3.5. 图像分类数据集(Fashion_Mnist).ipynb

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        "<a href=\"https://colab.research.google.com/github/chongzicbo/Dive-into-Deep-Learning-tf.keras/blob/master/3_5_%E5%9B%BE%E5%83%8F%E5%88%86%E7%B1%BB%E6%95%B0%E6%8D%AE%E9%9B%86(Fashion_Mnist).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "##3.5. 图像分类数据集（Fashion-MNIST）\n",
        "&emsp;&emsp;在介绍softmax回归的实现前我们先引入一个多类图像分类数据集。它将在后面的章节中被多次使用，以方便我们观察比较算法之间在模型精度和计算效率上的区别。图像分类数据集中最常用的是手写数字识别数据集MNIST [1]。但大部分模型在MNIST上的分类精度都超过了95%。为了更直观地观察算法之间的差异，我们将使用一个图像内容更加复杂的数据集Fashion-MNIST [2]。\n",
        "\n",
        "### 3.5.1. 获取数据集\n",
        "&emsp;&emsp;首先导入本节需要的包或模块。"
      ]
    },
    {
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      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 64
        }
      },
      "source": [
        "%matplotlib inline\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets\n",
        "import sys\n",
        "import time\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython import display\n",
        "import tensorflow.data as tfdata"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
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              "<IPython.core.display.HTML object>"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "Bu1_qLJ7FmEA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf.enable_eager_execution()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pGGNhlGNcGF2",
        "colab_type": "text"
      },
      "source": [
        "&emsp;&emsp;下面，我们通过keras的dataset包来下载这个数据集。第一次调用时会自动从网上获取数据。返回训练数据集和测试数据集（testing data set）。测试数据集也叫测试集（testing set），只用来评价模型的表现，并不用来训练模型。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5VehaS589a0Z",
        "colab_type": "code",
        "outputId": "d5cd7890-c6a0-48ef-a948-6a0d94c8c9b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        }
      },
      "source": [
        "(x_train,y_train),(x_test,y_test)=datasets.fashion_mnist.load_data()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8fp1bIMtcj_2",
        "colab_type": "text"
      },
      "source": [
        "&emsp;&emsp;训练集中和测试集中的每个类别的图像数分别为6,000和1,000。因为有10个类别，所以训练集和测试集的样本数分别为60,000和10,000。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hiKIkSdW9r70",
        "colab_type": "code",
        "outputId": "cda6453c-0963-43a9-9710-0d14383428e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "x_train.shape,y_train.shape,x_test.shape,y_test.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 28, 28), (60000,), (10000, 28, 28), (10000,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "onlSq2sf9uj0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "feature,label=x_train[0],y_train[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BdjVSMjRcwBo",
        "colab_type": "text"
      },
      "source": [
        "&emsp;&emsp;变量feature对应高和宽均为28像素的图像。每个像素的数值为0到255之间8位无符号整数（uint8）。它使用三维的Tensor存储。其中的最后一维是通道数。因为数据集中是灰度图像，所以通道数为1。为了表述简洁，我们将高和宽分别为 h 和 w 像素的图像的形状记为 h×w 或（h，w）。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j-md9ub1-tAW",
        "colab_type": "code",
        "outputId": "1cd0f67b-0df8-4cde-962b-0b0a9d48adde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "feature.shape,feature.dtype"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((28, 28), dtype('uint8'))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RIhIsPxI-z71",
        "colab_type": "code",
        "outputId": "134e5146-4a9d-443a-c17f-61896267fbbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "label,type(label),label.dtype"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9, numpy.uint8, dtype('uint8'))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cmvW9ekkc-QB",
        "colab_type": "text"
      },
      "source": [
        "&emsp;&emsp;Fashion-MNIST中一共包括了10个类别，分别为t-shirt（T恤）、trouser（裤子）、pullover（套衫）、dress（连衣裙）、coat（外套）、sandal（凉鞋）、shirt（衬衫）、sneaker（运动鞋）、bag（包）和ankle boot（短靴）。以下函数可以将数值标签转成相应的文本标签。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iUYEGR9V-7M9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_fashion_mnist_labels(labels):\n",
