RCGAN_5-by-5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FINALIZED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import pickle\n",
    "import keras\n",
    "import os, time\n",
    "from keras import backend as K\n",
    "from keras.utils import np_utils\n",
    "from IPython.display import clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "from functools import partial\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.85)\n",
    "os.environ['PYTHONHASHSEED'] = '0'\n",
    "np.random.seed(int(time.time()))\n",
    "random.seed(1)\n",
    "session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1, gpu_options = gpu_options)\n",
    "tf.set_random_seed(1)\n",
    "sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)\n",
    "K.set_session(sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('train.pickle', 'rb') as f:\n",
    "    X_train, Y_train = pickle.load(f)\n",
    "    \n",
    "X_train = X_train.reshape([80647, 5, 5, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "IQR = - np.quantile(Y_train, 0.25) + np.quantile(Y_train, 0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_bound, upper_bound = np.quantile(Y_train, 0.25) - 1.5 * IQR, np.quantile(Y_train, 0.75) + 1.5 * IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx, val = np.where((Y_train >= lower_bound) & (Y_train <= upper_bound))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train = Y_train[idx]\n",
    "X_train = X_train[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((78035, 5, 5, 1), (78035, 1))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create placeholders\n",
    "real_data = tf.placeholder(tf.float32, shape = [None, 5, 5, 1])\n",
    "z = tf.placeholder(tf.float32, shape = [None, 128])\n",
    "y = tf.placeholder(tf.float32, [None, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4787, 1.948)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(Y_train), np.max(Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lrelu(X, leak = 0.2):\n",
    "    f1 = 0.5 * (1 + leak)\n",
    "    f2 = 0.5 * (1 - leak)\n",
    "    return f1 * X + f2 * tf.abs(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generator(z, y_, reuse=None):\n",
    "    with tf.variable_scope('G',reuse=reuse):      \n",
    "        \n",
    "        y_ = tf.concat([z, y_], axis = 1)\n",
    "        \n",
    "        hidden2 = tf.layers.dense(y_, units = 1 * 1 * 512)\n",
    "        R1 = tf.reshape(hidden2, shape = [-1,1,1,512])\n",
    "        \n",
    "        for i in range(5):\n",
    "            C1 = tf.layers.conv2d_transpose(R1, filters = 512, kernel_size = 3,\n",
    "                                            strides = (1, 1), padding = 'same')\n",
    "            B1 = tf.layers.batch_normalization(C1)\n",
    "            R1 = lrelu(B1)\n",
    "        \n",
    "        C1 = tf.layers.conv2d_transpose(R1, filters = 256, kernel_size = 3,\n",
    "                                        strides = (2, 2), padding = 'same')\n",
    "        B1 = tf.layers.batch_normalization(C1)\n",
    "        R1 = lrelu(B1)\n",
    "        \n",
    "        C2 = tf.layers.conv2d_transpose(R1, filters = 128, kernel_size = 3,\n",
    "                                       strides = (2, 2), padding = 'same')\n",
    "        B2 = tf.layers.batch_normalization(C2)\n",
    "        R2 = lrelu(B2)\n",
    "        \n",
    "        C3 = tf.layers.conv2d_transpose(R2, filters = 1, kernel_size = 2,\n",
    "                                       strides = (1, 1))\n",
    "        O = tf.nn.tanh(C3)\n",
    "        \n",
    "        return O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discriminator(X, y_, reuse=None):\n",
    "    with tf.variable_scope('D',reuse=reuse):\n",
    "  \n",
    "        y1 = tf.concat([X, y_], axis = 1)\n",
    "        \n",
    "        y2 = tf.layers.dense(y1, units = 256)\n",
    "        y2 = lrelu(y2)\n",
    "        y2 = tf.layers.dropout(y2, 0.5)\n",
    "        \n",
    "        yout = tf.layers.dense(y2, units = 1)\n",
    "        O = tf.nn.sigmoid(yout)\n",
    "        \n",
    "        return O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regressor for inception score\n",
    "def regressor(X, reuse = None):\n",
    "    with tf.variable_scope('R', reuse = reuse):\n",
    "        tower0 = tf.layers.conv2d(X, filters = 32, kernel_size = (1, 1),\n",
    "                                 padding = 'same')\n",
    "        \n",
    "        tower1 = tf.layers.conv2d(X, filters = 64, kernel_size = (1, 1),\n",
    "                                 padding = 'same')\n",
    "        tower1 = tf.layers.conv2d(tower1, filters = 64, kernel_size = (3, 3),\n",
    "                                 padding = 'same')\n",
    "        \n",
    "        tower2 = tf.layers.conv2d(X, filters = 32, kernel_size = (1, 1),\n",
    "                                 padding = 'same')\n",
    "        tower2 = tf.layers.conv2d(tower2, filters = 32, kernel_size = (5, 5),\n",
    "                                 padding = 'same')\n",
    "        \n",
    "        tower3 = tf.layers.max_pooling2d(X, pool_size = (3, 3), strides = (1, 1),\n",
    "                                        padding = 'same')\n",
    "        tower3 = tf.layers.conv2d(tower3, filters = 32, kernel_size = (1, 1),\n",
    "                                 padding = 'same')\n",
    "        \n",
    "        h = tf.concat([tower0, tower1, tower2, tower3], axis = -1)\n",
    "        h = tf.nn.relu(h)\n",
    "        h = tf.layers.max_pooling2d(h, pool_size = (2, 2), strides = (1, 1), padding = 'same')\n",
    "      \n",
    "        for i in range(6):\n",
    "            tower0 = tf.layers.conv2d(h, filters = 32, kernel_size = (1, 1),\n",
    "                                 padding = 'same')\n",
    "        \n",
    "            tower1 = tf.layers.conv2d(h, filters = 64, kernel_size = (1, 1),\n",
    "                                     padding = 'same')\n",
    "            tower1 = tf.layers.conv2d(tower1, filters = 64, kernel_size = (3, 3),\n",
    "                                     padding = 'same')\n",
    "\n",
    "            tower2 = tf.layers.conv2d(h, filters = 32, kernel_size = (1, 1),\n",
    "                                     padding = 'same')\n",
    "            tower2 = tf.layers.conv2d(tower2, filters = 32, kernel_size = (5, 5),\n",
    "                                     padding = 'same')\n",
    "\n",
    "            tower3 = tf.layers.max_pooling2d(h, pool_size = (3, 3), strides = (1, 1),\n",
    "                                            padding = 'same')\n",
    "            tower3 = tf.layers.conv2d(tower3, filters = 32, kernel_size = (1, 1),\n",
    "                                     padding = 'same')\n",
    "\n",
    "            h = tf.concat([tower0, tower1, tower2, tower3], axis = -1)\n",
    "            h = tf.nn.relu(h)\n",
    "            if i % 2 == 0 and i != 0:\n",
    "                h = tf.layers.max_pooling2d(h, pool_size = (2, 2), strides = (1, 1), padding = 'same')\n",
    "        \n",
    "        Z0 = tf.layers.flatten(h)\n",
    "        Z0 = tf.layers.dense(Z0, units = 512, activation = 'relu')\n",
    "        Z0 = tf.layers.dropout(Z0, 0.2)\n",
    "        \n",
    "        Z0 = tf.layers.dense(Z0, units = 32)\n",
    "        Z_out = Z0\n",
    "\n",
    "        O = tf.layers.dense(Z0, units = 1)\n",
    "        \n",
    "        return O, Z_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def L2_Loss(y, y_hat):\n",
    "    return tf.reduce_mean((y - y_hat) ** 2)\n",
    "\n",
    "def L1_Loss(y, y_hat):\n",
    "    return tf.reduce_mean(tf.abs(y - y_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-14-d70f6cf1e7f6>:6: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.Dense instead.\n",
      "WARNING:tensorflow:From D:\\ProgramData\\Anaconda3\\envs\\chemistry\\lib\\site-packages\\tensorflow_core\\python\\layers\\core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.__call__` method instead.\n",
      "WARNING:tensorflow:From <ipython-input-14-d70f6cf1e7f6>:11: conv2d_transpose (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.keras.layers.Conv2DTranspose` instead.\n",
      "WARNING:tensorflow:From <ipython-input-14-d70f6cf1e7f6>:12: batch_normalization (from tensorflow.python.layers.normalization) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.BatchNormalization instead.  In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used (consult the `tf.keras.layers.batch_normalization` documentation).\n",
      "WARNING:tensorflow:From <ipython-input-16-c0380acb54ee>:5: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.keras.layers.Conv2D` instead.\n",
      "WARNING:tensorflow:From <ipython-input-16-c0380acb54ee>:18: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.MaxPooling2D instead.\n",
      "WARNING:tensorflow:From <ipython-input-16-c0380acb54ee>:50: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.flatten instead.\n",
      "WARNING:tensorflow:From <ipython-input-16-c0380acb54ee>:52: dropout (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.dropout instead.\n",
      "WARNING:tensorflow:From D:\\ProgramData\\Anaconda3\\envs\\chemistry\\lib\\site-packages\\tensorflow_core\\python\\ops\\math_grad.py:1375: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    }
   ],
   "source": [
    "fake_data = generator(z, y)\n",
    "\n",
    "true_pred, true_logit = regressor(real_data)\n",
    "fake_pred, fake_logit = regressor(fake_data, reuse = True)\n",
    "\n",
    "true_label_pred = discriminator(true_logit, y)\n",
    "fake_label_pred = discriminator(fake_logit, y, reuse = True)\n",
    "\n",
    "gen_sample = generator(z, y, reuse = True)\n",
    "gen_pred, gen_logit = regressor(gen_sample, reuse = True)\n",
    "\n",
    "# Regressor helper loss:\n",
    "R_loss = L2_Loss(true_pred, y)\n",
    "\n",
    "# Discriminator losses:\n",
    "D_real_loss = L2_Loss(true_label_pred, 0.9 * tf.ones_like(true_label_pred))\n",
    "D_fake_loss = L2_Loss(fake_label_pred, tf.zeros_like(fake_label_pred))\n",
    "D_loss = (D_real_loss + D_fake_loss)/2\n",
    "\n",
    "# Generator loss:\n",
    "G_loss = L2_Loss(fake_label_pred, 0.9 * tf.ones_like(fake_label_pred)) + 25 * L2_Loss(fake_pred, y)\n",
    "\n",
    "# Opimizer\n",
    "lr = 1e-4\n",
    "\n",
    "# Getting variables\n",
    "tvars = tf.trainable_variables()\n",
    "d_vars = [var for var in tvars if 'D' in var.name]\n",
    "g_vars = [var for var in tvars if 'G' in var.name]\n",
    "r_vars = [var for var in tvars if 'R' in var.name]\n",
    "\n",
    "# Setting up the optimizers\n",
    "opt_d = tf.train.AdamOptimizer(learning_rate = lr, beta1 = 0.5).minimize(D_loss, var_list = d_vars)\n",
    "opt_g = tf.train.AdamOptimizer(learning_rate = lr, beta1 = 0.5).minimize(G_loss, var_list = g_vars)\n",
    "opt_r = tf.train.AdamOptimizer(learning_rate= lr).minimize(R_loss, var_list = r_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06007875307629205"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "73.236/1219"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Currently on epoch 0 of 256\n",
      "G Loss: 1.0908817052841187, D Loss: 0.18948087096214294, R Loss: 0.01698073372244835\n",
      "Current number of unique samples: 1\n",
      "------------\n",
      "Currently Generating [[1.8593]]\n",
      "Predicted bandgap: 1.6938369274139404\n",
      "Samples not in database :100\n",
      "------------\n",
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 10 of 256\n",
      "G Loss: 0.23051394522190094, D Loss: 0.20087964832782745, R Loss: 0.004045507870614529\n",
      "Current number of unique samples: 3\n",
      "------------\n",
      "Currently Generating [[1.4004]]\n",
      "Predicted bandgap: 1.397233009338379\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 20 of 256\n",
      "G Loss: 0.21844221651554108, D Loss: 0.19724369049072266, R Loss: 0.004723524674773216\n",
      "Current number of unique samples: 2\n",
      "------------\n",
      "Currently Generating [[0.8658]]\n",
      "Predicted bandgap: 0.8779079914093018\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3de3xcZb3v8c9v7rlO2iRN0qalLbZg70AoLQgIiAIi6L5oqQJuOZsX54i4ZR+PcNyypZ7jEbYbFeyWg268oFJ0q1ikHlRA0S1gi5ZLL9AbpektadrcM5nbc/5YayaTySSZJDOZZM3vnde81u2ZNU/WrPmuNc+sixhjUEopNf25Cl0BpZRSuaGBrpRSDqGBrpRSDqGBrpRSDqGBrpRSDuEp1AvX1NSY+fPnF+rllVJqWnrppZdOGGNqM00rWKDPnz+fbdu2FerllVJqWhKRg8NN0yYXpZRyCA10pZRyCA10pZRyiIK1oSulVKpIJEJzczOhUKjQVZkSAoEAjY2NeL3erJ+jga6UmhKam5upqKhg/vz5iEihq1NQxhja2tpobm5mwYIFWT9Pm1yUUlNCKBSiurq66MMcQESorq4e87cVDXSl1JShYT5gPMti2gX61jdPcs//241e9lcppQabdoH+SnMH3/jtPtp7I4WuilLKYY4fP8769etZuHAh55xzDmvXruVnP/vZkHJvvvkmy5YtK0ANRzbtAr0hGADgWKf+Eq6Uyh1jDO9///u56KKL2L9/Py+99BKbNm2iubm50FXL2rQL9LpKO9A7NNCVUrnzzDPP4PP5uOWWW5LjTjvtND7xiU9kPY/t27ezZs0aVqxYwQc+8AFOnToFwP3338+SJUtYsWIF69atA+B3v/sdq1atYtWqVZx11ll0dXVN+H+YdoctJvbQj2qgK+VYdz+xg51HOnM6zyWzK/nn9y0ddvqOHTs4++yzJ/QaN9xwAw888AAXX3wxd911F3fffTdf/epX+dKXvsSBAwfw+/20t7cD8OUvf5mNGzdywQUX0N3dTSAQmNBrwzTcQ6+t8OMSONbRV+iqKKUc7OMf/zgrV67k3HPPzap8R0cH7e3tXHzxxQDceOONPPfccwCsWLGCD3/4w3z/+9/H47H2oy+44AJuv/127r//ftrb25PjJ2La7aF73S5qK/zahq6Ug420J50vS5cu5Sc/+UlyeOPGjZw4cYKmpqYJz/vJJ5/kueeeY/PmzXzhC19gx44d3HHHHbz3ve9ly5YtrFmzht/85jeceeaZE3qdabeHDlBfGdAmF6VUTl166aWEQiG+8Y1vJMf19vZm/fxgMMiMGTP4/e9/D8AjjzzCxRdfTDwe59ChQ1xyySXce++9tLe3093dzb59+1i+fDmf+cxnaGpqYvfu3RP+H6bdHjpAfTDA/taeQldDKeUgIsLjjz/Opz71Ke69915qa2spKyvjnnvuyVj+9ddfp7GxMTn8la98he9+97vccsst9Pb2snDhQr797W8Ti8X4yEc+QkdHB8YYPvWpT1FVVcXnPvc5nn32WdxuN0uWLOHKK6+c8P8wLQO9IVjCH/e1FboaSimHaWhoYNOmTaOWmz9/PpFI5nNhXnjhhSHj/vCHPwwZ98ADD4y9gqOYlk0udZUBukJRuvujha6KUkpNGdMy0JMnF2k7ulJKJU3LQK/XQFdKqSGmZaDr6f9KKTXUtAz0gdP/9eQipZRKGDXQReRhEWkRkdeGmS4icr+I7BWRV0RkYufOZiHgdTOj1KvHoiulVIps9tC/A1wxwvQrgUX242bgGyOUzZn6YAnHtclFKZVDbrebVatWsXTpUlauXMl9991HPB4fUm6qXj531OPQjTHPicj8EYpcC3zPWHeceEFEqkSkwRhzNEd1zKghqGeLKqVyq6SkhO3btwPQ0tLC+vXr6ejo4O677y5wzbKTizb0OcChlOFme9wQInKziGwTkW2tra0TetG6yoAe5aKUyptZs2bx0EMP8fWvfz3rO6Q54fK5mW58l/G/N8Y8BDwE0NTUNKF7yDUEA7T1hOmPxvB73BOZlVJqqvnlHXDs1dzOs345XPmlMT1l4cKFxONxWlpaqKurG7W8Ey6f2wzMTRluBI7kYL4jShyL3tLZn++XUkoVsWz3zp1y+dzNwK0isgk4D+jId/s5WFdcBOtGF3Nnlub75ZRSk2mMe9L5sn//ftxuN7NmzZrQfCbr8rmjBrqIPAq8E6gRkWbgnwEvgDHmQWALcBWwF+gF/m5CNcrSwJ2L9Fh0pVTutba2csstt3DrrbcikqllebDUy+deeOGFGS+f+453vIMf/vCHdHd309bWxvLly1m+fDnPP/88u3fvzn+gG2OuG2W6AT4+oVqMg57+r5TKtb6+PlatWkUkEsHj8XD99ddz++23Zyyrl8/NoYqAl3K/R0//V0rlTCwWy6qcXj43D+oq/bqHrpRStmkd6A3BEj25SCmlbNM60OuDAT39XymlbNM60BuCAVq6+onGhl5rQSmlis20DvS6ygCxuOFEd7jQVVFKqYKb1oGux6IrpdSAaR3oiWPRtR1dKZULicvnLlu2jPe9733J666kKy8vn+SaZWd6B3rK6f9KKTVRicvnvvbaa8ycOZONGzcWukpjMq0DfWaZD5/bpceiK6Vybu3atRw+fDjr8gcPHuSyyy5jxYoVXHbZZbz11lsA/PjHP2bZsmWsXLmSiy66CIAdO3awevVqVq1axYoVK9izZ09O6jxtzxQFEBHq9UYXSjnOPX+6h90nd+d0nmfOPJPPrP5MVmVjsRhPP/00N910U9bzv/XWW7nhhhu48cYbefjhh7ntttt4/PHH2bBhA0