Repository: open-reaction-database/ord-schema (language: python)

Issue:
ord-schema tests are failing
https://github.com/open-reaction-database/ord-schema/actions/runs/8633224441

@greglandrum This seems to be at least partially related to the disappearance of the ARM builds for the rdkit-postgresql conda package.


Discussion / hints:

Fix patch:
diff --git a/examples/applications/Perera_Science_Granda_Nature_Suzuki/Granda_Perera_ml_example.ipynb b/examples/applications/Perera_Science_Granda_Nature_Suzuki/Granda_Perera_ml_example.ipynb
index 9db5fcb08..e235f6411 100644
--- a/examples/applications/Perera_Science_Granda_Nature_Suzuki/Granda_Perera_ml_example.ipynb
+++ b/examples/applications/Perera_Science_Granda_Nature_Suzuki/Granda_Perera_ml_example.ipynb
@@ -20,7 +20,12 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-12T18:12:21.317248Z",
+     "start_time": "2024-04-12T18:12:18.513877Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Import modules\n",
@@ -45,8 +50,21 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-12T18:12:21.844198Z",
+     "start_time": "2024-04-12T18:12:21.318299Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100% [............................................................................] 269731 / 269731"
+     ]
+    }
+   ],
    "source": [
     "# Download dataset from ord-data\n",
     "url = \"https://github.com/open-reaction-database/ord-data/blob/main/data/68/ord_dataset-68cb8b4b2b384e3d85b5b1efae58b203.pb.gz?raw=true\"\n",
@@ -56,7 +74,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-12T18:12:22.127988Z",
+     "start_time": "2024-04-12T18:12:21.844967Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Load Dataset message\n",
@@ -66,7 +89,12 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-12T18:12:22.129418Z",
+     "start_time": "2024-04-12T18:12:22.129369Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Ensure dataset validates\n",
@@ -99,8 +127,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>inputs[\"Boronate in Solvent\"].components[0].identifiers[0].value</th>\n",
-       "      <th>inputs[\"Boronate in Solvent\"].components[1].identifiers[0].value</th>\n",
+       "      <th>inputs[\"Aryl Halide\"].components[0].identifiers[0].value</th>\n",
        "      <th>inputs[\"Solvent_1\"].components[0].identifiers[0].value</th>\n",
        "      <th>inputs[\"Ligand in Solvent\"].components[0].identifiers[0].value</th>\n",
        "      <th>inputs[\"Ligand in Solvent\"].components[0].amount.moles.value</th>\n",
@@ -109,9 +136,10 @@
        "      <th>inputs[\"Ligand in Solvent\"].components[1].identifiers[0].type</th>\n",
        "      <th>inputs[\"Ligand in Solvent\"].components[1].identifiers[0].value</th>\n",
        "      <th>inputs[\"Ligand in Solvent\"].components[1].amount.volume.value</th>\n",
+       "      <th>inputs[\"Ligand in Solvent\"].components[1].amount.volume.units</th>\n",
        "      <th>...</th>\n",
-       "      <th>inputs[\"Base in Solvent\"].components[1].amount.volume.units</th>\n",
-       "      <th>inputs[\"Base in Solvent\"].components[1].reaction_role</th>\n",
+       "      <th>inputs[\"Boronate in Solvent\"].components[0].identifiers[0].value</th>\n",
+       "      <th>inputs[\"Boronate in Solvent\"].components[1].identifiers[0].value</th>\n",
        "      <th>outcomes[0].products[0].measurements[0].percentage.value</th>\n",
        "      <th>provenance.record_modified[1].time.value</th>\n",
        "      <th>reaction_id</th>\n",
@@ -125,8 +153,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O</td>\n",
-       "      <td>CN(C)C=O</td>\n",
+       "      <td>ClC1=CC=C2N=CC=CC2=C1</td>\n",
        "      <td>CC#N</td>\n",
        "      <td>CC(C)(C)P(C(C)(C)C)C(C)(C)C</td>\n",
        "      <td>0.05</td>\n",
@@ -135,9 +162,10 @@
        "      <td>SMILES</td>\n",
        "      <td>Cc1ccccc1</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
        "      <td>MICROLITER</td>\n",
-       "      <td>SOLVENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O</td>\n",
+       "      <td>CN(C)C=O</td>\n",
        "      <td>4.760000</td>\n",
        "      <td>2021-03-04 13:56:15.292871</td>\n",
        "      <td>ord-61b2819b15824f1fa131ed1b2ea1dae4</td>\n",
@@ -149,8 +177,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O</td>\n",
-       "      <td>CN(C)C=O</td>\n",
+       "      <td>ClC1=CC=C2N=CC=CC2=C1</td>\n",
        "      <td>CC#N</td>\n",
        "      <td>c1ccc(cc1)P(c2ccccc2)c3ccccc3</td>\n",
        "      <td>0.05</td>\n",
@@ -159,9 +186,10 @@
        "      <td>SMILES</td>\n",
        "      <td>Cc1ccccc1</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
        "      <td>MICROLITER</td>\n",
-       "      <td>SOLVENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O</td>\n",
+       "      <td>CN(C)C=O</td>\n",
        "      <td>4.120000</td>\n",
        "      <td>2021-03-04 13:56:15.298402</td>\n",
        "      <td>ord-f18c2b2d0bd04d2c96a3a16dfd8b8bef</td>\n",
@@ -173,8 +201,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O</td>\n",
-       "      <td>CN(C)C=O</td>\n",
+       "      <td>ClC1=CC=C2N=CC=CC2=C1</td>\n",
        "      <td>CC#N</td>\n",
        "      <td>CC(C)(C)P(C1=CC=C(C=C1)N(C)C)C(C)(C)C.CC(C)(C)...</td>\n",
        "      <td>0.05</td>\n",
@@ -183,9 +210,10 @@
        "      <td>SMILES</td>\n",
        "      <td>Cc1ccccc1</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
        "      <td>MICROLITER</td>\n",
-       "      <td>SOLVENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O</td>\n",
+       "      <td>CN(C)C=O</td>\n",
        "      <td>2.580000</td>\n",
        "      <td>2021-03-04 13:56:15.303978</td>\n",
        "      <td>ord-1061f43c594b4de68345c010b2aef94b</td>\n",
@@ -197,8 +225,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O</td>\n",
-       "      <td>CN(C)C=O</td>\n",
+       "      <td>ClC1=CC=C2N=CC=CC2=C1</td>\n",
        "      <td>CC#N</td>\n",
        "      <td>C1CCC(CC1)P(C2CCCCC2)C3CCCCC3</td>\n",
        "      <td>0.05</td>\n",
@@ -207,9 +234,10 @@
        "      <td>SMILES</td>\n",
        "      <td>Cc1ccccc1</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
        "      <td>MICROLITER</td>\n",
-       "      <td>SOLVENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O</td>\n",
+       "      <td>CN(C)C=O</td>\n",
        "      <td>4.440000</td>\n",
        "      <td>2021-03-04 13:56:15.309572</td>\n",
        "      <td>ord-e61614af430845179c392e478d958383</td>\n",
@@ -221,8 +249,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O</td>\n",
-       "      <td>CN(C)C=O</td>\n",
+       "      <td>ClC1=CC=C2N=CC=CC2=C1</td>\n",
        "      <td>CC#N</td>\n",
        "      <td>Cc1ccccc1P(c2ccccc2C)c3ccccc3C</td>\n",
        "      <td>0.05</td>\n",
@@ -231,9 +258,10 @@
        "      <td>SMILES</td>\n",
        "      <td>Cc1ccccc1</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
        "      <td>MICROLITER</td>\n",
-       "      <td>SOLVENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O</td>\n",
+       "      <td>CN(C)C=O</td>\n",
        "      <td>1.950000</td>\n",
        "      <td>2021-03-04 13:56:15.315080</td>\n",
        "      <td>ord-3fede496f4284a5ea6656f083e2399ae</td>\n",
@@ -269,8 +297,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5755</th>\n",
-       "      <td>CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C</td>\n",
-       "      <td>O=CN(C)C</td>\n",
+       "      <td>BrC1=C(C)C=CC2=C1C=NN2C3CCCCO3</td>\n",
        "      <td>CN(C)C=O</td>\n",
        "      <td>CC(C)(C)P([C]1[CH][CH][CH][CH]1)C(C)(C)C.CC(C)...</td>\n",
        "      <td>0.05</td>\n",
@@ -279,9 +306,10 @@
        "      <td>SMILES</td>\n",
        "      <td>Cc1ccccc1</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
        "      <td>MICROLITER</td>\n",
-       "      <td>SOLVENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C</td>\n",
+       "      <td>O=CN(C)C</td>\n",
        "      <td>47.209999</td>\n",
        "      <td>2021-03-04 13:56:49.737623</td>\n",
        "      <td>ord-b498385d41664713aaef79a290409614</td>\n",
@@ -293,8 +321,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5756</th>\n",
-       "      <td>CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C</td>\n",
-       "      <td>O=CN(C)C</td>\n",
+       "      <td>BrC1=C(C)C=CC2=C1C=NN2C3CCCCO3</td>\n",
        "      <td>CN(C)C=O</td>\n",
        "      <td>CC(C)c1cc(c(c(c1)C(C)C)c2ccccc2P(C3CCCCC3)C4CC...</td>\n",
        "      <td>0.05</td>\n",
@@ -303,9 +330,10 @@
        "      <td>SMILES</td>\n",
        "      <td>Cc1ccccc1</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
        "      <td>MICROLITER</td>\n",
-       "      <td>SOLVENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C</td>\n",
+       "      <td>O=CN(C)C</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>2021-03-04 13:56:49.743574</td>\n",
        "      <td>ord-2ef787f9faac4077bff2d903866aa209</td>\n",
@@ -317,8 +345,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5757</th>\n",
-       "      <td>CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C</td>\n",
-       "      <td>O=CN(C)C</td>\n",
+       "      <td>BrC1=C(C)C=CC2=C1C=NN2C3CCCCO3</td>\n",
        "      <td>CN(C)C=O</td>\n",
        "      <td>c1ccc(cc1)P([c-]2cccc2)c3ccccc3.c1ccc(cc1)P([c...</td>\n",
        "      <td>0.05</td>\n",
@@ -327,9 +354,10 @@
        "      <td>SMILES</td>\n",
        "      <td>Cc1ccccc1</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
        "      <td>MICROLITER</td>\n",
-       "      <td>SOLVENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C</td>\n",
+       "      <td>O=CN(C)C</td>\n",
        "      <td>31.440001</td>\n",
        "      <td>2021-03-04 13:56:49.749090</td>\n",
        "      <td>ord-654740d21331471cafdfda41ff19d57a</td>\n",
@@ -341,8 +369,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5758</th>\n",
-       "      <td>CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C</td>\n",
-       "      <td>O=CN(C)C</td>\n",
+       "      <td>BrC1=C(C)C=CC2=C1C=NN2C3CCCCO3</td>\n",
        "      <td>CN(C)C=O</td>\n",
        "      <td>CC1(c2cccc(c2Oc3c1cccc3P(c4ccccc4)c5ccccc5)P(c...</td>\n",
        "      <td>0.05</td>\n",
@@ -351,9 +378,10 @@
        "      <td>SMILES</td>\n",
        "      <td>Cc1ccccc1</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
        "      <td>MICROLITER</td>\n",
-       "      <td>SOLVENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C</td>\n",
+       "      <td>O=CN(C)C</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>2021-03-04 13:56:49.754742</td>\n",
        "      <td>ord-8e53efc193e548ba888b47da3e91ced5</td>\n",
@@ -365,8 +393,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5759</th>\n",
-       "      <td>CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C</td>\n",
-       "      <td>O=CN(C)C</td>\n",
+       "      <td>BrC1=C(C)C=CC2=C1C=NN2C3CCCCO3</td>\n",
        "      <td>CN(C)C=O</td>\n",
        "      <td>Cc1ccccc1</td>\n",
        "      <td>NaN</td>\n",
@@ -375,9 +402,10 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
-       "      <td>MICROLITER</td>\n",
-       "      <td>SOLVENT</td>\n",
+       "      <td>CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C</td>\n",
+       "      <td>O=CN(C)C</td>\n",
        "      <td>54.200001</td>\n",
        "      <td>2021-03-04 13:56:49.759986</td>\n",
        "      <td>ord-0c525e5dde954d91b6dc2fe0bb849655</td>\n",
@@ -393,31 +421,18 @@
        "</div>"
       ],
       "text/plain": [
-       "     inputs[\"Boronate in Solvent\"].components[0].identifiers[0].value  \\\n",
-       "0                       B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O                 \n",
-       "1                       B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O                 \n",
-       "2                       B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O                 \n",
-       "3                       B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O                 \n",
-       "4                       B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O                 \n",
-       "...                                                 ...                 \n",
-       "5755               CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C                 \n",
-       "5756               CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C                 \n",
-       "5757               CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C                 \n",
-       "5758               CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C                 \n",
-       "5759               CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C                 \n",
-       "\n",
-       "     inputs[\"Boronate in Solvent\"].components[1].identifiers[0].value  \\\n",
-       "0                                              CN(C)C=O                 \n",
-       "1                                              CN(C)C=O                 \n",
-       "2                                              CN(C)C=O                 \n",
-       "3                                              CN(C)C=O                 \n",
-       "4                                              CN(C)C=O                 \n",
-       "...                                                 ...                 \n",
-       "5755                                           O=CN(C)C                 \n",
-       "5756                                           O=CN(C)C                 \n",
-       "5757                                           O=CN(C)C                 \n",
-       "5758                                           O=CN(C)C                 \n",
-       "5759                                           O=CN(C)C                 \n",
+       "     inputs[\"Aryl Halide\"].components[0].identifiers[0].value  \\\n",
+       "0                                 ClC1=CC=C2N=CC=CC2=C1         \n",
+       "1                                 ClC1=CC=C2N=CC=CC2=C1         \n",
+       "2                                 ClC1=CC=C2N=CC=CC2=C1         \n",
+       "3                                 ClC1=CC=C2N=CC=CC2=C1         \n",
+       "4                                 ClC1=CC=C2N=CC=CC2=C1         \n",
+       "...                                                 ...         \n",
+       "5755                     BrC1=C(C)C=CC2=C1C=NN2C3CCCCO3         \n",
+       "5756                     BrC1=C(C)C=CC2=C1C=NN2C3CCCCO3         \n",
+       "5757                     BrC1=C(C)C=CC2=C1C=NN2C3CCCCO3         \n",
+       "5758                     BrC1=C(C)C=CC2=C1C=NN2C3CCCCO3         \n",
+       "5759                     BrC1=C(C)C=CC2=C1C=NN2C3CCCCO3         \n",
        "\n",
        "     inputs[\"Solvent_1\"].components[0].identifiers[0].value  \\\n",
        "0                                                  CC#N       \n",
@@ -510,44 +525,57 @@
        "5758                                          Cc1ccccc1               \n",
        "5759                                                NaN               \n",
        "\n",
-       "      inputs[\"Ligand in Solvent\"].components[1].amount.volume.value  ...  \\\n",
-       "0                                                   1.0              ...   \n",
-       "1                                                   1.0              ...   \n",
-       "2                                                   1.0              ...   \n",
-       "3                                                   1.0              ...   \n",
-       "4                                                   1.0              ...   \n",
-       "...                                                 ...              ...   \n",
-       "5755                                                1.0              ...   \n",
-       "5756                                                1.0              ...   \n",
-       "5757                                                1.0              ...   \n",
-       "5758                                                1.0              ...   \n",
-       "5759                                                NaN              ...   \n",
+       "      inputs[\"Ligand in Solvent\"].components[1].amount.volume.value  \\\n",
+       "0                                                   1.0               \n",
+       "1                                                   1.0               \n",
+       "2                                                   1.0               \n",
+       "3                                                   1.0               \n",
+       "4                                                   1.0               \n",
+       "...                                                 ...               \n",
+       "5755                                                1.0               \n",
+       "5756                                                1.0               \n",
+       "5757                                                1.0               \n",
+       "5758                                                1.0               \n",
+       "5759                                                NaN               \n",
        "\n",
-       "     inputs[\"Base in Solvent\"].components[1].amount.volume.units  \\\n",
-       "0                                            MICROLITER            \n",
-       "1                                            MICROLITER            \n",
-       "2                                            MICROLITER            \n",
-       "3                                            MICROLITER            \n",
-       "4                                            MICROLITER            \n",
-       "...                                                 ...            \n",
-       "5755                                         MICROLITER            \n",
-       "5756                                         MICROLITER            \n",
-       "5757                                         MICROLITER            \n",
-       "5758                                         MICROLITER            \n",
-       "5759                                         MICROLITER            \n",
+       "     inputs[\"Ligand in Solvent\"].components[1].amount.volume.units  ...  \\\n",
+       "0                                            MICROLITER             ...   \n",
+       "1                                            MICROLITER             ...   \n",
+       "2                                            MICROLITER             ...   \n",
+       "3                                            MICROLITER             ...   \n",
+       "4                                            MICROLITER             ...   \n",
+       "...                                                 ...             ...   \n",
+       "5755                                         MICROLITER             ...   \n",
+       "5756                                         MICROLITER             ...   \n",
+       "5757                                         MICROLITER             ...   \n",
+       "5758                                         MICROLITER             ...   \n",
+       "5759                                                NaN             ...   \n",
        "\n",
-       "     inputs[\"Base in Solvent\"].components[1].reaction_role  \\\n",
-       "0                                               SOLVENT      \n",
-       "1                                               SOLVENT      \n",
-       "2                                               SOLVENT      \n",
-       "3                                               SOLVENT      \n",
-       "4                                               SOLVENT      \n",
-       "...                                                 ...      \n",
-       "5755                                            SOLVENT      \n",
-       "5756                                            SOLVENT      \n",
-       "5757                                            SOLVENT      \n",
-       "5758                                            SOLVENT      \n",
-       "5759                                            SOLVENT      \n",
+       "     inputs[\"Boronate in Solvent\"].components[0].identifiers[0].value  \\\n",
+       "0                       B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O                 \n",
+       "1                       B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O                 \n",
+       "2                       B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O                 \n",
+       "3                       B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O                 \n",
+       "4                       B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O                 \n",
+       "...                                                 ...                 \n",
+       "5755               CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C                 \n",
+       "5756               CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C                 \n",
+       "5757               CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C                 \n",
+       "5758               CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C                 \n",
+       "5759               CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C                 \n",
+       "\n",
+       "     inputs[\"Boronate in Solvent\"].components[1].identifiers[0].value  \\\n",
+       "0                                              CN(C)C=O                 \n",
+       "1                                              CN(C)C=O                 \n",
+       "2                                              CN(C)C=O                 \n",
