{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Neural Networks\n",
    "\n",
    "## TO DO: Almost all the figues and schematics will be replaced or improved slowly\n",
    "\n",
    "<img src=\"./images/neuralnets/Colored_neural_network.svg\"/>\n",
    "source: https://en.wikipedia.org/wiki/Artificial_neural_network\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## History of Neural networks\n",
    "\n",
    "**TODO: Make it more complete and format properly**\n",
    "\n",
    "1943 - Threshold Logic\n",
    "\n",
    "1940s - Hebbian Learning\n",
    "\n",
    "1958 - Perceptron\n",
    "\n",
    "1975 - Backpropagation\n",
    "\n",
    "1980s - Neocognitron\n",
    "\n",
    "1982: Hopfield Network\n",
    "\n",
    "1986: Convolutional Neural Networks\n",
    "\n",
    "1997: Long-short term memory (LSTM) model\n",
    "\n",
    "2014: Gated Recurrent Units, Generative Adversarial Networks(Check)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why the boom now?\n",
    "* Data\n",
    "* Data\n",
    "* Data\n",
    "* Availability of GPUs\n",
    "* Algorithmic developments which allow for efficient training and training for deeper networks\n",
    "* Much easier access than a decade ago"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building blocks\n",
    "### Perceptron\n",
    "\n",
    "Smallest unit of a neural network is a **perceptron** like node.\n",
    "\n",
    "**What is a Perceptron?**\n",
    "\n",
    "It is a simple function which has multiple inputs and a single output.\n",
    "\n",
    "Step 1: Weighted sum of the inputs is calculated\n",
    "\n",
    "\\begin{equation*}\n",
    "weighted\\_sum = \\sum_{k=1}^{num\\_inputs} w_{i} x_{i}\n",
    "\\end{equation*}\n",
    "\n",
    "Step 2: The following activation function is applied\n",
    "\n",
    "$$\n",
    "f(weighted\\_sum) = \\left\\{\n",
    "        \\begin{array}{ll}\n",
    "            0 & \\quad weighted\\_sum < threshold \\\\\n",
    "            1 & \\quad weighted\\_sum \\geq threshold\n",
    "        \\end{array}\n",
    "    \\right.\n",
    "$$\n",
    "\n",
    "You can see that this is also a linear classifier as we introduced in script 02."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config IPCompleter.greedy=True\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['lines.linewidth'] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "def perceptron(X, w, threshold=1):\n",
    "    # This function computes sum(w_i*x_i) and \n",
    "    # applies a perceptron activation\n",
    "    linear_sum = np.dot(X,w)\n",
    "    output=0\n",
    "    if linear_sum >= threshold:\n",
    "        output = 1\n",
    "        # print(\"The perceptron has peaked\")\n",
    "    return output\n",
    "X = [1,0]\n",
    "w = [1,1]\n",
    "perceptron(X,w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boolean AND\n",
    "\n",
    "| x$_1$ | x$_2$ | output |\n",
    "| --- | --- | --- |\n",
    "| 0 | 0 | 0 |\n",
    "| 1 | 0 | 0 |\n",
    "| 0 | 1 | 0 |\n",
    "| 1 | 1 | 1 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perceptron output for x1, x2 =  [0, 0]  is  0\n",
      "Perceptron output for x1, x2 =  [1, 0]  is  0\n",
      "Perceptron output for x1, x2 =  [0, 1]  is  0\n",
      "Perceptron output for x1, x2 =  [1, 1]  is  1\n"
     ]
    }
   ],
   "source": [
    "# Calculating Boolean AND using a perceptron\n",
    "import matplotlib.pyplot as plt\n",
    "threshold = 1.5\n",
    "w=[1,1]\n",
    "X=[[0,0],[1,0],[0,1],[1,1]]\n",
    "for i in X:\n",
    "    print(\"Perceptron output for x1, x2 = \" , i , \" is \" , perceptron(i,w,threshold))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this simple case we can rewrite our equation to $x_2 = ...... $ which describes a line in 2D:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6ceb34da0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 252,
       "width": 388
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the decision boundary\n",
    "plt.xlim(-1,2)\n",
    "plt.ylim(-1,2)\n",
    "for i in X:\n",
    "    plt.plot(i,\"o\",color=\"b\");\n",
    "# Plotting the decision boundary\n",
    "# that is a line given by w_1*x_1+w_2*x_2-threshold=0\n",
    "x1 = np.arange(-3,4)\n",
    "x2 = threshold - np.arange(-3,4)\n",
    "plt.plot(x1, x2 , \"--\" ,color=\"black\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise :Can you compute a Boolean \"OR\" using a perceptron?**\n",
    "\n",
    "Hint: copy the code from the \"AND\" example and edit the weights and/or threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boolean OR\n",
    "\n",
    "| x$_1$ | x$_2$ | output |\n",
    "| --- | --- | --- |\n",
    "| 0 | 0 | 0 |\n",
    "| 1 | 0 | 1 |\n",
    "| 0 | 1 | 1 |\n",
    "| 1 | 1 | 1 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating Boolean OR using a perceptron\n",
    "# Edit the code below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perceptron output for x1, x2 =  [0, 0]  is  0\n",
      "Perceptron output for x1, x2 =  [1, 0]  is  1\n",
      "Perceptron output for x1, x2 =  [0, 1]  is  1\n",
      "Perceptron output for x1, x2 =  [1, 1]  is  1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6cef41860>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 252,
       "width": 388
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "# Calculating Boolean OR using a perceptron\n",
    "import matplotlib.pyplot as plt\n",
    "threshold=0.6\n",
    "w=[1,1]\n",
    "X=[[0,0],[1,0],[0,1],[1,1]]\n",
    "for i in X:\n",
    "    print(\"Perceptron output for x1, x2 = \" , i , \" is \" , perceptron(i,w,threshold))\n",
    "# Plotting the decision boundary\n",
    "plt.xlim(-1,2)\n",
    "plt.ylim(-1,2)\n",
    "for i in X:\n",
    "    plt.plot(i,\"o\",color=\"b\");\n",
    "# Plotting the decision boundary\n",
    "# that is a line given by w_1*x_1+w_2*x_2-threshold=0\n",
    "x1 = np.arange(-3,4)\n",
    "x2 = threshold - np.arange(-3,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Optional exercise: Create a NAND gate using a perceptron**\n",
    "\n",
    "#### Boolean NAND\n",
    "\n",
    "| x$_1$ | x$_2$ | output |\n",
    "| --- | --- | --- |\n",
    "| 0 | 0 | 1 |\n",
    "| 1 | 0 | 1 |\n",
    "| 0 | 1 | 1 |\n",
    "| 1 | 1 | 0 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating Boolean NAND using a perceptron\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact a single perceptron can compute \"AND\", \"OR\" and \"NOT\" boolean functions.\n",
    "However, it cannot compute some other boolean functions such as \"XOR\"\n",
    "\n",
    "WHAT CAN WE DO?\n",
    "Hint: What is the significance of the NAND gate we created above\n",
    "\n",
    "We said a single perceptron can't compute these functions. We didn't say that about **multiple Perceptrons**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**XOR function**\n",
    "\n",
    "**TO DO: INSERT IMAGE HERE!!!!!!!!!!!!!!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Google Playground\n",
    "\n",
    "UWE: move up before discussing gradient stuff etc\n",
    "\n",
    "https://playground.tensorflow.org/\n",
    "\n",
    "<img src=\"./images/neuralnets/google_playground.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning\n",
    "\n",
    "Now we know that we can compute complex functions if we stack together a number of perceptrons.\n",
    "\n",
    "However, we can DO NOT want to set the weights and thresholds by hand as we did in the examples above.\n",
    "\n",
    "We want some algorithm to do this for us!\n",
    "\n",
    "In order to achieve this we first need to choose a loss function for the problem at hand\n",
    "\n",
    "\n",
    "### Loss function\n",
    "As in the case of other machine learning algorithms we need to define a so-called \"Loss function\". In simple words this function measures how close are the predictions of our network to the supplied labels. Once we have this function we need an algorithm to update the weights of the network such that this loss decreases. As one can already imagine the choice of an appropriate loss function is very important to the success of the trained model. Fortunately, for classification and regression (which comprise of a large range of probelms) these loss functions are well known. Generally **crossentropy** and **mean squared error** loss functions are chosen for classification and regression problems, respectively.\n",
    "\n",
    "### Gradient based learning\n",
    "Once we have a loss function we want to solve an **optimization problem** which minimizes this loss by updating the weights of the network and this is how the learning actually happens.\n",
    "\n",
    "One of the most popular optimization method used in machine learning is **Gradient-descent**\n",
    "\n",
    "INSERT MORE EXPLAINATIONS HERE\n",
    "\n",
    "### Activation Functions\n",
    "\n",
    "In order to train the network we need to change Perceptron's **step** activation function as it does not allow training using the back-propagation algorithm among other drawbacks.\n",
    "\n",
    "Non-Linear functions such as:\n",
    "\n",
    "* ReLU (Rectified linear unit)\n",
    "\n",
    "\\begin{equation*}\n",
    "f(z) = \\mathrm{max}(0,z)\n",
    "\\end{equation*}\n",
    "\n",
    "* Sigmoid\n",
    "\n",
    "\\begin{equation*}\n",
    "f(z) = \\frac{1}{1+e^{-z}}\n",
    "\\end{equation*}\n",
    "\n",
    "* tanh\n",
    "\n",
    "\\begin{equation*}\n",
    "f(z) = \\frac{e^{z} - e^{-z}}{e^{z} + e^{-z}}\n",
    "\\end{equation*}\n",
    "\n",
    "\n",
    "are some of the most popular choices used as activation functions.\n",
    "\n",
    "Linear activations are **NOT** used because it can be mathematically shown that if linear activations are used then output is just a linear function of the input. So adding any number of hidden layers does not help to learn interesting functions.\n",
    "\n",
    "Non-linear activation functions allow the network to learn more complex representations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6cef06fd0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 597
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "\n",
    "pts=np.arange(-20,20, 0.1)\n",
    "\n",
    "plt.subplot(1, 3, 1)\n",
    "# Sigmoid\n",
    "plt.plot(pts, 1/(1+np.exp(-pts))) ;\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "# tanh\n",
    "plt.plot(pts, np.tanh(pts*np.pi)) ;\n",
    "\n",
    "# Rectified linear unit (ReLu)\n",
    "plt.subplot(1, 3, 3)\n",
    "pts_relu=[max(0,i) for i in pts];\n",
    "plt.plot(pts, pts_relu) ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suggestion Uwe:\n",
    "\n",
    "1. more layers might improve power of single perctptron.\n",
    "\n",
    "2. regrettably math show that just \"stacking\" perceptrons only adds little improvements\n",
    "\n",
    "3. way around: look at nature how neuron works and introduce non linear activation functions.\n",
    "\n",
    "4. theoretical background: universal approximation theorem.\n",
    "\n",
    "\n",
    "\n",
    "### Multi-layer preceptron neural network\n",
    "Universal function theorem\n",
    "\n",
    "epochs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is **Keras**?\n",
    "\n",
    "* It is a high level API to create and work with neural networks\n",
    "* Supports multiple backends such as TensorFlow from Google, Theano (Although Theano is dead now) and CNTK (Microsoft Cognitive Toolkit)\n",
    "* Very good for creating neural nets very quickly and hides away a lot of tedious work\n",
    "* Has been incorporated into official TensorFlow (which obviously only works with tensforflow) and as of TensorFlow 2.0 this will the main api to use TensorFlow (check reference)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_9 (Dense)              (None, 4)                 36        \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 4)                 0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 4)                 20        \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 1)                 5         \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 61\n",
      "Trainable params: 61\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Say hello to keras\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "\n",
    "# Creating a model\n",
    "model = Sequential()\n",
    "\n",
    "# Adding layers to this model\n",
    "# 1st Hidden layer\n",
    "# A Dense/fully-connected layer which takes as input a \n",
    "# feature array of shape (samples, num_features)\n",
    "# Here input_shape = (8,) means that the layer expects an input with num_features = 8 \n",
    "# and the sample size could be anything\n",
    "# Then we specify an activation function\n",
    "model.add(Dense(units=4, input_shape=(8,)))\n",
    "model.add(Activation(\"relu\"))\n",
    "\n",
    "# 2nd Hidden layer\n",
    "# This is also a fully-connected layer and we do not need to specify the\n",
    "# shape of the input anymore (We need to do that only for the first layer)\n",
    "# NOTE: Now we didn't add the activation seperately. Instead we just added it\n",
    "# while calling Dense(). This and the way used for the first layer are Equivalent!\n",
    "model.add(Dense(units=4, activation=\"relu\"))\n",
    "\n",
    "          \n",
    "# The output layer\n",
    "model.add(Dense(units=1))\n",
    "model.add(Activation(\"sigmoid\"))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting the model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO: Move the MNIST example after the previous dataset examples**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MNIST Dataset\n",
    "\n",
    "MNIST datasets is a very common dataset used in machine learning. It is widely used to train and validate models.\n",
    "\n",
    "\n",
    ">The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a >test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size->normalized and centered in a fixed-size image.\n",
    ">It is a good database for people who want to try learning techniques and pattern recognition methods on real-world >data while spending minimal efforts on preprocessing and formatting.\n",
    ">source: http://yann.lecun.com/exdb/mnist/\n",
    "\n",
    "The problem we want to solve using this dataset is: multi-class classification\n",
    "This dataset consists of images of handwritten digits between 0-9 and their corresponsing labels. We want to train a neural network which is able to predict the correct digit on the image. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the dataset in keras\n",
    "# Later you can explore and play with other datasets with come with Keras\n",
    "from keras.datasets import mnist\n",
    "\n",
    "# Loading the train and test data\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Looking at the dataset\n",
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This digit is:  8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe8e68579e8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can see that the training set consists of 60,000 images of size 28x28 pixels\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "i=np.random.randint(0,X_train.shape[0])\n",
    "plt.imshow(X_train[i], cmap=\"gray_r\") ;\n",
    "print(\"This digit is: \" , y_train[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 255\n"
     ]
    }
   ],
   "source": [
    "# Look at the data values for a couple of images\n",
    "print(X_train[0].min(), X_train[1].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data consists of values between 0-255 representing the **grayscale level**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000,)\n"
     ]
    }
   ],
   "source": [
    "# The labels are the digit on the image\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling the data\n",
    "# It is important to normalize the input data to (0-1) before providing it to a neural net\n",
    "# We could use the previously introduced function from SciKit learn. However, here it is sufficient to\n",
    "# just divide the input data by 255\n",
    "X_train_norm = X_train/255.\n",
    "X_test_norm = X_test/255.\n",
    "\n",
    "# Also we need to reshape the input data such that each sample is a vector and not a 2D matrix\n",
    "X_train_prep = X_train_norm.reshape(X_train_norm.shape[0],28*28)\n",
    "X_test_prep = X_test_norm.reshape(X_test_norm.shape[0],28*28)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**IMPORTANT: One-Hot encoding**\n",
    "\n",
    "**TODO: Better frame the explaination**\n",
    "In such problems the labels are provided as something called **One-hot encodings**. What this does is to convert a categorical label to a vector.\n",
    "\n",
    "For the MNIST problem where we have **10 categories** one-hot encoding will create a vector of length 10 for each of the labels. All the entries of this vector will be zero **except** for the index which is equal to the integer value of the label.\n",
    "\n",
    "For example:\n",
    "if label is 4. The one-hot vector will look like **[0 0 0 0 1 0 0 0 0 0]**\n",
    "\n",
    "Fortunately, we don't have to code this ourselves because Keras has a built-in function for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 10)\n"
     ]
    }
   ],
   "source": [
    "from keras.utils.np_utils import to_categorical\n",
    "\n",
    "y_train_onehot = to_categorical(y_train, num_classes=10)\n",
    "y_test_onehot = to_categorical(y_test, num_classes=10)\n",
    "\n",
    "print(y_train_onehot.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 2s 34us/step - loss: 0.5888 - acc: 0.8434\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 1s 20us/step - loss: 0.2569 - acc: 0.9267\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 1s 16us/step - loss: 0.2024 - acc: 0.9416\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 1s 17us/step - loss: 0.1706 - acc: 0.9497\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.1475 - acc: 0.9563\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 1s 20us/step - loss: 0.1290 - acc: 0.9627\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.1162 - acc: 0.9651\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 1s 19us/step - loss: 0.1035 - acc: 0.9691\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 2s 28us/step - loss: 0.0939 - acc: 0.9716\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0848 - acc: 0.9743\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.0777 - acc: 0.9763\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0720 - acc: 0.9780\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0655 - acc: 0.9808\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0610 - acc: 0.9817\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 1s 16us/step - loss: 0.0563 - acc: 0.9832\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0527 - acc: 0.9842\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 1s 21us/step - loss: 0.0478 - acc: 0.9854\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 1s 15us/step - loss: 0.0453 - acc: 0.9864\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 1s 18us/step - loss: 0.0419 - acc: 0.9874\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0387 - acc: 0.9885\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fe8e7465438>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building the keras model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(64,input_shape=(28*28,), activation=\"relu\"))\n",
    "\n",
    "model.add(Dense(64, activation = \"relu\"))\n",
    "\n",
    "model.add(Dense(10, activation = \"softmax\"))\n",
    "\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "\n",
    "model_history = model.fit(X_train_prep, y_train_cat, epochs=20, batch_size=512);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 85us/step\n",
      "The [loss, accuracy] are:  [0.08737125840586377, 0.974]\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the model on test dataset\n",
    "print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Work in Progress\n",
    "\n",
    "## Network results on dataset used in previous notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff759315080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a network to solve the XOR problem\n",
