2_1.Tensorflow_Basic_More.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3.0\n"
     ]
    }
   ],
   "source": [
    "print (tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = tf.constant(2, name='a')\n",
    "b = tf.constant(3, name='b')\n",
    "x = tf.add(a, b, name='add')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "writer = tf.summary.FileWriter('./graphs', tf.get_default_graph())\n",
    "with tf.Session() as sess:\n",
    "    print(sess.run(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#tensorboard --logdir=\"./graphs\" --port 6100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import clear_output, Image, display, HTML\n",
    "\n",
    "def strip_consts(graph_def, max_const_size=32):\n",
    "    \"\"\"Strip large constant values from graph_def.\"\"\"\n",
    "    strip_def = tf.GraphDef()\n",
    "    for n0 in graph_def.node:\n",
    "        n = strip_def.node.add() \n",
    "        n.MergeFrom(n0)\n",
    "        if n.op == 'Const':\n",
    "            tensor = n.attr['value'].tensor\n",
    "            size = len(tensor.tensor_content)\n",
    "            if size > max_const_size:\n",
    "                tensor.tensor_content = \"<stripped %d bytes>\"%size\n",
    "    return strip_def\n",
    "\n",
    "def show_graph(graph_def, max_const_size=32):\n",
    "    \"\"\"Visualize TensorFlow graph.\"\"\"\n",
    "    if hasattr(graph_def, 'as_graph_def'):\n",
    "        graph_def = graph_def.as_graph_def()\n",
    "    strip_def = strip_consts(graph_def, max_const_size=max_const_size)\n",
    "    code = \"\"\"\n",
    "        <script>\n",
    "          function load() {{\n",
    "            document.getElementById(\"{id}\").pbtxt = {data};\n",
    "          }}\n",
    "        </script>\n",
    "        <link rel=\"import\" href=\"https://tensorboard.appspot.com/tf-graph-basic.build.html\" onload=load()>\n",
    "        <div style=\"height:600px\">\n",
    "          <tf-graph-basic id=\"{id}\"></tf-graph-basic>\n",
    "        </div>\n",
    "    \"\"\".format(data=repr(str(strip_def)), id='graph'+str(np.random.rand()))\n",
    "\n",
    "    iframe = \"\"\"\n",
    "        <iframe seamless style=\"width:1200px;height:620px;border:0\" srcdoc=\"{}\"></iframe>\n",
    "    \"\"\".format(code.replace('\"', '&quot;'))\n",
    "    display(HTML(iframe))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe seamless style=\"width:1200px;height:620px;border:0\" srcdoc=\"\n",
       "        <script>\n",
       "          function load() {\n",
       "            document.getElementById(&quot;graph0.7607954656788475&quot;).pbtxt = 'node {\\n  name: &quot;a&quot;\\n  op: &quot;Const&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_INT32\\n    }\\n  }\\n  attr {\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_INT32\\n        tensor_shape {\\n        }\\n        int_val: 2\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;b&quot;\\n  op: &quot;Const&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_INT32\\n    }\\n  }\\n  attr {\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_INT32\\n        tensor_shape {\\n        }\\n        int_val: 3\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;add&quot;\\n  op: &quot;Add&quot;\\n  input: &quot;a&quot;\\n  input: &quot;b&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_INT32\\n    }\\n  }\\n}\\n';\n",
       "          }\n",
       "        </script>\n",
       "        <link rel=&quot;import&quot; href=&quot;https://tensorboard.appspot.com/tf-graph-basic.build.html&quot; onload=load()>\n",
       "        <div style=&quot;height:600px&quot;>\n",
       "          <tf-graph-basic id=&quot;graph0.7607954656788475&quot;></tf-graph-basic>\n",
       "        </div>\n",
       "    \"></iframe>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_graph(tf.get_default_graph().as_graph_def())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "node {\n",
      "  name: \"a\"\n",
      "  op: \"Const\"\n",
      "  attr {\n",
      "    key: \"dtype\"\n",
      "    value {\n",
      "      type: DT_INT32\n",
      "    }\n",
      "  }\n",
      "  attr {\n",
      "    key: \"value\"\n",
      "    value {\n",
      "      tensor {\n",
      "        dtype: DT_INT32\n",
      "        tensor_shape {\n",
      "        }\n",
      "        int_val: 2\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "node {\n",
      "  name: \"b\"\n",
      "  op: \"Const\"\n",
      "  attr {\n",
      "    key: \"dtype\"\n",
      "    value {\n",
      "      type: DT_INT32\n",
      "    }\n",
      "  }\n",
      "  attr {\n",
      "    key: \"value\"\n",
      "    value {\n",
      "      tensor {\n",
      "        dtype: DT_INT32\n",
      "        tensor_shape {\n",
      "        }\n",
      "        int_val: 3\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "node {\n",
      "  name: \"add\"\n",
      "  op: \"Add\"\n",
      "  input: \"a\"\n",
      "  input: \"b\"\n",
      "  attr {\n",
      "    key: \"T\"\n",
      "    value {\n",
      "      type: DT_INT32\n",
      "    }\n",
      "  }\n",
