4BAADojna+ZkZvPwj9Wq1wV9uLwl2UNvu2cBcj+LorRq/sNXpnRi9kRSwGhizT6EgMP0JjH4Zt7tNGwRQKhY7WRYHYZES1fcmIZEQZz4jqnn9sFHn0/Ef2UhWM7AV6jPvyAQBxVH0F7YSqbx11v51W9l7+OtSoAtKo0NCo6FBeJeRbXEgtoKUhqi3TxOulpZGJEk+UwQwPqXI8K7to+q0y9h6P0mZ5rXjtZGF8aruLqNXgQ6KKfpDW7sHw/Vv3iEhcyCaHf27U/XAI35VN3xvd/MBpqm6PEY+U8lbUDkijDazqNDwMoH2yuLp9xf5QlcXVir3Ho7RZ+S8mp+NT210UTf+B1j1MQloo/u7Zr4hhxCDjakTGBQAAQBWFu7p9xf5urnBXK/Yej9JmZaUip+NT211ErQYfElX0g7R2D4bv3+4UA6Po8j4FckdGVLcvGVHdhWuXqX1YS0bUahhRNFoy+6cyo5C9AB1iLh8A0LJSuSH8q0XIV69O9N6ou7rfjiq/4DUqjoQHEN5moyCjaDWSqhJA05GJUh2IPZiNYqjVzmZGDyZi4zHiqbtKF8ansot4h1g7wbfUS9N3ZqOFq6RSwkjw46v9Rtp5X8X7FGrzzVCouf5TeB258Krgd4cr2fckUCaLC2+zUZBRyOLqxiOLq+2izUOsMpKIgxnyHzY87I6ScTXtMXx1GRcAAECPiV2V6lzvCneNOlW4a9p4jHjqrpKLwl2fnH6t3Zyk2gk6uU+B3JERhbfZKMgoZER145ER1XbRM6cyZS9A9g2mHQAAkD/pfnOI0nvdr9ndFPu7X+X3usqaSJReQjptZwRa2pa6Cxfbu8N7N99vna52dWJ82tGhHlsqqYS/bztapW16iFUtFqXBRrsyYl/xdGKIQmpnIZtQte3FmotjNRqZRm2m+FPym266qbd/yL58+fLly5enHQUpkMW1GkBEsrj2A2hT/2RxEfuqjK3peVMZV8R+E29TxiXjAgAAKFO4azWAiBTu2g+gTf1TuAuJJ/FmM3v6NRFZ26eBKhZ0kYyo1QAikhG1H0Cb+icjyt2pzERkZ/zLZC+QC+byAQDJy9rFQrITSSPl76VV39/KI9nml6vsjEBvf0tsX/hvdoMs7cqOanREJC6pQ6xRy93pqxPqDnv4JjQtroV3V7eAleIb/uijjz7nnHPS6r3Tli9ffvbZZx9zzDFpB9LX7rrrriuvvDLtKOrI2r+b7ETSiCyOkuxncXX/z8q40iXjknHRaZnNuAAAoAekXutoSuGOkuwX7rqjnWJg1gaq+/tUFYtOU8XKr+x8NjYiI6Ik+xlRsqcyM7JRZbKXZMleskD2kghz+QCAhGXtu1Be1H7/7KWRrKz+9MYWJavp+PTSmyGiLvy4vBOj2mhX9sAebLoJdX8dXn4yfN1Gq6foda973SmnnJJ2FJ2yfPnyY445poc3MC8yWNXqgQ+rVMji+lmWs7iq/8J145RxZY2Mq5fIuDIigxkXAADQNQp3/SzLhbu0xCsGtjRQHa0+JbtPW1pYFYsuUMWic2RE/SzLGVHnTmXKXgLZC10he2nfQNoBAAA9peo7Q0aueVMVRkaiqtTpL8YZGYEMjnymNBqfbB5WHZX3umF+91E58ngfGpVl32TjiRh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Xicfdj7ZH2W6xTznOiafOcQLBc64K2sfGMtlvCoPBID8U2u7s8znlAABAnUhrzoUJRFoDAK3T/cTqLJhTLV3cDxgAACAASURBVHMWAABqQ/hx+Vt/6pF5ZIBcBAAAgibUIp9KPlci7wEAoGP6zlcqSVMyeXuWjn0zpPP9ZUY4kp5Y9PIBAEKCQ+qyz52+ccigvo2gnhToLaVSrD8bwfPHxtrdtgAAhCbSmrJIawAQ4phYAQBASCH8BI5uVgQAAGgdkS+g+OwGAIAQRL5SkKizdCz/ITk9dxrdkiGFXj4AAPRPEwfdZP4N4PDlE4GsCAAAIEhIawAABJQmploAAAClBCj88Gc+AACASnCwCwAAwB+aSFNeHYuzb+dzvigi1IlePgAAQoKCyUzxFOvDUO4eoomEDQAA4Iy0BgBAQDH7AACAkKJs+CFKAQAAqI2aP1cCAABQPzWnKf+HMtix3eJ3XVAYvXwAAMALaj6Ep+baAAAAgkPNiUjNtQEAAAAAAAAAAIQaPrsBAADwB2kKPqOXDwAA/VMqJjqETqW+p0GRLBug2gAAAIKAtAYAQEDx+RkAAAgpiocfzkkCAABQFZV/rgQAAKByKk9T8o/FuVyi5X/ZblSkNiiIXj4AQEiwphCHiwXbRxPrLe6ClPNv7W/xbRz7ixcHmvOyPC/d5VWV7W90eLhvo7nbVu5Gc77cs3MCdq4NAABoAmmNtAYAUFCIT6wuK/F/qgUAAKpF+HGuRJHjDJ7XFwAAIJiIfA5lBPRzJQAAEApCOV8FOk0peyzO5WgOJ+Q4b3aoH718AIBQYcudDpHFPtA4n23s8rfW2xUZR0HKflGEux+VHc22DeUX7/wMykdOBQBAzUhrMpHWAABy6H5idRbMqZY5CwAAtSH8+MzPIwPkIgAAEDShFvlU8rkSeQ8AAB3Td75SSZryajSvztJx2KScpaM54aILAAAgeOR80Oh8H/kfT3o1ToAykMtQ6/IWd4HSh49jgzOa54e4+5WVn+kZAAAEB2nN/hbSGgDAT6EwsToL3FRrG9+f8gAAQOAQfjwsXdkjA7ZFy6sRAABAMSEY+ch7AAAgoHSfr3R8lo7nR3m+D0lPDbguHwAAoUjZHKbm0QAAALRIzfmKtAYA0AGmMwAAEFIIPwAAAPpG3gMAAPCHms+rIenpFb18AADoh+2C157v5vP1lDU3WuDGBAAA8AFpLWhjAgBCh8FgsM2tgZ5qHR5ov2gAAIDgCGb4kT8muQgAAEAp5D0AAAClcJZO0MaED8JFFwAAgJbYIp3Lyy6rgfViyp7LU7ZyNY8mEToBAAgxpDVtjSaR1gBA3VQ+sbosKXBTrfOcpcJtAgAA/EH4kYlcBAAAtEvNkY+8BwAAtEjN+UriLB0nnKWjHvTyAQDgBU3EF00UGTRsDQAAQoompn5NFBk0bA0AUDON7qUDVLZGtwYAAJBPo9N98MvW6IYCAACQtJlkyHsAAEDN1J8c1F9hMLE11MMougAAAAAAAAAAAAAAAAAAAAAAAAAAAHSOXj4AAAAAAAAAAAAAAAAAAAAAAAAAAAKLXj4AAAAAAAAAAAAAAAAAAAAAAAAAAAKLXj4AAAAAAAAAAAAAAAAAAAAAAAAAAAIr3PmmYcOGBb8OAAA8OHjwYIMGDZo0adK4cePIyEiH3546dUrS9fy1a9cuSdcrqG88cQDkuH79eqNGjURXAQQKaQ2ADuTm5hYUFIiuQo2uXLly6dKlqKgo0YXoSnV1dUREhOf76Pg1qfvkAADe0vE+X38sFsulS5d+8pOfiC5EV2Rmg4sXL2p0y3NUAQCgEkxGAlkslqtXrzZr1kx0IcIEKBFduXKlSZMmRqMqLncRgn/ZWZM8oFEh+J4FoDOhcMSJfXXQkOsCxGCxWGw/7Ny5c+nSpQKrAQDAmdls/tvf/nbt2rWamhpJkurXr9/kfzl39wF+mj59ep8+ffwchGQFQI6ampp9+/YdO3bsiSeeaNKkiehyAEAbFElrgHz6/pDDN1euXDl16tSpU6euX7/esGHDAQMGqOScGB0oKSk5c+bM448/HhYWJroWBASzGACvkEO0wmw2l5eXl5aWXr9+feDAgQ0aNBBdUWi5cePGJ5980rhx465du3bo0MFgMIiuCLKQiwBAJfhcW6xTp06VlJRUVlYOGjSIw0EKMpvNH3/8cXh4eHR0NBFRIM6whxZxNAZQif3790uSdN9994kuBIAkkesC4H96+QAAULPLly+XlJQcPHjQ+u++ffvOnTsnSVLz5s2jo6NjYmKioqJs/xFdLAAAddu1a9cLL7xw/vz55cuXjxkzRnQ5AAAAdSgpKSkoKNi8efPhw4c7dOjw61//etiwYbGxsZwKo6Djx4/36tXr8ccf37JlCxsWAAD1u3HjxjvvvLN06dLTp0+PHDly9uzZ0dHRoosKRQcOHFi0aNGmTZvat2+flJQ0ceJEOioBAIDKFRUVzZ49e8+ePUOHDs3Ozu7SpYvoivSmvLw8Ozs7Pz+/S5cumZmZ8fHxHG0DAEBDhg8fLknSli1bRBcCAAFBLx8AQMMcuvv2799/9uxZSZKaN29u6+uz/nv33XdzSA4AoB7V1dXZ2dlZWVlxcXFr165t166d6IoAAADcsm/ha9++/bPPPksLX0B99tlnTz75ZFZWVnJysuhaAACAW9euXVu3bt3ChQsrKirGjx8/Y8aMjh07ii4q1B0/fjw3N3fNmjWNGzeeNGlSUlJSs2bNRBcFAADg6KuvvkpLS/vb3/4WFxe3ePHiBx54QHRFenbs2LGFCxeuXbs2Ojp6zpw5XG4LAACtoJcPgL7RywcA0BWH7r6SkpLTp09LktSsWbNOnTrR3QcAUIMDBw688MILpaWlOTk5U6dOZT4CAADqZG3h27Jly6FDh2jhC7Jly5bNmDHjgw8+GDx4sOhaAACAo7Nnz7711lvLli2zWCxjx45NSUlp3bq16KLwX+fOnVu5cmVeXl5tbW1iYuLs2bPbtGkjuigAAABJkqT9+/fPnz+/oKAgNjZ2wYIFffv2FV1RqCgpKcnMzNy6devPfvazrKysfv36ia4IAADUgV4+APpGLx8AQOds3X1lZWW2/0iS1KRJky5dulj7+qwX8evWrZvRaBRdLwBAz2pra5csWZKRkfHTn/50/fr1Xbp0EV0RAACAI1r4VGLChAmbN2/euXNnTEyM6FoAAMC/lZWV5eXlrV69umnTpi+//PKrr77atGlT0UXBtYqKivz8fJPJdOnSpYSEhLS0NC6cCAAABDp06FBGRsbWrVsffvjh7Ozs/v37i64oFO3evTsrK6uwsJBeSgAA1I9ePgD6Ri8fACDkXLly5fvvv7e/fN+xY8csFku9evU6depku3Af3X0AAGUdO3YsISFh165dqampc+bMCQsLE10RAADAf1lb+AoKCg4ePNiuXbuhQ4cOGzbskUce4e9iUaqqquLi4s6ePbt79+5mzZqJLgcAgFC3d+/eN9544/e///2dd945derUl156qX79+qKLQt0qKyvXr1+fnZ19+vTpkSNHpqSkdOvWTXRRAAAgtJw4cSInJ2ft2rVdu3bNyMiIj4/nO7PE2rFjR1pa2ueffx4XF7do0aKePXuKrggAALhALx8AfaOXDwAA6erVq0ePHnXu7ouMjOzcubN9d1/Xrl1pvQAAeMtisaxZs2bGjBl33333hg0bHnjgAdEVAQAA/FtZWdlHH31UUFCwY8eOFi1aPPXUU8OGDRs4cCB//KrBmTNnevXqFR0d/fHHH/OMAAAgSnFxsclk2r59+/333z99+vTnn3+eeVlzqqurN23atHDhwtLS0oEDB6anp/fu3Vt0UQAAQP/OnTu3dOnSZcuWtWnTJiUlZfz48SRJ9SgqKpo9e/a3334bHx+flZV1zz33iK4IAAD8D3r5AOgbvXwAALhw7dq1I0eOWPv6ysrKSkpKDh8+bDab6e4DAHjrzJkzEyZM+OSTT2bMmDFv3rzIyEjRFQEAAEjHjh378MMPHVr4nnrqqfDwcNGl4X98++23ffv2nTJlislkEl0LAAChxWKxFBYWLliwYOfOnbGxscnJyYMHD+YKKppmNpu3b9+enZ29e/du63M6ZMgQ0UUBAAB9unjx4uLFi5cvX96iRYv09PRx48Zx2E2FrJl/zpw5+/fvHzp06MKFC6OiokQXBQAA/o1ePgD6Ri8fAACyVFZWHj161HbhPlt3X0RERIcOHWytfTExMTExMfXr1xddLwBAFQoKCl5++eVmzZq9++67ffv2FV0OAAAIdfYtfD/5yU8GDhxIC5/6/e53vxszZkx+fn5iYqLoWgAACAnWa7iZTKZDhw4NGjQoNTW1T58+oouCkqzXWiwsLOzZs2dSUtLo0aONRqPoogAAgE5cv359xYoVCxYsiIiImDlz5rRp0ziBROXMZvO2bdtSU1PLy8vHjh2bkZHRtm1b0UUBAAB6+QDoHL18AAD4qKqq6siRI/bdfaWlpbW1tc7dfdHR0Q0aNBBdLwAgqK5cuTJlypTf//73EyZMWLp0acOGDUVXBAAAQtfx48c/+OADWvi0a+bMmStWrPj8888ffvhh0bUAAKBnlZWV69evz87OPn369MiRI2fPnh0dHS26KATKnj17cnNzN27cGB0dPWvWrFGjRhGPAQCAP6qqqt59993XX3/9xx9/nDx5cmpqapMmTUQXBbmqq6vXrVs3b968y5cvv/jii2lpaXfccYfoogAACGn08gHQN3r5AABQjEN3X1lZ2YEDByorK8PDwzt27Gjf3detW7fbbrtNdL0AgED55JNPxo8fbzab33nnnUGDBokuBwAAhCha+HTDbDYPGTJkz549f//739u1aye6HAAAdOjatWvr1q0zmUyXLl0aPnz466+/3rlzZ9FFIRgOHDiwaNGiTZs2tW/fPikpaeLEiXw5IwAA8JZ9G9grr7ySnJzcvHlz0UXBF9aGzIyMjOvXr//mN79JSUlp2rSp6KIAAAhR9PIB0Dd6+QAACKDq6uqTJ0/aLtxn/ffWrVvW7r6oqChbg1/Pnj25ZBMA6EBFRcXMmTPXrFkTHx//9ttv33777aIrAgAAIefEiRN/+tOfrC18t99++6BBg2jh04HLly/37t27RYsWf/3rX+vVqye6HAAA9OPs2bNvvfVWXl6e2WweO3bs7Nmz27RpI7ooBNvx48dzc3PXrFnTuHHjSZMmJSUlNWvWTHRRAABAA8xm87Zt21JTU8vLy8eOHTt37lzCpA7cuHHjzTffNJlMRqNx1qxZr7zyCt/WDQBA8NHLB0Df6OUDACConLv7Dh48ePPmTUmS2rRpY7twX3R09AMPPNCoUSPR9QIAvPDVV18lJCRcvXr17bfffvbZZ0WXAwAAQouthe+rr75q3ry5tYXvySefjIiIEF0alFFaWtq7d+9nnnnm3XffFV0LAAB6UFZWlpeXt2bNmiZNmrz88su0b+HcuXMrV67My8urra1NTEyksRMAAHhWVFQ0a9asffv2DR06dOHChVFRUaIrgpIqKipWrlyZk5NTr169GTNmTJs2rX79+qKLAgAghNDLB0Df6OUDAECwmpqa8vJy++6+Q4cO/fjjj5JTd1+PHj0aN24sul4AgAu3bt2aO3fukiVLnnjiibVr17Zt21Z0RQAAIFTQwhdSPv3000GDBuXm5r7yyiuiawEAQMP27t37xhtvbNq0qWPHjlOnTp04cWKDBg1EFwW1qKioyM/PN5lMly5dSkhISEtL69ixo+iiAACAuhQVFaWmpv7jH/+Ij4+fP3/+vffeK7oiBMqFCxeWLFmyfPnyli1bpqWljRs3Ljw8XHRRAACEBHr5AOgbvXwAAKhObW3tiRMnysrKbA1+33333Y0bN6T/dPdFRUVZG/x69OjRsmVL0fUCQKjbt2/fCy+8cOzYscWLF0+cOFF0OQAAICSUl5f/8Y9/pIUvBOXk5GRkZHz66af9+vUTXQsAANpTXFxsMpm2b99+3333zZgxY9SoUZyJC5cqKyvXr1+fnZ19+vTpkSNHpqSkdOvWTXRRAABAvF27dqWlpf3lL3+Ji4szmUw//elPRVeEYDh16tTixYtXrVrVtm3b2bNnjx8/PiwsTHRRAADoHL18APSNXj4AALThhx9+sF24r6SkZO/evdevX5ckqXnz5rYL98XExNx3332tWrUSXSwAhIqampo33njj9ddff+ihh9avX9+5c2fRFQEAAJ2zb+Fr1qzZ4MGDaeELNRaL5fnnn//00093795N/gQAQCaLxVJYWLhgwYKdO3fGxsYmJycPHjzYYDCIrgtqV11dvWnTpoULF5aWlg4cODA9Pb13796iiwIAAGIcOHBg3rx5BQUFjzzySE5OzmOPPSa6IgTbiRMncnJy1q5d27Vr14yMjPj4eP6mAAAgcOjlA6Bv9PIBAKBVDt19+/btq6iokJy6+7p37966dWvRxQKADn3//fcJCQnffPPN3LlzZ82aZTQaRVcEAAB06+TJk++//75DC9+AAQMiIyNFlwYBbt68+fOf//zGjRu7du1q0qSJ6HIAAFA1s9m8bdu2zMzMgwcPDho0KDU1tU+fPqKLgsaYzebt27dnZ2fv3r3b2gs6ZMgQ0UUBAIDgKS0tzc7O3rhxY/fu3dPT04cNGya6Ioh06NChjIyMrVu3Pvzww2lpaSRDAAAChF4+APpGLx8AAPph7e4rKyuzNvjt3bv3/PnzklN3X1RUVFRUlOhiAUDDLBbLmjVrpk+f3qlTp/fee+/+++8XXREAANAnWvjgTnl5ea9evR555JFt27bxpRIAALhUWVm5efPm+fPnl5WVDR06NCMjIyYmRnRR0Lbi4mKTyVRYWNizZ8+kpKTRo0eTxAAA0LeTJ09mZWXl5+d36dIlMzOT67DBZv/+/fPnzy8oKIiNjc3Kynr88cdFVwQAgN7QywdA3+jlAwBAzy5fvmy7cN/Bgwf3799/9uxZSZKaN28eFRVl3+B39913c9AZAOQoLy9PTEz84osvZsyYMW/ePM6kBwAAirNv4WvatOmQIUNo4YOzr7766he/+EVKSsrcuXNF1wIAgLpcu3Zt3bp1JpPp0qVLw4cPnzNnTpcuXUQXBf3Ys2dPbm7uxo0bo6OjZ82aNWrUqPDwcNFFAQAAhV24cGHJkiV5eXmtWrVKTU0dP358WFiY6KKgOjt37kxPT//LX/4SFxe3cOHCBx98UHRFAADoB718APSNXj4AAEKLQ3dfSUnJ6dOnJUlq1qxZp06d6O4DAM8KCgpeeuml1q1bb9iw4aGHHhJdDgAA0JVTp05t27aNFj7It27duvHjx//hD3+wfpwJAADOnTu3cuXKvLy82traxMTE2bNnt2nTRnRR0KcDBw4sWrRo06ZN7du3T0pKmjhxYoMGDUQXBQAAFFBRUbFy5cqcnJz69etPnz49KSmpXr16oouCqhUXF6emphYXFw8aNCgrK6tHjx6iKwIAQA/o5QOgb/TyAQAQ6mzdfWVlZbb/SJLUtGnTzp0723f33XXXXUajUXS9ACDGuXPnXn755T/96U8TJkxYunRpw4YNRVcEAAB0ghY++GPy5Mnvvffejh077r//ftG1AAAg0rFjx5YtW7ZmzZrGjRtPmjQpKSmpWbNmoouC/h0/fjw3N5cXHgAA+nDjxo0333zTZDIZjcZZs2ZNnTqVXn3IV1RU9Nprr+3du3fo0KE5OTmdO3cWXREAANpGLx8AfaOXDwAAOLp8+bKtr8/677FjxywWS7169Tp16mRr7YuOju7WrRvdfQBCwccff/ziiy9GRkauW7fuF7/4hehyAACAHvzrX//aunVrQUHBzp07Gzdu/Ktf/WrYsGG//OUv+Z5veKW6unrAgAFlZWV///vfW7ZsKbocAAAE2Ldv35IlSzZt2tShQ4dp06ZxeTQEHxeEBABA66qqqt599925c+dWVFT85je/SUlJadq0qeiioD0Wi2Xr1q3p6enHjh1LTEx8/fXX27VrJ7ooAAC0il4+APpGLx8AAKjblStXvv/+e+fuvsjIyM6dO9t393Xt2jUsLEx0vQCgmGvXrs2aNWv16tXDhg1btWpV8+bNRVcEAAC07cKFCx9//HFBQcH//d//1atXr1+/fsOGDYuPj7/ttttElwatunjx4sMPP9yuXbuioiIu5wgACCnFxcUmk2n79u333XffjBkzRo0aFR4eLroohK6Kior8/HyTyXTp0qWEhIS0tLSOHTuKLgoAANTBbDZv27Zt9uzZp06dGjt27Lx581q1aiW6KGibw4sqMzOzdevWoosCAEB76OUDoG/08gEAAF9cu3btyJEj9t19x48fN5vNdPcB0JPPPvssMTGxsrJy1apVzzzzjOhyAACAhtla+D755JPIyEhrC9/QoUMbNmwoujTowd69e2NjY8eNG7d8+XLRtQAAEHAWi6WwsHDhwoVfffVVbGxscnLy4MGDDQaD6LoASZKkysrK9evXZ2dnnz59euTIkSkpKd26dRNdFAAAcIFLqCGgrBd7zMzMvHr16pQpU5KTk/nSWAAAvEIvHwB9o5cPAAAoo7Ky8ujRo7bWvpKSksOHD5vN5oiIiA4dOtha+2JiYmJiYurXry+6XgDw5NatW3Pnzl28ePGTTz75zjvvtGnTRnRFAABAk2jhQ9C8//778fHxb7/99sSJE0XXAgBAoJjN5u3bt2dmZn777beDBg1KSUl55JFHRBcFuFBdXb1p06aFCxeWlpYOHDgwPT29d+/eoosCAAD/VVRUlJyc/N133w0dOjQnJ6dz586iK4I+/fjjj2vWrMnJybl58+bkyZNTU1ObNGkiuigAALSBXj4A+kYvHwAACJSqqqojR47Yd/eVlpbW1tY6d/dFR0c3aNBAdL0A8G9ff/11QkLC6dOnFy1axJnQAADABxcvXty+fbu1hS8iIqJ///608CEI5syZYzKZioqKfv7zn4uuBQAAhVVWVm7evDkrK+v7778fOHDg3LlzH3zwQdFFAXWwdp9mZ2fv3r3beg3JIUOGiC4KAIBQt2PHjrS0tM8//zwuLm7JkiU9evQQXRH07/r16ytWrFiwYEFERMTMmTOnTp3KGTIAANSJXj4A+kYvHwAACB533X3h4eEdO3a07+7r1q3bbbfdJrpeACGnpqbmjTfemDNnzmOPPZafn9+hQwfRFQEAAC2hhQ9iWSyW4cOHf/nll3//+9+JsgAA3aioqMjPz1+0aNHFixeHDx8+Z86cLl26iC4K8E5xcbHJZCosLOzZs2dSUtLo0aONRqPoogAACDlff/31/PnzCwsL4+LicnJyevXqJboihJaLFy8uXrz4t7/97e233z5z5syXX365Xr16oosCAEC96OUDoG/08gEAAJGqq6tPnjxpa+2z/nvr1i377r6oqKjo6OiePXty/iuAgDp48OALL7xw8ODBjIyMWbNmcT4NAACQyWUL37PPPtuoUSPRpSHkXL9+vU+fPhEREcXFxXxFDgBA686dO7dy5cq8vLza2trExMTk5OS2bduKLgrw3Z49e3Jzczdu3BgdHT1r1qxRo0aFh4eLLgoAgJBw8ODBuXPnbt26tXfv3tnZ2f369RNdEULX+fPn33jjjby8vFatWqWmpo4fPz4sLEx0UQAAqBG9fAD0jV4+AACgLs7dfQcPHrx586YkSW3atLFduC86OvqBBx7g1FgAirBYLGvWrHn11Ve7d+++YcOGe++9V3RFAABAAy5dulRYWFhQUPDpp5+Gh4fTwgeVOH78eK9evR5//PEtW7YYDAbR5QAA4Itjx44tW7ZszZo1jRs3njRpUlJSUrNmzUQXBSjjwIEDixYt2rRpU/v27ZOSkiZOnNigQQPRRQEAoFsnTpzIyclZu3Ztt27dXn/99WHDhomuCJAkSSovL8/Ozs7Pz+/SpUtmZmZ8fDzH8QAAcEAvHwB9o5cPAACoXU1NTXl5uX1336FDh3788UfJqbuvR48ejRs3Fl0vAI05ceLE2LFji4uLZ8yYMX/+/IiICNEVAQAAVbNv4QsLC4uLi6OFD2pTVFT01FNPZWdnv/baa6JrAQDAO/v371+8ePGmTZs6dOgwbdo02pygV8ePH8/NzaVhFQCAwPnXv/61aNGiVatW3XXXXampqaNHjzYajaKLAv7HsWPHFi5cuHbt2ujo6Dlz5tBrCgCAPXr5AOgbvXwAAEB7rN19ZWVltga/77777saNG9L/dvdFRUX16NGjZcuWousFoF4bNmyYMmVKhw4dNmzY8OCDD4ouBwAAqJfLFr5f//rXfJ8I1Ck3N3fmzJkffPDB4MGDRdcCAIAsxcXFJpNp+/bt3bt3nzlz5qhRo8LDw0UXBQTWuXPnVq5cmZeXV1tbm5iYOHv27DZt2oguCgAAzbt48eLixYuXL1/eokWL9PT0cePGESyhZiUlJZmZmVu3bv3Zz36WlZXVr18/0RUBAKAK9PIB0Dd6+QAAgE788MMPtgv3lZSU7N279/r165IkNW/e3HbhvpiYmPvvv/+OO+4QXSwA8c6ePfvSSy99+OGHr7zyyqJFi+rVqye6IgAAoEaXL1/+6KOPaOGDFk2YMGHz5s07d+6MiYkRXQsAAJ4UFRXNnTt3x44dsbGxycnJgwcPNhgMoosCgqeioiI/P99kMl26dCkhISEtLa1jx46iiwIAQJOuX7++YsWKBQsWREREzJw5c9q0afXr1xddFCDL7t27s7KyCgsLY2NjFyxY0LdvX9EVAQAgGL18APSNXj4AAKBbDt19+/btq6iokJy6+7p37966dWvRxQIIqq1bt06aNKlRo0br1q17/PHHRZcDAABUhxY+6EBVVVVcXNzZs2d3797drFkz0eUAAODIbDZv3749MzPzm2++iYuLmzt3bmxsrOiiAGEqKyvXr1+fnZ19+vTpkSNHpqSkdOvWTXRRAABoxo8//rhmzZqcnJybN29Onjw5NTW1SZMmoosCvLZjx4709PS//e1vcXFxixYt6tmzp+iKAAAQhl4+APpGLx8AAAghOXGqMQAAIABJREFU1u6+srIya4Pf3r17z58/Lzl190VFRUVFRYkuFkBAXL169bXXXlu9evWYMWNWrFjB6fgAAMCerYXv//2//2c0Gq0tfM888wyn/kCjzpw506tXr+jo6I8//jgsLEx0OQAA/FtlZeXmzZuzs7OPHj06cODAuXPnPvjgg6KLAlShurp606ZNCxcuLC0tHThwYHp6eu/evUUXBQCAqlVXV69bty4zM/Pq1atTpkxJTk5u3ry56KIAvxQVFc2ePfvbb7+Nj4/Pysq65557RFcEAIAA9PIB0Dd6+QAAQEi7fPmy7cJ9Bw8e3L9//9mzZyVJat68eVRUlH2D3913320wGETXC8Avf/7zn8eNG1ddXb169epf/epXossBAABqceXKlQ8//NDawmcwGJ544gla+KAb3377bd++fadMmWIymexvt1gsVVVV9erVE1UYACA0VVRU5OfnL1q06OLFi8OHD09PT+e0VMCZ9aqV2dnZu3fvjo2NTU5OHjJkiOiiAABQHbPZvG3bttTU1PLy8rFjx2ZmZrZu3Vp0UYAyLBZLYWHhnDlz9u/fP3To0IULF/KF1ACAUEMvHwB9o5cPAADgfzh09x04cODMmTOSJDVr1qxTp0509wEadfPmzczMzMWLFw8dOnTlypUtWrQQXREAABCPFj6EiN/97ndjxozJz89PTEy03nLjxo2EhIQnnnjipZdeElsbACB0nD9/fsWKFcuXL6+pqUlMTExOTm7btq3oogC1Ky4uNplMhYWFPXv2TEpKGj16tNFoFF0UAACqUFRUNHPmTNqcoG8ODasZGRn8GQUA0LENGzYsXbq0trbW+uOFCxckSbKd4hUWFjZ9+vQXXnhBWH0AoCh6+QAAAOrg0N1XVlZWVlYmSVLTpk07d+5s391311138Tk6IMq1a9ciIiIaNGjg/Ktdu3YlJCScPXt20aJFEydODH5tAABAVVy28D399NNNmzYVXRoQKDNmzFi5cuXnn3/+8MMPHz9+fNCgQQcPHuzdu/euXbtElwYA0L/jx4/n5uauWbOmUaNGkydPnjZtWvPmzUUXBWjJnj17cnNzN27cGB0dPWvWrFGjRoWHh4suCgAAYYqKilJSUr755pv4+PisrCyu8wzdq66uXrdu3bx58y5fvvziiy+mpaXdcccdoosCAEB5//znP++9914PdygtLSX7AdANevkAAAC8dvny5bKyMvsGv2PHjlksliZNmnTp0iUqKsrW4NetWze6+4DgGDFiRKtWrZYvX25/Y3V1dXZ2dlZWVv/+/deuXdu+fXtR5QEAAOGuXr36wQcfFBQU/PnPf5YkiRY+hBSz2TxkyJA9e/asXLnyxRdfvHbtWnV1tcFgOHr0KN9bDwAInP379y9evHjTpk3t27dPSkqaOHGiy69hAiDHgQMHFi1axBsKAKBjFRUVjRs39nCHnTt3pqWl/fWvf42LizOZTD/96U+DVhsgXFVV1bvvvpuRkXH9+vXf/OY3KSkpHo5s//jjj7fddlswywMAQBEPPPDAvn37nNtbDAbD/fff/9133wmpCgACgV4+AAAABVy5cuX777937u6LjIzs3Lmz7cJ90dHRXbt2DQsLE10voDf5+fnjx483GAxFRUX9+vWz3lhSUjJmzJjDhw8vWLBg6tSpBoNBbJEAAECIH3/88bPPPnvvvfc+/PDDmpqan/3sZ8OGDXv++edbtGghujQgqC5fvtytW7fz588bDIba2lpJkiIiIl5//fX09HTRpQEAtOfrr78+derUs88+6+4OxcXFJpNp+/bt3bt3nzlzJpcRA5Riu9Bl48aNJ02alJSU1KxZM5f3rKioeOONNzIyMjguCgDQhIqKiv79+8+fP3/AgAHOvz1w4MC8efMKCgpiY2Ozs7Mfe+yx4FcIqMGNGzfefPNNk8lkNBpnzZr1yiuvuOzZGzFixGOPPTZ58uTgVwgAgD+WLl2anJxcU1PjcHtERITJZHr11VeFVAUAgUAvHwAAQEBcvXr16NGj9t19x48fN5vNdPcBijty5EiPHj1u3rxpNBpbtGhx+PDhpk2b/va3v01OTu7Zs+f69evvuece0TUCAAAFXLt27cqVKx07dpRzZ2sLX0FBwbZt2yorK60tfKNGjWrZsmWg6wRUqKam5tVXX33zzTcdbr/zzjuPHz8uoiIAgIbt3LnziSeeaNeu3eHDh517hKxdfIWFhbGxscnJyYMHD6aPCFDcuXPnVq5cmZeXV1tbm5iYOHv27DZt2jjcZ/Hixa+99torr7ySl5fH2xAAoHK3bt0aMGDAF198Yb3civ3Mdfjw4ZycnI0bNz700EPp6elDhgwRWCegEhUVFStXrszJyalXr96MGTOmTZtWv35922/37t3bs2dPSZI2bNgwevRocWUCAOC1H374oUOHDmaz2eF2g8Fw8uTJdu3aCakKAAKBXj4AAIAgqaysPHr0qK21r6Sk5PDhw2azOSIiokuXLjExMVFRUdYGv+7du9erV090vYA23Lp1q1evXqWlpdXV1ZIkRUREDB48+OzZs998801mZubMmTPplQUAQB9KSkqefvrpX//614sXL/ZwN1sL3/vvv3/r1i1a+ABJki5cuPDss8/u3LnT+XtMJUn6+uuve/XqFfyqAAAaVVxcPGDAgMrKytra2j/96U9PP/209Xaz2bx9+/Z58+b94x//sHbxcZo1EGgVFRX5+fkmk+nSpUsJCQlpaWm2rz65detWhw4dLly4YDQaX3rppRUrVtDOBwBQrdra2vj4+MLCwpqaGoPBUFBQMHToUEmSysvLs7Oz8/Pz77nnnrlz58bHxzOdAfYuXLiwZMmS5cuXt2zZMi0tbdy4cdbLoQ8aNOjPf/5zdXW10Wjctm3bM888I7pSAAC80Ldv36+++sq+nc9oND7yyCNffvmlwKoAQHH08gEAAAhTVVV15MgR++6+0tLS2traiIiIDh062C7cFxMTExMTY/89av47ceJETU1Np06dFBwTEGLSpEnvvPOOw0nJXbt23bJly3333SeqKgAAoKxNmzaNGzeusrKyTZs2p06dcj5r5+bNm0VFRQ4tfM8999wdd9whpGBAPQ4cOPDkk0+ePn3a+UtMJUmKjIx86aWXli9fHvzCAABa9OWXXw4YMKCqqqq2tjYsLKxHjx7ffPNNVVXVH/7wh+zs7KNHjw4cODAjI+Ohhx4SXSkQQiorK9evX5+dnX369OmRI0empKR069btrbfemjJlijUBGo3GxMTE1atXG41G0cUCAODIYrEkJib+7ne/q62tlSTJaDTeddddxcXFeXl5eXl5rVq1Sk1NHT9+PN/dCbhz6tSpxYsXr1q1qm3btrNnz+7Ro0efPn2sZwUbDIawsLCPPvroySefFF0mAAByrV69etKkSfafaoWFhb311lsTJkwQWBUAKI5ePgAAABVx190XHh7esWNH++6+6OjoBg0a+LygtWvXTpo0afLkyenp6S1atFBwFYBgev/9961fzGnPaDQ2adLk8OHDrVq1ElIVAABQUE1NTXp6uslkMhj+fSRz9/9n797joyjvxY/P3hJIAkm4BBZRJFrQYLm4iIRdQXAFesyiUgLWC0rVWOyReDkKnhaLtVVS24PgS61obctpvGC9Jr68RbwlhCoR9IiJHokiGG4Cm3AJkmz298ee7m+7t8zuzs4zM/t5/8GL7O48853Lfp+ZZ+c7849/TJo0KfAuJXxArzo7O++9997Al+jEiRORHygoKNi3b5/NZlM/NgCAvrz33nuzZ88OFPIFX7zpppueffZZr9d7zTXX3H777cXFxQIjBDJZV1dXdXV1VVXVF198cckll/zjH/9oa2sLXg1isVgWLlz4+OOPU84HANCaW265Zc2aNaHXaptMJpvNVlRUtHz58kWLFjFkAcixffv2FStWPPnkk4MHDz5w4EDwTrhms9lms7355pvnnXee2AgBAJDp0KF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vNTc3+/3+4uJip9N59913u1yuCRMmmM1m0TECAPB/Dh069Oqrr/7xj38UHQgAQCX33Xff0KFDr7rqKtGBAIC2UMsHAAAAQAGTJ0/+3e9+d+jQocLCQtGxAACgY3a7nVo+AHpXUFDw7bffio4CAETyer08qggAYDxDhgxZuHDhwoULjx8/Xl9fX1dX99JLL1VVVQ0cOHDGjBlut3vOnDlDhw4VHabxHT16dMuWLYHivcbGxgMHDuTm5o4fP97tdq9YsWL69OmDBg0SHSMAANGtX7/eZDJdeumlogMBAKhh586df/7zn9esWZOVlSU6FgDQFmr5AAAAAChgypQpfr//gw8+mDVrluhYAADQMWr5ABhAQUGB1+sVHQUAiOT1enkuHwDAwPr06eN2u91u98qVK1tbW2tqampra2+66aYbb7xx/PjxZWVlHo/n7LPPNplMoiM1jra2tkDxXlNT04cffnjixAm73e5wOG6//Xan0zlp0iQujQUA6EJ1dfXFF1+cn58vOhAAgBqqqqqKioquueYa0YEAgOZQywcAAABAAUVFRSNHjmxsbKSWDwCAVAwbNqy1tVV0FACQkvz8fGr5AGQ4r9fLhYkAgAxRXFxcWVlZWVl59OjRDRs21NbWPvbYY3ffffeQIUNmzpzp8XhmzZrVv39/0WHqT3d398cffxwo3nvvvfd27NhhtVrHjRvndDorKiqmTp166qmnio4RAIDEfPPNNw0NDS+++KLoQAAAati9e/cTTzzxhz/8gTuPAEAkavkAAAAAKKO0tHTTpk2iowAAQN/sdntDQ4PoKAAgJdTyAQC1fACADJSbm+vxeDwejyRJ27Ztq62trampueyyy7Kyslwul9vtvvjii8844wzRYWranj17Pvzww6ampsAj+I4fPz5kyJBzzjnnmmuucblcTqezb9++omMEACB5f/vb3woKCrg7MABkiJUrVxYUFCxatEh0IACgRdTyAQAAAFBGaWnp8uXLe3p6zGaz6FgAANAru92+e/du0VEAQEoKCgo6Ojo4NQCQsXp6eg4fPlxQUCA6EAAAhBkzZsyYMWOWLl26f//+d955p6amZuXKlcuWLSsuLna73WVlZbNmzeLJDJIk+Xy+lpaWYPFec3Oz2WwePXq0w+FYvXq10+ksKSkxmUyiwwQAQBlPPfVUoM5fdCAAgLTbs2fPY489VlVV1adPH9GxAIAWUcsHAAAAQBmlpaVer7elpaWkpER0LAAA6JXdbj906FBnZyf3WQegXwUFBYE6Fp5JBSAzBeqZqeUDAECSpMGDB5eXl5eXl/t8vsbGxtra2rq6urVr1+bm5k6fPt3j8Vx00UUnnXSS6DBVdfjw4Y8//jhQvNfQ0HDo0KG8vLxx48Z5PJ6VK1e6XK7CwkLRMQIAoLwtW7Z8+umnjz76qOhAAABq+N3vfpefn3/dddeJDgQANIpaPgAAAADKGDt2bE5OTmNjI7V8AAAkzW63S5K0Z8+ekSNHio4FAJIUKF/xer3U8gHITF6vV/pnMgQAAAEWi8XlcrlcLkmSvvrqqzfffLOmpqaysvKGG24oKSnxeDxlZWVTpkwx6sO9W1tb6+vrA8/f27JlS09Pj91ud7lcv/rVr1wu14QJE4y64AAABFVXV48YMaK0tFR0IACAtPvuu+8ee+yxe+65h9vXAkAs1PIBAAAAUIbNZnM4HI2Njddee63oWAAA0Kthw4ZJkrR7925q+QDoV6CEz+v1jhgxQnQsACBAoJaPemYAAGIZOXJkRUVFRUVFZ2dnQ0NDTU3NU089VVVVNWjQoOnTp5eVlc2ZM0fvVfHHjh376KOPAsV777zzzv79+20229ixY51O59KlS6dNm1ZUVCQ6RgAA1NPT0/P000//9Kc/NZlMomMBAKRdVVVVbm5uRUWF6EAAQLuo5QMAAACgmNLS0traWtFRAACgY0OGDDGbzW1tbaIDAYDkBS66bW9vFx0IAIgRSIB6r0AAAEAFffv2dbvdbrd79erVra2tNTU1tbW11113XU9Pz+TJkz0ej9vtdjgcosOUq62tLVC8V19fv3nz5u+//95utzscjttuu83pdJ5zzjnZ2dmiYwQAQIwNGzZ8++23l19+uehAAABpd+DAgT/+8Y8rVqzIyckRHQsAaBe1fAAAAAAUU1paev/99x86dKiwsFB0LAAA6JLNZhs0aNDu3btFBwIAyQucDgQeSwUAGYjn8gEAkITi4uLKysrKysqDBw++9dZbdXV1DzzwwLJly0499dSZM2e63e4f/ehHeXl5osP8F93d3Z9//nmgeK++vv6rr76yWq2jRo1yuVwVFRUOh2PMmDGiYwQAQBOqq6snTpx4xhlniA4EAJB2v//97/v06XPDDTeIDgQANI1aPgAAAACKmTJlit/v/+CDD2bNmiU6FgAA9Mput1PLB0DXsrKy+vbtSy0fgIzl9XpzcnJ48A4AAMkZMGBAeXl5eXn5I488smXLlsDD+h577LE+ffo4nc6ysrJLL730lFNOERVee3v7hx9+WF9f39DQ0NDQ0NnZ2b9//0mTJi1cuNDlck2ZMoVHTwAAEOb48eMvvPDCihUrRAcCAEi7AwcOPPTQQ7/85S+1disWANAaavkAAAAAKKaoqGjkyJGbNm2ilg8AgKRRywfAAPLz86nlA5CxvF4vD+UDACB1ZrPZ4XA4HI4VK1bs27fvtddeq62tveuuu26++ebi4uKysjKPxzNt2jSbzZbuSFpbWwPFe/X19c3NzX6/v7i42Ol0PvDAA06ns6SkxGQypTsGAAD066WXXjpy5Mhll10mOhAAQNqtWrUqKytr8eLFogMBAK2jlg8AAACAkkpLSxsbG0VHAQCAjtnt9ra2NtFRAEBKCgoKqOUDkLG8Xm9BQYHoKHwZIUoAACAASURBVAAAMJSioqKFCxcuXLiwu7t706ZNtbW1L7/88po1awYMGHDBBRe43W6Px2O325Wa3ZEjR7Zu3Roo3tu4cePBgwdzc3PHjx/v8XhWrlw5ZcqUgQMHKjUvAAAMr7q62u12Dx06VHQgAID08nq9Dz300B133NGvXz/RsQCA1lHLBwAAAEBJpaWly5cv7+npMZvNomMBAECXhg0btnnzZtFRAEBKCgoK2tvbRUcBAGK0t7dTywcAQJpYrVaXy+VyuVauXNna2lpXV1dTU7NkyZLFixdPmDDB7XaXlZU5nc4kHpTX1tYWKN5ramr64IMPurq67Ha7y+W66667HA7HpEmTsrKy0rFEAAAY28GDB19//fXHH39cdCAAgLRbtWqV2Wz+93//d9GBAIAOUMsHAAAAQEmTJ0/2er0tLS0lJSWiYwEAQJfsdvvu3btFRwEAKaGWD0Amo5YPAAB1FBcXV1RUVFRUHDt27K233qqtra2urq6qqioqKpo1a5bH45k1a1b//v1jTd7V1fXJJ5/U19c3NDS8++67+/bts1qt48aNczqdFRUV06ZNGzFihJqLAwCAIT399NNWq/XSSy8VHQgAIL3a29sffPDBW265hYfyAYAc1PIBAAAAUNK4ceNycnIaGxup5QMAIDl2u/3AgQPff/99dna26FgAIEkFBQVer1d0FAAghtfrpZYPAAA15eTkeDwej8cjSdK2bdtqa2vr6uouv/xySZLOPffcwFuB3yx27969efPm4PP3jh8/PnTo0IkTJy5evDjwuL8+ffoIXhgAAIylurr6kksuycvLEx0IACC9HnjggZ6enptuukl0IACgD9TyAQAAAFCSzWZzOByNjY3XXnut6FgAANAlu93u9/v37t17yimniI4FAJKUn5//5Zdfio4CAMTwer2jRo0SHQUAABlqzJgxY8aMWbp06Xfffff222+//PLL995777Jly/Ly8mw226FDhywWy+jRox0Ox8KFC51OZ0lJiclkEh01AADGtGPHjsbGxl/84heiAwEApFdHR8eaNWtuvvlm7nEGADJRywcAAABAYaWlpa+88oroKAAA0Kthw4ZJkrR7925q+QDoF8/lA5DJeC4fAABidXR0fPDBB4En79XX13d0dOTk5BQVFXV2dppMpqysrGHDhjkcjn/7t38bPny46GABADCydevWDRo06MILLxQdCAAgvR588MHu7u7KykrRgQCAbphFBwAAAADAaEpLSz/77LNDhw6JDgQAAF0aOnSoyWTavXu36EAAIHn5+fnU8gHIWF6vNz8/X3QUAABkltbW1nXr1lVWVk6cOLGwsPDCCy/87//+78LCwhUrVmzevPnw4cPbt29va2trbW194IEHCgsLly9ffvLJJ48ZM2bZsmV1dXXd3d2ilwAAAAN6+umnL7vsMpvNJjoQAEAaHT16dPXq1ZWVlYWFhaJjAQDd4Ll8AAAAABQ2ZcoUv9//4Ycfzpw5U3QsAADoT3Z2dmFhYVtbm+hAACB5BQUF7e3toqMAADHa29t5Lh8AAOl29OjRLVu2NDU1NTQ0vP322999911OTs6ECROcTufSpUvPP//8wYMHR0516qmnVlRUVFRUdHZ2NjQ01NXVvfjii1VVVQMHDpwxY0ZZWZnH4+HyUwAAFLF58+bPPvvsiSeeEB0IACC9Hnzwwc7OziVLlogOBAD0hFo+AAAAAAorKioaOXJkY2MjtXwAACRn2LBhPJcPgK4VFBR4vV6/328ymUTHAgCq8vv91PIBAJAmbW1tgeK9+vr6Dz/88MSJE3a73eFw/Md//IfT6TznnHOys7NlNtW3b1+32+12u1euXNna2lpTU1NbW3v99df7fL7x48cHivrOPvtszmgAAEhadXX1aaedNmnSJNGBAADS6OjRo6tWrbrpppsGDRokOhYA0BNq+QAAAAAor7S0tLGxUXQUAADold1up5YPgK4VFBR0d3cfPXo0Ly9PdCwAoKojR450d3dTywcAgCK6u7s//vjj+vr6pqam999//+uvv7ZaraNGjXK5XBUVFeedd97IkSNTn0txcXFlZWVlZeXRo0c3bNhQW1u7du3au+++e8iQITNnzvR4PLNnz+7Xr1/qMwIAIHP4fL6nn376hhtuoDAeAIzt4YcfPnr06C233CI6EADQGWr5AAAAAChv8uTJd911V09Pj9lsFh0LAAD6Qy0fAL3Lz8+XJMnr9VLLByDTeL1e6Z9pEAAAJGHv3r0ffPBB4Pl7DQ0NnZ2d+fn555xzztVXX+1yuZxOZ9++fdM069zcXI/H4/F4HnnkkS1bttTV1dXU1CxYsCA7O9vlcrnd7ksuuWT06NFpmjsAAEZSV1e3Z8+eyy+/XHQgAIA0On78+KpVq37+858PHjxYdCwAoDPU8gEAAABQXmlpqdfrbWlpKSkpER0LAAD6Y7fbP/nkE9FRAEDyAg+kam9vHz58uOhYAEBV7e3t0j/TIAAAkMPn87W0tASK9+rr65ubm/1+f3FxsdPpfOCBB5xOZ0lJicqP9DGbzQ6Hw+FwLF26dP/+/a+++mptbe199923bNmy4uLisrIyj8czderUrKwsNaMCAEBHqqurzz333FGjRokOBACQRg8//HB7e/ttt90mOhAA0B9q+QAAAAAob9y4cTk5OY2NjdTyAQCQBJ7LB0DvAkUsgYdTAUBGCaQ+avkAAIjv8OHDH3/8caB4r6Gh4dChQ3l5eePGjfN4PCtXrnQ6nQMGDBAd4/8ZPHjwwoULFy5c6PP5Ghsba2tr6+rq1qxZk5ubO336dI/HU1ZWNmzYMNFhAgAg0uHDh3NyciwWS+DPY8eOvfjii7/97W/FRgUAUFZnZ2foY9KPHz/+hz/8YfHixUVFRQKjAgCdopYPAAAAgPJsNpvD4di0adMFF1zQ2NjY2NjY0NBQXl6+bNky0aEBAKADdrt9//79H3300Z49e/bu3btr167u7u67775bdFwAIJfNZpMk6ZNPPjGZTF6v1+v1tre3//jHP+YHXQDGs2/fvueeey4/P7+goKCgoCDwdOVAGgQAAKHa2tqCxXtbtmzp6emx2+0ul+tXv/qVy+WaMGGC2WwWHWM8FovF5XK5XC5JklpbW+vq6mpqapYsWbJ48eIJEya43e6ysrIpU6ZofCkAAEiHV155pbKy8oorrrjyyivPPvvsF198sbOzc/78+aLjAgAo6corr+zbt+/y5ctHjx4tSdKjjz568ODBW2+9VXRcAKBLJr/fLzoGAAAAAMbR2dnZ1NTU2Nj417/+tbW1tbOz02QyZWdnHz9+/C9/+cvVV18tOkAAALTo6NGjt99++86dO3ft2vXtt98ePHjQ5/MF3rJYLD09PXPmzHnxxRfFBgkA8V1zzTUbNmzo6Og4cuRIMIkF5eTkHDp0KCsrS0hsAJA+J06cKCwsPHbsWNjrFoslLy+vf//+M2bM+Mtf/iIiNAAABOvq6vrkk08CxXvvvPPO/v37bTbb2LFjnU6ny+WaNm2aAW72cezYsY0bN9bU1Lzwwgs7d+4cPHjw+eefX1ZWdvHFF+fn54uODgAAlTz55JNXXnmlxWLp7u4+7bTTbDbb0KFD3377bdFxAQCUVFJS0tLSYjKZysvL77zzzosuuqi8vHzVqlWi4wIAXeK5fAAAAACU8eWXXy5YsODjjz/2+Xw2m83n8/X09EiS5Pf7jx8/LknSiBEjRMcIAIBG5ebmfvHFFxs2bIi88VagY502bZqQwABAvsmTJ//1r3+N+pbZbJ4+fTqFfAAMKSsra/r06a+99lpYGbPP52tvb29vb588ebKo2AAAUF9bW1tTU1Pg+XubN2/+/vvv7Xa7w+G47bbbnE7nxIkT+/TpIzpGJeXk5LjdbrfbvXr16m3bttXW1tbV1V133XU//elPJ0+e7PF43G63w+EQHSYAAOnl8/lMJlN3d7ckSdu3b7darS0tLePHj1+0aNFPfvITA1TvAwAkSdq5c6ff7/f7/S+88ML69euHDx8+e/Zs0UEBgF7xXD4AAAAAipk1a1ZdXV2ghC/S9u3bi4uLVQ4JAAC92LBhwwUXXBDr3c2bN3PhFwCNO3bs2JAhQ44cORL5ltVqXbNmzeLFi9WPCgBU8PDDD1dWVgauWQyTm5u7d+/e3Nxc9aMCAEAd3d3dn3/+eaB4r6mp6bPPPrNYLKNHj3a5XE6n0+FwjBkzRnSMajtw4MCGDRvq6upeeumlvXv3jhw58sILLywrK7vwwgsNVsoIAEDAunXrFi1aFHadgNlsNplMJpOprKysuro6JydHVHgAgNR1dHSEPXs8Kyurq6tr9uzZ99xzDz9kA0CiqOUDAAAAoJgdO3acccYZgafwhTGZTMePH+dBHAAAxDFx4sStW7eGPdFFkqScnJyOjg6LxSIkKgCQ7+abb3744Ye7uroi3+LWHgAM7Kuvvoqa4mw2289//vNVq1apHxIAAGnV0dHxwQcf1NfXNzQ0bNy48dixY/379580aVKgeO+8884rKCgQHaMm+Hy+rVu31tTU1NbWfvTRR3369HE6nWVlZXPnzj355JNFRwcAgGL+8pe/XHfddZG/bgRUVVXdcccdKocEAFDW//zP/4wdOzbydavV6vP5ysrKHnzwwREjRqgfGADoFLV8AAAAAJR077333nXXXZHD9IMHD963b5+QkAAA0Iu///3v8+fPDxuvM5lMbrf7jTfeEBUVAMj35Zdfjho1KvJ3h+Li4u3btwsJCQDUUVxc/NVXX4W9aDKZmpubR48eLSQkAACU1draGijeq6+vb25u9vv9xcXFgeI9l8s1YcIEs9ksOkZN27t37+uvv15bW/vaa68dPny4uLi4rKzM4/FMmzbNZrPJb2f//v0DBgzglk8AAE154oknKioqIi8SsFqtHo/nueeeM5lMQgIDACilpqZmzpw5Ud8ymUw/+tGPnn/++ezsbJWjAgD9YhwNAAAAgJLuuOOOUaNGRf6KfMoppwiJBwAAHZk7d+6IESPCftK22WwzZswQFRIAJOT000+fMWOG1WoNfTErKyvW77sAYBhz5szJysoKfcVqtV5wwQUU8gEABOro6HjrrbeSnvzo0aP19fVVVVUej2fQoEGnnXbajTfeuG3bNrfb/cwzz+zfv3/79u3r1q2rrKx0OBwU8vVqyJAhCxcuXL9+/b59+958883y8vI33njjwgsvHDp06Pz589euXbtnzx457axatcrlcn399ddpjhcAgAT09PREVutZrdbRo0f/7W9/o5APAAzg66+/jnoXEqvVOnv2bAr5ACBRDKUBAAAAUJLVal27dm1PT0/oi2az+fTTTxcVEgAAemE2m++8886wX7VPnDgxdepUUSEBQKIqKyu7u7tDXzlx4sSsWbNExQMA6pg1a9aJEydCX/H5fJWVlaLiAQDg+eef/8EPfvCf//mfCU3V1tb27LPPVlZWulyuAQMGnHfeeatXr+7bt+/y5cvff//9gwcP1tfXr169ury8fNCgQWmK3PD69OnjdrtXrlzZ3Ny8ffv2++67r7Oz86abbho+fPjEiRNXrFjR1NQU+bTzoBdffHHTpk1nnXXWU089pWbYAADEEXZ5gCRJFoslNze3pqYmJydHSEgAAGXt2LEjsjbbYrHMmTPnpZdeopAPABJlijP6AwAAAADJWbRoUXV1dVdXV+DP7OzsysrKqqoqsVEBAKB933///fDhw7/77rvgK1lZWYcPHw57zAsAaFZPT8+IESN27doVfCUrK+vQoUNctQPA2Do7OwsKCkLL+YYNG/bNN99YLBaBUQEAMtM333xz4403vvLKKyaTyWq1Hj58OM5Vld3d3R9//HF9fX1TU9N77723Y8cOq9U6btw4p9PpcDimTp166qmnqhh75jp69OiGDRtqa2tfeeWVb7/9tqioaNasWR6PZ9asWf379w9+bNeuXaecckrgWi+TyTR37tzHH3+8oKBAXOAAAEiSJP3xj39csmRJ8PIASZIsFktdXd35558vLigAgJLmzZv3/PPPhxaeWCyWuXPnPvnkk1arVWBgAKBTPJcPAAAAgPL+8Ic/5OXlBW/I1NPTc/LJJ4sNCQAAXcjOzr7tttuCP3iYTKbJkydTyAdAR8xm80033RTMY2azeerUqRTyATC8vn37Tp061Wz+v99ebTbbzTffTCEfAEBlPT09a9euPfPMM9944w1Jkvx+f1dXV1NTU9jH9uzZU1NTs2LFigsvvLBfv34TJ0687777Dh06dM0117z55psdHR2bN29evXr1woULKeRTTW5ursfjefTRR3ft2vXpp5/eeuutra2tl1122cCBA10uV1VVVXNzsyRJr7zySvB4w+/3v/zyy2PGjKmvrxcaOwAA4c/lM5lMDz74IIV8AGAkX375ZVgh37x58yjkA4CkUcsHAAAAQHkDBgxYtWpV8M+urq4RI0YIjAcAAB258cYbg/fLt9ls06dPFxsPACTq+uuvD5avWCyWsrIysfEAgDouuuii4LX1JpPppz/9qdh4AACZZuvWrQ6HY/HixceOHQs+FScrK2vjxo0+n2/btm3r1q274YYbxowZM2zYsEsvvfTZZ5+12+2rV6/+9NNPd+/eHajuc7vdffv2FbsgGDNmzNKlS+vr69va2tauXWu32++7776SkpIzzjjjkUceCf1kV1fX3r17p02btmLFCp/PJypgAABCa/msVutVV121ePFigfEAABS3Y8eO4P8tFsvChQsp5AOAVJhCK6QBAAAAQCl+v//8889vbGwMXDSwdevWcePGiQ4KAAB9uOOOOx544IFAH7phwwbK+QDozqJFi6qrqwN5rKWlZfTo0aIjAoC0a2lpOfPMMyVJstlsV1xxxZ///GfREQEAMsWxY8d+/etf33///Wazubu7O/Qts9lcXFy8Z8+eI0eO5Ofnl5aWlpaWTpky5dxzz+3Xr5+ogJGorq6u+vr6mpqahx566MSJE5EfsFgsEyZMeOaZZ4qLi9UPDwCANWvW3H777SdOnLDZbOPHj3///feDtywEABhAZ2dnbm5uoOrEbDYvWrRo7dq1wfuaAQCSQC0fAAAAgHRpaWkZO3Zs4Pr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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "h11_model_path = path.join(MOD_PATH, 'h11_inception_same_conv_no_fc.h5')\n",
    "h21_model_path = path.join(MOD_PATH, 'h21_inception_same_conv_no_fc.h5')\n",
    "\n",
    "log.debug('Building the full convolutional model...')\n",
    "K.clear_session() #--------------------------------------------------- clear the Tensorflow session\n",
