SubsurfaceDataAnalytics_NeuralNet.ipynb

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    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Subsurface Data Analytics \n",
    "\n",
    "## Artificial Neural Networks for Prediction in Python\n",
    "\n",
    "#### Honggeun Jo, Graduate Student, The University of Texas at Austin\n",
    "\n",
    "##### [LinkedIn](https://www.linkedin.com/in/honggeun-jo/?originalSubdomain=kr) | [Twitter](https://twitter.com/HonggeunJ)\n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "### PGE 383 Exercise: Support Vector Machine for Subsurface Modeling in Python \n",
    "\n",
    "Here's a simple workflow, demonstration of neural networks for subsurface modeling workflows. This should help you get started with building subsurface models that use data analytics and machine learning. Here's some basic details about neural networks.  \n",
    "\n",
    "#### Neural Networks\n",
    "\n",
    "Machine learning method for supervised learning for classification and regression analysis.  Here are some key aspects of support vector machines.\n",
    "\n",
    "**Basic Design** *\"...a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.\"* Caudill (1989). \n",
    "\n",
    "**Nature-inspire Computing** based on the neuronal structure in the brain, including many interconnected simple, processing units, known as nodes that are capable of complicated emergent pattern detection due to a large number of nodes and interconnectivity.\n",
    "\n",
    "**Training and Testing** just like and other predictive model (e.g. linear regression, decision trees and support vector machines) we perform training to fit parameters and testing to tune hyper parameters.\n",
    "\n",
    "**Parameters** are the weights applied to each connection and a bias term applied to each node.  For a single node artificial neural network, we could compare the weights to the $\\beta$ slope term and the bias to the $\\beta_{0}$ intercept term.\n",
    "\n",
    "\\begin{equation}\n",
    "Y = \\beta X + \\beta_0\n",
    "\\end{equation}\n",
    "\n",
    "the number of parameters increases rapidly as we increase the number of nodes and the connectivity between the nodes.\n",
    "\n",
    "**Layers** the typical artificial neural net is structured with an **input layer**, with one node for each $m$ predictor feature, $X_1,\\ldots,X_m$. There is an **ouput layer**, with one node for each $r$ response feature, $Y_1,\\ldots,Y_r$.  There may be one or more layers of nodes between the input and output layers, known as **hidden layer(s)**.  \n",
    "\n",
    "**Connections** are the linkages between the nodes in adjacent layers.  For example, in a fully connected artificial neural network, all the input nodes are connected to all of the nodes in the first layer, then all of the nodes in the first layer are connected to the next layer and so forth. Each connection includes a weight parameter as indicated above.\n",
    "\n",
    "**Nodes** receive the weighted signal from the connected previous layer nodes, sum and then apply this result to the **activation** function in the node. Some example activation functions include:\n",
    "\n",
    "* **Binary** the node fires or not.  This is represented by a Heaviside step function.\n",
    "\n",
    "* **Identify** the input is passed to the output $f(x) = x$\n",
    "\n",
    "* **Linear** the node passes a signal that increases linearly with the weighted input.\n",
    "\n",
    "* **Logistic** also known as sigmoid or soft step $f(x) = \\frac{1}{1+e^{-x}}$\n",
    "\n",
    "the result of the linearly weighted inputs applied to the nonlinear activation is output from the node to the next layer of connected nodes.\n",
    "\n",
    "**Training Cycles / Epochs** include the presentation of new data, forward application of the current prediction model to make estimates, calculation of error and then backpropagation of error to correct the network weights to reduce the error.\n",
    "\n",
    "**Batch** is the set of training data drawn to train for each epoch. There is a trade-off, a larger batch results in more computational time per epoch, but a more accurate estimate of the error to adjust the weights.  Smaller batches result in a nosier estimate of the error, but faster epochs, this results in faster learning and even possibly more robust models.\n",
    "\n",
    "**Local Minimums** if one calculated the error hypersurface for a range of model parameters it would be hyparabolic, there is a global minimium error solution.  But this error hyper surface is rough and it is possible to be stuck in a local minimum. **Learning Rate** and **Mommentum** coefficients are introduced to avoid getting stuck in local minimums.\n",
    "\n",
    "* **Mommentum** is a hyperparameter to control the use of information from the weight update over the last epoch for consideration in the current epoch.  This can be accomplished with an update vector, $v_i$, a mommentum parameter, $\\alpha$, to calculate the current weight update, $v_{i+1}$ given the new update $\\theta_{i+1}$.\n",
    "\n",
    "\\begin{equation}\n",
    "v_{i+1} = \\alpha v_i + \\theta_{i+1}\n",
    "\\end{equation}\n",
    "\n",
    "* **Learning Rate** is a hyperparameter that controls the adjustment of the weights in response to the gradient indicated by backpropagation of error \n",
    "\n",
    "##### Applications to subsurface modeling\n",
    "\n",
    "We demonstrate the estimation of porosity between wells from seismic accoustic impedance. \n",
    "\n",
    "* modeling a nonlinear relationship between acoustic impedance and porosity \n",
    "\n",
    "#### Limitations of Neural Network Estimation\n",
    "\n",
    "Since we demonstrate the use of a neural network to estimate porosity inter-well from seismic, we should comment on limitations of neural networks for estimation:\n",
    "\n",
    "* does not honor the well data\n",
    "\n",
    "* does not honor the histogram of the data\n",
    "\n",
    "* does not honor spaital correlation \n",
    "\n",
    "* does not honor the multivariate relationship\n",
    "\n",
    "* generally low interpretability models\n",
    "\n",
    "* requires a large number of data for effective training\n",
    "\n",
    "* high model complexity with high model variance\n",
    "\n",
    "\n",
    "#### Workflow Goals\n",
    "\n",
    "Learn the basics of machine learning in python to predict subsurface features. This includes:\n",
    "\n",
    "* Loading and visualizing sample data\n",
    "* Trying out neural nets\n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Subsurface Machine Learning Class, I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - 12_sample_data.csv found [here](https://github.com/GeostatsGuy/GeoDataSets/blob/master/12_sample_data.csv).\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Import Required Packages\n",
    "\n",
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB          # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats    # GSLIB methods convert to Python        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                        # ndarrys for gridded data\n",
    "import pandas as pd                       # DataFrames for tabular data\n",
    "import os                                 # set working directory, run executables\n",
    "import matplotlib.pyplot as plt           # for plotting\n",
    "import seaborn as sns                     # for plotting\n",
    "import warnings                           # supress warnings from seaborn pairplot\n",
    "from sklearn.model_selection import train_test_split # train / test DatFrame split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need the following packages to train and test our artificial neural nets:\n",
    "\n",
    "* Tensorflow - open source machine learning \n",
    "\n",
    "* Keras - high level application programing interface (API) to build and train models\n",
    "\n",
    "More information is available at [tensorflow install](https://www.tensorflow.org/install).\n",
    "\n",
    "* This workflow was designed with tensorflow version 1.13.1 and does not work with version 2.0.0 alpha.\n",
    "\n",
    "To check your current version of tensorflow you could run the next block of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.13.1'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.__version__                            # check the installed version of tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import all of the tensorflow and keras methods that we will need in our workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from keras.models import Sequential, load_model\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from keras.utils import np_utils\n",
    "import keras\n",
    "from tensorflow.python.keras import backend as k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"C:/PGE383\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Data\n",
    "Let's load the provided multivariate, spatial dataset '12_sample_data.csv'.  It is a comma delimited file with: \n",
    "\n",
    "* X and Y coordinates ($m$)\n",
    "* facies 0 and 1 \n",
    "* porosity (fraction)\n",
    "* permeability ($mD$)\n",
    "* acoustic impedance ($\\frac{kg}{m^3} \\cdot \\frac{m}{s} \\cdot 10^3$). \n",
    "\n",
    "We load it with the pandas 'read_csv' function into a data frame we called 'df' and then preview it to make sure it loaded correctly.\n",
    "\n",
    "**Python Tip: using functions from a package** just type the label for the package that we declared at the beginning:\n",
    "\n",
    "```python\n",
    "import pandas as pd\n",
    "```\n",
    "\n",
    "so we can access the pandas function 'read_csv' with the command: \n",
    "\n",
    "```python\n",
    "pd.read_csv()\n",
    "```\n",
    "\n",
    "but read csv has required input parameters. The essential one is the name of the file. For our circumstance all the other default parameters are fine. If you want to see all the possible parameters for this function, just go to the docs [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html).  \n",
    "\n",
    "* The docs are always helpful\n",
    "* There is often a lot of flexibility for Python functions, possible through using various inputs parameters\n",
    "\n",
    "also, the program has an output, a pandas DataFrame loaded from the data.  So we have to specficy the name / variable representing that new object.\n",
    "\n",
    "```python\n",
    "df = pd.read_csv(\"12_sample_data.csv\")  \n",
    "```\n",
    "\n",
    "Let's run this command to load the data and then look at the resulting DataFrame to ensure that we loaded it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('12_sample_data.csv')     # load our data table (wrong name!)\n",
    "df = df.sample(frac=.30, random_state = 73073); df = df.reset_index()\n",
