03 2D RNN - Keras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "In this study, we will start by studying the applicability of using the raw data from the seismometer as an input to the neural network for recognition and prediction of earthquakes. The raw data is a time series data of 100 Hz frequency which discribes the vertical motion of the earth. The figure below show a simple illustration of how it is measured. Note that the current tools rely on a more sophisticated and accurate tools but the concept is still the same.\n",
    "\n",
    "As mentioned above, two problems are studied described in the figure above. Obviously, the second problem is more complicated as there is no known method to do this. In fact, it is not known that the signal contain information for the prediction. The two problems are:\n",
    "1. The recognition of the earthquakes signal after they occur.\n",
    "2. The prediction of earthquakes signal before they occur.\n",
    "\n",
    "<img src=\"Figures/For Notbooks/Seismometer.png\" width=\"700\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset\n",
    "The dataset was harvested from servers specifically for this study. After compiling numerous datasets with varying [minimum earthquake magnitude](https://en.wikipedia.org/wiki/Richter_magnitude_scale), lateral coverage, and number of stations, this study area ([The Geysers](https://en.wikipedia.org/wiki/The_Geysers)) of the  is specifically chosen because:\n",
    "1. It contains a spatially dense seismometers coverage.\n",
    "2. The seismicmometers have been operating for a relatively long time.\n",
    "3. The area is tectonically active because of the existance of the geysers.\n",
    "4. Enhanced geothermal system are operating in the area creating more earthquakes.\n",
    "\n",
    "<img src=\"Figures/For Notbooks/Map.png\" width=\"600\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Package and helper functions\n",
    "We will use [Keras](https://keras.io/) to build the neural network, [Matplotlib](https://matplotlib.org/) for plotting results if needed, and [Numpy](http://www.numpy.org/) for matrix manipulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Numpy (The God package)\n",
    "import numpy as np\n",
    "\n",
    "# MatplotLib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import mlab\n",
    "from matplotlib.colors import Normalize\n",
    "\n",
    "# Other\n",
    "import math as M\n",
    "from skimage.transform import resize\n",
    "from obspy.imaging.cm import obspy_sequential\n",
    "\n",
    "# Keras\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.models import Model, load_model, Sequential\n",
    "from keras.layers import Dense, Activation, Dropout, Input, Masking, TimeDistributed, LSTM, Conv1D, Conv2D\n",
    "from keras.layers import GRU, Bidirectional, BatchNormalization, Reshape, Flatten, MaxPooling1D, MaxPooling2D\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "# Sklearn\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use this cell to create some helper functions that will be used in the analysis. We decided to have all the functions needed in one script to facilitate readibility and understanding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def splitDataset2D(X, Y, proportions=np.array([.8, .2])):\n",
    "    \"\"\"\n",
    "    Splits the dataset into a number of smaller datasets with certain given ratios.\n",
    "    \n",
    "    Argument:\n",
    "    X -- Input dataset\n",
    "    Y -- Labels\n",
    "\n",
    "    Returns:\n",
    "    Xs -- Splitted dataset (dictionary with numbers as keys)\n",
    "    Ys -- Splitted labels  (dictionary with numbers as keys)\n",
    "    \"\"\"\n",
    "    \n",
    "    # Make sure proportions are a numpy array\n",
    "    proportions = np.array(proportions)\n",
    "    \n",
    "    # Handle an option to provide one ratio\n",
    "    if (np.sum(proportions)!=1):\n",
    "        ratios = np.concatenate((proportions, np.array([1-np.sum(proportions)])))\n",
    "        \n",
    "    # Create the random indices\n",
    "    m = X.shape[0] \n",
    "    randomIndecies = np.random.permutation(m)\n",
    "    \n",
    "    # Calculate the number of boundaries of indecies in each set\n",
    "    nSamples = np.round(proportions * m)\n",
    "    nSamples = np.concatenate((np.array([0]), np.round(proportions * m)))\n",
