cicy3f_nn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning for Complete Intersection Calabi-Yau Manifolds\n",
    "\n",
    "We then separately consider another ML approach to predict the **Hodge numbers** from the configuration matrix of CICY 3-folds: we implement **neural networks** (NN) in order to improve previous predictions using different kinds of architectures. In particular we will use **convolutional** NN (CNN) to process the configuration matrix using kernel convolutions (as if we were considering a \"computer vision\" task). We try different architectures such as a CNN with only convolutional hidden layers, as opposed to a CNN which uses a **fully connected** (FC) network to process the output.\n",
    "\n",
    "We will use the [Tensorflow](https://www.tensorflow.org/) backend to implement NNs on the GPU, using the [Keras](https://www.tensorflow.org/api_docs/python/tf/keras) high level API.\n",
    "\n",
    "This is the Jupyter notebook for the neural network analysis on the **favourable** dataset."
   ]
  },
  {
   "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: 5.6.12-arch1-1, architecture: x86_64)\n",
      "Number of available threads: 8\n",
      "Current CPU frequency:       2751 MHz (max: 2800 MHz)\n",
      "Available RAM memory:        8431 MB (tot: 15758 MB)\n",
      "Fri May 15 17:45:34 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   1400850      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 json\n",
    "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 of outputs, data, csv files, etc.\n",
    "\n",
    "DB_NAME = 'cicy3f' #---------------------------------------------- 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": [
      "2020-05-15 17:45:38,092: INFO ==> New logging session started. Log is at ./log/favourable/cicy3f_nn.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.log'\n",
    "                       ) #----------------------------------------------------- path to the log file\n",
    "log = create_logfile(path_to_log,\n",
    "                     name=DB_NAME + '_nn',\n",
    "                     level=logging.DEBUG\n",
    "                    ) #-------------------------------------------------------- create a log file and a log session\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": [
      "2020-05-15 17:45:38,780: DEBUG ==> Database loaded.\n",
      "2020-05-15 17:45:38,782: INFO ==> Shape is 7852 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": [
    "We print the `dtypes` and the name of the keys inside the dataframe as a reference:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "h11            int16\n",
       "h21            int16\n",
       "num_cp          int8\n",
       "dim_cp        object\n",
       "dim_h0_amb    object\n",
       "matrix        object\n",
       "pca           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-15 17:45:40,587: DEBUG ==> Matrix shape: (7852, 15, 18, 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 also extract num_cp for the plots (not for training)\n",
    "num_cp = np.array(ExtractTensor(flatten=True).fit_transform(df['num_cp']))\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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-15 17:45:40,649: DEBUG ==> Train set size: 6280\n",
      "2020-05-15 17:45:40,654: DEBUG ==> Validation set size: 786\n",
      "2020-05-15 17:45:40,657: DEBUG ==> Test set size: 786\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# divide into training and test sets\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(matrix, num_cp, h11, h21, test_size=0.1, shuffle=False)\n",
    "\n",
    "# keep a holdout validation set\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(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": 10,
   "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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input Visualisation\n",
    "\n",
    "Using _matplotlib_ we can try to visualise a few samples of the training set to better understand what kind of input we are dealing with and come up with a good strategy for the architecture of the neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1080 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-15 17:45:46,591: DEBUG ==> Plot saved in ./img/favourable/nn_input_visualisation.pdf.\n"
     ]
    }
   ],
   "source": [
    "from mltools.libplot import Plot\n",
    "\n",
    "# choose 4 random indices\n",
    "rnd = np.random.randint(low=0, high=np.shape(matrix_train)[0], size=(9,))\n",
    "\n",
    "# decide the colour map\n",
    "cmap = 'plasma'\n",
    "\n",
    "# plot the matrices\n",
    "plot = Plot(rows=3, columns=3)\n",
    "\n",
    "plot.matrix(data=np.reshape(matrix_train[rnd[0]], (np.shape(matrix_train[rnd[0]])[0], np.shape(matrix_train[rnd[0]])[1])),\n",
    "            axis=(0,0),\n",
    "            title='$h_{} = {:d}$, $h_{} = {:d}$'.format('{11}', h11_train[rnd[0]], '{21}', h21_train[rnd[0]]),\n",
    "            cmap=cmap,\n",
    "            vmin=0.0,\n",
    "            vmax=1.0\n",
    "           )\n",
    "plot.matrix(data=np.reshape(matrix_train[rnd[1]], (np.shape(matrix_train[rnd[1]])[0], np.shape(matrix_train[rnd[1]])[1])),\n",
    "            axis=(0,1),\n",
    "            title='$h_{} = {:d}$, $h_{} = {:d}$'.format('{11}', h11_train[rnd[1]], '{21}', h21_train[rnd[1]]),\n",
    "            cmap=cmap,\n",
    "            vmin=0.0,\n",
    "            vmax=1.0\n",
    "           )\n",
    "plot.matrix(data=np.reshape(matrix_train[rnd[2]], (np.shape(matrix_train[rnd[2]])[0], np.shape(matrix_train[rnd[2]])[1])),\n",
    "            axis=(0,2),\n",
    "            title='$h_{} = {:d}$, $h_{} = {:d}$'.format('{11}', h11_train[rnd[2]], '{21}', h21_train[rnd[2]]),\n",
    "            cmap=cmap,\n",
    "            vmin=0.0,\n",
    "            vmax=1.0\n",
    "           )\n",
    "plot.matrix(data=np.reshape(matrix_train[rnd[3]], (np.shape(matrix_train[rnd[3]])[0], np.shape(matrix_train[rnd[3]])[1])),\n",
    "            axis=(1,0),\n",
    "            title='$h_{} = {:d}$, $h_{} = {:d}$'.format('{11}', h11_train[rnd[3]], '{21}', h21_train[rnd[3]]),\n",
    "            cmap=cmap,\n",
    "            vmin=0.0,\n",
    "            vmax=1.0\n",
    "           )\n",
    "plot.matrix(data=np.reshape(matrix_train[rnd[4]], (np.shape(matrix_train[rnd[4]])[0], np.shape(matrix_train[rnd[4]])[1])),\n",
    "            axis=(1,1),\n",
    "            title='$h_{} = {:d}$, $h_{} = {:d}$'.format('{11}', h11_train[rnd[4]], '{21}', h21_train[rnd[4]]),\n",
    "            cmap=cmap,\n",
    "            vmin=0.0,\n",
    "            vmax=1.0\n",
    "           )\n",
    "plot.matrix(data=np.reshape(matrix_train[rnd[5]], (np.shape(matrix_train[rnd[5]])[0], np.shape(matrix_train[rnd[5]])[1])),\n",
    "            axis=(1,2),\n",
    "            title='$h_{} = {:d}$, $h_{} = {:d}$'.format('{11}', h11_train[rnd[5]], '{21}', h21_train[rnd[5]]),\n",
    "            cmap=cmap,\n",
    "            vmin=0.0,\n",
    "            vmax=1.0\n",
    "           )\n",
    "plot.matrix(data=np.reshape(matrix_train[rnd[6]], (np.shape(matrix_train[rnd[6]])[0], np.shape(matrix_train[rnd[6]])[1])),\n",
    "            axis=(2,0),\n",
    "            title='$h_{} = {:d}$, $h_{} = {:d}$'.format('{11}', h11_train[rnd[6]], '{21}', h21_train[rnd[6]]),\n",
    "            cmap=cmap,\n",
    "            vmin=0.0,\n",
    "            vmax=1.0\n",
    "           )\n",
    "plot.matrix(data=np.reshape(matrix_train[rnd[7]], (np.shape(matrix_train[rnd[7]])[0], np.shape(matrix_train[rnd[7]])[1])),\n",
    "            axis=(2,1),\n",
    "            title='$h_{} = {:d}$, $h_{} = {:d}$'.format('{11}', h11_train[rnd[7]], '{21}', h21_train[rnd[7]]),\n",
    "            cmap=cmap,\n",
    "            vmin=0.0,\n",
    "            vmax=1.0\n",
    "           )\n",
    "plot.matrix(data=np.reshape(matrix_train[rnd[8]], (np.shape(matrix_train[rnd[8]])[0], np.shape(matrix_train[rnd[8]])[1])),\n",
    "            axis=(2,2),\n",
    "            title='$h_{} = {:d}$, $h_{} = {:d}$'.format('{11}', h11_train[rnd[8]], '{21}', h21_train[rnd[8]]),\n",
    "            cmap=cmap,\n",
    "            vmin=0.0,\n",
    "            vmax=1.0\n",
    "           )\n",
    "\n",
    "\n",
    "plot.save_and_close(path.join(IMG_PATH, 'nn_input_visualisation'))\n",
    "log.debug('Plot saved in {}.'.format(path.join(IMG_PATH, 'nn_input_visualisation.pdf')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Architecture Building\n",
    "\n",
    "We then move to the problem of building the correct architecture. We will first implement a function which will allow us to build different models using similar structure blocks: the idea is to first process the images through convolutional layers, then add a FC network at the end (we will also add dropout and normalization layers)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models       import Model\n",
    "from tensorflow.keras.layers       import Input, Conv2D, Dense, LeakyReLU, Dropout, ZeroPadding2D, BatchNormalization, Flatten, concatenate\n",
    "from tensorflow.keras.regularizers import l1_l2\n",
    "from tensorflow.keras.optimizers   import Adam\n",
    "from tensorflow.keras.utils        import model_to_dot\n",
    "\n",
    "def cnn_model(input_shape,\n",
    "              model_name='cnn_model',\n",
    "              learning_rate=0.01,\n",
    "              conv_layers=[32],\n",
    "              conv_paddings=['same'],\n",
    "              conv_kernels=[(2,2)],\n",
    "              conv_alpha=0.0,\n",
    "              fc_layers=[],\n",
    "              fc_alpha=0.0,\n",
    "              dropout=0.2,\n",
    "              full_dropout=0.0,\n",
    "              normalization=0.99,\n",
    "              last_relu=True,\n",
    "              out_name='output',\n",
    "              l1_reg=0.0,\n",
    "              l2_reg=0.0\n",
    "             ):\n",
    "    '''\n",
    "    Build a CNN model.\n",
    "    \n",
    "    Required arguments:\n",
    "        input_size:    the size of the input tensor.\n",
    "        \n",
    "    Optional arguments:\n",
    "        model_name:    the name of the model,\n",
    "        learning_rate: the learning rate of the gradient descent,\n",
    "        conv_layers:   a list-like object with the no. of filters for each hiddend convolution layer,\n",
    "        conv_paddings: a list-like object with the corresponding padding for each layer,\n",
    "        conv_kernels:  a list-like object with the kernel size of each layer,\n",
    "        conv_alpha:    the slope of the LeakyReLU activation (ReLU if 0.0) of the convolution layers,\n",
    "        fc_layers:     a list-like object with the no. of units for each hidden dense layer,\n",
    "        fc_alpha:      the slope of the LeakyReLU activation (ReLU if 0.0) of the FC network,\n",
    "        dropout:       the dropout rate (do not use dropout if <= 0.0),\n",
    "        full_dropout:  use this dropout rate after every layer (disabled if <= 0.0),\n",
    "        normalization: the momentum of the batch normalization (do not use normalization if <= 0.0),\n",
    "        last_relu:     whether to use ReLU activation in the output layer (force positive output),\n",
    "        out_name:      the name of the output layer,\n",
    "        l1_reg:        the L1 kernel regularization factor,\n",
    "        l2_reg:        the L2 kernel regularization factor.\n",
    "        \n",
    "    Returns:\n",
    "        the compiled model.\n",
    "    '''\n",
    "    \n",
    "    # define the regularizer\n",
    "    regularizer = l1_l2(l1=l1_reg, l2=l2_reg) #--------------------------------------------- regularizer\n",
    " \n",
    "    # build the model\n",
    "    I = Input(shape=input_shape, name=model_name + '_input') #------------------------------ input layer\n",
    "    x = I\n",
    "    \n",
    "    # build convolutional layers\n",
    "    for n in range(np.shape(conv_layers)[0]): #--------------------------------------------- loop through the conv. layers\n",
    "        x = Conv2D(filters=conv_layers[n],\n",
