tensorFlow_22NOV18.ipynb

{
 "cells": [
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   "cell_type": "code",
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   "metadata": {
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   "source": [
    "### Own - Conda venv --- dc_info_venv\n",
    "# \n",
    "import tensorflow as tf\n",
    "#from tf.keras import layers ### Fails - We have TF version == 1.5.0 \n",
    "\n",
    "import math\n",
    "import numpy as np\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.python.framework import ops\n",
    "#from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict\n",
    "\n",
    "%matplotlib inline\n",
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5.0\n",
      "2.1.2-tf\n",
      "Keras: 2.2.4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "print(tf.VERSION)\n",
    "print(tf.keras.__version__)\n",
    "import keras\n",
    "print('Keras: {}'.format(keras.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "# Source --- https://github.com/larsmaaloee/nvidia-workshop-2017/blob/master/tf-tutorial.ipynb\n",
    "\n",
    "Computational Graphs\n",
    "\n",
    "Computational graphs can be seen as a nice way of structuring mathematical expressions wrt order of operations, \n",
    "declarative programming. Let us say that we have following expression:\n",
    "\n",
    "\n",
    "This equation needs to be performed in the correct order to give the correct result. \n",
    "Therefore we define 3 operations, in this example with fixed values (a=3, b=4):\n",
    "\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Const:0\", shape=(), dtype=float32) Tensor(\"Const_1:0\", shape=(), dtype=float32) Tensor(\"Sqrt:0\", shape=(), dtype=float32)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nTensor(\"Const:0\", shape=(), dtype=float32) \\nTensor(\"Const_1:0\", shape=(), dtype=float32) \\nTensor(\"Sqrt:0\", shape=(), dtype=float32)\\n'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Source --- https://github.com/larsmaaloee/nvidia-workshop-2017/blob/master/tf-tutorial.ipynb\n",
    "\n",
    "\n",
    "a = tf.constant(3.0, tf.float32)\n",
    "b = tf.constant(4.0, tf.float32)\n",
    "c = tf.sqrt(tf.add(tf.square(a), tf.square(b)))\n",
    "\n",
    "print(a, b, c)\n",
    "\n",
    "\"\"\"\n",
    "Tensor(\"Const:0\", shape=(), dtype=float32) \n",
    "Tensor(\"Const_1:0\", shape=(), dtype=float32) \n",
    "Tensor(\"Sqrt:0\", shape=(), dtype=float32)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Const_2:0\", shape=(), dtype=float32) Tensor(\"Const_3:0\", shape=(), dtype=float32) Tensor(\"Sqrt_1:0\", shape=(), dtype=float32)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nTensor(\"Const_11:0\", shape=(), dtype=float32)\\nTensor(\"Const_12:0\", shape=(), dtype=float32)\\nTensor(\"Sqrt_1:0\", shape=(), dtype=float32)\\n'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "av = tf.constant(3.0, tf.float32)\n",
    "bv = tf.constant(4.0, tf.float32)\n",
    "cv = tf.sqrt(tf.add(tf.square(av), tf.square(bv)))\n",
    "\n",
    "print(av, bv, cv)\n",
    "\n",
    "\"\"\"\n",
    "Tensor(\"Const_11:0\", shape=(), dtype=float32)\n",
    "Tensor(\"Const_12:0\", shape=(), dtype=float32)\n",
    "Tensor(\"Sqrt_1:0\", shape=(), dtype=float32)\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"\n",
    "Source -- https://jacobbuckman.com/post/tensorflow-the-confusing-parts-1/\n",
    "\n",
    "Every time we call tf.constant, we create a new node in the graph.\n",
    "This is true even if the node is functionally identical to an existing node,\n",
    "even if we re-assign a node to the same variable, or even if we don’t assign it to a variable at all.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0 4.0 5.0\n"
     ]
    }
   ],
   "source": [
    "sess = tf.Session()\n",
    "print(*sess.run([av, bv, cv]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Placeholder:0\", dtype=float32) Tensor(\"Placeholder_1:0\", dtype=float32) Tensor(\"Sqrt_1:0\", dtype=float32)\n",
      "4.0 3.0 5.0\n"
     ]
    }
   ],
   "source": [
    "# earlier created Constants \n",
    "# Now creating placeHolders\n",
    "\n",
    "a = tf.placeholder(tf.float32)\n",
    "b = tf.placeholder(tf.float32)\n",
    "c = tf.sqrt(tf.add(tf.square(a), tf.square(b)))\n",
    "\n",
    "print(a, b, c)\n",
    "\n",
    "sess = tf.Session()\n",
    "print(*sess.run([a, b, c], feed_dict={a: 4., b: 3.}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.0 4.0 5.656854\n"
     ]
    }
   ],
   "source": [
    "print(*sess.run([a, b, c], feed_dict={a: 4., b: 4.}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.0 9.0 9.848858\n"
     ]
    }
   ],
   "source": [
    "print(*sess.run([a, b, c], feed_dict={a: 4., b: 9.}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "\n",
    "Differentiation\n",
    "\n",
    "Backpropagation is one of the most essential building blocks in deep learning. \n",
    "An optimal way of performing backpropagation is by using the computational graph to take\n",
    "the partial derivatives between variabels. This way we sum over the derivatives of the nodes in the graph.\n",
    "\n",
    "So let's consider a linear regression problem.\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "W = tf.Variable([.3], tf.float32)\n",
    "b = tf.Variable([-.3], tf.float32)\n",
    "x = tf.placeholder(tf.float32)\n",
    "linear_model = W * x + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "                                                                                                                                                                                    \n",
      "[2, 5]\n"
     ]
    }
   ],
   "source": [
    "## SOURCE --- https://jacobbuckman.com/post/tensorflow-the-confusing-parts-1/#fnref:1\n",
    "\n",
    "two_node = tf.constant(2)\n",
    "three_node = tf.constant(3)\n",
    "\n",
    "sum_node = two_node + three_node\n",
    "sess = tf.Session()\n",
    "print(sess.run(sum_node)) # 5 \n",
    "#\n",
    "print(\"  \"*90)\n",
    "#\n",
    "print(sess.run([two_node, sum_node]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "In general, sess.run() calls tend to be one of the biggest TensorFlow bottlenecks, \n",
    "so the fewer times you call it, the better. \n",
    "Whenever possible, return multiple items in a single sess.run() call instead of making multiple calls.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "InvalidArgumentError",
