3op_nn-all data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import cv2\n",
    "import glob\n",
    "# this first cell is to import the database from my computer\n",
    "\n",
    "#label: [mult add sub]\n",
    "fnames_mult=[]\n",
    "fnames_mult = [os.path.basename(x) for x in glob.glob('/Users/hellen/workspace/supercamera/data/pdf2jpg-melhor resolucao/multiplicacao/train/*.jpg')]\n",
    "train_mult = [];\n",
    "labels_mult = [];\n",
    "for i in range(len(fnames_mult)):\n",
    "    filepath = '/Users/hellen/workspace/supercamera/data/pdf2jpg-melhor resolucao/multiplicacao/train/' + fnames_mult[i];\n",
    "    image = cv2.imread(filepath,cv2.IMREAD_GRAYSCALE);\n",
    "    image = np.reshape(image,[784,])\n",
    "    train_mult.append(image);\n",
    "    labels_mult.append([1,0,0]);\n",
    "\n",
    "fnames_add = [os.path.basename(x) for x in glob.glob('/Users/hellen/workspace/supercamera/data/pdf2jpg-melhor resolucao/adicao/train/*.jpg')]\n",
    "train_add = [];\n",
    "labels_add = [];\n",
    "for i in range(len(fnames_add)):\n",
    "    filepath = '/Users/hellen/workspace/supercamera/data/pdf2jpg-melhor resolucao/adicao/train/' + fnames_add[i];\n",
    "    image = cv2.imread(filepath,cv2.IMREAD_GRAYSCALE);\n",
    "    image = np.reshape(image,[784,])\n",
    "    train_add.append(image);\n",
    "    labels_add.append([0,1,0]);\n",
    "\n",
    "fnames_sub = [os.path.basename(x) for x in glob.glob('/Users/hellen/workspace/supercamera/data/pdf2jpg-melhor resolucao/subtracao/train/*.jpg')]\n",
    "train_sub = [];\n",
    "labels_sub = [];\n",
    "for i in range(len(fnames_sub)):\n",
    "    filepath = '/Users/hellen/workspace/supercamera/data/pdf2jpg-melhor resolucao/subtracao/train/' + fnames_sub[i];\n",
    "    image = cv2.imread(filepath,cv2.IMREAD_GRAYSCALE);\n",
    "    image = np.reshape(image,[784,]);\n",
    "    train_sub.append(image);\n",
    "    labels_sub.append([0,0,1]);\n",
    "\n",
    "input_x = np.concatenate((train_mult, train_add, train_sub), axis=0)\n",
    "labels = np.concatenate((labels_mult, labels_add, labels_sub), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# importing test sample\n",
    "fnames_mult = [os.path.basename(x) for x in glob.glob('/Users/hellen/workspace/supercamera/data/pdf2jpg-melhor resolucao/multiplicacao/test/*.jpg')]\n",
    "test_mult = [];\n",
    "labels_mult_tst = [];\n",
    "for i in range(len(fnames_mult)):\n",
    "    filepath = '/Users/hellen/workspace/supercamera/data/pdf2jpg-melhor resolucao/multiplicacao/test/' + fnames_mult[i];\n",
    "    image = cv2.imread(filepath,cv2.IMREAD_GRAYSCALE);\n",
    "    image = np.reshape(image,[784,])\n",
    "    test_mult.append(image);\n",
    "    labels_mult_tst.append([1,0,0]);\n",
    "\n",
    "fnames_add = [os.path.basename(x) for x in glob.glob('/Users/hellen/workspace/supercamera/data/pdf2jpg-melhor resolucao/adicao/test/*.jpg')]\n",
    "test_add = [];\n",
    "labels_add_tst = [];\n",
    "for i in range(len(fnames_add)):\n",
    "    filepath = '/Users/hellen/workspace/supercamera/data/pdf2jpg-melhor resolucao/adicao/test/' + fnames_add[i];\n",
    "    image = cv2.imread(filepath,cv2.IMREAD_GRAYSCALE);\n",
    "    image = np.reshape(image,[784,])\n",
    "    test_add.append(image);\n",
    "    labels_add_tst.append([0,1,0]);\n",
    "\n",
    "fnames_sub = [os.path.basename(x) for x in glob.glob('/Users/hellen/workspace/supercamera/data/pdf2jpg-melhor resolucao/subtracao/test/*.jpg')]\n",
    "test_sub = [];\n",
    "labels_sub_tst = [];\n",
    "for i in range(len(fnames_sub)):\n",
    "    filepath = '/Users/hellen/workspace/supercamera/data/pdf2jpg-melhor resolucao/subtracao/test/' + fnames_sub[i];\n",
    "    image = cv2.imread(filepath,cv2.IMREAD_GRAYSCALE);\n",
    "    image = np.reshape(image,[784,])\n",
    "    test_sub.append(image);\n",
    "    labels_sub_tst.append([0,0,1]);\n",
    "    \n",
    "input_tst = np.concatenate((test_mult, test_add, test_sub), axis=0)\n",
    "labels_tst = np.concatenate((labels_mult_tst, labels_add_tst, labels_sub_tst), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#concatenating train + test\n",
    "\n",
    "input_all = np.concatenate((input_x, input_tst), axis=0)\n",
    "labels_all = np.concatenate((labels, labels_tst), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11590, 784)\n",
      "(1948, 784)\n",
      "(13538, 784)\n",
      "(11590, 3)\n",
      "(1948, 3)\n",
      "(13538, 3)\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(input_x));\n",