+       "3                                              CN(C)C=O                 \n",
+       "4                                              CN(C)C=O                 \n",
+       "...                                                 ...                 \n",
+       "5755                                           O=CN(C)C                 \n",
+       "5756                                           O=CN(C)C                 \n",
+       "5757                                           O=CN(C)C                 \n",
+       "5758                                           O=CN(C)C                 \n",
+       "5759                                           O=CN(C)C                 \n",
        "\n",
        "     outcomes[0].products[0].measurements[0].percentage.value  \\\n",
        "0                                              4.760000         \n",
@@ -562,18 +590,18 @@
        "5758                                           0.000000         \n",
        "5759                                          54.200001         \n",
        "\n",
-       "     provenance.record_modified[1].time.value  \\\n",
-       "0                  2021-03-04 13:56:15.292871   \n",
-       "1                  2021-03-04 13:56:15.298402   \n",
-       "2                  2021-03-04 13:56:15.303978   \n",
-       "3                  2021-03-04 13:56:15.309572   \n",
-       "4                  2021-03-04 13:56:15.315080   \n",
-       "...                                       ...   \n",
-       "5755               2021-03-04 13:56:49.737623   \n",
-       "5756               2021-03-04 13:56:49.743574   \n",
-       "5757               2021-03-04 13:56:49.749090   \n",
-       "5758               2021-03-04 13:56:49.754742   \n",
-       "5759               2021-03-04 13:56:49.759986   \n",
+       "      provenance.record_modified[1].time.value  \\\n",
+       "0                   2021-03-04 13:56:15.292871   \n",
+       "1                   2021-03-04 13:56:15.298402   \n",
+       "2                   2021-03-04 13:56:15.303978   \n",
+       "3                   2021-03-04 13:56:15.309572   \n",
+       "4                   2021-03-04 13:56:15.315080   \n",
+       "...                                        ...   \n",
+       "5755                2021-03-04 13:56:49.737623   \n",
+       "5756                2021-03-04 13:56:49.743574   \n",
+       "5757                2021-03-04 13:56:49.749090   \n",
+       "5758                2021-03-04 13:56:49.754742   \n",
+       "5759                2021-03-04 13:56:49.759986   \n",
        "\n",
        "                               reaction_id  \\\n",
        "0     ord-61b2819b15824f1fa131ed1b2ea1dae4   \n",
@@ -588,18 +616,18 @@
        "5758  ord-8e53efc193e548ba888b47da3e91ced5   \n",
        "5759  ord-0c525e5dde954d91b6dc2fe0bb849655   \n",
        "\n",
-       "      inputs[\"Ligand in Solvent\"].components[0].amount.volume.value  \\\n",
-       "0                                                   NaN               \n",
-       "1                                                   NaN               \n",
-       "2                                                   NaN               \n",
-       "3                                                   NaN               \n",
-       "4                                                   NaN               \n",
-       "...                                                 ...               \n",
-       "5755                                                NaN               \n",
-       "5756                                                NaN               \n",
-       "5757                                                NaN               \n",
-       "5758                                                NaN               \n",
-       "5759                                                1.0               \n",
+       "     inputs[\"Ligand in Solvent\"].components[0].amount.volume.value  \\\n",
+       "0                                                   NaN              \n",
+       "1                                                   NaN              \n",
+       "2                                                   NaN              \n",
+       "3                                                   NaN              \n",
+       "4                                                   NaN              \n",
+       "...                                                 ...              \n",
+       "5755                                                NaN              \n",
+       "5756                                                NaN              \n",
+       "5757                                                NaN              \n",
+       "5758                                                NaN              \n",
+       "5759                                                1.0              \n",
        "\n",
        "     inputs[\"Ligand in Solvent\"].components[0].amount.volume.units  \\\n",
        "0                                                   NaN              \n",
@@ -627,18 +655,18 @@
        "5758                                                NaN                         \n",
        "5759                                               True                         \n",
        "\n",
-       "     inputs[\"Base in Solvent\"].components[0].amount.volume.value  \\\n",
-       "0                                                   NaN            \n",
-       "1                                                   NaN            \n",
-       "2                                                   NaN            \n",
-       "3                                                   NaN            \n",
-       "4                                                   NaN            \n",
-       "...                                                 ...            \n",
-       "5755                                                NaN            \n",
-       "5756                                                NaN            \n",
-       "5757                                                NaN            \n",
-       "5758                                                NaN            \n",
-       "5759                                                NaN            \n",
+       "      inputs[\"Base in Solvent\"].components[0].amount.volume.value  \\\n",
+       "0                                                   NaN             \n",
+       "1                                                   NaN             \n",
+       "2                                                   NaN             \n",
+       "3                                                   NaN             \n",
+       "4                                                   NaN             \n",
+       "...                                                 ...             \n",
+       "5755                                                NaN             \n",
+       "5756                                                NaN             \n",
+       "5757                                                NaN             \n",
+       "5758                                                NaN             \n",
+       "5759                                                NaN             \n",
        "\n",
        "     inputs[\"Base in Solvent\"].components[0].amount.volume.units  \n",
        "0                                                   NaN           \n",
@@ -679,8 +707,7 @@
     {
      "data": {
       "text/plain": [
-       "['inputs[\"Boronate in Solvent\"].components[0].identifiers[0].value',\n",
-       " 'inputs[\"Boronate in Solvent\"].components[1].identifiers[0].value',\n",
+       "['inputs[\"Aryl Halide\"].components[0].identifiers[0].value',\n",
        " 'inputs[\"Solvent_1\"].components[0].identifiers[0].value',\n",
        " 'inputs[\"Ligand in Solvent\"].components[0].identifiers[0].value',\n",
        " 'inputs[\"Ligand in Solvent\"].components[0].amount.moles.value',\n",
@@ -692,7 +719,6 @@
        " 'inputs[\"Ligand in Solvent\"].components[1].amount.volume.units',\n",
        " 'inputs[\"Ligand in Solvent\"].components[1].amount.volume_includes_solutes',\n",
        " 'inputs[\"Ligand in Solvent\"].components[1].reaction_role',\n",
-       " 'inputs[\"Aryl Halide\"].components[0].identifiers[0].value',\n",
        " 'inputs[\"Base in Solvent\"].components[0].identifiers[0].value',\n",
        " 'inputs[\"Base in Solvent\"].components[0].amount.moles.value',\n",
        " 'inputs[\"Base in Solvent\"].components[0].amount.moles.units',\n",
@@ -702,6 +728,8 @@
        " 'inputs[\"Base in Solvent\"].components[1].amount.volume.value',\n",
        " 'inputs[\"Base in Solvent\"].components[1].amount.volume.units',\n",
        " 'inputs[\"Base in Solvent\"].components[1].reaction_role',\n",
+       " 'inputs[\"Boronate in Solvent\"].components[0].identifiers[0].value',\n",
+       " 'inputs[\"Boronate in Solvent\"].components[1].identifiers[0].value',\n",
        " 'outcomes[0].products[0].measurements[0].percentage.value',\n",
        " 'provenance.record_modified[1].time.value',\n",
        " 'reaction_id',\n",
@@ -1136,122 +1164,122 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
        "      <td>0.0476</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
        "      <td>0.0412</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
        "      <td>0.0258</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
        "      <td>0.0444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
        "      <td>0.0195</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1280,122 +1308,122 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5755</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "      <td>0.4721</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5756</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "      <td>0.0000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5757</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "      <td>0.3144</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5758</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "      <td>0.0000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5759</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "      <td>0.5420</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1405,160 +1433,160 @@
       ],
       "text/plain": [
        "      electrophile_BrC1=C(C)C=CC2=C1C=NN2C3CCCCO3  \\\n",
-       "0                                               0   \n",
-       "1                                               0   \n",
-       "2                                               0   \n",
-       "3                                               0   \n",
-       "4                                               0   \n",
+       "0                                           False   \n",
+       "1                                           False   \n",
+       "2                                           False   \n",
+       "3                                           False   \n",
+       "4                                           False   \n",
        "...                                           ...   \n",
-       "5755                                            1   \n",
-       "5756                                            1   \n",
-       "5757                                            1   \n",
-       "5758                                            1   \n",
-       "5759                                            1   \n",
+       "5755                                         True   \n",
+       "5756                                         True   \n",
+       "5757                                         True   \n",
+       "5758                                         True   \n",
+       "5759                                         True   \n",
        "\n",
        "      electrophile_BrC1=CC=C2N=CC=CC2=C1  electrophile_ClC1=CC=C2N=CC=CC2=C1  \\\n",
-       "0                                      0                                   1   \n",
-       "1                                      0                                   1   \n",
-       "2                                      0                                   1   \n",
-       "3                                      0                                   1   \n",
-       "4                                      0                                   1   \n",
+       "0                                  False                                True   \n",
+       "1                                  False                                True   \n",
+       "2                                  False                                True   \n",
+       "3                                  False                                True   \n",
+       "4                                  False                                True   \n",
        "...                                  ...                                 ...   \n",
-       "5755                                   0                                   0   \n",
-       "5756                                   0                                   0   \n",
-       "5757                                   0                                   0   \n",
-       "5758                                   0                                   0   \n",
-       "5759                                   0                                   0   \n",
+       "5755                               False                               False   \n",
+       "5756                               False                               False   \n",
+       "5757                               False                               False   \n",
+       "5758                               False                               False   \n",
+       "5759                               False                               False   \n",
        "\n",
        "      electrophile_IC1=CC=C2N=CC=CC2=C1  \\\n",
-       "0                                     0   \n",
-       "1                                     0   \n",
-       "2                                     0   \n",
-       "3                                     0   \n",
-       "4                                     0   \n",
+       "0                                 False   \n",
+       "1                                 False   \n",
+       "2                                 False   \n",
+       "3                                 False   \n",
+       "4                                 False   \n",
        "...                                 ...   \n",
-       "5755                                  0   \n",
-       "5756                                  0   \n",
-       "5757                                  0   \n",
-       "5758                                  0   \n",
-       "5759                                  0   \n",
+       "5755                              False   \n",
+       "5756                              False   \n",
+       "5757                              False   \n",
+       "5758                              False   \n",
+       "5759                              False   \n",
        "\n",
        "      electrophile_O=S(OC1=CC=C2N=CC=CC2=C1)(C(F)(F)F)=O  \\\n",
-       "0                                                     0    \n",
-       "1                                                     0    \n",
-       "2                                                     0    \n",
-       "3                                                     0    \n",
-       "4                                                     0    \n",
+       "0                                                 False    \n",
+       "1                                                 False    \n",
+       "2                                                 False    \n",
+       "3                                                 False    \n",
+       "4                                                 False    \n",
        "...                                                 ...    \n",
-       "5755                                                  0    \n",
-       "5756                                                  0    \n",
-       "5757                                                  0    \n",
-       "5758                                                  0    \n",
-       "5759                                                  0    \n",
+       "5755                                              False    \n",
+       "5756                                              False    \n",
+       "5757                                              False    \n",
+       "5758                                              False    \n",
+       "5759                                              False    \n",
        "\n",
        "      nucleophile_B(c1c(ccc2c1cnn2C3CCCCO3)C)(O)O  \\\n",
-       "0                                               1   \n",
-       "1                                               1   \n",
-       "2                                               1   \n",
-       "3                                               1   \n",
-       "4                                               1   \n",
+       "0                                            True   \n",
+       "1                                            True   \n",
+       "2                                            True   \n",
+       "3                                            True   \n",
+       "4                                            True   \n",
        "...                                           ...   \n",
-       "5755                                            0   \n",
-       "5756                                            0   \n",
-       "5757                                            0   \n",
-       "5758                                            0   \n",
-       "5759                                            0   \n",
+       "5755                                        False   \n",
+       "5756                                        False   \n",
+       "5757                                        False   \n",
+       "5758                                        False   \n",
+       "5759                                        False   \n",
        "\n",
        "      nucleophile_CC1(OB(C2=CC=C3N=CC=CC3=C2)OC1(C)C)C  \\\n",
-       "0                                                    0   \n",
-       "1                                                    0   \n",
-       "2                                                    0   \n",
-       "3                                                    0   \n",
-       "4                                                    0   \n",
+       "0                                                False   \n",
+       "1                                                False   \n",
+       "2                                                False   \n",
+       "3                                                False   \n",
+       "4                                                False   \n",
        "...                                                ...   \n",
-       "5755                                                 1   \n",
-       "5756                                                 1   \n",
-       "5757                                                 1   \n",
-       "5758                                                 1   \n",
-       "5759                                                 1   \n",
+       "5755                                              True   \n",
+       "5756                                              True   \n",
+       "5757                                              True   \n",
+       "5758                                              True   \n",
+       "5759                                              True   \n",
        "\n",
        "      nucleophile_Cc1ccc2n(C3CCCCO3)ncc2c1B4OC(C)(C)C(C)(C)O4  \\\n",
-       "0                                                     0         \n",
-       "1                                                     0         \n",
-       "2                                                     0         \n",
-       "3                                                     0         \n",
-       "4                                                     0         \n",
+       "0                                                 False         \n",
+       "1                                                 False         \n",
+       "2                                                 False         \n",
+       "3                                                 False         \n",
+       "4                                                 False         \n",
        "...                                                 ...         \n",
-       "5755                                                  0         \n",
-       "5756                                                  0         \n",
-       "5757                                                  0         \n",
-       "5758                                                  0         \n",
-       "5759                                                  0         \n",
+       "5755                                              False         \n",
+       "5756                                              False         \n",
+       "5757                                              False         \n",
+       "5758                                              False         \n",
+       "5759                                              False         \n",
        "\n",
        "      nucleophile_F[B-](F)(C1=C(C)C=CC2=C1C=NN2C3CCCCO3)F.[K+]  \\\n",
-       "0                                                     0          \n",
-       "1                                                     0          \n",
-       "2                                                     0          \n",
