diff --git a/nn_regression.ipynb b/nn_regression.ipynb
index d8f1d311c69dcc3af5965be9db4355959d3737e9..00879dcee6a1bb6b0a8b34e0ff0083ee35632406 100644
--- a/nn_regression.ipynb
+++ b/nn_regression.ipynb
@@ -75,8 +75,8 @@
"execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-05-22T14:48:01.350451Z",
- "start_time": "2020-05-22T14:47:59.404436Z"
+ "end_time": "2021-01-29T13:36:50.877631Z",
+ "start_time": "2021-01-29T13:36:50.869148Z"
},
"scrolled": true
},
@@ -84,6 +84,10 @@
"source": [
"# Plotting\n",
"%matplotlib inline\n",
+ "\n",
+ "import warnings\n",
+ "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
+ "\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
@@ -94,16 +98,16 @@
"# Keras for neural networks\n",
"from keras.models import load_model\n",
"from keras.layers import Input, Dense, Dropout\n",
- "from keras.models import Model\n",
- "\n",
- "# pymatgen\n",
- "from pymatgen.core.periodic_table import Element\n",
+ "from keras.models import Model, load_model\n",
"\n",
"# other packages\n",
"import os\n",
"import numpy as np\n",
"from collections import Counter\n",
"\n",
+ "# pymatgen\n",
+ "from pymatgen.core.periodic_table import Element\n",
+ "\n",
"# sklearn\n",
"from sklearn.metrics import r2_score, mean_absolute_error\n",
"from sklearn.model_selection import KFold\n",
@@ -111,7 +115,10 @@
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"# pandas\n",
- "import pandas as pd"
+ "import pandas as pd\n",
+ "\n",
+ "# json\n",
+ "import json"
]
},
{
@@ -356,8 +363,8 @@
"execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-05-22T14:08:25.380307Z",
- "start_time": "2020-05-22T14:08:25.007228Z"
+ "end_time": "2021-01-29T13:36:51.267156Z",
+ "start_time": "2021-01-29T13:36:50.879337Z"
},
"scrolled": true
},
@@ -391,8 +398,8 @@
"execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-05-22T14:08:25.710996Z",
- "start_time": "2020-05-22T14:08:25.382690Z"
+ "end_time": "2021-01-29T13:36:51.693354Z",
+ "start_time": "2021-01-29T13:36:51.269399Z"
}
},
"outputs": [],
@@ -415,8 +422,8 @@
"execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-05-22T14:08:26.547127Z",
- "start_time": "2020-05-22T14:08:25.713822Z"
+ "end_time": "2021-01-29T13:36:53.555987Z",
+ "start_time": "2021-01-29T13:36:51.695774Z"
}
},
"outputs": [],
@@ -465,8 +472,8 @@
"execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-05-22T14:08:26.640466Z",
- "start_time": "2020-05-22T14:08:26.548884Z"
+ "end_time": "2021-01-29T13:36:53.646679Z",
+ "start_time": "2021-01-29T13:36:53.558131Z"
}
},
"outputs": [],
@@ -488,8 +495,8 @@
"execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-05-22T14:08:26.645227Z",
- "start_time": "2020-05-22T14:08:26.642010Z"
+ "end_time": "2021-01-29T13:36:53.651474Z",
+ "start_time": "2021-01-29T13:36:53.648126Z"
}
},
"outputs": [],
@@ -516,8 +523,8 @@
"execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-05-22T14:08:26.793126Z",
- "start_time": "2020-05-22T14:08:26.646886Z"
+ "end_time": "2021-01-29T13:36:53.812557Z",
+ "start_time": "2021-01-29T13:36:53.654271Z"
}
},
"outputs": [],
@@ -544,8 +551,8 @@
"execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-05-22T14:08:26.799383Z",
- "start_time": "2020-05-22T14:08:26.795789Z"
+ "end_time": "2021-01-29T13:36:53.818325Z",
+ "start_time": "2021-01-29T13:36:53.815133Z"
}
},
"outputs": [],
@@ -569,8 +576,8 @@
"execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-05-22T14:08:26.921060Z",
- "start_time": "2020-05-22T14:08:26.801212Z"
+ "end_time": "2021-01-29T13:36:53.954336Z",
+ "start_time": "2021-01-29T13:36:53.820003Z"
}
},
"outputs": [],
@@ -605,8 +612,8 @@
"execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-05-22T14:08:26.928103Z",
- "start_time": "2020-05-22T14:08:26.922685Z"
+ "end_time": "2021-01-29T13:36:53.963378Z",
+ "start_time": "2021-01-29T13:36:53.956231Z"
}
},
"outputs": [],
@@ -678,8 +685,8 @@
"execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-05-22T14:08:27.092710Z",
- "start_time": "2020-05-22T14:08:26.930206Z"
+ "end_time": "2021-01-29T13:36:54.130309Z",
+ "start_time": "2021-01-29T13:36:53.965483Z"
}
},
"outputs": [],
