Ch5 - CNN Training on CSTR data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import multiprocessing\n",
    "import tensorflow as tf\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from domain_adaptation import CNNClassifier\n",
    "from domain_adaptation.utils import EvalOnDomains\n",
    "from utils.preprocessing import feature_normalization\n",
    "\n",
    "plt.style.use('science')\n",
    "\n",
    "plt.rcParams['mathtext.fontset'] = 'custom' \n",
    "plt.rcParams['mathtext.rm'] = 'Bitstream Vera Sans' \n",
    "plt.rcParams['mathtext.it'] = 'Bitstream Vera Sans:italic' \n",
    "plt.rcParams['mathtext.bf'] = 'Bitstream Vera Sans:bold' \n",
    "plt.rcParams['font.size'] = 16 \n",
    "plt.rcParams['mathtext.fontset'] = 'stix' \n",
    "plt.rcParams['font.family'] = 'STIXGeneral' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cores = multiprocessing.cpu_count()\n",
    "num_folds = 5\n",
    "use_gpu = False\n",
    "\n",
    "if use_gpu:\n",
    "    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)\n",
    "    config = tf.ConfigProto(gpu_options=gpu_options)\n",
    "    config.gpu_options.allow_growth = True\n",
    "else:\n",
    "    config = tf.ConfigProto(intra_op_parallelism_threads=num_cores,\n",
    "                            inter_op_parallelism_threads=num_cores, \n",
    "                            allow_soft_placement=True,\n",
    "                            device_count = {'CPU' : 1,\n",
    "                                            'GPU' : 0}\n",
    "                        )\n",
    "\n",
    "session = tf.Session(config=config)\n",
    "tf.keras.backend.set_session(session)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Source-Only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "crossval_index = np.load('./data/crossval_index.npy')\n",
    "data_mat = np.load('./data/cstr_rawdata.npy')\n",
    "X = data_mat[:, :-4]\n",
    "y = data_mat[:, -4]\n",
    "d = data_mat[:, -3]\n",
    "X = X[np.where(d == 0)[0]]\n",
    "y = y[np.where(d == 0)[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1040, 1400, 1) (1040, 13) (260, 1400, 1) (260, 13)\n"
     ]
    }
   ],
   "source": [
    "Xtr, Xts, ytr, yts = train_test_split(X, y, train_size=0.8, stratify=y)\n",
    "\n",
    "Xtr = feature_normalization(Xtr)[..., np.newaxis]\n",
    "ytr = tf.keras.utils.to_categorical(ytr)\n",
    "\n",
    "Xts = feature_normalization(Xts)[..., np.newaxis]\n",
    "yts = tf.keras.utils.to_categorical(yts)\n",
    "\n",
    "print(Xtr.shape, ytr.shape, Xts.shape, yts.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/eduardo/.conda/envs/OptimalTransport/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n"
     ]
    }
   ],
   "source": [
    "cnn = CNNClassifier(input_shape=(1400, 1), n_classes=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1040 samples, validate on 260 samples\n",
      "Epoch 1/100\n",
      "1040/1040 [==============================] - 15s 14ms/sample - loss: 1.5682 - acc: 0.5712 - val_loss: 0.3654 - val_acc: 0.8538\n",
      "Epoch 2/100\n",
      " 768/1040 [=====================>........] - ETA: 1s - loss: 0.2384 - acc: 0.9219"
     ]
    }
   ],
   "source": [
    "hist_dict = cnn.fit(Xtr, ytr, batch_size=256, epochs=100, validation_data=(Xts, yts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f34e04420d0>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, figsize=(9, 4))\n",
    "\n",
    "axes[0].plot(hist_dict.history['loss'])\n",
    "axes[0].plot(hist_dict.history['val_loss'])\n",
    "\n",
    "axes[1].plot(hist_dict.history['acc'])\n",
    "axes[1].plot(hist_dict.history['val_acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'cmat' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-45d9db9f4845>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maspect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auto'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'binary'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_yticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'cmat' is not defined"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2EAAAGxCAYAAADrrb45AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAABi2klEQVR4nO3de3Sb+X3f+c8XvEnUDaQ0kqi5UeDc7BnJNiX5fqEt0Ek3bZpkqFGSrRsn9ZDZze42PUnFqD275my3nZLpyXYvaUo625NNetqdkSZtd7t7WhOy6Xh8iyTGFmcczzgDccYzoqQZUdCNEm/47R94AIEQSAIggAcg3i8dHuJ58Dw/fPmI5MMvfr/f92fOOQEAAAAAyiPgdwAAAAAAUEtIwgAAAACgjEjCULXMbNjMrpqZM7MTZtbjd0yrMbNeMzvr02uHves0bGaD3kfIe+6YHzEBAADUonq/AwAK5ZzrM7OYpLBz7ojf8eTojKRguV/UzAYlhSQ965yLpe3vMbNOSX2ShsodFwAAQC0iCUO1u+J3APlwzo1LGi/na3oJWDBbouqcO+k9DwAAgDJhOCKwjnm9XL2S+pc7xjm37HMAAAAoPpIwoIzMrNPMwmV8yUFJkfQhiMuIJB+YWdBL3rTSPgAAABSG4YioGRmFOw5Jej5zfpT3sFWJ4XtDac91SvqKt3lEUnIe1REl5lp9RYm5XgckJZOsbkmDzrmo10ZI0nHv+ZaMdlc8Ny2OQUlveMdHvVglSc65kSxfdlgr9IKlGUyLZ1DSwYwYs+1b7nr0el+nJB1xzkW8c3oknZB00jl3ZKX/D++5mPdcUFK3c64vh68DAACg4pGEoSZ41f/G0xKCcUmnlEh8kn/0R9KSgF4zO5ZMxJxz42b2rBJJRMibS9UtqdV77oiks5IOOudOem3EJA0rkVDJORf12jifjCvXc719g5KuJJMtMxuV1O+1EczyNSf3RTOfy5RM9tLiyYyxz4sxfd9y12PIzKKS+pLX2zvnpJkNOef6V/r/SCZn6ed6rw8AALAuMBwR656XjBzPSAiSvVPJIXZHdbf3RkoMzzua0VRUiV6vM14bfWnJS1SJHpszacefUaL3KCXbsMBcz5XUo6VFPUaV6H3K2m6hlmlrOsu+la7HSUkH05NDryfwhRz+P0JK6+HznCjwywEAAKg49IRhXfP+4A9Lms4yFyuS9vhZ7/iwEglRq7KXko+tlPCsJRnK4dzM57ev1p6ZSYmkplRWuh4vKjE0MTmss9PrDevRyv8fJyWd9XrkTkh6cZmhlgAAAFWJnjCsW14CdlCJJCTmnItkfPR7JeOlRNJ1Skr14kSyNpq9RygptoZwczn3eSXmXCWF5c3lWkFEiflWK1rDQtcrXY9heT11GVb8//B6xfbqbk/fVRaTBgAA6wlJGNazkBJJwrhWXyA5Ob/qnuQr23wrn8QknfDmq/UqUfRitfle/ZLCOXwNmcP/1iyZ4KZVhExe2xX/P8ys0zkXc84NOecOKFEMpM8bzggAAFD1SMKwnh1VYt7SGUmhzD/ik2XXvf2hjAQsfV5SOUvKr6TT6zEa8T5yKbgxLmlEK/SYZSRIScGM7UIToGRvWDBt2OKK/x+SjqYnjd55g2uIAQAAoKKQhKHaZZ0X5SUWx7welZgSiUBmIvKMl6RMe+cE054L6l7L9hZ552Y7J7jacbmeK2l7Ib1B3mLMMTM7kdkj5iU9wSwJXTTj2INZ4sml92xE0jMZ8cS08v+HtLRIinRv4RIAAICqZc45v2MACmJmw0r8gR/U3eIPUmLNqrAkOecs7fgeJeZHnfZ2pZek71GiHPyoEsP+zuhuIvCC93nQa3dE0nD6cDvv2B7vuX4lem0y97V6bfRIGnHO9eV6rldkIyjpqu7OH0sOtezPpVfMS0yTc7TSr8H4Msd26m55+3El1icbV2JeWnC565GlrRPOuSNZ9mf9//CGWp7xrkPMe61YtqGiQCGS33veGxSrHXtMaWvyUSQGAFAMJU/Ccr3ZeceFlKiMNq1EVbWTufxxCax3Xg9Yn5YuaBxSIlEalHSgmGXqgfUo7c2FbknR1RYA99bmO522ft+SbQAAClWy4YhmFvbeQezT6kURpLu9BG8osVBslAQMSOmT9EJ6ouWci3p/DA6rcuatARXLm1M5pKXr7a2kNyPhSq3NBwDAWpRsnTBv6FDEzLYrtyRMSlRBayX5Au4xqkShkWx/PB5SYkgggCJJW8g93bR4wwMAUAQVtViz9y5/zOcwgIrjnIuYWfr8FOnugtL9DEUEiq5V966DF5MSxXT4mQMArEVFJWHehPxpeX9cesNGAOhu77LfcQA1IphlXzIpaxVvGAIA1qCSkrCIpOm0ogPDZtZLJSoAgA9iuncZhuR2Zg8ZAAB5qZgkLMs8sFElCnXck4T9zM/8jLt165Ykqb29Xe3t7QW95uTkZMHnVktbxW6vVmKrla+z2O3VSmy18nUWu71C25qcnNTk5KQk6Rvf+Mb/55z7maIEtLJp3dsbFpRSQ+eX+OhHP+o2bNggaW33paRKuO7V1Fax26Mtf9ujLX/bo63c20i7N33XOfexvBpwzpX0Q4lEaniVY4KSnBJDEJP7wonw7j3+y1/+siuGYrVTyW0Vu71aia1Wvs5it1crsdXK11ns9orRlqQBV4b7knfc1YztsKTRbMd+5jOfWfPXlq7Srnult1Xs9mjL3/Zoy9/2aCt/ksZcnveikpWoL0BmcYGQci8j7Luurq6KbKsU7RVTpV63WrlmxW6vVq5bJf8fFFslX7diM7OQt2Zl0kjGdrcSS0KUXKVe90r+P6zUr7NSr1kl/x6rhWtWyde/mCr1mlXC9SrHYs2DSvRw9WXsD0nqdHcXwTzm0gpxmNmoEu9U3rMo5sDAgBsYGFhzbGNjYxXxn1BNuGaF4boVhuuWv/V+zczsOefcQIHndirRm9WnxPyu5yVFnHPj3vO9ko4457rTzklWJA1Jirll5il/8YtfdH/0R39USFg1a71/r5YC1yx/XLP8cc3yZ2b/p3Pui3mdU6okLN+bnZkFJfUqMRm6Q9LpbAmYVLwkDABQXdaShJUS9yUAqF2F3JtKuVjzuBLDCbOWmffeTRxJ244tdywAAAAArBeVNCesrP7kz97Q9dvzfocBAAAAoMbUbBL20nff1Dd+eNHvMAAAAADUmJpNwsL79yhybsrvMAAAAADUmNpNwva1KTIxpVJXhwQAAACAdDWbhD2+Z6ucc3p96rrfoQAAAACoIVWZhE1OTmpgYEBjY2MFt2FmDEkEgCri/c5v9zcKAADWriqTsPb2dg0MDKx5IbnkkEQAQOXzfudP+hsFAABrV5VJWLF85v279L0fv6vbcwt+hwIAAACgRtR0EratuVH7H27Vyz+67HcoAAAAAGpETSdhkhTe38a8MAAVY3x8XGamvr4+DQ0Nqb+/X2amI0eOpLZbWlo0NDRU8Gv09fWpr6+v5OcAAIDs6v0OwG/d+9r0q3/wbQ36HQgASJqentaJEyfU09OT2jc0NKS+vj6Fw2FJ0vHjxzUyMlLwaxSSTJGAAQBQPDWfhO17qEXXZuY0+e5Ntd+32e9wANS4WCy2JAHLJhgMKhgMFvwanZ2dZTkHAABkV/PDEQMB0+Gn2nSKKokAKkAoFMrpuIMHDxbUfiwW0/j4uMbHx0t6Tq0pxtIpAIDqU+jyKTXfEyZJ3fvb9NL33tLf+dyjfocCoMbl2uM0PT2tAwcO6ODBg+ru7tYLL7ygo0ePqqenR9FoNJUwjY6Oqq+vL9Xu9PS0nn/+ecViMY2Ojmp8fFzPPvusQqFQasjh6OioOjo61NvbW/A5khSNRjU8PKxDhw7p9OnT6ujoSD2Xftx6kFw6BQBQWwpdPoUkTNJnn9qt3/yj05pbWFRjfZ3f4QAooi1/+9/4HYJu/PEvF73NcDisvr4+DQ4OanBwcEkP2pEjR3T8+HH19PSos7NTBw4c0NWrVyUplTgNDiZmwnZ2dur48ePq7+9XKBRSKBRSOByWmaUSpULOScZx4sQJhUIhBYNBDQ4OanR0tOjXAgCAalOVSVhy2EdXV9eaF2yWpB1bNuixtq363o/f06fet2vtAQKoGKVIgCpFa2trKsFJ70E7depUas5YMjmLRqPLDnXMPDYpFostO/csl3PGx8fV2tqaOu7MmTO5fmlZFTrkAwCASlOVSVgphn2E97dp9NwUSRiAqpItSQoGg4pEIopGo6kkaDWZyVQuhT9WO6enp0eRSEQ9PT0aHx/XM888k1Msyyl0yAcAAJWm5gtzJIX3tSkyccHvMABgzbq7uxWNRtXb27tqpcVSOnr0qKanp3Xy5ElJ0vDwsG+xAABQSaqyJ6wUDoS26+0rM5q6OqO2lma/wwGAgoyPj+vMmTNL5l7FYjFJ0smTJ5dNyqanp5dsJ89ZyWrnnD59OjWPDAAA3EVPmKe+LqDPPrlbp1656HcoACApsUjzkSNHJEn9/f0aGhpSNBqVlEi2hoeHFYlENDQ0lEqAOjs79cwzz2hoaEiRSESRSETDw8MaHBxUMBjU+Pi4BgcHdebMGY2MjKS2x8fHU+309/enXjNZaTHfcySpo6NDLS0t6ujoUEdHh7q7u1O9YgAA1DJzzvkdQ94GBgZcKUoB/8mfvaFTE1P6o9/4ZNHbBoBaMj4+rhdeeEHHjx9XMBhULBZTNBrVs88+m6qYWAgze845N1DcaNeuVPclAEDlK+TeRE9YmvC+Nn3tlYtajMf9DgUAqtqZM2d06NChVLGOZAXHgwcP5jTUEQCA9Yw5YWnaWpp1f2uzzkan9eFHdvgdDgBUrd7eXo2MjGhoaCiViMViMXV3d+e8IDUAAOsVSViG8P42Rc5dIAkDgDVKX7gZAADcxXDEDN379mh0YsrvMAAAAACsU1WZhE1OTmpgYEBjY2NFb/ujj+3Q6xeu68qN2aK3DQAonPc7v93fKAAAWLuqHI7Y3t6uUlWhaqyv0yef2Kmvvzqlno+2l+Q1AAD56+rqkqRJf6MAAGDtqrInrNS69+/R6DmGJAIAAAAoPpKwLA7va9OpiSnF49W3hhoAAACAykYSlsXenZu1ZWODXvlJzO9QAAAAAKwzJGHL6N7fptFzF/wOAwAAAMA6QxK2jPC+PYpQqh5AmY2Pj8vM1NfXp6GhIfX398vMdOTIkdR2S0uLhoaG/A4VaUpZtRcAULkKrdxbldURy+GTT+zUr/z+y7p+e15bNzb4HQ6AGjE9Pa0TJ06op6cntW9oaEh9fX0Kh8OSpOPHj2tkZKTg1xgZGWEh5SIrZdVeAEDlKrRyLz1hy2huqtdHHtmhb/zwot+hAKghsVhsSQKWTTAYVDAYLPg1zp49W/C5AABg7UjCVhDev0cRStUDKKNQKJTTcQcPHsy77Vgspr6+Pk1PT+d9LgAAKB6GI64gvK9Nf/DV1+Sck5n5HQ6AGtDZ2ZnzcbFYTM8//7wOHTqkaDSqzs7O1JDFoaGh1DGnT5/W0aNHFY1GNT09rfHxcQ0NDSkYDDIsEQAAH1RlEpacAN3V1ZUch1kSj+/ZKuecXp+6rsf3bCvZ6wAoHfvq5/0OQe7zXy1JuwcOHNDo6Giq96yjo0Nnz57Viy++qFAolErIkkMX04c5Hjt2rCQxlVKhk58BAKg0VZmElWsCtJmlhiSShAHVqVQJkN9Onjwpaenwxc7OTkUiEYVCodSww3A4nErGql2hk58BAKg0zAlbRXhfG6XqAVScaDSqYDCoSCSS+jh06JCCwaDC4bCGh4c1Ojqqjo4OHThwQLFYzO+QAQCAhyRsFZ95/y5978fv6vbcgt+hAEBKcu5YsqcrHA7r2LFjCofDikQiCofDOnHihJxzCofDWUvaR6NRjY+Plzt0AABqXsmTMDPrMbPBHI895h3fa2YVMVt8W3Oj9j/cqpd/dNnvUAAgJTnEMBqNpvbFYjGNj49rfHxckUgktf/o0aOpx6FQKHVOspgHAAAor5LNCTOzsKROSd2SoqscLi9RO+2cO5ncNrOe5LafwvvbFDk3pe79e/wOBUANGRoa0unTpyVJ/f39Onr0qHp6elLzwE6dOpWqjpjU09OjM2fOKBqNpuaNRaPRVCGOzs5OHTx4cE2LPQMAgLUpWRLmnItIipjZdknBHE7pdc71p22PSuqX5HsS1r2vTb/6B99WTt15AFAkq1UwDAaDGhy89zfTamXnh4eH1xQXAABYm4qYE2Zm2cbDTEuqiJJe+x5q0bWZOU2+e9PvUAAAAABUuYpIwiS1KpF0pYtJkpkFyx1MpkDAdPipxJBEAAAAAFiLSknCgln2JZOy1jLGsazu/W0aPXfB7zAAAAAAVLlKWaw5pnuTreR2Zg+ZJicnU4s1d3V1JRfwLKnPPrVbv/lHpzW3sKjG+rqSvx4AIGFsbExjY2PJzXb/IgEAoDgqJQmb1r29YUFJcs7FMg9ub29PJWHlsmPLBj3WtlXfff09ffr9u8r62gBQy9LfbHvuuecmfQ0GAIAiqIjhiM65cXlzwNK0Sorce7R/wvvbNDrBkEQAAAAAhfMtCTOzkJn1pO0aydjullRRdZTD+yjOAQC4V3KYfNqwSQBADfB+77fne14pF2vuVKLEfI+kVjN7Q1LE6/WS99wReeuAOef6zeyYl4iFJL1RCQs1pzsQ2q53pmc0dXVGbS3NfocDAKgQfgyTBwD4zxsuP5nveaVcrHlc0rikoWWeH5E0krEv67GVor4uoM8+uVuRiSl94dMdfocDAAAAoApVxJywahLez5BEAAAAAIUjCctTeF+bvv7qRS0sxv0OBQAAAEAVIgnLU1tLs+5vbdbZ6BW/QwEAAABQhUjCChDe36bIBEMSAQAAAOSvKpMwv0sBd+/bw7wwACizQssAAwBQaUpWHbGU/C4F/NHHduj1qet678Yd7diywbc4AKCWFFoGGACASlOVPWF+a6yv0yef2Kmvv3LR71AAAAAAVBmSsAJ179+jUYYkAgAAAMjTqkmYmX3QzD7oPd5qZr9tZr9d8sgq3OF9bTr1ypTiced3KAAAAACqSC49Yb8oqdV7fErSdkmnaj0R27tzs7ZubNDEW1f9DgUAAABAFcmlMMdp59zXzGyvpAPOuUOSZGah0oZW+br3t2l0YkofaG9d/WAAAAAAUG5JWLKrJyzpZNr+mh+HF963R7/3H3+o3/4bT/odCgAgR2Z2TFJU3igP59zIKsf3pm0GnXNDJQwPAFADchmOeMDMnpbUL+lfSpKZHdbdIYo165NP7NQP3pzWtZk5v0MBAOTAzAYlRZ1zJ73kq8PMelY4/phzbiT5ISniJXEAABRs1STMOfe7SiRcfd6wxMOSOkseWRVobqrXRx7ZoW/88JLfoQAActPrnEsf1TEqqW+F44+mbzjnxiUdKkVgAIDakVN1RCXmhZ0ys61KJGDOOfeHpQ6uGoT371FkglL1AFDpzCzbG4jTSgy3X860mZ1Ia6NX0gvFjg0AUFsKqY7YKp+rI05OTmpgYEBjY2N+hZAS3temyLkLcq7mp8gBQEl5v/Pb19BEqxJJV7qYJJlZcJlz+iR1mtlVbxjidEZPGgAAeavK6ojt7e0aGBjw6+WXeHzPVknSaxeu64n7t/kcDQCsX11dXZI0uYYmgln2JZOyVnkJWTrnXNTMhpUYljgoaUhLi1RJuvvmYDJOL1YAwDo1NjaW3iHUnu/5VEdcIzNLDUkkCQOAihbTvUWlktuZPWSSJC8BO+GcG/IKeHzFzELOuSPpx1XSm4MAgNJLf8Ptueeem8z3fKojFkFySCIAoKJN697esKAkOedimQcn55A55yLe55OSDkhatpoiAAC5oDpiEXzm/bv0vb96TzOzC36HAgBYhlfZMJaxu1VSZJlTWiW9kdFGVFmGIwIAkI9cesLknPuKpNNm9jkl5oj9LtUR79rW3KgPPNyql3902e9QAAArG8lYF6xb0nByw8xCyee9HrAl5ei9Ah7RMsQJAFjHckrCzOxfKvHu4UlJV82M8rwZwvvbFJlgSCIAVDLnXL+kkJn1eNUO38iodhjW0nXD+s1s0Mx6vfL0z3htAABQsFULc3il6E845349bd/TZvbbzrl/VtLoqkj3vjZ98V98y+8wAACrcM4NrfDciKSRtO2oEnOiAQAomlx6ws47506l73DOvSTpWmlCqk77HmrR9dvzOn/5pt+hAAAAAKhguSRhy5Wiv1LMQKpdIGA6/FSbTk1M+R0KAAAAgAqWSxLWYWZb03eYWbukD5ckohwkF8VMWyCtInTvb9MopeoBoCS83/nt/kYBAMDa5bJY84ikr5mZU2KNleT6YIdLFtUqKnVRzM8+tVu/+UenNbewqMb6Or/DAYB1xVsUc9LfKAAAWLtVkzDn3DVJB9PWB4t6c8KQYceWDXqsbau++/p7+vT7d/kdDgAAAIAKlEtPmCTJK85xatUDa1x4f5tGJy6QhAEAAADIKqd1wpK80vR/38y+amb/qVRBVbPwvjZFzlGcAwAAAEB2OfeESanS9DKzlySdKUlEVe5AaLvemZ7R1NUZtbU0+x0OAAAAgAqTV09Ykrd4ZaTIsawL9XUBffbJ3YpQqh4AAABAFvckYV75+VycLm4o60d4P0MSAQAAAGSXrSesJ8dzl1vEueaF97Xp669e1MJi3O9QAAAAAFSYbHPC+rw1wWyVc49K+mfFD6n6tbU06/7WZp2NXtFHHr3P73AAACU2OTmpgYEBdXV1JdczAwDUgLGxMUlqz/e8bElYh6T/Kodz9+b7YsVSDTe78P42RSamSMIAoEgKvdGVQ3t7uwYGBvwOAwBQZl4uMpnvedmSsCHn3O+sdqKZ/dN8X6xYquFm171vj7784vf1D39hv9+hAMC6UOiNDgCASpNtTthwjufmelxN+uhjO/T61HW9d+OO36EAAAAAqCD3JGHOufO5nJjrcbWqsb5On3xip77+ykW/QwEAAABQQQpaJwy56d6/R6OUqgcAAACQJtucsKIys2OSopJaJck5N7LCsT2SQpJOSpqW1CvppLc4dNU5vK9Nz//7CcXjToHAasUmAQAAANSCkvaEmdmgpKhz7qSXfHV4idZyWiUNSnpD0nnv3KpMwCRp787N2rqxQRNvXfU7FAAAAAAVotTDEXudcyfTtkcl9a1yToukDudcS8a5Val7f5tGJxiSCAAAACChZEmYmXVm2T0tKbzSec65WDX3fmUK79ujCPPCAAAAAHhK2RPWqkTSlS4mSWYWXO4kM+s1sx7v87HShVcen3xip37w5rSuzcz5HQoAAACAClDKJCyYZV8yKWtd5pyIpBcz5pD1liK4cmluqtdHHtmhb/zwkt+hAAAAAKgApayOGNO9yVZyO7OHTJKUZRjiqBKFOpZUVJycnNTAwIAkqaurS11dXWuLtMTC+/coMjGlnz34oN+hAEDVGRsb09jYWHKz3b9IAAAojlImYdO6tzcsKCXmfWUe7A1RvCqpJe35mBIl65dob29PJWHVILyvTf/iP/9IzjmZUaoeAPKR/mbbc889N+lrMAAAFEHJhiM658blzQFL06rEkMPl9GckaCFJ48WNrPwe37NVkvTahes+RwIAAADAb6UuUT+SsS5Yt6Th5IaZhZLPZ+sdk3RE0vMljbAMzCw1JBEAAABAbStpEuac65cU8qodHpP0RsbaX2EtXTdsxMyOeZURByUNr4e1wqTEkMTIuQt+hwEAAADAZ6WcEyZJcs4NrfDciNKKbni9YcseX80+8/5d6hv5jmZmF9TcVPLLDgAAAKBClXo4Ijzbmhv1gYdb9fKPLvsdCgAAAAAfkYSVUXh/myITDEkEgPUmuXRKWil9AEAN8H7vt+d7HuPiyqh7X5u++C++5XcYAIAiq7alUwAAxeEtoTKZ73n0hJXRvodadP32vM5fvul3KAAAAAB8UpVJWLUO+wgETIefatMpStUDQN4KHfIBAEClqcokLDnsw+v+qyrd+9s0Sql6AMhboUM+AACoNFWZhFWzzz61Wy//6LLmFhb9DgUAAACAD0jCymzHlg16rG2rvvv6e36HAgAAAMAHJGE+CO9v0yil6gEAAICaRBLmg/C+NkXOUZwDAAAAqEUkYT44ENqud6ZnNHV1xu9QAAAAAJQZSZgP6usC+uyTuxWhVD0AAABQc0jCfBLez5BEAAAAoBaRhPkkvK9NX3/1ohYW436HAgAAAKCMqjIJm5yc1MDAgMbGxvwOpWBtLc26v7VZZ6NX/A4FAKqC9zu/3d8oAABYu3q/AyhEe3u7BgYG/A5jzcL72xSZmNJHHr3P71AAoOJ1dXVJ0qS/UQAAsHZV2RO2XnTv28O8MAAAAKDGVGVP2Hrx0cd26PWp63rvxh3t2LJBc/F5/ejWT/RXMxfUFGjQlrqN2lLfrC31G7W5bqO21G1Uc90GmZnfoQMAAAAoEEmYD5xzujg3rXM3zmvHJ97QL5z+x7q24V29PvO22jfu0mPND2jeLejGwm3dWJzRzYU7urE4oxsLtzUbn9fm+g3aUpdIzu5+3qjNGdtb6pvv+by5buOS5zcEGknqAAAAgDIiCSuxO4tz+uGtN3XuxnmduxnVuRvn9YMbUcUV1wc2h3RfW6vmp3brX/2NL+j9mx7WxrqmFdtbiC/q5uLtVFJ2Y2FGNxYTn28u3lmy/d7ti6lELv3zzcXbqe0Ft7gkodtctyHV+5Yt0Xtww3366R2HVo0TAGpJsmBUV1dXcu4aAKAGFFo0iiSsSJxzenv23VSSlUy4zt++qEea9+gDW0Lavzmkz7cf0P7NIbU1tcrMdP7yTYX/0Vf1oV96VIHA6j1S9YE6BQObFWzYXJS45+MLWRO6G16idjNt++LsVf0/735Xv/bq7+nnd35CX9hzWJ9p2a+AMbUQQG1bLwWjAAD5KbRoFElYAW4t3NYrNyd17ub5JT1cGwKN2r9lr/Zv3quf2fERHd/7i3pi04NqCjQu29benZu1dWODJt66qg+0t5bxq0hoCNSrNbBVrQ1bcz7nwp0r+rcXv66/99q/1PT8Df2XbZ/TF9rCev/mh0sYKQAAALA+kIStIO7imrx9KZVknbsR1bmb5/X2nff0xKYHUwnXz+38uPZtbtfOppaCXqd7f5tGJ6Z8ScIKsWfDdv1We49+q71HEzfO60+mIuo++zva3diiL+wJ65d2f1a7CrwWAAAAwHpHEua5vnBLEzcm7yZcN6OauDGpbfXN+sCWDu3fslc9uz6l//GRX9GjzferIVC8Sxfet0e/9x9/qN/+G08Wrc1y2bdlr4a2PKvnH/01fX36B/qTCxENvPEn+ti29+kLe8L6uZ0fV3PdBr/DBAAAACpGzSZh/8/l7+jPr7+W6uF6d/6antz0sPZvCWn/lr36xd1d2relPa9heoX65BM79Su//7KuzcxpW/PyQxcrWZ3VKby9U+Htnbq1cFv/4d3v6E8uRPQbf/m/62/u/Ji+0BZWV+t+1Vmd36ECAAAAvqrKJKwYVahejr2qpkCDvtB2WB94rFeh5t2+JQjNTfX6yCM79I0fXtLPHnzQlxiKaVP9Rv1y2+f0y22f08XZaf3bi1/X33/9K7o8F/Pmjx3WU1v2+h0mgCpTaAUqAAAqTVUmYcWoQjX42JeKE0yRhPfvUWRial0kYel2N7Xq7z38tP7ew0/r1ZuT+pMLp/TXxv+htjdu1Rfawvrlts+qrWm732ECqAKFVqACAKDSUFu8QoT3tSly7oKcc36HUjJPbm7XP33s7+jNT/9r/d5jfXr15pt6/7ee1U+dPa5/fSGiWwu3/Q4RAAAAKDmSsArx+J7E3LPXLlz3OZLSC1hAn9v+If2rp35L73zm3+hX93xe//bimO7/s1/W354Y0uiVs1p0i36HCQAAAJREVQ5HXI/MLDUk8Yn7t/kdTtk0123QL7Z9Vr/Y9lldmr2q/+vimI7/+F9panZav7z7s/pbew7rA1s6/A4TAAAAKBp6wipIckhirdrV1KK/+/DP68xHf1+RA4NqCNTrZ//iy9r/7T797vkX9c6d9/wOEQAAAFgzkrAK8pn379L3/uo9zcwu+B2K7963+SH9k0d/Tec/9cf63574Db0287b2fbtP3Wf69ccXRnXTmz924/a8fjx1XS//6JJOv/Ge5hfiPkcOAAAArIzhiBVkW3OjPvBwq17+0WV9/gN7/A7HN845XZuZ18XYbe9ji56K/bS2xT6usz/5gX47+qf61U3/ixovPKimyUf00FyHdm/bpJt35nX+8k117t2ujz9+nz7+2E4demS7Nm9o8PtLAgAAAFJIwipMeH+bIhMX1mUS5pzT9M25tOQq8+OOLl27ramrt9VQZ9oV3Ki2lo3avW2jdgU36qGWbfpI8Ke0O/hzatg8q28+flon3/u6fnLntMJtXfrp7Qe1xT2qd96O69yPZ/RP/t2EJt66qifu36qPPnqfPv74Tn3ssft039YNfl8KAD4ys2OSopJaJck5N7LK8UFJvWnnnHHOjZc4TADAOkYSVmG697Xp6D//M+1paVZTQ52a6gNqqA+oqb5OTQ0Zj+sCyxyT2NfUEFBdoPQjTuNxpys3ZzV1dbnkKvFx6dodbWqq167gRu3etkG7gxu1u2Wj2u/brI8+et/dpCu4UZuaVv/W/Jge0rFHntZrt36ifz11SoOTL2pqdloXZq9otnVebV2tCjW0yuY36+UbTfqPr9bpnf/stKOuRR/e86C6QnvV/djDevi+zTKzkl8nAP4zs0FJp51zJ5PbZtaT3M5yfFDSKefcAW+7V9JxSUfKFDIAYB2qyiRscnJSAwMD6urqSi7euW7se6hF/81PP6ELV2c0vxDX7EJcs/OLSx8vxjU7H9fswqLm5uOaW4xrbn5RswtxzS3ENbewmHreZGpqCHiJ2t3kLPm4sT6gRi95Sz2uD6ixoU6N9QHvGO9xQ0DxuNOla3eWJFfvXp/V1uaGRFK1bYN2tzRrd3CDHmvbqk+/b5d2e4nVrm0btLGx+N9yj296UP/okS8u2TezeCeVkF2YvZJ6/M6d9/RX136sU7e/oxM3Ylo4u6D6O5vUEtimBzfu0Pta2vTU9jbt2bBde5oSH21NrdpWv4lEDfDZ2NiYJLWvsZle51x/2vaopH5JWZMwSYOShpMbzrkRM3txjTEAAGpcVSZh7e3tGhgY8DuMkggETP/NTz9RtPYWFuN3k7NUoraouYVEInf38aKXzHnJnXfO7PzSx2amfQ+1KLy/zUu6NmpXcIMa6+uKFnMxNNdtUEfzHnU0rzys8+bCbX3vnbd0Knpepy/8RF97bUr/IfC2gvctqmnrnOabbmo6HtOCi6cSskRy1qo273NifyJh21rfTLIGlIj3pttkoeebWWeW3dOSwiuc1itpyToZzrlYoTEAACBVaRKG3NXXBVRfF9CmJr8jqUyb6zfq8MOP6/DDj6f2XYzd1ndef1fffu2yvv3au7pz6Yb2dWzWk4816qGH6tQSXNRVF9OF2Wn94MYbupDW4xZ38bvJmdeb1tbYqh2N27StflPqI9hw93FjgMIhQJm0KpF0pYtJiWGHmcmVmYW8hyEvgWuVFHTODZU4TgDAOkcSBmTYHdyon//wQ/r5Dz8kSbo2M6fv/fg9ffv1y/pP/+ld/WByWo/v2aaPP75Xzzy2Ux9//G6xjxsLM6mhj+nDIV+99aauLdzStflbii3cSjz2PuqtTsH6zdpW35xIzBo2edvJRK1ZwYbNS5K4bfWbFPSO3Vq3SfWByuqJBCpUMMu+ZFLWKi8hS5NMwpQ2h+yYmQ1mDGlMDZOXtC6HygMAlhobG0sOk5cKGCpPEgasYltzoz7/gT2pipV35hY1fv6Kvv36u/rjb/yVfuP/+K52btuojz92nz72WKIK42fuuz+nYYnOOc0s3tG1hZlUUhZbuHk3SfOSttduvX33+fmbS46/vjCjjXWNuSVuDZmJ3GZtrW/WlvqNqjMSOax7MXkVEdMktzN7yNL3nUnbF5F0Vol5ZCnreZg8AOBe6W+4Pffcc5P5nl/yJKyAUsB5HQ+U24bGOn388Z36+OM7pb/xpBbjcb36k2v69muX9Z9/cEFffvH7CgRMH/cSso8/dp92t2yUc4nznXNyTnJLHm/UFrdBm9127ZHk6pxcneQavWOWOU+SFuOLuhW/o+uLM7qxeMv7nHh8Y3ZG12du6VL8im7G39KN+G3dWLylm/EZ3YrfTnx2t3U7PquNgUZtTeuNSz7eWt+srXXN2uold1vrM55Le7y5bqMCxhrwqFjTurc3LCgtO88rmuW5mJR9+CIAALkqaRJWQCngvI4HKkFdIKD9D7do/8Mt+vXPPy7nnM5fvqlve/PK/uCrr+nKjVmZmcwkk1Z+LCW2l3ks2YrPm5kCJpkaZLYt8ZE6R2qSqcmkHV78s/Nx3Zyd1834bc3EZ/RO/LbebphXU/OiGpoXVb9hXvVNV2RNF2WN83KN83L181qsn9VC3ZwWArOaDcxq1u5oTnPaqA1qDmzQ5kCzttRtvDvEsmGTWps2q7Vxs7Y1eIlbXXoSdzep21y3kQInKDrn3LiZxTJ2tyrRu5Xt+JiZxcws5JyLeruDyedKFScAYP0rdU9YvqWA8z0eqDhmptCuLQrt2qK/9anQ6idUoLmFRd2aXdTM7IJuzS4s+Xz38WLi8527z92YndP1+RldX7il64u3dSt+WxfjMzrvbuu2ZnXHrmmhblb1TQsKbFhQoGleapiXa5hXvGFOi3VzWqib1aItqME1qk51MpkCFlBApoACCpipTgEFLKA6S3yuT/tcl9wOBFRvdaoPJLbrAwHVB+pUlzxPyfMz2pOpzuoS+9OPS9sf0N0E0aVdN5e2teSxy+WY9P2FH2+SGgL1arB6NXqfG6zu3n2ButRzjYGGJcdk3ecdf7fNpfuqaDjrSMabe91KK0HvFePoTHv+eSWqJyZHZRxVxlBEAADyVbIkLN9SwAWWDgZQAom14erUsqmx6G0vxuOayZrgLaa2r9+Z1dW5W7qzsKjZhXnNLi5qdmFRswsLml1c1Nxi4vHdz3HNLS5qbnFB897z8/FFzcXjml9c0PxiPLG9GFegzqmhztTQYKqrk+rrvSqi9VJ9namuXqqvk+rqEs/f/XCqq1tUoM4pEJACZl6vY/JDqW0zJRLGQPq2pbYDXs/l3ecT5wcskGqnzkyJtdaTjxPt1GUcY4Gl58qkBbegRS1qfnFRC25Bt7SgRc1qwS1qwS1q3s2nHi9oQQtuUXNuwXtuQfPxxOe5+IK3vaB5l7nPO9bbJ2mZZO1uIvc/dPyXOrq7q+jfU/lwzvV7xTV6lCi88UbGaIuwEgsxn/SOH/KOP+Y9f4XqiACAtSplT1hepYALOB5AFaoLBLRlY0BbNpa/NL9zTguLLpHQzce9hc+99fOWWfA8faH0udTzcS3GneLxxOfFuNOic4rH5W3HFXfOO0Za9B4nz4m7xHFz8bT9qePv3bfS/uR2PC0O5+oVd3WKx53iLjmHMPE4eaxT8rH3OW2eYcBLAE2WSjjTE81Gk5rMliSZZk5WF5cFnAL1ThaIywXisrq4FHByAaeb2i7tLvt/+z1WSqK8ecgjGftIugAARVXKJCyYZd9KpYBzPp5SwAAKYWZqqDc11Ae0eYPf0VQm59yS5My5u0la3OluAhdPJm53E73k8/G0hC99/85thV30tZYBBgCg0pQyCYspv1LAOR9PKWAAKI3kMMe6CipyudYywAAAVJpS3mbzLQWc7/EAAAAAUHVKloQ558Z175DDlUoB53U8AAAAAFSjUg84GfEqUCXdUwo44/kVjy+2tDkGyBHXrDBct8Jw3fLHNQMAoPKVNAnz1vwKmVmPV943WyngvjyOLyr+WMkf16wwXLfCcN3yxzUDAKDylXzqtXNuyDl30vucWfZ3xDnXnevxlayYf/gU+4+oSv6jrFKvW61cs2K3VyvXrZL/D4qtkq8bAADVylxyYZgqYmZ/KOntIjTVLmmyCO1UclvFbq+YbRW7vUptq9jtFbOtYrdXzLaK3V6ltlXs9orZVrHbK0ZbDzjnvrT2UIprYGDAUbUXAGqTmT3nnBvI55xSlqgvmUq8AQMAAABALipoJRgAAAAAWP9IwoAyMbOSVfoEAABA9ajK4Yhr5VVejCqxDpmqqQCIX7xrJkmHJJ12zg35GU+1MbNBSSG/46gWZhaU1Ku7P6dnvLUEsQwz603bDPIzCgBA5aq5JMz7Y/h0svS9mQ2aWU8pS+FXOzMbds71pW2fNTPxR15uzIzkKw9eAnbKOXfA2+6VdFzSET/jqmRmdiz959HMOjP3obQmJyc1MDCgrq4udXV1+R0OAKBMvMq/7fmeV4vDEXszEq5Rpa1VhqW8P4hjGbuHxTXLR1iJ7zPkZlBpi7R7PdXP+hdOVTiavuH1Gh7yKZaa1N7enkrCAAC1w/u9P5nveTWVhJlZZ5bd00r8kYzsWiUdy9KbQ+9ODswsLOlFv+OoMr2SIuk7nHMxf0KpGtNmdiK54fUevuBjPAAAYAW1NhyxVYmkK11MSvT48IfevZxzUTM74JyLpu3uVsYfyVhW0DkXMzO/46gKacl