    "df['logPerm'] = np.log10(df['Perm'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add a facies label for shale and sand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Facies_Label']=np.zeros(len(df));\n",
    "df['Facies_Label']= np.where(df['Facies']==1, 'Sand', 'Shale')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get a quick preview of our new DataFrame, 'df' with the built in function.\n",
    "\n",
    "```python\n",
    "df.head()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>logPerm</th>\n",
       "      <th>Facies_Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72</td>\n",
       "      <td>250</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.139637</td>\n",
       "      <td>0.347182</td>\n",
       "      <td>4747.274043</td>\n",
       "      <td>-0.459442</td>\n",
       "      <td>Shale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>153</td>\n",
       "      <td>650</td>\n",
       "      <td>750</td>\n",
       "      <td>0</td>\n",
       "      <td>0.170732</td>\n",
       "      <td>10.720560</td>\n",
       "      <td>4535.625583</td>\n",
       "      <td>1.030217</td>\n",
       "      <td>Shale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>258</td>\n",
       "      <td>80</td>\n",
       "      <td>669</td>\n",
       "      <td>1</td>\n",
       "      <td>0.244345</td>\n",
       "      <td>3222.716042</td>\n",
       "      <td>2696.102930</td>\n",
       "      <td>3.508222</td>\n",
       "      <td>Sand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>200</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0.167125</td>\n",
       "      <td>3.042590</td>\n",
       "      <td>5500.997419</td>\n",
       "      <td>0.483243</td>\n",
       "      <td>Shale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>303</td>\n",
       "      <td>60</td>\n",
       "      <td>929</td>\n",
       "      <td>1</td>\n",
       "      <td>0.216253</td>\n",
       "      <td>400.298484</td>\n",
       "      <td>3959.934912</td>\n",
       "      <td>2.602384</td>\n",
       "      <td>Sand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>374</td>\n",
       "      <td>750</td>\n",
       "      <td>699</td>\n",
       "      <td>0</td>\n",
       "      <td>0.159171</td>\n",
       "      <td>9.224292</td>\n",
       "      <td>5263.064063</td>\n",
       "      <td>0.964933</td>\n",
       "      <td>Shale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>333</td>\n",
       "      <td>340</td>\n",
       "      <td>549</td>\n",
       "      <td>0</td>\n",
       "      <td>0.170881</td>\n",
       "      <td>84.471492</td>\n",
       "      <td>2918.232227</td>\n",
       "      <td>1.926710</td>\n",
       "      <td>Shale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>850</td>\n",
       "      <td>1</td>\n",
       "      <td>0.237154</td>\n",
       "      <td>39.837129</td>\n",
       "      <td>3074.562617</td>\n",
       "      <td>1.600288</td>\n",
       "      <td>Sand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>54</td>\n",
       "      <td>200</td>\n",
       "      <td>250</td>\n",
       "      <td>1</td>\n",
       "      <td>0.188043</td>\n",
       "      <td>57.804784</td>\n",
       "      <td>4997.078597</td>\n",
       "      <td>1.761964</td>\n",
       "      <td>Sand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>311</td>\n",
       "      <td>250</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>0.215039</td>\n",
       "      <td>625.510541</td>\n",
       "      <td>2693.691341</td>\n",
       "      <td>2.796235</td>\n",
       "      <td>Sand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "      <td>450</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200389</td>\n",
       "      <td>265.636019</td>\n",
       "      <td>3454.389302</td>\n",
       "      <td>2.424287</td>\n",
       "      <td>Sand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>269</td>\n",
       "      <td>540</td>\n",
       "      <td>859</td>\n",
       "      <td>0</td>\n",
       "      <td>0.173662</td>\n",
       "      <td>39.910675</td>\n",
       "      <td>3414.962471</td>\n",
       "      <td>1.601089</td>\n",
       "      <td>Shale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>279</td>\n",
       "      <td>580</td>\n",
       "      <td>779</td>\n",
       "      <td>0</td>\n",
       "      <td>0.168371</td>\n",
       "      <td>11.171392</td>\n",
       "      <td>3646.918994</td>\n",
       "      <td>1.048107</td>\n",
       "      <td>Shale</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index    X    Y  Facies  Porosity         Perm           AI   logPerm  \\\n",
       "0      72  250   50       0  0.139637     0.347182  4747.274043 -0.459442   \n",
       "1     153  650  750       0  0.170732    10.720560  4535.625583  1.030217   \n",
       "2     258   80  669       1  0.244345  3222.716042  2696.102930  3.508222   \n",
       "3      56  200  150       0  0.167125     3.042590  5500.997419  0.483243   \n",
       "4     303   60  929       1  0.216253   400.298484  3959.934912  2.602384   \n",
       "5     374  750  699       0  0.159171     9.224292  5263.064063  0.964933   \n",
       "6     333  340  549       0  0.170881    84.471492  2918.232227  1.926710   \n",
       "7       1   50  850       1  0.237154    39.837129  3074.562617  1.600288   \n",
       "8      54  200  250       1  0.188043    57.804784  4997.078597  1.761964   \n",
       "9     311  250  579       1  0.215039   625.510541  2693.691341  2.796235   \n",
       "10      8   50  450       1  0.200389   265.636019  3454.389302  2.424287   \n",
       "11    269  540  859       0  0.173662    39.910675  3414.962471  1.601089   \n",
       "12    279  580  779       0  0.168371    11.171392  3646.918994  1.048107   \n",
       "\n",
       "   Facies_Label  \n",
       "0         Shale  \n",
       "1         Shale  \n",
       "2          Sand  \n",
       "3         Shale  \n",
       "4          Sand  \n",
       "5         Shale  \n",
       "6         Shale  \n",
       "7          Sand  \n",
       "8          Sand  \n",
       "9          Sand  \n",
       "10         Sand  \n",
       "11        Shale  \n",
       "12        Shale  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(n=13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary Statistics for Tabular Data\n",
    "\n",
    "There are a lot of efficient methods to calculate summary statistics from tabular data in DataFrames. The describe command provides count, mean, minimum, maximum, and quartiles all in a nice data table. We use transpose just to flip the table so that features are on the rows and the statistics are on the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <td>144.0</td>\n",
       "      <td>261.048611</td>\n",
       "      <td>136.830267</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>144.750000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>381.250000</td>\n",
       "      <td>478.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>144.0</td>\n",
       "      <td>449.375000</td>\n",
       "      <td>263.691435</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>242.500000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>980.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>144.0</td>\n",
       "      <td>542.979167</td>\n",
       "      <td>289.228936</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>979.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>144.0</td>\n",
       "      <td>0.659722</td>\n",
       "      <td>0.475456</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>144.0</td>\n",
       "      <td>0.190700</td>\n",
       "      <td>0.031972</td>\n",
       "      <td>0.131230</td>\n",
       "      <td>0.166621</td>\n",
       "      <td>0.188733</td>\n",
       "      <td>0.217234</td>\n",
       "      <td>0.256172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>144.0</td>\n",
       "      <td>510.036736</td>\n",
       "      <td>1136.459068</td>\n",
       "      <td>0.039555</td>\n",
       "      <td>6.950509</td>\n",
       "      <td>56.886770</td>\n",
       "      <td>356.658709</td>\n",
       "      <td>7452.343369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>144.0</td>\n",
       "      <td>3746.825725</td>\n",
       "      <td>793.196589</td>\n",
       "      <td>1961.600397</td>\n",
       "      <td>3167.631744</td>\n",
       "      <td>3668.526774</td>\n",
       "      <td>4244.264532</td>\n",
       "      <td>6194.573653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logPerm</th>\n",
       "      <td>144.0</td>\n",
       "      <td>1.646059</td>\n",
       "      <td>1.210694</td>\n",
       "      <td>-1.402796</td>\n",
       "      <td>0.841941</td>\n",
       "      <td>1.754955</td>\n",
       "      <td>2.552252</td>\n",
       "      <td>3.872293</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count         mean          std          min          25%  \\\n",
       "index     144.0   261.048611   136.830267     1.000000   144.750000   \n",
       "X         144.0   449.375000   263.691435     0.000000   242.500000   \n",
       "Y         144.0   542.979167   289.228936    19.000000   300.000000   \n",
       "Facies    144.0     0.659722     0.475456     0.000000     0.000000   \n",
       "Porosity  144.0     0.190700     0.031972     0.131230     0.166621   \n",
       "Perm      144.0   510.036736  1136.459068     0.039555     6.950509   \n",
       "AI        144.0  3746.825725   793.196589  1961.600397  3167.631744   \n",
       "logPerm   144.0     1.646059     1.210694    -1.402796     0.841941   \n",
       "\n",
       "                  50%          75%          max  \n",
       "index      270.000000   381.250000   478.000000  \n",
       "X          400.000000   650.000000   980.000000  \n",
       "Y          579.000000   800.000000   979.000000  \n",
       "Facies       1.000000     1.000000     1.000000  \n",
       "Porosity     0.188733     0.217234     0.256172  \n",
       "Perm        56.886770   356.658709  7452.343369  \n",
       "AI        3668.526774  4244.264532  6194.573653  \n",
       "logPerm      1.754955     2.552252     3.872293  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this table we set some reasonable feature ranges for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pormin = 0.13; pormax = 0.26              # porosity min and max\n",
    "permmin = 0.01; permmax = 3000            # permeability min and max\n",
    "AImin = 1000.0; AImax = 8000              # acoustic impedance min and max\n",
    "Fmin = 0; Fmax = 1                        # facies min and max\n",
    "cmap = plt.cm.plasma                      # color map for plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Seismically-Derived Map\n",