      "}\n",
      "versions {\n",
      "  producer: 24\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(tf.get_default_graph().as_graph_def())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More about tf.constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 2]\n",
      " [4 6]]\n"
     ]
    }
   ],
   "source": [
    "a = tf.constant([2, 2], name='a')\n",
    "b = tf.constant([[0, 1], [2, 3]], name='b')\n",
    "x = tf.multiply(a, b, name='mul')\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    print(sess.run(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe seamless style=\"width:1200px;height:620px;border:0\" srcdoc=\"\n",
       "        <script>\n",
       "          function load() {\n",
       "            document.getElementById(&quot;graph0.6002228782285566&quot;).pbtxt = 'node {\\n  name: &quot;my_const&quot;\\n  op: &quot;Const&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_FLOAT\\n        tensor_shape {\\n          dim {\\n            size: 2\\n          }\\n        }\\n        tensor_content: &quot;\\\\000\\\\000\\\\200?\\\\000\\\\000\\\\000@&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;a&quot;\\n  op: &quot;Const&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_INT32\\n    }\\n  }\\n  attr {\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_INT32\\n        tensor_shape {\\n          dim {\\n            size: 2\\n          }\\n        }\\n        tensor_content: &quot;\\\\002\\\\000\\\\000\\\\000\\\\002\\\\000\\\\000\\\\000&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;b&quot;\\n  op: &quot;Const&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_INT32\\n    }\\n  }\\n  attr {\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_INT32\\n        tensor_shape {\\n          dim {\\n            size: 2\\n          }\\n          dim {\\n            size: 2\\n          }\\n        }\\n        tensor_content: &quot;\\\\000\\\\000\\\\000\\\\000\\\\001\\\\000\\\\000\\\\000\\\\002\\\\000\\\\000\\\\000\\\\003\\\\000\\\\000\\\\000&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;mul&quot;\\n  op: &quot;Mul&quot;\\n  input: &quot;a&quot;\\n  input: &quot;b&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_INT32\\n    }\\n  }\\n}\\n';\n",
       "          }\n",
       "        </script>\n",
       "        <link rel=&quot;import&quot; href=&quot;https://tensorboard.appspot.com/tf-graph-basic.build.html&quot; onload=load()>\n",
       "        <div style=&quot;height:600px&quot;>\n",
       "          <tf-graph-basic id=&quot;graph0.6002228782285566&quot;></tf-graph-basic>\n",
       "        </div>\n",
       "    \"></iframe>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_graph(tf.get_default_graph().as_graph_def())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "node {\n",
      "  name: \"my_const\"\n",
      "  op: \"Const\"\n",
      "  attr {\n",
      "    key: \"dtype\"\n",
      "    value {\n",
      "      type: DT_FLOAT\n",
      "    }\n",
      "  }\n",
      "  attr {\n",
      "    key: \"value\"\n",
      "    value {\n",
      "      tensor {\n",
      "        dtype: DT_FLOAT\n",
      "        tensor_shape {\n",
      "          dim {\n",
      "            size: 2\n",
      "          }\n",
      "        }\n",
      "        tensor_content: \"\\000\\000\\200?\\000\\000\\000@\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "node {\n",
      "  name: \"a\"\n",
      "  op: \"Const\"\n",
      "  attr {\n",
      "    key: \"dtype\"\n",
      "    value {\n",
      "      type: DT_INT32\n",
      "    }\n",
      "  }\n",
      "  attr {\n",
      "    key: \"value\"\n",
      "    value {\n",
      "      tensor {\n",
      "        dtype: DT_INT32\n",
      "        tensor_shape {\n",
      "          dim {\n",
      "            size: 2\n",
      "          }\n",
      "        }\n",
      "        tensor_content: \"\\002\\000\\000\\000\\002\\000\\000\\000\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "node {\n",
      "  name: \"b\"\n",
      "  op: \"Const\"\n",
      "  attr {\n",
      "    key: \"dtype\"\n",
      "    value {\n",
      "      type: DT_INT32\n",
      "    }\n",
      "  }\n",
      "  attr {\n",
      "    key: \"value\"\n",
      "    value {\n",
      "      tensor {\n",
      "        dtype: DT_INT32\n",
      "        tensor_shape {\n",
      "          dim {\n",
      "            size: 2\n",
      "          }\n",
      "          dim {\n",
      "            size: 2\n",
      "          }\n",
      "        }\n",
      "        tensor_content: \"\\000\\000\\000\\000\\001\\000\\000\\000\\002\\000\\000\\000\\003\\000\\000\\000\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "node {\n",
      "  name: \"mul\"\n",
      "  op: \"Mul\"\n",
      "  input: \"a\"\n",
      "  input: \"b\"\n",
      "  attr {\n",
      "    key: \"T\"\n",
      "    value {\n",
      "      type: DT_INT32\n",
      "    }\n",
      "  }\n",
      "}\n",
      "node {\n",
      "  name: \"my_const_1\"\n",
      "  op: \"Const\"\n",
      "  attr {\n",
      "    key: \"dtype\"\n",
      "    value {\n",
