Up and running with TensorFlow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "def reset_graph(seed=42):\n",
    "    tf.reset_default_graph()\n",
    "    tf.set_random_seed(seed)\n",
    "    np.random.seed(seed)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"tensorflow\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Create and running a graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "\n",
    "x = tf.Variable(3, name=\"x\")\n",
    "y = tf.Variable(4, name=\"y\")\n",
    "f = x*x*y + y + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42\n"
     ]
    }
   ],
   "source": [
    "sess = tf.Session()\n",
    "sess.run(x.initializer)\n",
    "sess.run(y.initializer)\n",
    "result = sess.run(f)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.Session() as sess:\n",
    "    x.initializer.run()\n",
    "    y.initializer.run()\n",
    "    result = f.eval()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    result = f.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42\n"
     ]
    }
   ],
   "source": [
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42\n"
     ]
    }
   ],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "init.run()\n",
    "result = f.eval()\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Managing Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = tf.Variable(1)\n",
    "x1.graph is tf.get_default_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    x2 = tf.Variable(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2.graph is graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2.graph is tf.get_default_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "w = tf.constant(3)\n",
    "x = w + 2\n",
    "y = x + 5\n",
    "z = x * 3\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    print(y.eval())  # 10\n",
    "    print(z.eval())  # 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    y_val, z_val = sess.run([y, z])\n",
    "    print(y_val)  # 10\n",
    "    print(z_val)  # 15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "m, n = housing.data.shape\n",
    "housing_data_plus_bias = np.c_[np.ones((m, 1)), housing.data]\n",
    "\n",
    "X = tf.constant(housing_data_plus_bias, dtype=tf.float32, name=\"X\")\n",
    "y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\"y\")\n",
    "XT = tf.transpose(X)\n",
    "theta = tf.matmul(tf.matmul(tf.matrix_inverse(tf.matmul(XT, X)), XT), y)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    theta_value = theta.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "compare with numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ -3.69419202e+01]\n",
      " [  4.36693293e-01]\n",
      " [  9.43577803e-03]\n",
      " [ -1.07322041e-01]\n",
      " [  6.45065694e-01]\n",
      " [ -3.97638942e-06]\n",
      " [ -3.78654265e-03]\n",
      " [ -4.21314378e-01]\n",
      " [ -4.34513755e-01]]\n"
     ]
    }
   ],
   "source": [
    "X = housing_data_plus_bias\n",
    "y = housing.target.reshape(-1, 1)\n",
    "theta_numpy = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)\n",
    "\n",
    "print(theta_numpy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare with Scikit learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ -3.69419202e+01]\n",
      " [  4.36693293e-01]\n",
      " [  9.43577803e-03]\n",
      " [ -1.07322041e-01]\n",
      " [  6.45065694e-01]\n",
      " [ -3.97638942e-06]\n",
      " [ -3.78654265e-03]\n",
      " [ -4.21314378e-01]\n",
      " [ -4.34513755e-01]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing.data, housing.target.reshape(-1, 1))\n",
    "\n",
    "print(np.r_[lin_reg.intercept_.reshape(-1, 1), lin_reg.coef_.T])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing GD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaled_housing_data = scaler.fit_transform(housing.data)\n",
    "scaled_housing_data_plus_bias = np.c_[np.ones((m, 1)), scaled_housing_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1.00000000e+00   6.60969987e-17   5.50808322e-18   6.60969987e-17\n",
      "  -1.06030602e-16  -1.10161664e-17   3.44255201e-18  -1.07958431e-15\n",
      "  -8.52651283e-15]\n",
      "[ 0.38915536  0.36424355  0.5116157  ..., -0.06612179 -0.06360587\n",
      "  0.01359031]\n",
      "0.111111111111\n",
      "(20640, 9)\n"
     ]
    }
   ],
   "source": [
    "print(scaled_housing_data_plus_bias.mean(axis=0))\n",
    "print(scaled_housing_data_plus_bias.mean(axis=1))\n",
    "print(scaled_housing_data_plus_bias.mean())\n",
    "print(scaled_housing_data_plus_bias.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manually computing the gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 MSE= 11.1832\n",
      "Epoch 100 MSE= 1.03757\n",
      "Epoch 200 MSE= 0.793744\n",
      "Epoch 300 MSE= 0.721674\n",
      "Epoch 400 MSE= 0.670696\n",
      "Epoch 500 MSE= 0.633282\n",
