Projeto_aula4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Projeto_aula4.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_aula4.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"
      ],
      "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": 67
        },
        "outputId": "1cb85424-710a-4f1b-ab75-59ec8dd8de8a"
      },
      "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.Dense(128, activation=tensorflow.nn.relu),\n",
        "    keras.layers.Dense(64, activation=tensorflow.nn.relu),\n",
        "    keras.layers.Dense(10, activation=tensorflow.nn.softmax)\n",
        "])\n",
        "\n",
        "modelo.compile(optimizer='adam', \n",
        "               loss='sparse_categorical_crossentropy')\n",
        "\n",
        "modelo.fit(imagens_treino, identificacoes_treino)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 12s 206us/step - loss: 14.5066\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9eafb29160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    }
  ]
}