899RRz5sxJNuE8+OCDfPKTn+TDH/4w4XA46zNURzOt99DBakfX0/+VUrmQuJZLdXU1J0+e5PLLL8/6uc8//zzr168H4Prrr0+e7n/BBRfw0Y9+lG9+85vJ4F67di1f/OIXueeeezh48CAlJSU5qf+03kMHqx19+6HMP1wopaanbPekcy3Rht7R0cHVV1/Nxo0bue2228Y1r8QVGh988EFefPFFnnzySVatWsX27dtZv3495513Hk8++STvec97+Na3vsWll1464fpP+z30BnsPPduL0Cul1GiCwSD3338/X/7yl4e9CFe6888/n02bNgHwgx/8gHe84x0A7Nu3j/POO48NGzZQU1PDoUOH2L9/PwsXLuS2227jmmuu4ZVXXslJvad9oNcHA4SjcU71ZrfQlVIqG2eddRYrV65MhnSq3t5eGhsbk4/77ruP+++/n29/+9usWLGCRx55hK997WsAfPrTn2b58uUsW7aMiy66iJUrV/LYY4+xbNkyVq1axe7du7nhhhtyUudp3+SSenLRzDJfgWujlJrOuru7Bw0/8cQTGcvF45kvN/LMM88MGffTn/50yLg777yTO++8cxw1HNm030Ovq9QbXSilFDgg0BuC1q/DeuiiUqrYTftAr63w43aJnv6vlAPowQ0DxrMspn2gu11Cbblf99CVmuYCgQBtbW0a6lhh3tbWRiAQGNPzpv2PomCfXKSBrtS01tjYSHNzM62trYWuypQQCAQG3YQ6G44I9IZggD0t3aMXVEpNWV6vlwULFhS6GtPatG9yAd1DV0opcEqgVwbo7o/SFdKTi5RSxcsZgR7UY9GVUsoRgZ44Fl2vuqiUKmYOCXS9c5FSSmUV6CJyhYi8LiJ7ReSODNPnicizIvIXEXlFRK7KfVWHN6vSD2iTi1KquI0a6CLiBjYCVwJLgOtEZElasX8CfmSMOQtYB/xbris6Er/HTXWZT/fQlVJFLZs99NXAXmPMfmNMGNgEXJtWxgCVdn8QOJK7KmanPhjQ0/+VUkUtm0CfAxxKGW62x6X6PPAREWkGtgCfyDQjEblZRLaJyLZcnw1WX6n3FlVKFbdsAl0yjEu/2MJ1wHeMMY3AVcAjIjJk3saYh4wxTcaYptra2rHXdgTWyUV9OZ2nUkpNJ9kEejMwN2W4kaFNKjcBPwIwxjwPBICaXFQwWw3BAKd6I4Qiubl7tlJKTTfZBPpWYJGILBARH9aPnpvTyrwFXAYgIm/HCvRJvcJOvX0surajK6WK1aiBboyJArcCTwG7sI5m2SEiG0TkGrvYPwJ/LyIvA48CHzWTfA3M+ko9Fl0pVdyyutqiMWYL1o+dqePuSunfCVyQ26qNjZ7+r5Qqdo44UxRSAl2bXJRSRcoxgV7u91AR8OgeulKqaDkm0CFxLLoeuqiUKk7OCnS90YVSqog5KtAbggFtQ1dKFS1HBXp9ZYCWrn4isXihq6KUUpPOWYEeLMEYaO3qL3RVlFJq0jkq0Bv00EWlVBFzVKDryUVKqWLmrEDX0/+VUkXMUYFeVerF73HpZXSVUkXJUYEuIvahi/qjqFKq+Dgq0EFvdKGUKl7OC3S9FZ1Sqkg5L9CDJbR09hOPT+rl2JVSquAcF+gNwQDhWJyTveFCV0UppSaV4wK9rlKPRVdKFSfHBXribFFtR1dKFRvHBrqe/q+UKjaOC/Tqcj8el+ihi0qpouO4QHe7hFkVfm1yUUoVHccFOuidi5RSxcmRgd4QLNE2dKVU0XFkoCf20I3Rk4uUUsXDmYFeGaA3HKMzFC10VZRSatI4M9DtQxePa7OLUqqIODLQ9eQipVQxyirQReQKEXldRPaKyB3DlPmgiOwUkR0i8sPcVnNsBk7/12PRlVLFwzNaARFxAxuBy4FmYKuIbDbG7Ewpswi4E7jAGHNKRGblq8LZqNNb0SmlilA2e+irgb3GmP3GmDCwCbg2rczfAxuNMacAjDEtua3m2Pg8LmrK/dqGrpQqKtkE+hzgUMpwsz0u1WJgsYj8p4i8ICJXZJqRiNwsIttEZFtra+v4apylhqDe6EIpVVyyCXTJMC79AG8PsAh4J3Ad8C0RqRryJGMeMsY0GWOaamtrx1rXMamr1LNFlVLFJZtAbwbmpgw3AkcylPm5MSZijDkAvI4V8AWje+hKqWKTTaBvBRaJyAIR8QHrgM1pZR4HLgEQkRqsJpj9uazoWNUHA3T0RegLxwpZDaWUmjSjBroxJgrcCjwF7AJ+ZIzZISIbROQau9hTQJuI7ASeBT5tjGnLV6WzoddFV0oVm1EPWwQwxmwBtqSNuyul3wC3248poT556GIfC2rKClwbpZTKP0eeKQp6+r9Sqvg4PtD1h1GlVLFwbKCX+jxUBjx66KJSqmg4NtDButGF7qErpYqFowO9PhjQNnSlVNFwdKDryUVKqWLi6ECvqwxworufSCxe6KoopVTeOTrQG4IBjIGWrv5CV0UppfLO0YGeOHRRb3ShlCoGjg70hmAJoMeiK6WKg6MDvT55KzoNdKWU8zk60CtLPJR43RroSqmi4OhAFxHr0EU9Fl0pVQQcHeigdy5SShUPxwd6Q1ADXSlVHBwf6InT/+Px9NugKqWUszg+0BuCAaJxw4kePblIKeVsjg/0Oj10USlVJBwf6ImTi3IR6E+8fEQ3DEqpKcvxgV6fo5tF/+6NVj7x6F+496nduaiWUkrlnOMDvbrMh9ctEzr9PxKL84Vf7ARgy6tH6eiL5Kp6SimVM44PdJdLmFUxsUMXf/DCQfa2dHPbZYsIReL8fPvhHNZQKaVyw/GBDhM7Fv1UT5iv/GYP559ezafetYhlcyp59E+HMEYPg1RKTS1FEej1wcC429C/+ps36ApFuOt9SxAR1p07j11HO3mluSPHtVRKqYkpjkCvDHC0o2/Me9VvHO/i+y++xXWr53FmfSUA166aTYnXzaatb+WjqkopNW7FEejBAKFIfEw/Zhpj+MIvdlLqc3P75YuT4ysCXq5e0cDm7Ufo6Y/mo7pKKTUuRRHoyWPRx9Ds8szuFn6/5wSfvGwR1eX+QdPWrZ5HTzjGEy8fyWk9lVJqIooi0BPHomd76GI4Gud/PbmLhTVl3LB2/pDpZ8+rYnFdOY9uPZTLaiql1IRkFegicoWIvC4ie0XkjhHK/Y2IGBFpyl0VJ27g3qLZBfr3nn+TAyd6+Ker347PM3QRJX4cfflQO7uOduayqkopNW6jBrqIuIGNwJXAEuA6EVmSoVwFcBvwYq4rOVGzKvyIZBfobd39fO3pPVy0uJZLzpg1bLm/OnsOPo+LTX/SH0eVUlNDNnvoq4G9xpj9xpgwsAm4NkO5LwD3AlPuYidet4vacn9Wgf6vv36D3nCMz7337YjIsOWqSn1cuayen/3lMKFILJfVVUqpcckm0OcAqY3Fzfa4JBE5C5hrjPnFSDMSkZtFZJuIbGttbR1zZScim1vR7TrayaY/vcX1a05jUV3FqPNcd+48OkNRtrx6NFfVVEqpccsm0DPtpiYP6BYRF/AV4B9Hm5Ex5iFjTJMxpqm2tjb7WuaAdSu6vmGnG2PY8MROKku8/MO7FmU1zzULZ7KgpoxNf9IfR5VShZdNoDcDc1OGG4HU4/UqgGXAb0XkTWANsHmq/TA62un/T+04zvP727j98sVUlfqymqeI8KFz5/KnN0+yt6U7V1VVSqlxySbQtwKLRGSBiPiAdcDmxERjTIcxpsYYM98YMx94AbjGGLMtLzUep/pgCZ2haMaTgfqjMb64ZReL68pZv3remOb712c34nEJj+mZo0qpAhs10I0xUeBW4ClgF/AjY8wOEdkgItfku4K5Uh+0Tg7KdHLRw394k7dO9vK5q5fgcY/t0PzaCj+XL6njJ38+TH9UfxxVShVOVulljNlijFlsjDndGPO/7XF3GWM2Zyj7zqm2dw5QX2mdLXo8rdmlpSvE15/Zw7vePosLF42vXX/d6nmc7Anz653HJ1xPpZQar6I4UxSsNnQYerbol596nXAszmffO+TQ+qxd+LYa5lSV8Kgek66UKqCiCfRMt6J77XAHP36pmY+eP58FNWXjnrfLZf04+p972zjY1jPhuiql1HgUTaAHvG6qSr0ctQ9dNMZw9xM7mFnq4xOXZXeY4kj+tqkRl8Bjen0XpVSBFE2gg3Vd9GMd/QA8+epRtr55in989xlUBrwTnndDsIRLzpjFj19qJhKLT3h+Sik1VkUV6A3BAMc6+whFYvyfLbt5e0MlHzp37uhPzNK61fNo7ernmd0tOZunUkplq6gCvT5YwrGOEN98bj+H2/u46+oluF3DX69lrC45o5a6Sr9esEspVRDFFeiVAU50h/m33+7jiqX1rD29Oqfz97hd/O05c/ndG60caR/+MgNKKZUPRRXoiUMXY3HD/7zq7Xl5jQ+dO5e4gR9t0x9HlVKTq7gCvcoK9JsuXMC86tK8vMbcmaVcuKiGH209RCw+tptSK6XURBRVoK9ZWM0X3r+M2y6d+GGKI1l37jyOdIR4bs/kXiJYKVXciirQvW4X1685jRKfO6+vc/mSOqrLfPrjqFJqUhVVoE8Wn8fFX5/TyNO7WmjpmnI3cFJKOZQGep586Ny5ROOG/3ipudBVUUoVCQ30PDm9tpzVC2by2NZDxPXHUaXUJNBAz6PrVs/lYFsvL+xvK3RVlFJFQAM9j65c1kCwxMujesEupdQk0EDPo4DXzQfOmsNTrx3jZE+40NVRSjmcBnqeXbd6HuFYnJ/+WX8cVUrllwZ6np1RX8FZ86rYtPUQxuiPo0qp/NFAnwTXnTuPvS3dvHTwVKGropRyME+hK1AMrl7ZwIZf7OTmR17ibbPKaawqYbb9mDOjhDlVAWZXlVDq07dDKTV+miCToNTn4YH1Z/HzvxzmSHuIFw+c5FhnaMjFu2aUegeC3n4kQv+0maXMKPMV6D9QSk0HGuiT5JIzZnHJGbOSw9FYnONd/Rxp7+NIex/Np/qS/Qfbevjj3hP0hGOD5tEQDLB0diVLZgdZOruSZXOCzA4GEMndTTqUUtOXBnqBeNyu5F54JsYYOkNRDttBv/9ENzuOdLLjSCfP7G4hsXNfVepl6exKltohv3R2JQtqynN6J6ZiYozhZE+Y3nCM3nCMvkiMvnCMUGSgvzcSI5SYZo/rs4f7ozEW11Ww9vRqzp43g4A3vxeCK4S+cIx9rd3saenijePdvHWyl1kVfhbWlLGgppwFtWU0VAZw5WkdNMbQ1hPmSHsfnX1RYsYQjxticUPM2N24IW73R+P29JRy0bjB53Exq8JPXWWA+mCA2nI/Hvf0/llRCnXkRVNTk9m2bVtBXnu66w1H2XW0i51HOpIh//qxLsL2zalLvG7ObKhIBv3iugqCJV7K/R5K/W7KfJ6iD/ye/igHTvSwr7WbAyd62N/aw/4T3Rxo7RnyzWgkPreLgNdFic9NideN2yUcONFD3FgXaTtn3gzOP72a899WzYrGKrzTKDASwf3G8S72tHSz57gV4IdO9ZKIDa9bmF1VQmtXP70py83vcbGgpmzQY2GtFfgzSr0jfquMxOIc6whxuL2Pw6f6OGx/cz3cPtAfiuT+RuwugZpyP/XBgBXydtDXVQaoq/RTXxmgLhigwu8p6LdiEXnJGNOUcZoGujNEYnH2tiT24q2g33mkk+7+aMbyAa/LCnifhzK/hzKfmzK/xx5n9Zf53ZTa4S+AS4TEeixijRMZGC/WhEFlI7E4oUiMUCSe3MsNReL0R2KEogPjk2WiMfrtcS6XMKPUy4xSn/Uo81JV6mNmqY+qxPgyX7JMsMQ7aK8wFjc0n+q1w7qH/a3d7G/t4cCJHo51DlwFUwTmVJWwsLachTVlnFZdSkXAS4nXTYnPRYnXkwzsEq+bgM+V7M+0R9cZirD1wEme39fGH/e1sfNoJwClPjfnzp/J2tOrOf/0apbODuZkw9oXjtHa1T/ovU7Nm+R7hmQYZ+lNhrcV3Htahgb3gpoyFtVVsGhWOYvrKlhcV85p1WV43S6MMbR09SeX74ET9obyRA9vtfUSTfm9KFjiZX5NWXJZ90fjg4L7eGeI9Msf1ZT7mVMVYM6MEmYHrd+VZleVUFXixeMWXCK4XSkPEVx2N3W8SwSPy5oWjsY53hnieGeIY50hjndY3WOd/cn+jr7IkOVd6nNTVxkgWOKlssRLZcBjd71UlnjsbubxufjGpoE+FSSW8yRu2eNxw1sne9nX2k13f5Se/hi94Sjd/VF6wzGr2x+luz9GT390yLSe/uiQD9ZEeFxCwOu2H66BrmdgnN/rtoddxOKGU71hTvVErG5vhPbe8KBwSCUCVSVWuIvAoZN9yW8tYAWJtZdYxul2eC+sLee06tK8No2c6gnz4gEr3J/f18aelm4AKgIezltQndyDXzyrIrlBCkWskD7R3c+J7nCyP1N3LN8oRpMa3ItnVbCornxQcI9HNBan+VRfMuATYX+gtYcjHSG8bqEhOPgggDlVAeZUlTLbPgKsUE1XfeHYQOAnwr+jn+NdITr7InSGonT1RegMRejoixCJjfyB8XlcVAa83Hnlmfz1OY3jqtOEA11ErgC+BriBbxljvpQ2/XbgvwBRoBX4mDHm4EjzLIpA7zwC+56BvU/D/t9CpBcqZ0PlHAg22t05UNlodYONEAgWutZJxhj6o3HixmAMGEj2Y8AwdLyxJiTHed12cHtcOWmfNMbQ1R+lPRnyYdp7I5zsCdNuh/6p3jDRmOG0mlJOrylPhvjMMt+U+AG5pSvE8/vaeGG/FfIH23oR4qwobaci4GF3Tzkn+jMvq6pSLzXlfmrL/dRUJLo+asv9VAQ8WPvcA5/pxMc79VM+MG5grNft4vTasgkF93iEIjF8blfe2tsnU+Lz0pkM+CidoUgy+BPjO/uiXLtqNmsWju8m9RMKdBFxA28AlwPNwFbgOmPMzpQylwAvGmN6ReS/Au80xnxopPk6MtAjfXDwj1aI73sGWuxFVF4Hp18KZTXQcRg6D0NHM3QdBZPWFuirsEM+LezL68BbCr5S8JZZXV+Z1e/JweGMxkC039roRPrsRy9EQ+DygMcPbr/V9fjB7RsY55o+7cJTQl+7tW4c3wHHXiV85FVcrbvwxPoGiniChErriZU3IJVz8M1spKR6Hp4qe92onA3+8gL+E6pQRgr0bI5yWQ3sNcbst2e2CbgWSAa6MebZlPIvAB8Zf3WnEWOgdffAXvjB/7QC0O2DeWvh8g1w+mVQtzRzU0ssCt3H7JBvHhz2nYfh2KvQ0zJ6PVyegZBPD31vqRW8ybAODQ3tRJdxtq+4vClB77c2MInwd3nsh9vqimtgWNz2+MQ0u+tyWf2S2FAkdinNyMODeo21sUx+ncjQb+L2sEmZZqz3qmQGlFZbG+HSaiitGegvqwFf+ejNZ7EonNwHx1+zwjvx6Ei5+mbJDHx1y6DpRpi1xFp3Og9T0nmEks4j1nrw1muw+8TQ+fuD9jc++1EyI61OGRrShx0v1rJ3J94vL7i99nvjtacNN+yBeMx+RO1HZGA4FkkZH7OnpQy73Pb6ErDWHU/A3mEIDKxXg8allHG5U17X7prY4PqY1Okp44xJWT9T//cMj9T/NfHIZp0cbthtL98cyybQ5wCp139tBs4bofxNwC8zTRCRm4GbAebNm5dlFaeY3pNW88m+p2Hfs9YHDqB6EZzzUSvA519g7T2Pxu2xmlmCjQy7SKP9VtNNzwmI9EC4F8I9A/3JbmJ878D4UCd0HbPm4QmAt8R6lNfZ/aUD45KP0sFdT8D6AERDEA1DrN+aX7Tf7g8PdKOhtHH9gz+4Jg6xcNoHLZ72oUsZNnGS4ZMaPCMOp45zWb3issaJpPS77Oek9CeGTRyOvmwt81j/MO+d3w74mSlhXwMlVdD+lhXiLbsHnu/yQM1imLcG6m6CumXWhr6iIbvfVSIh6xtd5xH7cXhw9/gOCLUPlB/0zdtkMT4+9Nuiyp/33gfn3pTz2WYT6JnWtoy7ciLyEaAJuDjTdGPMQ8BDYDW5ZFnHwfb8GnY8bgWmr8z62ukrHxj2VWSe5i0d/MGJRazAC7VDfyeEOuzhjuGHe09C6y5rxfcHYeHFcPH/sJpTqvK0gfL4YeYC66EmlzEQ7raCvfck9J6w+xPdtoHhUwegpw3CXdYGs24pnHfzQHDXLLbey/HyBvK/HiQ2pql72Mm961GGk3vu7mH2ar2Dv6m5vQPfykxsYIchuVPQnzbO7qbuTERCgBn6zS/5bc89uD7p45I7k+kAAAtZSURBVCDtW0XKIxYduiyS0+zhITsVpA2PsBPSmLHFZMKyCfRmYG7KcCNwJL2QiLwL+CxwsTFmmN2aHOg4ZO0hh7utRzzzYXlDiRXu3sDAnuxo/JXWj5SJbtVcePvV1l74nHOsPWzlXCLgr7Ae2QZpLJKXr9KTwuUClw+Y7EtMuKxlNoHtnbJkk0hbgUUisgA4DKwD1qcWEJGzgP8LXGGMyaLRdwKaPmY9wNqDioWtgO7vsrrhHmsvKdE/aHy3FeS+ciugU8M6UDl4nL/C2pIrNRbTNcyVI4wa6MaYqIjcCjyFddjiw8aYHSKyAdhmjNkM/AtQDvzYPizsLWPMNXmst0Vk4EeT0pl5fzmllJrKsmozMMZsAbakjbsrpf9dOa6XUkqpMdIDiJVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE00JVSyiE8ha7AWDV3NXO4+zDl3nLKvGWU+8op95bjd/sRkUJXTymlCiarQBeRK4CvAW7gW8aYL6VN9wPfA84B2oAPGWPezG1VLb86+Cu+8tJXhoz3iIcyX9lA0Kd2U8aXekoxGOImTszEiMVjyf64iRM1UeLxgeGYiQ30x2N4XB58bh9+tx+vy4vf7cfn9iUffrcfn2vwcKKc2+XGhQuXuEBI9osIguASexoM6XeL25qny4fb5c7HolVKTXOjBrqIuIGNwOVAM7BVRDYbY3amFLsJOGWMeZuIrAPuAT6UjwpfvfBqltcspyfSQ3ekm56w3Y2kdMNW92ToJIe6DiXH90X7Rpy3RzxWeLrcyRB1y0C/y+UiGo8SjoUJx8L0x/oxmHz8m6PW0+v2Dtl4JB8ue0Nil/G6vLjEhcflGfhfMnRT//fkshA3ceLETRxjDDETwxhDHGsDl9g4JqbFTRyDSU4TBI/Lg9flxePyDH7IQH9iulvcGcu5Xe7B/Sldj8vqT31u4n8SkUEbTpWd1Pc68b7GTdxaF+Lx5DqReCTWH494cLkGr1up78V0Z4xJ/s/JcSkZkJ4HxmSelljncy2bPfTVwF5jzH4AEdkEXAukBvq1wOft/v8Avi4iYlL/mxyZVTqLWaWzxvXcSDxCX7QPQawVzTU40MbKGEPURInEIvTH+pNBH46HMw4nQi6xUiTDMCUUU8elrjwxE0vOK3WDEolHBvoT9YiH6Qp3JfvDsXDyG0bqN49M3dQVdTQuceHC+qC6xW2FZ8o4l7isbz3xqPUw0THNPx/S6+cSV/LbUXr9EwRJDov9hwyeJsiQsolhYHCZlP7EcKJs6gYx8b6khupIw4nASH2dQXW2BoaMS5RLnVc+DAr9lJ2k1OWdujwTyzl9XHrZ4YwWP6nf1IdsuNKXbdoynqjPrfkcHzzjgzmZV6psAn0OcChluBk4b7gyxpioiHQA1cCJXFQyV7wuL15f7raKIoJXvHhdXkq9pTmbbyGlbkQSASxIsrkoNQjHIz3gk/0pj0g8QszEiMajQ7vxWPJ5iQ1UYl6DhuPRwRvFlD3K1HGp/2/qN4tEGBj7L7FsEuMSw+llU5djslx6GbtY+rwNJrlBSd/LTWw0Rxp2iStjHZN1SnutQXU2A4Gb/hqprzXoYdczsSEarhkz0WyZGJ+6bqUu7+SfGbzcUruDlrs9fqRQT91wZpo25H9K/8Y6wnLItFEerQ6JcitqVwxbfiKyCfRMNU3fTGVTBhG5GbgZYN68eVm8tJpsiZXWjRsvuf9K6BJXsmlIKZVb2exmNQNzU4YbgSPDlRERDxAETqbPyBjzkDGmyRjTVFtbO74aK6WUyiibQN8KLBKRBSLiA9YBm9PKbAZutPv/BngmH+3nSimlhjdqk4vdJn4r8BTWYYsPG2N2iMgGYJsxZjPw78AjIrIXa898XT4rrZRSaqisjkM3xmwBtqSNuyulPwT8bW6rppRSaiz01H+llHIIDXSllHIIDXSllHIIDXSllHIIKdTRhSLSChwc59NrmGJnodq0XmOj9Rq7qVo3rdfYTKRepxljMp7IU7BAnwgR2WaMaSp0PdJpvcZG6zV2U7VuWq+xyVe9tMlFKaUcQgNdKaUcYroG+kOFrsAwtF5jo/Uau6laN63X2OSlXtOyDV0ppdRQ03UPXSmlVBoNdKWUcogpHegicoWIvC4ie0XkjgzT/SLymD39RRGZPwl1misiz4rILhHZISKfzFDmnSLSISLb7cddmeaVh7q9KSKv2q+5LcN0EZH77eX1ioicPQl1OiNlOWwXkU4R+Ye0MpO2vETkYRFpEZHXUsbNFJFfi8geuztjmOfeaJfZIyI3ZiqTwzr9i4jstt+nn4lI1TDPHfE9z1PdPi8ih1Per6uGee6In9881OuxlDq9KSLbh3luXpbZcNkwqeuXMWZKPrAu1bsPWAj4gJeBJWll/hvwoN2/DnhsEurVAJxt91cAb2So1zuBXxRgmb0J1Iww/Srgl1h3mFoDvFiA9/QY1okRBVlewEXA2cBrKePuBe6w++8A7snwvJnAfrs7w+6fkcc6vRvw2P33ZKpTNu95nur2eeC/Z/Fej/j5zXW90qb/K3DXZC6z4bJhMtevqbyHnrw5tTEmDCRuTp3qWuC7dv9/AJfJSDf3ywFjzFFjzJ/t/i5gF9Y9VaeDa4HvGcsLQJWINEzi618G7DPGjPcM4QkzxjzH0Ltppa5H3wXen+Gp7wF+bYw5aYw5BfwauCJfdTLG/MoYE7UHX8C6U9ikG2Z5ZSObz29e6mVnwAeBR3P1elnWabhsmLT1ayoHeqabU6cH56CbUwOJm1NPCruJ5yzgxQyT14rIyyLySxFZOklVMsCvROQlse7fmi6bZZpP6xj+Q1aI5ZVQZ4w5CtaHEpiVoUwhl93HsL5ZZTLae54vt9rNQQ8P04RQyOV1IXDcGLNnmOl5X2Zp2TBp69dUDvSc3Zw6H0SkHPgJ8A/GmM60yX/GalZYCTwAPD4ZdQIuMMacDVwJfFxELkqbXsjl5QOuAX6cYXKhltdYFGTZichngSjwg2GKjPae58M3gNOBVcBRrOaNdAVb14DrGHnvPK/LbJRsGPZpGcaNeXlN5UDP2c2pc01EvFhv2A+MMT9Nn26M6TTGdNv9WwCviNTku17GmCN2twX4GdbX3lTZLNN8uRL4szHmePqEQi2vFMcTTU92tyVDmUlfdvYPY1cDHzZ2Q2u6LN7znDPGHDfGxIwxceCbw7xmQdY1Owf+CnhsuDL5XGbDZMOkrV9TOdCn5M2p7fa5fwd2GWPuG6ZMfaItX0RWYy3ntjzXq0xEKhL9WD+qvZZWbDNwg1jWAB2Jr4KTYNi9pkIsrzSp69GNwM8zlHkKeLeIzLCbGN5tj8sLEbkC+AxwjTGmd5gy2bzn+ahb6u8uHxjmNbP5/ObDu4DdxpjmTBPzucxGyIbJW79y/Utvjn81vgrrl+J9wGftcRuwVnKAANZX+L3An4CFk1Cnd2B9FXoF2G4/rgJuAW6xy9wK7MD6Zf8F4PxJqNdC+/Vetl87sbxS6yXARnt5vgo0TdL7WIoV0MGUcQVZXlgblaNABGuv6Cas312eBvbY3Zl22SbgWynP/Zi9ru0F/i7PddqL1aaaWMcSR3PNBraM9J5PwvJ6xF5/XsEKq4b0utnDQz6/+ayXPf47ifUqpeykLLMRsmHS1i899V8ppRxiKje5KKWUGgMNdKWUcggNdKWUcggNdKWUcggNdKWUcggNdKWUcggNdKWUcoj/D3frmWvZWejIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 30 of 256\n",
      "G Loss: 0.21693211793899536, D Loss: 0.19639185070991516, R Loss: 0.002510707126930356\n",
      "Current number of unique samples: 3\n",
      "------------\n",
      "Currently Generating [[1.4596]]\n",
      "Predicted bandgap: 1.454673171043396\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 40 of 256\n",
      "G Loss: 0.22245526313781738, D Loss: 0.19453319907188416, R Loss: 0.0025413604453206062\n",
      "Current number of unique samples: 4\n",
      "------------\n",
      "Currently Generating [[1.3214]]\n",
      "Predicted bandgap: 1.3152058124542236\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 50 of 256\n",
      "G Loss: 0.22029615938663483, D Loss: 0.1983814537525177, R Loss: 0.0015068029752001166\n",
      "Current number of unique samples: 2\n",
      "------------\n",
      "Currently Generating [[1.8549]]\n",
      "Predicted bandgap: 1.820641040802002\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 60 of 256\n",
      "G Loss: 0.22299572825431824, D Loss: 0.19767694175243378, R Loss: 0.0017466099234297872\n",
      "Current number of unique samples: 2\n",
      "------------\n",
      "Currently Generating [[0.9494]]\n",
      "Predicted bandgap: 0.9666085839271545\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 70 of 256\n",
      "G Loss: 0.22072367370128632, D Loss: 0.19867731630802155, R Loss: 0.001379733090288937\n",
      "Current number of unique samples: 2\n",
      "------------\n",
      "Currently Generating [[1.2276]]\n",