    "# Loading and plotting the data\n",
    "xor = pd.read_csv(\"xor.csv\")\n",
    "xv = xor[\"x\"]\n",
    "yv = xor[\"y\"]\n",
    "\n",
    "colors = [[\"steelblue\", \"chocolate\"][i] for i in xor[\"label\"]]\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.xlim([-2, 2])\n",
    "plt.ylim([-2, 2])\n",
    "plt.title(\"Blue points are False\")\n",
    "\n",
    "\n",
    "plt.scatter(xv, yv, color=colors, marker=\"o\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using x and y coordinates as featues\n",
    "features = xor.iloc[:, :-1]\n",
    "# Convert boolean to integer values (True->1 and False->0)\n",
    "labels = xor.iloc[:, -1].astype(int)\n",
    "\n",
    "# Building a Keras model\n",
    "\n",
    "def a_simple_NN():\n",
    "    \n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(4, input_shape = (2,), activation = \"relu\"))\n",
    "\n",
    "    #model.add(Dense(4, activation = \"relu\"))\n",
    "\n",
    "    model.add(Dense(1, activation = \"sigmoid\"))\n",
    "\n",
    "    model.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = a_simple_NN()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 350 samples, validate on 150 samples\n",
      "Epoch 1/100\n",
      "350/350 [==============================] - 1s 2ms/step - loss: 0.8305 - acc: 0.3571 - val_loss: 0.8120 - val_acc: 0.3667\n",
      "Epoch 2/100\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.8170 - acc: 0.3629 - val_loss: 0.8010 - val_acc: 0.3667\n",
      "Epoch 3/100\n",
      "350/350 [==============================] - 0s 121us/step - loss: 0.8060 - acc: 0.3657 - val_loss: 0.7904 - val_acc: 0.3733\n",
      "Epoch 4/100\n",
      "350/350 [==============================] - 0s 133us/step - loss: 0.7960 - acc: 0.3743 - val_loss: 0.7807 - val_acc: 0.3867\n",
      "Epoch 5/100\n",
      "350/350 [==============================] - 0s 121us/step - loss: 0.7866 - acc: 0.3800 - val_loss: 0.7716 - val_acc: 0.3867\n",
      "Epoch 6/100\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.7773 - acc: 0.3886 - val_loss: 0.7625 - val_acc: 0.3867\n",
      "Epoch 7/100\n",
      "350/350 [==============================] - 0s 97us/step - loss: 0.7682 - acc: 0.3914 - val_loss: 0.7536 - val_acc: 0.3867\n",
      "Epoch 8/100\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.7594 - acc: 0.4086 - val_loss: 0.7450 - val_acc: 0.4067\n",
      "Epoch 9/100\n",
      "350/350 [==============================] - 0s 81us/step - loss: 0.7507 - acc: 0.4143 - val_loss: 0.7367 - val_acc: 0.4200\n",
      "Epoch 10/100\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.7420 - acc: 0.4200 - val_loss: 0.7283 - val_acc: 0.4333\n",
      "Epoch 11/100\n",
      "350/350 [==============================] - 0s 130us/step - loss: 0.7335 - acc: 0.4343 - val_loss: 0.7200 - val_acc: 0.4533\n",
      "Epoch 12/100\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.7252 - acc: 0.4429 - val_loss: 0.7123 - val_acc: 0.4600\n",
      "Epoch 13/100\n",
      "350/350 [==============================] - 0s 138us/step - loss: 0.7172 - acc: 0.4514 - val_loss: 0.7043 - val_acc: 0.4733\n",
      "Epoch 14/100\n",
      "350/350 [==============================] - 0s 103us/step - loss: 0.7091 - acc: 0.4600 - val_loss: 0.6967 - val_acc: 0.4733\n",
      "Epoch 15/100\n",
      "350/350 [==============================] - 0s 144us/step - loss: 0.7014 - acc: 0.4800 - val_loss: 0.6894 - val_acc: 0.4933\n",
      "Epoch 16/100\n",
      "350/350 [==============================] - 0s 103us/step - loss: 0.6937 - acc: 0.4971 - val_loss: 0.6821 - val_acc: 0.5133\n",
      "Epoch 17/100\n",
      "350/350 [==============================] - 0s 101us/step - loss: 0.6862 - acc: 0.5229 - val_loss: 0.6750 - val_acc: 0.5467\n",
      "Epoch 18/100\n",
      "350/350 [==============================] - 0s 146us/step - loss: 0.6786 - acc: 0.5371 - val_loss: 0.6680 - val_acc: 0.5733\n",
      "Epoch 19/100\n",
      "350/350 [==============================] - 0s 149us/step - loss: 0.6711 - acc: 0.5514 - val_loss: 0.6611 - val_acc: 0.5933\n",
      "Epoch 20/100\n",
      "350/350 [==============================] - 0s 131us/step - loss: 0.6639 - acc: 0.5800 - val_loss: 0.6546 - val_acc: 0.6133\n",
      "Epoch 21/100\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.6569 - acc: 0.5886 - val_loss: 0.6482 - val_acc: 0.6333\n",
      "Epoch 22/100\n",
      "350/350 [==============================] - 0s 134us/step - loss: 0.6503 - acc: 0.6114 - val_loss: 0.6422 - val_acc: 0.6467\n",
      "Epoch 23/100\n",
      "350/350 [==============================] - 0s 110us/step - loss: 0.6436 - acc: 0.6257 - val_loss: 0.6360 - val_acc: 0.6533\n",
      "Epoch 24/100\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.6371 - acc: 0.6343 - val_loss: 0.6303 - val_acc: 0.6667\n",
      "Epoch 25/100\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.6308 - acc: 0.6486 - val_loss: 0.6244 - val_acc: 0.7000\n",
      "Epoch 26/100\n",
      "350/350 [==============================] - 0s 116us/step - loss: 0.6246 - acc: 0.6600 - val_loss: 0.6189 - val_acc: 0.7000\n",
      "Epoch 27/100\n",
      "350/350 [==============================] - 0s 85us/step - loss: 0.6185 - acc: 0.6771 - val_loss: 0.6135 - val_acc: 0.7133\n",
      "Epoch 28/100\n",
      "350/350 [==============================] - 0s 116us/step - loss: 0.6126 - acc: 0.6914 - val_loss: 0.6083 - val_acc: 0.7267\n",
      "Epoch 29/100\n",
      "350/350 [==============================] - 0s 115us/step - loss: 0.6069 - acc: 0.7114 - val_loss: 0.6032 - val_acc: 0.7333\n",
      "Epoch 30/100\n",
      "350/350 [==============================] - 0s 133us/step - loss: 0.6013 - acc: 0.7314 - val_loss: 0.5981 - val_acc: 0.7267\n",
      "Epoch 31/100\n",
      "350/350 [==============================] - 0s 104us/step - loss: 0.5960 - acc: 0.7400 - val_loss: 0.5933 - val_acc: 0.7333\n",
      "Epoch 32/100\n",
      "350/350 [==============================] - 0s 133us/step - loss: 0.5907 - acc: 0.7486 - val_loss: 0.5885 - val_acc: 0.7533\n",
      "Epoch 33/100\n",
      "350/350 [==============================] - 0s 104us/step - loss: 0.5854 - acc: 0.7571 - val_loss: 0.5839 - val_acc: 0.7733\n",
      "Epoch 34/100\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.5802 - acc: 0.7686 - val_loss: 0.5791 - val_acc: 0.7667\n",
      "Epoch 35/100\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.5753 - acc: 0.7743 - val_loss: 0.5747 - val_acc: 0.7667\n",
      "Epoch 36/100\n",
      "350/350 [==============================] - 0s 119us/step - loss: 0.5704 - acc: 0.7829 - val_loss: 0.5703 - val_acc: 0.7733\n",
      "Epoch 37/100\n",
      "350/350 [==============================] - 0s 154us/step - loss: 0.5658 - acc: 0.7857 - val_loss: 0.5661 - val_acc: 0.7733\n",
      "Epoch 38/100\n",
      "350/350 [==============================] - 0s 121us/step - loss: 0.5613 - acc: 0.7829 - val_loss: 0.5620 - val_acc: 0.7933\n",
      "Epoch 39/100\n",
      "350/350 [==============================] - 0s 141us/step - loss: 0.5570 - acc: 0.7800 - val_loss: 0.5581 - val_acc: 0.7933\n",
      "Epoch 40/100\n",
      "350/350 [==============================] - 0s 64us/step - loss: 0.5528 - acc: 0.7886 - val_loss: 0.5545 - val_acc: 0.8000\n",
      "Epoch 41/100\n",
      "350/350 [==============================] - 0s 124us/step - loss: 0.5489 - acc: 0.7914 - val_loss: 0.5511 - val_acc: 0.7933\n",
      "Epoch 42/100\n",
      "350/350 [==============================] - 0s 128us/step - loss: 0.5449 - acc: 0.7971 - val_loss: 0.5477 - val_acc: 0.7933\n",
      "Epoch 43/100\n",
      "350/350 [==============================] - 0s 140us/step - loss: 0.5411 - acc: 0.7971 - val_loss: 0.5444 - val_acc: 0.7867\n",
      "Epoch 44/100\n",
      "350/350 [==============================] - 0s 120us/step - loss: 0.5372 - acc: 0.8029 - val_loss: 0.5410 - val_acc: 0.7867\n",
      "Epoch 45/100\n",
      "350/350 [==============================] - 0s 108us/step - loss: 0.5335 - acc: 0.8057 - val_loss: 0.5379 - val_acc: 0.7867\n",
      "Epoch 46/100\n",
      "350/350 [==============================] - 0s 119us/step - loss: 0.5298 - acc: 0.8029 - val_loss: 0.5346 - val_acc: 0.7800\n",
      "Epoch 47/100\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.5261 - acc: 0.8057 - val_loss: 0.5315 - val_acc: 0.7800\n",
      "Epoch 48/100\n",
      "350/350 [==============================] - 0s 142us/step - loss: 0.5225 - acc: 0.8057 - val_loss: 0.5283 - val_acc: 0.7800\n",
      "Epoch 49/100\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.5189 - acc: 0.8114 - val_loss: 0.5251 - val_acc: 0.7800\n",
      "Epoch 50/100\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.5152 - acc: 0.8086 - val_loss: 0.5220 - val_acc: 0.7800\n",
      "Epoch 51/100\n",
      "350/350 [==============================] - 0s 114us/step - loss: 0.5116 - acc: 0.8143 - val_loss: 0.5187 - val_acc: 0.7800\n",
      "Epoch 52/100\n",
      "350/350 [==============================] - 0s 121us/step - loss: 0.5079 - acc: 0.8286 - val_loss: 0.5153 - val_acc: 0.7800\n",
      "Epoch 53/100\n",
      "350/350 [==============================] - 0s 138us/step - loss: 0.5043 - acc: 0.8286 - val_loss: 0.5120 - val_acc: 0.7867\n",
      "Epoch 54/100\n",
      "350/350 [==============================] - 0s 138us/step - loss: 0.5007 - acc: 0.8257 - val_loss: 0.5089 - val_acc: 0.7867\n",
      "Epoch 55/100\n",
      "350/350 [==============================] - 0s 135us/step - loss: 0.4974 - acc: 0.8314 - val_loss: 0.5060 - val_acc: 0.7933\n",
      "Epoch 56/100\n",
      "350/350 [==============================] - 0s 102us/step - loss: 0.4941 - acc: 0.8314 - val_loss: 0.5031 - val_acc: 0.7933\n",
      "Epoch 57/100\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.4906 - acc: 0.8371 - val_loss: 0.5000 - val_acc: 0.7933\n",
      "Epoch 58/100\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.4871 - acc: 0.8400 - val_loss: 0.4969 - val_acc: 0.7867\n",
      "Epoch 59/100\n",
      "350/350 [==============================] - 0s 116us/step - loss: 0.4838 - acc: 0.8400 - val_loss: 0.4939 - val_acc: 0.7867\n",
      "Epoch 60/100\n",
      "350/350 [==============================] - 0s 100us/step - loss: 0.4803 - acc: 0.8400 - val_loss: 0.4906 - val_acc: 0.8000\n",
      "Epoch 61/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "350/350 [==============================] - 0s 123us/step - loss: 0.4767 - acc: 0.8400 - val_loss: 0.4876 - val_acc: 0.8000\n",
      "Epoch 62/100\n",
      "350/350 [==============================] - 0s 123us/step - loss: 0.4733 - acc: 0.8343 - val_loss: 0.4846 - val_acc: 0.7933\n",
      "Epoch 63/100\n",
      "350/350 [==============================] - 0s 106us/step - loss: 0.4699 - acc: 0.8400 - val_loss: 0.4816 - val_acc: 0.7933\n",
      "Epoch 64/100\n",
      "350/350 [==============================] - 0s 142us/step - loss: 0.4667 - acc: 0.8400 - val_loss: 0.4786 - val_acc: 0.8000\n",
      "Epoch 65/100\n",
      "350/350 [==============================] - 0s 134us/step - loss: 0.4636 - acc: 0.8371 - val_loss: 0.4758 - val_acc: 0.8000\n",
      "Epoch 66/100\n",
      "350/350 [==============================] - 0s 103us/step - loss: 0.4604 - acc: 0.8371 - val_loss: 0.4730 - val_acc: 0.8000\n",
      "Epoch 67/100\n",
      "350/350 [==============================] - 0s 131us/step - loss: 0.4574 - acc: 0.8429 - val_loss: 0.4701 - val_acc: 0.8000\n",
      "Epoch 68/100\n",
      "350/350 [==============================] - 0s 134us/step - loss: 0.4545 - acc: 0.8457 - val_loss: 0.4677 - val_acc: 0.8000\n",
      "Epoch 69/100\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.4516 - acc: 0.8457 - val_loss: 0.4652 - val_acc: 0.8000\n",
      "Epoch 70/100\n",
      "350/350 [==============================] - 0s 123us/step - loss: 0.4486 - acc: 0.8457 - val_loss: 0.4625 - val_acc: 0.8000\n",
      "Epoch 71/100\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.4457 - acc: 0.8486 - val_loss: 0.4600 - val_acc: 0.8000\n",
      "Epoch 72/100\n",
      "350/350 [==============================] - 0s 106us/step - loss: 0.4426 - acc: 0.8457 - val_loss: 0.4574 - val_acc: 0.8067\n",
      "Epoch 73/100\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.4399 - acc: 0.8457 - val_loss: 0.4549 - val_acc: 0.8067\n",
      "Epoch 74/100\n",
      "350/350 [==============================] - 0s 107us/step - loss: 0.4369 - acc: 0.8486 - val_loss: 0.4523 - val_acc: 0.8067\n",
      "Epoch 75/100\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.4340 - acc: 0.8514 - val_loss: 0.4498 - val_acc: 0.8067\n",
      "Epoch 76/100\n",
      "350/350 [==============================] - 0s 116us/step - loss: 0.4311 - acc: 0.8514 - val_loss: 0.4472 - val_acc: 0.8200\n",
      "Epoch 77/100\n",
      "350/350 [==============================] - 0s 129us/step - loss: 0.4282 - acc: 0.8543 - val_loss: 0.4449 - val_acc: 0.8200\n",
      "Epoch 78/100\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.4255 - acc: 0.8571 - val_loss: 0.4425 - val_acc: 0.8267\n",
      "Epoch 79/100\n",
      "350/350 [==============================] - 0s 132us/step - loss: 0.4228 - acc: 0.8571 - val_loss: 0.4401 - val_acc: 0.8267\n",
      "Epoch 80/100\n",
      "350/350 [==============================] - 0s 157us/step - loss: 0.4201 - acc: 0.8571 - val_loss: 0.4377 - val_acc: 0.8200\n",
      "Epoch 81/100\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.4173 - acc: 0.8629 - val_loss: 0.4352 - val_acc: 0.8200\n",
      "Epoch 82/100\n",
      "350/350 [==============================] - 0s 109us/step - loss: 0.4146 - acc: 0.8600 - val_loss: 0.4328 - val_acc: 0.8200\n",
      "Epoch 83/100\n",
      "350/350 [==============================] - 0s 108us/step - loss: 0.4120 - acc: 0.8600 - val_loss: 0.4306 - val_acc: 0.8200\n",
      "Epoch 84/100\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.4095 - acc: 0.8629 - val_loss: 0.4284 - val_acc: 0.8200\n",
      "Epoch 85/100\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.4069 - acc: 0.8629 - val_loss: 0.4261 - val_acc: 0.8200\n",
      "Epoch 86/100\n",
      "350/350 [==============================] - 0s 131us/step - loss: 0.4043 - acc: 0.8657 - val_loss: 0.4238 - val_acc: 0.8200\n",
      "Epoch 87/100\n",
      "350/350 [==============================] - 0s 125us/step - loss: 0.4018 - acc: 0.8686 - val_loss: 0.4216 - val_acc: 0.8200\n",
      "Epoch 88/100\n",
      "350/350 [==============================] - 0s 117us/step - loss: 0.3993 - acc: 0.8686 - val_loss: 0.4193 - val_acc: 0.8200\n",
      "Epoch 89/100\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.3969 - acc: 0.8714 - val_loss: 0.4173 - val_acc: 0.8200\n",
      "Epoch 90/100\n",
      "350/350 [==============================] - 0s 137us/step - loss: 0.3945 - acc: 0.8771 - val_loss: 0.4151 - val_acc: 0.8200\n",
      "Epoch 91/100\n",
      "350/350 [==============================] - 0s 144us/step - loss: 0.3921 - acc: 0.8771 - val_loss: 0.4130 - val_acc: 0.8200\n",
      "Epoch 92/100\n",
      "350/350 [==============================] - 0s 98us/step - loss: 0.3899 - acc: 0.8743 - val_loss: 0.4109 - val_acc: 0.8200\n",
      "Epoch 93/100\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.3875 - acc: 0.8771 - val_loss: 0.4088 - val_acc: 0.8200\n",
      "Epoch 94/100\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.3854 - acc: 0.8800 - val_loss: 0.4068 - val_acc: 0.8200\n",
      "Epoch 95/100\n",
      "350/350 [==============================] - 0s 126us/step - loss: 0.3832 - acc: 0.8771 - val_loss: 0.4050 - val_acc: 0.8200\n",
      "Epoch 96/100\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.3811 - acc: 0.8771 - val_loss: 0.4030 - val_acc: 0.8200\n",
      "Epoch 97/100\n",
      "350/350 [==============================] - 0s 110us/step - loss: 0.3790 - acc: 0.8771 - val_loss: 0.4010 - val_acc: 0.8200\n",
      "Epoch 98/100\n",
      "350/350 [==============================] - 0s 150us/step - loss: 0.3768 - acc: 0.8743 - val_loss: 0.3990 - val_acc: 0.8200\n",
      "Epoch 99/100\n",
      "350/350 [==============================] - 0s 136us/step - loss: 0.3746 - acc: 0.8743 - val_loss: 0.3972 - val_acc: 0.8267\n",
      "Epoch 100/100\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.3726 - acc: 0.8743 - val_loss: 0.3953 - val_acc: 0.8333\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6dcd8fef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we split the dataset into training (80%) and validation sets (20%) \n",
    "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.3)\n",
    "\n",
    "num_epochs = 100\n",
    "\n",
    "model_run = model.fit(X_train, y_train, epochs=num_epochs, validation_data = (X_test,y_test))\n",
    "\n",
    "history_model = model_run.history\n",
    "\n",
    "plt.plot(np.arange(1,num_epochs+1)[5:], history_model[\"acc\"][5:], \"--\") ;\n",
    "\n",
    "plt.plot(np.arange(1,num_epochs+1)[5:], history_model[\"val_acc\"][5:]) ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using SciKit learn functions on Keras models\n",
    "\n",
    "As we have seen from the previous chapters, SciKit learn offers very handy functions for evaluating and tuning the machine learning models.\n",
    "\n",
    "So the question is: Can we somehow use those functions with the models we build in Keras?\n",
    "\n",
    "The Answer is **YES !**\n",
    "\n",
    "Keras offers wrappers which allow its Sequential models to be used with SciKit learn. There 2 such wrappers: **KerasClassifier** and **KerasRegressor**.\n",
    "\n",
    "For more information:\n",
    "https://keras.io/scikit-learn-api/\n",
    "\n",
    "**Now lets see how this works!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.61428571 0.6        0.88571429 0.7        0.67142857]\n",
      "0.6942857147966113\n"
     ]
    }
   ],
   "source": [
    "# We wrap the Keras model we created above with KerasClassifier\n",
    "from keras.wrappers.scikit_learn import KerasClassifier \n",
    "from sklearn.model_selection import cross_val_score\n",
    "model_scikit = KerasClassifier(build_fn=a_simple_NN, **{\"epochs\":num_epochs, \"verbose\":0})\n",
    "cross_validation = cross_val_score(model_scikit, X_train, y_train, cv=5, verbose=0)\n",
    "print(cross_validation)\n",
    "print(np.mean(cross_validation))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import numpy as np\n",
    "# We wrap the Keras model we created above with KerasClassifier\n",
    "from keras.wrappers.scikit_learn import KerasClassifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "def list_flatten(list_of_list):\n",
    "    flattened_list = [i for j in list_of_list for i in j]\n",
    "    return flattened_list\n",
    "\n",
    "def train_and_plot_decision_surface(\n",
    "    name, classifier, features_2d, labels, preproc=None, plt=plt, marker='o', N=400\n",
    "):\n",
    "\n",
    "    features_2d = np.array(features_2d)\n",
    "    xmin, ymin = features_2d.min(axis=0)\n",
    "    xmax, ymax = features_2d.max(axis=0)\n",
    "\n",
    "    x = np.linspace(xmin, xmax, N)\n",
    "    y = np.linspace(ymin, ymax, N)\n",
    "    points = np.array(np.meshgrid(x, y)).T.reshape(-1, 2)\n",
    "\n",
    "    if preproc is not None:\n",
    "        points_for_classifier = preproc.fit_transform(points)\n",
    "        features_2d = preproc.fit_transform(features_2d)\n",
    "    else:\n",
    "        points_for_classifier = points\n",
    "\n",
    "    classifier.fit(features_2d, labels, verbose=0)\n",
    "    predicted = classifier.predict(features_2d)\n",
    "    \n",
    "    if name == \"Neural Net\":\n",
    "        predicted = list_flatten(predicted)\n",
    "    \n",
    "    \n",
    "    if preproc is not None:\n",
    "        name += \" (w/ preprocessing)\"\n",
    "    print(name + \":\\t\", sum(predicted == labels), \"/\", len(labels), \"correct\")\n",
    "    \n",
    "    if name == \"Neural Net\":\n",
    "        classes = np.array(list_flatten(classifier.predict(points_for_classifier)), dtype=bool)\n",
    "    else:\n",
    "        classes = np.array(classifier.predict(points_for_classifier), dtype=bool)\n",
    "    plt.plot(\n",
    "        points[~classes][:, 0],\n",
    "        points[~classes][:, 1],\n",
    "        \"o\",\n",
    "        color=\"black\",\n",