-       "3                                                     0          \n",
-       "4                                                     0          \n",
+       "0                                                 False          \n",
+       "1                                                 False          \n",
+       "2                                                 False          \n",
+       "3                                                 False          \n",
+       "4                                                 False          \n",
        "...                                                 ...          \n",
-       "5755                                                  0          \n",
-       "5756                                                  0          \n",
-       "5757                                                  0          \n",
-       "5758                                                  0          \n",
-       "5759                                                  0          \n",
+       "5755                                              False          \n",
+       "5756                                              False          \n",
+       "5757                                              False          \n",
+       "5758                                              False          \n",
+       "5759                                              False          \n",
        "\n",
        "      nucleophile_OB(C1=CC=C2N=CC=CC2=C1)O.[H]Cl  ...  base_[F-].[Cs+]  \\\n",
-       "0                                              0  ...                0   \n",
-       "1                                              0  ...                0   \n",
-       "2                                              0  ...                0   \n",
-       "3                                              0  ...                0   \n",
-       "4                                              0  ...                0   \n",
+       "0                                          False  ...            False   \n",
+       "1                                          False  ...            False   \n",
+       "2                                          False  ...            False   \n",
+       "3                                          False  ...            False   \n",
+       "4                                          False  ...            False   \n",
        "...                                          ...  ...              ...   \n",
-       "5755                                           0  ...                0   \n",
-       "5756                                           0  ...                0   \n",
-       "5757                                           0  ...                0   \n",
-       "5758                                           0  ...                0   \n",
-       "5759                                           0  ...                0   \n",
+       "5755                                       False  ...            False   \n",
+       "5756                                       False  ...            False   \n",
+       "5757                                       False  ...            False   \n",
+       "5758                                       False  ...            False   \n",
+       "5759                                       False  ...            False   \n",
        "\n",
        "      base_[Li+].CC(C)(C)[O-]  base_[O-]P(=O)([O-])[O-].[K+].[K+].[K+]  \\\n",
-       "0                           0                                        0   \n",
-       "1                           0                                        0   \n",
-       "2                           0                                        0   \n",
-       "3                           0                                        0   \n",
-       "4                           0                                        0   \n",
+       "0                       False                                    False   \n",
+       "1                       False                                    False   \n",
+       "2                       False                                    False   \n",
+       "3                       False                                    False   \n",
+       "4                       False                                    False   \n",
        "...                       ...                                      ...   \n",
-       "5755                        0                                        1   \n",
-       "5756                        0                                        1   \n",
-       "5757                        0                                        1   \n",
-       "5758                        0                                        1   \n",
-       "5759                        0                                        1   \n",
+       "5755                    False                                     True   \n",
+       "5756                    False                                     True   \n",
+       "5757                    False                                     True   \n",
+       "5758                    False                                     True   \n",
+       "5759                    False                                     True   \n",
        "\n",
        "      base_[OH-].[K+]  base_[OH-].[Na+]  solvent_C1CCOC1  solvent_CC#N  \\\n",
-       "0                   0                 1                0             1   \n",
-       "1                   0                 1                0             1   \n",
-       "2                   0                 1                0             1   \n",
-       "3                   0                 1                0             1   \n",
-       "4                   0                 1                0             1   \n",
+       "0               False              True            False          True   \n",
+       "1               False              True            False          True   \n",
+       "2               False              True            False          True   \n",
+       "3               False              True            False          True   \n",
+       "4               False              True            False          True   \n",
        "...               ...               ...              ...           ...   \n",
-       "5755                0                 0                0             0   \n",
-       "5756                0                 0                0             0   \n",
-       "5757                0                 0                0             0   \n",
-       "5758                0                 0                0             0   \n",
-       "5759                0                 0                0             0   \n",
+       "5755            False             False            False         False   \n",
+       "5756            False             False            False         False   \n",
+       "5757            False             False            False         False   \n",
+       "5758            False             False            False         False   \n",
+       "5759            False             False            False         False   \n",
        "\n",
        "      solvent_CN(C)C=O  solvent_CO   yield  \n",
-       "0                    0           0  0.0476  \n",
-       "1                    0           0  0.0412  \n",
-       "2                    0           0  0.0258  \n",
-       "3                    0           0  0.0444  \n",
-       "4                    0           0  0.0195  \n",
+       "0                False       False  0.0476  \n",
+       "1                False       False  0.0412  \n",
+       "2                False       False  0.0258  \n",
+       "3                False       False  0.0444  \n",
+       "4                False       False  0.0195  \n",
        "...                ...         ...     ...  \n",
-       "5755                 1           0  0.4721  \n",
-       "5756                 1           0  0.0000  \n",
-       "5757                 1           0  0.3144  \n",
-       "5758                 1           0  0.0000  \n",
-       "5759                 1           0  0.5420  \n",
+       "5755              True       False  0.4721  \n",
+       "5756              True       False  0.0000  \n",
+       "5757              True       False  0.3144  \n",
+       "5758              True       False  0.0000  \n",
+       "5759              True       False  0.5420  \n",
        "\n",
        "[5760 rows x 36 columns]"
       ]
@@ -1689,733 +1717,617 @@
     "tags": []
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING 2024-04-12 15:13:14,769 optimizer.py:70: At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Epoch 1/300\n",
-      "35/35 [==============================] - ETA: 0s - loss: 0.2450 - root_mean_squared_error: 0.4950WARNING:tensorflow:From /Users/michaelmaser 1/.local/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
-      "WARNING:tensorflow:From /Users/michaelmaser 1/.local/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
-      "INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 2s 62ms/step - loss: 0.2450 - root_mean_squared_error: 0.4950 - val_loss: 0.0783 - val_root_mean_squared_error: 0.2799\n",
+      "35/35 [==============================] - 0s 2ms/step - loss: 0.1405 - root_mean_squared_error: 0.3749 - val_loss: 0.0648 - val_root_mean_squared_error: 0.2545\n",
       "Epoch 2/300\n",
-      "28/35 [=======================>......] - ETA: 0s - loss: 0.0970 - root_mean_squared_error: 0.3114INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 26ms/step - loss: 0.0955 - root_mean_squared_error: 0.3090 - val_loss: 0.0711 - val_root_mean_squared_error: 0.2666\n",
+      "35/35 [==============================] - 0s 756us/step - loss: 0.0721 - root_mean_squared_error: 0.2684 - val_loss: 0.0531 - val_root_mean_squared_error: 0.2304\n",
       "Epoch 3/300\n",
-      "21/35 [=================>............] - ETA: 0s - loss: 0.0863 - root_mean_squared_error: 0.2938INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 40ms/step - loss: 0.0838 - root_mean_squared_error: 0.2896 - val_loss: 0.0649 - val_root_mean_squared_error: 0.2547\n",
+      "35/35 [==============================] - 0s 674us/step - loss: 0.0593 - root_mean_squared_error: 0.2435 - val_loss: 0.0440 - val_root_mean_squared_error: 0.2099\n",
       "Epoch 4/300\n",
-      "19/35 [===============>..............] - ETA: 0s - loss: 0.0766 - root_mean_squared_error: 0.2769INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 37ms/step - loss: 0.0722 - root_mean_squared_error: 0.2688 - val_loss: 0.0575 - val_root_mean_squared_error: 0.2398\n",
+      "35/35 [==============================] - 0s 627us/step - loss: 0.0512 - root_mean_squared_error: 0.2263 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1987\n",
       "Epoch 5/300\n",
-      "19/35 [===============>..............] - ETA: 0s - loss: 0.0649 - root_mean_squared_error: 0.2547INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 30ms/step - loss: 0.0621 - root_mean_squared_error: 0.2492 - val_loss: 0.0503 - val_root_mean_squared_error: 0.2243\n",
+      "35/35 [==============================] - 0s 601us/step - loss: 0.0452 - root_mean_squared_error: 0.2127 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1919\n",
       "Epoch 6/300\n",
-      "26/35 [=====================>........] - ETA: 0s - loss: 0.0567 - root_mean_squared_error: 0.2381INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 29ms/step - loss: 0.0550 - root_mean_squared_error: 0.2346 - val_loss: 0.0441 - val_root_mean_squared_error: 0.2099\n",
+      "35/35 [==============================] - 0s 642us/step - loss: 0.0445 - root_mean_squared_error: 0.2111 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1911\n",
       "Epoch 7/300\n",
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-      "35/35 [==============================] - 1s 28ms/step - loss: 0.0486 - root_mean_squared_error: 0.2206 - val_loss: 0.0394 - val_root_mean_squared_error: 0.1984\n",
+      "35/35 [==============================] - 0s 633us/step - loss: 0.0423 - root_mean_squared_error: 0.2056 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n",
       "Epoch 8/300\n",
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-      "35/35 [==============================] - 1s 29ms/step - loss: 0.0456 - root_mean_squared_error: 0.2135 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1916\n",
+      "35/35 [==============================] - 0s 648us/step - loss: 0.0402 - root_mean_squared_error: 0.2006 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n",
       "Epoch 9/300\n",
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-      "35/35 [==============================] - 1s 28ms/step - loss: 0.0437 - root_mean_squared_error: 0.2089 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n",
+      "35/35 [==============================] - 0s 510us/step - loss: 0.0407 - root_mean_squared_error: 0.2016 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1856\n",
       "Epoch 10/300\n",
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-      "35/35 [==============================] - 1s 32ms/step - loss: 0.0420 - root_mean_squared_error: 0.2050 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n",
+      "35/35 [==============================] - 0s 649us/step - loss: 0.0391 - root_mean_squared_error: 0.1978 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1841\n",
       "Epoch 11/300\n",
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-      "35/35 [==============================] - 1s 38ms/step - loss: 0.0409 - root_mean_squared_error: 0.2022 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1839\n",
+      "35/35 [==============================] - 0s 511us/step - loss: 0.0400 - root_mean_squared_error: 0.1999 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1847\n",
       "Epoch 12/300\n",
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-      "35/35 [==============================] - 1s 20ms/step - loss: 0.0390 - root_mean_squared_error: 0.1974 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1835\n",
+      "35/35 [==============================] - 0s 493us/step - loss: 0.0382 - root_mean_squared_error: 0.1954 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1843\n",
       "Epoch 13/300\n",
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-      "35/35 [==============================] - 1s 27ms/step - loss: 0.0394 - root_mean_squared_error: 0.1986 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1827\n",
+      "35/35 [==============================] - 0s 832us/step - loss: 0.0382 - root_mean_squared_error: 0.1955 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1835\n",
       "Epoch 14/300\n",
-      "35/35 [==============================] - 0s 3ms/step - loss: 0.0389 - root_mean_squared_error: 0.1972 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1827\n",
+      "35/35 [==============================] - 0s 698us/step - loss: 0.0378 - root_mean_squared_error: 0.1944 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1835\n",
       "Epoch 15/300\n",
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-      "35/35 [==============================] - 1s 29ms/step - loss: 0.0381 - root_mean_squared_error: 0.1951 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1826\n",
+      "35/35 [==============================] - 0s 726us/step - loss: 0.0369 - root_mean_squared_error: 0.1921 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1825\n",
       "Epoch 16/300\n",
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-      "35/35 [==============================] - 1s 25ms/step - loss: 0.0387 - root_mean_squared_error: 0.1967 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1817\n",
+      "35/35 [==============================] - 0s 639us/step - loss: 0.0370 - root_mean_squared_error: 0.1923 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1824\n",
       "Epoch 17/300\n",
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-      "35/35 [==============================] - 1s 26ms/step - loss: 0.0383 - root_mean_squared_error: 0.1958 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1812\n",
+      "35/35 [==============================] - 0s 636us/step - loss: 0.0366 - root_mean_squared_error: 0.1914 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1814\n",
       "Epoch 18/300\n",
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-      "35/35 [==============================] - 1s 24ms/step - loss: 0.0373 - root_mean_squared_error: 0.1932 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1811\n",
+      "35/35 [==============================] - 0s 640us/step - loss: 0.0367 - root_mean_squared_error: 0.1916 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1804\n",
       "Epoch 19/300\n",
-      "35/35 [==============================] - ETA: 0s - loss: 0.0376 - root_mean_squared_error: 0.1940INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 24ms/step - loss: 0.0376 - root_mean_squared_error: 0.1940 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1806\n",
+      "35/35 [==============================] - 0s 459us/step - loss: 0.0361 - root_mean_squared_error: 0.1901 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1809\n",
       "Epoch 20/300\n",
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-      "35/35 [==============================] - 1s 26ms/step - loss: 0.0371 - root_mean_squared_error: 0.1927 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1802\n",
+      "35/35 [==============================] - 0s 494us/step - loss: 0.0361 - root_mean_squared_error: 0.1900 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1807\n",
       "Epoch 21/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0366 - root_mean_squared_error: 0.1913 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1803\n",
+      "35/35 [==============================] - 0s 635us/step - loss: 0.0366 - root_mean_squared_error: 0.1913 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1797\n",
       "Epoch 22/300\n",
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-      "35/35 [==============================] - 1s 22ms/step - loss: 0.0357 - root_mean_squared_error: 0.1890 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1797\n",
+      "35/35 [==============================] - 0s 447us/step - loss: 0.0365 - root_mean_squared_error: 0.1909 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1803\n",
       "Epoch 23/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0358 - root_mean_squared_error: 0.1892 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1797\n",
+      "35/35 [==============================] - 0s 637us/step - loss: 0.0355 - root_mean_squared_error: 0.1885 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1795\n",
       "Epoch 24/300\n",
-      "35/35 [==============================] - ETA: 0s - loss: 0.0356 - root_mean_squared_error: 0.1886INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 23ms/step - loss: 0.0356 - root_mean_squared_error: 0.1886 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1792\n",
+      "35/35 [==============================] - 0s 604us/step - loss: 0.0351 - root_mean_squared_error: 0.1872 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1788\n",
       "Epoch 25/300\n",
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-      "35/35 [==============================] - 1s 23ms/step - loss: 0.0363 - root_mean_squared_error: 0.1905 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1789\n",
+      "35/35 [==============================] - 0s 524us/step - loss: 0.0344 - root_mean_squared_error: 0.1855 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1788\n",
       "Epoch 26/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0359 - root_mean_squared_error: 0.1894 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1786\n",
+      "35/35 [==============================] - 0s 650us/step - loss: 0.0350 - root_mean_squared_error: 0.1870 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1786\n",
       "Epoch 27/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0361 - root_mean_squared_error: 0.1901 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1787\n",
+      "35/35 [==============================] - 0s 399us/step - loss: 0.0346 - root_mean_squared_error: 0.1860 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1789\n",
       "Epoch 28/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0355 - root_mean_squared_error: 0.1885 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1776\n",
+      "35/35 [==============================] - 0s 650us/step - loss: 0.0336 - root_mean_squared_error: 0.1834 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1786\n",
       "Epoch 29/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0353 - root_mean_squared_error: 0.1879 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1777\n",
+      "35/35 [==============================] - 0s 586us/step - loss: 0.0341 - root_mean_squared_error: 0.1847 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1785\n",
       "Epoch 30/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0346 - root_mean_squared_error: 0.1860 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1778\n",
+      "35/35 [==============================] - 0s 607us/step - loss: 0.0335 - root_mean_squared_error: 0.1832 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1771\n",
       "Epoch 31/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0350 - root_mean_squared_error: 0.1870 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1780\n",
+      "35/35 [==============================] - 0s 732us/step - loss: 0.0336 - root_mean_squared_error: 0.1833 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1768\n",