    "model = cnn_full_model(model_name='full_model',\n",
    "                       h11_model_path=h11_model_path,\n",
    "                       h21_model_path=h21_model_path,\n",
    "                       num_cp_shape=num_cp_train.shape[1:],\n",
    "                       dim_cp_shape=dim_cp_train.shape[1:],\n",
    "                       dim_h0_amb_shape=dim_h0_amb_train.shape[1:],\n",
    "                       num_cp_neurons_h11=[],\n",
    "                       num_cp_neurons_h21=[10],\n",
    "                       dim_cp_neurons_h11=[],\n",
    "                       dim_cp_neurons_h21=[10],\n",
    "                       dim_h0_amb_neurons_h11=[],\n",
    "                       dim_h0_amb_neurons_h21=[10],\n",
    "                       learning_rate=0.03,\n",
    "                       batch_norm_momentum=0.99,\n",
    "                       dropout_rate=0.3,\n",
    "                       leaky_alpha=0.0,\n",
    "                       l1_reg=1.0e-5,\n",
    "                       l2_reg=1.0e-4,\n",
    "                       last_relu=True,\n",
    "                       last_layer_h11=1,\n",
    "                       last_layer_h21=2\n",
    "                      )\n",
    "\n",
    "model.summary()\n",
    "model_dot = model_to_dot(model=model, show_shapes=True, show_layer_names=True)\n",
    "model_dot.write_pdf(path.join(IMG_PATH, model.name + '.pdf'))\n",
    "\n",
    "Image(model_dot.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally we fit the model. We have two options: the first is to monitor the **validation loss** to measure the stability of the algorithm and choose the best training epoch and the second is to separately monitor the **MSE** on $h_{11}$ and $h_{21}$ and separately produce predictions for the Hodge numbers. After many trials with the first option, we decide to use the second method (even though it takes longer) because we want to get the best possible predictions in a consistent way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-11 16:55:45,520: INFO ==> Training the full model with the h11 objectives...\n"
     ]
    }
   ],
   "source": [
    "# define the callbacks (NB: with restore_best_weights we do not need to reload the model for evaluation)\n",
    "callbacks_h11=[EarlyStopping(monitor='val_h11_mean_squared_error', patience=400, verbose=0, restore_best_weights=True),\n",
    "               ReduceLROnPlateau(monitor='val_h11_mean_squared_error', factor=0.33, patience=150, verbose=0),\n",
    "               ModelCheckpoint(monitor='val_h11_mean_squared_error', filepath=path.join(MOD_PATH, 'h11_transfer_learning.h5'), verbose=0, save_best_only=True)\n",
    "              ]\n",
    "\n",
    "# fit the model\n",
    "log.info('Training the full model with the h11 objectives...')\n",
    "model_hst_h11 = model.fit(x=[matrix_train, matrix_train, num_cp_train, dim_cp_train, dim_h0_amb_train],\n",
    "                          y=[h11_train, h21_train],\n",
    "                          batch_size=128,\n",
    "                          epochs=5000,\n",
    "                          verbose=0,\n",
    "                          callbacks=callbacks_h11,\n",
    "                          validation_data=([matrix_val, matrix_val, num_cp_val, dim_cp_val, dim_h0_amb_val], [h11_val, h21_val])\n",
    "                         )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then evaluate the model with the $h_{11}$ objectives:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of full_model on the validation set: 99.236%\n",
      "Accuracy of full_model on the test set: 99.491%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-11 18:18:49,105: DEBUG ==> Plot saved to ./img/original/full_model_h11.pdf.\n"
     ]
    }
   ],
   "source": [
    "from mltools.libplot  import Plot\n",
    "from mltools.libscore import Score\n",
    "\n",
    "# compute the predictions\n",
    "model_val_predictions  = model.predict([matrix_val, matrix_val, num_cp_val, dim_cp_val, dim_h0_amb_val])[0].reshape(-1,) #------- take only h11 predictions\n",
    "model_test_predictions = model.predict([matrix_test, matrix_test, num_cp_test, dim_cp_test, dim_h0_amb_test])[0].reshape(-1,)\n",
    "\n",
    "# compute the accuracy and score of the predictions\n",
    "rounding = np.rint\n",
    "model_score_val  = Score(y_true=h11_val, y_pred=model_val_predictions, rounding=rounding)\n",
    "model_score_test = Score(y_true=h11_test, y_pred=model_test_predictions, rounding=rounding)\n",
    "print('Accuracy of {} on the validation set: {:.3f}%'.format(model.name, model_score_val.accuracy()*100))\n",
    "print('Accuracy of {} on the test set: {:.3f}%'.format(model.name, model_score_test.accuracy()*100))\n",
    "\n",
    "# plot the distribution of the predictions and the error difference\n",
    "plot = Plot(rows=1, columns=5)\n",
    "\n",
    "plot.scatter2D(np.array(list(get_counts(num_cp_val, h11_val))).T,\n",
    "               axis=0,\n",
    "               title='Predictions for the Validation Set for $h_{11}$',\n",
    "               legend='real values',\n",
    "               xlabel='num_cp (validation set)',\n",
    "               ylabel='$h_{11}$',\n",
    "               colour=False,\n",
    "               alpha=0.65\n",
    "              )\n",
    "plot.scatter2D(np.array(list(get_counts(num_cp_val, rounding(model_val_predictions)))).T,\n",
    "               axis=0,\n",
