mlp_training_double_x2_10m.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
   "id": "f7fbfe8e-cce0-4ab7-948f-1aa0f94fb992",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 139 µs (started: 2023-07-24 13:39:42 +02:00)\n"
     ]
    }
   ],
   "source": [
    "%load_ext autotime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac794bb8-5583-4fc8-bcad-44094343c561",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 244 µs (started: 2023-07-24 13:39:42 +02:00)\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7e2cb2ec-cc01-42ff-9784-77cb6d134386",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-24 13:39:44.525911: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-07-24 13:39:44.823527: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2023-07-24 13:39:44.823557: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2023-07-24 13:39:46.628401: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2023-07-24 13:39:46.628848: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2023-07-24 13:39:46.628856: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 6.87 s (started: 2023-07-24 13:39:42 +02:00)\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import r2_score \n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Flatten, Dense, Dropout, Activation \n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, TerminateOnNaN, ReduceLROnPlateau, EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9d7721d5-aa98-421a-b1ed-1ddb9efef636",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 21.4 ms (started: 2023-07-24 13:39:49 +02:00)\n"
     ]
    }
   ],
   "source": [
    "# from kerastuner.tuners import RandomSearch\n",
    "from net import create_model, normalize, normalize1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "69998211-344c-4b57-bbe5-ca7e96cdb005",
   "metadata": {},
   "outputs": [
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       "          Extinction  Scattering       SSA      Asym  coating  core_dia  \\\n",
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       "\n",
       "           n_core    k_core   n_shell       k_shell  density    lambda  \\\n",
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       "...           ...       ...       ...           ...      ...       ...   \n",
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       "\n",
       "                 x  \n",
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       "...            ...  \n",
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       "44527839  0.431969  \n",
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 51.3 s (started: 2023-07-24 13:39:49 +02:00)\n"
     ]
    }
   ],
   "source": [
    "columns = ['Extinction', 'Scattering', 'SSA', 'Asym', 'coating', 'core_dia', 'n_core', 'k_core', 'n_shell', 'k_shell', 'density', 'lambda']\n",
    "df = pd.read_csv('/work/bb1070/b382177/mie/icon/24-07-2023/mie_all.csv', index_col=0).sort_index()\n",
    "df = df[columns].dropna().astype('float').drop_duplicates()\n",
    "df['x'] = np.pi * df['core_dia'] / df['lambda']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0baf5647-cce4-4a0d-b5f3-11bb29aa9434",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 320 ms (started: 2023-07-24 13:40:40 +02:00)\n"
     ]
    }
   ],
   "source": [
    "mx = df.max()\n",
    "mn = df.min()\n",
    "pd.concat([pd.DataFrame(mx), pd.DataFrame(mn)], axis=1).to_csv('/work/bb1070/b382177/mie/icon/24-07-2023/mlp_min_max_x2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "67080919-c157-47ce-ac75-1225738d7e57",
   "metadata": {},
   "outputs": [
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       "      <td>0.429602</td>\n",
       "      <td>0.161829</td>\n",
       "      <td>0.376696</td>\n",
       "      <td>0.179406</td>\n",
       "      <td>0.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.486000</td>\n",
       "      <td>0.106100</td>\n",
       "      <td>1.3960</td>\n",
       "      <td>1.100000e-07</td>\n",
       "      <td>2.6</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.942478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40204573</th>\n",
       "      <td>2.043812</td>\n",
       "      <td>1.162208</td>\n",
       "      <td>0.568647</td>\n",
       "      <td>0.574923</td>\n",
       "      <td>0.36</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>1.737322</td>\n",
       "      <td>0.323523</td>\n",
       "      <td>1.6200</td>\n",
       "      <td>5.000000e-02</td>\n",
       "      <td>1.7</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>1.151917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40204574</th>\n",
       "      <td>1.209879</td>\n",
       "      <td>0.585066</td>\n",
       "      <td>0.483573</td>\n",
       "      <td>0.313111</td>\n",
