Tensorflow 1.13.1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "<b><font size=100>TENSORFLOW</font></b>\n",
    "<br>\n",
    "<br>\n",
    "By अंkur गोswami\n",
    "____"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____\n",
    "# Tensorflow Version:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.13.1'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# Constants:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Constants:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.constant()\n",
    "**`tf.constant()`** takes a value and creates a constant of the value given."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "name=tf.constant('Ankur Goswami')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`print(name)`** doesn't return the value. But it returns type of data the constant holds & it's shape(in case of matrices).<br>\n",
    "Below is an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Const:0\", shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "print(name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# Sessions:\n",
    "Sessions are created and executed to do the actual computation. In tensorflow, creating constants/variables and applying operations doesn't actually compute those operations on them. We need to create and run sessions for them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Sessions:\n",
    "Sessions are created just by instantiating **`Session`** class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess=tf.Session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Executing Sessions:\n",
    "Sessions are executed using **`.run()`** method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'Ankur Goswami'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Terminating Sessions:\n",
    "Sessions can be terminated using **`.close()`** method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Note__: We can also use **`with-as`** python keyword to execute sessions. This avoids writing a terminating session statement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# Tensors\n",
    "A tensor is a generalization of vectors and matrices to potentially higher dimensions. Internally, TensorFlow represents tensors as n-dimensional arrays of base datatypes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Tensors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess=tf.Session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.fill()\n",
    "**``tf.fill()``** takes two arguments. One being the shape(tuple) of the tensor and other being the value. This functions creates a tensor of shape given, with all values equal to the value provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 10, 10, 10, 10],\n",
       "       [10, 10, 10, 10, 10],\n",
       "       [10, 10, 10, 10, 10]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(tf.fill((3,5),10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.zeros()\n",
    "**``tf.zeros()``** takes shape(tuple) as its argument. This functions creates a tensor of shape given, with all values equal to the 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0.],\n",
       "       [0., 0.],\n",
       "       [0., 0.],\n",
       "       [0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(tf.zeros((4,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.ones()\n",
    "**``tf.ones()``** takes shape(tuple) as its argument. This functions creates a tensor of shape given, with all values equal to the 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [1., 1.]], dtype=float32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(tf.ones((2,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.random_normal()\n",
    "**`tf.random_normal()`** takes shape(tuple) as its argument. In addition, it also takes **`mean`** and **`standard deviation`**.  This functions creates a tensor of shape given, with random values normally distributed having given mean & standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7.278775 ,  4.7681856,  6.584076 ,  8.958192 , 10.119011 ],\n",
       "       [ 9.529356 ,  3.311645 ,  5.220321 ,  5.740642 ,  7.057202 ],\n",
       "       [ 7.274617 ,  6.412084 ,  4.7838373, 11.256926 ,  4.960513 ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(tf.random_normal((3,5),mean=6,stddev=3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.random_uniform()\n",
    "**`tf.random_uniform()`** takes shape(tuple) as its argument. In addition, it also takes **`minimum value`** and **`maximum value`**.  This functions creates a tensor of shape given, with random values uniformly distributed from given minimum value to given maximum value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9.752785  ,  6.934698  , -6.720121  , -5.8778644 , -0.8086567 ],\n",
       "       [-0.57055664, -8.168974  ,  5.078869  ,  0.511179  ,  2.9104729 ],\n",
       "       [-3.6444902 , -5.0551987 ,  3.7317133 ,  4.796791  ,  1.7103024 ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(tf.random_uniform((3,5),minval=-10,maxval=10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.random_poisson()\n",
    "**`tf.random_uniform()`** takes shape(tuple) & **`lambda`** as its argument. This functions creates a tensor of shape given, with random values following poisson distribution with given lambda."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7., 3., 3., 3., 5.],\n",
       "       [1., 2., 4., 0., 1.],\n",
       "       [4., 1., 3., 3., 1.]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(tf.random_poisson(3,(3,5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tensor Operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tensor=tf.constant([[1,2,3],[4,5,6]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .get_shape()\n",