      "      type: DT_FLOAT\n",
      "    }\n",
      "  }\n",
      "  attr {\n",
      "    key: \"value\"\n",
      "    value {\n",
      "      tensor {\n",
      "        dtype: DT_FLOAT\n",
      "        tensor_shape {\n",
      "          dim {\n",
      "            size: 2\n",
      "          }\n",
      "        }\n",
      "        tensor_content: \"\\000\\000\\200?\\000\\000\\000@\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "versions {\n",
      "  producer: 24\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "my_const = tf.constant([1.0, 2.0], name='my_const')\n",
    "print(tf.get_default_graph().as_graph_def())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.32474852 -0.81394601 -0.91790313 ..., -1.08966351 -0.68882     1.00975597]\n",
      " [-0.18972902  0.55317867 -0.18734248 ...,  0.91431129  1.0509243\n",
      "   0.20342256]\n",
      " [-0.628699    1.03223825 -0.19864014 ..., -0.65045613  0.0114994\n",
      "   1.4502548 ]\n",
      " ..., \n",
      " [-0.63450354  0.00245895 -1.63297307 ..., -0.86257714  0.04046192\n",
      "   0.84565622]\n",
      " [-0.20935765  1.41112196 -1.06333303 ..., -0.81004685 -0.53228247\n",
      "   0.52511793]\n",
      " [ 0.03027365 -0.99078512  0.75863963 ..., -1.18009186  0.77311212\n",
      "  -0.2369678 ]]\n"
     ]
    }
   ],
   "source": [
    "# Example 1: creating variables\n",
    "s = tf.Variable(2, name='scalar') \n",
    "m = tf.Variable([[0, 1], [2, 3]], name='matrix') \n",
    "W = tf.Variable(tf.zeros([784,10]), name='big_matrix')\n",
    "V = tf.Variable(tf.truncated_normal([784, 10]), name='normal_matrix')\n",
    "\n",
    "s = tf.get_variable('scalar', initializer=tf.constant(2)) \n",
    "m = tf.get_variable('matrix', initializer=tf.constant([[0, 1], [2, 3]]))\n",
    "W = tf.get_variable('big_matrix', shape=(784, 10), initializer=tf.zeros_initializer())\n",
    "V = tf.get_variable('normal_matrix', shape=(784, 10), initializer=tf.truncated_normal_initializer())\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    print(V.eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "# Example 2: assigning values to variables\n",
    "W = tf.Variable(10)\n",
    "W.assign(100)\n",
    "with tf.Session() as sess:\n",
    "    sess.run(W.initializer)\n",
    "    print(sess.run(W))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    }
   ],
   "source": [
    "W = tf.Variable(10)\n",
    "assign_op = W.assign(100)\n",
    "with tf.Session() as sess:\n",
    "    sess.run(assign_op)\n",
    "    print(W.eval())    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "var_2:0\n",
      "[-0.0092819  -0.03676274 -0.06512789]\n"
     ]
    }
   ],
   "source": [
    "# variable will be initialized with normal distribution\n",
    "var = tf.Variable(tf.random_normal([3], stddev=0.1), name='var')\n",
    "print (var.name)\n",
    "tf.global_variables_initializer().run()\n",
    "print (var.eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "my_var:0\n",
      "my_var_1:0\n"
     ]
    }
   ],
   "source": [
    "var2 = tf.Variable(tf.random_normal([2, 3], stddev=0.1), name='my_var')\n",
    "var3 = tf.Variable(tf.random_normal([2, 3], stddev=0.1), name='my_var')\n",
    "print (var2.name)\n",
    "print (var3.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "var:0\n",
      "var_1:0\n",
      "var_2:0\n",
      "my_var:0\n",
      "my_var_1:0\n"
     ]
    }
   ],
   "source": [
    "for var in tf.global_variables():\n",
    "    print (var.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "function"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(tf.get_variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "function"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(tf.global_variables_initializer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Variable v already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:\n\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1204, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 2630, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 767, in apply_op\n    op_def=op_def)\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-31-49370462cc7f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmy_initializer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom_normal_initializer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstddev\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_variable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'v'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minitializer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmy_initializer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mglobal_variables_initializer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\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)\u001b[0m\n\u001b[1;32m   1063\u001b[0m       \u001b[0mcollections\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcollections\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcaching_device\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcaching_device\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1064\u001b[0m       \u001b[0mpartitioner\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpartitioner\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidate_shape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1065\u001b[0;31m       use_resource=use_resource, custom_getter=custom_getter)\n\u001b[0m\u001b[1;32m   1066\u001b[0m get_variable_or_local_docstring = (\n\u001b[1;32m   1067\u001b[0m     \"\"\"%s\n",