     "evalue": "You must feed a value for placeholder tensor 'Placeholder_2' with dtype int32\n\t [[Node: Placeholder_2 = Placeholder[dtype=DT_INT32, shape=<unknown>, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n\nCaused by op 'Placeholder_2', defined at:\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/__main__.py\", line 3, in <module>\n    app.launch_new_instance()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 505, in start\n    self.io_loop.start()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/platform/asyncio.py\", line 132, in start\n    self.asyncio_loop.run_forever()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/asyncio/base_events.py\", line 421, in run_forever\n    self._run_once()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/asyncio/base_events.py\", line 1425, in _run_once\n    handle._run()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/asyncio/events.py\", line 127, in _run\n    self._callback(*self._args)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/ioloop.py\", line 758, in _run_callback\n    ret = callback()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 1233, in inner\n    self.run()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 1147, in run\n    yielded = self.gen.send(value)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 357, in process_one\n    yield gen.maybe_future(dispatch(*args))\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 326, in wrapper\n    yielded = next(result)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 267, in dispatch_shell\n    yield gen.maybe_future(handler(stream, idents, msg))\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 326, in wrapper\n    yielded = next(result)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 534, in execute_request\n    user_expressions, allow_stdin,\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 326, in wrapper\n    yielded = next(result)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 294, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 536, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2819, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2845, in _run_cell\n    return runner(coro)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/async_helpers.py\", line 67, in _pseudo_sync_runner\n    coro.send(None)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 3020, in run_cell_async\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 3185, in run_ast_nodes\n    if (yield from self.run_code(code, result)):\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 3267, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-9-e6e5bcfa54ce>\", line 2, in <module>\n    input_placeholder = tf.placeholder(tf.int32)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py\", line 1680, in placeholder\n    return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 3141, in _placeholder\n    \"Placeholder\", dtype=dtype, shape=shape, name=name)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 3160, in create_op\n    op_def=op_def)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 1625, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n\nInvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_2' with dtype int32\n\t [[Node: Placeholder_2 = Placeholder[dtype=DT_INT32, shape=<unknown>, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1349\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1350\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1351\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1328\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1329\u001b[0;31m                                    status, run_metadata)\n\u001b[0m\u001b[1;32m   1330\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type_arg, value_arg, traceback_arg)\u001b[0m\n\u001b[1;32m    472\u001b[0m             \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\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--> 473\u001b[0;31m             c_api.TF_GetCode(self.status.status))\n\u001b[0m\u001b[1;32m    474\u001b[0m     \u001b[0;31m# Delete the underlying status object from memory otherwise it stays alive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: You must feed a value for placeholder tensor 'Placeholder_2' with dtype int32\n\t [[Node: Placeholder_2 = Placeholder[dtype=DT_INT32, shape=<unknown>, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-e6e5bcfa54ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0minput_placeholder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplaceholder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0msess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mprint\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[0minput_placeholder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    893\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 895\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    896\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    897\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~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1126\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   1127\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1128\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1129\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1130\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~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/client/session.py\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   1342\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1343\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1344\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1345\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1346\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~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1361\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   1362\u001b[0m           \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1363\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   1364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1365\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: You must feed a value for placeholder tensor 'Placeholder_2' with dtype int32\n\t [[Node: Placeholder_2 = Placeholder[dtype=DT_INT32, shape=<unknown>, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n\nCaused by op 'Placeholder_2', defined at:\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/__main__.py\", line 3, in <module>\n    app.launch_new_instance()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 505, in start\n    self.io_loop.start()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/platform/asyncio.py\", line 132, in start\n    self.asyncio_loop.run_forever()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/asyncio/base_events.py\", line 