    "               title='Predictions for the Validation Set for $h_{11}$',\n",
    "               legend='predictions',\n",
    "               xlabel='num_cp (validation set)',\n",
    "               ylabel='$h_{11}$',\n",
    "               colour=False,\n",
    "               alpha=0.65\n",
    "              )\n",
    "\n",
    "plot.scatter2D(np.array(list(get_counts(num_cp_test, h11_test))).T,\n",
    "               axis=1,\n",
    "               title='Predictions for the Test Set for $h_{11}$',\n",
    "               legend='real values',\n",
    "               xlabel='num_cp (test set)',\n",
    "               ylabel='$h_{11}$',\n",
    "               colour=False,\n",
    "               alpha=0.65\n",
    "              )\n",
    "plot.scatter2D(np.array(list(get_counts(num_cp_test, rounding(model_test_predictions)))).T,\n",
    "               axis=1,\n",
    "               title='Predictions for the Test Set for $h_{11}$',\n",
    "               legend='predictions',\n",
    "               xlabel='num_cp (test set)',\n",
    "               ylabel='$h_{11}$',\n",
    "               colour=False,\n",
    "               alpha=0.65\n",
    "              )\n",
    "\n",
    "plot.hist2D(model_score_val.error(),\n",
    "            axis=2,\n",
    "            title='Distance from the Real Value',\n",
    "            legend='validation set',\n",
    "            xlabel='error difference',\n",
    "            ylabel='#',\n",
    "            binstep=1\n",
    "           )\n",
    "plot.hist2D(model_score_test.error(),\n",
    "            axis=2,\n",
    "            title='Distance from the Real Value',\n",
    "            legend='test set',\n",
    "            xlabel='error difference',\n",
    "            ylabel='#',\n",
    "            binstep=1\n",
    "           )\n",
    "\n",
    "plot.series2D(data=model_hst_h11.history['h11_loss'],\n",
    "              axis=3,\n",
    "              title='Loss Function',\n",
    "              xlabel='epoch',\n",
    "              legend='training loss',\n",
    "              ylog=True,\n",
    "              binstep=int(np.shape(model_hst_h11.history['h11_loss'])[0] / 10)\n",
    "             )\n",
    "plot.series2D(data=model_hst_h11.history['val_h11_loss'],\n",
    "              axis=3,\n",
    "              title='Loss Function',\n",
    "              xlabel='epoch',\n",
    "              legend='validation loss',\n",
    "              ylog=True,\n",
    "              binstep=int(np.shape(model_hst_h11.history['val_h11_loss'])[0] / 10)\n",
    "             )\n",
    "\n",
    "plot.series2D(data=model_hst_h11.history['h11_mean_squared_error'],\n",
    "              axis=4,\n",
    "              title='Metric Function',\n",
    "              xlabel='epoch',\n",
    "              legend='training MSE',\n",
    "              ylog=True,\n",
    "              binstep=int(np.shape(model_hst_h11.history['h11_mean_squared_error'])[0] / 10)\n",
    "             )\n",
    "plot.series2D(data=model_hst_h11.history['val_h11_mean_squared_error'],\n",
    "              axis=4,\n",
    "              title='Metric Function',\n",
    "              xlabel='epoch',\n",
    "              legend='validation MSE',\n",
    "              ylog=True,\n",
    "              binstep=int(np.shape(model_hst_h11.history['val_h11_mean_squared_error'])[0] / 10)\n",
    "             )\n",
    "\n",
    "plot.save_and_close(path.join(IMG_PATH, model.name + '_h11'))\n",
    "log.debug('Plot saved to {}.'.format(path.join(IMG_PATH, model.name + '_h11.pdf')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then keep training to find the best $h_{21}$ result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-11 18:18:49,122: DEBUG ==> Training the full model with the h21 objectives...\n"
     ]
    }
   ],
   "source": [
    "# define the callbacks (NB: with restore_best_weights we do not need to reload the model for evaluation)\n",
    "callbacks_h21=[EarlyStopping(monitor='val_h21_mean_squared_error', patience=400, verbose=0, restore_best_weights=True),\n",
    "               ReduceLROnPlateau(monitor='val_h21_mean_squared_error', factor=0.33, patience=150, verbose=0),\n",
    "               ModelCheckpoint(monitor='val_h21_mean_squared_error', filepath=path.join(MOD_PATH, 'h21_transfer_learning.h5'), verbose=0, save_best_only=True)\n",
    "              ]\n",
    "\n",
    "# fit the model\n",
    "log.debug('Training the full model with the h21 objectives...')\n",
    "model_hst_h21 = model.fit(x=[matrix_train, matrix_train, num_cp_train, dim_cp_train, dim_h0_amb_train],\n",
    "                          y=[h11_train, h21_train],\n",
    "                          batch_size=256,\n",
    "                          epochs=5000,\n",
    "                          verbose=0,\n",
    "                          callbacks=callbacks_h21,\n",
    "                          validation_data=([matrix_val, matrix_val, num_cp_val, dim_cp_val, dim_h0_amb_val], [h11_val, h21_val])\n",
    "                         )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then evaluate the model with the $h_{21}$ objectives:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of full_model on the validation set: 45.732%\n",
      "Accuracy of full_model on the test set: 48.092%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-11 19:21:58,226: DEBUG ==> Plot saved to ./img/original/full_model_h21.pdf.\n"
