mlp_training_double_x1_10m.ipynb

{
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   "id": "f7fbfe8e-cce0-4ab7-948f-1aa0f94fb992",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 139 µs (started: 2023-07-24 16:51:49 +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: 239 µs (started: 2023-07-24 16:51:49 +02:00)\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "efe00312-69b4-4ebc-9c6b-e95b88bab3d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-24 16:51:52.385120: 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 16:51:52.881393: 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 16:51:52.881423: 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 16:51:54.852588: 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 16:51:54.853054: 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 16:51:54.853062: 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: 7.76 s (started: 2023-07-24 16:51:49 +02:00)\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "# from mei import Size_Distribution_Optics\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import glob\n",
    "import dask.dataframe as dd\n",
    "\n",
    "import sklearn\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\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.layers import Activation\n",
    "\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: 26.2 ms (started: 2023-07-24 16:51:57 +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",
       "0         1.48600  0.106100  1.464000  2.630000e-07      2.6     100.0   \n",
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       "11        1.45504  0.023075  1.336727  1.353750e-09      2.6     450.0   \n",
       "...           ...       ...       ...           ...      ...       ...   \n",
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       "\n",
       "                 x  \n",
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       "8         0.104720  \n",
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       "...            ...  \n",
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       "44527903  0.376991  \n",
       "\n",
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
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     "text": [
      "time: 52 s (started: 2023-07-24 16:51:57 +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": "67080919-c157-47ce-ac75-1225738d7e57",
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       "      <td>1.353750e-09</td>\n",
       "      <td>2.6</td>\n",
       "      <td>450.0</td>\n",
       "      <td>0.069813</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>44527776</th>\n",
       "      <td>0.844114</td>\n",
       "      <td>0.108977</td>\n",
       "      <td>0.129103</td>\n",
       "      <td>0.083386</td>\n",
       "      <td>0.4</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.61350</td>\n",
       "      <td>0.648098</td>\n",
       "      <td>2.017500</td>\n",
       "      <td>7.225000e-01</td>\n",
       "      <td>1.7</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>0.418879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44527837</th>\n",
       "      <td>1.117326</td>\n",
       "      <td>0.202738</td>\n",
       "      <td>0.181449</td>\n",
       "      <td>0.117843</td>\n",
       "      <td>0.4</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>1.61350</td>\n",
       "      <td>0.648098</td>\n",
       "      <td>2.015000</td>\n",
       "      <td>7.250000e-01</td>\n",
       "      <td>1.7</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>0.493679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44527839</th>\n",
       "      <td>0.886265</td>\n",
       "      <td>0.122657</td>\n",
       "      <td>0.138398</td>\n",
       "      <td>0.089080</td>\n",
       "      <td>0.4</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>1.61350</td>\n",
       "      <td>0.648098</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>7.200000e-01</td>\n",
       "      <td>1.7</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.431969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44527902</th>\n",
       "      <td>0.819451</td>\n",
       "      <td>0.106342</td>\n",
       "      <td>0.129773</td>\n",
       "      <td>0.083984</td>\n",
       "      <td>0.4</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1.61350</td>\n",
       "      <td>0.648098</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>6.850000e-01</td>\n",
       "      <td>1.7</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>0.418879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44527903</th>\n",
       "      <td>0.671211</td>\n",
       "      <td>0.068711</td>\n",
       "      <td>0.102369</td>\n",
       "      <td>0.067859</td>\n",
       "      <td>0.4</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1.61350</td>\n",
       "      <td>0.648098</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>6.500000e-01</td>\n",
       "      <td>1.7</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>0.376991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4154613 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Extinction  Scattering       SSA      Asym  coating  core_dia  \\\n",