    "\n",
    "Let's load a seismically-derived map of acoustic impedance.\n",
    "\n",
    "* This seismic acoustic impedance matches the well data acoustic impedance at the well locations\n",
    "\n",
    "* This seismic data has better coverage than the well data, but with larger scale and error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "seismic = np.loadtxt(open(\"12_AI.csv\", \"rb\"), delimiter=\",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's set the map parameters:\n",
    "\n",
    "* X min and max\n",
    "\n",
    "* Y min and max\n",
    "\n",
    "* number of cells in the X and Y directions\n",
    "\n",
    "* isotropic cell size\n",
    "\n",
    "and then plot the loaded seismic map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xmin = 0.0; xmax = 1000.0                 # range of x values\n",
    "ymin = 0.0; ymax = 1000.0                 # range of y values\n",
    "ny = 100; nx = 100                        # number of cell in Y and X\n",
    "cell_size = 10.0;                         # grid cell size\n",
    "\n",
    "plt.subplot(111)\n",
    "GSLIB.pixelplt_st(seismic,xmin,xmax,ymin,ymax,cell_size,AImin,AImax,'Acoustic Impedance','X(m)','Y(m)','Acoustic Impedance (kg/m2s*10^3)',cmap)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.2, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract Variables of Interest\n",
    "\n",
    "Let's extract the facies, porosity, log permeability and acoustic impedance.\n",
    "\n",
    "* the extracted DataFrame subset is a shallow copy, any changes in DataFrame 'df_subset' update the same entries in 'df'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_subset = df.iloc[:,[3,4,7,6]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Always good to preview the DataFrame everytime we work with it to make sure it is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "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>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>logPerm</th>\n",
       "      <th>AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.139637</td>\n",
       "      <td>-0.459442</td>\n",
       "      <td>4747.274043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.170732</td>\n",
       "      <td>1.030217</td>\n",
       "      <td>4535.625583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.244345</td>\n",
       "      <td>3.508222</td>\n",
       "      <td>2696.102930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.167125</td>\n",
       "      <td>0.483243</td>\n",
       "      <td>5500.997419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.216253</td>\n",
       "      <td>2.602384</td>\n",
       "      <td>3959.934912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0.159171</td>\n",
       "      <td>0.964933</td>\n",
       "      <td>5263.064063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0.170881</td>\n",
       "      <td>1.926710</td>\n",
       "      <td>2918.232227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0.237154</td>\n",
       "      <td>1.600288</td>\n",
       "      <td>3074.562617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0.188043</td>\n",
       "      <td>1.761964</td>\n",
       "      <td>4997.078597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0.215039</td>\n",
       "      <td>2.796235</td>\n",
       "      <td>2693.691341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>0.200389</td>\n",
       "      <td>2.424287</td>\n",
       "      <td>3454.389302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0.173662</td>\n",
       "      <td>1.601089</td>\n",
       "      <td>3414.962471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0.168371</td>\n",
       "      <td>1.048107</td>\n",
       "      <td>3646.918994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Facies  Porosity   logPerm           AI\n",
       "0        0  0.139637 -0.459442  4747.274043\n",
       "1        0  0.170732  1.030217  4535.625583\n",
       "2        1  0.244345  3.508222  2696.102930\n",
       "3        0  0.167125  0.483243  5500.997419\n",
       "4        1  0.216253  2.602384  3959.934912\n",
       "5        0  0.159171  0.964933  5263.064063\n",
       "6        0  0.170881  1.926710  2918.232227\n",
       "7        1  0.237154  1.600288  3074.562617\n",
       "8        1  0.188043  1.761964  4997.078597\n",
       "9        1  0.215039  2.796235  2693.691341\n",
       "10       1  0.200389  2.424287  3454.389302\n",
       "11       0  0.173662  1.601089  3414.962471\n",
       "12       0  0.168371  1.048107  3646.918994"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subset.head(n=13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multivariate Relationships\n",
    "\n",
    "Before we build a predictive model, it is a good idea to attempt to understand the multivariate relationships between the variables. Let's calculate a matrix scatter plots to understand how the data parameters are correlated to each other, 2 variables at a time: \n",
    "\n",
    "* linearility vs. non-linearity\n",
    "\n",
    "* homo- vs. heteroscedasticity, \n",
    "\n",
    "* constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 765.725x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings('ignore')         # supress warning from the seaborn.pairplot         \n",
    "sns.pairplot(df_subset, hue = 'Facies')\n",
    "sns.set(style=\"ticks\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Normalization\n",
    "\n",
    "We must normalize the features before we apply them in a neural network model.\n",
    "\n",
    "* We will set the features to range from -1 to 1.\n",
    "\n",
    "These are the motivations for this normalization:\n",
    "\n",
    "* remove effect of scale of different type of data (i.e., acoustic impedance varies between 2000~6000 but porosity only varies between 0~0.3)\n",
    "\n",
    "* activation functions in Neural networks have more sensivity when the values of nodes are closer to 0.0 (i.e., results in higher gradient and improves backpropagation in training)\n",
    "\n",
    "Let's normalize each feature.  \n",
    "\n",
    "* It is easy to backtransform given we keep track of the original min and max values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "por_min = df_subset['Porosity'].min(); por_max = df_subset['Porosity'].max();\n",
    "logperm_min = df_subset['logPerm'].min(); logperm_max = df_subset['logPerm'].max();\n",
    "AI_min = df_subset['AI'].min(); AI_max = df_subset['AI'].max();\n",
    "\n",
    "df_subset['norm_Porosity'] = (df_subset['Porosity'] - por_min)/(por_max - por_min) * 2 - 1\n",
    "df_subset['norm_logPerm'] = (df_subset['logPerm'] - logperm_min)/(logperm_max - logperm_min) * 2 - 1\n",
    "df_subset['norm_AI'] = (df_subset['AI'] - AI_min)/(AI_max - AI_min) * 2 - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the update to our DataFrame with a quick preview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "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>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>logPerm</th>\n",
       "      <th>AI</th>\n",
       "      <th>norm_Porosity</th>\n",
       "      <th>norm_logPerm</th>\n",
       "      <th>norm_AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.139637</td>\n",
       "      <td>-0.459442</td>\n",
       "      <td>4747.274043</td>\n",
       "      <td>-0.865422</td>\n",
       "      <td>-0.642337</td>\n",
       "      <td>0.316178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.170732</td>\n",
       "      <td>1.030217</td>\n",
       "      <td>4535.625583</td>\n",
       "      <td>-0.367672</td>\n",
       "      <td>-0.077546</td>\n",
       "      <td>0.216178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.244345</td>\n",
       "      <td>3.508222</td>\n",
       "      <td>2696.102930</td>\n",
       "      <td>0.810690</td>\n",
       "      <td>0.861966</td>\n",
       "      <td>-0.652961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.167125</td>\n",
       "      <td>0.483243</td>\n",
       "      <td>5500.997419</td>\n",
       "      <td>-0.425413</td>\n",
       "      <td>-0.284926</td>\n",
       "      <td>0.672298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.216253</td>\n",
       "      <td>2.602384</td>\n",
       "      <td>3959.934912</td>\n",
       "      <td>0.361002</td>\n",
       "      <td>0.518526</td>\n",
       "      <td>-0.055825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0.159171</td>\n",
       "      <td>0.964933</td>\n",
       "      <td>5263.064063</td>\n",
       "      <td>-0.552734</td>\n",
       "      <td>-0.102298</td>\n",
       "      <td>0.559879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0.170881</td>\n",
       "      <td>1.926710</td>\n",
       "      <td>2918.232227</td>\n",
       "      <td>-0.365287</td>\n",
       "      <td>0.262351</td>\n",
       "      <td>-0.548010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0.237154</td>\n",
       "      <td>1.600288</td>\n",
       "      <td>3074.562617</td>\n",
       "      <td>0.695581</td>\n",
       "      <td>0.138591</td>\n",
       "      <td>-0.474146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0.188043</td>\n",
       "      <td>1.761964</td>\n",
       "      <td>4997.078597</td>\n",
       "      <td>-0.090559</td>\n",
       "      <td>0.199889</td>\n",
       "      <td>0.434206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0.215039</td>\n",
       "      <td>2.796235</td>\n",
       "      <td>2693.691341</td>\n",
       "      <td>0.341574</td>\n",
       "      <td>0.592023</td>\n",
       "      <td>-0.654101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>0.200389</td>\n",
       "      <td>2.424287</td>\n",
       "      <td>3454.389302</td>\n",
       "      <td>0.107058</td>\n",
       "      <td>0.451002</td>\n",
       "      <td>-0.294685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0.173662</td>\n",
       "      <td>1.601089</td>\n",
       "      <td>3414.962471</td>\n",
       "      <td>-0.320773</td>\n",
       "      <td>0.138895</td>\n",
       "      <td>-0.313314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0.168371</td>\n",
       "      <td>1.048107</td>\n",
       "      <td>3646.918994</td>\n",
       "      <td>-0.405462</td>\n",
       "      <td>-0.070763</td>\n",
       "      <td>-0.203719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Facies  Porosity   logPerm           AI  norm_Porosity  norm_logPerm  \\\n",
       "0        0  0.139637 -0.459442  4747.274043      -0.865422     -0.642337   \n",
       "1        0  0.170732  1.030217  4535.625583      -0.367672     -0.077546   \n",
       "2        1  0.244345  3.508222  2696.102930       0.810690      0.861966   \n",
       "3        0  0.167125  0.483243  5500.997419      -0.425413     -0.284926   \n",
