From a392bc4407c3411ca436a40e7e29a107cd4350ff Mon Sep 17 00:00:00 2001
From: Luigi <luigi.sbailo@gmail.com>
Date: Mon, 25 May 2020 10:10:31 +0200
Subject: [PATCH] Change title

---
 nn_regression.ipynb | 461 +++++++++++---------------------------------
 1 file changed, 116 insertions(+), 345 deletions(-)

diff --git a/nn_regression.ipynb b/nn_regression.ipynb
index 38d72bb..63a0c4e 100644
--- a/nn_regression.ipynb
+++ b/nn_regression.ipynb
@@ -14,7 +14,7 @@
     "\n",
     "   \n",
     "   <div style=\"text-align:center\">\n",
-    "    <b><font size=\"6.4\">Regression using multilayer perceptron</font></b>    \n",
+    "    <b><font size=\"6.4\">Regression using multilayer perceptrons</font></b>    \n",
     "  </div>\n",
     "    \n",
     "<p>\n",
@@ -62,8 +62,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-22T14:48:01.350451Z",
+     "start_time": "2020-05-22T14:47:59.404436Z"
+    },
     "scrolled": true
    },
    "outputs": [],
@@ -312,92 +316,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-22T14:08:25.380307Z",
+     "start_time": "2020-05-22T14:08:25.007228Z"
+    },
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>vol_per_atom</th>\n",
-       "      <th>composition</th>\n",
-       "      <th>number_of_elements</th>\n",
-       "      <th>stoichiometry_dicts</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>17.8351</td>\n",
-       "      <td>Li1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>{'Li': 1}</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>22.9639</td>\n",
-       "      <td>Mg1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>{'Mg': 1}</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>41.4146</td>\n",
-       "      <td>Kr1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>{'Kr': 1}</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>32.9826</td>\n",
-       "      <td>Na1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>{'Na': 1}</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>15.2088</td>\n",
-       "      <td>Pd1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>{'Pd': 1}</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   vol_per_atom composition  number_of_elements stoichiometry_dicts\n",
-       "0       17.8351         Li1                   1           {'Li': 1}\n",
-       "1       22.9639         Mg1                   1           {'Mg': 1}\n",
-       "2       41.4146         Kr1                   1           {'Kr': 1}\n",
-       "3       32.9826         Na1                   1           {'Na': 1}\n",
-       "4       15.2088         Pd1                   1           {'Pd': 1}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df = pd.read_pickle('./data/nn_regression/OQMD_Ward_et_al_2016_df.pkl')\n",
     "\n",
@@ -424,40 +351,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Statistics of the target property:\n",
-      "\n",
-      "count    347227.000000\n",
-      "mean         22.157036\n",
-      "std           8.613623\n",
-      "min           2.723960\n",
-      "25%          16.116350\n",
-      "50%          20.881500\n",
-      "75%          26.504150\n",
-      "max          99.884500\n",
-      "Name: vol_per_atom, dtype: float64\n"
-     ]
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-22T14:08:25.710996Z",
+     "start_time": "2020-05-22T14:08:25.382690Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "df.hist()\n",
     "plt.show()\n",
@@ -474,46 +375,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total number of datapoints: 347227\n",
-      "\n",
-      "Compounds with 1 element(s) appear 2472 times in the dataset\n",
-      "Compounds with 2 element(s) appear 99411 times in the dataset\n",
-      "Compounds with 3 element(s) appear 235658 times in the dataset\n",
-      "Compounds with 4 element(s) appear 8248 times in the dataset\n",
-      "Compounds with 5 element(s) appear 1320 times in the dataset\n",
-      "Compounds with 6 element(s) appear 112 times in the dataset\n",
-      "Compounds with 7 element(s) appear 6 times in the dataset\n",
-      "\n",
-      "The following elements (in total 89) appear in the dataset:\n",
-      "\n",
-      " ['H' 'He' 'Li' 'Be' 'B' 'C' 'N' 'O' 'F' 'Ne' 'Na' 'Mg' 'Al' 'Si' 'P' 'S'\n",
-      " 'Cl' 'Ar' 'K' 'Ca' 'Sc' 'Ti' 'V' 'Cr' 'Mn' 'Fe' 'Co' 'Ni' 'Cu' 'Zn' 'Ga'\n",
-      " 'Ge' 'As' 'Se' 'Br' 'Kr' 'Rb' 'Sr' 'Y' 'Zr' 'Nb' 'Mo' 'Tc' 'Ru' 'Rh' 'Pd'\n",
-      " 'Ag' 'Cd' 'In' 'Sn' 'Sb' 'Te' 'I' 'Xe' 'Cs' 'Ba' 'La' 'Ce' 'Pr' 'Nd' 'Pm'\n",
-      " 'Sm' 'Eu' 'Gd' 'Tb' 'Dy' 'Ho' 'Er' 'Tm' 'Yb' 'Lu' 'Hf' 'Ta' 'W' 'Re' 'Os'\n",
-      " 'Ir' 'Pt' 'Au' 'Hg' 'Tl' 'Pb' 'Bi' 'Ac' 'Th' 'Pa' 'U' 'Np' 'Pu']\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1800x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-22T14:08:26.547127Z",
+     "start_time": "2020-05-22T14:08:25.713822Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "print(\"Total number of datapoints: {}\\n\".format(len(y_vol_per_atom)))\n",
     "\n",
@@ -556,8 +425,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-22T14:08:26.640466Z",
