diff --git a/src/POET_Training.ipynb b/src/POET_Training.ipynb
index 9f854bf..50726d9 100644
--- a/src/POET_Training.ipynb
+++ b/src/POET_Training.ipynb
@@ -27,19 +27,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-02-21 16:44:01.704481: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
-      "2025-02-21 16:44:01.724144: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
-      "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import keras\n",
     "from keras.layers import Dense, Dropout, Input,BatchNormalization, LeakyReLU\n",
@@ -53,6 +43,7 @@
     "from sklearn.cluster import KMeans\n",
     "from sklearn.pipeline import Pipeline, make_pipeline\n",
     "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
+    "from scipy import stats, special\n",
     "from imblearn.over_sampling import SMOTE\n",
     "from imblearn.under_sampling import RandomUnderSampler\n",
     "from imblearn.over_sampling import RandomOverSampler\n",
@@ -67,9 +58,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2"
@@ -84,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -191,2771 +191,17 @@
    "source": [
     "preprocess = preprocessing(np.log1p, np.expm1)\n",
     "X, y = preprocess.cluster(df_design[species_columns], df_results[species_columns])\n",
-    "# X, y = preprocess.funcTranform(X, y)\n",
+    "X, y = preprocess.funcTranform(X, y)\n",
     "\n",
     "X_train, X_test, y_train, y_test = preprocess.split(X, y, ratio = 0.2)\n",
+    "X_train_origin = X_train.copy()\n",
     "X_train, y_train = preprocess.balancer(X_train, y_train, strategy = \"off\")\n",
-    "X_train, y_train = preprocess.class_selection(X_train, y_train, 0)\n",
+    "X_train, y_train = preprocess.class_selection(X_train, y_train, class_label=0)\n",
     "preprocess.scale_fit(X_train, y_train, scaling = \"global\", type=\"minmax\")\n",
     "X_train, X_test, y_train, y_test = preprocess.scale_transform(X_train, X_test, y_train, y_test)\n",
     "X_train, X_val, y_train, y_val = preprocess.split(X_train, y_train, ratio = 0.1)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
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-       "               H         O        Ba        Cl         S        Sr  \\\n",
-       "478350  0.557224  0.140163  0.001119  0.555750  0.140160  0.557932   \n",
-       "366736  0.354555  0.155748  0.000697  0.352515  0.155747  0.351468   \n",
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-       "250138  0.510507  0.142898  0.001021  0.508943  0.142895  0.510372   \n",
-       "\n",
-       "              Barite  Celestite  Class  \n",
-       "478350  3.017125e-06   0.999778      0  \n",
-       "366736  1.880549e-06   0.999699      0  \n",
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-       "                 H          O        Ba        Cl         S        Sr  \\\n",
-       "0       111.012434  55.506567  0.000023  0.064477  0.000088  0.032303   \n",
-       "1       111.012434  55.506606  0.000014  0.040898  0.000097  0.020532   \n",
-       "2       111.012434  55.506559  0.000026  0.072091  0.000086  0.036106   \n",
-       "3       111.012434  55.506581  0.000019  0.053721  0.000091  0.026933   \n",
-       "4       111.012434  55.506562  0.000024  0.068847  0.000086  0.034485   \n",
-       "...            ...        ...       ...       ...       ...       ...   \n",
-       "431881  111.012434  55.506574  0.000021  0.059198  0.000089  0.029667   \n",
-       "431882  111.012434  55.506600  0.000015  0.043198  0.000096  0.021680   \n",
-       "431883  111.012434  55.506573  0.000021  0.059438  0.000089  0.029787   \n",
-       "431884  111.012434  55.506591  0.000017  0.048030  0.000094  0.024092   \n",
-       "431885  111.012434  55.506574  0.000021  0.059047  0.000089  0.029592   \n",
-       "\n",
-       "          Barite  Celestite  \n",
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-       "431885  111.012434  55.506574  0.000021  0.059047  0.000089  0.029592   \n",
-       "\n",
-       "          Barite  Celestite  Class  \n",
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-     "execution_count": 48,
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-    "df1 = pd.DataFrame(preprocess.scaler_X.inverse_transform(X_train.loc[:, X_train.columns != \"Class\"]), columns=X_train.columns[:-1])\n",
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-       "                 H          O        Ba        Cl         S        Sr  \\\n",
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-       "629999  111.012439  55.643904  1.975694e-03  2.288900e-02  6.403583e-07   \n",
-       "\n",
-       "              Sr    Barite  Celestite  Class  \n",
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-       "\n",
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-       "      <td>0.001001</td>\n",
-       "      <td>1.000674</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>111.012434</td>\n",
-       "      <td>55.506606</td>\n",
-       "      <td>0.000014</td>\n",
-       "      <td>0.040898</td>\n",
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-       "      <td>1.000634</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
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-       "      <td>55.506559</td>\n",
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-       "      <td>0.072091</td>\n",
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-       "      <td>0.036106</td>\n",
-       "      <td>0.001000</td>\n",
-       "      <td>1.000204</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>111.012434</td>\n",
-       "      <td>55.506581</td>\n",
-       "      <td>0.000019</td>\n",
-       "      <td>0.053721</td>\n",
-       "      <td>0.000091</td>\n",
-       "      <td>0.026933</td>\n",
-       "      <td>0.001000</td>\n",
-       "      <td>1.000540</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>111.012434</td>\n",
-       "      <td>55.506562</td>\n",
-       "      <td>0.000024</td>\n",
-       "      <td>0.068847</td>\n",
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-       "      <td>0.034485</td>\n",
-       "      <td>0.001003</td>\n",
-       "      <td>1.000766</td>\n",
-       "      <td>NaN</td>\n",
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-       "    <tr>\n",
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-       "    <tr>\n",
-       "      <th>568516</th>\n",
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-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
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-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>454744</th>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>583730</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>598582</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>459565</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>564481 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 H          O        Ba        Cl         S        Sr  \\\n",
-       "0       111.012434  55.506567  0.000023  0.064477  0.000088  0.032303   \n",
-       "1       111.012434  55.506606  0.000014  0.040898  0.000097  0.020532   \n",
-       "2       111.012434  55.506559  0.000026  0.072091  0.000086  0.036106   \n",
-       "3       111.012434  55.506581  0.000019  0.053721  0.000091  0.026933   \n",
-       "4       111.012434  55.506562  0.000024  0.068847  0.000086  0.034485   \n",
-       "...            ...        ...       ...       ...       ...       ...   \n",
-       "568516         NaN        NaN       NaN       NaN       NaN       NaN   \n",
-       "454744         NaN        NaN       NaN       NaN       NaN       NaN   \n",
-       "583730         NaN        NaN       NaN       NaN       NaN       NaN   \n",
-       "598582         NaN        NaN       NaN       NaN       NaN       NaN   \n",
-       "459565         NaN        NaN       NaN       NaN       NaN       NaN   \n",
-       "\n",
-       "          Barite  Celestite  Class  \n",
-       "0       0.001001   1.000674    0.0  \n",
-       "1       0.001000   1.000634    0.0  \n",
-       "2       0.001000   1.000204    NaN  \n",
-       "3       0.001000   1.000540    0.0  \n",
-       "4       0.001003   1.000766    NaN  \n",