       "4        1  0.216253  2.602384  3959.934912       0.361002      0.518526   \n",
       "5        0  0.159171  0.964933  5263.064063      -0.552734     -0.102298   \n",
       "6        0  0.170881  1.926710  2918.232227      -0.365287      0.262351   \n",
       "7        1  0.237154  1.600288  3074.562617       0.695581      0.138591   \n",
       "8        1  0.188043  1.761964  4997.078597      -0.090559      0.199889   \n",
       "9        1  0.215039  2.796235  2693.691341       0.341574      0.592023   \n",
       "10       1  0.200389  2.424287  3454.389302       0.107058      0.451002   \n",
       "11       0  0.173662  1.601089  3414.962471      -0.320773      0.138895   \n",
       "12       0  0.168371  1.048107  3646.918994      -0.405462     -0.070763   \n",
       "\n",
       "     norm_AI  \n",
       "0   0.316178  \n",
       "1   0.216178  \n",
       "2  -0.652961  \n",
       "3   0.672298  \n",
       "4  -0.055825  \n",
       "5   0.559879  \n",
       "6  -0.548010  \n",
       "7  -0.474146  \n",
       "8   0.434206  \n",
       "9  -0.654101  \n",
       "10 -0.294685  \n",
       "11 -0.313314  \n",
       "12 -0.203719  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subset.head(n=13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also a good idea to check the summary statistics.  \n",
    "\n",
    "* All continuous features should now range from -1.0 to 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>144.0</td>\n",
       "      <td>0.659722</td>\n",
       "      <td>0.475456</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>144.0</td>\n",
       "      <td>0.190700</td>\n",
       "      <td>0.031972</td>\n",
       "      <td>0.131230</td>\n",
       "      <td>0.166621</td>\n",
       "      <td>0.188733</td>\n",
       "      <td>0.217234</td>\n",
       "      <td>0.256172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logPerm</th>\n",
       "      <td>144.0</td>\n",
       "      <td>1.646059</td>\n",
       "      <td>1.210694</td>\n",
       "      <td>-1.402796</td>\n",
       "      <td>0.841941</td>\n",
       "      <td>1.754955</td>\n",
       "      <td>2.552252</td>\n",
       "      <td>3.872293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>144.0</td>\n",
       "      <td>3746.825725</td>\n",
       "      <td>793.196589</td>\n",
       "      <td>1961.600397</td>\n",
       "      <td>3167.631744</td>\n",
       "      <td>3668.526774</td>\n",
       "      <td>4244.264532</td>\n",
       "      <td>6194.573653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>norm_Porosity</th>\n",
       "      <td>144.0</td>\n",
       "      <td>-0.048027</td>\n",
       "      <td>0.511789</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.433483</td>\n",
       "      <td>-0.079520</td>\n",
       "      <td>0.376700</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>norm_logPerm</th>\n",
       "      <td>144.0</td>\n",
       "      <td>0.155945</td>\n",
       "      <td>0.459023</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.148929</td>\n",
       "      <td>0.197231</td>\n",
       "      <td>0.499519</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>norm_AI</th>\n",
       "      <td>144.0</td>\n",
       "      <td>-0.156515</td>\n",
       "      <td>0.374770</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.430173</td>\n",
       "      <td>-0.193509</td>\n",
       "      <td>0.078516</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count         mean         std          min          25%  \\\n",
       "Facies         144.0     0.659722    0.475456     0.000000     0.000000   \n",
       "Porosity       144.0     0.190700    0.031972     0.131230     0.166621   \n",
       "logPerm        144.0     1.646059    1.210694    -1.402796     0.841941   \n",
       "AI             144.0  3746.825725  793.196589  1961.600397  3167.631744   \n",
       "norm_Porosity  144.0    -0.048027    0.511789    -1.000000    -0.433483   \n",
       "norm_logPerm   144.0     0.155945    0.459023    -1.000000    -0.148929   \n",
       "norm_AI        144.0    -0.156515    0.374770    -1.000000    -0.430173   \n",
       "\n",
       "                       50%          75%          max  \n",
       "Facies            1.000000     1.000000     1.000000  \n",
       "Porosity          0.188733     0.217234     0.256172  \n",
       "logPerm           1.754955     2.552252     3.872293  \n",
       "AI             3668.526774  4244.264532  6194.573653  \n",
       "norm_Porosity    -0.079520     0.376700     1.000000  \n",
       "norm_logPerm      0.197231     0.499519     1.000000  \n",
       "norm_AI          -0.193509     0.078516     1.000000  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subset.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Separation of Training and Testing Data\n",
    "\n",
    "We also need to split our data into training / testing datasets so that we:\n",
    "\n",
    "* can train our neural networks using training data \n",
    "\n",
    "* while testing their performance with validating data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_subset.iloc[:,[6]]                 # extract the predictor feature - acoustic impedance\n",
    "y = df_subset.iloc[:,[4]]                 # extract the response feature - porosity\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=73073)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize are train and test data split on a single scatter plot.\n",
    "\n",
    "* we want to make sure it is fair\n",
    "\n",
    "* ensure that the test samples are not clustered or too far away from the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "plt.plot(X_train['norm_AI'].values,y_train['norm_Porosity'].values, 'o', markerfacecolor='red', markeredgecolor='black', alpha=0.4, label = \"Train\")\n",
    "plt.plot(X_test['norm_AI'].values,y_test['norm_Porosity'].values, 'o', markerfacecolor='blue', markeredgecolor='black', alpha=0.4, label = \"Test\")\n",
    "plt.legend(loc='upper right');\n",
    "plt.title('Train and Test: Porosity vs. Acoustic Impedance')\n",
    "plt.ylabel('Porosity (Normalized)')\n",
    "plt.xlabel('Acoustic Impedance (Normalized)')\n",
    "plt.xlim(-1,1); plt.ylim(-1,1)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.2, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build and Train a Simple Neural Network\n",
    "\n",
    "For our first model let's try something very simple.  \n",
    "\n",
    "* 1 predictor feature - acoustic impedance\n",
    "\n",
    "* 1 responce feature - porosity\n",
    "\n",
    "We will build a model to predict porosity from acoustic impedance over all locations in our model $\\bf{u} \\in AOI$. \n",
    "\n",
    "\\begin{equation}\n",
    "\\phi(\\bf{u}) = \\hat{f}(AI(\\bf{u})\n",
    "\\end{equation}\n",
    "\n",
    "We will use this model to inform porosity between the wells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build Neural Network for prediction model (input: AI - > Output: Porosity)\n",
    "model_1 = Sequential([\n",
    "    Dense(1, activation='relu', input_shape=(1,)),\n",
    "    Dense(3, activation='relu'),\n",
    "    Dense(1, activation='tanh'),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have specified a simple neural net with:\n",
    "\n",
    "* 1 input node \n",
    "\n",
    "* 3 nodes in the hidden layer\n",
    "\n",
    "* 1 output node\n",
    "\n",
    "Now we specify the optimization method and the related hyperparameters (e.g.,learning rate, etc)\n",
    "further information of Keras optimizers are available [here](https://keras.io/optimizers)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.optimizers.Adam(lr=0.003, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\n",
    "\n",
    "model_1.compile(optimizer='adam',\n",
    "              loss='mse',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are ready to supply our normalized predictor feature to predict the normalized responce feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 115 samples, validate on 29 samples\n",
      "Epoch 1/300\n",
      "115/115 [==============================] - 0s 4ms/step - loss: 0.2258 - acc: 0.0000e+00 - val_loss: 0.2715 - val_acc: 0.0000e+00\n",
      "Epoch 2/300\n",
      "115/115 [==============================] - 0s 270us/step - loss: 0.2179 - acc: 0.0000e+00 - val_loss: 0.2682 - val_acc: 0.0000e+00\n",
      "Epoch 3/300\n",
      "115/115 [==============================] - 0s 244us/step - loss: 0.2125 - acc: 0.0000e+00 - val_loss: 0.2650 - val_acc: 0.0000e+00\n",
      "Epoch 4/300\n",
      "115/115 [==============================] - 0s 239us/step - loss: 0.2080 - acc: 0.0000e+00 - val_loss: 0.2598 - val_acc: 0.0000e+00\n",
      "Epoch 5/300\n",
      "115/115 [==============================] - 0s 217us/step - loss: 0.2046 - acc: 0.0000e+00 - val_loss: 0.2578 - val_acc: 0.0000e+00\n",
      "Epoch 6/300\n",
      "115/115 [==============================] - 0s 226us/step - loss: 0.2013 - acc: 0.0000e+00 - val_loss: 0.2540 - val_acc: 0.0000e+00\n",
      "Epoch 7/300\n",
      "115/115 [==============================] - 0s 235us/step - loss: 0.1994 - acc: 0.0000e+00 - val_loss: 0.2500 - val_acc: 0.0000e+00\n",
      "Epoch 8/300\n",
      "115/115 [==============================] - 0s 209us/step - loss: 0.1966 - acc: 0.0000e+00 - val_loss: 0.2494 - val_acc: 0.0000e+00\n",
      "Epoch 9/300\n",
      "115/115 [==============================] - 0s 209us/step - loss: 0.1936 - acc: 0.0000e+00 - val_loss: 0.2446 - val_acc: 0.0000e+00\n",
      "Epoch 10/300\n",
      "115/115 [==============================] - 0s 209us/step - loss: 0.1915 - acc: 0.0000e+00 - val_loss: 0.2420 - val_acc: 0.0000e+00\n",
      "Epoch 11/300\n",
      "115/115 [==============================] - 0s 218us/step - loss: 0.1895 - acc: 0.0000e+00 - val_loss: 0.2394 - val_acc: 0.0000e+00\n",
      "Epoch 12/300\n",
      "115/115 [==============================] - 0s 209us/step - loss: 0.1886 - acc: 0.0000e+00 - val_loss: 0.2358 - val_acc: 0.0000e+00\n",
      "Epoch 13/300\n",
      "115/115 [==============================] - 0s 217us/step - loss: 0.1860 - acc: 0.0000e+00 - val_loss: 0.2334 - val_acc: 0.0000e+00\n",
      "Epoch 14/300\n",
      "115/115 [==============================] - ETA: 0s - loss: 0.2748 - acc: 0.0000e+0 - 0s 209us/step - loss: 0.1843 - acc: 0.0000e+00 - val_loss: 0.2319 - val_acc: 0.0000e+00\n",
      "Epoch 15/300\n",
      "115/115 [==============================] - 0s 209us/step - loss: 0.1835 - acc: 0.0000e+00 - val_loss: 0.2304 - val_acc: 0.0000e+00\n",
      "Epoch 16/300\n",
      "115/115 [==============================] - 0s 209us/step - loss: 0.1823 - acc: 0.0000e+00 - val_loss: 0.2278 - val_acc: 0.0000e+00\n",
      "Epoch 17/300\n",