421, in run_forever\n    self._run_once()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/asyncio/base_events.py\", line 1425, in _run_once\n    handle._run()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/asyncio/events.py\", line 127, in _run\n    self._callback(*self._args)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/ioloop.py\", line 758, in _run_callback\n    ret = callback()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 1233, in inner\n    self.run()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 1147, in run\n    yielded = self.gen.send(value)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 357, in process_one\n    yield gen.maybe_future(dispatch(*args))\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 326, in wrapper\n    yielded = next(result)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 267, in dispatch_shell\n    yield gen.maybe_future(handler(stream, idents, msg))\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 326, in wrapper\n    yielded = next(result)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 534, in execute_request\n    user_expressions, allow_stdin,\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 326, in wrapper\n    yielded = next(result)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 294, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 536, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2819, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2845, in _run_cell\n    return runner(coro)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/async_helpers.py\", line 67, in _pseudo_sync_runner\n    coro.send(None)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 3020, in run_cell_async\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 3185, in run_ast_nodes\n    if (yield from self.run_code(code, result)):\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 3267, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-9-e6e5bcfa54ce>\", line 2, in <module>\n    input_placeholder = tf.placeholder(tf.int32)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py\", line 1680, in placeholder\n    return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 3141, in _placeholder\n    \"Placeholder\", dtype=dtype, shape=shape, name=name)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 3160, in create_op\n    op_def=op_def)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 1625, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n\nInvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_2' with dtype int32\n\t [[Node: Placeholder_2 = Placeholder[dtype=DT_INT32, shape=<unknown>, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n"
     ]
    }
   ],
   "source": [
    "###  A placeholder is a type of node that is designed to accept external input.\n",
    "input_placeholder = tf.placeholder(tf.int32)\n",
    "sess = tf.Session()\n",
    "print(sess.run(input_placeholder))\n",
    "\"\"\"\n",
    "To provide a value to a PLACEHOLDER, we use the feed_dict attribute of sess.run().\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"\n",
    "InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_2' with dtype int32\n",
    "\t [[Node: Placeholder_2 = Placeholder[dtype=DT_INT32, shape=<unknown>, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "input_placeholder = tf.placeholder(tf.int32)\n",
    "sess = tf.Session()\n",
    "print(sess.run(input_placeholder, feed_dict={input_placeholder: 2}))\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "feed_dict...... The DICTIONARY keys should be variables corresponding to placeholder nodes from \n",
    "the graph (which, as discussed earlier, really means pointers to placeholder nodes in the graph). \n",
    "The corresponding values are the data elements to assign to each placeholder – typically scalars or Numpy arrays.\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    },
    {
     "ename": "InvalidArgumentError",
     "evalue": "You must feed a value for placeholder tensor 'Placeholder_4' with dtype int32\n\t [[Node: Placeholder_4 = Placeholder[dtype=DT_INT32, shape=<unknown>, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n\nCaused by op 'Placeholder_4', defined at:\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/__main__.py\", line 3, in <module>\n    app.launch_new_instance()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 505, in start\n    self.io_loop.start()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/platform/asyncio.py\", line 132, in start\n    self.asyncio_loop.run_forever()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/asyncio/base_events.py\", line 421, in run_forever\n    self._run_once()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/asyncio/base_events.py\", line 1425, in _run_once\n    handle._run()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/asyncio/events.py\", line 127, in _run\n    self._callback(*self._args)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/ioloop.py\", line 758, in _run_callback\n    ret = callback()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 1233, in inner\n    self.run()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 1147, in run\n    yielded = self.gen.send(value)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 357, in process_one\n    yield gen.maybe_future(dispatch(*args))\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 326, in wrapper\n    yielded = next(result)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 267, in dispatch_shell\n    yield gen.maybe_future(handler(stream, idents, msg))\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 326, in wrapper\n    yielded = next(result)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 534, in execute_request\n    user_expressions, allow_stdin,\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 326, in wrapper\n    yielded = next(result)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 294, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 536, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2819, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2845, in _run_cell\n    return runner(coro)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/async_helpers.py\", line 67, in _pseudo_sync_runner\n    coro.send(None)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 3020, in run_cell_async\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 3185, in run_ast_nodes\n    if (yield from self.run_code(code, result)):\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 