       "Epoch 32/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0343 - root_mean_squared_error: 0.1853 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1773\n",
+      "35/35 [==============================] - 0s 610us/step - loss: 0.0340 - root_mean_squared_error: 0.1845 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1773\n",
       "Epoch 33/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0345 - root_mean_squared_error: 0.1856 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1769\n",
+      "35/35 [==============================] - 0s 946us/step - loss: 0.0340 - root_mean_squared_error: 0.1843 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1764\n",
       "Epoch 34/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0351 - root_mean_squared_error: 0.1873 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1770\n",
+      "35/35 [==============================] - 0s 768us/step - loss: 0.0334 - root_mean_squared_error: 0.1829 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1755\n",
       "Epoch 35/300\n",
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-      "35/35 [==============================] - 1s 27ms/step - loss: 0.0338 - root_mean_squared_error: 0.1839 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1767\n",
+      "35/35 [==============================] - 0s 787us/step - loss: 0.0332 - root_mean_squared_error: 0.1822 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1753\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0342 - root_mean_squared_error: 0.1849 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1764\n",
+      "35/35 [==============================] - 0s 777us/step - loss: 0.0331 - root_mean_squared_error: 0.1819 - val_loss: 0.0305 - val_root_mean_squared_error: 0.1746\n",
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-      "35/35 [==============================] - 1s 22ms/step - loss: 0.0344 - root_mean_squared_error: 0.1855 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1758\n",
+      "35/35 [==============================] - 0s 740us/step - loss: 0.0330 - root_mean_squared_error: 0.1815 - val_loss: 0.0302 - val_root_mean_squared_error: 0.1739\n",
       "Epoch 38/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0334 - root_mean_squared_error: 0.1828 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1760\n",
+      "35/35 [==============================] - 0s 578us/step - loss: 0.0333 - root_mean_squared_error: 0.1825 - val_loss: 0.0304 - val_root_mean_squared_error: 0.1744\n",
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-      "35/35 [==============================] - 1s 23ms/step - loss: 0.0336 - root_mean_squared_error: 0.1833 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1753\n",
+      "35/35 [==============================] - 0s 632us/step - loss: 0.0327 - root_mean_squared_error: 0.1807 - val_loss: 0.0304 - val_root_mean_squared_error: 0.1744\n",
       "Epoch 40/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0341 - root_mean_squared_error: 0.1848 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1753\n",
+      "35/35 [==============================] - 0s 737us/step - loss: 0.0329 - root_mean_squared_error: 0.1815 - val_loss: 0.0301 - val_root_mean_squared_error: 0.1735\n",
       "Epoch 41/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0338 - root_mean_squared_error: 0.1838 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1755\n",
+      "35/35 [==============================] - 0s 747us/step - loss: 0.0320 - root_mean_squared_error: 0.1788 - val_loss: 0.0297 - val_root_mean_squared_error: 0.1723\n",
       "Epoch 42/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0337 - root_mean_squared_error: 0.1836 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1753\n",
+      "35/35 [==============================] - 0s 652us/step - loss: 0.0321 - root_mean_squared_error: 0.1791 - val_loss: 0.0296 - val_root_mean_squared_error: 0.1722\n",
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-      "35/35 [==============================] - 1s 23ms/step - loss: 0.0336 - root_mean_squared_error: 0.1832 - val_loss: 0.0306 - val_root_mean_squared_error: 0.1750\n",
+      "35/35 [==============================] - 0s 627us/step - loss: 0.0318 - root_mean_squared_error: 0.1784 - val_loss: 0.0293 - val_root_mean_squared_error: 0.1713\n",
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-      "35/35 [==============================] - 1s 23ms/step - loss: 0.0334 - root_mean_squared_error: 0.1827 - val_loss: 0.0305 - val_root_mean_squared_error: 0.1747\n",
+      "35/35 [==============================] - 0s 596us/step - loss: 0.0315 - root_mean_squared_error: 0.1776 - val_loss: 0.0291 - val_root_mean_squared_error: 0.1706\n",
       "Epoch 45/300\n",
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-      "35/35 [==============================] - 1s 20ms/step - loss: 0.0337 - root_mean_squared_error: 0.1836 - val_loss: 0.0303 - val_root_mean_squared_error: 0.1741\n",
+      "35/35 [==============================] - 0s 617us/step - loss: 0.0312 - root_mean_squared_error: 0.1767 - val_loss: 0.0286 - val_root_mean_squared_error: 0.1692\n",
       "Epoch 46/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0337 - root_mean_squared_error: 0.1836 - val_loss: 0.0303 - val_root_mean_squared_error: 0.1740\n",
+      "35/35 [==============================] - 0s 575us/step - loss: 0.0309 - root_mean_squared_error: 0.1759 - val_loss: 0.0286 - val_root_mean_squared_error: 0.1690\n",
       "Epoch 47/300\n",
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-      "35/35 [==============================] - 1s 26ms/step - loss: 0.0332 - root_mean_squared_error: 0.1822 - val_loss: 0.0300 - val_root_mean_squared_error: 0.1732\n",
+      "35/35 [==============================] - 0s 634us/step - loss: 0.0307 - root_mean_squared_error: 0.1753 - val_loss: 0.0282 - val_root_mean_squared_error: 0.1680\n",
       "Epoch 48/300\n",
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-      "35/35 [==============================] - 1s 22ms/step - loss: 0.0326 - root_mean_squared_error: 0.1806 - val_loss: 0.0299 - val_root_mean_squared_error: 0.1730\n",
+      "35/35 [==============================] - 0s 636us/step - loss: 0.0307 - root_mean_squared_error: 0.1752 - val_loss: 0.0279 - val_root_mean_squared_error: 0.1672\n",
       "Epoch 49/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0322 - root_mean_squared_error: 0.1794 - val_loss: 0.0297 - val_root_mean_squared_error: 0.1722\n",
+      "35/35 [==============================] - 0s 698us/step - loss: 0.0299 - root_mean_squared_error: 0.1728 - val_loss: 0.0278 - val_root_mean_squared_error: 0.1668\n",
       "Epoch 50/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0327 - root_mean_squared_error: 0.1809 - val_loss: 0.0295 - val_root_mean_squared_error: 0.1719\n",
+      "35/35 [==============================] - 0s 603us/step - loss: 0.0300 - root_mean_squared_error: 0.1733 - val_loss: 0.0277 - val_root_mean_squared_error: 0.1664\n",
       "Epoch 51/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0327 - root_mean_squared_error: 0.1809 - val_loss: 0.0297 - val_root_mean_squared_error: 0.1722\n",
+      "35/35 [==============================] - 0s 642us/step - loss: 0.0298 - root_mean_squared_error: 0.1726 - val_loss: 0.0271 - val_root_mean_squared_error: 0.1646\n",
       "Epoch 52/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0323 - root_mean_squared_error: 0.1796 - val_loss: 0.0295 - val_root_mean_squared_error: 0.1719\n",
+      "35/35 [==============================] - 0s 427us/step - loss: 0.0297 - root_mean_squared_error: 0.1723 - val_loss: 0.0272 - val_root_mean_squared_error: 0.1650\n",
       "Epoch 53/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0320 - root_mean_squared_error: 0.1788 - val_loss: 0.0293 - val_root_mean_squared_error: 0.1713\n",
+      "35/35 [==============================] - 0s 736us/step - loss: 0.0290 - root_mean_squared_error: 0.1702 - val_loss: 0.0269 - val_root_mean_squared_error: 0.1641\n",
       "Epoch 54/300\n",
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-      "35/35 [==============================] - 1s 20ms/step - loss: 0.0319 - root_mean_squared_error: 0.1785 - val_loss: 0.0291 - val_root_mean_squared_error: 0.1705\n",
+      "35/35 [==============================] - 0s 653us/step - loss: 0.0291 - root_mean_squared_error: 0.1706 - val_loss: 0.0268 - val_root_mean_squared_error: 0.1638\n",
       "Epoch 55/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0316 - root_mean_squared_error: 0.1777 - val_loss: 0.0290 - val_root_mean_squared_error: 0.1702\n",
+      "35/35 [==============================] - 0s 686us/step - loss: 0.0295 - root_mean_squared_error: 0.1716 - val_loss: 0.0265 - val_root_mean_squared_error: 0.1628\n",
       "Epoch 56/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0310 - root_mean_squared_error: 0.1761 - val_loss: 0.0286 - val_root_mean_squared_error: 0.1691\n",
+      "35/35 [==============================] - 0s 626us/step - loss: 0.0292 - root_mean_squared_error: 0.1709 - val_loss: 0.0260 - val_root_mean_squared_error: 0.1613\n",
       "Epoch 57/300\n",
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-      "35/35 [==============================] - 1s 27ms/step - loss: 0.0314 - root_mean_squared_error: 0.1771 - val_loss: 0.0284 - val_root_mean_squared_error: 0.1686\n",
+      "35/35 [==============================] - 0s 460us/step - loss: 0.0283 - root_mean_squared_error: 0.1681 - val_loss: 0.0266 - val_root_mean_squared_error: 0.1631\n",
       "Epoch 58/300\n",
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-      "35/35 [==============================] - 1s 20ms/step - loss: 0.0309 - root_mean_squared_error: 0.1757 - val_loss: 0.0284 - val_root_mean_squared_error: 0.1685\n",
+      "35/35 [==============================] - 0s 748us/step - loss: 0.0286 - root_mean_squared_error: 0.1691 - val_loss: 0.0258 - val_root_mean_squared_error: 0.1605\n",
       "Epoch 59/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0314 - root_mean_squared_error: 0.1773 - val_loss: 0.0286 - val_root_mean_squared_error: 0.1692\n",
+      "35/35 [==============================] - 0s 682us/step - loss: 0.0283 - root_mean_squared_error: 0.1681 - val_loss: 0.0257 - val_root_mean_squared_error: 0.1604\n",
       "Epoch 60/300\n",
-      "34/35 [============================>.] - ETA: 0s - loss: 0.0312 - root_mean_squared_error: 0.1767INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0311 - root_mean_squared_error: 0.1762 - val_loss: 0.0281 - val_root_mean_squared_error: 0.1676\n",
+      "35/35 [==============================] - 0s 733us/step - loss: 0.0278 - root_mean_squared_error: 0.1666 - val_loss: 0.0254 - val_root_mean_squared_error: 0.1593\n",
       "Epoch 61/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0299 - root_mean_squared_error: 0.1728 - val_loss: 0.0275 - val_root_mean_squared_error: 0.1659\n",
+      "35/35 [==============================] - 0s 792us/step - loss: 0.0282 - root_mean_squared_error: 0.1681 - val_loss: 0.0252 - val_root_mean_squared_error: 0.1589\n",
       "Epoch 62/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0304 - root_mean_squared_error: 0.1743 - val_loss: 0.0277 - val_root_mean_squared_error: 0.1664\n",
+      "35/35 [==============================] - 0s 743us/step - loss: 0.0279 - root_mean_squared_error: 0.1671 - val_loss: 0.0250 - val_root_mean_squared_error: 0.1583\n",
       "Epoch 63/300\n",
-      "35/35 [==============================] - ETA: 0s - loss: 0.0296 - root_mean_squared_error: 0.1721INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0296 - root_mean_squared_error: 0.1721 - val_loss: 0.0274 - val_root_mean_squared_error: 0.1656\n",
+      "35/35 [==============================] - 0s 757us/step - loss: 0.0274 - root_mean_squared_error: 0.1656 - val_loss: 0.0250 - val_root_mean_squared_error: 0.1581\n",
       "Epoch 64/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0295 - root_mean_squared_error: 0.1718 - val_loss: 0.0272 - val_root_mean_squared_error: 0.1648\n",
+      "35/35 [==============================] - 0s 662us/step - loss: 0.0274 - root_mean_squared_error: 0.1654 - val_loss: 0.0247 - val_root_mean_squared_error: 0.1571\n",
       "Epoch 65/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0292 - root_mean_squared_error: 0.1710 - val_loss: 0.0266 - val_root_mean_squared_error: 0.1630\n",
+      "35/35 [==============================] - 0s 584us/step - loss: 0.0278 - root_mean_squared_error: 0.1667 - val_loss: 0.0247 - val_root_mean_squared_error: 0.1570\n",
       "Epoch 66/300\n",
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-      "35/35 [==============================] - 1s 23ms/step - loss: 0.0288 - root_mean_squared_error: 0.1697 - val_loss: 0.0265 - val_root_mean_squared_error: 0.1626\n",
+      "35/35 [==============================] - 0s 433us/step - loss: 0.0268 - root_mean_squared_error: 0.1638 - val_loss: 0.0247 - val_root_mean_squared_error: 0.1572\n",
       "Epoch 67/300\n",
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-      "35/35 [==============================] - 1s 27ms/step - loss: 0.0291 - root_mean_squared_error: 0.1705 - val_loss: 0.0261 - val_root_mean_squared_error: 0.1615\n",
+      "35/35 [==============================] - 0s 635us/step - loss: 0.0274 - root_mean_squared_error: 0.1655 - val_loss: 0.0243 - val_root_mean_squared_error: 0.1560\n",
       "Epoch 68/300\n",
-      "34/35 [============================>.] - ETA: 0s - loss: 0.0286 - root_mean_squared_error: 0.1690INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0285 - root_mean_squared_error: 0.1688 - val_loss: 0.0258 - val_root_mean_squared_error: 0.1606\n",
+      "35/35 [==============================] - 0s 624us/step - loss: 0.0264 - root_mean_squared_error: 0.1625 - val_loss: 0.0236 - val_root_mean_squared_error: 0.1536\n",
       "Epoch 69/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0289 - root_mean_squared_error: 0.1699 - val_loss: 0.0256 - val_root_mean_squared_error: 0.1600\n",
+      "35/35 [==============================] - 0s 425us/step - loss: 0.0259 - root_mean_squared_error: 0.1608 - val_loss: 0.0238 - val_root_mean_squared_error: 0.1542\n",
       "Epoch 70/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0278 - root_mean_squared_error: 0.1667 - val_loss: 0.0256 - val_root_mean_squared_error: 0.1599\n",
+      "35/35 [==============================] - 0s 625us/step - loss: 0.0258 - root_mean_squared_error: 0.1607 - val_loss: 0.0235 - val_root_mean_squared_error: 0.1534\n",
       "Epoch 71/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0278 - root_mean_squared_error: 0.1667 - val_loss: 0.0259 - val_root_mean_squared_error: 0.1609\n",
+      "35/35 [==============================] - 0s 451us/step - loss: 0.0266 - root_mean_squared_error: 0.1630 - val_loss: 0.0236 - val_root_mean_squared_error: 0.1538\n",
       "Epoch 72/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0285 - root_mean_squared_error: 0.1687 - val_loss: 0.0254 - val_root_mean_squared_error: 0.1594\n",
+      "35/35 [==============================] - 0s 649us/step - loss: 0.0259 - root_mean_squared_error: 0.1608 - val_loss: 0.0226 - val_root_mean_squared_error: 0.1504\n",
       "Epoch 73/300\n",
-      "35/35 [==============================] - ETA: 0s - loss: 0.0272 - root_mean_squared_error: 0.1648INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0272 - root_mean_squared_error: 0.1648 - val_loss: 0.0245 - val_root_mean_squared_error: 0.1565\n",
+      "35/35 [==============================] - 0s 437us/step - loss: 0.0256 - root_mean_squared_error: 0.1600 - val_loss: 0.0230 - val_root_mean_squared_error: 0.1518\n",
       "Epoch 74/300\n",
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-      "35/35 [==============================] - 1s 22ms/step - loss: 0.0269 - root_mean_squared_error: 0.1640 - val_loss: 0.0242 - val_root_mean_squared_error: 0.1554\n",
+      "35/35 [==============================] - 0s 622us/step - loss: 0.0251 - root_mean_squared_error: 0.1586 - val_loss: 0.0225 - val_root_mean_squared_error: 0.1501\n",
       "Epoch 75/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0268 - root_mean_squared_error: 0.1638 - val_loss: 0.0242 - val_root_mean_squared_error: 0.1557\n",
+      "35/35 [==============================] - 0s 463us/step - loss: 0.0256 - root_mean_squared_error: 0.1599 - val_loss: 0.0226 - val_root_mean_squared_error: 0.1503\n",
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-      "35/35 [==============================] - 1s 22ms/step - loss: 0.0269 - root_mean_squared_error: 0.1639 - val_loss: 0.0240 - val_root_mean_squared_error: 0.1548\n",
+      "35/35 [==============================] - 0s 652us/step - loss: 0.0256 - root_mean_squared_error: 0.1600 - val_loss: 0.0222 - val_root_mean_squared_error: 0.1489\n",
       "Epoch 77/300\n",
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-      "35/35 [==============================] - 1s 28ms/step - loss: 0.0264 - root_mean_squared_error: 0.1626 - val_loss: 0.0240 - val_root_mean_squared_error: 0.1548\n",
+      "35/35 [==============================] - 0s 588us/step - loss: 0.0252 - root_mean_squared_error: 0.1586 - val_loss: 0.0222 - val_root_mean_squared_error: 0.1489\n",
       "Epoch 78/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0256 - root_mean_squared_error: 0.1601 - val_loss: 0.0236 - val_root_mean_squared_error: 0.1538\n",
+      "35/35 [==============================] - 0s 594us/step - loss: 0.0248 - root_mean_squared_error: 0.1574 - val_loss: 0.0217 - val_root_mean_squared_error: 0.1472\n",
       "Epoch 79/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0262 - root_mean_squared_error: 0.1619 - val_loss: 0.0232 - val_root_mean_squared_error: 0.1523\n",
+      "35/35 [==============================] - 0s 422us/step - loss: 0.0252 - root_mean_squared_error: 0.1588 - val_loss: 0.0218 - val_root_mean_squared_error: 0.1478\n",
       "Epoch 80/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0265 - root_mean_squared_error: 0.1628 - val_loss: 0.0234 - val_root_mean_squared_error: 0.1530\n",
+      "35/35 [==============================] - 0s 668us/step - loss: 0.0251 - root_mean_squared_error: 0.1584 - val_loss: 0.0210 - val_root_mean_squared_error: 0.1449\n",
       "Epoch 81/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0257 - root_mean_squared_error: 0.1602 - val_loss: 0.0235 - val_root_mean_squared_error: 0.1534\n",
+      "35/35 [==============================] - 0s 606us/step - loss: 0.0242 - root_mean_squared_error: 0.1556 - val_loss: 0.0214 - val_root_mean_squared_error: 0.1464\n",
       "Epoch 82/300\n",
-      "34/35 [============================>.] - ETA: 0s - loss: 0.0255 - root_mean_squared_error: 0.1597INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0254 - root_mean_squared_error: 0.1593 - val_loss: 0.0229 - val_root_mean_squared_error: 0.1513\n",
+      "35/35 [==============================] - 0s 603us/step - loss: 0.0245 - root_mean_squared_error: 0.1566 - val_loss: 0.0212 - val_root_mean_squared_error: 0.1455\n",
       "Epoch 83/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0254 - root_mean_squared_error: 0.1593 - val_loss: 0.0225 - val_root_mean_squared_error: 0.1502\n",
+      "35/35 [==============================] - 0s 823us/step - loss: 0.0244 - root_mean_squared_error: 0.1563 - val_loss: 0.0201 - val_root_mean_squared_error: 0.1419\n",
       "Epoch 84/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0255 - root_mean_squared_error: 0.1596 - val_loss: 0.0222 - val_root_mean_squared_error: 0.1488\n",
+      "35/35 [==============================] - 0s 830us/step - loss: 0.0236 - root_mean_squared_error: 0.1535 - val_loss: 0.0200 - val_root_mean_squared_error: 0.1416\n",
       "Epoch 85/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0250 - root_mean_squared_error: 0.1581 - val_loss: 0.0224 - val_root_mean_squared_error: 0.1498\n",
+      "35/35 [==============================] - 0s 769us/step - loss: 0.0235 - root_mean_squared_error: 0.1532 - val_loss: 0.0196 - val_root_mean_squared_error: 0.1401\n",
       "Epoch 86/300\n",
-      "35/35 [==============================] - ETA: 0s - loss: 0.0247 - root_mean_squared_error: 0.1571INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0247 - root_mean_squared_error: 0.1571 - val_loss: 0.0219 - val_root_mean_squared_error: 0.1480\n",
+      "35/35 [==============================] - 0s 770us/step - loss: 0.0229 - root_mean_squared_error: 0.1512 - val_loss: 0.0190 - val_root_mean_squared_error: 0.1379\n",
       "Epoch 87/300\n",
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-      "35/35 [==============================] - 1s 27ms/step - loss: 0.0241 - root_mean_squared_error: 0.1553 - val_loss: 0.0218 - val_root_mean_squared_error: 0.1477\n",
+      "35/35 [==============================] - 0s 567us/step - loss: 0.0234 - root_mean_squared_error: 0.1530 - val_loss: 0.0197 - val_root_mean_squared_error: 0.1403\n",
       "Epoch 88/300\n",
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-      "35/35 [==============================] - 1s 28ms/step - loss: 0.0243 - root_mean_squared_error: 0.1558 - val_loss: 0.0216 - val_root_mean_squared_error: 0.1469\n",
+      "35/35 [==============================] - 0s 612us/step - loss: 0.0234 - root_mean_squared_error: 0.1530 - val_loss: 0.0192 - val_root_mean_squared_error: 0.1386\n",
       "Epoch 89/300\n",
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-      "35/35 [==============================] - 1s 22ms/step - loss: 0.0240 - root_mean_squared_error: 0.1550 - val_loss: 0.0215 - val_root_mean_squared_error: 0.1465\n",