+yHvTpFXMb8pFn6RRM7sq6XlJUYZYAwBQuWqqJ0xSMMu+ZFLWWsY4qkp6QQRveGJYDEdcFXMNC5LqYXXOnUwWzfHmcmIZ3pskw0oUMhkUQxEBAKhotZaExXRvspXczuwhQ3YnJB3O6BlDBq9HJ+Z3HFUo+XN4Jm1fRNKxLMfC4y1/MO4VMzkiqTd9eCIAAKgstTYccVr39oYFJeac5MLrjeinVHhOOiW1ppUN71ZiiF2vpAhJ7LKi0j0/jzGJIcPLSc51dc5FvM8nzWxc0hu+BgYAAJZVU0mYc27czGIZu1vF/KZVmVmPpNFkAmZmnSRjy8schujNCQuxJt3KvPlzMTMLpSWqweRzvgVW2VqVkXB5czkZCgsAQIWqteGIkjTiJRRJ3Uorh417eRX+WiWdMbOgN9Tu6CqnweP1fh1RoifsmDevDst7Xksrlh6V1O9TLBXP6wFbMgfM+x6jtxUAgApVUz1hkuSc6/f+EO5RogjAGxRPWJ73x1xyjav0ZJVrliOv94sesBw554a8n9HkPLArVEdcVb83XDjVI+acI3EFAKBC1VwSJiX+yPM7hmrhDQGjvjrKip/R/HhDN0m6AACoErU4HBEAAAAAfEMSBgAAAABlRBIGAAAAAGVEEgYAAAAAZUQSBgAAAABlVJPVEYFy8dZU65N0TNK4pBfSnu6Q1Cupr9SLOJtZpxLrbb3BgtEAAAD+IgkDSihZOtxbl+6FzNLrZjaspQsTlyqOcW/R7SNizTIAAABfMRwR8JFzblxSrEwvN16m1wFqzuTkpAYGBjQ2NuZ3KACAMvJ+77fnex49YYAPzCzsnIt4m1FfgwGwZu3t7RoYGPA7DABAmXV1dUnSZL7nkYQB/uiWFJGkZDLmDRccVCIpG5U0LSkkKZY+j8vMgkrMJUsmb6EswxwHJZ1Wopct6Jw7mfZccvhjt5gjBgAAUHYkYUD5dJuZdLcgR3/6k865iJk9L2nQOXckud/MTpiZ0pKlU5IOO+di3vNhMxt2zvV526OS+r2hjjKzq2aWTNgOSop6c9UiZubEHDEAAICyIgkDymc02WNlZrEVjsucuzUs6YSkkWQvVjIB8x5HzGzUzPqV6Dk7mEzAPAecc1Hv3GkvAUsxs2B6ewAAACgtCnMA/kiVqvd6soIrHBuVlHy+U4lhipliSvRyHVTGHLOMpCtz/lksl2ABAABQPCRhgA8yeqo6V+mJCulushSV1JrlmKCkM95HaO0RAgAAoFRIwgAfeT1ghzJ2d2Zs98mbP5YssOEtAp1so0fSSedczEvuomnFN2RmIW+xZuneBC641q8BAAAA+WFOGFBCXrLUp0Tv1FGvMEdSh6RnJL2YcVrUS6xiSiRkoxkVDA9LOm5mp5VIqoLphTy85we915722plWIpHrNLNjShTjOO4dP2hmg5lzxQAAAFAaJGFACXmJTb8yKiGm6cuyL5ZWUj6S+aQ3dHG59pLPZ2u3O2N7pbgAAABQIgxHBCpP0O8AAAAAUDokYUCF8OZt9Uk6aGa9fscDAACA0mA4IlAhvKIamUMGAQAAsM7QEwYAAAAAZUQSBgAAAABlRBIGAAAAAGVEEgYAAAAAZUQSBgAAAABlRBIGAAAAAGVEEgYAwBpNTk5qYGBAY2NjfocCACgj7/d+e77nsU4YAABr1N7eroGBAb/DAACUWVdXlyRN5nsePWEAAAAAUEYkYQAAAABQRlU5HPFLX/qSe+CBB9bczuTkpNrb29ceUAW3Vez2aiW2Wvk6i91ercRWK19nsdsrRlvPPffc/+Gc+1JRAgIAwC/OuZJ+SOqRNJjjsce843sl9S533Je//GVXDMVqp5LbKnZ7tRJbrXydxW6vVmKrla+z2O0Voy1JA67E961CPop93QEA1aOQe1PJhiOaWdjMjknqkxTM4fhBSVHn3Enn3IikDjPrKVV8xeZNyqu4tkrRXjFV6nWrlWtW7PZq5bpV8v9BsVXydQMAoFqVLAlzzkWcc0OSxnM8pdc5dzJte1SJBK5kKvWPi0r+A69WYquVr7PY7dVKbLXydRa7PZIwAAASKqIwh5l1Ztk9LSlcytflD4L8cc0Kw3UrDNctf1wzAAAqX0UkYZJalUi60sUkycyC5Q4GAAAAAEqlUpKwYJZ9yaSstYxxAAAAAEBJVUqJ+pjuTbaS25k9ZJqcnNTAwICkxNAbht8A1c85p7hzWlh0Wog7LSzGtRh3WvQeJ/cvxhOPU/vTjkl89o5Jf5w8Pp7WTvL4tHbiaY8Tx8W9fUvPjcfjijspHk/EHHfJ+JXYztwfT3vOObnUcen7lm4n23e6+zoubX884/WSxzm//yNX8NwzH9SvfKYj7/PGxsY0NjaW3GwvYkgAAPiiUpKwad3bGxaUJOdcLPPg9vb2VBIGYHnxuNONO/O6PjOvazNzunY78fj67Tnv87zmFuKJBCaZAKUnP2kJ0GI8PWnJPC6e2peZSC0sJpOr+JJz7x53N0myurjqNswr0LSgQOO86gIBBQJSXSCg+oApEDDVedt13na9mQJ1pjoLqK5OqjPznguo3js2cZ6pPiAFAoHEMXWW9jn5Ot5x9YntgJnq6wJqDJg2es8FzIvDTGZSICCZTGamgElmgdTjxLalfU6cY8lzJQUCaecq0XbAJJkUUPKxt19eO4G77QZkiWO9YyrZo1uCBZ2X/mbbc889N7nWOLzKvVF5b/Z5FXlzPXfYOVfSolEAgPWvIpIw59y4mcUydrdKivgQDlAR4nGnm7MLuj4zp+u353UtLXm65iVV19OSqmszS4+5fnteN+8saFNTnbY2N2rrxgZtbW7Qto0N2tbcqK3NDdqysUFN9XVqqA9oQyCg+jpTfTIRqUskPnV1AS+BMe/5QCJxCSQeJ/e5QFx3dNv7d0e33YxmdEe33G3dis+kPt+I39KNxRndjN/WjcVbur4wo+uLtxRbuKV5t6Bg/WZtq9+kLfUbFchxxHTc+5gv6f9Ijpz3gXv0731GD23u8jUGbzmU08lqvGY2aGY9GdV5Vzo3VOoYAQDrn29JmJmFJHWm3fhGMm6E3ZKG/YkOKI6Z2QW9Mz2jKzdnl/RAJXukrqUSrPSEat5LvBbU3FTnJU+JJGpbc0NqO/G4Ufe3NqcSrK0bG1PHbGtu1JaN9aoLrJ7ILMQXdX3xlq7N39K1hRldW7iV+ogt3Ew8nk/uSzwfm7t597j5W5p189pat0nb6pu1rWGTttVvSiVUiY9m7a6/T9sa2rM8l3i+uW6DzCq7NwdVr9c515+2PSqpX9KKSZh3zwIAoChKloR5ZefDknoktZrZG5IizrnkumFhSUfk3ficc/1mdsxboDkk6Y1c3pkE/JJMsJIfF67O6O0rM0v2zcwtaE9Ls3ZsaUr0PmUkS7u2bdW2TY3atKFOzRulDRtMjRucGhul+kanBS1oNj6nO/H5tM/zuhO/rjvxOc3G53U9Pqd34/Op7Tszc7pzw3t8z3lzd4/zHt9YuK2ZxVltqd+Ylhw1JxKjhrtJ0o7GbXqkec/dpCntuWD9Zm0igUKFW+NyKGElErZsbQAAkJeSJWFesjUuaWiZ50ckjWTsy3osUG6ZCVa2j5m5Bd3f0qw9rc16oDXx+akHg/r8B/bogdZmBbfV6fTsD/Tv3/22zt++qLcWlyZDqc8z85q/taANgUY1BRqWfL77uGHpc3WNWY/f0tCcdnyjNnjHNpn3OeP4pkCDttY3a3PdRgWsUoqlAiWz4nIo2eYge8+FJb0o6WApgwMA1I6KmBMGlNMtL8G6MD2jt73P72Q8TiZY929vTiVa6QnWntZE71Zmz8/1hVv6f9/9c/2jS9/UaHRcH976uJ7e9Sn9+gN/fWlyVdewJDFqtAZ6kYDSC2bZl74cSmy585xzMX5GAQDFQhKGdSWZYL1zZUbvXL2baKWGDGZJsO7fnluCtZyr8zf0f1/+jl66/LLGps/pUy1P6eldn9Tw+39T2xu3lvgrBpCHmPJYDkWSci3awdIpAFBb1rp8CklYBfqzH17Stdtzisfvrp2UuabQknWJ4i7rGkWptYiW7E9rc8kaRLrnddySNrKvYXTvcdnXMEpuK0s82dpf0uYKr5u+ltKN2/NZE6x9Dwb1UwUmWMt5dy6mf3/523rp0sv6duyHOrz9g3pm16f1x08dU7BhczG+DQAUX17LoXjFOO7Znw1LpwBAbVnr8ikkYRVm8t2b+sV//g19+v27l6wxlFwDKPnY7tn2jvHWEEo+TqxjZEu3k216++vrLW2tIivsddP2pW9bWkyJNZDSjkmL1TJiWvL1LPkalCWGxOPmpvqiJFjLuXDniv7d5W/ppUvf1NkbP9ZPbz+kX7v/p3TyA/+9NtdvLMlrAiieApZD6VSisFSvt90tKeRtR5xz0dJECgBY70jCKkzk3JT++oEHNNL3cb9DgaS3bl/WS5e/qZcuvaxXb76pv37fR/TfPfxz+qntB7Wxrsnv8ADkb8XlUNKXT8kchui9wRPKZ3FnAACyIQmrMJGJKf38hx/0O4ya9lcz7+ilSy/rpUsvK3p7Sn9z58f0D/b+kg5v/6CaAo1+hwdgDXJYDmXJ8ilJXu/XESV6wo5JGlmumiIAAKshCasgcwuLevlHl/S//dqH/Q6l5vzw5pt66dI39dLll3Vx9qp+fucn9E8e/VV9pmW/GgL8mADryUrLoWRbPmWl/QAAFIK/LivIn//Vewrt3KL7tm7wLYbZ+Jwuz8V0eS6mS7MxXZq7qktzV73tq7odn9MDG3bo4Q279NCGnXpow316eOMu7WwMVtU6U845nbsZ1clLiaGG1xdm9PSuT+p/ffy/1idanlSd1fkdIgAAANYpkrAKEpmYUnh/W9HbvblwO5FEeQnVpVkvqUruS9u+tXhH9zVu067GFu1qDGpnY1C7mlp0f9MOfWjLI9oYaNTbs+/prTuX9Z3YD/Xmnct6685lXVu4pQea7kslZekJ2kMbdurBDff5PofKOafT119LDTVcdIt6eten9K+e/C19eNvjVZVEAgAAoHpVZRKWXI9lva3FEjk3pcG/dWDV45xzurpwY9neqkvJniwvwXKSdnnJ1M7GYCrBenzTA/pUy1Pedot2NQUVrN9cUDJye3FWP7nzrt7ykrI3b1/Wy7FX9W8ufl1v3r6st2ff1da65qwJ2kMbdurhjTu1o2Fb0Ssbxl1c34n9pU5e+jP96eVvaUOgUU/v+qRe/MA/1Ie2PMICyUAV8dZjafc3CgAA1q4qk7D1uB7L5Wu3df7dG3rwAdOpK3+hqdkry/ZWXZ6LqbmuSbsak0lVIsHa1diizq2PLtne2RjU5rqNJU82NtY16bFND+ixTQ9kfT7u4ro8F9Obty/pLS9Zi96e0tj0D1LbM4uzenBDZm9aIkF7aMNOPbBhR06FMRbii/pmbEInL31T/+7yt7S9Yaue3vlJ/b8f+kd6cnM7iRdQpbw33Sb9jQIAgLWryiRsPbi1cFuv3npTEzfO69zN84q8/Zd672ffUuefv6gnNz+s+5t2pIYDPr7pgVRv1U5v34a66qrSF7CAdje1andTqz6i92U95ubC7VRv2pt3Lumt25f1tenvpx6/M3tF2xu2ppKyzB61y3Mxnbz0Tf2Hy9/Wgxvu09O7PqWvH/xdPb6JapMAAACoHCRhJbboFhWduahzN6OphGvi5nm9c+eKntj0oPZtbte+LXv14Dub9Xce/KL+XldnzfbUbK7fqPdtfkjv2/xQ1ucX3aKmZqcTQx5vX9abdy7rtZm3NXplXG/euazNdRv1C7s+oe995H/V3ubiz60DAAAAioEkrIjenYstSbQmbkzq1ZuT2tkY1L4te7V/814d3f0Z/ePNv6pHm+9XfSBRgS8ed/r9P/9TjfzMEzWbgOWizur0wIb79MCG+/Tx4JN+hwMAAAAUhCSsAHcW5/SXt97SuRtRTdyc9Hq5JnU7Pqv9m/dq35a9+vDWx/V37v9pPbW5XVvrN63Y3g/evKqWzU16aMfKxwEAAACofiRhK4i7uN68fWlJonXuZlSTty/pkeY92rd5r/Zv2au/+9DPa/+WvXqg6b6CerIiExfUvY/hcwAAAEAtIAnzxOZvauLmeZ27cT71+ZWbk9pa36z9W/Zq3+a9+tmdH9U/DP2Sntj0oBoDDUV77cjElH7rrzO8DgAAAKgFNZuE/emll/Xn117zEq6oYgu39NTmdu3b3K79W0L6pd2f1b4t7Wpt2FrSOK7NzOncm1f1ySd2lvR1AAAAAFSGmk3Cxq//WJvqmtT7wH+hfZv3qn3jroIWKV6rb/zwkj78yA41N9XsfwUAAABQU2r2L///6dFf9TsESYmhiGHmgwEAAAA1o/xdP0hxzukUSRgAVL3JyUkNDAxobGzM71AAAGXk/d5vz/e8mu0JqwSvT13XYtzpifu3+R0KAGAN2tvbNTAw4HcYAIAy6+rqkqTJfM+jJ8xHpyamdHhfGws0AwAAADWEJMxHpyamWB8MAAAAqDFVmYSth7H3d+YW9e3X39VnntztdygAUBUKHXcPAEClqco5Yeth7P23X7+sJx8MqmVTo9+hAEBVKHTcPQAAlaYqe8LWg9FzVEUEAAAAahFJmE8oTQ8AAADUJpIwH7x95ZYuXbujD+1t9TsUAAAAAGVGEuaDU69c1Oee2q26AJcfAAAAqDVkAT6InLugwwxFBAAAAGoSSViZLSzG9Y0fXmI+GAAAAFCjSMLK7Ez0iu5vbdbu4Ea/QwEAAADgA5KwMjs1McVQRAAAAKCGkYSVWeTclLr3k4QBAAAAtYokrIyu3JjVaxeu6aOP3ud3KAAAAAB8smoSZmbPlyOQWvD1V6f0iSd2qqmhzu9QAAAAAPgkl56wPjP7kpltLXk061xk4iJVEQEAAIAal0sSdsQ594eSDpnZ02b2wRLHtC4553RqYkrh/Xv8DgUAUGSTk5MaGBjQ2NiY36EAAMrI+73fnu95qyZhzrlTyc/OuZckycz+s5l9Kd8XK5ZqvNm9+pOYNjbWqWPXFr9DAYCqVOiNrhza29s1MDCgrq4uv0MBAJSR93t/Mt/z6lc7wMw+6Jz7vtcD9g8khSW9KOmsmT0tyTnn/jTfF16L5M2umoxOTDEUEQDWoNAbHQAAlWbVJEzSSTNz3uMh59wzac/9hSSZ2Ze8IYtYxqmJKf1Xn3/c7zAAAAAA+CyXJEySfj05LDGTmX2uiPGsSzfvzOts9Io+/b5dfocCAAAAwGe5FOboXy4B8xyQFC1SPOvSN//ysj60t1VbNjb4HQoAAAAAn+VSmOOlzIqI6dvOud91zn2t+KGtH6cmphTeR1VEAAAAALkt1vy0pFNm1p62+xrDEHMXmbhAUQ4AAAAAknIbjhhzzm13zk0mdzjnzksKliqo9SR66YZu3lnQvoeCfocCAAAAoALkkoRtW2Z/azEDWa9OTUzpc0+1ycz8DgUAAABABcilOuKHzSzqnPt+coc3FPGApFXL0pvZMSUKd7RKknNuZJXje9M2g865oRxirFiRiSn1fPRhv8MAAAAAUCFWTcKcc79jZl81s2QVxJD3+fBq55rZoKTTzrmTyW0z60luZzn+WHrSZWadmfuqydzCol7+0WX9/pc+4ncoAAAAACpELsMR5Zz7vKRuSSOSws65Q8656zmc2puRcI1K6lvh+KMZrzsu6VAuMVai7/34PT3atkU7tmzwOxQAAAAAFSLXxZqTCdG4lKqYeHWl0vRm1pll97Sk8AovM21mJ5xzR7w2eiW9kGuMlWb03JQOP0VVRAAAAAB35dQTJklm1p78kHRW0pFVTmlVIulKF/PaCi5zTp+kTjO76s0lm15u6GI1ODUxpcOUpgcAAACQJpd1wj5kZtNKJF5nlegNe8N7vJJgln3JpCxrZUXnXFTSsBJzzgZVxUMRL8Vu6633burDj+zwOxQAAAAAFSSX4Yh9kg44586b2dPOuZckycxWK8wR073JVnI7s4dMXpvDkk4454bMrEfSV8wslByemDQ5OamBgQFJUldXl7q6unL4Msrr1CtT+vT7d6u+LufORgBAFmNjYxobG0tutvsXCQAAxZFLEjbqLc4sSS1p+90q503r3t6woCQ552KZByfnkDnnIt7nk2aW7HVbor29PZWEVarIuSmFGYoIAGuW/mbbc889N+lrMAAAFEFO3TRm9gvew/Nm9iXvcbbCGyleIY9Yxu5WSZFlTmlVRsLlDU+sujlhi/G4vvbqRZIwAAAAAPfIJQkbl/QPzGyrc+6UpGfM7IqkjhzOHfGGFSZ1KzHnS5JkZqHk814P2JI5YF4Bj2gOr1NRvj95VTu2NOnBHZv8DgUAUAbJYfJpwyYBADXA+73fnu95uSzWfF7SwbTtz5vZh5xzf5HDuf1mdsxLtEKS3siodhhWospicl+/t8DzG+lt5PalVI5TEwxFBIBaUg3D5AEAxecNl5/M97xVkzBvKGLUOff95L5cErC0Y4dWeG5EiQWgk9tRSVWXdGUaPTel/r/5pN9hAAAAAKhAuQxH/MVsO81sa5FjWReuzczplZ9c1See2Ol3KAAAAAAqUC5J2AvKPi+rt8ixrAtjr17SRx+9Txsbcyk8CQAAAKDW5JIpdEv6p2YW1d1qhybpsKR/VqK4qlZk4oIOMx8MAAAAwDJyScIOShrSvQssB4seTZVzzunUxJR+46ee8DsUAAAAABUqlySs3ytNv4TXM4Y0r124Luekx/cwXQ4AAABAdqvOCcuWgHneWGZ/zTo1MaXw/jaZmd+hAAAAAKhQqyZhZrY124ekwTLEV1UirA8GAAAAYBUrJmFmtk2JYhxXJZ1XYiGy5PYzpQ2tutyeW9B3f/yuPvP+3X6HAgAAAKCCrTgnzDl3zcxGnHO/nr7fzD5U2rCqz7d+dFlPPdii4KZGv0MBAAAAUMFyWSesP3OHc+4vJO0tfji5mZyc1MDAgMbGxvwK4R6JoYj0ggFAqXi/89v9jQIAgLVbtTqic+7aMk+1FjmWnLW3t2tgYMCvl88qMjGl4d6P+R0GAKxbXV1dUmJY/JqY2TFJUXn3MefcSA7HS9IhSaedc0NrjQEAUNtWTMK8OWEvZnkqJOlkSSKqQj9575bevT6rD7a3+B0KAGAFZjaoRCJ1MrltZj3J7SzHDzvn+tK2z5qZSMQAAGuRy5yw7ZKeV6IgR1LUOXe+lIFVk8jElA4/tVt1gVxGdwIAfNTrnEsfZj+qxLD7e5IwMwtq6b1Pkoa940nCAAAFK3ixZtx16pUp/bUP3u93GACAFZhZZ5bd05LCy5zSKumY1xsWTdsfKnpwAICaktNizWb2wfR9mdu1bGExrm+8elGHWR8MACpdqxJJV7qYlOr1WsJLvA5kJGDdkiIlig8AUCNW7Qkzs6cljZjZAefcpLf7mpl9zjn3tZJGVwVOv3FFD+7YpN3BjX6HAgBYWTDLvmRS1qp7hx7KOTeefOwlamFJBzKPS1btlRIFRLwiIgCAdWpsbCy9Unt7vufnMhwx5pzbnr7DOXeetcISIucuKLxvj99hAABWF9O9lX2T25k9ZNmckHQ4o2dMUmVW7QUAlE76G27PPffcZL7n51JJYtsy+30rUV9JTr0ypTBDEQGgGkzr3t6woCQ552IrnehVVexP7xkDAKBQuSRhH84yJ+xzyjIco9a8d+OOfjx1Qx99bIffoQAAVuElULGM3a1aZY6XmfVIGk0mYMsU+AAAIGe5FOb4HUlDZnbFzE6b2RVJg0qU6K1pX3/loj7xxE411tf5HQoAIDcjXlKV1K1E2XlJkpmF0p83s7ASidoZMwuaWUjS0bJFCwBYl3KZEybn3Oe9d/4OSDrjnPuL0oZVHSITU+pmKCIAVA3nXL+ZHfMSrZCkNzIWag5LOiLppFeIY9TbP5x2TNaFnQEAyFVOSZiZfdAbhjGetv39UgZW6ZxzOjUxpf6/+ZTfoQAA8uCcW3ahZefciKQR73FMkpUpLABADVl1OKJXov6UmbWn7b7mzQurWa/8JKbNG+oV2rXF71AAAAAAVJFcCnPEnHPb09YIk3PuvLKvt1IzRs9NsUAzAAAAgLxVZYn65KKYaQukld2piSnWBwOAMvJ+57f7GwUAAGuXy5ywD5tZNH0OWFqJ+j8sVWAr8XtRzJt35jV+/oo+9b6dvsUAALXGWxRz0t8oAABYu1WTMOfc75jZV83sgKSoEtWkopIOlzq4SvVnf3lJB0LbtXlDg9+hAAAAAKgylKgvQIT5YAAAAAAKlMucMEmSc27cOfeVZAJmZltLF1ZlS8wHIwkDAAAAkL+ck7AseosWRRV549IN3Zpd0FMPBv0OBQAAAEAVyjsJM7N2M/v7kgZLEE/FOzWRGIpoxvqdAAAAAPKXUxJmZlvN7EtmdlrSG5L6JMVKGVilGj13Qd37GYoIAAAAoDArJmFm9gtm9oKkq5JGlFig+fPOuUck/U7pw6sss/OL+taPLuuzT5KEAQAAACjMPUmYmX3QzP7AzBYlnZTUIunXnXMBSUPOuVOS5Jz7SnlD9d93f/yuHt+zTdu3NPkdCgAAAIAqla0n7BclHZX0kqSgc+7zaQmXK1tkFSgyQWl6AMC9JicnNTAwoLGxMb9DAQCUkfd7vz3f8+5ZJ8w59zuSfsfMDks6bmZXJJ10zk2uLcTqFzk3pf/5i4f8DgMAUGHa29s1MDDgdxgAgDLr6uqSpMl8z1t2sWZv2OEpSTKzp83s1yXtNbMXnXPXzewXnHN/Wli41Wfq6ozevnJLB0Pb/Q4FAAAAQBVbNglL55x7SdJLZrZN0lEz65D0rKSaScJOvXJRXU/uVn3dWpZWAwAAAFDrckrCkpxz1yR9RZLMrLMkEVWoU8wHAwAAAFAEa+nW6S9aFHkq9wToxXhcX3vlosIkYQBKbHx8XGamvr4+DQ0Nqb+/X2amI0eOpLZbWlo0NDRU8Gv09fWpr6+v5OcUW6GTnwEAqDR59YSlc879RTEDyUe5J0D/xflp7dy2QQ9s31S21wRQm6anp3XixAn19PSk9g0NDamvr0/hcFiSdPz4cY2MjBT8GoUkU34nYFLhk58BAKg0BSdhteTUxBS9YADKIhaLLUnAsgkGgwoGgwW/Rmdn/qPJCzkHAABkR5WJHIyShAEok1AolNNxBw8eLKj9WCym8fFxjY+Pl/QcAACwPHrCVnH11px++JOYPvH4Tr9DAVADcu1xmp6e1oEDB3Tw4EF1d3frhRde0NGjR9XT06NoNJpKmEZHR9XX15dqd3p6Ws8//7xisZhGR0c1Pj6uZ599VqFQKDXkcHR0VB0dHert7S34HEmKRqMaHh7WoUOHdPr0aXV0dKSeSz8OAIBaQxK2im+8elEffew+bWis8zsUAAXY8rf/jd8h6MYf/3LR2wyHw+rr69Pg4KAGBweX9KAdOXJEx48fV09Pjzo7O3XgwAFdvXpVklKJ0+DgoKRE0nf8+HH19/crFAopFAopHA7LzFKJUiHnJOM4ceKEQqGQgsGgBgcHNTo6WvRrAQBAtSEJW0WEoYhAVStFAlQpWltbUwlOeg/aqVOnUnPGkslZNBpddqhj5rFJsVhs2blnuZwzPj6u1tbW1HFnzpzJ9UsDAGBdIwlbgXNOkYkp/bd/7Qm/QwGArLIlScFgUJFIRNFoNJUErSYzmcql8Mdq5/T09CgSiainp0fj4+N65plncooFAID1jsIcK3jtwnUFTHqsbavfoQBAzrq7uxWNRtXb27tqpcVSOnr0qKanp3Xy5ElJ0vDwsG+xAABQSUreE2ZmxyRFJbVKknNuxcVtzCwoqTftnDPOOV9Kco2eu6Dw/j0yMz9eHgDyNj4+rjNnziyZexWLxSRJJ0+eXDYpm56eXrKdPGclq51z+vTp1DwyAABwV0l7wsxsUFLUOXfSS746zGzZt2W9BOyUc27IOXfS2328lDGu5NTElA4/xXwwAP4YGhrSkSNHJEn9/f0aGhpSNBqVlEi2hoeHFYlENDQ0lEqAOjs79cwzz2hoaEiRSESRSETDw8MaHBxUMBjU+Pi4BgcHdebMGY2MjKS2x8fHU+309/enXjNZaTHfcySpo6NDLS0t6ujoUEdHh7q7u1O9YgAA1DJzzpWucbOrzrmWtO2wpH7nXPcyxw9LOpveW2ZmQedcLP24gYEBNzAwUJqgPTOzC+r4b/9UP/rnP6dtzY0lfS0AWG/Gx8f1wgsv6Pjx4woGg4rFYopGo3r22WdTFRMLYWbPOecGihvt2pXjvgQAqEyF3JtKNhzRzLItdjMtKbzCab2SOtJ3ZCZg5fKt1y5r30MtJGAAUIAzZ87o0KFDqWIdyQqOBw8ezGmoIwAA61kp54S1KpF0pYtJ2Xu3zCz5tmjIS+BaJQWdc0MljHFZkXNT6t7PUEQAKERvb69GRkY0NDSUSsRisZi6u7tzXpC6mkxOTmpgYEBdXV3q6uryOxwAQJmMjY1JUnu+55UyCQtm2ZdMylrlJWRpUmNTkvPBzOyYmQ065/rTD0ze7CSV7IYXmZjSV/o+VvR2AaBWpC/cvBZjY2PJm5xUwI2uHNrb28VwRACoPV4eMpnveaVMwmLyKiKmSW5n9pCl70tfzTMi6aykJUlYqW92b713S9M3Z/XB9tzW1wEAlE76m23PPffcpK/BAABQBKWsjjite3vDgtKy87yiWZ6LSamqiWUTmZjS557arUCA0vQAAAAAiqtkSZi3tlcsY3erEr1b2Y6PSYqlzQ2TVk7aSibirQ8GAAAAAMVW0nXCJI1krAvWLWk4uWFmoYznn9fS6olHlTEUsdTmF+L6s7+8pMNP7S7nywIAAACoESVNwryCGiEz6zGzY5LeSFuEWUokXH1pxw9JCnoFOY5JulLu6oin33hP7fdt1s5tG8v5sgAAAABqRCkLc0hKJVbLPTciaSRjny8l6ZMiE1M6vI/S9AAAAABKo9TDEavOqQnWBwMAAABQOiRhad69fkd/dfGGPvzIDr9DAVCjxsfHZWbq6+vT0NCQ+vv7ZWY6cuRIarulpUVDQ74OGgAAAGtQ8uGI1eTrr1zUp963S431dX6HAqBGTU9P68SJE+rpuVuzaGhoSH19fQqHE3WLjh8/rpGRkeWaWNXIyEjRFlIGAAD5oycsTWTigsLMBwPgo1gstiQByyYYDCoYDBb8GmfPni34XAAAsHYkYZ543OnUKxcpygHAV6FQaPWDJB08eDDvtmOxmPr6+jQ9PZ33uQAAoHgYjuiZeOuqtmxo0N6dm/0OBUAN6+zszPm4WCym559/XocOHVI0GlVnZ2dqyOLQ0FDqmNOnT+vo0aOKRqOanp7W+Pi4hoaGFAwGGZYIAIAPSMI8kYkphiIC65B99fN+hyD3+a+WpN0DBw5odHQ01XvW0dGhs2fP6sUXX1QoFEolZMmhi+nDHI8dO1aSmAAAwOpIwjyRiSn93f/ifX6HAaDISpUA+e3kycS69+nDFzs7OxWJRBQKhVLDDsPhcCoZAwAAlaEq54RNTk5qYGBAY2NjRWnvxu15fX9yWp96366itAcApRaNRhUMBhWJRFIfhw4dUjAYVDgc1vDwsEZHR9XR0aEDBw4oFov5HfKaeb/z2/2NAgCAtavKnrD29nYNDAwUrb1v/PCSDoa2a1NTVV4OADWos7NTL7zwwpJeruTjSCSypAesv79fIyMj9wxBjEajisViOc9D81tXV5ckTfobBQAAa1eVPWHFdmpiiqqIAKpKMsGKRqOpfbFYTOPj4xofH1ckEkntP3r0aOpxKBRKnZMs5gEAAMqr5rt+nHOKTFzQ//Wbn/E7FABYYmhoSKdPn5aU6M06evSoenp6UvPATp06laqOmNTT06MzZ84oGo2m5o1Fo9FUL1hnZ6cOHjy4psWeca/kMPmurq5kjx0AoAYUOlS+5pOwNy7d0OxCXO9/YJvfoQDAEqtVMAwGgxocHLxn/2pl54eHh9cUF+5V7GHyAIDqUOhQ+Zofjhg5N6XDT7XJzPwOBQAAAEANIAljfTAAAAAAZVTTSdjs/KK+/dpldT252+9QAAAAANSImk7CvvP6u3r8/m3avqXJ71AAAAAA1IiaTsIiE1PqZigiAAAAgDKq+SSM9cEAAAAAlFPNJmFTV2d0YXpGB0Lb/Q4FAAAAQA2p2SQsMjGlrid3q76uZi8BAAAAAB/UbAZyitL0AAAAAHxQs0lY6+Ym5oMBAAAAKLuqTMImJyc1MDCgsbGxgtv4vV85pPtbm4sXFACgpLzf+e3+RgEAwNrV+x1AIdrb2zUwMOB3GACAMurq6pKkybW2Y2bHJEUltUqSc26kmMcDALCaquwJAwCgEGY2KCnqnDvpJVMdZtZTrOMBAMgFSRgAoJb0OudOpm2PSuor4vEAAKyqppOwtcwpq1Vcs8Jw3QrDdcsf12x5ZtaZZfe0pPBaj5+cnFxTbLWI79X8cc3yxzXLH9esIO35nkAShrxwzQrDdSsM1y1/XLMVtSqRRKWLSZKZBddyPElY/vhezR/XLH9cs/xxzQrSnu8JNZ2EFVMxv2GL/c1fyT9MlXrdauWaFbu9Wrlulfx/UGyVfN0KEMyyL5lktRbh+KKp1OteAf+Hy6rUr7NSr1kl/x6rhWtWyde/mCr1mlXC9TLnnN8x5M3M/lDS20Voql1FqLRV4W0Vu71itlXs9iq1rWK3V8y2it1eMdsqdnuV2lax2ytmW8VurxhtPeCc+1IhJ5pZWNIJ51xL2r6QpDcktTjnYoUeb2bfkTTrbU5q7V9nexHaqKW2it0ebfnbHm352x5t5d5Gu/e4yTn3sXxOrsoS9YXegAEANW1a9/ZuBSUpMwHL9/h8b74AgNpWlUkYUI3MbNg5R1U1wCfOuXEzi2XsbpUUKcbxwFqtZU067jFA6XhLkxxyzvXncGxOP8c1mYSx8Gb+vGsmSYcknXbODfkZT7Xx1hoK+R1HtfCKHvTq7s/pGefcuK9BVTgz603bDPIzuqwRM+tJKzvfLWk4+aQ33LAz7fkR7+YbUuL78UtKDEdcEfeZghfFlmr0PuPdJ04nv/fMbDDje3W1c2vyHlPA91lQNX5/KeCa1ez9xRuW3qnEvSKaw/E5/xxX5Zywtch2cdK3ca/Md9fM7KykF2rph3AtvD/q+pT4w67b73gqnXeDPOWcO+Bt90rqds4d8TWwCmZmx9J/Hr3S6mF+RrNL+wMkJCmW/geI9/12JP1n1cy+LumspMtKVEfs0Ar3De4z+V8D7jOSmV3NmH8YltS/2n2jlu8xBXyfBVXj95cCrhn3F6WuU3C13uZ8fo5rsToiC2/mwfuFFcvYPSyuWT7CSnyfITeDSuuZ8P5Afta/cKrC0fQN713dQz7FUvGcc0POuZPe55GM50ay3Cw/6Jz77bTjWeB5dTlfA+4z+a9hl6GW7zH5/qxxf8n/mnF/yVG+P8c1lYSt8ZdcrWqVdMx7py1dTQ57yJf3DsiLfsdRZXqVMedmmaIJuGvazE4kN7x3d1/wMZ51o5QLPK9XBVwD7jP5r2En77mavccU+LNW0/eXAq8Z95fc5fVzXFNJmAr8JVfLnHNRSQe8z0ndYmJ6roK19At+rdL+CAuZWY+Z9abNE8Hy+iR1mtlV73pN19LQtxIr2QLP61he14D7jKTC16Sr5XtMXt9n3F8kFfb7iftL7oJZ9i37c1xrSVgwy76yLLxZzdInrHo/pGHV0DCRQuU6oRpLpN759oaLjUipsdhYhvfH67AS85wGxVCRYgpm2VeRCzxXkGCWfSteA+4ziunea5PczvyjWRL3GOX/fcb9pbCfTe4vuYspj5/jWkvCYsrzlxzucULS4Yx3LJHBe8ct5nccVSj5c3gmbV9EUq29W5kXMxuWNO5NNj8iqTd9+AjWJKb87hv5Hr8exbS2a1CL95m81rDjHiMp/+8z7i+FJfvcX3KX189xrZWoz3ehTqTx3i3qr7VSrgXqlNSaVta1W4khEL2SIjX2x0U+otI9P48xKfHuOD+n90qO8XfORbzPJ81sXDmUUUdOSrbA8zpW8DWo1ftMAWvScY/J//uM+0v+yT73lzzk+3NcU0kYC28WzlsnZzR5YzSzzlq7SeYjc4iImUlSqBbXCsqHcy5mZjEzC6X9ERFMPudbYJWtVRk3ROdc1MxqeZhS0eR73+A+U/g14D6T+xp23GMK+tms+ftLAT+b3F9WYcusLbncz3G6WhuOKN1deDNp2YuDBK/6UqukM2YW9L7hjq5yGjzJdYeUeJfyWA1Nzi/U81paqemopFVXqK9V3juUS8boe99jtfBOeLmseN8ws1DG89xn8rxm3Gck51y/7haNOCbpjYxkK+s8uRq/x+T7s8n9JY9rxv0l8WaQ9/PYI+kZ72csvcrkkp/LHH6O77btamyxZmnlhTqxlPfDdjXLUydraXFDlFdmxapaWxQyX3Z3sdbUO5b8Xiuule4bln2B55q/z+R6zbjPYC0K/NlMqcX7Sz7XjPtL6dRkEgYAAAAAfqnF4YgAAAAA4BuSMAAAAAAoI5IwAAAAACgjkjAAAAAAKCOSMAAAAAAoI5IwAAAAACijer8DANaztPU1jkkal/RC2tMdknol9ZV6zQ1vYcGjSiwayPoeAAAAPmKdMKAMzOwNScOZi0J6yVG4HItFeoszdqcvWgkAAIDyYzgi4CPn3LikWJlebrxMrwMAAIAVkIQBPjCzcNpm1LdAAAAAUHbMCQP80S0pIknOuYiUSswGlUjKRiVNSwpJiqXP4zKzoBJzyZLJWyjLMMdBSaeV6GULOudOpj2XTAC7xRwxAACAsmNOGFAG3pywZHLVIanXOWdZjuuRNOic60jbd0LSaDJZMrOzkg4752LedljSEedcn7c9KqnfG+ooM7sq6bCkVkknJB1wzkW951y2OAAAAFA69IQB5TOa7LEys9gKx2XO3RpWInkaSfZiJRMw73HEzEbNrF+JnrODyQTMc8A5F/XOnU4mYElmFkxvDwAAAKXFnDDAH6lS9WYW9oYYLicqKfl8pxLDFDPFJB30PpYkWRlJV+b8s1guwQIAAKB4SMIAH2T0VHWu0hMV0t1kKarEsMJMQUlnvI/Q2iMEAABAqZCEAT7yesAOZezuzNjuk9QvSckCG94i0Mk2eiSddM7FvOQuml590cxC3npk0r0JXHCtXwMAAADyw5wwoIS8ZKlPid6po2ZLamB0SHpG0osZp0W9xCqmREI2mlHB8LCk42Z2WomkKuicO5Lx/KD32tNeO9NKJHKd3qLNI5KOe8cPmtlg5lwxAAAAlAbVEYEK4iVfRzOSKgAAAKwjDEcEKk/Q7wAAAABQOiRhQIXw5m31STpoZr1+xwMAAIDSYDgiAAAAAJQRPWEAAAAAUEYkYQAAAABQRiRhAAAAAFBG/z/wVh4ekLeIxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3 = plt.figure(constrained_layout=True, figsize=(12, 6))\n",
    "\n",
    "gs = fig3.add_gridspec(2, 2)\n",
    "ax = fig3.add_subplot(gs[0, 0])\n",
    "ax.plot(hist_dict.history['loss'][:10], label='Training')\n",
    "ax.plot(hist_dict.history['val_loss'][:10], label='Test')\n",
    "ax.set_xlabel('Epoch')\n",
    "ax.set_ylabel('Loss')\n",
    "ax.set_title('Learning Curves')\n",
    "ax.legend()\n",
    "ax = fig3.add_subplot(gs[1, 0])\n",
    "ax.plot(hist_dict.history['acc'][:10], label='Training')\n",
    "ax.plot(hist_dict.history['val_acc'][:10], label=\"Test\")\n",
    "ax.set_xlabel('Epoch')\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.legend()\n",
    "ax = fig3.add_subplot(gs[:, 1:])\n",
    "\n",
    "norm = mpl.colors.Normalize(vmin=0, vmax=20)\n",
    "cmap = mpl.cm.ScalarMappable(norm=norm, cmap='binary')\n",
    "cmap.set_array([])\n",
    "\n",
    "ax.imshow(cmat, aspect='auto', cmap='binary')\n",
    "ax.set_xticks([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
    "ax.set_yticks([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
    "ax.set_xticklabels([r\"$F_{\" + str(int(i)) + \"}$\" for i in range(12)] + [\"N\"])\n",
    "ax.set_yticklabels([r\"$F_{\" + str(int(i)) + \"}$\" for i in range(12)] + [\"N\"])\n",
    "ax.set_ylabel('Ground-Truth')\n",
    "ax.set_xlabel('Predictions')\n",
    "plt.tight_layout()\n",
    "\n",
    "cbar =  fig.colorbar(cmap, ax=ax)\n",
    "cbar.set_ticks([2 * i for i in range(11)])\n",
    "plt.savefig('./Figures/Presentation/Cmat_CNN.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmat = confusion_matrix(yts.argmax(axis=1), yp)\n",
    "\n",
    "norm = mpl.colors.Normalize(vmin=0, vmax=20)\n",
    "cmap = mpl.cm.ScalarMappable(norm=norm, cmap='binary')\n",
    "cmap.set_array([])\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(7, 6))\n",
    "ax.imshow(cmat, aspect='auto', cmap='binary')\n",
    "ax.set_xticks([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
    "ax.set_yticks([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
    "ax.set_xticklabels([r\"$F_{\" + str(int(i)) + \"}$\" for i in range(12)] + [\"N\"])\n",
    "ax.set_yticklabels([r\"$F_{\" + str(int(i)) + \"}$\" for i in range(12)] + [\"N\"])\n",
    "ax.set_ylabel('Ground-Truth')\n",
    "ax.set_xlabel('Predictions')\n",
    "\n",
    "cbar =  fig.colorbar(cmap, ax=ax)\n",
    "cbar.set_ticks([2 * i for i in range(11)])\n",
    "\n",
    "plt.savefig('./Figures/Ch6/CMat_CNN.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Domain Adaptation Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/cstr_rawdata.csv')\n",
    "crossval_index = np.load('./data/crossval_index.npy')\n",
    "\n",
    "data_mat = df.values\n",
    "X = data_mat[:, :-4]\n",
    "y = data_mat[:, -4]\n",
    "d = data_mat[:, -3]\n",
    "\n",
    "Xtr = feature_normalization(X[np.where(d == 0)[0], :, np.newaxis])\n",
    "ytr = tf.keras.utils.to_categorical(y[np.where(d == 0)[0]])\n",
    "\n",
    "Xtargets = [feature_normalization(X[np.where(d == domain)[0], :, np.newaxis]) for domain in range(1, 7)]\n",
    "ytargets = [y[np.where(d == domain)[0]] for domain in range(1, 7)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/eduardo/.conda/envs/OptimalTransport/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n"