    "print(np.shape(input_tst));\n",
    "print(np.shape(input_all));\n",
    "\n",
    "print(np.shape(labels));\n",
    "print(np.shape(labels_tst));\n",
    "print(np.shape(labels_all));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#from tensorflow.examples.tutorials.mnist import input_data\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "#training process using mult, add and sub\n",
    "#label: [mult add sub]\n",
    "\n",
    "  # Create the model\n",
    "x = tf.placeholder(tf.float32, [None, 784])\n",
    "#W = tf.Variable(tf.random_normal([784, 10], stddev=0.2))\n",
    "W = tf.Variable(tf.zeros([784, 3]))\n",
    "b = tf.Variable(tf.zeros([3]))\n",
    "y = tf.matmul(x, W) + b\n",
    "#y = tf.nn.softmax(y) no activation function\n",
    "\n",
    "# some train samples, 3 classes\n",
    "\n",
    "  # Define loss and optimizer\n",
    "y_ = tf.placeholder(tf.float32, [None, 3])\n",
    "\n",
    "cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))\n",
    "train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\n",
    "\n",
    "# labels: [mult add sub]\n",
    "\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "tf.initialize_all_variables().run()\n",
    "for _ in range(1000):\n",
    "    batch_xs = input_all\n",
    "    batch_ys = labels_all\n",
    "    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.992096\n"
     ]
    }
   ],
   "source": [
    "#calculating accuracy?\n",
    "\n",
    "weights = np.array(sess.run(W))\n",
    "biases = np.array(sess.run(b))\n",
    "\n",
    "outputs_test = np.dot(input_all,weights)+biases\n",
    "n_tst_samples = len(outputs_test);\n",
    "correct_prediction = 0;\n",
    "for i in range(n_tst_samples):\n",
    "    if outputs_test[i].argmax() == labels_all[i].argmax():\n",
    "        correct_prediction = correct_prediction + 1\n",
    "        #print(correct_prediction)\n",
    "\n",
    "accuracy = float(correct_prediction)/float(n_tst_samples);\n",
    "print(\"Test accuracy: %f\" % accuracy);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1042683d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f4e4e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "#img_raw = cv2.imread('mini_test_sample/mais.jpg',cv2.IMREAD_GRAYSCALE)\n",
    "img_raw = cv2.imread('operador.jpg',cv2.IMREAD_GRAYSCALE)\n",
    "img_raw = cv2.resize(img_raw, (28, 28)) \n",
    "\n",
    "inv_mat = np.ones([28,28])*255\n",
    "img_inv = abs(np.subtract(img_raw, inv_mat))\n",
    "img_inv[img_inv>20] = 255\n",
    "img_inv[img_inv<20] = 0\n",
    "img_norm = img_inv/255\n",
    "\n",
    "#image_norm = input_tst[0]\n",
    "#image_norm = np.reshape(image_norm,[28,28])\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.legend_handler import HandlerLine2D\n",
    "\n",
    "plt.rcParams['lines.linewidth'] = 2\n",
    "plt.rcParams['legend.handlelength'] = 3\n",
    "plt.rcParams['legend.borderpad'] = 0.3\n",
    "plt.rcParams['legend.numpoints'] = 1\n",
    "plt.rcParams['xtick.labelsize'] = 18\n",
    "plt.rcParams['font.weight'] = 'bold'\n",
    "plt.rcParams['ytick.labelsize'] = 18\n",
    "\n",
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(figsize=(28,28),nrows=1, ncols=1)\n",
    "\n",
    "ax.imshow(img_raw,cmap='gray',interpolation=\"nearest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 25895.08203125  10507.85058594 -36402.9609375 ]]\n",
      "Resposta: multiplicar\n",
      "(1, 784)\n"
     ]
    }
   ],
   "source": [
    "test_in = np.reshape(img_raw,[1,784])\n",
    "out_teste = np.dot(test_in,weights)+biases\n",
    "#label: [mult add sub]\n",
    "\n",
    "print(out_teste)\n",
    "#print (\"Resposta: %d\" % out_teste.argmax())\n",
    "if out_teste.argmax() == 0:\n",
    "    print (\"Resposta: multiplicar\")\n",
    "if out_teste.argmax() == 1:\n",
    "    print (\"Resposta: somar\")\n",
    "if out_teste.argmax() == 2:\n",
    "    print (\"Resposta: subtrair\")\n",
    "    \n",
    "print(np.shape(test_in))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(784, 3)\n",
      "(3,)\n"
     ]
    }
   ],
   "source": [
    "np.savetxt('weights_op_all.txt', weights, fmt='%7.2f')\n",
    "print(np.shape(weights))\n",
    "\n",
    "np.savetxt('biases_op_all.txt', biases, fmt='%7.2f')\n",
    "print(np.shape(biases))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  0.  0.]\n",
      "(3,)\n"
     ]
    }
   ],
   "source": [
    "a = np.zeros([3])\n",
    "print(a)\n",
    "print(np.shape(a))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}