     ]
    }
   ],
   "source": [
    "from mltools.libplot  import Plot\n",
    "from mltools.libscore import Score\n",
    "\n",
    "# compute the predictions\n",
    "model_val_predictions  = model.predict([matrix_val, matrix_val, num_cp_val, dim_cp_val, dim_h0_amb_val])[1].reshape(-1,) #---------- take only h21 predictions\n",
    "model_test_predictions = model.predict([matrix_test, matrix_test, num_cp_test, dim_cp_test, dim_h0_amb_test])[1].reshape(-1,)\n",
    "\n",
    "# compute the accuracy and score of the predictions\n",
    "rounding = np.rint\n",
    "model_score_val  = Score(y_true=h21_val, y_pred=model_val_predictions, rounding=rounding)\n",
    "model_score_test = Score(y_true=h21_test, y_pred=model_test_predictions, rounding=rounding)\n",
    "print('Accuracy of {} on the validation set: {:.3f}%'.format(model.name, model_score_val.accuracy()*100))\n",
    "print('Accuracy of {} on the test set: {:.3f}%'.format(model.name, model_score_test.accuracy()*100))\n",
    "\n",
    "# plot the distribution of the predictions and the error difference\n",
    "plot = Plot(rows=1, columns=5)\n",
    "\n",
    "plot.scatter2D(np.array(list(get_counts(num_cp_val, h21_val))).T,\n",
    "               axis=0,\n",
    "               title='Predictions for the Validation Set for $h_{21}$',\n",
    "               legend='real values',\n",
    "               xlabel='num_cp (validation set)',\n",
    "               ylabel='$h_{21}$',\n",
    "               colour=False,\n",
    "               alpha=0.65\n",
    "              )\n",
    "plot.scatter2D(np.array(list(get_counts(num_cp_val, rounding(model_val_predictions)))).T,\n",
    "               axis=0,\n",
    "               title='Predictions for the Validation Set for $h_{21}$',\n",
    "               legend='predictions',\n",
    "               xlabel='num_cp (validation set)',\n",
    "               ylabel='$h_{21}$',\n",
    "               colour=False,\n",
    "               alpha=0.65\n",
    "              )\n",
    "\n",
    "plot.scatter2D(np.array(list(get_counts(num_cp_test, h21_test))).T,\n",
    "               axis=1,\n",
    "               title='Predictions for the Test Set for $h_{21}$',\n",
    "               legend='real values',\n",
    "               xlabel='num_cp (test set)',\n",
    "               ylabel='$h_{21}$',\n",
    "               colour=False,\n",
    "               alpha=0.65\n",
    "              )\n",
    "plot.scatter2D(np.array(list(get_counts(num_cp_test, rounding(model_test_predictions)))).T,\n",
    "               axis=1,\n",
    "               title='Predictions for the Test Set for $h_{21}$',\n",
    "               legend='predictions',\n",
    "               xlabel='num_cp (test set)',\n",
    "               ylabel='$h_{21}$',\n",
    "               colour=False,\n",
    "               alpha=0.65\n",
    "              )\n",
    "\n",
    "plot.hist2D(model_score_val.error(),\n",
    "            axis=2,\n",
    "            title='Distance from the Real Value',\n",
    "            legend='validation set',\n",
    "            xlabel='error difference',\n",
    "            ylabel='#',\n",
    "            binstep=1\n",
    "           )\n",
    "plot.hist2D(model_score_test.error(),\n",
    "            axis=2,\n",
    "            title='Distance from the Real Value',\n",
    "            legend='test set',\n",
    "            xlabel='error difference',\n",
    "            ylabel='#',\n",
    "            binstep=1\n",
    "           )\n",
    "\n",
    "plot.series2D(data=model_hst_h21.history['h21_loss'],\n",
    "              axis=3,\n",
    "              title='Loss Function',\n",
    "              xlabel='epoch',\n",
    "              legend='training loss',\n",
    "              ylog=True,\n",
    "              binstep=int(np.shape(model_hst_h21.history['h21_loss'])[0] / 10)\n",
    "             )\n",
    "plot.series2D(data=model_hst_h21.history['val_h21_loss'],\n",
    "              axis=3,\n",
    "              title='Loss Function',\n",
    "              xlabel='epoch',\n",
    "              legend='validation loss',\n",
    "              ylog=True,\n",
    "              binstep=int(np.shape(model_hst_h21.history['val_h21_loss'])[0] / 10)\n",
    "             )\n",
    "\n",
    "plot.series2D(data=model_hst_h21.history['h21_mean_squared_error'],\n",
    "              axis=4,\n",
    "              title='Metric Function',\n",
    "              xlabel='epoch',\n",
    "              legend='training MSE',\n",
    "              ylog=True,\n",
    "              binstep=int(np.shape(model_hst_h21.history['h21_mean_squared_error'])[0] / 10)\n",
    "             )\n",
    "plot.series2D(data=model_hst_h21.history['val_h21_mean_squared_error'],\n",
    "              axis=4,\n",
    "              title='Metric Function',\n",
    "              xlabel='epoch',\n",
    "              legend='validation MSE',\n",
    "              ylog=True,\n",
    "              binstep=int(np.shape(model_hst_h21.history['val_h21_mean_squared_error'])[0] / 10)\n",
    "             )\n",
    "\n",
    "plot.save_and_close(path.join(IMG_PATH, model.name + '_h21'))\n",
    "log.debug('Plot saved to {}.'.format(path.join(IMG_PATH, model.name + '_h21.pdf')))"
   ]
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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