      "Predicted bandgap: 1.2103164196014404\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3de5wcdZ3v/9enqq8zmcxM7iEhJIEohpAEiFxEQUEWUBfXc87+FlDAc9zD4fcQcXV//oTH7rIr+3jsEQ8PL2h+clgPrrq68c5mhbMcBVzXXUCCIhIgkoSETIKZySRzn77W9/dHVfX0XJJ0khlmuvN+Ph796Ft116eqq9/1rW9XV5lzDhERqX/edBcgIiKTQ4EuItIgFOgiIg1CgS4i0iAU6CIiDSIxXSOeN2+eW758+XSNXkSkLj3zzDMHnHPzJ3pu2gJ9+fLlbNmyZbpGLyJSl8xs9+GeU5eLiEiDUKCLiDQIBbqISIOYtj50EZFqxWKRjo4OcrncdJcyI2QyGZYuXUoymaz5NQp0EZkROjo6aGlpYfny5ZjZdJczrZxzdHd309HRwYoVK2p+nbpcRGRGyOVyzJ0796QPcwAzY+7cuce8taJAF5EZQ2E+4njmRd0F+tO7DnLPI9solYPpLkVEZEapu0D/1auH+NLj28mVFOgiMrn279/P9ddfz8qVKznvvPO46KKL+OEPfzhuuF27drFmzZppqPDI6i7Q0wkfgHyxPM2ViEgjcc7xB3/wB1xyySXs3LmTZ555hk2bNtHR0THdpdWsDgM9LDmvFrqITKLHHnuMVCrFLbfcUnnstNNO4yMf+UjN7/Hss89y4YUXsnbtWt73vvdx6NAhAO69915Wr17N2rVrufbaawH4l3/5F9avX8/69es555xz6O/vP+FpqLvdFtNJBbpIo/vUP23lhX19k/qeq0+ZzV/+/lmHfX7r1q2ce+65JzSOG2+8kS9+8Ytceuml3HnnnXzqU5/i85//PJ/+9Kd55ZVXSKfT9PT0AHDPPfewceNGLr74YgYGBshkMic0bqjLFnrY5VJQoIvIFPrwhz/MunXrePOb31zT8L29vfT09HDppZcCcNNNN/Gzn/0MgLVr1/L+97+fv//7vyeRCNvRF198MR//+Me599576enpqTx+IuqvhV7pclEfukijOlJLeqqcddZZfP/736/c37hxIwcOHGDDhg0n/N4PPfQQP/vZz9i8eTN//dd/zdatW7n99tt597vfzcMPP8yFF17IT37yE84888wTGk/dttDV5SIik+myyy4jl8vx5S9/ufLY0NBQza9vbW2lvb2df/3XfwXgG9/4BpdeeilBELBnzx7e8Y538JnPfIaenh4GBgbYsWMHZ599Np/85CfZsGEDL7300glPQ/210OM+9KICXUQmj5nx4IMP8rGPfYzPfOYzzJ8/n+bmZu6+++4Jh9+2bRtLly6t3P/c5z7H1772NW655RaGhoZYuXIlX/3qVymXy3zgAx+gt7cX5xwf+9jHaGtr4y/+4i94/PHH8X2f1atXc/XVV5/wNNRfoKvLRUSmyOLFi9m0adNRh1u+fDnFYnHC55588slxj/385z8f99gXv/jFYy/wKNTlIiLSIOow0NVCFxGZSP0FuvrQRUQmVH+Bri4XEZEJ1WGgq8tFRGQiRw10M3vAzDrN7PnDPG9mdq+ZbTez58zsxP47exSphLpcREQmUksL/e+Aq47w/NXAquhyM/DlIwx7whKe4Zm6XERk8vm+z/r16znrrLNYt24dn/3sZwmC8VkzUw+fe9T90J1zPzOz5UcY5L3A151zDnjSzNrMbLFz7rVJqnEUMyOd8NXlIiKTLpvN8uyzzwLQ2dnJ9ddfT29vL5/61KemubLaTEYf+hJgT9X9juixcczsZjPbYmZburq6jnuE6aSnFrqITKkFCxZw//3386UvfYmwvXp0jXD43IlOfDfh1Dvn7gfuB9iwYUNtc2gC6YSnPnSRRva/b4ff/WZy33PR2XD1p4/pJStXriQIAjo7O1m4cOFRh2+Ew+d2AKdW3V8K7JuE9z0sdbmIyOul1tZ5oxw+dzNwq5ltAi4Aeqeq/zyWTqjLRaShHWNLeqrs3LkT3/dZsGDBCb3P63X43KMGupn9A/B2YJ6ZdQB/CSQBnHP3AQ8D7wK2A0PAfz6himqQTno6wYWITKmuri5uueUWbr31Vswm6lkerfrwuW9729smPHzuW9/6Vr71rW8xMDBAd3c3Z599NmeffTZPPPEEL7300tQHunPuuqM874APn1AVxyjsclGgi8jkGh4eZv369RSLRRKJBDfccAMf//jHJxxWh8+dJGGXi/rQRWRylcu15YoOnzuJ1IcuIjJenQa6r90WRUTGqM9AT6rLRURkrPoMdHW5iIiMU6eBrr1cRETGqtNA98gX1eUiIlKtPgNdB+cSkSkQHz53zZo1/P7v/37luCtjzZo163WurDZ1Gegp36cUOEplhbqITJ748LnPP/88c+bMYePGjdNd0jGpy0CPTxRdUKCLyBS56KKL2Lt3b83D7969m8svv5y1a9dy+eWX8+qrrwLw3e9+lzVr1rBu3TouueQSALZu3cr555/P+vXrWbt2LS+//PKk1Fy3/xSF8DR0TalpLkZEJt3dv7iblw6+NKnveeacM/nk+Z+sadhyucyjjz7Khz70oZrf/9Zbb+XGG2/kpptu4oEHHuC2227jwQcf5K677uKRRx5hyZIllS6c++67j49+9KO8//3vp1Ao1PwP1aOpzxZ6wgd0GjoRmVzxsVzmzp3LwYMHueKKK2p+7RNPPMH1118PwA033FD5u//FF1/MBz/4Qf72b/+2EtwXXXQRf/M3f8Pdd9/N7t27yWazk1J/fbfQ9ecikYZUa0t6ssV96L29vbznPe9h48aN3Hbbbcf1XvERGu+77z6eeuopHnroIdavX8+zzz7L9ddfzwUXXMBDDz3ElVdeyVe+8hUuu+yyE66/PlvoyTjQ1UIXkcnX2trKvffeyz333HPYg3CN9Za3vIVNmzYB8M1vfpO3vvWtAOzYsYMLLriAu+66i3nz5rFnzx527tzJypUrue2227jmmmt47rnnJqXu+gz0uMtFx3MRkSlyzjnnsG7dukpIVxsaGmLp0qWVy2c/+1nuvfdevvrVr7J27Vq+8Y1v8IUvfAGAT3ziE5x99tmsWbOGSy65hHXr1vHtb3+bNWvWsH79el566SVuvPHGSam5rrtcCpP0Q4KICMDAwMCo+//0T/804XBBMHFj8rHHHhv32A9+8INxj91xxx3ccccdx1HhkdVpC31kLxcREQnVZ6AntZeLiMhY9Rno2stFpCGFZ7QUOL55UeeBrha6SKPIZDJ0d3cr1AnDvLu7m0wmc0yvq88fRZPay0Wk0SxdupSOjg66urqmu5QZIZPJjDoJdS3qM9DV5SLScJLJJCtWrJjuMuqaulxERBpEnQa69nIRERmrLgM96Rtm6KxFIiJV6jLQzYyUr7MWiYhUq8tAh+i8ogp0EZGKmgLdzK4ys21mtt3Mbp/g+WVm9riZ/crMnjOzd01+qaOlk772chERqXLUQDczH9gIXA2sBq4zs9VjBvtz4DvOuXOAa4H/b7ILHSud8LQfuohIlVpa6OcD251zO51zBWAT8N4xwzhgdnS7Fdg3eSVOTF0uIiKj1RLoS4A9Vfc7oseq/RXwATPrAB4GPjLRG5nZzWa2xcy2nOi/wdIJdbmIiFSrJdBtgsfGHmzhOuDvnHNLgXcB3zCzce/tnLvfObfBObdh/vz5x15tlXRSLXQRkWq1BHoHcGrV/aWM71L5EPAdAOfcE0AGmDcZBR6O+tBFREarJdCfBlaZ2QozSxH+6Ll5zDCvApcDmNmbCAN9So+wk0745MsKdBGR2FED3TlXAm4FHgFeJNybZauZ3WVm10SD/SnwX83s18A/AB90U3wMzLCFrj50EZFYTUdbdM49TPhjZ/Vjd1bdfgG4eHJLO7J00qegPnQRkQr9U1REpEHUeaCry0VEJFbHge5rLxcRkSr1G+jaD11EZJT6DfSER6EcEAQ6oayICNRxoKei09AVtC+6iAhQx4FeOQ2d+tFFRIC6DvT4RNHa00VEBBoi0NVCFxGBeg70ZNTloha6iAhQz4EetdBz6kMXEQEaINDV5SIiEqrjQFeXi4hItfoN9KRa6CIi1eo30OM/FinQRUSAug70uMtFgS4iAnUd6FGXi85aJCIC1HOgqw9dRGSU+g10dbmIiIxSx4GuY7mIiFSr/0DXP0VFRIA6DnQzI6UTRYuIVNRtoAOkfZ0oWkQkVt+BrvOKiohU1HegJ3z1oYuIROo80NXlIiISq+tA14+iIiIjagp0M7vKzLaZ2XYzu/0ww/xfZvaCmW01s29NbpkTSyd9BbqISCRxtAHMzAc2AlcAHcDTZrbZOfdC1TCrgDuAi51zh8xswVQVXC2d8HQsFxGRSC0t9POB7c65nc65ArAJeO+YYf4rsNE5dwjAOdc5uWVOLK0uFxGRiloCfQmwp+p+R/RYtTcAbzCzfzOzJ83sqoneyMxuNrMtZralq6vr+Cqukk6oy0VEJFZLoNsEj7kx9xPAKuDtwHXAV8ysbdyLnLvfObfBObdh/vz5x1rrOOmkR0F7uYiIALUFegdwatX9pcC+CYb5R+dc0Tn3CrCNMOCnlLpcRERG1BLoTwOrzGyFmaWAa4HNY4Z5EHgHgJnNI+yC2TmZhU5EXS4iIiOOGujOuRJwK/AI8CLwHefcVjO7y8yuiQZ7BOg2sxeAx4FPOOe6p6romPZyEREZcdTdFgGccw8DD4957M6q2w74eHR53ehYLiIiI+r6n6Jxl0u4PhERObnVeaCH5RfKaqWLiDREoKvbRUSkUQJdh9AVEan3QPcBnShaRATqPdCT6nIREYnVd6Cry0VEpKLOA11dLiIisToPdHW5iIjE6jvQ1YcuIlJR34Eed7noeC4iIvUe6Gqhi4jE6jzQ4x9FFegiIvUd6FEfekGBLiJS54Fe6XJRH7qISJ0HurpcRERidR3oKf1TVESkoq4D3feMpG/qchERoc4DHSY+UfTXn9jFt556dXoKEhGZJg0Q6N64Fvr/+vkrfPOp3dNUkYjI9KjpJNEzWSrhjepDzxXLvHpwiPam1DRWJSLy+muQFvpIoO/oGsA5ODhYIKdDAojISaQBAt0f1eWyvXOgcvu13tx0lCQiMi3qP9CTo1voL++vDvTh6ShJRGRa1H+gj+lD/+3+frLJ8A9Hr/WohS4iJ48GCPTxXS4XrpwDqIUuIieXBgj0kS6XfKnMru5Bzl7SypzmFPvUhy4iJ5GaAt3MrjKzbWa23cxuP8Jw/8nMnJltmLwSj6y6D/2VA4MEDs5Y2MLi1gyv9aiFLiInj6MGupn5wEbgamA1cJ2ZrZ5guBbgNuCpyS7ySKq7XOIfRFctmMXi1qz2chGRk0otLfTzge3OuZ3OuQKwCXjvBMP9NfAZ4HVN0eofRV/uHMAzWDGvmVPaMuxTC11ETiK1BPoSYE/V/Y7osQozOwc41Tn3o0msrSbphEehHAb69s5+TpvbTCbps7g1S1+uxGC+9HqXJCIyLWoJdJvgMVd50swDPgf86VHfyOxmM9tiZlu6urpqr/II0kl/pIW+f4AzFswC4JS2DKA9XUTk5FFLoHcAp1bdXwrsq7rfAqwBfmpmu4ALgc0T/TDqnLvfObfBObdh/vz5x191lfjgXMVywCsHBlkVBfri1iwA+7QvuoicJGoJ9KeBVWa2wsxSwLXA5vhJ51yvc26ec265c2458CRwjXNuy5RUPEY64RG4cP/zUuBYtTAOdLXQReTkctRAd86VgFuBR4AXge8457aa2V1mds1UF3g08WnofrO3F4BVC1oAWNSawUwtdBE5edR0+Fzn3MPAw2Meu/Mww779xMuqXToZrpO27u3FDE6fH7bQk77H/FlptdBF5KTREP8UBXh+Xx9L27NkU37lucVt2hddRE4edR/o8YmiX9jXV+luiS2enVGgi8hJo+4DPe5DHy6WKz+Ixha3hX//d85N9FIRkYbSAIE+MgljW+intGYZLJTpy+nPRSLS+Bog0Ef6zON90GOL9eciETmJ1H+gJ0cm4fSxgR79uUgnuhCRk0H9B3rU5bKkLcus9Oi9MOO//+9TC11ETgINEOhhl8sZY1rnAAtaMvieqYUuIieFBgj0cBLG9p8D+J6xsCWtFrqInBTqPtDbm1Nkkh7nndY+4fOL27JqoYvISaGmv/7PZK3ZJE//2TvH9Z/HFrdmeD46zouISCOr+xY6QEsmidlEh22HU6K//+vPRSLS6Boi0I9kcWuGfCng4GBhuksREZlSJ0GgR/ui65guItLgGj7QK/ui64TRItLgGj7Q1UIXkZNFwwf63OYUKd9ToItIw2v4QPc8Y1FrRgfoEpGG1/CBDuGeLvpzkYg0upMi0E9py+rv/yLS8E6KQF/cmmF/X45iOZjuUkREpsxJEeirT5lNsex4292Pc++jL9PZp+4XEWk8J0Wgv/vsxfzPG85j1cJZfPbHv+Utn36MD3/zl2zv7J/u0kREJk3dH5yrFmbGlWct4sqzFvHKgUG++eRuvr1lDz9+cT9/esUb+OO3rcT3Jj4WzGTLl8r4ZiT8E1uXOucOe/waETk52XQdtGrDhg1uy5Yt0zJugK7+PH/+4G94ZOt+zlnWxj1/uI7T548/pvqJyBXL/OCXe/nN3l52dw+yu3uIfb3DLJ6d4YH//GbOXDT7mN+zsy/HZx7Zxg9+2cGVZy3iY1e8gTcsHH1y7P5cke8/08Evdh1kMF9muFBmqFiiVHa89Yx5/Idzl7L6lGMft4hMPzN7xjm3YcLnTtZAh7CVu/nX+/jLzVsZLpS57vxlrFnSyhsXtnDGgllkU/6Er8sVy+zrGWZvzzAp32P9srZRJ6sulQO+90wHX3j0ZV7rzdHelGT5vGZOm9PEqXOa+O6WDgYLJf72xg1cuHJuTbXmS2W++m+7+OKjL1MoB1x51iJ+uq2LwUKJ9647hT955xvIlwK+/sQufvirvQwVyiyb00RbU5KmlE9TKkGxHPDkzm6KZceZi1r4j+cu5Z2rF7J8bpNa+1XKgcMzJnWeOOcolh0Jz/AmYWuwP1dke+cAO7oGCQLHotYMi1szLGrN0JJJTkLFow0VSvQMFckVy+SKAflSmWLZ4XtGyvdI+EbS92hO+7RkkjSn/ClfpoLAMVQsM1QoEQQwO5sgm5z68VYrlQMc4NvkfK61UKAfRWd/jr/avJWfvNBJIdoTxgxOac2STnj4nuF7hmfGgYE8nf35Ua/PJD3evHwObzl9HnNnpbjvpzvYeWCQdae28ckr38hbzpg3avi9PcPc9MAveLV7iM/90XrevXYxEH7pn+vo5dEX93NwqIBhmIEB//LbLnZ1D3H5mQv48/esZsW8Zg4NFrjvZzv42r/volAKCBykEh7XrDuFGy86jbVL28ZN68HBAj96bh8/+OVent3TA0B7U5JzlrVz7rI21p3axlmntDKnOTUFczoUBI6DQwV2RIG0o2uA3d2DzM4kOXVOE8vmNLFsbhNzm1Mko7BIeF4lDH3P8C2cN6XAkSuWyZcCcsUyAM2pBNmUT1PKJ+EZg4UyhwYL9A4X6Rkq0jNcoGeoSO9wkb7hIt2DBfb35ejsy7O/P0fPUBHfMzIJj2zKJ5P0mducikIzy+LWDLOzSXLFMkOFchRyZTyzUctKz1CBjkPhir/j0DAD+RIAnkHC80j6xuxsktZskram8Dqb9PHMMDM8AwcUSmGA5ksBQ4Uyu7sH2d+XP+z8zSZ9ZmcTzEonaMkkackkaEr5o+ZL0veIc88wHI5CKYjGFV4ODOT5XW+O3/Xl6M+Vjukz9gxmpRO0N6dY0JJmQUuG+S1p5s1KkUn6pBMe6YRPasz3y/eMwDkG8yUGC2WG8iX6cyW6+vN09ufo7M/T1Z+nL1ckVxy/11rSN2ZnkszOJmlvSjKnOc3c5hRzZ6VY3JZl+dwmTpvTzCltmZq7PYcKJfb15NjXM8yeQ0Ps6Bxk54EBdnQN0HFomOoI9T0j4RnphEcqEU1nMpzWcJo90kmfD77lNC47c+ExzdOYAn2wG7b/BN7we5Cd+MxGEK5tdx8c4re/62fb/n5ePThEsewoBwGlsqMcOObOSrG0vYml7VmWtjfRN1zk33Yc4N+3d7Ntf/gj66oFs/h/rnwjv7d64WFbCz1DBf74a1t45tVDfOSyVfQNF/k/W3/Hvt4cvme0ZpM453CEAbikvYlPXvVG3v7GBePeq7M/x9f/fTezswn+8LxTaa8xjHd2DfDkzoP86tVD/GpPD9s7ByrPLW7NsHrxbFYtbCFXLFe+UF39eYaj4AQqK53q0PW9sLUWXof3C6UgCtMiPUMFgqrFLpv0OW1uE/25Evt6R39BTpRnjBrXWOmEx5zmFAtnZ1g4O83C2RnmNKcolR3DxTLDxTK5QpmuKNxe681VgrkyDwwyCR9HuIyUAodz0JJOsCRaTpa2Z5nTnCJwjlI5HKZQCujPhfOkN1rR5EsBgXMEAZVj+KeTo8Pg1PYmTl/QzBnzZ3HGglkko0NbvNY7zO96w8+oP1diIF+iL1ekP1eqdLsN5csMFsLuNwhXGLGUH4ZPfD23Oc2i2WGrf+HsDO1NSTJJn0wUUEnfoxR9N4rlgEI5YDBfpj8aZ3+uyMGhIp19uWj5yY+bd7V+hnNnpaMVQ7hyaK1seYZbn2bQnytVVtI9w0UODRY4OFige7DAocECpaoFIeEZC2dnaMkkaMmEK7+mVIJ8qcxgvsxQscxgvsSBgTw9Q8VR9WSSHivnzeL0BbNYMbeJpO9Rdo4gcJSjzzdfWTGGK+LKijJqfNxy6UquWrP4mOcFTEKgm9lVwBcAH/iKc+7TY57/OPDHQAnoAv6Lc273kd7zdQv0vtfg6++FA9sgkYHVfwDnfRCWXQiTvGnW2Z9jd/cQ5y5rr+lH1lyxzH//2g+Zv2szz9hZJM94B7+35hTe+aYFtDVNXQv5cHqHizy/t5cX9vWxdV8vL7zWx46uQZqSPvNnh1+m+S0ZmqOuqHjRCVwYZMXAUSoHFMuOwIVf8nIQLuDJhNHWlKK9KUl7U4o5zSlWRoG0eHamsrmaL5XZ15Njd/cgvcNFiuX4PQNKQTiewDkCF3aNxOGTTnhkkj7OwVAh3AwfKpQplAJmZxO0ZVO0NiVpyyZpa0pVWsSZ5MTdakcSB1ZT1HpPJ7xxK+4gcK/bJng9ibem4q2OQilcRuIgDJzDM6M5naA5HW1VJP0TnpdB4Ojsz7Ore7Dye1a8ch6IVn5DhRLphE9zOlxJNKX8sGXfmmVJW5ZT2rIsac+OWl6nwwkFupn5wG+BK4AO4GngOufcC1XDvAN4yjk3ZGb/N/B259wfHel9X5dA79kDX78GBjrhXffA3i3w3Hcg3wfz3ginvQXmrYK5Z4QXLwGHdkWXV2CwC5rmQstimLUQWhZBphVSzZCaFV77aXBBeMGF1+UiBKXw2pXD13pjgqOYg3+9B/fzz2NB1AJoPRXWXQfrr4c5K6Z23tRIwSQNIShDcRjKhfB7WS5AUGTc5qCfDL/Tiejip8GbWXt3n2igXwT8lXPuyuj+HQDOuf9+mOHPAb7knLv4SO875YF+8BX42jWQ64UPfB9OfXP4eGEQtv4Qfr0J9j8Pw4cmfr2XDMN8qDv84E9Epg1OvwxWXQFnvBM6X4QffQwO7oC118LlfwF7noJffRN2PAa4cIWBRVsRFi5omdboMjt8vjAI+f5wBZXvh2Q2XPm0LB5Z+RQGoTAQXheHINkcvj49O7z2klRWRPHKqJQLF/7KdR7KeSgVwsc8PxxXsjm8TlSt1IJyuDLL9cLQwXD+DXWHXyA/CX4qvE42Q9symLMyXHm1Lw/fa9TyWFWXc9FzbuQ+LhxXUBoZLxbWE9flp0e/H4RbapnWkXmQqB7mMMolyPWEy8vQQSgOgnlVFx+SmXC6Uk2QbArf10uG0xu34IMgnIfxvC0ORZ9R9PmUC+G0uCBsDEz0/TQv/AzMDxshuJHpjy/FISgMReMYjG5H4ykMVS3T0TJWPR1ePD1NkJ410oCpfM7RZwCjX4tF01U1TfF0japnaPQyFr8X0Tzy/PHBmpoF6ZbwM0u3RJ9tMpx+LxHWlOuFfG94neuNvhsD4XXpBA79kchGy3tT+NnGy02mNawFq5ov0ecQrzhK+Wi5jD63+Pt8wX+DN1x5XOUcKdBr2Q99CbCn6n4HcMERhv8Q8L8PU8jNwM0Ay5Ytq2HUE8gPjATKRIIAXnsWNr0//BBv+kc45ZyR51PNcM4HwguEX84DL0P3y+GMb4/CpXVpOB7nwi9x/2vQ/7tw4agsrAPhB2d+uCzGC7WfjL7I0ezteCbsw9/6g5E62lfADQ/C6e8I77cuhTX/EXo74DffC7cqqAqxUj4M7lwv5PrC55NN4YLVujRcsIpDYY17nwnrLeXCL0X8hUxmw2FyfeF7cYSVeSITXqqDMZGKAtJB796RL2qpEC6olZDxw4W9aS7MOwOy54fvUS5GWy/FcD4e2gU7f3piX7bJ4CWj6U2H135y5EtZyoc1FwdPcByJcPkoT8OpEL1EuAwkm6NloSlcsVZWkNUrzmil7MphABcGwktwDH3f5o1swcYhGDckWhZFy1Rm5Dr+nsXirdtyPO+HRxovPa+GoV2KWtjlUnhtXtVKujXcKp57RrQSaIFUSzi+uEHhp6LPJArYWLkw0ngp58Mt6coKaChaQfSF36GePWFNuCgDohWbnxg/HqhaEUbf5ylQSwv9D4ErnXN/HN2/ATjfOfeRCYb9AHArcKlz7ogVH3cL/ckvwz/fEbbq5r8JFpwZtvIOvhIG2b5fhqHXNA9u/EdYtObYxzEVggD2/wZe/nH4QZ9/8+FXSpPBRa1X/zC7sAXByBd1bAstkXn9NjOdC1dCPburwq7qC+b54f24dROvNC0aLm6heYmRYIi/iKVceHvsexaHR76U+d7wSxqHdylftTURb1GkwlDIzgl/VM+2h2FVvQURlEa+/NWt7XIhDJ1yIRwuDrF4BRIHX3zxU6YAXjUAAAukSURBVCOfRTzto2fY6NANSlUt62g++IkwSONW5eGWgWP5jOKtNPOrPgfGrwzi6dJusFPmRFvoHcCpVfeXAvsmGMk7gT+jhjA/IadeAJd+ErpehM6X4OVHooXah4VnwVn/AZacF3ZvtCyasjKOmefB4nXh5fVgduQvsueFLabpZgazF4cXmZnMou6kzHRXIkdRS6A/DawysxXAXuBa4PrqAaJ+8/8JXOWc65z0KqstOTe8xEoF6N0T9hunmqZ01CIiM9lRA905VzKzW4FHCHdbfMA5t9XM7gK2OOc2A/8DmAV8N9p961Xn3DVTWPeIRArmnv66jEpEZCar6eBczrmHgYfHPHZn1e13TnJdIiJyjGbWDpYiInLcFOgiIg1CgS4i0iAU6CIiDUKBLiLSIBToIiINQoEuItIgFOgiIg1CgS4i0iAU6CIiDUKBLiLSIBToIiINQoEuItIgFOgiIg1CgS4i0iAU6CIiDUKBLiLSIBToIiINQoEuItIgFOgiIg1CgS4i0iAU6CIiDUKBLiLSIBToIiINQoEuItIgFOgiIg0iMd0FHKt/fuWf+e5vv8uZc87kzDln8qY5b2J563ISXoJiUGS4NMxQcYi0n6Yt3YaZTXfJIiKvi5oC3cyuAr4A+MBXnHOfHvN8Gvg6cB7QDfyRc27X5JYacjiGikNsemkThaAAQNJLAlAMiqOGTXpJFjQtYH52PnMyc0j7aZJ+koSXIOklac+0Mz87PxymaT5t6TayiSzZRJaMn8HMcM5RCkoUggKFcoG0nyabyGpFISIzzlED3cx8YCNwBdABPG1mm51zL1QN9iHgkHPuDDO7Frgb+KOpKPjqFVdz9YqrKQUlXul9hZcOvsTLPS9jGNlElqZEE9lklkK5wP6h/XQNddE11MWr/a9SCkoUgyLFcpF8kKcv34fDHXZcKS9FMSiOGybhJZidmh1e0rNpSbYwKzWLWcnwkvJTpPwUaT9Nyk/hm49nHp55+OZjZnjmYYxc58t5cuUc+VKe4fIwOCrDeeYRuIBcKUehXCBXzlEKSqT81Mg0J7Kk/BSeeSS8RGWcQKX+wAXh60s5cuUcuVKOkisRDYTDYWZk/AxpP00mkSHjZ0ZfJ8Lnkl6yMn3xNMbjTXrJUdMYr/ycczgcgQsoBkV6cj0cyh+iJ9dDT76HsisD4XQbhu/5pLyR+Zj0kiS9ZGU88bWZVa7j8Y0dbzwf4hrKrkw5KFN2ZQIXUApKBC4gcAFlV8b3fBJWNR7Px7foEt2ujCsaX/Xr49vVF4erLAPx9dj3jT+zaArARj6b6vqdC+djQFBZjqov1Z//iYrnU9mVK/NyLM+8ymc+meOuRVyTGlm1tdDPB7Y753YCmNkm4L1AdaC/F/ir6Pb3gC+ZmbnDffqTIOElWNW+ilXtq477PYpBke7hbg4MH2D/0H768n0MlYbIlXIMl4YplAsk/TC40n6ahJcgV8rRV+gLL/k++gv99BX62Duwl4HiAAOFAQpBgcAFkzi1Ic+8Si1JL0m+nGe4NDxuy+RY3i9h4SJQHUrxls9kMaJgPcLKU6ZGvLKoDtzKyqLKRCuMkitRDsrH9bkZNrLi8vxRK7GxK/sJa4lWWPHKNwiCSj3xSnNsXfH7J7zEhLerxx2PH0ZWBIaNmwfxiqwy/qrbcR3V0zy2QTF2GuPrT7z5E7xv1fuOeb4eTS2BvgTYU3W/A7jgcMM450pm1gvMBQ5UD2RmNwM3Ayxbtuw4S548SS/JouZFLGpexBrWTOp7l4IShXLYTVNypcrCUbmuWmAcrtIqziaypP10pVUeEOCcwzASXmLCVkj820GxXBzX4qx8WSxcmNJ+mnQiTdbPHvb9AheQL+fJl/KVlnx8Ha/oCkGBfDlPsVysTGM5KFNyJUpBOL1x7WVXHtkaMcMj3IpoS7fRlmljTmYOralWEl6iMl8q8zDq6iqUw/GVXZlSUBo1nni+Vs+vmHNu1DTG4x8bMnHrOGHhPInnYTyewAWV6Yu/0PH7x1sd1Vti1S3V+BLP2+pWfPWWQikoVUKqOlTGbnF45uHhjQqKeNrjOqvHUXIlcKOHGRuiseqgjedL3EU5KqSqVtLx9Md1VLfo4wCs3HbBqGVjooaPw42ad/Gy75uP543M1+p6HG7UdFc+s+j22HHHt8fO7+qtZmxkhRivGKq3gKtXlJXPK9ycGlkpjVkeY8tbl084/09ULYE+0Sc/tsJahsE5dz9wP8CGDRsaurmW8BIkvARNyabjfg/ffHz8ow6X9JIkU8njHs9YnnmV3xJEpH7U0tHVAZxadX8psO9ww5hZAmgFDk5GgSIiUptaAv1pYJWZrTCzFHAtsHnMMJuBm6Lb/wl4bCr7z0VEZLyjdrlEfeK3Ao8Q7rb4gHNuq5ndBWxxzm0G/hfwDTPbTtgyv3YqixYRkfFq2g/dOfcw8PCYx+6sup0D/nBySxMRkWOhv/6LiDQIBbqISINQoIuINAgFuohIg7Dp2rvQzLqA3cf58nmM+RfqDFdP9dZTrVBf9dZTrVBf9dZTrXBi9Z7mnJs/0RPTFugnwsy2OOc2THcdtaqneuupVqiveuupVqiveuupVpi6etXlIiLSIBToIiINol4D/f7pLuAY1VO99VQr1Fe99VQr1Fe99VQrTFG9ddmHLiIi49VrC11ERMZQoIuINIi6C3Qzu8rMtpnZdjO7fbrrGcvMHjCzTjN7vuqxOWb2YzN7Obpun84aY2Z2qpk9bmYvmtlWM/to9PiMq9fMMmb2CzP7dVTrp6LHV5jZU1Gt344O8TwjmJlvZr8ysx9F92dyrbvM7Ddm9qyZbYkem3HLQczM2szse2b2UrT8XjQT6zWzN0bzNL70mdmfTFWtdRXoNnLC6quB1cB1ZrZ6eqsa5++Aq8Y8djvwqHNuFfBodH8mKAF/6px7E3Ah8OFofs7EevPAZc65dcB64Cozu5DwhOSfi2o9RHjC8pnio8CLVfdncq0A73DOra/aP3omLgexLwD/7Jw7E1hHOJ9nXL3OuW3RPF0PnAcMAT9kqmp1ztXNBbgIeKTq/h3AHdNd1wR1Lgeer7q/DVgc3V4MbJvuGg9T9z8CV8z0eoEm4JeE57Y9ACQmWj6mucal0Rf1MuBHhKdpnJG1RvXsAuaNeWxGLgfAbOAVop06Znq9VfX9HvBvU1lrXbXQmfiE1UumqZZjsdA59xpAdL1gmusZx8yWA+cATzFD6426MJ4FOoEfAzuAHueiMzbPrOXh88D/C8RnQZ7LzK0VwnMA/x8zeyY6mTvM0OUAWAl0AV+NurS+YmbNzNx6Y9cC/xDdnpJa6y3QazoZtRwbM5sFfB/4E+dc33TXczjOubILN12XAucDb5posNe3qvHM7D1Ap3PumeqHJxh02mutcrFz7lzC7swPm9kl013QESSAc4EvO+fOAQaZAd0rRxL9XnIN8N2pHE+9BXotJ6yeifab2WKA6LpzmuupMLMkYZh/0zn3g+jhGVsvgHOuB/gpYb9/W3Ricpg5y8PFwDVmtgvYRNjt8nlmZq0AOOf2RdedhH285zNzl4MOoMM591R0/3uEAT9T64VwRflL59z+6P6U1FpvgV7LCatnouqTaN9E2Fc97czMCM8H+6Jz7rNVT824es1svpm1RbezwDsJfwh7nPDE5DBDanXO3eGcW+qcW064jD7mnHs/M7BWADNrNrOW+DZhX+/zzMDlAMA59ztgj5m9MXrocuAFZmi9kesY6W6Bqap1un8oOI4fFt4F/Jaw//TPprueCer7B+A1oEjYkvgQYf/po8DL0fWc6a4zqvWthJv9zwHPRpd3zcR6gbXAr6JanwfujB5fCfwC2E64OZue7lrH1P124Eczudaorl9Hl63x92omLgdVNa8HtkTLw4NA+0ytl/BH/G6gteqxKalVf/0XEWkQ9dblIiIih6FAFxFpEAp0EZEGoUAXEWkQCnQRkQahQBcRaRAKdBGRBvH/A4xiSPFh4nuXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 80 of 256\n",
      "G Loss: 0.21717673540115356, D Loss: 0.2006036788225174, R Loss: 0.0009651595610193908\n",
      "Current number of unique samples: 2\n",
      "------------\n",
      "Currently Generating [[1.1514]]\n",
      "Predicted bandgap: 1.1607898473739624\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 90 of 256\n",
      "G Loss: 0.2195497453212738, D Loss: 0.2001829296350479, R Loss: 0.001174130360595882\n",
      "Current number of unique samples: 5\n",
      "------------\n",
      "Currently Generating [[1.1279]]\n",
      "Predicted bandgap: 1.1207804679870605\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 100 of 256\n",
      "G Loss: 0.2151733636856079, D Loss: 0.20090703666210175, R Loss: 0.0008492088527418673\n",
      "Current number of unique samples: 3\n",
      "------------\n",
      "Currently Generating [[0.7696]]\n",
      "Predicted bandgap: 0.8038581013679504\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 110 of 256\n",
      "G Loss: 0.21508987247943878, D Loss: 0.2010233998298645, R Loss: 0.0008530315826646984\n",