      "115/115 [==============================] - 0s 209us/step - loss: 0.1812 - acc: 0.0000e+00 - val_loss: 0.2260 - val_acc: 0.0000e+00\n",
      "Epoch 18/300\n",
      "115/115 [==============================] - 0s 213us/step - loss: 0.1803 - acc: 0.0000e+00 - val_loss: 0.2261 - val_acc: 0.0000e+00\n",
      "Epoch 19/300\n",
      "115/115 [==============================] - 0s 222us/step - loss: 0.1792 - acc: 0.0000e+00 - val_loss: 0.2243 - val_acc: 0.0000e+00\n",
      "Epoch 20/300\n",
      "115/115 [==============================] - 0s 226us/step - loss: 0.1794 - acc: 0.0000e+00 - val_loss: 0.2217 - val_acc: 0.0000e+00\n",
      "Epoch 21/300\n",
      "115/115 [==============================] - 0s 209us/step - loss: 0.1779 - acc: 0.0000e+00 - val_loss: 0.2229 - val_acc: 0.0000e+00\n",
      "Epoch 22/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1771 - acc: 0.0000e+00 - val_loss: 0.2196 - val_acc: 0.0000e+00\n",
      "Epoch 23/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1758 - acc: 0.0000e+00 - val_loss: 0.2191 - val_acc: 0.0000e+00\n",
      "Epoch 24/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1752 - acc: 0.0000e+00 - val_loss: 0.2179 - val_acc: 0.0000e+00\n",
      "Epoch 25/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1746 - acc: 0.0000e+00 - val_loss: 0.2180 - val_acc: 0.0000e+00\n",
      "Epoch 26/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1739 - acc: 0.0000e+00 - val_loss: 0.2172 - val_acc: 0.0000e+00\n",
      "Epoch 27/300\n",
      "115/115 [==============================] - 0s 187us/step - loss: 0.1732 - acc: 0.0000e+00 - val_loss: 0.2150 - val_acc: 0.0000e+00\n",
      "Epoch 28/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1730 - acc: 0.0000e+00 - val_loss: 0.2139 - val_acc: 0.0000e+00\n",
      "Epoch 29/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1726 - acc: 0.0000e+00 - val_loss: 0.2145 - val_acc: 0.0000e+00\n",
      "Epoch 30/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1722 - acc: 0.0000e+00 - val_loss: 0.2124 - val_acc: 0.0000e+00\n",
      "Epoch 31/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1716 - acc: 0.0000e+00 - val_loss: 0.2123 - val_acc: 0.0000e+00\n",
      "Epoch 32/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1727 - acc: 0.0000e+00 - val_loss: 0.2136 - val_acc: 0.0000e+00\n",
      "Epoch 33/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1710 - acc: 0.0000e+00 - val_loss: 0.2109 - val_acc: 0.0000e+00\n",
      "Epoch 34/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1703 - acc: 0.0000e+00 - val_loss: 0.2117 - val_acc: 0.0000e+00\n",
      "Epoch 35/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1702 - acc: 0.0000e+00 - val_loss: 0.2105 - val_acc: 0.0000e+00\n",
      "Epoch 36/300\n",
      "115/115 [==============================] - 0s 187us/step - loss: 0.1695 - acc: 0.0000e+00 - val_loss: 0.2109 - val_acc: 0.0000e+00\n",
      "Epoch 37/300\n",
      "115/115 [==============================] - 0s 209us/step - loss: 0.1695 - acc: 0.0000e+00 - val_loss: 0.2120 - val_acc: 0.0000e+00\n",
      "Epoch 38/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1690 - acc: 0.0000e+00 - val_loss: 0.2102 - val_acc: 0.0000e+00\n",
      "Epoch 39/300\n",
      "115/115 [==============================] - 0s 187us/step - loss: 0.1690 - acc: 0.0000e+00 - val_loss: 0.2096 - val_acc: 0.0000e+00\n",
      "Epoch 40/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1690 - acc: 0.0000e+00 - val_loss: 0.2084 - val_acc: 0.0000e+00\n",
      "Epoch 41/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1691 - acc: 0.0000e+00 - val_loss: 0.2094 - val_acc: 0.0000e+00\n",
      "Epoch 42/300\n",
      "115/115 [==============================] - 0s 209us/step - loss: 0.1683 - acc: 0.0000e+00 - val_loss: 0.2096 - val_acc: 0.0000e+00\n",
      "Epoch 43/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1680 - acc: 0.0000e+00 - val_loss: 0.2080 - val_acc: 0.0000e+00\n",
      "Epoch 44/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1676 - acc: 0.0000e+00 - val_loss: 0.2089 - val_acc: 0.0000e+00\n",
      "Epoch 45/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1676 - acc: 0.0000e+00 - val_loss: 0.2073 - val_acc: 0.0000e+00\n",
      "Epoch 46/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1681 - acc: 0.0000e+00 - val_loss: 0.2100 - val_acc: 0.0000e+00\n",
      "Epoch 47/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1668 - acc: 0.0000e+00 - val_loss: 0.2081 - val_acc: 0.0000e+00\n",
      "Epoch 48/300\n",
      "115/115 [==============================] - 0s 187us/step - loss: 0.1671 - acc: 0.0000e+00 - val_loss: 0.2076 - val_acc: 0.0000e+00\n",
      "Epoch 49/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1667 - acc: 0.0000e+00 - val_loss: 0.2075 - val_acc: 0.0000e+00\n",
      "Epoch 50/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1667 - acc: 0.0000e+00 - val_loss: 0.2068 - val_acc: 0.0000e+00\n",
      "Epoch 51/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1665 - acc: 0.0000e+00 - val_loss: 0.2070 - val_acc: 0.0000e+00\n",
      "Epoch 52/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1664 - acc: 0.0000e+00 - val_loss: 0.2059 - val_acc: 0.0000e+00\n",
      "Epoch 53/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1660 - acc: 0.0000e+00 - val_loss: 0.2068 - val_acc: 0.0000e+00\n",
      "Epoch 54/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1657 - acc: 0.0000e+00 - val_loss: 0.2068 - val_acc: 0.0000e+00\n",
      "Epoch 55/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1658 - acc: 0.0000e+00 - val_loss: 0.2060 - val_acc: 0.0000e+00\n",
      "Epoch 56/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1658 - acc: 0.0000e+00 - val_loss: 0.2062 - val_acc: 0.0000e+00\n",
      "Epoch 57/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115/115 [==============================] - 0s 183us/step - loss: 0.1653 - acc: 0.0000e+00 - val_loss: 0.2058 - val_acc: 0.0000e+00\n",
      "Epoch 58/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1658 - acc: 0.0000e+00 - val_loss: 0.2039 - val_acc: 0.0000e+00\n",
      "Epoch 59/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1660 - acc: 0.0000e+00 - val_loss: 0.2059 - val_acc: 0.0000e+00\n",
      "Epoch 60/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1654 - acc: 0.0000e+00 - val_loss: 0.2038 - val_acc: 0.0000e+00\n",
      "Epoch 61/300\n",
      "115/115 [==============================] - 0s 204us/step - loss: 0.1648 - acc: 0.0000e+00 - val_loss: 0.2049 - val_acc: 0.0000e+00\n",
      "Epoch 62/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1650 - acc: 0.0000e+00 - val_loss: 0.2043 - val_acc: 0.0000e+00\n",
      "Epoch 63/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1649 - acc: 0.0000e+00 - val_loss: 0.2034 - val_acc: 0.0000e+00\n",
      "Epoch 64/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1657 - acc: 0.0000e+00 - val_loss: 0.2065 - val_acc: 0.0000e+00\n",
      "Epoch 65/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1649 - acc: 0.0000e+00 - val_loss: 0.2034 - val_acc: 0.0000e+00\n",
      "Epoch 66/300\n",
      "115/115 [==============================] - 0s 187us/step - loss: 0.1644 - acc: 0.0000e+00 - val_loss: 0.2033 - val_acc: 0.0000e+00\n",
      "Epoch 67/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1643 - acc: 0.0000e+00 - val_loss: 0.2038 - val_acc: 0.0000e+00\n",
      "Epoch 68/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1641 - acc: 0.0000e+00 - val_loss: 0.2031 - val_acc: 0.0000e+00\n",
      "Epoch 69/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1673 - acc: 0.0000e+00 - val_loss: 0.2058 - val_acc: 0.0000e+00\n",
      "Epoch 70/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1638 - acc: 0.0000e+00 - val_loss: 0.2018 - val_acc: 0.0000e+00\n",
      "Epoch 71/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1642 - acc: 0.0000e+00 - val_loss: 0.2031 - val_acc: 0.0000e+00\n",
      "Epoch 72/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1639 - acc: 0.0000e+00 - val_loss: 0.2025 - val_acc: 0.0000e+00\n",
      "Epoch 73/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1640 - acc: 0.0000e+00 - val_loss: 0.2026 - val_acc: 0.0000e+00\n",
      "Epoch 74/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1642 - acc: 0.0000e+00 - val_loss: 0.2037 - val_acc: 0.0000e+00\n",
      "Epoch 75/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1640 - acc: 0.0000e+00 - val_loss: 0.2019 - val_acc: 0.0000e+00\n",
      "Epoch 76/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1649 - acc: 0.0000e+00 - val_loss: 0.2054 - val_acc: 0.0000e+00\n",
      "Epoch 77/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1633 - acc: 0.0000e+00 - val_loss: 0.2027 - val_acc: 0.0000e+00\n",
      "Epoch 78/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1651 - acc: 0.0000e+00 - val_loss: 0.2038 - val_acc: 0.0000e+00\n",
      "Epoch 79/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1648 - acc: 0.0000e+00 - val_loss: 0.2005 - val_acc: 0.0000e+00\n",
      "Epoch 80/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1637 - acc: 0.0000e+00 - val_loss: 0.2017 - val_acc: 0.0000e+00\n",
      "Epoch 81/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1638 - acc: 0.0000e+00 - val_loss: 0.2027 - val_acc: 0.0000e+00\n",
      "Epoch 82/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1640 - acc: 0.0000e+00 - val_loss: 0.2014 - val_acc: 0.0000e+00\n",
      "Epoch 83/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1639 - acc: 0.0000e+00 - val_loss: 0.2034 - val_acc: 0.0000e+00\n",
      "Epoch 84/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1638 - acc: 0.0000e+00 - val_loss: 0.2016 - val_acc: 0.0000e+00\n",
      "Epoch 85/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1639 - acc: 0.0000e+00 - val_loss: 0.2011 - val_acc: 0.0000e+00\n",
      "Epoch 86/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1636 - acc: 0.0000e+00 - val_loss: 0.2035 - val_acc: 0.0000e+00\n",
      "Epoch 87/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1637 - acc: 0.0000e+00 - val_loss: 0.2029 - val_acc: 0.0000e+00\n",
      "Epoch 88/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1643 - acc: 0.0000e+00 - val_loss: 0.2006 - val_acc: 0.0000e+00\n",
      "Epoch 89/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1638 - acc: 0.0000e+00 - val_loss: 0.2011 - val_acc: 0.0000e+00\n",
      "Epoch 90/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1638 - acc: 0.0000e+00 - val_loss: 0.2043 - val_acc: 0.0000e+00\n",
      "Epoch 91/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1650 - acc: 0.0000e+00 - val_loss: 0.2004 - val_acc: 0.0000e+00\n",
      "Epoch 92/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.2019 - val_acc: 0.0000e+00\n",
      "Epoch 93/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.2015 - val_acc: 0.0000e+00\n",
      "Epoch 94/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1637 - acc: 0.0000e+00 - val_loss: 0.2007 - val_acc: 0.0000e+00\n",
