Segundo_projeto_aula1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Segundo_projeto_aula1.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "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\n",
        "from tensorflow.keras.models import load_model"
      ],
      "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": "61176d4b-23cf-4b16-887b-9c6a5532ad9f"
      },
      "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": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "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": "c23e4dd9-50c3-4863-8813-8aeb793c49cf"
      },
      "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.colorbar()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7f99d2827a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "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 0x7f99d28f0f60>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "uU6ZySpIcoc5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Normalizando as imagens"
      ]
    },
    {
      "metadata": {
        "id": "Ral_hdl9ulGG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "imagens_treino = imagens_treino/float(255)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2WABnvOJdCNl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Criando, compilando, treinando e normalizando o modelo"
      ]
    },
    {
      "metadata": {
        "id": "uFedwlfFc0ii",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "7772c07b-077d-43e8-a9cd-7f7055df320f"
      },
      "cell_type": "code",
      "source": [
        "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": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/5\n",
            "48000/48000 [==============================] - 9s 189us/step - loss: 0.5252 - acc: 0.8134 - val_loss: 0.4049 - val_acc: 0.8524\n",
            "Epoch 2/5\n",
            "48000/48000 [==============================] - 9s 183us/step - loss: 0.3959 - acc: 0.8569 - val_loss: 0.3694 - val_acc: 0.8693\n",
            "Epoch 3/5\n",
            "48000/48000 [==============================] - 9s 182us/step - loss: 0.3605 - acc: 0.8683 - val_loss: 0.3779 - val_acc: 0.8627\n",
            "Epoch 4/5\n",
            "48000/48000 [==============================] - 9s 183us/step - loss: 0.3377 - acc: 0.8757 - val_loss: 0.3443 - val_acc: 0.8755\n",
            "Epoch 5/5\n",
            "48000/48000 [==============================] - 9s 182us/step - loss: 0.3209 - acc: 0.8805 - val_loss: 0.3495 - val_acc: 0.8765\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "weeyzt2QuDXS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Sumário do modelo"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "5bf36159-d992-43e3-9f62-a2950eeb7628",
        "id": "QLG_CH6Lr2vp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        }
      },
      "cell_type": "code",
      "source": [
        "sumario_do_modelo = modelo.summary()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 256)               200960    \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                2570      \n",
            "=================================================================\n",
            "Total params: 203,530\n",
            "Trainable params: 203,530\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "GVuSQg42udch",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Pesos da primeira camada Dense"
      ]
    },
    {
      "metadata": {
        "id": "YRzqJi5gr-ug",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1354
        },
        "outputId": "fb501c8a-69f6-49c1-ae48-176d4cf06592"
      },
      "cell_type": "code",
      "source": [
        "pesos_camada_dense = modelo.layers[1].get_weights()\n",
        "pesos_camada_dense"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[ 0.00524871, -0.11004062,  0.13758828, ..., -0.1579878 ,\n",
              "         -0.00142127,  0.00939471],\n",
              "        [ 0.03461614, -0.04760416,  0.1779013 , ..., -0.14755748,\n",
              "         -0.10598074, -0.05744827],\n",
              "        [-0.04720343,  0.01652463, -0.03128776, ..., -0.13856664,\n",
              "          0.01710287, -0.05725543],\n",
              "        ...,\n",
              "        [ 0.03753441,  0.21444741,  0.01582495, ..., -0.2257942 ,\n",
              "          0.2782449 , -0.08317742],\n",
              "        [-0.0208508 ,  0.22376445,  0.00300794, ..., -0.17215742,\n",
              "          0.04819855, -0.11253677],\n",