    "                   kernel_size=conv_kernels[n],\n",
    "                   padding=conv_paddings[n],\n",
    "                   kernel_regularizer=regularizer,\n",
    "                   name=model_name + '_conv2d_' + str(n)\n",
    "                  )(x) #-------------------------------------------------------------------- add conv. layer\n",
    "        x = LeakyReLU(alpha=conv_alpha,\n",
    "                      name=model_name + '_conv2d_' + str(n) + '_activation'\n",
    "                     )(x) #----------------------------------------------------------------- add activation\n",
    "        \n",
    "        if normalization > 0.0:\n",
    "            x = BatchNormalization(momentum=normalization,\n",
    "                                   name=model_name + '_conv2d_' + str(n) + '_normalization'\n",
    "                                  )(x) #---------------------------------------------------- add batch normalization (if requested)\n",
    "        if full_dropout > 0.0:\n",
    "            x = Dropout(rate=full_dropout,\n",
    "                        name=model_name + '_conv2d_' + str(n) + '_full_dropout'\n",
    "                       )(x) #--------------------------------------------------------------- add dropout (if requested)\n",
    "        \n",
    "    # add dropout\n",
    "    if dropout > 0.0 and full_dropout <= 0.0:\n",
    "        x = Dropout(rate=dropout,\n",
    "                    name=model_name + '_dropout'\n",
    "                   )(x) #------------------------------------------------------------------- add dropout (if requested)\n",
    "        \n",
    "    # flatten the output\n",
    "    x = Flatten(name=model_name + '_flatten')(x) #------------------------------------------ flatten the output\n",
    "    \n",
    "    # build FC network\n",
    "    for n in range(np.shape(fc_layers)[0]):\n",
    "        x = Dense(units=fc_layers[n],\n",
    "                  kernel_regularizer=regularizer,\n",
    "                  name=model_name + '_fc_' + str(n)\n",
    "                 )(x) #--------------------------------------------------------------------- add dense layers\n",
    "        x = LeakyReLU(alpha=fc_alpha,\n",
    "                      name=model_name + '_fc_' + str(n) + '_activation'\n",
    "                     )(x) #----------------------------------------------------------------- add activation\n",
    "        \n",
    "        if normalization > 0.0:\n",
    "            x = BatchNormalization(momentum=normalization,\n",
    "                                   name=model_name + '_fc_' + str(n) + '_normalization'\n",
    "                                  )(x) #---------------------------------------------------- add batch normalization (if requested)\n",
    "        if full_dropout > 0.0:\n",
    "            x = Dropout(rate=full_dropout,\n",
    "                        name=model_name + '_fc_' + str(n) + '_full_dropout'\n",
    "                       )(x) #--------------------------------------------------------------- add dropout (if requested)\n",
    "        \n",
    "    if last_relu: #------------------------------------------------------------------------- output layer\n",
    "        F = Dense(1, activation='relu', name=model_name + '_' + out_name)(x)\n",
    "    else:\n",
    "        F = Dense(1, name=model_name + '_' + out_name)(x)\n",
    "    \n",
    "    # define the model\n",
    "    model = Model(inputs=I, outputs=F, name=model_name)\n",
    "    \n",
    "    # compile the model\n",
    "    model.compile(optimizer=Adam(learning_rate=learning_rate),\n",
    "                  loss='mean_squared_error',\n",
    "                  metrics=['mean_squared_error']\n",
    "                 )\n",
    "    \n",
    "    # return the compiled model\n",
    "    return model\n",
    "\n",
    "def scan_inception_model(input_shape,\n",
    "                         model_name='inception_model',\n",
    "                         learning_rate=0.01,\n",
    "                         conv_layers=[32],\n",
    "                         conv_padding='same',\n",
    "                         conv_alpha=0.0,\n",
    "                         fc_layers=[],\n",
    "                         fc_alpha=0.0,\n",
    "                         dropout=0.2,\n",
    "                         full_dropout=0.0,\n",
    "                         normalization=0.99,\n",
    "                         last_relu=True,\n",
    "                         out_name='output',\n",
    "                         l1_reg=0.0,\n",
    "                         l2_reg=0.0\n",
    "                        ):\n",
    "    '''\n",
    "    Build a CNN 'scan-inception' model: scan over rows and columns and merge.\n",
    "    \n",
    "    Required arguments:\n",
    "        input_size:    the size of the input tensor.\n",
    "        \n",
    "    Optional arguments:\n",
    "        model_name:    the name of the model,\n",
    "        learning_rate: the learning rate of the gradient descent,\n",
    "        conv_layers:   a list-like object with the no. of filters for each 'inception' modules,\n",
    "        conv_padding:  the padding to use for the convolutional scans,\n",
    "        conv_alpha:    the slope of the LeakyReLU activation (ReLU if 0.0) of the convolution layers,\n",
    "        fc_layers:     a list-like object with the no. of units for each hidden dense layer,\n",
    "        fc_alpha:      the slope of the LeakyReLU activation (ReLU if 0.0) of the FC network,\n",
    "        dropout:       the dropout rate (do not use dropout if <= 0.0),\n",
    "        full_dropout:  use this dropout rate after every layer (disabled if <= 0.0),\n",
    "        normalization: the momentum of the batch normalization (do not use normalization if <= 0.0),\n",
    "        last_relu:     whether to use ReLU activation in the output layer (force positive output),\n",
    "        out_name:      the name of the output layer,\n",
    "        l1_reg:        the L1 kernel regularization factor,\n",
    "        l2_reg:        the L2 kernel regularization factor.\n",
    "        \n",
    "    Returns:\n",
    "        the compiled model.\n",
    "    '''\n",
    "    \n",
    "    # define the regularizer\n",
    "    regularizer = l1_l2(l1=l1_reg, l2=l2_reg) #--------------------------------------------- regularizer\n",
    " \n",
    "    # build the model\n",
    "    I = Input(shape=input_shape, name=model_name + '_input') #------------------------------ input layer\n",
    "    if conv_padding == 'same':\n",
    "        x = I\n",
    "    else:\n",
    "        x = ZeroPadding2D(padding=((0,3),(0,0)), data_format='channels_last')(I)\n",
    "    \n",
    "    # build convolutional layers\n",
    "    for n in range(np.shape(conv_layers)[0]): #--------------------------------------------- loop through the conv. layers\n",
    "        a = Conv2D(filters=conv_layers[n],\n",
    "                   kernel_size=(x.shape[1],1),\n",
    "                   padding=conv_padding,\n",
    "                   kernel_regularizer=regularizer,\n",
    "                   name=model_name + '_conv2d_rows_' + str(n)\n",
    "                  )(x) #-------------------------------------------------------------------- add conv. layer over rows\n",
    "        a = LeakyReLU(alpha=conv_alpha,\n",
    "                      name=model_name + '_conv2d_rows_' + str(n) + '_activation'\n",
    "                     )(a) #----------------------------------------------------------------- add activation\n",
    "        b = Conv2D(filters=conv_layers[n],\n",
    "                   kernel_size=(1,x.shape[2]),\n",
    "                   padding=conv_padding,\n",
    "                   kernel_regularizer=regularizer,\n",
    "                   name=model_name + '_conv2d_columns_' + str(n)\n",
    "                  )(x) #-------------------------------------------------------------------- add conv. layer over columns\n",
    "        b = LeakyReLU(alpha=conv_alpha,\n",
    "                      name=model_name + '_conv2d_columns_' + str(n) + '_activation'\n",
    "                     )(b) #----------------------------------------------------------------- add activation\n",
    "        \n",
    "        x = concatenate([a, b],\n",
    "                        name=model_name + '_concatenation_' + str(n)\n",
    "                       ) if conv_padding == 'same' \\\n",
    "                         else concatenate([a, tf.einsum('bij...->bji...',b)], #------------- swap columns and rows\n",
    "                                          name=model_name + '_concatenation_' + str(n)\n",
    "                                         ) #------------------------------------------------ concatenate layers\n",
    "        \n",
    "        if normalization > 0.0:\n",
    "            x = BatchNormalization(momentum=normalization,\n",
    "                                   name=model_name + '_conv2d_' + str(n) + '_normalization'\n",
    "                                  )(x) #---------------------------------------------------- add batch normalization (if requested)\n",
    "        if full_dropout > 0.0:\n",
    "            x = Dropout(rate=full_dropout,\n",
    "                        name=model_name + '_conv2d_' + str(n) + '_full_dropout'\n",
    "                       )(x) #--------------------------------------------------------------- add dropout (if requested)\n",
    "        \n",
    "    # add dropout\n",
    "    if dropout > 0.0 and full_dropout <= 0.0:\n",
    "        x = Dropout(rate=dropout,\n",
    "                    name=model_name + '_dropout'\n",
    "                   )(x) #------------------------------------------------------------------- add dropout (if requested)\n",
    "        \n",
    "    # flatten the output\n",
    "    x = Flatten(name=model_name + '_flatten')(x) #------------------------------------------ flatten the output\n",
    "    \n",
    "    # build FC network\n",
    "    for n in range(np.shape(fc_layers)[0]):\n",
    "        x = Dense(units=fc_layers[n],\n",
    "                  kernel_regularizer=regularizer,\n",
    "                  name=model_name + '_fc_' + str(n)\n",
    "                 )(x) #--------------------------------------------------------------------- add dense layers\n",
    "        x = LeakyReLU(alpha=fc_alpha,\n",
    "                      name=model_name + '_fc_' + str(n) + '_activation'\n",
    "                     )(x) #----------------------------------------------------------------- add activation\n",
    "        \n",
    "        if normalization > 0.0:\n",
    "            x = BatchNormalization(momentum=normalization,\n",
    "                                   name=model_name + '_fc_' + str(n) + '_normalization'\n",
    "                                  )(x) #---------------------------------------------------- add batch normalization (if requested)\n",
    "        if full_dropout > 0.0:\n",
    "            x = Dropout(rate=full_dropout,\n",
    "                        name=model_name + '_fc_' + str(n) + '_full_dropout'\n",
    "                       )(x) #--------------------------------------------------------------- add dropout (if requested)\n",
    "        \n",
    "    if last_relu: #------------------------------------------------------------------------- output layer\n",
    "        F = Dense(1, activation='relu', name=model_name + '_' + out_name)(x)\n",
    "    else:\n",
    "        F = Dense(1, name=model_name + '_' + out_name)(x)\n",
    "    \n",
    "    # define the model\n",
    "    model = Model(inputs=I, outputs=F, name=model_name)\n",
    "    \n",
    "    # compile the model\n",
    "    model.compile(optimizer=Adam(learning_rate=learning_rate),\n",
    "                  loss='mean_squared_error',\n",
    "                  metrics=['mean_squared_error']\n",
    "                 )\n",
    "    \n",
    "    # return the compiled model\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will consider different models and train them all and then compare and discuss the results. The models we implement will compare these peculiarities:\n",
    "\n",