     ]
    }
   ],
   "source": [
    "cnn = CNNClassifier(input_shape=(1400, 1), n_classes=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 1400, 1)]         0         \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, 1400, 32)          192       \n",
      "_________________________________________________________________\n",
      "max_pooling1d (MaxPooling1D) (None, 700, 32)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 700, 64)           12352     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 350, 64)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 350, 128)          49280     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1 (None, 175, 128)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 22400)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1200)              26881200  \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 200)               240200    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 13)                2613      \n",
      "=================================================================\n",
      "Total params: 27,185,837\n",
      "Trainable params: 27,185,837\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn.model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1300 samples\n",
      "Epoch 1/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.3652 - acc: 0.5203Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.7346153846153847\n",
      "Domain: 2, Acc: 0.7653846153846153\n",
      "Domain: 3, Acc: 0.6730769230769231\n",
      "Domain: 4, Acc: 0.5692307692307692\n",
      "Domain: 5, Acc: 0.5230769230769231\n",
      "1300/1300 [==============================] - 7s 6ms/sample - loss: 1.3485 - acc: 0.5269\n",
      "Epoch 2/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 0.1217 - acc: 0.9711Domain: 0, Acc: 0.85\n",
      "Domain: 1, Acc: 0.7576923076923077\n",
      "Domain: 2, Acc: 0.7346153846153847\n",
      "Domain: 3, Acc: 0.7153846153846154\n",
      "Domain: 4, Acc: 0.5692307692307692\n",
      "Domain: 5, Acc: 0.46153846153846156\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 0.1229 - acc: 0.9708\n",
      "Epoch 3/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 0.0649 - acc: 0.9734Domain: 0, Acc: 0.8423076923076923\n",
      "Domain: 1, Acc: 0.8\n",
      "Domain: 2, Acc: 0.7846153846153846\n",
      "Domain: 3, Acc: 0.6807692307692308\n",
      "Domain: 4, Acc: 0.7384615384615385\n",
      "Domain: 5, Acc: 0.6076923076923076\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 0.0644 - acc: 0.9738\n",
      "Epoch 4/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 0.0473 - acc: 0.9883Domain: 0, Acc: 0.85\n",
      "Domain: 1, Acc: 0.7807692307692308\n",
      "Domain: 2, Acc: 0.7769230769230769\n",
      "Domain: 3, Acc: 0.6653846153846154\n",
      "Domain: 4, Acc: 0.7307692307692307\n",
      "Domain: 5, Acc: 0.6269230769230769\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 0.0466 - acc: 0.9885\n",
      "Epoch 5/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 0.0197 - acc: 0.9953Domain: 0, Acc: 0.8269230769230769\n",
      "Domain: 1, Acc: 0.7423076923076923\n",
      "Domain: 2, Acc: 0.75\n",
      "Domain: 3, Acc: 0.6269230769230769\n",
      "Domain: 4, Acc: 0.7269230769230769\n",
      "Domain: 5, Acc: 0.6192307692307693\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 0.0194 - acc: 0.9954\n",
      "Epoch 6/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 0.0091 - acc: 0.9969Domain: 0, Acc: 0.8384615384615385\n",
      "Domain: 1, Acc: 0.7653846153846153\n",
      "Domain: 2, Acc: 0.7384615384615385\n",
      "Domain: 3, Acc: 0.6192307692307693\n",
      "Domain: 4, Acc: 0.7615384615384615\n",
      "Domain: 5, Acc: 0.6923076923076923\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 0.0092 - acc: 0.9969\n",
      "Epoch 7/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 0.0091 - acc: 0.9961Domain: 0, Acc: 0.8423076923076923\n",
      "Domain: 1, Acc: 0.7730769230769231\n",
      "Domain: 2, Acc: 0.7538461538461538\n",
      "Domain: 3, Acc: 0.6269230769230769\n",
      "Domain: 4, Acc: 0.7538461538461538\n",
      "Domain: 5, Acc: 0.6846153846153846\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 0.0090 - acc: 0.9962\n",
      "Epoch 8/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.9984Domain: 0, Acc: 0.8307692307692308\n",
      "Domain: 1, Acc: 0.7576923076923077\n",
      "Domain: 2, Acc: 0.7423076923076923\n",
      "Domain: 3, Acc: 0.6269230769230769\n",
      "Domain: 4, Acc: 0.7653846153846153\n",
      "Domain: 5, Acc: 0.6615384615384615\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 0.0057 - acc: 0.9985\n",
      "Epoch 9/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 0.0017 - acc: 1.0000Domain: 0, Acc: 0.8307692307692308\n",
      "Domain: 1, Acc: 0.7615384615384615\n",
      "Domain: 2, Acc: 0.7461538461538462\n",
      "Domain: 3, Acc: 0.6269230769230769\n",
      "Domain: 4, Acc: 0.7576923076923077\n",
      "Domain: 5, Acc: 0.6230769230769231\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 0.0017 - acc: 1.0000\n",
      "Epoch 10/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 7.3364e-04 - acc: 1.0000Domain: 0, Acc: 0.8153846153846154\n",
      "Domain: 1, Acc: 0.7461538461538462\n",
      "Domain: 2, Acc: 0.7346153846153847\n",
      "Domain: 3, Acc: 0.6230769230769231\n",
      "Domain: 4, Acc: 0.7653846153846153\n",
      "Domain: 5, Acc: 0.5846153846153846\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 9.2771e-04 - acc: 1.0000\n",
      "Epoch 11/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 1.0000Domain: 0, Acc: 0.8346153846153846\n",
      "Domain: 1, Acc: 0.75\n",
      "Domain: 2, Acc: 0.7576923076923077\n",
      "Domain: 3, Acc: 0.6423076923076924\n",
      "Domain: 4, Acc: 0.7269230769230769\n",
      "Domain: 5, Acc: 0.5538461538461539\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 0.0046 - acc: 1.0000\n",
      "Epoch 12/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 0.0063 - acc: 0.9977Domain: 0, Acc: 0.823076923076923\n",
      "Domain: 1, Acc: 0.7423076923076923\n",
      "Domain: 2, Acc: 0.7384615384615385\n",
      "Domain: 3, Acc: 0.6269230769230769\n",
      "Domain: 4, Acc: 0.7615384615384615\n",
      "Domain: 5, Acc: 0.573076923076923\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 0.0062 - acc: 0.9977\n",
      "Epoch 13/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 5.5893e-04 - acc: 1.0000Domain: 0, Acc: 0.8\n",
      "Domain: 1, Acc: 0.7269230769230769\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7692307692307693\n",
      "Domain: 5, Acc: 0.5769230769230769\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 5.5037e-04 - acc: 1.0000\n",
      "Epoch 14/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 1.0000Domain: 0, Acc: 0.8153846153846154\n",
      "Domain: 1, Acc: 0.7384615384615385\n",
      "Domain: 2, Acc: 0.7384615384615385\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7653846153846153\n",
      "Domain: 5, Acc: 0.5884615384615385\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 0.0012 - acc: 1.0000\n",
      "Epoch 15/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.7738e-04 - acc: 1.0000Domain: 0, Acc: 0.8269230769230769\n",
      "Domain: 1, Acc: 0.7346153846153847\n",
      "Domain: 2, Acc: 0.7346153846153847\n",
      "Domain: 3, Acc: 0.6115384615384616\n",
      "Domain: 4, Acc: 0.7461538461538462\n",
      "Domain: 5, Acc: 0.5961538461538461\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 1.7471e-04 - acc: 1.0000\n",
      "Epoch 16/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 2.3961e-04 - acc: 1.0000Domain: 0, Acc: 0.8269230769230769\n",
      "Domain: 1, Acc: 0.7346153846153847\n",
      "Domain: 2, Acc: 0.7384615384615385\n",
      "Domain: 3, Acc: 0.6153846153846154\n",
      "Domain: 4, Acc: 0.7307692307692307\n",
      "Domain: 5, Acc: 0.5961538461538461\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 2.3593e-04 - acc: 1.0000\n",
      "Epoch 17/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 2.0214e-04 - acc: 1.0000Domain: 0, Acc: 0.8269230769230769\n",
      "Domain: 1, Acc: 0.7346153846153847\n",
      "Domain: 2, Acc: 0.7346153846153847\n",
      "Domain: 3, Acc: 0.6153846153846154\n",
      "Domain: 4, Acc: 0.7346153846153847\n",
      "Domain: 5, Acc: 0.5884615384615385\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 1.9999e-04 - acc: 1.0000\n",
      "Epoch 18/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.1855e-04 - acc: 1.0000Domain: 0, Acc: 0.8269230769230769\n",
      "Domain: 1, Acc: 0.7307692307692307\n",
      "Domain: 2, Acc: 0.7346153846153847\n",
      "Domain: 3, Acc: 0.6153846153846154\n",
      "Domain: 4, Acc: 0.7269230769230769\n",
      "Domain: 5, Acc: 0.5884615384615385\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 1.1701e-04 - acc: 1.0000\n",
      "Epoch 19/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 6.7976e-05 - acc: 1.0000Domain: 0, Acc: 0.8153846153846154\n",
      "Domain: 1, Acc: 0.7230769230769231\n",
      "Domain: 2, Acc: 0.7346153846153847\n",
      "Domain: 3, Acc: 0.6153846153846154\n",
      "Domain: 4, Acc: 0.7307692307692307\n",
      "Domain: 5, Acc: 0.5769230769230769\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 7.3187e-05 - acc: 1.0000\n",
      "Epoch 20/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 5.8761e-05 - acc: 1.0000Domain: 0, Acc: 0.8153846153846154\n",
      "Domain: 1, Acc: 0.7153846153846154\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7423076923076923\n",
      "Domain: 5, Acc: 0.5615384615384615\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 5.7888e-05 - acc: 1.0000\n",
      "Epoch 21/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 5.2639e-05 - acc: 1.0000Domain: 0, Acc: 0.8115384615384615\n",
      "Domain: 1, Acc: 0.7115384615384616\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7423076923076923\n",
      "Domain: 5, Acc: 0.5576923076923077\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 5.1934e-05 - acc: 1.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 4.8967e-05 - acc: 1.0000Domain: 0, Acc: 0.8115384615384615\n",
