01. Scoring.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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   "source": [
    "import keras\n",
    "import numpy as np\n",
    "from keras import losses\n",
    "from keras import metrics\n",
    "from keras import optimizers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing IMDB Dataset\n",
    "from keras.datasets import imdb\n",
    "\n",
    "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 1., ..., 0., 0., 0.],\n",
       "       [0., 1., 1., ..., 0., 0., 0.],\n",
       "       [0., 1., 1., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 1., 1., ..., 0., 0., 0.],\n",
       "       [0., 1., 1., ..., 0., 0., 0.],\n",
       "       [0., 1., 1., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#Vectorize Test Data\n",
    "\n",
    "def vectorize_sequences(sequences, dimension=10000):\n",
    "    # Create an all-zero matrix of shape (len(sequences), dimension)\n",
    "    results = np.zeros((len(sequences), dimension))\n",
    "    for i, sequence in enumerate(sequences):\n",
    "        results[i, sequence] = 1.  # set specific indices of results[i] to 1s\n",
    "    return results\n",
    "\n",
    "# Our vectorized test data\n",
    "x_test = vectorize_sequences(test_data)\n",
    "x_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading our Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the model\n",
    "def init():\n",
    "    from keras.models import load_model\n",
    "    global model\n",
    "    model = load_model('BestModel.h5')\n",
    "  \n",
    "    model.compile(optimizer=optimizers.RMSprop(lr=0.001),\n",
    "              loss=losses.binary_crossentropy,\n",
    "              metrics=[metrics.binary_accuracy])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Score Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run(input_np):\n",
    "    import json\n",
    "   \n",
    "    pred = model.predict(input_np)\n",
    "    return json.dumps(str(pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\"[[0.1356007 ]\\\\n [0.9997118 ]\\\\n [0.27816272]\\\\n ...\\\\n [0.07139964]\\\\n [0.04283792]\\\\n [0.47383887]]\"'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init()\n",
    "run(x_test)"
   ]
  }
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