-       "...          ...        ...    ...  \n",
-       "568516       NaN        NaN    0.0  \n",
-       "454744       NaN        NaN    0.0  \n",
-       "583730       NaN        NaN    0.0  \n",
-       "598582       NaN        NaN    0.0  \n",
-       "459565       NaN        NaN    0.0  \n",
-       "\n",
-       "[564481 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "preprocess.scale_inverse(X_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scale_X = tf.convert_to_tensor(preprocess.scaler_X.scale_, dtype=tf.float32)\n",
-    "min_X = tf.convert_to_tensor(preprocess.scaler_X.min_, dtype=tf.float32)\n",
-    "scale_y = tf.convert_to_tensor(preprocess.scaler_y.scale_, dtype=tf.float32)\n",
-    "min_y = tf.convert_to_tensor(preprocess.scaler_y.min_, dtype=tf.float32)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_inverse = X_train.loc[:, X_train.columns != \"Class\"] * scale_X + min_X"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
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-       "               H         O        Ba        Cl         S        Sr  \\\n",
-       "478350  0.557224  0.140163  0.001119  0.555750  0.140160  0.557932   \n",
-       "366736  0.354555  0.155748  0.000697  0.352515  0.155747  0.351468   \n",
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-       "390646  0.594795  0.138235  0.001199  0.593417  0.138231  0.596207   \n",
-       "...          ...       ...       ...       ...       ...       ...   \n",
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-       "459565  0.513986  0.142663  0.001028  0.512319  0.142660  0.513803   \n",
-       "301794  0.415823  0.149779  0.000823  0.413985  0.149777  0.413901   \n",
-       "250138  0.510507  0.142898  0.001021  0.508943  0.142895  0.510372   \n",
-       "\n",
-       "              Barite  Celestite  Class  \n",
-       "478350  3.017125e-06   0.999778      0  \n",
-       "366736  1.880549e-06   0.999699      0  \n",
-       "618649  5.809254e-07   0.998845      0  \n",
-       "435954  1.695287e-06   0.999513      0  \n",
-       "390646  6.463547e-06   0.999962      0  \n",
-       "...              ...        ...    ...  \n",
-       "598582  5.984834e-07   0.998902      0  \n",
-       "395666  1.730543e-06   0.999638      0  \n",
-       "459565  3.197008e-06   0.999831      0  \n",
-       "301794  3.439337e-06   0.999885      0  \n",
-       "250138  5.957527e-06   0.999955      0  \n",
-       "\n",
-       "[431886 rows x 9 columns]"
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-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
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-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
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-     "name": "stdout",
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-     "text": [
-      "Class column found\n"
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-       "1       111.012434  55.506606  0.000014  0.040898  0.000097  0.020532   \n",
-       "2       111.012434  55.506559  0.000026  0.072091  0.000086  0.036106   \n",
-       "3       111.012434  55.506581  0.000019  0.053721  0.000091  0.026933   \n",
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-       "[431886 rows x 8 columns]"
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-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
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-  },
   {
    "cell_type": "code",
    "execution_count": 29,
@@ -2963,8 +209,202 @@
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+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>453600 rows × 9 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "111.01243365971465"
+       "                 H          O        Ba        Cl             S        Sr  \\\n",
+       "0       111.012434  55.506628  0.000011  0.033119  1.027055e-04  0.016651   \n",
+       "1       111.012434  55.506609  0.000014  0.039354  9.817041e-05  0.019762   \n",
+       "2       111.012434  55.506591  0.000017  0.047946  9.354856e-05  0.024050   \n",
+       "3       111.012434  55.506564  0.000024  0.066873  8.688250e-05  0.033500   \n",
+       "4       111.012434  55.506805  0.000004  0.011483  1.469930e-04  0.005884   \n",
+       "...            ...        ...       ...       ...           ...       ...   \n",
+       "453595  111.012434  55.506591  0.000017  0.048120  9.347094e-05  0.024137   \n",
+       "453596  111.012434  55.506776  0.000005  0.013076  1.397029e-04  0.006673   \n",
+       "453597  111.012434  55.506474  0.000274  0.108422  6.420915e-05  0.054001   \n",
+       "453598  111.012434  55.506577  0.000020  0.056594  9.007128e-05  0.028367   \n",
+       "453599  111.012434  55.506217  0.041677  0.150324  1.392182e-07  0.033485   \n",
+       "\n",
+       "          Barite  Celestite  Class  \n",
+       "0       0.001000   1.000506      0  \n",
+       "1       0.001000   1.000637      0  \n",
+       "2       0.001000   1.000489      0  \n",
+       "3       0.001000   1.000388      0  \n",
+       "4       0.001000   1.000565      0  \n",
+       "...          ...        ...    ...  \n",
+       "453595  0.001001   1.000615      0  \n",
+       "453596  0.001000   1.000591      0  \n",
+       "453597  0.104655   0.892149      0  \n",
+       "453598  0.001000   1.000080      0  \n",
+       "453599  1.006763   0.000000      1  \n",
+       "\n",
+       "[453600 rows x 9 columns]"
       ]
      },
      "execution_count": 29,
@@ -2973,63 +413,24 @@
     }
    ],
    "source": [
-    "df_design.loc[589946, \"H\"]"
+    "preprocess.funcInverse(preprocess.scale_inverse(X_train)[0])[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4.718498871295094"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "np.log1p(111)"
+    "scale_X = tf.convert_to_tensor(preprocess.scaler_X.data_range_, dtype=tf.float32)\n",
+    "min_X = tf.convert_to_tensor(preprocess.scaler_X.data_min_, dtype=tf.float32)\n",
+    "scale_y = tf.convert_to_tensor(preprocess.scaler_y.data_range_, dtype=tf.float32)\n",
+    "min_y = tf.convert_to_tensor(preprocess.scaler_y.data_min_, dtype=tf.float32)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "336563   -1.0\n",
-       "354285   -1.0\n",
-       "435530   -1.0\n",
-       "628987   -1.0\n",
-       "118289   -1.0\n",
-       "         ... \n",
-       "368446   -1.0\n",
-       "79409    -1.0\n",
-       "527845   -1.0\n",
-       "512874   -1.0\n",
-       "589946   -1.0\n",
-       "Name: H, Length: 453600, dtype: float64"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.expm1(results_inverse[\"H\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -3066,58 +467,58 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>336563</th>\n",
-       "      <td>-5.869538e+09</td>\n",
-       "      <td>-88058.882645</td>\n",
-       "      <td>0.001342</td>\n",
-       "      <td>1.004960</td>\n",
-       "      <td>266.542724</td>\n",
-       "      <td>5.068195</td>\n",
-       "      <td>-0.001429</td>\n",
-       "      <td>1.441588</td>\n",
+       "      <td>111.012426</td>\n",
+       "      <td>55.506639</td>\n",
+       "      <td>0.000011</td>\n",
+       "      <td>0.033119</td>\n",
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+       "      <td>0.016651</td>\n",
+       "      <td>0.001000</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>354285</th>\n",
-       "      <td>-5.828789e+09</td>\n",
-       "      <td>-88225.165400</td>\n",
-       "      <td>0.001602</td>\n",
-       "      <td>1.190561</td>\n",
-       "      <td>254.765439</td>\n",
-       "      <td>6.036488</td>\n",
-       "      <td>-0.001429</td>\n",
-       "      <td>1.441725</td>\n",
+       "      <td>111.012426</td>\n",
+       "      <td>55.506621</td>\n",
+       "      <td>0.000014</td>\n",
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+       "      <td>9.817042e-05</td>\n",
+       "      <td>0.019762</td>\n",
+       "      <td>0.001000</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>435530</th>\n",
-       "      <td>-5.772645e+09</td>\n",
-       "      <td>-88394.623588</td>\n",
-       "      <td>0.001965</td>\n",
-       "      <td>1.444481</td>\n",