              "        [ 0.02667857,  0.08448928,  0.23865685, ..., -0.05406902,\n",
              "         -0.14307678,  0.01467239]], dtype=float32),\n",
              " array([ 1.16563626e-01,  2.00383574e-01,  2.16542244e-01, -1.04050189e-01,\n",
              "         2.22290292e-01,  2.03423440e-01,  1.78649172e-01,  1.04543686e-01,\n",
              "         3.23054373e-01,  2.97243536e-01, -5.07429475e-03,  1.30689308e-01,\n",
              "         3.12031865e-01,  3.39405507e-01,  1.46878883e-01,  2.59472758e-01,\n",
              "        -1.03004798e-01,  3.85944843e-01,  3.57965261e-01, -7.44848475e-02,\n",
              "         1.53556302e-01,  2.76009977e-01,  1.60143286e-01,  3.65746707e-01,\n",
              "         3.35957766e-01, -4.07021679e-02,  3.53741157e-03,  1.49494678e-01,\n",
              "        -1.18720653e-02,  2.86895782e-01,  1.71697333e-01,  2.70660341e-01,\n",
              "        -1.90745622e-01, -1.26834903e-02, -3.37902158e-02,  1.94578290e-01,\n",
              "         3.59597266e-01,  1.14150092e-01,  1.12235427e-01,  3.16841990e-01,\n",
              "         9.54611879e-03, -1.95544466e-01,  2.29236320e-01,  1.89695671e-01,\n",
              "         4.73570004e-02,  9.87034738e-02,  2.32090458e-01,  2.12860946e-02,\n",
              "         6.61147758e-02, -4.24835980e-01,  3.15535158e-01,  3.21921289e-01,\n",
              "         7.37581030e-02,  3.09438467e-01, -1.87978446e-01, -2.61927731e-02,\n",
              "        -1.60711899e-01,  2.42159218e-01,  1.56536087e-01,  9.22217965e-03,\n",
              "         1.82041883e-01,  9.23544243e-02,  1.35896012e-01,  2.39720523e-01,\n",
              "        -8.12186971e-02,  1.23139266e-02, -3.39500867e-02,  1.22392870e-01,\n",
              "         2.00472146e-01, -2.17560562e-03, -2.20949188e-01,  2.72897154e-01,\n",
              "        -1.08623579e-02,  1.96204364e-01,  3.35872024e-01, -1.97613165e-01,\n",
              "         2.76348889e-01,  1.17945582e-01,  1.88796625e-01,  2.31201984e-02,\n",
              "        -1.41431298e-02,  3.27661216e-01,  2.72685587e-02,  2.58330017e-01,\n",
              "         4.87888791e-02,  2.10950270e-01,  3.46322268e-01,  3.43774706e-01,\n",
              "        -4.27812524e-02,  2.40779400e-01, -6.12564832e-02, -1.78985372e-01,\n",
              "        -8.94419942e-03,  2.07404166e-01,  5.10557322e-03, -1.01498179e-02,\n",
              "         1.30236566e-01,  2.50320852e-01,  2.00364500e-01,  6.64655566e-02,\n",
              "        -1.01419687e-01,  2.57371038e-01, -2.33354792e-01,  3.01758826e-01,\n",
              "         2.40178391e-01,  1.14074878e-01, -1.13115385e-02, -3.27479141e-03,\n",
              "         2.65459508e-01, -1.20592214e-01,  3.37435186e-01, -7.84784369e-03,\n",
              "         2.28633240e-01,  9.78518352e-02,  3.25709015e-01,  2.96632439e-01,\n",
              "         4.44317120e-04,  1.59170657e-01,  4.48305488e-01,  1.08523689e-01,\n",
              "         7.20590055e-02, -9.18169916e-02,  9.71861929e-02, -2.16058847e-02,\n",
              "         5.89288995e-02,  1.77676991e-01, -3.08794882e-02,  2.08451599e-01,\n",
              "         3.34183648e-02,  1.13854900e-01,  1.96839243e-01,  1.40792608e-01,\n",
              "         5.55239134e-02,  1.56214744e-01,  7.90987816e-03,  3.63177717e-01,\n",
              "        -1.56363193e-02,  3.68518591e-01,  9.49568376e-02, -2.08511874e-01,\n",
              "         2.18438044e-01,  6.51510805e-02,  5.28218560e-02, -1.42783836e-01,\n",
              "         1.68985445e-02,  2.90053546e-01,  6.10899851e-02, -1.64991133e-02,\n",
              "         2.13183105e-01,  1.58428669e-01, -6.41118810e-02,  2.65895650e-02,\n",
              "         1.05100922e-01,  1.29719332e-01,  1.71137169e-01,  4.18035805e-01,\n",
              "        -1.75026402e-01, -1.79482233e-02,  1.49668813e-01,  2.35473365e-01,\n",
              "        -2.79365368e-02,  9.41284597e-02, -1.01978900e-02,  1.24944352e-01,\n",
              "         1.08707353e-01, -7.94230867e-03, -3.24166775e-01, -3.66591185e-01,\n",
              "         2.68817842e-01,  2.87170887e-01,  1.16077222e-01, -1.23165967e-02,\n",
              "         1.69344917e-01,  2.15018451e-01, -5.96200190e-02,  1.81544319e-01,\n",
              "        -3.78576550e-03,  2.07346842e-01,  2.60249138e-01, -1.85766026e-01,\n",
              "         1.19951367e-01,  6.72665611e-02,  2.02264652e-01,  1.97026134e-01,\n",
              "         1.78503782e-01,  2.66332686e-01, -2.97352634e-02,  1.04686908e-01,\n",