+      "35/35 [==============================] - 0s 712us/step - loss: 0.0224 - root_mean_squared_error: 0.1497 - val_loss: 0.0187 - val_root_mean_squared_error: 0.1367\n",
       "Epoch 90/300\n",
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-      "35/35 [==============================] - 1s 22ms/step - loss: 0.0243 - root_mean_squared_error: 0.1558 - val_loss: 0.0209 - val_root_mean_squared_error: 0.1445\n",
+      "35/35 [==============================] - 0s 743us/step - loss: 0.0226 - root_mean_squared_error: 0.1503 - val_loss: 0.0187 - val_root_mean_squared_error: 0.1367\n",
       "Epoch 91/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0236 - root_mean_squared_error: 0.1535 - val_loss: 0.0210 - val_root_mean_squared_error: 0.1448\n",
+      "35/35 [==============================] - 0s 757us/step - loss: 0.0222 - root_mean_squared_error: 0.1491 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1350\n",
       "Epoch 92/300\n",
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-      "35/35 [==============================] - 1s 22ms/step - loss: 0.0229 - root_mean_squared_error: 0.1512 - val_loss: 0.0204 - val_root_mean_squared_error: 0.1429\n",
+      "35/35 [==============================] - 0s 787us/step - loss: 0.0224 - root_mean_squared_error: 0.1498 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1346\n",
       "Epoch 93/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0238 - root_mean_squared_error: 0.1542 - val_loss: 0.0209 - val_root_mean_squared_error: 0.1447\n",
+      "35/35 [==============================] - 0s 710us/step - loss: 0.0216 - root_mean_squared_error: 0.1470 - val_loss: 0.0176 - val_root_mean_squared_error: 0.1326\n",
       "Epoch 94/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0227 - root_mean_squared_error: 0.1506 - val_loss: 0.0203 - val_root_mean_squared_error: 0.1424\n",
+      "35/35 [==============================] - 0s 555us/step - loss: 0.0212 - root_mean_squared_error: 0.1457 - val_loss: 0.0179 - val_root_mean_squared_error: 0.1339\n",
       "Epoch 95/300\n",
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-      "35/35 [==============================] - 1s 22ms/step - loss: 0.0230 - root_mean_squared_error: 0.1518 - val_loss: 0.0201 - val_root_mean_squared_error: 0.1419\n",
+      "35/35 [==============================] - 0s 791us/step - loss: 0.0217 - root_mean_squared_error: 0.1472 - val_loss: 0.0175 - val_root_mean_squared_error: 0.1322\n",
       "Epoch 96/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0226 - root_mean_squared_error: 0.1503 - val_loss: 0.0205 - val_root_mean_squared_error: 0.1431\n",
+      "35/35 [==============================] - 0s 733us/step - loss: 0.0213 - root_mean_squared_error: 0.1461 - val_loss: 0.0172 - val_root_mean_squared_error: 0.1311\n",
       "Epoch 97/300\n",
-      "35/35 [==============================] - ETA: 0s - loss: 0.0226 - root_mean_squared_error: 0.1504INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 22ms/step - loss: 0.0226 - root_mean_squared_error: 0.1504 - val_loss: 0.0200 - val_root_mean_squared_error: 0.1413\n",
+      "35/35 [==============================] - 0s 645us/step - loss: 0.0207 - root_mean_squared_error: 0.1440 - val_loss: 0.0174 - val_root_mean_squared_error: 0.1320\n",
       "Epoch 98/300\n",
-      "35/35 [==============================] - ETA: 0s - loss: 0.0222 - root_mean_squared_error: 0.1490INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 22ms/step - loss: 0.0222 - root_mean_squared_error: 0.1490 - val_loss: 0.0198 - val_root_mean_squared_error: 0.1406\n",
+      "35/35 [==============================] - 0s 606us/step - loss: 0.0208 - root_mean_squared_error: 0.1442 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1314\n",
       "Epoch 99/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0221 - root_mean_squared_error: 0.1488 - val_loss: 0.0202 - val_root_mean_squared_error: 0.1422\n",
+      "35/35 [==============================] - 0s 563us/step - loss: 0.0202 - root_mean_squared_error: 0.1420 - val_loss: 0.0180 - val_root_mean_squared_error: 0.1342\n",
       "Epoch 100/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0219 - root_mean_squared_error: 0.1481 - val_loss: 0.0199 - val_root_mean_squared_error: 0.1411\n",
+      "35/35 [==============================] - 0s 496us/step - loss: 0.0211 - root_mean_squared_error: 0.1451 - val_loss: 0.0174 - val_root_mean_squared_error: 0.1319\n",
       "Epoch 101/300\n",
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-      "35/35 [==============================] - 1s 26ms/step - loss: 0.0218 - root_mean_squared_error: 0.1476 - val_loss: 0.0195 - val_root_mean_squared_error: 0.1397\n",
+      "35/35 [==============================] - 0s 727us/step - loss: 0.0197 - root_mean_squared_error: 0.1405 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1303\n",
       "Epoch 102/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0219 - root_mean_squared_error: 0.1480 - val_loss: 0.0197 - val_root_mean_squared_error: 0.1405\n",
+      "35/35 [==============================] - 0s 757us/step - loss: 0.0200 - root_mean_squared_error: 0.1415 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1303\n",
       "Epoch 103/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0220 - root_mean_squared_error: 0.1482 - val_loss: 0.0198 - val_root_mean_squared_error: 0.1407\n",
+      "35/35 [==============================] - 0s 693us/step - loss: 0.0203 - root_mean_squared_error: 0.1426 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1302\n",
       "Epoch 104/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0221 - root_mean_squared_error: 0.1485 - val_loss: 0.0192 - val_root_mean_squared_error: 0.1385\n",
+      "35/35 [==============================] - 0s 720us/step - loss: 0.0197 - root_mean_squared_error: 0.1403 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1296\n",
       "Epoch 105/300\n",
-      " 1/35 [..............................] - ETA: 0s - loss: 0.0243 - root_mean_squared_error: 0.1560INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 20ms/step - loss: 0.0221 - root_mean_squared_error: 0.1487 - val_loss: 0.0191 - val_root_mean_squared_error: 0.1381\n",
+      "35/35 [==============================] - 0s 655us/step - loss: 0.0191 - root_mean_squared_error: 0.1383 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1292\n",
       "Epoch 106/300\n",
-      "35/35 [==============================] - ETA: 0s - loss: 0.0219 - root_mean_squared_error: 0.1481INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0219 - root_mean_squared_error: 0.1481 - val_loss: 0.0190 - val_root_mean_squared_error: 0.1379\n",
+      "35/35 [==============================] - 0s 658us/step - loss: 0.0196 - root_mean_squared_error: 0.1400 - val_loss: 0.0166 - val_root_mean_squared_error: 0.1288\n",
       "Epoch 107/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0218 - root_mean_squared_error: 0.1478 - val_loss: 0.0189 - val_root_mean_squared_error: 0.1376\n",
+      "35/35 [==============================] - 0s 506us/step - loss: 0.0191 - root_mean_squared_error: 0.1383 - val_loss: 0.0166 - val_root_mean_squared_error: 0.1289\n",
       "Epoch 108/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0217 - root_mean_squared_error: 0.1471 - val_loss: 0.0193 - val_root_mean_squared_error: 0.1390\n",
+      "35/35 [==============================] - 0s 687us/step - loss: 0.0196 - root_mean_squared_error: 0.1400 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1279\n",
       "Epoch 109/300\n",
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-      "35/35 [==============================] - 1s 22ms/step - loss: 0.0211 - root_mean_squared_error: 0.1451 - val_loss: 0.0187 - val_root_mean_squared_error: 0.1369\n",
+      "35/35 [==============================] - 0s 620us/step - loss: 0.0194 - root_mean_squared_error: 0.1393 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1272\n",
       "Epoch 110/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0209 - root_mean_squared_error: 0.1447 - val_loss: 0.0189 - val_root_mean_squared_error: 0.1373\n",
+      "35/35 [==============================] - 0s 566us/step - loss: 0.0195 - root_mean_squared_error: 0.1396 - val_loss: 0.0160 - val_root_mean_squared_error: 0.1264\n",
       "Epoch 111/300\n",
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-      "35/35 [==============================] - 1s 23ms/step - loss: 0.0208 - root_mean_squared_error: 0.1442 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1357\n",
+      "35/35 [==============================] - 0s 435us/step - loss: 0.0188 - root_mean_squared_error: 0.1370 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1292\n",
       "Epoch 112/300\n",
-      "35/35 [==============================] - 0s 3ms/step - loss: 0.0209 - root_mean_squared_error: 0.1446 - val_loss: 0.0190 - val_root_mean_squared_error: 0.1380\n",
+      "35/35 [==============================] - 0s 483us/step - loss: 0.0191 - root_mean_squared_error: 0.1383 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1282\n",
       "Epoch 113/300\n",
-      "35/35 [==============================] - 0s 3ms/step - loss: 0.0207 - root_mean_squared_error: 0.1440 - val_loss: 0.0187 - val_root_mean_squared_error: 0.1367\n",
+      "35/35 [==============================] - 0s 432us/step - loss: 0.0189 - root_mean_squared_error: 0.1375 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1269\n",
       "Epoch 114/300\n",
-      "35/35 [==============================] - 0s 3ms/step - loss: 0.0206 - root_mean_squared_error: 0.1435 - val_loss: 0.0188 - val_root_mean_squared_error: 0.1372\n",
+      "35/35 [==============================] - 0s 609us/step - loss: 0.0190 - root_mean_squared_error: 0.1379 - val_loss: 0.0166 - val_root_mean_squared_error: 0.1289\n",
       "Epoch 115/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0204 - root_mean_squared_error: 0.1427 - val_loss: 0.0185 - val_root_mean_squared_error: 0.1359\n",
+      "35/35 [==============================] - 0s 664us/step - loss: 0.0186 - root_mean_squared_error: 0.1364 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1248\n",
       "Epoch 116/300\n",
-      "35/35 [==============================] - 0s 4ms/step - loss: 0.0201 - root_mean_squared_error: 0.1419 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1358\n",
+      "35/35 [==============================] - 0s 670us/step - loss: 0.0178 - root_mean_squared_error: 0.1336 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1246\n",
       "Epoch 117/300\n",
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-      "35/35 [==============================] - 1s 24ms/step - loss: 0.0208 - root_mean_squared_error: 0.1441 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1348\n",
+      "35/35 [==============================] - 0s 429us/step - loss: 0.0184 - root_mean_squared_error: 0.1358 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1274\n",
       "Epoch 118/300\n",
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-      "35/35 [==============================] - 1s 36ms/step - loss: 0.0198 - root_mean_squared_error: 0.1407 - val_loss: 0.0179 - val_root_mean_squared_error: 0.1338\n",
+      "35/35 [==============================] - 0s 507us/step - loss: 0.0187 - root_mean_squared_error: 0.1366 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1269\n",
       "Epoch 119/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0200 - root_mean_squared_error: 0.1416 - val_loss: 0.0186 - val_root_mean_squared_error: 0.1362\n",
+      "35/35 [==============================] - 0s 652us/step - loss: 0.0173 - root_mean_squared_error: 0.1315 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1243\n",
       "Epoch 120/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0203 - root_mean_squared_error: 0.1423 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1347\n",
+      "35/35 [==============================] - 0s 464us/step - loss: 0.0182 - root_mean_squared_error: 0.1348 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1270\n",
       "Epoch 121/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0195 - root_mean_squared_error: 0.1398 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1347\n",
+      "35/35 [==============================] - 0s 470us/step - loss: 0.0180 - root_mean_squared_error: 0.1343 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1249\n",
       "Epoch 122/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0197 - root_mean_squared_error: 0.1403 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1356\n",
+      "35/35 [==============================] - 0s 459us/step - loss: 0.0182 - root_mean_squared_error: 0.1348 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1251\n",
       "Epoch 123/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0193 - root_mean_squared_error: 0.1388 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1349\n",
+      "35/35 [==============================] - 0s 470us/step - loss: 0.0181 - root_mean_squared_error: 0.1344 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1244\n",
       "Epoch 124/300\n",
-      "30/35 [========================>.....] - ETA: 0s - loss: 0.0200 - root_mean_squared_error: 0.1414INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 31ms/step - loss: 0.0198 - root_mean_squared_error: 0.1407 - val_loss: 0.0179 - val_root_mean_squared_error: 0.1336\n",
+      "35/35 [==============================] - 0s 482us/step - loss: 0.0174 - root_mean_squared_error: 0.1318 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1250\n",
       "Epoch 125/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0194 - root_mean_squared_error: 0.1392 - val_loss: 0.0180 - val_root_mean_squared_error: 0.1341\n",
+      "35/35 [==============================] - 0s 694us/step - loss: 0.0180 - root_mean_squared_error: 0.1342 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1234\n",
       "Epoch 126/300\n",
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-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0195 - root_mean_squared_error: 0.1395 - val_loss: 0.0178 - val_root_mean_squared_error: 0.1333\n",
+      "35/35 [==============================] - 0s 464us/step - loss: 0.0170 - root_mean_squared_error: 0.1305 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1247\n",
       "Epoch 127/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0195 - root_mean_squared_error: 0.1395 - val_loss: 0.0180 - val_root_mean_squared_error: 0.1340\n",
+      "35/35 [==============================] - 0s 468us/step - loss: 0.0171 - root_mean_squared_error: 0.1309 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1239\n",
       "Epoch 128/300\n",
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-      "35/35 [==============================] - 1s 30ms/step - loss: 0.0192 - root_mean_squared_error: 0.1387 - val_loss: 0.0175 - val_root_mean_squared_error: 0.1324\n",
+      "35/35 [==============================] - 0s 453us/step - loss: 0.0175 - root_mean_squared_error: 0.1323 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1245\n",
       "Epoch 129/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0189 - root_mean_squared_error: 0.1375 - val_loss: 0.0178 - val_root_mean_squared_error: 0.1335\n",
+      "35/35 [==============================] - 0s 643us/step - loss: 0.0167 - root_mean_squared_error: 0.1292 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1222\n",
       "Epoch 130/300\n",
-      "30/35 [========================>.....] - ETA: 0s - loss: 0.0188 - root_mean_squared_error: 0.1371INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 24ms/step - loss: 0.0186 - root_mean_squared_error: 0.1365 - val_loss: 0.0174 - val_root_mean_squared_error: 0.1319\n",
+      "35/35 [==============================] - 0s 584us/step - loss: 0.0170 - root_mean_squared_error: 0.1303 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1242\n",
       "Epoch 131/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0188 - root_mean_squared_error: 0.1372 - val_loss: 0.0179 - val_root_mean_squared_error: 0.1338\n",
+      "35/35 [==============================] - 0s 478us/step - loss: 0.0176 - root_mean_squared_error: 0.1326 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1228\n",
       "Epoch 132/300\n",
-      "30/35 [========================>.....] - ETA: 0s - loss: 0.0190 - root_mean_squared_error: 0.1379INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 33ms/step - loss: 0.0188 - root_mean_squared_error: 0.1371 - val_loss: 0.0174 - val_root_mean_squared_error: 0.1318\n",
+      "35/35 [==============================] - 0s 465us/step - loss: 0.0170 - root_mean_squared_error: 0.1303 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1232\n",
       "Epoch 133/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0188 - root_mean_squared_error: 0.1372 - val_loss: 0.0179 - val_root_mean_squared_error: 0.1339\n",
+      "35/35 [==============================] - 0s 448us/step - loss: 0.0171 - root_mean_squared_error: 0.1309 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1246\n",
       "Epoch 134/300\n",
-      " 1/35 [..............................] - ETA: 0s - loss: 0.0203 - root_mean_squared_error: 0.1426INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 20ms/step - loss: 0.0186 - root_mean_squared_error: 0.1366 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1316\n",
+      "35/35 [==============================] - 0s 491us/step - loss: 0.0170 - root_mean_squared_error: 0.1302 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1241\n",
       "Epoch 135/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0188 - root_mean_squared_error: 0.1372 - val_loss: 0.0178 - val_root_mean_squared_error: 0.1334\n",
+      "35/35 [==============================] - 0s 663us/step - loss: 0.0163 - root_mean_squared_error: 0.1279 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1217\n",
       "Epoch 136/300\n",
-      " 1/35 [..............................] - ETA: 0s - loss: 0.0190 - root_mean_squared_error: 0.1377INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 20ms/step - loss: 0.0184 - root_mean_squared_error: 0.1356 - val_loss: 0.0172 - val_root_mean_squared_error: 0.1313\n",
+      "35/35 [==============================] - 0s 464us/step - loss: 0.0165 - root_mean_squared_error: 0.1285 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1224\n",
       "Epoch 137/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0189 - root_mean_squared_error: 0.1373 - val_loss: 0.0174 - val_root_mean_squared_error: 0.1320\n",
+      "35/35 [==============================] - 0s 462us/step - loss: 0.0163 - root_mean_squared_error: 0.1279 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1244\n",
       "Epoch 138/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0182 - root_mean_squared_error: 0.1351 - val_loss: 0.0177 - val_root_mean_squared_error: 0.1330\n",
+      "35/35 [==============================] - 0s 443us/step - loss: 0.0166 - root_mean_squared_error: 0.1289 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1229\n",
       "Epoch 139/300\n",
-      "34/35 [============================>.] - ETA: 0s - loss: 0.0182 - root_mean_squared_error: 0.1349INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 2s 45ms/step - loss: 0.0181 - root_mean_squared_error: 0.1347 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1309\n",
+      "35/35 [==============================] - 0s 659us/step - loss: 0.0160 - root_mean_squared_error: 0.1265 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1212\n",
       "Epoch 140/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0183 - root_mean_squared_error: 0.1352 - val_loss: 0.0174 - val_root_mean_squared_error: 0.1320\n",
+      "35/35 [==============================] - 0s 495us/step - loss: 0.0165 - root_mean_squared_error: 0.1284 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1223\n",
       "Epoch 141/300\n",
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-      "35/35 [==============================] - 1s 30ms/step - loss: 0.0180 - root_mean_squared_error: 0.1340 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1307\n",
+      "35/35 [==============================] - 0s 474us/step - loss: 0.0162 - root_mean_squared_error: 0.1275 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1227\n",
       "Epoch 142/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0178 - root_mean_squared_error: 0.1335 - val_loss: 0.0174 - val_root_mean_squared_error: 0.1319\n",
+      "35/35 [==============================] - 0s 460us/step - loss: 0.0166 - root_mean_squared_error: 0.1287 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1254\n",
       "Epoch 143/300\n",
-      "29/35 [=======================>......] - ETA: 0s - loss: 0.0179 - root_mean_squared_error: 0.1336INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 30ms/step - loss: 0.0178 - root_mean_squared_error: 0.1335 - val_loss: 0.0169 - val_root_mean_squared_error: 0.1302\n",
+      "35/35 [==============================] - 0s 479us/step - loss: 0.0163 - root_mean_squared_error: 0.1279 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1213\n",
       "Epoch 144/300\n",
-      "29/35 [=======================>......] - ETA: 0s - loss: 0.0179 - root_mean_squared_error: 0.1336INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 31ms/step - loss: 0.0180 - root_mean_squared_error: 0.1343 - val_loss: 0.0169 - val_root_mean_squared_error: 0.1301\n",
+      "35/35 [==============================] - 0s 645us/step - loss: 0.0163 - root_mean_squared_error: 0.1275 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1205\n",
       "Epoch 145/300\n",
-      "35/35 [==============================] - 0s 3ms/step - loss: 0.0179 - root_mean_squared_error: 0.1336 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1308\n",
