image_generate_description.ipynb

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   "source": [
    "# 如何使用深度学习为照片自动生成文本描述\n",
    "\n",
    "## 定义\n",
    "对图像搜索和帮助视觉障碍者「查看」世界等应用而言，让图像带有文本描述是非常有价值的。使用人力标注显然不现实，而随着深度学习技术的发展，使用机器为图像自动生成准确的文本描述成为了可能。\n",
    "## 难点\n",
    "图像描述涉及到为给定图像（比如照片）生成人类可读的文本描述。这个问题对人类而言非常简单，但对机器来说却非常困难，因为它既涉及到理解图像的内容，还涉及到将理解到的内容翻译成自然语言。\n",
    "\n",
    "## 如何描述图像\n",
    "描述图像是指为图像（比如某个物体或场景的照片生成人类可读的文本描述）。这个问题有时成为自动图像标注或图像标注，这个问题对人类来说轻而易举，但对机器来说非常困难。要解决这个问题，既需要理解图像的内容，也需要将其中的含义用词语表达出来，并且所表达的词语必须以正确的方式串接起来才能被理解。这需要计算机视觉和自然语言处理结合起来，是广义的人工智能领域的一大难题。\n",
    "\n",
    "### 神经描述图像\n",
    "神经网络模型已经主导了自动描述生成领域；这主要是因为这种方法得到了当前最佳的结果。在端到端的神经网络模型之前，生成图像描述的两种主要方法是基于模板的方法和基于最近邻并修改已有描述的方法。\n",
    "用于描述的神经网络模型涉及到两个主要元素：\n",
    "\n",
    "* 特征提取\n",
    "* 语言模型\n",
    "\n",
    "#### 特征提取\n",
    "特征提取模型是一种神经网络。给定一张图像，它可以提取出显著的特征，通常用固定长度的向量表示。提取出的特征是该图像的内部表征，不是人类可以直接理解的东西。用作特征提取子模型的通常是深度卷积神经网络（CNN）。这种网络可以在图像描述数据集中的图像上直接训练。或者可以使用预训练的模型（比如用于图像分类的当前最佳的模型），或者也可以使用混合方法，即使用预训练的模型并根据实际问题进行微调。\n",
    "![image.png](http://ata2-img.cn-hangzhou.img-pub.aliyun-inc.com/0ee9d33d69285e3466eb5199fd8650e8.png)\n",
    "\n",
    "#### 语言模型\n",
    "一般而言，当一个序列已经给出了一些词时，语言模型可以预测该序列的下一个词的概率。\n",
    "对于图像描述，语言模型这种神经网络可以基于网络提取出的特征预测描述中的词序列并根据已经生成的词构建描述。\n",
    "常用的方法是使用循环神经网络作为语言模型，比如长短期记忆网络（LSTM）。每个输出时间步骤都会在序列中生成一个新词。\n",
    "然后每个生成的词都会使用一个词嵌入（比如 word2vec）进行编码，该编码会作为输入被传递给解码器以生成后续的词。\n",
    "对该模型的一种改进方法是为输出序列收集词在词汇库中的概率分布并搜索它以生成多个可能的描述。这些描述可以根据似然（likelihood）进行评分和排序。常见的方式是使用波束搜索（Beam Search）进行这种搜索。\n",
    "语言模型可以使用从图像数据集提取出的预计算的特征单独训练得到；也可以使用特征提取网络或某些组合方法来联合训练得到。\n",
    "![image.png](http://ata2-img.cn-hangzhou.img-pub.aliyun-inc.com/98647607cde8bd5e55be6d69f8f4f2bd.png)\n",
    "\n",
    "#### 编码器解码器架构\n",
    "构建子模型的一种常用方法是使用编码器-解码器架构，其中两个模型是联合训练的。\n",
    "\n",
    ">这种模型的基础是将图像编码成紧凑的表征的卷积神经网络，后面跟着一个循环神经网络来生成对应的句子。这种模型的训练目标是最大化给定图像的句子的似然。——《Show and Tell：一种神经图像描述生成器》，2015\n",
    "\n",
    "这种架构原本是为机器翻译开发的，其中输入的序列（比如法语）会被一个编码器网络编码成固定长度的向量。然后一个分立的解码器网络会读取这些编码并用另一种语言（比如英语）生成输出序列。\n",
    "\n",
    "除了能力出色外，这种方法的好处是可以在该问题上训练单个端到端模型。\n",
    "\n",
    "当将该方法用于图像描述时，编码器网络使用了深度卷积神经网络，解码器网络则是 LSTM 层的堆叠。\n",
    "\n",
    "> 在机器翻译中，「编码器」RNN 会读取源句子并将其转换成信息丰富的固定长度的向量表征，这种表征又会被用作「解码器」RNN 的初始隐藏状态，进而生成目标句子。我们在这里提出遵循这种优雅的方案，并使用深度卷积神经网络（CNN）替代编码器 RNN。——《Show and Tell：一种神经图像描述生成器》，2015\n",
    "\n",
    " ![image.png](http://ata2-img.cn-hangzhou.img-pub.aliyun-inc.com/f26e5b2fe0999db864680b439f747247.png)"
   ]
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   "source": [
    "## keras生成图像描述实战\n",
    "\n",
    "![image.png](http://ata2-img.cn-hangzhou.img-pub.aliyun-inc.com/1fd83e8552e584ecbdc3460eae410c6c.png)\n",
    "\n",
    "### 准备图像和描述数据集\n",
    "图像字幕生成可使用的优秀数据集有 Flickr8K 数据集。原因在于它逼真且相对较小，即使你的工作站使用的是 CPU 也可以下载它，并用于构建模型。\n",
    "作者对该数据集的描述如下：\n",
    "> 我们介绍了一种用于基于句子的图像描述和搜索的新型基准集合，包括 8000 张图像，每个图像有五个不同的字幕描述对突出实体和事件提供清晰描述。\n",
    "图像选自六个不同的 Flickr 组，往往不包含名人或有名的地点，而是手动选择多种场景和情形。\n",
    "\n",
    "该数据集的地址：\n",
    "* image数据：<http://nlp.cs.illinois.edu/HockenmaierGroup/Framing_Image_Description/Flickr8k_Dataset.zip>\n",
    "* 描述文本数据：<http://nlp.cs.illinois.edu/HockenmaierGroup/Framing_Image_Description/Flickr8k_text.zip>\n",
    "\n",
    "\n",
    "下载数据集，并在当前工作文件夹里进行解压缩。你将得到两个目录：\n",
    "\n",
    "* Flicker8k_Dataset：包含 8092 张 JPEG 格式图像。\n",
    "* Flickr8k_text：包含大量不同来源的图像描述文件。\n",
    "该数据集包含一个预制训练数据集（6000 张图像）、开发数据集（1000 张图像）和测试数据集（1000 张图像）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
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   "source": [
    "import numpy as np\n",
    "from os import listdir\n",
    "from pickle import dump\n",
    "from pickle import load\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.preprocessing.image import load_img\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "from keras.applications.vgg16 import preprocess_input\n",
    "from keras.models import Model\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.utils import to_categorical\n",
    "from keras.utils import plot_model\n",
    "from keras.models import Model\n",
    "from keras.layers import Input\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Embedding\n",
    "from keras.layers import Dropout\n",
    "from keras.layers.merge import add\n",
    "from keras.callbacks import ModelCheckpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 针对训练集中的图像进行特征提取\n",
    "\n",
    "由上文神经网络生成描述可得，第一步加载训练图像，我们需要用卷积神经网络对训练图像进行特征提取，生成固定长度的向量表示，该向量作为图像描述网络的编码器。Keras内置了已经训练好的cnn网络，我们不需要重新训练网络提取特征，直接使用训练好的vgg网络，去掉softmax分类层。因为我们对图像分类不感兴趣，我们感兴趣的是分类之前图像的内部表征，及图像的特征。\n",
    "    \n",
    "![image.png](http://ata2-img.cn-hangzhou.img-pub.aliyun-inc.com/c5736385fbbe08b2bc3807a8d8e75216.png)\n",
    "\n",
    "Keras 还提供工具将加载图像改造成模型的偏好大小（如 3 通道 224 x 224 像素图像）。\n",
    "\n",
    "下面是 extract_features() 函数，即给出一个目录名，该函数将加载每个图像、为 VGG 准备图像数据，并从 VGG 模型中收集预测到的特征。图像特征是包含 4096 个元素的向量，该函数向图像特征返回一个图像标识符（identifier）词典。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "source": [
    "\"\"\"\n",
    "给一个目录名，该函数将加载每个图像，为VGG模型中收集预测到的特征，并从VGG模型中收集预测到的特征。\n",
    "图像特征是包含4096个元素的向量 ，该函数向图像特征返回一个图像标识符字典.\n",
    "\n",
    "\"\"\"\n",
    "def extract_features(directory):\n",
    "    # 加载VGG模型\n",
    "    model = VGG16()\n",
    "    \"\"\"弹出模型最后一层,最后一层是softmax的分类，这里我们只需要vgg全连接层\"\"\" \n",
    "    model.layers.pop()\n",