      "Epoch 95/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1645 - acc: 0.0000e+00 - val_loss: 0.2040 - val_acc: 0.0000e+00\n",
      "Epoch 96/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1633 - acc: 0.0000e+00 - val_loss: 0.2012 - val_acc: 0.0000e+00\n",
      "Epoch 97/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.2004 - val_acc: 0.0000e+00\n",
      "Epoch 98/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1638 - acc: 0.0000e+00 - val_loss: 0.2017 - val_acc: 0.0000e+00\n",
      "Epoch 99/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.2012 - val_acc: 0.0000e+00\n",
      "Epoch 100/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.2015 - val_acc: 0.0000e+00\n",
      "Epoch 101/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1636 - acc: 0.0000e+00 - val_loss: 0.2002 - val_acc: 0.0000e+00\n",
      "Epoch 102/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1635 - acc: 0.0000e+00 - val_loss: 0.2016 - val_acc: 0.0000e+00\n",
      "Epoch 103/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1636 - acc: 0.0000e+00 - val_loss: 0.2015 - val_acc: 0.0000e+00\n",
      "Epoch 104/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1639 - acc: 0.0000e+00 - val_loss: 0.2026 - val_acc: 0.0000e+00\n",
      "Epoch 105/300\n",
      "115/115 [==============================] - ETA: 0s - loss: 0.0826 - acc: 0.0000e+0 - 0s 187us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.2005 - val_acc: 0.0000e+00\n",
      "Epoch 106/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.2002 - val_acc: 0.0000e+00\n",
      "Epoch 107/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1639 - acc: 0.0000e+00 - val_loss: 0.2016 - val_acc: 0.0000e+00\n",
      "Epoch 108/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.2007 - val_acc: 0.0000e+00\n",
      "Epoch 109/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1633 - acc: 0.0000e+00 - val_loss: 0.2008 - val_acc: 0.0000e+00\n",
      "Epoch 110/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1633 - acc: 0.0000e+00 - val_loss: 0.2011 - val_acc: 0.0000e+00\n",
      "Epoch 111/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.2006 - val_acc: 0.0000e+00\n",
      "Epoch 112/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1633 - acc: 0.0000e+00 - val_loss: 0.2006 - val_acc: 0.0000e+00\n",
      "Epoch 113/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115/115 [==============================] - 0s 200us/step - loss: 0.1642 - acc: 0.0000e+00 - val_loss: 0.1996 - val_acc: 0.0000e+00\n",
      "Epoch 114/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.2004 - val_acc: 0.0000e+00\n",
      "Epoch 115/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1642 - acc: 0.0000e+00 - val_loss: 0.2024 - val_acc: 0.0000e+00\n",
      "Epoch 116/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.1999 - val_acc: 0.0000e+00\n",
      "Epoch 117/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1635 - acc: 0.0000e+00 - val_loss: 0.2009 - val_acc: 0.0000e+00\n",
      "Epoch 118/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1635 - acc: 0.0000e+00 - val_loss: 0.1995 - val_acc: 0.0000e+00\n",
      "Epoch 119/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1639 - acc: 0.0000e+00 - val_loss: 0.2012 - val_acc: 0.0000e+00\n",
      "Epoch 120/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1635 - acc: 0.0000e+00 - val_loss: 0.1995 - val_acc: 0.0000e+00\n",
      "Epoch 121/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1635 - acc: 0.0000e+00 - val_loss: 0.2015 - val_acc: 0.0000e+00\n",
      "Epoch 122/300\n",
      "115/115 [==============================] - 0s 187us/step - loss: 0.1638 - acc: 0.0000e+00 - val_loss: 0.2010 - val_acc: 0.0000e+00\n",
      "Epoch 123/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1635 - acc: 0.0000e+00 - val_loss: 0.1999 - val_acc: 0.0000e+00\n",
      "Epoch 124/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1648 - acc: 0.0000e+00 - val_loss: 0.2005 - val_acc: 0.0000e+00\n",
      "Epoch 125/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.2004 - val_acc: 0.0000e+00\n",
      "Epoch 126/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1636 - acc: 0.0000e+00 - val_loss: 0.2002 - val_acc: 0.0000e+00\n",
      "Epoch 127/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1635 - acc: 0.0000e+00 - val_loss: 0.2003 - val_acc: 0.0000e+00\n",
      "Epoch 128/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1633 - acc: 0.0000e+00 - val_loss: 0.2019 - val_acc: 0.0000e+00\n",
      "Epoch 129/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1640 - acc: 0.0000e+00 - val_loss: 0.1999 - val_acc: 0.0000e+00\n",
      "Epoch 130/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.2019 - val_acc: 0.0000e+00\n",
      "Epoch 131/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1638 - acc: 0.0000e+00 - val_loss: 0.1994 - val_acc: 0.0000e+00\n",
      "Epoch 132/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1644 - acc: 0.0000e+00 - val_loss: 0.2025 - val_acc: 0.0000e+00\n",
      "Epoch 133/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1639 - acc: 0.0000e+00 - val_loss: 0.1993 - val_acc: 0.0000e+00\n",
      "Epoch 134/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.2002 - val_acc: 0.0000e+00\n",
      "Epoch 135/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.2016 - val_acc: 0.0000e+00\n",
      "Epoch 136/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1631 - acc: 0.0000e+00 - val_loss: 0.2000 - val_acc: 0.0000e+00\n",
      "Epoch 137/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1637 - acc: 0.0000e+00 - val_loss: 0.1991 - val_acc: 0.0000e+00\n",
      "Epoch 138/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.2001 - val_acc: 0.0000e+00\n",
      "Epoch 139/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1633 - acc: 0.0000e+00 - val_loss: 0.2006 - val_acc: 0.0000e+00\n",
      "Epoch 140/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1637 - acc: 0.0000e+00 - val_loss: 0.1989 - val_acc: 0.0000e+00\n",
      "Epoch 141/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1641 - acc: 0.0000e+00 - val_loss: 0.2018 - val_acc: 0.0000e+00\n",
      "Epoch 142/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.2008 - val_acc: 0.0000e+00\n",
      "Epoch 143/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1633 - acc: 0.0000e+00 - val_loss: 0.1989 - val_acc: 0.0000e+00\n",
      "Epoch 144/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1647 - acc: 0.0000e+00 - val_loss: 0.1975 - val_acc: 0.0000e+00\n",
      "Epoch 145/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1640 - acc: 0.0000e+00 - val_loss: 0.2016 - val_acc: 0.0000e+00\n",
      "Epoch 146/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1633 - acc: 0.0000e+00 - val_loss: 0.1997 - val_acc: 0.0000e+00\n",
      "Epoch 147/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.2003 - val_acc: 0.0000e+00\n",
      "Epoch 148/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1635 - acc: 0.0000e+00 - val_loss: 0.2005 - val_acc: 0.0000e+00\n",
      "Epoch 149/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1641 - acc: 0.0000e+00 - val_loss: 0.2000 - val_acc: 0.0000e+00\n",
      "Epoch 150/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1648 - acc: 0.0000e+00 - val_loss: 0.1973 - val_acc: 0.0000e+00\n",
      "Epoch 151/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1641 - acc: 0.0000e+00 - val_loss: 0.2010 - val_acc: 0.0000e+00\n",
      "Epoch 152/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.2004 - val_acc: 0.0000e+00\n",
      "Epoch 153/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1635 - acc: 0.0000e+00 - val_loss: 0.1985 - val_acc: 0.0000e+00\n",
      "Epoch 154/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1635 - acc: 0.0000e+00 - val_loss: 0.2004 - val_acc: 0.0000e+00\n",
      "Epoch 155/300\n",
      "115/115 [==============================] - 0s 204us/step - loss: 0.1631 - acc: 0.0000e+00 - val_loss: 0.1990 - val_acc: 0.0000e+00\n",
      "Epoch 156/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1639 - acc: 0.0000e+00 - val_loss: 0.2011 - val_acc: 0.0000e+00\n",
      "Epoch 157/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.2000 - val_acc: 0.0000e+00\n",
      "Epoch 158/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1637 - acc: 0.0000e+00 - val_loss: 0.1995 - val_acc: 0.0000e+00\n",
      "Epoch 159/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1635 - acc: 0.0000e+00 - val_loss: 0.2001 - val_acc: 0.0000e+00\n",
      "Epoch 160/300\n",
      "115/115 [==============================] - 0s 187us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1994 - val_acc: 0.0000e+00\n",
      "Epoch 161/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1636 - acc: 0.0000e+00 - val_loss: 0.1979 - val_acc: 0.0000e+00\n",
      "Epoch 162/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1631 - acc: 0.0000e+00 - val_loss: 0.2001 - val_acc: 0.0000e+00\n",
      "Epoch 163/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1635 - acc: 0.0000e+00 - val_loss: 0.1994 - val_acc: 0.0000e+00\n",
      "Epoch 164/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1635 - acc: 0.0000e+00 - val_loss: 0.2005 - val_acc: 0.0000e+00\n",
      "Epoch 165/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1631 - acc: 0.0000e+00 - val_loss: 0.1999 - val_acc: 0.0000e+00\n",
      "Epoch 166/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1636 - acc: 0.0000e+00 - val_loss: 0.1989 - val_acc: 0.0000e+00\n",
      "Epoch 167/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1988 - val_acc: 0.0000e+00\n",
      "Epoch 168/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1631 - acc: 0.0000e+00 - val_loss: 0.1999 - val_acc: 0.0000e+00\n",
      "Epoch 169/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115/115 [==============================] - 0s 200us/step - loss: 0.1638 - acc: 0.0000e+00 - val_loss: 0.1984 - val_acc: 0.0000e+00\n",
      "Epoch 170/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1631 - acc: 0.0000e+00 - val_loss: 0.2006 - val_acc: 0.0000e+00\n",
      "Epoch 171/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.2004 - val_acc: 0.0000e+00\n",
      "Epoch 172/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1640 - acc: 0.0000e+00 - val_loss: 0.1986 - val_acc: 0.0000e+00\n",
      "Epoch 173/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1628 - acc: 0.0000e+00 - val_loss: 0.1998 - val_acc: 0.0000e+00\n",
      "Epoch 174/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1636 - acc: 0.0000e+00 - val_loss: 0.1995 - val_acc: 0.0000e+00\n",
      "Epoch 175/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1995 - val_acc: 0.0000e+00\n",
      "Epoch 176/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1633 - acc: 0.0000e+00 - val_loss: 0.1987 - val_acc: 0.0000e+00\n",