+      "35/35 [==============================] - 0s 462us/step - loss: 0.0162 - root_mean_squared_error: 0.1274 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1217\n",
       "Epoch 146/300\n",
-      "31/35 [=========================>....] - ETA: 0s - loss: 0.0174 - root_mean_squared_error: 0.1319INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 28ms/step - loss: 0.0174 - root_mean_squared_error: 0.1320 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1298\n",
+      "35/35 [==============================] - 0s 567us/step - loss: 0.0160 - root_mean_squared_error: 0.1266 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1207\n",
       "Epoch 147/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0173 - root_mean_squared_error: 0.1313 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1307\n",
+      "35/35 [==============================] - 0s 448us/step - loss: 0.0157 - root_mean_squared_error: 0.1251 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1214\n",
       "Epoch 148/300\n",
-      " 1/35 [..............................] - ETA: 0s - loss: 0.0191 - root_mean_squared_error: 0.1384INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0180 - root_mean_squared_error: 0.1343 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1298\n",
+      "35/35 [==============================] - 0s 630us/step - loss: 0.0157 - root_mean_squared_error: 0.1255 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1202\n",
       "Epoch 149/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0174 - root_mean_squared_error: 0.1318 - val_loss: 0.0172 - val_root_mean_squared_error: 0.1312\n",
+      "35/35 [==============================] - 0s 460us/step - loss: 0.0156 - root_mean_squared_error: 0.1250 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1225\n",
       "Epoch 150/300\n",
-      " 1/35 [..............................] - ETA: 0s - loss: 0.0178 - root_mean_squared_error: 0.1333INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0172 - root_mean_squared_error: 0.1312 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1297\n",
+      "35/35 [==============================] - 0s 443us/step - loss: 0.0154 - root_mean_squared_error: 0.1241 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1217\n",
       "Epoch 151/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0175 - root_mean_squared_error: 0.1322 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1305\n",
+      "35/35 [==============================] - 0s 450us/step - loss: 0.0160 - root_mean_squared_error: 0.1263 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1206\n",
       "Epoch 152/300\n",
-      "35/35 [==============================] - ETA: 0s - loss: 0.0176 - root_mean_squared_error: 0.1327INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 22ms/step - loss: 0.0176 - root_mean_squared_error: 0.1327 - val_loss: 0.0166 - val_root_mean_squared_error: 0.1288\n",
+      "35/35 [==============================] - 0s 474us/step - loss: 0.0153 - root_mean_squared_error: 0.1237 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1216\n",
       "Epoch 153/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0175 - root_mean_squared_error: 0.1323 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1307\n",
+      "35/35 [==============================] - 0s 486us/step - loss: 0.0157 - root_mean_squared_error: 0.1255 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1207\n",
       "Epoch 154/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0175 - root_mean_squared_error: 0.1322 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1302\n",
+      "35/35 [==============================] - 0s 692us/step - loss: 0.0155 - root_mean_squared_error: 0.1245 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1182\n",
       "Epoch 155/300\n",
-      "35/35 [==============================] - 0s 3ms/step - loss: 0.0171 - root_mean_squared_error: 0.1307 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1295\n",
+      "35/35 [==============================] - 0s 460us/step - loss: 0.0154 - root_mean_squared_error: 0.1239 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1188\n",
       "Epoch 156/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0174 - root_mean_squared_error: 0.1318 - val_loss: 0.0166 - val_root_mean_squared_error: 0.1290\n",
+      "35/35 [==============================] - 0s 447us/step - loss: 0.0156 - root_mean_squared_error: 0.1250 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1224\n",
       "Epoch 157/300\n",
-      "33/35 [===========================>..] - ETA: 0s - loss: 0.0179 - root_mean_squared_error: 0.1337INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 30ms/step - loss: 0.0176 - root_mean_squared_error: 0.1325 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1282\n",
+      "35/35 [==============================] - 0s 467us/step - loss: 0.0150 - root_mean_squared_error: 0.1223 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1209\n",
       "Epoch 158/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0167 - root_mean_squared_error: 0.1292 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1298\n",
+      "35/35 [==============================] - 0s 500us/step - loss: 0.0150 - root_mean_squared_error: 0.1225 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1211\n",
       "Epoch 159/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0168 - root_mean_squared_error: 0.1294 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1297\n",
+      "35/35 [==============================] - 0s 628us/step - loss: 0.0147 - root_mean_squared_error: 0.1212 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1179\n",
       "Epoch 160/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0172 - root_mean_squared_error: 0.1311 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1291\n",
+      "35/35 [==============================] - 0s 707us/step - loss: 0.0159 - root_mean_squared_error: 0.1261 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1176\n",
       "Epoch 161/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0169 - root_mean_squared_error: 0.1300 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1304\n",
+      "35/35 [==============================] - 0s 436us/step - loss: 0.0145 - root_mean_squared_error: 0.1203 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1185\n",
       "Epoch 162/300\n",
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-      "35/35 [==============================] - 1s 22ms/step - loss: 0.0168 - root_mean_squared_error: 0.1295 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1279\n",
+      "35/35 [==============================] - 0s 452us/step - loss: 0.0146 - root_mean_squared_error: 0.1209 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1192\n",
       "Epoch 163/300\n",
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-      "35/35 [==============================] - 1s 22ms/step - loss: 0.0169 - root_mean_squared_error: 0.1299 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1279\n",
+      "35/35 [==============================] - 0s 669us/step - loss: 0.0150 - root_mean_squared_error: 0.1224 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1174\n",
       "Epoch 164/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0169 - root_mean_squared_error: 0.1298 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1280\n",
+      "35/35 [==============================] - 0s 455us/step - loss: 0.0149 - root_mean_squared_error: 0.1219 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1191\n",
       "Epoch 165/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0161 - root_mean_squared_error: 0.1269 - val_loss: 0.0165 - val_root_mean_squared_error: 0.1284\n",
+      "35/35 [==============================] - 0s 459us/step - loss: 0.0146 - root_mean_squared_error: 0.1208 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1179\n",
       "Epoch 166/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0166 - root_mean_squared_error: 0.1287 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1290\n",
+      "35/35 [==============================] - 0s 472us/step - loss: 0.0145 - root_mean_squared_error: 0.1204 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1182\n",
       "Epoch 167/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0160 - root_mean_squared_error: 0.1264 - val_loss: 0.0165 - val_root_mean_squared_error: 0.1284\n",
+      "35/35 [==============================] - 0s 490us/step - loss: 0.0147 - root_mean_squared_error: 0.1212 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1182\n",
       "Epoch 168/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0164 - root_mean_squared_error: 0.1282 - val_loss: 0.0165 - val_root_mean_squared_error: 0.1283\n",
+      "35/35 [==============================] - 0s 512us/step - loss: 0.0145 - root_mean_squared_error: 0.1203 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1189\n",
       "Epoch 169/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0160 - root_mean_squared_error: 0.1263 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1292\n",
+      "35/35 [==============================] - 0s 669us/step - loss: 0.0149 - root_mean_squared_error: 0.1220 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1168\n",
       "Epoch 170/300\n",
-      " 1/35 [..............................] - ETA: 0s - loss: 0.0153 - root_mean_squared_error: 0.1238INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0163 - root_mean_squared_error: 0.1275 - val_loss: 0.0160 - val_root_mean_squared_error: 0.1265\n",
+      "35/35 [==============================] - 0s 629us/step - loss: 0.0145 - root_mean_squared_error: 0.1205 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1158\n",
       "Epoch 171/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0162 - root_mean_squared_error: 0.1272 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1275\n",
+      "35/35 [==============================] - 0s 448us/step - loss: 0.0147 - root_mean_squared_error: 0.1211 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1183\n",
       "Epoch 172/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0167 - root_mean_squared_error: 0.1291 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1276\n",
+      "35/35 [==============================] - 0s 537us/step - loss: 0.0149 - root_mean_squared_error: 0.1222 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1169\n",
       "Epoch 173/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0162 - root_mean_squared_error: 0.1274 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1280\n",
+      "35/35 [==============================] - 0s 628us/step - loss: 0.0142 - root_mean_squared_error: 0.1191 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1192\n",
       "Epoch 174/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0161 - root_mean_squared_error: 0.1270 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1272\n",
+      "35/35 [==============================] - 0s 796us/step - loss: 0.0148 - root_mean_squared_error: 0.1215 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1190\n",
       "Epoch 175/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0160 - root_mean_squared_error: 0.1265 - val_loss: 0.0160 - val_root_mean_squared_error: 0.1266\n",
+      "35/35 [==============================] - 0s 642us/step - loss: 0.0144 - root_mean_squared_error: 0.1200 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1167\n",
       "Epoch 176/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0161 - root_mean_squared_error: 0.1270 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1277\n",
+      "35/35 [==============================] - 0s 563us/step - loss: 0.0142 - root_mean_squared_error: 0.1193 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1169\n",
       "Epoch 177/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0157 - root_mean_squared_error: 0.1254 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1281\n",
+      "35/35 [==============================] - 0s 654us/step - loss: 0.0142 - root_mean_squared_error: 0.1191 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1174\n",
       "Epoch 178/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0158 - root_mean_squared_error: 0.1256 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1279\n",
+      "35/35 [==============================] - 0s 611us/step - loss: 0.0140 - root_mean_squared_error: 0.1181 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1178\n",
       "Epoch 179/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0161 - root_mean_squared_error: 0.1268 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1269\n",
+      "35/35 [==============================] - 0s 572us/step - loss: 0.0140 - root_mean_squared_error: 0.1185 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1168\n",
       "Epoch 180/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0152 - root_mean_squared_error: 0.1234 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1273\n",
+      "35/35 [==============================] - 0s 623us/step - loss: 0.0143 - root_mean_squared_error: 0.1195 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1192\n",
       "Epoch 181/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0162 - root_mean_squared_error: 0.1272 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1278\n",
+      "35/35 [==============================] - 0s 517us/step - loss: 0.0146 - root_mean_squared_error: 0.1210 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1171\n",
       "Epoch 182/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0165 - root_mean_squared_error: 0.1285 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1275\n",
+      "35/35 [==============================] - 0s 471us/step - loss: 0.0137 - root_mean_squared_error: 0.1169 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1159\n",
       "Epoch 183/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0161 - root_mean_squared_error: 0.1268 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1269\n",
+      "35/35 [==============================] - 0s 486us/step - loss: 0.0137 - root_mean_squared_error: 0.1172 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1165\n",
       "Epoch 184/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0156 - root_mean_squared_error: 0.1248 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1276\n",
+      "35/35 [==============================] - 0s 676us/step - loss: 0.0145 - root_mean_squared_error: 0.1204 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1153\n",
       "Epoch 185/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0153 - root_mean_squared_error: 0.1238 - val_loss: 0.0160 - val_root_mean_squared_error: 0.1265\n",
+      "35/35 [==============================] - 0s 673us/step - loss: 0.0140 - root_mean_squared_error: 0.1185 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1153\n",
       "Epoch 186/300\n",
-      "35/35 [==============================] - ETA: 0s - loss: 0.0151 - root_mean_squared_error: 0.1227INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0151 - root_mean_squared_error: 0.1227 - val_loss: 0.0160 - val_root_mean_squared_error: 0.1264\n",
+      "35/35 [==============================] - 0s 427us/step - loss: 0.0138 - root_mean_squared_error: 0.1176 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1162\n",
       "Epoch 187/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0152 - root_mean_squared_error: 0.1233 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1274\n",
+      "35/35 [==============================] - 0s 591us/step - loss: 0.0138 - root_mean_squared_error: 0.1175 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1170\n",
       "Epoch 188/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0155 - root_mean_squared_error: 0.1247 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1270\n",
+      "35/35 [==============================] - 0s 499us/step - loss: 0.0143 - root_mean_squared_error: 0.1195 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1159\n",
       "Epoch 189/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0152 - root_mean_squared_error: 0.1234 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1268\n",
+      "35/35 [==============================] - 0s 445us/step - loss: 0.0140 - root_mean_squared_error: 0.1185 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1170\n",
       "Epoch 190/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0155 - root_mean_squared_error: 0.1245 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1276\n",
+      "35/35 [==============================] - 0s 641us/step - loss: 0.0141 - root_mean_squared_error: 0.1186 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1153\n",
       "Epoch 191/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0154 - root_mean_squared_error: 0.1240 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1271\n",
+      "35/35 [==============================] - 0s 630us/step - loss: 0.0133 - root_mean_squared_error: 0.1151 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1146\n",
       "Epoch 192/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0151 - root_mean_squared_error: 0.1230 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1273\n",
+      "35/35 [==============================] - 0s 437us/step - loss: 0.0136 - root_mean_squared_error: 0.1167 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1163\n",
       "Epoch 193/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0149 - root_mean_squared_error: 0.1222 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1281\n",
+      "35/35 [==============================] - 0s 474us/step - loss: 0.0132 - root_mean_squared_error: 0.1147 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1146\n",
       "Epoch 194/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0151 - root_mean_squared_error: 0.1229 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1275\n",
+      "35/35 [==============================] - 0s 631us/step - loss: 0.0131 - root_mean_squared_error: 0.1145 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1160\n",
       "Epoch 195/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0149 - root_mean_squared_error: 0.1223 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1272\n",
+      "35/35 [==============================] - 0s 496us/step - loss: 0.0137 - root_mean_squared_error: 0.1170 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1151\n",
       "Epoch 196/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0148 - root_mean_squared_error: 0.1217 - val_loss: 0.0160 - val_root_mean_squared_error: 0.1266\n",
+      "35/35 [==============================] - 0s 431us/step - loss: 0.0135 - root_mean_squared_error: 0.1163 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1187\n",
       "Epoch 197/300\n",
-      "35/35 [==============================] - ETA: 0s - loss: 0.0148 - root_mean_squared_error: 0.1215INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 23ms/step - loss: 0.0148 - root_mean_squared_error: 0.1215 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1261\n",
+      "35/35 [==============================] - 0s 676us/step - loss: 0.0136 - root_mean_squared_error: 0.1166 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1143\n",
       "Epoch 198/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0146 - root_mean_squared_error: 0.1207 - val_loss: 0.0160 - val_root_mean_squared_error: 0.1265\n",
+      "35/35 [==============================] - 0s 480us/step - loss: 0.0134 - root_mean_squared_error: 0.1156 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1147\n",
       "Epoch 199/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0150 - root_mean_squared_error: 0.1224 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1270\n",
+      "35/35 [==============================] - 0s 470us/step - loss: 0.0138 - root_mean_squared_error: 0.1174 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1166\n",
       "Epoch 200/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0145 - root_mean_squared_error: 0.1204 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1271\n",
+      "35/35 [==============================] - 0s 537us/step - loss: 0.0134 - root_mean_squared_error: 0.1157 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1144\n",
       "Epoch 201/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0147 - root_mean_squared_error: 0.1213 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1271\n",
+      "35/35 [==============================] - 0s 513us/step - loss: 0.0137 - root_mean_squared_error: 0.1169 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1171\n",
       "Epoch 202/300\n",
-      "32/35 [==========================>...] - ETA: 0s - loss: 0.0151 - root_mean_squared_error: 0.1228INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0150 - root_mean_squared_error: 0.1224 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1253\n",
+      "35/35 [==============================] - 0s 474us/step - loss: 0.0134 - root_mean_squared_error: 0.1156 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1151\n",
       "Epoch 203/300\n",
-      " 1/35 [..............................] - ETA: 0s - loss: 0.0167 - root_mean_squared_error: 0.1291INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0145 - root_mean_squared_error: 0.1203 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1249\n",
+      "35/35 [==============================] - 0s 475us/step - loss: 0.0132 - root_mean_squared_error: 0.1148 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1165\n",
       "Epoch 204/300\n",
-      " 1/35 [..............................] - ETA: 0s - loss: 0.0167 - root_mean_squared_error: 0.1294INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 27ms/step - loss: 0.0148 - root_mean_squared_error: 0.1217 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1249\n",