    "        markersize=1,\n",
    "        alpha=0.1,\n",
    "    )\n",
    "    plt.plot(\n",
    "        points[classes][:, 0],\n",
    "        points[classes][:, 1],\n",
    "        \"o\",\n",
    "        color=\"blue\",\n",
    "        markersize=1,\n",
    "        alpha=0.1,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "def a_simple_NN():\n",
    "    \n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(8, input_shape = (2,), activation = \"relu\"))\n",
    "\n",
    "    model.add(Dense(2, activation = \"relu\"))\n",
    "\n",
    "    model.add(Dense(1, activation = \"sigmoid\"))\n",
    "\n",
    "    model.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = a_simple_NN()\n",
    "\n",
    "num_epochs = 400\n",
    "model_scikit = KerasClassifier(build_fn=a_simple_NN, epochs=num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Net:\t 487 / 500 correct\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6ff946320>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#color=\"steelblue\",color=\"chocolate\" marker=marker,\n",
    "\n",
    "\n",
    "def plot_points(plt=plt, marker='o'):\n",
    "    colors = [[\"steelblue\", \"chocolate\"][i] for i in labels]\n",
    "    plt.scatter(features.iloc[:, 0], features.iloc[:, 1], color=colors, marker=marker);\n",
    "\n",
    "_, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "xor = pd.read_csv(\"xor.csv\")\n",
    "features = xor.iloc[:, :-1]\n",
    "# Convert boolean to integer values (True->1 and False->0)\n",
    "labels = xor.iloc[:, -1]\n",
    "\n",
    "train_and_plot_decision_surface(\"Neural Net\", model_scikit, features, labels, plt=ax)\n",
    "plot_points(plt=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise: Create a neural network to classify the 2d points example from chapter 2 and **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"2d_points.csv\")\n",
    "features = df.iloc[:, :-1]\n",
    "labels = df.iloc[:, -1]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Might Remove the following: This beer example is not good for neural networks. Basically the dataset is far too small**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(225, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Revisiting the beer example\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Loading the beer data\n",
    "beer = pd.read_csv(\"beers.csv\")\n",
    "\n",
    "# Extracting the features and labels\n",
    "#beer_data.describe()\n",
    "features = beer.iloc[:, :-1]\n",
    "labels = beer.iloc[:, -1]\n",
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Revisiting the beer example\n",
    "\n",
    "# Loading and preparing the data\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Loading the beer data\n",
    "beer = pd.read_csv(\"beers.csv\")\n",
    "\n",
    "# Extracting the features and labels\n",
    "#beer_data.describe()\n",
    "features = beer.iloc[:, :-1]\n",
    "labels = beer.iloc[:, -1]\n",
    "\n",
    "# Here we split the dataset into training (70%) and validation sets (30%) \n",
    "#X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.5, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.3)\n",
    "\n",
    "# Scaling the data\n",
    "# NOTE: The features should be normalized before being fed into the neural network\n",
    "scaling = MinMaxScaler()\n",
    "scaling.fit(X_train)\n",
    "\n",
    "X_train_scaled = scaling.transform(X_train)\n",
    "X_test_scaled = scaling.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 157 samples, validate on 68 samples\n",
      "Epoch 1/1000\n",
      "157/157 [==============================] - 1s 6ms/step - loss: 0.6730 - acc: 0.5350 - val_loss: 0.6769 - val_acc: 0.5147\n",
      "Epoch 2/1000\n",
      "157/157 [==============================] - 0s 406us/step - loss: 0.6704 - acc: 0.5350 - val_loss: 0.6754 - val_acc: 0.5147\n",
      "Epoch 3/1000\n",
      "157/157 [==============================] - 0s 256us/step - loss: 0.6693 - acc: 0.5350 - val_loss: 0.6740 - val_acc: 0.5147\n",
      "Epoch 4/1000\n",
      "157/157 [==============================] - 0s 215us/step - loss: 0.6679 - acc: 0.5350 - val_loss: 0.6728 - val_acc: 0.5147\n",
      "Epoch 5/1000\n",
      "157/157 [==============================] - 0s 168us/step - loss: 0.6668 - acc: 0.5350 - val_loss: 0.6716 - val_acc: 0.5147\n",
      "Epoch 6/1000\n",
      "157/157 [==============================] - 0s 107us/step - loss: 0.6658 - acc: 0.5350 - val_loss: 0.6704 - val_acc: 0.5147\n",
      "Epoch 7/1000\n",
      "157/157 [==============================] - 0s 303us/step - loss: 0.6652 - acc: 0.5350 - val_loss: 0.6693 - val_acc: 0.5147\n",
      "Epoch 8/1000\n",
      "157/157 [==============================] - 0s 98us/step - loss: 0.6637 - acc: 0.5350 - val_loss: 0.6682 - val_acc: 0.5147\n",
      "Epoch 9/1000\n",
      "157/157 [==============================] - 0s 92us/step - loss: 0.6626 - acc: 0.5350 - val_loss: 0.6670 - val_acc: 0.5147\n",
      "Epoch 10/1000\n",
      "157/157 [==============================] - 0s 90us/step - loss: 0.6616 - acc: 0.5350 - val_loss: 0.6657 - val_acc: 0.5147\n",
      "Epoch 11/1000\n",
      "157/157 [==============================] - 0s 92us/step - loss: 0.6605 - acc: 0.5350 - val_loss: 0.6644 - val_acc: 0.5147\n",
      "Epoch 12/1000\n",
      "157/157 [==============================] - 0s 305us/step - loss: 0.6596 - acc: 0.5350 - val_loss: 0.6633 - val_acc: 0.5147\n",
      "Epoch 13/1000\n",
      "157/157 [==============================] - 0s 142us/step - loss: 0.6587 - acc: 0.5350 - val_loss: 0.6622 - val_acc: 0.5147\n",
      "Epoch 14/1000\n",
      "157/157 [==============================] - 0s 144us/step - loss: 0.6578 - acc: 0.5350 - val_loss: 0.6612 - val_acc: 0.5147\n",
      "Epoch 15/1000\n",
      "157/157 [==============================] - 0s 137us/step - loss: 0.6567 - acc: 0.5350 - val_loss: 0.6601 - val_acc: 0.5147\n",
      "Epoch 16/1000\n",
      "157/157 [==============================] - 0s 179us/step - loss: 0.6558 - acc: 0.5350 - val_loss: 0.6591 - val_acc: 0.5147\n",
      "Epoch 17/1000\n",
      "157/157 [==============================] - 0s 98us/step - loss: 0.6551 - acc: 0.5350 - val_loss: 0.6580 - val_acc: 0.5147\n",
      "Epoch 18/1000\n",
      "157/157 [==============================] - 0s 106us/step - loss: 0.6540 - acc: 0.5350 - val_loss: 0.6570 - val_acc: 0.5147\n",
      "Epoch 19/1000\n",
      "157/157 [==============================] - 0s 97us/step - loss: 0.6531 - acc: 0.5350 - val_loss: 0.6559 - val_acc: 0.5147\n",
      "Epoch 20/1000\n",
      "157/157 [==============================] - 0s 131us/step - loss: 0.6523 - acc: 0.5350 - val_loss: 0.6549 - val_acc: 0.5147\n",
      "Epoch 21/1000\n",
      "157/157 [==============================] - 0s 141us/step - loss: 0.6512 - acc: 0.5350 - val_loss: 0.6537 - val_acc: 0.5147\n",
      "Epoch 22/1000\n",
      "157/157 [==============================] - 0s 288us/step - loss: 0.6506 - acc: 0.5350 - val_loss: 0.6527 - val_acc: 0.5147\n",
      "Epoch 23/1000\n",
      "157/157 [==============================] - 0s 128us/step - loss: 0.6496 - acc: 0.5414 - val_loss: 0.6517 - val_acc: 0.5147\n",
      "Epoch 24/1000\n",
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      "Epoch 28/1000\n",
      "157/157 [==============================] - 0s 257us/step - loss: 0.6447 - acc: 0.5541 - val_loss: 0.6461 - val_acc: 0.5147\n",
      "Epoch 29/1000\n",
      "157/157 [==============================] - 0s 134us/step - loss: 0.6437 - acc: 0.5541 - val_loss: 0.6449 - val_acc: 0.5147\n",
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      "Epoch 31/1000\n",
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      "Epoch 32/1000\n",
      "157/157 [==============================] - 0s 103us/step - loss: 0.6407 - acc: 0.5669 - val_loss: 0.6414 - val_acc: 0.5147\n",
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      "Epoch 36/1000\n",
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      "Epoch 38/1000\n",
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      "Epoch 39/1000\n",
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      "Epoch 45/1000\n",
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      "Epoch 50/1000\n",
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      "Epoch 51/1000\n",
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      "Epoch 52/1000\n",
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      "Epoch 55/1000\n",
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      "Epoch 56/1000\n",
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      "Epoch 57/1000\n",
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      "Epoch 58/1000\n",
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      "Epoch 59/1000\n",
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      "Epoch 60/1000\n",
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      "Epoch 61/1000\n",
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      "Epoch 62/1000\n",
      "157/157 [==============================] - 0s 409us/step - loss: 0.6006 - acc: 0.6306 - val_loss: 0.5943 - val_acc: 0.6176\n",
      "Epoch 63/1000\n",
      "157/157 [==============================] - 0s 104us/step - loss: 0.5991 - acc: 0.6306 - val_loss: 0.5922 - val_acc: 0.6324\n",
      "Epoch 64/1000\n",
      "157/157 [==============================] - 0s 193us/step - loss: 0.5981 - acc: 0.6369 - val_loss: 0.5906 - val_acc: 0.6324\n",
      "Epoch 65/1000\n",
      "157/157 [==============================] - 0s 104us/step - loss: 0.5958 - acc: 0.6433 - val_loss: 0.5889 - val_acc: 0.6471\n",
      "Epoch 66/1000\n",
      "157/157 [==============================] - 0s 172us/step - loss: 0.5945 - acc: 0.6433 - val_loss: 0.5871 - val_acc: 0.6471\n",
      "Epoch 67/1000\n",
      "157/157 [==============================] - 0s 378us/step - loss: 0.5929 - acc: 0.6433 - val_loss: 0.5852 - val_acc: 0.6471\n",
      "Epoch 68/1000\n",
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      "Epoch 69/1000\n",
      "157/157 [==============================] - 0s 155us/step - loss: 0.5901 - acc: 0.6497 - val_loss: 0.5816 - val_acc: 0.6471\n",
      "Epoch 70/1000\n",
      "157/157 [==============================] - 0s 180us/step - loss: 0.5885 - acc: 0.6497 - val_loss: 0.5797 - val_acc: 0.6765\n",
      "Epoch 71/1000\n",
      "157/157 [==============================] - 0s 208us/step - loss: 0.5867 - acc: 0.6561 - val_loss: 0.5778 - val_acc: 0.6765\n",
      "Epoch 72/1000\n",
      "157/157 [==============================] - 0s 200us/step - loss: 0.5850 - acc: 0.6561 - val_loss: 0.5755 - val_acc: 0.6765\n",
      "Epoch 73/1000\n",
      "157/157 [==============================] - 0s 279us/step - loss: 0.5831 - acc: 0.6624 - val_loss: 0.5733 - val_acc: 0.6765\n",
      "Epoch 74/1000\n",
      "157/157 [==============================] - 0s 263us/step - loss: 0.5812 - acc: 0.6688 - val_loss: 0.5712 - val_acc: 0.6912\n",
      "Epoch 75/1000\n",
      "157/157 [==============================] - 0s 263us/step - loss: 0.5791 - acc: 0.6752 - val_loss: 0.5688 - val_acc: 0.7059\n",
      "Epoch 76/1000\n",
      "157/157 [==============================] - 0s 223us/step - loss: 0.5771 - acc: 0.6752 - val_loss: 0.5665 - val_acc: 0.7059\n",
      "Epoch 77/1000\n",
      "157/157 [==============================] - 0s 252us/step - loss: 0.5750 - acc: 0.6879 - val_loss: 0.5643 - val_acc: 0.7059\n",
      "Epoch 78/1000\n",
      "157/157 [==============================] - 0s 217us/step - loss: 0.5728 - acc: 0.6879 - val_loss: 0.5619 - val_acc: 0.7059\n",
      "Epoch 79/1000\n",
      "157/157 [==============================] - 0s 123us/step - loss: 0.5708 - acc: 0.6943 - val_loss: 0.5596 - val_acc: 0.7059\n",
      "Epoch 80/1000\n",
      "157/157 [==============================] - 0s 149us/step - loss: 0.5687 - acc: 0.7006 - val_loss: 0.5570 - val_acc: 0.7206\n",
      "Epoch 81/1000\n",
      "157/157 [==============================] - 0s 181us/step - loss: 0.5666 - acc: 0.7070 - val_loss: 0.5545 - val_acc: 0.7206\n",
      "Epoch 82/1000\n",
      "157/157 [==============================] - 0s 109us/step - loss: 0.5643 - acc: 0.7006 - val_loss: 0.5519 - val_acc: 0.7206\n",
      "Epoch 83/1000\n",
      "157/157 [==============================] - 0s 258us/step - loss: 0.5623 - acc: 0.7134 - val_loss: 0.5495 - val_acc: 0.7206\n",
      "Epoch 84/1000\n",
      "157/157 [==============================] - 0s 123us/step - loss: 0.5600 - acc: 0.7197 - val_loss: 0.5469 - val_acc: 0.7206\n",
      "Epoch 85/1000\n",
      "157/157 [==============================] - 0s 120us/step - loss: 0.5577 - acc: 0.7197 - val_loss: 0.5443 - val_acc: 0.7206\n",
      "Epoch 86/1000\n",
      "157/157 [==============================] - 0s 166us/step - loss: 0.5550 - acc: 0.7197 - val_loss: 0.5411 - val_acc: 0.7353\n",
      "Epoch 87/1000\n",
      "157/157 [==============================] - 0s 134us/step - loss: 0.5529 - acc: 0.7325 - val_loss: 0.5383 - val_acc: 0.7353\n",
      "Epoch 88/1000\n",
      "157/157 [==============================] - 0s 185us/step - loss: 0.5498 - acc: 0.7325 - val_loss: 0.5347 - val_acc: 0.7353\n",
      "Epoch 89/1000\n",
      "157/157 [==============================] - 0s 194us/step - loss: 0.5471 - acc: 0.7516 - val_loss: 0.5314 - val_acc: 0.7647\n",
      "Epoch 90/1000\n",
      "157/157 [==============================] - 0s 163us/step - loss: 0.5451 - acc: 0.7452 - val_loss: 0.5283 - val_acc: 0.7941\n",
      "Epoch 91/1000\n",
      "157/157 [==============================] - 0s 292us/step - loss: 0.5430 - acc: 0.7580 - val_loss: 0.5258 - val_acc: 0.8088\n",
      "Epoch 92/1000\n",
      "157/157 [==============================] - 0s 137us/step - loss: 0.5399 - acc: 0.7580 - val_loss: 0.5234 - val_acc: 0.8088\n",
      "Epoch 93/1000\n",
      "157/157 [==============================] - 0s 193us/step - loss: 0.5383 - acc: 0.7643 - val_loss: 0.5210 - val_acc: 0.8088\n",
      "Epoch 94/1000\n",
      "157/157 [==============================] - 0s 231us/step - loss: 0.5356 - acc: 0.7643 - val_loss: 0.5184 - val_acc: 0.8088\n",
      "Epoch 95/1000\n",
      "157/157 [==============================] - 0s 96us/step - loss: 0.5334 - acc: 0.7643 - val_loss: 0.5158 - val_acc: 0.8235\n",
      "Epoch 96/1000\n",
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      "Epoch 97/1000\n",
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      "Epoch 98/1000\n",
      "157/157 [==============================] - 0s 179us/step - loss: 0.5263 - acc: 0.7898 - val_loss: 0.5074 - val_acc: 0.8235\n",
      "Epoch 99/1000\n",
      "157/157 [==============================] - 0s 182us/step - loss: 0.5243 - acc: 0.7962 - val_loss: 0.5044 - val_acc: 0.8088\n",
      "Epoch 100/1000\n",
      "157/157 [==============================] - 0s 144us/step - loss: 0.5234 - acc: 0.7834 - val_loss: 0.5024 - val_acc: 0.8088\n",
      "Epoch 101/1000\n",
      "157/157 [==============================] - 0s 154us/step - loss: 0.5198 - acc: 0.8025 - val_loss: 0.5002 - val_acc: 0.8088\n",
      "Epoch 102/1000\n",
      "157/157 [==============================] - 0s 320us/step - loss: 0.5184 - acc: 0.7962 - val_loss: 0.4978 - val_acc: 0.8088\n",
      "Epoch 103/1000\n",
      "157/157 [==============================] - 0s 142us/step - loss: 0.5162 - acc: 0.8025 - val_loss: 0.4956 - val_acc: 0.8088\n",
      "Epoch 104/1000\n",
      "157/157 [==============================] - 0s 131us/step - loss: 0.5136 - acc: 0.8025 - val_loss: 0.4932 - val_acc: 0.8088\n",
      "Epoch 105/1000\n",
      "157/157 [==============================] - 0s 142us/step - loss: 0.5115 - acc: 0.7962 - val_loss: 0.4903 - val_acc: 0.8235\n",
      "Epoch 106/1000\n",
      "157/157 [==============================] - 0s 144us/step - loss: 0.5091 - acc: 0.8025 - val_loss: 0.4877 - val_acc: 0.8382\n",
      "Epoch 107/1000\n",
      "157/157 [==============================] - 0s 351us/step - loss: 0.5065 - acc: 0.8089 - val_loss: 0.4851 - val_acc: 0.8382\n",
      "Epoch 108/1000\n",
      "157/157 [==============================] - 0s 370us/step - loss: 0.5041 - acc: 0.8025 - val_loss: 0.4822 - val_acc: 0.8529\n",
      "Epoch 109/1000\n",
      "157/157 [==============================] - 0s 345us/step - loss: 0.5016 - acc: 0.8089 - val_loss: 0.4795 - val_acc: 0.8529\n",
      "Epoch 110/1000\n",
      "157/157 [==============================] - 0s 121us/step - loss: 0.4996 - acc: 0.8025 - val_loss: 0.4765 - val_acc: 0.8529\n",
      "Epoch 111/1000\n",
      "157/157 [==============================] - 0s 135us/step - loss: 0.4972 - acc: 0.8089 - val_loss: 0.4739 - val_acc: 0.8529\n",
      "Epoch 112/1000\n",
      "157/157 [==============================] - 0s 266us/step - loss: 0.4944 - acc: 0.8280 - val_loss: 0.4716 - val_acc: 0.8529\n",
      "Epoch 113/1000\n",
      "157/157 [==============================] - 0s 218us/step - loss: 0.4918 - acc: 0.8153 - val_loss: 0.4686 - val_acc: 0.8529\n",
      "Epoch 114/1000\n",
      "157/157 [==============================] - 0s 174us/step - loss: 0.4894 - acc: 0.8471 - val_loss: 0.4656 - val_acc: 0.8529\n",
      "Epoch 115/1000\n",
      "157/157 [==============================] - 0s 157us/step - loss: 0.4869 - acc: 0.8408 - val_loss: 0.4624 - val_acc: 0.8676\n",
      "Epoch 116/1000\n",
      "157/157 [==============================] - 0s 276us/step - loss: 0.4846 - acc: 0.8089 - val_loss: 0.4592 - val_acc: 0.8676\n",
      "Epoch 117/1000\n",
      "157/157 [==============================] - 0s 146us/step - loss: 0.4818 - acc: 0.8408 - val_loss: 0.4565 - val_acc: 0.8676\n",
      "Epoch 118/1000\n",
      "157/157 [==============================] - 0s 246us/step - loss: 0.4792 - acc: 0.8535 - val_loss: 0.4539 - val_acc: 0.8676\n",
      "Epoch 119/1000\n",
      "157/157 [==============================] - 0s 116us/step - loss: 0.4768 - acc: 0.8408 - val_loss: 0.4506 - val_acc: 0.8676\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 120/1000\n",
      "157/157 [==============================] - 0s 229us/step - loss: 0.4756 - acc: 0.8471 - val_loss: 0.4482 - val_acc: 0.8676\n",
      "Epoch 121/1000\n",
      "157/157 [==============================] - 0s 204us/step - loss: 0.4726 - acc: 0.8599 - val_loss: 0.4463 - val_acc: 0.8676\n",
      "Epoch 122/1000\n",
      "157/157 [==============================] - 0s 162us/step - loss: 0.4708 - acc: 0.8471 - val_loss: 0.4438 - val_acc: 0.8676\n",
      "Epoch 123/1000\n",
      "157/157 [==============================] - 0s 245us/step - loss: 0.4682 - acc: 0.8599 - val_loss: 0.4415 - val_acc: 0.8676\n",
      "Epoch 124/1000\n",
      "157/157 [==============================] - 0s 200us/step - loss: 0.4658 - acc: 0.8535 - val_loss: 0.4390 - val_acc: 0.8676\n",
      "Epoch 125/1000\n",
      "157/157 [==============================] - 0s 178us/step - loss: 0.4635 - acc: 0.8599 - val_loss: 0.4361 - val_acc: 0.8824\n",
      "Epoch 126/1000\n",
      "157/157 [==============================] - 0s 156us/step - loss: 0.4614 - acc: 0.8535 - val_loss: 0.4332 - val_acc: 0.8824\n",
      "Epoch 127/1000\n",
      "157/157 [==============================] - 0s 327us/step - loss: 0.4584 - acc: 0.8726 - val_loss: 0.4307 - val_acc: 0.8824\n",
      "Epoch 128/1000\n",
      "157/157 [==============================] - 0s 181us/step - loss: 0.4571 - acc: 0.8535 - val_loss: 0.4279 - val_acc: 0.8824\n",
      "Epoch 129/1000\n",
      "157/157 [==============================] - 0s 268us/step - loss: 0.4550 - acc: 0.8726 - val_loss: 0.4258 - val_acc: 0.8824\n",
      "Epoch 130/1000\n",
      "157/157 [==============================] - 0s 176us/step - loss: 0.4517 - acc: 0.8599 - val_loss: 0.4230 - val_acc: 0.8824\n",
      "Epoch 131/1000\n",
      "157/157 [==============================] - 0s 281us/step - loss: 0.4497 - acc: 0.8726 - val_loss: 0.4204 - val_acc: 0.8824\n",
      "Epoch 132/1000\n",
      "157/157 [==============================] - 0s 149us/step - loss: 0.4476 - acc: 0.8662 - val_loss: 0.4178 - val_acc: 0.8824\n",
      "Epoch 133/1000\n",
      "157/157 [==============================] - 0s 177us/step - loss: 0.4456 - acc: 0.8726 - val_loss: 0.4153 - val_acc: 0.8824\n",
      "Epoch 134/1000\n",