      "Epoch 177/300\n",
      "115/115 [==============================] - 0s 187us/step - loss: 0.1631 - acc: 0.0000e+00 - val_loss: 0.1994 - val_acc: 0.0000e+00\n",
      "Epoch 178/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1638 - acc: 0.0000e+00 - val_loss: 0.2004 - val_acc: 0.0000e+00\n",
      "Epoch 179/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.2005 - val_acc: 0.0000e+00\n",
      "Epoch 180/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1987 - val_acc: 0.0000e+00\n",
      "Epoch 181/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1631 - acc: 0.0000e+00 - val_loss: 0.1995 - val_acc: 0.0000e+00\n",
      "Epoch 182/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1986 - val_acc: 0.0000e+00\n",
      "Epoch 183/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1990 - val_acc: 0.0000e+00\n",
      "Epoch 184/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.1982 - val_acc: 0.0000e+00\n",
      "Epoch 185/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1636 - acc: 0.0000e+00 - val_loss: 0.2013 - val_acc: 0.0000e+00\n",
      "Epoch 186/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1992 - val_acc: 0.0000e+00\n",
      "Epoch 187/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.1992 - val_acc: 0.0000e+00\n",
      "Epoch 188/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1640 - acc: 0.0000e+00 - val_loss: 0.2012 - val_acc: 0.0000e+00\n",
      "Epoch 189/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.1981 - val_acc: 0.0000e+00\n",
      "Epoch 190/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1636 - acc: 0.0000e+00 - val_loss: 0.1988 - val_acc: 0.0000e+00\n",
      "Epoch 191/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.1994 - val_acc: 0.0000e+00\n",
      "Epoch 192/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1986 - val_acc: 0.0000e+00\n",
      "Epoch 193/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1631 - acc: 0.0000e+00 - val_loss: 0.1988 - val_acc: 0.0000e+00\n",
      "Epoch 194/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.1992 - val_acc: 0.0000e+00\n",
      "Epoch 195/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.1986 - val_acc: 0.0000e+00\n",
      "Epoch 196/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1997 - val_acc: 0.0000e+00\n",
      "Epoch 197/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1995 - val_acc: 0.0000e+00\n",
      "Epoch 198/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1636 - acc: 0.0000e+00 - val_loss: 0.2004 - val_acc: 0.0000e+00\n",
      "Epoch 199/300\n",
      "115/115 [==============================] - 0s 187us/step - loss: 0.1628 - acc: 0.0000e+00 - val_loss: 0.1980 - val_acc: 0.0000e+00\n",
      "Epoch 200/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1635 - acc: 0.0000e+00 - val_loss: 0.1973 - val_acc: 0.0000e+00\n",
      "Epoch 201/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1991 - val_acc: 0.0000e+00\n",
      "Epoch 202/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.1984 - val_acc: 0.0000e+00\n",
      "Epoch 203/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1993 - val_acc: 0.0000e+00\n",
      "Epoch 204/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1640 - acc: 0.0000e+00 - val_loss: 0.1999 - val_acc: 0.0000e+00\n",
      "Epoch 205/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.1981 - val_acc: 0.0000e+00\n",
      "Epoch 206/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1993 - val_acc: 0.0000e+00\n",
      "Epoch 207/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1637 - acc: 0.0000e+00 - val_loss: 0.1977 - val_acc: 0.0000e+00\n",
      "Epoch 208/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1627 - acc: 0.0000e+00 - val_loss: 0.1989 - val_acc: 0.0000e+00\n",
      "Epoch 209/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1995 - val_acc: 0.0000e+00\n",
      "Epoch 210/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1631 - acc: 0.0000e+00 - val_loss: 0.1987 - val_acc: 0.0000e+00\n",
      "Epoch 211/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1631 - acc: 0.0000e+00 - val_loss: 0.1987 - val_acc: 0.0000e+00\n",
      "Epoch 212/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.1992 - val_acc: 0.0000e+00\n",
      "Epoch 213/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1628 - acc: 0.0000e+00 - val_loss: 0.1992 - val_acc: 0.0000e+00\n",
      "Epoch 214/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1633 - acc: 0.0000e+00 - val_loss: 0.1976 - val_acc: 0.0000e+00\n",
      "Epoch 215/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.2008 - val_acc: 0.0000e+00\n",
      "Epoch 216/300\n",
      "115/115 [==============================] - 0s 187us/step - loss: 0.1636 - acc: 0.0000e+00 - val_loss: 0.1988 - val_acc: 0.0000e+00\n",
      "Epoch 217/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1642 - acc: 0.0000e+00 - val_loss: 0.1973 - val_acc: 0.0000e+00\n",
      "Epoch 218/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1627 - acc: 0.0000e+00 - val_loss: 0.1995 - val_acc: 0.0000e+00\n",
      "Epoch 219/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1652 - acc: 0.0000e+00 - val_loss: 0.2021 - val_acc: 0.0000e+00\n",
      "Epoch 220/300\n",
      "115/115 [==============================] - 0s 192us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.1968 - val_acc: 0.0000e+00\n",
      "Epoch 221/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.1976 - val_acc: 0.0000e+00\n",
      "Epoch 222/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1628 - acc: 0.0000e+00 - val_loss: 0.1986 - val_acc: 0.0000e+00\n",
      "Epoch 223/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1631 - acc: 0.0000e+00 - val_loss: 0.1981 - val_acc: 0.0000e+00\n",
      "Epoch 224/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1982 - val_acc: 0.0000e+00\n",
      "Epoch 225/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115/115 [==============================] - 0s 187us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1984 - val_acc: 0.0000e+00\n",
      "Epoch 226/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1999 - val_acc: 0.0000e+00\n",
      "Epoch 227/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1633 - acc: 0.0000e+00 - val_loss: 0.1981 - val_acc: 0.0000e+00\n",
      "Epoch 228/300\n",
      "115/115 [==============================] - 0s 187us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.1994 - val_acc: 0.0000e+00\n",
      "Epoch 229/300\n",
      "115/115 [==============================] - 0s 187us/step - loss: 0.1635 - acc: 0.0000e+00 - val_loss: 0.1990 - val_acc: 0.0000e+00\n",
      "Epoch 230/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1631 - acc: 0.0000e+00 - val_loss: 0.1989 - val_acc: 0.0000e+00\n",
      "Epoch 231/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.1990 - val_acc: 0.0000e+00\n",
      "Epoch 232/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1987 - val_acc: 0.0000e+00\n",
      "Epoch 233/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1982 - val_acc: 0.0000e+00\n",
      "Epoch 234/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1628 - acc: 0.0000e+00 - val_loss: 0.1980 - val_acc: 0.0000e+00\n",
      "Epoch 235/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1973 - val_acc: 0.0000e+00\n",
      "Epoch 236/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1982 - val_acc: 0.0000e+00\n",
      "Epoch 237/300\n",
      "115/115 [==============================] - 0s 187us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1983 - val_acc: 0.0000e+00\n",
      "Epoch 238/300\n",
      "115/115 [==============================] - 0s 187us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1978 - val_acc: 0.0000e+00\n",
      "Epoch 239/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1993 - val_acc: 0.0000e+00\n",
      "Epoch 240/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1628 - acc: 0.0000e+00 - val_loss: 0.1980 - val_acc: 0.0000e+00\n",
      "Epoch 241/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1639 - acc: 0.0000e+00 - val_loss: 0.1995 - val_acc: 0.0000e+00\n",
      "Epoch 242/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1628 - acc: 0.0000e+00 - val_loss: 0.1966 - val_acc: 0.0000e+00\n",
      "Epoch 243/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1643 - acc: 0.0000e+00 - val_loss: 0.1989 - val_acc: 0.0000e+00\n",
      "Epoch 244/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1639 - acc: 0.0000e+00 - val_loss: 0.1977 - val_acc: 0.0000e+00\n",
      "Epoch 245/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1984 - val_acc: 0.0000e+00\n",
      "Epoch 246/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1984 - val_acc: 0.0000e+00\n",
      "Epoch 247/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.1998 - val_acc: 0.0000e+00\n",
      "Epoch 248/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1974 - val_acc: 0.0000e+00\n",
      "Epoch 249/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1639 - acc: 0.0000e+00 - val_loss: 0.1964 - val_acc: 0.0000e+00\n",
      "Epoch 250/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1631 - acc: 0.0000e+00 - val_loss: 0.1986 - val_acc: 0.0000e+00\n",
      "Epoch 251/300\n",
      "115/115 [==============================] - 0s 187us/step - loss: 0.1628 - acc: 0.0000e+00 - val_loss: 0.1983 - val_acc: 0.0000e+00\n",
      "Epoch 252/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1633 - acc: 0.0000e+00 - val_loss: 0.1984 - val_acc: 0.0000e+00\n",
      "Epoch 253/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1993 - val_acc: 0.0000e+00\n",
      "Epoch 254/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1631 - acc: 0.0000e+00 - val_loss: 0.1974 - val_acc: 0.0000e+00\n",
      "Epoch 255/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1976 - val_acc: 0.0000e+00\n",
      "Epoch 256/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1654 - acc: 0.0000e+00 - val_loss: 0.1969 - val_acc: 0.0000e+00\n",
      "Epoch 257/300\n",
      "115/115 [==============================] - 0s 183us/step - loss: 0.1635 - acc: 0.0000e+00 - val_loss: 0.2006 - val_acc: 0.0000e+00\n",
      "Epoch 258/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.1993 - val_acc: 0.0000e+00\n",
      "Epoch 259/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1995 - val_acc: 0.0000e+00\n",
      "Epoch 260/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1638 - acc: 0.0000e+00 - val_loss: 0.1970 - val_acc: 0.0000e+00\n",
      "Epoch 261/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1626 - acc: 0.0000e+00 - val_loss: 0.1980 - val_acc: 0.0000e+00\n",
      "Epoch 262/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1637 - acc: 0.0000e+00 - val_loss: 0.2005 - val_acc: 0.0000e+00\n",
      "Epoch 263/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1633 - acc: 0.0000e+00 - val_loss: 0.1972 - val_acc: 0.0000e+00\n",
