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   "source": [
    "import numpy as np\n",
    "from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams\n",
    "import theano\n",
    "import theano.tensor as T\n",
    "from theano.ifelse import ifelse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Dropout:\n",
    "\n",
    "    def __init__(self):\n",
    "        \n",
    "        # Set seed for the random numbers\n",
    "        np.random.seed(1234)\n",
    "        rng = np.random.RandomState(1234)\n",
    "        \n",
    "        # Generate a theano RandomStreams\n",
    "        self.srng = RandomStreams(rng.randint(999999))        \n",
    "        \n",
    "    \n",
    "    def __dropit(self, weight, drop):\n",
    "        \"\"\"\n",
    "        Drops some of the cells of 'weight' matrix based on a binomial representation with prob = drop\n",
    "        \n",
    "        # Arguments:\n",
    "        \n",
    "        :param weight : Parameter matrix of the model\n",
    "        :type : theano.tensor\n",
    "        \n",
    "        :param drop : proportion to dropout from the 'weight'\n",
    "        :type : float32\n",
    "       \n",
    "        # Returns: Dropped out matrix\n",
    "        \"\"\"\n",
    "        retain_prob = 1 - drop\n",
    "        mask = self.srng.binomial(n=1, p=retain_prob, size=weight.shape, dtype='floatX')\n",
    "        \n",
    "        return theano.tensor.cast(weight * mask, theano.config.floatX)\n",
    "\n",
    "    def __dont_dropit(self, weight, drop):\n",
    "        \"\"\"\n",
    "        Scale the weight parameters if train = 0\n",
    "        \n",
    "        # Arguments:\n",
    "        \n",
    "        :param weight : Parameter matrix of the model\n",
    "        :type : theano.tensor\n",
    "        \n",
    "        :param drop : proportion to dropout from the 'weight'\n",
    "        :type : float32\n",
    "       \n",
    "        # Returns: Scaled Dropped out matrix\n",
    "        \"\"\"\n",
    "        return (1 - drop) * theano.tensor.cast(weight, theano.config.floatX)\n",
    "\n",
    "    def dropout_layer(self, weight, drop, train = 1):        \n",
    "        \"\"\"\n",
    "        Drops some of the cells of 'weight' matrix based on a binomial representation with prob = drop\n",
    "        \n",
    "        # Arguments:\n",
    "        \n",
    "        :param weight : Parameter matrix of the model\n",
    "        :type : theano.tensor\n",
    "        \n",
    "        :param drop : proportion to dropout from the 'weight'\n",
    "        :type : float32\n",
    "        \n",
    "        :param train : Flag to switch on/off the dropout\n",
    "        :type : int32\n",
    "        \n",
    "        # Returns: Dropped out matrix if train = 1\n",
    "        \"\"\"\n",
    "        result = theano.ifelse.ifelse(theano.tensor.eq(train, 1), self.__dropit(weight, drop), self.__dont_dropit(weight, drop))\n",
    "        return result"
   ]
  }
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