      "Epoch 600 MSE= 0.605732\n",
      "Epoch 700 MSE= 0.58539\n",
      "Epoch 800 MSE= 0.570326\n",
      "Epoch 900 MSE= 0.559134\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 1000\n",
    "learning_rate = 0.01\n",
    "\n",
    "X = tf.constant(scaled_housing_data_plus_bias, dtype=tf.float32, name=\"X\")\n",
    "y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\"y\")\n",
    "theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0), name=\"theta\")\n",
    "y_pred = tf.matmul(X, theta, name=\"predictions\")\n",
    "error = y_pred - y\n",
    "mse = tf.reduce_mean(tf.square(error), name=\"mse\")\n",
    "gradients = 2/m * tf.matmul(tf.transpose(X), error)\n",
    "training_op = tf.assign(theta, theta - learning_rate * gradients)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    \n",
    "    for epoch in range(n_epochs):\n",
    "        if epoch % 100 == 0:\n",
    "            print(\"Epoch\", epoch, \"MSE=\", mse.eval())\n",
    "        sess.run(training_op)\n",
    "    \n",
    "    best_theta = theta.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.06855226],\n",
       "       [ 0.78346634],\n",
       "       [ 0.15487331],\n",
       "       [-0.09458195],\n",
       "       [ 0.12751007],\n",
       "       [ 0.00923653],\n",
       "       [-0.04139781],\n",
       "       [-0.67990863],\n",
       "       [-0.64114094]], dtype=float32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 MSE= 6.85776\n",
      "Epoch 100 MSE= 0.647279\n",
      "Epoch 200 MSE= 0.56007\n",
      "Epoch 300 MSE= 0.550706\n",
      "Epoch 400 MSE= 0.544657\n",
      "Epoch 500 MSE= 0.540094\n",
      "Epoch 600 MSE= 0.536618\n",
      "Epoch 700 MSE= 0.533958\n",
      "Epoch 800 MSE= 0.531911\n",
      "Epoch 900 MSE= 0.530328\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 1000\n",
    "learning_rate = 0.01\n",
    "\n",
    "X = tf.constant(scaled_housing_data_plus_bias, dtype=tf.float32, name=\"X\")\n",
    "y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\"y\")\n",
    "theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0), name=\"theta\")\n",
    "y_pred = tf.matmul(X, theta, name=\"predictions\")\n",
    "error = y_pred - y\n",
    "mse = tf.reduce_mean(tf.square(error), name=\"mse\")\n",
    "gradients = tf.gradients(mse, [theta])[0]\n",
    "training_op = tf.assign(theta, theta - learning_rate * gradients)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    \n",
    "    for epoch in range(n_epochs):\n",
    "        if epoch % 100 == 0:\n",
    "            print(\"Epoch\", epoch, \"MSE=\", mse.eval())\n",
    "        sess.run(training_op)\n",
    "    \n",
    "    best_theta = theta.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.06855226],\n",
       "       [ 0.83494025],\n",
       "       [ 0.12018571],\n",
       "       [-0.27476469],\n",
       "       [ 0.31297702],\n",
       "       [-0.00404983],\n",
       "       [-0.03956117],\n",
       "       [-0.88444185],\n",
       "       [-0.85567713]], dtype=float32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 MSE= 8.09504\n",
      "Epoch 100 MSE= 0.731992\n",
      "Epoch 200 MSE= 0.619389\n",
      "Epoch 300 MSE= 0.592017\n",
      "Epoch 400 MSE= 0.573145\n",
      "Epoch 500 MSE= 0.559546\n",
      "Epoch 600 MSE= 0.549736\n",
      "Epoch 700 MSE= 0.542658\n",
      "Epoch 800 MSE= 0.537552\n",
      "Epoch 900 MSE= 0.533869\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 1000\n",
    "learning_rate = 0.01\n",
    "\n",
    "X = tf.constant(scaled_housing_data_plus_bias, dtype=tf.float32, name=\"X\")\n",
    "y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\"y\")\n",
    "theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0), name=\"theta\")\n",
    "y_pred = tf.matmul(X, theta, name=\"predictions\")\n",
    "error = y_pred - y\n",
    "mse = tf.reduce_mean(tf.square(error), name=\"mse\")\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(mse)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    \n",
    "    for epoch in range(n_epochs):\n",
    "        if epoch % 100 == 0:\n",
    "            print(\"Epoch\", epoch, \"MSE=\", mse.eval())\n",
    "        sess.run(training_op)\n",
    "    \n",
    "    best_theta = theta.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.06855226],\n",
       "       [ 0.77515411],\n",
       "       [ 0.14126606],\n",
       "       [-0.10141296],\n",
       "       [ 0.14306943],\n",
       "       [ 0.00439436],\n",
       "       [-0.04006951],\n",
       "       [-0.78690732],\n",
       "       [-0.74826258]], dtype=float32)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 MSE= 5.03215\n",
      "Epoch 100 MSE= 0.541943\n",
      "Epoch 200 MSE= 0.525815\n",
      "Epoch 300 MSE= 0.524505\n",
      "Epoch 400 MSE= 0.524345\n",
      "Epoch 500 MSE= 0.524324\n",
      "Epoch 600 MSE= 0.524321\n",