-       "      <td>242.762760</td>\n",
-       "      <td>7.366351</td>\n",
-       "      <td>-0.001429</td>\n",
-       "      <td>1.441571</td>\n",
+       "      <td>111.012426</td>\n",
+       "      <td>55.506602</td>\n",
+       "      <td>0.000017</td>\n",
+       "      <td>0.047946</td>\n",
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+       "      <td>0.024050</td>\n",
+       "      <td>0.001000</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>628987</th>\n",
-       "      <td>-5.648861e+09</td>\n",
-       "      <td>-88639.013486</td>\n",
-       "      <td>0.002780</td>\n",
-       "      <td>1.996571</td>\n",
-       "      <td>225.451322</td>\n",
-       "      <td>10.277376</td>\n",
-       "      <td>-0.001429</td>\n",
-       "      <td>1.441465</td>\n",
+       "      <td>111.012426</td>\n",
+       "      <td>55.506576</td>\n",
+       "      <td>0.000024</td>\n",
+       "      <td>0.066873</td>\n",
+       "      <td>8.688250e-05</td>\n",
+       "      <td>0.033500</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>1.000388</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>118289</th>\n",
-       "      <td>-6.010781e+09</td>\n",
-       "      <td>-86434.937962</td>\n",
-       "      <td>0.000465</td>\n",
-       "      <td>0.352152</td>\n",
-       "      <td>381.551364</td>\n",
-       "      <td>1.694147</td>\n",
-       "      <td>-0.001430</td>\n",
-       "      <td>1.441650</td>\n",
+       "      <td>111.012426</td>\n",
+       "      <td>55.506816</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.011483</td>\n",
+       "      <td>1.469930e-04</td>\n",
+       "      <td>0.005884</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>1.000565</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -3132,57 +533,57 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>368446</th>\n",
-       "      <td>-5.771538e+09</td>\n",
-       "      <td>-88397.469420</td>\n",
-       "      <td>0.001973</td>\n",
-       "      <td>1.449608</td>\n",
-       "      <td>242.561186</td>\n",
-       "      <td>7.393257</td>\n",
-       "      <td>-0.001428</td>\n",
-       "      <td>1.441701</td>\n",
+       "      <td>111.012426</td>\n",
+       "      <td>55.506602</td>\n",
+       "      <td>0.000017</td>\n",
+       "      <td>0.048120</td>\n",
+       "      <td>9.347094e-05</td>\n",
+       "      <td>0.024137</td>\n",
+       "      <td>0.001001</td>\n",
+       "      <td>1.000615</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>79409</th>\n",
-       "      <td>-5.999915e+09</td>\n",
-       "      <td>-86702.258273</td>\n",
-       "      <td>0.000527</td>\n",
-       "      <td>0.400695</td>\n",
-       "      <td>362.620297</td>\n",
-       "      <td>1.942555</td>\n",
-       "      <td>-0.001429</td>\n",
-       "      <td>1.441677</td>\n",
+       "      <td>111.012426</td>\n",
+       "      <td>55.506787</td>\n",
+       "      <td>0.000005</td>\n",
+       "      <td>0.013076</td>\n",
+       "      <td>1.397029e-04</td>\n",
+       "      <td>0.006673</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>1.000591</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>527845</th>\n",
-       "      <td>-5.378014e+09</td>\n",
-       "      <td>-89470.324171</td>\n",
-       "      <td>0.032076</td>\n",
-       "      <td>3.174978</td>\n",
-       "      <td>166.568829</td>\n",
-       "      <td>16.502590</td>\n",
-       "      <td>0.200593</td>\n",
-       "      <td>1.325815</td>\n",
+       "      <td>111.012426</td>\n",
+       "      <td>55.506485</td>\n",
+       "      <td>0.000274</td>\n",
+       "      <td>0.108422</td>\n",
+       "      <td>6.420915e-05</td>\n",
+       "      <td>0.054001</td>\n",
+       "      <td>0.104655</td>\n",
+       "      <td>0.892149</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>512874</th>\n",
-       "      <td>-5.716136e+09</td>\n",
-       "      <td>-88522.110180</td>\n",
-       "      <td>0.002335</td>\n",
-       "      <td>1.697977</td>\n",
-       "      <td>233.732459</td>\n",
-       "      <td>8.699688</td>\n",
-       "      <td>-0.001430</td>\n",
-       "      <td>1.441145</td>\n",
+       "      <td>111.012426</td>\n",
+       "      <td>55.506589</td>\n",
+       "      <td>0.000020</td>\n",
+       "      <td>0.056594</td>\n",
+       "      <td>9.007128e-05</td>\n",
+       "      <td>0.028367</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>1.000080</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>589946</th>\n",
-       "      <td>-5.105040e+09</td>\n",
-       "      <td>-91819.640470</td>\n",
-       "      <td>4.783127</td>\n",
-       "      <td>4.319467</td>\n",
-       "      <td>0.172592</td>\n",
-       "      <td>10.272858</td>\n",
-       "      <td>1.424595</td>\n",
+       "      <td>111.012426</td>\n",
+       "      <td>55.506229</td>\n",
+       "      <td>0.041677</td>\n",
+       "      <td>0.150324</td>\n",
+       "      <td>1.392182e-07</td>\n",
+       "      <td>0.033485</td>\n",
+       "      <td>1.006763</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -3191,472 +592,48 @@
        "</div>"
       ],
       "text/plain": [
-       "                   H             O        Ba        Cl           S         Sr  \\\n",
-       "336563 -5.869538e+09 -88058.882645  0.001342  1.004960  266.542724   5.068195   \n",
-       "354285 -5.828789e+09 -88225.165400  0.001602  1.190561  254.765439   6.036488   \n",
-       "435530 -5.772645e+09 -88394.623588  0.001965  1.444481  242.762760   7.366351   \n",
-       "628987 -5.648861e+09 -88639.013486  0.002780  1.996571  225.451322  10.277376   \n",
-       "118289 -6.010781e+09 -86434.937962  0.000465  0.352152  381.551364   1.694147   \n",
-       "...              ...           ...       ...       ...         ...        ...   \n",
-       "368446 -5.771538e+09 -88397.469420  0.001973  1.449608  242.561186   7.393257   \n",
-       "79409  -5.999915e+09 -86702.258273  0.000527  0.400695  362.620297   1.942555   \n",
-       "527845 -5.378014e+09 -89470.324171  0.032076  3.174978  166.568829  16.502590   \n",
-       "512874 -5.716136e+09 -88522.110180  0.002335  1.697977  233.732459   8.699688   \n",
-       "589946 -5.105040e+09 -91819.640470  4.783127  4.319467    0.172592  10.272858   \n",
+       "                 H          O        Ba        Cl             S        Sr  \\\n",
+       "336563  111.012426  55.506639  0.000011  0.033119  1.027055e-04  0.016651   \n",
+       "354285  111.012426  55.506621  0.000014  0.039354  9.817042e-05  0.019762   \n",
+       "435530  111.012426  55.506602  0.000017  0.047946  9.354856e-05  0.024050   \n",
+       "628987  111.012426  55.506576  0.000024  0.066873  8.688250e-05  0.033500   \n",
+       "118289  111.012426  55.506816  0.000004  0.011483  1.469930e-04  0.005884   \n",
+       "...            ...        ...       ...       ...           ...       ...   \n",
+       "368446  111.012426  55.506602  0.000017  0.048120  9.347094e-05  0.024137   \n",
+       "79409   111.012426  55.506787  0.000005  0.013076  1.397029e-04  0.006673   \n",
+       "527845  111.012426  55.506485  0.000274  0.108422  6.420915e-05  0.054001   \n",
+       "512874  111.012426  55.506589  0.000020  0.056594  9.007128e-05  0.028367   \n",
+       "589946  111.012426  55.506229  0.041677  0.150324  1.392182e-07  0.033485   \n",
        "\n",
        "          Barite  Celestite  \n",
-       "336563 -0.001429   1.441588  \n",
-       "354285 -0.001429   1.441725  \n",
-       "435530 -0.001429   1.441571  \n",
-       "628987 -0.001429   1.441465  \n",
-       "118289 -0.001430   1.441650  \n",
+       "336563  0.001000   1.000506  \n",
+       "354285  0.001000   1.000637  \n",
+       "435530  0.001000   1.000489  \n",
+       "628987  0.001000   1.000388  \n",
+       "118289  0.001000   1.000565  \n",
        "...          ...        ...  \n",
-       "368446 -0.001428   1.441701  \n",
-       "79409  -0.001429   1.441677  \n",
-       "527845  0.200593   1.325815  \n",
-       "512874 -0.001430   1.441145  \n",
-       "589946  1.424595   0.000000  \n",
+       "368446  0.001001   1.000615  \n",
+       "79409   0.001000   1.000591  \n",
+       "527845  0.104655   0.892149  \n",
