cp_generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 彩票预测\n",
    "本项目使用RNN进行彩票预测。\n",
    "\n",
    "使用人工智能技术来预测彩票，是这次的主题，那么预测哪种彩票呢？我们先选择简单一些的，就是排列组合少一些的，如果证明我们的模型work，再扩展到其他的彩票上。最终我选择了[`排列三`](https://baike.baidu.com/item/排列三/343981?fr=aladdin), 从000-999的数字中选取1个3位数，一共有1000种，中奖概率就是千分之一，够简单了吧。\n",
    "\n",
    "历史数据在[`这里`](https://datachart.500.com/)。\n",
    "\n",
    "数据是按照每期一组数的顺序排列的，从第一期到最新的一期，实际上是时间序列的数据。跟回归预测有很大的区别，因为特征上没有特殊的意义，不具备一组特征x映射到label y的条件。但是按照时间序列来训练的话就不一样了，输入x是一期的开奖结果，要学习的y是下一期的开奖结果。\n",
    "\n",
    "彩票的开奖结果是一个随机分布，跟投骰子、抛硬币差不多，从数学角度看没有规律可言。我的预期是，虽然数学模型是随机的，但是一旦跟现实世界的物体发生关系，总会受到某种影响吧，比如量子纠缠，万有引力，动力学，空气阻力，空气湿度，开奖时刻的机器电压强度，开奖器材的损耗，每个球的质量的差异，吹球设备的物理特性，装球器皿的特定形状等等因素所产生的规律性的东西。\n",
    "\n",
    "看得出来以上所列出的和没列出的都是增加不确定性、随机性的因素，但是也有可能每次开奖这些相同的特点可能造成某种规律性的结果出来，比如根据这些物理特性的影响，某个球特别容易开出来。一旦是这样，那么就有迹可循，让我们的学习器学到规律。\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM介绍\n",
    "我们需要从过往的历史数据中寻找规律，[`LSTM`](https://en.wikipedia.org/wiki/Long_short-term_memory)再适合不过了。如果你对LSTM不熟悉的话，以下几篇文章建议你阅读：\n",
    "\n",
    "[`Understanding LSTM Networks`](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)\n",
    "\n",
    "[`[译] 理解 LSTM 网络`](http://www.jianshu.com/p/9dc9f41f0b29)\n",
    "\n",
    "[`RNN以及LSTM的介绍和公式梳理`](http://blog.csdn.net/Dark_Scope/article/details/47056361)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import os\n",
    "# 加载数据集\n",
    "def load_data(path):\n",
    "    input_file = os.path.join(path)\n",
    "    with open(input_file, \"r\") as f:\n",
    "        data = f.read()\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = './data/cp.txt'\n",
    "text = load_data(data_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一共4656条记录，4600多期了。共出现了988个不重复的结果，就是说还有（1000 – 988）12组数到现在还没有开出来过。文件中第一行是最新的一期，第二行是之前的一期，。。。，最后一行是第一期。\n",
    "\n",
    "我们可以把三个数组合成一组数，就像数据集中体现的那样，并且把一组数当作一个数或者说当作一个单词。这样在预处理数据集的时候会简单一些，从索引到单词（0 -> ‘000’）和从单词到索引（‘012’-> 12）其实都是同一个数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 预测网络介绍"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "网络的输入是每一期的开奖结果，总共有1000组数，用one hot编码是一个1000维的稀疏向量：\n",
    "\n",
    "<img src=\"assets/OneHot.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用one hot稀疏向量在输入层与网络第一层做矩阵乘法时会很没有效率，因为向量里面大部分都是0， 矩阵乘法浪费了大量的计算，最终矩阵运算得出的结果是向量中值为1的列所对应的矩阵中的行向量。\n",
    "<img src=\"assets/Matrix_multiplication.png\">\n",
    "\n",
    "这看起来很像用索引查表一样，one hot向量中值为1的位置作为下标，去索引参数矩阵中的行向量。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了代替矩阵乘法，我们将参数矩阵当作一个查找表（lookup table）或者叫做嵌入矩阵（embedding matrix），将每组开奖数据所对应的数作为索引，比如“958”，对应索引就是958，然后在查找表中找第958行。\n",
    "<img src=\"assets/lookup_table.png\">\n",
    "这其实跟替换之前的模型没有什么不同，嵌入矩阵就是参数矩阵，嵌入层仍然是隐层。查找表只是矩阵乘法的一种便捷方式，它会像参数矩阵一样被训练，是要学习的参数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面就是我们要构建的网络架构，从嵌入层输出的向量进入LSTM层进行时间序列的学习，然后经过softmax预测出下一期的开奖结果。\n",
    "<img src=\"assets/model.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "网络训练的代码，使用了几个trick，在下文<[`构建计算图`](#构建计算图)>和<[`训练`](#训练)>章节会做说明，<[`结论`](#结论)>在最后。\n",
    "\n",
    "几个图表的位置："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [`真实值在预测值中的距离图表`](#真实值在预测值中的距离图表)\n",
    "- [`显示训练Loss`](#显示训练Loss)\n",
    "- [`显示测试Loss`](#显示测试Loss)\n",
    "- [`显示准确率`](#显示准确率)\n",
    "- [`显示预测结果和实际开奖结果`](#显示预测结果和实际开奖结果)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 编码实现"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 实现数据预处理\n",
    "我们需要先准备好彩票开奖记录和ID之间的转换关系。在这个函数中，创建并返回两个字典：\n",
    "- 单词到ID的转换字典： `vocab_to_int`\n",
    "- ID到单词的转换字典： `int_to_vocab`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def create_lookup_tables():\n",
    "    vocab_to_int = {str(ii).zfill(3) : ii for ii in range(1000)}\n",
    "    int_to_vocab = {ii : str(ii).zfill(3) for ii in range(1000)}\n",
    "    return vocab_to_int, int_to_vocab\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 处理所有数据并保存\n",
    "将每期结果按照从第一期开始的顺序保存到文件中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "text = load_data(data_dir)\n",
    "\n",
    "words = [word for word in text.split()]\n",
    "\n",
    "reverse_words = [text.split()[idx] for idx in (range(len(words)-1, 0, -1))]\n",
    "vocab_to_int, int_to_vocab = create_lookup_tables()\n",
    "\n",
    "int_text = [vocab_to_int[word] for word in reverse_words]\n",
    "pickle.dump((int_text, vocab_to_int, int_to_vocab), open('preprocess.p', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取保存的数据\n",
    "int_text, vocab_to_int, int_to_vocab = pickle.load(open('preprocess.p', mode='rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_batches(int_text, batch_size, seq_length):\n",
    "    batchCnt = len(int_text) // (batch_size * seq_length)\n",
    "    int_text_inputs = int_text[:batchCnt * (batch_size * seq_length)]\n",
    "    int_text_targets = int_text[1:batchCnt * (batch_size * seq_length)+1]\n",
    "\n",
    "    result_list = []\n",
    "    x = np.array(int_text_inputs).reshape(1, batch_size, -1)\n",
    "    y = np.array(int_text_targets).reshape(1, batch_size, -1)\n",
    "\n",
    "    x_new = np.dsplit(x, batchCnt)\n",
    "    y_new = np.dsplit(y, batchCnt)\n",
    "\n",
    "    for ii in range(batchCnt):\n",
    "        x_list = []\n",
    "        x_list.append(x_new[ii][0])\n",
    "        x_list.append(y_new[ii][0])\n",
    "        result_list.append(x_list)\n",
    "\n",
    "    return np.array(result_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 超参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练迭代次数\n",
    "epochs = 50\n",
    "# 批次大小\n",
    "batch_size = 32\n",
    "# RNN的大小（隐藏节点的维度）\n",
    "rnn_size = 512\n",
    "# 嵌入层的维度\n",
    "embed_dim = 512\n",
    "# 序列的长度，始终为1\n",
    "seq_length = 1\n",
    "# 学习率\n",
    "learning_rate = 0.01\n",
    "# 过多少batch以后打印训练信息\n",
    "show_every_n_batches = 10\n",
    "\n",
    "save_dir = './save'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建计算图\n",
    "使用实现的神经网络构建计算图。\n",
    "\n",
    "使用normalized_embedding做相似度上距离的计算。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "tf.reset_default_graph()\n",
    "train_graph = tf.Graph()\n",
    "with train_graph.as_default():\n",
    "    vocab_size = len(int_to_vocab)\n",
    "    # 定义输入、目标和学习率占位符\n",
    "    input_text = tf.placeholder(tf.int32, [None, None], name=\"input\")\n",
    "    targets = tf.placeholder(tf.int32, [None, None], name=\"targets\")\n",
    "    lr = tf.placeholder(tf.float32)\n",
    "    \n",
    "    input_data_shape = tf.shape(input_text)\n",
    "    # 构建RNN单元并初始化\n",
    "    # 将一个或多个BasicLSTMCells 叠加在MultiRNNCell中，这里我们使用2层LSTM cell\n",
    "    cell = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.BasicLSTMCell(num_units=rnn_size) for _ in range(2)])\n",
    "    initial_state = cell.zero_state(input_data_shape[0], tf.float32)\n",
    "    initial_state = tf.identity(initial_state, name=\"initial_state\")\n",
    "    \n",
    "    # embed_matrix是嵌入矩阵，后面计算相似度(距离)的时候会用到\n",
    "    embed_matrix = tf.Variable(tf.random_uniform([vocab_size, embed_dim], -1, 1))\n",
    "    # embed_layer是从嵌入矩阵（查找表）中索引到的向量\n",
    "    embed_layer = tf.nn.embedding_lookup(embed_matrix, input_text)\n",
    "    \n",
    "    # 使用RNN单元构建RNN\n",
    "    outputs, state = tf.nn.dynamic_rnn(cell, embed_layer, dtype=tf.float32)\n",
    "    final_state = tf.identity(state, name=\"final_state\")\n",
    "    \n",
    "    logits = tf.layers.dense(outputs, vocab_size)\n",
    "\n",
    "    probs = tf.nn.softmax(logits, name='probs')\n",
    "\n",
    "    cost = tf.contrib.seq2seq.sequence_loss(\n",
    "        logits,\n",
    "        targets,\n",
    "        tf.ones([input_data_shape[0], input_data_shape[1]]))\n",
    "\n",
    "    norm = tf.sqrt(tf.reduce_sum(tf.square(embed_matrix), 1, keep_dims=True))\n",
    "    normalized_embedding = embed_matrix / norm\n",
    "    \n",
    "    optimizer = tf.train.AdamOptimizer(lr)\n",
    "\n",
    "    gradients = optimizer.compute_gradients(cost)  \n",
    "    capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]  #clip_by_norm\n",
    "    train_op = optimizer.apply_gradients(capped_gradients)\n",
    "    \n",
    "    correct_pred = tf.equal(tf.argmax(probs, 2), tf.cast(targets, tf.int64))#logits <--> probs  tf.argmax(targets, 1) <--> targets\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练\n",
    "在预处理过的数据上训练神经网络。 \n",
