TensorFlow Linear Regression.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "#### Tensorflow Operations (Ops)\n",
    "* They take any number of inputs & produce any number of outputs\n",
    "* Examples are multiplication & addition - they take inputs & produce outputs\n",
    "\n",
    "#### Tensorflow Constants & Variables ( Source Ops ) \n",
    "* They take no inputs\n",
    "\n",
    "#### Tensors\n",
    "* Similar to NumPy, tensors have type & shape\n",
    "* The TensorFlow Operations takes Tensors as inputs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Abs:0' shape=() dtype=int32>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.abs(-9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import fetch_california_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading Cal. housing from http://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.tgz to C:\\Users\\ZekeLabs\\scikit_learn_data\n"
     ]
    }
   ],
   "source": [
    "housing = fetch_california_housing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m,n = housing.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20640"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1, 3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[[1,2,3]]]).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_data_plus_bias = np.c_[np.ones((m,1)),housing.data]\n",
    "#help(np.c_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = tf.constant(housing_data_plus_bias, dtype=tf.float32, name=\"X\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'X:0' shape=(20640, 9) dtype=float32>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   1.        ,    8.3252    ,   41.        , ...,    2.55555556,\n",
       "          37.88      , -122.23      ],\n",
       "       [   1.        ,    8.3014    ,   21.        , ...,    2.10984183,\n",
       "          37.86      , -122.22      ],\n",
       "       [   1.        ,    7.2574    ,   52.        , ...,    2.80225989,\n",
       "          37.85      , -122.24      ],\n",
       "       ..., \n",
       "       [   1.        ,    1.7       ,   17.        , ...,    2.3256351 ,\n",
       "          39.43      , -121.22      ],\n",
       "       [   1.        ,    1.8672    ,   18.        , ...,    2.12320917,\n",
       "          39.43      , -121.32      ],\n",
       "       [   1.        ,    2.3886    ,   16.        , ...,    2.61698113,\n",
       "          39.37      , -121.24      ]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_data_plus_bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\"y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.526],\n",
       "       [ 3.585],\n",
       "       [ 3.521],\n",
       "       ..., \n",
       "       [ 0.923],\n",
       "       [ 0.847],\n",
       "       [ 0.894]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.target.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "XT = tf.transpose(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta = tf.matmul(tf.matmul(tf.matrix_inverse(tf.matmul(XT,X)),XT),y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.Session() as sess:\n",
    "    theta_val = theta.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -3.74651413e+01],\n",
       "       [  4.35734153e-01],\n",
       "       [  9.33829229e-03],\n",
       "       [ -1.06622010e-01],\n",
       "       [  6.44106984e-01],\n",
       "       [ -4.25131839e-06],\n",
       "       [ -3.77322501e-03],\n",
       "       [ -4.26648885e-01],\n",
       "       [ -4.40514028e-01]], dtype=float32)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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