+       "512874  0.001000   1.000080  \n",
+       "589946  1.006763   0.000000  \n",
        "\n",
        "[453600 rows x 8 columns]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "results_inverse"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>H</th>\n",
-       "      <th>O</th>\n",
-       "      <th>Ba</th>\n",
-       "      <th>Cl</th>\n",
-       "      <th>S</th>\n",
-       "      <th>Sr</th>\n",
-       "      <th>Barite</th>\n",
-       "      <th>Celestite</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
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-       "      <td>-0.001428</td>\n",
-       "      <td>3.227403</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-1.0</td>\n",
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-       "      <td>0.001604</td>\n",
-       "      <td>2.288925</td>\n",
-       "      <td>4.397687e+110</td>\n",
-       "      <td>4.174209e+02</td>\n",
-       "      <td>-0.001428</td>\n",
-       "      <td>3.227981</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>0.001967</td>\n",
-       "      <td>3.239653</td>\n",
-       "      <td>2.694804e+105</td>\n",
-       "      <td>1.580850e+03</td>\n",
-       "      <td>-0.001428</td>\n",
-       "      <td>3.227331</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>0.002784</td>\n",
-       "      <td>6.363765</td>\n",
-       "      <td>8.170811e+97</td>\n",
-       "      <td>2.906650e+04</td>\n",
-       "      <td>-0.001428</td>\n",
-       "      <td>3.226885</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>0.000465</td>\n",
-       "      <td>0.422125</td>\n",
-       "      <td>5.077526e+165</td>\n",
-       "      <td>4.441999e+00</td>\n",
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-      "text/plain": [
-       "          H    O          Ba         Cl              S            Sr  \\\n",
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-       "1      -1.0 -1.0    0.001604   2.288925  4.397687e+110  4.174209e+02   \n",
-       "2      -1.0 -1.0    0.001967   3.239653  2.694804e+105  1.580850e+03   \n",
-       "3      -1.0 -1.0    0.002784   6.363765   8.170811e+97  2.906650e+04   \n",
-       "4      -1.0 -1.0    0.000465   0.422125  5.077526e+165  4.441999e+00   \n",
-       "...     ...  ...         ...        ...            ...           ...   \n",
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-       "453596 -1.0 -1.0    0.000528   0.492862  3.047853e+157  5.976555e+00   \n",
-       "453597 -1.0 -1.0    0.032596  22.926292   2.187376e+72  1.468872e+07   \n",
-       "453598 -1.0 -1.0    0.002338   4.462883  3.226392e+101  6.000041e+03   \n",
-       "453599 -1.0 -1.0  118.477362  74.148597   1.883815e-01  2.893546e+04   \n",
-       "\n",
-       "          Barite  Celestite  \n",
-       "0      -0.001428   3.227403  \n",
-       "1      -0.001428   3.227981  \n",
-       "2      -0.001428   3.227331  \n",
-       "3      -0.001428   3.226885  \n",
-       "4      -0.001429   3.227666  \n",
-       "...          ...        ...  \n",
-       "453595 -0.001427   3.227882  \n",
-       "453596 -0.001428   3.227780  \n",
-       "453597  0.222128   2.765252  \n",
-       "453598 -0.001429   3.225532  \n",
-       "453599  3.156173   0.000000  \n",
-       "\n",
-       "[453600 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(tf.math.expm1(results_inverse), columns = X.columns[:-1])"
+    "np.expm1(X_train.loc[:, species_columns] * scale_X + min_X)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
-   "outputs": [
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-       "\n",
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-       "\n",
-       "[453600 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(preprocess.scaler_X.inverse_transform(X_train.loc[:, X.columns != \"Class\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plt.hist(X_train[\"Barite\"], bins = 100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
    "outputs": [],
    "source": [
     "# timestep=251\n",
@@ -3670,7 +647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -3706,7 +683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3719,34 +696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'H': 0,\n",
-       " 'O': 1,\n",
-       " 'Ba': 2,\n",
-       " 'Cl': 3,\n",
-       " 'S': 4,\n",
-       " 'Sr': 5,\n",
-       " 'Barite': 6,\n",
-       " 'Celestite': 7}"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "column_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3771,7 +721,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3796,12 +746,12 @@
     "\n",
     "\n",
     "optimizer = keras.optimizers.Adam(learning_rate=lr_schedule)\n",
-    "model_minmax.compile(optimizer=optimizer, loss=custom_loss(preprocess, column_dict, 1, 1, 1, scaler_type, loss_variant, 1), metrics=[huber_metric(preprocess, scaler_type, delta), mass_balance_metric(preprocess, column_dict, scaler_type)])"
+    "model_minmax.compile(optimizer=optimizer, loss=custom_loss(preprocess, column_dict, h1, h2, h3, scaler_type, loss_variant, 1), metrics=[huber_metric(preprocess, scaler_type, delta), mass_balance_metric(preprocess, column_dict, scaler_type)])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3813,27 +763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function preprocessing.custom_loss.<locals>.loss(results, predicted)>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "custom_loss(preprocess, column_dict, 1, 2, 3, scaler_type, loss_variant, delta)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -3841,44 +771,213 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/100\n",
-      "\u001b[1m547/886\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - huber: nan - loss: nan - mass_balance: nan"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[30], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m history_standard \u001b[38;5;241m=\u001b[39m model_training(model_minmax)\n",
-      "Cell \u001b[0;32mIn[27], line 5\u001b[0m, in \u001b[0;36mmodel_training\u001b[0;34m(model)\u001b[0m\n\u001b[1;32m      3\u001b[0m start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m      4\u001b[0m callback \u001b[38;5;241m=\u001b[39m keras\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mEarlyStopping(monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mloss\u001b[39m\u001b[38;5;124m'\u001b[39m, patience\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m30\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m history \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mfit(X_train\u001b[38;5;241m.\u001b[39mloc[:, X_train\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mClass\u001b[39m\u001b[38;5;124m\"\u001b[39m], \n\u001b[1;32m      6\u001b[0m                     y_train\u001b[38;5;241m.\u001b[39mloc[:, y_train\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mClass\u001b[39m\u001b[38;5;124m\"\u001b[39m], \n\u001b[1;32m      7\u001b[0m                     batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m512\u001b[39m, \n\u001b[1;32m      8\u001b[0m                     epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m, \n\u001b[1;32m      9\u001b[0m                     validation_data\u001b[38;5;241m=\u001b[39m(X_val\u001b[38;5;241m.\u001b[39mloc[:, X_val\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mClass\u001b[39m\u001b[38;5;124m\"\u001b[39m], y_val\u001b[38;5;241m.\u001b[39mloc[:, y_val\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mClass\u001b[39m\u001b[38;5;124m\"\u001b[39m]),\n\u001b[1;32m     10\u001b[0m                     callbacks\u001b[38;5;241m=\u001b[39m[callback])\n\u001b[1;32m     13\u001b[0m end \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTraining took \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m seconds\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(end \u001b[38;5;241m-\u001b[39m start))\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:320\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[1;32m    318\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator\u001b[38;5;241m.