    "    model = Model(inputs=model.inputs, outputs=model.layers[-1].output)\n",
    "    # summarize\n",
    "    print(model.summary())\n",
    "    # extract features from each photo\n",
    "    features = dict()\n",
    "    for name in listdir(directory):\n",
    "        # load an image from file\n",
    "        filename = directory + '/' + name\n",
    "        image = load_img(filename, target_size=(224, 224))\n",
    "        # 将图像数据转化为numpy array\n",
    "        image = img_to_array(image)\n",
    "        # reshape data for the model\n",
    "        image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))\n",
    "        # prepare the image for the VGG model\n",
    "        image = preprocess_input(image)\n",
    "        # get features\n",
    "        feature = model.predict(image, verbose=0)\n",
    "        # get image id\n",
    "        image_id = name.split('.')[0]\n",
    "        # store feature\n",
    "        features[image_id] = feature\n",
    "        print('>%s' % name)\n",
    "    return features"
   ]
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   "execution_count": 3,
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "fc1 (Dense)                  (None, 4096)              102764544 \n",
      "_________________________________________________________________\n",
      "fc2 (Dense)                  (None, 4096)              16781312  \n",
      "=================================================================\n",
      "Total params: 134,260,544\n",
      "Trainable params: 134,260,544\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
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      ">957230475_48f4285ffe.jpg\n",
      ">95728660_d47de66544.jpg\n",
      ">95728664_06c43b90f1.jpg\n",
      ">95734035_84732a92c1.jpg\n",
      ">95734036_bef6d1a871.jpg\n",
      ">95734038_2ab5783da7.jpg\n",
      ">957682378_46c3b07bcd.jpg\n",
      ">95783195_e1ba3f57ca.jpg\n",
      ">958326692_6210150354.jpg\n",
      ">961189263_0990f3bcb5.jpg\n",
      ">961611340_251081fcb8.jpg\n",
      ">963730324_0638534227.jpg\n",
      ">96399948_b86c61bfe6.jpg\n",
      ">964197865_0133acaeb4.jpg\n",
      ">96420612_feb18fc6c6.jpg\n",
      ">965444691_fe7e85bf0e.jpg\n",
      ">967719295_3257695095.jpg\n",
      ">968081289_cdba83ce2e.jpg\n",
      ">96973080_783e375945.jpg\n",
      ">96978713_775d66a18d.jpg\n",
      ">96985174_31d4c6f06d.jpg\n",
      ">970641406_9a20ee636a.jpg\n",
      ">97105139_fae46fe8ef.jpg\n",
      ">972381743_5677b420ab.jpg\n",
      ">973827791_467d83986e.jpg\n",
      ">97406261_5eea044056.jpg\n",
      ">974924582_10bed89b8d.jpg\n",
      ">975131015_9acd25db9c.jpg\n",
      ">97577988_65e2eae14a.jpg\n",
      ">976392326_082dafc3c5.jpg\n",
      ">97731718_eb7ba71fd3.jpg\n",
      ">977856234_0d9caee7b2.jpg\n",
      ">978580450_e862715aba.jpg\n",
      ">979201222_75b6456d34.jpg\n",
      ">979383193_0a542a059d.jpg\n",
      ">98377566_e4674d1ebd.jpg\n",
      ">985067019_705fe4a4cc.jpg\n",
      ">987907964_5a06a63609.jpg\n",
      ">989754491_7e53fb4586.jpg\n",
      ">989851184_9ef368e520.jpg\n",
      ">990890291_afc72be141.jpg\n",
      ">99171998_7cc800ceef.jpg\n",
      ">99679241_adc853a5c0.jpg\n",
      ">997338199_7343367d7f.jpg\n",
      ">997722733_0cb5439472.jpg\n",
      "Extracted Features: 8091\n"
     ]
    }
   ],
   "source": [
    "# extract features from all images\n",
    "directory = 'Flicker8k_Dataset'\n",
    "features = extract_features(directory)\n",
    "print('Extracted Features: %d' % len(features))\n",
    "# save to file\n",
    "dump(features, open('features.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 准备文本数据\n",
    "\n",
    "**文本数据预处理步骤:**\n",
    "![image.png](http://ata2-img.cn-hangzhou.img-pub.aliyun-inc.com/6989091d971bbbb62063fdb30f18f3ce.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded: 8092 \n",
      "Vocabulary Size: 8763\n"
     ]
    }
   ],
   "source": [
    "import string\n",
    "\n",
    "# load doc into memory\n",
    "def load_doc(filename):\n",
    "    # open the file as read only\n",
    "    file = open(filename, 'r')\n",
    "    # read all text\n",
    "    text = file.read()\n",
    "    # close the file\n",
    "    file.close()\n",
    "    return text\n",
    "\n",
    "# extract descriptions for images\n",
    "def load_descriptions(doc):\n",
    "    mapping = dict()\n",
    "    # process lines\n",
    "    for line in doc.split('\\n'):\n",
    "        # split line by white space\n",
    "        tokens = line.split()\n",
    "        if len(line) < 2:\n",
    "            continue\n",
    "        # take the first token as the image id, the rest as the description\n",
    "        image_id, image_desc = tokens[0], tokens[1:]\n",
    "        # remove filename from image id\n",
    "        image_id = image_id.split('.')[0]\n",
    "        # convert description tokens back to string\n",
    "        image_desc = ' '.join(image_desc)\n",
    "        # create the list if needed\n",
    "        if image_id not in mapping:\n",
    "            mapping[image_id] = list()\n",
    "        # store description\n",
    "        mapping[image_id].append(image_desc)\n",
    "    return mapping\n",
    "\n",
    "def clean_descriptions(descriptions):\n",