    "\n",
    "这里除了保存预测准确率之外，还保存了三类准确率：\n",
    "\n",
    " - Top K准确率：\n",
    " \n",
    " 预测结果中，前K个结果的预测准确率。\n",
    "\n",
    " - 与预测结果距离最近的Top K准确率：\n",
    " \n",
    " 先得到预测结果，使用嵌入矩阵计算与预测结果Top 1距离最近的相似度向量，取这个相似度向量中前K个结果的预测准确率。\n",
    "\n",
    " - 浮动距离中位数范围K准确率：\n",
    " \n",
    " 得到预测结果之后，计算正确结果在预测结果中的距离中位数，这个距离实际上是元素在向量中的位置与第一个元素位置的距离。这个距离数据告诉我们真正的结果在我们的预测向量中的位置在哪。每次训练之后，距离中位数都会有变化，所以是浮动的，当然也可以考虑使用众数或均值。使用中位数表示真正的结果通常会在我们的预测向量中大部分时候（平均、或者说更具代表性的）位置在哪。所以这个准确率就是以中位数为中心，范围K为半径预测准确的概率。\n",
    "\n",
    "这里距离中位数准确率我分别在预测结果向量和与预测结果Top 1距离最近的相似度向量中都做了统计，从结果来看在相似度向量中的距离中位数准确率要稍好一些。\n",
    "\n",
    "浮动距离中位数的概率越高，说明我们的模型训练的不好，理想情况下应该是Top K准确率越来越高，说明模型预测的越来越准确。一旦模型预测的很差，那么预测向量中一定会有一部分区域是热点区域，也就是距离中位数指示的区域，这样可以通过距离中位数来进行预测。我们使用距离中位数来帮助我们进行预测，相当于为预测做了第二套方案，一旦模型预测不准确的时候，可以尝试使用距离中位数来预测。\n",
    "\n",
    "这三类准确率都是范围的，我们只能知道在某个范围内猜中的概率会高一些，但是到底是范围内的哪一个是准确值则很难说。\n",
    "\n",
    "\n",
    " - batches：是训练批数据\n",
    " - test_batches：是测试批数据\n",
    " - topk_acc：是预测结果的Top K准确率\n",
    " - sim_topk_acc：是与预测结果距离最近的Top K准确率\n",
    " - range_k：表示k值是一个范围，不像Top K是最开始的K个。\n",
    " - floating_median_acc_range_k：是以每次训练得出的距离中位数为中心，以范围K为半径的准确率，使用预测结果向量。\n",
    " - floating_median_sim_acc_range_k：同上，使用的是相似度向量。\n",
    " - losses：保存训练损失和测试损失\n",
    " - accuracies：保存各类准确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch   0 Batch    0/144   train_loss = 6.908\n",
      "Epoch   0 Batch   10/144   train_loss = 6.906\n",
      "Epoch   0 Batch   20/144   train_loss = 7.018\n",
      "Epoch   0 Batch   30/144   train_loss = 7.142\n",
      "Epoch   0 Batch   40/144   train_loss = 7.038\n",
      "Epoch   0 Batch   50/144   train_loss = 7.060\n",
      "Epoch   0 Batch   60/144   train_loss = 7.189\n",
      "Epoch   0 Batch   70/144   train_loss = 7.239\n",
      "Epoch   0 Batch   80/144   train_loss = 7.255\n",
      "Epoch   0 Batch   90/144   train_loss = 6.977\n",
      "Epoch   0 Batch  100/144   train_loss = 6.919\n",
      "Epoch   0 Batch  110/144   train_loss = 7.017\n",
      "Epoch   0 Batch  120/144   train_loss = 7.003\n",
      "Epoch   0 Batch  130/144   train_loss = 6.942\n",
      "Epoch   0 Batch  140/144   train_loss = 7.064\n",
      "Epoch   0 Batch    0/1   test_loss = 6.968\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 946\n",
      "min distance : 54\n",
      "平均距离 : 506.28125\n",
      "距离中位数 : 427.5\n",
      "距离标准差 : 272.7510580152486\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 999\n",
      "min distance : 1\n",
      "平均距离 : 389.71875\n",
      "距离中位数 : 328.0\n",
      "距离标准差 : 268.07907349966257\n",
      "Epoch   0 floating median sim range k accuracy 0.0625 \n",
      "Epoch   0 floating median range k accuracy 0.03125 \n",
      "Epoch   0 similar top k accuracy 0.03125 \n",
      "Epoch   0 top k accuracy 0.0 \n",
      "Epoch   0 accuracy 0.0 \n",
      "Epoch   1 Batch    6/144   train_loss = 6.742\n",
      "Epoch   1 Batch   16/144   train_loss = 6.764\n",
      "Epoch   1 Batch   26/144   train_loss = 6.688\n",
      "Epoch   1 Batch   36/144   train_loss = 6.524\n",
      "Epoch   1 Batch   46/144   train_loss = 6.466\n",
      "Epoch   1 Batch   56/144   train_loss = 6.031\n",
      "Epoch   1 Batch   66/144   train_loss = 5.953\n",
      "Epoch   1 Batch   76/144   train_loss = 6.772\n",
      "Epoch   1 Batch   86/144   train_loss = 7.267\n",
      "Epoch   1 Batch   96/144   train_loss = 7.106\n",
      "Epoch   1 Batch  106/144   train_loss = 7.304\n",
      "Epoch   1 Batch  116/144   train_loss = 6.818\n",
      "Epoch   1 Batch  126/144   train_loss = 7.621\n",
      "Epoch   1 Batch  136/144   train_loss = 6.939\n",
      "Epoch   1 Batch    0/1   test_loss = 7.568\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 969\n",
      "min distance : 41\n",
      "平均距离 : 475.6875\n",
      "距离中位数 : 463.5\n",
      "距离标准差 : 266.68326689867513\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 867\n",
      "min distance : 7\n",
      "平均距离 : 407.25\n",
      "距离中位数 : 390.0\n",
      "距离标准差 : 274.5967497986821\n",
      "Epoch   1 floating median sim range k accuracy 0.0625 \n",
      "Epoch   1 floating median range k accuracy 0.0625 \n",
      "Epoch   1 similar top k accuracy 0.03125 \n",
      "Epoch   1 top k accuracy 0.0 \n",
      "Epoch   1 accuracy 0.0 \n",
      "Epoch   2 Batch    2/144   train_loss = 7.138\n",
      "Epoch   2 Batch   12/144   train_loss = 6.904\n",
      "Epoch   2 Batch   22/144   train_loss = 6.723\n",
      "Epoch   2 Batch   32/144   train_loss = 6.388\n",
      "Epoch   2 Batch   42/144   train_loss = 6.382\n",
      "Epoch   2 Batch   52/144   train_loss = 6.750\n",
      "Epoch   2 Batch   62/144   train_loss = 6.605\n",
      "Epoch   2 Batch   72/144   train_loss = 6.343\n",
      "Epoch   2 Batch   82/144   train_loss = 5.943\n",
      "Epoch   2 Batch   92/144   train_loss = 5.875\n",
      "Epoch   2 Batch  102/144   train_loss = 6.008\n",
      "Epoch   2 Batch  112/144   train_loss = 5.486\n",
      "Epoch   2 Batch  122/144   train_loss = 5.396\n",
      "Epoch   2 Batch  132/144   train_loss = 5.714\n",
      "Epoch   2 Batch  142/144   train_loss = 5.869\n",
      "Epoch   2 Batch    0/1   test_loss = 9.128\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 954\n",
      "min distance : 36\n",
      "平均距离 : 523.09375\n",
      "距离中位数 : 539.5\n",
      "距离标准差 : 249.77244135600208\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 13\n",
      "平均距离 : 495.0\n",
      "距离中位数 : 501.5\n",
      "距离标准差 : 286.13993517158696\n",
      "Epoch   2 floating median sim range k accuracy 0.0625 \n",
      "Epoch   2 floating median range k accuracy 0.0625 \n",
      "Epoch   2 similar top k accuracy 0.020833333333333332 \n",
      "Epoch   2 top k accuracy 0.0 \n",
      "Epoch   2 accuracy 0.0 \n",
      "Epoch   3 Batch    8/144   train_loss = 5.578\n",
      "Epoch   3 Batch   18/144   train_loss = 5.513\n",
      "Epoch   3 Batch   28/144   train_loss = 5.726\n",
      "Epoch   3 Batch   38/144   train_loss = 5.435\n",
      "Epoch   3 Batch   48/144   train_loss = 5.995\n",
      "Epoch   3 Batch   58/144   train_loss = 5.712\n",
      "Epoch   3 Batch   68/144   train_loss = 5.495\n",
      "Epoch   3 Batch   78/144   train_loss = 5.708\n",
      "Epoch   3 Batch   88/144   train_loss = 5.318\n",
      "Epoch   3 Batch   98/144   train_loss = 5.072\n",
      "Epoch   3 Batch  108/144   train_loss = 5.474\n",
      "Epoch   3 Batch  118/144   train_loss = 5.114\n",
      "Epoch   3 Batch  128/144   train_loss = 4.700\n",
      "Epoch   3 Batch  138/144   train_loss = 4.313\n",
      "Epoch   3 Batch    0/1   test_loss = 9.763\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 986\n",
      "min distance : 36\n",
      "平均距离 : 487.375\n",
      "距离中位数 : 447.5\n",
      "距离标准差 : 282.9582290285971\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 15\n",
      "平均距离 : 419.0\n",
      "距离中位数 : 430.0\n",
      "距离标准差 : 290.8948478402462\n",
      "Epoch   3 floating median sim range k accuracy 0.046875 \n",
      "Epoch   3 floating median range k accuracy 0.046875 \n",
      "Epoch   3 similar top k accuracy 0.015625 \n",
      "Epoch   3 top k accuracy 0.0 \n",
      "Epoch   3 accuracy 0.0 \n",
      "Epoch   4 Batch    4/144   train_loss = 4.341\n",
      "Epoch   4 Batch   14/144   train_loss = 4.397\n",
      "Epoch   4 Batch   24/144   train_loss = 4.513\n",
      "Epoch   4 Batch   34/144   train_loss = 4.691\n",
      "Epoch   4 Batch   44/144   train_loss = 4.329\n",
      "Epoch   4 Batch   54/144   train_loss = 4.692\n",
      "Epoch   4 Batch   64/144   train_loss = 4.638\n",
      "Epoch   4 Batch   74/144   train_loss = 4.468\n",
      "Epoch   4 Batch   84/144   train_loss = 4.419\n",
      "Epoch   4 Batch   94/144   train_loss = 4.287\n",
      "Epoch   4 Batch  104/144   train_loss = 4.696\n",
      "Epoch   4 Batch  114/144   train_loss = 4.232\n",
      "Epoch   4 Batch  124/144   train_loss = 4.209\n",
      "Epoch   4 Batch  134/144   train_loss = 4.179\n",
      "Epoch   4 Batch    0/1   test_loss = 9.093\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 945\n",
      "min distance : 19\n",
      "平均距离 : 466.34375\n",
      "距离中位数 : 449.5\n",
      "距离标准差 : 296.0969656817467\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 995\n",