\u001b[39menumerate_epoch():\n\u001b[1;32m    319\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m--> 320\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_function(iterator)\n\u001b[1;32m    321\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n\u001b[1;32m    322\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_training:\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    830\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    832\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 833\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m    835\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    836\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    876\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[1;32m    877\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[0;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m tracing_compilation\u001b[38;5;241m.\u001b[39mcall_function(\n\u001b[1;32m    879\u001b[0m     args, kwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_config\n\u001b[1;32m    880\u001b[0m )\n\u001b[1;32m    881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[1;32m    882\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    883\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[0;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[1;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[0;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m function\u001b[38;5;241m.\u001b[39m_call_flat(  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m    140\u001b[0m     flat_inputs, captured_inputs\u001b[38;5;241m=\u001b[39mfunction\u001b[38;5;241m.\u001b[39mcaptured_inputs\n\u001b[1;32m    141\u001b[0m )\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[1;32m   1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1320\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1321\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1322\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_inference_function\u001b[38;5;241m.\u001b[39mcall_preflattened(args)\n\u001b[1;32m   1323\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1324\u001b[0m     args,\n\u001b[1;32m   1325\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1326\u001b[0m     executing_eagerly)\n\u001b[1;32m   1327\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    215\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 216\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcall_flat(\u001b[38;5;241m*\u001b[39margs)\n\u001b[1;32m    217\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[1;32m    250\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[0;32m--> 251\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mcall_function(\n\u001b[1;32m    252\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname,\n\u001b[1;32m    253\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[1;32m    254\u001b[0m         \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mflat_outputs),\n\u001b[1;32m    255\u001b[0m     )\n\u001b[1;32m    256\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    257\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[1;32m    258\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    259\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[1;32m    260\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[1;32m    261\u001b[0m     )\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/tensorflow/python/eager/context.py:1552\u001b[0m, in \u001b[0;36mContext.call_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m   1550\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[1;32m   1551\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1552\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute(\n\u001b[1;32m   1553\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1554\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[1;32m   1555\u001b[0m       inputs\u001b[38;5;241m=\u001b[39mtensor_inputs,\n\u001b[1;32m   1556\u001b[0m       attrs\u001b[38;5;241m=\u001b[39mattrs,\n\u001b[1;32m   1557\u001b[0m       ctx\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   1558\u001b[0m   )\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1560\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m   1561\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1562\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1566\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[1;32m   1567\u001b[0m   )\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/tensorflow/python/eager/execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[1;32m     54\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 0.0075 - loss: 0.0390 - mass_balance: 0.0736 - val_huber: 0.0010 - val_loss: 0.0084 - val_mass_balance: 0.0160\n",
+      "Epoch 2/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 8.1524e-04 - loss: 0.0113 - mass_balance: 0.0218 - val_huber: 5.2982e-04 - val_loss: 0.0174 - val_mass_balance: 0.0339\n",
+      "Epoch 3/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 14ms/step - huber: 4.1672e-04 - loss: 0.0107 - mass_balance: 0.0208 - val_huber: 2.3004e-04 - val_loss: 0.0117 - val_mass_balance: 0.0227\n",
+      "Epoch 4/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 16ms/step - huber: 2.2715e-04 - loss: 0.0082 - mass_balance: 0.0159 - val_huber: 2.1695e-04 - val_loss: 0.0050 - val_mass_balance: 0.0096\n",
+      "Epoch 5/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 21ms/step - huber: 1.7457e-04 - loss: 0.0076 - mass_balance: 0.0147 - val_huber: 1.0786e-04 - val_loss: 0.0098 - val_mass_balance: 0.0192\n",
+      "Epoch 6/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 25ms/step - huber: 1.3785e-04 - loss: 0.0061 - mass_balance: 0.0118 - val_huber: 1.4024e-04 - val_loss: 0.0059 - val_mass_balance: 0.0114\n",
+      "Epoch 7/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - huber: 1.1500e-04 - loss: 0.0051 - mass_balance: 0.0098 - val_huber: 4.9100e-05 - val_loss: 0.0039 - val_mass_balance: 0.0075\n",
+      "Epoch 8/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - huber: 7.5216e-05 - loss: 0.0038 - mass_balance: 0.0074 - val_huber: 6.9350e-05 - val_loss: 0.0100 - val_mass_balance: 0.0195\n",
+      "Epoch 9/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 22ms/step - huber: 9.0445e-05 - loss: 0.0047 - mass_balance: 0.0092 - val_huber: 7.8162e-05 - val_loss: 0.0053 - val_mass_balance: 0.0101\n",
+      "Epoch 10/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 14ms/step - huber: 7.5558e-05 - loss: 0.0038 - mass_balance: 0.0073 - val_huber: 4.7583e-05 - val_loss: 0.0025 - val_mass_balance: 0.0049\n",
+      "Epoch 11/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 6.1357e-05 - loss: 0.0031 - mass_balance: 0.0060 - val_huber: 3.8443e-05 - val_loss: 0.0028 - val_mass_balance: 0.0054\n",
+      "Epoch 12/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 4.9394e-05 - loss: 0.0030 - mass_balance: 0.0058 - val_huber: 3.4726e-05 - val_loss: 8.9427e-04 - val_mass_balance: 0.0017\n",
+      "Epoch 13/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 15ms/step - huber: 4.1436e-05 - loss: 0.0026 - mass_balance: 0.0050 - val_huber: 3.8143e-05 - val_loss: 0.0041 - val_mass_balance: 0.0080\n",
+      "Epoch 14/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - huber: 3.4605e-05 - loss: 0.0026 - mass_balance: 0.0050 - val_huber: 2.1593e-05 - val_loss: 8.6975e-04 - val_mass_balance: 0.0017\n",
+      "Epoch 15/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 26ms/step - huber: 2.5910e-05 - loss: 0.0017 - mass_balance: 0.0033 - val_huber: 1.8322e-05 - val_loss: 0.0026 - val_mass_balance: 0.0050\n",
+      "Epoch 16/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 27ms/step - huber: 2.6867e-05 - loss: 0.0017 - mass_balance: 0.0033 - val_huber: 5.0203e-05 - val_loss: 8.3505e-04 - val_mass_balance: 0.0016\n",
+      "Epoch 17/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 28ms/step - huber: 2.0973e-05 - loss: 0.0014 - mass_balance: 0.0027 - val_huber: 1.4719e-05 - val_loss: 0.0020 - val_mass_balance: 0.0040\n",
+      "Epoch 18/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 18ms/step - huber: 2.4551e-05 - loss: 0.0016 - mass_balance: 0.0030 - val_huber: 1.4335e-05 - val_loss: 0.0012 - val_mass_balance: 0.0023\n",
