tensorflow_basics.ipynb

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     "text": [
      "Tensor(\"add_3:0\", shape=(), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from numpy import genfromtxt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "#building a computational graph\n",
    "a=tf.constant(2)\n",
    "b=tf.constant(4)\n",
    "c=a+b\n",
    "\n",
    "\n",
    "print(c)\n"
   ]
  },
  {
   "cell_type": "code",
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     "data": {
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       "6"
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     "execution_count": 2,
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   "source": [
    "#running a computational graph\n",
    "ssn=tf.Session()\n",
    "\n",
    "ssn.run(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"mul:0\", shape=(4,), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "x1=tf.constant([1,2,4,5])\n",
    "x2=tf.constant([2,3,4,5])\n",
    "#result = tf.multiply(x1,x2)\n",
    "result=x1*x2\n",
    "\n",
    "print(result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  6, 16, 25])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssn.run(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Placeholder  : not providing values at the time of declaration but providing values later at the time of execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'add_1:0' shape=<unknown> dtype=float32>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=tf.placeholder(tf.float32)\n",
    "b=tf.placeholder(tf.float32)\n",
    "\n",
    "c=a+b\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ssn.run(c,{a:1,b:4})  # assigning values to a abd b here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'add_2:0' shape=<unknown> dtype=float32>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=tf.placeholder(tf.float32)\n",
    "b=tf.placeholder(tf.float32)\n",
    "\n",
    "c=a+b\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Placeholder is very useful when we want to give different input values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5., 4., 4.], dtype=float32)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssn.run(c,{a:[1,2,3],b:[4,2,1]})  # assigning values to a abd b here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5., 4.], dtype=float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssn.run(c,{a:[1,2],b:[4,2]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssn.run(c,{a:1,b:2})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q=tf.Variable([1])\n",
    "#q=tf.Variable([.1],tf.float32)\n",
    "w=tf.Variable([3])\n",
    "\n",
    "c=q*w\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "ssn.run(init)\n",
    "ssn.run(c)\n"
   ]
  }
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