    "- small vs. large kernels,\n",
    "- FC network vs. no FC network,\n",
    "- padding vs. no padding.\n",
    "\n",
    "The idea behind the _small kernels_ is to have a small no. of operations and to be fairly quick to train (smaller kernels will probably need more layers, though), while larger kernels usually improve the results in the field of [semantic segmentation](https://arxiv.org/abs/1703.02719) and may be worth the try (we will also need less hidden layers). The fully connected layers can then help in processing the output, however the no. of parameters will increase substantially since they are dense layers (one of the goals will be to build the **smallest NN**). The padding operation will instead guide the convolution operation by reducing the output (and input) size of each layer (when using padding we could also add a FC network to a very small convolutional layer and keep the no. of parameters small).\n",
    "\n",
    "We will then compare the results with [Bull et al](https://arxiv.org/abs/1806.03121)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.backend   import clear_session\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
    "from mltools.libscore           import Score\n",
    "\n",
    "def cnn_fit(model,\n",
    "            X,\n",
    "            y,\n",
    "            X_val,\n",
    "            y_val,\n",
    "            batch_size=32,\n",
    "            epochs=100,\n",
    "            early_stopping=10,\n",
    "            reduce_lr=5,\n",
    "            verbose=0\n",
    "           ):\n",
    "    '''\n",
    "    Fit the model.\n",
    "    \n",
    "    Required arguments:\n",
    "        model:          the model to fit,\n",
    "        X:              the training features,\n",
    "        y:              the training labels,\n",
    "        X_val:          the validation features,\n",
    "        y_val:          the validation labels.\n",
    "        \n",
    "    Optional arguments:\n",
    "        batch_size:     the size of the batch used in forward pass,\n",
    "        epochs:         the number of epochs,\n",
    "        early_stopping: patience of early stopping,\n",
    "        reduce_lr:      patience to reduce learning rate,\n",
    "        verbose:        verbosity level (debug)\n",
    "    \n",
    "    Returns:\n",
    "        the fitted model, the history of the model.\n",
    "    '''\n",
    "    \n",
    "    # clear the TF graph\n",
    "    clear_session()\n",
    "    \n",
    "    # define callbacks\n",
    "    callbacks = [EarlyStopping(monitor='val_mean_squared_error',\n",
    "                               patience=early_stopping,\n",
    "                               verbose=verbose,\n",
    "                               restore_best_weights=True\n",
    "                              ),\n",
    "                 ReduceLROnPlateau(monitor='val_mean_squared_error',\n",
    "                                   factor=0.3,\n",
    "                                   patience=reduce_lr,\n",
    "                                   verbose=verbose\n",
    "                                  ),\n",
    "                 ModelCheckpoint(filepath=path.join(MOD_PATH, model.name + '.h5'),\n",
    "                                 monitor='val_mean_squared_error',\n",
    "                                 verbose=verbose,\n",
    "                                 save_best_only=True,\n",
    "                                 save_format='h5'\n",
    "                                )\n",
    "                ]\n",
    "    \n",
    "    # fit the model\n",
    "    history = model.fit(x=X,\n",
    "                        y=y,\n",
    "                        batch_size=batch_size,\n",
    "                        epochs=epochs,\n",
    "                        verbose=verbose,\n",
    "                        callbacks=callbacks,\n",
    "                        validation_data=(X_val, y_val)\n",
    "                       )\n",
    "    \n",
    "    # return the fitted model and its history\n",
    "    return model, history.history"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The test results will then be saved to file along with other quantities which can be reused later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_results = {'h11_true': h11_test.tolist(),\n",
    "                'h21_true': h21_test.tolist(),\n",
    "                'num_cp':   num_cp_test.tolist(),\n",
    "                'matrix':   matrix_test.tolist()\n",
    "               } #--------- dictionary with the predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis for $h_{11}$\n",
    "\n",
    "We now have the instruments to proceed with the analysis, starting with the prediction of $h_{11}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Base Analysis and Comparison\n",
    "\n",
    "First of all we build the architecture of the network in [Bull et al.](https://arxiv.org/abs/1806.03121) and train it as a reference. Even though this network was not designed for the analysis on the favourable dataset, it might nonetheless represent a good baseline for comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-15 17:45:50,227: DEBUG ==> Training base model...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"h11_bull_et_al\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "h11_bull_et_al_input (InputL [(None, 15, 18, 1)]       0         \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_flatten (Flat (None, 270)               0         \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_0 (Dense)  (None, 876)               237396    \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_0_activati (None, 876)               0         \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_0_normaliz (None, 876)               3504      \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_0_full_dro (None, 876)               0         \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_1 (Dense)  (None, 461)               404297    \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_1_activati (None, 461)               0         \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_1_normaliz (None, 461)               1844      \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_1_full_dro (None, 461)               0         \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_2 (Dense)  (None, 437)               201894    \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_2_activati (None, 437)               0         \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_2_normaliz (None, 437)               1748      \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_2_full_dro (None, 437)               0         \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_3 (Dense)  (None, 929)               406902    \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_3_activati (None, 929)               0         \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_3_normaliz (None, 929)               3716      \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_3_full_dro (None, 929)               0         \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_4 (Dense)  (None, 404)               375720    \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_4_activati (None, 404)               0         \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_4_normaliz (None, 404)               1616      \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_fc_4_full_dro (None, 404)               0         \n",
      "_________________________________________________________________\n",
      "h11_bull_et_al_output (Dense (None, 1)                 405       \n",
      "=================================================================\n",
      "Total params: 1,639,042\n",
      "Trainable params: 1,632,828\n",
      "Non-trainable params: 6,214\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-15 17:55:22,113: DEBUG ==> Base model has been trained.\n",
      "2020-05-15 17:55:22,618: DEBUG ==> History of h11_bull_et_al has been saved to ./output/favourable/h11_bull_et_al.json.\n",
      "2020-05-15 17:55:24,506: DEBUG ==> History of h11_bull_et_al has been saved to ./output/favourable/convnet_test_results_h11_h21.json.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of h11_bull_et_al on the validation set: 97.201%\n",
      "Accuracy of h11_bull_et_al on the test set: 97.455%\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": [
      "2020-05-15 17:55:27,759: DEBUG ==> Plot saved to ./img/favourable/h11_bull_et_al.\n"
     ]
    }
   ],
   "source": [
    "h11_base_model = cnn_model(input_shape=input_shape,\n",
    "                           model_name='h11_bull_et_al',\n",
    "                           learning_rate=1.0e-3,\n",
    "                           conv_layers=[],\n",
    "                           conv_paddings=[],\n",
    "                           conv_kernels=[],\n",
    "                           conv_alpha=0.0,\n",
    "                           fc_layers=[876, 461, 437, 929, 404],\n",
    "                           fc_alpha=0.0,\n",
    "                           dropout=0.0,\n",
    "                           full_dropout=0.2072,\n",
    "                           normalization=0.99,\n",
    "                           last_relu=True,\n",
    "                           l1_reg=0.0,\n",
    "                           l2_reg=0.0\n",
    "                          )\n",
    "\n",
    "# print characteristics\n",
    "h11_base_model.summary()\n",
    "\n",
    "# print architecture\n",
    "h11_base_model_dot = model_to_dot(model=h11_base_model, show_shapes=True, show_layer_names=False)\n",
    "h11_base_model_dot.write_pdf(path.join(IMG_PATH, h11_base_model.name + '.pdf'))\n",
    "\n",
    "# train the model\n",
    "log.debug('Training base model...')\n",
    "h11_base_model, h11_base_model_history = cnn_fit(model=h11_base_model,\n",
    "                                                 X=matrix_train, y=h11_train,\n",
    "                                                 X_val=matrix_val, y_val=h11_val,\n",
    "                                                 batch_size=np.shape(matrix_train)[0],\n",
    "                                                 epochs=5000,\n",
    "                                                 early_stopping=200,\n",
    "                                                 reduce_lr=75,\n",
    "                                                 verbose=0\n",
    "                                                )\n",
    "log.debug('Base model has been trained.')\n",
    "\n",
    "# compute the predictions\n",
    "h11_base_model_val_predictions  = np.reshape(h11_base_model.predict(matrix_val), (-1,)) #---- reshape to match the labels\n",
    "h11_base_model_test_predictions = np.reshape(h11_base_model.predict(matrix_test), (-1,))\n",
    "test_results[str(h11_base_model.name)] = h11_base_model_test_predictions.tolist()\n",
    "\n",
    "# save the history of the model\n",
    "with open(path.join(OUT_PATH, h11_base_model.name + '.json'), 'w') as f:\n",
    "    json.dump(str(h11_base_model_history), f)\n",
    "    log.debug('History of {} has been saved to {}.'.format(h11_base_model.name, path.join(OUT_PATH, h11_base_model.name + '.json')))\n",
    "\n",
    "# save results to file\n",
    "with open(path.join(OUT_PATH, 'convnet_test_results_h11_h21.json'), 'w') as f:\n",
    "    json.dump(test_results, f)\n",
    "    log.debug('History of {} has been saved to {}.'.format(h11_base_model.name, path.join(OUT_PATH, 'convnet_test_results_h11_h21.json')))\n",
    "\n",
    "# compute the accuracy and score of the predictions\n",
    "rounding = np.rint\n",
    "h11_base_model_score_val  = Score(y_true=h11_val, y_pred=h11_base_model_val_predictions, rounding=rounding)\n",
    "h11_base_model_score_test = Score(y_true=h11_test, y_pred=h11_base_model_test_predictions, rounding=rounding)\n",
    "print('Accuracy of {} on the validation set: {:.3f}%'.format(h11_base_model.name, h11_base_model_score_val.accuracy()*100))\n",