      "157/157 [==============================] - 0s 137us/step - loss: 0.4433 - acc: 0.8790 - val_loss: 0.4131 - val_acc: 0.8824\n",
      "Epoch 135/1000\n",
      "157/157 [==============================] - 0s 121us/step - loss: 0.4409 - acc: 0.8854 - val_loss: 0.4108 - val_acc: 0.8824\n",
      "Epoch 136/1000\n",
      "157/157 [==============================] - 0s 167us/step - loss: 0.4381 - acc: 0.8726 - val_loss: 0.4082 - val_acc: 0.8824\n",
      "Epoch 137/1000\n",
      "157/157 [==============================] - 0s 272us/step - loss: 0.4357 - acc: 0.8854 - val_loss: 0.4053 - val_acc: 0.8824\n",
      "Epoch 138/1000\n",
      "157/157 [==============================] - 0s 286us/step - loss: 0.4338 - acc: 0.8726 - val_loss: 0.4025 - val_acc: 0.8824\n",
      "Epoch 139/1000\n",
      "157/157 [==============================] - 0s 164us/step - loss: 0.4308 - acc: 0.8726 - val_loss: 0.3994 - val_acc: 0.8824\n",
      "Epoch 140/1000\n",
      "157/157 [==============================] - 0s 160us/step - loss: 0.4286 - acc: 0.8790 - val_loss: 0.3968 - val_acc: 0.8824\n",
      "Epoch 141/1000\n",
      "157/157 [==============================] - 0s 196us/step - loss: 0.4266 - acc: 0.8726 - val_loss: 0.3944 - val_acc: 0.8824\n",
      "Epoch 142/1000\n",
      "157/157 [==============================] - 0s 285us/step - loss: 0.4241 - acc: 0.8790 - val_loss: 0.3924 - val_acc: 0.8824\n",
      "Epoch 143/1000\n",
      "157/157 [==============================] - 0s 136us/step - loss: 0.4224 - acc: 0.8726 - val_loss: 0.3902 - val_acc: 0.8824\n",
      "Epoch 144/1000\n",
      "157/157 [==============================] - 0s 243us/step - loss: 0.4204 - acc: 0.8726 - val_loss: 0.3882 - val_acc: 0.8824\n",
      "Epoch 145/1000\n",
      "157/157 [==============================] - 0s 155us/step - loss: 0.4177 - acc: 0.8726 - val_loss: 0.3860 - val_acc: 0.8824\n",
      "Epoch 146/1000\n",
      "157/157 [==============================] - 0s 210us/step - loss: 0.4167 - acc: 0.8854 - val_loss: 0.3840 - val_acc: 0.8824\n",
      "Epoch 147/1000\n",
      "157/157 [==============================] - 0s 155us/step - loss: 0.4133 - acc: 0.8726 - val_loss: 0.3815 - val_acc: 0.8824\n",
      "Epoch 148/1000\n",
      "157/157 [==============================] - 0s 150us/step - loss: 0.4112 - acc: 0.8790 - val_loss: 0.3791 - val_acc: 0.8824\n",
      "Epoch 149/1000\n",
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      "Epoch 150/1000\n",
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      "Epoch 200/1000\n",
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      "Epoch 201/1000\n",
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      "Epoch 209/1000\n",
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      "Epoch 211/1000\n",
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      "Epoch 212/1000\n",
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      "Epoch 216/1000\n",
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      "Epoch 217/1000\n",
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      "Epoch 220/1000\n",
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      "Epoch 223/1000\n",
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      "Epoch 224/1000\n",
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      "Epoch 225/1000\n",
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      "Epoch 226/1000\n",
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      "Epoch 227/1000\n",
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      "Epoch 228/1000\n",
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      "Epoch 229/1000\n",
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      "Epoch 230/1000\n",
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      "Epoch 231/1000\n",
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      "Epoch 232/1000\n",
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      "Epoch 233/1000\n",
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      "Epoch 234/1000\n",
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      "Epoch 235/1000\n",
      "157/157 [==============================] - 0s 192us/step - loss: 0.2584 - acc: 0.9490 - val_loss: 0.2292 - val_acc: 0.9118\n",
      "Epoch 236/1000\n",
      "157/157 [==============================] - 0s 294us/step - loss: 0.2579 - acc: 0.9427 - val_loss: 0.2271 - val_acc: 0.9118\n",
      "Epoch 237/1000\n",
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      "Epoch 238/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 251us/step - loss: 0.2542 - acc: 0.9490 - val_loss: 0.2261 - val_acc: 0.9118\n",
      "Epoch 239/1000\n",
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      "Epoch 240/1000\n",
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      "Epoch 241/1000\n",
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      "Epoch 242/1000\n",
      "157/157 [==============================] - 0s 171us/step - loss: 0.2530 - acc: 0.9427 - val_loss: 0.2201 - val_acc: 0.9118\n",
      "Epoch 243/1000\n",
      "157/157 [==============================] - 0s 185us/step - loss: 0.2502 - acc: 0.9554 - val_loss: 0.2198 - val_acc: 0.9118\n",
      "Epoch 244/1000\n",
      "157/157 [==============================] - 0s 125us/step - loss: 0.2478 - acc: 0.9490 - val_loss: 0.2190 - val_acc: 0.9118\n",
      "Epoch 245/1000\n",
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      "Epoch 246/1000\n",
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      "Epoch 247/1000\n",
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      "Epoch 248/1000\n",
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      "Epoch 249/1000\n",
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      "Epoch 250/1000\n",
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      "Epoch 251/1000\n",
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      "Epoch 252/1000\n",
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      "Epoch 253/1000\n",
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      "Epoch 254/1000\n",
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      "Epoch 255/1000\n",
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      "Epoch 256/1000\n",
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      "Epoch 257/1000\n",
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      "Epoch 258/1000\n",
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      "Epoch 259/1000\n",
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      "Epoch 260/1000\n",
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      "Epoch 261/1000\n",
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      "Epoch 262/1000\n",
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      "Epoch 263/1000\n",
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      "Epoch 265/1000\n",
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      "Epoch 266/1000\n",
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      "Epoch 267/1000\n",
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      "Epoch 268/1000\n",
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      "Epoch 269/1000\n",
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      "Epoch 270/1000\n",
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      "Epoch 271/1000\n",
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      "Epoch 272/1000\n",
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      "Epoch 273/1000\n",
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      "Epoch 274/1000\n",
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      "Epoch 275/1000\n",
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      "Epoch 276/1000\n",
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      "Epoch 310/1000\n",
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      "Epoch 311/1000\n",
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      "Epoch 315/1000\n",
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      "Epoch 316/1000\n",
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      "Epoch 317/1000\n",
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      "Epoch 318/1000\n",
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      "Epoch 319/1000\n",
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      "Epoch 320/1000\n",
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      "Epoch 321/1000\n",
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      "Epoch 322/1000\n",
      "157/157 [==============================] - ETA: 0s - loss: 0.1695 - acc: 0.968 - 0s 170us/step - loss: 0.1819 - acc: 0.9554 - val_loss: 0.1626 - val_acc: 0.9118\n",
      "Epoch 323/1000\n",
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      "Epoch 327/1000\n",
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      "Epoch 329/1000\n",
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      "Epoch 330/1000\n",
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      "Epoch 334/1000\n",
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      "Epoch 335/1000\n",
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      "Epoch 337/1000\n",
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      "Epoch 339/1000\n",
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      "Epoch 341/1000\n",
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      "Epoch 342/1000\n",
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      "Epoch 343/1000\n",
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      "Epoch 344/1000\n",
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      "Epoch 345/1000\n",
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      "Epoch 346/1000\n",
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      "Epoch 347/1000\n",
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      "Epoch 348/1000\n",
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      "Epoch 349/1000\n",
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      "Epoch 350/1000\n",
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      "Epoch 351/1000\n",
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      "Epoch 352/1000\n",
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      "Epoch 353/1000\n",
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      "Epoch 354/1000\n",
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      "Epoch 355/1000\n",
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      "Epoch 356/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 169us/step - loss: 0.1626 - acc: 0.9554 - val_loss: 0.1579 - val_acc: 0.9118\n",
      "Epoch 357/1000\n",
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      "Epoch 358/1000\n",
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      "Epoch 359/1000\n",
      "157/157 [==============================] - 0s 236us/step - loss: 0.1603 - acc: 0.9618 - val_loss: 0.1535 - val_acc: 0.9118\n",
      "Epoch 360/1000\n",
      "157/157 [==============================] - 0s 242us/step - loss: 0.1589 - acc: 0.9618 - val_loss: 0.1526 - val_acc: 0.9118\n",
      "Epoch 361/1000\n",
      "157/157 [==============================] - 0s 129us/step - loss: 0.1592 - acc: 0.9618 - val_loss: 0.1506 - val_acc: 0.9118\n",
      "Epoch 362/1000\n",
      "157/157 [==============================] - 0s 115us/step - loss: 0.1594 - acc: 0.9682 - val_loss: 0.1509 - val_acc: 0.9118\n",
      "Epoch 363/1000\n",
      "157/157 [==============================] - 0s 370us/step - loss: 0.1597 - acc: 0.9618 - val_loss: 0.1533 - val_acc: 0.9118\n",
      "Epoch 364/1000\n",
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      "Epoch 365/1000\n",
      "157/157 [==============================] - 0s 266us/step - loss: 0.1576 - acc: 0.9682 - val_loss: 0.1506 - val_acc: 0.9118\n",
      "Epoch 366/1000\n",
      "157/157 [==============================] - 0s 177us/step - loss: 0.1570 - acc: 0.9618 - val_loss: 0.1503 - val_acc: 0.9118\n",
      "Epoch 367/1000\n",
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      "Epoch 368/1000\n",
      "157/157 [==============================] - 0s 170us/step - loss: 0.1570 - acc: 0.9618 - val_loss: 0.1474 - val_acc: 0.9118\n",
      "Epoch 369/1000\n",
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      "Epoch 370/1000\n",
      "157/157 [==============================] - 0s 217us/step - loss: 0.1558 - acc: 0.9618 - val_loss: 0.1481 - val_acc: 0.9118\n",
      "Epoch 371/1000\n",
      "157/157 [==============================] - 0s 191us/step - loss: 0.1538 - acc: 0.9682 - val_loss: 0.1487 - val_acc: 0.9118\n",
      "Epoch 372/1000\n",
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      "Epoch 374/1000\n",
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      "Epoch 375/1000\n",
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      "Epoch 376/1000\n",
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      "Epoch 377/1000\n",
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      "Epoch 378/1000\n",
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      "Epoch 379/1000\n",
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      "Epoch 380/1000\n",
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      "Epoch 381/1000\n",
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      "Epoch 382/1000\n",
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      "Epoch 383/1000\n",
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      "Epoch 384/1000\n",
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      "Epoch 385/1000\n",
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      "Epoch 386/1000\n",
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      "Epoch 387/1000\n",
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      "Epoch 388/1000\n",
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      "Epoch 389/1000\n",
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      "Epoch 390/1000\n",
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      "Epoch 391/1000\n",
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      "Epoch 392/1000\n",
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      "Epoch 393/1000\n",
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      "Epoch 394/1000\n",
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      "Epoch 395/1000\n",
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      "Epoch 397/1000\n",
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      "Epoch 399/1000\n",
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      "Epoch 400/1000\n",
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      "Epoch 401/1000\n",
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      "Epoch 402/1000\n",
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      "Epoch 417/1000\n",
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      "Epoch 418/1000\n",
      "157/157 [==============================] - ETA: 0s - loss: 0.1272 - acc: 0.968 - 0s 196us/step - loss: 0.1331 - acc: 0.9682 - val_loss: 0.1357 - val_acc: 0.9118\n",
      "Epoch 419/1000\n",
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      "Epoch 427/1000\n",
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      "Epoch 431/1000\n",
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      "Epoch 432/1000\n",
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      "Epoch 433/1000\n",
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      "Epoch 434/1000\n",
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      "Epoch 435/1000\n",
      "157/157 [==============================] - ETA: 0s - loss: 0.1275 - acc: 0.968 - 0s 161us/step - loss: 0.1285 - acc: 0.9618 - val_loss: 0.1295 - val_acc: 0.9265\n",
      "Epoch 436/1000\n",
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      "Epoch 437/1000\n",
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      "Epoch 438/1000\n",
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      "Epoch 439/1000\n",
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      "Epoch 443/1000\n",
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      "Epoch 444/1000\n",
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      "Epoch 445/1000\n",
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      "Epoch 446/1000\n",
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      "Epoch 447/1000\n",
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      "Epoch 448/1000\n",
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      "Epoch 449/1000\n",
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      "Epoch 450/1000\n",
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      "Epoch 451/1000\n",
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      "Epoch 452/1000\n",
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      "Epoch 453/1000\n",
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      "Epoch 454/1000\n",
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      "Epoch 455/1000\n",
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      "Epoch 456/1000\n",
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      "Epoch 457/1000\n",
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      "Epoch 458/1000\n",
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      "Epoch 459/1000\n",
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      "Epoch 460/1000\n",
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      "Epoch 461/1000\n",
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      "Epoch 462/1000\n",
      "157/157 [==============================] - 0s 124us/step - loss: 0.1192 - acc: 0.9809 - val_loss: 0.1270 - val_acc: 0.9118\n",
      "Epoch 463/1000\n",
      "157/157 [==============================] - 0s 146us/step - loss: 0.1189 - acc: 0.9809 - val_loss: 0.1244 - val_acc: 0.9265\n",
      "Epoch 464/1000\n",
      "157/157 [==============================] - 0s 128us/step - loss: 0.1176 - acc: 0.9682 - val_loss: 0.1269 - val_acc: 0.9118\n",
      "Epoch 465/1000\n",
      "157/157 [==============================] - 0s 122us/step - loss: 0.1179 - acc: 0.9809 - val_loss: 0.1228 - val_acc: 0.9265\n",
      "Epoch 466/1000\n",
      "157/157 [==============================] - 0s 168us/step - loss: 0.1171 - acc: 0.9682 - val_loss: 0.1279 - val_acc: 0.9118\n",
      "Epoch 467/1000\n",
      "157/157 [==============================] - 0s 111us/step - loss: 0.1173 - acc: 0.9745 - val_loss: 0.1255 - val_acc: 0.9265\n",
      "Epoch 468/1000\n",
      "157/157 [==============================] - 0s 116us/step - loss: 0.1161 - acc: 0.9745 - val_loss: 0.1241 - val_acc: 0.9265\n",
      "Epoch 469/1000\n",
      "157/157 [==============================] - 0s 221us/step - loss: 0.1147 - acc: 0.9745 - val_loss: 0.1247 - val_acc: 0.9265\n",
      "Epoch 470/1000\n",
      "157/157 [==============================] - 0s 211us/step - loss: 0.1158 - acc: 0.9809 - val_loss: 0.1220 - val_acc: 0.9265\n",
      "Epoch 471/1000\n",
      "157/157 [==============================] - 0s 126us/step - loss: 0.1143 - acc: 0.9682 - val_loss: 0.1254 - val_acc: 0.9118\n",
      "Epoch 472/1000\n",
      "157/157 [==============================] - ETA: 0s - loss: 0.0983 - acc: 1.000 - 0s 138us/step - loss: 0.1152 - acc: 0.9745 - val_loss: 0.1270 - val_acc: 0.9118\n",
      "Epoch 473/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 162us/step - loss: 0.1152 - acc: 0.9745 - val_loss: 0.1234 - val_acc: 0.9265\n",
      "Epoch 474/1000\n",
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      "Epoch 475/1000\n",
      "157/157 [==============================] - 0s 150us/step - loss: 0.1132 - acc: 0.9745 - val_loss: 0.1195 - val_acc: 0.9265\n",
      "Epoch 476/1000\n",
      "157/157 [==============================] - 0s 140us/step - loss: 0.1137 - acc: 0.9682 - val_loss: 0.1242 - val_acc: 0.9265\n",
      "Epoch 477/1000\n",
      "157/157 [==============================] - 0s 165us/step - loss: 0.1127 - acc: 0.9682 - val_loss: 0.1263 - val_acc: 0.9118\n",
      "Epoch 478/1000\n",
      "157/157 [==============================] - ETA: 0s - loss: 0.0833 - acc: 0.968 - 0s 168us/step - loss: 0.1156 - acc: 0.9682 - val_loss: 0.1246 - val_acc: 0.9118\n",
      "Epoch 479/1000\n",
