WatsonIoTPlatformKerasTFLSTMModelCreate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from numpy import concatenate\n",
    "from matplotlib import pyplot\n",
    "from pandas import read_csv\n",
    "from pandas import DataFrame\n",
    "from pandas import concat\n",
    "import sklearn\n",
    "from  sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.callbacks import Callback\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense, Activation\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import ibmiotf.application\n",
    "from Queue import Queue\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!pip install h5py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#!pip install keras\n",
    "#!pip install ibmiotf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm watsoniotp.*\n",
    "!wget http://www.romeokienzler.com/watsoniotp.healthy.pickle\n",
    "!wget http://www.romeokienzler.com/watsoniotp.broken.pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_healthy = pickle.load(open('watsoniotp.healthy.pickle', 'rb'))\n",
    "data_broken = pickle.load(open('watsoniotp.broken.pickle', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_healthy = data_healthy.reshape(3000,3)\n",
    "data_broken = data_broken.reshape(3000,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def scaleData(data):\n",
    "    # normalize features\n",
    "    scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "    return scaler.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_healthy_scaled = scaleData(data_healthy)\n",
    "data_broken_scaled = scaleData(data_broken)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "timesteps = 10\n",
    "dim = 3\n",
    "samples = 3000\n",
    "data_healthy_scaled_reshaped = data_healthy_scaled\n",
    "#reshape to (300,10,3)\n",
    "data_healthy_scaled_reshaped.shape = (samples/timesteps,timesteps,dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "losses = []\n",
    "\n",
    "def handleLoss(loss):\n",
    "        global losses\n",
    "        losses+=[loss]\n",
    "        print loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class LossHistory(Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        self.losses = []\n",
    "\n",
    "    def on_batch_end(self, batch, logs={}):\n",
    "        self.losses.append(logs.get('loss'))\n",
    "        handleLoss(logs.get('loss'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# design network\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))\n",
    "model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))\n",
    "model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))\n",
    "model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))\n",
    "model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))\n",
    "model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))\n",
    "model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))\n",
    "model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))\n",
    "model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))\n",
    "model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))\n",
    "model.add(LSTM(50,input_shape=(timesteps,dim),return_sequences=True))\n",
    "model.add(Dense(3))\n",
    "model.compile(loss='mae', optimizer='adam')\n",
    "\n",
    "def train(data):\n",
    "    data.shape = (300, 10, 3)\n",
    "    model.fit(data, data, epochs=50, batch_size=72, validation_data=(data, data), verbose=0, shuffle=False,callbacks=[LossHistory()])\n",
    "    data.shape = (3000, 3)\n",
    "\n",
    "def score(data):\n",
    "    data.shape = (300, 10, 3)\n",
    "    yhat =  model.predict(data)\n",
    "    yhat.shape = (3000, 3)\n",
    "    return yhat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "----------------\n",
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      "0.0123134\n",
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      "0.0124227\n",
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      "0.0138069\n",
      "0.011035\n",
      "0.0263109\n",
      "0.00761452\n",
      "0.0188885\n",
      "0.016863\n",
      "0.0224258\n",
      "0.0240573\n",
      "0.0119561\n"
     ]
    }
   ],
   "source": [
    "for i in range(20):\n",
    "    \n",
    "    print \"----------------\"\n",
    "    train(data_healthy_scaled)\n",
    "    yhat_healthy = score(data_healthy_scaled)\n",
    "    yhat_broken = score(data_broken_scaled)\n",
    "    data_healthy_scaled.shape = (3000, 3)\n",
    "    data_broken_scaled.shape = (3000, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "model_json = model.to_json()\n",
    "with open(\"model.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)\n",
    "# serialize weights to HDF5\n",
    "model.save_weights(\"model.h5\")\n",
    "print(\"Saved model to disk\")"
   ]
  }
 ],
 "metadata": {
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   "display_name": "Python 2 with Spark 2.1",
   "language": "python",
   "name": "python2-spark21"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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 },
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