      "\u001b[0;32mC:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\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)\u001b[0m\n\u001b[1;32m    960\u001b[0m           \u001b[0mcollections\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcollections\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcaching_device\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcaching_device\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    961\u001b[0m           \u001b[0mpartitioner\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpartitioner\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidate_shape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 962\u001b[0;31m           use_resource=use_resource, custom_getter=custom_getter)\n\u001b[0m\u001b[1;32m    963\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m   def _get_partitioned_variable(self,\n",
      "\u001b[0;32mC:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\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)\u001b[0m\n\u001b[1;32m    365\u001b[0m           \u001b[0mreuse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreuse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrainable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrainable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcollections\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcollections\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m           \u001b[0mcaching_device\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcaching_device\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpartitioner\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpartitioner\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 367\u001b[0;31m           validate_shape=validate_shape, use_resource=use_resource)\n\u001b[0m\u001b[1;32m    368\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m   def _get_partitioned_variable(\n",
      "\u001b[0;32mC:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\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)\u001b[0m\n\u001b[1;32m    350\u001b[0m           \u001b[0mtrainable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrainable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcollections\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcollections\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m           \u001b[0mcaching_device\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcaching_device\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidate_shape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 352\u001b[0;31m           use_resource=use_resource)\n\u001b[0m\u001b[1;32m    353\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcustom_getter\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\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)\u001b[0m\n\u001b[1;32m    662\u001b[0m                          \u001b[1;34m\" Did you mean to set reuse=True in VarScope? \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    663\u001b[0m                          \"Originally defined at:\\n\\n%s\" % (\n\u001b[0;32m--> 664\u001b[0;31m                              name, \"\".join(traceback.format_list(tb))))\n\u001b[0m\u001b[1;32m    665\u001b[0m       \u001b[0mfound_var\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_vars\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    666\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_compatible_with\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfound_var\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_shape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Variable v already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:\n\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1204, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 2630, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 767, in apply_op\n    op_def=op_def)\n"
     ]
    }
   ],
   "source": [
    "my_initializer = tf.random_normal_initializer(mean=0, stddev=0.1)\n",
    "v = tf.get_variable('v', shape=[2, 3], initializer=my_initializer)\n",
    "tf.global_variables_initializer.run()\n",
    "print (v.eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer1/v:0\n",
      "layer2/v:0\n"
     ]
    }
   ],
   "source": [
    "with tf.variable_scope('layer1'):\n",
    "    w = tf.get_variable('v', shape=[2, 3], initializer=my_initializer)\n",
    "    print (w.name)\n",
    "    \n",
    "with tf.variable_scope('layer2'):\n",
    "    w = tf.get_variable('v', shape=[2, 3], initializer=my_initializer)\n",
    "    print (w.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer1/v:0\n",