      "Epoch 264/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1638 - acc: 0.0000e+00 - val_loss: 0.1973 - val_acc: 0.0000e+00\n",
      "Epoch 265/300\n",
      "115/115 [==============================] - 0s 200us/step - loss: 0.1639 - acc: 0.0000e+00 - val_loss: 0.2007 - val_acc: 0.0000e+00\n",
      "Epoch 266/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1975 - val_acc: 0.0000e+00\n",
      "Epoch 267/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1633 - acc: 0.0000e+00 - val_loss: 0.1986 - val_acc: 0.0000e+00\n",
      "Epoch 268/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1636 - acc: 0.0000e+00 - val_loss: 0.1968 - val_acc: 0.0000e+00\n",
      "Epoch 269/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.1981 - val_acc: 0.0000e+00\n",
      "Epoch 270/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.1984 - val_acc: 0.0000e+00\n",
      "Epoch 271/300\n",
      "115/115 [==============================] - 0s 204us/step - loss: 0.1631 - acc: 0.0000e+00 - val_loss: 0.1995 - val_acc: 0.0000e+00\n",
      "Epoch 272/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1626 - acc: 0.0000e+00 - val_loss: 0.1987 - val_acc: 0.0000e+00\n",
      "Epoch 273/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1638 - acc: 0.0000e+00 - val_loss: 0.1957 - val_acc: 0.0000e+00\n",
      "Epoch 274/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1644 - acc: 0.0000e+00 - val_loss: 0.1988 - val_acc: 0.0000e+00\n",
      "Epoch 275/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1983 - val_acc: 0.0000e+00\n",
      "Epoch 276/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1966 - val_acc: 0.0000e+00\n",
      "Epoch 277/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1977 - val_acc: 0.0000e+00\n",
      "Epoch 278/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.1982 - val_acc: 0.0000e+00\n",
      "Epoch 279/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1989 - val_acc: 0.0000e+00\n",
      "Epoch 280/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1987 - val_acc: 0.0000e+00\n",
      "Epoch 281/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115/115 [==============================] - 0s 191us/step - loss: 0.1625 - acc: 0.0000e+00 - val_loss: 0.1970 - val_acc: 0.0000e+00\n",
      "Epoch 282/300\n",
      "115/115 [==============================] - ETA: 0s - loss: 0.2754 - acc: 0.0000e+0 - 0s 191us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1963 - val_acc: 0.0000e+00\n",
      "Epoch 283/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1976 - val_acc: 0.0000e+00\n",
      "Epoch 284/300\n",
      "115/115 [==============================] - 0s 187us/step - loss: 0.1632 - acc: 0.0000e+00 - val_loss: 0.1987 - val_acc: 0.0000e+00\n",
      "Epoch 285/300\n",
      "115/115 [==============================] - 0s 196us/step - loss: 0.1628 - acc: 0.0000e+00 - val_loss: 0.1987 - val_acc: 0.0000e+00\n",
      "Epoch 286/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1986 - val_acc: 0.0000e+00\n",
      "Epoch 287/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1627 - acc: 0.0000e+00 - val_loss: 0.1979 - val_acc: 0.0000e+00\n",
      "Epoch 288/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1975 - val_acc: 0.0000e+00\n",
      "Epoch 289/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1966 - val_acc: 0.0000e+00\n",
      "Epoch 290/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1630 - acc: 0.0000e+00 - val_loss: 0.1987 - val_acc: 0.0000e+00\n",
      "Epoch 291/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1628 - acc: 0.0000e+00 - val_loss: 0.1971 - val_acc: 0.0000e+00\n",
      "Epoch 292/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1628 - acc: 0.0000e+00 - val_loss: 0.1975 - val_acc: 0.0000e+00\n",
      "Epoch 293/300\n",
      "115/115 [==============================] - 0s 187us/step - loss: 0.1628 - acc: 0.0000e+00 - val_loss: 0.1987 - val_acc: 0.0000e+00\n",
      "Epoch 294/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1628 - acc: 0.0000e+00 - val_loss: 0.1977 - val_acc: 0.0000e+00\n",
      "Epoch 295/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1634 - acc: 0.0000e+00 - val_loss: 0.1966 - val_acc: 0.0000e+00\n",
      "Epoch 296/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1627 - acc: 0.0000e+00 - val_loss: 0.1983 - val_acc: 0.0000e+00\n",
      "Epoch 297/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1628 - acc: 0.0000e+00 - val_loss: 0.1975 - val_acc: 0.0000e+00\n",
      "Epoch 298/300\n",
      "115/115 [==============================] - 0s 187us/step - loss: 0.1628 - acc: 0.0000e+00 - val_loss: 0.1986 - val_acc: 0.0000e+00\n",
      "Epoch 299/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1627 - acc: 0.0000e+00 - val_loss: 0.1976 - val_acc: 0.0000e+00\n",
      "Epoch 300/300\n",
      "115/115 [==============================] - 0s 191us/step - loss: 0.1629 - acc: 0.0000e+00 - val_loss: 0.1989 - val_acc: 0.0000e+00\n"
     ]
    }
   ],
   "source": [
    "hist_1 = model_1.fit(X_train, y_train,\n",
    "          batch_size=5, epochs=300,\n",
    "          validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the training curve, loss function for our model.\n",
    "\n",
    "* this is a measure of the inaccuracy\n",
    "\n",
    "* **square loss** ($L_2$ loss) is the:\n",
    "\n",
    "\\begin{equation}\n",
    "L_2 = \\sum_{\\bf{u}_{\\alpha} \\in AOI} \\left(y(\\bf{u}_{\\alpha}) - \\hat{f}(x_1(\\bf{u}_{\\alpha}),\\ldots,x_m(\\bf{u}_{\\alpha})\\right)\n",
    "\\end{equation}\n",
    "\n",
    "We can see the progress of the model over epochs in reduction of training and testing error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,1,1)\n",
    "plt.plot(hist_1.history['loss'])\n",
    "plt.plot(hist_1.history['val_loss'])\n",
    "plt.title('Loss function of Model 1')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the model in prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "AIbins = np.linspace(df_subset['AI'].values.min(), df_subset['AI'].values.max(), 30) # set the bins for prediction\n",
    "norm_AIbins = (AIbins-AI_min)/(AI_max-AI_min)*2-1 # use normalized bins\n",
    "\n",
    "pred_norm_porsity = model_1.predict(np.array(norm_AIbins)) # predict with our ANN\n",
    "\n",
    "pred_porosity = (pred_norm_porsity + 1)/2*(por_max - por_min)+por_min # transform it back to original range\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(df_subset['AI'].values,df_subset['Porosity'].values,'ko', alpha = 0.4)\n",
    "plt.plot(AIbins,pred_porosity,'r-',linewidth=5)\n",
    "plt.xlabel('Acoustic Impedance (kg/m2s*10^6)')\n",
    "plt.ylabel('Porosity (faction)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Neural Net-Based Map of Porosity from Seismic Acoustic Impedance\n",
    "\n",
    "Now let's predict porosity at all locations from the seismic data. First we normalize the seismic data.  \n",
    "\n",
    "* We may have some values below -1.0 and above 1.0 as we are using the transform applied to the acoustic impedance at the wells.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm_seismic = (seismic-AI_min)/(AI_max-AI_min)*2-1\n",
    "\n",
    "pred_norm_porosity_map = model_1.predict(np.array(norm_seismic.flatten())) # predict with our ANN\n",
    "\n",
    "pred_porosity_map = (pred_norm_porosity_map + 1)/2*(por_max - por_min)+por_min # transform it back to original range\n",
    "pred_porosity_map = np.reshape(pred_porosity_map,(ny,nx))\n",
    "\n",
    "plt.subplot(121)\n",
    "GSLIB.pixelplt_st(norm_seismic,xmin,xmax,ymin,ymax,cell_size,-1,1,'Acoustic Impedance','X(m)','Y(m)','Acoustic Impedance (normalized)',cmap)\n",
    "\n",
    "plt.subplot(122)\n",
    "GSLIB.pixelplt_st(pred_porosity_map,xmin,xmax,ymin,ymax,cell_size,por_min,por_max,'Predicted Porosity','X(m)','Y(m)','Porosity (fraction)',cmap)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.2, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing the Neural Net\n",
    "\n",
    "There are some methods available to interogate the artificial neural net.\n",
    "\n",
    "* neural net summary\n",
    "\n",
    "* weights\n",
    "\n",
    "Here's the summary from our neural net.  It lists by layers the number of nodes and number of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_24 (Dense)             (None, 1)                 2         \n",
      "_________________________________________________________________\n",
      "dense_25 (Dense)             (None, 3)                 6         \n",
      "_________________________________________________________________\n",
      "dense_26 (Dense)             (None, 1)                 4         \n",
      "=================================================================\n",
      "Total params: 12\n",
      "Trainable params: 12\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see the actual trained weights for each node in each layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'dense_24', 'trainable': True, 'batch_input_shape': (None, 1), 'dtype': 'float32', 'units': 1, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
      "[array([[-0.70692515]], dtype=float32), array([0.30242074], dtype=float32)]\n",
      "\n",
      "\n",
      "{'name': 'dense_25', 'trainable': True, 'units': 3, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
      "[array([[ 0.0549653, -0.8047614,  1.0055766]], dtype=float32), array([ 0.26931858,  0.        , -0.00066453], dtype=float32)]\n",
      "\n",
      "\n",
      "{'name': 'dense_26', 'trainable': True, 'units': 1, 'activation': 'tanh', 'use_bias': True, 'kernel_initializer': {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
      "[array([[-1.2730278],\n",
      "       [-0.4338343],\n",
      "       [ 1.3375224]], dtype=float32), array([-0.2667548], dtype=float32)]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Weights of Model_1\n",
    "\n",
    "for layer in model_1.layers:\n",
    "    g = layer.get_config()\n",
    "    h = layer.get_weights()\n",
    "    print(g)\n",
    "    print(h)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe the weights and bias terms associate with our neural network.\n",
    "\n",
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of artificial neural networks. \n",
    "\n",
    "The Texas Center for Geostatistics has many other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available [here](https://github.com/GeostatsGuy/PythonNumericalDemos), along with a package for geostatistics in Python called [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy). \n",
    "  \n",
    "We hope this was helpful,\n",
    "\n",
    "*Michael* and *Honggeun*\n",
    "\n",
    "***\n",
    "\n",
    "#### More on Michael Pyrcz and the Texas Center for Data Analytics and Geostatistics:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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