    "print('Accuracy of {} on the test set: {:.3f}%'.format(h11_base_model.name, h11_base_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(h11_base_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(h11_base_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(h11_base_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(h11_base_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=h11_base_model_history['loss'],\n",
    "              axis=3,\n",
    "              title='Loss Function',\n",
    "              xlabel='epoch',\n",
    "              legend='training loss',\n",
    "              ylog=True,\n",
    "              binstep=int(np.shape(h11_base_model_history['loss'])[0] / 10)\n",
    "             )\n",
    "plot.series2D(data=h11_base_model_history['val_loss'],\n",
    "              axis=3,\n",
    "              title='Loss Function',\n",
    "              xlabel='epoch',\n",
    "              legend='validation loss',\n",
    "              ylog=True,\n",
    "              binstep=int(np.shape(h11_base_model_history['val_loss'])[0] / 10)\n",
    "             )\n",
    "\n",
    "plot.series2D(data=h11_base_model_history['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(h11_base_model_history['mean_squared_error'])[0] / 10)\n",
    "             )\n",
    "plot.series2D(data=h11_base_model_history['val_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(h11_base_model_history['val_mean_squared_error'])[0] / 10)\n",
    "             )\n",
    "\n",
    "plot.save_and_close(path.join(IMG_PATH, h11_base_model.name))\n",
    "log.debug('Plot saved to {}.'.format(path.join(IMG_PATH, h11_base_model.name)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Improved Analysis and Differential Models\n",
    "\n",
    "We then implement our own models and compare their performance to the base model. We are interested in smaller networks which can give same or better performance or network comparable in size but which can give better overall performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"h11_inception_same_conv_no_fc\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "h11_inception_same_conv_no_fc_i [(None, 15, 18, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 32)   512         h11_inception_same_conv_no_fc_inp\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 32)   608         h11_inception_same_conv_no_fc_inp\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 32)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 32)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 64)   0           h11_inception_same_conv_no_fc_con\n",
      "                                                                 h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 64)   256         h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 64)   61504       h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 64)   73792       h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 64)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 64)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 128)  0           h11_inception_same_conv_no_fc_con\n",
      "                                                                 h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 128)  512         h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 32)   61472       h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 32)   73760       h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 32)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 32)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 64)   0           h11_inception_same_conv_no_fc_con\n",
      "                                                                 h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 64)   256         h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_d (None, 15, 18, 64)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_f (None, 17280)        0           h11_inception_same_conv_no_fc_dro\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_o (None, 1)            17281       h11_inception_same_conv_no_fc_fla\n",
      "==================================================================================================\n",
      "Total params: 289,953\n",
      "Trainable params: 289,441\n",
      "Non-trainable params: 512\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-15 17:55:34,178: DEBUG ==> Training model h11_inception_same_conv_no_fc...\n",
      "2020-05-15 19:08:31,487: DEBUG ==> Model h11_inception_same_conv_no_fc has been trained.\n",
      "2020-05-15 19:08:32,562: DEBUG ==> History of h11_inception_same_conv_no_fc has been saved to ./output/favourable/h11_inception_same_conv_no_fc.json.\n",
      "2020-05-15 19:08:34,273: DEBUG ==> History of h11_inception_same_conv_no_fc has been saved to ./output/favourable/convnet_test_results_h11_h21.json.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of h11_inception_same_conv_no_fc on the validation set: 98.855%\n",
      "Accuracy of h11_inception_same_conv_no_fc on the test set: 99.237%\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": [
      "2020-05-15 19:08:38,787: DEBUG ==> Plot saved to ./img/favourable/h11_inception_same_conv_no_fc.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"h11_small_kernel_valid_conv_no_fc\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "h11_small_kernel_valid_conv_ [(None, 15, 18, 1)]       0         \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 13, 15, 350)       4550      \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 13, 15, 350)       0         \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 13, 15, 350)       1400      \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 11, 12, 300)       1260300   \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 11, 12, 300)       0         \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 11, 12, 300)       1200      \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 9, 9, 250)         900250    \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 9, 9, 250)         0         \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 9, 9, 250)         1000      \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 7, 7, 200)         450200    \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 7, 7, 200)         0         \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 7, 7, 200)         800       \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 6, 6, 150)         120150    \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 6, 6, 150)         0         \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 6, 6, 150)         600       \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 5, 5, 100)         60100     \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 5, 5, 100)         0         \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 5, 5, 100)         400       \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 4, 4, 50)          20050     \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 4, 4, 50)          0         \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 4, 4, 50)          200       \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 4, 4, 50)          0         \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 800)               0         \n",
      "_________________________________________________________________\n",
      "h11_small_kernel_valid_conv_ (None, 1)                 801       \n",
      "=================================================================\n",
      "Total params: 2,822,001\n",
      "Trainable params: 2,819,201\n",
      "Non-trainable params: 2,800\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-15 19:08:39,276: DEBUG ==> Training model h11_small_kernel_valid_conv_no_fc...\n",
      "2020-05-16 02:39:02,284: DEBUG ==> Model h11_small_kernel_valid_conv_no_fc has been trained.\n",
      "2020-05-16 02:39:04,498: DEBUG ==> History of h11_small_kernel_valid_conv_no_fc has been saved to ./output/favourable/h11_small_kernel_valid_conv_no_fc.json.\n",
      "2020-05-16 02:39:05,963: DEBUG ==> History of h11_small_kernel_valid_conv_no_fc has been saved to ./output/favourable/convnet_test_results_h11_h21.json.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of h11_small_kernel_valid_conv_no_fc on the validation set: 99.109%\n",
      "Accuracy of h11_small_kernel_valid_conv_no_fc on the test set: 98.982%\n"
     ]
    },
    {
     "data": {
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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": [
      "2020-05-16 02:39:09,362: DEBUG ==> Plot saved to ./img/favourable/h11_small_kernel_valid_conv_no_fc.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"h11_large_kernel_same_conv_no_fc\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "h11_large_kernel_same_conv_n [(None, 15, 18, 1)]       0         \n",
      "_________________________________________________________________\n",
      "h11_large_kernel_same_conv_n (None, 15, 18, 180)       4680      \n",
      "_________________________________________________________________\n",
      "h11_large_kernel_same_conv_n (None, 15, 18, 180)       0         \n",
      "_________________________________________________________________\n",
      "h11_large_kernel_same_conv_n (None, 15, 18, 180)       720       \n",
      "_________________________________________________________________\n",
      "h11_large_kernel_same_conv_n (None, 15, 18, 100)       450100    \n",
      "_________________________________________________________________\n",
      "h11_large_kernel_same_conv_n (None, 15, 18, 100)       0         \n",
      "_________________________________________________________________\n",
      "h11_large_kernel_same_conv_n (None, 15, 18, 100)       400       \n",
      "_________________________________________________________________\n",
      "h11_large_kernel_same_conv_n (None, 15, 18, 40)        100040    \n",
      "_________________________________________________________________\n",
      "h11_large_kernel_same_conv_n (None, 15, 18, 40)        0         \n",
      "_________________________________________________________________\n",
      "h11_large_kernel_same_conv_n (None, 15, 18, 40)        160       \n",
      "_________________________________________________________________\n",
      "h11_large_kernel_same_conv_n (None, 15, 18, 20)        20020     \n",
      "_________________________________________________________________\n",
      "h11_large_kernel_same_conv_n (None, 15, 18, 20)        0         \n",
      "_________________________________________________________________\n",
      "h11_large_kernel_same_conv_n (None, 15, 18, 20)        80        \n",
      "_________________________________________________________________\n",
      "h11_large_kernel_same_conv_n (None, 15, 18, 20)        0         \n",
      "_________________________________________________________________\n",
      "h11_large_kernel_same_conv_n (None, 5400)              0         \n",
      "_________________________________________________________________\n",
      "h11_large_kernel_same_conv_n (None, 1)                 5401      \n",
      "=================================================================\n",
      "Total params: 581,601\n",
      "Trainable params: 580,921\n",
      "Non-trainable params: 680\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-16 02:39:09,688: DEBUG ==> Training model h11_large_kernel_same_conv_no_fc...\n",
      "2020-05-16 04:30:36,980: DEBUG ==> Model h11_large_kernel_same_conv_no_fc has been trained.\n",
      "2020-05-16 04:30:37,959: DEBUG ==> History of h11_large_kernel_same_conv_no_fc has been saved to ./output/favourable/h11_large_kernel_same_conv_no_fc.json.\n",
      "2020-05-16 04:30:39,473: DEBUG ==> History of h11_large_kernel_same_conv_no_fc has been saved to ./output/favourable/convnet_test_results_h11_h21.json.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of h11_large_kernel_same_conv_no_fc on the validation set: 98.601%\n",
      "Accuracy of h11_large_kernel_same_conv_no_fc on the test set: 98.982%\n"
     ]
    },
    {
     "data": {