      "157/157 [==============================] - 0s 169us/step - loss: 0.1123 - acc: 0.9745 - val_loss: 0.1227 - val_acc: 0.9265\n",
      "Epoch 480/1000\n",
      "157/157 [==============================] - 0s 144us/step - loss: 0.1129 - acc: 0.9809 - val_loss: 0.1195 - val_acc: 0.9265\n",
      "Epoch 481/1000\n",
      "157/157 [==============================] - 0s 142us/step - loss: 0.1133 - acc: 0.9682 - val_loss: 0.1225 - val_acc: 0.9265\n",
      "Epoch 482/1000\n",
      "157/157 [==============================] - 0s 205us/step - loss: 0.1130 - acc: 0.9745 - val_loss: 0.1259 - val_acc: 0.9118\n",
      "Epoch 483/1000\n",
      "157/157 [==============================] - 0s 140us/step - loss: 0.1113 - acc: 0.9745 - val_loss: 0.1233 - val_acc: 0.9265\n",
      "Epoch 484/1000\n",
      "157/157 [==============================] - 0s 150us/step - loss: 0.1114 - acc: 0.9745 - val_loss: 0.1219 - val_acc: 0.9265\n",
      "Epoch 485/1000\n",
      "157/157 [==============================] - 0s 161us/step - loss: 0.1110 - acc: 0.9809 - val_loss: 0.1184 - val_acc: 0.9265\n",
      "Epoch 486/1000\n",
      "157/157 [==============================] - 0s 161us/step - loss: 0.1107 - acc: 0.9745 - val_loss: 0.1186 - val_acc: 0.9265\n",
      "Epoch 487/1000\n",
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      "Epoch 488/1000\n",
      "157/157 [==============================] - 0s 146us/step - loss: 0.1108 - acc: 0.9745 - val_loss: 0.1222 - val_acc: 0.9265\n",
      "Epoch 489/1000\n",
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      "Epoch 490/1000\n",
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      "Epoch 491/1000\n",
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      "Epoch 492/1000\n",
      "157/157 [==============================] - 0s 191us/step - loss: 0.1094 - acc: 0.9809 - val_loss: 0.1200 - val_acc: 0.9265\n",
      "Epoch 493/1000\n",
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      "Epoch 494/1000\n",
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      "Epoch 495/1000\n",
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      "Epoch 496/1000\n",
      "157/157 [==============================] - 0s 178us/step - loss: 0.1081 - acc: 0.9809 - val_loss: 0.1146 - val_acc: 0.9412\n",
      "Epoch 497/1000\n",
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      "Epoch 498/1000\n",
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      "Epoch 500/1000\n",
      "157/157 [==============================] - 0s 134us/step - loss: 0.1057 - acc: 0.9745 - val_loss: 0.1216 - val_acc: 0.9265\n",
      "Epoch 501/1000\n",
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      "Epoch 502/1000\n",
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      "Epoch 503/1000\n",
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      "Epoch 504/1000\n",
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      "Epoch 505/1000\n",
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      "Epoch 506/1000\n",
      "157/157 [==============================] - 0s 153us/step - loss: 0.1035 - acc: 0.9745 - val_loss: 0.1202 - val_acc: 0.9265\n",
      "Epoch 507/1000\n",
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      "Epoch 508/1000\n",
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      "Epoch 509/1000\n",
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      "Epoch 510/1000\n",
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      "Epoch 511/1000\n",
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      "Epoch 512/1000\n",
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      "Epoch 513/1000\n",
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      "Epoch 514/1000\n",
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      "Epoch 515/1000\n",
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      "Epoch 516/1000\n",
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      "Epoch 517/1000\n",
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      "Epoch 518/1000\n",
      "157/157 [==============================] - 0s 139us/step - loss: 0.1040 - acc: 0.9745 - val_loss: 0.1156 - val_acc: 0.9265\n",
      "Epoch 519/1000\n",
      "157/157 [==============================] - 0s 114us/step - loss: 0.1007 - acc: 0.9809 - val_loss: 0.1185 - val_acc: 0.9265\n",
      "Epoch 520/1000\n",
      "157/157 [==============================] - 0s 133us/step - loss: 0.1000 - acc: 0.9809 - val_loss: 0.1175 - val_acc: 0.9265\n",
      "Epoch 521/1000\n",
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      "Epoch 522/1000\n",
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      "Epoch 523/1000\n",
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      "Epoch 524/1000\n",
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      "Epoch 525/1000\n",
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      "Epoch 526/1000\n",
      "157/157 [==============================] - 0s 137us/step - loss: 0.0992 - acc: 0.9745 - val_loss: 0.1177 - val_acc: 0.9265\n",
      "Epoch 527/1000\n",
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      "Epoch 528/1000\n",
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      "Epoch 529/1000\n",
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      "Epoch 530/1000\n",
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      "Epoch 531/1000\n",
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      "Epoch 532/1000\n",
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      "Epoch 533/1000\n",
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      "Epoch 534/1000\n",
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      "Epoch 535/1000\n",
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      "Epoch 536/1000\n",
      "157/157 [==============================] - 0s 350us/step - loss: 0.0962 - acc: 0.9745 - val_loss: 0.1169 - val_acc: 0.9265\n",
      "Epoch 537/1000\n",
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      "Epoch 538/1000\n",
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      "Epoch 539/1000\n",
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      "Epoch 540/1000\n",
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      "Epoch 541/1000\n",
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      "Epoch 542/1000\n",
      "157/157 [==============================] - 0s 139us/step - loss: 0.0943 - acc: 0.9809 - val_loss: 0.1129 - val_acc: 0.9265\n",
      "Epoch 543/1000\n",
      "157/157 [==============================] - 0s 209us/step - loss: 0.0951 - acc: 0.9809 - val_loss: 0.1135 - val_acc: 0.9265\n",
      "Epoch 544/1000\n",
      "157/157 [==============================] - 0s 130us/step - loss: 0.0950 - acc: 0.9809 - val_loss: 0.1144 - val_acc: 0.9265\n",
      "Epoch 545/1000\n",
      "157/157 [==============================] - 0s 141us/step - loss: 0.0944 - acc: 0.9745 - val_loss: 0.1093 - val_acc: 0.9559\n",
      "Epoch 546/1000\n",
      "157/157 [==============================] - 0s 172us/step - loss: 0.0939 - acc: 0.9745 - val_loss: 0.1086 - val_acc: 0.9559\n",
      "Epoch 547/1000\n",
      "157/157 [==============================] - 0s 208us/step - loss: 0.0935 - acc: 0.9745 - val_loss: 0.1047 - val_acc: 0.9559\n",
      "Epoch 548/1000\n",
      "157/157 [==============================] - 0s 180us/step - loss: 0.0938 - acc: 0.9809 - val_loss: 0.1097 - val_acc: 0.9559\n",
      "Epoch 549/1000\n",
      "157/157 [==============================] - 0s 151us/step - loss: 0.0938 - acc: 0.9809 - val_loss: 0.1163 - val_acc: 0.9265\n",
      "Epoch 550/1000\n",
      "157/157 [==============================] - ETA: 0s - loss: 0.1062 - acc: 0.968 - 0s 177us/step - loss: 0.0944 - acc: 0.9809 - val_loss: 0.1127 - val_acc: 0.9265\n",
      "Epoch 551/1000\n",
      "157/157 [==============================] - 0s 160us/step - loss: 0.0920 - acc: 0.9809 - val_loss: 0.1087 - val_acc: 0.9559\n",
      "Epoch 552/1000\n",
      "157/157 [==============================] - 0s 159us/step - loss: 0.0917 - acc: 0.9873 - val_loss: 0.1094 - val_acc: 0.9559\n",
      "Epoch 553/1000\n",
      "157/157 [==============================] - 0s 133us/step - loss: 0.0946 - acc: 0.9809 - val_loss: 0.1089 - val_acc: 0.9559\n",
      "Epoch 554/1000\n",
      "157/157 [==============================] - 0s 181us/step - loss: 0.0914 - acc: 0.9809 - val_loss: 0.1117 - val_acc: 0.9265\n",
      "Epoch 555/1000\n",
      "157/157 [==============================] - 0s 194us/step - loss: 0.0918 - acc: 0.9873 - val_loss: 0.1154 - val_acc: 0.9265\n",
      "Epoch 556/1000\n",
      "157/157 [==============================] - 0s 141us/step - loss: 0.0916 - acc: 0.9745 - val_loss: 0.1064 - val_acc: 0.9559\n",
      "Epoch 557/1000\n",
      "157/157 [==============================] - 0s 150us/step - loss: 0.0930 - acc: 0.9809 - val_loss: 0.1102 - val_acc: 0.9559\n",
      "Epoch 558/1000\n",
      "157/157 [==============================] - 0s 186us/step - loss: 0.0911 - acc: 0.9809 - val_loss: 0.1076 - val_acc: 0.9559\n",
      "Epoch 559/1000\n",
      "157/157 [==============================] - 0s 143us/step - loss: 0.0908 - acc: 0.9809 - val_loss: 0.1167 - val_acc: 0.9118\n",
      "Epoch 560/1000\n",
      "157/157 [==============================] - 0s 143us/step - loss: 0.0902 - acc: 0.9745 - val_loss: 0.1082 - val_acc: 0.9559\n",
      "Epoch 561/1000\n",
      "157/157 [==============================] - 0s 151us/step - loss: 0.0918 - acc: 0.9745 - val_loss: 0.1033 - val_acc: 0.9559\n",
      "Epoch 562/1000\n",
      "157/157 [==============================] - 0s 161us/step - loss: 0.0896 - acc: 0.9873 - val_loss: 0.1126 - val_acc: 0.9265\n",
      "Epoch 563/1000\n",
      "157/157 [==============================] - 0s 132us/step - loss: 0.0903 - acc: 0.9809 - val_loss: 0.1043 - val_acc: 0.9559\n",
      "Epoch 564/1000\n",
      "157/157 [==============================] - 0s 193us/step - loss: 0.0901 - acc: 0.9745 - val_loss: 0.1031 - val_acc: 0.9559\n",
      "Epoch 565/1000\n",
      "157/157 [==============================] - 0s 146us/step - loss: 0.0889 - acc: 0.9873 - val_loss: 0.1165 - val_acc: 0.9118\n",
      "Epoch 566/1000\n",
      "157/157 [==============================] - 0s 120us/step - loss: 0.0889 - acc: 0.9809 - val_loss: 0.1029 - val_acc: 0.9559\n",
      "Epoch 567/1000\n",
      "157/157 [==============================] - 0s 138us/step - loss: 0.0892 - acc: 0.9873 - val_loss: 0.1111 - val_acc: 0.9265\n",
      "Epoch 568/1000\n",
      "157/157 [==============================] - 0s 146us/step - loss: 0.0887 - acc: 0.9809 - val_loss: 0.1072 - val_acc: 0.9559\n",
      "Epoch 569/1000\n",
      "157/157 [==============================] - 0s 151us/step - loss: 0.0903 - acc: 0.9809 - val_loss: 0.1057 - val_acc: 0.9559\n",
      "Epoch 570/1000\n",
      "157/157 [==============================] - 0s 117us/step - loss: 0.0879 - acc: 0.9873 - val_loss: 0.1071 - val_acc: 0.9559\n",
      "Epoch 571/1000\n",
      "157/157 [==============================] - 0s 254us/step - loss: 0.0902 - acc: 0.9745 - val_loss: 0.1026 - val_acc: 0.9559\n",
      "Epoch 572/1000\n",
      "157/157 [==============================] - 0s 228us/step - loss: 0.0894 - acc: 0.9809 - val_loss: 0.1060 - val_acc: 0.9559\n",
      "Epoch 573/1000\n",
      "157/157 [==============================] - 0s 233us/step - loss: 0.0878 - acc: 0.9809 - val_loss: 0.1063 - val_acc: 0.9559\n",
      "Epoch 574/1000\n",
      "157/157 [==============================] - 0s 143us/step - loss: 0.0903 - acc: 0.9809 - val_loss: 0.1055 - val_acc: 0.9559\n",
      "Epoch 575/1000\n",
      "157/157 [==============================] - 0s 155us/step - loss: 0.0873 - acc: 0.9809 - val_loss: 0.1051 - val_acc: 0.9559\n",
      "Epoch 576/1000\n",
      "157/157 [==============================] - 0s 191us/step - loss: 0.0896 - acc: 0.9745 - val_loss: 0.1032 - val_acc: 0.9559\n",
      "Epoch 577/1000\n",
      "157/157 [==============================] - 0s 190us/step - loss: 0.0873 - acc: 0.9809 - val_loss: 0.1079 - val_acc: 0.9559\n",
      "Epoch 578/1000\n",
      "157/157 [==============================] - 0s 176us/step - loss: 0.0868 - acc: 0.9745 - val_loss: 0.1049 - val_acc: 0.9559\n",
      "Epoch 579/1000\n",
      "157/157 [==============================] - 0s 605us/step - loss: 0.0879 - acc: 0.9809 - val_loss: 0.1005 - val_acc: 0.9559\n",
      "Epoch 580/1000\n",
      "157/157 [==============================] - 0s 201us/step - loss: 0.0862 - acc: 0.9873 - val_loss: 0.1069 - val_acc: 0.9559\n",
      "Epoch 581/1000\n",
      "157/157 [==============================] - 0s 175us/step - loss: 0.0891 - acc: 0.9809 - val_loss: 0.1084 - val_acc: 0.9559\n",
      "Epoch 582/1000\n",
      "157/157 [==============================] - 0s 262us/step - loss: 0.0857 - acc: 0.9809 - val_loss: 0.1103 - val_acc: 0.9265\n",
      "Epoch 583/1000\n",
      "157/157 [==============================] - 0s 147us/step - loss: 0.0861 - acc: 0.9809 - val_loss: 0.1083 - val_acc: 0.9559\n",
      "Epoch 584/1000\n",
      "157/157 [==============================] - 0s 243us/step - loss: 0.0890 - acc: 0.9809 - val_loss: 0.1060 - val_acc: 0.9559\n",
      "Epoch 585/1000\n",
      "157/157 [==============================] - 0s 180us/step - loss: 0.0853 - acc: 0.9809 - val_loss: 0.1076 - val_acc: 0.9559\n",
      "Epoch 586/1000\n",
      "157/157 [==============================] - 0s 294us/step - loss: 0.0869 - acc: 0.9745 - val_loss: 0.1027 - val_acc: 0.9559\n",
      "Epoch 587/1000\n",
      "157/157 [==============================] - 0s 279us/step - loss: 0.0859 - acc: 0.9809 - val_loss: 0.1073 - val_acc: 0.9559\n",
      "Epoch 588/1000\n",
      "157/157 [==============================] - 0s 215us/step - loss: 0.0849 - acc: 0.9873 - val_loss: 0.1131 - val_acc: 0.9265\n",
      "Epoch 589/1000\n",
      "157/157 [==============================] - 0s 357us/step - loss: 0.0856 - acc: 0.9745 - val_loss: 0.1021 - val_acc: 0.9559\n",
      "Epoch 590/1000\n",
      "157/157 [==============================] - 0s 113us/step - loss: 0.0865 - acc: 0.9873 - val_loss: 0.1045 - val_acc: 0.9559\n",
      "Epoch 591/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 132us/step - loss: 0.0840 - acc: 0.9809 - val_loss: 0.1031 - val_acc: 0.9559\n",
      "Epoch 592/1000\n",
      "157/157 [==============================] - 0s 133us/step - loss: 0.0847 - acc: 0.9873 - val_loss: 0.1088 - val_acc: 0.9559\n",
      "Epoch 593/1000\n",
      "157/157 [==============================] - 0s 241us/step - loss: 0.0844 - acc: 0.9809 - val_loss: 0.1104 - val_acc: 0.9412\n",
      "Epoch 594/1000\n",
      "157/157 [==============================] - 0s 289us/step - loss: 0.0846 - acc: 0.9873 - val_loss: 0.1109 - val_acc: 0.9265\n",
      "Epoch 595/1000\n",
      "157/157 [==============================] - 0s 119us/step - loss: 0.0841 - acc: 0.9809 - val_loss: 0.1089 - val_acc: 0.9559\n",
      "Epoch 596/1000\n",
      "157/157 [==============================] - 0s 166us/step - loss: 0.0842 - acc: 0.9809 - val_loss: 0.1101 - val_acc: 0.9265\n",
      "Epoch 597/1000\n",
      "157/157 [==============================] - 0s 188us/step - loss: 0.0845 - acc: 0.9809 - val_loss: 0.1052 - val_acc: 0.9559\n",
      "Epoch 598/1000\n",
      "157/157 [==============================] - 0s 239us/step - loss: 0.0824 - acc: 0.9809 - val_loss: 0.1039 - val_acc: 0.9559\n",
      "Epoch 599/1000\n",
      "157/157 [==============================] - 0s 203us/step - loss: 0.0832 - acc: 0.9936 - val_loss: 0.1032 - val_acc: 0.9559\n",
      "Epoch 600/1000\n",
      "157/157 [==============================] - 0s 172us/step - loss: 0.0825 - acc: 0.9809 - val_loss: 0.1041 - val_acc: 0.9559\n",
      "Epoch 601/1000\n",
      "157/157 [==============================] - 0s 224us/step - loss: 0.0831 - acc: 0.9809 - val_loss: 0.1038 - val_acc: 0.9559\n",
      "Epoch 602/1000\n",
      "157/157 [==============================] - 0s 143us/step - loss: 0.0829 - acc: 0.9809 - val_loss: 0.1097 - val_acc: 0.9412\n",
      "Epoch 603/1000\n",
      "157/157 [==============================] - 0s 259us/step - loss: 0.0830 - acc: 0.9809 - val_loss: 0.1023 - val_acc: 0.9559\n",
      "Epoch 604/1000\n",
      "157/157 [==============================] - 0s 224us/step - loss: 0.0824 - acc: 0.9873 - val_loss: 0.1132 - val_acc: 0.9118\n",
      "Epoch 605/1000\n",
      "157/157 [==============================] - 0s 96us/step - loss: 0.0827 - acc: 0.9809 - val_loss: 0.1121 - val_acc: 0.9265\n",
      "Epoch 606/1000\n",
      "157/157 [==============================] - 0s 170us/step - loss: 0.0831 - acc: 0.9873 - val_loss: 0.1072 - val_acc: 0.9559\n",
      "Epoch 607/1000\n",
      "157/157 [==============================] - 0s 117us/step - loss: 0.0813 - acc: 0.9809 - val_loss: 0.1062 - val_acc: 0.9559\n",
      "Epoch 608/1000\n",
      "157/157 [==============================] - 0s 235us/step - loss: 0.0817 - acc: 0.9809 - val_loss: 0.1049 - val_acc: 0.9559\n",
      "Epoch 609/1000\n",
      "157/157 [==============================] - 0s 112us/step - loss: 0.0817 - acc: 0.9809 - val_loss: 0.1018 - val_acc: 0.9559\n",
      "Epoch 610/1000\n",
      "157/157 [==============================] - 0s 133us/step - loss: 0.0808 - acc: 0.9873 - val_loss: 0.1021 - val_acc: 0.9559\n",
      "Epoch 611/1000\n",
      "157/157 [==============================] - 0s 235us/step - loss: 0.0812 - acc: 0.9873 - val_loss: 0.1007 - val_acc: 0.9559\n",
      "Epoch 612/1000\n",
      "157/157 [==============================] - 0s 502us/step - loss: 0.0817 - acc: 0.9873 - val_loss: 0.1089 - val_acc: 0.9412\n",
      "Epoch 613/1000\n",
      "157/157 [==============================] - 0s 312us/step - loss: 0.0810 - acc: 0.9809 - val_loss: 0.1070 - val_acc: 0.9559\n",
      "Epoch 614/1000\n",
      "157/157 [==============================] - 0s 203us/step - loss: 0.0806 - acc: 0.9873 - val_loss: 0.1112 - val_acc: 0.9265\n",
      "Epoch 615/1000\n",
      "157/157 [==============================] - 0s 243us/step - loss: 0.0797 - acc: 0.9809 - val_loss: 0.1005 - val_acc: 0.9559\n",
      "Epoch 616/1000\n",
      "157/157 [==============================] - 0s 152us/step - loss: 0.0810 - acc: 0.9809 - val_loss: 0.1057 - val_acc: 0.9559\n",
      "Epoch 617/1000\n",
      "157/157 [==============================] - 0s 189us/step - loss: 0.0825 - acc: 0.9809 - val_loss: 0.1116 - val_acc: 0.9265\n",
      "Epoch 618/1000\n",
      "157/157 [==============================] - 0s 171us/step - loss: 0.0801 - acc: 0.9809 - val_loss: 0.1021 - val_acc: 0.9559\n",
      "Epoch 619/1000\n",
      "157/157 [==============================] - 0s 198us/step - loss: 0.0788 - acc: 0.9809 - val_loss: 0.1018 - val_acc: 0.9559\n",
      "Epoch 620/1000\n",
      "157/157 [==============================] - 0s 129us/step - loss: 0.0816 - acc: 0.9809 - val_loss: 0.1011 - val_acc: 0.9559\n",
      "Epoch 621/1000\n",
      "157/157 [==============================] - 0s 150us/step - loss: 0.0807 - acc: 0.9809 - val_loss: 0.1004 - val_acc: 0.9559\n",
      "Epoch 622/1000\n",
      "157/157 [==============================] - 0s 203us/step - loss: 0.0795 - acc: 0.9809 - val_loss: 0.0986 - val_acc: 0.9559\n",
      "Epoch 623/1000\n",
      "157/157 [==============================] - 0s 168us/step - loss: 0.0804 - acc: 0.9809 - val_loss: 0.1027 - val_acc: 0.9559\n",
      "Epoch 624/1000\n",
      "157/157 [==============================] - 0s 295us/step - loss: 0.0787 - acc: 0.9873 - val_loss: 0.1072 - val_acc: 0.9559\n",
      "Epoch 625/1000\n",
      "157/157 [==============================] - 0s 215us/step - loss: 0.0798 - acc: 0.9809 - val_loss: 0.1022 - val_acc: 0.9559\n",
      "Epoch 626/1000\n",
      "157/157 [==============================] - 0s 288us/step - loss: 0.0779 - acc: 0.9873 - val_loss: 0.1063 - val_acc: 0.9559\n",
      "Epoch 627/1000\n",
      "157/157 [==============================] - 0s 257us/step - loss: 0.0777 - acc: 0.9809 - val_loss: 0.1014 - val_acc: 0.9559\n",
      "Epoch 628/1000\n",
      "157/157 [==============================] - 0s 230us/step - loss: 0.0779 - acc: 0.9873 - val_loss: 0.0965 - val_acc: 0.9706\n",
      "Epoch 629/1000\n",
      "157/157 [==============================] - 0s 198us/step - loss: 0.0782 - acc: 0.9809 - val_loss: 0.1018 - val_acc: 0.9559\n",