    "    nSamples = np.cumsum(nSamples)\n",
    "    nSamples = nSamples.astype(int)\n",
    "    \n",
    "    # Extract teh sets\n",
    "    Xs = []\n",
    "    Ys = []\n",
    "    for i in range(0, proportions.size):\n",
    "        chosenSamples = randomIndecies[nSamples[i]:nSamples[i+1]]\n",
    "        Xs.append(X[chosenSamples, :, :, :])\n",
    "        Ys.append(Y[chosenSamples, :])\n",
    "        \n",
    "    return Xs, Ys\n",
    "\n",
    "# ======================================================================\n",
    "def normalizeData(data, axis=None, keepdims=True):\n",
    "    \"\"\"\n",
    "    Computes the zscore of data along a certain axis.\n",
    "    \n",
    "    Argument:\n",
    "    data -- Data to be normalized\n",
    "    axis -- Axis to normalized (if no axis is given, we\n",
    "\n",
    "    Returns:\n",
    "    normalizedData -- normalized dataset\n",
    "    Ys -- Splitted labels  (dictionary with numbers as keys)\n",
    "    \"\"\"\n",
    "    \n",
    "    # Zscore normalization\n",
    "    normalizedData = (data - np.mean(data, axis = axis, keepdims= keepdims)) / np.std(data, axis = axis, keepdims = keepdims)\n",
    "    return normalizedData\n",
    "\n",
    "# ======================================================================\n",
    "def spectrogramAnalysis(signal, fs, newShape=None, isNormalize=False, isPlot=False):\n",
    "    \"\"\"\n",
    "    Runs the spectogram analysis, resize, and normalize in one go.\n",
    "    \n",
    "    Argument:\n",
    "    signal      -- Data to be analyzed (1D)\n",
    "    fs          -- Sampling frequency\n",
    "    newSahpe    -- New size to scale histogram\n",
    "    isNormalize -- Normalize the result or not\n",
    "    isPlot      -- To show the results in a figure\n",
    "\n",
    "    Returns:\n",
    "    specgram -- Histogram (2D image, 1 channel)\n",
    "    freq     -- Frequency vector\n",
    "    time     -- Time vector\n",
    "    \"\"\"\n",
    "    \n",
    "    specgram, freq, time = getSpectrogram(signal, fs, dbscale=True)\n",
    "    \n",
    "    if (newShape != None):\n",
    "        specgram = resize(specgram,newShape)\n",
    "        \n",
    "    if (isNormalize == True):\n",
    "        specgram = normalizeData(specgram)\n",
    "        \n",
    "    if (isPlot == True):\n",
    "        fig = plt.figure(num=None, figsize=(10, 8), dpi=100)\n",
    "        plt.imshow(specgram)\n",
    "        plt.gca().invert_yaxis()\n",
    "        \n",
    "    return specgram, freq, time\n",
    "\n",
    "# ======================================================================\n",
    "def plotAccuracy(history, isError=True):\n",
    "    trainValues = np.array(history.history[\"acc\"])\n",
    "    validValues = np.array(history.history[\"val_acc\"])\n",
    "    \n",
    "    if (isError == True):\n",
    "        trainValues = 1-trainValues\n",
    "        validValues = 1-validValues\n",
    "    \n",
    "    trainAxe, = plt.plot(trainValues, label=\"Training set\")\n",
    "    validAxe, = plt.plot(validValues, label=\"Validation set\")\n",
    "    \n",
    "    if (isError == True):\n",
    "        plt.ylabel('Error (fraction)')\n",
    "    else:\n",
    "        plt.ylabel('Accuracy (fraction)')\n",
    "\n",
    "    plt.xlabel('Epoch')\n",
    "    \n",
    "    plt.legend(handles=[trainAxe, validAxe])\n",
    "\n",
    "# ======================================================================\n",
    "def getSpectrogram(data, samp_rate, per_lap=0.9, wlen=None, log=False,\n",
    "                outfile=None, fmt=None, axes=None, dbscale=False,\n",
    "                mult=8.0, zorder=None, title=None,\n",
    "                show=True, sphinx=False, clip=[0.0, 1.0]):\n",
    "    \"\"\"\n",
    "    Computes Spectogram of a 1D signal. This functions are based on Obspy internal functions\n",
    "    \"\"\"\n",
    "    \n",
    "    # enforce float for samp_rate\n",
    "    samp_rate = float(samp_rate)\n",
    "\n",
    "    # set wlen from samp_rate if not specified otherwise\n",
    "    if not wlen:\n",
    "        wlen = samp_rate / 100.\n",
    "\n",
    "    npts = len(data)\n",
    "    # nfft needs to be an integer, otherwise a deprecation will be raised\n",
    "    # XXX add condition for too many windows => calculation takes for ever\n",