      "image/png": 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E3OuiLzCfFnIuEs3vckVydWma8eMrQYuU+JCBc26v/99oc62IiFTMc8BFeD0m/SVsWnOgAbA45Bh9jj++NKU9yBVtPrk+JJ/1jGK5WOezFkBRkUqRsh6aS4nywQKRUqlAQxYBu81skpnV9y/EdTOzPnjvnj0EXG9mdcxsNCW/7mIR3g2tqWbW0MxSzGxAyPRteF3fRfIxXhetN5lZspmdDIzk8K5pD2NmJ5jZKWZWD+/9iPvwLvCVpcLrDFHaNoUrbT+X5C/AqXhVjc+GTWuId/NmO4CZXcGPN5JKU9p3Wp42q+R7DGVmnczsf82slf+5NV6XuB/5s5S1L6P5XmIRdybwX/8JiCI9gM+jiEFEqp/yX+Lmv6jbKmN/lBqv8o6I1Cb+b1NLvFd9FeM/6fq/zrn2eL87v7Ifu3ANLwy7CK8r21PxujFvW7SKcoSxETiulGmnhxW9pTjnoinqDo11C14xgBec92qNplSySDzCespjI/CvsG1Ldc5NKHNFzv0XuBN4xC8QLautF/C62W3tF3g8Rvm+myIlPQwwHe9Ju47OuUZ4PUaV1O4evAu8RUKLLyq8L0REaqBiuQiv+HoLlJ57K1hwH6q03+GylJarS8t/O/B6BQnf3ljkXRERqSS/94ivgTOA18Im78C7XtY15Bg93TlXVOxQ0u9/aXmhtHxSXvHIZ1uAJua9nrOI8plUKxVoHOH87nlG4nUJ9DXej/SfgXTn3A9478HNwXs38AUc/qMe3k4HvHc7bfLnL/IH4Dd+Zd6NYcv+gPc+p9P99T8KXObK0bUeXneAU/3ltuK93/aWcmx3ZdZZpMRtirC+EvdzKcusAz7EK5z4W9i0FXhdCC7EuzHUHfh3WQGX9p2Ws82q+h5D5eG9r/JjM9uDd4NsGd57O8uzL8v9vcQo7u6E3BQz7/3RR/sxi0iCUv5L3PxXwbZK2x9lxau8IyI1npk1MrMz8Yq9nvdv+IfPc6aZdfALAHbjFbKVVMyWBhzA642iAV5PCuX1d+BoM/ulmdUzszQz6+tPewy4y7ze+zCz5mZ2VhRth3sBuMLMsv0ivd8DH/vnUpW1DWhlZnXLOf/fgePN7FK/AC/ZLyLsXM7ln8XLX6PK0VYa3hNf+83sRLyCmmiU9DBAGt7fRr6ZdQJKK6hYAow2swZm1gGvu/wild0XIiKJKtkvyi8a6uC9Zuo3fk5rBtwGPA8l595KFNyHWgKcYWZNzOxovB45yuspvPw51MySzKyl/7sPpRSg++dGs/ByeZqfz39VtL0iIpIQfg6c4pzbEzrSea/afRK438z+B8D//R/uz7INaGrFX2tZlpnAqWZ2vv+QW1Mzyy5zqeJKO68oy5/xXgPWyzwdis41KT2fbcS79/YHP59n+uudGWXsIhXnnNOgQYMGDVU84HeXX9Y4DRo0aNCgobJDSflFeUeDhto14L12ZB9eoVkuXqH1tUAgZJ4ZwJ3+/2/wl9mDV1B4a8h8Z+EVGu4CbsTrMvYNv+31eL0tOKBDeLv+55OBTSGfuwH/xCt03ApM9scn4d3I+cJvew3w+xK2r62/zjoh44IxhIy72m/nO7zCgFYlzY/Xs0hOyOc7gT+XsP66wFt+uzvKud0n+MtsxytueRfILqH9BcC4sHGTgE/LagsY438vef42P4xXmBNxv5Wy/u+BeiHjBuH1oJEPvA/cgffu5cP2J14393P9GP6N103/BxXZFxo0aNBQEwa8HOrChjuBFOBBvJ4Vv/H/n+IvEzH34vWKt8j/DS3KXy1KWG+x3BMyPgV4Ga/wY6m/rk1h8Z4a8nlKUa7wP5/jL5eH9xqW4f74/sCXfo540B8X+vufgVeQsR3vyeXbgCR/Wk5oLghfVoMGDRo0VM0Q/psfMr6O/zvc1v+cglfUvtbPHyvxXjtSNP/T/rH7LrzXgBTLHf484fnkJLzeYnf7eeHyEmJcQNj5jz++rPOK8HO6YnkR73zwC7xzmGVAD398+DluW0LOk/BeL/l3Pw+vAa4uZRuLLatBQywGcy7aXjtFRCQaZvY23pPX64HHnXMzIo2LY4giIlJLlJRflHdERERERERERERE4k8FGiIiIiIiIiIiIiIiIiIiIiJVLCneAYiIiIiIiIiIiIiIiIiIiIjUdirQEBEREREREREREREREREREaliKtAQERERERERERERERERERERqWJ14h1ArDRr1sy1bdu2Um0UFhaSlFR7ala0PYlN25PYatv2QM3apsWLF+9wzjWPdxxVSXnrcLVte6D2bZO2J7Fpe+LnSMhbULtzVyLGlYgxgeKKRiLGBIorGokYU6wod5VfovwdJEIciRCD4lAcNSGORIihNsah3FU+te17VxyKozbHoDhqfxwl5a5aU6DRtm1bPv3000q1kZeXR1paWowiij9tT2LT9iS22rY9ULO2yczWxzuGqqa8dbjatj1Q+7ZJ25PYtD3xcyTkLajduSsR40rEmEBxRSMRYwLFFY1EjClWlLvKL1H+DhIhjkSIQXEojpoQRyLEUBvjUO4qn9r2vSsOxVGbY1ActT+OknJX/EtQRERERERERERERERERERERGo5FWiIiIiIiIjUQGbW1szeNrPvzWyrmT1sZnX8aUPNbJWZ7TWz+WbWJmQ5M7O7zWynP/zRzCx+WyIiIiIiIiIiInJkUIGGiIiIiIhIzfQo8C1wDJANDAauMbNmwGvArUAT4FPg5ZDlrgTOBrKATOBM4KrqC1tEREREREREROTIVCfeAVSlgwcPsmnTJvbv31+u+QsLC0lKqj01K9W1PSkpKbRq1Yrk5OQqX5eISG2mvFV926PcJSK1RDvgYefcfmCrmc0BugKjgeXOuVcAzGwKsMPMOjnnVgGXA/c65zb50+8FxgOPxWEbRKQWiPY4NpJEPLZNxJiipeNeERFPLHJVeSVK/qipcSh3iciRrjpzVpGamjMSJY5oc1etLtDYtGkTaWlptG3blvL02FtQUEAgEKiGyKpHdWyPc46dO3eyadMm2rVrV6XrEhGp7ZS3qmd7lLtEpBb5EzDWzBYAGcDpeL1mnAx8XjSTc26Pma3BK95Y5f/7eUg7n/vjDmNmV+L1uEHr1q3Jy8urVMB79uyp1PJVJRHjSsSYQHFFIxFjgqqJa+vWraSnp9O6detyHcdGkigX4kIlYkzRcM7x3XffsW7dOo4++uh4h1OtzGwkMLJDhw7xDkVEEkS011wqI1Gu19TEOI7kazbKXSJSpDpzVpGamDMSJY6K5K5aXaCxf//+av3jPRKZGU2bNmX79u3xDkVEpMZT3qoeyl0iUov8C6/ni91AAHgWeB3vlSXhP3K5QJr//1T/c+i0VDMz55wLXcg59wTwBEDv3r1dWloalRWLNqpCIsaViDGB4opGIsYEsY9r06ZNNG/evNLHsYlwIS5cIsYUjebNm7Nz586E/VusKs65N4E3e/fuPT7esYhIYtA1l5rhSL5mo9wlIkWUs2qWiuSumvsYQDnpj7fqaR+LiMSOflOrh/aziNR0ZpYEvAO8BjQEmuH1onE3kA80ClukEVDU/UX49EZAfnhxhohINHR8lZj0vYiI/Ei/iTWDvicREf0W1jTRfl+1vkBDRERERESkFmoCtAYeds4dcM7tBJ4BzgCWA1lFM5pZQ+A4fzzh0/3/L0dERERERERERESqVK1+xUm0nHOs27GH3H0HSa+fTJumDeJeodS2bVs+/fRTmjVrlhDtiIhI4jhwsIBlG3LZve8gjeonk9kqnZTk+HbBrLwlIlI9nHM7zOxrYIKZTcN7bcnlwOfAbOAeMzsXeAu4DVjqnFvlL/4X4Fdm9jbggP8FHqrubRCR2mvA1HfZvGtfzNpr2bg+/558SszaA0hNTSU/P58tW7Zw/fXX8+qrrx42zymnnMK9995L7969S2zngQce4Morr6RBgwYAnHHGGbzwwgs0btw4pvGWZt26dXz44YdcdNFF1bZOERGJzq5du3jhhRe45pprol62PLnltttuY9CgQZx66qmVCRPQNRkRkSNdTctZrVu35v333w+Oy87O5tChQyxbtoy9e/cyfvx4li5dinOOxo0bM2fOHFJTUwkEAnTv3j243NixY5k8eXKlYyoPFWj4lm/J5cn31rI1dz9mRqFztGhcn3EntaNri/RKt++cwzlHUpI6LRERkcpxzjF3xTaeX7iOHwoczoEZ1KuTxMX92jCsy1GVLjBU3hIRqRFGAw8Ak4ACYD5wg3Nuu1+c8TDwPPAxMDZkuceB9sB//c9/9seJiMTE5l37WDd1RFTLFBQUEAhELjZuO/mtWIQVUYsWLSIWZ5TXAw88wCWXXBIs0Hj77bdjFVq5rVu3jhdeeEEFGiIiCWzXrl08+uijEW92lZYDoXy55Y477qhUfCIiIkVqWs7Ky8tj48aNtG7dmpUrVxab9qc//YmjjjqK//7XuwT2xRdfkJycDED9+vVZsmRJTGMpL911wSvO+N2bK/h+zw80T6tH87R6/E9aPb7b8wO/e3MFy7fkVqjddevW0blzZ6655hp69uzJxo0bueeee+jTpw+ZmZncfvvtwXnPPvtsevXqRdeuXXniiSdKbXf69OncdNNNwc8zZszgF7/4xWHtPPnkkxFj6tatW/DztGnTmDJlCgBr1qzhtNNOo1evXpx00kmsWuU9YPfKK6/QrVs3srKyGDRoUIX2hYjUfnm537Hw2VtYu+zjeIdS681dsY0n31tL/boBjmqUwtHpKRzVKIX6dQM8+d5a5q7YVqF2lbdE5EizcfXnLHx6Mt9v/ybeoVSIc26Jc+5k51yGc66Zc+4859y3/rR5zrlOzrn6/jzrQpZzzrmbnHNN/OEm55yL24aIiFTSpEmTePTRR4Ofp0yZwr333kt+fj5Dhw6lZ8+edO/enTfeeOOwZUOPN/ft28fYsWPJzMzkggsuYN++H3sBmTBhAr1796Zr167B4+IHH3yQLVu2MGTIEIYMGQJ4T3Dt2LEDgPvuu49u3brRrVs3HnjggeD6OnfuzPjx4+natSvDhg0rtp4ikY5pCwoKmDhxYvD4/PHHvdq6yZMn8/7775Odnc39999f6f0pvsJCeP8+ApsWxTsSEakFJk+ezJo1a8jOzmbixIksWLCAIUOGcNFFFwWf3i3pWktRbikth+Tk5AQLDtu2bcvtt99Onz596N69e/B6yfbt2/npT39Kz549ueqqq2jTpk0wZ5UkUi7bs2cPI0aMICsri27duvHyyy8Ht7FLly5kZmZy4403xnYHSvl8v566Hz8MuZviHYmI1GDxyFlF52wVyVnnn39+MBe9+OKLXHjhhcFp33zzDS1btgx+PuGEE6hXr14M91bFHPE9aDjnePK9tdRLDtAopU7wiWMzI71+MrnAn9//mvvOz6rQ08hffPEFzzzzDI8++ihz585l9erVLFq0COcco0aN4r333mPQoEE8/fTTNGnShH379tGnTx/OPfdcmjZtGrHNMWPG0L9/f/74xz8C8PLLL/PrX/8a4LB2zjvvvBLbCXfllVfy2GOP0bFjRz7++GOuueYa3n33Xe644w7eeecdWrZsya5du6LeByJyZNixeQ1NNs7j25R02nfrG+9waq39BwuY+dF6mqbWpW6geJ1lvToBmqbWZebH6xl8fPMKve5EeUtEjiRbVy6k6eZ5bFv/UzKaHxPvcEREpILGjh3LL3/5y+ATXrNmzWLOnDmkpKQwe/ZsGjVqxI4dO+jXrx+jRo0q8frO9OnTadCgAUuXLmXp0qX07NkzOO2uu+6iSZMmFBQUMHToUJYuXcr111/Pfffdx/z58w/rBn7x4sU888wzfPzxxzjn6Nu3L4MHDyYjI4PVq1fz4osv8uSTT3L++efz17/+lUsuuaTY8pGOaZ966inS09P55JNPOHDgAAMGDGDYsGFMnTqVadOm8fe//z2Wu1UA/vlbAv1/BZ2HxjsSEYmh3765nBVbdse0zS4tGnH7yK4lTp86dSrLli0LPqm7YMECFi1axLJly2jXrh1w+DWSSNdaypNDAJo1a8Ynn3zC448/zrRp0/jzn//Mb3/7W0455RRuvvlm5syZU+YDNyXlsrVr19KiRQveesvr4So3N5fvvvuO2bNns/wlP3MAACAASURBVGrVKsxM12Pi5fuvqffBVOgwCNJbxTsaEYmBIyVnffbZZzz66KPcd999PPXUU1HlrDFjxpCTk8ONN97Im2++ycyZM3nuuecA+NnPfsawYcN49dVXGTp0KJdffjkdO3YEvAL97OzsYDs333wzF1xwQTn3YuUc8QUa63fuZWvufpqnRa6WaZRShy279rF+517aNmsYdftt2rShX79+AMydO5e5c+fSo0cPAPLz81m9ejWDBg3iwQcfZPbs2QBs3LiR1atXl3iDqnnz5rRv356PPvqIjh078sUXXzBgwACAqNoJlZ+fz4cffsh5550XHHfgwAEABgwYQE5ODueffz6jR4+Oeh+IyJGhbadebBr7FG1ato93KLXa0k25HDhUSOMGdYn0sHO9OgFy9x1k6aZcTmzXJOr2lbdE5EiSfdoVbO3cn1bHdS97ZpGKuL875G6o2LL1WsCBLRVbNv1YuOG/Zc8nUkv06NGDb7/9li1btrB9+3YyMjI49thjOXjwILfccgvvvfceSUlJbN68mW3btnH00UdHbOe9997j+uuvByAzM5PMzMzgtFmzZvHEE09w6NAhvvnmG1asWFFsergPPviAc845h4YNvWtJo0eP5v3332fUqFG0a9cueCGwV69erFu37rDlIx3Tzp07l6VLlwafNsvNzWX16tXUrVs3+p0mZSt63aMrjG8cIlJrnXjiicEbXVC+ayTlySFAMHf06tWL1157DfByU1H7p512GhkZGaXGV1IuO+2007jxxhuZNGkSZ555JieddBKHDh0iJSWFcePGMWLECM4888wo94bEhPkPa7mC+MYhIrVOdeWsv/71r0B0OatJkyZkZGTw0ksv0blz5+DrJwGys7NZu3Ytc+fOZd68efTp04eFCxfSuXPnuL7i5Igv0MjddxAzw8wi3ugyM5KSjNx9ByvUftHBC3i9ddx8881cddVVxeZZsGAB8+bNY+HChTRo0ICTTz6Z/fv3l9ruBRdcwKxZs+jUqRPnnHMOZnZYO4MHDz6snTp16lBY+OOJZdH0wsJCGjduHPEP8bHHHuPjjz/mrbfeIjs7myVLlpT76WYROXJYUhKtO+gGV1Xbve8gZXVCX1jozVcRylsiciRJrluP1h2z4h2G1Ga5G2BKya/MHDD1XTbvOvzVBgBHmWPb/uh7cQRYx0W0nfxWuedv2bg+/558SoXWJZIoxowZw6uvvsrWrVsZO3YsADNnzmT79u0sXryY5ORk2rZtW+Zxa6TeNb7++mumTZvGJ598QkZGBjk5OWW2U9qbo0K71A0EAhFfcRLpmNY5x0MPPcTw4cOLzbtgwYJSY5FKsCQVaIjUQqU9NVydQq/BlPdaS3lySOh8gUCAQ4cOAaXnpkhKmv/4449n8eLFvP3229x8880MGzaM2267jUWLFvHPf/6Tl156iYcffph33303qvVJDCT5BRqFKtAQqS2Us8rnggsu4Nprr2XGjBmHTUtNTWX06NGMHj2apKQk3n77bTp37hxV+7F2xBdopNdPptC5Er9o5xyFhY70+smVXtfw4cO59dZbufjii0lNTWXz5s0kJyeTm5tLRkYGDRo0YNWqVXz00UdltjV69Gjuuusu2rRpw9133w1wWDsff/zxYcsdddRRfPvtt+zcuZPU1FT+/ve/c9ppp9GoUSPatWvHK6+8wnnnnYdzjqVLl5KVlcWaNWvo27cvffv25c0332Tjxo260SUiEieN6idT1hu3kpK8+SpLeUtERKRqbd61j3VTR0SclpeXR1paWsUankKJ7UYSTTGHSKIaO3Ys48ePZ8eOHfzrX/8CvOPN//mf/yE5OZn58+ezfv36UtsYNGgQM2fOZMiQISxbtoylS5cCsHv3bho2bEh6ejrbtm3jH//4ByeffDIAaWlp5OXlHfaKk0GDBpGTk8PkyZNxzjF79uxgN7vlEemYdvjw4UyfPp1TTjmF5ORkvvzyS1q2bBmMQaqABfQUsojERFm/1RW51hKtgQMHMmvWLCZNmsTcuXP5/vvvS52/pFy2ZcsWmjRpwiWXXEJqaiozZswgPz+fvXv3csYZZ9CvXz86dOgQ8/ilHII9aKi4UEQqribmrHPOOYdvvvmG4cOHs2XLj72R/vvf/6ZLly5kZGTwww8/sGLFiuC5XDwd8QUabZo24Jj0FL7fe5BGKYfvjt37D9GicX3aNG0QYenoDBs2jJUrV9K/f3/Aq9h5/vnnOe2003jsscfIzMzkhBNOCHYtX5qMjAy6dOnCihUrOPHEEwEOa6dv376HLZecnMxtt91G3759adeuHZ06dQpOmzlzJhMmTODOO+/k4MGDjB07lqysLCZOnMjq1atxzjF06FCysvSUoYhIvGS2SqdenSQOHCqgbiDpsOkHDhVQt04Sma3SK70u5S0RERERqYiWjevHtPCnZeP6Zc7TtWtX8vLyaNmyJccccwwAF198MSNHjqR3795kZ2cXO5aMZMKECVxxxRVkZmaSnZ1Nnz59AMjKyqJHjx507dqV9u3bB1/XB3DllVdy+umnc8wxxzB//vzg+J49e5KTkxM89h03bhw9evQosVvfcJGOaTMzM1m3bh09e/bEOUfz5s15/fXXyczMpE6dOmRlZZGTk8MNN9xQrnVIOSQFMD2FLCIx0LRpUwYMGEC3bt04/fTTGTGieDFtRa61ROv222/nwgsv5OWXX2bw4MEcc8wxpRYEl5TL3nnnHSZOnEhSUhLJyclMnz6dvLw8zjrrLPbv349zjvvvvz/m8Us5mF7PJSKVVxNzVlpaGpMmTTps/Jo1a5gwYYLfIUMhI0aM4NxzzwVg3759wVewFG3X1KlTY74tkVi0XYQkqt69e7tPP/202LiVK1eWq4uS5Vty+d2bK6hXJ8l/Mtl73cnu/Yc4cLCAW0d2oWuLyt/oqm4FBQUEAoFqWVd593VlVOoJsgSk7UlstW17oGZtk5ktds71jnccVakyeeud5Vt58r21NGmYTEryj8WFBw4VsDP/B8YPas/wrpHfq53IqjNvgXJXRWh7Epu2J36OhLwFkXNXtBL1e62yuKakl/qKk7aT36qiHjRKX280ccQ0riqUiHElYkxQNXHF4riquo8FyyMRY6qISN+Pclc53dWCHzIvoe7IP8YuqApKhN+URIhBcSiOisZRHdcAiiRK/giP48CBAwQCAerUqcPChQuZMGFCxFfHVnUc5aHcVcHctXkxPHkKXPgynHBabAOLUqL/JigOxZEIMZQUR3XmrCKJlrvilbPC44hGNLnriO9BA6Bri3RuHdmFJ99byze5+0lKMgoLHS0a12fcSe1qZHGGiIjUXsO6HAXA8wvXsXv/IQoLvdea1A0kMX5Q++B0ERERERERqQRL0itORKTW2LBhA+effz6FhYXUrVuXJ598Mt4hSawFX3Gi3CUiNVttz1kq0PB1bZHOtDHd2bTrALn7DpJeP5k2TRtgZvEOTUREpBgzY3jXoznpuCYs+yaf3fsO0qh+Mpmt0klJjn+Vq4iIiIiISK2QpAINEak9OnbsyH/+8594hyFVKcm/LqjXc4lIDVfbc5YKNEKYGW2bNYx3GCIiIuVSLznAie2axDsMERERERGR2skCUFgY7yhERETKRz1oiIjUCEnxDkBEREREREREREQk4SQFABVoiIhIDaEeNEREagQVaIiIiIiIiIiIiIiEsyTd5BIRkZoj2IOGigtFRBKZCjREREREREREREREwllA3cSLiEiFmVm6mS0ys3wz61blK0zyb/mpuFBEJKGpQCOUc7BzDWxe7P3rXLwjClqwYAFnnnkmAH/729+YOnVqifPu2rWLRx99NPh5y5YtjBkzpspjFBGRanZoP6z/EL74h/fvwf3xjqiYaHPX9OnTg5+Vu0RERERqsPu7w5T0qIbA75qUPP3+7qWuLvw6SLQeeOAB9u7dW+HliyxYsIAPP/yw0u1IAkkKYHoKWUTiJDU1FSj9GsnJJ5/Mp59+Wmo74XnujDPOYNeuXZWOb8qUKUybNq3S7dRye4ERwKvVsjb1oCEicRKrnPWnP/2pynKWmfHVV18Fx91///2YWTCmp59+mu7du5OZmUlWVhZvvPEGADk5ObRr147s7Gyys7P5yU9+Uul46lS6hdrim6XYhw/B7i1g5iWw9FbQ/zo4JrPKVltQUEAgEIhqmVGjRjFq1KgSpxddmLjqqqsAaNGiBa++Wj35X0REqoFzsOot7JOnoOCA99kM6tSD3j+HTiO8z1WkqnLXY489xnXXXQcod4mIiIjUaLkbYEpuVIuUeow5Jb3UZYuug1xzzTVRrbPIAw88wCWXXEKDBg0qtHyRBQsWkJqaGpMLdnI4M0sH/g/oAvRzzi2r+pXqFSciEn+VvUYSnufefvvtWIUmZXDOHQS2WxVepyvG/Gey1fuTiMRJZXPWgw8+yGWXXVYlOat79+689NJL/OY3vwHg1VdfpUuXLgBs2rSJu+66i88++4z09HRyc3P57rvvgsvec889MX2gVD1oAHyzFObcDHu/g9Sj/OFo2LvTG//N0go1u27dOjp16sTll19OZmYmY8aMYe/evbRt25Y77riDgQMH8sorrzB37lz69+9Pz549Oe+888jPzwdgzpw5dOrUiYEDB/Laa68F250xY0bwBta2bds455xzyMrKIisriw8//JDJkyezZs0aevXqxcSJE1m3bh3dunm9Z+3fv58rrriC7t2706NHD+bPnx9sc/To0Zx22ml07NiRm266CfAukOTk5NCtWze6d+/O/fffX+HdLCIiMbLqLfjwIUhuAGnHQKMW3r/JDb3xq96qcNPxzl3Z2dnKXSIiIiISlfBjSfAuoPXp04fMzExuv/12APbs2cOIESPIysqiW7duvPzyyzz44INs2bKFIUOGMGTIkIhtd+nShczMTG688UYAtm/fzrnnnkufPn3o06cP//73v1m3bh2PPfYY999/P9nZ2bz//vvVtwOOHNX7FDJ4N7r0FLKIxMCkSZOK9fY0ZcoU7r33XvLz8xk6dCg9e/ake/fuwad1Q4VeI9m3bx9jx44lMzOTCy64gH379gXnmzBhAr1796Zr167B3Bcpz7Vt25YdO3YAcN9999GtWze6devGAw88EFxf586dGT9+PF27dmXYsGHF1hPJkiVL6NevH5mZmZxzzjl8//33wfUXPY08duxYAP71r38Fn0Lu0aMHeXl5Fdqn1cnMrjOzT83sgJnNCJvWxMxmm9keM1tvZhfFKUxI8otdVVwoIpVQW3PW2WefHYx57dq1pKen07x5cwC+/fZb0tLSgj2BpKam0q5du4rvxDKoBw3nYOFDkJwC9dJ/fOLYDFIaA7tg4cNwzuMVehr5iy++4KmnnmLAgAH87Gc/C/5Bp6Sk8MEHH7Bjxw5Gjx7NvHnzaNiwIXfffTf33XcfN910E+PHj+fdd9+lQ4cOXHDBBRHbv/766xk8eDCzZ8+moKCA/Px8pk6dyrJly1i8eDGBQIB169YF53/kkUcA+O9//8uqVasYNmwYX375JeAdRP3nP/+hXr16nHDCCfziF7/g22+/ZfPmzSxb5j2UEItuZEREpBIO7odPn4KGzSFQt/i0OvW88Z8+DR1O9XJbBcQzdy1ZsgRAuUtEREREyi38WHLu3LmsXr2aRYsW4Zxj1KhRvPfee2zfvp0WLVrw1lteQXNubi7p6encd999zJ8/n2bNmhVr97vvvmP27NmsWrUKMwseV/6///f/uOGGGxg4cCAbNmxg+PDhrFy5kquvvprU1NRgIYfEVrU/hQzejS4VaIjUPv+YDFv/G9s2j+4Op5f8atexY8fyy1/+Mtjb06xZs5gzZw4pKSnMnj2bRo0asWPHDvr168eoUaMo6bdu+vTpNGjQgKVLl7J06VJ69uwZnHbXXXfRpEkTCgoKGDp0KEuXLuX6668vMc8tXryYZ555ho8//hjnHH379mXw4MFkZGSwevVqXnzxRZ588knOP/98/vrXv3LJJZeUuH2XXXYZDz30EIMHD+a2227jt7/9LQ888ABTp07lq6++okGDBsE8Om3aNB555BEGDBhAfn4+KSkVu35VzbYAdwLDgfph0x4BfgCOArKBt8zsc+fc8uoNkZBXnKhAQ6TWUM