      "min distance : 17\n",
      "平均距离 : 484.0\n",
      "距离中位数 : 471.5\n",
      "距离标准差 : 291.5366615024601\n",
      "Epoch   4 floating median sim range k accuracy 0.0375 \n",
      "Epoch   4 floating median range k accuracy 0.0375 \n",
      "Epoch   4 similar top k accuracy 0.0125 \n",
      "Epoch   4 top k accuracy 0.0 \n",
      "Epoch   4 accuracy 0.0 \n",
      "Epoch   5 Batch    0/144   train_loss = 4.240\n",
      "Epoch   5 Batch   10/144   train_loss = 3.895\n",
      "Epoch   5 Batch   20/144   train_loss = 3.767\n",
      "Epoch   5 Batch   30/144   train_loss = 3.871\n",
      "Epoch   5 Batch   40/144   train_loss = 3.716\n",
      "Epoch   5 Batch   50/144   train_loss = 3.919\n",
      "Epoch   5 Batch   60/144   train_loss = 4.153\n",
      "Epoch   5 Batch   70/144   train_loss = 3.945\n",
      "Epoch   5 Batch   80/144   train_loss = 3.898\n",
      "Epoch   5 Batch   90/144   train_loss = 3.734\n",
      "Epoch   5 Batch  100/144   train_loss = 3.743\n",
      "Epoch   5 Batch  110/144   train_loss = 4.112\n",
      "Epoch   5 Batch  120/144   train_loss = 3.766\n",
      "Epoch   5 Batch  130/144   train_loss = 3.652\n",
      "Epoch   5 Batch  140/144   train_loss = 3.621\n",
      "Epoch   5 Batch    0/1   test_loss = 9.289\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 970\n",
      "min distance : 33\n",
      "平均距离 : 442.78125\n",
      "距离中位数 : 377.5\n",
      "距离标准差 : 294.74700320518525\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 994\n",
      "min distance : 24\n",
      "平均距离 : 450.5625\n",
      "距离中位数 : 382.0\n",
      "距离标准差 : 304.41243994579133\n",
      "Epoch   5 floating median sim range k accuracy 0.041666666666666664 \n",
      "Epoch   5 floating median range k accuracy 0.03125 \n",
      "Epoch   5 similar top k accuracy 0.010416666666666666 \n",
      "Epoch   5 top k accuracy 0.0 \n",
      "Epoch   5 accuracy 0.0 \n",
      "Epoch   6 Batch    6/144   train_loss = 3.503\n",
      "Epoch   6 Batch   16/144   train_loss = 3.705\n",
      "Epoch   6 Batch   26/144   train_loss = 3.351\n",
      "Epoch   6 Batch   36/144   train_loss = 3.226\n",
      "Epoch   6 Batch   46/144   train_loss = 3.248\n",
      "Epoch   6 Batch   56/144   train_loss = 3.289\n",
      "Epoch   6 Batch   66/144   train_loss = 3.206\n",
      "Epoch   6 Batch   76/144   train_loss = 3.292\n",
      "Epoch   6 Batch   86/144   train_loss = 3.045\n",
      "Epoch   6 Batch   96/144   train_loss = 3.262\n",
      "Epoch   6 Batch  106/144   train_loss = 3.142\n",
      "Epoch   6 Batch  116/144   train_loss = 3.426\n",
      "Epoch   6 Batch  126/144   train_loss = 3.289\n",
      "Epoch   6 Batch  136/144   train_loss = 3.211\n",
      "Epoch   6 Batch    0/1   test_loss = 9.682\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 967\n",
      "min distance : 23\n",
      "平均距离 : 479.65625\n",
      "距离中位数 : 505.0\n",
      "距离标准差 : 299.28995236381974\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 24\n",
      "平均距离 : 461.0625\n",
      "距离中位数 : 435.5\n",
      "距离标准差 : 292.60211225100545\n",
      "Epoch   6 floating median sim range k accuracy 0.03571428571428571 \n",
      "Epoch   6 floating median range k accuracy 0.026785714285714284 \n",
      "Epoch   6 similar top k accuracy 0.008928571428571428 \n",
      "Epoch   6 top k accuracy 0.0 \n",
      "Epoch   6 accuracy 0.0 \n",
      "Epoch   7 Batch    2/144   train_loss = 3.358\n",
      "Epoch   7 Batch   12/144   train_loss = 3.285\n",
      "Epoch   7 Batch   22/144   train_loss = 3.455\n",
      "Epoch   7 Batch   32/144   train_loss = 3.258\n",
      "Epoch   7 Batch   42/144   train_loss = 3.309\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch   7 Batch   52/144   train_loss = 3.153\n",
      "Epoch   7 Batch   62/144   train_loss = 3.228\n",
      "Epoch   7 Batch   72/144   train_loss = 3.050\n",
      "Epoch   7 Batch   82/144   train_loss = 2.938\n",
      "Epoch   7 Batch   92/144   train_loss = 2.897\n",
      "Epoch   7 Batch  102/144   train_loss = 2.941\n",
      "Epoch   7 Batch  112/144   train_loss = 2.760\n",
      "Epoch   7 Batch  122/144   train_loss = 3.071\n",
      "Epoch   7 Batch  132/144   train_loss = 3.212\n",
      "Epoch   7 Batch  142/144   train_loss = 2.885\n",
      "Epoch   7 Batch    0/1   test_loss = 10.445\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 995\n",
      "min distance : 9\n",
      "平均距离 : 491.34375\n",
      "距离中位数 : 563.5\n",
      "距离标准差 : 316.73901020546475\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 21\n",
      "平均距离 : 473.71875\n",
      "距离中位数 : 484.0\n",
      "距离标准差 : 297.05042021252467\n",
      "Epoch   7 floating median sim range k accuracy 0.03125 \n",
      "Epoch   7 floating median range k accuracy 0.02734375 \n",
      "Epoch   7 similar top k accuracy 0.0078125 \n",
      "Epoch   7 top k accuracy 0.00390625 \n",
      "Epoch   7 accuracy 0.0 \n",
      "Epoch   8 Batch    8/144   train_loss = 2.888\n",
      "Epoch   8 Batch   18/144   train_loss = 2.912\n",
      "Epoch   8 Batch   28/144   train_loss = 2.989\n",
      "Epoch   8 Batch   38/144   train_loss = 2.953\n",
      "Epoch   8 Batch   48/144   train_loss = 2.991\n",
      "Epoch   8 Batch   58/144   train_loss = 3.007\n",
      "Epoch   8 Batch   68/144   train_loss = 2.707\n",
      "Epoch   8 Batch   78/144   train_loss = 2.972\n",
      "Epoch   8 Batch   88/144   train_loss = 2.808\n",
      "Epoch   8 Batch   98/144   train_loss = 2.961\n",
      "Epoch   8 Batch  108/144   train_loss = 2.823\n",
      "Epoch   8 Batch  118/144   train_loss = 2.976\n",
      "Epoch   8 Batch  128/144   train_loss = 2.835\n",
      "Epoch   8 Batch  138/144   train_loss = 2.651\n",
      "Epoch   8 Batch    0/1   test_loss = 10.507\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 997\n",
      "min distance : 10\n",
      "平均距离 : 486.4375\n",
      "距离中位数 : 477.0\n",
      "距离标准差 : 296.9015974927552\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 20\n",
      "平均距离 : 472.65625\n",
      "距离中位数 : 477.5\n",
      "距离标准差 : 292.22012351297354\n",
      "Epoch   8 floating median sim range k accuracy 0.027777777777777776 \n",
      "Epoch   8 floating median range k accuracy 0.024305555555555556 \n",
      "Epoch   8 similar top k accuracy 0.006944444444444444 \n",
      "Epoch   8 top k accuracy 0.003472222222222222 \n",
      "Epoch   8 accuracy 0.0 \n",
      "Epoch   9 Batch    4/144   train_loss = 2.856\n",
      "Epoch   9 Batch   14/144   train_loss = 2.735\n",
      "Epoch   9 Batch   24/144   train_loss = 2.593\n",
      "Epoch   9 Batch   34/144   train_loss = 2.869\n",
      "Epoch   9 Batch   44/144   train_loss = 2.602\n",
      "Epoch   9 Batch   54/144   train_loss = 2.628\n",
      "Epoch   9 Batch   64/144   train_loss = 2.702\n",
      "Epoch   9 Batch   74/144   train_loss = 2.657\n",
      "Epoch   9 Batch   84/144   train_loss = 2.578\n",
      "Epoch   9 Batch   94/144   train_loss = 2.533\n",
      "Epoch   9 Batch  104/144   train_loss = 2.685\n",
      "Epoch   9 Batch  114/144   train_loss = 2.766\n",
      "Epoch   9 Batch  124/144   train_loss = 2.680\n",
      "Epoch   9 Batch  134/144   train_loss = 2.793\n",
      "Epoch   9 Batch    0/1   test_loss = 10.463\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 995\n",
      "min distance : 12\n",
      "平均距离 : 479.3125\n",
      "距离中位数 : 447.0\n",
      "距离标准差 : 298.1227135656557\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 20\n",
      "平均距离 : 441.46875\n",
      "距离中位数 : 457.5\n",
      "距离标准差 : 289.31708214939107\n",
      "Epoch   9 floating median sim range k accuracy 0.025 \n",
      "Epoch   9 floating median range k accuracy 0.021875 \n",
      "Epoch   9 similar top k accuracy 0.00625 \n",
      "Epoch   9 top k accuracy 0.003125 \n",
      "Epoch   9 accuracy 0.0 \n",
      "Epoch  10 Batch    0/144   train_loss = 2.699\n",
      "Epoch  10 Batch   10/144   train_loss = 2.589\n",
      "Epoch  10 Batch   20/144   train_loss = 2.637\n",
      "Epoch  10 Batch   30/144   train_loss = 2.536\n",
      "Epoch  10 Batch   40/144   train_loss = 2.753\n",
      "Epoch  10 Batch   50/144   train_loss = 2.565\n",
      "Epoch  10 Batch   60/144   train_loss = 2.660\n",