+      "35/35 [==============================] - 0s 490us/step - loss: 0.0137 - root_mean_squared_error: 0.1168 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1149\n",
       "Epoch 205/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0146 - root_mean_squared_error: 0.1210 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1270\n",
+      "35/35 [==============================] - 0s 445us/step - loss: 0.0132 - root_mean_squared_error: 0.1151 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1144\n",
       "Epoch 206/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0144 - root_mean_squared_error: 0.1199 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1259\n",
+      "35/35 [==============================] - 0s 574us/step - loss: 0.0137 - root_mean_squared_error: 0.1170 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1184\n",
       "Epoch 207/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0152 - root_mean_squared_error: 0.1233 - val_loss: 0.0165 - val_root_mean_squared_error: 0.1286\n",
+      "35/35 [==============================] - 0s 607us/step - loss: 0.0130 - root_mean_squared_error: 0.1142 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1130\n",
       "Epoch 208/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0143 - root_mean_squared_error: 0.1195 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1259\n",
+      "35/35 [==============================] - 0s 498us/step - loss: 0.0133 - root_mean_squared_error: 0.1155 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1139\n",
       "Epoch 209/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0145 - root_mean_squared_error: 0.1203 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1260\n",
+      "35/35 [==============================] - 0s 450us/step - loss: 0.0123 - root_mean_squared_error: 0.1109 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1139\n",
       "Epoch 210/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0148 - root_mean_squared_error: 0.1215 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1257\n",
+      "35/35 [==============================] - 0s 422us/step - loss: 0.0132 - root_mean_squared_error: 0.1148 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1138\n",
       "Epoch 211/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0149 - root_mean_squared_error: 0.1221 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1270\n",
+      "35/35 [==============================] - 0s 497us/step - loss: 0.0136 - root_mean_squared_error: 0.1166 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1163\n",
       "Epoch 212/300\n",
-      " 1/35 [..............................] - ETA: 0s - loss: 0.0123 - root_mean_squared_error: 0.1108INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0147 - root_mean_squared_error: 0.1212 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1247\n",
+      "35/35 [==============================] - 0s 535us/step - loss: 0.0128 - root_mean_squared_error: 0.1130 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1162\n",
       "Epoch 213/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0145 - root_mean_squared_error: 0.1202 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1256\n",
+      "35/35 [==============================] - 0s 502us/step - loss: 0.0128 - root_mean_squared_error: 0.1131 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1149\n",
       "Epoch 214/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0143 - root_mean_squared_error: 0.1197 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1249\n",
+      "35/35 [==============================] - 0s 480us/step - loss: 0.0128 - root_mean_squared_error: 0.1131 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1169\n",
       "Epoch 215/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0142 - root_mean_squared_error: 0.1193 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1254\n",
+      "35/35 [==============================] - 0s 461us/step - loss: 0.0127 - root_mean_squared_error: 0.1128 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1137\n",
       "Epoch 216/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0144 - root_mean_squared_error: 0.1200 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1252\n",
+      "35/35 [==============================] - 0s 472us/step - loss: 0.0128 - root_mean_squared_error: 0.1131 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1154\n",
       "Epoch 217/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0148 - root_mean_squared_error: 0.1216 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1254\n",
+      "35/35 [==============================] - 0s 650us/step - loss: 0.0129 - root_mean_squared_error: 0.1135 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1127\n",
       "Epoch 218/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0138 - root_mean_squared_error: 0.1176 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1251\n",
+      "35/35 [==============================] - 0s 570us/step - loss: 0.0127 - root_mean_squared_error: 0.1127 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1166\n",
       "Epoch 219/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0144 - root_mean_squared_error: 0.1199 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1259\n",
+      "35/35 [==============================] - 0s 453us/step - loss: 0.0128 - root_mean_squared_error: 0.1130 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1130\n",
       "Epoch 220/300\n",
-      " 1/35 [..............................] - ETA: 0s - loss: 0.0140 - root_mean_squared_error: 0.1182INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0144 - root_mean_squared_error: 0.1199 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1235\n",
+      "35/35 [==============================] - 0s 474us/step - loss: 0.0131 - root_mean_squared_error: 0.1145 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1143\n",
       "Epoch 221/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0139 - root_mean_squared_error: 0.1179 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1257\n",
+      "35/35 [==============================] - 0s 477us/step - loss: 0.0124 - root_mean_squared_error: 0.1111 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1133\n",
       "Epoch 222/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0136 - root_mean_squared_error: 0.1167 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1248\n",
+      "35/35 [==============================] - 0s 462us/step - loss: 0.0128 - root_mean_squared_error: 0.1131 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1164\n",
       "Epoch 223/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0141 - root_mean_squared_error: 0.1188 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1254\n",
+      "35/35 [==============================] - 0s 512us/step - loss: 0.0131 - root_mean_squared_error: 0.1144 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1127\n",
       "Epoch 224/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0138 - root_mean_squared_error: 0.1176 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1255\n",
+      "35/35 [==============================] - 0s 738us/step - loss: 0.0128 - root_mean_squared_error: 0.1131 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1124\n",
       "Epoch 225/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0136 - root_mean_squared_error: 0.1164 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1252\n",
+      "35/35 [==============================] - 0s 512us/step - loss: 0.0127 - root_mean_squared_error: 0.1126 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1155\n",
       "Epoch 226/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0141 - root_mean_squared_error: 0.1187 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1240\n",
+      "35/35 [==============================] - 0s 680us/step - loss: 0.0130 - root_mean_squared_error: 0.1140 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1119\n",
       "Epoch 227/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0138 - root_mean_squared_error: 0.1175 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1241\n",
+      "35/35 [==============================] - 0s 453us/step - loss: 0.0125 - root_mean_squared_error: 0.1118 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1129\n",
       "Epoch 228/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0136 - root_mean_squared_error: 0.1166 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1245\n",
+      "35/35 [==============================] - 0s 469us/step - loss: 0.0128 - root_mean_squared_error: 0.1130 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1141\n",
       "Epoch 229/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0138 - root_mean_squared_error: 0.1175 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1241\n",
+      "35/35 [==============================] - 0s 743us/step - loss: 0.0125 - root_mean_squared_error: 0.1117 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1116\n",
       "Epoch 230/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0138 - root_mean_squared_error: 0.1175 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1245\n",
+      "35/35 [==============================] - 0s 467us/step - loss: 0.0124 - root_mean_squared_error: 0.1114 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1136\n",
       "Epoch 231/300\n",
-      " 1/35 [..............................] - ETA: 0s - loss: 0.0115 - root_mean_squared_error: 0.1072INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0141 - root_mean_squared_error: 0.1187 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1234\n",
+      "35/35 [==============================] - 0s 483us/step - loss: 0.0126 - root_mean_squared_error: 0.1125 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1125\n",
       "Epoch 232/300\n",
-      "35/35 [==============================] - ETA: 0s - loss: 0.0132 - root_mean_squared_error: 0.1148INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0132 - root_mean_squared_error: 0.1148 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1231\n",
+      "35/35 [==============================] - 0s 472us/step - loss: 0.0121 - root_mean_squared_error: 0.1098 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1144\n",
       "Epoch 233/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0140 - root_mean_squared_error: 0.1184 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1234\n",
+      "35/35 [==============================] - 0s 494us/step - loss: 0.0121 - root_mean_squared_error: 0.1099 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1129\n",
       "Epoch 234/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0137 - root_mean_squared_error: 0.1170 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1258\n",
+      "35/35 [==============================] - 0s 461us/step - loss: 0.0122 - root_mean_squared_error: 0.1105 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1129\n",
       "Epoch 235/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0138 - root_mean_squared_error: 0.1176 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1240\n",
+      "35/35 [==============================] - 0s 430us/step - loss: 0.0122 - root_mean_squared_error: 0.1105 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1125\n",
       "Epoch 236/300\n",
-      " 1/35 [..............................] - ETA: 0s - loss: 0.0148 - root_mean_squared_error: 0.1217INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 21ms/step - loss: 0.0138 - root_mean_squared_error: 0.1176 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1221\n",
+      "35/35 [==============================] - 0s 551us/step - loss: 0.0125 - root_mean_squared_error: 0.1116 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1122\n",
       "Epoch 237/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0145 - root_mean_squared_error: 0.1203 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1226\n",
+      "35/35 [==============================] - 0s 458us/step - loss: 0.0121 - root_mean_squared_error: 0.1101 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1138\n",
       "Epoch 238/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0140 - root_mean_squared_error: 0.1182 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1241\n",
+      "35/35 [==============================] - 0s 628us/step - loss: 0.0125 - root_mean_squared_error: 0.1116 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1113\n",
       "Epoch 239/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0138 - root_mean_squared_error: 0.1173 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1232\n",
+      "35/35 [==============================] - 0s 430us/step - loss: 0.0117 - root_mean_squared_error: 0.1083 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1135\n",
       "Epoch 240/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0135 - root_mean_squared_error: 0.1161 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1234\n",
+      "35/35 [==============================] - 0s 444us/step - loss: 0.0119 - root_mean_squared_error: 0.1093 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1114\n",
       "Epoch 241/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0135 - root_mean_squared_error: 0.1160 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1230\n",
+      "35/35 [==============================] - 0s 446us/step - loss: 0.0122 - root_mean_squared_error: 0.1104 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1138\n",
       "Epoch 242/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0137 - root_mean_squared_error: 0.1170 - val_loss: 0.0153 - val_root_mean_squared_error: 0.1238\n",
+      "35/35 [==============================] - 0s 422us/step - loss: 0.0120 - root_mean_squared_error: 0.1095 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1118\n",
       "Epoch 243/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0133 - root_mean_squared_error: 0.1154 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1230\n",
+      "35/35 [==============================] - 0s 624us/step - loss: 0.0118 - root_mean_squared_error: 0.1085 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1101\n",
       "Epoch 244/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0134 - root_mean_squared_error: 0.1160 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1228\n",
+      "35/35 [==============================] - 0s 486us/step - loss: 0.0123 - root_mean_squared_error: 0.1108 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1135\n",
       "Epoch 245/300\n",
-      "35/35 [==============================] - 0s 3ms/step - loss: 0.0138 - root_mean_squared_error: 0.1177 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1242\n",
+      "35/35 [==============================] - 0s 533us/step - loss: 0.0116 - root_mean_squared_error: 0.1077 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1119\n",
       "Epoch 246/300\n",
-      "35/35 [==============================] - 0s 3ms/step - loss: 0.0134 - root_mean_squared_error: 0.1160 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1231\n",
+      "35/35 [==============================] - 0s 497us/step - loss: 0.0121 - root_mean_squared_error: 0.1099 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1118\n",
       "Epoch 247/300\n",
-      "23/35 [==================>...........] - ETA: 0s - loss: 0.0133 - root_mean_squared_error: 0.1154INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 27ms/step - loss: 0.0137 - root_mean_squared_error: 0.1169 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1218\n",
+      "35/35 [==============================] - 0s 459us/step - loss: 0.0116 - root_mean_squared_error: 0.1076 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1112\n",
       "Epoch 248/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0140 - root_mean_squared_error: 0.1184 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1232\n",
+      "35/35 [==============================] - 0s 467us/step - loss: 0.0119 - root_mean_squared_error: 0.1092 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1110\n",
       "Epoch 249/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0135 - root_mean_squared_error: 0.1163 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1221\n",
+      "35/35 [==============================] - 0s 442us/step - loss: 0.0118 - root_mean_squared_error: 0.1087 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1117\n",
       "Epoch 250/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0132 - root_mean_squared_error: 0.1149 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1233\n",
+      "35/35 [==============================] - 0s 459us/step - loss: 0.0119 - root_mean_squared_error: 0.1089 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1108\n",
       "Epoch 251/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0138 - root_mean_squared_error: 0.1175 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1240\n",
+      "35/35 [==============================] - 0s 447us/step - loss: 0.0116 - root_mean_squared_error: 0.1075 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1123\n",
       "Epoch 252/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0138 - root_mean_squared_error: 0.1173 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1228\n",
+      "35/35 [==============================] - 0s 921us/step - loss: 0.0115 - root_mean_squared_error: 0.1072 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1125\n",
       "Epoch 253/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0133 - root_mean_squared_error: 0.1153 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1221\n",
+      "35/35 [==============================] - 0s 675us/step - loss: 0.0109 - root_mean_squared_error: 0.1046 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1103\n",
       "Epoch 254/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0132 - root_mean_squared_error: 0.1148 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1219\n",
+      "35/35 [==============================] - 0s 529us/step - loss: 0.0117 - root_mean_squared_error: 0.1080 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1119\n",
       "Epoch 255/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0132 - root_mean_squared_error: 0.1150 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1221\n",
+      "35/35 [==============================] - 0s 465us/step - loss: 0.0117 - root_mean_squared_error: 0.1083 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1118\n",
       "Epoch 256/300\n",
-      "32/35 [==========================>...] - ETA: 0s - loss: 0.0130 - root_mean_squared_error: 0.1142INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 26ms/step - loss: 0.0129 - root_mean_squared_error: 0.1136 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1215\n",
+      "35/35 [==============================] - 0s 436us/step - loss: 0.0115 - root_mean_squared_error: 0.1071 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1126\n",
       "Epoch 257/300\n",
-      "35/35 [==============================] - 0s 3ms/step - loss: 0.0135 - root_mean_squared_error: 0.1162 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1233\n",
+      "35/35 [==============================] - 0s 470us/step - loss: 0.0114 - root_mean_squared_error: 0.1070 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1117\n",
       "Epoch 258/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0131 - root_mean_squared_error: 0.1145 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1218\n",
+      "35/35 [==============================] - 0s 449us/step - loss: 0.0116 - root_mean_squared_error: 0.1076 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1108\n",
       "Epoch 259/300\n",
-      "35/35 [==============================] - 0s 3ms/step - loss: 0.0135 - root_mean_squared_error: 0.1161 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1221\n",
+      "35/35 [==============================] - 0s 606us/step - loss: 0.0116 - root_mean_squared_error: 0.1076 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1100\n",
       "Epoch 260/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0128 - root_mean_squared_error: 0.1133 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1232\n",
+      "35/35 [==============================] - 0s 481us/step - loss: 0.0118 - root_mean_squared_error: 0.1088 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1128\n",
       "Epoch 261/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0133 - root_mean_squared_error: 0.1152 - val_loss: 0.0153 - val_root_mean_squared_error: 0.1238\n",
+      "35/35 [==============================] - 0s 471us/step - loss: 0.0114 - root_mean_squared_error: 0.1069 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1111\n",
       "Epoch 262/300\n",
-      " 1/35 [..............................] - ETA: 0s - loss: 0.0104 - root_mean_squared_error: 0.1020INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 35ms/step - loss: 0.0128 - root_mean_squared_error: 0.1130 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1214\n",
+      "35/35 [==============================] - 0s 479us/step - loss: 0.0116 - root_mean_squared_error: 0.1077 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1134\n",
       "Epoch 263/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0128 - root_mean_squared_error: 0.1130 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1221\n",
+      "35/35 [==============================] - 0s 585us/step - loss: 0.0118 - root_mean_squared_error: 0.1086 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1107\n",
       "Epoch 264/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0129 - root_mean_squared_error: 0.1138 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1239\n",