       "      <td>0.36</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>1.737109</td>\n",
       "      <td>0.368913</td>\n",
       "      <td>1.6200</td>\n",
       "      <td>5.000000e-02</td>\n",
       "      <td>1.7</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.863938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40204576</th>\n",
       "      <td>0.488016</td>\n",
       "      <td>0.155189</td>\n",
       "      <td>0.318000</td>\n",
       "      <td>0.123113</td>\n",
       "      <td>0.36</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>1.750163</td>\n",
       "      <td>0.352284</td>\n",
       "      <td>1.6200</td>\n",
       "      <td>5.000000e-02</td>\n",
       "      <td>1.7</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.575959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40204584</th>\n",
       "      <td>2.048849</td>\n",
       "      <td>1.135797</td>\n",
       "      <td>0.554358</td>\n",
       "      <td>0.938225</td>\n",
       "      <td>0.36</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1.471186</td>\n",
       "      <td>0.029172</td>\n",
       "      <td>1.6200</td>\n",
       "      <td>3.700000e-02</td>\n",
       "      <td>1.7</td>\n",
       "      <td>200.0</td>\n",
       "      <td>188.495559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40204585</th>\n",
       "      <td>2.050470</td>\n",
       "      <td>1.136432</td>\n",
       "      <td>0.554230</td>\n",
       "      <td>0.938228</td>\n",
       "      <td>0.36</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1.469323</td>\n",
       "      <td>0.027791</td>\n",
       "      <td>1.6200</td>\n",
       "      <td>3.700000e-02</td>\n",
       "      <td>1.7</td>\n",
       "      <td>210.0</td>\n",
       "      <td>179.519580</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5531457 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Extinction  Scattering       SSA      Asym  coating  core_dia  \\\n",
       "192         0.110201    0.016399  0.148813  0.053173     0.00      40.0   \n",
       "249         0.536998    0.230364  0.428984  0.226929     0.00      50.0   \n",
       "249         0.536998    0.230364  0.428984  0.226929     0.00      50.0   \n",
       "254         0.163354    0.039507  0.241849  0.083190     0.00      50.0   \n",
       "312         0.429602    0.161829  0.376696  0.179406     0.00      60.0   \n",
       "...              ...         ...       ...       ...      ...       ...   \n",
       "40204573    2.043812    1.162208  0.568647  0.574923     0.36   11000.0   \n",
       "40204574    1.209879    0.585066  0.483573  0.313111     0.36   11000.0   \n",
       "40204576    0.488016    0.155189  0.318000  0.123113     0.36   11000.0   \n",
       "40204584    2.048849    1.135797  0.554358  0.938225     0.36   12000.0   \n",
       "40204585    2.050470    1.136432  0.554230  0.938228     0.36   12000.0   \n",
       "\n",
       "            n_core    k_core  n_shell       k_shell  density   lambda  \\\n",
       "192       1.476800  0.078020   1.3688  4.880000e-08      2.6    240.0   \n",
       "249       1.486000  0.106100   1.4300  1.865000e-07      2.6    150.0   \n",
       "249       1.486000  0.106100   1.4300  1.865000e-07      2.6    150.0   \n",
       "254       1.476800  0.078020   1.3688  4.880000e-08      2.6    240.0   \n",
       "312       1.486000  0.106100   1.3960  1.100000e-07      2.6    200.0   \n",
       "...            ...       ...      ...           ...      ...      ...   \n",
       "40204573  1.737322  0.323523   1.6200  5.000000e-02      1.7  30000.0   \n",
       "40204574  1.737109  0.368913   1.6200  5.000000e-02      1.7  40000.0   \n",
       "40204576  1.750163  0.352284   1.6200  5.000000e-02      1.7  60000.0   \n",
       "40204584  1.471186  0.029172   1.6200  3.700000e-02      1.7    200.0   \n",
       "40204585  1.469323  0.027791   1.6200  3.700000e-02      1.7    210.0   \n",
       "\n",
       "                   x  \n",
       "192         0.523599  \n",
       "249         1.047198  \n",
       "249         1.047198  \n",
       "254         0.654498  \n",
       "312         0.942478  \n",
       "...              ...  \n",
       "40204573    1.151917  \n",
       "40204574    0.863938  \n",
       "40204576    0.575959  \n",
       "40204584  188.495559  \n",
       "40204585  179.519580  \n",
       "\n",
       "[5531457 rows x 13 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 882 ms (started: 2023-07-24 13:40:40 +02:00)\n"
     ]
    }
   ],
   "source": [
    "df1 = df[df.x > 0.5]\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "af78f502-bd3b-4e14-b514-db822920d5ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Extinction</th>\n",
       "      <th>Scattering</th>\n",
       "      <th>Asym</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.450873</td>\n",