      "Epoch  10 Batch   70/144   train_loss = 2.611\n",
      "Epoch  10 Batch   80/144   train_loss = 2.605\n",
      "Epoch  10 Batch   90/144   train_loss = 2.535\n",
      "Epoch  10 Batch  100/144   train_loss = 2.681\n",
      "Epoch  10 Batch  110/144   train_loss = 2.733\n",
      "Epoch  10 Batch  120/144   train_loss = 2.679\n",
      "Epoch  10 Batch  130/144   train_loss = 2.639\n",
      "Epoch  10 Batch  140/144   train_loss = 2.595\n",
      "Epoch  10 Batch    0/1   test_loss = 10.725\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 9\n",
      "平均距离 : 475.25\n",
      "距离中位数 : 440.5\n",
      "距离标准差 : 301.7716437970937\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 932\n",
      "min distance : 20\n",
      "平均距离 : 420.28125\n",
      "距离中位数 : 434.5\n",
      "距离标准差 : 275.32177020431476\n",
      "Epoch  10 floating median sim range k accuracy 0.028409090909090908 \n",
      "Epoch  10 floating median range k accuracy 0.019886363636363636 \n",
      "Epoch  10 similar top k accuracy 0.005681818181818182 \n",
      "Epoch  10 top k accuracy 0.005681818181818182 \n",
      "Epoch  10 accuracy 0.0 \n",
      "Epoch  11 Batch    6/144   train_loss = 2.629\n",
      "Epoch  11 Batch   16/144   train_loss = 2.754\n",
      "Epoch  11 Batch   26/144   train_loss = 2.561\n",
      "Epoch  11 Batch   36/144   train_loss = 2.459\n",
      "Epoch  11 Batch   46/144   train_loss = 2.553\n",
      "Epoch  11 Batch   56/144   train_loss = 2.678\n",
      "Epoch  11 Batch   66/144   train_loss = 2.501\n",
      "Epoch  11 Batch   76/144   train_loss = 2.661\n",
      "Epoch  11 Batch   86/144   train_loss = 2.384\n",
      "Epoch  11 Batch   96/144   train_loss = 2.553\n",
      "Epoch  11 Batch  106/144   train_loss = 2.465\n",
      "Epoch  11 Batch  116/144   train_loss = 2.666\n",
      "Epoch  11 Batch  126/144   train_loss = 2.504\n",
      "Epoch  11 Batch  136/144   train_loss = 2.527\n",
      "Epoch  11 Batch    0/1   test_loss = 10.986\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 994\n",
      "min distance : 13\n",
      "平均距离 : 475.4375\n",
      "距离中位数 : 461.0\n",
      "距离标准差 : 297.516694647124\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 20\n",
      "平均距离 : 429.09375\n",
      "距离中位数 : 441.5\n",
      "距离标准差 : 283.2539010515786\n",
      "Epoch  11 floating median sim range k accuracy 0.026041666666666668 \n",
      "Epoch  11 floating median range k accuracy 0.018229166666666668 \n",
      "Epoch  11 similar top k accuracy 0.005208333333333333 \n",
      "Epoch  11 top k accuracy 0.005208333333333333 \n",
      "Epoch  11 accuracy 0.0 \n",
      "Epoch  12 Batch    2/144   train_loss = 2.439\n",
      "Epoch  12 Batch   12/144   train_loss = 2.496\n",
      "Epoch  12 Batch   22/144   train_loss = 2.712\n",
      "Epoch  12 Batch   32/144   train_loss = 2.611\n",
      "Epoch  12 Batch   42/144   train_loss = 2.602\n",
      "Epoch  12 Batch   52/144   train_loss = 2.610\n",
      "Epoch  12 Batch   62/144   train_loss = 2.642\n",
      "Epoch  12 Batch   72/144   train_loss = 2.507\n",
      "Epoch  12 Batch   82/144   train_loss = 2.629\n",
      "Epoch  12 Batch   92/144   train_loss = 2.523\n",
      "Epoch  12 Batch  102/144   train_loss = 2.486\n",
      "Epoch  12 Batch  112/144   train_loss = 2.389\n",
      "Epoch  12 Batch  122/144   train_loss = 2.634\n",
      "Epoch  12 Batch  132/144   train_loss = 2.704\n",
      "Epoch  12 Batch  142/144   train_loss = 2.534\n",
      "Epoch  12 Batch    0/1   test_loss = 11.082\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 20\n",
      "平均距离 : 476.84375\n",
      "距离中位数 : 451.0\n",
      "距离标准差 : 294.51168370021844\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 20\n",
      "平均距离 : 430.1875\n",
      "距离中位数 : 443.5\n",
      "距离标准差 : 283.1464326876643\n",
      "Epoch  12 floating median sim range k accuracy 0.028846153846153848 \n",
      "Epoch  12 floating median range k accuracy 0.016826923076923076 \n",
      "Epoch  12 similar top k accuracy 0.004807692307692308 \n",
      "Epoch  12 top k accuracy 0.004807692307692308 \n",
      "Epoch  12 accuracy 0.0 \n",
      "Epoch  13 Batch    8/144   train_loss = 2.512\n",
      "Epoch  13 Batch   18/144   train_loss = 2.486\n",
      "Epoch  13 Batch   28/144   train_loss = 2.645\n",
      "Epoch  13 Batch   38/144   train_loss = 2.626\n",
      "Epoch  13 Batch   48/144   train_loss = 2.577\n",
      "Epoch  13 Batch   58/144   train_loss = 2.646\n",
      "Epoch  13 Batch   68/144   train_loss = 2.380\n",
      "Epoch  13 Batch   78/144   train_loss = 2.549\n",
      "Epoch  13 Batch   88/144   train_loss = 2.499\n",
      "Epoch  13 Batch   98/144   train_loss = 2.620\n",
      "Epoch  13 Batch  108/144   train_loss = 2.475\n",
      "Epoch  13 Batch  118/144   train_loss = 2.681\n",
      "Epoch  13 Batch  128/144   train_loss = 2.620\n",
      "Epoch  13 Batch  138/144   train_loss = 2.417\n",
      "Epoch  13 Batch    0/1   test_loss = 11.157\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 21\n",
      "平均距离 : 474.53125\n",
      "距离中位数 : 444.5\n",
      "距离标准差 : 293.84221790518376\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 20\n",
      "平均距离 : 429.5\n",
      "距离中位数 : 441.0\n",
      "距离标准差 : 283.4915122186201\n",
      "Epoch  13 floating median sim range k accuracy 0.026785714285714284 \n",
      "Epoch  13 floating median range k accuracy 0.015625 \n",
      "Epoch  13 similar top k accuracy 0.004464285714285714 \n",
      "Epoch  13 top k accuracy 0.004464285714285714 \n",
      "Epoch  13 accuracy 0.0 \n",
      "Epoch  14 Batch    4/144   train_loss = 2.569\n",
      "Epoch  14 Batch   14/144   train_loss = 2.601\n",
      "Epoch  14 Batch   24/144   train_loss = 2.404\n",
      "Epoch  14 Batch   34/144   train_loss = 2.623\n",
      "Epoch  14 Batch   44/144   train_loss = 2.381\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  14 Batch   54/144   train_loss = 2.388\n",
      "Epoch  14 Batch   64/144   train_loss = 2.501\n",
      "Epoch  14 Batch   74/144   train_loss = 2.382\n",
      "Epoch  14 Batch   84/144   train_loss = 2.372\n",
      "Epoch  14 Batch   94/144   train_loss = 2.396\n",
      "Epoch  14 Batch  104/144   train_loss = 2.440\n",
      "Epoch  14 Batch  114/144   train_loss = 2.578\n",
      "Epoch  14 Batch  124/144   train_loss = 2.464\n",
      "Epoch  14 Batch  134/144   train_loss = 2.603\n",
      "Epoch  14 Batch    0/1   test_loss = 11.306\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 997\n",
      "min distance : 23\n",
      "平均距离 : 474.4375\n",
      "距离中位数 : 443.0\n",
      "距离标准差 : 293.04222578623376\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 20\n",
      "平均距离 : 429.8125\n",
      "距离中位数 : 440.5\n",
      "距离标准差 : 283.0450977207519\n",
      "Epoch  14 floating median sim range k accuracy 0.027083333333333334 \n",
      "Epoch  14 floating median range k accuracy 0.014583333333333334 \n",
      "Epoch  14 similar top k accuracy 0.004166666666666667 \n",
      "Epoch  14 top k accuracy 0.004166666666666667 \n",
      "Epoch  14 accuracy 0.0 \n",
      "Epoch  15 Batch    0/144   train_loss = 2.516\n",
      "Epoch  15 Batch   10/144   train_loss = 2.417\n",
      "Epoch  15 Batch   20/144   train_loss = 2.465\n",
      "Epoch  15 Batch   30/144   train_loss = 2.340\n",
      "Epoch  15 Batch   40/144   train_loss = 2.587\n",
      "Epoch  15 Batch   50/144   train_loss = 2.445\n",
      "Epoch  15 Batch   60/144   train_loss = 2.504\n",
      "Epoch  15 Batch   70/144   train_loss = 2.489\n",
      "Epoch  15 Batch   80/144   train_loss = 2.424\n",
      "Epoch  15 Batch   90/144   train_loss = 2.422\n",
      "Epoch  15 Batch  100/144   train_loss = 2.567\n",
      "Epoch  15 Batch  110/144   train_loss = 2.597\n",
      "Epoch  15 Batch  120/144   train_loss = 2.556\n",
      "Epoch  15 Batch  130/144   train_loss = 2.477\n",
      "Epoch  15 Batch  140/144   train_loss = 2.461\n",
      "Epoch  15 Batch    0/1   test_loss = 11.384\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 997\n",
      "min distance : 24\n",
      "平均距离 : 473.34375\n",
      "距离中位数 : 441.0\n",
      "距离标准差 : 291.57756358461035\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 20\n",
      "平均距离 : 429.90625\n",