4CYpOzGjVqROvWrVm2bBlvvPEGF1xwAc888wwAWVlZHHXUUbRr146hQ4dy1llncfbZZweXnThxInfeeScAXbt2ZebMmeXc2ZGpB43v1kLuFq84I5J66ZC7yZuvAlq3bs2AAQMAuOSSS/jggw8AgjetPvroI1asWMGAAQPIzs7m2WefZf369axatYp27drRsWNHzKzEg593332XCRMmABAIBEhPL73bzw8++IBLL70UgE6dOtGmTZvgTa6hQ4eSnp5OSkoKXbp0Yf369bRv3561a9fyi1/8gjlz5tCoUaMK7QcREYmRLZ/BoQNeMUYkderBof3efBWk3CUiIiIiNdncuXOZO3cuPXr0oGfPnqxatYrVq1fTvXt35s2bx6RJk3j//ffLPA5t1KgRKSkpjBs3jtdeey3Yze68efO47rrryM7OZtSoUezevbtGPP2bKGrMU8jg3ejSTS4RiYEePXrw7bffsmXLFj7//HMyMjI49thjcc5xyy23kJmZyamnnsrmzZvZtm1bie289957westmZmZZGb++Hr2WbNm0bNnT3r06MHy5ctZuXJlqTF98MEHnHPOOTRs2JDU1FRGjx4d7AGqXbt2ZGdnA9CrV69iD9KEy83NZdeuXQwePBiAyy+/nPfeey8Y46WXXsrzzz9PnTre88IDBgzgV7/6FQ8++CC7du0Kjk9kzrnXnHOvAztDx5tZQ+Bc4FbnXL5z7gPgb8ClcQgzpAcNFReKSMXFI2etWLGi1JhilbPGjh3LSy+9xOuvv84555wTHB8IBJgzZw6vvvoqxx9/PDfeeCNTpkwJTr/nnntYsmQJS5YsqXRxBsSpBw0zuw7IAboDLzrnciLMczswBfipc25elQWzf5fXM4aZ15vG4YF4J2P7K/b0bXjVUNHnhg0bAuCc46c//SkvvvhisfmWLFlSYsVRZbhI2+irV+/Hm32BQIBDhw6RkZHB559/zjvvvMMjjzzCrFmzePrpp2Mel4hIIkusvJUbOV+Fcs6br4KUu0RERESkJnPOcfPNN3PVVVcdNm3x4sW8/fbb3HzzzQwbNozbbrutxHbq1KnDokWL+Oc//8lLL73Eww8/zLvvvkthYSELFy6kfv3wB2ilnGrGU8jg3ehSN/EitU8pTw1XpTFjxvDqq6+ydevW4Os+Zs6cyfbt21m8eDHJycm0bduW/fv3l9pOpGsvX3/9NdOmTeOTTz4hIyODnJycMtuJ5npLWa84Kclbb73F/Pnzeeutt/jd737H8uXLmTx5MiNGjODtt9+mX79+zJs3j06dOlWo/QRwPFDgnPsyZNznwOCiD2b2Nl5OO8HMHnfOzQhvxMyuBK4E78GpChd+7ttLGrB//x4Oxrl4dM+ePXFdfxHFUZziSKwYIHIchYWFFBT4x6DD7qqaFRcUP8YtDCvsGj16NLNmzWLr1q2cf/75FBQU8Nxzz/Htt9+yaNEikpOTOe6449izZ08w1oKCgmL/d84V3xZ//FdffcW0adP46KOPyMjI4Gc/+xl79+6N2E7o59C2CgsLg5/r1asXHG9m/PDDD8WWD53/jDPOYOLEifTq1YuGDRvinCu2vl69etGrVy+GDBnC+PHjufXWWyNuRySFhYXl/v2OV2liaSdimNlxwBjgmyqPJKWx11VhSQcjznmV8imNK9T8hg0bWLhwIf379+fFF19k4MCB/Oc//wlO79evH9deey1fffUVHTp0YO/evWzatIlOnTrx9ddfs2bNGo477rjDboIVGTp0KNOnT+eXv/wlBQUF7Nmzh7S0tBL/AAYNGsTMmTM55ZRT+PLLL9mwYQMnnHACn30W+UnrHTt2ULduXc4991yOO+44cnJyKrQfRERquATKW+llv3LLzJuvgpS7RCTeXGEh+/bmkZaWFu9QRESkBgg/lhw+fDi33norF198MampqWzevJnk5GQOHTpEkyZNuOSSS0hNTWXGjBnFlg/vRjc/P58DBw5wxhln0K9fPzp06ADAsGHDePjhh5k4cSLgFSpnZ2eTlpbG7t27q2ejazDn3GsAZtYbaFU0PuQp5G7OuXzgAzMregp5cnnbj9lNLqCBg4JDB9mfAD2kJMJNhESIARRHOMVRXElxlOfGSqyE3+Qqct5553HVVVexc+dO3n33XQoKCvj+++9p3rw5SUlJzJs3j/Xr1x92gyv0/wMHDuT5559n0KBBLFu2jKVLlwbbKXqqeMuWLfzjH//gpJNOoqCggLS0NHbt2kVGRkYwloKCAgYMGMDPf/5zJk6ciHOO2bNnM2PGjGLrK9qeSPuvaHxqaioZGRksWLCAk046iWeffZaTTjqJgwcPsmHDBgYPHszAgQN54YUXyM3NZefOnXTp0oUuXbrw4YcfsmLFCjp27HhY2zWkd6pUIPwpqVwgeDLrnDujrEacc08ATwD07t3bVfhcONn720tJTiYlAc6nE+WcXnEUpzgSKwY4PI6kpCQCgUC1xxG6zosuuojx48ezY8cO/vWvfxEIBMjLy+Ooo44iJSWF+fPns379egKBQHC58P8PHjyYl156iVNPPTWYswKBAHv27KFhw4Y0adKE7du3M2fOHIYMGUIgECAtLY29e/cWiyUQCHDyySeTk5PDLbfcgnOON954g+eee67Y+sDbd5H2X9H41NRU7r77bo4//ngCgQBmRiAQYNu2bWzdujX4GpalS5fSpk2b4Dzl+U6SkpLK/TcVlwKNkk7EQjwMTAIerfJgmrSH9Ja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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": [
      "2020-05-16 04:30:42,940: DEBUG ==> Plot saved to ./img/favourable/h11_large_kernel_same_conv_no_fc.\n"
     ]
    }
   ],
   "source": [
    "h11_deep_model = cnn_model(input_shape=input_shape,\n",
    "                           model_name='h11_large_kernel_same_conv_fc',\n",
    "                           learning_rate=1.0e-3,\n",
    "                           conv_layers=[80, 40, 20],\n",
    "                           conv_paddings=['same']*3,\n",
    "                           conv_kernels=[(5,5)]*3,\n",
    "                           conv_alpha=0.0,\n",
    "                           fc_layers=[1000, 1000, 100],\n",
    "                           fc_alpha=0.0,\n",
    "                           dropout=0.4,\n",
    "                           full_dropout=0.0,\n",
    "                           normalization=0.99,\n",
    "                           last_relu=True,\n",
    "                           l1_reg=1.0e-5,\n",
    "                           l2_reg=1.0e-5\n",
    "                          )\n",
    "\n",
    "h11_deep_model_no_fc = cnn_model(input_shape=input_shape,\n",
    "                                 model_name='h11_large_kernel_same_conv_no_fc',\n",
    "                                 learning_rate=1.0e-3,\n",
    "                                 conv_layers=[180, 100, 40, 20],\n",
    "                                 conv_paddings=['same']*4,\n",
    "                                 conv_kernels=[(5,5)]*4,\n",
    "                                 conv_alpha=0.0,\n",
    "                                 fc_layers=[],\n",
    "                                 fc_alpha=0.0,\n",
    "                                 dropout=0.4,\n",
    "                                 full_dropout=0.0,\n",
    "                                 normalization=0.99,\n",
    "                                 last_relu=True,\n",
    "                                 l1_reg=1.0e-5,\n",
    "                                 l2_reg=1.0e-5\n",
    "                                )\n",
    "\n",
    "h11_model_padding = cnn_model(input_shape=input_shape,\n",
    "                              model_name='h11_valid_conv_no_fc',\n",
    "                              learning_rate=1.0e-3,\n",
    "                              conv_layers=[180, 100, 75, 40, 20],\n",
    "                              conv_paddings=['valid']*5,\n",
    "                              conv_kernels=[(4,5), (4,5), (3,4), (3,3), (2,2)],\n",
    "                              conv_alpha=0.0,\n",
    "                              fc_layers=[],\n",
    "                              fc_alpha=0.0,\n",
    "                              dropout=0.2,\n",
    "                              full_dropout=0.0,\n",
    "                              normalization=0.99,\n",
    "                              last_relu=True,\n",
    "                              l1_reg=1.0e-5,\n",
    "                              l2_reg=1.0e-5\n",
    "                             )\n",
    "\n",
    "h11_small_kernel_deep_model = cnn_model(input_shape=input_shape,\n",
    "                                        model_name='h11_small_kernel_same_conv_fc',\n",
    "                                        learning_rate=1.0e-3,\n",
    "                                        conv_layers=[80, 40, 20],\n",
    "                                        conv_paddings=['same']*3,\n",
    "                                        conv_kernels=[(3,3), (3,3), (2,2)],\n",
    "                                        conv_alpha=0.0,\n",
    "                                        fc_layers=[1000, 1000, 100],\n",
    "                                        fc_alpha=0.0,\n",
    "                                        dropout=0.4,\n",
    "                                        full_dropout=0.0,\n",
    "                                        normalization=0.99,\n",
    "                                        last_relu=True,\n",
    "                                        l1_reg=1.0e-5,\n",
    "                                        l2_reg=1.0e-5\n",
    "                                       )\n",
    "\n",
    "h11_small_kernel_deep_model_padding = cnn_model(input_shape=input_shape,\n",
    "                                                model_name='h11_small_kernel_valid_conv_no_fc',\n",
    "                                                learning_rate=1.0e-3,\n",
    "                                                conv_layers=[350, 300, 250, 200, 150, 100, 50],\n",
    "                                                conv_paddings=['valid']*7,\n",
    "                                                conv_kernels=[(3,4), (3,4), (3,4), (3,3), (2,2), (2,2), (2,2)],\n",
    "                                                conv_alpha=0.0,\n",
    "                                                fc_layers=[],\n",
    "                                                fc_alpha=0.0,\n",
    "                                                dropout=0.4,\n",
    "                                                full_dropout=0.0,\n",
    "                                                normalization=0.99,\n",
    "                                                last_relu=True,\n",
    "                                                l1_reg=1.0e-5,\n",
    "                                                l2_reg=1.0e-5\n",
    "                                               )\n",
    "\n",
    "h11_inception_model_padding = scan_inception_model(input_shape=input_shape,\n",
    "                                                   model_name='h11_inception_valid_conv_fc',\n",
    "                                                   learning_rate=1.0e-2,\n",
    "                                                   conv_layers=[60],\n",
    "                                                   conv_padding='valid',\n",
    "                                                   conv_alpha=0.0,\n",
    "                                                   fc_layers=[500, 100],\n",
    "                                                   fc_alpha=0.0,\n",
    "                                                   dropout=0.2,\n",
    "                                                   full_dropout=0.0,\n",
    "                                                   normalization=0.99,\n",
    "                                                   last_relu=True,\n",
    "                                                   l1_reg=1.0e-5,\n",
    "                                                   l2_reg=1.0e-5\n",
    "                                                  )\n",
    "\n",
    "h11_deep_inception_model = scan_inception_model(input_shape=input_shape,\n",
    "                                                model_name='h11_inception_same_conv_no_fc',\n",
    "                                                learning_rate=1.0e-3,\n",
    "                                                conv_layers=[32, 64, 32],\n",
    "                                                conv_padding='same',\n",
    "                                                conv_alpha=0.0,\n",
    "                                                fc_layers=[],\n",
    "                                                fc_alpha=0.0,\n",
    "                                                dropout=0.3,\n",
    "                                                full_dropout=0.0,\n",
    "                                                normalization=0.99,\n",
    "                                                last_relu=True,\n",
    "                                                l1_reg=1.0e-4,\n",
    "                                                l2_reg=1.0e-4\n",
    "                                               )\n",
    "\n",
    "# create a list of models to train\n",
    "models = [h11_deep_inception_model,\n",
    "          #h11_inception_model_padding,\n",
    "          h11_small_kernel_deep_model_padding,\n",
    "          #h11_small_kernel_deep_model,\n",
    "          #h11_deep_model,\n",
    "          h11_deep_model_no_fc,\n",
    "          #h11_model_padding\n",
    "         ]\n",
    "\n",
    "# train and display properties for each of them\n",
    "rounding = np.rint\n",
    "for model in models:\n",
    "\n",
    "    # print characteristics\n",
    "    model.summary()\n",
    "\n",
    "    # print architecture\n",
    "    model_dot = model_to_dot(model=model, show_shapes=True, show_layer_names=False)\n",
    "    model_dot.write_pdf(path.join(IMG_PATH, model.name + '_model.pdf'))\n",
    "\n",
    "    # train the model\n",
    "    log.debug('Training model {}...'.format(model.name))\n",
    "    model, model_history = cnn_fit(model=model,\n",
    "                                   X=matrix_train, y=h11_train,\n",
    "                                   X_val=matrix_val, y_val=h11_val,\n",
    "                                   batch_size=32,\n",
    "                                   epochs=5000,\n",
    "                                   early_stopping=400,\n",
    "                                   reduce_lr=150,\n",
    "                                   verbose=0\n",
    "                                  )\n",
    "    log.debug('Model {} has been trained.'.format(model.name))\n",
    "\n",
    "    # compute the predictions\n",