    "    # prepare translation table for removing punctuation\n",
    "    table = str.maketrans('', '', string.punctuation)\n",
    "    for key, desc_list in descriptions.items():\n",
    "        for i in range(len(desc_list)):\n",
    "            desc = desc_list[i]\n",
    "            # tokenize\n",
    "            desc = desc.split()\n",
    "            # convert to lower case\n",
    "            desc = [word.lower() for word in desc]\n",
    "            # remove punctuation from each token\n",
    "            desc = [w.translate(table) for w in desc]\n",
    "            # remove hanging 's' and 'a'\n",
    "            desc = [word for word in desc if len(word)>1]\n",
    "            # remove tokens with numbers in them\n",
    "            desc = [word for word in desc if word.isalpha()]\n",
    "            # store as string\n",
    "            desc_list[i] =  ' '.join(desc)\n",
    "\n",
    "# convert the loaded descriptions into a vocabulary of words\n",
    "def to_vocabulary(descriptions):\n",
    "    # build a list of all description strings\n",
    "    all_desc = set()\n",
    "    for key in descriptions.keys():\n",
    "        [all_desc.update(d.split()) for d in descriptions[key]]\n",
    "    return all_desc\n",
    "\n",
    "# save descriptions to file, one per line\n",
    "def save_descriptions(descriptions, filename):\n",
    "    lines = list()\n",
    "    for key, desc_list in descriptions.items():\n",
    "        for desc in desc_list:\n",
    "            lines.append(key + ' ' + desc)\n",
    "    data = '\\n'.join(lines)\n",
    "    file = open(filename, 'w')\n",
    "    file.write(data)\n",
    "    file.close()\n",
    "    \n",
    "    \n",
    "\"\"\"\n",
    "打印出加载图像描述的数量（8092）和干净词汇量的规模(8763个单词)，最后把干净的单词写入descriptions.txt\n",
    "\"\"\"    \n",
    "\n",
    "filename = 'Flickr8k_text/Flickr8k.token.txt'\n",
    "# load descriptions\n",
    "doc = load_doc(filename)\n",
    "# parse descriptions\n",
    "descriptions = load_descriptions(doc)\n",
    "print('Loaded: %d ' % len(descriptions))\n",
    "# clean descriptions\n",
    "clean_descriptions(descriptions)\n",
    "# summarize vocabulary\n",
    "vocabulary = to_vocabulary(descriptions)\n",
    "print('Vocabulary Size: %d' % len(vocabulary))\n",
    "# save to file\n",
    "save_descriptions(descriptions, 'descriptions.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 开发深度学习模型\n",
    "\n",
    "我们将定义深度学习模型，在训练数据集上进行拟合。\n",
    "\n",
    "![image.png](http://ata2-img.cn-hangzhou.img-pub.aliyun-inc.com/47808f818b3ab690365631d9d40f58a5.png)\n",
    "\n",
    "#### 加载数据\n",
    "\n",
    "必须加载准备好的图像和文本数据来拟合模型。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: 6000\n",
      "Descriptions: train=6000\n",
      "Photos: train=6000\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "  加载数据，我们必须加载准备好的图像和文本数据来拟合模型。我们将在训练数据集中的所有图像和描述上训练数据，训练过程中，我们将在开发数据集上监控模型\n",
    "  性能，使用该模型能确定什么时候保存模型至文件。\n",
    "  训练和开发数据集已经预制好，并分别保存在 Flickr_8k.trainImages.txt 和 Flickr_8k.devImages.txt 文件中，\n",
    "  二者均包含图像文件名列表。从这些文件名中，我们可以提取图像标识符，并使用它们为每个集过滤图像和描述。\n",
    "\"\"\"\n",
    "# load a pre-defined list of photo identifiers\n",
    "def load_set(filename):\n",
    "    doc = load_doc(filename)\n",
    "    dataset = list()\n",
    "    # process line by line\n",
    "    for line in doc.split('\\n'):\n",
    "        # skip empty lines\n",
    "        if len(line) < 1:\n",
    "            continue\n",
    "        # get the image identifier\n",
    "        identifier = line.split('.')[0]\n",
    "        dataset.append(identifier)\n",
    "    return set(dataset)\n",
    "\n",
    "'''\n",
    "该函数从给定标识符集的 descriptions.txt 中加载干净的文本描述，并向文本描述列表返回标识符词典。\n",
    "'''\n",
    "def load_clean_descriptions(filename, dataset):\n",
    "    # load document\n",
    "    doc = load_doc(filename)\n",
    "    descriptions = dict()\n",
    "    for line in doc.split('\\n'):\n",
    "        # split line by white space\n",
    "        tokens = line.split()\n",
    "        # split id from description\n",
    "        image_id, image_desc = tokens[0], tokens[1:]\n",
    "        # skip images not in the set\n",
    "        if image_id in dataset:\n",
    "            # create list\n",
    "            if image_id not in descriptions:\n",
    "                descriptions[image_id] = list()\n",
    "            # wrap description in tokens\n",
    "            desc = 'startseq ' + ' '.join(image_desc) + ' endseq'\n",
    "            # store\n",
    "            descriptions[image_id].append(desc)\n",
    "    return descriptions\n",
    "\n",
    "'''\n",
    "下面定义了 load_photo_features() 函数，该函数加载了整个图像描述集，然后返回给定图像标识符集你感兴趣的子集。\n",
    "\n",
    "'''\n",
    "def load_photo_features(filename, dataset):\n",
    "    # load all features\n",
    "    all_features = load(open(filename, 'rb'))\n",
    "    # filter features\n",
    "    features = {k: all_features[k] for k in dataset}\n",
    "    return features\n",
    "\n",
    "\n",
    "'''\n",
    "运行该示例首先在测试数据集中加载 6000 张图像标识符。这些特征之后将用于加载干净描述文本和预计算的图像特征。\n",
    "\n",
    "'''\n",
    "# load training dataset (6K)\n",
    "filename = 'Flickr8k_text/Flickr_8k.trainImages.txt'\n",
    "train = load_set(filename)\n",
    "print('Dataset: %d' % len(train))\n",
    "# descriptions\n",
    "train_descriptions = load_clean_descriptions('descriptions.txt', train)\n",