+      "Epoch 19/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 14ms/step - huber: 1.8725e-05 - loss: 0.0016 - mass_balance: 0.0031 - val_huber: 1.3809e-05 - val_loss: 6.9777e-04 - val_mass_balance: 0.0013\n",
+      "Epoch 20/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 1.6265e-05 - loss: 0.0013 - mass_balance: 0.0026 - val_huber: 1.6385e-05 - val_loss: 2.8956e-04 - val_mass_balance: 5.5450e-04\n",
+      "Epoch 21/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 21ms/step - huber: 1.7133e-05 - loss: 0.0014 - mass_balance: 0.0026 - val_huber: 1.6281e-05 - val_loss: 3.9446e-04 - val_mass_balance: 7.5917e-04\n",
+      "Epoch 22/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - huber: 1.6131e-05 - loss: 0.0013 - mass_balance: 0.0026 - val_huber: 1.6597e-05 - val_loss: 9.6432e-04 - val_mass_balance: 0.0019\n",
+      "Epoch 23/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - huber: 1.3699e-05 - loss: 0.0012 - mass_balance: 0.0023 - val_huber: 1.1870e-05 - val_loss: 0.0018 - val_mass_balance: 0.0035\n",
+      "Epoch 24/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 25ms/step - huber: 1.4064e-05 - loss: 0.0012 - mass_balance: 0.0024 - val_huber: 1.0972e-05 - val_loss: 4.3153e-04 - val_mass_balance: 8.2718e-04\n",
+      "Epoch 25/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 18ms/step - huber: 1.3869e-05 - loss: 0.0012 - mass_balance: 0.0023 - val_huber: 9.4804e-06 - val_loss: 6.0883e-04 - val_mass_balance: 0.0012\n",
+      "Epoch 26/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 15ms/step - huber: 1.0623e-05 - loss: 8.5861e-04 - mass_balance: 0.0017 - val_huber: 9.9264e-06 - val_loss: 8.0563e-04 - val_mass_balance: 0.0016\n",
+      "Epoch 27/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 21ms/step - huber: 1.1598e-05 - loss: 8.2072e-04 - mass_balance: 0.0016 - val_huber: 6.8338e-06 - val_loss: 5.9611e-04 - val_mass_balance: 0.0011\n",
+      "Epoch 28/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 26ms/step - huber: 9.6297e-06 - loss: 8.0581e-04 - mass_balance: 0.0016 - val_huber: 7.0362e-06 - val_loss: 8.4991e-04 - val_mass_balance: 0.0017\n",
+      "Epoch 29/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - huber: 8.4862e-06 - loss: 7.6189e-04 - mass_balance: 0.0015 - val_huber: 7.6760e-06 - val_loss: 7.2745e-04 - val_mass_balance: 0.0014\n",
+      "Epoch 30/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - huber: 8.2719e-06 - loss: 7.1722e-04 - mass_balance: 0.0014 - val_huber: 6.2657e-06 - val_loss: 5.8498e-04 - val_mass_balance: 0.0011\n",
+      "Epoch 31/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 16ms/step - huber: 7.8455e-06 - loss: 7.1416e-04 - mass_balance: 0.0014 - val_huber: 8.7057e-06 - val_loss: 8.8044e-04 - val_mass_balance: 0.0017\n",
+      "Epoch 32/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 7.4936e-06 - loss: 6.4668e-04 - mass_balance: 0.0013 - val_huber: 5.9705e-06 - val_loss: 2.6550e-04 - val_mass_balance: 5.1172e-04\n",
+      "Epoch 33/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 15ms/step - huber: 6.9179e-06 - loss: 5.9576e-04 - mass_balance: 0.0012 - val_huber: 6.0079e-06 - val_loss: 4.0377e-04 - val_mass_balance: 7.7999e-04\n",
+      "Epoch 34/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - huber: 6.5149e-06 - loss: 6.0484e-04 - mass_balance: 0.0012 - val_huber: 4.6533e-06 - val_loss: 3.3134e-04 - val_mass_balance: 6.4487e-04\n",
+      "Epoch 35/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - huber: 6.6793e-06 - loss: 5.2434e-04 - mass_balance: 0.0010 - val_huber: 5.2271e-06 - val_loss: 0.0011 - val_mass_balance: 0.0022\n",
+      "Epoch 36/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 24ms/step - huber: 7.3776e-06 - loss: 6.7876e-04 - mass_balance: 0.0013 - val_huber: 5.8002e-06 - val_loss: 6.2378e-04 - val_mass_balance: 0.0012\n",
+      "Epoch 37/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - huber: 5.7621e-06 - loss: 5.2411e-04 - mass_balance: 0.0010 - val_huber: 4.3628e-06 - val_loss: 2.7846e-04 - val_mass_balance: 5.3314e-04\n",
+      "Epoch 38/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 15ms/step - huber: 6.3009e-06 - loss: 5.4680e-04 - mass_balance: 0.0011 - val_huber: 4.2194e-06 - val_loss: 4.9326e-04 - val_mass_balance: 9.5801e-04\n",
+      "Epoch 39/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 14ms/step - huber: 5.4980e-06 - loss: 3.6781e-04 - mass_balance: 7.1075e-04 - val_huber: 4.2870e-06 - val_loss: 2.5454e-04 - val_mass_balance: 4.8891e-04\n",
+      "Epoch 40/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 16ms/step - huber: 5.4379e-06 - loss: 4.2794e-04 - mass_balance: 8.2848e-04 - val_huber: 4.0493e-06 - val_loss: 2.2829e-04 - val_mass_balance: 4.4034e-04\n",
+      "Epoch 41/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 25ms/step - huber: 4.7769e-06 - loss: 3.6726e-04 - mass_balance: 7.1109e-04 - val_huber: 4.0265e-06 - val_loss: 3.9770e-04 - val_mass_balance: 7.7458e-04\n",
+      "Epoch 42/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 24ms/step - huber: 4.9517e-06 - loss: 3.1500e-04 - mass_balance: 6.0869e-04 - val_huber: 4.5054e-06 - val_loss: 1.7977e-04 - val_mass_balance: 3.4522e-04\n",
+      "Epoch 43/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 25ms/step - huber: 4.1640e-06 - loss: 3.3998e-04 - mass_balance: 6.5738e-04 - val_huber: 4.2401e-06 - val_loss: 1.6275e-04 - val_mass_balance: 3.1338e-04\n",
+      "Epoch 44/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 22ms/step - huber: 4.2278e-06 - loss: 3.0799e-04 - mass_balance: 5.9549e-04 - val_huber: 4.0452e-06 - val_loss: 2.1512e-04 - val_mass_balance: 4.1303e-04\n",
+      "Epoch 45/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 15ms/step - huber: 4.5296e-06 - loss: 3.3064e-04 - mass_balance: 6.3933e-04 - val_huber: 3.0928e-06 - val_loss: 4.1098e-04 - val_mass_balance: 7.9791e-04\n",
+      "Epoch 46/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 15ms/step - huber: 4.4066e-06 - loss: 3.3437e-04 - mass_balance: 6.4758e-04 - val_huber: 3.2996e-06 - val_loss: 1.7694e-04 - val_mass_balance: 3.4062e-04\n",
+      "Epoch 47/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 22ms/step - huber: 3.4459e-06 - loss: 2.6240e-04 - mass_balance: 5.0729e-04 - val_huber: 2.8129e-06 - val_loss: 1.7577e-04 - val_mass_balance: 3.4087e-04\n",
+      "Epoch 48/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - huber: 3.3382e-06 - loss: 2.3380e-04 - mass_balance: 4.5158e-04 - val_huber: 2.9951e-06 - val_loss: 1.6209e-04 - val_mass_balance: 3.1329e-04\n",
+      "Epoch 49/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - huber: 3.5292e-06 - loss: 2.7273e-04 - mass_balance: 5.2766e-04 - val_huber: 2.7400e-06 - val_loss: 1.9260e-04 - val_mass_balance: 3.7353e-04\n",
+      "Epoch 50/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 22ms/step - huber: 3.6774e-06 - loss: 2.5005e-04 - mass_balance: 4.8293e-04 - val_huber: 2.5461e-06 - val_loss: 1.9857e-04 - val_mass_balance: 3.8430e-04\n",
+      "Epoch 51/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 17ms/step - huber: 3.1892e-06 - loss: 2.2465e-04 - mass_balance: 4.3453e-04 - val_huber: 2.4246e-06 - val_loss: 2.2622e-04 - val_mass_balance: 4.3613e-04\n",
+      "Epoch 52/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 14ms/step - huber: 3.3681e-06 - loss: 2.1544e-04 - mass_balance: 4.1591e-04 - val_huber: 2.5782e-06 - val_loss: 2.8258e-04 - val_mass_balance: 5.4094e-04\n",
+      "Epoch 53/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 14ms/step - huber: 3.3544e-06 - loss: 2.1320e-04 - mass_balance: 4.1200e-04 - val_huber: 2.4744e-06 - val_loss: 1.9178e-04 - val_mass_balance: 3.7319e-04\n",
+      "Epoch 54/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 19ms/step - huber: 3.5877e-06 - loss: 2.0713e-04 - mass_balance: 3.9988e-04 - val_huber: 2.3547e-06 - val_loss: 1.5024e-04 - val_mass_balance: 2.9036e-04\n",
+      "Epoch 55/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - huber: 3.3519e-06 - loss: 2.0048e-04 - mass_balance: 3.8775e-04 - val_huber: 2.2808e-06 - val_loss: 1.8056e-04 - val_mass_balance: 3.4956e-04\n",
+      "Epoch 56/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - huber: 2.9392e-06 - loss: 1.8254e-04 - mass_balance: 3.5299e-04 - val_huber: 2.5235e-06 - val_loss: 1.2856e-04 - val_mass_balance: 2.4840e-04\n",