      "layer1/v:0\n"
     ]
    }
   ],
   "source": [
    "with tf.variable_scope('layer1', reuse=True):\n",
    "    w = tf.get_variable('v')   # Unlike above, we don't need to specify shape and initializer \n",
    "    print (w.name)\n",
    "\n",
    "# or\n",
    "\n",
    "with tf.variable_scope('layer1') as scope:\n",
    "    scope.reuse_variables()\n",
    "    w = tf.get_variable('v')\n",
    "    print (w.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Placeholders and feed_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "InvalidArgumentError",
     "evalue": "You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [3]\n\t [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[3], _device=\"/job:localhost/replica:0/task:0/gpu:0\"]()]]\n\t [[Node: add/_1 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device_incarnation=1, tensor_name=\"edge_8_add\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\n\nCaused by op 'Placeholder', defined at:\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\runpy.py\", line 184, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\ipykernel\\__main__.py\", line 3, in <module>\n    app.launch_new_instance()\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n    app.start()\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 474, in start\n    ioloop.IOLoop.instance().start()\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\zmq\\eventloop\\ioloop.py\", line 177, in start\n    super(ZMQIOLoop, self).start()\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tornado\\ioloop.py\", line 887, in start\n    handler_func(fd_obj, events)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tornado\\stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tornado\\stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 276, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 228, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 390, in execute_request\n    user_expressions, allow_stdin)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 501, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2717, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2821, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2881, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-41-7218b3042dca>\", line 2, in <module>\n    a = tf.placeholder(tf.float32, shape=[3])\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tensorflow\\python\\ops\\array_ops.py\", line 1548, in placeholder\n    return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tensorflow\\python\\ops\\gen_array_ops.py\", line 2094, in _placeholder\n    name=name)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 767, in apply_op\n    op_def=op_def)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 2630, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1204, 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' with dtype float and shape [3]\n\t [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[3], _device=\"/job:localhost/replica:0/task:0/gpu:0\"]()]]\n\t [[Node: add/_1 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device_incarnation=1, tensor_name=\"edge_8_add\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32mC:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\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   1326\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1327\u001b[0;31m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1328\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\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   1305\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1306\u001b[0;31m                                    status, run_metadata)\n\u001b[0m\u001b[1;32m   1307\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m                 \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tensorflow\\python\\framework\\errors_impl.py\u001b[0m in \u001b[0;36mraise_exception_on_not_ok_status\u001b[0;34m()\u001b[0m\n\u001b[1;32m    465\u001b[0m           \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpywrap_tensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 466\u001b[0;31m           pywrap_tensorflow.TF_GetCode(status))\n\u001b[0m\u001b[1;32m    467\u001b[0m   \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [3]\n\t [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[3], _device=\"/job:localhost/replica:0/task:0/gpu:0\"]()]]\n\t [[Node: add/_1 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device_incarnation=1, tensor_name=\"edge_8_add\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/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-41-7218b3042dca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msess\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[1;31m# compute the value of c given the value of a is [1, 2, 3]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m                 \u001b[1;31m# [6. 7. 8.]