      "Current number of unique samples: 3\n",
      "------------\n",
      "Currently Generating [[0.7185]]\n",
      "Predicted bandgap: 0.7288240194320679\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3de5zcdX3v8dfn9/vNzM7ec9ncgSSCQAi54MpFEBTlFJSLfdTTIgjWo6WcY8RiT4+cXugB++gR61EBc7CUSi1WsdZK0xIPVqSirSABUQkBCYGQJSG7Sfa+Ozu37/nj95vZ2c0mmWR3WX6z72ce88jOzO/3ne/v9v595zu/iznnEBGR+PNmugIiIjI1FOgiIjVCgS4iUiMU6CIiNUKBLiJSI4KZ+uD58+e75cuXz9THi4jE0pNPPrnPOdc20XszFujLly9ny5YtM/XxIiKxZGY7D/WeulxERGqEAl1EpEYo0EVEasSM9aGLiFTK5XJ0dHSQyWRmuipvCHV1dSxbtoxEIlH1OAp0EXlD6OjooKmpieXLl2NmM12dGeWcY//+/XR0dLBixYqqx1OXi4i8IWQyGebNmzfrwxzAzJg3b95Rf1tRoIvIG4bCfNSxzIvYBfoTLx/g/3zveXKF4kxXRUTkDSV2gf7Uzm7u/MF2snkFuohMrb1793LVVVexcuVK3vKWt3DOOefwne9856DhXn75ZVavXj0DNTy82AV64IdVzhd0Yw4RmTrOOd73vvdx/vnns2PHDp588knuv/9+Ojo6ZrpqVYtdoCf8sF8pV1QLXUSmzg9+8AOSySTXX399+bUTTjiBj3/841WX8fTTT3P22WezZs0afv3Xf53u7m4A7rjjDlatWsWaNWu48sorAfjhD3/IunXrWLduHevXr6e/v3/S0xC7wxYDTy10kVp3yz9v5dndfVNa5qolzfzpZacd8v2tW7dyxhlnTOozrr32Wu68804uuOACbr75Zm655Ra++MUv8pnPfIaXXnqJVCpFT08PAJ/73OfYuHEj5557LgMDA9TV1U3qsyGGLfSg1ELXj6IiMo0+9rGPsXbtWt761rdWNXxvby89PT1ccMEFAHzoQx/i0UcfBWDNmjVcffXVfO1rXyMIwnb0ueeeyyc/+UnuuOMOenp6yq9PRuxa6KUul3xRLXSRWnW4lvR0Oe200/j2t79dfr5x40b27dtHe3v7pMt+8MEHefTRR9m0aROf/vSn2bp1KzfddBPvfe972bx5M2effTbf//73OeWUUyb1OfFroZe7XNRCF5Gpc+GFF5LJZLjrrrvKrw0NDVU9fktLC3PmzOFHP/oRAPfddx8XXHABxWKRXbt28c53vpPPfvaz9PT0MDAwwIsvvsjpp5/Opz71Kdrb23nuuecmPQ2xbaFnFegiMoXMjAceeIAbb7yRz372s7S1tdHQ0MBtt9024fDPP/88y5YtKz//whe+wFe/+lWuv/56hoaGWLlyJffeey+FQoEPfvCD9Pb24pzjxhtvpLW1lT/5kz/hkUcewfd9Vq1axSWXXDLpaYhdoOtHURGZLosXL+b+++8/4nDLly8nl8tN+N5jjz120Gs//vGPD3rtzjvvPPoKHkHsulwSQRToOmxRRGSM+AW6VzrKRS10EZFKsQt0nSkqIjKxGAa6zhQVEZlI7AI9oR9FRUQmdMRAN7OvmFmnmT1ziPfNzO4ws+1m9gszm9y5s0dQaqHrOHQRkbGqaaH/DXDxYd6/BDgpelwH3HWYYSdt9OJcaqGLyNTyfZ9169Zx2mmnsXbtWj7/+c9TnKB79416+dwjHofunHvUzJYfZpArgL91zjngMTNrNbPFzrk9U1THMXSmqIhMl3Q6zdNPPw1AZ2cnV111Fb29vdxyyy0zXLPqTEUf+lJgV8Xzjui1g5jZdWa2xcy2dHV1HdOHjXa5qIUuItNnwYIF3H333XzpS18ibK8eWS1cPneiG99NOPXOubuBuwHa29uPKZET0WGLOspFpIZ99yZ47ZdTW+ai0+GSzxzVKCtXrqRYLNLZ2cnChQuPOHwtXD63Aziu4vkyYPcUlDuhwFMLXUReP9W2zmvl8rmbgA1mdj9wFtA7Xf3nMHpika6HLlLDjrIlPV127NiB7/ssWLBgUuW8XpfPPWKgm9k3gHcA882sA/hTIAHgnPsysBl4D7AdGAI+PKkaHYGuhy4ir4euri6uv/56NmzYgNlEPctjVV4+9+1vf/uEl88977zz+PrXv87AwAD79+/n9NNP5/TTT+cnP/kJzz333PQHunPuA0d43wEfm1QtjoKOchGR6TI8PMy6devI5XIEQcA111zDJz/5yQmH1eVzp0D5OHT1oYvIFCsUClUNp8vnThEzw/dMl88VERkndoEO4ZEuOspFRGSsWAZ6wvd0CzoRkXFiGeiBrxa6iMh4sQz0hO+pD11EZJx4BrpnOspFRGScWAZ64Hs6Dl1Eplzp8rmrV6/msssuK193ZbzGxsbXuWbViWmgm66HLiJTrnT53GeeeYa5c+eycePGma7SUYlloCc8tdBFZHqdc845vPrqq1UPv3PnTt71rnexZs0a3vWud/HKK68A8K1vfYvVq1ezdu1azj//fAC2bt3KmWeeybp161izZg0vvPDClNQ5dmeKgo5yEal1t/30Np478NyUlnnK3FP41JmfqmrYQqHAww8/zEc+8pGqy9+wYQPXXnstH/rQh/jKV77CDTfcwAMPPMCtt97KQw89xNKlS8tdOF/+8pf5xCc+wdVXX002m636DNUjiWULPfA9dbmIyJQrXctl3rx5HDhwgIsuuqjqcX/yk59w1VVXAXDNNdeUT/c/99xz+e3f/m3+6q/+qhzc55xzDn/+53/Obbfdxs6dO0mn01NS/1i20BOeqctFpIZV25KeaqU+9N7eXi699FI2btzIDTfccExlla7Q+OUvf5nHH3+cBx98kHXr1vH0009z1VVXcdZZZ/Hggw/ya7/2a9xzzz1ceOGFk65/TFvo6nIRkenT0tLCHXfcwec+97lDXoRrvLe97W3cf//9APzd3/0d5513HgAvvvgiZ511Frfeeivz589n165d7Nixg5UrV3LDDTdw+eWX84tf/GJK6h3LQE/4nm5BJyLTav369axdu7Yc0pWGhoZYtmxZ+fH5z3+eO+64g3vvvZc1a9Zw3333cfvttwPwB3/wB5x++umsXr2a888/n7Vr1/LNb36T1atXs27dOp577jmuvfbaKamzVXt7panW3t7utmzZckzjfvjen7JvIMs/f/y8Ka6ViMyUbdu2ceqpp850Nd5QJponZvakc659ouFj2UIPfE+3oBMRGSeWgZ7wTbegExEZJ5aBHujEIpGaNFNdwG9ExzIv4hnovi7OJVJr6urq2L9/v0KdMMz3799PXV3dUY0X0+PQdflckVqzbNkyOjo66OrqmumqvCHU1dWNuQl1NWIZ6DoOXaT2JBIJVqxYMdPViLVYdrnoFnQiIgeLZaDrJtEiIgeLZaAnAvWhi4iMF89Aj25Bp1/DRURGxTLQAz+sdkEnF4mIlFUV6GZ2sZk9b2bbzeymCd4/3sweMbOfmdkvzOw9U1/VUYEfXpZSZ4uKiIw6YqCbmQ9sBC4BVgEfMLNV4wb7Y+DvnXPrgSuB/zvVFa2U8MJq63ouIiKjqmmhnwlsd87tcM5lgfuBK8YN44Dm6O8WYPfUVfFg5Ra6jnQRESmrJtCXArsqnndEr1X6X8AHzawD2Ax8fKKCzOw6M9tiZlsmczZYqQ9d10QXERlVTaDbBK+Nbxp/APgb59wy4D3AfWZ2UNnOubudc+3Oufa2trajr20k4amFLiIyXjWB3gEcV/F8GQd3qXwE+HsA59xPgDpg/lRUcCKlFroCXURkVDWB/gRwkpmtMLMk4Y+em8YN8wrwLgAzO5Uw0KftCjuJqA9dXS4iIqOOGOjOuTywAXgI2EZ4NMtWM7vVzC6PBvt94HfM7OfAN4DfdtN41k/gqYUuIjJeVVdbdM5tJvyxs/K1myv+fhY4d2qrdmilo1x02KKIyKhYnima0IlFIiIHiWWgj3a5qIUuIlISz0Avd7mohS4iUhLLQE+UDlvUUS4iImWxDPTA04+iIiLjxTLQSy10dbmIiIyKdaDrOHQRkVGxDPTR66Gry0VEpCSWgT56PXS10EVESmIZ6KPXQ1cLXUSkJNaBntOZoiIiZbEM9ITOFBUROUgsA123oBMROVgsAz2hW9CJiBwkloEe6BZ0IiIHiWWg+56OchERGS+WgW5mJHzTUS4iIhViGegQXhNdLXQRkVHxDXTfdKaoiEiF2AZ6wvd0LRcRkQqxDfTAMx3lIiJSIbaBnvA9dbmIiFSIbaAHvqnLRUSkQnwD3TPdgk5EpEJsA11dLiIiY8U60HUcuojIqNgGetiHrha6iEhJVYFuZheb2fNmtt3MbjrEML9pZs+a2VYz+/rUVvNgCc9TH7qISIXgSAOYmQ9sBC4COoAnzGyTc+7ZimFOAv4ncK5zrtvMFkxXhUsC38jmFegiIiXVtNDPBLY753Y457LA/cAV44b5HWCjc64bwDnXObXVPFjge7o4l4hIhWoCfSmwq+J5R/RapTcDbzazfzezx8zs4okKMrPrzGyLmW3p6uo6thpHEp7pR1ERkQrVBLpN8Nr4pnEAnAS8A/gAcI+ZtR40knN3O+fanXPtbW1tR1vXsR/o69R/EZFK1QR6B3BcxfNlwO4Jhvkn51zOOfcS8DxhwE+bsMtFLXQRkZJqAv0J4CQzW2FmSeBKYNO4YR4A3glgZvMJu2B2TGVFx0vo4lwiImMcMdCdc3lgA/AQsA34e+fcVjO71cwujwZ7CNhvZs8CjwB/4JzbP12VhrCFrj50EZFRRzxsEcA5txnYPO61myv+dsAno8frQregExEZK75niuoWdCIiY8Q30HWUi4jIGLEN9ISOchERGSO2ga5b0ImIjBXfQPc98kVH+HusiIjENtATXngCqy6hKyISim2gB35YdV1CV0QkFNtAT/hhC123oRMRCcU40MOq61h0EZFQbAM98NWHLiJSKbaBnvDUhy4iUim2gV5uoasPXUQEiHWgR33oOltURASIcaCXjkPXUS4iIqHYBnq5ha5AFxEBYh3oUQtdXS4iIkCMA710lIta6CIiodgG+uhRLmqhi4hAjAO9fOp/dGLR//7uNh55rnMmqyQiMqNiG+iBN3rqf7Ho+OsfvcTmX+6Z4VqJiMyc+AZ6xcW59g2OkC86eoZzM1wrEZGZE9tAT1ScWNTZNwJAz1B2JqskIjKjYhvogTd66v9rvRkAeobUQheR2Su2gZ6ouMHFa31hoHcr0EVkFottoFf2oe+NAr13OKt7jIrIrBXfQPdG+9BLXS65gmMoW5jJaomIzJjYBnqy3OXiyl0uAN36YVREZqmqAt3MLjaz581su5nddJjh3m9mzszap66KE6s8U7Szb6T8I6l+GBWR2eqIgW5mPrARuARYBXzAzFZNMFwTcAPw+FRXciKVt6B7rS/DiQsaAQW6iMxe1bTQzwS2O+d2OOeywP3AFRMM92ngs0BmgvemXOniXP2ZPL3DOU5e1ARAz7C6XERkdqom0JcCuyqed0SvlZnZeuA459y/TGHdDsvzDM/g1Z5hgHKg69BFEZmtqgl0m+C18rGBZuYBXwB+/4gFmV1nZlvMbEtXV1f1tTyEwPfo6B4C4JQo0Hv1o6iIzFLVBHoHcFzF82XA7ornTcBq4N/M7GXgbGDTRD+MOufuds61O+fa29rajr3WkYRndHSHLfTj59ZTn/TVhy4is1Y1gf4EcJKZrTCzJHAlsKn0pnOu1zk33zm33Dm3HHgMuNw5t2Vaalwh8D26+sPruCxorqM1nVCXi4jMWkcMdOdcHtgAPARsA/7eObfVzG41s8unu4KHU7omen3SpykV0FqfpFc/iorILBVUM5BzbjOwedxrNx9i2HdMvlrVKZ0tuqi5DjOjtV4tdBGZvWJ7piiMHou+sLkOgDn1SV1CV0RmrVgHeumKi4tawkBvqU/oR1ERmbViHeil0/0XNKcAmFOfoGc4pysuisisFO9A90f70AFa00kKRcfASH4mqyUiMiNiHeilo1xKgd5SnwB0PRcRmZ1iHeilLpeFLaM/ioICXURmp3gH+vgul6iFrmuii8hsFOtAT/iGGbQ1jf4oCtAzrBa6iMw+sQ70wPOY15AqH77Yki51uaiFLiKzT1Vnir5RLZ2TpvIAxVb9KCois1isA/3TV6ymUByN9ITv0ZgK1IcuIrNSrAPd9wzfG3u59tb6BL1qoYvILBTrPvSJtEZni4qIzDa1F+jppLpcRGRWqr1AV5eLiMxSNRnoaqGLyGxUc4E+pz5J73COYlFXXBSR2aXmAr0lnaDooD+jKy6KyOxSc4FevkCX7i0qIrNMzQW6zhYVkdmqBgM9bKHrh1ERmW1qLtCXzUkTeMbN/7SV/3hx30xXR0TkdVNzgb6wuY6vffQsPIOr/upx/vA7vySbL850tUREpl3NBTrA2Svn8d1PnM9156/k64+/wo3ffJp8YfKh3pep/gbUmVxBN6sWkddVrC/OdTjppM8fvudUFjSl+LMHt5FO+nz2N9bgjbuY15H867N7+eef7+Znu7rZdWCY685fyR++59TDjvPEywf46Fe3sGpxMxuvPoO5DWG//iPPd/KNx1+heyhLz1COJa1p/sfFJ3PakpZjnk4RkZKaDfSSj759JQMjeb74/RcYzhb4jbcs5awV82hIjU56Nl/kV3v7ea03w9tOnEd9MqBYdPzF957nrn97kQVNKdqXz+HEtkbufnQHbz9pPm8/qW3Cz/v+s3v52Nefoq0pxZOvdHP5l37MX7x/Ld96chf/+NSrLG6p44R59axsa+CnLx3g0jt/zJVvPY6PX3gSS1rTr9dsmRTnHLmCIxkc3Re8QtGRzRdJBt5BV8k81Oe82DXIlpcPkE76vHlhEyvbGkgF/lF/7u6eYfozeQazeXKFIqnAJxV4tKQTtDWlqEscXZmVRvIFeodzjOSKky6rssxC0RF4XnRnrqNriMjsZDPVLdDe3u62bNnyunyWc44v/Ouv+MtHdzCSLxJ4xsLmOpKBhxnsOjBErhDOh6a6gN84Yxkd3cN8f9tePnDm8dxy+WkkA49MrsBld/6Y3uEc/+/3zqc1neB7z77Gvz3fBUCu4Hjg6VdZvaSZez98Jq8cGOJ379vC3r4RfM/4b+94ExsuPLEcSL3DOe54+AW++h8vky86zlw+l8vWLeE/rVrIwug+qUcrXyjy0r5BHnvpAI/v2E/ROdYsa2XNshYWNKVIBT7JwCPwrHxP1sGRPAMjeQxoqU/Qkk4wOFJgd88wr/Vm2NOXYW9vho7uIV7sGuTFrgGGsgWSgUdzXcCS1jQnL2zipIWN5ekaHCnge0bC9+jP5Ni6u49te/oYiX7PCDxjbkOSxa1pFjSlynVyDoayYX22dw6yb2BkzPR5BnMbksxvTDG/MUVLfYLWdIL6pI+ZYcBIvlguY0fXIDv2DR7xd5SmVMBxc8Md7Yr5DTTXJUgnfdIJn8APp2MkX6B7MMeBwSw7Dwyxo2uAnfuHGBgZexJbc13A4pY0x81Ns2xOPacubmL98WGDYKJviMWiY1f3ENv29PHkzm5++tIBntndN+Za//VJn/pkQEMq/L8x5dOQCmiMHs3pcLk11wUQhX/CM+Y1ppjfmGROfZKGVEB90ieTK9A9lKNnKEv3UI7uwWy4Q8oXyOaLFJzDN8OLxl8xr4Hj59bjeeG8zRWKJHyPpB9uPyP5IiO5Ynn8XMGRLxYpOodz4bflxlRAKvBxOApFx0i+yOBInsGRPN1D4Twt3WnM84yU7zG/KUVbY4oFzSkWNNVN2c4y7szsSedc+4Tv1VygFwvwzD/CivOhaeGYtzK5Ak/u7ObH2/fR2TdCrlAkXyxy/NwGVi9tprkuwbef6mDzL/dQdHDzpau49pwTxrSOnt3dx/s2/jvrjm+lP5Nn254+WtIJUoFH0cFbl8/hc/95bfkbQGdfhr98dAe/vn4pq5dO3LWy68AQD/zsVTb9fDcvdA4AsHppM+0nzGVP7zAv7Rtk30C4shvhdeCTgRc+fI9U4GFmdPZl2Ns/Ug6CRdFO65UDQ5Oerb5nLGquY2VbA29qa2R+Y5L+kTx9w3leOTDI868NlMPX94z6pE+xGLbkUwmPVYubWb20hfmNKbL5Ipl8gf0DI+zpzYTLolgsX66hFFxLW9OcvXIeZ66YS67geH5vP9s7B+jqz9DVn2X/4Ai9wzl6h3IM5wo4B0UXfnNoSAbUp3xOmFvPmxc2sWJ+A631CRpSAYEXhnMmV6RvOEfXwAidfZkopAfp6B7icFeO8AyWtKZ5U1sjK+Y3ML8xSUt9kqRv7BvIsrcvw+6ecAe468AQg9kCAA1Jn8WtaeY2JGlKBfRn8nQPZdnTmynvFJKBx7rjWmk/YQ5NdQkKxSLZfJHhXIHBbCEKwfD/oWye/pE8/Zk8fcO58s5ysjzjsNM/XeoSHoZRcI5cochE0dRcF9DWFO7Mm9MJmlIB6aQf7lTyRbL5AvmCI1909Azn6OrLsG8gS64YzhvPjPpEuDMs7RzTCZ/GuoDWdILmdALfM4rOUSw6Ci7cARWKYZnOhTnSl8nRN5wPv0X5RuAZbU0plrbWs6glVS7X8yCTK4Y70cEsnf0j7O3LcO3blvPOkxcc03yadKCb2cXA7YAP3OOc+8y49z8JfBTIA13Af3HO7TxcmdMW6P/xJfjeH0GqBd79p/CWD4N3dF0D+wZG6BnKceKCxgnf//q/PsbgD2/nJ02/xmX/6d1ctmZJubU7Wb/a28/D2zp5eNtentndy5LWNCvnN7KoJbwRtnOjXRcjhXBjz+bD1lBbU4olLWlOmFfPmSvmcvzcesyMA4NZnnm1l57hXBimuULFCurCVl5dgHNh67pnKEt9MmBxSx2LWupY0ppmfmPqiN0kvUM5At/KreW4KhQdw7kCQyN5hnOFcosz4XvMa0jSXJeo+rcY5xwv7RvkZ6/08IuOHjr7R9g/mGUgk6c5HdCaTrKopY5TFjVx6uJmTl7UdMyt0EyuQH8mj8NhGNlCkf0DI3T1j0TfmvIMZgvUBR5zGpK0pBPMa0gxpyERNUr8Md07haKjq3+El/YNsqs7bBSkAo+E75GL1j3nIJXwwi6sRNjASPjemJvPDEc7opF8Ec/AzEgFXjlU59QnmduQHDPdhaJj/2BY987+Ebr6wiDsGhhh38AI+/qz9GVy9GfyZHIFUoFHKuGT9L1ywDZH3WltTSlS0fZZcI6hih3jUDbPULbAwEienqEcfcM5is7heYZnYTmeZ/hm5WlKBV64M6kLCDyjEDVcXuvL8Gr3MMO5woTLxzOY15hiQVOKDe88kUtOX3xMy3lSgW5mPvAr4CKgA3gC+IBz7tmKYd4JPO6cGzKz/wq8wzn3W4crd1oCff+LcNfb4Pizw5b6yz+CxevglEvD1+rnwqtPwe6nwHyY/2aYfyI0LoT0XKhrAVw4bjEPhSzkM+H7iah/u/dV3FcvxQ7swJmPnfW78I6bonFFxsllIDsAuSHIj4TrliuG3SKJekg2QLIREsfWxXZIhVz4ma5IualrBuaNe/jg+eHfzkXDF8P130XbQbEY/j1hWdH4XhC+X8hBMRf+DVGZUTkAQV348PzRsvwE+Cnwo9+1itHnF0Ygnw23w0I2fC0/Mvq6Wfj5ZtE2G50d7ifDMr0AGD/NE+yIS3Us1b0QbfvF3Og0+okwAxL14d+l8oqFaNjw810hx+DICNlsnpF8AVd00bdpn/q6JEEQ1a1pEaTnHNOiPVygV/Oj6JnAdufcjqiw+4ErgHKgO+ceqRj+MeCDx1TTySgWYdPHwxXjfV8OZ9jP74f/uBMe+bOxw6ai4B7pq67sulZo/3C4Y/j2R7DB/XDVt7BffRceuwse/8tw5TEv3EAbF0LjgnDlG9gLmR5oWQbzT4aWpZDpg+ED4UrQuDB8BMloYy/AyEBYt5F+yA6GD1cMdxp1reGKX8yHK2D/a9DzCgx0hq8HaaifB0vXw7K3Qstx4bQ6RjfWMY/C6EZjHqQaw4DxgnA8LFzxGheG5foTrDKZXuh9Ffr3RGHlhSttqhnSreH/pQ3ZFaLpGoDhnnA+ZHqjjSma/hLzwnr4idGwyGfC8TO94fiZHhjuhtxwNI6NboBeIvz8+rmjO1znwocXbaj5DAwdCMvI9IyWmYnmf354tD5+MtygE/XhPGmYHz4S6XC9g3B5D3TCYCcM7gunsxp+Kqxj/bxw3WlcENavFDT5TDiNhdxoMBXz4bqRG46CLmqA5IbCv+PGvNEdQUwZMPH3+nHe+3/grR+d+s+vooX+fuBi59xHo+fXAGc55zYcYvgvAa855/5sgveuA64DOP7449+yc+dhe2UmlukN/69sETsHj/1feOgP4YqNsH7c/mToAOx6PNxAl6yHuW8KN4iBvbB/Owx2hcOM9AE22uIIUmEovPAQbPsXwIXhdM13YFm0g9z9s/C9UotmZCDaqPeG4drYFta1Zxd0PR+GXmnDNQs3/kzP2Pr6yfBzSuGabAjrNdIXBo4rhPXzfGhYAK3HQ9PiqAUzDH174NUnw7CcaqVWpfnhZ+UyYYtpJqRawsBOzwnrBRUtzFwYfsM9MLQvDLoJWbg80nOiR2u042yBVNPYckut3uxQOG8Hu2Bo/+g8cC4K44XQ0BY95oX1TKRHW6ZmYXm54TCQR/rCHUimNyyvtENwxWg5B+G4iXS4bpRalH5idAcT1EUt3WRFqz89tlVaaoET/V/agRaLoztis/DhJUa3g1IreHxZ5W+zUeu4vCON1s3S/C234F3Uuh6JdkxRWaWWd34k2on7YVlBKnyUpstLhA2f0rQ6Rr85lOYThGUXom9DpWkt7cjH7zBKdSg1QrzEuBZ+NI2FXLS+RzvP0uueH42TGG18mF/xjSAcdMw3/2IOFq+FuSuPabWfbAt9os7CCfcCZvZBoB24YKL3nXN3A3dD2OVSxWcf7Gdfg+/9MSw5A1ZeEM6kZ/8Jul+CE98N664+eJz6uXDyJQe/3rQofBzJGZz8HRUAAAtySURBVNfAgR3w9Nfh1MvChVGyZH34qJZzB3/tK63gpRXfT1Rf3uE+58COMBzKG2rF187KlS5IRUFR+nbQH7WUo+AZPhCWM7Q/bHFmB8MVM0iHXQX186F5Sfjwk6PdVZm+cGc10h+1HDPh9KWinVRdqfXcGm2w/tjQKHd9RfOmtKGlmsOwLYdGFcpf0b3Rsl1h9HNFakA1gd4BHFfxfBmwe/xAZvZu4I+AC5xz09dkW3EBvP2/w0s/hB9/MXxt5QVw3o1w+vsn7iObCnNXwoV/PPlyJqpfqSUylcxg3pvCh4Qtu0oKcalB1QT6E8BJZrYCeBW4EriqcgAzWw/8JWHXTOeU17LSotXhgz8KW4CuGH5NFhGZ5Y4Y6M65vJltAB4iPGzxK865rWZ2K7DFObcJ+AvC3wK+FR3y9Ipz7vJprHeornnaP0JEJC6qOvXfObcZ2DzutZsr/n73FNdLRESOUk1ebVFEZDZSoIuI1AgFuohIjVCgi4jUCAW6iEiNUKCLiNQIBbqISI1QoIuI1AgFuohIjVCgi4jUCAW6iEiNUKCLiNQIBbqISI1QoIuI1AgFuohIjVCgi4jUCAW6iEiNUKCLiNQIBbqISI1QoIuI1AgFuohIjVCgi4jUCAW6iEiNUKCLiNQIBbqISI1QoIuI1IjYBfp3X/ou12y+hnt+eQ/bu7dTdEV6R3rZ1b+L/mz/TFdPRGTGBNUMZGYXA7cDPnCPc+4z495PAX8LvAXYD/yWc+7lqa1qKPACRgoj3P7U7dz+1O0YhsOV329Lt7GiZQVNySZSfoqmZBMnNJ/AipYVLGtcRnOqmaZkEwBDuSEy+QzNqWbSQXo6qisi8ro5YqCbmQ9sBC4COoAnzGyTc+7ZisE+AnQ75040syuB24Dfmo4KX3TCRVx0wkW8Nvgaj3Y8SudQJy2pFpqSTewf3s+O3h3s7NvJgb4DZPIZ+rJ99GX7jlhuU7KJhfULmZeex/z0fOak5pAO0tQn6kl6SXzPxze//L9hDOYG6c/1M5IfwTMPzzwKrsBAdoDB3CBJP0lLqoXmZDMpP0XCS+B7fnkHlC1kGcwNMpAdIFfMUXRFANJBmsZkI42JRpqSTTQlm2hINFAf1JMO0tQFdSS8BEk/Wa6TZx5FV6RQLDCUH2L3wG52D+xmIDeAYXjmkfJT1CfqqQ/qSfpJEn5izLR55mEYZoZzLqyng7zLky9WPFwe51y5DgkvQcJLEHhBeT6YGYVigbzLj/nfMHzPJ/CCg8Yb/9lFV6TgChRdEcMIvIDAC/DNL/9vZoddrvlinmwhS66Yw7nRHb9Z+FmBBeVyPTu6L6zOOQqugHOOIqN1PNpyJlJaF0rzRKQa1bTQzwS2O+d2AJjZ/cAVQGWgXwH8r+jvfwC+ZGbmKregKbaoYRG/efJvHnE45xzdI93s6NnBnsE99Gf7ywHfkGgg5afoy/axd3AvnUOd7M/s5+edP6dnpIfh/DAFVzjiZwReUN64AwtoTDZSH9STLWbpGekhX8wfdvyklyTlpyDabofzw0ccR0KlHVEpREs7g3LYUv0qWLmj8M3H8zw8vHKglnaYpR1crpibsBzPvDE7q1J5vuePCejSjqBQLJAr5spl5ov5MetdaYdsZpT+AaPPo/8rd4qVf5fKqPzc0nwpz69x8+mg8vDK62e4n4/GryjnUCrrXaQ4ZhmV/hVdkXIVbPTzyw+8Mctj/I6uNE9KZZXmbeW0jp++Ut0qyyzN58phKsutLLuyrPH1rZzuymktFMN1csP6DVy68tJDzrNjVU2gLwV2VTzvAM461DDOubyZ9QLzgH2VA5nZdcB1AMcff/wxVvnomBlz6+Yyd9Hcox7XOUe2mCVbyJY35NJG7XA0JBpoSDQQeMGYcSpXNOccmUKm3ErMF/PllTHwApoSTST8xISf25/tpz/bz0B2gP5cP8P5YYZyQ2QL2dF6RS3YoivimYdvPnVBHUsalrCkcUm5e6noimQKmbFlROUUXZF8MZy20spXudFUtmJLDxht/ZaCqPQto7TSl1r+41vAla390niVG3nlxlV6lDaG0jeE0vilVrxzrhw4HqPfEiq/RVS2nJ0b3UDzLk+uENajVHblcq5UKqcU1pXfFJxz5bIqp620jEo76dK0lqatNI8qdwKBF5TDoDR95eCD8rencoBVBqSb4LWK4K0M2MMFfuVnThT45b8nCMAx63NFWeN3QMDot7qK7tPK9ai0Yy4tj/GBOn5nUloe4wN6/PDj51lpXkdvlutbmqzSN/PKbaNyh1G5LlaWOX5H65tPW7ptwnk1WdUE+kRLafwur5phcM7dDdwN0N7ePm2t96liZqT8VNh6Popxxj9PB+mj6qMvf246xfz0/KrHE5HZrZrOvg7guIrny4DdhxrGzAKgBTgwFRUUEZHqVBPoTwAnmdkKM0sCVwKbxg2zCfhQ9Pf7gR9MZ/+5iIgc7IhdLlGf+AbgIcLDFr/inNtqZrcCW5xzm4C/Bu4zs+2ELfMrp7PSIiJysKqOQ3fObQY2j3vt5oq/M8B/ntqqiYjI0YjdmaIiIjIxBbqISI1QoIuI1AgFuohIjbCZOrrQzLqAncc4+nzGnYVaY2p5+jRt8VXL0xenaTvBOTfhqaYzFuiTYWZbnHPtM12P6VLL06dpi69anr5amTZ1uYiI1AgFuohIjYhroN890xWYZrU8fZq2+Krl6auJaYtlH7qIiBwsri10EREZR4EuIlIjYhfoZnaxmT1vZtvN7KaZrs9kmNlxZvaImW0zs61m9ono9blm9q9m9kL0/5yZruuxMjPfzH5mZv8SPV9hZo9H0/bN6JLMsWRmrWb2D2b2XLQMz6mVZWdmN0br5DNm9g0zq4vzsjOzr5hZp5k9U/HahMvKQndEGfMLMztj5mp+dGIV6BU3rL4EWAV8wMxWzWytJiUP/L5z7lTgbOBj0fTcBDzsnDsJeDh6HlefALZVPL8N+EI0bd2ENxiPq9uB/+ecOwVYSzidsV92ZrYUuAFod86tJrxsdunm73Fddn8DXDzutUMtq0uAk6LHdcBdr1MdJy1WgU7FDaudc1mgdMPqWHLO7XHOPRX93U8YCEsJp+mr0WBfBd43MzWcHDNbBrwXuCd6bsCFhDcSh3hPWzNwPuG9AHDOZZ1zPdTIsiO8tHY6ugNZPbCHGC8759yjHHwXtUMtqyuAv3Whx4BWM1v8+tR0cuIW6BPdsHrpDNVlSpnZcmA98Diw0Dm3B8LQBxbMXM0m5YvA/4DyHX3nAT3OuXz0PM7LbyXQBdwbdSndY2YN1MCyc869CnwOeIUwyHuBJ6mdZVdyqGUV25yJW6BXdTPquDGzRuDbwO855/pmuj5TwcwuBTqdc09WvjzBoHFdfgFwBnCXc249MEgMu1cmEvUlXwGsAJYADYTdEOPFddkdSWzX07gFejU3rI4VM0sQhvnfOef+MXp5b+krXvR/50zVbxLOBS43s5cJu8YuJGyxt0Zf4yHey68D6HDOPR49/wfCgK+FZfdu4CXnXJdzLgf8I/A2amfZlRxqWcU2Z+IW6NXcsDo2oj7lvwa2Oec+X/FW5U23PwT80+tdt8lyzv1P59wy59xywuX0A+fc1cAjhDcSh5hOG4Bz7jVgl5mdHL30LuBZamDZEXa1nG1m9dE6Wpq2mlh2FQ61rDYB10ZHu5wN9Ja6Zt7wnHOxegDvAX4FvAj80UzXZ5LTch7hV7lfAE9Hj/cQ9jU/DLwQ/T93pus6yel8B/Av0d8rgZ8C24FvAamZrt8kpmsdsCVafg8Ac2pl2QG3AM8BzwD3Aak4LzvgG4S/B+QIW+AfOdSyIuxy2RhlzC8Jj/aZ8Wmo5qFT/0VEakTculxEROQQFOgiIjVCgS4iUiMU6CIiNUKBLiJSIxToIiI1QoEuIlIj/j/kvRdFk6DZiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 120 of 256\n",
