Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    " \n",
    "import os\n",
    "import random\n",
    "import sklearn\n",
    "\n",
    "\n",
    "\n",
    "words = {}\n",
    "\n",
    "def load_input_sentences(filepath):\n",
    "    sentence_file = open(filepath,'r')\n",
    "    sentence = sentence_file.readline()\n",
    "    sentences = []\n",
    "    solutions = []\n",
    "    while sentence != '':\n",
    "        substrings = sentence.split(' ')\n",
    "        solution = substrings[-1].strip()\n",
    "        sentence = ''\n",
    "        for i in range(len(substrings)-1):\n",
    "            sentence = sentence + ' '+ substrings[i]    \n",
    "        solutions.append(int(solution))\n",
    "        sentences.append(sentence)\n",
    "        sentence = sentence_file.readline()\n",
    "        \n",
    "    return sentences, solutions\n",
    "\n",
    "\n",
    "\n",
    "def create_vocabulary():\n",
    "    num = 0\n",
    "    vocab_size = 0\n",
    "    for sentence in sentences:\n",
    "        substrings = sentence.split(' ')\n",
    "        for string in substrings:\n",
    "            if words.get(string) == None:\n",
    "                #print num\n",
    "                words[string] = num\n",
    "                num += 1\n",
    "                vocab_size = num\n",
    "    \n",
    "    return vocab_size\n",
    "    \n",
    "\n",
    "def fc(input, num_output, name = 'fc'):\n",
    "    with tf.variable_scope(name):\n",
    "        num_input = input.get_shape()[1]\n",
    "        W = tf.get_variable('w', [num_input, num_output], tf.float32, tf.random_normal_initializer(0.0, 0.02))\n",
    "        b = tf.get_variable('b', [num_output], initializer = tf.constant_initializer(0.0))\n",
    "        return tf.matmul(input, W) + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
     ]
    }
   ],
   "source": [
    "sentences, solutions = load_input_sentences('sentences_nlp.txt')\n",
    "vocab_size = create_vocabulary()\n",
    "print vocab_size\n",
    "test_sentences, test_solutions = load_input_sentences('sentences_nlp_test.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(?, 100)\n",
      "(?, 100)\n",
      "(?, 100)\n",
      "[]\n",
      "(TensorShape([Dimension(None)]), (0,))\n"
     ]
    },
    {
     "ename": "InvalidArgumentError",
     "evalue": "Incompatible shapes: [32] vs. [0]\n\t [[Node: Equal = Equal[T=DT_INT64, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](ArgMax, Equal/y)]]\n\nCaused by op u'Equal', defined at:\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/runpy.py\", line 174, in _run_module_as_main\n    \"__main__\", fname, loader, pkg_name)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/runpy.py\", line 72, in _run_code\n    exec code in run_globals\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/ipykernel/kernelapp.py\", line 477, in start\n    ioloop.IOLoop.instance().start()\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\n    super(ZMQIOLoop, self).start()\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/tornado/ioloop.py\", line 888, in start\n    handler_func(fd_obj, events)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/ipykernel/kernelbase.py\", line 235, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/ipykernel/ipkernel.py\", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/IPython/core/interactiveshell.py\", line 2718, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/IPython/core/interactiveshell.py\", line 2822, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/IPython/core/interactiveshell.py\", line 2882, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-8-eb73aea2f96d>\", line 125, in <module>\n    dcgan = Text_Arithematic()\n  File \"<ipython-input-8-eb73aea2f96d>\", line 26, in __init__\n    self._init_ops()\n  File \"<ipython-input-8-eb73aea2f96d>\", line 66, in _init_ops\n    correct = tf.equal(predict, labels)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.py\", line 1476, in equal\n    \"Equal\", x=x, y=y, name=name)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/tensorflow/python/framework/ops.py\", line 2956, in create_op\n    op_def=op_def)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/tensorflow/python/framework/ops.py\", line 1470, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n\nInvalidArgumentError (see above for traceback): Incompatible shapes: [32] vs. [0]\n\t [[Node: Equal = Equal[T=DT_INT64, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](ArgMax, Equal/y)]]\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-eb73aea2f96d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    127\u001b[0m         \u001b[0mtrain_sentences\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msentences\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m         \u001b[0mtrain_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolutions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 129\u001b[0;31m         \u001b[0mdcgan\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrain_sentences\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrain_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    130\u001b[0m         \u001b[0mvar_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_collection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGraphKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGLOBAL_VARIABLES\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'net'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m         \u001b[0msaver\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSaver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-8-eb73aea2f96d>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, sess, train_sentences, train_labels)\u001b[0m\n\u001b[1;32m    103\u001b[0m                 \u001b[0mfeed_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal_sentences\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0membeddings\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal_labels\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_train\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m                 \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccuracy_op\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_step\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    887\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 889\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    890\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1118\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1119\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1120\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1121\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1122\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1315\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1317\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1318\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1319\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1334\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1335\u001b[0m           \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1336\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1338\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: Incompatible shapes: [32] vs. [0]\n\t [[Node: Equal = Equal[T=DT_INT64, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](ArgMax, Equal/y)]]\n\nCaused by op u'Equal', defined at:\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/runpy.py\", line 174, in _run_module_as_main\n    \"__main__\", fname, loader, pkg_name)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/runpy.py\", line 72, in _run_code\n    exec code in run_globals\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/ipykernel/kernelapp.py\", line 477, in start\n    ioloop.IOLoop.instance().start()\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\n    super(ZMQIOLoop, self).start()\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/tornado/ioloop.py\", line 888, in start\n    handler_func(fd_obj, events)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/ipykernel/kernelbase.py\", line 235, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/ipykernel/ipkernel.py\", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/IPython/core/interactiveshell.py\", line 2718, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/IPython/core/interactiveshell.py\", line 2822, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/IPython/core/interactiveshell.py\", line 2882, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-8-eb73aea2f96d>\", line 125, in <module>\n    dcgan = Text_Arithematic()\n  File \"<ipython-input-8-eb73aea2f96d>\", line 26, in __init__\n    self._init_ops()\n  File \"<ipython-input-8-eb73aea2f96d>\", line 66, in _init_ops\n    correct = tf.equal(predict, labels)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.py\", line 1476, in equal\n    \"Equal\", x=x, y=y, name=name)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/tensorflow/python/framework/ops.py\", line 2956, in create_op\n    op_def=op_def)\n  File \"/Users/madhvikannan/anaconda/envs/MNIST_Classify/lib/python2.7/site-packages/tensorflow/python/framework/ops.py\", line 1470, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n\nInvalidArgumentError (see above for traceback): Incompatible shapes: [32] vs. [0]\n\t [[Node: Equal = Equal[T=DT_INT64, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](ArgMax, Equal/y)]]\n"
     ]
    }
   ],
   "source": [
    "def batch_norm(input, is_training):\n",
    "    out = tf.contrib.layers.batch_norm(input, decay = 0.99, center = True, scale = True,\n",
    "                                       is_training = is_training, updates_collections = None)\n",
    "    return out\n",
    "\n",
    "def leaky_relu(input, alpha = 0.2):\n",
    "    return tf.maximum(alpha * input, input)\n",
    "\n",
    "\n",
    "class Text_Arithematic(object):\n",
    "\n",
    "    def __init__(self):\n",
    "        self.num_epoch = 50\n",
    "        self.batch_size = 32\n",
    "        self.log_step = 50\n",
    "        self.learning_rate = 1e-4\n",
    "        \n",
    "        self._net_called = False\n",
    "\n",
    "\n",
    "        self.real_sentences = tf.placeholder(tf.float32, [None, vocab_size])\n",
    "        \n",
    "        self.real_labels = tf.placeholder(tf.float32, [None, 100])\n",