    "    model_val_predictions  = np.reshape(model.predict(matrix_val), (-1,)) #---- reshape to match the labels\n",
    "    model_test_predictions = np.reshape(model.predict(matrix_test), (-1,))\n",
    "    test_results[str(model.name)] = model_test_predictions.tolist()\n",
    "\n",
    "    # save the history of the model\n",
    "    with open(path.join(OUT_PATH, model.name + '.json'), 'w') as f:\n",
    "        json.dump(str(model_history), f)\n",
    "        log.debug('History of {} has been saved to {}.'.format(model.name, path.join(OUT_PATH, model.name + '.json')))\n",
    "\n",
    "    # save results to file\n",
    "    with open(path.join(OUT_PATH, 'convnet_test_results_h11_h21.json'), 'w') as f:\n",
    "        json.dump(test_results, f)\n",
    "        log.debug('History of {} has been saved to {}.'.format(model.name, path.join(OUT_PATH, 'convnet_test_results_h11_h21.json')))\n",
    "\n",
    "    # compute the accuracy and score of the predictions\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_history['loss'],\n",
    "                  axis=3,\n",
    "                  title='Loss Function',\n",
    "                  xlabel='epoch',\n",
    "                  legend='training loss',\n",
    "                  ylog=True,\n",
    "                  binstep=int(np.shape(model_history['loss'])[0] / 10)\n",
    "                 )\n",
    "    plot.series2D(data=model_history['val_loss'],\n",
    "                  axis=3,\n",
    "                  title='Loss Function',\n",
    "                  xlabel='epoch',\n",
    "                  legend='validation loss',\n",
    "                  ylog=True,\n",
    "                  binstep=int(np.shape(model_history['val_loss'])[0] / 10)\n",
    "                 )\n",
    "\n",
    "    plot.series2D(data=model_history['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_history['mean_squared_error'])[0] / 10)\n",
    "                 )\n",
    "    plot.series2D(data=model_history['val_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_history['val_mean_squared_error'])[0] / 10)\n",
    "                 )\n",
    "\n",
    "    plot.save_and_close(path.join(IMG_PATH, model.name))\n",
    "    log.debug('Plot saved to {}.'.format(path.join(IMG_PATH, model.name)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis for $h_{21}$\n",
    "\n",
    "We can then follow the same line of ideas and implement models of neural networks to predict $h_{21}$. In general they will have to be deeper with respect to the previous models since the dependences on the features are more complicated in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-16 04:30:47,513: DEBUG ==> Training model h21_inception_same_conv_no_fc...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"h21_inception_same_conv_no_fc\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "h21_inception_same_conv_no_fc_i [(None, 15, 18, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 128)  2048        h21_inception_same_conv_no_fc_inp\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 128)  2432        h21_inception_same_conv_no_fc_inp\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 128)  0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 128)  0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 256)  0           h21_inception_same_conv_no_fc_con\n",
      "                                                                 h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 256)  1024        h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 128)  491648      h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 128)  589952      h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 128)  0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 128)  0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 256)  0           h21_inception_same_conv_no_fc_con\n",
      "                                                                 h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 256)  1024        h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 32)   122912      h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 32)   147488      h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 32)   0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 32)   0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 64)   0           h21_inception_same_conv_no_fc_con\n",
      "                                                                 h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 64)   256         h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_d (None, 15, 18, 64)   0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_f (None, 17280)        0           h21_inception_same_conv_no_fc_dro\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_o (None, 1)            17281       h21_inception_same_conv_no_fc_fla\n",
      "==================================================================================================\n",
      "Total params: 1,376,065\n",
      "Trainable params: 1,374,913\n",
      "Non-trainable params: 1,152\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-16 15:16:06,347: DEBUG ==> Model h21_inception_same_conv_no_fc has been trained.\n",
      "2020-05-16 15:16:08,725: DEBUG ==> History of h21_inception_same_conv_no_fc has been saved to ./output/favourable/h21_inception_same_conv_no_fc.json.\n",
      "2020-05-16 15:16:10,304: DEBUG ==> History of h21_inception_same_conv_no_fc has been saved to ./output/favourable/convnet_test_results_h11_h21.json.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of h21_inception_same_conv_no_fc on the validation set: 47.583%\n",
      "Accuracy of h21_inception_same_conv_no_fc on the test set: 47.710%\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": [
      "2020-05-16 15:16:14,262: DEBUG ==> Plot saved to ./img/favourable/h21_inception_same_conv_no_fc.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"h21_large_kernel_same_conv_fc\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "h21_large_kernel_same_conv_f [(None, 15, 18, 1)]       0         \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 15, 18, 250)       9250      \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 15, 18, 250)       0         \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 15, 18, 250)       1000      \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 15, 18, 150)       1350150   \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 15, 18, 150)       0         \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 15, 18, 150)       600       \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 15, 18, 100)       540100    \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 15, 18, 100)       0         \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 15, 18, 100)       400       \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 15, 18, 20)        72020     \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 15, 18, 20)        0         \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 15, 18, 20)        80        \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 15, 18, 20)        0         \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 5400)              0         \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 1500)              8101500   \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 1500)              0         \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 1500)              6000      \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 1500)              2251500   \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 1500)              0         \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 1500)              6000      \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 100)               150100    \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 100)               400       \n",
      "_________________________________________________________________\n",
      "h21_large_kernel_same_conv_f (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 12,489,201\n",
      "Trainable params: 12,481,961\n",
      "Non-trainable params: 7,240\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-16 15:16:14,587: DEBUG ==> Training model h21_large_kernel_same_conv_fc...\n",
      "2020-05-16 22:05:18,104: DEBUG ==> Model h21_large_kernel_same_conv_fc has been trained.\n",
      "2020-05-16 22:05:21,001: DEBUG ==> History of h21_large_kernel_same_conv_fc has been saved to ./output/favourable/h21_large_kernel_same_conv_fc.json.\n",
      "2020-05-16 22:05:22,452: DEBUG ==> History of h21_large_kernel_same_conv_fc has been saved to ./output/favourable/convnet_test_results_h11_h21.json.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of h21_large_kernel_same_conv_fc on the validation set: 31.934%\n",
      "Accuracy of h21_large_kernel_same_conv_fc on the test set: 31.170%\n"
     ]
    },
    {
     "data": {
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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": [
      "2020-05-16 22:05:26,480: DEBUG ==> Plot saved to ./img/favourable/h21_large_kernel_same_conv_fc.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"h21_small_kernel_same_conv_fc\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "h21_small_kernel_same_conv_f [(None, 15, 18, 1)]       0         \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 250)       2500      \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 250)       0         \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 250)       1000      \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 200)       450200    \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 200)       0         \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 200)       800       \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 150)       270150    \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 150)       0         \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 150)       600       \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 100)       135100    \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 100)       0         \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 100)       400       \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 50)        45050     \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 50)        0         \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 50)        200       \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 10)        4510      \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 10)        0         \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 10)        40        \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 15, 18, 10)        0         \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 2700)              0         \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 800)               2160800   \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 800)               0         \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 800)               3200      \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 800)               640800    \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 800)               0         \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 800)               3200      \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 100)               80100     \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 100)               400       \n",
      "_________________________________________________________________\n",
      "h21_small_kernel_same_conv_f (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 3,799,151\n",
      "Trainable params: 3,794,231\n",
      "Non-trainable params: 4,920\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-16 22:05:26,973: DEBUG ==> Training model h21_small_kernel_same_conv_fc...\n",
      "2020-05-17 01:34:13,255: DEBUG ==> Model h21_small_kernel_same_conv_fc has been trained.\n",
      "2020-05-17 01:34:15,207: DEBUG ==> History of h21_small_kernel_same_conv_fc has been saved to ./output/favourable/h21_small_kernel_same_conv_fc.json.\n",