       "      <td>0.250176</td>\n",
       "      <td>0.954549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.451206</td>\n",
       "      <td>0.244508</td>\n",
       "      <td>0.975758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.454677</td>\n",
       "      <td>0.248138</td>\n",
       "      <td>0.841665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.528779</td>\n",
       "      <td>0.272999</td>\n",
       "      <td>0.932618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.449394</td>\n",
       "      <td>0.246708</td>\n",
       "      <td>0.963596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4425160</th>\n",
       "      <td>0.152194</td>\n",
       "      <td>0.128570</td>\n",
       "      <td>0.447660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4425161</th>\n",
       "      <td>0.464915</td>\n",
       "      <td>0.248701</td>\n",
       "      <td>0.976729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4425162</th>\n",
       "      <td>0.587328</td>\n",
       "      <td>0.285657</td>\n",
       "      <td>0.608381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4425163</th>\n",
       "      <td>0.468391</td>\n",
       "      <td>0.247999</td>\n",
       "      <td>0.980577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4425164</th>\n",
       "      <td>0.453044</td>\n",
       "      <td>0.255870</td>\n",
       "      <td>0.938459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4425165 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Extinction  Scattering      Asym\n",
       "0          0.450873    0.250176  0.954549\n",
       "1          0.451206    0.244508  0.975758\n",
       "2          0.454677    0.248138  0.841665\n",
       "3          0.528779    0.272999  0.932618\n",
       "4          0.449394    0.246708  0.963596\n",
       "...             ...         ...       ...\n",
       "4425160    0.152194    0.128570  0.447660\n",
       "4425161    0.464915    0.248701  0.976729\n",
       "4425162    0.587328    0.285657  0.608381\n",
       "4425163    0.468391    0.247999  0.980577\n",
       "4425164    0.453044    0.255870  0.938459\n",
       "\n",
       "[4425165 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.5 s (started: 2023-07-24 13:40:41 +02:00)\n"
     ]
    }
   ],
   "source": [
    "df_shuffled = df1.sample(frac=1, random_state=40).reset_index(drop=True)\n",
    "df_shuffled = normalize1(df_shuffled)\n",
    "\n",
    "out_col = [\"Extinction\", \"Scattering\", \"Asym\"]\n",
    "in_col = ['coating', 'x', 'n_core', 'k_core', 'n_shell', 'k_shell']\n",
    "\n",
    "y = df_shuffled[out_col]\n",
    "x = df_shuffled[in_col]\n",
    "\n",
    "split = 0.8\n",
    "train_size = int(split * len(x))\n",
    "train_x = x.iloc[:train_size, :].reset_index(drop=True)\n",
    "train_y = y.iloc[:train_size, :].reset_index(drop=True)\n",
    "test_x = x.iloc[train_size:, :].reset_index(drop=True)\n",
    "test_y = y.iloc[train_size:, :].reset_index(drop=True)\n",
    "train_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ac07d99f-d716-49d4-80a2-f6d471d25965",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: (6,)\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " hidden_layer_1 (Dense)      (None, 16)                112       \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 16)                0         \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-24 13:40:43.189640: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2023-07-24 13:40:43.189677: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2023-07-24 13:40:43.189695: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (l40007.lvt.dkrz.de): /proc/driver/nvidia/version does not exist\n",
      "2023-07-24 13:40:43.190625: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 16)               64        \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " hidden_layer_2 (Dense)      (None, 32)                544       \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 32)                0         \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 32)               128       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " hidden_layer_3 (Dense)      (None, 32)                1056      \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 32)                0         \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 32)               128       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " hidden_layer_4 (Dense)      (None, 16)                528       \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 16)                0         \n",
      "                                                                 \n",