    "print('Descriptions: train=%d' % len(train_descriptions))\n",
    "# photo features\n",
    "train_features = load_photo_features('features.pkl', train)\n",
    "print('Photos: train=%d' % len(train_features))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### keras单词数据预处理\n",
    "keras中文文档：http://keras-cn.readthedocs.io/en/latest/preprocessing/text/\n",
    "\n",
    "#### 简介\n",
    "在进行自然语言处理之前，需要对文本进行处理。 \n",
    "本文介绍keras提供的预处理包keras.preproceing下的text与序列处理模块sequence模块\n",
    "\n",
    "#### text模块提供的方法\n",
    "* text_to_word_sequence(text,fileter) 可以简单理解此函数功能类str.split\n",
    "* one_hot(text,vocab_size) 基于hash函数(桶大小为vocab_size)，将一行文本转换向量表示（把单词数字化，vocab_size=5表示所有单词全都数字化在5以内）\n",
    "#### text.Tokenizer类\n",
    "这个类用来对文本中的词进行统计计数，生成文档词典，以支持基于词典位序生成文本的向量表示。 \n",
    "init(num_words) 构造函数，传入词典的最大值\n",
    "\n",
    "##### 成员函数\n",
    "fit_on_text(texts) 使用一系列文档来生成token词典，texts为list类，每个元素为一个文档。\n",
    "texts_to_sequences(texts) 将多个文档转换为word下标的向量形式,shape为[len(texts)，len(text)] -- (文档数，每条文档的长度)\n",
    "texts_to_matrix(texts) 将多个文档转换为矩阵表示,shape为[len(texts),num_words]\n",
    "##### 成员变量\n",
    "document_count 处理的文档数量\n",
    "word_index 一个dict，保存所有word对应的编号id，从1开始\n",
    "word_counts 一个dict，保存每个word在所有文档中出现的次数\n",
    "word_docs 一个dict，保存每个word出现的文档的数量\n",
    "index_docs 一个dict，保存word的id出现的文档的数量\n",
    "##### 示例\n",
    "\n",
    "<p><code>\n",
    "    import keras.preprocessing.text as T\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "\n",
    "text1='some thing to eat'\n",
    "text2='some thing to drink'\n",
    "texts=[text1,text2]\n",
    "\n",
    "print T.text_to_word_sequence(text1)  #以空格区分，中文也不例外 ['some', 'thing', 'to', 'eat']\n",
    "print T.one_hot(text1,10)  #[7, 9, 3, 4] -- （10表示数字化向量为10以内的数字）\n",
    "print T.one_hot(text2,10)  #[7, 9, 3, 1]\n",
    "\n",
    "tokenizer = Tokenizer(num_words=None) #num_words:None或整数,处理的最大单词数量。少于此数的单词丢掉\n",
    "tokenizer.fit_on_texts(texts)\n",
    "print( tokenizer.word_counts) #[('some', 2), ('thing', 2), ('to', 2), ('eat', 1), ('drink', 1)]\n",
    "print( tokenizer.word_index) #{'some': 1, 'thing': 2,'to': 3 ','eat': 4, drink': 5}\n",
    "print( tokenizer.word_docs) #{'some': 2, 'thing': 2, 'to': 2, 'drink': 1,  'eat': 1}\n",
    "print( tokenizer.index_docs) #{1: 2, 2: 2, 3: 2, 4: 1, 5: 1}\n",
    "\n",
    "####### num_words=多少会影响下面的结果，行数=num_words\n",
    "print( tokenizer.texts_to_sequences(texts)) #得到词索引[[1, 2, 3, 4], [1, 2, 3, 5]]\n",
    "print( tokenizer.texts_to_matrix(texts))  # 矩阵化=one_hot\n",
    "[[ 0.,  1.,  1.,  1.,  1.,  0.,  0.,  0.,  0.,  0.],\n",
    " [ 0.,  1.,  1.,  1.,  0.,  1.,  0.,  0.,  0.,  0.]]\n",
    "\n",
    "\n",
    "'''将新闻文档处理成单词索引序列，单词与序号之间的对应关系靠单词的索引表word_index来记录'''\n",
    "#######例-------------------------------------------------------------------------\n",
    "\n",
    "tokenizer = Tokenizer(num_words=None) # 分词MAX_NB_WORDS\n",
    "tokenizer.fit_on_texts(all_texts)\n",
    "sequences = tokenizer.texts_to_sequences(all_texts) #受num_words影响\n",
    "word_index = tokenizer.word_index # 词_索引\n",
    "print('Found %s unique tokens.' % len(word_index))\n",
    "data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)  #将长度不足 100 的新闻用 0 填充（在前端填充）\n",
    "labels = to_categorical(np.asarray(all_labels)) #最后将标签处理成 one-hot 向量，比如 6 变成了 [0,0,0,0,0,0,1,0,0,0,0,0,0]，\n",
    "print('Shape of data tensor:', data.shape)\n",
    "print('Shape of label tensor:', labels.shape)\n",
    "####### Shape of data tensor: (81, 1000)  -- 81条数据\n",
    "####### Shape of label tensor: (81, 14)\n",
    "</p></code>    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary Size: 7579\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "描述文本在作为输入馈送至模型或与模型预测进行对比之前需要先编码成数值。\n",
    "编码数据的第一步是创建单词到唯一整数值之间的持续映射。Keras 提供 Tokenizer class，可根据加载的描述数据学习该映射。\n",
    "\n",
    "下面定义了用于将描述词典转换成字符串列表的 to_lines() 函数，和对加载图像描述文本拟合 Tokenizer 的 create_tokenizer() 函数。\n",
    "\n",
    "'''\n",
    "# convert a dictionary of clean descriptions to a list of descriptions\n",
    "def to_lines(descriptions):\n",
    "    all_desc = list()\n",
    "    for key in descriptions.keys():\n",
    "        [all_desc.append(d) for d in descriptions[key]]\n",
    "    return all_desc\n",
    "\n",
    "# 这个类用来对文本中的词进行统计计数，生成文档词典，以支持基于词典位序生成文本的向量表示\n",
    "def create_tokenizer(descriptions):\n",
    "    lines = to_lines(descriptions)\n",
    "    tokenizer = Tokenizer()\n",
    "    tokenizer.fit_on_texts(lines)\n",
    "    return tokenizer\n",
    "\n",
    "# prepare tokenizer\n",
    "tokenizer = create_tokenizer(train_descriptions)\n",
    "vocab_size = len(tokenizer.word_index) + 1\n",
    "print('Vocabulary Size: %d' % vocab_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们现在对文本进行编码。\n",
    "\n",
    "每个描述将被分割成单词。我们向该模型提供一个单词和图像，然后模型生成下一个单词。描述的前两个单词和图像将作为模型输入以生成下一个单词，这就是该模型的训练方式。\n",
    "\n",
    "例如，输入序列「a little girl running in field」将被分割成 6 个输入-输出对来训练该模型：\n",
    "<pre><code>\n",
    "X1,\t\tX2 (text sequence), \t\t\t\t\t\t   y (word)\n",
    "photo\tstartseq, \t\t\t\t\t\t\t\t\t   little\n",
    "photo\tstartseq, little,\t\t\t\t\t\t\t   girl\n",
    "photo\tstartseq, little, girl, \t\t\t\t\t   running\n",
    "photo\tstartseq, little, girl, running, \t\t\t   in\n",
    "photo\tstartseq, little, girl, running, in, \t\t   field\n",