3267, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-11-88f4d021ac22>\", line 2, in <module>\n    input_placeholder = tf.placeholder(tf.int32)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py\", line 1680, in placeholder\n    return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 3141, in _placeholder\n    \"Placeholder\", dtype=dtype, shape=shape, name=name)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 3160, in create_op\n    op_def=op_def)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 1625, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n\nInvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_4' with dtype int32\n\t [[Node: Placeholder_4 = Placeholder[dtype=DT_INT32, shape=<unknown>, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1349\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1350\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1351\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1328\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1329\u001b[0;31m                                    status, run_metadata)\n\u001b[0m\u001b[1;32m   1330\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type_arg, value_arg, traceback_arg)\u001b[0m\n\u001b[1;32m    472\u001b[0m             \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\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--> 473\u001b[0;31m             c_api.TF_GetCode(self.status.status))\n\u001b[0m\u001b[1;32m    474\u001b[0m     \u001b[0;31m# Delete the underlying status object from memory otherwise it stays alive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: You must feed a value for placeholder tensor 'Placeholder_4' with dtype int32\n\t [[Node: Placeholder_4 = Placeholder[dtype=DT_INT32, shape=<unknown>, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-88f4d021ac22>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mprint\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[0mthree_node\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mprint\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[0msum_node\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    893\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 895\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    896\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    897\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~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1126\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   1127\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1128\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1129\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1130\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~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/client/session.py\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   1342\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1343\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1344\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1345\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1346\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~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1361\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   1362\u001b[0m           \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1363\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   1364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1365\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: You must feed a value for placeholder tensor 'Placeholder_4' with dtype int32\n\t [[Node: Placeholder_4 = Placeholder[dtype=DT_INT32, shape=<unknown>, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n\nCaused by op 'Placeholder_4', defined at:\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/__main__.py\", line 3, in <module>\n    app.launch_new_instance()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 505, in start\n    self.io_loop.start()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/platform/asyncio.py\", line 132, in start\n    self.asyncio_loop.run_forever()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/asyncio/base_events.py\", line 421, in run_forever\n    self._run_once()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/asyncio/base_events.py\", line 1425, in _run_once\n    handle._run()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/asyncio/events.py\", line 127, in _run\n    self._callback(*self._args)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/ioloop.py\", line 758, in _run_callback\n    ret = callback()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 1233, in inner\n    self.run()\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 1147, in run\n    yielded = self.gen.send(value)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 357, in process_one\n    yield gen.maybe_future(dispatch(*args))\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 326, in wrapper\n    yielded = next(result)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 267, in dispatch_shell\n    yield gen.maybe_future(handler(stream, idents, msg))\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 326, in wrapper\n    yielded = next(result)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 534, in execute_request\n    user_expressions, allow_stdin,\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tornado/gen.py\", line 326, in wrapper\n    yielded = next(result)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 294, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 536, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2819, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2845, in _run_cell\n    return runner(coro)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/async_helpers.py\", line 67, in _pseudo_sync_runner\n    coro.send(None)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 3020, in run_cell_async\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 3185, in run_ast_nodes\n    if (yield from self.run_code(code, result)):\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 3267, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-11-88f4d021ac22>\", line 2, in <module>\n    input_placeholder = tf.placeholder(tf.int32)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py\", line 1680, in placeholder\n    return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 3141, in _placeholder\n    \"Placeholder\", dtype=dtype, shape=shape, name=name)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 3160, in create_op\n    op_def=op_def)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 1625, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n\nInvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_4' with dtype int32\n\t [[Node: Placeholder_4 = Placeholder[dtype=DT_INT32, shape=<unknown>, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n"
