Neural Network with TensorFlow.ipynb

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      "/home/faiz/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(101)\n",
    "tf.set_random_seed(101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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       "array([[51.63986277, 57.06675869,  2.84742265, 17.15216562, 68.52769817],\n",
       "       [83.38968626, 30.69662197, 89.36130797, 72.15438618, 18.99389542],\n",
       "       [55.42275911, 35.2131954 , 18.18924027, 78.56017619, 96.54832224],\n",
       "       [23.23536618,  8.35614337, 60.35484223, 72.89927573, 27.62388285],\n",
       "       [68.53063288, 51.78674742,  4.84845374, 13.78692376, 18.69674261]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_a=np.random.uniform(0,100,(5,5))\n",
    "rand_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "rand_b=np.random.uniform(0,100,(5,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=tf.placeholder(tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=tf.placeholder(tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "add_op = a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mult_op =a * b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "[[151.07166  156.49855  102.27921  116.58396  167.95949 ]\n",
      " [135.45622   82.76316  141.42784  124.22093   71.06043 ]\n",
      " [113.30171   93.09215   76.06819  136.43912  154.42728 ]\n",
      " [ 96.71727   81.83804  133.83675  146.38118  101.10579 ]\n",
      " [122.72681  105.982925  59.044632  67.9831    72.89292 ]]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    addResult=sess.run(add_op,feed_dict={a:rand_a,b:rand_b})\n",
    "    print(addResult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5134.644   5674.25     283.12433 1705.4707  6813.8315 ]\n",
      " [4341.8125  1598.267   4652.734   3756.8293   988.94635]\n",
      " [3207.8113  2038.1029  1052.7742  4546.9805  5588.1157 ]\n",
      " [1707.379    614.02527 4434.989   5356.7773  2029.8555 ]\n",
      " [3714.0984  2806.6438   262.76764  747.19855 1013.292  ]]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    multResult=sess.run(mult_op,feed_dict={a:rand_a,b:rand_b})\n",
    "    print(multResult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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