smart_camera_interV1.2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Smart Camera 5000\n",
    "#### Hellen Lima e Victor Barros\n",
    "<a id=\"topo\"></a>\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "<font size = 4><strong>Projeto Integrado 2016/2 - Apresentação Parcial: </strong></font>\n",
    "<p><a href=\"#1\">1. Rede neural para os operandos: </a></p>\n",
    " .\n",
    "<p><a href=\"#1a\"> 1a. Importando Dados de Treinamento e Criando o Modelo</a></p>\n",
    "<p><a href=\"#1b\"> 1b. Cálculo de Acurácia</a></p>\n",
    "<p><a href=\"#1c\"> 1c. Classificando um Novo Dígito</a></p>\n",
    "<p><a href=\"#1d\"> 1d. Resposta do Modelo</a></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"1a\"></a>\n",
    "# Importando Dados de Treinamento e Criando o Modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "#training using 11 classes, the last one is random\n",
    "\n",
    "mnist = input_data.read_data_sets(\"/tmp/data\", one_hot=True)\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, 11]))\n",
    "b = tf.Variable(tf.zeros([11]))\n",
    "y = tf.matmul(x, W) + b\n",
    "#y = tf.nn.softmax(y) no activation function\n",
    "\n",
    "rand = abs(np.random.normal(0,0.5,(2000,784)))\n",
    "#mnist_train = np.array(mnist.train.images[0:20000])\n",
    "mnist_train = np.array(mnist.train.images) #55000 entries\n",
    "input_x = np.concatenate((mnist_train, rand), axis=0) #57000 entries\n",
    "\n",
    "  # Define loss and optimizer\n",
    "y_ = tf.placeholder(tf.float32, [None, 11])\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",
    "label_mnist_last = np.zeros([55000,1])\n",
    "labels_mnist = np.concatenate((mnist.train.labels, label_mnist_last), axis=1)\n",
    "labels_rnd = np.concatenate((np.zeros([2000,10]), np.ones([2000,1])), axis=1)\n",
    "labels = np.concatenate((labels_mnist, labels_rnd), axis=0)\n",
    "\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "tf.initialize_all_variables().run()\n",
    "for _ in range(1000):\n",
    "    batch_xs = input_x\n",
    "    batch_ys = labels\n",
    "    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a href=\"#topo\">[retornar ao topo]</a></p>\n",
    "<hr>\n",
    "<a id=\"1b\"></a>\n",
    "# Cálculo de Acurácia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#calculating accuracy for test\n",
    "rand_in_tst = abs(np.random.normal(0,0.5,(1000,784)))\n",
    "mnist_test = np.array(mnist.test.images)\n",
    "input_tst = np.concatenate((mnist_test, rand_in_tst), axis=0)\n",
    "\n",
    "label_mnist_last_tst = np.zeros([10000,1])\n",
    "labels_mnist_tst = np.concatenate((mnist.test.labels, label_mnist_last_tst), axis=1)\n",
    "labels_rnd_tst = np.concatenate((np.zeros([1000,10]), np.ones([1000,1])), axis=1)\n",
    "labels_tst = np.concatenate((labels_mnist_tst, labels_rnd_tst), axis=0)\n",
    "\n",
    "weights = np.array(sess.run(W))\n",
    "biases = np.array(sess.run(b))\n",
    "\n",
    "outputs_test = np.dot(input_tst,weights)+biases\n",
    "\n",
    "correct_prediction = tf.equal(tf.argmax(outputs_test, 1), tf.argmax(labels_tst, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "print(\"Test accuracy: %f\" % sess.run(accuracy, feed_dict={x:input_tst,\n",
    "                                      y_: labels_tst}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a href=\"#topo\">[retornar ao topo]</a></p>\n",
    "<hr>\n",
    "<a id=\"1c\"></a>\n",
    "# Classificando um Novo Dígito"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10d700b50>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d700bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#showing character\n",
    "import cv2\n",
    "import numpy as np\n",
    "img_raw = cv2.imread('mini_test_sample/doisdahellen.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",
    "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",
    "mnist_test = np.reshape(mnist.test.images[10,:],[28,28])\n",
    "ax.imshow(img_norm,cmap='gray',interpolation=\"nearest\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a href=\"#topo\">[retornar ao topo]</a></p>\n",
    "<hr>\n",
    "<a id=\"1d\"></a>\n",
    "# Resposta do Modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-ef910e9eeeee>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_in\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_norm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m784\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mout_teste\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_in\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mbiases\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mrand_inp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m784\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mout_rand\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrand_inp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mbiases\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "test_in = np.reshape(img_norm,[784,])\n",
    "out_teste = np.dot(test_in,weights)+biases\n",
    "\n",
    "rand_inp = np.random.random([1,784])\n",
    "out_rand = np.dot(rand_inp,weights)+biases\n",
    "\n",
    "print(out_teste)\n",
    "print (\"Resposta: %d\" % out_teste.argmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(784, 11)\n",
      "(11,)\n"
     ]
    }
   ],
   "source": [
    "np.savetxt('weights.txt', weights, fmt='%7.2f')\n",
    "print(np.shape(weights))\n",
    "\n",
    "np.savetxt('biases.txt', biases, fmt='%7.2f')\n",
    "print(np.shape(biases))\n"
   ]
  }
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