       "0           0.072795    0.002253  0.030951  0.019297      0.0      10.0   \n",
       "2           0.034139    0.000141  0.004118  0.004843      0.0      10.0   \n",
       "8           0.007768    0.000024  0.003152  0.002135      0.0      10.0   \n",
       "11          0.003332    0.000005  0.001404  0.000946      0.0      10.0   \n",
       "11          0.003332    0.000005  0.001404  0.000946      0.0      10.0   \n",
       "...              ...         ...       ...       ...      ...       ...   \n",
       "44527776    0.844114    0.108977  0.129103  0.083386      0.4   10000.0   \n",
       "44527837    1.117326    0.202738  0.181449  0.117843      0.4   11000.0   \n",
       "44527839    0.886265    0.122657  0.138398  0.089080      0.4   11000.0   \n",
       "44527902    0.819451    0.106342  0.129773  0.083984      0.4   12000.0   \n",
       "44527903    0.671211    0.068711  0.102369  0.067859      0.4   12000.0   \n",
       "\n",
       "           n_core    k_core   n_shell       k_shell  density    lambda  \\\n",
       "0         1.48600  0.106100  1.464000  2.630000e-07      2.6     100.0   \n",
       "2         1.48600  0.106100  1.396000  1.100000e-07      2.6     200.0   \n",
       "8         1.46300  0.035900  1.349000  1.600000e-08      2.6     300.0   \n",
       "11        1.45504  0.023075  1.336727  1.353750e-09      2.6     450.0   \n",
       "11        1.45504  0.023075  1.336727  1.353750e-09      2.6     450.0   \n",
       "...           ...       ...       ...           ...      ...       ...   \n",
       "44527776  1.61350  0.648098  2.017500  7.225000e-01      1.7   75000.0   \n",
       "44527837  1.61350  0.648098  2.015000  7.250000e-01      1.7   70000.0   \n",
       "44527839  1.61350  0.648098  2.020000  7.200000e-01      1.7   80000.0   \n",
       "44527902  1.61350  0.648098  2.020000  6.850000e-01      1.7   90000.0   \n",
       "44527903  1.61350  0.648098  2.020000  6.500000e-01      1.7  100000.0   \n",
       "\n",
       "                 x  \n",
       "0         0.314159  \n",
       "2         0.157080  \n",
       "8         0.104720  \n",
       "11        0.069813  \n",
       "11        0.069813  \n",
       "...            ...  \n",
       "44527776  0.418879  \n",
       "44527837  0.493679  \n",
       "44527839  0.431969  \n",
       "44527902  0.418879  \n",
       "44527903  0.376991  \n",
       "\n",
       "[4154613 rows x 13 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.03 s (started: 2023-07-24 16:52:49 +02:00)\n"
     ]
    }
   ],
   "source": [
    "df1 = df[df.x <= 0.5]\n",
    "\n",
    "mx = df1.max()\n",
    "mn = df1.min()\n",
    "\n",
    "pd.concat([pd.DataFrame(mx), pd.DataFrame(mn)], axis=1).to_csv('/work/bb1070/b382177/mie/icon/24-07-2023/mlp_min_max_x1.csv')\n",
    "\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "af78f502-bd3b-4e14-b514-db822920d5ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</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>0.000985</td>\n",
       "      <td>4.804669e-07</td>\n",
       "      <td>0.001390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.010004</td>\n",
       "      <td>1.511571e-02</td>\n",
       "      <td>0.235466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.007343</td>\n",
       "      <td>5.153546e-06</td>\n",
       "      <td>0.005008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.008859</td>\n",
       "      <td>7.119190e-07</td>\n",
       "      <td>0.001371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.006145</td>\n",
       "      <td>3.678805e-04</td>\n",
       "      <td>0.034010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3323685</th>\n",
       "      <td>0.100276</td>\n",
       "      <td>6.947009e-03</td>\n",
       "      <td>0.092239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3323686</th>\n",
       "      <td>0.011105</td>\n",
       "      <td>9.013835e-08</td>\n",
       "      <td>0.000346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3323687</th>\n",
       "      <td>0.002645</td>\n",
       "      <td>3.198423e-04</td>\n",
       "      <td>0.033000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3323688</th>\n",
       "      <td>0.006881</td>\n",
       "      <td>7.575481e-04</td>\n",
       "      <td>0.048339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3323689</th>\n",
       "      <td>0.005077</td>\n",
       "      <td>8.915148e-07</td>\n",
       "      <td>0.001907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3323690 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Extinction    Scattering      Asym\n",
       "0          0.000985  4.804669e-07  0.001390\n",
       "1          0.010004  1.511571e-02  0.235466\n",
       "2          0.007343  5.153546e-06  0.005008\n",
       "3          0.008859  7.119190e-07  0.001371\n",
       "4          0.006145  3.678805e-04  0.034010\n",
       "...             ...           ...       ...\n",
       "3323685    0.100276  6.947009e-03  0.092239\n",
