AbordagemInicial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "[View in Colaboratory](https://colab.research.google.com/github/cassiass/keras-tensorflow/blob/master/Projeto_aula7.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sNc3NouWYrN7"
   },
   "source": [
    "Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4bQCahYjYdUB"
   },
   "outputs": [],
   "source": [
    "import tensorflow\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from tensorflow.keras.models import load_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "f8uuzUjVYvkf"
   },
   "source": [
    "Carregando o dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QoIys3wMYirH"
   },
   "outputs": [],
   "source": [
    "dataset = keras.datasets.fashion_mnist\n",
    "((imagens_treino, identificacoes_treino), (imagens_teste, identificacoes_teste)) = dataset.load_data()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oFr0526ZY5E_"
   },
   "source": [
    "Exploração dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fPIT36hpYl5A"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(imagens_treino)\n",
    "imagens_treino.shape\n",
    "imagens_teste.shape\n",
    "len(identificacoes_teste)\n",
    "identificacoes_treino.min()\n",
    "identificacoes_treino.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yTWqT9DIY-iB"
   },
   "source": [
    "Exibição dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bGESm49JVahh"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x19ef3f4f8d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_de_classificacoes = 10\n",
    "nomes_de_classificacoes = ['Camiseta', 'Calça', 'Pullover', \n",
    "                           'Vestido', 'Casaco', 'Sandália', 'Camisa',\n",
    "                           'Tênis', 'Bolsa', 'Bota']\n",
    "\n",
    "plt.imshow(imagens_treino[0])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uU6ZySpIcoc5"
   },
   "source": [
    "Normalizando as imagens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ral_hdl9ulGG"
   },
   "outputs": [],
   "source": [
    "imagens_treino = imagens_treino/float(255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2WABnvOJdCNl"
   },
   "source": [
    "Criando, compilando, treinando e normalizando o modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uFedwlfFc0ii"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0728 16:07:50.619428  9828 deprecation.py:506] From C:\\Users\\Matheus\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 48000 samples, validate on 12000 samples\n",
      "Epoch 1/5\n",
      "48000/48000 [==============================] - 5s 114us/sample - loss: 0.5326 - acc: 0.8120 - val_loss: 0.3974 - val_acc: 0.8548\n",
      "Epoch 2/5\n",
      "48000/48000 [==============================] - 5s 108us/sample - loss: 0.3997 - acc: 0.8548 - val_loss: 0.3787 - val_acc: 0.8623\n",
      "Epoch 3/5\n",
      "48000/48000 [==============================] - 5s 111us/sample - loss: 0.3619 - acc: 0.8672 - val_loss: 0.3399 - val_acc: 0.8759\n",
      "Epoch 4/5\n",
      "48000/48000 [==============================] - 6s 118us/sample - loss: 0.3398 - acc: 0.8742 - val_loss: 0.3585 - val_acc: 0.8683\n",
      "Epoch 5/5\n",
      "48000/48000 [==============================] - 6s 118us/sample - loss: 0.3235 - acc: 0.8808 - val_loss: 0.3517 - val_acc: 0.8738\n"
     ]
    }
   ],
   "source": [
    "modelo = keras.Sequential([ \n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(256, activation=tensorflow.nn.relu),\n",
    "#     keras.layers.Dense(128, activation=tensorflow.nn.relu),\n",
    "#     keras.layers.Dense(64, activation=tensorflow.nn.relu),\n",
    "    keras.layers.Dropout(0.2),\n",
    "    keras.layers.Dense(10, activation=tensorflow.nn.softmax)\n",
    "])\n",
    "\n",
    "modelo.compile(optimizer='adam', \n",
    "               loss='sparse_categorical_crossentropy',\n",
    "               metrics=['accuracy'])\n",
    "\n",
    "historico = modelo.fit(imagens_treino, identificacoes_treino, epochs=5, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4-RqzS88dgk_"
   },
   "source": [
    "Salvando e carregando o modelo treinado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SVjmU41IELzX"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0728 16:08:18.935421  9828 deprecation.py:506] From C:\\Users\\Matheus\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\init_ops.py:97: calling GlorotUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "W0728 16:08:18.939744  9828 deprecation.py:506] From C:\\Users\\Matheus\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
     ]
    }
   ],
   "source": [
    "modelo.save('modelo.h5')\n",
    "modelo_salvo = load_model('modelo.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SVxHO3opdnMb"
   },
   "source": [
    "Visualizando as acurácias de treino e validação por época"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pNc0JsWZY1Ie"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19ef103f9b0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(historico.history['acc'])\n",
    "plt.plot(historico.history['val_acc'])\n",
    "plt.title('Acurácia por épocas')\n",
    "plt.xlabel('épocas')\n",
    "plt.ylabel('acurácia')\n",
    "plt.legend(['treino', 'validação'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZKxkWNawdzZh"
   },
   "source": [
    "Visualizando as perdas de treino e validação por época"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2ugG3Vusg_Va"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19ee7bbd860>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(historico.history['loss'])\n",
    "plt.plot(historico.history['val_loss'])\n",
    "plt.title('Perda por épocas')\n",
    "plt.xlabel('épocas')\n",
    "plt.ylabel('perda')\n",
    "plt.legend(['treino', 'validação'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JFXG1oibd8E8"
   },
   "source": [
    "Testando o modelo e o modelo salvo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "I2kLEHPYUdhd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resultado teste: 2\n",
      "número da imagem de teste: 2\n",
      "resultado teste modelo salvo: 2\n",
      "número da imagem de teste: 2\n"
     ]
    }
   ],
   "source": [
    "testes = modelo.predict(imagens_teste)\n",
    "print('resultado teste:', np.argmax(testes[1]))\n",
    "print('número da imagem de teste:', identificacoes_teste[1])\n",
    "\n",
    "testes_modelo_salvo = modelo_salvo.predict(imagens_teste)\n",
    "print('resultado teste modelo salvo:', np.argmax(testes_modelo_salvo[1]))\n",
    "print('número da imagem de teste:', identificacoes_teste[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U64QMV1GeCUr"
   },
   "source": [
    "Avaliando o modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wsbr9WqyXQ2G"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 39us/sample - loss: 54.2225 - acc: 0.8654\n",
      "Perda do teste: 54.22252141304016\n",
      "Acurácia do teste: 0.8654\n"
     ]
    }
   ],
   "source": [
    "perda_teste, acuracia_teste = modelo.evaluate(imagens_teste, identificacoes_teste)\n",
    "print('Perda do teste:', perda_teste)\n",
    "print('Acurácia do teste:', acuracia_teste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "Projeto_aula7.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}