      "Epoch 630/1000\n",
      "157/157 [==============================] - 0s 171us/step - loss: 0.0772 - acc: 0.9873 - val_loss: 0.1039 - val_acc: 0.9559\n",
      "Epoch 631/1000\n",
      "157/157 [==============================] - 0s 190us/step - loss: 0.0771 - acc: 0.9873 - val_loss: 0.1081 - val_acc: 0.9559\n",
      "Epoch 632/1000\n",
      "157/157 [==============================] - 0s 162us/step - loss: 0.0780 - acc: 0.9809 - val_loss: 0.0956 - val_acc: 0.9706\n",
      "Epoch 633/1000\n",
      "157/157 [==============================] - 0s 153us/step - loss: 0.0770 - acc: 0.9809 - val_loss: 0.0983 - val_acc: 0.9559\n",
      "Epoch 634/1000\n",
      "157/157 [==============================] - 0s 166us/step - loss: 0.0787 - acc: 0.9809 - val_loss: 0.1033 - val_acc: 0.9559\n",
      "Epoch 635/1000\n",
      "157/157 [==============================] - 0s 168us/step - loss: 0.0765 - acc: 0.9809 - val_loss: 0.0989 - val_acc: 0.9559\n",
      "Epoch 636/1000\n",
      "157/157 [==============================] - 0s 170us/step - loss: 0.0778 - acc: 0.9809 - val_loss: 0.1014 - val_acc: 0.9559\n",
      "Epoch 637/1000\n",
      "157/157 [==============================] - 0s 125us/step - loss: 0.0762 - acc: 0.9873 - val_loss: 0.1015 - val_acc: 0.9559\n",
      "Epoch 638/1000\n",
      "157/157 [==============================] - 0s 129us/step - loss: 0.0757 - acc: 0.9873 - val_loss: 0.1073 - val_acc: 0.9559\n",
      "Epoch 639/1000\n",
      "157/157 [==============================] - 0s 163us/step - loss: 0.0777 - acc: 0.9873 - val_loss: 0.1046 - val_acc: 0.9559\n",
      "Epoch 640/1000\n",
      "157/157 [==============================] - 0s 128us/step - loss: 0.0768 - acc: 0.9873 - val_loss: 0.1076 - val_acc: 0.9559\n",
      "Epoch 641/1000\n",
      "157/157 [==============================] - 0s 147us/step - loss: 0.0783 - acc: 0.9745 - val_loss: 0.1049 - val_acc: 0.9559\n",
      "Epoch 642/1000\n",
      "157/157 [==============================] - 0s 256us/step - loss: 0.0751 - acc: 0.9873 - val_loss: 0.1052 - val_acc: 0.9559\n",
      "Epoch 643/1000\n",
      "157/157 [==============================] - 0s 252us/step - loss: 0.0759 - acc: 0.9809 - val_loss: 0.0991 - val_acc: 0.9559\n",
      "Epoch 644/1000\n",
      "157/157 [==============================] - 0s 302us/step - loss: 0.0753 - acc: 0.9809 - val_loss: 0.0980 - val_acc: 0.9559\n",
      "Epoch 645/1000\n",
      "157/157 [==============================] - 0s 325us/step - loss: 0.0758 - acc: 0.9809 - val_loss: 0.0942 - val_acc: 0.9706\n",
      "Epoch 646/1000\n",
      "157/157 [==============================] - 0s 242us/step - loss: 0.0751 - acc: 0.9873 - val_loss: 0.0975 - val_acc: 0.9559\n",
      "Epoch 647/1000\n",
      "157/157 [==============================] - 0s 148us/step - loss: 0.0740 - acc: 0.9873 - val_loss: 0.1068 - val_acc: 0.9559\n",
      "Epoch 648/1000\n",
      "157/157 [==============================] - 0s 146us/step - loss: 0.0754 - acc: 0.9745 - val_loss: 0.0966 - val_acc: 0.9559\n",
      "Epoch 649/1000\n",
      "157/157 [==============================] - 0s 142us/step - loss: 0.0736 - acc: 0.9873 - val_loss: 0.1073 - val_acc: 0.9559\n",
      "Epoch 650/1000\n",
      "157/157 [==============================] - 0s 119us/step - loss: 0.0754 - acc: 0.9809 - val_loss: 0.0996 - val_acc: 0.9559\n",
      "Epoch 651/1000\n",
      "157/157 [==============================] - 0s 125us/step - loss: 0.0730 - acc: 0.9873 - val_loss: 0.1035 - val_acc: 0.9559\n",
      "Epoch 652/1000\n",
      "157/157 [==============================] - 0s 100us/step - loss: 0.0745 - acc: 0.9873 - val_loss: 0.1131 - val_acc: 0.9118\n",
      "Epoch 653/1000\n",
      "157/157 [==============================] - 0s 176us/step - loss: 0.0778 - acc: 0.9809 - val_loss: 0.1017 - val_acc: 0.9559\n",
      "Epoch 654/1000\n",
      "157/157 [==============================] - 0s 191us/step - loss: 0.0732 - acc: 0.9809 - val_loss: 0.0970 - val_acc: 0.9559\n",
      "Epoch 655/1000\n",
      "157/157 [==============================] - 0s 214us/step - loss: 0.0734 - acc: 0.9873 - val_loss: 0.1025 - val_acc: 0.9559\n",
      "Epoch 656/1000\n",
      "157/157 [==============================] - 0s 240us/step - loss: 0.0732 - acc: 0.9809 - val_loss: 0.1020 - val_acc: 0.9559\n",
      "Epoch 657/1000\n",
      "157/157 [==============================] - 0s 255us/step - loss: 0.0733 - acc: 0.9809 - val_loss: 0.0967 - val_acc: 0.9559\n",
      "Epoch 658/1000\n",
      "157/157 [==============================] - 0s 187us/step - loss: 0.0724 - acc: 0.9873 - val_loss: 0.0989 - val_acc: 0.9559\n",
      "Epoch 659/1000\n",
      "157/157 [==============================] - 0s 228us/step - loss: 0.0740 - acc: 0.9873 - val_loss: 0.0978 - val_acc: 0.9559\n",
      "Epoch 660/1000\n",
      "157/157 [==============================] - 0s 378us/step - loss: 0.0744 - acc: 0.9873 - val_loss: 0.0978 - val_acc: 0.9559\n",
      "Epoch 661/1000\n",
      "157/157 [==============================] - 0s 243us/step - loss: 0.0719 - acc: 0.9873 - val_loss: 0.0981 - val_acc: 0.9559\n",
      "Epoch 662/1000\n",
      "157/157 [==============================] - 0s 143us/step - loss: 0.0727 - acc: 0.9873 - val_loss: 0.0941 - val_acc: 0.9706\n",
      "Epoch 663/1000\n",
      "157/157 [==============================] - 0s 114us/step - loss: 0.0730 - acc: 0.9873 - val_loss: 0.1029 - val_acc: 0.9559\n",
      "Epoch 664/1000\n",
      "157/157 [==============================] - 0s 411us/step - loss: 0.0738 - acc: 0.9809 - val_loss: 0.0987 - val_acc: 0.9559\n",
      "Epoch 665/1000\n",
      "157/157 [==============================] - 0s 329us/step - loss: 0.0722 - acc: 0.9809 - val_loss: 0.0947 - val_acc: 0.9559\n",
      "Epoch 666/1000\n",
      "157/157 [==============================] - 0s 335us/step - loss: 0.0732 - acc: 0.9873 - val_loss: 0.0993 - val_acc: 0.9559\n",
      "Epoch 667/1000\n",
      "157/157 [==============================] - 0s 206us/step - loss: 0.0710 - acc: 0.9809 - val_loss: 0.0935 - val_acc: 0.9706\n",
      "Epoch 668/1000\n",
      "157/157 [==============================] - 0s 101us/step - loss: 0.0709 - acc: 0.9873 - val_loss: 0.0988 - val_acc: 0.9559\n",
      "Epoch 669/1000\n",
      "157/157 [==============================] - 0s 180us/step - loss: 0.0705 - acc: 0.9873 - val_loss: 0.1039 - val_acc: 0.9559\n",
      "Epoch 670/1000\n",
      "157/157 [==============================] - 0s 122us/step - loss: 0.0711 - acc: 0.9809 - val_loss: 0.1019 - val_acc: 0.9559\n",
      "Epoch 671/1000\n",
      "157/157 [==============================] - 0s 105us/step - loss: 0.0704 - acc: 0.9809 - val_loss: 0.1007 - val_acc: 0.9559\n",
      "Epoch 672/1000\n",
      "157/157 [==============================] - 0s 160us/step - loss: 0.0749 - acc: 0.9873 - val_loss: 0.0974 - val_acc: 0.9559\n",
      "Epoch 673/1000\n",
      "157/157 [==============================] - 0s 151us/step - loss: 0.0726 - acc: 0.9873 - val_loss: 0.1020 - val_acc: 0.9559\n",
      "Epoch 674/1000\n",
      "157/157 [==============================] - 0s 144us/step - loss: 0.0713 - acc: 0.9809 - val_loss: 0.0986 - val_acc: 0.9559\n",
      "Epoch 675/1000\n",
      "157/157 [==============================] - 0s 216us/step - loss: 0.0705 - acc: 0.9873 - val_loss: 0.1031 - val_acc: 0.9559\n",
      "Epoch 676/1000\n",
      "157/157 [==============================] - 0s 291us/step - loss: 0.0723 - acc: 0.9809 - val_loss: 0.1020 - val_acc: 0.9559\n",
      "Epoch 677/1000\n",
      "157/157 [==============================] - 0s 242us/step - loss: 0.0709 - acc: 0.9809 - val_loss: 0.0967 - val_acc: 0.9559\n",
      "Epoch 678/1000\n",
      "157/157 [==============================] - 0s 142us/step - loss: 0.0705 - acc: 0.9873 - val_loss: 0.1041 - val_acc: 0.9559\n",
      "Epoch 679/1000\n",
      "157/157 [==============================] - 0s 226us/step - loss: 0.0711 - acc: 0.9873 - val_loss: 0.1024 - val_acc: 0.9559\n",
      "Epoch 680/1000\n",
      "157/157 [==============================] - 0s 330us/step - loss: 0.0696 - acc: 0.9873 - val_loss: 0.0955 - val_acc: 0.9559\n",
      "Epoch 681/1000\n",
      "157/157 [==============================] - 0s 133us/step - loss: 0.0697 - acc: 0.9809 - val_loss: 0.0930 - val_acc: 0.9706\n",
      "Epoch 682/1000\n",
      "157/157 [==============================] - 0s 131us/step - loss: 0.0700 - acc: 0.9809 - val_loss: 0.0955 - val_acc: 0.9559\n",
      "Epoch 683/1000\n",
      "157/157 [==============================] - 0s 282us/step - loss: 0.0687 - acc: 0.9873 - val_loss: 0.1031 - val_acc: 0.9559\n",
      "Epoch 684/1000\n",
      "157/157 [==============================] - 0s 129us/step - loss: 0.0697 - acc: 0.9873 - val_loss: 0.1033 - val_acc: 0.9559\n",
      "Epoch 685/1000\n",
      "157/157 [==============================] - 0s 111us/step - loss: 0.0702 - acc: 0.9809 - val_loss: 0.0906 - val_acc: 0.9706\n",
      "Epoch 686/1000\n",
      "157/157 [==============================] - 0s 121us/step - loss: 0.0692 - acc: 0.9873 - val_loss: 0.0927 - val_acc: 0.9706\n",
      "Epoch 687/1000\n",
      "157/157 [==============================] - 0s 216us/step - loss: 0.0679 - acc: 0.9873 - val_loss: 0.1006 - val_acc: 0.9559\n",
      "Epoch 688/1000\n",
      "157/157 [==============================] - 0s 204us/step - loss: 0.0714 - acc: 0.9809 - val_loss: 0.0951 - val_acc: 0.9559\n",
      "Epoch 689/1000\n",
      "157/157 [==============================] - 0s 271us/step - loss: 0.0684 - acc: 0.9873 - val_loss: 0.1033 - val_acc: 0.9559\n",
      "Epoch 690/1000\n",
      "157/157 [==============================] - 0s 239us/step - loss: 0.0685 - acc: 0.9873 - val_loss: 0.0967 - val_acc: 0.9559\n",
      "Epoch 691/1000\n",
      "157/157 [==============================] - 0s 189us/step - loss: 0.0684 - acc: 0.9873 - val_loss: 0.1023 - val_acc: 0.9559\n",
      "Epoch 692/1000\n",
      "157/157 [==============================] - 0s 161us/step - loss: 0.0690 - acc: 0.9809 - val_loss: 0.0969 - val_acc: 0.9559\n",
      "Epoch 693/1000\n",
      "157/157 [==============================] - 0s 237us/step - loss: 0.0699 - acc: 0.9745 - val_loss: 0.0944 - val_acc: 0.9559\n",
      "Epoch 694/1000\n",
      "157/157 [==============================] - 0s 131us/step - loss: 0.0678 - acc: 0.9873 - val_loss: 0.0987 - val_acc: 0.9559\n",
      "Epoch 695/1000\n",
      "157/157 [==============================] - 0s 163us/step - loss: 0.0668 - acc: 0.9873 - val_loss: 0.0994 - val_acc: 0.9559\n",
      "Epoch 696/1000\n",
      "157/157 [==============================] - 0s 146us/step - loss: 0.0692 - acc: 0.9809 - val_loss: 0.1031 - val_acc: 0.9559\n",
      "Epoch 697/1000\n",
      "157/157 [==============================] - 0s 148us/step - loss: 0.0689 - acc: 0.9809 - val_loss: 0.0985 - val_acc: 0.9559\n",
      "Epoch 698/1000\n",
      "157/157 [==============================] - 0s 299us/step - loss: 0.0676 - acc: 0.9873 - val_loss: 0.0983 - val_acc: 0.9559\n",
      "Epoch 699/1000\n",
      "157/157 [==============================] - 0s 154us/step - loss: 0.0671 - acc: 0.9809 - val_loss: 0.0983 - val_acc: 0.9559\n",
      "Epoch 700/1000\n",
      "157/157 [==============================] - 0s 209us/step - loss: 0.0695 - acc: 0.9809 - val_loss: 0.1002 - val_acc: 0.9559\n",
      "Epoch 701/1000\n",
      "157/157 [==============================] - 0s 175us/step - loss: 0.0665 - acc: 0.9873 - val_loss: 0.0976 - val_acc: 0.9559\n",
      "Epoch 702/1000\n",
      "157/157 [==============================] - 0s 168us/step - loss: 0.0687 - acc: 0.9873 - val_loss: 0.0940 - val_acc: 0.9559\n",
      "Epoch 703/1000\n",
      "157/157 [==============================] - 0s 133us/step - loss: 0.0663 - acc: 0.9873 - val_loss: 0.0949 - val_acc: 0.9559\n",
      "Epoch 704/1000\n",
      "157/157 [==============================] - 0s 153us/step - loss: 0.0661 - acc: 0.9873 - val_loss: 0.0946 - val_acc: 0.9559\n",
      "Epoch 705/1000\n",
      "157/157 [==============================] - 0s 155us/step - loss: 0.0677 - acc: 0.9809 - val_loss: 0.0947 - val_acc: 0.9559\n",
      "Epoch 706/1000\n",
      "157/157 [==============================] - 0s 127us/step - loss: 0.0674 - acc: 0.9809 - val_loss: 0.0942 - val_acc: 0.9559\n",
      "Epoch 707/1000\n",
      "157/157 [==============================] - 0s 173us/step - loss: 0.0664 - acc: 0.9873 - val_loss: 0.0948 - val_acc: 0.9559\n",
      "Epoch 708/1000\n",
      "157/157 [==============================] - 0s 125us/step - loss: 0.0664 - acc: 0.9873 - val_loss: 0.0964 - val_acc: 0.9559\n",
      "Epoch 709/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 150us/step - loss: 0.0652 - acc: 0.9873 - val_loss: 0.0961 - val_acc: 0.9559\n",
      "Epoch 710/1000\n",
      "157/157 [==============================] - 0s 120us/step - loss: 0.0680 - acc: 0.9809 - val_loss: 0.0943 - val_acc: 0.9559\n",
      "Epoch 711/1000\n",
      "157/157 [==============================] - 0s 138us/step - loss: 0.0652 - acc: 0.9809 - val_loss: 0.0913 - val_acc: 0.9706\n",
      "Epoch 712/1000\n",
      "157/157 [==============================] - 0s 127us/step - loss: 0.0655 - acc: 0.9873 - val_loss: 0.0945 - val_acc: 0.9559\n",
      "Epoch 713/1000\n",
      "157/157 [==============================] - 0s 160us/step - loss: 0.0666 - acc: 0.9873 - val_loss: 0.0895 - val_acc: 0.9706\n",
      "Epoch 714/1000\n",
      "157/157 [==============================] - 0s 148us/step - loss: 0.0658 - acc: 0.9936 - val_loss: 0.0982 - val_acc: 0.9559\n",
      "Epoch 715/1000\n",
      "157/157 [==============================] - 0s 152us/step - loss: 0.0653 - acc: 0.9873 - val_loss: 0.0988 - val_acc: 0.9559\n",
      "Epoch 716/1000\n",
      "157/157 [==============================] - 0s 310us/step - loss: 0.0642 - acc: 0.9873 - val_loss: 0.0943 - val_acc: 0.9559\n",
      "Epoch 717/1000\n",
      "157/157 [==============================] - 0s 223us/step - loss: 0.0660 - acc: 0.9809 - val_loss: 0.0910 - val_acc: 0.9706\n",
      "Epoch 718/1000\n",
      "157/157 [==============================] - 0s 237us/step - loss: 0.0649 - acc: 0.9873 - val_loss: 0.0950 - val_acc: 0.9559\n",
      "Epoch 719/1000\n",
      "157/157 [==============================] - 0s 221us/step - loss: 0.0636 - acc: 0.9873 - val_loss: 0.0951 - val_acc: 0.9559\n",
      "Epoch 720/1000\n",
      "157/157 [==============================] - 0s 163us/step - loss: 0.0661 - acc: 0.9809 - val_loss: 0.0948 - val_acc: 0.9559\n",
      "Epoch 721/1000\n",
      "157/157 [==============================] - 0s 219us/step - loss: 0.0644 - acc: 0.9809 - val_loss: 0.0915 - val_acc: 0.9559\n",
      "Epoch 722/1000\n",
      "157/157 [==============================] - 0s 127us/step - loss: 0.0644 - acc: 0.9936 - val_loss: 0.0999 - val_acc: 0.9559\n",
      "Epoch 723/1000\n",
      "157/157 [==============================] - 0s 183us/step - loss: 0.0641 - acc: 0.9809 - val_loss: 0.0966 - val_acc: 0.9559\n",
      "Epoch 724/1000\n",
      "157/157 [==============================] - 0s 190us/step - loss: 0.0634 - acc: 0.9873 - val_loss: 0.0916 - val_acc: 0.9559\n",
      "Epoch 725/1000\n",
      "157/157 [==============================] - 0s 139us/step - loss: 0.0638 - acc: 0.9873 - val_loss: 0.0897 - val_acc: 0.9706\n",
      "Epoch 726/1000\n",
      "157/157 [==============================] - 0s 229us/step - loss: 0.0631 - acc: 0.9873 - val_loss: 0.0922 - val_acc: 0.9559\n",
      "Epoch 727/1000\n",
      "157/157 [==============================] - 0s 141us/step - loss: 0.0635 - acc: 0.9809 - val_loss: 0.0877 - val_acc: 0.9706\n",
      "Epoch 728/1000\n",
      "157/157 [==============================] - 0s 222us/step - loss: 0.0632 - acc: 0.9936 - val_loss: 0.0944 - val_acc: 0.9559\n",
      "Epoch 729/1000\n",
      "157/157 [==============================] - 0s 143us/step - loss: 0.0631 - acc: 0.9809 - val_loss: 0.0848 - val_acc: 0.9706\n",
      "Epoch 730/1000\n",
      "157/157 [==============================] - 0s 152us/step - loss: 0.0641 - acc: 0.9873 - val_loss: 0.0898 - val_acc: 0.9706\n",
      "Epoch 731/1000\n",
      "157/157 [==============================] - 0s 132us/step - loss: 0.0624 - acc: 0.9873 - val_loss: 0.0859 - val_acc: 0.9706\n",
      "Epoch 732/1000\n",
      "157/157 [==============================] - 0s 129us/step - loss: 0.0627 - acc: 0.9936 - val_loss: 0.0920 - val_acc: 0.9559\n",
      "Epoch 733/1000\n",
      "157/157 [==============================] - 0s 147us/step - loss: 0.0618 - acc: 0.9936 - val_loss: 0.0944 - val_acc: 0.9559\n",
      "Epoch 734/1000\n",
      "157/157 [==============================] - 0s 163us/step - loss: 0.0655 - acc: 0.9745 - val_loss: 0.0916 - val_acc: 0.9559\n",
      "Epoch 735/1000\n",
      "157/157 [==============================] - 0s 131us/step - loss: 0.0619 - acc: 0.9936 - val_loss: 0.0957 - val_acc: 0.9559\n",
      "Epoch 736/1000\n",
      "157/157 [==============================] - 0s 136us/step - loss: 0.0646 - acc: 0.9809 - val_loss: 0.0965 - val_acc: 0.9559\n",
      "Epoch 737/1000\n",
      "157/157 [==============================] - 0s 132us/step - loss: 0.0623 - acc: 0.9809 - val_loss: 0.0920 - val_acc: 0.9559\n",
      "Epoch 738/1000\n",
      "157/157 [==============================] - 0s 137us/step - loss: 0.0618 - acc: 0.9873 - val_loss: 0.0971 - val_acc: 0.9559\n",
      "Epoch 739/1000\n",
      "157/157 [==============================] - 0s 133us/step - loss: 0.0612 - acc: 0.9936 - val_loss: 0.0957 - val_acc: 0.9559\n",
      "Epoch 740/1000\n",
      "157/157 [==============================] - 0s 160us/step - loss: 0.0626 - acc: 0.9873 - val_loss: 0.0922 - val_acc: 0.9559\n",
      "Epoch 741/1000\n",
      "157/157 [==============================] - 0s 158us/step - loss: 0.0620 - acc: 0.9936 - val_loss: 0.0915 - val_acc: 0.9559\n",
      "Epoch 742/1000\n",
      "157/157 [==============================] - ETA: 0s - loss: 0.0297 - acc: 1.000 - 0s 213us/step - loss: 0.0619 - acc: 0.9936 - val_loss: 0.0895 - val_acc: 0.9706\n",
      "Epoch 743/1000\n",
      "157/157 [==============================] - 0s 179us/step - loss: 0.0621 - acc: 0.9936 - val_loss: 0.0893 - val_acc: 0.9706\n",
      "Epoch 744/1000\n",
      "157/157 [==============================] - 0s 329us/step - loss: 0.0629 - acc: 0.9809 - val_loss: 0.0915 - val_acc: 0.9559\n",
      "Epoch 745/1000\n",
      "157/157 [==============================] - 0s 194us/step - loss: 0.0617 - acc: 0.9809 - val_loss: 0.0920 - val_acc: 0.9559\n",
      "Epoch 746/1000\n",
      "157/157 [==============================] - 0s 199us/step - loss: 0.0602 - acc: 0.9936 - val_loss: 0.0965 - val_acc: 0.9559\n",
      "Epoch 747/1000\n",
      "157/157 [==============================] - 0s 271us/step - loss: 0.0611 - acc: 0.9745 - val_loss: 0.0925 - val_acc: 0.9559\n",
      "Epoch 748/1000\n",
      "157/157 [==============================] - 0s 189us/step - loss: 0.0606 - acc: 0.9936 - val_loss: 0.1003 - val_acc: 0.9559\n",
      "Epoch 749/1000\n",
      "157/157 [==============================] - 0s 173us/step - loss: 0.0601 - acc: 0.9873 - val_loss: 0.0938 - val_acc: 0.9559\n",
      "Epoch 750/1000\n",
      "157/157 [==============================] - 0s 103us/step - loss: 0.0618 - acc: 0.9809 - val_loss: 0.0885 - val_acc: 0.9706\n",
      "Epoch 751/1000\n",
      "157/157 [==============================] - 0s 196us/step - loss: 0.0602 - acc: 0.9936 - val_loss: 0.0954 - val_acc: 0.9559\n",
      "Epoch 752/1000\n",
      "157/157 [==============================] - 0s 164us/step - loss: 0.0627 - acc: 0.9873 - val_loss: 0.0938 - val_acc: 0.9559\n",
      "Epoch 753/1000\n",
      "157/157 [==============================] - 0s 134us/step - loss: 0.0601 - acc: 0.9873 - val_loss: 0.0922 - val_acc: 0.9559\n",
      "Epoch 754/1000\n",
      "157/157 [==============================] - 0s 172us/step - loss: 0.0621 - acc: 0.9809 - val_loss: 0.0881 - val_acc: 0.9706\n",
      "Epoch 755/1000\n",
      "157/157 [==============================] - 0s 119us/step - loss: 0.0610 - acc: 0.9936 - val_loss: 0.0950 - val_acc: 0.9559\n",
      "Epoch 756/1000\n",
      "157/157 [==============================] - 0s 110us/step - loss: 0.0597 - acc: 0.9936 - val_loss: 0.0900 - val_acc: 0.9706\n",
