{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.8.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Describe TF Graph: standard normalization of inputs and ouputs (z-score) + linear regression\n",
    "\n",
    "batch_size = 100\n",
    "\n",
    "a = tf.get_variable(\"a\", [1], dtype=tf.float32)\n",
    "b = tf.get_variable(\"b\", [1], dtype=tf.float32)\n",
    "\n",
    "x = tf.placeholder(tf.float32, shape=[batch_size])\n",
    "x_mean = tf.placeholder(tf.float32, shape=[1])\n",
    "x_std = tf.placeholder(tf.float32, shape=[1])\n",
    "x_norm = (x - x_mean) / x_std\n",
    "\n",
    "y = a * x_norm + b\n",
    "\n",
    "y_true = tf.placeholder(tf.float32, shape=[batch_size])\n",
    "y_mean = tf.placeholder(tf.float32, shape=[1])\n",
    "y_std = tf.placeholder(tf.float32, shape=[1])\n",
    "y_true_norm = (y_true - y_mean) / y_std\n",
    "\n",
    "loss = tf.losses.mean_squared_error(labels=y_true_norm, predictions=y)\n",
    "\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.01)\n",
    "train = optimizer.minimize(loss)\n",
    "\n",
    "# note: this part of the graph will be used only at inference time\n",
    "y_denorm = y * y_std + y_mean\n",
    "\n",
    "# write Graph to TensorBoard\n",
    "writer = tf.summary.FileWriter('.')\n",
    "writer.add_graph(tf.get_default_graph())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 3.5527137e-13\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([  0.9941406,   2.9941406,   4.9941406,   6.9941406,   8.994141 ,\n",
       "        10.994141 ,  12.993164 ,  14.993164 ,  16.993164 ,  18.99414  ,\n",
       "        20.99414  ,  22.99414  ,  24.99414  ,  26.99414  ,  28.99414  ,\n",
       "        30.99414  ,  32.993164 ,  34.993164 ,  36.993164 ,  38.99414  ,\n",
       "        40.99414  ,  42.99414  ,  44.99414  ,  46.99414  ,  48.99414  ,\n",
       "        50.99414  ,  52.993164 ,  54.993164 ,  56.993164 ,  58.99414  ,\n",
       "        60.99414  ,  62.99414  ,  64.99414  ,  66.99414  ,  68.99414  ,\n",
       "        70.99414  ,  72.993164 ,  74.993164 ,  76.993164 ,  78.99414  ,\n",
       "        80.99414  ,  82.99414  ,  84.99414  ,  86.99414  ,  88.99414  ,\n",
       "        90.99414  ,  92.993164 ,  94.993164 ,  96.99414  ,  98.99414  ,\n",
       "       100.99414  , 102.99414  , 104.99414  , 106.99414  , 108.99414  ,\n",
       "       110.99414  , 112.993164 , 114.993164 , 116.99414  , 118.99414  ,\n",
       "       120.99414  , 122.99414  , 124.99414  , 126.99414  , 128.99414  ,\n",
       "       130.99414  , 132.99316  , 134.99316  , 136.99414  , 138.99414  ,\n",
       "       140.99414  , 142.99414  , 144.99414  , 146.99414  , 148.99414  ,\n",
       "       150.99316  , 152.99316  , 154.99316  , 156.99414  , 158.99414  ,\n",
       "       160.99414  , 162.99414  , 164.99414  , 166.99414  , 168.99414  ,\n",
       "       170.99316  , 172.99316  , 174.99316  , 176.99414  , 178.99414  ,\n",
       "       180.99414  , 182.99414  , 184.99414  , 186.99414  , 188.99414  ,\n",
       "       190.99316  , 192.99316  , 194.99316  , 196.99414  , 198.99414  ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data\n",
    "\n",
    "x_values = np.arange(10000)\n",
    "y_true_values = x_values * 2 + 1\n",
    "\n",
    "epoch = 100\n",
    "\n",
    "training_data_moments = {\n",
    "    x_mean:[np.mean(x_values)],\n",
    "    x_std:[np.std(x_values)],\n",
    "    y_mean:[np.mean(y_true_values)],\n",
    "    y_std:[np.std(y_true_values)]\n",
    "}\n",
    "\n",
    "# Run training\n",
    "sess = tf.Session()\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)\n",
    "\n",
    "for _ in range(epoch):\n",
    "    for i in range(0, len(x_values), batch_size):\n",
    "        x_batch = x_values[i:i+batch_size]\n",
    "        y_batch = y_true_values[i:i+batch_size]\n",
    "        _, current_loss = sess.run((train, loss), feed_dict={x:x_batch, y_true:y_batch, **training_data_moments})\n",
    "        \n",
    "print(\"Loss: \" + str(current_loss))\n",
    "        \n",
    "# Inference (note: x normalization and y denormalization has been serialized into TF Graph)\n",
    "y_test = sess.run(y_denorm, feed_dict={x:x_values[0:batch_size], **training_data_moments})\n",
    "\n",
    "y_test"
   ]
  }
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