    "    nfft = int(_nearest_pow_2(wlen * samp_rate))\n",
    "    if nfft > npts:\n",
    "        nfft = int(_nearest_pow_2(npts / 8.0))\n",
    "\n",
    "    if mult is not None:\n",
    "        mult = int(_nearest_pow_2(mult))\n",
    "        mult = mult * nfft\n",
    "    nlap = int(nfft * float(per_lap))\n",
    "\n",
    "    data = data - data.mean()\n",
    "    end = npts / samp_rate\n",
    "\n",
    "    \n",
    "    specgram, freq, time = mlab.specgram(data, Fs=samp_rate, NFFT=nfft,\n",
    "                                         pad_to=mult, noverlap=nlap)\n",
    "    # db scale and remove zero/offset for amplitude\n",
    "    if dbscale:\n",
    "        specgram = 10 * np.log10(specgram[1:, :])\n",
    "    else:\n",
    "        specgram = np.sqrt(specgram[1:, :])\n",
    "    freq = freq[1:]\n",
    "\n",
    "    vmin, vmax = clip\n",
    "    if vmin < 0 or vmax > 1 or vmin >= vmax:\n",
    "        msg = \"Invalid parameters for clip option.\"\n",
    "        raise ValueError(msg)\n",
    "    _range = float(specgram.max() - specgram.min())\n",
    "    vmin = specgram.min() + vmin * _range\n",
    "    vmax = specgram.min() + vmax * _range\n",
    "    norm = Normalize(vmin, vmax, clip=True)\n",
    "    \n",
    "    return specgram, freq, time\n",
    "\n",
    "# ======================================================================\n",
    "def _nearest_pow_2(x):\n",
    "    \"\"\"\n",
    "    Finds power of two nearest to x.\n",
    "\n",
    "    Argument:\n",
    "    x -- number\n",
    "    \n",
    "    Returns:\n",
    "    a/b -- Nearest power of 2 to x\n",
    "    \"\"\"\n",
    "    a = M.pow(2, M.ceil(np.log2(x)))\n",
    "    b = M.pow(2, M.floor(np.log2(x)))\n",
    "    if abs(a - x) < abs(b - x):\n",
    "        return a\n",
    "    else:\n",
    "        return b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will load the dataset. The dataset is already saved in binary numpy format using another script written specifically for this study. The data loaded is the time series data and the labels. Note that we have two datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "folderName = \"FinalDatasets\"\n",
    "datasetNumber = 9\n",
    "testPercent   = .1\n",
    "fs = 100\n",
    "spectogramShape = [128, 256]\n",
    "\n",
    "data = np.load(folderName + \"\\Data_D\" + str(datasetNumber) + \".npy\")\n",
    "label = np.load(folderName + \"\\Label_D\" + str(datasetNumber) + \".npy\")\n",
    "\n",
    "data = data[27000:30000,:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will calculate the spectogram for each 1D signal. The spectogram images are then collected as a dataset that will be used in the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\skimage\\transform\\_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d708617748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "specgram, freq, time = spectrogramAnalysis(data[:,30,0], fs, spectogramShape, isPlot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\skimage\\transform\\_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n"
     ]
    }
   ],
   "source": [
    "dataSpectrogram = np.zeros((data.shape[1], spectogramShape[0], spectogramShape[1], data.shape[2]))\n",
    "for i in range(data.shape[1]):\n",
    "    for k in range(data.shape[2]):\n",
    "        dataToAnalyze = data[:,i,k]\n",
    "        specgram, freq, time = spectrogramAnalysis(dataToAnalyze, fs, spectogramShape)\n",
    "        dataSpectrogram[i,:, :,k] = specgram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the training percentage\n",
    "trainingPercent = 1 - testPercent;\n",
    "\n",
    "# Split the dataset\n",
    "Xs, Ys = splitDataset2D(dataSpectrogram, label, proportions=[trainingPercent, testPercent])\n",
    "\n",
    "# Get training data\n",
    "xTrain = Xs[0][:,:,:,0]\n",
    "yTrain = Ys[0][:,0]\n",
    "\n",
    "# Get test data\n",
    "xTest = Xs[1][:,:,:,0]\n",
    "yTest = Ys[1][:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural network model\n",
    "\n",
    "The model that will be used for this 1D convolutional network is as discribed in the figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_30 (InputLayer)        (None, 128, 256)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_30 (Conv1D)           (None, 126, 100)          76900     \n",