     ]
    }
   ],
   "source": [
    "### Computation Paths\n",
    "\n",
    "input_placeholder = tf.placeholder(tf.int32)\n",
    "three_node = tf.constant(3)\n",
    "sum_node = input_placeholder + three_node\n",
    "sess = tf.Session()\n",
    "print(sess.run(three_node))\n",
    "print(sess.run(sum_node))\n",
    "\n",
    "\"\"\"\n",
    "InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_4' with dtype int32\n",
    "\t [[Node: Placeholder_4 = Placeholder[dtype=DT_INT32, shape=<unknown>, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "\n",
    "The fact that Tensorflow automatically routes computation only through nodes that are necessary \n",
    "is a huge strength of the framework. It saves a lot of runtime on calls if the graph is very big\n",
    "and has many nodes that are not necessary. \n",
    "It allows us to construct large, “multi-purpose” graphs, which use a single, shared set of core nodes\n",
    "to do different things depending on which computation path is taken.\n",
    "For almost every application, it’s important to think about sess.run() calls in terms of the computation \n",
    "path taken.\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Variables & Side Effects --- FATT \n",
    "\n",
    "\"\"\"\n",
    "So far, we’ve seen two types of “no-ancestor” nodes: tf.constant, which is the same for every run, \n",
    "and tf.placeholder, which is different for every run. \n",
    "\n",
    "There’s a third case that we often want to consider: a node which generally has the same value \n",
    "between runs, but can also be updated to have a new value. That’s where variables come in.\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"\n",
    "Understanding variables is essential to doing deep learning with Tensorflow, \n",
    "because the parameters of your model fall into this category. \n",
    "\n",
    "During training, you want to update your parameters at every step, via gradient descent; \n",
    "but during evaluation, you want to keep your parameters fixed, and pass a bunch of different \n",
    "test-set inputs into the model.\n",
    "\n",
    "More than likely, all of your model’s trainable parameters will\n",
    "be implemented as variables.\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "To create variables, use tf.get_variable().\n",
    "\n",
    "The first two arguments to tf.get_variable() are required; the rest are optional. \n",
    "They are tf.get_variable(name, shape). \n",
    "\n",
    "name is a string which uniquely identifies this variable object. \n",
    "It must be unique relative to the global graph, so be careful to keep track of all names you \n",
    "have used to ensure there are no duplicates.\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "shape is an array of integers corresponding to the shape of a tensor; \n",
    "the syntax of this is intuitive – just one integer per dimension, in order.\n",
    "\n",
    "For example, a 3x8 matrix would have shape [3, 8]. To create a scalar, use an empty list as your shape: [].\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "FailedPreconditionError",
     "evalue": "Attempting to use uninitialized value count\n\t [[Node: _retval_count_0_0 = _Retval[T=DT_FLOAT, index=0, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](count)]]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFailedPreconditionError\u001b[0m                   Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1349\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1350\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1351\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1328\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1329\u001b[0;31m                                    status, run_metadata)\n\u001b[0m\u001b[1;32m   1330\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type_arg, value_arg, traceback_arg)\u001b[0m\n\u001b[1;32m    472\u001b[0m             \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\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--> 473\u001b[0;31m             c_api.TF_GetCode(self.status.status))\n\u001b[0m\u001b[1;32m    474\u001b[0m     \u001b[0;31m# Delete the underlying status object from memory otherwise it stays alive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFailedPreconditionError\u001b[0m: Attempting to use uninitialized value count\n\t [[Node: _retval_count_0_0 = _Retval[T=DT_FLOAT, index=0, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](count)]]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mFailedPreconditionError\u001b[0m                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-185adff38bd7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mcount_variable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"count\"\u001b[0m\u001b[0;34m,\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[1;32m      4\u001b[0m \u001b[0msess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mprint\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[0mcount_variable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    893\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 895\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    896\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    897\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~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1126\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   1127\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1128\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1129\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1130\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~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/client/session.py\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   1342\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1343\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1344\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1345\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1346\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~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1361\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   1362\u001b[0m           \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1363\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   1364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1365\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;31mFailedPreconditionError\u001b[0m: Attempting to use uninitialized value count\n\t [[Node: _retval_count_0_0 = _Retval[T=DT_FLOAT, index=0, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](count)]]"