      "Domain: 1, Acc: 0.7115384615384616\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7423076923076923\n",
      "Domain: 5, Acc: 0.55\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 4.8319e-05 - acc: 1.0000\n",
      "Epoch 23/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 4.3452e-05 - acc: 1.0000Domain: 0, Acc: 0.8115384615384615\n",
      "Domain: 1, Acc: 0.7115384615384616\n",
      "Domain: 2, Acc: 0.7346153846153847\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7423076923076923\n",
      "Domain: 5, Acc: 0.55\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 4.3471e-05 - acc: 1.0000\n",
      "Epoch 24/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 3.9793e-05 - acc: 1.0000Domain: 0, Acc: 0.8076923076923077\n",
      "Domain: 1, Acc: 0.7115384615384616\n",
      "Domain: 2, Acc: 0.7346153846153847\n",
      "Domain: 3, Acc: 0.6115384615384616\n",
      "Domain: 4, Acc: 0.7384615384615385\n",
      "Domain: 5, Acc: 0.5461538461538461\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 3.9420e-05 - acc: 1.0000\n",
      "Epoch 25/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 3.6363e-05 - acc: 1.0000Domain: 0, Acc: 0.8115384615384615\n",
      "Domain: 1, Acc: 0.7115384615384616\n",
      "Domain: 2, Acc: 0.7346153846153847\n",
      "Domain: 3, Acc: 0.6115384615384616\n",
      "Domain: 4, Acc: 0.7423076923076923\n",
      "Domain: 5, Acc: 0.5461538461538461\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 3.6213e-05 - acc: 1.0000\n",
      "Epoch 26/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 2.8698e-05 - acc: 1.0000Domain: 0, Acc: 0.8115384615384615\n",
      "Domain: 1, Acc: 0.7115384615384616\n",
      "Domain: 2, Acc: 0.7346153846153847\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7423076923076923\n",
      "Domain: 5, Acc: 0.5461538461538461\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 3.3690e-05 - acc: 1.0000\n",
      "Epoch 27/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 3.1113e-05 - acc: 1.0000Domain: 0, Acc: 0.8115384615384615\n",
      "Domain: 1, Acc: 0.7115384615384616\n",
      "Domain: 2, Acc: 0.7307692307692307\n",
      "Domain: 3, Acc: 0.6038461538461538\n",
      "Domain: 4, Acc: 0.7307692307692307\n",
      "Domain: 5, Acc: 0.5346153846153846\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 3.0689e-05 - acc: 1.0000\n",
      "Epoch 28/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 2.7810e-05 - acc: 1.0000Domain: 0, Acc: 0.8115384615384615\n",
      "Domain: 1, Acc: 0.7115384615384616\n",
      "Domain: 2, Acc: 0.7307692307692307\n",
      "Domain: 3, Acc: 0.6038461538461538\n",
      "Domain: 4, Acc: 0.7269230769230769\n",
      "Domain: 5, Acc: 0.5307692307692308\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 2.7463e-05 - acc: 1.0000\n",
      "Epoch 29/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 2.6664e-05 - acc: 1.0000Domain: 0, Acc: 0.8115384615384615\n",
      "Domain: 1, Acc: 0.7115384615384616\n",
      "Domain: 2, Acc: 0.7307692307692307\n",
      "Domain: 3, Acc: 0.6038461538461538\n",
      "Domain: 4, Acc: 0.7269230769230769\n",
      "Domain: 5, Acc: 0.5307692307692308\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 2.6402e-05 - acc: 1.0000\n",
      "Epoch 30/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 2.5219e-05 - acc: 1.0000Domain: 0, Acc: 0.8115384615384615\n",
      "Domain: 1, Acc: 0.7115384615384616\n",
      "Domain: 2, Acc: 0.7307692307692307\n",
      "Domain: 3, Acc: 0.6038461538461538\n",
      "Domain: 4, Acc: 0.7269230769230769\n",
      "Domain: 5, Acc: 0.5269230769230769\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 2.5206e-05 - acc: 1.0000\n",
      "Epoch 31/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 2.4305e-05 - acc: 1.0000Domain: 0, Acc: 0.8076923076923077\n",
      "Domain: 1, Acc: 0.7115384615384616\n",
      "Domain: 2, Acc: 0.7307692307692307\n",
      "Domain: 3, Acc: 0.6038461538461538\n",
      "Domain: 4, Acc: 0.7269230769230769\n",
      "Domain: 5, Acc: 0.5269230769230769\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 2.3988e-05 - acc: 1.0000\n",
      "Epoch 32/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 2.2884e-05 - acc: 1.0000Domain: 0, Acc: 0.8076923076923077\n",
      "Domain: 1, Acc: 0.7115384615384616\n",
      "Domain: 2, Acc: 0.7307692307692307\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7307692307692307\n",
      "Domain: 5, Acc: 0.5230769230769231\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 2.2846e-05 - acc: 1.0000\n",
      "Epoch 33/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 2.1978e-05 - acc: 1.0000Domain: 0, Acc: 0.8076923076923077\n",
      "Domain: 1, Acc: 0.7115384615384616\n",
      "Domain: 2, Acc: 0.7346153846153847\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7307692307692307\n",
      "Domain: 5, Acc: 0.5230769230769231\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 2.1726e-05 - acc: 1.0000\n",
      "Epoch 34/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 2.1275e-05 - acc: 1.0000Domain: 0, Acc: 0.8076923076923077\n",
      "Domain: 1, Acc: 0.7076923076923077\n",
      "Domain: 2, Acc: 0.7346153846153847\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7307692307692307\n",
      "Domain: 5, Acc: 0.5230769230769231\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 2.0982e-05 - acc: 1.0000\n",
      "Epoch 35/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 2.0321e-05 - acc: 1.0000Domain: 0, Acc: 0.8115384615384615\n",
      "Domain: 1, Acc: 0.7076923076923077\n",
      "Domain: 2, Acc: 0.7346153846153847\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7307692307692307\n",
      "Domain: 5, Acc: 0.5230769230769231\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 2.0166e-05 - acc: 1.0000\n",
      "Epoch 36/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.8795e-05 - acc: 1.0000Domain: 0, Acc: 0.8115384615384615\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7346153846153847\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7307692307692307\n",
      "Domain: 5, Acc: 0.5192307692307693\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 1.9289e-05 - acc: 1.0000\n",
      "Epoch 37/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.9050e-05 - acc: 1.0000Domain: 0, Acc: 0.8115384615384615\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7346153846153847\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7307692307692307\n",
      "Domain: 5, Acc: 0.5153846153846153\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 1.8790e-05 - acc: 1.0000\n",
      "Epoch 38/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.8292e-05 - acc: 1.0000Domain: 0, Acc: 0.8076923076923077\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7346153846153847\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7307692307692307\n",
      "Domain: 5, Acc: 0.5038461538461538\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 1.8121e-05 - acc: 1.0000\n",
      "Epoch 39/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.7695e-05 - acc: 1.0000Domain: 0, Acc: 0.8076923076923077\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7307692307692307\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7307692307692307\n",
      "Domain: 5, Acc: 0.5\n",
      "1300/1300 [==============================] - 8s 6ms/sample - loss: 1.7475e-05 - acc: 1.0000\n",
      "Epoch 40/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.5785e-05 - acc: 1.0000Domain: 0, Acc: 0.8076923076923077\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7307692307692307\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7307692307692307\n",
      "Domain: 5, Acc: 0.5\n",
      "1300/1300 [==============================] - 7s 6ms/sample - loss: 1.6915e-05 - acc: 1.0000\n",
      "Epoch 41/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.6818e-05 - acc: 1.0000Domain: 0, Acc: 0.8076923076923077\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7230769230769231\n",
      "Domain: 5, Acc: 0.5\n",
      "1300/1300 [==============================] - 7s 6ms/sample - loss: 1.6600e-05 - acc: 1.0000\n",
      "Epoch 42/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.6528e-05 - acc: 1.0000Domain: 0, Acc: 0.8076923076923077\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7230769230769231\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Domain: 5, Acc: 0.5\n",
      "1300/1300 [==============================] - 8s 6ms/sample - loss: 1.6274e-05 - acc: 1.0000\n",
      "Epoch 43/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.6264e-05 - acc: 1.0000Domain: 0, Acc: 0.8076923076923077\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7230769230769231\n",
      "Domain: 5, Acc: 0.5\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 1.6035e-05 - acc: 1.0000\n",
      "Epoch 44/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.5125e-05 - acc: 1.0000Domain: 0, Acc: 0.8076923076923077\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7230769230769231\n",
      "Domain: 5, Acc: 0.5\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 1.5479e-05 - acc: 1.0000\n",
      "Epoch 45/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.4980e-05 - acc: 1.0000Domain: 0, Acc: 0.8076923076923077\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7192307692307692\n",
      "Domain: 5, Acc: 0.49615384615384617\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 1.4968e-05 - acc: 1.0000\n",
      "Epoch 46/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.4762e-05 - acc: 1.0000Domain: 0, Acc: 0.8076923076923077\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7153846153846154\n",
      "Domain: 5, Acc: 0.49230769230769234\n",
      "1300/1300 [==============================] - 8s 6ms/sample - loss: 1.4634e-05 - acc: 1.0000\n",
      "Epoch 47/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.4304e-05 - acc: 1.0000Domain: 0, Acc: 0.8076923076923077\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7192307692307692\n",
      "Domain: 5, Acc: 0.49230769230769234\n",
      "1300/1300 [==============================] - 8s 6ms/sample - loss: 1.4131e-05 - acc: 1.0000\n",
      "Epoch 48/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.3457e-05 - acc: 1.0000Domain: 0, Acc: 0.8038461538461539\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7192307692307692\n",