+     "start_time": "2020-05-22T14:08:26.548884Z"
+    }
+   },
    "outputs": [],
    "source": [
     "X_ElemNet = np.load('./data/nn_regression/X_ElemNet.npy')"
@@ -574,17 +448,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input shape = (347227, 89), target shape = (347227,)\n"
-     ]
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-22T14:08:26.645227Z",
+     "start_time": "2020-05-22T14:08:26.642010Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "print(\"Input shape = {}, target shape = {}\".format(X_ElemNet.shape, y_vol_per_atom.shape))"
    ]
@@ -605,8 +476,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-22T14:08:26.793126Z",
+     "start_time": "2020-05-22T14:08:26.646886Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Very important for reproducibility!!\n",
@@ -628,20 +504,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\nscaler = StandardScaler()\\nX = scaler.fit_transform(X)\\nX_test = scaler.transform(X_test)\\n'"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-22T14:08:26.799383Z",
+     "start_time": "2020-05-22T14:08:26.795789Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "\"\"\"\n",
     "scaler = StandardScaler()\n",
@@ -659,8 +529,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-22T14:08:26.921060Z",
+     "start_time": "2020-05-22T14:08:26.801212Z"
+    }
+   },
    "outputs": [],
    "source": [
     "X_train, X_val, y_train, y_val = train_test_split(X, y,\n",
@@ -690,8 +565,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-22T14:08:26.928103Z",
+     "start_time": "2020-05-22T14:08:26.922685Z"
+    }
+   },
    "outputs": [],
    "source": [
     "n_feat = X_train.shape[1]\n",
@@ -735,61 +615,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"model_1\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "x_input (InputLayer)         (None, 89)                0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 512)               46080     \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 512)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 256)               131328    \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 256)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_3 (Dense)              (None, 128)               32896     \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 128)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_4 (Dense)              (None, 64)                8256      \n",
-      "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "dense_5 (Dense)              (None, 32)                2080      \n",
-      "_________________________________________________________________\n",
-      "dropout_5 (Dropout)          (None, 32)                0         \n",
-      "_________________________________________________________________\n",
-      "dense_6 (Dense)              (None, 18)                594       \n",
-      "_________________________________________________________________\n",
-      "dropout_6 (Dropout)          (None, 18)                0         \n",
-      "_________________________________________________________________\n",
-      "dense_7 (Dense)              (None, 8)                 152       \n",
-      "_________________________________________________________________\n",
-      "dropout_7 (Dropout)          (None, 8)                 0         \n",
-      "_________________________________________________________________\n",
-      "dense_8 (Dense)              (None, 4)                 36        \n",
-      "_________________________________________________________________\n",
-      "dropout_8 (Dropout)          (None, 4)                 0         \n",
-      "_________________________________________________________________\n",
-      "dense_9 (Dense)              (None, 1)                 5         \n",
-      "=================================================================\n",
-      "Total params: 221,427\n",
-      "Trainable params: 221,427\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n",
-      "None\n"
-     ]
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-22T14:08:27.092710Z",
+     "start_time": "2020-05-22T14:08:26.930206Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "batch_size = 64\n",
     "epochs = 30\n",
@@ -817,40 +650,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-22T14:15:47.803232Z",
+     "start_time": "2020-05-22T14:08:27.094355Z"
+    },
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 222224 samples, validate on 55557 samples\n",
-      "Epoch 1/30\n",
-      "222224/222224 [==============================] - 28s 126us/step - loss: 11.4482 - val_loss: 6.0274\n",
-      "Epoch 2/30\n",
-      "222224/222224 [==============================] - 28s 127us/step - loss: 6.3603 - val_loss: 5.8549\n",
-      "Epoch 3/30\n",