     ]
    }
   ],
   "source": [
    "## Create a VARIABLE == count_variable\n",
    "\n",
    "count_variable = tf.get_variable(\"count\", [])\n",
    "sess = tf.Session()\n",
    "print(sess.run(count_variable))\n",
    "#\n",
    "# FailedPreconditionError: Attempting to use uninitialized value count\n",
    "#\n",
    "\"\"\"\n",
    "FailedPreconditionError: Attempting to use uninitialized value count\n",
    "\t [[Node: _retval_count_0_0 = _Retval[T=DT_FLOAT, index=0, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](count)]]\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "When a variable node is first created, it basically stores “null”, and any attempts to evaluate it will \n",
    "result in this exception. \n",
    "\n",
    "We can only evaluate a variable after putting a value into it first. \n",
    "There are two main ways to put a value into a variable: initializers and tf.assign(). \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Variable count already exists, disallowed. Did you mean to set reuse=True or reuse=tf.AUTO_REUSE in VarScope? Originally defined at:\n\n  File \"<ipython-input-12-185adff38bd7>\", line 3, in <module>\n    count_variable = tf.get_variable(\"count\", [])\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 3267, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 3185, in run_ast_nodes\n    if (yield from self.run_code(code, result)):\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-c978890fabad>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcount_variable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"count\"\u001b[0m\u001b[0;34m,\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[0m\u001b[1;32m      2\u001b[0m \u001b[0mzero_node\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstant\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[1;32m      3\u001b[0m \u001b[0massign_node\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcount_variable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzero_node\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#sess = tf.Session() ## Dhank - Commented out - do we need to INIT Session everytime ?? NO.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0massign_node\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36mget_variable\u001b[0;34m(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint)\u001b[0m\n\u001b[1;32m   1260\u001b[0m       \u001b[0mpartitioner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpartitioner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1261\u001b[0m       \u001b[0muse_resource\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_resource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_getter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_getter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1262\u001b[0;31m       constraint=constraint)\n\u001b[0m\u001b[1;32m   1263\u001b[0m get_variable_or_local_docstring = (\n\u001b[1;32m   1264\u001b[0m     \"\"\"%s\n",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36mget_variable\u001b[0;34m(self, var_store, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint)\u001b[0m\n\u001b[1;32m   1095\u001b[0m           \u001b[0mpartitioner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpartitioner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1096\u001b[0m           \u001b[0muse_resource\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_resource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_getter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_getter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1097\u001b[0;31m           constraint=constraint)\n\u001b[0m\u001b[1;32m   1098\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1099\u001b[0m   def _get_partitioned_variable(self,\n",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36mget_variable\u001b[0;34m(self, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint)\u001b[0m\n\u001b[1;32m    433\u001b[0m           \u001b[0mcaching_device\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcaching_device\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpartitioner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpartitioner\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m           \u001b[0mvalidate_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_resource\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_resource\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 435\u001b[0;31m           constraint=constraint)\n\u001b[0m\u001b[1;32m    436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m   def _get_partitioned_variable(\n",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36m_true_getter\u001b[0;34m(name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, constraint)\u001b[0m\n\u001b[1;32m    402\u001b[0m           \u001b[0mtrainable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrainable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcollections\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    403\u001b[0m           \u001b[0mcaching_device\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcaching_device\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 404\u001b[0;31m           use_resource=use_resource, constraint=constraint)\n\u001b[0m\u001b[1;32m    405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcustom_getter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36m_get_single_variable\u001b[0;34m(self, name, shape, dtype, initializer, regularizer, partition_info, reuse, trainable, collections, caching_device, validate_shape, use_resource, constraint)\u001b[0m\n\u001b[1;32m    741\u001b[0m                          \u001b[0;34m\"reuse=tf.AUTO_REUSE in VarScope? \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    742\u001b[0m                          \"Originally defined at:\\n\\n%s\" % (\n\u001b[0;32m--> 743\u001b[0;31m                              name, \"\".join(traceback.format_list(tb))))\n\u001b[0m\u001b[1;32m    744\u001b[0m       \u001b[0mfound_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_vars\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    745\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_compatible_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfound_var\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_shape\u001b[0m\u001b[0;34m(\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;31mValueError\u001b[0m: Variable count already exists, disallowed. Did you mean to set reuse=True or reuse=tf.AUTO_REUSE in VarScope? Originally defined at:\n\n  File \"<ipython-input-12-185adff38bd7>\", line 3, in <module>\n    count_variable = tf.get_variable(\"count\", [])\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 3267, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"/home/dhankar/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 3185, in run_ast_nodes\n    if (yield from self.run_code(code, result)):\n"