      "Domain: 5, Acc: 0.49230769230769234\n",
      "1300/1300 [==============================] - 7s 6ms/sample - loss: 1.3453e-05 - acc: 1.0000\n",
      "Epoch 49/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.2699e-05 - acc: 1.0000Domain: 0, Acc: 0.8038461538461539\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7230769230769231\n",
      "Domain: 5, Acc: 0.49230769230769234\n",
      "1300/1300 [==============================] - 7s 6ms/sample - loss: 1.2982e-05 - acc: 1.0000\n",
      "Epoch 50/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.2775e-05 - acc: 1.0000Domain: 0, Acc: 0.8038461538461539\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7307692307692307\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7230769230769231\n",
      "Domain: 5, Acc: 0.49230769230769234\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 1.2607e-05 - acc: 1.0000\n",
      "Epoch 51/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.2699e-05 - acc: 1.0000Domain: 0, Acc: 0.8038461538461539\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7307692307692307\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7230769230769231\n",
      "Domain: 5, Acc: 0.49230769230769234\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 1.2518e-05 - acc: 1.0000\n",
      "Epoch 52/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.2324e-05 - acc: 1.0000Domain: 0, Acc: 0.8038461538461539\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7307692307692307\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7192307692307692\n",
      "Domain: 5, Acc: 0.49230769230769234\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 1.2142e-05 - acc: 1.0000\n",
      "Epoch 53/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.1851e-05 - acc: 1.0000Domain: 0, Acc: 0.8038461538461539\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7307692307692307\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7192307692307692\n",
      "Domain: 5, Acc: 0.49230769230769234\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 1.1748e-05 - acc: 1.0000\n",
      "Epoch 54/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.1478e-05 - acc: 1.0000Domain: 0, Acc: 0.8\n",
      "Domain: 1, Acc: 0.7038461538461539\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7153846153846154\n",
      "Domain: 5, Acc: 0.48846153846153845\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 1.1310e-05 - acc: 1.0000\n",
      "Epoch 55/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.0808e-05 - acc: 1.0000Domain: 0, Acc: 0.8\n",
      "Domain: 1, Acc: 0.7\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7153846153846154\n",
      "Domain: 5, Acc: 0.4846153846153846\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 1.0971e-05 - acc: 1.0000\n",
      "Epoch 56/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.0880e-05 - acc: 1.0000Domain: 0, Acc: 0.8\n",
      "Domain: 1, Acc: 0.7\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7153846153846154\n",
      "Domain: 5, Acc: 0.4846153846153846\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 1.0713e-05 - acc: 1.0000\n",
      "Epoch 57/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.0644e-05 - acc: 1.0000Domain: 0, Acc: 0.8\n",
      "Domain: 1, Acc: 0.6961538461538461\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7153846153846154\n",
      "Domain: 5, Acc: 0.4846153846153846\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 1.0545e-05 - acc: 1.0000\n",
      "Epoch 58/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 1.0119e-05 - acc: 1.0000Domain: 0, Acc: 0.8\n",
      "Domain: 1, Acc: 0.6961538461538461\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7153846153846154\n",
      "Domain: 5, Acc: 0.4807692307692308\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 1.0232e-05 - acc: 1.0000\n",
      "Epoch 59/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 9.9381e-06 - acc: 1.0000Domain: 0, Acc: 0.8\n",
      "Domain: 1, Acc: 0.6961538461538461\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7153846153846154\n",
      "Domain: 5, Acc: 0.4807692307692308\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 9.8713e-06 - acc: 1.0000\n",
      "Epoch 60/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 9.6733e-06 - acc: 1.0000Domain: 0, Acc: 0.8\n",
      "Domain: 1, Acc: 0.6961538461538461\n",
      "Domain: 2, Acc: 0.7307692307692307\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7153846153846154\n",
      "Domain: 5, Acc: 0.4807692307692308\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 9.7519e-06 - acc: 1.0000\n",
      "Epoch 61/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 9.6342e-06 - acc: 1.0000Domain: 0, Acc: 0.8\n",
      "Domain: 1, Acc: 0.6961538461538461\n",
      "Domain: 2, Acc: 0.7307692307692307\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7153846153846154\n",
      "Domain: 5, Acc: 0.4807692307692308\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 9.5192e-06 - acc: 1.0000\n",
      "Epoch 62/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 9.4779e-06 - acc: 1.0000Domain: 0, Acc: 0.8\n",
      "Domain: 1, Acc: 0.6961538461538461\n",
      "Domain: 2, Acc: 0.7307692307692307\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7153846153846154\n",
      "Domain: 5, Acc: 0.4807692307692308\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 9.3862e-06 - acc: 1.0000\n",
      "Epoch 63/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 9.2317e-06 - acc: 1.0000Domain: 0, Acc: 0.8\n",
      "Domain: 1, Acc: 0.6961538461538461\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7153846153846154\n",
      "Domain: 5, Acc: 0.4807692307692308\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 9.1401e-06 - acc: 1.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 64/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 8.6254e-06 - acc: 1.0000Domain: 0, Acc: 0.8\n",
      "Domain: 1, Acc: 0.6961538461538461\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7153846153846154\n",
      "Domain: 5, Acc: 0.4807692307692308\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 8.8270e-06 - acc: 1.0000\n",
      "Epoch 65/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 8.7446e-06 - acc: 1.0000Domain: 0, Acc: 0.8\n",
      "Domain: 1, Acc: 0.6961538461538461\n",
      "Domain: 2, Acc: 0.7269230769230769\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7153846153846154\n",
      "Domain: 5, Acc: 0.47692307692307695\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 8.6246e-06 - acc: 1.0000\n",
      "Epoch 66/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 7.7680e-06 - acc: 1.0000Domain: 0, Acc: 0.7923076923076923\n",
      "Domain: 1, Acc: 0.6961538461538461\n",
      "Domain: 2, Acc: 0.7230769230769231\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7115384615384616\n",
      "Domain: 5, Acc: 0.47692307692307695\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 8.4502e-06 - acc: 1.0000\n",
      "Epoch 67/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 8.1529e-06 - acc: 1.0000Domain: 0, Acc: 0.7961538461538461\n",
      "Domain: 1, Acc: 0.6961538461538461\n",
      "Domain: 2, Acc: 0.7230769230769231\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7115384615384616\n",
      "Domain: 5, Acc: 0.4807692307692308\n",
      "1300/1300 [==============================] - 7s 6ms/sample - loss: 8.3336e-06 - acc: 1.0000\n",
      "Epoch 68/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 8.3945e-06 - acc: 1.0000Domain: 0, Acc: 0.7923076923076923\n",
      "Domain: 1, Acc: 0.6961538461538461\n",
      "Domain: 2, Acc: 0.7230769230769231\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7115384615384616\n",
      "Domain: 5, Acc: 0.4807692307692308\n",
      "1300/1300 [==============================] - 7s 6ms/sample - loss: 8.2781e-06 - acc: 1.0000\n",
      "Epoch 69/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 8.0983e-06 - acc: 1.0000Domain: 0, Acc: 0.7923076923076923\n",
      "Domain: 1, Acc: 0.6961538461538461\n",
      "Domain: 2, Acc: 0.7230769230769231\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7115384615384616\n",
      "Domain: 5, Acc: 0.4807692307692308\n",
      "1300/1300 [==============================] - 8s 6ms/sample - loss: 8.1006e-06 - acc: 1.0000\n",
      "Epoch 70/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 7.8882e-06 - acc: 1.0000Domain: 0, Acc: 0.7923076923076923\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7230769230769231\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7115384615384616\n",
      "Domain: 5, Acc: 0.47692307692307695\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 7.9120e-06 - acc: 1.0000\n",
      "Epoch 71/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 7.7390e-06 - acc: 1.0000Domain: 0, Acc: 0.7923076923076923\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7230769230769231\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7115384615384616\n",
      "Domain: 5, Acc: 0.47692307692307695\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 7.6793e-06 - acc: 1.0000\n",
      "Epoch 72/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 7.5022e-06 - acc: 1.0000Domain: 0, Acc: 0.7923076923076923\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7230769230769231\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7115384615384616\n",
      "Domain: 5, Acc: 0.47692307692307695\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 7.5046e-06 - acc: 1.0000\n",
      "Epoch 73/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 7.3349e-06 - acc: 1.0000Domain: 0, Acc: 0.7923076923076923\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7230769230769231\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7115384615384616\n",
      "Domain: 5, Acc: 0.47692307692307695\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 7.3298e-06 - acc: 1.0000\n",
      "Epoch 74/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 7.3310e-06 - acc: 1.0000Domain: 0, Acc: 0.7923076923076923\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7230769230769231\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7115384615384616\n",
