regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/training/input.py:187: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "To construct input pipelines, use the `tf.data` module.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/training/input.py:187: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "To construct input pipelines, use the `tf.data` module.\n",
      "WARNING:tensorflow:From <ipython-input-1-2852c4321d42>:42: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "To construct input pipelines, use the `tf.data` module.\n",
      "[0.3963168] [0.00575532] 0.02594387\n"
     ]
    }
   ],
   "source": [
    "from __future__ import division, print_function, absolute_import\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "%pylab inline\n",
    "\n",
    "pwd = os.getcwd()\n",
    "tfrecord_file_path = pwd + \"/stft_2.tfrecord\"\n",
    "filename_queue = tf.train.string_input_producer(\n",
    "                              tf.train.match_filenames_once(tfrecord_file_path),\n",
    "                              shuffle=True, num_epochs=None)\n",
    "\n",
    "reader = tf.TFRecordReader()\n",
    "_, serialized_example = reader.read(filename_queue)\n",
    "\n",
    "features = tf.parse_single_example(serialized_example,\n",
    "                                   features = {\n",
    "                                       'theory': tf.FixedLenFeature([1], tf.float32),\n",
    "                                       'cross': tf.FixedLenFeature([1], tf.float32)\n",
    "                                   }  \n",
    "                                  )  \n",
    "\n",
    "y_data = tf.train.shuffle_batch([features['cross']],batch_size=530,capacity=530,min_after_dequeue=100,num_threads=1)\n",
    "x_data = tf.train.shuffle_batch([features['theory']],batch_size=530,capacity=530,min_after_dequeue=100,num_threads=1)\n",
    "\n",
    "W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))\n",
    "b = tf.Variable(tf.zeros([1]))\n",
    "y = W * x_data + b\n",
    " \n",
    "loss = tf.reduce_mean(tf.square(y - y_data))\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.01).minimize(loss)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    tf.local_variables_initializer().run()\n",
    "    tf.global_variables_initializer().run()\n",
    "    coord = tf.train.Coordinator()\n",
    "    thread = tf.train.start_queue_runners(sess=sess,coord=coord)\n",
    "    \n",
    "    for i in range(100):\n",
    "        ratio,bias,_,l = sess.run([W,b,optimizer,loss])\n",
    "    print(ratio,bias,l)\n",
    "    \n",
    "    coord.request_stop()\n",
    "    coord.join(thread)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[0.7249909] [0.00085748] 0.04375537"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f384ce2fda0>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pwd = os.getcwd()\n",
    "readname = \"stft_2\"\n",
    "filepath = pwd + \"/txt_data/\" + readname + \".txt\"\n",
    "fr = open(filepath,'r')\n",
    "datax = []\n",
    "datay = []\n",
    "while True:\n",
    "    line = fr.readline()\n",
    "    if not line:\n",
    "        break\n",
    "    line = line.strip().split()\n",
    "    if len(line) >= 3:\n",
    "        datax.append(float(line[1]))\n",
    "        datay.append(float(line[2]))\n",
    "fr.close()\n",
    "plt.plot(datax,datay,'+')\n",
    "datax = np.array(datax)\n",
    "y = datax * ratio + bias \n",
    "plt.plot(datax, y, '-')"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}