      "Epoch 757/1000\n",
      "157/157 [==============================] - 0s 126us/step - loss: 0.0595 - acc: 0.9809 - val_loss: 0.0857 - val_acc: 0.9706\n",
      "Epoch 758/1000\n",
      "157/157 [==============================] - 0s 110us/step - loss: 0.0592 - acc: 0.9936 - val_loss: 0.0940 - val_acc: 0.9559\n",
      "Epoch 759/1000\n",
      "157/157 [==============================] - 0s 117us/step - loss: 0.0606 - acc: 0.9936 - val_loss: 0.0983 - val_acc: 0.9559\n",
      "Epoch 760/1000\n",
      "157/157 [==============================] - 0s 135us/step - loss: 0.0590 - acc: 0.9873 - val_loss: 0.0945 - val_acc: 0.9559\n",
      "Epoch 761/1000\n",
      "157/157 [==============================] - 0s 172us/step - loss: 0.0595 - acc: 0.9809 - val_loss: 0.0845 - val_acc: 0.9706\n",
      "Epoch 762/1000\n",
      "157/157 [==============================] - 0s 130us/step - loss: 0.0599 - acc: 0.9873 - val_loss: 0.0842 - val_acc: 0.9706\n",
      "Epoch 763/1000\n",
      "157/157 [==============================] - 0s 136us/step - loss: 0.0598 - acc: 0.9936 - val_loss: 0.0896 - val_acc: 0.9706\n",
      "Epoch 764/1000\n",
      "157/157 [==============================] - 0s 133us/step - loss: 0.0580 - acc: 0.9936 - val_loss: 0.0914 - val_acc: 0.9559\n",
      "Epoch 765/1000\n",
      "157/157 [==============================] - 0s 152us/step - loss: 0.0588 - acc: 0.9936 - val_loss: 0.0892 - val_acc: 0.9706\n",
      "Epoch 766/1000\n",
      "157/157 [==============================] - 0s 200us/step - loss: 0.0588 - acc: 0.9936 - val_loss: 0.0994 - val_acc: 0.9559\n",
      "Epoch 767/1000\n",
      "157/157 [==============================] - 0s 249us/step - loss: 0.0592 - acc: 0.9936 - val_loss: 0.0977 - val_acc: 0.9559\n",
      "Epoch 768/1000\n",
      "157/157 [==============================] - 0s 222us/step - loss: 0.0583 - acc: 0.9809 - val_loss: 0.0848 - val_acc: 0.9706\n",
      "Epoch 769/1000\n",
      "157/157 [==============================] - 0s 163us/step - loss: 0.0591 - acc: 0.9936 - val_loss: 0.0839 - val_acc: 0.9706\n",
      "Epoch 770/1000\n",
      "157/157 [==============================] - 0s 146us/step - loss: 0.0587 - acc: 0.9873 - val_loss: 0.0837 - val_acc: 0.9706\n",
      "Epoch 771/1000\n",
      "157/157 [==============================] - 0s 225us/step - loss: 0.0594 - acc: 0.9936 - val_loss: 0.0918 - val_acc: 0.9559\n",
      "Epoch 772/1000\n",
      "157/157 [==============================] - 0s 410us/step - loss: 0.0575 - acc: 0.9809 - val_loss: 0.0849 - val_acc: 0.9706\n",
      "Epoch 773/1000\n",
      "157/157 [==============================] - 0s 458us/step - loss: 0.0594 - acc: 0.9936 - val_loss: 0.0926 - val_acc: 0.9559\n",
      "Epoch 774/1000\n",
      "157/157 [==============================] - 0s 380us/step - loss: 0.0587 - acc: 0.9936 - val_loss: 0.0882 - val_acc: 0.9706\n",
      "Epoch 775/1000\n",
      "157/157 [==============================] - 0s 278us/step - loss: 0.0587 - acc: 0.9936 - val_loss: 0.0944 - val_acc: 0.9559\n",
      "Epoch 776/1000\n",
      "157/157 [==============================] - 0s 210us/step - loss: 0.0579 - acc: 0.9936 - val_loss: 0.0958 - val_acc: 0.9559\n",
      "Epoch 777/1000\n",
      "157/157 [==============================] - 0s 221us/step - loss: 0.0574 - acc: 0.9873 - val_loss: 0.0911 - val_acc: 0.9559\n",
      "Epoch 778/1000\n",
      "157/157 [==============================] - 0s 187us/step - loss: 0.0602 - acc: 0.9809 - val_loss: 0.0920 - val_acc: 0.9559\n",
      "Epoch 779/1000\n",
      "157/157 [==============================] - 0s 159us/step - loss: 0.0567 - acc: 0.9873 - val_loss: 0.0883 - val_acc: 0.9706\n",
      "Epoch 780/1000\n",
      "157/157 [==============================] - 0s 209us/step - loss: 0.0570 - acc: 0.9936 - val_loss: 0.0914 - val_acc: 0.9559\n",
      "Epoch 781/1000\n",
      "157/157 [==============================] - 0s 214us/step - loss: 0.0562 - acc: 0.9936 - val_loss: 0.0909 - val_acc: 0.9559\n",
      "Epoch 782/1000\n",
      "157/157 [==============================] - 0s 203us/step - loss: 0.0574 - acc: 0.9873 - val_loss: 0.0836 - val_acc: 0.9706\n",
      "Epoch 783/1000\n",
      "157/157 [==============================] - 0s 179us/step - loss: 0.0572 - acc: 0.9936 - val_loss: 0.0966 - val_acc: 0.9559\n",
      "Epoch 784/1000\n",
      "157/157 [==============================] - 0s 137us/step - loss: 0.0567 - acc: 0.9873 - val_loss: 0.0908 - val_acc: 0.9559\n",
      "Epoch 785/1000\n",
      "157/157 [==============================] - 0s 400us/step - loss: 0.0568 - acc: 0.9936 - val_loss: 0.0955 - val_acc: 0.9559\n",
      "Epoch 786/1000\n",
      "157/157 [==============================] - 0s 459us/step - loss: 0.0569 - acc: 0.9936 - val_loss: 0.0988 - val_acc: 0.9559\n",
      "Epoch 787/1000\n",
      "157/157 [==============================] - 0s 170us/step - loss: 0.0567 - acc: 0.9745 - val_loss: 0.0899 - val_acc: 0.9559\n",
      "Epoch 788/1000\n",
      "157/157 [==============================] - 0s 153us/step - loss: 0.0572 - acc: 0.9936 - val_loss: 0.0927 - val_acc: 0.9559\n",
      "Epoch 789/1000\n",
      "157/157 [==============================] - 0s 190us/step - loss: 0.0556 - acc: 0.9936 - val_loss: 0.0914 - val_acc: 0.9559\n",
      "Epoch 790/1000\n",
      "157/157 [==============================] - 0s 141us/step - loss: 0.0571 - acc: 0.9809 - val_loss: 0.0939 - val_acc: 0.9559\n",
      "Epoch 791/1000\n",
      "157/157 [==============================] - 0s 220us/step - loss: 0.0582 - acc: 0.9745 - val_loss: 0.0904 - val_acc: 0.9559\n",
      "Epoch 792/1000\n",
      "157/157 [==============================] - 0s 97us/step - loss: 0.0563 - acc: 0.9936 - val_loss: 0.0966 - val_acc: 0.9559\n",
      "Epoch 793/1000\n",
      "157/157 [==============================] - 0s 124us/step - loss: 0.0577 - acc: 0.9873 - val_loss: 0.0959 - val_acc: 0.9559\n",
      "Epoch 794/1000\n",
      "157/157 [==============================] - 0s 129us/step - loss: 0.0564 - acc: 0.9809 - val_loss: 0.0863 - val_acc: 0.9706\n",
      "Epoch 795/1000\n",
      "157/157 [==============================] - 0s 188us/step - loss: 0.0560 - acc: 0.9936 - val_loss: 0.0951 - val_acc: 0.9559\n",
      "Epoch 796/1000\n",
      "157/157 [==============================] - 0s 229us/step - loss: 0.0563 - acc: 0.9936 - val_loss: 0.0908 - val_acc: 0.9559\n",
      "Epoch 797/1000\n",
      "157/157 [==============================] - 0s 117us/step - loss: 0.0545 - acc: 0.9936 - val_loss: 0.0927 - val_acc: 0.9559\n",
      "Epoch 798/1000\n",
      "157/157 [==============================] - 0s 142us/step - loss: 0.0572 - acc: 0.9873 - val_loss: 0.0914 - val_acc: 0.9559\n",
      "Epoch 799/1000\n",
      "157/157 [==============================] - 0s 180us/step - loss: 0.0559 - acc: 0.9936 - val_loss: 0.0895 - val_acc: 0.9559\n",
      "Epoch 800/1000\n",
      "157/157 [==============================] - 0s 220us/step - loss: 0.0555 - acc: 0.9936 - val_loss: 0.0900 - val_acc: 0.9559\n",
      "Epoch 801/1000\n",
      "157/157 [==============================] - 0s 103us/step - loss: 0.0555 - acc: 0.9936 - val_loss: 0.0897 - val_acc: 0.9559\n",
      "Epoch 802/1000\n",
      "157/157 [==============================] - 0s 172us/step - loss: 0.0569 - acc: 0.9873 - val_loss: 0.0896 - val_acc: 0.9559\n",
      "Epoch 803/1000\n",
      "157/157 [==============================] - 0s 161us/step - loss: 0.0556 - acc: 0.9936 - val_loss: 0.0899 - val_acc: 0.9559\n",
      "Epoch 804/1000\n",
      "157/157 [==============================] - 0s 116us/step - loss: 0.0539 - acc: 0.9936 - val_loss: 0.0897 - val_acc: 0.9559\n",
      "Epoch 805/1000\n",
      "157/157 [==============================] - 0s 184us/step - loss: 0.0559 - acc: 0.9873 - val_loss: 0.0864 - val_acc: 0.9706\n",
      "Epoch 806/1000\n",
      "157/157 [==============================] - 0s 233us/step - loss: 0.0539 - acc: 0.9936 - val_loss: 0.0901 - val_acc: 0.9559\n",
      "Epoch 807/1000\n",
      "157/157 [==============================] - 0s 151us/step - loss: 0.0538 - acc: 0.9936 - val_loss: 0.0855 - val_acc: 0.9706\n",
      "Epoch 808/1000\n",
      "157/157 [==============================] - 0s 223us/step - loss: 0.0547 - acc: 0.9936 - val_loss: 0.0852 - val_acc: 0.9706\n",
      "Epoch 809/1000\n",
      "157/157 [==============================] - 0s 216us/step - loss: 0.0545 - acc: 0.9936 - val_loss: 0.0941 - val_acc: 0.9559\n",
      "Epoch 810/1000\n",
      "157/157 [==============================] - 0s 191us/step - loss: 0.0549 - acc: 0.9936 - val_loss: 0.0935 - val_acc: 0.9559\n",
      "Epoch 811/1000\n",
      "157/157 [==============================] - 0s 162us/step - loss: 0.0560 - acc: 0.9873 - val_loss: 0.0872 - val_acc: 0.9706\n",
      "Epoch 812/1000\n",
      "157/157 [==============================] - 0s 180us/step - loss: 0.0557 - acc: 0.9809 - val_loss: 0.0842 - val_acc: 0.9706\n",
      "Epoch 813/1000\n",
      "157/157 [==============================] - 0s 156us/step - loss: 0.0540 - acc: 0.9936 - val_loss: 0.0859 - val_acc: 0.9706\n",
      "Epoch 814/1000\n",
      "157/157 [==============================] - 0s 183us/step - loss: 0.0529 - acc: 0.9936 - val_loss: 0.0885 - val_acc: 0.9559\n",
      "Epoch 815/1000\n",
      "157/157 [==============================] - 0s 96us/step - loss: 0.0549 - acc: 0.9873 - val_loss: 0.0855 - val_acc: 0.9706\n",
      "Epoch 816/1000\n",
      "157/157 [==============================] - 0s 105us/step - loss: 0.0535 - acc: 0.9936 - val_loss: 0.0943 - val_acc: 0.9559\n",
      "Epoch 817/1000\n",
      "157/157 [==============================] - 0s 178us/step - loss: 0.0554 - acc: 0.9809 - val_loss: 0.0882 - val_acc: 0.9559\n",
      "Epoch 818/1000\n",
      "157/157 [==============================] - 0s 135us/step - loss: 0.0531 - acc: 0.9936 - val_loss: 0.0934 - val_acc: 0.9559\n",
      "Epoch 819/1000\n",
      "157/157 [==============================] - 0s 149us/step - loss: 0.0549 - acc: 0.9936 - val_loss: 0.0899 - val_acc: 0.9559\n",
      "Epoch 820/1000\n",
      "157/157 [==============================] - 0s 182us/step - loss: 0.0555 - acc: 0.9936 - val_loss: 0.0914 - val_acc: 0.9559\n",
      "Epoch 821/1000\n",
      "157/157 [==============================] - 0s 173us/step - loss: 0.0533 - acc: 0.9873 - val_loss: 0.0841 - val_acc: 0.9706\n",
      "Epoch 822/1000\n",
      "157/157 [==============================] - 0s 221us/step - loss: 0.0531 - acc: 0.9936 - val_loss: 0.0844 - val_acc: 0.9706\n",
      "Epoch 823/1000\n",
      "157/157 [==============================] - 0s 112us/step - loss: 0.0531 - acc: 0.9873 - val_loss: 0.0826 - val_acc: 0.9706\n",
      "Epoch 824/1000\n",
      "157/157 [==============================] - 0s 202us/step - loss: 0.0533 - acc: 0.9936 - val_loss: 0.0897 - val_acc: 0.9559\n",
      "Epoch 825/1000\n",
      "157/157 [==============================] - 0s 263us/step - loss: 0.0525 - acc: 1.0000 - val_loss: 0.0800 - val_acc: 0.9706\n",
      "Epoch 826/1000\n",
      "157/157 [==============================] - 0s 191us/step - loss: 0.0544 - acc: 0.9936 - val_loss: 0.0937 - val_acc: 0.9559\n",
      "Epoch 827/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 183us/step - loss: 0.0521 - acc: 0.9936 - val_loss: 0.0847 - val_acc: 0.9706\n",
      "Epoch 828/1000\n",
      "157/157 [==============================] - 0s 266us/step - loss: 0.0524 - acc: 0.9936 - val_loss: 0.0902 - val_acc: 0.9559\n",
      "Epoch 829/1000\n",
      "157/157 [==============================] - 0s 228us/step - loss: 0.0525 - acc: 0.9873 - val_loss: 0.0839 - val_acc: 0.9706\n",
      "Epoch 830/1000\n",
      "157/157 [==============================] - 0s 228us/step - loss: 0.0528 - acc: 0.9809 - val_loss: 0.0829 - val_acc: 0.9706\n",
      "Epoch 831/1000\n",
      "157/157 [==============================] - 0s 251us/step - loss: 0.0530 - acc: 0.9936 - val_loss: 0.0832 - val_acc: 0.9706\n",
      "Epoch 832/1000\n",
      "157/157 [==============================] - 0s 275us/step - loss: 0.0544 - acc: 0.9873 - val_loss: 0.0876 - val_acc: 0.9706\n",
      "Epoch 833/1000\n",
      "157/157 [==============================] - 0s 233us/step - loss: 0.0526 - acc: 0.9936 - val_loss: 0.0859 - val_acc: 0.9706\n",
      "Epoch 834/1000\n",
      "157/157 [==============================] - 0s 366us/step - loss: 0.0527 - acc: 0.9936 - val_loss: 0.0853 - val_acc: 0.9706\n",
      "Epoch 835/1000\n",
      "157/157 [==============================] - 0s 220us/step - loss: 0.0514 - acc: 0.9936 - val_loss: 0.0911 - val_acc: 0.9559\n",
      "Epoch 836/1000\n",
      "157/157 [==============================] - 0s 172us/step - loss: 0.0529 - acc: 0.9873 - val_loss: 0.0923 - val_acc: 0.9559\n",
      "Epoch 837/1000\n",
      "157/157 [==============================] - 0s 255us/step - loss: 0.0513 - acc: 0.9936 - val_loss: 0.0843 - val_acc: 0.9706\n",
      "Epoch 838/1000\n",
      "157/157 [==============================] - 0s 117us/step - loss: 0.0514 - acc: 0.9936 - val_loss: 0.0900 - val_acc: 0.9559\n",
      "Epoch 839/1000\n",
      "157/157 [==============================] - 0s 104us/step - loss: 0.0509 - acc: 0.9936 - val_loss: 0.0868 - val_acc: 0.9706\n",
      "Epoch 840/1000\n",
      "157/157 [==============================] - 0s 153us/step - loss: 0.0530 - acc: 0.9873 - val_loss: 0.0874 - val_acc: 0.9706\n",
      "Epoch 841/1000\n",
      "157/157 [==============================] - 0s 129us/step - loss: 0.0510 - acc: 0.9809 - val_loss: 0.0826 - val_acc: 0.9706\n",
      "Epoch 842/1000\n",
      "157/157 [==============================] - 0s 180us/step - loss: 0.0512 - acc: 0.9936 - val_loss: 0.0835 - val_acc: 0.9706\n",
      "Epoch 843/1000\n",
      "157/157 [==============================] - 0s 174us/step - loss: 0.0505 - acc: 0.9936 - val_loss: 0.0914 - val_acc: 0.9559\n",
      "Epoch 844/1000\n",
      "157/157 [==============================] - 0s 116us/step - loss: 0.0551 - acc: 0.9936 - val_loss: 0.0913 - val_acc: 0.9559\n",
      "Epoch 845/1000\n",
      "157/157 [==============================] - 0s 224us/step - loss: 0.0502 - acc: 0.9936 - val_loss: 0.0861 - val_acc: 0.9706\n",
      "Epoch 846/1000\n",
      "157/157 [==============================] - 0s 178us/step - loss: 0.0504 - acc: 0.9936 - val_loss: 0.0863 - val_acc: 0.9706\n",
      "Epoch 847/1000\n",
      "157/157 [==============================] - 0s 150us/step - loss: 0.0500 - acc: 0.9873 - val_loss: 0.0803 - val_acc: 0.9706\n",
      "Epoch 848/1000\n",
      "157/157 [==============================] - 0s 198us/step - loss: 0.0511 - acc: 0.9936 - val_loss: 0.0844 - val_acc: 0.9706\n",
      "Epoch 849/1000\n",
      "157/157 [==============================] - 0s 182us/step - loss: 0.0497 - acc: 0.9936 - val_loss: 0.0830 - val_acc: 0.9706\n",
      "Epoch 850/1000\n",
      "157/157 [==============================] - 0s 173us/step - loss: 0.0515 - acc: 0.9936 - val_loss: 0.0926 - val_acc: 0.9559\n",
      "Epoch 851/1000\n",
      "157/157 [==============================] - 0s 185us/step - loss: 0.0502 - acc: 0.9809 - val_loss: 0.0829 - val_acc: 0.9706\n",
      "Epoch 852/1000\n",
      "157/157 [==============================] - 0s 188us/step - loss: 0.0501 - acc: 0.9936 - val_loss: 0.0916 - val_acc: 0.9559\n",
      "Epoch 853/1000\n",
      "157/157 [==============================] - 0s 206us/step - loss: 0.0509 - acc: 0.9809 - val_loss: 0.0803 - val_acc: 0.9706\n",
      "Epoch 854/1000\n",
      "157/157 [==============================] - 0s 131us/step - loss: 0.0507 - acc: 0.9936 - val_loss: 0.0835 - val_acc: 0.9706\n",
      "Epoch 855/1000\n",
      "157/157 [==============================] - 0s 235us/step - loss: 0.0497 - acc: 0.9936 - val_loss: 0.0848 - val_acc: 0.9706\n",
      "Epoch 856/1000\n",
      "157/157 [==============================] - 0s 106us/step - loss: 0.0501 - acc: 0.9936 - val_loss: 0.0889 - val_acc: 0.9559\n",
      "Epoch 857/1000\n",
      "157/157 [==============================] - ETA: 0s - loss: 0.0745 - acc: 1.000 - 0s 99us/step - loss: 0.0493 - acc: 0.9936 - val_loss: 0.0840 - val_acc: 0.9706\n",
      "Epoch 858/1000\n",
      "157/157 [==============================] - 0s 106us/step - loss: 0.0512 - acc: 0.9936 - val_loss: 0.0918 - val_acc: 0.9559\n",
      "Epoch 859/1000\n",
      "157/157 [==============================] - 0s 169us/step - loss: 0.0507 - acc: 0.9936 - val_loss: 0.0846 - val_acc: 0.9706\n",
      "Epoch 860/1000\n",
      "157/157 [==============================] - 0s 180us/step - loss: 0.0494 - acc: 0.9873 - val_loss: 0.0875 - val_acc: 0.9559\n",
      "Epoch 861/1000\n",
      "157/157 [==============================] - 0s 154us/step - loss: 0.0507 - acc: 0.9873 - val_loss: 0.0861 - val_acc: 0.9706\n",
      "Epoch 862/1000\n",
      "157/157 [==============================] - 0s 178us/step - loss: 0.0524 - acc: 0.9873 - val_loss: 0.0799 - val_acc: 0.9706\n",
      "Epoch 863/1000\n",
      "157/157 [==============================] - 0s 252us/step - loss: 0.0493 - acc: 0.9936 - val_loss: 0.0896 - val_acc: 0.9559\n",
      "Epoch 864/1000\n",
      "157/157 [==============================] - 0s 216us/step - loss: 0.0494 - acc: 0.9936 - val_loss: 0.0929 - val_acc: 0.9559\n",
      "Epoch 865/1000\n",
      "157/157 [==============================] - 0s 167us/step - loss: 0.0505 - acc: 0.9936 - val_loss: 0.0885 - val_acc: 0.9559\n",
      "Epoch 866/1000\n",
      "157/157 [==============================] - 0s 177us/step - loss: 0.0513 - acc: 0.9936 - val_loss: 0.0919 - val_acc: 0.9559\n",
      "Epoch 867/1000\n",
      "157/157 [==============================] - 0s 121us/step - loss: 0.0500 - acc: 0.9873 - val_loss: 0.0859 - val_acc: 0.9706\n",
      "Epoch 868/1000\n",
      "157/157 [==============================] - 0s 170us/step - loss: 0.0502 - acc: 0.9936 - val_loss: 0.0889 - val_acc: 0.9559\n",
      "Epoch 869/1000\n",
      "157/157 [==============================] - 0s 186us/step - loss: 0.0502 - acc: 0.9936 - val_loss: 0.0886 - val_acc: 0.9559\n",
      "Epoch 870/1000\n",
      "157/157 [==============================] - 0s 241us/step - loss: 0.0486 - acc: 0.9936 - val_loss: 0.0878 - val_acc: 0.9559\n",
      "Epoch 871/1000\n",
      "157/157 [==============================] - 0s 252us/step - loss: 0.0483 - acc: 0.9873 - val_loss: 0.0808 - val_acc: 0.9706\n",
      "Epoch 872/1000\n",
      "157/157 [==============================] - 0s 239us/step - loss: 0.0482 - acc: 0.9936 - val_loss: 0.0869 - val_acc: 0.9559\n",
      "Epoch 873/1000\n",
      "157/157 [==============================] - 0s 199us/step - loss: 0.0500 - acc: 0.9936 - val_loss: 0.0901 - val_acc: 0.9559\n",
      "Epoch 874/1000\n",
      "157/157 [==============================] - 0s 175us/step - loss: 0.0495 - acc: 0.9873 - val_loss: 0.0881 - val_acc: 0.9559\n",
      "Epoch 875/1000\n",
      "157/157 [==============================] - 0s 155us/step - loss: 0.0485 - acc: 0.9936 - val_loss: 0.0923 - val_acc: 0.9559\n",
      "Epoch 876/1000\n",
      "157/157 [==============================] - 0s 149us/step - loss: 0.0482 - acc: 0.9873 - val_loss: 0.0821 - val_acc: 0.9706\n",
      "Epoch 877/1000\n",
      "157/157 [==============================] - 0s 177us/step - loss: 0.0493 - acc: 0.9936 - val_loss: 0.0955 - val_acc: 0.9559\n",
      "Epoch 878/1000\n",
      "157/157 [==============================] - 0s 218us/step - loss: 0.0499 - acc: 0.9809 - val_loss: 0.0851 - val_acc: 0.9706\n",
      "Epoch 879/1000\n",
      "157/157 [==============================] - 0s 204us/step - loss: 0.0475 - acc: 0.9936 - val_loss: 0.0855 - val_acc: 0.9706\n",
      "Epoch 880/1000\n",
      "157/157 [==============================] - 0s 209us/step - loss: 0.0490 - acc: 0.9936 - val_loss: 0.0826 - val_acc: 0.9706\n",
      "Epoch 881/1000\n",
      "157/157 [==============================] - 0s 349us/step - loss: 0.0474 - acc: 0.9936 - val_loss: 0.0813 - val_acc: 0.9706\n",
      "Epoch 882/1000\n",
      "157/157 [==============================] - 0s 153us/step - loss: 0.0479 - acc: 0.9936 - val_loss: 0.0924 - val_acc: 0.9559\n",
      "Epoch 883/1000\n",
      "157/157 [==============================] - 0s 188us/step - loss: 0.0527 - acc: 0.9873 - val_loss: 0.0853 - val_acc: 0.9706\n",