      "Domain: 5, Acc: 0.47692307692307695\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 7.2360e-06 - acc: 1.0000\n",
      "Epoch 75/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 7.1845e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7230769230769231\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7115384615384616\n",
      "Domain: 5, Acc: 0.47692307692307695\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 7.1054e-06 - acc: 1.0000\n",
      "Epoch 76/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 7.0385e-06 - acc: 1.0000Domain: 0, Acc: 0.7923076923076923\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7230769230769231\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7115384615384616\n",
      "Domain: 5, Acc: 0.47692307692307695\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 6.9649e-06 - acc: 1.0000\n",
      "Epoch 77/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 6.8811e-06 - acc: 1.0000Domain: 0, Acc: 0.7923076923076923\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7230769230769231\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7115384615384616\n",
      "Domain: 5, Acc: 0.47692307692307695\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 6.8239e-06 - acc: 1.0000\n",
      "Epoch 78/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 6.7547e-06 - acc: 1.0000Domain: 0, Acc: 0.7923076923076923\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7230769230769231\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7115384615384616\n",
      "Domain: 5, Acc: 0.47692307692307695\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 6.6889e-06 - acc: 1.0000\n",
      "Epoch 79/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 6.6340e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7230769230769231\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7115384615384616\n",
      "Domain: 5, Acc: 0.47692307692307695\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 6.5432e-06 - acc: 1.0000\n",
      "Epoch 80/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 6.5357e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7230769230769231\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7115384615384616\n",
      "Domain: 5, Acc: 0.47692307692307695\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 6.4417e-06 - acc: 1.0000\n",
      "Epoch 81/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 6.3944e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7230769230769231\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.47692307692307695\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 6.3022e-06 - acc: 1.0000\n",
      "Epoch 82/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 6.3021e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.47692307692307695\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 6.2129e-06 - acc: 1.0000\n",
      "Epoch 83/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 6.0852e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.47307692307692306\n",
      "1300/1300 [==============================] - 7s 6ms/sample - loss: 6.0851e-06 - acc: 1.0000\n",
      "Epoch 84/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 6.0087e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.47307692307692306\n",
      "1300/1300 [==============================] - 7s 6ms/sample - loss: 5.9895e-06 - acc: 1.0000\n",
      "Epoch 85/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 5.9638e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.47307692307692306\n",
      "1300/1300 [==============================] - 8s 6ms/sample - loss: 5.8979e-06 - acc: 1.0000\n",
      "Epoch 86/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 5.8361e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.46923076923076923\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 5.8058e-06 - acc: 1.0000\n",
      "Epoch 87/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 5.7542e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.46923076923076923\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 5.6997e-06 - acc: 1.0000\n",
      "Epoch 88/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 5.6259e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.4653846153846154\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 5.5978e-06 - acc: 1.0000\n",
      "Epoch 89/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 5.5983e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.4653846153846154\n",
      "1300/1300 [==============================] - 8s 6ms/sample - loss: 5.5133e-06 - acc: 1.0000\n",
      "Epoch 90/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 5.1168e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.4653846153846154\n",
      "1300/1300 [==============================] - 8s 6ms/sample - loss: 5.4205e-06 - acc: 1.0000\n",
      "Epoch 91/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 5.3373e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.46923076923076923\n",
      "1300/1300 [==============================] - 8s 6ms/sample - loss: 5.2876e-06 - acc: 1.0000\n",
      "Epoch 92/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 5.3156e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.46923076923076923\n",
      "1300/1300 [==============================] - 7s 6ms/sample - loss: 5.2377e-06 - acc: 1.0000\n",
      "Epoch 93/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 5.2584e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.46923076923076923\n",
      "1300/1300 [==============================] - 8s 6ms/sample - loss: 5.1842e-06 - acc: 1.0000\n",
      "Epoch 94/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 5.1766e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.46923076923076923\n",
      "1300/1300 [==============================] - 8s 6ms/sample - loss: 5.1048e-06 - acc: 1.0000\n",
      "Epoch 95/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 4.8516e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.4653846153846154\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 5.0374e-06 - acc: 1.0000\n",
      "Epoch 96/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 5.0084e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.46923076923076923\n",
      "1300/1300 [==============================] - 6s 5ms/sample - loss: 4.9327e-06 - acc: 1.0000\n",
      "Epoch 97/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 4.9193e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.4653846153846154\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 4.8506e-06 - acc: 1.0000\n",
      "Epoch 98/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 4.8247e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.4653846153846154\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 4.7643e-06 - acc: 1.0000\n",
      "Epoch 99/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 4.7221e-06 - acc: 1.0000Domain: 0, Acc: 0.7846153846153846\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.4653846153846154\n",
      "1300/1300 [==============================] - 7s 6ms/sample - loss: 4.6762e-06 - acc: 1.0000\n",
      "Epoch 100/100\n",
      "1280/1300 [============================>.] - ETA: 0s - loss: 4.6012e-06 - acc: 1.0000Domain: 0, Acc: 0.7884615384615384\n",
      "Domain: 1, Acc: 0.6923076923076923\n",
      "Domain: 2, Acc: 0.7192307692307692\n",
      "Domain: 3, Acc: 0.6076923076923076\n",
      "Domain: 4, Acc: 0.7076923076923077\n",
      "Domain: 5, Acc: 0.4653846153846154\n",
      "1300/1300 [==============================] - 7s 5ms/sample - loss: 4.5940e-06 - acc: 1.0000\n"
     ]
    }
   ],
   "source": [
    "cback = EvalOnDomains(model=cnn.model, Xtargets=Xtargets, ytargets=ytargets)\n",
    "hist_dict = cnn.fit(Xtr, ytr, batch_size=256, epochs=100, callbacks=[cback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(9, 5))\n",
    "ax.plot(100 * np.array(hist_dict.history['acc']), label='Source Domain')\n",
    "ax.plot(100 * np.array(hist_dict.history['dom0_acc']), label='Target 1')\n",
    "ax.plot(100 * np.array(hist_dict.history['dom1_acc']), label='Target 2')\n",
    "ax.plot(100 * np.array(hist_dict.history['dom2_acc']), label='Target 3')\n",
    "ax.plot(100 * np.array(hist_dict.history['dom3_acc']), label='Target 4')\n",
    "ax.plot(100 * np.array(hist_dict.history['dom4_acc']), label='Target 5')\n",
    "ax.plot(100 * np.array(hist_dict.history['dom5_acc']), label='Target 6')\n",
    "ax.set_ylabel('Accuracy (\\%)')\n",
    "ax.set_xlabel('Epoch')\n",
    "ax.legend(bbox_to_anchor=(1, 1))\n",
    "plt.savefig('./Figures/Ch6/CNN_Training.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmats = []\n",
    "for Xt, yt in zip(Xtargets, ytargets):\n",
    "    yp = cnn.predict(Xt)\n",
    "    cmat = confusion_matrix(yt, yp)\n",
    "    cmats.append(cmat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 2, figsize=(12, 12))\n",
    "eps = [0.1, 0.15, 0.2, 0.15, 0.15, 0.15]\n",
    "Ns = [1, 1, 1, 0.5, 1.5, 2.0]\n",
    "for cmat, ax, ei, Ni in zip(cmats, axes.flatten(), eps, Ns):\n",
    "    sns.heatmap(cmat, annot=False, ax=ax, cmap='binary', cbar=False,\n",
    "                xticklabels=[r\"$F_{\" + str(int(i)) + \"}$\" for i in range(1, 13)] + [\"N\"],\n",
    "                yticklabels=[r\"$F_{\" + str(int(i)) + \"}$\" for i in range(1, 13)] + [\"N\"])\n",
    "    ax.set_title(r'$\\epsilon = {}$, $N = {}$'.format(ei, Ni))\n",
    "    ax.set_ylabel('Ground-Truth')\n",
    "    ax.set_xlabel('Preidctions')\n",
    "plt.tight_layout()\n",
    "\n",
    "norm = mpl.colors.Normalize(vmin=0, vmax=20)\n",
    "cmap = mpl.cm.ScalarMappable(norm=norm, cmap='binary')\n",
    "cmap.set_array([])\n",
    "cbar =  fig.colorbar(cmap, ax=axes.ravel().tolist())\n",
    "cbar.set_ticks([2 * i for i in range(11)])\n",
    "plt.savefig('./Figures/Ch6/CNNFeatures_Errs.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7730769230769231"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 1\n",
    "sum(np.diag(cmats[n])) / sum(sum(cmats[n]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.7 64-bit ('OptimalTransport': conda)",
   "language": "python",
   "name": "python37764bitoptimaltransportconda38cd42aab3c640dc8d34473d4e53c23f"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}