    "**`.get_shape()`** method returns shape of the tensor created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([Dimension(2), Dimension(3)])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor.get_shape()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .eval()\n",
    "**`.eval()`** method simply executes the tensor.<br>\n",
    "__Note:__ To use **`.eval()`** method one must execute an **`Interactive session`** before using **`.eval()`** method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess=tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.reshape()\n",
    "**`tf.reshape()`** takes 2 arguments a **`tensor`** and **`shape`**. The function changes shape of given **`tensor`** to **`shape`** given."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(tf.reshape(tensor,(3,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.rank()\n",
    "**`tf.rank()`** takes a **`tensor`** as its argument. The function provides number of dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(tf.rank(tensor))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.device()\n",
    "**`tf.device()`** lets you to place tensors on different devices such as CPUs, GPUs,etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.device(\"/device:GPU:0\"):\n",
    "    const=tf.constant('Ankur Goswami')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .dtype\n",
    "**`.dtype`** attribute returns data type of the tensor object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tf.int32"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .shape\n",
    "**`.shape`** attribute returns shape of the tensor created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([Dimension(2), Dimension(3)])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# Mathematical Operations:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mathematical operations of Tensorflow are as same as NumPy. The only difference is that constants & variables need to be executed using a session in tensorflow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# Tensorflow Graphs:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you start TensorFlow, a default Graph is created, you can create additional graphs easily:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Default Graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.framework.ops.Graph at 0x1fe52c09588>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.get_default_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Graphs:\n",
    "**`tf.Graph()`** function lets you to create graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.framework.ops.Graph at 0x1fe52c09a58>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph=tf.Graph()\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph Operations:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__To check whether the Graph is default or not__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph is tf.get_default_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .as_default()\n",
    "**`.as_default()`** method is sets a graph as a default graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "with graph.as_default():\n",
    "    print(graph is tf.get_default_graph())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# Tensorflow Variables :\n",
    "A TensorFlow variable is the best way to represent shared, persistent state manipulated by your program."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Variables:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.Variable()\n",
    "**`tf.Variable()`** takes a value as its argument and creates a variable with that value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Ankur Goswami\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "var=tf.Variable([[9,8,7],[6,5,4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "FailedPreconditionError",
     "evalue": "Attempting to use uninitialized value Variable\n\t [[{{node _retval_Variable_0_0}}]]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFailedPreconditionError\u001b[0m                   Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1333\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1334\u001b[1;33m       \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[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1335\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[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1318\u001b[0m       return self._call_tf_sessionrun(\n\u001b[1;32m-> 1319\u001b[1;33m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[0;32m   1320\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m   1406\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\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[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1407\u001b[1;33m         run_metadata)\n\u001b[0m\u001b[0;32m   1408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFailedPreconditionError\u001b[0m: Attempting to use uninitialized value Variable\n\t [[{{node _retval_Variable_0_0}}]]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mFailedPreconditionError\u001b[0m                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-29-9f35e6729087>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    927\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    928\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 929\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    930\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    931\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[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1150\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1151\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1152\u001b[1;33m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m   1153\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1154\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[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1326\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1327\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[1;32m-> 1328\u001b[1;33m                            run_metadata)\n\u001b[0m\u001b[0;32m   1329\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1330\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[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[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1346\u001b[0m           \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1347\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0merror_interpolation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1348\u001b[1;33m       \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[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1349\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1350\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[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFailedPreconditionError\u001b[0m: Attempting to use uninitialized value Variable\n\t [[{{node _retval_Variable_0_0}}]]"
     ]
    }
   ],
   "source": [
    "sess.run(var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.global_variables_initializer()\n",
    "__Note:__ Declared and assigned variables do not get executed using a session, until and unless they are initialized. To intialize, we use **`tf.global_variables_initializer()`** function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess.run(var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.get_variable()\n",
    "**`tf.get_variable()`** function takes 3 arguments **`name`**, **`shape`** & **`initializer`**. This function creates a variable with given **`shape`** and value(**`initializer`**) provided. If **`initializer`** is not provided, then it initializes with random values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "variable = tf.get_variable(\"variable\",(1,2,3),initializer=tf.constant_initializer(7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sess.run(tf.global_variables_initializer())\n",
    "sess.run(variable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# Tensorflow Placeholders:\n",
    "Placeholders are initially empty & are used to feed in the actual training data. However, they do need a declared expected data type with an optional shape argument."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating placeholders:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.placeholder()\n",
    "**`tf.placeholder()`** takes **`data type`** & **`shape`** as its arguments & creates a placeholder of given data type & shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "placeholder=tf.placeholder(tf.float32,(None,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Note:__ In above example, we have set **`None`** as we exactly don't know how many samples we are feeding in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2., 3., 4., 5., 6.]], dtype=float32)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(placeholder,feed_dict={placeholder:[[1,2,3,4,5,6]]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TensorFlow's Model Training & Testing Procedure:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loading & Cleaning:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data:\n",
    "https://archive.ics.uci.edu/ml/datasets/pima+indians+diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes = pd.read_csv('./data/tensorflow/pima-indians-diabetes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number_pregnant</th>\n",
       "      <th>Glucose_concentration</th>\n",
       "      <th>Blood_pressure</th>\n",
       "      <th>Triceps</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Pedigree</th>\n",
       "      <th>Age</th>\n",
       "      <th>Class</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>0.743719</td>\n",
       "      <td>0.590164</td>\n",
       "      <td>0.353535</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500745</td>\n",
       "      <td>0.234415</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.427136</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.292929</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.396423</td>\n",
       "      <td>0.116567</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>0.919598</td>\n",
       "      <td>0.524590</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.347243</td>\n",
       "      <td>0.253629</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.447236</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.232323</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.418778</td>\n",
       "      <td>0.038002</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.688442</td>\n",
       "      <td>0.327869</td>\n",
       "      <td>0.353535</td>\n",
       "      <td>0.198582</td>\n",
       "      <td>0.642325</td>\n",
       "      <td>0.943638</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number_pregnant  Glucose_concentration  Blood_pressure   Triceps   Insulin  \\\n",
       "0                6               0.743719        0.590164  0.353535  0.000000   \n",
       "1                1               0.427136        0.540984  0.292929  0.000000   \n",
       "2                8               0.919598        0.524590  0.000000  0.000000   \n",
       "3                1               0.447236        0.540984  0.232323  0.111111   \n",
       "4                0               0.688442        0.327869  0.353535  0.198582   \n",
       "\n",
       "        BMI  Pedigree  Age  Class Group  \n",
       "0  0.500745  0.234415   50      1     B  \n",
       "1  0.396423  0.116567   31      0     C  \n",
       "2  0.347243  0.253629   32      1     B  \n",
       "3  0.418778  0.038002   21      0     B  \n",
       "4  0.642325  0.943638   33      1     C  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Number_pregnant', 'Glucose_concentration', 'Blood_pressure', 'Triceps',\n",