      "G Loss: 0.21476882696151733, D Loss: 0.20119433104991913, R Loss: 0.0007300396100617945\n",
      "Current number of unique samples: 3\n",
      "------------\n",
      "Currently Generating [[1.1177]]\n",
      "Predicted bandgap: 1.14271879196167\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 130 of 256\n",
      "G Loss: 0.21397706866264343, D Loss: 0.20103324949741364, R Loss: 0.0007540572551079094\n",
      "Current number of unique samples: 5\n",
      "------------\n",
      "Currently Generating [[0.871]]\n",
      "Predicted bandgap: 0.8757418990135193\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3de5zbdZ3v8dfn90smmWvbaacFOi1tBYTSK5abIAjICoqgexOKgGfxcDhHxCMeFY5HVvDsrrAuINKVg6yoiFbwglW6i4ug6AosRS5SWqAU2g69TS9zn0xu3/PHL5nJzGRm0nbamV/m/Xw88kjyyy+/fPJL8v5980nyiznnEBGR8PPGugARERkdCnQRkTKhQBcRKRMKdBGRMqFAFxEpE5GxuuFp06a5OXPmjNXNi4iE0nPPPbfLOddQ7LIxC/Q5c+awZs2asbp5EZFQMrNNQ12mlouISJlQoIuIlAkFuohImRizHrqISKFUKkVTUxOJRGKsSxkX4vE4jY2NRKPRkq+jQBeRcaGpqYna2lrmzJmDmY11OWPKOcfu3btpampi7ty5JV9PLRcRGRcSiQRTp06d8GEOYGZMnTp1n9+tKNBFZNxQmPfZn3URukB/9q09/NOvXiWVyY51KSIi40roAv35zXv5xuMbSKYV6CIyunbs2MHy5cuZN28e73rXuzj11FP52c9+Nmi+t956iwULFoxBhcMLXaD7XlByOqs/5hCR0eOc48Mf/jBnnHEGGzdu5LnnnmPlypU0NTWNdWklC12gR7ygr5RWy0VERtHjjz9ORUUFV199de+0I488kk996lMlL+OFF17glFNOYdGiRXzkIx9h7969ANx5553Mnz+fRYsWcfHFFwPw29/+liVLlrBkyRKWLl1Ke3v7Ad+H0H1t0c8FekYjdJGyddMv1vLK1rZRXeb8I+r42w8dP+Tla9eu5YQTTjig27j88sv5xje+wZlnnsmNN97ITTfdxB133MFXv/pV3nzzTWKxGC0tLQB87WtfY8WKFZx22ml0dHQQj8cP6LYhhCP0qJ8boSvQReQg+uQnP8nixYs58cQTS5q/tbWVlpYWzjzzTACuuOIKnnzySQAWLVrEpZdeyve//30ikWAcfdppp3Hddddx55130tLS0jv9QIRwhB5sgzRCFylfw42kD5bjjz+en/zkJ73nV6xYwa5du1i2bNkBL/uRRx7hySefZNWqVXzlK19h7dq1XH/99Xzwgx9k9erVnHLKKTz22GMce+yxB3Q7oRuh53vo+tqiiIyms88+m0QiwTe/+c3eaV1dXSVff9KkSUyZMoXf/e53ANx///2ceeaZZLNZtmzZwllnncWtt95KS0sLHR0dvPHGGyxcuJAvfOELLFu2jPXr1x/wfQjdCD3iq4cuIqPPzHj44Yf5zGc+w6233kpDQwPV1dXccsstRed/9dVXaWxs7D1/++23893vfperr76arq4u5s2bx3333Ucmk+FjH/sYra2tOOf4zGc+w+TJk/nSl77EE088ge/7zJ8/n/PPP/+A70P4At1TD11EDo7DDz+clStXjjjfnDlzSKVSRS97+umnB037/e9/P2jaN77xjX0vcASha7mohy4iUlzoAl09dBGR4sIX6Oqhi4gUFbpA99VDFxEpKnSBHsnvyyWjQBcRKTRioJvZt81sp5m9PMTlZmZ3mtkGM3vJzA7st7Mj6Buhq4cuIlKolBH6d4Dzhrn8fODo3OEq4JvDzHvAouqhi8hB4vs+S5Ys4fjjj2fx4sXcdtttZIsMHsfr7nNH/B66c+5JM5szzCwXAd9zzjngaTObbGaHO+e2jVKN/aiHLiIHS2VlJS+88AIAO3fuZPny5bS2tnLTTTeNcWWlGY0e+kxgS8H5pty0QczsKjNbY2Zrmpub9+vG1EMXkUNh+vTp3HPPPdx1110E49WRlcPuc4v98V3Re++cuwe4B2DZsmX7lcgRXz10kbL3r9fD9j+N7jIPWwjnf3WfrjJv3jyy2Sw7d+5kxowZI85fDrvPbQJmFZxvBLaOwnKLimh/6CJyCJU6Oi+X3eeuAq4xs5XAyUDrweqfg3roIhPCPo6kD5aNGzfi+z7Tp08/oOUcqt3njhjoZvZD4L3ANDNrAv4WiAI45+4GVgMfADYAXcB/OaCKRqAeuogcCs3NzVx99dVcc801mBXrLPdXuPvc97znPUV3n3v66afzgx/8gI6ODnbv3s3ChQtZuHAhTz31FOvXrz/4ge6cu2SEyx3wyQOqYh/0/fRfPXQRGV3d3d0sWbKEVCpFJBLhsssu47rrris6r3afOwq0+1wROVgymUxJ82n3uaOkt4eulouISD+hC/TeHrpG6CIi/YQv0NVDFxEpKnSB7pt66CIixYQu0D3P8Ew9dBGRgUIX6AAR39MIXURkgHAGumfqoYvIqMvvPnfBggV86EMf6t3vykA1NTWHuLLShDLQfc80QheRUZfffe7LL79MfX09K1asGOuS9kkoAz3imXroInJQnXrqqbz99tslz79p0ybOOeccFi1axDnnnMPmzZsBeOihh1iwYAGLFy/mjDPOAGDt2rWcdNJJLFmyhEWLFvH666+PSs2h+6UoqIcuUu5u+c9bWL9n/agu89j6Y/nCSV8oad5MJsOvf/1rrrzyypKXf80113D55ZdzxRVX8O1vf5trr72Whx9+mJtvvplHH32UmTNn9rZw7r77bj796U9z6aWXkkwmS/6F6khCO0JXD11ERlt+Xy5Tp05lz549nHvuuSVf96mnnmL58uUAXHbZZb0/9z/ttNP4+Mc/zre+9a3e4D711FP5+7//e2655RY2bdpEZWXlqNQfyhG6r5aLSFkrdSQ92vI99NbWVi644AJWrFjBtddeu1/Lyu+h8e677+aZZ57hkUceYcmSJbzwwgssX76ck08+mUceeYT3v//93HvvvZx99tkHXH9oR+hquYjIwTJp0iTuvPNOvva1rw25E66B3v3ud7Ny5UoAHnjgAU4//XQA3njjDU4++WRuvvlmpk2bxpYtW9i4cSPz5s3j2muv5cILL+Sll14albrDGei+p38sEpGDaunSpSxevLg3pAt1dXXR2NjYe7jtttu48847ue+++1i0aBH3338/X//61wH43Oc+x8KFC1mwYAFnnHEGixcv5kc/+hELFixgyZIlrF+/nssvv3xUag5lyyUYoauHLiKjq6Ojo9/5X/ziF0Xnyw6RP48//vigaT/96U8HTbvhhhu44YYb9qPC4YVyhK4euojIYKEMdH1tUURksHAGumfqoYuUoeAfLQX2b12EMtB99dBFyk48Hmf37t0KdYIw3717N/F4fJ+uF9oPRZNpBbpIOWlsbKSpqYnm5uaxLmVciMfj/f6EuhThDHTfoys5Oj+VFZHxIRqNMnfu3LEuI9RC2XJRD11EZLBQBrrvGamMWi4iIoVCGegaoYuIDBbOQNdP/0VEBglnoGvnXCIig5QU6GZ2npm9amYbzOz6IpfPNrMnzOx5M3vJzD4w+qX2CX76rx66iEihEQPdzHxgBXA+MB+4xMzmD5jt/wAPOueWAhcD/zzahRaK+hqhi4gMVMoI/SRgg3Nuo3MuCawELhowjwPqcqcnAVtHr8TBfH0oKiIySCmBPhPYUnC+KTet0JeBj5lZE7Aa+FSxBZnZVWa2xszWHMivwSKeds4lIjJQKYFuRaYNTNNLgO845xqBDwD3m9mgZTvn7nHOLXPOLWtoaNj3anPUQxcRGayUQG8CZhWcb2RwS+VK4EEA59xTQByYNhoFFhNRD11EZJBSAv1Z4Ggzm2tmFQQfeq4aMM9m4BwAMzuOINAP2h529MMiEZHBRgx051wauAZ4FFhH8G2WtWZ2s5ldmJvts8B/NbMXgR8CH3cHcR+Yfq6Hrt1sioj0KWlvi8651QQfdhZOu7Hg9CvAaaNb2tAiXtDWz2QdEb9Yi19EZOIJ5y9FcyGuPrqISJ9wBnrBCF1ERAKhDHTfC8pOZxToIiJ5oQz0aG/LRd9FFxHJC2Wg+2q5iIgMEspAz/fQ9aGoiEifUAa6eugiIoOFMtDVQxcRGSyUga4euojIYKEM9HwPPaWWi4hIr1AGer6HrhG6iEifUAZ6RD10EZFBwhno6qGLiAwSykD31UMXERkklIEe9dVDFxEZKJSB7nvqoYuIDBTKQFcPXURksFAGunroIiKDhTLQ1UMXERkslIGuHrqIyGChDPTe3eeq5SIi0iuUga6dc4mIDBbKQM/30PUHFyIifUIZ6H0jdPXQRUTyQhno2n2uiMhg4Qx0fW1RRGSQcAa6/iRaRGSQkgLdzM4zs1fNbIOZXT/EPH9tZq+Y2Voz+8HoltmfeugiIoNFRprBzHxgBXAu0AQ8a2arnHOvFMxzNHADcJpzbq+ZTT9YBYN66CIixZQyQj8J2OCc2+icSwIrgYsGzPNfgRXOub0Azrmdo1tmf2aG75l66CIiBUoJ9JnAloLzTblphY4BjjGz/zCzp83svGILMrOrzGyNma1pbm7ev4pzfM/UQxcRKVBKoFuRaQOTNAIcDbwXuAS418wmD7qSc/c455Y555Y1NDTsa639b9Az0hn10EVE8koJ9CZgVsH5RmBrkXl+7pxLOefeBF4lCPiDRiN0EZH+Sgn0Z4GjzWyumVUAFwOrBszzMHAWgJlNI2jBbBzNQgeK+p566CIiBUYMdOdcGrgGeBRYBzzonFtrZjeb2YW52R4FdpvZK8ATwOecc7sPVtGgEbqIyEAjfm0RwDm3Glg9YNqNBacdcF3ucEiohy4i0l8ofykKEPH1tUURkULhDXTPU8tFRKRAaANdPywSEekvtIEe8YyUeugiIr3CG+jqoYuI9BPaQPfVQxcR6Se0gR7xjLR2nysi0iu0ge57Rlq7zxUR6RXaQI+qhy4i0k9oA109dBGR/kIb6Oqhi4j0F+5AVw9dRKRXeANdPXQRkX5CG+i+p/2hi4gUCm2gRzwjpR66iEivUAd6Rj10EZFe4Q10X/9YJCJSKLSBrr+gExHpL7SBHvG83r+gu/d3G3ngmU1jXJGIyNgKcaD3fW3xwTVb+PnzW8e4IhGRsVXSn0SPR35BD31XRxLPbIwrEhEZW6EN9Eiuh57OZNnblaQy6o91SSIiYyrELZfgh0V7OpM4B23dqbEuSURkTIU40IMWy462HgA6kmmy+taLiExgoQ103w8CfXtbAgDnoL0nPZYliYiMqdAGen6Eng90UNtFRCa2EAd6UPqO1r5Ab09ohC4iE1dJgW5m55nZq2a2wcyuH2a+vzQzZ2bLRq/E4iJ+kRF6QiN0EZm4Rgx0M/OBFcD5wHzgEjObX2S+WuBa4JnRLrIYv/dDUY3QRUSgtBH6ScAG59xG51wSWAlcVGS+rwC3Aokil426fA99W2uCSZVRQD10EZnYSgn0mcCWgvNNuWm9zGwpMMs598vhFmRmV5nZGjNb09zcvM/FFurtobclmNdQDajlIiITWymBXuw39b1f+DYzD7gd+OxIC3LO3eOcW+acW9bQ0FB6lUXke+jtiTRzp1b3nhYRmahKCfQmYFbB+UagcE9YtcAC4Ddm9hZwCrDqYH8wmu+hA8yYFKeqwlfLRUQmtFIC/VngaDOba2YVwMXAqvyFzrlW59w059wc59wc4GngQufcmoNScU6+5QIwrSZGbTyiEbqITGgjBrpzLg1cAzwKrAMedM6tNbObzezCg13gUCIFI/RpNRXUxaPqoYvIhFbS3hadc6uB1QOm3TjEvO898LJGlv/pPwQj9LpKBbqITGwh/qVo/0BXy0VEJroQB3phDz3XctGHoiIygYU30HMtF89gclWFRugiMuGFNtDzX1usr47he9bbQ3dO+0QXkYkptIGe76FPq6kAoDYeIZVx9KSzY1mWiMiYCXGgB6VPq4kBUBfX/lxEZGILb6D7/UfodfkddOmriyIyQYU20PM99Km5EXptPPhKfZs+GBWRCSq0gR5Vy0VEpJ/QBvrk6ihVFT7HHl4LQF1uhK6vLorIRFXST//Ho7p4lBdu/DMqIsE2ST10EZnoQjtCB3rDHApbLhqhi8jEFOpALxSPekQ8o10jdBGZoMom0M1Me1wUkQmtbAId0P5cRGRCK6tA1x4XRWQiK6tAr41H9MMiEZmwyirQ6+JRfSgqIhNWeQV6ZURfWxSRCausAr1WI3QRmcDKKtDr4lE6kxnSGe0TXUQmnrIK9COnVgFw3YMv0tmj1ouITCyh3ZdLMRcuPoK3W7r5p1+9yivb2rjv4ycyq75qrMsSETkkymqE7nnGJ886iu9feTLN7T184rtr9nuk3p3MkEhlRpyvK6l3AiIyPpRVoOe9+6hprFh+Aq/vbOd/PfRiyX8cnck67nnyDf7q7j+w6KZH+fCK/yA5zH+U3vHYayz88q948NktADjnuP/pTVx679Ocd8eTvP/2J/m3l7eNyn0SERlJWbVcCp1+9DRuOP84/m71Oj7/45dYfvJsFjdOxsv901FbIsXzm1uYXBll8azJpDNZPvvQi/z8ha0snDmJCxfP5Cd/bOI7f3iTq854x6Dlf/v3b3LHY68zrSbG53/yEq3dKV5oauGRl7Zx7GG1NE6pomlvF1d//498eMkRfPGD82mojR3q1TAk5xzdqQyVUR8zG3H+VCZLTzpLLOIR9YceBzjneG1HB09v3M2MuhgLZk5i5uTKkm7DOceOth52dfTQ1p0i4xyVUZ+aeIQZtXEmV0VLWk7h8tq60+zpSjKpMsqUfbx+Xjbr6EimiUd8or7t1zJEDoWyDXSAT7xnLk17u3jgmc089FwTdfEItfEongdNe7vJD9xPnTeV6liEx9bt4HPvfyefPOsoAFq6ktzx2Ot8aPERRH2Pbz25kV0dSVKZLKte3Mp5xx/G7R9dwrUrn+fvVq/DM7j+/GP5b2fMw8xIZbKseGIDdz2+gUf+tI3zFhzOJSfO4uR5U3v/Qq8UyXSWP73dyhPrd/JiUwtHT6/llHn1NE6poiLiEcsdIr5HZ0+atkSKCt+jvrqCiOexZW8XTXu72Lyniy17unl1RzvrtrXRnkgT8Ywp1RW8c0YtixonURuP0tKVpDOZ7g3utW+38UJTS++7lVjEY+aUShqnVFEV9fF9I5nO0pFIs3lPF2+3dPerPx71aKiN0VATo6E2xrSaGBURD8NIZjJ09mTY1dHD2q1t7OlMDrke4lGPedNqOO7wOhqnVFJZ4VMZDQ6xqEdbIs2u9h427+li3bY2NjZ3kiz4xlNtLMLsqVUcObWKOVOrOXFuPSfNqac61v9l4Jxjy55u/rh5L0++3syTrzWzqyOoy/eMyqhPPOpTE/OpjUepq4xQG8sdx6PUxaPUxiPUVeaOc+cn5c73pLM0t/fQ3NHDrvYe9nQmSWcd2awj4xxZF9zXoxpqeMf0GgzoTmUwjFhur6L5DWwyf8gEx1kH1TGf2lgUM8g6Fzw2PWm6khk6c8fB+TQRz2NGXZwZdTFm1MWZXhdjanWs5OdnJtv37ndfntOjJZt1tPekSWWyZJ1jcmVFv91qjwfZrCOVzeKZDTsYGg1WajtitC1btsytWbNmdBfauRs2PAYL/gL8vhdpa1eKx1/dwZq39pJIBU/+dzRUs+zIetZvb+OeJzeys72H//2BY/uNxjfv7uLc23/LMTNqeWt3J93JDDPq4mSd44Qjp3DbXy8mFvFJZbL8v9++wYlz6jl53tRBZb3R3MEDT2/mx89toS2RZkZdjDOPaWB7Ww+vbW+nO5Uh6hsRz8P3rDekKyIeuzuSbGvtJuuCF8zR02t4c1cnPcO0goZTG4/wjoYajj+ijplTKulIpHvD9NXt7aSzjljEozoWIZXJksk6jplRy7IjpzCjLk4ilaEtkaJpbzdvt3TTk8qSymap8D1q4xGm1cQ445gGTj9qGrs7k/ypqYXNe7p6A6y5vSfYKObqj/hGdSzClKoK5h9ex/Ez6zisLk5dZRTfM7qTGdoTaXa0Jdja0s1rOztYt62N5vaeIe/jjLoYxx1exztn1NJQG2NKVQV7u5Js3hNs1DbvDo7TWUfEM2ZOqWR6bYxYxGd3Z5Ltrd3s7Qp+zzC5KsoZRzewYGYdyXSW7lSG7mSW7lSajp4M7YkU7Yk0bd2540SKruTIn72MB75n/QK5cHpDTYwZdTGm18Wpr6oglQ02ID2pLD3pDC1dKba2dLO7YAMci3hMqoxSVxmlLrdBy2/EfLNgo+Uc6Ywjk9t4pbOO9kSalq4kiVQG3wue941TKpkztYr66hjVFT5e7rnQ0ZOmaW83TXu7gufg3u5+G20IHrN3zqjl5HlTWdw4iSnVFdTFo0RyG5yNuzp4fnML67a1sXlPF1tbEqRyy6iNR5hRF+ewujgzJsWZURunOuYTi/rEIh7xqI9vRncqQ1cyGEDsbO9hR1uCHW097O0KBnzJdJZUxpHKZEnn1rHvGcceVsvS2ZP58xMaOWH2lP163MzsOefcsqKXlRLoZnYe8HXAB+51zn11wOXXAZ8A0kAz8DfOuU3DLfOgBPqPLoN1q2DWyfAX/wKTZ5V0tUQqw6bdXbzzsNpBl33vF4+RfOZeXpr1MT795+/lHQ01+11eIpXhsXU7+PkLW3lm425m1VfxzsNqqY1FSGUd6dyDn8o4kukMPeksU6oqmFVfxTEzanjPUQ1MqorSk87wp6ZWdnX09I7SetJZ0pks1bFglJjMZNnT0UM665g5uZJZ9VXMmlLFpKrosPU5B5UV/n7fx0Mlm3Uk0hkSqXzIZqiNR6ivrihpFNSdzPDcpr08vXE3m/Z0sbMtQTKTZWp18C7i+CPqWDJrMscdXrfPI890Jkt7It0b8G2JFG3d6d7wj0U9ptUE71QaamLU11QQ9Q3fDM8MM+joSbNhZwdv7urEMyMeDe5T8Dg7KnIb/IqIR8wPjqO+h2dGe0+KjkQaB/gWDBCqYxGqYz7VFRGqYxGqKoKASmUcuzr6Aqm5PTje0ZZgR3sPO9sS7O1KEvXz7wSDd0N18ShHTA42hL5nZJ2jO5mhtbvv/gbHKVq7U721+J4R8Qyv4Lg2FmFSVQWVUY9MFnrSGbbs6WLL3u6iG5z66gpm5d4hNtZX0tD7jg/2dKbY2Z7gpaZW1m5tpcjVgSBcj2qoYfbUKmZOriSWG9W3JVJsb02wPbcOhnvHCOBZ8Ef1M+piTK+NM6WqgljUo8L3iPrBiDyae3w6etK81NTCi1taufmi4/nzExr36XmVd0CBbmY+8BpwLtAEPAtc4px7pWCes4BnnHNdZvbfgfc65z463HJHPdA3PQX3nQfHnA9v/R48H064DI56H3jRYNqeN2Dq0TDjeJjUCDXTIVoJmRRkksEhm4Epc4Lrd+7CfetsrGUTLj4Z+9DX4fgPj17NcuCyGTAP9qWvnc1CogXSPX2Pe7onWE58UnCoqN63ZY50e9lU3/OsFM4BrvRjACxX8xDH5vUdPB/MB5cN7ns6kTv09B1n07n5Cq43cDmFB+eC5bksuEzBaTf4NA78iuAQiUOkIqgnmw7WUzZNOp0kkUiSSCbIZjLEKqLEYxXEotFg3vx9yNdYeD8zSTo629i6czfdnR30dHeSwcgSob6uirnT64jHq3OPd11wnWxhzcEhlcnQk0qTLDhks1liPsQjRm3MiBhBHZGKgvsUAz8WTHOu33rNpLrJVB9GRV3Dfj2dDjTQTwW+7Jx7f+78DcHzzf3DEPMvBe5yzp023HJHNdCdg3vPgbat8KnnoGMHrP48vPnbgheQQe3h0L515OU1HAdn3QB/uAu2vwQXrYCnVsDWP/Y9eSuqoa4Rquqhsxnat0PtYXDYQqhuCKb1tAen644IHuRsJnhQE63Q0waJtuA4EoPKeqiogkwaUl2w9y3Y82bwBK+ogprDYM7pMPsUiA1+JzEiM6ieDnUzoXIKeAWj2EwaWrcEt5nqDl4gfgXE6oIne6yu7zZ72nOHtr7TyY7gCRs8GH2PSf58qhu6dhcc9gYvGK/gRRmJQfW0oMZI4YfHFqyD7r3Bdbv35JaxJzikOoN5Cl9Asdrgsa5uCB4nvyII8Lat0LYN2rcFATvs+vKDF3vNjODxq5zSF/6ZZBA6hRuEwumZZLBOM8ngdrL6aqsMcMHtsOxv9uuqBxrofwmc55z7RO78ZcDJzrlrhpj/LmC7c+7/FrnsKuAqgNmzZ79r06ZhuzLFde8NRjzVBb3qF38EP7sKLvpnWHpp3/RkJ7z1H8FoYfapUDk5CNHm9cGLumNn8KKMxMCPBi/8VBc8/U3YvSFYxl9/D+ZfFLxQ//i94HouG4Ry69tBwFQ3BC/8tq2w7cXgsuppQRB2NgcbmHzQeZEBQVkHmZ5cOHUHvf9IHCbPhvp5QW3JLtizETY/Den+HzjuHwtCL39/U12jsMyRbtKHqqnBoXJKEOTZTPDYZNPB49DZHBxckc8H4pOCjV7V1GAjWjU1OB+v67t+PlQTrcEGtmNn38gzPikI+bojguOaGcG7s/yIyo8GtSTagusnWoONQMdOaHs7OO/nnieRWN91Cqd50WCD4uWeS34kOPaifc8vP5obTZa00vreJQw38ib/TmKEkXzvSDnTNxo1g0hlUH8k3v/Y8weMWB0DR7C9h953SgPfBeRGzl5+BO313f9MKnhsMj2QTgZ1edFgvXmR4LQX6VuOywSv/X73IXcM/Wv0oxCtCgZD0argPuXrzKaDDW0qETzGidbg+oPejezDwWUGv+MrfOfXu15z6/bwRUEnYD8MF+ilfMul2PvOolsBM/sYsAw4s9jlzrl7gHsgGKGXcNuDPf8A/PuXgoCeewZs/C1s/gMctggWX9x/3opqOObP+k+L18Gsk4a/jRM+Dn96MFj58y8KpvlROPHK/SqZTLpvRLqv7YFC6SQ0rwteCPsqmw42LG1bg41ioi14IUWroKImaEFNmRMEff6dRO9ovDWY3zyI1fSN2POHippgXeX13r/ccTQO8cml3e9s7u049I3yzYJ1JyLDKiXQm4DCTxcbgUF9CzN7H/BF4Ezn3NBfQThQR58bbFHXPwK/+YdgFPu+L8MJV4zei96PwJLlo7Os/PJGQ6QCDl88Ossar7xSR68iMlApLbeNhXsAAAp5SURBVJcIwYei5wBvE3woutw5t7ZgnqXAjwlaM6+XcsOj0kPv2hO8fdcPPURkghiu5TLicMg5lwauAR4F1gEPOufWmtnNZnZhbrZ/BGqAh8zsBTNbNUq1D6+qXmEuIpJTUi/AObcaWD1g2o0Fp983ynWJiMg+UsNSRKRMKNBFRMqEAl1EpEwo0EVEyoQCXUSkTCjQRUTKhAJdRKRMKNBFRMqEAl1EpEwo0EVEyoQCXUSkTCjQRUTKhAJdRKRMKNBFRMqEAl1EpEwo0EVEyoQCXUSkTCjQRUTKhAJdRKRMKNBFRMqEAl1EpEwo0EVEyoQCXUSkTCjQRUTKhAJdRKRMhC7QnXNjXYKIyLgUukBf/eZqLv7lxdz/yv00dzXTlmxjU9smdnXvGuvSRETGVKSUmczsPODrgA/c65z76oDLY8D3gHcBu4GPOufeGt1SA5WRSrIuy63P3sqtz97a77JpldN4x6R3UB2tpipaxZF1R3Jc/XHMqptFfayeqmgVnalOOlOdTI5Npqai5mCUKCIyJmykFoaZ+cBrwLlAE/AscIlz7pWCef4HsMg5d7WZXQx8xDn30eGWu2zZMrdmzZr9Lnxjy0Z+0/QbfPOpj9fT0tPC+j3reavtLbrT3XQkO9jWuW3YZdRGazms5jAOrz6chsoGKiOVxCNxYn6MuB8n6keJWASHoz3ZTnuynazL4ns+6WyatmQbPeke6mJ1TIlPIebH8MzDNx/Pgjc/+eulsimyLkvEi1ATraE6Wh0cV1RTG60NzlcE06siVfjm96u1PdXO5rbNvN3xNlmXxTOPqBelKlpFZaSSykglVZEqPPMwMwzrPfbMwzBS2RTpbJpUNkUqmyLjMkS9KBEvQtSL9jsd8SJkXRaHw7ncAUfWZTEzfPOJeBEiXqT3/g68PaB/PRi5yQC98wA4HJlshnQ2HRxcGufcoGWaBQcPr3cd52/DY0ANZowm5xwZl+mtvXA9ixwqZvacc25ZsctKGaGfBGxwzm3MLWwlcBHwSsE8FwFfzp3+MXCXmZk7iA3veZPnMW/yvGHn6Ux18tre19jasZWWnhY6U529gbm3Zy/bO7ezrXMb2zu3s3bXWhKZBD3pHtIuXXR5+cBOZ9P45lNXUUc8Eqc12UprT+uQdVRHq4n5MQDS2TSdqc7eYJCDKx+4xcJ+0GW50wAZlyHrsmRcsJHJnx/pdgo3XAM3REPNN1L9w15eZGNS7DpFp5W4ISr1usPervU/n18HA6cVu27h9HykOAZHS7HL8qeLXs8xeFrhbRfU3O/xG6Leoeop5lMnfIoL5l0w7PX3RymBPhPYUnC+CTh5qHmcc2kzawWmAv0a22Z2FXAVwOzZs/ez5NJVR6tZOn0pS6cv3afrpbNpejI9JDNJMi6Dc47ailrikfiQ18lkM6Rdmky2LwjyNUS8/qvZOUcik6Az1UlHsoPOVCftqXY6k510pILzA59kVZEqZtfNZmbNTCJeBOccyWySrlQX3enu3kPWZfuNprNkwUHWZYn6wSi8wqsg6kcxjLRLk8r0H7nnA6zoKNsM54Jl5+fLv/vI3y7Qf3RfcFy4DgbyPb/33UHEIn23RbbfcgqXXeyy3vucuywfxAPXR34d9V6Wm5a/z/l3Ib75+J7fWxPkQsDRu4z87ecv661pwDucgfMNZ6Tx0HChNtJ8pd5e0dso8XYHzj9w/RRb1sDphdctDNXCYxh6Y1B4WbEgHmqDWXi7xU4PrGlfTa+cvl/XG0kpgV6s4oGPXinz4Jy7B7gHgpZLCbc9JvKthOpodcnX8T0fHz/4lGEEZtbbJplWOe0AKhUR6VPKt1yagFkF5xuBrUPNY2YRYBKwZzQKFBGR0pQS6M8CR5vZXDOrAC4GVg2YZxVwRe70XwKPH8z+uYiIDDZiyyXXE78GeJSgofBt59xaM7sZWOOcWwX8C3C/mW0gGJlffDCLFhGRwUr6HrpzbjWwesC0GwtOJ4C/Gt3SRERkX4Tul6IiIlKcAl1EpEwo0EVEyoQCXUSkTIy4L5eDdsNmzcCm/bz6NAb8CjVEVPvYUO2HXljrhvFd+5HOuYZiF4xZoB8IM1sz1M5pxjvVPjZU+6EX1rohvLWr5SIiUiYU6CIiZSKsgX7PWBdwAFT72FDth15Y64aQ1h7KHrqIiAwW1hG6iIgMoEAXESkToQt0MzvPzF41sw1mdv1Y1zMcM5tlZk+Y2TozW2tmn85Nrzezfzez13PHU8a61mLMzDez583sl7nzc83smVzdP8rtTnncMbPJZvZjM1ufW/enhmidfyb3XHnZzH5oZvHxut7N7NtmttPMXi6YVnQ9W+DO3Ov2JTM7YewqH7L2f8w9Z14ys5+Z2eSCy27I1f6qmb1/bKoeWagC3YI/rF4BnA/MBy4xs/ljW9Ww0sBnnXPHAacAn8zVez3wa+fc0cCvc+fHo08D6wrO3wLcnqt7L3DlmFQ1sq8D/+acOxZYTHAfxv06N7OZwLXAMufcAoLdVV/M+F3v3wHOGzBtqPV8PnB07nAV8M1DVONQvsPg2v8dWOCcWwS8BtwAkHvNXgwcn7vOP+eyaNwJVaBT8IfVzrkkkP/D6nHJObfNOffH3Ol2gmCZSVDzd3OzfRf48NhUODQzawQ+CNybO2/A2QR/Ag7jt+464AyCffTjnEs651oIwTrPiQCVuX/+qgK2MU7Xu3PuSQb/M9lQ6/ki4Hsu8DQw2cwOPzSVDlasdufcr5zr/Yf4pwn+nQ2C2lc653qcc28CGwiyaNwJW6AX+8PqmWNUyz4xsznAUuAZYIZzbhsEoQ8cnH+MPTB3AJ8H8n91PxVoKXjCj9d1Pw9oBu7LtYvuNbNqQrDOnXNvA18DNhMEeSvwHOFY73lDreewvXb/BvjX3OnQ1B62QC/pz6jHGzOrAX4C/E/nXNtY1zMSM7sA2Omce65wcpFZx+O6jwAnAN90zi0FOhmH7ZVicv3mi4C5wBFANUGrYqDxuN5HEpbnD2b2RYJ26QP5SUVmG5e1hy3QS/nD6nHFzKIEYf6Ac+6nuck78m83c8c7x6q+IZwGXGhmbxG0tc4mGLFPzrUCYPyu+yagyTn3TO78jwkCfryvc4D3AW8655qdcyngp8C7Ccd6zxtqPYfitWtmVwAXAJcW/C9yKGqH8AV6KX9YPW7k+s7/Aqxzzt1WcFHhn2pfAfz8UNc2HOfcDc65RufcHIJ1/Lhz7lLgCYI/AYdxWDeAc247sMXM3pmbdA7wCuN8nedsBk4xs6rccydf+7hf7wWGWs+rgMtz33Y5BWjNt2bGCzM7D/gCcKFzrqvgolXAxWYWM7O5BB/s/udY1Dgi51yoDsAHCD6BfgP44ljXM0KtpxO8NXsJeCF3+ABBP/rXwOu54/qxrnWY+/Be4Je50/MInsgbgIeA2FjXN0TNS4A1ufX+MDAlLOscuAlYD7wM3A/Exut6B35I0OtPEYxirxxqPRO0LVbkXrd/Ivgmz3irfQNBrzz/Wr27YP4v5mp/FTh/rNf9UAf99F9EpEyEreUiIiJDUKCLiJQJBbqISJlQoIuIlAkFuohImVCgi4iUCQW6iEiZ+P98rkAeaXQwyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 140 of 256\n",