+      "Epoch 57/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - huber: 2.7906e-06 - loss: 1.7508e-04 - mass_balance: 3.3794e-04 - val_huber: 2.5248e-06 - val_loss: 1.8699e-04 - val_mass_balance: 3.6292e-04\n",
+      "Epoch 58/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 18ms/step - huber: 2.8318e-06 - loss: 1.7877e-04 - mass_balance: 3.4473e-04 - val_huber: 2.1862e-06 - val_loss: 2.3205e-04 - val_mass_balance: 4.4626e-04\n",
+      "Epoch 59/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 14ms/step - huber: 2.7693e-06 - loss: 1.6680e-04 - mass_balance: 3.2246e-04 - val_huber: 2.2441e-06 - val_loss: 1.2467e-04 - val_mass_balance: 2.4221e-04\n",
+      "Epoch 60/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 2.6047e-06 - loss: 1.5568e-04 - mass_balance: 3.0085e-04 - val_huber: 2.1657e-06 - val_loss: 1.1823e-04 - val_mass_balance: 2.2892e-04\n",
+      "Epoch 61/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 17ms/step - huber: 2.4983e-06 - loss: 1.6571e-04 - mass_balance: 3.2043e-04 - val_huber: 2.3236e-06 - val_loss: 1.4562e-04 - val_mass_balance: 2.8067e-04\n",
+      "Epoch 62/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - huber: 2.5143e-06 - loss: 1.5320e-04 - mass_balance: 2.9590e-04 - val_huber: 2.0950e-06 - val_loss: 2.1408e-04 - val_mass_balance: 4.1179e-04\n",
+      "Epoch 63/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 28ms/step - huber: 2.7199e-06 - loss: 1.6342e-04 - mass_balance: 3.1520e-04 - val_huber: 2.1094e-06 - val_loss: 1.2267e-04 - val_mass_balance: 2.3728e-04\n",
+      "Epoch 64/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 28ms/step - huber: 2.5061e-06 - loss: 1.4948e-04 - mass_balance: 2.8907e-04 - val_huber: 2.0398e-06 - val_loss: 1.8459e-04 - val_mass_balance: 3.5698e-04\n",
+      "Epoch 65/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 31ms/step - huber: 2.5435e-06 - loss: 1.4227e-04 - mass_balance: 2.7519e-04 - val_huber: 2.0541e-06 - val_loss: 1.9828e-04 - val_mass_balance: 3.8181e-04\n",
+      "Epoch 66/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - huber: 2.5066e-06 - loss: 1.5777e-04 - mass_balance: 3.0448e-04 - val_huber: 1.9378e-06 - val_loss: 1.2588e-04 - val_mass_balance: 2.4382e-04\n",
+      "Epoch 67/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 18ms/step - huber: 2.5894e-06 - loss: 1.4670e-04 - mass_balance: 2.8397e-04 - val_huber: 1.9249e-06 - val_loss: 1.1284e-04 - val_mass_balance: 2.1878e-04\n",
+      "Epoch 68/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 26ms/step - huber: 2.6530e-06 - loss: 1.4706e-04 - mass_balance: 2.8417e-04 - val_huber: 1.9059e-06 - val_loss: 1.2299e-04 - val_mass_balance: 2.3856e-04\n",
+      "Epoch 69/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 24ms/step - huber: 2.2359e-06 - loss: 1.3067e-04 - mass_balance: 2.5302e-04 - val_huber: 1.9152e-06 - val_loss: 1.7301e-04 - val_mass_balance: 3.3409e-04\n",
+      "Epoch 70/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 24ms/step - huber: 2.5769e-06 - loss: 1.3953e-04 - mass_balance: 2.6978e-04 - val_huber: 1.8918e-06 - val_loss: 1.2590e-04 - val_mass_balance: 2.4435e-04\n",
+      "Epoch 71/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 17ms/step - huber: 2.2142e-06 - loss: 1.3009e-04 - mass_balance: 2.5191e-04 - val_huber: 1.8140e-06 - val_loss: 1.5228e-04 - val_mass_balance: 2.9534e-04\n",
+      "Epoch 72/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 15ms/step - huber: 2.2749e-06 - loss: 1.3118e-04 - mass_balance: 2.5412e-04 - val_huber: 1.8351e-06 - val_loss: 1.1974e-04 - val_mass_balance: 2.3162e-04\n",
+      "Epoch 73/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 22ms/step - huber: 2.2190e-06 - loss: 1.3127e-04 - mass_balance: 2.5395e-04 - val_huber: 1.7942e-06 - val_loss: 1.0903e-04 - val_mass_balance: 2.1127e-04\n",
+      "Epoch 74/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 27ms/step - huber: 2.3982e-06 - loss: 1.2534e-04 - mass_balance: 2.4267e-04 - val_huber: 1.8305e-06 - val_loss: 1.0127e-04 - val_mass_balance: 1.9655e-04\n",
+      "Epoch 75/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 24ms/step - huber: 2.0604e-06 - loss: 1.2235e-04 - mass_balance: 2.3708e-04 - val_huber: 1.8266e-06 - val_loss: 1.2604e-04 - val_mass_balance: 2.4344e-04\n",
+      "Epoch 76/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 21ms/step - huber: 2.3165e-06 - loss: 1.1998e-04 - mass_balance: 2.3248e-04 - val_huber: 1.7778e-06 - val_loss: 1.1033e-04 - val_mass_balance: 2.1423e-04\n",
+      "Epoch 77/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 14ms/step - huber: 2.0192e-06 - loss: 1.1901e-04 - mass_balance: 2.3076e-04 - val_huber: 1.7642e-06 - val_loss: 1.2262e-04 - val_mass_balance: 2.3711e-04\n",
+      "Epoch 78/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 16ms/step - huber: 1.9410e-06 - loss: 1.1909e-04 - mass_balance: 2.3076e-04 - val_huber: 1.6967e-06 - val_loss: 1.2829e-04 - val_mass_balance: 2.4769e-04\n",
+      "Epoch 79/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 20ms/step - huber: 2.2415e-06 - loss: 1.1771e-04 - mass_balance: 2.2803e-04 - val_huber: 1.6993e-06 - val_loss: 1.2866e-04 - val_mass_balance: 2.4925e-04\n",
+      "Epoch 80/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 24ms/step - huber: 2.0585e-06 - loss: 1.1747e-04 - mass_balance: 2.2764e-04 - val_huber: 1.6712e-06 - val_loss: 1.1262e-04 - val_mass_balance: 2.1807e-04\n",
+      "Epoch 81/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - huber: 2.0641e-06 - loss: 1.1335e-04 - mass_balance: 2.1972e-04 - val_huber: 1.6921e-06 - val_loss: 1.3330e-04 - val_mass_balance: 2.5897e-04\n",
+      "Epoch 82/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - huber: 2.4290e-06 - loss: 1.1190e-04 - mass_balance: 2.1678e-04 - val_huber: 1.6846e-06 - val_loss: 1.2090e-04 - val_mass_balance: 2.3408e-04\n",
+      "Epoch 83/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 22ms/step - huber: 2.1889e-06 - loss: 1.0847e-04 - mass_balance: 2.1029e-04 - val_huber: 1.6704e-06 - val_loss: 9.8870e-05 - val_mass_balance: 1.9208e-04\n",
+      "Epoch 84/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 16ms/step - huber: 2.1365e-06 - loss: 1.1127e-04 - mass_balance: 2.1576e-04 - val_huber: 1.6899e-06 - val_loss: 1.0984e-04 - val_mass_balance: 2.1367e-04\n",
+      "Epoch 85/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 1.9194e-06 - loss: 1.1274e-04 - mass_balance: 2.1861e-04 - val_huber: 1.6626e-06 - val_loss: 1.1977e-04 - val_mass_balance: 2.3172e-04\n",
+      "Epoch 86/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 16ms/step - huber: 1.9864e-06 - loss: 1.1020e-04 - mass_balance: 2.1364e-04 - val_huber: 1.6328e-06 - val_loss: 9.9944e-05 - val_mass_balance: 1.9447e-04\n",
+      "Epoch 87/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 28ms/step - huber: 2.2566e-06 - loss: 1.0508e-04 - mass_balance: 2.0383e-04 - val_huber: 1.6217e-06 - val_loss: 1.0180e-04 - val_mass_balance: 1.9751e-04\n",
+      "Epoch 88/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - huber: 1.9429e-06 - loss: 1.0397e-04 - mass_balance: 2.0171e-04 - val_huber: 1.6278e-06 - val_loss: 1.0268e-04 - val_mass_balance: 1.9916e-04\n",
+      "Epoch 89/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - huber: 1.9575e-06 - loss: 1.0253e-04 - mass_balance: 1.9897e-04 - val_huber: 1.6036e-06 - val_loss: 1.1626e-04 - val_mass_balance: 2.2494e-04\n",
+      "Epoch 90/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - huber: 1.8867e-06 - loss: 1.0403e-04 - mass_balance: 2.0183e-04 - val_huber: 1.5811e-06 - val_loss: 1.0117e-04 - val_mass_balance: 1.9684e-04\n",
+      "Epoch 91/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 15ms/step - huber: 1.9020e-06 - loss: 1.0284e-04 - mass_balance: 1.9955e-04 - val_huber: 1.5778e-06 - val_loss: 1.1690e-04 - val_mass_balance: 2.2632e-04\n",
+      "Epoch 92/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 2.0789e-06 - loss: 1.0025e-04 - mass_balance: 1.9458e-04 - val_huber: 1.5698e-06 - val_loss: 1.0207e-04 - val_mass_balance: 1.9854e-04\n",
+      "Epoch 93/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 1.8589e-06 - loss: 1.0170e-04 - mass_balance: 1.9733e-04 - val_huber: 1.5623e-06 - val_loss: 9.9011e-05 - val_mass_balance: 1.9261e-04\n",
