02.2-breast-cancer-test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-1-b787bdd856f4>:8: RuntimeWarning: overflow encountered in exp\n",
      "  return 1 / ( 1 + np.exp ( -soma ) )      # formula função sigmoid\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    }
   ],
   "source": [
    "# importando as bibliotecas necessárias\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "\n",
    "# definindo funções\n",
    "# definindo a função de ativação sigmoid\n",
    "def sigmoid(soma):\n",
    "    return 1 / ( 1 + np.exp ( -soma ) )      # formula função sigmoid\n",
    "\n",
    "\n",
    "# derivada da função sigmoid\n",
    "def derivadaSigmoid(sig):\n",
    "    return sig * (1 - sig)\n",
    "\n",
    "# parametros da rede\n",
    "momentum = 1\n",
    "taxaAprendizagem = 0.6\n",
    "epocas = 1_000\n",
    "\n",
    "# inicializando pesos aleatoriamente\n",
    "pesos0 = 2 * np.random.random((30, 3)) - 1\n",
    "pesos1 = 2 * np.random.random((3, 1)) - 1\n",
    "\n",
    "# dataset_breast_cancer\n",
    "base = datasets.load_breast_cancer()\n",
    "\n",
    "# entradas\n",
    "entradas = base.data\n",
    "\n",
    "# resultados esperados(classes)\n",
    "valoresSaida = base.target\n",
    "saidas = np.empty([569, 1], dtype=int)\n",
    "for i in range(569):\n",
    "    saidas[i] = valoresSaida[i]\n",
    "\n",
    "# treinamento running time\n",
    "for i in range(epocas):\n",
    "    # camada de entrada\n",
    "    camadaEntrada = entradas                                             # variavel auxiliar para camada de entrada\n",
    "    \n",
    "    # primeira camada\n",
    "    somaSinapse0 = np.dot(camadaEntrada, pesos0)                         # produto escalar entradas e pesos0\n",
    "    camadaOculta = sigmoid(somaSinapse0)                                 # função sigmoid para camada oculta\n",
    "    \n",
    "    # segunda camada\n",
    "    somaSinapse1 = np.dot(camadaOculta, pesos1)                          # produto escalar camada oculta e pesos1\n",
    "    camadaSaida = sigmoid(somaSinapse1)                                  # função sigmoid para camada de saída\n",
    "    \n",
    "    # erro \n",
    "    erroCamadaSaida = saidas - camadaSaida                               # erro da camada de saída\n",
    "    mediaAbsoluta = np.mean(np.abs(erroCamadaSaida))                     # média absoluta do erro\n",
    "    print(\"Erro: \" + str(mediaAbsoluta))                                 # imprimindo erro\n",
    "\n",
    "    derivadaSaida = derivadaSigmoid(camadaSaida)                         # derivada da função sigmoid para camada de saída\n",
    "    deltaSaida = erroCamadaSaida * derivadaSaida                         # delta para camada de saída\n",
    "\n",
    "    # backpropagation\n",
    "    pesos1Transposta = pesos1.T                                          # matriz transposta de pesos1 para fazer a multiplicação de matrizes\n",
    "    deltaSaidaPeso = deltaSaida.dot(pesos1Transposta)                    # produto escalar deltaSaida e pesos1Transposta\n",
    "    deltaCamadaOculta = deltaSaidaPeso * derivadaSigmoid(camadaOculta)   # delta para camada oculta\n",
    "\n",
    "    camadaOcultaTransposta = camadaOculta.T                              # matriz transposta de camada oculta para fazer a multiplicação de matrizes\n",
    "    pesosnovo1 = camadaOcultaTransposta.dot(deltaSaida)                  # produto escalar camada oculta e deltaSaida\n",
    "    pesos1 = (pesos1 * momentum) + (pesosnovo1 * taxaAprendizagem)       # ajustando pesos1\n",
    "\n",
    "    camadaEntradaTransposta = camadaEntrada.T                             # matriz transposta de camada de entrada para fazer a multiplicação de matrizes\n",
    "    pesosnovo0 = camadaEntradaTransposta.dot(deltaCamadaOculta)           # produto escalar camada de entrada e deltaCamadaOculta\n",
    "    pesos0 = (pesos0 * momentum) + (pesosnovo0 * taxaAprendizagem)        # ajustando pesos0\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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