       "3323686    0.011105  9.013835e-08  0.000346\n",
       "3323687    0.002645  3.198423e-04  0.033000\n",
       "3323688    0.006881  7.575481e-04  0.048339\n",
       "3323689    0.005077  8.915148e-07  0.001907\n",
       "\n",
       "[3323690 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.12 s (started: 2023-07-24 16:52:50 +02:00)\n"
     ]
    }
   ],
   "source": [
    "df_shuffled = df1.sample(frac=1, random_state=25).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": 8,
   "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",
      "                                                                 \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, 16)                528       \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 16)                0         \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 16)               64        \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " output_layer (Dense)        (None, 3)                 51        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,491\n",
      "Trainable params: 1,363\n",
      "Non-trainable params: 128\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-24 16:52:51.676115: 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 16:52:51.676150: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2023-07-24 16:52:51.676169: 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 16:52:51.677664: 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",
      "Epoch 1: val_loss improved from inf to 0.00032, saving model to model/model_24-07-2023_x1.h5\n",
      "\n",
      "Epoch 2: val_loss improved from 0.00032 to 0.00010, saving model to model/model_24-07-2023_x1.h5\n",
      "\n",
      "Epoch 3: val_loss improved from 0.00010 to 0.00009, saving model to model/model_24-07-2023_x1.h5\n",
      "\n",
      "Epoch 4: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 5: val_loss improved from 0.00009 to 0.00009, saving model to model/model_24-07-2023_x1.h5\n",
      "\n",
      "Epoch 6: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 7: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 8: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 9: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 10: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 11: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 12: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 13: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 14: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 15: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 16: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 17: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 18: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 19: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 20: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 21: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 22: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 23: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 24: val_loss did not improve from 0.00009\n",
      "\n",
      "Epoch 25: val_loss did not improve from 0.00009\n",
      "Epoch 25: early stopping\n",
      "25967/25967 [==============================] - 15s 564us/step\n",
      "time: 4min 56s (started: 2023-07-24 16:52:51 +02:00)\n"
     ]
    }
   ],
   "source": [
    "opt = keras.optimizers.Adam(learning_rate=0.01)\n",
    "model = create_model(\n",
    "    6,\n",
    "    3,\n",
    "    # hidden=[15, 30, 60, 30, 15],\n",
    "    hidden=[16, 32, 16],\n",
    "    dropout=[0.05, 0.05, 0.05, 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_x1.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=20)\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": 9,
   "id": "70fcb6d5-5e00-4331-ab1e-328e7ad447b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 779 ms (started: 2023-07-24 16:57:48 +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_x1.png\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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: 23.8 s (started: 2023-07-24 16:57:49 +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_x1.png\", bbox_inches=\"tight\")\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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: 1.24 s (started: 2023-07-24 16:58:12 +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, 0.33, 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_x1.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "11733fae-68f5-4521-b287-46fe547bd3b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 32.9 ms (started: 2023-07-24 16:58:14 +02:00)\n"
     ]
    }
   ],
   "source": [
    "model.save('/work/bb1070/b382177/mie/icon/24-07-2023/model/model_24_07_2023_x1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d72c91a7-d5d8-4875-95f4-9350192a363d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 127 ms (started: 2023-07-24 16:58:14 +02:00)\n"
     ]
    }
   ],
   "source": [
    "lmodel = tf.keras.models.load_model('/work/bb1070/b382177/mie/icon/24-07-2023/model/model_24_07_2023_x1.h5')"
   ]
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