      "距离中位数 : 440.0\n",
      "距离标准差 : 283.2653746241102\n",
      "Epoch  15 floating median sim range k accuracy 0.029296875 \n",
      "Epoch  15 floating median range k accuracy 0.013671875 \n",
      "Epoch  15 similar top k accuracy 0.00390625 \n",
      "Epoch  15 top k accuracy 0.00390625 \n",
      "Epoch  15 accuracy 0.0 \n",
      "Epoch  16 Batch    6/144   train_loss = 2.519\n",
      "Epoch  16 Batch   16/144   train_loss = 2.589\n",
      "Epoch  16 Batch   26/144   train_loss = 2.426\n",
      "Epoch  16 Batch   36/144   train_loss = 2.336\n",
      "Epoch  16 Batch   46/144   train_loss = 2.437\n",
      "Epoch  16 Batch   56/144   train_loss = 2.556\n",
      "Epoch  16 Batch   66/144   train_loss = 2.394\n",
      "Epoch  16 Batch   76/144   train_loss = 2.542\n",
      "Epoch  16 Batch   86/144   train_loss = 2.287\n",
      "Epoch  16 Batch   96/144   train_loss = 2.465\n",
      "Epoch  16 Batch  106/144   train_loss = 2.381\n",
      "Epoch  16 Batch  116/144   train_loss = 2.577\n",
      "Epoch  16 Batch  126/144   train_loss = 2.412\n",
      "Epoch  16 Batch  136/144   train_loss = 2.434\n",
      "Epoch  16 Batch    0/1   test_loss = 11.460\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 997\n",
      "min distance : 25\n",
      "平均距离 : 472.75\n",
      "距离中位数 : 437.0\n",
      "距离标准差 : 291.6592618107644\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 20\n",
      "平均距离 : 429.5\n",
      "距离中位数 : 437.0\n",
      "距离标准差 : 283.51576234840985\n",
      "Epoch  16 floating median sim range k accuracy 0.03125 \n",
      "Epoch  16 floating median range k accuracy 0.012867647058823529 \n",
      "Epoch  16 similar top k accuracy 0.003676470588235294 \n",
      "Epoch  16 top k accuracy 0.003676470588235294 \n",
      "Epoch  16 accuracy 0.0 \n",
      "Epoch  17 Batch    2/144   train_loss = 2.322\n",
      "Epoch  17 Batch   12/144   train_loss = 2.377\n",
      "Epoch  17 Batch   22/144   train_loss = 2.631\n",
      "Epoch  17 Batch   32/144   train_loss = 2.507\n",
      "Epoch  17 Batch   42/144   train_loss = 2.466\n",
      "Epoch  17 Batch   52/144   train_loss = 2.521\n",
      "Epoch  17 Batch   62/144   train_loss = 2.541\n",
      "Epoch  17 Batch   72/144   train_loss = 2.416\n",
      "Epoch  17 Batch   82/144   train_loss = 2.514\n",
      "Epoch  17 Batch   92/144   train_loss = 2.398\n",
      "Epoch  17 Batch  102/144   train_loss = 2.399\n",
      "Epoch  17 Batch  112/144   train_loss = 2.303\n",
      "Epoch  17 Batch  122/144   train_loss = 2.550\n",
      "Epoch  17 Batch  132/144   train_loss = 2.624\n",
      "Epoch  17 Batch  142/144   train_loss = 2.450\n",
      "Epoch  17 Batch    0/1   test_loss = 11.546\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 24\n",
      "平均距离 : 469.0\n",
      "距离中位数 : 438.0\n",
      "距离标准差 : 292.24711290276247\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 20\n",
      "平均距离 : 429.53125\n",
      "距离中位数 : 436.0\n",
      "距离标准差 : 283.90678315855274\n",
      "Epoch  17 floating median sim range k accuracy 0.03298611111111111 \n",
      "Epoch  17 floating median range k accuracy 0.012152777777777778 \n",
      "Epoch  17 similar top k accuracy 0.003472222222222222 \n",
      "Epoch  17 top k accuracy 0.003472222222222222 \n",
      "Epoch  17 accuracy 0.0 \n",
      "Epoch  18 Batch    8/144   train_loss = 2.448\n",
      "Epoch  18 Batch   18/144   train_loss = 2.417\n",
      "Epoch  18 Batch   28/144   train_loss = 2.561\n",
      "Epoch  18 Batch   38/144   train_loss = 2.557\n",
      "Epoch  18 Batch   48/144   train_loss = 2.503\n",
      "Epoch  18 Batch   58/144   train_loss = 2.568\n",
      "Epoch  18 Batch   68/144   train_loss = 2.299\n",
      "Epoch  18 Batch   78/144   train_loss = 2.475\n",
      "Epoch  18 Batch   88/144   train_loss = 2.432\n",
      "Epoch  18 Batch   98/144   train_loss = 2.539\n",
      "Epoch  18 Batch  108/144   train_loss = 2.394\n",
      "Epoch  18 Batch  118/144   train_loss = 2.598\n",
      "Epoch  18 Batch  128/144   train_loss = 2.556\n",
      "Epoch  18 Batch  138/144   train_loss = 2.352\n",
      "Epoch  18 Batch    0/1   test_loss = 11.690\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 22\n",
      "平均距离 : 471.34375\n",
      "距离中位数 : 442.5\n",
      "距离标准差 : 291.9212874833514\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 20\n",
      "平均距离 : 429.28125\n",
      "距离中位数 : 436.5\n",
      "距离标准差 : 284.37620707161403\n",
      "Epoch  18 floating median sim range k accuracy 0.03453947368421053 \n",
      "Epoch  18 floating median range k accuracy 0.011513157894736841 \n",
      "Epoch  18 similar top k accuracy 0.003289473684210526 \n",
      "Epoch  18 top k accuracy 0.003289473684210526 \n",
      "Epoch  18 accuracy 0.0 \n",
      "Epoch  19 Batch    4/144   train_loss = 2.505\n",
      "Epoch  19 Batch   14/144   train_loss = 2.537\n",
      "Epoch  19 Batch   24/144   train_loss = 2.343\n",
      "Epoch  19 Batch   34/144   train_loss = 2.561\n",
      "Epoch  19 Batch   44/144   train_loss = 2.332\n",
      "Epoch  19 Batch   54/144   train_loss = 2.330\n",
      "Epoch  19 Batch   64/144   train_loss = 2.446\n",
      "Epoch  19 Batch   74/144   train_loss = 2.333\n",
      "Epoch  19 Batch   84/144   train_loss = 2.309\n",
      "Epoch  19 Batch   94/144   train_loss = 2.348\n",
      "Epoch  19 Batch  104/144   train_loss = 2.388\n",
      "Epoch  19 Batch  114/144   train_loss = 2.512\n",
      "Epoch  19 Batch  124/144   train_loss = 2.409\n",
      "Epoch  19 Batch  134/144   train_loss = 2.561\n",
      "Epoch  19 Batch    0/1   test_loss = 11.712\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 995\n",
      "min distance : 20\n",
      "平均距离 : 471.53125\n",
      "距离中位数 : 426.0\n",
      "距离标准差 : 287.6068262810142\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 20\n",
      "平均距离 : 429.5\n",
      "距离中位数 : 439.5\n",
      "距离标准差 : 284.46331046375735\n",
      "Epoch  19 floating median sim range k accuracy 0.0359375 \n",
      "Epoch  19 floating median range k accuracy 0.0109375 \n",
      "Epoch  19 similar top k accuracy 0.003125 \n",
      "Epoch  19 top k accuracy 0.003125 \n",
      "Epoch  19 accuracy 0.0 \n",
      "Epoch  20 Batch    0/144   train_loss = 2.462\n",
      "Epoch  20 Batch   10/144   train_loss = 2.360\n",
      "Epoch  20 Batch   20/144   train_loss = 2.412\n",
      "Epoch  20 Batch   30/144   train_loss = 2.267\n",
      "Epoch  20 Batch   40/144   train_loss = 2.532\n",
      "Epoch  20 Batch   50/144   train_loss = 2.408\n",
      "Epoch  20 Batch   60/144   train_loss = 2.439\n",
      "Epoch  20 Batch   70/144   train_loss = 2.444\n",
      "Epoch  20 Batch   80/144   train_loss = 2.382\n",
      "Epoch  20 Batch   90/144   train_loss = 2.368\n",
      "Epoch  20 Batch  100/144   train_loss = 2.526\n",
      "Epoch  20 Batch  110/144   train_loss = 2.541\n",
      "Epoch  20 Batch  120/144   train_loss = 2.527\n",
      "Epoch  20 Batch  130/144   train_loss = 2.434\n",
      "Epoch  20 Batch  140/144   train_loss = 2.409\n",
      "Epoch  20 Batch    0/1   test_loss = 11.660\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 20\n",
      "平均距离 : 470.96875\n",
      "距离中位数 : 417.5\n",
      "距离标准差 : 286.11202836203427\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 20\n",
      "平均距离 : 429.34375\n",
      "距离中位数 : 435.0\n",
      "距离标准差 : 284.4397573932616\n",
      "Epoch  20 floating median sim range k accuracy 0.03720238095238095 \n",
      "Epoch  20 floating median range k accuracy 0.010416666666666666 \n",
      "Epoch  20 similar top k accuracy 0.002976190476190476 \n",
      "Epoch  20 top k accuracy 0.002976190476190476 \n",
      "Epoch  20 accuracy 0.0 \n",
      "Epoch  21 Batch    6/144   train_loss = 2.481\n",
      "Epoch  21 Batch   16/144   train_loss = 2.525\n",
      "Epoch  21 Batch   26/144   train_loss = 2.377\n",
      "Epoch  21 Batch   36/144   train_loss = 2.301\n",
      "Epoch  21 Batch   46/144   train_loss = 2.397\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  21 Batch   56/144   train_loss = 2.485\n",
      "Epoch  21 Batch   66/144   train_loss = 2.315\n",
      "Epoch  21 Batch   76/144   train_loss = 2.499\n",
      "Epoch  21 Batch   86/144   train_loss = 2.237\n",