    "photo\tstartseq, little, girl, running, in, field,    endseq\n",
    "</pre></code>\n",
    "\n",
    "下面是 create_sequences() 函数，给出 tokenizer、最大序列长度和所有描述和图像的词典，该函数将这些数据转换成输入-输出对来训练模型。该模型有两个输入数组：一个用于图像特征，一个用于编码文本。模型输出是文本序列中编码的下一个单词。\n",
    "输入文本被编码为整数，被馈送至词嵌入层。图像特征将被直接馈送至模型的另一部分。\n",
    "该模型输出的预测是所有单词在词汇表中的概率分布。因此，输出数据是每个单词的 one-hot 编码，它表示一种理想化的概率分布，即除了实际词位置之外所有词位置的值都为 0，实际词位置的值为 1。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "outputs": [],
   "source": [
    "def create_sequences(tokenizer, max_length, descriptions, photos):\n",
    "    X1, X2, y = list(), list(), list()\n",
    "    # walk through each image identifier\n",
    "    for key, desc_list in descriptions.items():\n",
    "        # walk through each description for the image\n",
    "        for desc in desc_list:\n",
    "            # encode the sequence\n",
    "            seq = tokenizer.texts_to_sequences([desc])[0]\n",
    "            # split one sequence into multiple X,y pairs\n",
    "            for i in range(1, len(seq)):\n",
    "                # split into input and output pair\n",
    "                in_seq, out_seq = seq[:i], seq[i]\n",
    "                # pad input sequence\n",
    "                in_seq = pad_sequences([in_seq], maxlen=max_length)[0]\n",
    "                # encode output sequence\n",
    "                out_seq = to_categorical([out_seq], num_classes=vocab_size)[0]\n",
    "                # store\n",
    "                X1.append(photos[key][0])\n",
    "                X2.append(in_seq)\n",
    "                y.append(out_seq)\n",
    "    return np.array(X1), np.array(X2), np.array(y)\n",
    "'''\n",
    "我们需要计算最长描述中单词的最大数量。下面是一个有帮助的函数 max_length()。\n",
    "'''\n",
    "def max_length(descriptions):\n",
    "    lines = to_lines(descriptions)\n",
    "    return max(len(d.split()) for d in lines)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### keras Embedding层\n",
    "嵌入层Embedding层将正整数（下标）转换为具有固定大小的向量，如[[4],[20]]->[[0.25,0.1],[0.6,-0.2]]\n",
    "\n",
    "![image.png](http://ata2-img.cn-hangzhou.img-pub.aliyun-inc.com/0fe93c90a11d261bb9a87aa965e05a94.png)\n",
    "\n",
    "上图的流程是把文章的单词使用词向量来表示。 \n",
    "(1)提取文章所有的单词，把其按其出现的次数降许(这里只取前50000个)，比如单词‘network’出现的次数最多，编号ID为0，依次类推…\n",
    "\n",
    "(2)每个编号ID都可以使用50000维的二进制(one-hot)表示\n",
    "\n",
    "(3)最后，我们会生产一个矩阵M，行大小为词的个数50000，列大小为词向量的维度(通常取128或300)，比如矩阵的第一行就是编号ID=0，即network对应的词向量。\n",
    "\n",
    "那这个矩阵M怎么获得呢？在Skip-Gram 模型中，我们会随机初始化它，然后使用神经网络来训练这个权重矩阵 \n",
    "\n",
    "![image.png](http://ata2-img.cn-hangzhou.img-pub.aliyun-inc.com/ef183e8d29f371a6b5fea1293933aa12.png)\n",
    "那我们的输入数据和标签是什么？如下图，输入数据就是中间的哪个蓝色的词对应的one-hot编码，标签就是它附近词的one-hot编码(这里windown_size=2,左右各取2个) \n",
    "![image.png](http://ata2-img.cn-hangzhou.img-pub.aliyun-inc.com/c8ed067ecda4e45a15e7c35657c95514.png)\n",
    "就上述的Word2Vec中的demo而言，它的单词表大小为1000，词向量的维度为300，所以Embedding的参数 input_dim=10000，output_dim=300\n",
    "\n",
    "回到最初的问题：嵌入层将正整数（下标）转换为具有固定大小的向量，如[[4],[20]]->[[0.25,0.1],[0.6,-0.2]]\n",
    "\n",
    "举个栗子：假如单词表的大小为1000，词向量维度为2，经单词频数统计后，tom对应的id=4，而jerry对应的id=20，经上述的转换后，我们会得到一个M1000×2的矩阵，而tom对应的是该矩阵的第4行，取出该行的数据就是[0.25,0.1]\n",
    "\n",
    "如果输入数据不需要词的语义特征语义，简单使用Embedding层就可以得到一个对应的词向量矩阵，但如果需要语义特征，我们大可把以及训练好的词向量权重直接扔到Embedding层中即可，具体看参考keras提供的栗子:在Keras模型中使用预训练的词向量\n",
    "<https://github.com/MoyanZitto/keras-cn/blob/master/docs/legacy/blog/word_embedding.md>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义模型\n",
    "![1.jpg](https://image.jiqizhixin.com/uploads/wangeditor/d5fd5480-0b2c-462a-b71d-cc15d1a56f86/80570Schematic-of-the-Merge-Model-For-Image-Captioning.png)\n",
    "* 图像特征提取器：这是一个在 ImageNet 数据集上预训练的 16 层 VGG 模型。我们已经使用 VGG 模型（没有输出层）对图像进行预处理，并将使用该模型预测的提取特征作为输入。\n",
    "* 序列处理器：合适一个词嵌入层，用于处理文本输入，后面是长短期记忆（LSTM）循环神经网络层。\n",
    "* 解码器：特征提取器和序列处理器输出一个固定长度向量。这些向量由密集层（Dense layer）融合和处理，来进行最终预测。\n",
    "图像特征提取器模型的输入图像特征是维度为 4096 的向量，这些向量经过全连接层处理并生成图像的 256 元素表征。\n",
    "序列处理器模型期望馈送至嵌入层的预定义长度（34 个单词）输入序列使用掩码来忽略 padded 值。之后是具备 256 个循环单元的 LSTM 层。\n",
    "两个输入模型均输出 256 元素的向量。此外，输入模型以 50% 的 dropout 率使用正则化，旨在减少训练数据集的过拟合情况，因为该模型配置学习非常快。\n",
    "解码器模型使用额外的操作融合来自两个输入模型的向量。然后将其馈送至 256 个神经元的密集层，然后输送至最终输出密集层，从而在所有输出词汇上对序列中的下一个单词进行 softmax 预测。\n",
    "\n",
    "\n",
    "**该模型的网络结构为**\n",
    "![2.png](https://image.jiqizhixin.com/uploads/wangeditor/d5fd5480-0b2c-462a-b71d-cc15d1a56f86/87019Plot-of-the-Caption-Generation-Deep-Learning-Model.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "outputs": [],
   "source": [
    "# define the captioning model\n",
    "def define_model(vocab_size, max_length):\n",
    "    # feature extractor model\n",
    "    inputs1 = Input(shape=(4096,))\n",
    "    fe1 = Dropout(0.5)(inputs1)\n",
    "    fe2 = Dense(256, activation='relu')(fe1)\n",
    "    # sequence model\n",
    "    inputs2 = Input(shape=(max_length,))\n",
    "    se1 = Embedding(vocab_size, 256, mask_zero=True)(inputs2)\n",
    "    se2 = Dropout(0.5)(se1)\n",
    "    se3 = LSTM(256)(se2)\n",
    "    # decoder model\n",
    "    decoder1 = add([fe2, se3])\n",
    "    decoder2 = Dense(256, activation='relu')(decoder1)\n",
    "    outputs = Dense(vocab_size, activation='softmax')(decoder2)\n",
    "    # tie it together [image, seq] [word]\n",
    "    model = Model(inputs=[inputs1, inputs2], outputs=outputs)\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam')\n",