     ]
    }
   ],
   "source": [
    "count_variable = tf.get_variable(\"count\", [])\n",
    "# DHANK - FATT --- from FILE --variable_scope.py---\n",
    "##  ~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py in _get_single_variable(self, name, shape, dtype, initializer, regularizer, partition_info, reuse, trainable, collections, caching_device, validate_shape, use_resource, constraint)\n",
    "# ValueError: Variable count already exists, disallowed. \n",
    "# Did you mean to set reuse=True or reuse=tf.AUTO_REUSE in VarScope? Originally defined at:\n",
    "\n",
    "zero_node = tf.constant(0.)\n",
    "assign_node = tf.assign(count_variable, zero_node)\n",
    "#sess = tf.Session() ## Dhank - Commented out - do we need to INIT Session everytime ?? NO. \n",
    "sess.run(assign_node)\n",
    "print(sess.run(count_variable))\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "#count_variable = tf.get_variable(\"count\", [])\n",
    "### if we restart the Notebook we get === NameError: name 'count_variable' is not defined\n",
    "# DHANK - FATT --- if we RE-RUN the JupyterNotebook CELL --- ValueError: Variable count already exists, disallowed. \n",
    "##Did you mean to set reuse=True or reuse=tf.AUTO_REUSE in VarScope? Originally defined at:\n",
    "\n",
    "zero_node = tf.constant(0.) ## Graph Node which is storing the CONSTANT value == ZERO. \n",
    "assign_node = tf.assign(count_variable, zero_node)\n",
    "sess = tf.Session() ## Dhank - Commented out - do we need to INIT Session everytime ?? NO. \n",
    "##### if we restart the Notebook we get === NameError: name 'sess' is not defined\n",
    "sess.run(assign_node)\n",
    "print(sess.run(count_variable))\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### FATT --- Gone to Notebook === tensorFlow_25NOV18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Using a `tf.Tensor` as a Python `bool` is not allowed. Use `if t is not None:` instead of `if t:` to test if a tensor is defined, and use TensorFlow ops such as tf.cond to execute subgraphs conditioned on the value of a tensor.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-66bb0a15d4c3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m7.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\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[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/ops/gen_math_ops.py\u001b[0m in \u001b[0;36madd\u001b[0;34m(x, y, name)\u001b[0m\n\u001b[1;32m    181\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0m_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0min_graph_mode\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[1;32m    182\u001b[0m     _, _, _op = _op_def_lib._apply_op_helper(\n\u001b[0;32m--> 183\u001b[0;31m         \"Add\", x=x, y=y, name=name)\n\u001b[0m\u001b[1;32m    184\u001b[0m     \u001b[0m_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\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[1;32m    185\u001b[0m     \u001b[0m_inputs_flat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py\u001b[0m in \u001b[0;36m_apply_op_helper\u001b[0;34m(self, op_type_name, name, **keywords)\u001b[0m\n\u001b[1;32m    392\u001b[0m     \u001b[0minputs\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[1;32m    393\u001b[0m     \u001b[0minput_types\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--> 394\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mscope\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    395\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m       \u001b[0;31m# Perform input type inference\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   5384\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_g_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5385\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name_scope\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5386\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name_scope\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5387\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5388\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__exit__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype_arg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue_arg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback_arg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/contextlib.py\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__enter__\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[1;32m     58\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"generator didn't yield\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mname_scope\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   3797\u001b[0m         \u001b[0mabove\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3798\u001b[0m     \"\"\"\n\u001b[0;32m-> 3799\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3800\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbytes_or_text_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3801\u001b[0m         \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/dc_info_venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m__bool__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    604\u001b[0m       \u001b[0;31m`\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    605\u001b[0m     \"\"\"\n\u001b[0;32m--> 606\u001b[0;31m     raise TypeError(\"Using a `tf.Tensor` as a Python `bool` is not allowed. \"\n\u001b[0m\u001b[1;32m    607\u001b[0m                     \u001b[0;34m\"Use `if t is not None:` instead of `if t:` to test if a \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    608\u001b[0m                     \u001b[0;34m\"tensor is defined, and use TensorFlow ops such as \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Using a `tf.Tensor` as a Python `bool` is not allowed. Use `if t is not None:` instead of `if t:` to test if a tensor is defined, and use TensorFlow ops such as tf.cond to execute subgraphs conditioned on the value of a tensor."