      "Epoch  21 Batch   96/144   train_loss = 2.412\n",
      "Epoch  21 Batch  106/144   train_loss = 2.359\n",
      "Epoch  21 Batch  116/144   train_loss = 2.560\n",
      "Epoch  21 Batch  126/144   train_loss = 2.376\n",
      "Epoch  21 Batch  136/144   train_loss = 2.363\n",
      "Epoch  21 Batch    0/1   test_loss = 11.758\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 997\n",
      "min distance : 22\n",
      "平均距离 : 470.71875\n",
      "距离中位数 : 421.0\n",
      "距离标准差 : 290.56520722281516\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 996\n",
      "min distance : 20\n",
      "平均距离 : 430.53125\n",
      "距离中位数 : 429.0\n",
      "距离标准差 : 284.4316684608757\n",
      "Epoch  21 floating median sim range k accuracy 0.03835227272727273 \n",
      "Epoch  21 floating median range k accuracy 0.009943181818181818 \n",
      "Epoch  21 similar top k accuracy 0.002840909090909091 \n",
      "Epoch  21 top k accuracy 0.002840909090909091 \n",
      "Epoch  21 accuracy 0.0 \n",
      "Epoch  22 Batch    2/144   train_loss = 2.282\n",
      "Epoch  22 Batch   12/144   train_loss = 2.361\n",
      "Epoch  22 Batch   22/144   train_loss = 2.580\n",
      "Epoch  22 Batch   32/144   train_loss = 2.476\n",
      "Epoch  22 Batch   42/144   train_loss = 2.440\n",
      "Epoch  22 Batch   52/144   train_loss = 2.476\n",
      "Epoch  22 Batch   62/144   train_loss = 2.521\n",
      "Epoch  22 Batch   72/144   train_loss = 2.398\n",
      "Epoch  22 Batch   82/144   train_loss = 2.504\n",
      "Epoch  22 Batch   92/144   train_loss = 2.382\n",
      "Epoch  22 Batch  102/144   train_loss = 2.356\n",
      "Epoch  22 Batch  112/144   train_loss = 2.247\n",
      "Epoch  22 Batch  122/144   train_loss = 2.501\n",
      "Epoch  22 Batch  132/144   train_loss = 2.570\n",
      "Epoch  22 Batch  142/144   train_loss = 2.452\n",
      "Epoch  22 Batch    0/1   test_loss = 12.015\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 997\n",
      "min distance : 18\n",
      "平均距离 : 485.125\n",
      "距离中位数 : 474.0\n",
      "距离标准差 : 286.1385973178033\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 994\n",
      "min distance : 20\n",
      "平均距离 : 455.15625\n",
      "距离中位数 : 453.0\n",
      "距离标准差 : 284.95614897021875\n",
      "Epoch  22 floating median sim range k accuracy 0.036684782608695655 \n",
      "Epoch  22 floating median range k accuracy 0.009510869565217392 \n",
      "Epoch  22 similar top k accuracy 0.002717391304347826 \n",
      "Epoch  22 top k accuracy 0.002717391304347826 \n",
      "Epoch  22 accuracy 0.0 \n",
      "Epoch  23 Batch    8/144   train_loss = 2.425\n",
      "Epoch  23 Batch   18/144   train_loss = 2.369\n",
      "Epoch  23 Batch   28/144   train_loss = 2.522\n",
      "Epoch  23 Batch   38/144   train_loss = 2.518\n",
      "Epoch  23 Batch   48/144   train_loss = 2.465\n",
      "Epoch  23 Batch   58/144   train_loss = 2.524\n",
      "Epoch  23 Batch   68/144   train_loss = 2.272\n",
      "Epoch  23 Batch   78/144   train_loss = 2.465\n",
      "Epoch  23 Batch   88/144   train_loss = 2.386\n",
      "Epoch  23 Batch   98/144   train_loss = 2.544\n",
      "Epoch  23 Batch  108/144   train_loss = 2.363\n",
      "Epoch  23 Batch  118/144   train_loss = 2.593\n",
      "Epoch  23 Batch  128/144   train_loss = 2.546\n",
      "Epoch  23 Batch  138/144   train_loss = 2.334\n",
      "Epoch  23 Batch    0/1   test_loss = 11.939\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 997\n",
      "min distance : 24\n",
      "平均距离 : 484.71875\n",
      "距离中位数 : 480.0\n",
      "距离标准差 : 277.6566263362672\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 995\n",
      "min distance : 19\n",
      "平均距离 : 457.46875\n",
      "距离中位数 : 418.0\n",
      "距离标准差 : 282.908883783167\n",
      "Epoch  23 floating median sim range k accuracy 0.037760416666666664 \n",
      "Epoch  23 floating median range k accuracy 0.009114583333333334 \n",
      "Epoch  23 similar top k accuracy 0.0026041666666666665 \n",
      "Epoch  23 top k accuracy 0.0026041666666666665 \n",
      "Epoch  23 accuracy 0.0 \n",
      "Epoch  24 Batch    4/144   train_loss = 2.553\n",
      "Epoch  24 Batch   14/144   train_loss = 2.543\n",
      "Epoch  24 Batch   24/144   train_loss = 2.367\n",
      "Epoch  24 Batch   34/144   train_loss = 2.575\n",
      "Epoch  24 Batch   44/144   train_loss = 2.403\n",
      "Epoch  24 Batch   54/144   train_loss = 2.359\n",
      "Epoch  24 Batch   64/144   train_loss = 2.395\n",
      "Epoch  24 Batch   74/144   train_loss = 2.304\n",
      "Epoch  24 Batch   84/144   train_loss = 2.275\n",
      "Epoch  24 Batch   94/144   train_loss = 2.361\n",
      "Epoch  24 Batch  104/144   train_loss = 2.381\n",
      "Epoch  24 Batch  114/144   train_loss = 2.502\n",
      "Epoch  24 Batch  124/144   train_loss = 2.524\n",
      "Epoch  24 Batch  134/144   train_loss = 2.621\n",
      "Epoch  24 Batch    0/1   test_loss = 11.228\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 947\n",
      "min distance : 25\n",
      "平均距离 : 485.03125\n",
      "距离中位数 : 384.0\n",
      "距离标准差 : 286.9245855158416\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 954\n",
      "min distance : 30\n",
      "平均距离 : 430.4375\n",
      "距离中位数 : 424.5\n",
      "距离标准差 : 268.32989694357576\n",
      "Epoch  24 floating median sim range k accuracy 0.03625 \n",
      "Epoch  24 floating median range k accuracy 0.01125 \n",
      "Epoch  24 similar top k accuracy 0.0025 \n",
      "Epoch  24 top k accuracy 0.0025 \n",
      "Epoch  24 accuracy 0.0 \n",
      "Epoch  25 Batch    0/144   train_loss = 2.533\n",
      "Epoch  25 Batch   10/144   train_loss = 2.533\n",
      "Epoch  25 Batch   20/144   train_loss = 2.507\n",
      "Epoch  25 Batch   30/144   train_loss = 2.642\n",
      "Epoch  25 Batch   40/144   train_loss = 2.753\n",
      "Epoch  25 Batch   50/144   train_loss = 2.915\n",
      "Epoch  25 Batch   60/144   train_loss = 3.150\n",
      "Epoch  25 Batch   70/144   train_loss = 3.123\n",
      "Epoch  25 Batch   80/144   train_loss = 4.025\n",
      "Epoch  25 Batch   90/144   train_loss = 4.286\n",
      "Epoch  25 Batch  100/144   train_loss = 4.936\n",
      "Epoch  25 Batch  110/144   train_loss = 5.031\n",
      "Epoch  25 Batch  120/144   train_loss = 5.556\n",
      "Epoch  25 Batch  130/144   train_loss = 5.280\n",
      "Epoch  25 Batch  140/144   train_loss = 5.737\n",
      "Epoch  25 Batch    0/1   test_loss = 9.241\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 899\n",
      "min distance : 28\n",
      "平均距离 : 483.5\n",
      "距离中位数 : 503.5\n",
      "距离标准差 : 259.45640288880907\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 945\n",
      "min distance : 27\n",
      "平均距离 : 439.625\n",
      "距离中位数 : 367.5\n",
      "距离标准差 : 278.5914066783109\n",
      "Epoch  25 floating median sim range k accuracy 0.03485576923076923 \n",
      "Epoch  25 floating median range k accuracy 0.010817307692307692 \n",
      "Epoch  25 similar top k accuracy 0.002403846153846154 \n",
      "Epoch  25 top k accuracy 0.002403846153846154 \n",
      "Epoch  25 accuracy 0.0 \n",
      "Epoch  26 Batch    6/144   train_loss = 5.473\n",
      "Epoch  26 Batch   16/144   train_loss = 4.668\n",
      "Epoch  26 Batch   26/144   train_loss = 5.130\n",
      "Epoch  26 Batch   36/144   train_loss = 5.225\n",
      "Epoch  26 Batch   46/144   train_loss = 5.337\n",
      "Epoch  26 Batch   56/144   train_loss = 4.858\n",
      "Epoch  26 Batch   66/144   train_loss = 5.211\n",
      "Epoch  26 Batch   76/144   train_loss = 5.037\n",
      "Epoch  26 Batch   86/144   train_loss = 4.969\n",
      "Epoch  26 Batch   96/144   train_loss = 4.993\n",
      "Epoch  26 Batch  106/144   train_loss = 4.780\n",
      "Epoch  26 Batch  116/144   train_loss = 5.711\n",
      "Epoch  26 Batch  126/144   train_loss = 4.352\n",
      "Epoch  26 Batch  136/144   train_loss = 4.769\n",
      "Epoch  26 Batch    0/1   test_loss = 9.874\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 947\n",
      "min distance : 91\n",
      "平均距离 : 520.0\n",
      "距离中位数 : 512.0\n",
      "距离标准差 : 289.8747574384496\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 15\n",
      "平均距离 : 404.5625\n",
      "距离中位数 : 375.5\n",