      "Epoch 884/1000\n",
      "157/157 [==============================] - 0s 122us/step - loss: 0.0473 - acc: 0.9936 - val_loss: 0.0863 - val_acc: 0.9706\n",
      "Epoch 885/1000\n",
      "157/157 [==============================] - 0s 152us/step - loss: 0.0469 - acc: 0.9873 - val_loss: 0.0787 - val_acc: 0.9706\n",
      "Epoch 886/1000\n",
      "157/157 [==============================] - 0s 161us/step - loss: 0.0477 - acc: 0.9936 - val_loss: 0.0853 - val_acc: 0.9706\n",
      "Epoch 887/1000\n",
      "157/157 [==============================] - 0s 176us/step - loss: 0.0475 - acc: 0.9936 - val_loss: 0.0957 - val_acc: 0.9559\n",
      "Epoch 888/1000\n",
      "157/157 [==============================] - 0s 173us/step - loss: 0.0478 - acc: 0.9936 - val_loss: 0.0922 - val_acc: 0.9559\n",
      "Epoch 889/1000\n",
      "157/157 [==============================] - 0s 223us/step - loss: 0.0477 - acc: 0.9873 - val_loss: 0.0834 - val_acc: 0.9706\n",
      "Epoch 890/1000\n",
      "157/157 [==============================] - 0s 204us/step - loss: 0.0477 - acc: 0.9873 - val_loss: 0.0862 - val_acc: 0.9706\n",
      "Epoch 891/1000\n",
      "157/157 [==============================] - 0s 386us/step - loss: 0.0466 - acc: 0.9873 - val_loss: 0.0829 - val_acc: 0.9706\n",
      "Epoch 892/1000\n",
      "157/157 [==============================] - 0s 254us/step - loss: 0.0477 - acc: 0.9936 - val_loss: 0.0824 - val_acc: 0.9706\n",
      "Epoch 893/1000\n",
      "157/157 [==============================] - 0s 115us/step - loss: 0.0466 - acc: 0.9873 - val_loss: 0.0840 - val_acc: 0.9706\n",
      "Epoch 894/1000\n",
      "157/157 [==============================] - 0s 122us/step - loss: 0.0480 - acc: 0.9936 - val_loss: 0.0870 - val_acc: 0.9559\n",
      "Epoch 895/1000\n",
      "157/157 [==============================] - 0s 176us/step - loss: 0.0477 - acc: 0.9873 - val_loss: 0.0866 - val_acc: 0.9559\n",
      "Epoch 896/1000\n",
      "157/157 [==============================] - 0s 177us/step - loss: 0.0467 - acc: 0.9873 - val_loss: 0.0824 - val_acc: 0.9706\n",
      "Epoch 897/1000\n",
      "157/157 [==============================] - 0s 319us/step - loss: 0.0467 - acc: 0.9873 - val_loss: 0.0852 - val_acc: 0.9706\n",
      "Epoch 898/1000\n",
      "157/157 [==============================] - 0s 133us/step - loss: 0.0483 - acc: 0.9936 - val_loss: 0.0878 - val_acc: 0.9559\n",
      "Epoch 899/1000\n",
      "157/157 [==============================] - 0s 138us/step - loss: 0.0482 - acc: 0.9936 - val_loss: 0.0899 - val_acc: 0.9559\n",
      "Epoch 900/1000\n",
      "157/157 [==============================] - 0s 239us/step - loss: 0.0455 - acc: 0.9873 - val_loss: 0.0804 - val_acc: 0.9706\n",
      "Epoch 901/1000\n",
      "157/157 [==============================] - 0s 212us/step - loss: 0.0491 - acc: 0.9873 - val_loss: 0.0852 - val_acc: 0.9706\n",
      "Epoch 902/1000\n",
      "157/157 [==============================] - 0s 244us/step - loss: 0.0457 - acc: 0.9936 - val_loss: 0.0920 - val_acc: 0.9559\n",
      "Epoch 903/1000\n",
      "157/157 [==============================] - 0s 236us/step - loss: 0.0473 - acc: 0.9809 - val_loss: 0.0789 - val_acc: 0.9706\n",
      "Epoch 904/1000\n",
      "157/157 [==============================] - 0s 172us/step - loss: 0.0469 - acc: 0.9936 - val_loss: 0.0858 - val_acc: 0.9706\n",
      "Epoch 905/1000\n",
      "157/157 [==============================] - 0s 232us/step - loss: 0.0461 - acc: 0.9936 - val_loss: 0.0868 - val_acc: 0.9559\n",
      "Epoch 906/1000\n",
      "157/157 [==============================] - 0s 192us/step - loss: 0.0456 - acc: 0.9936 - val_loss: 0.0847 - val_acc: 0.9706\n",
      "Epoch 907/1000\n",
      "157/157 [==============================] - 0s 162us/step - loss: 0.0467 - acc: 0.9936 - val_loss: 0.0896 - val_acc: 0.9559\n",
      "Epoch 908/1000\n",
      "157/157 [==============================] - 0s 94us/step - loss: 0.0500 - acc: 0.9873 - val_loss: 0.0832 - val_acc: 0.9706\n",
      "Epoch 909/1000\n",
      "157/157 [==============================] - 0s 221us/step - loss: 0.0453 - acc: 0.9936 - val_loss: 0.0872 - val_acc: 0.9559\n",
      "Epoch 910/1000\n",
      "157/157 [==============================] - 0s 348us/step - loss: 0.0455 - acc: 0.9936 - val_loss: 0.0890 - val_acc: 0.9559\n",
      "Epoch 911/1000\n",
      "157/157 [==============================] - 0s 149us/step - loss: 0.0463 - acc: 0.9873 - val_loss: 0.0857 - val_acc: 0.9706\n",
      "Epoch 912/1000\n",
      "157/157 [==============================] - 0s 144us/step - loss: 0.0452 - acc: 0.9936 - val_loss: 0.0939 - val_acc: 0.9559\n",
      "Epoch 913/1000\n",
      "157/157 [==============================] - 0s 158us/step - loss: 0.0465 - acc: 0.9873 - val_loss: 0.0809 - val_acc: 0.9706\n",
      "Epoch 914/1000\n",
      "157/157 [==============================] - 0s 123us/step - loss: 0.0448 - acc: 0.9936 - val_loss: 0.0851 - val_acc: 0.9706\n",
      "Epoch 915/1000\n",
      "157/157 [==============================] - 0s 148us/step - loss: 0.0480 - acc: 0.9873 - val_loss: 0.0852 - val_acc: 0.9706\n",
      "Epoch 916/1000\n",
      "157/157 [==============================] - 0s 179us/step - loss: 0.0450 - acc: 0.9936 - val_loss: 0.0950 - val_acc: 0.9559\n",
      "Epoch 917/1000\n",
      "157/157 [==============================] - 0s 141us/step - loss: 0.0466 - acc: 0.9936 - val_loss: 0.0868 - val_acc: 0.9559\n",
      "Epoch 918/1000\n",
      "157/157 [==============================] - 0s 184us/step - loss: 0.0452 - acc: 0.9873 - val_loss: 0.0825 - val_acc: 0.9706\n",
      "Epoch 919/1000\n",
      "157/157 [==============================] - 0s 107us/step - loss: 0.0457 - acc: 0.9936 - val_loss: 0.0792 - val_acc: 0.9706\n",
      "Epoch 920/1000\n",
      "157/157 [==============================] - 0s 104us/step - loss: 0.0446 - acc: 0.9936 - val_loss: 0.0843 - val_acc: 0.9706\n",
      "Epoch 921/1000\n",
      "157/157 [==============================] - 0s 102us/step - loss: 0.0462 - acc: 0.9873 - val_loss: 0.0818 - val_acc: 0.9706\n",
      "Epoch 922/1000\n",
      "157/157 [==============================] - ETA: 0s - loss: 0.0544 - acc: 1.000 - 0s 102us/step - loss: 0.0451 - acc: 0.9873 - val_loss: 0.0853 - val_acc: 0.9706\n",
      "Epoch 923/1000\n",
      "157/157 [==============================] - 0s 99us/step - loss: 0.0448 - acc: 0.9873 - val_loss: 0.0818 - val_acc: 0.9706\n",
      "Epoch 924/1000\n",
      "157/157 [==============================] - 0s 105us/step - loss: 0.0449 - acc: 0.9873 - val_loss: 0.0841 - val_acc: 0.9706\n",
      "Epoch 925/1000\n",
      "157/157 [==============================] - 0s 275us/step - loss: 0.0452 - acc: 0.9873 - val_loss: 0.0823 - val_acc: 0.9706\n",
      "Epoch 926/1000\n",
      "157/157 [==============================] - 0s 167us/step - loss: 0.0441 - acc: 0.9873 - val_loss: 0.0808 - val_acc: 0.9706\n",
      "Epoch 927/1000\n",
      "157/157 [==============================] - 0s 118us/step - loss: 0.0447 - acc: 0.9936 - val_loss: 0.0785 - val_acc: 0.9706\n",
      "Epoch 928/1000\n",
      "157/157 [==============================] - 0s 161us/step - loss: 0.0447 - acc: 0.9873 - val_loss: 0.0766 - val_acc: 0.9706\n",
      "Epoch 929/1000\n",
      "157/157 [==============================] - 0s 193us/step - loss: 0.0449 - acc: 0.9936 - val_loss: 0.0836 - val_acc: 0.9706\n",
      "Epoch 930/1000\n",
      "157/157 [==============================] - 0s 191us/step - loss: 0.0446 - acc: 0.9936 - val_loss: 0.0850 - val_acc: 0.9706\n",
      "Epoch 931/1000\n",
      "157/157 [==============================] - 0s 163us/step - loss: 0.0439 - acc: 0.9936 - val_loss: 0.0868 - val_acc: 0.9559\n",
      "Epoch 932/1000\n",
      "157/157 [==============================] - 0s 168us/step - loss: 0.0434 - acc: 0.9936 - val_loss: 0.0844 - val_acc: 0.9706\n",
      "Epoch 933/1000\n",
      "157/157 [==============================] - 0s 171us/step - loss: 0.0443 - acc: 0.9936 - val_loss: 0.0793 - val_acc: 0.9706\n",
      "Epoch 934/1000\n",
      "157/157 [==============================] - 0s 197us/step - loss: 0.0441 - acc: 0.9873 - val_loss: 0.0855 - val_acc: 0.9706\n",
      "Epoch 935/1000\n",
      "157/157 [==============================] - 0s 142us/step - loss: 0.0447 - acc: 0.9873 - val_loss: 0.0878 - val_acc: 0.9559\n",
      "Epoch 936/1000\n",
      "157/157 [==============================] - 0s 190us/step - loss: 0.0439 - acc: 0.9809 - val_loss: 0.0763 - val_acc: 0.9706\n",
      "Epoch 937/1000\n",
      "157/157 [==============================] - 0s 170us/step - loss: 0.0427 - acc: 0.9936 - val_loss: 0.0870 - val_acc: 0.9559\n",
      "Epoch 938/1000\n",
      "157/157 [==============================] - ETA: 0s - loss: 0.0191 - acc: 1.000 - 0s 138us/step - loss: 0.0439 - acc: 0.9873 - val_loss: 0.0760 - val_acc: 0.9706\n",
      "Epoch 939/1000\n",
      "157/157 [==============================] - 0s 188us/step - loss: 0.0442 - acc: 0.9936 - val_loss: 0.0777 - val_acc: 0.9706\n",
      "Epoch 940/1000\n",
      "157/157 [==============================] - 0s 186us/step - loss: 0.0445 - acc: 0.9936 - val_loss: 0.0877 - val_acc: 0.9559\n",
      "Epoch 941/1000\n",
      "157/157 [==============================] - 0s 170us/step - loss: 0.0441 - acc: 0.9873 - val_loss: 0.0819 - val_acc: 0.9706\n",
      "Epoch 942/1000\n",
      "157/157 [==============================] - 0s 204us/step - loss: 0.0433 - acc: 0.9936 - val_loss: 0.0863 - val_acc: 0.9559\n",
      "Epoch 943/1000\n",
      "157/157 [==============================] - 0s 235us/step - loss: 0.0430 - acc: 0.9873 - val_loss: 0.0843 - val_acc: 0.9706\n",
      "Epoch 944/1000\n",
      "157/157 [==============================] - 0s 207us/step - loss: 0.0436 - acc: 0.9873 - val_loss: 0.0817 - val_acc: 0.9706\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 945/1000\n",
      "157/157 [==============================] - 0s 178us/step - loss: 0.0470 - acc: 0.9873 - val_loss: 0.0836 - val_acc: 0.9706\n",
      "Epoch 946/1000\n",
      "157/157 [==============================] - 0s 170us/step - loss: 0.0432 - acc: 0.9936 - val_loss: 0.0858 - val_acc: 0.9559\n",
      "Epoch 947/1000\n",
      "157/157 [==============================] - 0s 327us/step - loss: 0.0428 - acc: 0.9873 - val_loss: 0.0818 - val_acc: 0.9706\n",
      "Epoch 948/1000\n",
      "157/157 [==============================] - 0s 219us/step - loss: 0.0433 - acc: 0.9873 - val_loss: 0.0807 - val_acc: 0.9706\n",
      "Epoch 949/1000\n",
      "157/157 [==============================] - 0s 253us/step - loss: 0.0438 - acc: 0.9936 - val_loss: 0.0792 - val_acc: 0.9706\n",
      "Epoch 950/1000\n",
      "157/157 [==============================] - 0s 369us/step - loss: 0.0430 - acc: 0.9936 - val_loss: 0.0869 - val_acc: 0.9559\n",
      "Epoch 951/1000\n",
      "157/157 [==============================] - 0s 241us/step - loss: 0.0435 - acc: 0.9936 - val_loss: 0.0822 - val_acc: 0.9706\n",
      "Epoch 952/1000\n",
      "157/157 [==============================] - 0s 82us/step - loss: 0.0430 - acc: 0.9873 - val_loss: 0.0797 - val_acc: 0.9706\n",
      "Epoch 953/1000\n",
      "157/157 [==============================] - 0s 86us/step - loss: 0.0431 - acc: 0.9873 - val_loss: 0.0845 - val_acc: 0.9706\n",
      "Epoch 954/1000\n",
      "157/157 [==============================] - 0s 112us/step - loss: 0.0433 - acc: 0.9873 - val_loss: 0.0792 - val_acc: 0.9706\n",
      "Epoch 955/1000\n",
      "157/157 [==============================] - 0s 152us/step - loss: 0.0437 - acc: 0.9936 - val_loss: 0.0863 - val_acc: 0.9559\n",
      "Epoch 956/1000\n",
      "157/157 [==============================] - 0s 323us/step - loss: 0.0438 - acc: 0.9873 - val_loss: 0.0850 - val_acc: 0.9706\n",
      "Epoch 957/1000\n",
      "157/157 [==============================] - 0s 229us/step - loss: 0.0424 - acc: 0.9936 - val_loss: 0.0862 - val_acc: 0.9559\n",
      "Epoch 958/1000\n",
      "157/157 [==============================] - 0s 332us/step - loss: 0.0422 - acc: 0.9873 - val_loss: 0.0763 - val_acc: 0.9706\n",
      "Epoch 959/1000\n",
      "157/157 [==============================] - 0s 369us/step - loss: 0.0434 - acc: 0.9873 - val_loss: 0.0755 - val_acc: 0.9706\n",
      "Epoch 960/1000\n",
      "157/157 [==============================] - 0s 314us/step - loss: 0.0421 - acc: 0.9936 - val_loss: 0.0840 - val_acc: 0.9706\n",
      "Epoch 961/1000\n",
      "157/157 [==============================] - 0s 358us/step - loss: 0.0423 - acc: 0.9873 - val_loss: 0.0861 - val_acc: 0.9559\n",
      "Epoch 962/1000\n",
      "157/157 [==============================] - 0s 363us/step - loss: 0.0416 - acc: 0.9936 - val_loss: 0.0824 - val_acc: 0.9706\n",
      "Epoch 963/1000\n",
      "157/157 [==============================] - 0s 227us/step - loss: 0.0443 - acc: 0.9873 - val_loss: 0.0831 - val_acc: 0.9706\n",
      "Epoch 964/1000\n",
      "157/157 [==============================] - 0s 347us/step - loss: 0.0440 - acc: 0.9873 - val_loss: 0.0839 - val_acc: 0.9706\n",
      "Epoch 965/1000\n",
      "157/157 [==============================] - 0s 256us/step - loss: 0.0411 - acc: 0.9936 - val_loss: 0.0864 - val_acc: 0.9559\n",
      "Epoch 966/1000\n",
      "157/157 [==============================] - 0s 331us/step - loss: 0.0416 - acc: 0.9873 - val_loss: 0.0843 - val_acc: 0.9706\n",
      "Epoch 967/1000\n",
      "157/157 [==============================] - 0s 286us/step - loss: 0.0419 - acc: 0.9873 - val_loss: 0.0754 - val_acc: 0.9706\n",
      "Epoch 968/1000\n",
      "157/157 [==============================] - 0s 299us/step - loss: 0.0425 - acc: 0.9936 - val_loss: 0.0783 - val_acc: 0.9706\n",
      "Epoch 969/1000\n",
      "157/157 [==============================] - 0s 314us/step - loss: 0.0417 - acc: 0.9936 - val_loss: 0.0784 - val_acc: 0.9706\n",
      "Epoch 970/1000\n",
      "157/157 [==============================] - 0s 348us/step - loss: 0.0418 - acc: 0.9936 - val_loss: 0.0836 - val_acc: 0.9706\n",
      "Epoch 971/1000\n",
      "157/157 [==============================] - 0s 310us/step - loss: 0.0428 - acc: 0.9873 - val_loss: 0.0837 - val_acc: 0.9706\n",
      "Epoch 972/1000\n",
      "157/157 [==============================] - 0s 357us/step - loss: 0.0416 - acc: 0.9936 - val_loss: 0.0867 - val_acc: 0.9559\n",
      "Epoch 973/1000\n",
      "157/157 [==============================] - 0s 317us/step - loss: 0.0430 - acc: 0.9936 - val_loss: 0.0851 - val_acc: 0.9559\n",
      "Epoch 974/1000\n",
      "157/157 [==============================] - 0s 296us/step - loss: 0.0413 - acc: 0.9873 - val_loss: 0.0855 - val_acc: 0.9559\n",
      "Epoch 975/1000\n",
      "157/157 [==============================] - 0s 249us/step - loss: 0.0414 - acc: 0.9936 - val_loss: 0.0873 - val_acc: 0.9559\n",
      "Epoch 976/1000\n",
      "157/157 [==============================] - 0s 279us/step - loss: 0.0416 - acc: 0.9873 - val_loss: 0.0879 - val_acc: 0.9559\n",
      "Epoch 977/1000\n",
      "157/157 [==============================] - 0s 98us/step - loss: 0.0414 - acc: 0.9936 - val_loss: 0.0799 - val_acc: 0.9706\n",
      "Epoch 978/1000\n",
      "157/157 [==============================] - 0s 90us/step - loss: 0.0418 - acc: 0.9936 - val_loss: 0.0793 - val_acc: 0.9706\n",
      "Epoch 979/1000\n",
      "157/157 [==============================] - 0s 149us/step - loss: 0.0414 - acc: 0.9873 - val_loss: 0.0807 - val_acc: 0.9706\n",
      "Epoch 980/1000\n",
      "157/157 [==============================] - 0s 120us/step - loss: 0.0405 - acc: 0.9873 - val_loss: 0.0741 - val_acc: 0.9706\n",
      "Epoch 981/1000\n",
      "157/157 [==============================] - 0s 129us/step - loss: 0.0413 - acc: 0.9936 - val_loss: 0.0755 - val_acc: 0.9706\n",
      "Epoch 982/1000\n",
      "157/157 [==============================] - 0s 252us/step - loss: 0.0409 - acc: 0.9936 - val_loss: 0.0803 - val_acc: 0.9706\n",
      "Epoch 983/1000\n",
      "157/157 [==============================] - 0s 263us/step - loss: 0.0404 - acc: 0.9873 - val_loss: 0.0769 - val_acc: 0.9706\n",
      "Epoch 984/1000\n",
      "157/157 [==============================] - 0s 158us/step - loss: 0.0419 - acc: 0.9936 - val_loss: 0.0744 - val_acc: 0.9706\n",
      "Epoch 985/1000\n",
      "157/157 [==============================] - 0s 220us/step - loss: 0.0410 - acc: 0.9936 - val_loss: 0.0833 - val_acc: 0.9706\n",
      "Epoch 986/1000\n",
      "157/157 [==============================] - 0s 115us/step - loss: 0.0417 - acc: 0.9873 - val_loss: 0.0915 - val_acc: 0.9559\n",
      "Epoch 987/1000\n",
      "157/157 [==============================] - 0s 141us/step - loss: 0.0403 - acc: 0.9873 - val_loss: 0.0797 - val_acc: 0.9706\n",
      "Epoch 988/1000\n",
      "157/157 [==============================] - 0s 115us/step - loss: 0.0405 - acc: 0.9873 - val_loss: 0.0821 - val_acc: 0.9706\n",
      "Epoch 989/1000\n",
      "157/157 [==============================] - 0s 102us/step - loss: 0.0397 - acc: 0.9936 - val_loss: 0.0813 - val_acc: 0.9706\n",
      "Epoch 990/1000\n",
      "157/157 [==============================] - 0s 161us/step - loss: 0.0402 - acc: 0.9936 - val_loss: 0.0899 - val_acc: 0.9559\n",
      "Epoch 991/1000\n",
      "157/157 [==============================] - 0s 299us/step - loss: 0.0421 - acc: 0.9809 - val_loss: 0.0819 - val_acc: 0.9706\n",
      "Epoch 992/1000\n",
      "157/157 [==============================] - 0s 218us/step - loss: 0.0400 - acc: 0.9873 - val_loss: 0.0787 - val_acc: 0.9706\n",
      "Epoch 993/1000\n",
      "157/157 [==============================] - 0s 195us/step - loss: 0.0410 - acc: 0.9936 - val_loss: 0.0817 - val_acc: 0.9706\n",
      "Epoch 994/1000\n",
      "157/157 [==============================] - 0s 114us/step - loss: 0.0392 - acc: 0.9936 - val_loss: 0.0889 - val_acc: 0.9559\n",
      "Epoch 995/1000\n",
      "157/157 [==============================] - 0s 186us/step - loss: 0.0399 - acc: 0.9873 - val_loss: 0.0750 - val_acc: 0.9706\n",
      "Epoch 996/1000\n",
      "157/157 [==============================] - 0s 474us/step - loss: 0.0406 - acc: 0.9936 - val_loss: 0.0791 - val_acc: 0.9706\n",
      "Epoch 997/1000\n",
      "157/157 [==============================] - 0s 267us/step - loss: 0.0396 - acc: 0.9936 - val_loss: 0.0862 - val_acc: 0.9559\n",
      "Epoch 998/1000\n",
      "157/157 [==============================] - 0s 310us/step - loss: 0.0395 - acc: 0.9873 - val_loss: 0.0734 - val_acc: 0.9706\n",
      "Epoch 999/1000\n",
      "157/157 [==============================] - 0s 457us/step - loss: 0.0398 - acc: 0.9936 - val_loss: 0.0901 - val_acc: 0.9559\n",
      "Epoch 1000/1000\n",
      "157/157 [==============================] - 0s 332us/step - loss: 0.0395 - acc: 0.9873 - val_loss: 0.0871 - val_acc: 0.9559\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "# Building a Keras model\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(8, input_shape = (4,), activation = \"relu\"))\n",
    "\n",
    "model.add(Dense(8, activation = \"relu\"))\n",
    "\n",
    "model.add(Dense(1, activation = \"sigmoid\"))\n",
    "\n",
    "model.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "\n",
    "num_epochs = 1000\n",
    "\n",
    "model_run = model.fit(X_train_scaled, y_train, epochs=num_epochs, validation_data = (X_test_scaled,y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe91c78a208>]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe91c78a748>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 373
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "history_model = model_run.history\n",
    "\n",
    "fig, ax = plt.subplot()\n",
    "\n",
    "plt.plot(np.arange(1,num_epochs+1), history_model[\"acc\"], \"--\")\n",
    "\n",
    "plt.plot(np.arange(1,num_epochs+1), history_model[\"val_acc\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Network Architecture\n",
    "\n",
    "## CNN examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: \n",
    "\n",
    "- does keras support scikit-learn api ? (.fit and .predict methods) ?\n",
    "- if yes: we could use cross validation and hyper parameter optimzation for scikit-learn to evaluae / improve keras network.    \n",
    "      \n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "latex_envs": {
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