-      " 14208/222224 [>.............................] - ETA: 24s - loss: 6.4522"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-17-c63294c180f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m                     \u001b[0mvalidation_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                     \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"epochs\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m                     batch_size=params[\"batch_size\"], verbose=True)\n\u001b[0m",
-      "\u001b[0;32m~/anaconda3/envs/tf_1/lib/python3.7/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m   1237\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1238\u001b[0m                                         \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m                                         validation_freq=validation_freq)\n\u001b[0m\u001b[1;32m   1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1241\u001b[0m     def evaluate(self,\n",
-      "\u001b[0;32m~/anaconda3/envs/tf_1/lib/python3.7/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, fit_function, fit_inputs, out_labels, batch_size, epochs, verbose, callbacks, val_function, val_inputs, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq)\u001b[0m\n\u001b[1;32m    194\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    197\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/envs/tf_1/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   3474\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3475\u001b[0m     fetched = self._callable_fn(*array_vals,\n\u001b[0;32m-> 3476\u001b[0;31m                                 run_metadata=self.run_metadata)\n\u001b[0m\u001b[1;32m   3477\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_fetch_callbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3478\u001b[0m     output_structure = nest.pack_sequence_as(\n",
-      "\u001b[0;32m~/anaconda3/envs/tf_1/lib/python3.7/site-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1470\u001b[0m         ret = tf_session.TF_SessionRunCallable(self._session._session,\n\u001b[1;32m   1471\u001b[0m                                                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1472\u001b[0;31m                                                run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1473\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1474\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "history = model.fit(X_train, y_train,\n",
     "                    validation_data = (X_val, y_val),\n",
@@ -867,21 +675,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'history' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-18-ce2b5c471917>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# summarize history for loss: A plot of loss on the training and validation datasets over training epochs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'model loss'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'history' is not defined"
-     ]
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-22T14:15:47.945244Z",
+     "start_time": "2020-05-22T14:15:47.805420Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -919,22 +720,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training: MAE: 1.195 | MSE: 5.275 | RMSE: 2.297 | Pearson: 0.964\n",
-      "Dataset: Min: 2.726 | Max: 98.717 | Mean: 22.149 | Std.dev.: 8.588\n",
-      "\n",
-      "\n",
-      "Validation: MAE: 1.208 | MSE: 5.527 | RMSE: 2.351 | Pearson: 0.963\n",
-      "Dataset: Min: 2.724 | Max: 99.106 | Mean: 22.167 | Std.dev.: 8.672\n"
-     ]
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-22T14:15:53.924363Z",
+     "start_time": "2020-05-22T14:15:47.947273Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "def rmse(y_pred, y_true):\n",
     "    return np.sqrt(((y_pred - y_true) ** 2).mean())\n",
@@ -989,20 +782,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x432 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-22T14:15:56.919219Z",
+     "start_time": "2020-05-22T14:15:53.926422Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "y_true = y_true_val\n",
     "y_pred = y_pred_val\n",
@@ -1042,8 +829,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-22T14:15:56.923919Z",
+     "start_time": "2020-05-22T14:15:56.921595Z"
+    }
+   },
    "outputs": [],
    "source": [
     "investigate_test_set = True"
@@ -1051,29 +843,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-22T14:16:02.573238Z",
+     "start_time": "2020-05-22T14:15:56.925596Z"
+    },
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test: MAE: 1.223 | MSE: 5.557 | RMSE: 2.357 | Pearson: 0.963\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x432 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "if investigate_test_set:\n",
     "    y_pred_test = model.predict(X_test).flatten()\n",
@@ -1126,13 +904,6 @@
     "dimensions for vision architectures. In Proceedings of the 30th International Conference on International Conference on\n",
     "Machine Learning - Volume 28, ICML’13, I–115–I–123 (JMLR.org, 2013)."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -1151,7 +922,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
-- 
GitLab