    "    # summarize model\n",
    "    print(model.summary())\n",
    "    plot_model(model, to_file='model.png', show_shapes=True)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练模型\n",
    "现在我们已经了解如何定义模型了，那么接下来我们要在训练数据集上拟合模型。\n",
    "该模型学习速度快，很快就会对训练数据集产生过拟合。因此，我们需要在留出的开发数据集上监控训练模型的泛化情况。如果模型在开发数据集上的技能在每个 epoch 结束时有所提升，则我们将整个模型保存至文件。\n",
    "在运行结束时，我们能够使用训练数据集上具备最优技能的模型作为最终模型。\n",
    "通过在 Keras 中定义 ModelCheckpoint，使之监控验证数据集上的最小损失，我们可以实现以上目的。然后将该模型保存至文件名中包含训练损失和验证损失的文件中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: 6000\n",
      "Descriptions: train=6000\n",
      "Photos: train=6000\n",
      "Vocabulary Size: 7579\n",
      "Description Length: 34\n",
      "Dataset: 1000\n",
      "Descriptions: test=1000\n",
      "Photos: test=1000\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            (None, 34)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_1 (InputLayer)            (None, 4096)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, 34, 256)      1940224     input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 4096)         0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 34, 256)      0           embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 256)          1048832     dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lstm_1 (LSTM)                   (None, 256)          525312      dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 256)          0           dense_1[0][0]                    \n",
      "                                                                 lstm_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 256)          65792       add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 7579)         1947803     dense_2[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 5,527,963\n",
      "Trainable params: 5,527,963\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "Train on 306404 samples, validate on 50903 samples\n",
      "Epoch 1/20\n",
      " - 1390s - loss: 4.5010 - val_loss: 4.0511\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 4.05109, saving model to model-ep001-loss4.501-val_loss4.051.h5\n",
      "Epoch 2/20\n",
      " - 1385s - loss: 3.8697 - val_loss: 3.8949\n",
      "\n",
      "Epoch 00002: val_loss improved from 4.05109 to 3.89486, saving model to model-ep002-loss3.870-val_loss3.895.h5\n",
      "Epoch 3/20\n",
      " - 1364s - loss: 3.6820 - val_loss: 3.8535\n",
      "\n",
      "Epoch 00003: val_loss improved from 3.89486 to 3.85347, saving model to model-ep003-loss3.682-val_loss3.853.h5\n",
      "Epoch 4/20\n",
      " - 1614s - loss: 3.5875 - val_loss: 3.8408\n",
      "\n",
      "Epoch 00004: val_loss improved from 3.85347 to 3.84077, saving model to model-ep004-loss3.588-val_loss3.841.h5\n",
      "Epoch 5/20\n",
      " - 1572s - loss: 3.5325 - val_loss: 3.8343\n",
      "\n",
      "Epoch 00005: val_loss improved from 3.84077 to 3.83431, saving model to model-ep005-loss3.533-val_loss3.834.h5\n",
      "Epoch 6/20\n",
      " - 1552s - loss: 3.5025 - val_loss: 3.8543\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 3.83431\n",
      "Epoch 7/20\n",
      " - 1617s - loss: 3.4805 - val_loss: 3.8688\n",
      "\n",
      "Epoch 00007: val_loss did not improve from 3.83431\n",
      "Epoch 8/20\n",
      " - 1726s - loss: 3.4639 - val_loss: 3.8639\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 3.83431\n",
      "Epoch 9/20\n",
      " - 1373s - loss: 3.4541 - val_loss: 3.8878\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 3.83431\n",
      "Epoch 10/20\n",
      " - 1640s - loss: 3.4479 - val_loss: 3.9128\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 3.83431\n",
      "Epoch 11/20\n",
      " - 1390s - loss: 3.4451 - val_loss: 3.9144\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 3.83431\n",
      "Epoch 12/20\n",
      " - 2180s - loss: 3.4382 - val_loss: 3.9270\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 3.83431\n",
      "Epoch 13/20\n",
      " - 2119s - loss: 3.4357 - val_loss: 3.9446\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 3.83431\n",
      "Epoch 14/20\n",
      " - 1605s - loss: 3.4354 - val_loss: 3.9430\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 3.83431\n",
      "Epoch 15/20\n",
      " - 2170s - loss: 3.4337 - val_loss: 3.9536\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 3.83431\n",
      "Epoch 16/20\n",
      " - 2008s - loss: 3.4349 - val_loss: 3.9722\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 3.83431\n",
      "Epoch 17/20\n",
      " - 1589s - loss: 3.4323 - val_loss: 3.9803\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 3.83431\n",
      "Epoch 18/20\n",
      " - 1774s - loss: 3.4365 - val_loss: 3.9955\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 3.83431\n",
      "Epoch 19/20\n",
      " - 2214s - loss: 3.4387 - val_loss: 4.0062\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 3.83431\n",
      "Epoch 20/20\n",
      " - 2028s - loss: 3.4375 - val_loss: 3.9937\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 3.83431\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x118003f28>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "我们还需要 fit() 中的 validation_data 参数指定开发数据集。\n",
    "我们仅拟合模型 20 epoch，给出一定量的训练数据，在一般硬件上每个 epoch 可能需要 30 分钟。\n",
