{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#output random values from a truncated normal distribution\n",
    "a = tf.truncated_normal([16,128,128,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.Session()\n",
    "sess.run(tf.initialize_all_variables())\n",
    "sess.run(tf.shape(a)) #create a in session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = tf.reshape(a,[16,49152])\n",
    "sess.run(tf.shape(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "################ starting point ###############\n",
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "from random import shuffle\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAIN_DIR = 'C:/Users/Awannaphasch2016/Documents/tensorflow cat and dog/train'\n",
    "TEST_DIR = 'C:/Users/Awannaphasch2016/Documents/tensorflow cat and dog/test1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_SIZE = 50\n",
    "LR = 1e-3 # what is this ?? \n",
    "#just so we remember which saved model is which, sized must match\n",
    "#to call\n",
    "MODEL_NAME = 'dogvscats-{}-{}.model'.format(LR, '2conv-basic')"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}