       "       'Insulin', 'BMI', 'Pedigree', 'Age', 'Class', 'Group'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_norm = ['Number_pregnant','Glucose_concentration',\n",
    "                'Blood_pressure','Triceps','Insulin','BMI','Pedigree']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes[cols_to_norm]=diabetes[cols_to_norm].apply(lambda x: (x-x.min())/(x.max()-x.min()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number_pregnant</th>\n",
       "      <th>Glucose_concentration</th>\n",
       "      <th>Blood_pressure</th>\n",
       "      <th>Triceps</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Pedigree</th>\n",
       "      <th>Age</th>\n",
       "      <th>Class</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.743719</td>\n",
       "      <td>0.590164</td>\n",
       "      <td>0.353535</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500745</td>\n",
       "      <td>0.234415</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.427136</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.292929</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.396423</td>\n",
       "      <td>0.116567</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.919598</td>\n",
       "      <td>0.524590</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.347243</td>\n",
       "      <td>0.253629</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.447236</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.232323</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.418778</td>\n",
       "      <td>0.038002</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.688442</td>\n",
       "      <td>0.327869</td>\n",
       "      <td>0.353535</td>\n",
       "      <td>0.198582</td>\n",
       "      <td>0.642325</td>\n",
       "      <td>0.943638</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number_pregnant  Glucose_concentration  Blood_pressure   Triceps   Insulin  \\\n",
       "0         0.352941               0.743719        0.590164  0.353535  0.000000   \n",
       "1         0.058824               0.427136        0.540984  0.292929  0.000000   \n",
       "2         0.470588               0.919598        0.524590  0.000000  0.000000   \n",
       "3         0.058824               0.447236        0.540984  0.232323  0.111111   \n",
       "4         0.000000               0.688442        0.327869  0.353535  0.198582   \n",
       "\n",
       "        BMI  Pedigree  Age  Class Group  \n",
       "0  0.500745  0.234415   50      1     B  \n",
       "1  0.396423  0.116567   31      0     C  \n",
       "2  0.347243  0.253629   32      1     B  \n",
       "3  0.418778  0.038002   21      0     B  \n",
       "4  0.642325  0.943638   33      1     C  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continuous Features\n",
    "\n",
    "* Number of times pregnant\n",
    "* Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n",
    "* Diastolic blood pressure (mm Hg)\n",
    "* Triceps skin fold thickness (mm)\n",
    "* 2-Hour serum insulin (mu U/ml)\n",
    "* Body mass index (weight in kg/(height in m)^2)\n",
    "* Diabetes pedigree function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.feature_column.numeric_column()\n",
    "**`tf.feature_column.numeric_column()`** takes in **`key`** and **`shape`** of the input. The given column must be a numeric column. This function considers the given column as a Numerical Feauture column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_preg=tf.feature_column.numeric_column('Number_pregnant')\n",
    "plasma_gluc=tf.feature_column.numeric_column('Glucose_concentration')\n",
    "dias_press=tf.feature_column.numeric_column('Blood_pressure')\n",
    "tricep=tf.feature_column.numeric_column('Triceps')\n",
    "insulin=tf.feature_column.numeric_column('Insulin')\n",
    "bmi=tf.feature_column.numeric_column('BMI')\n",
    "diabetes_pedigree=tf.feature_column.numeric_column('Pedigree')\n",
    "age=tf.feature_column.numeric_column('Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical Features\n",
    "If you know the set of all possible feature values of a column and there are only a few of them, you can use **`categorical_column_with_vocabulary_list`**. If you don't know the set of possible values in advance you can use **`categorical_column_with_hash_bucket`**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.feature_column.categorical_column_with_vocabulary_list()\n",
    "**`tf.feature_column.categorical_column_with_vocabulary_list()`** takes a **`key`** and a **`vocabulary list`** as its input arguments. **`Vocabulary list`** contains unique values that comes in the gven column. The given column must be a categorical column. This function considers the given column as a Categorical Feauture column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "assigned_group_1=tf.feature_column.categorical_column_with_vocabulary_list('Group',['A','B','C','D'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.feature_column.categorical_column_with_hash_bucket()\n",
    "**`tf.feature_column.categorical_column_with_hash_bucket()`** takes a **`key`** & **`hash bucket size`** as its arguments. **`Hash bucket size`** is the number of unique values that the given colummn has. One can even provide a number which is greater than the number of categories. The given column must be a categorical column. This function considers the given column as a Categorical Feauture column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "assigned_group_2=tf.feature_column.categorical_column_with_hash_bucket('Group', hash_bucket_size=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.feature_column.embedding_column()\n",
