rajshah4/RNN_Simple_Addition_Public.ipynb

## RNN_Simple_Addition_Public.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Teaching a computer to add (using memorization)**\n",
    "The goal here is to take advantage of Recurrent Neural Networks, for more background see my blog post at http://projects.rajivshah.com/blog/2016/04/05/rnn_addition/  This code was partially derived from https://github.com/yankev/tensorflow_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Import basic libraries\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "#from tensorflow.models.rnn import rnn_cell\n",
    "#from tensorflow.models.rnn import rnn\n",
    "#from tensorflow.models.rnn import seq2seq\n",
    "from numpy import sum\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Defining some hyper-params\n",
    "num_units = 50       #this is the parameter for input_size in the basic LSTM cell\n",
    "input_size = 1      \n",
    "batch_size = 50    \n",
    "seq_len = 15\n",
    "drop_out = 0.6 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Creates our random sequences\n",
    "def gen_data(min_length=5, max_length=15, n_batch=50):\n",
    "\n",
    "    X = np.concatenate([np.random.randint(10,size=(n_batch, max_length, 1))],\n",
    "                       axis=-1)\n",
    "    y = np.zeros((n_batch,))\n",
    "    # Compute masks and correct values\n",
    "    for n in range(n_batch):\n",
    "        # Randomly choose the sequence length\n",
    "        length = np.random.randint(min_length, max_length)\n",
    "        X[n, length:, 0] = 0\n",
    "        # Sum the dimensions of X to get the target value\n",
    "        y[n] = np.sum(X[n, :, 0]*1)\n",
    "    return (X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Model Construction\n",
    "num_layers = 2\n",
    "cell = tf.nn.rnn_cell.BasicLSTMCell(num_units)\n",
    "cell = tf.nn.rnn_cell.MultiRNNCell([cell] * num_layers)\n",
    "cell = tf.nn.rnn_cell.DropoutWrapper(cell,output_keep_prob=drop_out)\n",
    "\n",
    "#create placeholders for X and y\n",
    "inputs = [tf.placeholder(tf.float32,shape=[batch_size,1]) for _ in range(seq_len)]\n",
    "result = tf.placeholder(tf.float32, shape=[batch_size])\n",
    "initial_state = cell.zero_state(batch_size, tf.float32)\n",
    "\n",
    "outputs, states = tf.nn.seq2seq.rnn_decoder(inputs, initial_state, cell, scope ='rnnln')\n",
    "outputs2 = outputs[-1]\n",
    "\n",
    "W_o = tf.Variable(tf.random_normal([num_units,input_size], stddev=0.01))     \n",
    "b_o = tf.Variable(tf.random_normal([input_size], stddev=0.01))\n",
    "\n",
    "outputs3 = tf.matmul(outputs2, W_o) + b_o\n",
    "\n",
    "cost = tf.pow(tf.sub(tf.reshape(outputs3, [-1]), result),2)\n",
    "\n",
    "train_op = tf.train.RMSPropOptimizer(0.005, 0.2).minimize(cost) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Generate Validation Data\n",
    "tempX,y_val = gen_data(5,seq_len,batch_size)\n",
    "X_val = []\n",
    "for i in range(seq_len):\n",
    "    X_val.append(tempX[:,i,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "##Run this cell to see what the inputs look like  \n",
    "print (tempX[1])  \n",
    "print (y_val[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "##Session\n",
    "sess = tf.Session()\n",
    "sess.run(tf.initialize_all_variables())\n",
    "train_score =[]\n",
    "val_score= []\n",
    "x_axis=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "num_epochs=1000\n",
    " \n",
    "for k in range(1,num_epochs):\n",
    "\n",
    "    #Generate Data for each epoch\n",
    "    tempX,y = gen_data(5,seq_len,batch_size)\n",
    "    X = []\n",
    "    for i in range(seq_len):\n",
    "        X.append(tempX[:,i,:])\n",
    "\n",
    "    #Create the dictionary of inputs to feed into sess.run\n",
    "    temp_dict = {inputs[i]:X[i] for i in range(seq_len)}\n",
    "    temp_dict.update({result: y})\n",
    "\n",
    "    _,c_train = sess.run([train_op,cost],feed_dict=temp_dict)   #perform an update on the parameters\n",
    "\n",
    "    val_dict = {inputs[i]:X_val[i] for i in range(seq_len)}  #create validation dictionary\n",
    "    val_dict.update({result: y_val})\n",
    "    c_val = sess.run([cost],feed_dict = val_dict )            #compute the cost on the validation set\n",
    "    if (k%100==0):\n",
    "        train_score.append(sum(c_train))\n",
    "        val_score.append(sum(c_val))\n",
    "        x_axis.append(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Train cost: 3086.54125977, on Epoch 999\n",
      "Final Validation cost: 2445.63671875, on Epoch 999\n"
     ]
    },
    {
     "data": {
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3SiDP3TdG3T7z3P1oMxsL4O63ROfNBm4E1kbndIvKLwBOcffR0Tnj3X2BmTUA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1102cbe90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print \"Final Train cost: {}, on Epoch {}\".format(train_score[-1],k)\n",
    "print \"Final Validation cost: {}, on Epoch {}\".format(val_score[-1],k)\n",
    "plt.plot(train_score, 'r-', val_score, 'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "##This part generates a new validation set to test against\n",
    "val_score_v =[]\n",
    "num_epochs=1\n",
    "\n",
    "for k in range(num_epochs):\n",
    "\n",
    "    #Generate Data for each epoch\n",
    "    tempX,y = gen_data(5,seq_len,batch_size)\n",
    "    X = []\n",
    "    for i in range(seq_len):\n",
    "        X.append(tempX[:,i,:])\n",
    "\n",
    "    val_dict = {inputs[i]:X[i] for i in range(seq_len)}\n",
    "    val_dict.update({result: y})\n",
    "    outv, c_val = sess.run([outputs3,cost],feed_dict = val_dict ) \n",
    "    val_score_v.append([c_val])\n",
    "#print \"Validation cost: {}, on Epoch {}\".format(c_val,k)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[8],\n",
       "        [2],\n",
       "        [8],\n",
       "        [8],\n",
       "        [9],\n",
       "        [6],\n",
       "        [0],\n",
       "        [0],\n",
       "        [0],\n",
       "        [0],\n",
       "        [0],\n",
       "        [0],\n",
       "        [0],\n",
       "        [0],\n",
       "        [0]]), 41.0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Target\n",
    "tempX[3],y[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 44.25109482], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Prediction\n",
    "outv[3]"
   ]
  }
 ],
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