DSA-Python-Cap10-Lab04.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 10</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": [
    "# Lab 4 - Construindo um Modelo de Regressão Linear com TensorFlow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use como referência o Deep Learning Book: http://www.deeplearningbook.com.br/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obs: Embora a versão 2.x do TensorFlow já esteja disponível, este Jupyter Notebook usar a versão 1.15, que também é mantida pela equipe do Google.\n",
    "\n",
    "Caso queira aprender TensorFlow 2.0, esta versão já está disponível nos cursos da Formação IA, aqui na DSA.\n",
    "\n",
    "Execute a célula abaixo para instalar o TensorFlow na sua máquina."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Versão do TensorFlow a ser usada \n",
    "!pip install -q tensorflow==1.15.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definindo os hyperparâmetros do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparâmetros do modelo\n",
    "learning_rate = 0.01\n",
    "training_epochs = 2000\n",
    "display_step = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definindo os datasets de treino e de teste\n",
    "\n",
    "## Considere X como o tamanho de uma casa e y o preço de uma casa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset de treino\n",
    "train_X = np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1])\n",
    "train_y = np.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n",
    "n_samples = train_X.shape[0]\n",
    " \n",
    "# Dataset de teste\n",
    "test_X = np.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])\n",
    "test_y = np.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Placeholders e variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Placeholders para as variáveis preditoras (x) e para  variável target (y)\n",
    "X = tf.placeholder(tf.float32)\n",
    "y = tf.placeholder(tf.float32)\n",
    " \n",
    "# Pesos e bias do modelo\n",
    "W = tf.Variable(np.random.randn(), name=\"weight\")\n",
    "b = tf.Variable(np.random.randn(), name=\"bias\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Construindo o modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construindo o modelo linear\n",
    "# Fórmula do modelo linear: y = W*X + b\n",
    "linear_model = W*X + b\n",
    " \n",
    "# Mean squared error (erro quadrado médio)\n",
    "cost = tf.reduce_sum(tf.square(linear_model - y)) / (2*n_samples)\n",
    " \n",
    "# Otimização com Gradient descent\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Executando o grafo computacional, treinando e testando o modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   200 \t Custo (Erro):    0.2628 \t W:0.4961 \t b:-0.934\n",
      "Epoch:   400 \t Custo (Erro):    0.1913 \t W:0.4433 \t b:-0.5603\n",
      "Epoch:   600 \t Custo (Erro):    0.1473 \t W: 0.402 \t b:-0.2672\n",
      "Epoch:   800 \t Custo (Erro):    0.1202 \t W:0.3696 \t b:-0.03732\n",
      "Epoch:  1000 \t Custo (Erro):    0.1036 \t W:0.3441 \t b: 0.143\n",
      "Epoch:  1200 \t Custo (Erro):   0.09331 \t W:0.3242 \t b:0.2844\n",
      "Epoch:  1400 \t Custo (Erro):     0.087 \t W:0.3085 \t b:0.3954\n",
      "Epoch:  1600 \t Custo (Erro):   0.08313 \t W:0.2963 \t b:0.4824\n",
      "Epoch:  1800 \t Custo (Erro):   0.08074 \t W:0.2866 \t b:0.5506\n",
      "Epoch:  2000 \t Custo (Erro):   0.07927 \t W:0.2791 \t b:0.6041\n",
      "\n",
      "Otimização Concluída!\n",
      "Custo Final de Treinamento: 0.07927451  - W Final: 0.2790933  - b Final: 0.60413384 \n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Custo Final em Teste: 0.07531277\n",
      "Diferença Média Quadrada Absoluta: 0.003961742\n"
     ]
    },
    {
     "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": [
    "# Definindo a inicialização das variáveis\n",
    "init = tf.global_variables_initializer()\n",
    " \n",
    "# Iniciando a sessão\n",
    "with tf.Session() as sess:\n",
    "    # Iniciando as variáveis\n",
    "    sess.run(init)\n",
    " \n",
    "    # Treinamento do modelo\n",
    "    for epoch in range(training_epochs):\n",
    " \n",
    "        # Otimização com Gradient Descent\n",
    "        sess.run(optimizer, feed_dict={X: train_X, y: train_y})\n",
    "         \n",
    "        # Display de cada epoch\n",
    "        if (epoch+1) % display_step == 0:\n",
    "            c = sess.run(cost, feed_dict={X: train_X, y: train_y})\n",
    "            print(\"Epoch:{0:6} \\t Custo (Erro):{1:10.4} \\t W:{2:6.4} \\t b:{3:6.4}\".format(epoch+1, c, sess.run(W), sess.run(b)))\n",
    "             \n",
    "    # Imprimindo os parâmetros finais do modelo\n",
    "    print(\"\\nOtimização Concluída!\")\n",
    "    training_cost = sess.run(cost, feed_dict={X: train_X, y: train_y})\n",
    "    print(\"Custo Final de Treinamento:\", training_cost, \" - W Final:\", sess.run(W), \" - b Final:\", sess.run(b), '\\n')\n",
    "     \n",
    "    # Visualizando o resultado\n",
    "    plt.plot(train_X, train_y, 'ro', label='Dados Originais')\n",
    "    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Linha de Regressão')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    " \n",
    "    # Testando o modelo\n",
    "    testing_cost = sess.run(tf.reduce_sum(tf.square(linear_model - y)) / (2 * test_X.shape[0]), \n",
    "                            feed_dict={X: test_X, y: test_y})\n",
    "     \n",
    "    print(\"Custo Final em Teste:\", testing_cost)\n",
    "    print(\"Diferença Média Quadrada Absoluta:\", abs(training_cost - testing_cost))\n",
    " \n",
    "    # Display em Teste\n",
    "    plt.plot(test_X, test_y, 'bo', label='Dados de Teste')\n",
    "    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Linha de Regressão')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conheça a Formação Inteligência Artificial, um programa completo, 100% online e 100% em português, com 402 horas em 9 cursos de nível intermediário/avançado, que vão ajudá-lo a se tornar um dos profissionais mais cobiçados do mercado de tecnologia. Clique no link abaixo, faça sua inscrição, comece hoje mesmo e aumente sua empregabilidade:\n",
    "\n",
    "https://www.datascienceacademy.com.br/pages/formacao-inteligencia-artificial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Fim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Obrigado - Data Science Academy - <a href=\"http://facebook.com/dsacademybr\">facebook.com/dsacademybr</a>"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}