      " batch_normalization_3 (Batc  (None, 16)               64        \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " output_layer (Dense)        (None, 3)                 51        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2,675\n",
      "Trainable params: 2,483\n",
      "Non-trainable params: 192\n",
      "_________________________________________________________________\n",
      "None\n",
      "\n",
      "Epoch 1: val_loss improved from inf to 0.00370, saving model to model/model_24-07-2023_x2.h5\n",
      "\n",
      "Epoch 2: val_loss did not improve from 0.00370\n",
      "\n",
      "Epoch 3: val_loss did not improve from 0.00370\n",
      "\n",
      "Epoch 4: val_loss did not improve from 0.00370\n",
      "\n",
      "Epoch 5: val_loss improved from 0.00370 to 0.00153, saving model to model/model_24-07-2023_x2.h5\n",
      "\n",
      "Epoch 6: val_loss improved from 0.00153 to 0.00052, saving model to model/model_24-07-2023_x2.h5\n",
      "\n",
      "Epoch 7: val_loss did not improve from 0.00052\n",
      "\n",
      "Epoch 8: val_loss did not improve from 0.00052\n",
      "\n",
      "Epoch 9: val_loss did not improve from 0.00052\n",
      "\n",
      "Epoch 10: val_loss did not improve from 0.00052\n",
      "\n",
      "Epoch 11: val_loss improved from 0.00052 to 0.00027, saving model to model/model_24-07-2023_x2.h5\n",
      "\n",
      "Epoch 12: val_loss did not improve from 0.00027\n",
      "\n",
      "Epoch 13: val_loss did not improve from 0.00027\n",
      "\n",
      "Epoch 14: val_loss improved from 0.00027 to 0.00018, saving model to model/model_24-07-2023_x2.h5\n",
      "\n",
      "Epoch 15: val_loss did not improve from 0.00018\n",
      "\n",
      "Epoch 16: val_loss did not improve from 0.00018\n",
      "\n",
      "Epoch 17: val_loss did not improve from 0.00018\n",
      "\n",
      "Epoch 18: val_loss did not improve from 0.00018\n",
      "\n",
      "Epoch 19: val_loss did not improve from 0.00018\n",
      "\n",
      "Epoch 20: val_loss did not improve from 0.00018\n",
      "\n",
      "Epoch 21: val_loss did not improve from 0.00018\n",
      "\n",
      "Epoch 22: val_loss improved from 0.00018 to 0.00009, saving model to model/model_24-07-2023_x2.h5\n",
      "\n",
      "Epoch 23: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 24: val_loss improved from 0.00009 to 0.00009, saving model to model/model_24-07-2023_x2.h5\n",
      "\n",
      "Epoch 25: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 26: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 27: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 28: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 29: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 30: val_loss improved from 0.00009 to 0.00009, saving model to model/model_24-07-2023_x2.h5\n",
      "\n",
      "Epoch 31: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 32: val_loss improved from 0.00009 to 0.00009, saving model to model/model_24-07-2023_x2.h5\n",
      "\n",
      "Epoch 33: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 34: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 35: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 36: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 37: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 38: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 39: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 40: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 41: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 42: val_loss did not improve from 0.00009\n",
      "Epoch 42: early stopping\n",
      "34572/34572 [==============================] - 21s 594us/step\n",
      "time: 10min 36s (started: 2023-07-24 13:40:43 +02:00)\n"
     ]
    }
   ],
   "source": [
    "opt = keras.optimizers.Adam(learning_rate=0.001)\n",
    "\n",
    "model = create_model(\n",
    "    6,\n",
    "    3,\n",
    "    hidden=[16, 32, 32, 16],\n",
    "    # dropout=[0.1, 0.3, 0.3, 0],\n",
    "    dropout=[0, 0, 0, 0, 0],\n",
    "    # activation=[\"relu\", \"tanh\", \"tanh\", \"tanh\"],\n",
    "    activation=[\"gelu\", \"gelu\", \"gelu\", 'gelu', 'gelu'],\n",
    "    # activation=[\"relu\", \"relu\", \"relu\", 'relu'],\n",
    "    loss=\"mse\",\n",
    "    optimizer=opt,\n",
    "    metrics=['mse', 'mae', 'mape']\n",
    ")\n",
    "\n",
    "mc = ModelCheckpoint('model/model_24-07-2023_x2.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True, save_weights_only=False)\n",
    "es = EarlyStopping(monitor=\"val_loss\", mode=\"min\", verbose=1, patience=10)\n",
    "rlr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.00001)\n",
    "tnan = TerminateOnNaN()\n",
    "callbacks = [es, mc, tnan, rlr]\n",
    "\n",
    "h = model.fit(\n",
    "    train_x,\n",
    "    train_y,\n",
    "    epochs=100,\n",
    "    batch_size=500,\n",
    "    verbose=0,\n",