      "_________________________________________________________________\n",
      "batch_normalization_88 (Batc (None, 126, 100)          400       \n",
      "_________________________________________________________________\n",
      "activation_30 (Activation)   (None, 126, 100)          0         \n",
      "_________________________________________________________________\n",
      "dropout_88 (Dropout)         (None, 126, 100)          0         \n",
      "_________________________________________________________________\n",
      "lstm_59 (LSTM)               (None, 126, 20)           9680      \n",
      "_________________________________________________________________\n",
      "dropout_89 (Dropout)         (None, 126, 20)           0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_89 (Batc (None, 126, 20)           80        \n",
      "_________________________________________________________________\n",
      "lstm_60 (LSTM)               (None, 126, 30)           6120      \n",
      "_________________________________________________________________\n",
      "dropout_90 (Dropout)         (None, 126, 30)           0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_90 (Batc (None, 126, 30)           120       \n",
      "_________________________________________________________________\n",
      "time_distributed_30 (TimeDis (None, 126, 1)            31        \n",
      "_________________________________________________________________\n",
      "flatten_30 (Flatten)         (None, 126)               0         \n",
      "_________________________________________________________________\n",
      "dense_60 (Dense)             (None, 1)                 127       \n",
      "=================================================================\n",
      "Total params: 93,458\n",
      "Trainable params: 93,158\n",
      "Non-trainable params: 300\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def constructModel(inputShape):\n",
    "    \"\"\"\n",
    "    Function creating the model's graph in Keras.\n",
    "    \n",
    "    Argument:\n",
    "    input_shape -- shape of the model's input data (using Keras conventions)\n",
    "\n",
    "    Returns:\n",
    "    model -- Keras model instance\n",
    "    \"\"\"\n",
    "    \n",
    "    xInput = Input(shape = inputShape)\n",
    "    \n",
    "    X = Conv1D(filters=100,kernel_size=3,strides=1)(xInput)                          \n",
    "    X = BatchNormalization()(X)                          \n",
    "    X = Activation(activation='relu')(X)                              \n",
    "    X = Dropout(rate=0.3)(X)                         \n",
    "\n",
    "    X = LSTM(units=20, return_sequences=True)(X)                              \n",
    "    X = Dropout(rate=.3)(X)                                 \n",
    "    X = BatchNormalization()(X)                              \n",
    "    \n",
    "    X = LSTM(units=30, return_sequences=True)(X)\n",
    "    X = Dropout(rate=0.3)(X)                                 \n",
    "    X = BatchNormalization()(X)                     \n",
    "    \n",
    "    X = TimeDistributed(Dense(1, activation = \"relu\"))(X) \n",
    "    X = Flatten()(X)\n",
    "    X = Dense(1, activation = \"sigmoid\")(X)\n",
    "\n",
    "    model = Model(inputs = xInput, outputs = X)\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = constructModel(inputShape = (xTrain.shape[1], xTrain.shape[2]))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 309 samples, validate on 35 samples\n",
      "Epoch 1/25\n",
      "309/309 [==============================] - 11s 36ms/step - loss: 0.7537 - acc: 0.5081 - val_loss: 0.7386 - val_acc: 0.5429\n",
      "Epoch 2/25\n",
      "309/309 [==============================] - 2s 5ms/step - loss: 0.7916 - acc: 0.5307 - val_loss: 0.7748 - val_acc: 0.4857\n",
      "Epoch 3/25\n",
      "309/309 [==============================] - 2s 5ms/step - loss: 0.7459 - acc: 0.4951 - val_loss: 0.7687 - val_acc: 0.4857\n",
      "Epoch 4/25\n",
      "309/309 [==============================] - 2s 5ms/step - loss: 0.7467 - acc: 0.5307 - val_loss: 0.7589 - val_acc: 0.5143\n",
      "Epoch 5/25\n",
      "309/309 [==============================] - 2s 5ms/step - loss: 0.7301 - acc: 0.4984 - val_loss: 0.7647 - val_acc: 0.4000\n",
      "Epoch 6/25\n",