+      "Epoch 94/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 19ms/step - huber: 1.9282e-06 - loss: 9.9660e-05 - mass_balance: 1.9340e-04 - val_huber: 1.5526e-06 - val_loss: 9.0676e-05 - val_mass_balance: 1.7642e-04\n",
+      "Epoch 95/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 24ms/step - huber: 2.1101e-06 - loss: 9.7466e-05 - mass_balance: 1.8913e-04 - val_huber: 1.5413e-06 - val_loss: 9.6417e-05 - val_mass_balance: 1.8739e-04\n",
+      "Epoch 96/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - huber: 1.6935e-06 - loss: 9.6747e-05 - mass_balance: 1.8790e-04 - val_huber: 1.5203e-06 - val_loss: 9.2831e-05 - val_mass_balance: 1.8054e-04\n",
+      "Epoch 97/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - huber: 1.9700e-06 - loss: 9.6101e-05 - mass_balance: 1.8657e-04 - val_huber: 1.5266e-06 - val_loss: 1.0302e-04 - val_mass_balance: 2.0030e-04\n",
+      "Epoch 98/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 25ms/step - huber: 1.7164e-06 - loss: 9.5325e-05 - mass_balance: 1.8514e-04 - val_huber: 1.5184e-06 - val_loss: 8.6008e-05 - val_mass_balance: 1.6730e-04\n",
+      "Epoch 99/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 15ms/step - huber: 1.9702e-06 - loss: 9.4756e-05 - mass_balance: 1.8396e-04 - val_huber: 1.5249e-06 - val_loss: 1.0126e-04 - val_mass_balance: 1.9700e-04\n",
+      "Epoch 100/100\n",
+      "\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 15ms/step - huber: 2.1157e-06 - loss: 9.2913e-05 - mass_balance: 1.8043e-04 - val_huber: 1.5028e-06 - val_loss: 9.1530e-05 - val_mass_balance: 1.7779e-04\n",
+      "Training took 1789.720398426056 seconds\n"
      ]
     }
    ],
    "source": [
-    "history_standard = model_training(model_minmax)"
+    "history_minmax = model_training(model_minmax)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -3888,22 +987,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x900 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[35], line 9\u001b[0m\n\u001b[1;32m      7\u001b[0m handles, labels \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mget_legend_handles_labels()\n\u001b[1;32m      8\u001b[0m order \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m0\u001b[39m]  \u001b[38;5;66;03m# Manually specify the order of the legend items\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m ax\u001b[38;5;241m.\u001b[39mlegend([handles[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m order], [labels[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m order], loc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mupper right\u001b[39m\u001b[38;5;124m'\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m14\u001b[39m) \u001b[38;5;66;03m# bbox_to_anchor=(0.5, -0.4),\u001b[39;00m\n\u001b[1;32m     10\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEpoch\u001b[39m\u001b[38;5;124m\"\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m12\u001b[39m)\n\u001b[1;32m     11\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMass balance metric\u001b[39m\u001b[38;5;124m\"\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m12\u001b[39m)\n",
+      "Cell \u001b[0;32mIn[35], line 9\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      7\u001b[0m handles, labels \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mget_legend_handles_labels()\n\u001b[1;32m      8\u001b[0m order \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m0\u001b[39m]  \u001b[38;5;66;03m# Manually specify the order of the legend items\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m ax\u001b[38;5;241m.\u001b[39mlegend([handles[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m order], [labels[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m order], loc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mupper right\u001b[39m\u001b[38;5;124m'\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m14\u001b[39m) \u001b[38;5;66;03m# bbox_to_anchor=(0.5, -0.4),\u001b[39;00m\n\u001b[1;32m     10\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEpoch\u001b[39m\u001b[38;5;124m\"\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m12\u001b[39m)\n\u001b[1;32m     11\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMass balance metric\u001b[39m\u001b[38;5;124m\"\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m12\u001b[39m)\n",
+      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
+     ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1500x900 with 1 Axes>"
       ]
@@ -3914,9 +1015,9 @@
    ],
    "source": [
     "fig, ax = plt.subplots(figsize=(10, 6), dpi=150)\n",
-    "ax.plot(history_standard.history[\"val_mass_balance\"], label = \"standardized, ext. huber loss\")\n",
+    "# ax.plot(history_standard.history[\"val_mass_balance\"], label = \"standardized, ext. huber loss\")\n",
     "ax.plot(history_minmax.history[\"val_mass_balance\"], label = \"minmax, ext. huber loss\")\n",
-    "ax.plot(history_origin.history[\"val_mass_balance\"], label = \"minmax, huber loss\")\n",
+    "# ax.plot(history_origin.history[\"val_mass_balance\"], label = \"minmax, huber loss\")\n",
     "\n",
     "ax.grid()\n",
     "handles, labels = ax.get_legend_handles_labels()\n",
@@ -3929,7 +1030,7 @@
     "plt.yticks(fontsize=12)\n",
     "\n",
     "plt.yscale('log')\n",
-    "plt.savefig(\"/Users/hannessigner/Documents/Work/BMBF/GreenHPC2021UP/Treffen/2025-02-20-PERFACCT/Vorbereitung/images/mass_balance_metric.pdf\")\n",
+    "# plt.savefig(\"/Users/hannessigner/Documents/Work/BMBF/GreenHPC2021UP/Treffen/2025-02-20-PERFACCT/Vorbereitung/images/mass_balance_metric.pdf\")\n",
     "plt.show()\n",
     "\n",
     "\n",
@@ -3951,7 +1052,7 @@
     "plt.xticks(fontsize=12)\n",
     "plt.yticks(fontsize=12)\n",
     "plt.yscale('log')\n",
-    "plt.savefig(\"/Users/hannessigner/Documents/Work/BMBF/GreenHPC2021UP/Treffen/2025-02-20-PERFACCT/Vorbereitung/images/huber_metric.pdf\")\n",
+    "# plt.savefig(\"/Users/hannessigner/Documents/Work/BMBF/GreenHPC2021UP/Treffen/2025-02-20-PERFACCT/Vorbereitung/images/huber_metric.pdf\")\n",
     "plt.show()\n",
     "\n"
    ]
@@ -3965,7 +1066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4196,7 +1297,7 @@
     }
    ],
    "source": [
-    "pd.DataFrame(preprocess.scaler_X.inverse_transform(model_standard.predict(X_train[species_columns])), columns=species_columns)"
+    "pd.DataFrame(preprocess.scaler_X.inverse_transform(model_minmax.predict(X_train[species_columns])), columns=species_columns)"
    ]
   },
   {
@@ -4403,26 +1504,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m3938/3938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 958us/step\n",
-      "1.9593958053287786e-10\n",
-      "4.6533221720324036e-11\n"
+      "\u001b[1m3938/3938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 1ms/step\n",
+      "4.684881100246174e-09\n",
+      "1.640090285803808e-10\n"
      ]
     }
    ],
    "source": [
-    "dBa, dSr, prediction, classes = mass_balance(model_standard, X_test, preprocess)"
+    "dBa, dSr, prediction, classes = mass_balance(model_minmax, X_test, preprocess)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4431,7 +1532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4440,7 +1541,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4451,44 +1552,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.727858131891964\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0.0"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(mass_balance_ratio(mass_balance_results_non_reactive))\n",
-    "mass_balance_ratio((mass_balance_results_reactive))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.787420634920635"
+       "0.0014682539682539682"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4678,7 +1751,7 @@
     "y_design = pd.DataFrame(y_design)\n",
     "y_results = pd.DataFrame(y_results)\n",
     "\n",
-    "prediction = model_standard.predict(y_design.iloc[:, y_design.columns != \"Class\"])\n",
+    "prediction = model_minmax.predict(y_design.iloc[:, y_design.columns != \"Class\"])\n",
     "prediction = pd.DataFrame(prediction, columns = y_results.columns)\n",
     "\n",
     "# y_results_back, prediction = preprocess.funcInverse(y_results, prediction)\n",