DSA-Python-Cap12-02-Deep-Learning-Teste.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 12</font>\n",
    "\n",
    "## Download: http://github.com/dsacademybr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Versão da Linguagem Python Usada Neste Jupyter Notebook: 3.7.6\n"
     ]
    }
   ],
   "source": [
    "# Versão da Linguagem Python\n",
    "from platform import python_version\n",
    "print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detecção de Emoções em Imagens com Inteligência Artificial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q tensorflow==1.15.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import misc\n",
    "import numpy as np\n",
    "import matplotlib.cm as cm\n",
    "import tensorflow as tf\n",
    "import os, sys, inspect\n",
    "from datetime import datetime\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib as mat\n",
    "import matplotlib.image as mpimg\n",
    "from modulos import utils\n",
    "from modulos.utils import testResult\n",
    "from tensorflow.python.framework import ops\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "import sklearn as sk\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.18.2'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.15.2'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.2.1'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.22.2'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "ops.reset_default_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "emotion = {0:'anger', \n",
    "           1:'disgust',\n",
    "           2:'fear',\n",
    "           3:'happy',\n",
    "           4:'sad',\n",
    "           5:'surprise',\n",
    "           6:'neutral'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rgb2gray(rgb):\n",
    "    return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 12,
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      "INFO:tensorflow:Restoring parameters from modelo/model.ckpt-900\n"
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       "  input: \"^save/Assign\"\n",
       "  input: \"^save/Assign_1\"\n",
       "  input: \"^save/Assign_10\"\n",
       "  input: \"^save/Assign_11\"\n",
       "  input: \"^save/Assign_12\"\n",
       "  input: \"^save/Assign_13\"\n",
       "  input: \"^save/Assign_14\"\n",
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       "  input: \"^save/Assign_20\"\n",
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       "  input: \"^save/Assign_22\"\n",
       "  input: \"^save/Assign_23\"\n",
       "  input: \"^save/Assign_24\"\n",
       "  input: \"^save/Assign_25\"\n",
       "  input: \"^save/Assign_26\"\n",
       "  input: \"^save/Assign_3\"\n",
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       "  input: \"^save/Assign_5\"\n",
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       "}\n",
       "versions {\n",
       "  producer: 134\n",
       "}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_saver = tf.train.import_meta_graph('modelo/model.ckpt-900.meta')\n",
    "new_saver.restore(sess, 'modelo/model.ckpt-900')\n",
    "tf.get_default_graph().as_graph_def()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = sess.graph.get_tensor_by_name(\"input:0\")\n",
    "y_conv = sess.graph.get_tensor_by_name(\"output:0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = mpimg.imread('images_teste/image05.jpg')     \n",
    "gray = rgb2gray(img)\n",
    "plt.imshow(gray, cmap = plt.get_cmap('gray'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_0 = np.resize(gray,(1,48,48,1))\n",
    "tResult = testResult()\n",
    "num_evaluations = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "anger = 0.0%\n",
      "disgust = 0.0%\n",
      "fear = 100.0%\n",
      "happy = 0.0%\n",
      "sad = 0.0%\n",
      "surprise = 0.0%\n",
      "neutral = 0.0%\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, num_evaluations):\n",
    "    result = sess.run(y_conv, feed_dict={x:image_0})\n",
    "    label = sess.run(tf.argmax(result, 1))\n",
    "    label = label[0]\n",
    "    label = int(label)\n",
    "    tResult.evaluate(label)\n",
    "tResult.display_result(num_evaluations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para adquirir conhecimento técnico sólido e especializado em Deep Learning, Visão Computacional, Processamento de Linguagem Natural e outros temas relacionados à Inteligência Artificial, confira nosso programa completo: <a href=\"https://www.datascienceacademy.com.br/pages/formacao-inteligencia-artificial\">Formação Inteligência Artificial</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obrigado - Data Science Academy - <a href=\"http://facebook.com/dsacademybr\">facebook.com/dsacademybr</a>"
   ]
  }
 ],
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