      "309/309 [==============================] - 2s 5ms/step - loss: 0.7321 - acc: 0.4984 - val_loss: 0.7645 - val_acc: 0.3714\n",
      "Epoch 7/25\n",
      "309/309 [==============================] - 2s 6ms/step - loss: 0.7022 - acc: 0.5502 - val_loss: 0.7479 - val_acc: 0.4286\n",
      "Epoch 8/25\n",
      "309/309 [==============================] - 2s 6ms/step - loss: 0.7078 - acc: 0.5340 - val_loss: 0.7330 - val_acc: 0.3714\n",
      "Epoch 9/25\n",
      "309/309 [==============================] - 2s 6ms/step - loss: 0.6841 - acc: 0.5793 - val_loss: 0.7251 - val_acc: 0.4000\n",
      "Epoch 10/25\n",
      "309/309 [==============================] - 2s 5ms/step - loss: 0.7193 - acc: 0.5534 - val_loss: 0.7193 - val_acc: 0.4286\n",
      "Epoch 11/25\n",
      "309/309 [==============================] - 2s 5ms/step - loss: 0.6991 - acc: 0.5599 - val_loss: 0.7296 - val_acc: 0.4286\n",
      "Epoch 12/25\n",
      "309/309 [==============================] - 2s 5ms/step - loss: 0.6775 - acc: 0.5825 - val_loss: 0.7427 - val_acc: 0.4000\n",
      "Epoch 13/25\n",
      "309/309 [==============================] - 2s 5ms/step - loss: 0.6951 - acc: 0.5696 - val_loss: 0.7486 - val_acc: 0.3714\n",
      "Epoch 14/25\n",
      "309/309 [==============================] - 2s 5ms/step - loss: 0.6582 - acc: 0.6084 - val_loss: 0.7536 - val_acc: 0.3714\n",
      "Epoch 15/25\n",
      "309/309 [==============================] - 2s 5ms/step - loss: 0.6739 - acc: 0.6084 - val_loss: 0.7568 - val_acc: 0.4000\n",
      "Epoch 16/25\n",
      "309/309 [==============================] - 2s 5ms/step - loss: 0.6517 - acc: 0.5987 - val_loss: 0.7609 - val_acc: 0.4000\n",
      "Epoch 17/25\n",
      "309/309 [==============================] - 2s 5ms/step - loss: 0.6554 - acc: 0.5858 - val_loss: 0.7640 - val_acc: 0.3714\n",
      "Epoch 18/25\n",
      "309/309 [==============================] - 2s 5ms/step - loss: 0.6304 - acc: 0.6505 - val_loss: 0.7623 - val_acc: 0.3714\n",
      "Epoch 19/25\n",
      "309/309 [==============================] - 2s 5ms/step - loss: 0.6507 - acc: 0.6084 - val_loss: 0.7599 - val_acc: 0.3714\n",
      "Epoch 20/25\n",
      "309/309 [==============================] - 1s 5ms/step - loss: 0.6320 - acc: 0.6408 - val_loss: 0.7604 - val_acc: 0.3714\n",
      "Epoch 21/25\n",
      "309/309 [==============================] - 1s 5ms/step - loss: 0.6404 - acc: 0.6084 - val_loss: 0.7615 - val_acc: 0.3429\n",
      "Epoch 22/25\n",
      "309/309 [==============================] - 1s 5ms/step - loss: 0.6267 - acc: 0.6602 - val_loss: 0.7628 - val_acc: 0.3714\n",
      "Epoch 23/25\n",
      "309/309 [==============================] - 2s 5ms/step - loss: 0.6260 - acc: 0.6537 - val_loss: 0.7699 - val_acc: 0.3429\n",
      "Epoch 24/25\n",
      "309/309 [==============================] - 1s 5ms/step - loss: 0.5944 - acc: 0.6699 - val_loss: 0.7799 - val_acc: 0.3429\n",
      "Epoch 25/25\n",
      "309/309 [==============================] - 2s 5ms/step - loss: 0.6037 - acc: 0.6440 - val_loss: 0.7928 - val_acc: 0.3429\n"
     ]
    }
   ],
   "source": [
    "model = constructModel(inputShape = (xTrain.shape[1], xTrain.shape[2]))\n",
    "opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, decay=0.01)\n",
    "model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[\"accuracy\"])\n",
    "history = model.fit(xTrain, yTrain, batch_size = 256, epochs=25, validation_split=.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38/38 [==============================] - 0s 3ms/step\n",
      "Test set accuracy =  0.5526315789473685\n",
      "Guess Accuracy =  0.5392670157068062\n",
      "[[0.39473684 0.13157895]\n",
      " [0.31578947 0.15789474]]\n",
      "Test set accuracy (C) =  0.5526315789473684\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d726ee9cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss, acc = model.evaluate(xTest, yTest)\n",
    "print(\"Test set accuracy = \", acc)\n",
    "print(\"Guess Accuracy = \", 1-np.sum(label)/label.size)\n",
    "\n",
    "yPred = model.predict(xTest) > .5\n",
    "confusionMatrix = confusion_matrix(yTest, yPred)/yTest.size\n",
    "print(confusionMatrix)\n",
    "print(\"Test set accuracy (C) = \", confusionMatrix[0,0]+confusionMatrix[1,1])\n",
    "\n",
    "plotAccuracy(history, False)   "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}