      "距离标准差 : 271.5628308398445\n",
      "Epoch  26 floating median sim range k accuracy 0.034722222222222224 \n",
      "Epoch  26 floating median range k accuracy 0.01273148148148148 \n",
      "Epoch  26 similar top k accuracy 0.0023148148148148147 \n",
      "Epoch  26 top k accuracy 0.0023148148148148147 \n",
      "Epoch  26 accuracy 0.0 \n",
      "Epoch  27 Batch    2/144   train_loss = 4.957\n",
      "Epoch  27 Batch   12/144   train_loss = 4.174\n",
      "Epoch  27 Batch   22/144   train_loss = 4.342\n",
      "Epoch  27 Batch   32/144   train_loss = 4.221\n",
      "Epoch  27 Batch   42/144   train_loss = 4.064\n",
      "Epoch  27 Batch   52/144   train_loss = 4.423\n",
      "Epoch  27 Batch   62/144   train_loss = 3.812\n",
      "Epoch  27 Batch   72/144   train_loss = 3.669\n",
      "Epoch  27 Batch   82/144   train_loss = 3.826\n",
      "Epoch  27 Batch   92/144   train_loss = 3.893\n",
      "Epoch  27 Batch  102/144   train_loss = 4.665\n",
      "Epoch  27 Batch  112/144   train_loss = 3.559\n",
      "Epoch  27 Batch  122/144   train_loss = 3.523\n",
      "Epoch  27 Batch  132/144   train_loss = 3.638\n",
      "Epoch  27 Batch  142/144   train_loss = 3.401\n",
      "Epoch  27 Batch    0/1   test_loss = 10.358\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 929\n",
      "min distance : 88\n",
      "平均距离 : 542.1875\n",
      "距离中位数 : 601.5\n",
      "距离标准差 : 301.01250363357\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 999\n",
      "min distance : 6\n",
      "平均距离 : 496.0\n",
      "距离中位数 : 490.5\n",
      "距离标准差 : 316.05151874338463\n",
      "Epoch  27 floating median sim range k accuracy 0.033482142857142856 \n",
      "Epoch  27 floating median range k accuracy 0.012276785714285714 \n",
      "Epoch  27 similar top k accuracy 0.0033482142857142855 \n",
      "Epoch  27 top k accuracy 0.002232142857142857 \n",
      "Epoch  27 accuracy 0.0 \n",
      "Epoch  28 Batch    8/144   train_loss = 3.359\n",
      "Epoch  28 Batch   18/144   train_loss = 3.419\n",
      "Epoch  28 Batch   28/144   train_loss = 3.239\n",
      "Epoch  28 Batch   38/144   train_loss = 3.507\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  28 Batch   48/144   train_loss = 3.067\n",
      "Epoch  28 Batch   58/144   train_loss = 3.501\n",
      "Epoch  28 Batch   68/144   train_loss = 3.447\n",
      "Epoch  28 Batch   78/144   train_loss = 3.241\n",
      "Epoch  28 Batch   88/144   train_loss = 3.312\n",
      "Epoch  28 Batch   98/144   train_loss = 2.913\n",
      "Epoch  28 Batch  108/144   train_loss = 2.918\n",
      "Epoch  28 Batch  118/144   train_loss = 3.019\n",
      "Epoch  28 Batch  128/144   train_loss = 3.242\n",
      "Epoch  28 Batch  138/144   train_loss = 3.369\n",
      "Epoch  28 Batch    0/1   test_loss = 10.943\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 941\n",
      "min distance : 126\n",
      "平均距离 : 531.78125\n",
      "距离中位数 : 519.0\n",
      "距离标准差 : 261.47236163395456\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 427.65625\n",
      "距离中位数 : 390.0\n",
      "距离标准差 : 308.684163970129\n",
      "Epoch  28 floating median sim range k accuracy 0.032327586206896554 \n",
      "Epoch  28 floating median range k accuracy 0.014008620689655173 \n",
      "Epoch  28 similar top k accuracy 0.004310344827586207 \n",
      "Epoch  28 top k accuracy 0.0021551724137931034 \n",
      "Epoch  28 accuracy 0.0 \n",
      "Epoch  29 Batch    4/144   train_loss = 3.130\n",
      "Epoch  29 Batch   14/144   train_loss = 3.107\n",
      "Epoch  29 Batch   24/144   train_loss = 2.748\n",
      "Epoch  29 Batch   34/144   train_loss = 2.842\n",
      "Epoch  29 Batch   44/144   train_loss = 2.707\n",
      "Epoch  29 Batch   54/144   train_loss = 2.888\n",
      "Epoch  29 Batch   64/144   train_loss = 2.820\n",
      "Epoch  29 Batch   74/144   train_loss = 2.914\n",
      "Epoch  29 Batch   84/144   train_loss = 2.986\n",
      "Epoch  29 Batch   94/144   train_loss = 2.572\n",
      "Epoch  29 Batch  104/144   train_loss = 2.919\n",
      "Epoch  29 Batch  114/144   train_loss = 2.719\n",
      "Epoch  29 Batch  124/144   train_loss = 2.563\n",
      "Epoch  29 Batch  134/144   train_loss = 2.678\n",
      "Epoch  29 Batch    0/1   test_loss = 11.319\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 945\n",
      "min distance : 93\n",
      "平均距离 : 529.90625\n",
      "距离中位数 : 479.5\n",
      "距离标准差 : 270.6381762814284\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 19\n",
      "平均距离 : 449.90625\n",
      "距离中位数 : 397.0\n",
      "距离标准差 : 279.12086980542585\n",
      "Epoch  29 floating median sim range k accuracy 0.03229166666666667 \n",
      "Epoch  29 floating median range k accuracy 0.015625 \n",
      "Epoch  29 similar top k accuracy 0.004166666666666667 \n",
      "Epoch  29 top k accuracy 0.0020833333333333333 \n",
      "Epoch  29 accuracy 0.0 \n",
      "Epoch  30 Batch    0/144   train_loss = 2.722\n",
      "Epoch  30 Batch   10/144   train_loss = 2.525\n",
      "Epoch  30 Batch   20/144   train_loss = 2.577\n",
      "Epoch  30 Batch   30/144   train_loss = 2.531\n",
      "Epoch  30 Batch   40/144   train_loss = 2.911\n",
      "Epoch  30 Batch   50/144   train_loss = 2.426\n",
      "Epoch  30 Batch   60/144   train_loss = 2.619\n",
      "Epoch  30 Batch   70/144   train_loss = 2.548\n",
      "Epoch  30 Batch   80/144   train_loss = 2.432\n",
      "Epoch  30 Batch   90/144   train_loss = 2.522\n",
      "Epoch  30 Batch  100/144   train_loss = 2.617\n",
      "Epoch  30 Batch  110/144   train_loss = 2.586\n",
      "Epoch  30 Batch  120/144   train_loss = 2.518\n",
      "Epoch  30 Batch  130/144   train_loss = 2.430\n",
      "Epoch  30 Batch  140/144   train_loss = 2.427\n",
      "Epoch  30 Batch    0/1   test_loss = 11.826\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 939\n",
      "min distance : 84\n",
      "平均距离 : 525.21875\n",
      "距离中位数 : 496.5\n",
      "距离标准差 : 276.3021143575226\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 457.53125\n",
      "距离中位数 : 475.5\n",
      "距离标准差 : 280.9834274889491\n",
      "Epoch  30 floating median sim range k accuracy 0.03125 \n",
      "Epoch  30 floating median range k accuracy 0.015120967741935484 \n",
      "Epoch  30 similar top k accuracy 0.005040322580645161 \n",
      "Epoch  30 top k accuracy 0.0020161290322580645 \n",
      "Epoch  30 accuracy 0.0 \n",
      "Epoch  31 Batch    6/144   train_loss = 2.580\n",
      "Epoch  31 Batch   16/144   train_loss = 2.529\n",
      "Epoch  31 Batch   26/144   train_loss = 2.376\n",
      "Epoch  31 Batch   36/144   train_loss = 2.284\n",
      "Epoch  31 Batch   46/144   train_loss = 2.398\n",
      "Epoch  31 Batch   56/144   train_loss = 2.476\n",
      "Epoch  31 Batch   66/144   train_loss = 2.372\n",
      "Epoch  31 Batch   76/144   train_loss = 2.405\n",
      "Epoch  31 Batch   86/144   train_loss = 2.220\n",
      "Epoch  31 Batch   96/144   train_loss = 2.400\n",
      "Epoch  31 Batch  106/144   train_loss = 2.276\n",
      "Epoch  31 Batch  116/144   train_loss = 2.505\n",
      "Epoch  31 Batch  126/144   train_loss = 2.343\n",
      "Epoch  31 Batch  136/144   train_loss = 2.385\n",
      "Epoch  31 Batch    0/1   test_loss = 12.112\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 944\n",
      "min distance : 91\n",
      "平均距离 : 527.375\n",
      "距离中位数 : 505.0\n",
      "距离标准差 : 272.64763775796774\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 456.125\n",
      "距离中位数 : 474.0\n",
      "距离标准差 : 291.2660671533847\n",
      "Epoch  31 floating median sim range k accuracy 0.0302734375 \n",
      "Epoch  31 floating median range k accuracy 0.0146484375 \n",
      "Epoch  31 similar top k accuracy 0.005859375 \n",
      "Epoch  31 top k accuracy 0.001953125 \n",
      "Epoch  31 accuracy 0.0 \n",
      "Epoch  32 Batch    2/144   train_loss = 2.237\n",
      "Epoch  32 Batch   12/144   train_loss = 2.291\n",
      "Epoch  32 Batch   22/144   train_loss = 2.518\n",
      "Epoch  32 Batch   32/144   train_loss = 2.360\n",
      "Epoch  32 Batch   42/144   train_loss = 2.370\n",
      "Epoch  32 Batch   52/144   train_loss = 2.409\n",
      "Epoch  32 Batch   62/144   train_loss = 2.396\n",