      "G Loss: 0.21299481391906738, D Loss: 0.20156848430633545, R Loss: 0.0006353664211928844\n",
      "Current number of unique samples: 3\n",
      "------------\n",
      "Currently Generating [[1.7062]]\n",
      "Predicted bandgap: 1.6992601156234741\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 150 of 256\n",
      "G Loss: 0.2108565866947174, D Loss: 0.20159165561199188, R Loss: 0.0005497806705534458\n",
      "Current number of unique samples: 2\n",
      "------------\n",
      "Currently Generating [[0.9088]]\n",
      "Predicted bandgap: 0.9235255718231201\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 160 of 256\n",
      "G Loss: 0.21197164058685303, D Loss: 0.20140570402145386, R Loss: 0.0006428966880775988\n",
      "Current number of unique samples: 1\n",
      "------------\n",
      "Currently Generating [[1.6964]]\n",
      "Predicted bandgap: 1.6963385343551636\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 170 of 256\n",
      "G Loss: 0.2114877998828888, D Loss: 0.2017674744129181, R Loss: 0.0005806992412544787\n",
      "Current number of unique samples: 1\n",
      "------------\n",
      "Currently Generating [[1.8585]]\n",
      "Predicted bandgap: 1.8722926378250122\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 180 of 256\n",
      "G Loss: 0.21259236335754395, D Loss: 0.20119963586330414, R Loss: 0.0005781676154583693\n",
      "Current number of unique samples: 5\n",
      "------------\n",
      "Currently Generating [[1.2656]]\n",
      "Predicted bandgap: 1.2868233919143677\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 190 of 256\n",
      "G Loss: 0.2110615223646164, D Loss: 0.20134858787059784, R Loss: 0.00044895661994814873\n",
      "Current number of unique samples: 3\n",
      "------------\n",
      "Currently Generating [[0.8659]]\n",
      "Predicted bandgap: 0.8906625509262085\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 200 of 256\n",
      "G Loss: 0.21154439449310303, D Loss: 0.20133212208747864, R Loss: 0.0005510778282769024\n",
      "Current number of unique samples: 1\n",
      "------------\n",
      "Currently Generating [[0.6852]]\n",
      "Predicted bandgap: 0.6915310621261597\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 210 of 256\n",
      "G Loss: 0.21202541887760162, D Loss: 0.1997968554496765, R Loss: 0.0008092036005109549\n",
      "Current number of unique samples: 3\n",
      "------------\n",
      "Currently Generating [[1.1841]]\n",
      "Predicted bandgap: 1.196600079536438\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3deZwc5X3n8c+vqq+5R6OZEUIjIQlxGHRhZA5zGRMv4At710kAY/DGu4RdY7Imm5dhs2Ftsq/EsD5isGIvdnyEOCHOOsFyUEJiwMYHEIQtYwQSutFISDMaaUZz9VnP/lE9Mz09h1poxEyNvm+pX91d9XT1089Uf/upp6uqzTmHiIhEnzfdFRARkamhQBcRmSUU6CIis4QCXURkllCgi4jMErHpeuLm5ma3ePHi6Xp6EZFIeuGFFw4651rGmzdtgb548WI2bNgwXU8vIhJJZrZ7onkachERmSUU6CIis4QCXURklpi2MXQRkVK5XI729nbS6fR0V2VGSKVStLW1EY/HK36MAl1EZoT29nbq6upYvHgxZjbd1ZlWzjm6urpob29nyZIlFT9OQy4iMiOk02nmzp170oc5gJkxd+7cY95aUaCLyIyhMB/xRtoicoH+/K5DfP5ftpArBNNdFRGRGSVygf6L3Yd58MltZPMKdBGZWgcOHODGG29k6dKlnH/++Vx88cX8wz/8w5hyu3btYvny5dNQw8lFLtC94mZIoB/mEJEp5JzjAx/4AJdffjk7duzghRde4JFHHqG9vX26q1ax6AW6Vwx0ddBFZAo9+eSTJBIJbrvttuFpp512Gp/4xCcqXsbGjRu56KKLWLlyJR/84Ac5fPgwAA888ADnnHMOK1eu5Prrrwfgxz/+MatXr2b16tWcd9559Pb2HvdriNxui37xewL10EVmr8/8YBMv7zsypcs859R6/tf7zp1w/qZNm3jrW996XM9x88038+CDD3LFFVdwzz338JnPfIY/+7M/47Of/Sw7d+4kmUzS3d0NwOc+9znWrl3LJZdcQl9fH6lU6rieGyLcQy8o0EXkBPr4xz/OqlWreNvb3lZR+Z6eHrq7u7niiisAuOWWW3j66acBWLlyJR/+8If5q7/6K2KxsB99ySWXcOedd/LAAw/Q3d09PP14RK6HPjyGHijQRWaryXrSJ8q5557L9773veH7a9eu5eDBg6xZs+a4l/3YY4/x9NNPs27dOv74j/+YTZs2cdddd/Ge97yH9evXc9FFF/HDH/6Qs88++7ieJ3I9dH9oDF15LiJT6J3vfCfpdJqvfOUrw9MGBgYqfnxDQwNz5szhJz/5CQAPP/wwV1xxBUEQsGfPHq688kruv/9+uru76evrY/v27axYsYJPfepTrFmzhs2bNx/3a4hgDz281pCLiEwlM+PRRx/lk5/8JPfffz8tLS3U1NRw3333jVt+y5YttLW1Dd//4he/yLe//W1uu+02BgYGWLp0Kd/85jcpFArcdNNN9PT04Jzjk5/8JI2NjfzRH/0RTz31FL7vc84553Dttdce92uIYKBryEVEToz58+fzyCOPHLXc4sWLyeVy48579tlnx0z76U9/Ombagw8+eOwVPIoID7ko0EVESkUu0Id66AX10EVERoleoKuHLiIyrsgFum/ay0VEZDyRC/ThvVyU6CIioxw10M3sG2bWYWYvTTDfzOwBM9tmZi+a2fEdO3sUw0eKKtBFREappIf+LeCaSeZfC5xRvNwKfGWSssdtaMhFQ+giMtV832f16tWce+65rFq1ii984QsE45wJcKaePveo+6E75542s8WTFLkO+EvnnAOeNbNGM5vvnHt9iuo4ilf8CNKBRSIy1aqqqti4cSMAHR0d3HjjjfT09PCZz3xmmmtWmakYQ18A7Cm5316cNoaZ3WpmG8xsQ2dn5xt6Mu22KCJvhtbWVh566CG+/OUv4yrsQM6G0+eO98N3475659xDwEMAa9aseUOJ7A0PuSjQRWatf7oL9v96apd5ygq49rPH9JClS5cSBAEdHR3MmzfvqOVnw+lz24GFJffbgH1TsNxx+fpSVETeRJV2HmfL6XPXAbeb2SPAhUDPiRo/h9KfoDtRzyAi0+4Ye9Inyo4dO/B9n9bW1uNazpt1+tyjBrqZ/Q3wDqDZzNqB/wXEAZxzXwXWA+8GtgEDwH88rhodhadfLBKRN0FnZye33XYbt99+O2bjjSyPVnr63Msuu2zc0+deeuml/PVf/zV9fX10dXWxYsUKVqxYwTPPPMPmzZtPfKA75244ynwHfPy4anEMNOQiIifK4OAgq1evJpfLEYvF+MhHPsKdd945blmdPncK6FwuInKiFAqFisrp9LlTZGQMXYEuIlIqcoHuD++HPs0VERGZYSIX6ENHiqqHLiIyWvQCXT9BJyIyrsgF+vBeLuqhi4iMErlA14FFIiLji2Cgh9cachGRqTZ0+tzly5fzvve9b/i8K+Vqa2vf5JpVJnKBrgOLROREGTp97ksvvURTUxNr166d7iodk8gFuvZDF5E3w8UXX8zevXsrLr97926uuuoqVq5cyVVXXcVrr70GwN/93d+xfPlyVq1axeWXXw7Apk2buOCCC1i9ejUrV65k69atU1JnHSkqIjPOff92H5sPbZ7SZZ7ddDafuuBTFZUtFAo88cQTfOxjH6t4+bfffjs333wzt9xyC9/4xje44447ePTRR7n33nt5/PHHWbBgwfAQzle/+lV+7/d+jw9/+MNks9mKj1A9msj10HVgkYicKEPncpk7dy6HDh3iXe96V8WPfeaZZ7jxxhsB+MhHPjJ8uP8ll1zCRz/6Ub72ta8NB/fFF1/Mn/zJn3Dfffexe/duqqqqpqT+Eeyhh9fqoYvMXpX2pKfa0Bh6T08P733ve1m7di133HHHG1rW0Bkav/rVr/Lcc8/x2GOPsXr1ajZu3MiNN97IhRdeyGOPPcbVV1/N17/+dd75znced/0j10PXGLqInGgNDQ088MADfO5zn5vwJFzl3v72t/PII48A8J3vfIdLL70UgO3bt3PhhRdy77330tzczJ49e9ixYwdLly7ljjvu4P3vfz8vvvjilNQ7coHu60hREXkTnHfeeaxatWo4pEsNDAzQ1tY2fPnCF77AAw88wDe/+U1WrlzJww8/zJe+9CUA/uAP/oAVK1awfPlyLr/8clatWsXf/u3fsnz5clavXs3mzZu5+eabp6TO0RtyGRpDV56LyBTr6+sbdf8HP/jBuOWCYPwv8Z588skx0/7+7/9+zLS7776bu++++w3UcHKR66EPj6Grhy4iMkrkAt3XbosiIuOKXKCPDLko0EVmG6f39bA30haRDXQNuYjMLqlUiq6uLoU6YZh3dXWRSqWO6XGR+1J0ZMhlmisiIlOqra2N9vZ2Ojs7p7sqM0IqlRr1I9SViFygD51tUSfnEpld4vE4S5Ysme5qRFrkhlzMDDN9KSoiUi5ygQ7hwUUKdBGR0SIZ6J6ZTs4lIlImmoHuachFRKRcJAPdN9NuiyIiZSoKdDO7xsy2mNk2M7trnPmLzOwpM/ulmb1oZu+e+qqO8Mx0YJGISJmjBrqZ+cBa4FrgHOAGMzunrNj/BL7rnDsPuB7486muaCnPUw9dRKRcJT30C4Btzrkdzrks8AhwXVkZB9QXbzcA+6auimP5nunAIhGRMpUE+gJgT8n99uK0Up8GbjKzdmA98InxFmRmt5rZBjPbcDxHg3mmc7mIiJSrJNBtnGnlaXoD8C3nXBvwbuBhMxuzbOfcQ865Nc65NS0tLcde2yLPTOd7EBEpU0mgtwMLS+63MXZI5WPAdwGcc88AKaB5Kio4nnA/dAW6iEipSgL9eeAMM1tiZgnCLz3XlZV5DbgKwMzeQhjoJ+wMO76nA4tERModNdCdc3ngduBx4BXCvVk2mdm9Zvb+YrHfB/6zmf0K+Bvgo+4Ejol4ns6bLCJSrqKzLTrn1hN+2Vk67Z6S2y8Dl0xt1Sam/dBFRMaK7JGiGkMXERktkoHueYY66CIio0Uz0E0/cCEiUi6iga4xdBGRcpEMdN/TgUUiIuUiGeg6sEhEZKxoBrpnFJTnIiKjRDLQfdOBRSIi5SIZ6BpyEREZK5qB7inQRUTKRTLQfdOBRSIi5SIZ6J6nH7gQESkXzUA3I1Cgi4iMEt1A1xi6iMgokQx039Oh/yIi5SIZ6GEPfbprISIys0Q00NEYuohImUgGuq/90EVExohkoHue9nIRESkXzUA3Qx10EZHRIhnovn6xSERkjEgGuoZcRETGimag68AiEZExIhnovn5TVERkjEgGejjkMt21EBGZWaIZ6IaGXEREykQy0HUuFxGRsSoKdDO7xsy2mNk2M7trgjK/ZWYvm9kmM/vrqa3maPpSVERkrNjRCpiZD6wF3gW0A8+b2Trn3MslZc4A7gYucc4dNrPWE1Vh0IFFIiLjqaSHfgGwzTm3wzmXBR4Brisr85+Btc65wwDOuY6preZovqcDi0REylUS6AuAPSX324vTSp0JnGlmPzOzZ83smvEWZGa3mtkGM9vQ2dn5xmqMfrFIRGQ8lQS6jTOtPE1jwBnAO4AbgK+bWeOYBzn3kHNujXNuTUtLy7HWdZiOFBURGauSQG8HFpbcbwP2jVPm+865nHNuJ7CFMOBPCF9j6CIiY1QS6M8DZ5jZEjNLANcD68rKPApcCWBmzYRDMDumsqKlPJ2cS0RkjKMGunMuD9wOPA68AnzXObfJzO41s/cXiz0OdJnZy8BTwB8457pOWKW9cBRIuy6KiIw46m6LAM659cD6smn3lNx2wJ3FywnnWzHQncMbd4hfROTkE8kjRYd66DpaVERkRDQDfaiHHkxzRUREZpBIBrpfrLV2XRQRGRHJQB/qoWvIRURkRKQDXXu5iIiMiGSg+0O7LSrPRUSGRTLQi3mug4tEREpEM9C9kf3QRUQkFMlALz2wSEREQpEM9OG9XDTkIiIyLJqB7unAIhGRcpEMdB1YJCIyViQDXQcWiYiMFelAdwp0EZFhkQ70gsbQRUSGRTLQh8bQtZeLiMiISAa6p/3QRUTGUKCLiMwSkQz0oZNz/fCVDj69btM010ZEZGaIZKAPHVj0L5v288jzr01zbUREZoZoBnrxbItHBnOkc4HOiy4iQkQDfejkXD2DOQAyee2/KCISyUAfGnLpzxYASOcK01kdEZEZIZqBXuyhDxlUoIuIRDPQ/bJaK9BFRCIa6OU9dA25iIgo0EVEZo2KAt3MrjGzLWa2zczumqTch8zMmdmaqaviWEMHFg0ZzGovFxGRowa6mfnAWuBa4BzgBjM7Z5xydcAdwHNTXcly6qGLiIxVSQ/9AmCbc26Hcy4LPAJcN065PwbuB9JTWL9xefpSVERkjEoCfQGwp+R+e3HaMDM7D1jonPvHyRZkZrea2QYz29DZ2XnMlR3ia7dFEZExKgl0G2fa8LH2ZuYBXwR+/2gLcs495Jxb45xb09LSUnktyytUFugZBbqISEWB3g4sLLnfBuwruV8HLAd+ZGa7gIuAdSfyi9ExX4oq0EVEKgr054EzzGyJmSWA64F1QzOdcz3OuWbn3GLn3GLgWeD9zrkNJ6TGjDPkor1cRESOHujOuTxwO/A48ArwXefcJjO718zef6IrOJ6yPCedVw9dRCRWSSHn3Hpgfdm0eyYo+47jr9bkSodcEjGPwawCXUQkkkeKDgV6zDMaq+LaD11EhIgG+tCQS20qRlXCV6CLiBDRQB/6UrQuFaMq7msvFxERKhxDn2mGhlxqk/FwDD2nvVxERCIZ6EMHFtUlY3iezuUiIgJRHXIZ6qGnYqTiGkMXEYGI9tCHxtBrkzFyhUC7LYqIENEe+qi9XOK+DiwSESGqPXRvZAzdOR36LyICEe2hxzxjfkOK01trwx66xtBFRKIZ6GbGz+96J795fhupuKdAFxEhooEOYaibGVVxn3zgyBU07CIiJ7fIBvqQqoQP6JzoIiKRD/RkPAx0DbuIyMku8oFeNRTo2tNFRE5ysybQNeQiIie7yAd6Kh6+BA25iMjJLvKBrh66iEgo8oGeVKCLiACzINCHeugZBbqInOSiH+jaD11EBJgFgd5UkyDuG9/fuI9C4Ka7OiIi0ybygd5QFeee953Lj7Z08qUfvjrd1RERmTaRD3SAmy5cxL8/bwFf+fF29nYPTlo2Vwg4ks6NOz2j86qLSITNikA3M/771WcB8OdPbZuw3Dd+upNL73uSK+5/iv5Mfni6c46bvv4c1335Z2TyBdY+tY33PvgT3vPAT9jW0XvC6y8iMhVmRaADnNpYxW+tWch3N+zhqS0dOOfI5gN+srWT17oGeOKVA9z7jy/TUpfk8ECOf3pp//Bjv79xH8/tPMTm/b3c8NCz/J/Ht5CM+Rw4kubmv/g32g8PnJA65wrBhAdEOefoHsiOu9Wwt3uQb/1sJ7947TDBBN8bZPIFXtrbw1NbOvj5toNs7+wjP8kZKZ1zdPSm2d3VP+EyS/UM5HQwl8gMY85NzxeJa9ascRs2bJjSZb7eM8hv/d9n2HNokKaaBLlCQG86T3XCpzoRo6kmzg8+cSlXf/Fp5jdU8bFLl/CDF/fxs20Hmd9QxektNTy6cR/vOKuFv7jlbWzef4Tr/++zAPz+vzuTj1y8ePjXksr1pnP87fN7eHrrQa48q4W3LW6iOuFTlfDpTefJ5gPm1afY3dXPr9p7+MXuwzz9aie9mTzNtQnecVYr8+qTHOrP4pnxs20H2dUVfpAsbKri9JZaYp5Hz2CWX77WTb4YutUJnyXNNSxtqaWxKk4+cHQcSfPczkP0lWyFACRjHhctncvKtgYaquLUpWL0DObYtO8IP361k+6BcCiqoSrO6oWNvP30uXzo/Dbm1iaB8EPiR1s6+Yuf7uTfdh4aXmZDVZyWuiStdUla61K01oe3W0puZ/MBOw/2s/NgP73pPIFzFALH6S21LF/QQDpXIO57xHxjIFtgIJNnIFsgWwiYU50gFffI5AO6B3IcHshyqD9Lz2CO5toEZ7TWsay1lgWNVXjj/H0KxdMr+54R94+/D1MIHIf6s+QKAc21SRKxqe8XBYEjkw/wPEjG/ClfvkSXmb3gnFsz7rzIB7pz8PKjsPgyqGkmky/wvRf28uu9PYDj0mUtfLPYm/3u717MmsVNPPjEVj7/r69iBnNrkjTXJrj/Qys5ramGv3puNzddeBoN1XEAdh7s557vv8RPth7kLfPrifvG9o4+4jGPZMwjFffxPWPXwX4CBwsaq446jg9wakOKy89sYWFTNVsP9PLE5g4GsgXmVIcfRG+ZX8eVZ7WSzgVsOXCE1w4NkC84GqrirFjQwG+9bSGb9vXwqz097DjYz86DffRnCnhmNNXEOf+0OVx2Rgvz6lNk8wF7uwd5aW8PT7/ayc6ufkr/7C11SS5b1syqhY0kYh4vtnfzi93dbDnQS9w3Tm+pJRn32d7RR18mz6kNKX77bYuI+caRwTBgO3ozdBzJ0NGboas/w9FWK8/CobLj2TMpEfPI5ke2OqriPstaazltbjWBc/RnChzqz/LqgV4yxXJNNQnm1aeYV/yg8T0jm3dkCwG5fED3YJbO3gyeGQvmVLGqrZHmuiT5QsC+7kFebO/hpb099Bd/mNz3jOULGvjQ+W2cM7+O+lScg31Z1v/6dZ7b2cXO4npxSn2KM+fVcua8OubWJqhNxqlO+PRl8uzrHmRrRx/bO/s41J8lnSuQzgXDr/GdZ7Wyoq2BefUpWuuS5AoBfZk8PYM59vek2d+TZl/PIAeOZIj7xrz6FKfUp1jYVE1zbZJMPvywTOcK7DzYz66ufnIFR0tdkpbaZHhdvJzWVM3CpmpintGXyfOrPT08sfkA6VwBMyPmGc21SU5vqWXN4jnMq0+N+psMZPNs7+hnMFdgMFcgnSuQyQf4ZsR8I+4bMc+jvtgJaCl+IOYKAft70uw5PIBvxupFjeN+kDkXfphu7+xn/5H08HIbq+KsaGsgGfPJ5MP2y+QLdPUV/56eEfeMZNyjOhGjJhGjOulTm4yRivvDy03FfQLn6OzN0NmbYU5NgtNbasd05ILAsf9Imtd7BmmoSrCgsWp4F+rJ9KZzvHZogNa6FC11yWNb4YuOO9DN7BrgS4APfN0599my+XcC/wnIA53A7zjndk+2zCkL9N0/h29eC6eeBx9dD4nqMUVyhYADR9K0zQnntXcdYd2X7uD1U9/F//id64/6h3DOsf7X+/n8v26hqTrBirYGCoEjkwsYzBXI5gPOnFfLlWe3ct6iOWzZ38vurv6wp5ktUJuKkfCN13vCOqxqa6C17I0wNBwSm4Ie5NEEgaMvm+fIYI66ZHz4w6vcto5evveLvWx+/QjZQsCS5hp+4y3zuGRZ86Q93XwhoKs/S8eRDJ19aTqOZIj7Houba1jSXMOc6jhmRhA4Xn79CNs7+6hNxsgVAvKBC7ds4jGqEz5x3wuHngoBCd+jsTpOU02i2Gv3OdyfZVtnH1sP9LG1o5dtHX3s7hogEfOoSfjUV8U5a14dc4vBFn7wpNl/JKwXQNz3SMQ84r5Rl4ozrz5JEMD2zj62dvQNv65EzOOc+fWsbGvg9JZa4r5H++EBnnilgy0HRn/XkoqHW0NnnVKHb8be7kG27O9lR2c/2bKhr5hnLG6uYVlLLa31SariPsm4T1XcZ3/PIP+8aT8HinUtF/PCAJ/fkGJeQ4pcPlzXX+9J09E79jGN1XEWz60hEfM42BeGVm86P86SR1TFfWpT4e/35oNgeEsOwq3HUxuqSMQ8nIMXdh8+5mNCYp5RcG5UJyDhe9QkfWK+R8wLQzsIwkA8cpT6Hqs5xfX/8MDYnSUg/FueUp9iXn2KqoTPnkMD7Dk8OKozMbScOTUJGqvi1CRjZPPhkGp/tsCRwRxH0rnhD+r//YHl3HTRaW+ovscV6GbmA68C7wLageeBG5xzL5eUuRJ4zjk3YGb/BXiHc+63J1vulAX6P94Jv3wYCjlYchlcfDvsfBryGVh2FbScDfFqyA1AIQtNp8PG78AP7sBVz8U+9q/QtBRs/KGUijh3fI+fDvkM+ImJ613IQd8ByA2GbefFoe4UqJpT+Wst5CE/CLk0uHHe5M4BLrx2wejbrvhmMa94sZLbPngx8HzIpyHbD5leyPaFf2MvVizjF6+LjzEvrEcQQJAv3i6E185BvCpcV+JV4esvZMhmBunvHyDme1RXpfD9OPixUc/h8hn27X+dQ12dZLJZYvEUZ7c1k6pvhprm8LUE4XMFhTzpbJbBTJZ0JktVzFGf9IgZEE9BvAYSxUshC5k+yPaSSac51DdIz0CauDmqYlAdh7qEhx+LQ6I2fIwNdU4c6VyBI4NZkr5RCALiHtSlYiXtPlQuT89AjsMDGV7vHqSzN00QBFQnwiBbtbCRZCw23J6ZAF7rOMSOPfvYu38//eksWeeTcTHamhs4e2EryZoGEr6R8AKSVsDlcxSCgELgyBe3nroH83QP5MgWHL7nMbfYY8/kHZsP9JLOBQRBAS+fwYIsZhCLJ2ltqGVeUx3N9bUEXoxCocDhvkF2HejGXJ6kD3HfiMdi1CQTNNYkcBg5i5NxMbLZLOlMpnidpbMvS8EZC+bWkQvAmU9TdZxW6+ZI/wC7egI60kZ/Xz+FQpbquiZa66s4pS5GS3WM/nSa7r5BuvsGSWcy9GYd3fk4LlYFiWq8ZA3xVB1V1bXU1NWzoHkOqxfN4dTGqmN4w4443kC/GPi0c+7q4v27w/ei+9MJyp8HfNk5d8lky52SQC/k4fNnwpIrYOkV8C/3QKYnDCovDrn+sY9pexv07IXqJjiyDwYPhW/0ucvCN2nPXlh0IVQ3w8DBcHqyLgyNvgPQuz98s9fPBwwO74L9v4ZYCuavgtU3hKE3mWQ9tL4FqueGQZXugX2/DK9jVVDbArXzwnAZ6IL+zuLlYBhEQ5wL69/9WnjJp8PX7ceh/lSoXzAShpnekXLdr0G6O1xGvBpSDeHrrDslXH7nFji4FYJxeix+EuYsDssHuTDws/3F4B+6TodBHkxtT0pkVjAP3vN5WPM7b+zhkwR6rILHLwD2lNxvBy6cpPzHgH+aoCK3ArcCLFq0qIKnHkemFwYOwZzTYOePwsBb/h/gLe8Nr3f+BBZeEIbw3hega3vY04lXwWA3PHFvGDb/4WthaL+yLuyBdr4aBlDbmnAZ+ZfCwN3103B+rApqW8PQ8+Lw+othUNaeAhf+bhjyWx+H73+88tfiJ8IPkdzx7EVjUDcfGheGvbQgHy5vx4+h93WK3bCw59e4KCy38ILwdRRyYRgPHIKurdBe/IBtOQvOvBoaTyv2/KrDD4veA9C7L2zTQzsglgw/EKqbir3bmrCXGasae+1PtKrZyIcOVuyJ+yNbAaN67G6kRx0UwtcaS0KiDpLFHqqfLM7PF8sGJb3woKR3743uxZuN9PZz6bC+fjJcvp8I6xLkwmUVciM9fBeE60NVI6Qaw2UXMuEW0EBX2LbmjWxRDG81xEpuF59/+MNxALID4Qdzsm7kdZXXeeg6yIdbJ9n+4pZQse1K27SSa4auyudRbMdgpO1jyfD1phrCehVy4Wsu5ML6Z3rD1+3HRjoZ5jO8FTa0XpZupYUTRs83L+wsDf8N8uH7uZAr/h1yJe0bK3ueoGT5Qfi4fHak7UvLDq9XwcjWZG1r2O5DW5mxZPiYdE84f+g5Pb/kdqz4Hiz9W/aPbOkO3Z6/qpI39zGrJNDH274et1tvZjcBa4ArxpvvnHsIeAjCHnqFdRztua/Cj+6Dc66D7U9CVRMs+41wXrIOzn73SNnT3h5eSi2+FPa/GF4DtJ49+fMNrWiVDDMEn4XOV47eM+0/CAdfDbcQXBBulp+yMuyV5wbC3njfgfCPX9MSzq9pCT+Ahlbsofok68IVTUROepUEejuwsOR+G7CvvJCZ/Qbwh8AVzrnxv8GZCqtuDHuKv/h2+EXo+x4Ie4GVmr8yvFTqWMbGPQ/mnVtZ2WVXVb5cEZEKVDKGHiP8UvQqYC/hl6I3Ouc2lZQ5D/h/wDXOua2VPPFxj6EX8iObqiIiJ4nJxtCPuo+ccy4P3A48DrwCfNc5t8nM7jWz99lpKKAAAAi/SURBVBeL/R+gFvg7M9toZuumqO4T82MKcxGREpUMueCcWw+sL5t2T8nt35jieomIyDGaNedyERE52SnQRURmCQW6iMgsoUAXEZklFOgiIrOEAl1EZJZQoIuIzBIKdBGRWUKBLiIySyjQRURmCQW6iMgsoUAXEZklFOgiIrOEAl1EZJZQoIuIzBIKdBGRWUKBLiIySyjQRURmCQW6iMgsoUAXEZklFOgiIrOEAl1EZJZQoIuIzBIKdBGRWUKBLiIyS0Qu0J1z5IP8dFdDRGTGiVyg//Ouf+Y3f/Cb/Hzvz9nXt4+9fXunu0oiIjNCrJJCZnYN8CXAB77unPts2fwk8JfA+UAX8NvOuV1TW9VQXaKOdD7N7/7wd4enLa5fzKL6RSyqW8RlCy7j9MbTiXkx+nP9zK+dT9yLn4iqiIjMKOacm7yAmQ+8CrwLaAeeB25wzr1cUua/Aiudc7eZ2fXAB51zvz3ZctesWeM2bNjwhiqdKWR4bMdjBC4gU8jws70/4+DgQXb07CBTyIwqm/ASLJuzjGWNy6hP1FObqKU6Vk3Mi3E4fZi+XB+++XSlu/DM49SaU0n6SXzPxzlH52Ang/lBfPNpSjUxt2ouc1Nzh69r4jWYGc45Xut9jVcPv4pzjqpYFY3JRuqT9ST8BB4enufh4RG4gHQhTTqfxuFI+SlSsRRJP0nCT+CcI3BBeCEg5sVIeIlwOeaFFzzMDN98zAzDRr3uvMuTLWTJFrIAI48zD9/8UfdHXQivzUYvr1whKJALcjgchg