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0mwriter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\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[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\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[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\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   1122\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1124\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1125\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\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   1319\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1320\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1321\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1322\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1323\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\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   1338\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1339\u001b[0m           \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1340\u001b[0;31m       \u001b[1;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1341\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1342\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [3]\n\t [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[3], _device=\"/job:localhost/replica:0/task:0/gpu:0\"]()]]\n\t [[Node: add/_1 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device_incarnation=1, tensor_name=\"edge_8_add\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\n\nCaused by op 'Placeholder', defined at:\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\runpy.py\", line 184, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\ipykernel\\__main__.py\", line 3, in <module>\n    app.launch_new_instance()\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n    app.start()\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 474, in start\n    ioloop.IOLoop.instance().start()\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\zmq\\eventloop\\ioloop.py\", line 177, in start\n    super(ZMQIOLoop, self).start()\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tornado\\ioloop.py\", line 887, in start\n    handler_func(fd_obj, events)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tornado\\stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tornado\\stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 276, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 228, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 390, in execute_request\n    user_expressions, allow_stdin)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 501, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2717, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2821, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2881, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-41-7218b3042dca>\", line 2, in <module>\n    a = tf.placeholder(tf.float32, shape=[3])\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tensorflow\\python\\ops\\array_ops.py\", line 1548, in placeholder\n    return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tensorflow\\python\\ops\\gen_array_ops.py\", line 2094, in _placeholder\n    name=name)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 767, in apply_op\n    op_def=op_def)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 2630, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"C:\\Users\\jwlee\\Anaconda3\\envs\\tensorflow35\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1204, 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' with dtype float and shape [3]\n\t [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[3], _device=\"/job:localhost/replica:0/task:0/gpu:0\"]()]]\n\t [[Node: add/_1 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device_incarnation=1, tensor_name=\"edge_8_add\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\n"
     ]
    }
   ],
   "source": [
    "# a is a placeholderfor a vector of 3 elements, type tf.float32\n",
    "a = tf.placeholder(tf.float32, shape=[3])\n",
    "b = tf.constant([5, 5, 5], tf.float32)\n",
    "\n",
    "# use the placeholder as you would a constant\n",
    "c = a + b  # short for tf.add(a, b)\n",
    "\n",
    "writer = tf.summary.FileWriter('graphs/placeholders', tf.get_default_graph())\n",
    "with tf.Session() as sess:\n",
    "    # compute the value of c given the value of a is [1, 2, 3]\n",
    "    print(sess.run(c))                 # [6. 7. 8.]\n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 6.  7.  8.]\n"
     ]
    }
   ],
   "source": [
    "# a is a placeholderfor a vector of 3 elements, type tf.float32\n",
    "a = tf.placeholder(tf.float32, shape=[3])\n",
    "b = tf.constant([5, 5, 5], tf.float32)\n",
    "\n",
    "# use the placeholder as you would a constant\n",
    "c = a + b  # short for tf.add(a, b)\n",
    "\n",
    "writer = tf.summary.FileWriter('graphs/placeholders', tf.get_default_graph())\n",
    "with tf.Session() as sess:\n",
    "    # compute the value of c given the value of a is [1, 2, 3]\n",
    "    print(sess.run(c, {a: [1, 2, 3]}))                 # [6. 7. 8.]\n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n",