      "Epoch 700 MSE= 0.524321\n",
      "Epoch 800 MSE= 0.524321\n",
      "Epoch 900 MSE= 0.524321\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 1000\n",
    "learning_rate = 0.01\n",
    "\n",
    "X = tf.constant(scaled_housing_data_plus_bias, dtype=tf.float32, name=\"X\")\n",
    "y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\"y\")\n",
    "theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0), name=\"theta\")\n",
    "y_pred = tf.matmul(X, theta, name=\"predictions\")\n",
    "error = y_pred - y\n",
    "mse = tf.reduce_mean(tf.square(error), name=\"mse\")\n",
    "optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.9)\n",
    "training_op = optimizer.minimize(mse)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    \n",
    "    for epoch in range(n_epochs):\n",
    "        if epoch % 100 == 0:\n",
    "            print(\"Epoch\", epoch, \"MSE=\", mse.eval())\n",
    "        sess.run(training_op)\n",
    "    \n",
    "    best_theta = theta.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.06855798],\n",
       "       [ 0.82963264],\n",
       "       [ 0.1187541 ],\n",
       "       [-0.26555187],\n",
       "       [ 0.30571693],\n",
       "       [-0.00450229],\n",
       "       [-0.03932676],\n",
       "       [-0.89985532],\n",
       "       [-0.87051219]], dtype=float32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " best_theta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feeding data to the training algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "A = tf.placeholder(tf.float32, shape=(None, 3))\n",
    "B = A + 5\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    B_val_1 = B.eval(feed_dict={A: [[1, 2, 3]]})\n",
    "    B_val_2 = B.eval(feed_dict={A: [[4, 5, 6], [7, 8, 9]]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 6.  7.  8.]]\n"
     ]
    }
   ],
   "source": [
    "print(B_val_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  9.  10.  11.]\n",
      " [ 12.  13.  14.]]\n"
     ]
    }
   ],
   "source": [
    "print(B_val_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_epochs = 1000\n",
    "learning_rate = 0.01\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=(None, n + 1), name=\"X\")\n",
    "y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")\n",
    "theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0), name=\"theta\")\n",
    "y_pred = tf.matmul(X, theta, name=\"predictions\")\n",
    "error = y_pred - y\n",
    "mse = tf.reduce_mean(tf.square(error), name=\"mse\")\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(mse)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "batch_size = 100\n",
    "n_batches = int(np.ceil(m / batch_size))\n",
    "\n",
    "def fetch_batch(epoch, batch_index, batch_size):\n",
    "    np.random.seed(epoch * n_batches + batch_index)  # not shown in the book\n",
    "    indices = np.random.randint(m, size=batch_size)  # not shown\n",
    "    X_batch = scaled_housing_data_plus_bias[indices] # not shown\n",
    "    y_batch = housing.target.reshape(-1, 1)[indices] # not shown\n",
    "    return X_batch, y_batch\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    \n",
    "    for epoch in range(n_epochs):\n",
    "        for batch_index in range(n_batches):\n",
    "            X_batch, y_batch = fetch_batch(epoch, batch_index, batch_size)\n",
    "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
    "    \n",
    "    best_theta = theta.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.07144761],\n",
       "       [ 0.84620118],\n",
       "       [ 0.11558536],\n",
       "       [-0.26835835],\n",
       "       [ 0.32982785],\n",
       "       [ 0.00608358],\n",
       "       [ 0.07052912],\n",
       "       [-0.87988579],\n",
       "       [-0.86342514]], dtype=float32)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " best_theta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving and restoring a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 MSE = 9.16154\n",
      "Epoch 100 MSE = 0.7145\n",
      "Epoch 200 MSE = 0.566705\n",
      "Epoch 300 MSE = 0.555572\n",
      "Epoch 400 MSE = 0.548811\n",
      "Epoch 500 MSE = 0.543636\n",
      "Epoch 600 MSE = 0.539629\n",
      "Epoch 700 MSE = 0.536509\n",
      "Epoch 800 MSE = 0.534068\n",
      "Epoch 900 MSE = 0.532147\n"
     ]
    }
   ],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_epochs = 1000                                                                       # not shown in the book\n",
    "learning_rate = 0.01                                                                  # not shown\n",
    "\n",
    "X = tf.constant(scaled_housing_data_plus_bias, dtype=tf.float32, name=\"X\")            # not shown\n",
    "y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\"y\")            # not shown\n",
    "theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\"theta\")\n",