      "2020-05-17 01:34:16,742: DEBUG ==> History of h21_small_kernel_same_conv_fc has been saved to ./output/favourable/convnet_test_results_h11_h21.json.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of h21_small_kernel_same_conv_fc on the validation set: 31.807%\n",
      "Accuracy of h21_small_kernel_same_conv_fc on the test set: 27.863%\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": [
      "2020-05-17 01:34:20,418: DEBUG ==> Plot saved to ./img/favourable/h21_small_kernel_same_conv_fc.\n"
     ]
    }
   ],
   "source": [
    "h21_deep_model = cnn_model(input_shape=input_shape,\n",
    "                           model_name='h21_large_kernel_same_conv_fc',\n",
    "                           learning_rate=1.0e-3,\n",
    "                           conv_layers=[250, 150, 100, 20],\n",
    "                           conv_paddings=['same']*4,\n",
    "                           conv_kernels=[(6,6)]*4,\n",
    "                           conv_alpha=0.0,\n",
    "                           fc_layers=[1500, 1500, 100],\n",
    "                           fc_alpha=0.0,\n",
    "                           dropout=0.4,\n",
    "                           full_dropout=0.0,\n",
    "                           normalization=0.99,\n",
    "                           last_relu=True,\n",
    "                           l1_reg=1.0e-5,\n",
    "                           l2_reg=1.0e-5\n",
    "                          )\n",
    "\n",
    "h21_deep_model_no_fc = cnn_model(input_shape=input_shape,\n",
    "                                 model_name='h21_large_kernel_same_conv_no_fc',\n",
    "                                 learning_rate=1.0e-3,\n",
    "                                 conv_layers=[250, 150, 100, 50],\n",
    "                                 conv_paddings=['same']*4,\n",
    "                                 conv_kernels=[(6,6)]*4,\n",
    "                                 conv_alpha=0.0,\n",
    "                                 fc_layers=[],\n",
    "                                 fc_alpha=0.0,\n",
    "                                 dropout=0.4,\n",
    "                                 full_dropout=0.0,\n",
    "                                 normalization=0.99,\n",
    "                                 last_relu=True,\n",
    "                                 l1_reg=1.0e-5,\n",
    "                                 l2_reg=1.0e-5\n",
    "                                )\n",
    "\n",
    "h21_model_padding = cnn_model(input_shape=input_shape,\n",
    "                              model_name='h21_large_kernel_valid_conv_no_fc',\n",
    "                              learning_rate=1.0e-3,\n",
    "                              conv_layers=[360, 180, 100, 50],\n",
    "                              conv_paddings=['valid']*4,\n",
    "                              conv_kernels=[(5,6), (5,6), (3,4), (2,2)],\n",
    "                              conv_alpha=0.0,\n",
    "                              fc_layers=[],\n",
    "                              fc_alpha=0.0,\n",
    "                              dropout=0.4,\n",
    "                              full_dropout=0.0,\n",
    "                              normalization=0.99,\n",
    "                              last_relu=True,\n",
    "                              l1_reg=1.0e-5,\n",
    "                              l2_reg=1.0e-5\n",
    "                             )\n",
    "\n",
    "h21_small_kernel_deep_model = cnn_model(input_shape=input_shape,\n",
    "                                        model_name='h21_small_kernel_same_conv_fc',\n",
    "                                        learning_rate=1.0e-3,\n",
    "                                        conv_layers=[250, 200, 150, 100, 50, 10],\n",
    "                                        conv_paddings=['same']*6,\n",
    "                                        conv_kernels=[(3,3)]*6,\n",
    "                                        conv_alpha=0.0,\n",
    "                                        fc_layers=[800, 800, 100],\n",
    "                                        fc_alpha=0.0,\n",
    "                                        dropout=0.4,\n",
    "                                        full_dropout=0.0,\n",
    "                                        normalization=0.99,\n",
    "                                        last_relu=True,\n",
    "                                        l1_reg=1.0e-5,\n",
    "                                        l2_reg=1.0e-5\n",
    "                                       )\n",
    "\n",
    "h21_small_kernel_deep_model_padding = cnn_model(input_shape=input_shape,\n",
    "                                                model_name='h21_small_kernel_valid_conv_no_fc',\n",
    "                                                learning_rate=1.0e-3,\n",
    "                                                conv_layers=[350, 300, 250, 200, 150, 100, 50],\n",
    "                                                conv_paddings=['valid']*7,\n",
    "                                                conv_kernels=[(3,4), (3,4), (3,4), (3,3), (2,2), (2,2), (2,2)],\n",
    "                                                conv_alpha=0.0,\n",
    "                                                fc_layers=[],\n",
    "                                                fc_alpha=0.0,\n",
    "                                                dropout=0.4,\n",
    "                                                full_dropout=0.0,\n",
    "                                                normalization=0.99,\n",
    "                                                last_relu=True,\n",
    "                                                l1_reg=1.0e-5,\n",
    "                                                l2_reg=1.0e-5\n",
    "                                               )\n",
    "\n",
    "h21_inception_model_padding = scan_inception_model(input_shape=input_shape,\n",
    "                                                   model_name='h21_inception_valid_conv_fc',\n",
    "                                                   learning_rate=1.0e-2,\n",
    "                                                   conv_layers=[120],\n",
    "                                                   conv_padding='valid',\n",
    "                                                   conv_alpha=0.0,\n",
    "                                                   fc_layers=[500, 100],\n",
    "                                                   fc_alpha=0.0,\n",
    "                                                   dropout=0.2,\n",
    "                                                   full_dropout=0.0,\n",
    "                                                   normalization=0.99,\n",
    "                                                   last_relu=True,\n",
    "                                                   l1_reg=1.0e-5,\n",
    "                                                   l2_reg=1.0e-5\n",
    "                                                  )\n",
    "\n",
    "h21_deep_inception_model = scan_inception_model(input_shape=input_shape,\n",
    "                                                model_name='h21_inception_same_conv_no_fc',\n",
    "                                                learning_rate=1.0e-3,\n",
    "                                                conv_layers=[128, 128, 32],\n",
    "                                                conv_padding='same',\n",
    "                                                conv_alpha=0.0,\n",
    "                                                fc_layers=[],\n",
    "                                                fc_alpha=0.0,\n",
    "                                                dropout=0.2,\n",
    "                                                full_dropout=0.0,\n",
    "                                                normalization=0.99,\n",
    "                                                last_relu=True,\n",
    "                                                l1_reg=1.0e-4,\n",
    "                                                l2_reg=1.0e-4\n",
    "                                               )\n",
    "\n",
    "# create a list of models to train\n",
    "models = [h21_deep_inception_model,\n",
    "          #h21_inception_model_padding,\n",
    "          h21_deep_model,\n",
    "          #h21_deep_model_no_fc,\n",
    "          #h21_model_padding,\n",
    "          h21_small_kernel_deep_model,\n",
    "          #h21_small_kernel_deep_model_padding\n",
    "         ]\n",
    "\n",
    "# train and display properties for each of them\n",
    "rounding = np.rint\n",
    "for model in models:\n",
    "\n",
    "    # print characteristics\n",
    "    model.summary()\n",
    "\n",
    "    # print architecture\n",
    "    model_dot = model_to_dot(model=model, show_shapes=True, show_layer_names=False)\n",
    "    model_dot.write_pdf(path.join(IMG_PATH, model.name + '_model.pdf'))\n",
    "\n",
    "    # train the model\n",
    "    log.debug('Training model {}...'.format(model.name))\n",
    "    model, model_history = cnn_fit(model=model,\n",
    "                                   X=matrix_train, y=h21_train,\n",
    "                                   X_val=matrix_val, y_val=h21_val,\n",
    "                                   batch_size=32,\n",
    "                                   epochs=5000,\n",
    "                                   early_stopping=400,\n",
    "                                   reduce_lr=125,\n",
    "                                   verbose=0\n",
    "                                  )\n",
    "    log.debug('Model {} has been trained.'.format(model.name))\n",
    "\n",
    "    # compute the predictions\n",
    "    model_val_predictions  = np.reshape(model.predict(matrix_val), (-1,)) #---- reshape to match the labels\n",
    "    model_test_predictions = np.reshape(model.predict(matrix_test), (-1,))\n",
    "    test_results[str(model.name)] = model_test_predictions.tolist()\n",
    "\n",
    "    # save the history of the model\n",
    "    with open(path.join(OUT_PATH, model.name + '.json'), 'w') as f:\n",
    "        json.dump(str(model_history), f)\n",
    "        log.debug('History of {} has been saved to {}.'.format(model.name, path.join(OUT_PATH, model.name + '.json')))\n",
    "\n",
    "    # save results to file\n",
    "    with open(path.join(OUT_PATH, 'convnet_test_results_h11_h21.json'), 'w') as f:\n",
    "        json.dump(test_results, f)\n",
    "        log.debug('History of {} has been saved to {}.'.format(model.name, path.join(OUT_PATH, 'convnet_test_results_h11_h21.json')))\n",
    "\n",
    "    # compute the accuracy and score of the predictions\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 $h_{11}$',\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, h21_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_history['loss'],\n",
    "                  axis=3,\n",
    "                  title='Loss Function',\n",
    "                  xlabel='epoch',\n",
    "                  legend='training loss',\n",
    "                  ylog=True,\n",
    "                  binstep=int(np.shape(model_history['loss'])[0] / 10)\n",
    "                 )\n",
    "    plot.series2D(data=model_history['val_loss'],\n",
    "                  axis=3,\n",
    "                  title='Loss Function',\n",
    "                  xlabel='epoch',\n",
    "                  legend='validation loss',\n",
    "                  ylog=True,\n",
    "                  binstep=int(np.shape(model_history['val_loss'])[0] / 10)\n",
    "                 )\n",
    "\n",
    "    plot.series2D(data=model_history['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_history['mean_squared_error'])[0] / 10)\n",
    "                 )\n",
    "    plot.series2D(data=model_history['val_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_history['val_mean_squared_error'])[0] / 10)\n",
    "                 )\n",
    "\n",
    "    plot.save_and_close(path.join(IMG_PATH, model.name))\n",
    "    log.debug('Plot saved to {}.'.format(path.join(IMG_PATH, model.name)))"
   ]
  }
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