      "45\n"
     ]
    }
   ],
   "source": [
    "a = tf.add(2, 5)\n",
    "b = tf.multiply(a, 3)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    print(sess.run(b))                                 # >> 21\n",
    "    # compute the value of b given the value of a is 15\n",
    "    print(sess.run(b, feed_dict={a: 15}))              # >> 45"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lazy Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
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   "source": [
    "tf.reset_default_graph()"
   ]
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      "node {\n",
      "  name: \"x/initial_value\"\n",
      "  op: \"Const\"\n",
      "  attr {\n",
      "    key: \"dtype\"\n",
      "    value {\n",
      "      type: DT_INT32\n",
      "    }\n",
      "  }\n",
      "  attr {\n",
      "    key: \"value\"\n",
      "    value {\n",
      "      tensor {\n",
      "        dtype: DT_INT32\n",
      "        tensor_shape {\n",
      "        }\n",
      "        int_val: 10\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "node {\n",
      "  name: \"x\"\n",
      "  op: \"VariableV2\"\n",
      "  attr {\n",
      "    key: \"container\"\n",
      "    value {\n",
      "      s: \"\"\n",
      "    }\n",
      "  }\n",
      "  attr {\n",
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      "  attr {\n",
      "    key: \"shape\"\n",
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      "    }\n",
      "  }\n",
      "  attr {\n",
      "    key: \"shared_name\"\n",
      "    value {\n",
      "      s: \"\"\n",
      "    }\n",
      "  }\n",
      "}\n",
      "node {\n",
      "  name: \"x/Assign\"\n",
      "  op: \"Assign\"\n",
      "  input: \"x\"\n",
      "  input: \"x/initial_value\"\n",
      "  attr {\n",
      "    key: \"T\"\n",
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      "  attr {\n",
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      "  attr {\n",
      "    key: \"use_locking\"\n",
      "    value {\n",
      "      b: true\n",
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      "  attr {\n",
      "    key: \"validate_shape\"\n",
      "    value {\n",
      "      b: true\n",
      "    }\n",
      "  }\n",
      "}\n",
      "node {\n",
      "  name: \"x/read\"\n",
      "  op: \"Identity\"\n",
      "  input: \"x\"\n",
      "  attr {\n",
      "    key: \"T\"\n",
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      "        s: \"loc:@x\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "node {\n",
      "  name: \"y/initial_value\"\n",
      "  op: \"Const\"\n",
      "  attr {\n",
      "    key: \"dtype\"\n",
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      "  attr {\n",
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      "node {\n",
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      "  op: \"Assign\"\n",
      "  input: \"y\"\n",
      "  input: \"y/initial_value\"\n",
      "  attr {\n",
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      "  attr {\n",
      "    key: \"use_locking\"\n",
      "    value {\n",
      "      b: true\n",
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      "  attr {\n",
      "    key: \"validate_shape\"\n",
      "    value {\n",
      "      b: true\n",
      "    }\n",
      "  }\n",
      "}\n",
      "node {\n",
      "  name: \"y/read\"\n",
      "  op: \"Identity\"\n",
      "  input: \"y\"\n",
      "  attr {\n",
      "    key: \"T\"\n",
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      "      list {\n",
      "        s: \"loc:@y\"\n",
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      "  }\n",
      "}\n",
      "node {\n",
      "  name: \"Add\"\n",
      "  op: \"Add\"\n",
      "  input: \"x/read\"\n",
      "  input: \"y/read\"\n",
      "  attr {\n",
      "    key: \"T\"\n",
      "    value {\n",
      "      type: DT_INT32\n",
      "    }\n",
      "  }\n",
      "}\n",
      "node {\n",
      "  name: \"init\"\n",
      "  op: \"NoOp\"\n",
      "  input: \"^x/Assign\"\n",
      "  input: \"^y/Assign\"\n",
      "}\n",
      "versions {\n",
      "  producer: 24\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## Normal Loading\n",
    "x = tf.Variable(10, name='x')\n",
    "y = tf.Variable(20, name='y')\n",
    "z = tf.add(x, y)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    writer = tf.summary.FileWriter('graphs/normal_loading', sess.graph)\n",
    "    for _ in range(10):\n",
    "        sess.run(z)\n",
    "    print(tf.get_default_graph().as_graph_def())\n",
    "    writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Lazy Loading\n",
    "x = tf.Variable(10, name='x')\n",
    "y = tf.Variable(20, name='y')\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    writer = tf.summary.FileWriter('graphs/lazy_loading', sess.graph)\n",
    "    for _ in range(10):\n",
    "\t\tsess.run(tf.add(x, y))\n",
    "\tprint(tf.get_default_graph().as_graph_def()) \n",
    "\twriter.close()"
   ]
  }
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