    "y_pred = tf.matmul(X, theta, name=\"predictions\")                                      # not shown\n",
    "error = y_pred - y                                                                    # not shown\n",
    "mse = tf.reduce_mean(tf.square(error), name=\"mse\")                                    # not shown\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)            # not shown\n",
    "training_op = optimizer.minimize(mse)                                                 # not shown\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "\n",
    "    for epoch in range(n_epochs):\n",
    "        if epoch % 100 == 0:\n",
    "            print(\"Epoch\", epoch, \"MSE =\", mse.eval())                                # not shown\n",
    "            save_path = saver.save(sess, \"/tmp/my_model.ckpt\")\n",
    "        sess.run(training_op)\n",
    "    \n",
    "    best_theta = theta.eval()\n",
    "    save_path = saver.save(sess, \"/tmp/my_model_final.ckpt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from /tmp/my_model_final.ckpt\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, \"/tmp/my_model_final.ckpt\")\n",
    "    best_theta_restored = theta.eval() # not shown in the book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(best_theta, best_theta_restored)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visulizing the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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 = b\"<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": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe seamless style=\"width:1200px;height:620px;border:0\" srcdoc=\"\n",
       "        <script>\n",
       "          function load() {\n",
       "            document.getElementById(&quot;graph0.3745401188473625&quot;).pbtxt = 'node {\\n  name: &quot;X&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: 20640\\n          }\\n          dim {\\n            size: 9\\n          }\\n        }\\n        tensor_content: &quot;<stripped 743040 bytes>&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;y&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: 20640\\n          }\\n          dim {\\n            size: 1\\n          }\\n        }\\n        tensor_content: &quot;<stripped 82560 bytes>&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;random_uniform/shape&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;\\\\t\\\\000\\\\000\\\\000\\\\001\\\\000\\\\000\\\\000&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;random_uniform/min&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        }\\n        float_val: -1.0\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;random_uniform/max&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        }\\n        float_val: 1.0\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;random_uniform/RandomUniform&quot;\\n  op: &quot;RandomUniform&quot;\\n  input: &quot;random_uniform/shape&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_INT32\\n    }\\n  }\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;seed&quot;\\n    value {\\n      i: 42\\n    }\\n  }\\n  attr {\\n    key: &quot;seed2&quot;\\n    value {\\n      i: 42\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;random_uniform/sub&quot;\\n  op: &quot;Sub&quot;\\n  input: &quot;random_uniform/max&quot;\\n  input: &quot;random_uniform/min&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n}\\nnode {\\n  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   s: &quot;&quot;\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;theta/Assign&quot;\\n  op: &quot;Assign&quot;\\n  input: &quot;theta&quot;\\n  input: &quot;random_uniform&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;_class&quot;\\n    value {\\n      list {\\n        s: &quot;loc:@theta&quot;\\n      }\\n    }\\n  }\\n  attr {\\n    key: &quot;use_locking&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n  attr {\\n    key: &quot;validate_shape&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;theta/read&quot;\\n  op: &quot;Identity&quot;\\n  input: &quot;theta&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;_class&quot;\\n    value {\\n      list {\\n        s: &quot;loc:@theta&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;predictions&quot;\\n  op: &quot;MatMul&quot;\\n  input: &quot;X&quot;\\n  input: &quot;theta/read&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;transpose_a&quot;\\n    value {\\n      b: false\\n    }\\n  }\\n  attr {\\n    key: &quot;transpose_b&quot;\\n    value {\\n      b: false\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;sub&quot;\\n  op: &quot;Sub&quot;\\n  input: &quot;predictions&quot;\\n  input: &quot;y&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;Square&quot;\\n  op: &quot;Square&quot;\\n  input: &quot;sub&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;Const&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;\\\\000\\\\000\\\\000\\\\000\\\\001\\\\000\\\\000\\\\000&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;mse&quot;\\n  op: &quot;Mean&quot;\\n  input: &quot;Square&quot;\\n  input: &quot;Const&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;Tidx&quot;\\n    value {\\n      type: DT_INT32\\n    }\\n  }\\n  attr {\\n    key: &quot;keep_dims&quot;\\n    value {\\n      b: false\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;gradients/Shape&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          }\\n        }\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;gradients/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        }\\n        float_val: 1.0\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;gradients/Fill&quot;\\n  op: &quot;Fill&quot;\\n  input: &quot;gradients/Shape&quot;\\n  input: &quot;gradients/Const&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;gradients/mse_grad/Reshape/shape&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;\\\\001\\\\000\\\\000\\\\000\\\\001\\\\000\\\\000\\\\000&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;gradients/mse_grad/Reshape&quot;\\n  op: &quot;Reshape&quot;\\n  input: &quot;gradients/Fill&quot;\\n  input: &quot;gradients/mse_grad/Reshape/shape&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;Tshape&quot;\\n    value {\\n      type: DT_INT32\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;gradients/mse_grad/Tile/multiples&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;\\\\240P\\\\000\\\\000\\\\001\\\\000\\\\000\\\\000&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;gradients/mse_grad/Tile&quot;\\n  op: &quot;Tile&quot;\\n  input: &quot;gradients/mse_grad/Reshape&quot;\\n  input: 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       "          }\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;graph0.3745401188473625&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())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "now = datetime.utcnow().strftime(\"%Y%m%d%H%M%S\")\n",
    "root_logdir = \"tf_logs\"\n",
    "logdir = \"{}/run-{}/\".format(root_logdir, now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_epochs = 1000\n",
    "learning_rate = 0.01\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=(None, n + 1), name=\"X\")\n",
    "y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")\n",
    "theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\"theta\")\n",
    "y_pred = tf.matmul(X, theta, name=\"predictions\")\n",
    "error = y_pred - y\n",
    "mse = tf.reduce_mean(tf.square(error), name=\"mse\")\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(mse)\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "mse_summary = tf.summary.scalar('MSE', mse)\n",
    "file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_epochs = 10\n",
    "batch_size = 100\n",
    "n_batches = int(np.ceil(m / batch_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.Session() as sess:                                                        # not shown in the book\n",
    "    sess.run(init)                                                                # not shown\n",
    "\n",
    "    for epoch in range(n_epochs):                                                 # not shown\n",
    "        for batch_index in range(n_batches):\n",
    "            X_batch, y_batch = fetch_batch(epoch, batch_index, batch_size)\n",
    "            if batch_index % 10 == 0:\n",
    "                summary_str = mse_summary.eval(feed_dict={X: X_batch, y: y_batch})\n",
    "                step = epoch * n_batches + batch_index\n",
    "                file_writer.add_summary(summary_str, step)\n",
    "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
    "\n",
    "    best_theta = theta.eval()                                                     # not shown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.07033372],\n",
       "       [ 0.86371452],\n",
       "       [ 0.12255152],\n",
       "       [-0.31211877],\n",
       "       [ 0.38510376],\n",
       "       [ 0.00434168],\n",
       "       [-0.0123295 ],\n",
       "       [-0.83376896],\n",
       "       [-0.80304712]], dtype=float32)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_theta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Name scopes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "now = datetime.utcnow().strftime(\"%Y%m%d%H%M%S\")\n",
    "root_logdir = \"tf_logs\"\n",
    "logdir = \"{}/run-{}/\".format(root_logdir, now)\n",
    "\n",
    "n_epochs = 1000\n",
    "learning_rate = 0.01\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=(None, n + 1), name=\"X\")\n",
    "y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")\n",
    "theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\"theta\")\n",