0/ptLHi8gbY2YvOOfWjDuvgkC/GPi0c+7q4v27AZxzf1pS5vFimWfMLAbsB1rcJAs/nkCfyGB+kF8e+CV7evdQcAWqYlXs6NnB5kOb2XVkF73ZXvpz/cPlffOpidcQuIDGZCOBC9g/sJ/ABcNl6uJ1VMeryQd5ujPdFFxhSus8U5WG+9DFzMgHeXJBblQbTaQ86Mf74AGOGv6ljyktO2p66XLLFjfZ4w0bnjZUbqiehhH+D6c7HBTXaFf8B+H3OkO3y++XTh/v+UY9VyUi+jlZ2raj7pe0x0Rly9v3jTzveM9dqvzvN970MY+ZoNxEyxq6n3d5Pnn+J/nAsg9U8hLGmCzQKxlyWQDsKbnfDlw4URnnXN7MeoC5wMGyitwK3AqwaNGiiip/LKpiVbx9wdsnLRO4gMH8IPkgT228Ft/zR813zlFwheEvXlOx1KjHdme6OTR4iK50F12DXfTnRz4gWqtaObf5XOJenIHcAD3ZHnoyPWQLWRyOQlAgIMAwqmJVVMWqAEgX0mTyGdKFNNlCdnSIEoZoNgh72845AoKRHrwLhlea0hCJe/HhXv1Q3QuuMOpxpVsBQ7cLrjDcBuOVjftx4l54iXkxPPOGw720PgHB8JaGw42q56j2HucN80beKBMtb8JlufED2eFGtefQvPKwHzIqnMvCqfQx4z1f6XNV4o2G2nQbs366sR92463DQ9PLP/Qm6wAcbd0pn3/MnYVJnr+iZZkRsxhttW0TvobjUUmgj1f78jWrkjI45x4CHoKwh17Bc085zzxq4jUTzh9q8Jg3tmk882hKNdGUamIZyyZ9noZkA/OZf9z1FRGpVCV7ubQDC0vutwH7JipTHHJpAA5NRQVFRKQylQT688AZZrbEzBLA9cC6sjLrgFuKtz8EPDnZ+LmIiEy9ow65FMfEbwceJ9xt8RvOuU1mdi+wwTm3DvgL4GEz20bYM7/+RFZaRETGqmg/dOfcemB92bR7Sm6ngd+c2qqJiMixiNyRoiIiMj4FuojILKFAFxGZJRToIiKzxFEP/T9hT2zWCex+gw9vpuwoVBlDbTQ5tc/RqY0mN13tc5pzrmW8GdMW6MfDzDZMdC4DCamNJqf2OTq10eRmYvtoyEVEZJZQoIuIzBJRDfSHprsCEaA2mpza5+jURpObce0TyTF0EREZK6o9dBERKaNAFxGZJSIX6GZ2jZltMbNtZnbXdNdnJjCzXWb2azPbaGYbitOazOxfzWxr8XrOdNfzzWRm3zCzDjN7qWTauG1ioQeK69SLZvbW6av5m2OC9vm0me0trkcbzezdJfPuLrbPFjO7enpq/eYxs4Vm9pSZvWJmm8zs94rTZ/Q6FKlAL/5g9VrgWuAc4AYzO2d6azVjXOmcW12yX+xdwBPOuTOAJ4r3TybfAq4pmzZRm1wLnFG83Ap85U2q43T6FmPbB+CLxfVodfEsqxTfY9cD5xYf8+fF9+Jslgd+3zn3FuAi4OPFdpjR61CkAh24ANjmnNvhnMsCjwDXTXOdZqrrgG8Xb38beGO/SBtRzrmnGfurWRO1yXXAX7rQs0Cjmc3q3w+coH0mch3wiHMu45zbCWwjfC/OWs65151zvyje7gVeIfzt5Bm9DkUt0Mf7weoF01SXmcQB/2JmLxR/iBtgnnPudQhXTqB12mo3c0zUJlqvRtxeHDL4Rskw3UndPma2GDgPeI4Zvg5FLdAr+jHqk9Alzrm3Em72fdzMLp/uCkWM1qvQV4DTgdXA68Dni9NP2vYxs1rge8B/c84dmazoONPe9DaKWqBX8oPVJx3n3L7idQfwD4SbwweGNvmK1x3TV8MZY6I20XoFOOcOOOcKzrkA+BojwyonZfuYWZwwzL/jnPv74uQZvQ5FLdAr+cHqk4qZ1ZhZ3dBt4N8BLzH6h7tvAb4/PTWcUSZqk3XAzcU9FS4CeoY2q08mZWO+HyRcjyBsn+vNLGlmSwi/+Pu3N7t+byYzM8LfSn7FOfeFklkzex1yzkXqArwbeBXYDvzhdNdnui/AUuBXxcumoTYB5hJ+C7+1eN003XV9k9vlbwiHDXKEvaePTdQmhJvLa4vr1K+BNdNd/2lqn4eLr/9FwoCaX1L+D4vtswW4drrr/ya0z6WEQyYvAhuLl3fP9HVIh/6LiMwSURtyERGRCSjQRURmCQW6iMgsoUAXEZklFOgiIrOEAl1EZJZQoIuIzBL/H4DqT4DhauLJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 220 of 256\n",
      "G Loss: 0.210361048579216, D Loss: 0.2010033130645752, R Loss: 0.000549832358956337\n",
      "Current number of unique samples: 2\n",
      "------------\n",
      "Currently Generating [[0.5535]]\n",
      "Predicted bandgap: 0.581807553768158\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 230 of 256\n",
      "G Loss: 0.2106723040342331, D Loss: 0.20104078948497772, R Loss: 0.00042264978401362896\n",
      "Current number of unique samples: 3\n",
      "------------\n",
      "Currently Generating [[0.638]]\n",
      "Predicted bandgap: 0.6361410021781921\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3deZxcZZ3v8c+vTm29d5Luzh6SQFhCVogsgqCgI6CCvma8LCo4F4cXMyJe8XqF11y5gnMdcbioYJRBrxvqRB0Vo+DFURTUASWsJpBAErI0WbrT3em9urbn/nGqO9Vd1Z1K0p3OqXzfSb2q6tRT5zzn9Knveeqpp06Zcw4REQm+0GRXQERExocCXUSkTCjQRUTKhAJdRKRMKNBFRMpEeLIW3NDQ4ObPnz9ZixcRCaRnnnlmn3Ousdhjkxbo8+fPZ926dZO1eBGRQDKz7aM9pi4XEZEyoUAXESkTCnQRkTIxaX3oIiL5UqkUzc3NJBKJya7KMSEejzNnzhwikUjJz1Ggi8gxobm5mZqaGubPn4+ZTXZ1JpVzjra2Npqbm1mwYEHJz1OXi4gcExKJBNOmTTvuwxzAzJg2bdohv1tRoIvIMUNhfsDhbIvABfrT29q551ebSGWyk10VEZFjSuAC/dntHdz72GYFuoiMu71793LNNdewcOFCzjzzTM4991x++tOfFpTbtm0bS5YsmYQaji1wgR7KvQ3JZPXDHCIyfpxzvPvd7+aCCy5g69atPPPMM6xZs4bm5ubJrlrJghfoIT/QleciMp4ee+wxotEoN95449C0E044gY985CMlz+P555/nnHPOYdmyZbznPe+ho6MDgHvvvZfFixezbNkyrrrqKgAef/xxVqxYwYoVK1i5ciXd3d1HvA6BG7aYy3P003ki5euOn2/gpV1d4zrPxbNq+V/vOn3Uxzds2MAZZ5xxRMu49tprue+++7jwwgu5/fbbueOOO/jiF7/I5z73OV577TVisRj79+8H4O6772b16tWcd9559PT0EI/Hj2jZEMQWurpcROQo+PCHP8zy5ct5wxveUFL5zs5O9u/fz4UXXgjAddddxxNPPAHAsmXLeN/73sd3v/tdwmG/HX3eeedxyy23cO+997J///6h6UcieC10dbmIlL2xWtIT5fTTT+fHP/7x0P3Vq1ezb98+Vq1adcTzfvjhh3niiSdYu3Ytn/nMZ9iwYQO33nor73jHO3jkkUc455xz+PWvf82pp556RMsJYAvdv86qy0VExtFFF11EIpHgq1/96tC0vr6+kp9fV1fHlClT+P3vfw/Agw8+yIUXXkg2m2Xnzp285S1v4fOf/zz79++np6eHLVu2sHTpUj75yU+yatUqNm7ceMTrELgWumeDLXQFuoiMHzPjoYce4mMf+xif//znaWxspKqqirvuuqto+U2bNjFnzpyh+1/4whf49re/zY033khfXx8LFy7km9/8JplMhve///10dnbinONjH/sY9fX1fOpTn+K3v/0tnuexePFiLr300iNeh8AFuvrQRWSizJw5kzVr1hy03Pz580mlUkUfe+qppwqm/eEPfyiYdt999x16BQ8icF0uNjTKZXLrISJyrAlcoHshdbmIiBQTuEBXl4uISHHBC3QNWxQRKSp4ga5hiyIiRR000M3sG2bWYmbrR3nczOxeM9tsZi+a2ZF9d/YgNGxRRKS4Ulro3wIuGePxS4FFucsNwFfHKHvEBk/6ntXZc0VknHmex4oVKzj99NNZvnw599xzD9kiYXOsnj73oOPQnXNPmNn8MYpcAXzH+WfLesrM6s1spnNu9zjVcRh1uYjIRKmoqOD5558HoKWlhWuuuYbOzk7uuOOOSa5ZacajD302sDPvfnNuWgEzu8HM1pnZutbW1sNamIYtisjR0NTUxAMPPMCXv/zlks/uWg6nzy32w3dF19459wDwAMCqVasOK5E1bFHkOPDLW2HPX8Z3njOWwqWfO6SnLFy4kGw2S0tLC9OnTz9o+XI4fW4zMDfv/hxg1zjMtygNWxSRo6nU1nm5nD53LXCTma0BzgY6J6r/HNSHLnJcOMSW9ETZunUrnufR1NR0RPM5WqfPPWigm9m/AW8GGsysGfhfQATAOXc/8AhwGbAZ6AP+9ohqdBBDwxbVRBeRCdTa2sqNN97ITTfdNDS6biz5p89905veVPT0ueeffz7f//736enpoa2tjaVLl7J06VKefPJJNm7cOPGB7py7+iCPO+DDR1SLQzA0bFF5LiLjrL+/nxUrVpBKpQiHw3zgAx/glltuKVpWp88dB+pyEZGJkslkSiqn0+eOEw1bFBEpLnCBbhq2KCJSVOACfbCFrga6iMhwgQv0wT50tdBFRIYLYKCrD11EpJgAB/okV0RE5BgTvEDP1VgtdBEZb4Onz12yZAnvete7hs67MlJ1dfVRrllpAhfo+oELEZkog6fPXb9+PVOnTmX16tWTXaVDErhA17BFETkazj33XF5//fWSy2/fvp2LL76YZcuWcfHFF7Njxw4AfvSjH7FkyRKWL1/OBRdcAMCGDRs466yzWLFiBcuWLePVV18dlzoH7puiGrYoUv7u+vNdbGzfOK7zPHXqqXzyrE+WVDaTyfCb3/yG66+/vuT533TTTVx77bVcd911fOMb3+Dmm2/moYce4s477+TRRx9l9uzZQ104999/Px/96Ed53/veRzKZLPkbqgcTuBa6vvovIhNl8Fwu06ZNo729nbe97W0lP/fJJ5/kmmuuAeADH/jA0Nf9zzvvPD74wQ/yta99bSi4zz33XD772c9y1113sX37dioqKsal/oFroesHLkTKX6kt6fE22Ife2dnJO9/5TlavXs3NN998WPMa7B6+//77+dOf/sTDDz/MihUreP7557nmmms4++yzefjhh3n729/O17/+dS666KIjrn/wWujqchGRCVZXV8e9997L3XffPepJuEZ64xvfyJo1awD43ve+x/nnnw/Ali1bOPvss7nzzjtpaGhg586dbN26lYULF3LzzTdz+eWX8+KLL45LvYMX6IPfFFWii8gEWrlyJcuXLx8K6Xx9fX3MmTNn6HLPPfdw77338s1vfpNly5bx4IMP8qUvfQmAT3ziEyxdupQlS5ZwwQUXsHz5cn7wgx+wZMkSVqxYwcaNG7n22mvHpc6B63LRsEURmSg9PT3D7v/85z8vWi6bzRad/thjjxVM+8lPflIw7bbbbuO22247jBqOLXAtdNMvFomIFBW4QPf0I9EiIkUFLtA1bFGkfDm9rocczrYIXKDrm6Ii5Skej9PW1qZQxw/ztrY24vH4IT0veB+KatiiSFmaM2cOzc3NtLa2TnZVjgnxeHzYj1CXInCBrmGLIuUpEomwYMGCya5GoAWuy0U/cCEiUlxwA1196CIiwwQw0P1r5bmIyHCBC/QD49CV6CIi+QIX6PqmqIhIcSUFupldYmabzGyzmd1a5PF5ZvZbM3vOzF40s8vGv6oHeCFTl4uIyAgHDXQz84DVwKXAYuBqM1s8otj/BH7onFsJXAV8Zbwrmi9kGrYoIjJSKS30s4DNzrmtzrkksAa4YkQZB9TmbtcBu8avioVCZupDFxEZoZRAnw3szLvfnJuW79PA+82sGXgE+EixGZnZDWa2zszWHcm3wUJm+qaoiMgIpQS6FZk2Mk6vBr7lnJsDXAY8aGYF83bOPeCcW+WcW9XY2Hjotc0Jmc7lIiIyUimB3gzMzbs/h8IuleuBHwI4554E4kDDeFSwmFBIXS4iIiOVEuhPA4vMbIGZRfE/9Fw7oswO4GIAMzsNP9An7Aw7ITMNWxQRGeGgge6cSwM3AY8CL+OPZtlgZnea2eW5Yh8H/s7MXgD+Dfigm8BzYGrYoohIoZLOtuicewT/w878abfn3X4JOG98qzY6DVsUESkUuG+KwuAoFwW6iEi+wAb6KD+6LSJy3ApooKvLRURkpGAGuoYtiogUCGaga9iiiEiBQAa6hi2KiBQKZKCb+tBFRAoEMtA1bFFEpFAgA93TsEURkQKBDHR1uYiIFApkoHshdbmIiIwUyEAPmel86CIiIwQz0DVsUUSkQDAD3dA3RUVERghooOur/yIiIwUy0DVsUUSkUCADXcMWRUQKBTLQNWxRRKRQIANdwxZFRAoFM9A1bFFEpEAwA91Ql4uIyAgBDXTTh6IiIiMENtA1bFFEZLiABrq+KSoiMlIgA93Tj0SLiBQIZKBr2KKISKFABroZqIEuIjJcSYFuZpeY2SYz22xmt45S5r+Y2UtmtsHMvj++1RxOXS4iIoXCBytgZh6wGngb0Aw8bWZrnXMv5ZVZBNwGnOec6zCzpomqMGjYoohIMaW00M8CNjvntjrnksAa4IoRZf4OWO2c6wBwzrWMbzWH07BFEZFCpQT6bGBn3v3m3LR8JwMnm9kfzewpM7uk2IzM7AYzW2dm61pbWw+vxmjYoohIMaUEuhWZNjJNw8Ai4M3A1cDXzay+4EnOPeCcW+WcW9XY2HiodR2iPnQRkUKlBHozMDfv/hxgV5EyP3POpZxzrwGb8AN+Qpjp5FwiIiOVEuhPA4vMbIGZRYGrgLUjyjwEvAXAzBrwu2C2jmdF84UMskp0EZFhDhrozrk0cBPwKPAy8EPn3AYzu9PMLs8VexRoM7OXgN8Cn3DOtU1UpdXlIiJS6KDDFgGcc48Aj4yYdnvebQfckrtMOH1TVESkUCC/KRoy0zdFRURGCGig60eiRURGCmSgqw9dRKRQIANdwxZFRAoFMtA1bFFEpFAgA11dLiIihQIZ6INdLk6hLiIyJJCB7pl/ehnluYjIAYEM9FDudGEauigickAwAz2X6OpHFxE5IJiBri4XEZECAQ10/1rncxEROSCQge6py0VEpEAgA91yXS76XVERkQMCGeherstFLXQRkQMCGega5SIiUiiQgT7Y5aJx6CIiBwQy0PVNURGRQoEM9MFhi/c99io/eHrH5FZGROQYEcxAzyX6j9Y18//W75nk2oiIHBuCGei5LpeBdJZESmMXRUQgoIHu5dU6kc5MXkVERI4hgQz0wRY6oBa6iEhOIAPd8gJ9QC10EREgoIHu5Qe6WugiIkBAAz10IM9JpNRCFxGBoAZ6KL8PXYEuIgIlBrqZXWJmm8xss5ndOka5vzEzZ2arxq+KhYZ9KJpWl4uICJQQ6GbmAauBS4HFwNVmtrhIuRrgZuBP413JkfK7XDJZRyqjUBcRKaWFfhaw2Tm31TmXBNYAVxQp9xng80BiHOtXVH6XC/hfMBIROd6VEuizgZ1595tz04aY2UpgrnPuF2PNyMxuMLN1ZrautbX1kCs7KL/LBdSPLiICpQW6FZk2dJ5DMwsBXwA+frAZOececM6tcs6tamxsLL2WI3gKdBGRAqUEejMwN+/+HGBX3v0aYAnwOzPbBpwDrJ3ID0ZH9Ljo26IiIpQW6E8Di8xsgZlFgauAtYMPOuc6nXMNzrn5zrn5wFPA5c65dRNSYwr70NVCFxEpIdCdc2ngJuBR4GXgh865DWZ2p5ldPtEVLGZkH7q+/i8iAuFSCjnnHgEeGTHt9lHKvvnIqzW2kV0u+vq/iEgZfFMUdApdEREIaqDnulxq4/4bDH0oKiIS0EAfHLbYUB0D9KGoiAgENNAHPxOdVh0F1EIXEYGABrqX60OfWuUHuka5iIgENNAH+9CnVg12uaiFLiIS0ED3r6dVDXa5qIUuIhLMQM8lenU8TDQc0rBFERGCGui5LpeqWJh4OKQvFomIENBAn1oZpTLqcWJjFfGIpy4XERFK/Or/saauMsKGO96OmRGLhPQDFyIiBLSFDmC5bpd4WC10EREIcKAPUpeLiIivDAI9pHHoIiKUQaDHwp6GLYqIUAaBHo9o2KKICJRBoMciaqGLiEAZBHo87KmFLiJCOQR6JKRRLiIilEGgxzQOXUQEKINAj0dCJPRNURGR4Af69No4mazjNy/vneyqiIhMqsAH+pVvmMvimbX89x+9wN6uxGRXR0Rk0gQ+0OMRj/uuWUl3Is39j28Zs2zPQJpUZnj3TDbr6E+qD15Egi/wgQ5wYmM1ly+fxQ+e3klnX6rg8f5khlt//CIr7/wV//SLl4Y99j9+/CJvvedxOvtSfHrtBv7qC4/z9999Rh+0ikjglEWgA3zoTQvpS2b4l19tZCCdIZ3J8rtNLWzb18vq325mzdM7mVlXwUPP7yKZ+xB1w65O/v2ZZl7f38+7v/JHvvWf25hWFeOX6/dw20/+QjbrxrWOfcl00Xk659jflyx495BMZ/npc838asOeUX8Iu6UrwVNb2/jdphb+0tw55oEokcqwpbXnoO9IkuksXYnUuK+/iEysQJ4PvZjFs2q5ctVcvvvUDn723C5CIaOzP0VNLMxAJsu7V8ziXctncf231/GTZ5t5obmTp7e1U1cR4fxFDTz84m4uXz6LL121gvse28w9//EKu/b38y9/s5x50yqLLnNHWx9ffXwLiVSGvz5jDtNrY1TGwgykMvQOZJheG2NzSw/rtnfw+1dbWbe9g3DIWD6nnnNPnEZbb5KQwR83t/Havl7M4JTpNcyZUkE669i4u5s9uc8FouEQCxuqWDS9hvqKCAPpDJtbenh2x/5hdfJCxpnzpnDWgqnUV0aoiYfZ0znAH7fsY922drIOwiFj6Zw63njiNK45+wRm11cAsL8vyQ/X7eRfH986VLe6ighNNXGm18WZWetfz6iNM7MuzvTaOFOrojR39LG5pYc9XQkyWUcm6zipqZrTZ9XRn8pQEfHIZB19yTS9yQx9A2mqYmFq4mEG0lk6epO09SZp60nSn8qwsLGK02bUsmh6NfGIV7DdU5ksmawj6oWGfo7wcGSzjrbeJJmso7EmhncE8yo27/5UhnjEG9f5iozFnJucVtiqVavcunXrjnxGnc2w/T9h6XvBjN+/2sqvNuxlIJ3hnIXT+NfHt9Lc0cevP34h06pirPqn/6ArkSYWDjG9Ns7fv/lELlsyk+//eQfvP2ceNfEIzjl+tK6Zz/ziJTLOcca8Kbywcz+eZ8TCIT+gnGNnez+xcIhoOER3Ij1mNU+dUcNbT5tOKpPlsY0tvNrSw7SqKBnnWNRUzVtPm053Is0Lzftp700SMmNmXZyrz54HwJNb2nh1bzevtvTQl8wQ8YyZdRW89bQmVs6bQjzi0dqd4MXmTh7b2MKmvd3k/2lPmV7Dxac1sbCxmq2tPfz5tXae2+kfDE6eXoMXglf29JDMZDn/pAYuPLmRrkSK9t4kLd0D7O1KsLszwb6eAcbaZULmn6s+c5it+4hnpDJuaF7zG6o4ZXoNITO6B9K0dCV4taVnKNBn1MWZURdnVl2cimiYZDpLMpMlmc7Q1pOkvS9JLOyxbHYdi6ZXE4t4tHYleL65k+d2dAz93eKREBcsauSvz5zD3Cn+AfzXL+/lNxtbeHl3F5msY1Z9nMUzazlleg31lVFq4n57qKV7gFf2drNxdzct3Qn6kpmhH11pqI7xjqUzWNhYzaz6CiKe0Z1I09mfYndnP6939PP6/n72dg1QXxlhVl0Fs6dUMH9aJVnn/35uMp1lS2sPW1t7CJkxozZOU22c6bUxZtTGmT2lgkVNNcQjIXqTGZ7a0sbjr7SSzjrMoCrqMXdqJWeeMIXTZtQOOwh29qV4ra2X/mSGRCpDXzJD1jkiXoiIZ4S9ELXxMDPq4jRUx4h4IXoG0uxo62Nvd4LTZ9XSVBMv+Dtms47mjn5e2dtNf8rfX2MRj6Wz65hSGSWR8pfXn8qwtytBZ38qt8wQVdEw1fEwVTGPmliEiqg3dPCtiYfpTqTZ25UgkcqweFYtldHh7dJUJsvO9j46+lLMrq+gqSY25oHfOce+niS79vfT1jvAvKlVzKiLUxnxDqnBkM5k6U9l8ELmv1a6B+hLZehP+tu1OhbmpKZqTphWScQ7/M4RM3vGObeq6GOlBLqZXQJ8CfCArzvnPjfi8VuADwFpoBX4r8657WPNc9wCfe1H4NnvwLu+BGd+sODhRCpDZ3+K6bX+TvfAd79HzytPcP4HP8tZC6eNOetd+/u5/Wfr2bqvl3MXTiMcMgbSWXqTGbJZx/K5dVy+fDY18TBPb2unO5GmdyBNLBKiIhJmT2c/JzRUccbcKdRVRobm65wjmckSCxe2PsdLNuvoHkjT1Z+ioTpGRbRwWa/v72fNn3fwYnMnDjhtRg2Xr5jF6bPqRp1vKpOltXuAPV0J9nQmaOtNMrs+zkmNNcyqjxP2QmSzjvW7OtnW1kdV1KM/lSEcMqpiYSqjYSoiHj0DaXoGUkQ9j6lVURqqo0ypihIyY0d7Hxt3d/Hynm427eni1b09mEF1LMyUqiiLZ9ZSHQ/T1Z9md2c/u/cn2NXZz0A6S9TzD7ARz5hSGaWhJkZPIs1zOzroyoX34DuhlfOmcOqMGkIhY/Pebta+sIuOEZ/BnHnCFFbOrScaDrG9vY+Xd3fx2r7egoParLo4p8yoYc6USiqjHhVRj3jE49ntHTz+SmvRX9XyQv5Be3Z9BdNr43T0JdndmWBne19B+bqKCCc2VgGwt2uAlu7E0IGvmOpYmIqoh3PQnUgNza++MsLJTTVEwyGyzvH0tvYx5zNS/gF3UH1lhHBo8ABgZLPQ3uu/2xoPUyojpDP+/jySF/IPcLPq41TFwmxv62NHe9+wBkU0HKKpJsbUqih1FRGSaT94e3Kvj/19KdJFGiCxcIgTG6s5samagVSGVCZLY02MvV0D7Ozoo6M3STr3jjSddUNduQcT8Yw7r1jC1WfNO6ztcUSBbmYe8ArwNqAZeBq42jn3Ul6ZtwB/cs71mdnfA292zl051nzHJdCzGbj7ZOhvh1AE3v6/IZ2Alo1w0kUwcwVEqyDZCyEPaueQ/fIbCHVshYs+BW/6uP/qPhzOHf5zj5b0AHjR4vXMZqFnr79tkj0QqYDaWRCrOfh8nYNMClJ9/jJwhY/j/GuXzd3O5i657Wah4ZdQ2L+AX6eBbv+S6st73POvB+/j/H0gm4Fs2r+4jL+MSAVEqyEcg/QALp2gt6+XVDJJVUWcaDQOXm6Z5kHIY6Cvmx2vN9PT2U4ajxOaptLU2AQ10wHLLSNDOp2kPzFA70ASMhlq40alB4Tj/v42eEn1w0A3LtlDZ28/+zr7yGTTVIehMmLURsGLRCFaA7Fqfxk4stksHX1JQuZwWYcXgtpYGGNwu0I2m6U7kaS9N8nuzj527+8nlc5QEQkxp76C5XPrCIcjYB7OQrT2JNj0WjPbXn+dru5eEs5jwIVZMH0qJ8+dTqSihrjniIcyhLJpMpkU6Ywj4xw9Axna+1J09qdIZqAi6jG9toLaiiib9vbQ0Zckk8limQFCmQHMjHg8zowptcxqqKOqIk7aGX39Cbbs6SCVShL3IBL2iIZD1FfGqI5HSTkjaTG/9T6QIDGQJJEYoKUniYVCzJxSTX/aqIhFaYwkqUh3sLUjza5eo6MnQSbVT3XdNGbWxZlVG6U26ujo6ae9q5ee/oS/Hskw6XAlFq0kFKshVllDvKqW+ro6Zk2toSGa5PW9++jq7aO3cx+J1q3s6MrSFOqmkiSbB+pI18xiVk2ExngWLwReKEQoFKIilKLGkqQIU1NdRXXDXJi2kNp0O5XZXnp7e2je18XOzgHOWLacJSefdFgv6yMN9HOBTzvn3p67f5v/mnX/PEr5lcCXnXPnjTXfcQn07U/CNy+By+6G9T+GHU/602O1MNBVWP6E82D7H6FpMbTkjkd18yBeC507Yf6bwIv4YdV4iv94byt07YbEfqiZ6QfAvlf958fr4ORL4JRL/bAZTfV0aDrNf5FnM/5z27b4YVPV6M83Wgk9rdCzB3paoL9j+DwSndDxGrRvg0zSD+pIBUxd6NcD/On7d0DHNr9sfwdgfrDVTIfGU/3Abn8N9q73g3ykWJ1f16oG//Fknx+wqV7/dqrfD1mnUUAiB+cfpAu84x54w/WHN8cxAr2UD0VnAzvz7jcDZ49R/nrgl6NU5AbgBoB58w7v7QbJXj/wpi6Ajb/wg23ZlfCGD8HOP/vB2LQYdj0HrRv9FmS0yg/yZ7/jP/ahX8OzD0JfG7S9CgM9fmt+6+8OtNo2Puy3JCsboHamH5otLwPOb8m+8SY/gF96CF74fml1j1bnwvAwT1VQPR2mLPDXJ5PyW9g7nvS3Cc6vd91cmDIfTn8P1MyCzIC/fp07/YPIQDfUz4UV10DDyRCv97dZqt//PKJzJ+xZ74d+tMp/rLoJIpX+7UilfyCJVPi3wzG/xVxgsBVu/u2Qd6DcUAs+12of1sJ2fms1VuNfIpXDy7jsgbLYgZZ9KHSgtW3mr0+yB9JJv47huH8d8vznZlL+ZbBVn834y6qc6v+ts2l/3+nv8Lfz0LK8EdfhA+uWTkKyO/eupw8icX8dotV57y7ynm8eZFP+3yfZ428T7MA2K/maItPJ2265g2+8DiqmQDjqr3t6wG8EJHty72IjfoPGix5ooAz+rYb93fKvOVDvSIX/XMht32TukvK3cSjiv768qL/uQ/tAbn7ZtP93w3Kvw8iBd235f6dsxt8Xqxr9dUj1+fMLxw405AafO/h6DkX8ZaRyDZRil2za3+dj1X4do9X+aymThIqp/mNdu6DrdX87RSrytgu551Tl9p0EdGyH9q1QN9t/nUUq/Hq4LDSdWvrr/hCUEujF+hWKNuvN7P3AKuDCYo875x4AHgC/hV5iHYd78ivw+F1+q3jTL2HR2/wWNsC8vOPMnFX+ZdCyK2HeuTBjmb/Rz7lx7OVks35IHMwl/wz7x/i4wDk/KPdt8g8A0SqYdhJMP91/Mfe0QvcuPwCqp/vhWTPD3wEGA9ByL5Zo1cHrIyITp3YmcGZpZeefP6FVKaaUQG8G5ubdnwPsGlnIzN4K/CNwoXNuYHyqV8TK9/sB+vz34LR3wTu/WNrzzPxWaalKCXOAinr/MpZZK4B3lr5sEZHDUEqgPw0sMrMFwOvAVcCwZMz1m/8rcIlzrmXca5mvdiZc8WW/ZVzKB3giIseJgzZDnXNp4CbgUeBl4IfOuQ1mdqeZXZ4r9i9ANfAjM3vezNZOWI0HKcxFRIYp6ZuizrlHgGzk27YAAAiqSURBVEdGTLs97/Zbx7leIiJyiMrmXC4iIsc7BbqISJlQoIuIlAkFuohImVCgi4iUCQW6iEiZUKCLiJQJBbqISJlQoIuIlAkFuohImVCgi4iUCQW6iEiZUKCLiJQJBbqISJlQoIuIlAkFuohImVCgi4iUCQW6iEiZUKCLiJQJBbqISJlQoIuIlAkFuohImVCgi4iUCQW6iEiZUKCLiJSJwAW6c45UNjXZ1RAROeYELtAf3fYo7137XtbtWce2zm3sT+yf7CqJiBwTwqUUMrNLgC8BHvB159znRjweA74DnAm0AVc657aNb1V9tdFa+tJ9/O2jfwuAZx7LGpfRVNnE2TPP5qwZZ1EXraMr2cXMqplEvMhEVENE5JhjzrmxC5h5wCvA24Bm4GngaufcS3ll/gFY5py70cyuAt7jnLtyrPmuWrXKrVu37rAq3Zfq4+dbfk4sHGNb5zaea3mO3b272d27e1i5aCjKKVNP4aT6k6iN1lITraEiXEEym2Rf/z4Mo3Ogk7RLM6d6DjEvRjgUJpFJ0NrXSsZlqInW0FjRSENFA42VjTRWNFIdqcbMSGVTrN+3nr29e4l4Eepj9dTH6qkIV+CZRygUIkSIdDZNIpMgkU4Q8SLEvTgV4QpiXgyArMv6F7JEQ1FiXoyIF8EwPPMwM0IWImQhDBu2jslMkkQmQcZl/GXmyoUsNHS/2HSz4fPJN9itlc6mDyzXjBAHbovI5DCzZ5xzq4o9VkoL/Sxgs3Nua25ma4ArgJfyylwBfDp3+9+BL5uZuYMdLQ5TZaSSK08dfrxwzvFKxytsbN9IV7KL6kg1Wzu3sn7fev7w+h/oSfXQn+4fKl8TqSFLlppoDZ55PLrtUbIuC4BhTI1PJRwK05XsGva8cmFYQfCbGclMsqTPKELmH6wGDzYjDzTAmMGfXz6/3LDp+fMcdtOKPs+woWmDt4fKYvj/DYeD3J7pBv/ldlWXeyD//uC0kcsZdl1k/YusdGCM3JYjpw2bnpvm3PBtdSjLyp9P/rRB+fPNj5XRlleszJjzcMP/1vl/58HXCTDs9sHqUyz+vJCHYfzDin/g0gWXFq37kSgl0GcDO/PuNwNnj1bGOZc2s05gGrAvv5CZ3QDcADBv3rzDrHJxZsYpU0/hlKmnjFomlUmRyCTwzKMyUjnsMeccGZchlU0RDoWJhCJD03tTvbT2t7Kvfx+tfa30pnv9ZWKcPOVkTqg9gVQ2RddAF+2JdhKZBFmXJZPNkHVZwqGw3yIPx4bqkEgnSGQSw4LV8Fv9yUySZCZJFr/l7pwbasXD8J0m6vktes88f5kuM7Qug/eH3gGMcX/weVEv6l9CUbyQN/QiHSzvnBu1XsO2Z5EX2sjQLLg9yvG/WJmR0/KDeGRID5YffGxk2AMF4V9wsDArWE6W7Kh1HmsdjnUj1xPG2N555UYe3EY7oJcatoN/q0GlHPhHW+bBDhrD/v55f+vBfXzwNeBwZLPZgoNzKfUcmodz1MXqitbzSJUS6MW20Mi9s5QyOOceAB4Av8ulhGWPq4gXGbVP3cwIW5hwKFwwvTpaTXW0mgV1C8acf0NFAwtZOG71FRE5FKWMcmkG5ubdnwPsGq2MmYWBOqB9PCooIiKlKSXQnwYWmdkCM4sCVwFrR5RZC1yXu/03wGMT1X8uIiLFHbTLJdcnfhPwKP6wxW845zaY2Z3AOufcWuD/Ag+a2Wb8lvlVE1lpEREpVNI4dOfcI8AjI6bdnnc7Abx3fKsmIiKHInDfFBURkeIU6CIiZUKBLiJSJhToIiJl4qDncpmwBZu1AtsP8+kNjPgW6nFK2+EAbQuftoOvnLfDCc65xmIPTFqgHwkzWzfayWmOJ9oOB2hb+LQdfMfrdlCXi4hImVCgi4iUiaAG+gOTXYFjhLbDAdoWPm0H33G5HQLZhy4iIoWC2kIXEZERFOgiImUicIFuZpeY2SYz22xmt052fY4mM9tmZn8xs+fNbF1u2lQz+w8zezV3PWWy6znezOwbZtZiZuvzphVdb/Pdm9s/XjSzMyav5uNrlO3waTN7PbdPPG9ml+U9dltuO2wys7dPTq3Hn5nNNbPfmtnLZrbBzD6am37c7RMjBSrQcz9YvRq4FFgMXG1miye3VkfdW5xzK/LG2N4K/MY5twj4Te5+ufkWcMmIaaOt96XAotzlBuCrR6mOR8O3KNwOAF/I7RMrcmdGJfe6uAo4Pfecr+ReP+UgDXzcOXcacA7w4dz6Ho/7xDCBCnTyfrDaOZcEBn+w+nh2BfDt3O1vA++exLpMCOfcExT+AtZo630F8B3newqoN7OZR6emE2uU7TCaK4A1zrkB59xrwGb810/gOed2O+eezd3uBl7G/13j426fGClogV7sB6tnT1JdJoMDfmVmz+R+cBtgunNuN/g7OtA0abU7ukZb7+NxH7kp15Xwjbwut+NiO5jZfGAl8Ce0TwQu0Ev6Meoydp5z7gz8t5AfNrMLJrtCx6DjbR/5KnAisALYDfyf3PSy3w5mVg38GPhvzrmusYoWmVZW22JQ0AK9lB+sLlvOuV256xbgp/hvofcOvn3MXbdMXg2PqtHW+7jaR5xze51zGedcFvgaB7pVyno7mFkEP8y/55z7SW7ycb9PBC3QS/nB6rJkZlVmVjN4G/grYD3Df6D7OuBnk1PDo2609V4LXJsb2XAO0Dn4NrwcjegLfg/+PgH+drjKzGJmtgD/A8E/H+36TQQzM/zfMX7ZOXdP3kPaJ5xzgboAlwGvAFuAf5zs+hzF9V4IvJC7bBhcd2Aa/if6r+aup052XSdg3f8Nvzshhd/aun609cZ/e706t3/8BVg12fWf4O3wYG49X8QPrpl55f8xtx02AZdOdv3HcTucj99l8iLwfO5y2fG4T4y86Kv/IiJlImhdLiIiMgoFuohImVCgi4iUCQW6iEiZUKCLiJQJBbqISJlQoIuIlIn/D2zIJ05Ct3D7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 240 of 256\n",