        "  text_labels=['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n",
        "                   'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
        "  return [text_labels[int(i)]  for i in labels]                   "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b6DUoXc2dH8H",
        "colab_type": "text"
      },
      "source": [
        "现在，我们看一下训练数据集中前9个样本的图像内容和文本标签。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zrc0Xe0eAHfm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def use_svg_display():\n",
        "  #用矢量图显示\n",
        "  display.set_matplotlib_formats('svg')\n",
        "def set_figsize(figsize=(3.5,2.5)):\n",
        "  use_svg_display()\n",
        "  #设置图的尺寸\n",
        "  plt.rcParams['figure.figsize']=figsize\n",
        "\n",
        "def show_fashion_mnist(images,labels):\n",
        "  use_svg_display()\n",
        "  _,figs=plt.subplots(1,len(images),figsize=(12,12))\n",
        "  for f,img,lbl in zip(figs,images,labels):\n",
        "    f.imshow(img.reshape((28,28)))\n",
        "    f.set_title(lbl)\n",
        "    f.axes.get_xaxis().set_visible(False)\n",
        "    f.axes.get_yaxis().set_visible(False)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ykClriifBkP8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X,y=x_train[0:9],y_train[0:9]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JunFc9q9B0eV",
        "colab_type": "code",
        "outputId": "96f04870-e19b-4724-da42-58fe8a2a8162",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        }
      },
      "source": [
        "show_fashion_mnist(X,get_fashion_mnist_labels(y))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
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          "metadata": {
            "tags": []
          }
        }
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EGxxDfbudNvY",
        "colab_type": "text"
      },
      "source": [
        "###3.5.2. 读取小批量\n",
        "&emsp;&emsp;我们将在训练数据集上训练模型，并将训练好的模型在测试数据集上评价模型的表现。虽然我们可以像“线性回归的从零开始实现”一节中那样通过yield来定义读取小批量数据样本的函数，但为了代码简洁，这里我们直接通过from_tensor_slices创建Dataset实例。该实例每次读取一个样本数为batch_size的小批量数据。这里的批量大小batch_size是一个超参数。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lfCxFSsaB278",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size=256\n",
        "buffer_size=10000\n",
        "train_iter=tfdata.Dataset.from_tensor_slices((x_train,y_train)).map(lambda x,y:(x/255,y)).shuffle(buffer_size).batch(batch_size)\n",
        "test_iter=tfdata.Dataset.from_tensor_slices((x_test,y_test)).map(lambda x,y:(x/255,y)).batch(batch_size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_I39oL8FKpZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for x,y in iter(train_iter):\n",
        "  print(x[0])\n",
        "  print(y)\n",
        "  break"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MgJPk4FOFeKG",
        "colab_type": "code",
        "outputId": "48a3f30b-af7e-4ab3-9636-e45cd4b58e0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "start=time.time()\n",
        "for X,y in train_iter:\n",
        "  continue\n",
        "\n",
        "'%.2f sec'%(time.time()-start)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'3.75 sec'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRLBJVQ0GiOU",
        "colab_type": "code",
        "outputId": "52bb0710-bfe1-4f0a-8162-c39a0896c131",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "type(train_iter)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensorflow.python.data.ops.dataset_ops.DatasetV1Adapter"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JHbL06xXGl3Y",
        "colab_type": "text"
      },
      "source": [
        "### 3.5.3. 小结\n",
        "&emsp;&emsp;Fashion-MNIST是一个10类服饰分类数据集，之后章节里将使用它来检验不同算法的表现。\n",
        "我们将高和宽分别为 h 和 w 像素的图像的形状记为 h×w 或（h，w）。"
      ]
    }
  ]
}