+      "35/35 [==============================] - 0s 491us/step - loss: 0.0114 - root_mean_squared_error: 0.1066 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1111\n",
       "Epoch 265/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0123 - root_mean_squared_error: 0.1109 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1220\n",
+      "35/35 [==============================] - 0s 647us/step - loss: 0.0115 - root_mean_squared_error: 0.1073 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1100\n",
       "Epoch 266/300\n",
-      " 1/35 [..............................] - ETA: 0s - loss: 0.0122 - root_mean_squared_error: 0.1103INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 31ms/step - loss: 0.0130 - root_mean_squared_error: 0.1139 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1202\n",
+      "35/35 [==============================] - 0s 459us/step - loss: 0.0113 - root_mean_squared_error: 0.1064 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1122\n",
       "Epoch 267/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0123 - root_mean_squared_error: 0.1109 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1230\n",
+      "35/35 [==============================] - 0s 604us/step - loss: 0.0111 - root_mean_squared_error: 0.1051 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1094\n",
       "Epoch 268/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0126 - root_mean_squared_error: 0.1124 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1225\n",
+      "35/35 [==============================] - 0s 441us/step - loss: 0.0116 - root_mean_squared_error: 0.1076 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1111\n",
       "Epoch 269/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0129 - root_mean_squared_error: 0.1136 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1221\n",
+      "35/35 [==============================] - 0s 533us/step - loss: 0.0116 - root_mean_squared_error: 0.1076 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1107\n",
       "Epoch 270/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0129 - root_mean_squared_error: 0.1136 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1228\n",
+      "35/35 [==============================] - 0s 616us/step - loss: 0.0112 - root_mean_squared_error: 0.1056 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1112\n",
       "Epoch 271/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0124 - root_mean_squared_error: 0.1112 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1213\n",
+      "35/35 [==============================] - 0s 553us/step - loss: 0.0114 - root_mean_squared_error: 0.1070 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1094\n",
       "Epoch 272/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0127 - root_mean_squared_error: 0.1125 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1213\n",
+      "35/35 [==============================] - 0s 565us/step - loss: 0.0118 - root_mean_squared_error: 0.1085 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1110\n",
       "Epoch 273/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0135 - root_mean_squared_error: 0.1161 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1223\n",
+      "35/35 [==============================] - 0s 483us/step - loss: 0.0107 - root_mean_squared_error: 0.1035 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1125\n",
       "Epoch 274/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0126 - root_mean_squared_error: 0.1122 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1216\n",
+      "35/35 [==============================] - 0s 484us/step - loss: 0.0111 - root_mean_squared_error: 0.1053 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1108\n",
       "Epoch 275/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0129 - root_mean_squared_error: 0.1136 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1241\n",
+      "35/35 [==============================] - 0s 443us/step - loss: 0.0110 - root_mean_squared_error: 0.1050 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1107\n",
       "Epoch 276/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0128 - root_mean_squared_error: 0.1131 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1219\n",
+      "35/35 [==============================] - 0s 474us/step - loss: 0.0115 - root_mean_squared_error: 0.1073 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1110\n",
       "Epoch 277/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0129 - root_mean_squared_error: 0.1136 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1223\n",
+      "35/35 [==============================] - 0s 459us/step - loss: 0.0109 - root_mean_squared_error: 0.1045 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1110\n",
       "Epoch 278/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0127 - root_mean_squared_error: 0.1129 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1210\n",
+      "35/35 [==============================] - 0s 476us/step - loss: 0.0114 - root_mean_squared_error: 0.1068 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1107\n",
       "Epoch 279/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0125 - root_mean_squared_error: 0.1119 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1222\n",
+      "35/35 [==============================] - 0s 595us/step - loss: 0.0108 - root_mean_squared_error: 0.1039 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1113\n",
       "Epoch 280/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0128 - root_mean_squared_error: 0.1132 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1223\n",
+      "35/35 [==============================] - 0s 483us/step - loss: 0.0105 - root_mean_squared_error: 0.1026 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1095\n",
       "Epoch 281/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0127 - root_mean_squared_error: 0.1128 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1214\n",
+      "35/35 [==============================] - 0s 420us/step - loss: 0.0113 - root_mean_squared_error: 0.1061 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1107\n",
       "Epoch 282/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0129 - root_mean_squared_error: 0.1137 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1205\n",
+      "35/35 [==============================] - 0s 482us/step - loss: 0.0108 - root_mean_squared_error: 0.1037 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1108\n",
       "Epoch 283/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0124 - root_mean_squared_error: 0.1114 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1207\n",
+      "35/35 [==============================] - 0s 458us/step - loss: 0.0108 - root_mean_squared_error: 0.1041 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1116\n",
       "Epoch 284/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0126 - root_mean_squared_error: 0.1124 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1223\n",
+      "35/35 [==============================] - 0s 455us/step - loss: 0.0115 - root_mean_squared_error: 0.1074 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1095\n",
       "Epoch 285/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0125 - root_mean_squared_error: 0.1118 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1213\n",
+      "35/35 [==============================] - 0s 474us/step - loss: 0.0107 - root_mean_squared_error: 0.1035 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1112\n",
       "Epoch 286/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0122 - root_mean_squared_error: 0.1106 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1207\n",
+      "35/35 [==============================] - 0s 535us/step - loss: 0.0114 - root_mean_squared_error: 0.1067 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1095\n",
       "Epoch 287/300\n",
-      " 1/35 [..............................] - ETA: 0s - loss: 0.0092 - root_mean_squared_error: 0.0961INFO:tensorflow:Assets written to: /Users/michaelmaser 1/Documents/git/forks/ord/ord-schema/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/assets\n",
-      "35/35 [==============================] - 1s 20ms/step - loss: 0.0122 - root_mean_squared_error: 0.1106 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1196\n",
+      "35/35 [==============================] - 0s 483us/step - loss: 0.0112 - root_mean_squared_error: 0.1059 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1103\n",
       "Epoch 288/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0126 - root_mean_squared_error: 0.1122 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1216\n",
+      "35/35 [==============================] - 0s 435us/step - loss: 0.0106 - root_mean_squared_error: 0.1030 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1106\n",
       "Epoch 289/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0126 - root_mean_squared_error: 0.1123 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1224\n",
+      "35/35 [==============================] - 0s 453us/step - loss: 0.0112 - root_mean_squared_error: 0.1061 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1113\n",
       "Epoch 290/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0123 - root_mean_squared_error: 0.1111 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1221\n",
+      "35/35 [==============================] - 0s 442us/step - loss: 0.0111 - root_mean_squared_error: 0.1052 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1112\n",
       "Epoch 291/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0122 - root_mean_squared_error: 0.1103 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1222\n",
+      "35/35 [==============================] - 0s 443us/step - loss: 0.0107 - root_mean_squared_error: 0.1034 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1094\n",
       "Epoch 292/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0122 - root_mean_squared_error: 0.1105 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1223\n",
+      "35/35 [==============================] - 0s 445us/step - loss: 0.0108 - root_mean_squared_error: 0.1038 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1113\n",
       "Epoch 293/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0121 - root_mean_squared_error: 0.1100 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1207\n",
+      "35/35 [==============================] - 0s 436us/step - loss: 0.0103 - root_mean_squared_error: 0.1017 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1110\n",
       "Epoch 294/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0122 - root_mean_squared_error: 0.1104 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1210\n",
+      "35/35 [==============================] - 0s 441us/step - loss: 0.0105 - root_mean_squared_error: 0.1026 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1101\n",
       "Epoch 295/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0118 - root_mean_squared_error: 0.1088 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1208\n",
+      "35/35 [==============================] - 0s 430us/step - loss: 0.0105 - root_mean_squared_error: 0.1026 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1115\n",
       "Epoch 296/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0128 - root_mean_squared_error: 0.1129 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1207\n",
+      "35/35 [==============================] - 0s 435us/step - loss: 0.0101 - root_mean_squared_error: 0.1007 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1111\n",
       "Epoch 297/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0124 - root_mean_squared_error: 0.1112 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1227\n",
+      "35/35 [==============================] - 0s 560us/step - loss: 0.0104 - root_mean_squared_error: 0.1018 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1123\n",
       "Epoch 298/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0120 - root_mean_squared_error: 0.1094 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1231\n",
+      "35/35 [==============================] - 0s 474us/step - loss: 0.0107 - root_mean_squared_error: 0.1035 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1104\n",
       "Epoch 299/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0121 - root_mean_squared_error: 0.1100 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1205\n",
+      "35/35 [==============================] - 0s 440us/step - loss: 0.0110 - root_mean_squared_error: 0.1051 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1094\n",
       "Epoch 300/300\n",
-      "35/35 [==============================] - 0s 2ms/step - loss: 0.0121 - root_mean_squared_error: 0.1102 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1204\n"
+      "35/35 [==============================] - 0s 438us/step - loss: 0.0110 - root_mean_squared_error: 0.1048 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1102\n"
      ]
     }
    ],
@@ -2426,7 +2338,7 @@
     "# Construct model\n",
     "model = tf.keras.Sequential(\n",
     "    [\n",
-    "        tf.keras.layers.InputLayer(input_shape=X_train.shape[1]),\n",
+    "        tf.keras.Input((X_train.shape[1],)),\n",
     "        tf.keras.layers.Dense(50, activation=\"sigmoid\"),\n",
     "        tf.keras.layers.Dense(7, activation=\"sigmoid\"),\n",
     "        tf.keras.layers.Dropout(0.2),\n",
@@ -2443,7 +2355,7 @@
     "\n",
     "# Setup callback to load weights from best epoch later on\n",
     "# Saving model weights from epoch with lowest validation loss\n",
-    "checkpoint_filepath = os.path.join(os.getcwd(), \"saved_model\")\n",
+    "checkpoint_filepath = os.path.join(os.getcwd(), \"saved_model.keras\")\n",
     "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
     "    filepath=checkpoint_filepath, monitor=\"val_loss\", mode=\"min\", save_best_only=True\n",
     ")\n",
@@ -2460,7 +2372,7 @@
     {
      "data": {
       "text/plain": [
-       "286"
+       "266"
       ]
      },
      "execution_count": 15,
@@ -2483,27 +2395,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "18/18 [==============================] - 0s 2ms/step - loss: 0.0143 - root_mean_squared_error: 0.1197\n",
-      "Test RMSE: {'loss': 0.014322989620268345, 'root_mean_squared_error': 0.11967869102954865}\n",
-      "Test R2: 0.8212893817541477\n"
+      "18/18 [==============================] - 0s 306us/step\n",
+      "18/18 [==============================] - 0s 312us/step - loss: 0.0120 - root_mean_squared_error: 0.1096\n",
+      "Test RMSE: {'loss': 0.012003862299025059, 'root_mean_squared_error': 0.10956213623285294}\n",
+      "Test R2: 0.8502255811673648\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 1296x432 with 2 Axes>"
+       "<Figure size 1800x600 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "# Load model weights from best epoch\n",
-    "model.load_weights(os.path.join(checkpoint_filepath, \"variables/variables\"))\n",
+    "model.load_weights(checkpoint_filepath)\n",
     "\n",
     "# Generate predictions for test set and measure test RMSE and R2\n",
     "preds = model.predict(test_dataset)\n",
@@ -2545,14 +2456,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 864x432 with 2 Axes>"
+       "<Figure size 1200x600 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -2580,7 +2489,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2594,7 +2503,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/saved_model.pb b/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/saved_model.pb
deleted file mode 100644
index e7b1d2074..000000000
Binary files a/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/saved_model.pb and /dev/null differ
diff --git a/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/variables/variables.data-00000-of-00001 b/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/variables/variables.data-00000-of-00001
deleted file mode 100644
index 4ab6680b0..000000000
Binary files a/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/variables/variables.data-00000-of-00001 and /dev/null differ
diff --git a/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/variables/variables.index b/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/variables/variables.index
deleted file mode 100644
index 8177f46fb..000000000
Binary files a/examples/applications/Perera_Science_Granda_Nature_Suzuki/saved_model/variables/variables.index and /dev/null differ
diff --git a/ord_schema/orm/database.py b/ord_schema/orm/database.py
index 7b382da55..f8200f34c 100644
--- a/ord_schema/orm/database.py
+++ b/ord_schema/orm/database.py
@@ -20,7 +20,7 @@
 from sqlalchemy import cast, delete, func, select, text, update
 from sqlalchemy.dialects.postgresql import insert
 from sqlalchemy.engine import Engine
-from sqlalchemy.exc import OperationalError
+from sqlalchemy.exc import OperationalError, NotSupportedError
 from sqlalchemy.orm import Session
 
 from ord_schema.logging import get_logger
@@ -55,14 +55,16 @@ def prepare_database(engine: Engine) -> bool:
     with engine.begin() as connection:
         connection.execute(text("CREATE SCHEMA IF NOT EXISTS ord"))
         connection.execute(text("CREATE SCHEMA IF NOT EXISTS rdkit"))
-        try:
+    try:
+        with engine.begin() as connection:
             # NOTE(skearnes): The RDKit PostgreSQL extension works best in the public schema.
             connection.execute(text("CREATE EXTENSION IF NOT EXISTS rdkit"))
-            rdkit_cartridge = True
-        except OperationalError:
+        rdkit_cartridge = True
+    except (OperationalError, NotSupportedError):
+        with engine.begin() as connection:
             logger.warning("RDKit PostgreSQL cartridge is not installed; structure search will be disabled")
             connection.execute(text("CREATE EXTENSION IF NOT EXISTS btree_gist"))
-            rdkit_cartridge = False
+        rdkit_cartridge = False
     with patch.dict(os.environ, {"ORD_POSTGRES_RDKIT": "1" if rdkit_cartridge else "0"}):
         Base.metadata.create_all(engine)
     return rdkit_cartridge


Test patch:
diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml
index a0f1e1218..b74724f6c 100644
--- a/.github/workflows/run_tests.yml
+++ b/.github/workflows/run_tests.yml
@@ -33,7 +33,8 @@ jobs:
         shell: bash -l {0}
         run: |
           # NOTE(skearnes): conda is only used for postgres (not python).
-          conda install -c rdkit rdkit-postgresql==2020.03.3.0
+          # NOTE(skearnes): rdkit-postgresql may not be available for ARM.
+          conda install -c rdkit rdkit-postgresql==2020.03.3.0 || conda install -c conda-forge postgresql
           initdb
       - uses: actions/setup-python@v4
         with:
diff --git a/ord_schema/orm/rdkit_mappers_test.py b/ord_schema/orm/rdkit_mappers_test.py
index 6c00f2554..a4890e97e 100644
--- a/ord_schema/orm/rdkit_mappers_test.py
+++ b/ord_schema/orm/rdkit_mappers_test.py
@@ -15,30 +15,37 @@
 """Tests for ord_schema.orm.rdkit_mappers."""
 import pytest
 from sqlalchemy import select
+from sqlalchemy.exc import ProgrammingError
 from ord_schema.orm.mappers import Mappers
 from ord_schema.orm.rdkit_mappers import FingerprintType, RDKitMol
 
 
 def test_tanimoto_operator(test_session):
-    query = (
-        select(Mappers.Reaction)
-        .join(Mappers.ReactionInput)
-        .join(Mappers.Compound)
-        .join(RDKitMol)
-        .where(RDKitMol.morgan_bfp % FingerprintType.MORGAN_BFP("c1ccccc1CCC(O)C"))
-    )
-    results = test_session.execute(query)
-    assert len(results.fetchall()) == 20
+    try:
+        query = (
+            select(Mappers.Reaction)
+            .join(Mappers.ReactionInput)
+            .join(Mappers.Compound)
+            .join(RDKitMol)
+            .where(RDKitMol.morgan_bfp % FingerprintType.MORGAN_BFP("c1ccccc1CCC(O)C"))
+        )
+        results = test_session.execute(query)
+        assert len(results.fetchall()) == 20
+    except ProgrammingError as error:
+        pytest.skip(f"RDKit cartridge is required: {error}")
 
 
 @pytest.mark.parametrize("fp_type", list(FingerprintType))
 def test_tanimoto(test_session, fp_type):
-    query = (
-        select(Mappers.Reaction)
-        .join(Mappers.ReactionInput)
-        .join(Mappers.Compound)
-        .join(RDKitMol)
-        .where(RDKitMol.tanimoto("c1ccccc1CCC(O)C", fp_type=fp_type) > 0.5)
-    )
-    results = test_session.execute(query)
-    assert len(results.fetchall()) == 20
+    try:
+        query = (
+            select(Mappers.Reaction)
+            .join(Mappers.ReactionInput)
+            .join(Mappers.Compound)
+            .join(RDKitMol)
+            .where(RDKitMol.tanimoto("c1ccccc1CCC(O)C", fp_type=fp_type) > 0.5)
+        )
+        results = test_session.execute(query)
+        assert len(results.fetchall()) == 20
+    except ProgrammingError as error:
+        pytest.skip(f"RDKit cartridge is required: {error}")
diff --git a/ord_schema/orm/scripts/add_datasets_test.py b/ord_schema/orm/scripts/add_datasets_test.py
index 5944718a5..30ebb374a 100644
--- a/ord_schema/orm/scripts/add_datasets_test.py
+++ b/ord_schema/orm/scripts/add_datasets_test.py
@@ -16,6 +16,7 @@
 import os
 
 import docopt
+import pytest
 from sqlalchemy import create_engine
 from testing.postgresql import Postgresql
 
@@ -26,7 +27,8 @@
 def test_main():
     with Postgresql() as postgres:
         engine = create_engine(postgres.url(), future=True)
-        assert prepare_database(engine)  # Requires RDKit.
+        if not prepare_database(engine):
+            pytest.skip("RDKit cartridge is required")
         argv = [
             "--url",
             postgres.url(),