    "'''\n",
    "# train dataset\n",
    "\n",
    "# load training dataset (6K)\n",
    "filename = 'Flickr8k_text/Flickr_8k.trainImages.txt'\n",
    "train = load_set(filename)\n",
    "print('Dataset: %d' % len(train))\n",
    "# descriptions\n",
    "train_descriptions = load_clean_descriptions('descriptions.txt', train)\n",
    "print('Descriptions: train=%d' % len(train_descriptions))\n",
    "# photo features\n",
    "train_features = load_photo_features('features.pkl', train)\n",
    "print('Photos: train=%d' % len(train_features))\n",
    "# prepare tokenizer\n",
    "tokenizer = create_tokenizer(train_descriptions)\n",
    "vocab_size = len(tokenizer.word_index) + 1\n",
    "print('Vocabulary Size: %d' % vocab_size)\n",
    "# determine the maximum sequence length\n",
    "max_length = max_length(train_descriptions)\n",
    "print('Description Length: %d' % max_length)\n",
    "# prepare sequences\n",
    "X1train, X2train, ytrain = create_sequences(tokenizer, max_length, train_descriptions, train_features)\n",
    "\n",
    "# dev dataset\n",
    "\n",
    "# load test set\n",
    "filename = 'Flickr8k_text/Flickr_8k.devImages.txt'\n",
    "test = load_set(filename)\n",
    "print('Dataset: %d' % len(test))\n",
    "# descriptions\n",
    "test_descriptions = load_clean_descriptions('descriptions.txt', test)\n",
    "print('Descriptions: test=%d' % len(test_descriptions))\n",
    "# photo features\n",
    "test_features = load_photo_features('features.pkl', test)\n",
    "print('Photos: test=%d' % len(test_features))\n",
    "# prepare sequences\n",
    "X1test, X2test, ytest = create_sequences(tokenizer, max_length, test_descriptions, test_features)\n",
    "\n",
    "# fit model\n",
    "\n",
    "# define the model\n",
    "model = define_model(vocab_size, max_length)\n",
    "# define checkpoint callback\n",
    "filepath = 'model-ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5'\n",
    "checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
    "# fit model\n",
    "model.fit([X1train, X2train], ytrain, epochs=20, verbose=2, callbacks=[checkpoint], validation_data=([X1test, X2test], ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "outputs": [],
   "source": [
    "'''\n",
    "模型拟合之后，我们可以在留出的测试数据集上评估它的预测技能。\n",
    "\n",
    "使模型对测试数据集中的所有图像生成描述，使用标准代价函数评估预测，从而评估模型。\n",
    "\n",
    "首先，我们需要使用训练模型对图像生成描述。输入开始描述的标记 『startseq『，生成一个单词，然后递归地用生成单词作为输入启用模型直到序列标记到 『endseq『或达到最大描述长度。\n",
    "\n",
    "下面的 generate_desc() 函数实现该行为，并基于给定训练模型和作为输入的准备图像生成文本描述。它启用 word_for_id() 函数以映射整数预测至单词。\n",
    "\n",
    "'''\n",
    "# map an integer to a word\n",
    "def word_for_id(integer, tokenizer):\n",
    "    for word, index in tokenizer.word_index.items():\n",
    "        if index == integer:\n",
    "            return word\n",
    "    return None\n",
    "\n",
    "# generate a description for an image\n",
    "def generate_desc(model, tokenizer, photo, max_length):\n",
    "    # seed the generation process\n",
    "    in_text = 'startseq'\n",
    "    # iterate over the whole length of the sequence\n",
    "    for i in range(max_length):\n",
    "        # integer encode input sequence\n",
    "        sequence = tokenizer.texts_to_sequences([in_text])[0]\n",
    "        # pad input\n",
    "        sequence = pad_sequences([sequence], maxlen=max_length)\n",
    "        # predict next word\n",
    "        yhat = model.predict([photo,sequence], verbose=0)\n",
    "        # convert probability to integer\n",
    "        yhat = argmax(yhat)\n",
    "        # map integer to word\n",
    "        word = word_for_id(yhat, tokenizer)\n",
    "        # stop if we cannot map the word\n",
    "        if word is None:\n",
    "            break\n",
    "        # append as input for generating the next word\n",
    "        in_text += ' ' + word\n",
    "        # stop if we predict the end of the sequence\n",
    "        if word == 'endseq':\n",
    "            break\n",
    "    return in_text\n",
    "\n",
    "'''\n",
    "我们将为测试数据集和训练数据集中的所有图像生成预测。\n",
    "下面的 evaluate_model() 基于给定图像描述数据集和图像特征评估训练模型。收集实际和预测描述，使用语料库 BLEU 值对它们进行评估。语料库 BLEU 值总结了生成文本和期望文本之间的相似度。\n",
    "'''\n",
    "# evaluate the skill of the model\n",
    "def evaluate_model(model, descriptions, photos, tokenizer, max_length):\n",
    "    actual, predicted = list(), list()\n",
    "    # step over the whole set\n",
    "    for key, desc_list in descriptions.items():\n",
    "        # generate description\n",
    "        yhat = generate_desc(model, tokenizer, photos[key], max_length)\n",
    "        # store actual and predicted\n",
    "        references = [d.split() for d in desc_list]\n",
    "        actual.append(references)\n",
    "        predicted.append(yhat.split())\n",
    "    # calculate BLEU score\n",
    "    print('BLEU-1: %f' % corpus_bleu(actual, predicted, weights=(1.0, 0, 0, 0)))\n",
    "    print('BLEU-2: %f' % corpus_bleu(actual, predicted, weights=(0.5, 0.5, 0, 0)))\n",
    "    print('BLEU-3: %f' % corpus_bleu(actual, predicted, weights=(0.3, 0.3, 0.3, 0)))\n",
    "    print('BLEU-4: %f' % corpus_bleu(actual, predicted, weights=(0.25, 0.25, 0.25, 0.25)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成新的图像字幕\n",
    "\n",
    "现在我们了解了如何开发和评估字幕生成模型，那么我们如何使用它呢？\n",
    "\n",