    "**`tf.feature_column.embedding_column()`** takes a **`categorical column`** & **`dimension`** as its arguments. This function is only useful when our model is dealing with the __neural networks__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedded_group_column = tf.feature_column.embedding_column(assigned_group_1, dimension=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting Continuous to Categorical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.feature_column.bucketized_column()\n",
    "**`tf.feature_column.bucketized_column()`** takes in **`source column`** & **`boundaries`** as its arguments. The function categorizes numerical data to categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_buckets = tf.feature_column.bucketized_column(age, boundaries=[20,30,40,50,60,70,80])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_cols=[num_preg,plasma_gluc,dias_press,tricep,insulin,bmi,diabetes_pedigree,assigned_group_1,age_buckets]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data=diabetes.drop('Class',axis=1)\n",
    "labels=diabetes['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(x_data,labels,test_size=0.33,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input Function:\n",
    "An input function defines the input of your model. It contains type of input (Pandas or NumPy), input values, batch size, number of iterations,etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.estimator.inputs.pandas_input_fn()\n",
    "**`tf.estimator.inputs.pandas_input_fn()`** takes **`feature matrix`**, **`vector of predictions`**, **`batch size`**, **`number of iterations`**, **`shuffle`**,etc. The **`feature matrix`** & **`vector of predictions`** must be of pandas type. This lets the model to train/test in **`batches`** in **`iterations`**. **`Shuffle`** lets to randomize inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_train_func=tf.estimator.inputs.pandas_input_fn(x=X_train,y=y_train,batch_size=10,num_epochs=1000,shuffle=True)\n",
    "input_test_func=tf.estimator.inputs.pandas_input_fn(x=X_test,y=y_test,batch_size=10,num_epochs=1000,shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.estimator.inputs.numpy_input_fn()\n",
    "This is same as **`tf.estimator.inputs.pandas_input_fn()`** but in place of pandas it takes numpy type of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Creation:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.estimator.LinearClassifier()\n",
    "**`tf.estimator.LinearClassifier()`** takes in **`featue columns`** & **`no of classes`** as its arguments. This instantiates a Linear Classifier model for given **`featue columns`** & **`no of classes`**. This classifier works linearly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: C:\\Users\\Ankur Goswami\\AppData\\Local\\Temp\\tmp0xwg8e4k\n",
      "INFO:tensorflow:Using config: {'_model_dir': 'C:\\\\Users\\\\Ankur Goswami\\\\AppData\\\\Local\\\\Temp\\\\tmp0xwg8e4k', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
      "graph_options {\n",
      "  rewrite_options {\n",
      "    meta_optimizer_iterations: ONE\n",
      "  }\n",
      "}\n",
      ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x000001FE54337128>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
     ]
    }
   ],
   "source": [
    "model=tf.estimator.LinearClassifier(feature_columns=feat_cols,n_classes=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.estimator.DNNClassifier()\n",
    "**`tf.estimator.DNNClassifier()`** is a Neural network based Classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.estimator.DNNLinearCombinedClassifier()\n",
    "**`tf.estimator.DNNLinearCombinedClassifier()`** is a Classifier which is a combination of Linear Classifier & Neural network based Classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.estimator.LinearRegressor()\n",
    "**`tf.estimator.LinearRegressor()`** is a Linear Regressor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.estimator.DNNRegressor()\n",
    "**`tf.estimator.DNNRegressor()`** is a Neural network based Regressor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf.estimator.DNNLinearCombinedRegressor()\n",
    "**`tf.estimator.DNNLinearCombinedRegressor()`** is a Regressor which is a combination of Linear Regressor & Neural network based Regressor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### model.train()\n",
    "**`.train()`** method takes an **`input function`** & **`number of steps`**  as its function parameters. This method fits the model for the given input data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Ankur Goswami\\Anaconda3\\lib\\site-packages\\tensorflow_estimator\\python\\estimator\\inputs\\queues\\feeding_queue_runner.py:62: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "To construct input pipelines, use the `tf.data` module.\n",
      "WARNING:tensorflow:From C:\\Users\\Ankur Goswami\\Anaconda3\\lib\\site-packages\\tensorflow_estimator\\python\\estimator\\inputs\\queues\\feeding_functions.py:500: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "To construct input pipelines, use the `tf.data` module.\n",
      "INFO:tensorflow:Calling model_fn.\n",
      "WARNING:tensorflow:From C:\\Users\\Ankur Goswami\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\feature_column\\feature_column_v2.py:2703: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "WARNING:tensorflow:From C:\\Users\\Ankur Goswami\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\feature_column\\feature_column_v2.py:2898: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "WARNING:tensorflow:From C:\\Users\\Ankur Goswami\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\monitored_session.py:809: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "To construct input pipelines, use the `tf.data` module.\n",
      "INFO:tensorflow:Saving checkpoints for 0 into C:\\Users\\Ankur Goswami\\AppData\\Local\\Temp\\tmp0xwg8e4k\\model.ckpt.\n",
      "INFO:tensorflow:loss = 6.931472, step = 1\n",
      "INFO:tensorflow:global_step/sec: 137.551\n",
      "INFO:tensorflow:loss = 4.346552, step = 101 (0.730 sec)\n",