@@ -705,7 +712,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now we fit the model to the training data, while computing the mean squared error on the validation set for each epoch:"
+ "Now we fit the model to the training data, while computing the mean squared error on the validation set for each epoch. A pretrained model is provided, while you can also start from a fresh one (just replace \"reload=True\" with \"reload=False\"):"
]
},
{
@@ -713,17 +720,29 @@
"execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-05-22T14:15:47.803232Z",
- "start_time": "2020-05-22T14:08:27.094355Z"
+ "end_time": "2021-01-29T13:36:55.675381Z",
+ "start_time": "2021-01-29T13:36:54.131740Z"
},
"scrolled": true
},
"outputs": [],
"source": [
- "history = model.fit(X_train, y_train,\n",
- " validation_data = (X_val, y_val),\n",
- " epochs=params[\"epochs\"],\n",
- " batch_size=params[\"batch_size\"], verbose=True)"
+ "reload = True\n",
+ "\n",
+ "if reload:\n",
+ " model = load_model('./data/nn_regression/model.h5')\n",
+ " with open('./data/nn_regression/history.json') as json_file:\n",
+ " history = json.load(json_file)\n",
+ "else:\n",
+ " history = model.fit(X_train, y_train,\n",
+ " validation_data = (X_val, y_val),\n",
+ " epochs=params[\"epochs\"],\n",
+ " batch_size=params[\"batch_size\"], verbose=True)\n",
+ " # save new model\n",
+ " model.save('./data/nn_regression/new_model.h5')\n",
+ " with open('./data/nn_regression/new_history.json', 'w') as outfile:\n",
+ " json.dump(history.history, outfile)\n",
+ " "
]
},
{
@@ -738,8 +757,8 @@
"execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-05-22T14:15:47.945244Z",
- "start_time": "2020-05-22T14:15:47.805420Z"
+ "end_time": "2021-01-29T13:36:55.892054Z",
+ "start_time": "2021-01-29T13:36:55.677104Z"
}
},
"outputs": [],
@@ -747,8 +766,8 @@
"import matplotlib.pyplot as plt\n",
"\n",
"# summarize history for loss: A plot of loss on the training and validation datasets over training epochs.\n",
- "plt.plot(history.history['loss'])\n",
- "plt.plot(history.history['val_loss'])\n",
+ "plt.plot(history['loss'])\n",
+ "plt.plot(history['val_loss'])\n",
"plt.title('model loss')\n",
"plt.ylabel('loss')\n",
"plt.xlabel('epoch')\n",
@@ -783,8 +802,8 @@
"execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-05-22T14:15:53.924363Z",
- "start_time": "2020-05-22T14:15:47.947273Z"
+ "end_time": "2021-01-29T13:37:02.714771Z",
+ "start_time": "2021-01-29T13:36:55.894010Z"
}
},
"outputs": [],
@@ -845,8 +864,8 @@
"execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-05-22T14:15:56.919219Z",
- "start_time": "2020-05-22T14:15:53.926422Z"
+ "end_time": "2021-01-29T13:37:06.094492Z",
+ "start_time": "2021-01-29T13:37:02.716800Z"
}
},
"outputs": [],
@@ -892,8 +911,8 @@
"execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-05-22T14:15:56.923919Z",
- "start_time": "2020-05-22T14:15:56.921595Z"
+ "end_time": "2021-01-29T13:37:06.098758Z",
+ "start_time": "2021-01-29T13:37:06.096204Z"
}
},
"outputs": [],
@@ -906,8 +925,8 @@
"execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-05-22T14:16:02.573238Z",
- "start_time": "2020-05-22T14:15:56.925596Z"
+ "end_time": "2021-01-29T13:37:12.129710Z",
+ "start_time": "2021-01-29T13:37:06.100997Z"
},
"scrolled": true
},
diff --git a/setup.py b/setup.py
index de85809933a517d13f09d6f0622f0657fb1a94b1..33e5bc18ff2cee2afeb4c661949173ebe462346a 100644
--- a/setup.py
+++ b/setup.py
@@ -1,6 +1,9 @@
import json
from setuptools import setup, find_packages
+# Succesfull test of this tutorial was conducted for:
+# 'tensorflow==1.13.1', 'keras==2.2.4', 'numpy==1.16.4', 'scipy==1.1.0', 'matplotlib', 'pandas', 'seaborn', 'pymatgen==2020.3.13', 'sklearn'
+
with open('metainfo.json') as file:
metainfo = json.load(file)
@@ -13,5 +16,5 @@ setup(
description=metainfo['title'],
long_description=metainfo['description'],
packages=find_packages(),
- install_requires=['tensorflow==1.13.1', 'keras==2.2.4', 'numpy==1.16.4', 'scipy==1.1.0', 'matplotlib', 'pandas', 'seaborn', 'pymatgen==2020.3.13', 'sklearn'],
+ install_requires=['tensorflow', 'keras', 'numpy', 'scipy', 'matplotlib', 'pandas', 'seaborn', 'pymatgen', 'sklearn'],
)