    "我们需要模型文件中全新的图像，还需要 Tokenizer 用于对模型生成单词进行编码，生成序列和定义模型时使用的输入序列最大长度。\n",
    "\n",
    "我们可以对最大序列长度进行硬编码。文本编码后，我们就可以创建 tokenizer，并将其保存至文件，这样我们可以在需要的时候快速加载，无需整个 Flickr8K 数据集。另一个方法是使用我们自己的词汇文件，在训练过程中将其映射到取整函数。\n",
    "\n",
    "我们可以按照之前的方式创建 Tokenizer，并将其保存为 pickle 文件 tokenizer.pkl。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: 6000\n",
      "Descriptions: train=6000\n"
     ]
    }
   ],
   "source": [
    "# load training dataset (6K)\n",
    "filename = 'Flickr8k_text/Flickr_8k.trainImages.txt'\n",
    "train = load_set(filename)\n",
    "print('Dataset: %d' % len(train))\n",
    "# descriptions\n",
    "train_descriptions = load_clean_descriptions('descriptions.txt', train)\n",
    "print('Descriptions: train=%d' % len(train_descriptions))\n",
    "# prepare tokenizer\n",
    "tokenizer = create_tokenizer(train_descriptions)\n",
    "# save the tokenizer\n",
    "dump(tokenizer, open('tokenizer.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在我们可以在需要的时候加载 tokenizer，无需加载整个标注训练数据集。下面，我们来为一个新图像生成描述，下面这张图是我从 Flickr 中随机选的一张图像。\n",
    "![image.png](http://ata2-img.cn-hangzhou.img-pub.aliyun-inc.com/bfcfbe37a06a0a61e0fb11b1e1d58e69.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FAhNTk8wuznPlyhVMQ+fk/Xej5XTMgg1ZSpplSCERusIoCNF0CxKVQq1Ef+TR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WqaSEtbO43OftZWR7EqNaLRFGZHJ4EXzzLuEJkTrTjdJjxmDxa/glOzUjZLmEWZgN1Oc1cv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\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GDEzu5INv+xrR4AAyBoasUWwodNoNzDYvnUqBwcgA7nAQj8dG0GPmxInzVIstbt/nQpAN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\n8BWucIUrvCT8v+4YnXmkOtwQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x52c6a8198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "startseq man in red shirt is riding bike on the road endseq\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "预测单幅图像的描述\n",
    "'''\n",
    "from pickle import load\n",
    "from numpy import argmax\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.preprocessing.image import load_img\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from keras.applications.vgg16 import preprocess_input\n",
    "from keras.models import Model\n",
    "from keras.models import load_model\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "\n",
    "# extract features from each photo in the directory\n",
    "def extract_features(filename):\n",
    "    # load the model\n",
    "    model = VGG16()\n",
    "    # re-structure the model\n",
    "    model.layers.pop()\n",
    "    model = Model(inputs=model.inputs, outputs=model.layers[-1].output)\n",
    "    # load the photo\n",
    "    image = load_img(filename, target_size=(224, 224))\n",
    "    # convert the image pixels to a numpy array\n",
    "    image = img_to_array(image)\n",
    "    # reshape data for the model\n",
    "    image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))\n",
    "    # prepare the image for the VGG model\n",
    "    image = preprocess_input(image)\n",
    "    # get features\n",
    "    feature = model.predict(image, verbose=0)\n",
    "    return feature\n",
    "\n",
    "# map an integer to a word\n",
    "def word_for_id(integer, tokenizer):\n",
    "    for word, index in tokenizer.word_index.items():\n",
    "        if index == integer:\n",
    "            return word\n",
    "    return None\n",
    "\n",
    "# generate a description for an image\n",
    "def generate_desc(model, tokenizer, photo, max_length):\n",
    "    # seed the generation process\n",
    "    in_text = 'startseq'\n",
    "    # iterate over the whole length of the sequence\n",
    "    for i in range(max_length):\n",
    "        # integer encode input sequence\n",
    "        sequence = tokenizer.texts_to_sequences([in_text])[0]\n",
    "        # pad input\n",
    "        sequence = pad_sequences([sequence], maxlen=max_length)\n",
    "        # predict next word\n",
    "        yhat = model.predict([photo,sequence], verbose=0)\n",
    "        # convert probability to integer\n",
    "        yhat = argmax(yhat)\n",
    "        # map integer to word\n",
    "        word = word_for_id(yhat, tokenizer)\n",
    "        # stop if we cannot map the word\n",
    "        if word is None:\n",
    "            break\n",
    "        # append as input for generating the next word\n",
    "        in_text += ' ' + word\n",
    "        # stop if we predict the end of the sequence\n",
    "        if word == 'endseq':\n",
    "            break\n",
    "    return in_text\n",
    "\n",
    "# load the tokenizer\n",
    "tokenizer = load(open('tokenizer.pkl', 'rb'))\n",
    "# pre-define the max sequence length (from training)\n",
    "max_length = 34\n",
    "# load the model\n",
    "model = load_model('model-ep005-loss3.533-val_loss3.834.h5')\n",
    "# load and prepare the photograph\n",
    "photo = extract_features('example.jpg')\n",
    "# generate description\n",
    "description = generate_desc(model, tokenizer, photo, max_length)\n",
    "img = Image.open('example.jpg')\n",
    "\n",
    "plt.figure(\"Image\") # 图像窗口名称\n",
    "plt.imshow(img)\n",
    "plt.axis('on') # 关掉坐标轴为 off\n",
    "plt.title('image') # 图像题目\n",
    "plt.show()\n",
    "print(description)"
   ]
  }
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