Projeto_aula6.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Projeto_aula6.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/cassiass/keras-tensorflow/blob/master/Projeto_aula6.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "sNc3NouWYrN7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Imports"
      ]
    },
    {
      "metadata": {
        "id": "4bQCahYjYdUB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "f8uuzUjVYvkf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Carregando o dataset"
      ]
    },
    {
      "metadata": {
        "id": "QoIys3wMYirH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dataset = keras.datasets.fashion_mnist\n",
        "((imagens_treino, identificacoes_treino), (imagens_teste, identificacoes_teste)) = dataset.load_data()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oFr0526ZY5E_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Exploração dos dados"
      ]
    },
    {
      "metadata": {
        "id": "fPIT36hpYl5A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c1115b73-38f8-44ee-d16b-9ed85fcea1be"
      },
      "cell_type": "code",
      "source": [
        "len(imagens_treino)\n",
        "imagens_treino.shape\n",
        "imagens_teste.shape\n",
        "len(identificacoes_teste)\n",
        "identificacoes_treino.min()\n",
        "identificacoes_treino.max()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "yTWqT9DIY-iB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Exibição dos dados"
      ]
    },
    {
      "metadata": {
        "id": "bGESm49JVahh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "11546b53-773f-453e-ce77-5105fe2cee6f"
      },
      "cell_type": "code",
      "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.title(identificacoes_treino[0])\n",
        "\n",
        "for imagem in range(10):\n",
        "  plt.subplot(2, 5, imagem+1)\n",
        "  plt.imshow(imagens_treino[imagem])\n",
        "  plt.title(nomes_de_classificacoes[identificacoes_treino[imagem]])\n",
        "'''  \n",
        "plt.imshow(imagens_treino[0])\n",
        "plt.colorbar()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7f665618a588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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f//znxjFfeOEF47qff/65Ub0+ffoEPFddXd3sOCkpyfj6Xbt2Nap35SRUW+xs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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f66561dfa20>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Ral_hdl9ulGG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "8fa5c8ac-db3b-4304-89b8-04e3cc003acb"
      },
      "cell_type": "code",
      "source": [
        "#normalização\n",
        "imagens_treino = imagens_treino/float(255)\n",
        "\n",
        "modelo = keras.Sequential([ \n",
        "    keras.layers.Flatten(input_shape=(28, 28)),\n",
        "    keras.layers.Dense(256, 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)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/5\n",
            "48000/48000 [==============================] - 9s 184us/step - loss: 1.0886 - acc: 0.6531 - val_loss: 0.6933 - val_acc: 0.7533\n",
            "Epoch 2/5\n",
            "48000/48000 [==============================] - 9s 177us/step - loss: 0.6539 - acc: 0.7629 - val_loss: 0.5897 - val_acc: 0.7833\n",
            "Epoch 3/5\n",
            "48000/48000 [==============================] - 9s 179us/step - loss: 0.5774 - acc: 0.7918 - val_loss: 0.5344 - val_acc: 0.8068\n",
            "Epoch 4/5\n",
            "48000/48000 [==============================] - 9s 178us/step - loss: 0.5335 - acc: 0.8095 - val_loss: 0.5026 - val_acc: 0.8203\n",
            "Epoch 5/5\n",
            "48000/48000 [==============================] - 9s 179us/step - loss: 0.5031 - acc: 0.8209 - val_loss: 0.4829 - val_acc: 0.8282\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SVjmU41IELzX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "\n",
        "modelo.save('modelo.h5')\n",
        "modelo_salvo = load_model('modelo.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pNc0JsWZY1Ie",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "outputId": "6b0ee96a-0484-43ab-ab73-aa30900313a9"
      },
      "cell_type": "code",
      "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"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbf9aff1ac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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srt1HfnUBJU0nAfBwc+fqsEmkR6QwXjtWNrXpg6Gpg4MnGyg6Uc+h0gZaO850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\n3F1XpDAlAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbf9b3d7860>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "2ugG3Vusg_Va",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "outputId": "27a42694-f172-4895-8a5e-ee0a0f1e4521"
      },
      "cell_type": "code",
      "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'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbf9a6fa6d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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R2SWjbyGEcGcS3iOEJdCHJdOiqDnbRmZ2udblCCGEuA4S3iPIV2bH4W3U88Hu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\n8SYiIhIMizcREZFg/h/KkzcsnLQbSwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbf9a7763c8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "I2kLEHPYUdhd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "65c79ae9-0f0d-4cbf-e2e8-24411da24834"
      },
      "cell_type": "code",
      "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])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "wsbr9WqyXQ2G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "2719b4f2-8d1c-4d63-b70c-f8ceaa402ef1"
      },
      "cell_type": "code",
      "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)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 0s 34us/step\n",
            "Perda do teste: 13.12789245300293\n",
            "Acurácia do teste: 0.1835\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}