    "        self.is_train = tf.placeholder(tf.bool)\n",
    "        \n",
    "        self._init_ops()\n",
    "\n",
    "    def _network(self,text_embedding, is_train):\n",
    "        with tf.variable_scope('net', reuse = self._net_called):\n",
    "            self._net_called = True\n",
    "            fc1 = fc(text_embedding, 256, 'fc1')\n",
    "            relu1 = leaky_relu(fc1)\n",
    "            #batch1 = batch_norm(relu1,is_train)\n",
    "            \n",
    "            fc2 = fc(relu1, 100, 'fc2')\n",
    "            relu2 = leaky_relu(fc2)\n",
    "            #batch2 = batch_norm(relu2,is_train)\n",
    "            \n",
    "            sigmoid3 = tf.sigmoid(relu2)\n",
    "            print(sigmoid3.shape)\n",
    "            return sigmoid3\n",
    "\n",
    "    def _loss(self, labels, logits):\n",
    "        loss = tf.nn.sigmoid_cross_entropy_with_logits(labels = labels, logits = logits)\n",
    "        \n",
    "        return tf.reduce_mean(loss)\n",
    "    \n",
    "    # Define operations\n",
    "    def _init_ops(self):\n",
    "        self.loss_op = self._loss(self.real_labels, self._network(self.real_sentences, self.is_train))\n",
    "        \n",
    "        logits = self._network(self.real_sentences, self.is_train)\n",
    "        print(logits.shape)\n",
    "        labels = []\n",
    "        for i in range(self.batch_size):\n",
    "            one_hot = self.real_labels[i]\n",
    "            for j in range(100):\n",
    "                if one_hot[j] == 1:\n",
    "                    labels.append(j)\n",
    "        print (labels)          \n",
    "        labels = np.array(labels)\n",
    "                    \n",
    "        predict = tf.argmax(logits, 1)\n",
    "        print (predict.shape, labels.shape)\n",
    "        correct = tf.equal(predict, labels)\n",
    "        self.accuracy_op = tf.reduce_mean(tf.cast(correct, tf.float32))\n",
    "        \n",
    "        \n",
    "        optimizer = tf.train.RMSPropOptimizer(self.learning_rate)\n",
    "        train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,\n",
    "                                     \"net\")\n",
    "        self.train_op = optimizer.minimize(self.loss_op,var_list = train_vars)\n",
    "        \n",
    "        \n",
    "    # Training function\n",
    "    def train(self, sess,train_sentences,train_labels ):\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "\n",
    "        num_train = train_sentences.shape[0]\n",
    "        step = 0\n",
    "        \n",
    "        dis_losses = []\n",
    "        gen_losses = []\n",
    "        for epoch in range(self.num_epoch):\n",
    "            for i in range(num_train // (10*self.batch_size)):\n",
    "                step += 1\n",
    "                sentence_batch = train_sentences[i * self.batch_size : (i + 1) * self.batch_size]\n",
    "                labels = np.zeros([self.batch_size, 100])\n",
    "                embeddings = np.zeros([self.batch_size, vocab_size])\n",
    "                #print embeddings.shape\n",
    "                for i in range(len(sentence_batch)):\n",
    "                    sentence = sentence_batch[i]\n",
    "                    substrings = sentence.split(' ') \n",
    "                    for string in substrings:\n",
    "                        index = words.get(string)\n",
    "                        #print(i, index)\n",
    "                        embeddings[i][index] = 1\n",
    "                \n",
    "                for j in range(self.batch_size ):\n",
    "                    labels[j][train_labels[i*self.batch_size + j]] = 1\n",
    "                \n",
    "                feed_dict = { self.real_sentences:embeddings, self.real_labels:labels,self.is_train:True}\n",
    "\n",
    "                _, loss, accuracy = sess.run([self.train_op, self.loss_op, self.accuracy_op], feed_dict = feed_dict)\n",
    "\n",
    "                if step % self.log_step == 0:\n",
    "                    print('Iteration {0}: loss = {1:.4f}, accuracy = {2:.4f}'.format(step,loss, accuracy))\n",
    "\n",
    "\n",
    "    # Generates a single sample from input code\n",
    "    def generate_one_sample(self, code, i):\n",
    "        \n",
    "        sentence_batch = sentences[i : i+1]\n",
    "        gen_vis_feed_dict = {self.real_labels:np.zeros(100) ,self.real_sentences:sentence_batch, self.is_train:True}\n",
    "\n",
    "        generated = sess.run(self.loss_op, feed_dict = gen_vis_feed_dict)\n",
    "        return generated\n",
    "\n",
    "    \n",
    "tf.reset_default_graph()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    with tf.device('/cpu:0'):\n",
    "        dcgan = Text_Arithematic()\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        train_sentences = np.array(sentences)\n",
    "        train_labels = np.array(solutions)\n",
    "        dcgan.train(sess,train_sentences,train_labels)\n",
    "        var_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, 'net')\n",
    "        saver = tf.train.Saver(var_list)\n",
    "        saver.save(sess, 'model/dcgan')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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