     ]
    }
   ],
   "source": [
    "### this cell FAILS - need to understand why ??\n",
    "\n",
    "a = tf.constant(3.0, tf.float32)\n",
    "b = tf.constant(4.0, tf.float32)\n",
    "z = tf.constant(7.0, tf.float32)\n",
    "\n",
    "c = tf.sqrt(tf.add(tf.square(a), tf.square(b),tf.square(z)))\n",
    "\n",
    "print(a,b,z,c)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Importing MNIST data set\n",
    "from keras.datasets import mnist\n",
    "\n",
    "# Assigning the data from the data set\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(60000, 28, 28)\n",
      "(10000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(type(X_train))\n",
    "print(X_train.shape)\n",
    "#\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000,)\n",
      "[5 0 4 1 9 2 1 3 1 4 3 5 3 6 1 7 2 8 6 9 4 0 9 1 1 2 4 3 2 7]\n",
      "[4 0 9 1 1 2 4 3 2 7]\n",
      "[5 0 4 ... 1 8 3]\n"
     ]
    }
   ],
   "source": [
    "# Print SHAPE of Labels\n",
    "print(y_train.shape)\n",
    "# Print First 30 labels\n",
    "print(y_train[0:30])\n",
    "# print 20-30 Labels\n",
    "print(y_train[20:30])\n",
    "# Last 1st to Last 3 Labels\n",
    "print(y_train[:-3])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x72 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Source == http://codewithmax.com/2018/03/06/basic-example-of-a-neural-network-with-tensorflow-and-keras/\n",
    "# https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html\n",
    "\n",
    "# Defining the figure for plotting, using 'xticks':[] and 'yticks':[]\n",
    "# to avoid the ticks on axes\n",
    "fig, axes = plt.subplots(1, 10, figsize=(10, 1),\n",
    "                         subplot_kw={'xticks':[], 'yticks':[]},\n",
    "                         gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
    "\n",
    "# Looping through the images and their corresponding labels\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(X_train[i], cmap='binary')\n",
    "    ax.text(0.05, 0.05, str(y_train[i]), transform=ax.transAxes, color='orangered')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 1 1]\n",
      " [2 2 2]\n",
      " [3 3 3]]\n"
     ]
    }
   ],
   "source": [
    "### Numpy BroadCasting ADDITION \n",
    "\n",
    "import numpy as np\n",
    "\n",
    "a = np.array([1,1,1])\n",
    "b = np.array([[0,0,0],[1,1,1],[2,2,2]])\n",
    "\n",
    "c= a + b \n",
    "\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (7,) (10,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-e757595d4c43>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0ma\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[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mb\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[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (7,) (10,) "
     ]
    }
   ],
   "source": [
    "### Numpy BroadCasting ADDITION \n",
    "import numpy as np\n",
    "\n",
    "a = np.array([1,2,3,4,5,6,7])\n",
    "b = np.array([1,2,3,4,5,6,7,8,9,10])\n",
    "c= a + b \n",
    "## Above -- ValueError: operands could not be broadcast together with shapes (7,) (10,) \n",
    "print(c)"
   ]
  }
 ],
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