      "INFO:tensorflow:global_step/sec: 351.816\n",
      "INFO:tensorflow:loss = 6.0716467, step = 201 (0.284 sec)\n",
      "INFO:tensorflow:global_step/sec: 394.752\n",
      "INFO:tensorflow:loss = 3.2757988, step = 301 (0.254 sec)\n",
      "INFO:tensorflow:global_step/sec: 315.307\n",
      "INFO:tensorflow:loss = 4.7444944, step = 401 (0.317 sec)\n",
      "INFO:tensorflow:global_step/sec: 339.889\n",
      "INFO:tensorflow:loss = 7.513871, step = 501 (0.293 sec)\n",
      "INFO:tensorflow:global_step/sec: 391.669\n",
      "INFO:tensorflow:loss = 4.6703453, step = 601 (0.255 sec)\n",
      "INFO:tensorflow:global_step/sec: 397.886\n",
      "INFO:tensorflow:loss = 4.3264713, step = 701 (0.250 sec)\n",
      "INFO:tensorflow:global_step/sec: 334.225\n",
      "INFO:tensorflow:loss = 4.6197658, step = 801 (0.304 sec)\n",
      "INFO:tensorflow:global_step/sec: 344.562\n",
      "INFO:tensorflow:loss = 3.9483936, step = 901 (0.286 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 1000 into C:\\Users\\Ankur Goswami\\AppData\\Local\\Temp\\tmp0xwg8e4k\\model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 3.8325036.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow_estimator.python.estimator.canned.linear.LinearClassifier at 0x1fe544264a8>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.train(input_fn=input_test_func,steps=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### model.evaluate()\n",
    "**`.evaluate()`** method is takes an **`input function`** & **`number of steps`** as its function parameters. This method evaluates various parameters such as **`accuracy`**, **`loss`**, **`precision`**,etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n",
      "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
      "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Starting evaluation at 2019-07-15T09:36:20Z\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\Ankur Goswami\\AppData\\Local\\Temp\\tmp0xwg8e4k\\model.ckpt-1000\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Finished evaluation at 2019-07-15-09:38:21\n",
      "INFO:tensorflow:Saving dict for global step 1000: accuracy = 0.7140078, accuracy_baseline = 0.64591444, auc = 0.7467563, auc_precision_recall = 0.58532095, average_loss = 0.56621677, global_step = 1000, label/mean = 0.3540856, loss = 5.6621675, precision = 0.6335878, prediction/mean = 0.3501416, recall = 0.45604396\n",
      "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1000: C:\\Users\\Ankur Goswami\\AppData\\Local\\Temp\\tmp0xwg8e4k\\model.ckpt-1000\n"
     ]
    }
   ],
   "source": [
    "results=model.evaluate(input_fn=input_train_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.7140078,\n",
       " 'accuracy_baseline': 0.64591444,\n",
       " 'auc': 0.7467563,\n",
       " 'auc_precision_recall': 0.58532095,\n",
       " 'average_loss': 0.56621677,\n",
       " 'label/mean': 0.3540856,\n",
       " 'loss': 5.6621675,\n",
       " 'precision': 0.6335878,\n",
       " 'prediction/mean': 0.3501416,\n",
       " 'recall': 0.45604396,\n",
       " 'global_step': 1000}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### model.predict()\n",
    "**`.predict()`** method takes an **`input function`** as its function parameters and generates predictions for given **`output function`**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=model.predict(input_fn=input_test_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\Ankur Goswami\\AppData\\Local\\Temp\\tmp0xwg8e4k\\model.ckpt-1000\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    },
    {
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       "  'classes': array([b'0'], dtype=object)},\n",
       " {'logits': array([-0.6689532], dtype=float32),\n",
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       "  'probabilities': array([0.6612688 , 0.33873132], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object)},\n",
       " {'logits': array([-1.7013524], dtype=float32),\n",
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       "  'probabilities': array([0.84571123, 0.15428872], dtype=float32),\n",
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       "  'classes': array([b'0'], dtype=object)},\n",
       " {'logits': array([-1.1136563], dtype=float32),\n",
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       "  'probabilities': array([0.75281006, 0.24718985], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object)},\n",
       " {'logits': array([-0.58492637], dtype=float32),\n",
       "  'logistic': array([0.35779983], dtype=float32),\n",
       "  'probabilities': array([0.6422002 , 0.35779983], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object)},\n",
       " {'logits': array([-1.1837863], dtype=float32),\n",
       "  'logistic': array([0.23437211], dtype=float32),\n",
       "  'probabilities': array([0.76562786, 0.2343721 ], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object)},\n",
       " {'logits': array([-0.66984445], dtype=float32),\n",
       "  'logistic': array([0.33853167], dtype=float32),\n",
       "  'probabilities': array([0.6614683 , 0.33853167], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object)},\n",
       " {'logits': array([0.01054072], dtype=float32),\n",
       "  'logistic': array([0.5026352], dtype=float32),\n",
       "  'probabilities': array([0.49736488, 0.5026352 ], dtype=float32),\n",
       "  'class_ids': array([1], dtype=int64),\n",
       "  'classes': array([b'1'], dtype=object)},\n",
       " {'logits': array([-1.075103], dtype=float32),\n",
       "  'logistic': array([0.25443384], dtype=float32),\n",
       "  'probabilities': array([0.7455662 , 0.25443384], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object)},\n",
       " ...]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  }
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