lab-04-4-tf_reader_linear_regression-tf2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO\n",
    "# Lab 4 Multi-variable linear regression\n",
    "# https://www.tensorflow.org/programmers_guide/reading_data\n",
    "\n",
    "import tensorflow as tf\n",
    "assert(tf.__version__.find('2') == 0)\n",
    "tf.random.set_seed(777)  # for reproducibility\n",
    "\n",
    "filename_queue = tf.train.string_input_producer(\n",
    "    ['data-01-test-score.csv'], shuffle=False, name='filename_queue')\n",
    "\n",
    "reader = tf.TextLineReader()\n",
    "key, value = reader.read(filename_queue)\n",
    "\n",
    "# Default values, in case of empty columns. Also specifies the type of the\n",
    "# decoded result.\n",
    "record_defaults = [[0.], [0.], [0.], [0.]]\n",
    "xy = tf.decode_csv(value, record_defaults=record_defaults)\n",
    "\n",
    "# collect batches of csv in\n",
    "train_x_batch, train_y_batch = \\\n",
    "    tf.train.batch([xy[0:-1], xy[-1:]], batch_size=10)\n",
    "\n",
    "# placeholders for a tensor that will be always fed.\n",
    "X = tf.placeholder(tf.float32, shape=[None, 3])\n",
    "Y = tf.placeholder(tf.float32, shape=[None, 1])\n",
    "\n",
    "W = tf.Variable(tf.random_normal([3, 1]), name='weight')\n",
    "b = tf.Variable(tf.random_normal([1]), name='bias')\n",
    "\n",
    "# Hypothesis\n",
    "hypothesis = tf.matmul(X, W) + b\n",
    "\n",
    "# Simplified cost/loss function\n",
    "cost = tf.reduce_mean(tf.square(hypothesis - Y))\n",
    "\n",
    "# Minimize\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-5)\n",
    "train = optimizer.minimize(cost)\n",
    "\n",
    "# Launch the graph in a session.\n",
    "sess = tf.Session()\n",
    "# Initializes global variables in the graph.\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "# Start populating the filename queue.\n",
    "coord = tf.train.Coordinator()\n",
    "threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n",
    "\n",
    "for step in range(2001):\n",
    "    x_batch, y_batch = sess.run([train_x_batch, train_y_batch])\n",
    "    cost_val, hy_val, _ = sess.run(\n",
    "        [cost, hypothesis, train], feed_dict={X: x_batch, Y: y_batch})\n",
    "    if step % 10 == 0:\n",
    "        print(step, \"Cost: \", cost_val, \"\\nPrediction:\\n\", hy_val)\n",
    "\n",
    "coord.request_stop()\n",
    "coord.join(threads)\n",
    "\n",
    "# Ask my score\n",
    "print(\"Your score will be \",\n",
    "      sess.run(hypothesis, feed_dict={X: [[100, 70, 101]]}))\n",
    "\n",
    "print(\"Other scores will be \",\n",
    "      sess.run(hypothesis, feed_dict={X: [[60, 70, 110], [90, 100, 80]]}))\n",
    "\n",
    "'''\n",
    "Your score will be  [[185.33531]]\n",
    "Other scores will be  [[178.36246]\n",
    " [177.03687]]\n",
    "'''\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}