    "y_pred = tf.matmul(X, theta, name=\"predictions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.name_scope(\"loss\") as scope:\n",
    "    error = y_pred - y\n",
    "    mse = tf.reduce_mean(tf.square(error), name=\"mse\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(mse)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "mse_summary = tf.summary.scalar('MSE', mse)\n",
    "file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best theta:\n",
      "[[ 2.07033372]\n",
      " [ 0.86371452]\n",
      " [ 0.12255152]\n",
      " [-0.31211877]\n",
      " [ 0.38510376]\n",
      " [ 0.00434168]\n",
      " [-0.0123295 ]\n",
      " [-0.83376896]\n",
      " [-0.80304712]]\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 10\n",
    "batch_size = 100\n",
    "n_batches = int(np.ceil(m / batch_size))\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "\n",
    "    for epoch in range(n_epochs):\n",
    "        for batch_index in range(n_batches):\n",
    "            X_batch, y_batch = fetch_batch(epoch, batch_index, batch_size)\n",
    "            if batch_index % 10 == 0:\n",
    "                summary_str = mse_summary.eval(feed_dict={X: X_batch, y: y_batch})\n",
    "                step = epoch * n_batches + batch_index\n",
    "                file_writer.add_summary(summary_str, step)\n",
    "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
    "\n",
    "    best_theta = theta.eval()\n",
    "\n",
    "file_writer.flush()\n",
    "file_writer.close()\n",
    "print(\"Best theta:\")\n",
    "print(best_theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss/sub\n"
     ]
    }
   ],
   "source": [
    "print(error.op.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss/mse\n"
     ]
    }
   ],
   "source": [
    "print(mse.op.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "a_1\n",
      "param/a\n",
      "param_1/a\n"
     ]
    }
   ],
   "source": [
    "reset_graph()\n",
    "\n",
    "a1 = tf.Variable(0, name=\"a\")      # name == \"a\"\n",
    "a2 = tf.Variable(0, name=\"a\")      # name == \"a_1\"\n",
    "\n",
    "with tf.name_scope(\"param\"):       # name == \"param\"\n",
    "    a3 = tf.Variable(0, name=\"a\")  # name == \"param/a\"\n",
    "\n",
    "with tf.name_scope(\"param\"):       # name == \"param_1\"\n",
    "    a4 = tf.Variable(0, name=\"a\")  # name == \"param_1/a\"\n",
    "\n",
    "for node in (a1, a2, a3, a4):\n",
    "    print(node.op.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_features = 3\n",
    "X = tf.placeholder(tf.float32, shape=(None, n_features), name=\"X\")\n",
    "\n",
    "w1 = tf.Variable(tf.random_normal((n_features, 1)), name=\"weights1\")\n",
    "w2 = tf.Variable(tf.random_normal((n_features, 1)), name=\"weights2\")\n",
    "b1 = tf.Variable(0.0, name=\"bias1\")\n",
    "b2 = tf.Variable(0.0, name=\"bias2\")\n",
    "\n",
    "z1 = tf.add(tf.matmul(X, w1), b1, name=\"z1\")\n",
    "z2 = tf.add(tf.matmul(X, w2), b2, name=\"z2\")\n",
    "\n",
    "relu1 = tf.maximum(z1, 0., name=\"relu1\")\n",
    "relu2 = tf.maximum(z1, 0., name=\"relu2\")  # Oops, cut&paste error! Did you spot it?\n",
    "\n",
    "output = tf.add(relu1, relu2, name=\"output\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "def relu(X):\n",
    "    w_shape = (int(X.get_shape()[1]), 1)\n",
    "    w = tf.Variable(tf.random_normal(w_shape), name=\"weights\")\n",
    "    b = tf.Variable(0.0, name=\"bias\")\n",
    "    z = tf.add(tf.matmul(X, w), b, name=\"z\")\n",
    "    return tf.maximum(z, 0., name=\"relu\")\n",
    "\n",
    "n_features = 3\n",
    "X = tf.placeholder(tf.float32, shape=(None, n_features), name=\"X\")\n",
    "relus = [relu(X) for i in range (5)]\n",
    "output = tf.add_n(relus, name=\"Output\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "def relu(X):\n",
    "    with tf.name_scope(\"relu\"):\n",
    "        w_shape = (int(X.get_shape()[1]), 1)                          # not shown in the book\n",
    "        w = tf.Variable(tf.random_normal(w_shape), name=\"weights\")    # not shown\n",
    "        b = tf.Variable(0.0, name=\"bias\")                             # not shown\n",
    "        z = tf.add(tf.matmul(X, w), b, name=\"z\")                      # not shown\n",
    "        return tf.maximum(z, 0., name=\"max\")                          # not shown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_features = 3\n",
    "X = tf.placeholder(tf.float32, shape=(None, n_features), name=\"X\")\n",
    "relus = [relu(X) for i in range(5)]\n",
    "output = tf.add_n(relus, name=\"output\")\n",
    "\n",
    "file_writer = tf.summary.FileWriter(\"logs/relu2\", tf.get_default_graph())\n",
    "file_writer.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sharing variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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