      "G Loss: 0.207736998796463, D Loss: 0.2017054408788681, R Loss: 0.00034975199378095567\n",
      "Current number of unique samples: 2\n",
      "------------\n",
      "Currently Generating [[0.9538]]\n",
      "Predicted bandgap: 0.9456529021263123\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n",
      "Currently on epoch 250 of 256\n",
      "G Loss: 0.20833705365657806, D Loss: 0.2016279399394989, R Loss: 0.00045503737055696547\n",
      "Current number of unique samples: 1\n",
      "------------\n",
      "Currently Generating [[0.8133]]\n",
      "Predicted bandgap: 0.8326677083969116\n",
      "Samples not in database :100\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Saving current model\n"
     ]
    }
   ],
   "source": [
    "epochs_gan = 256\n",
    "batch_size = 64\n",
    "\n",
    "batches = 78035 // batch_size\n",
    "\n",
    "D_Losses = []\n",
    "G_Losses = []\n",
    "R_Losses = []\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "tol_time = 0\n",
    "\n",
    "with tf.Session(config=session_conf) as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    \n",
    "    for e in range(epochs_gan+1):\n",
    "        a = time.time()\n",
    "        D_Losses_ = []\n",
    "        G_Losses_ = []\n",
    "        R_Losses_ = []\n",
    "\n",
    "        for b in range(batches):\n",
    "            idx = np.arange(b * batch_size, (b + 1) * batch_size)\n",
    "            train = X_train[idx,:,:,:]\n",
    "            batch_y = Y_train[idx,:]\n",
    "            batch_z = np.random.normal(0, 1, size = (batch_size, 128))\n",
    "\n",
    "            G_loss_, _ = sess.run([G_loss, opt_g], feed_dict = {y: batch_y, z: batch_z})\n",
    "            R_loss_, _= sess.run([R_loss, opt_r], feed_dict = {real_data: train, y: batch_y})\n",
    "            D_loss_, _ = sess.run([D_loss, opt_d], feed_dict = {real_data: train, y: batch_y, z: batch_z})\n",
    "            D_Losses_.append(D_loss_)\n",
    "            G_Losses_.append(G_loss_)\n",
    "            R_Losses_.append(R_loss_)\n",
    "        \n",
    "        b = time.time()\n",
    "        delta_t = b - a\n",
    "        tol_time += delta_t\n",
    "        \n",
    "        D_Losses.append(np.mean(D_Losses_))\n",
    "        G_Losses.append(np.mean(G_Losses_))\n",
    "        R_Losses.append(np.mean(R_Losses_))\n",
    "        \n",
    "        if e % 10 == 0:\n",
    "            print('Currently on epoch {} of {}'.format(e, epochs_gan))\n",
    "            print('G Loss: {}, D Loss: {}, R Loss: {}'.format(np.mean(G_Losses_), np.mean(D_Losses_), np.mean(R_Losses_)))\n",
    "            samples = []\n",
    "            scores = []\n",
    "            sample_y = np.random.uniform(0.48, 1.95, size = (1, 1))\n",
    "            sample_y = np.round(sample_y, 4)\n",
    "            sample_y_ = sample_y * np.ones([100, 1])\n",
    "            sample_z = np.random.normal(0, 1, size = (100, 128))\n",
    "                \n",
    "            gen_sample_, pred_score = sess.run([gen_sample, gen_pred], feed_dict = {y: sample_y_, z: sample_z})\n",
    "\n",
    "            samples = np.array(gen_sample_)\n",
    "            samples = samples.reshape([100,5,5,1])\n",
    "            samples = np.where(samples < 0, -1, 1)\n",
    "            \n",
    "            S_ = samples.tolist()\n",
    "            not_in = 0\n",
    "            for s_ in S_:\n",
    "                if s_ not in X_train.tolist():\n",
    "                    not_in += 1\n",
    "            \n",
    "            print('Current number of unique samples: {}'.format(np.shape(np.unique(samples[:,:,:,0], axis = 0))[0]))\n",
    "            print('------------')\n",
    "            print('Currently Generating {}'.format(sample_y))\n",
    "            print('Predicted bandgap: {}'.format(np.mean(pred_score)))\n",
    "            print('Samples not in database :{}'.format(not_in))\n",
    "            print('------------')\n",
    "            \n",
    "            if e != 0:\n",
    "                plt.plot(G_Losses)\n",
    "                plt.plot(D_Losses)\n",
    "                plt.plot(R_Losses)\n",
    "                plt.legend(['G Loss', 'D Loss', 'R Loss'])\n",
    "                plt.show()\n",
    "\n",
    "        if e % 10 == 0:\n",
    "            print('-----------')\n",
    "            print('Saving current model')\n",
    "            saver.save(sess, \"cGAN_model.ckpt\")\n",
    "            with open('cGAN_Losses.pickle', 'wb') as f:\n",
    "                pickle.dump((G_Losses, D_Losses, R_Losses), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average running time: 73.23594409227371\n"
     ]
    }
   ],
   "source": [
    "print('average running time: {}'.format(tol_time / 256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from cGAN_model.ckpt\n",
      "Model has been loaded!\n",
      "predicting: 0/1219\n",
      "predicting: 50/1219\n",
      "predicting: 100/1219\n",
      "predicting: 150/1219\n",
      "predicting: 200/1219\n",
      "predicting: 250/1219\n",
      "predicting: 300/1219\n",
      "predicting: 350/1219\n",
      "predicting: 400/1219\n",
      "predicting: 450/1219\n",
      "predicting: 500/1219\n",
      "predicting: 550/1219\n",
      "predicting: 600/1219\n",
      "predicting: 650/1219\n",
      "predicting: 700/1219\n",
      "predicting: 750/1219\n",
      "predicting: 800/1219\n",
      "predicting: 850/1219\n",
      "predicting: 900/1219\n",
      "predicting: 950/1219\n",
      "predicting: 1000/1219\n"
     ]
    }
   ],
   "source": [
    "saver = tf.train.Saver()\n",
    "batch_size = 64\n",
    "\n",
    "batches = 78035 // batch_size\n",
    "preds = []\n",
    "\n",
    "with tf.Session(config = session_conf) as sess:\n",
    "    saver.restore(sess, 'cGAN_model.ckpt')\n",
    "    print('Model has been loaded!')\n",
    "    for b in range(batches):\n",
    "        if b % 50 == 0:\n",
    "            print('predicting: {}/{}'.format(b, batches))\n",
    "        \n",
    "        idx = np.arange(b * batch_size, (b + 1) * batch_size)\n",
    "        data = X_train[idx,:,:,:]\n",
    "        predicted, _ = sess.run(regressor(real_data, reuse = True), feed_dict = {real_data: data})\n",
    "        \n",
    "        preds.extend(predicted.reshape([-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fractional MAE: 0.04206729962905385\n"
     ]
    }
   ],
   "source": [
    "preds = np.asarray(preds)\n",
    "print('Fractional MAE: {}'.format(np.mean(np.abs((preds - Y_train[:78016].reshape([-1])) / Y_train[:78016].reshape([-1])))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataframe for regressor predicted training data\n",
    "\n",
    "reg_out = {}\n",
    "\n",
    "reg_out['X'] = []\n",
    "reg_out['y'] = Y_train[:78016].reshape([-1])\n",
    "reg_out['y_pred'] = preds\n",
    "\n",
    "for x in X_train[:78016,:,:,:]:\n",
    "    xx = int('0b'+''.join(list(np.where(x<0, 0, 1).reshape([-1]).astype(str))), 2)\n",
    "    reg_out['X'].append(xx)\n",
    "    \n",
    "reg_out = pd.DataFrame(reg_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_out.to_csv('Reg_55_20200103.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#============================================#\n",
    "#===============GENERATION===================#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 0/5\n",
      "INFO:tensorflow:Restoring parameters from cGAN_model.ckpt\n",
      "------------\n",
      "Progress: 1/5\n",
      "INFO:tensorflow:Restoring parameters from cGAN_model.ckpt\n",
      "------------\n",
      "Progress: 2/5\n",
      "INFO:tensorflow:Restoring parameters from cGAN_model.ckpt\n",
      "------------\n",
      "Progress: 3/5\n",
      "INFO:tensorflow:Restoring parameters from cGAN_model.ckpt\n",
      "------------\n",
      "Progress: 4/5\n",
      "INFO:tensorflow:Restoring parameters from cGAN_model.ckpt\n",
      "------------\n",
      "Progress: 5/5\n",
      "INFO:tensorflow:Restoring parameters from cGAN_model.ckpt\n",
      "------------\n"
     ]
    }
   ],
   "source": [
    "OUT = pd.DataFrame()\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "for K in range(5 + 1):\n",
    "    print('Progress: {}/{}'.format(K, 5))\n",
    "    with tf.Session(config=config) as sess:\n",
    "        saver.restore(sess, 'cGAN_model.ckpt')\n",
    "        print('------------')\n",
    "        Samples = []\n",
    "        labels = []\n",
    "        Inception_scores = []\n",
    "        gen_preds = []\n",
    "\n",
    "        for i in range(200):\n",
    "            labels_pred = []\n",
    "            sample_y = np.random.uniform(0.45, 2.00, size = (1, 1))\n",
    "            sample_y = np.round(sample_y, 4)\n",
    "            sample_y_ = sample_y * np.ones([1000, 1])\n",
    "\n",
    "            sample_z = np.random.normal(0, 1, size = (1000, 128))\n",
    "            gen_sample = sess.run(generator(z, y, reuse = True), feed_dict = {z: sample_z, y: sample_y_})\n",
    "\n",
    "            gen_sample = np.where(gen_sample < 0, -1, 1)\n",
    "            gen_score = sess.run(R_loss, feed_dict = {real_data: gen_sample, y: sample_y_})\n",
    "\n",
    "            Inception_scores.extend(list(np.unique(gen_score)))\n",
    "\n",
    "            gen_pred_,_ = sess.run(regressor(real_data, reuse = True), feed_dict = {real_data: np.unique(gen_sample, axis = 0)})\n",
    "            gen_preds.append(gen_pred_)\n",
    "\n",
    "            # print('------------')\n",
    "            gen_sample = np.array(gen_sample)\n",
    "            gen_sample = gen_sample.reshape([1000,5,5,1])\n",
    "            Samples.extend(list(np.unique(gen_sample, axis = 0)))\n",
    "            labels.extend(list(sample_y * np.ones(shape = (1, np.shape(np.unique(gen_sample[:,:,:,0], axis = 0))[0]))))\n",
    "\n",
    "    labels_ = []\n",
    "    for l in labels:\n",
    "        for j in range(len(l)):\n",
    "            labels_.append(l[j])\n",
    "            \n",
    "    Gen_preds_ = []\n",
    "    for l in gen_preds:\n",
    "        for j in range(len(l)):\n",
    "            Gen_preds_.append(l[j])\n",
    "            \n",
    "    Samples_ = np.where(np.array(Samples)<0, 0, 1)\n",
    "    \n",
    "    summary = {}\n",
    "    summary['X'] = []\n",
    "    summary['require_label'] = []\n",
    "    summary['predicted_label'] = []\n",
    "\n",
    "    for s, l, gen_p in zip(Samples_, labels_, Gen_preds_):\n",
    "        summary['X'].append(int('0b'+''.join(list(s.reshape([25,]).astype('str').tolist())), 2))\n",
    "        summary['require_label'].append(l)\n",
    "        summary['predicted_label'].append(gen_p[0])\n",
    "        \n",
    "    out = pd.DataFrame(summary)\n",
    "    out = out.sort_values('X').reset_index().drop('index', axis = 1)\n",
    "\n",
    "    OUT = pd.concat([OUT, out], axis = 0, ignore_index = True)\n",
    "    \n",
    "    with open('summary.pickle', 'wb') as f:\n",
    "        pickle.dump(OUT, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fractional MAE: 0.08767840019011998\n"
     ]
    }
   ],
   "source": [
    "r = OUT.require_label.values\n",
    "p = OUT.predicted_label.values\n",
    "\n",
    "print('Fractional MAE: {}'.format(np.mean(np.abs((p - r) / r))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen_out = {}\n",
    "\n",
    "gen_out['X'] = []\n",
    "gen_out['y_true'] = Y_train.reshape([-1])\n",
    "\n",
    "for x in X_train:\n",
    "    xx = int('0b'+''.join(list(np.where(x<0, 0, 1).reshape([-1]).astype(str))), 2)\n",
    "    gen_out['X'].append(xx)\n",
    "    \n",
    "gen_out = pd.DataFrame(gen_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "OUT = pd.merge(OUT, gen_out, how = 'left')\n",
    "OUT.fillna(-1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "OUT.to_csv('generated_55_0129.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2090, 4)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OUT.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(OUT.X.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('summary.pickle', 'rb') as f:\n",
    "    OUT = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=====================================================#\n",
    "#========================LOSS=========================#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('cGAN_Losses.pickle', 'rb') as f:\n",
    "    G, D, R = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Losses = {}\n",
    "Losses['G Loss'] = np.asarray(G)\n",
    "Losses['D Loss'] = np.asarray(D)\n",
    "Losses['R Loss'] = np.asarray(R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Losses = pd.DataFrame(Losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Losses.to_csv('55_Losses_01072020.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}