    "    validation_split=0.2,\n",
    "    callbacks=callbacks,\n",
    "    shuffle=True\n",
    ")\n",
    "\n",
    "pred_y = model.predict(test_x)\n",
    "pred_y = pd.DataFrame(pred_y, columns=out_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "70fcb6d5-5e00-4331-ab1e-328e7ad447b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.12 s (started: 2023-07-24 13:51:19 +02:00)\n"
     ]
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 10))\n",
    "ax1.plot(h.history[\"loss\"], label=\"Training Loss\")\n",
    "ax1.plot(h.history[\"val_loss\"], label=\"Validation Loss\")\n",
    "ax1.semilogy()\n",
    "\n",
    "ax2.plot(h.history['mae'], label=\"Training MAE\")\n",
    "ax2.plot(h.history[\"val_mae\"], label=\"Validation MAE\")\n",
    "\n",
    "ax1.set_ylabel(\"Loss [MSE]\")\n",
    "ax2.set_ylabel(\"Error [MAE]\")\n",
    "\n",
    "for ax in [ax1, ax2]:\n",
    "    ax.minorticks_on()\n",
    "    ax.semilogy()\n",
    "    ax.legend(frameon=False)\n",
    "    ax.set_xlabel(\"Number of Epochs\")\n",
    "    # ax.set_yticks([0.01, 0.05, 0.1, 0.5])\n",
    "    ax.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "plt.savefig(\"/work/bb1070/b382177/mie/icon/24-07-2023/figs/opt_epochs_x2.png\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3353962b-8b87-4308-b915-e7d10b5bbf46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2300x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 31.8 s (started: 2023-07-24 13:51:21 +02:00)\n"
     ]
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(23, 7))\n",
    "for col, ax in zip(y.columns, axes):\n",
    "    ntest = test_y[col]\n",
    "    npred = pred_y[col]\n",
    "        \n",
    "    corr = np.corrcoef(ntest, npred)[0,1]\n",
    "    r2 = r2_score(ntest, npred)\n",
    "    \n",
    "    x1 = test_y[col]\n",
    "    y1 = pred_y[col]    \n",
    "\n",
    "    x1 = (mx[col] - mn[col]) * x1 + mn[col]\n",
    "    y1 = (mx[col] - mn[col]) * y1 + mn[col]\n",
    "        \n",
    "    ax.scatter(x1, y1)\n",
    "    ax.scatter(x1, x1)\n",
    "    ax.set_title('%s [$R^2$=%0.3f]'%(col, r2), fontsize=20)\n",
    "    ax.set_xlabel('Mie', fontsize=20)\n",
    "    ax.minorticks_on()\n",
    "    ax.tick_params(labelsize=18)\n",
    "axes[0].set_ylabel('MLP', fontsize=20)\n",
    "plt.savefig(\"/work/bb1070/b382177/mie/icon/24-07-2023/figs/mlp_accuracy_stats_x2.png\", bbox_inches=\"tight\")\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "542bd12f-b6b6-4643-bf4b-7174dcd2d219",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1900x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.35 s (started: 2023-07-24 13:51:53 +02:00)\n"
     ]
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(19, 6), sharey=True)\n",
    "axes = axes.flatten()\n",
    "for i, (col, ax) in enumerate(zip(y.columns, axes)):\n",
    "    ntest = test_y[col]\n",
    "    npred = pred_y[col]  \n",
    "    \n",
    "    x1 = test_y[col]\n",
    "    y1 = pred_y[col]  \n",
    "    \n",
    "    x1 = (mx[col] - mn[col]) * x1 + mn[col]\n",
    "    y1 = (mx[col] - mn[col]) * y1 + mn[col]\n",
    "    \n",
    "    corr = np.corrcoef(x1, y1)[0,1]    \n",
    "    error = 100 * (y1 - x1) / x1   \n",
    "    \n",
    "    ax.hist(error, bins=np.arange(-20, 21, 1), density=True, color='blue')\n",
    "    # ax.hist(error, bins=100, density=True)\n",
    "    \n",
    "    # ax.set_title('%s [R=%0.3f]'%(col, corr), fontsize=20)\n",
    "    ax.set_title('%s'%(col), fontsize=20, y=0.9)\n",
    "    ax.set_xlabel('Error [%]', fontsize=20)\n",
    "    ax.set_ylabel('Density', fontsize=20)\n",
    "    if i!=0:\n",
    "        ax.set_ylabel('', fontsize=20)\n",
    "        \n",
    "    if i!=1:\n",
    "        ax.set_xlabel('', fontsize=20)\n",
    "        \n",
    "    ax.minorticks_on()\n",
    "    ax.set_yticks(np.arange(0, 0.45, 0.05))\n",
    "    ax.tick_params(labelsize=16)\n",
    "    \n",
    "plt.subplots_adjust(hspace=0.01, wspace=0.05)\n",
    "plt.savefig('/work/bb1070/b382177/mie/icon/24-07-2023/figs/mlp_error_ana_x2.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "11733fae-68f5-4521-b287-46fe547bd3b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 109 ms (started: 2023-07-24 13:51:54 +02:00)\n"
     ]
    }
   ],
   "source": [
    "model.save('/work/bb1070/b382177/mie/icon/24-07-2023/model/model_24_07_2023_x2.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d72c91a7-d5d8-4875-95f4-9350192a363d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 162 ms (started: 2023-07-24 13:51:54 +02:00)\n"
     ]
    }
   ],
   "source": [
    "lmodel = tf.keras.models.load_model('/work/bb1070/b382177/mie/icon/24-07-2023/model/model_24_07_2023_x2.h5')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf2",
   "language": "python",
   "name": "tf"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}