              "         3.03999603e-01, -1.34745538e-02, -1.50592193e-01, -2.33385824e-02,\n",
              "        -2.67395973e-02,  3.34146231e-01,  4.63848084e-01,  9.08255056e-02,\n",
              "        -1.28224254e-01,  1.70217261e-01, -4.45119590e-02, -2.88670324e-02,\n",
              "        -2.16365922e-02,  3.31768245e-01,  4.08323035e-02,  1.36680275e-01,\n",
              "         8.81125554e-02, -6.27287403e-02,  2.18742967e-01,  1.92244291e-01,\n",
              "         2.81801503e-02,  1.92194059e-01,  5.07624567e-01, -2.77130216e-01,\n",
              "         5.13272882e-02,  2.02881262e-01,  2.07814813e-01,  2.32015803e-01,\n",
              "         8.77994075e-02,  3.72730494e-01, -8.68224353e-03,  8.90769064e-02,\n",
              "         2.78868377e-01, -1.33509599e-02, -1.02290526e-01,  9.12050828e-02,\n",
              "         9.56115499e-02, -1.45345896e-01,  9.74815190e-02,  3.14872265e-01,\n",
              "         4.59881350e-02,  7.03799501e-02,  1.48478866e-01, -2.41981402e-01,\n",
              "         2.21961141e-02, -3.69806541e-03, -9.29736793e-02,  2.39182755e-01,\n",
              "         2.17786700e-01,  1.10879233e-02,  2.37261593e-01,  5.18026769e-01,\n",
              "        -1.78707734e-01, -2.14400850e-02,  1.63810670e-01, -2.38751739e-01,\n",
              "         2.65253652e-02,  3.87158096e-02,  5.02756275e-02, -8.95725042e-02,\n",
              "        -1.45936176e-01, -9.95598454e-03, -4.72079590e-02,  3.96476686e-02,\n",
              "        -2.22098202e-01, -7.88760409e-02, -2.44239885e-02,  8.99515226e-02],\n",
              "       dtype=float32)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "metadata": {
        "id": "4-RqzS88dgk_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Salvando e carregando o modelo treinado"
      ]
    },
    {
      "metadata": {
        "id": "SVjmU41IELzX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "modelo.save('modelo.h5')\n",
        "modelo_salvo = load_model('modelo.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SVxHO3opdnMb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Visualizando as acurácias de treino e validação por época"
      ]
    },
    {
      "metadata": {
        "id": "pNc0JsWZY1Ie",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "19182697-90e1-4179-928a-18b420c48d8a"
      },
      "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": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f99cbdf73c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZgML3vvcwZWV1NDWZqalppK6uiYaGFsrK6mhoaMBqtVFWVsdzzy3gpz/9ORqNhsjIHgwe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\nERERi1Cpi4iIWMT/AT+ENTwu2bzXAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f99d28a4128>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ZKxkWNawdzZh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Visualizando as perdas de treino e validação por época"
      ]
    },
    {
      "metadata": {
        "id": "2ugG3Vusg_Va",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "outputId": "dbc526a0-5890-4ece-a0e0-0ab01e18034a"
      },
      "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": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f99cbd7bc18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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p9lbGyd6GX4zvTYOhmS+35Jo7HCGEEBZAir0VGhfuR5CvC7uyi8gpqDR3OEII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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f99cbdda630>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "JFXG1oibd8E8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Testando o modelo e o modelo salvo"
      ]
    },
    {
      "metadata": {
        "id": "I2kLEHPYUdhd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "c62dd99d-5620-437b-8cda-2553728c86c3"
      },
      "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": 10,
      "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": "U64QMV1GeCUr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Avaliando o modelo"
      ]
    },
    {
      "metadata": {
        "id": "wsbr9WqyXQ2G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "cbf2e2c1-699b-4688-e770-e7ecbe5c0fd5"
      },
      "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": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 0s 40us/step\n",
            "Perda do teste: 2.2868376407623274\n",
            "Acurácia do teste: 0.8565\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}