      "Epoch  32 Batch   72/144   train_loss = 2.371\n",
      "Epoch  32 Batch   82/144   train_loss = 2.430\n",
      "Epoch  32 Batch   92/144   train_loss = 2.315\n",
      "Epoch  32 Batch  102/144   train_loss = 2.365\n",
      "Epoch  32 Batch  112/144   train_loss = 2.204\n",
      "Epoch  32 Batch  122/144   train_loss = 2.408\n",
      "Epoch  32 Batch  132/144   train_loss = 2.562\n",
      "Epoch  32 Batch  142/144   train_loss = 2.360\n",
      "Epoch  32 Batch    0/1   test_loss = 12.276\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 955\n",
      "min distance : 93\n",
      "平均距离 : 524.5\n",
      "距离中位数 : 506.5\n",
      "距离标准差 : 275.13383107135337\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 455.875\n",
      "距离中位数 : 473.0\n",
      "距离标准差 : 290.75803406784826\n",
      "Epoch  32 floating median sim range k accuracy 0.029356060606060608 \n",
      "Epoch  32 floating median range k accuracy 0.017045454545454544 \n",
      "Epoch  32 similar top k accuracy 0.006628787878787879 \n",
      "Epoch  32 top k accuracy 0.001893939393939394 \n",
      "Epoch  32 accuracy 0.0 \n",
      "Epoch  33 Batch    8/144   train_loss = 2.353\n",
      "Epoch  33 Batch   18/144   train_loss = 2.288\n",
      "Epoch  33 Batch   28/144   train_loss = 2.410\n",
      "Epoch  33 Batch   38/144   train_loss = 2.451\n",
      "Epoch  33 Batch   48/144   train_loss = 2.357\n",
      "Epoch  33 Batch   58/144   train_loss = 2.469\n",
      "Epoch  33 Batch   68/144   train_loss = 2.201\n",
      "Epoch  33 Batch   78/144   train_loss = 2.352\n",
      "Epoch  33 Batch   88/144   train_loss = 2.304\n",
      "Epoch  33 Batch   98/144   train_loss = 2.383\n",
      "Epoch  33 Batch  108/144   train_loss = 2.291\n",
      "Epoch  33 Batch  118/144   train_loss = 2.452\n",
      "Epoch  33 Batch  128/144   train_loss = 2.428\n",
      "Epoch  33 Batch  138/144   train_loss = 2.271\n",
      "Epoch  33 Batch    0/1   test_loss = 12.452\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 958\n",
      "min distance : 83\n",
      "平均距离 : 524.40625\n",
      "距离中位数 : 495.0\n",
      "距离标准差 : 274.7088071229925\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 455.65625\n",
      "距离中位数 : 472.0\n",
      "距离标准差 : 290.65406944671787\n",
      "Epoch  33 floating median sim range k accuracy 0.02849264705882353 \n",
      "Epoch  33 floating median range k accuracy 0.016544117647058824 \n",
      "Epoch  33 similar top k accuracy 0.007352941176470588 \n",
      "Epoch  33 top k accuracy 0.001838235294117647 \n",
      "Epoch  33 accuracy 0.0 \n",
      "Epoch  34 Batch    4/144   train_loss = 2.342\n",
      "Epoch  34 Batch   14/144   train_loss = 2.474\n",
      "Epoch  34 Batch   24/144   train_loss = 2.233\n",
      "Epoch  34 Batch   34/144   train_loss = 2.417\n",
      "Epoch  34 Batch   44/144   train_loss = 2.225\n",
      "Epoch  34 Batch   54/144   train_loss = 2.231\n",
      "Epoch  34 Batch   64/144   train_loss = 2.323\n",
      "Epoch  34 Batch   74/144   train_loss = 2.238\n",
      "Epoch  34 Batch   84/144   train_loss = 2.243\n",
      "Epoch  34 Batch   94/144   train_loss = 2.185\n",
      "Epoch  34 Batch  104/144   train_loss = 2.286\n",
      "Epoch  34 Batch  114/144   train_loss = 2.322\n",
      "Epoch  34 Batch  124/144   train_loss = 2.263\n",
      "Epoch  34 Batch  134/144   train_loss = 2.444\n",
      "Epoch  34 Batch    0/1   test_loss = 12.595\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 962\n",
      "min distance : 70\n",
      "平均距离 : 523.90625\n",
      "距离中位数 : 489.0\n",
      "距离标准差 : 274.8547752194557\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 455.4375\n",
      "距离中位数 : 471.5\n",
      "距离标准差 : 290.7202196163005\n",
      "Epoch  34 floating median sim range k accuracy 0.027678571428571427 \n",
      "Epoch  34 floating median range k accuracy 0.016964285714285713 \n",
      "Epoch  34 similar top k accuracy 0.008035714285714285 \n",
      "Epoch  34 top k accuracy 0.0017857142857142857 \n",
      "Epoch  34 accuracy 0.0 \n",
      "Epoch  35 Batch    0/144   train_loss = 2.300\n",
      "Epoch  35 Batch   10/144   train_loss = 2.201\n",
      "Epoch  35 Batch   20/144   train_loss = 2.265\n",
      "Epoch  35 Batch   30/144   train_loss = 2.141\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  35 Batch   40/144   train_loss = 2.379\n",
      "Epoch  35 Batch   50/144   train_loss = 2.281\n",
      "Epoch  35 Batch   60/144   train_loss = 2.303\n",
      "Epoch  35 Batch   70/144   train_loss = 2.306\n",
      "Epoch  35 Batch   80/144   train_loss = 2.207\n",
      "Epoch  35 Batch   90/144   train_loss = 2.187\n",
      "Epoch  35 Batch  100/144   train_loss = 2.395\n",
      "Epoch  35 Batch  110/144   train_loss = 2.375\n",
      "Epoch  35 Batch  120/144   train_loss = 2.379\n",
      "Epoch  35 Batch  130/144   train_loss = 2.261\n",
      "Epoch  35 Batch  140/144   train_loss = 2.265\n",
      "Epoch  35 Batch    0/1   test_loss = 12.721\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 963\n",
      "min distance : 70\n",
      "平均距离 : 523.84375\n",
      "距离中位数 : 489.0\n",
      "距离标准差 : 274.1949704789231\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 455.34375\n",
      "距离中位数 : 471.0\n",
      "距离标准差 : 290.6358986531731\n",
      "Epoch  35 floating median sim range k accuracy 0.026909722222222224 \n",
      "Epoch  35 floating median range k accuracy 0.018229166666666668 \n",
      "Epoch  35 similar top k accuracy 0.008680555555555556 \n",
      "Epoch  35 top k accuracy 0.001736111111111111 \n",
      "Epoch  35 accuracy 0.0 \n",
      "Epoch  36 Batch    6/144   train_loss = 2.338\n",
      "Epoch  36 Batch   16/144   train_loss = 2.362\n",
      "Epoch  36 Batch   26/144   train_loss = 2.231\n",
      "Epoch  36 Batch   36/144   train_loss = 2.188\n",
      "Epoch  36 Batch   46/144   train_loss = 2.222\n",
      "Epoch  36 Batch   56/144   train_loss = 2.347\n",
      "Epoch  36 Batch   66/144   train_loss = 2.214\n",
      "Epoch  36 Batch   76/144   train_loss = 2.303\n",
      "Epoch  36 Batch   86/144   train_loss = 2.095\n",
      "Epoch  36 Batch   96/144   train_loss = 2.277\n",
      "Epoch  36 Batch  106/144   train_loss = 2.200\n",
      "Epoch  36 Batch  116/144   train_loss = 2.387\n",
      "Epoch  36 Batch  126/144   train_loss = 2.214\n",
      "Epoch  36 Batch  136/144   train_loss = 2.252\n",
      "Epoch  36 Batch    0/1   test_loss = 12.842\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 964\n",
      "min distance : 73\n",
      "平均距离 : 524.53125\n",
      "距离中位数 : 491.5\n",
      "距离标准差 : 273.99121614284917\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 455.25\n",
      "距离中位数 : 470.5\n",
      "距离标准差 : 290.62723117423116\n",
      "Epoch  36 floating median sim range k accuracy 0.026182432432432432 \n",
      "Epoch  36 floating median range k accuracy 0.019425675675675675 \n",
      "Epoch  36 similar top k accuracy 0.009290540540540541 \n",
      "Epoch  36 top k accuracy 0.0016891891891891893 \n",
      "Epoch  36 accuracy 0.0 \n",
      "Epoch  37 Batch    2/144   train_loss = 2.136\n",
      "Epoch  37 Batch   12/144   train_loss = 2.217\n",
      "Epoch  37 Batch   22/144   train_loss = 2.402\n",
      "Epoch  37 Batch   32/144   train_loss = 2.274\n",
      "Epoch  37 Batch   42/144   train_loss = 2.233\n",
      "Epoch  37 Batch   52/144   train_loss = 2.310\n",
      "Epoch  37 Batch   62/144   train_loss = 2.320\n",
      "Epoch  37 Batch   72/144   train_loss = 2.263\n",
      "Epoch  37 Batch   82/144   train_loss = 2.338\n",
      "Epoch  37 Batch   92/144   train_loss = 2.209\n",
      "Epoch  37 Batch  102/144   train_loss = 2.271\n",
      "Epoch  37 Batch  112/144   train_loss = 2.109\n",
      "Epoch  37 Batch  122/144   train_loss = 2.333\n",
      "Epoch  37 Batch  132/144   train_loss = 2.477\n",
      "Epoch  37 Batch  142/144   train_loss = 2.250\n",
      "Epoch  37 Batch    0/1   test_loss = 12.950\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 969\n",
      "min distance : 81\n",
      "平均距离 : 524.625\n",
      "距离中位数 : 495.0\n",
      "距离标准差 : 272.9896689895059\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 455.25\n",
      "距离中位数 : 470.5\n",
      "距离标准差 : 290.52796767264937\n",
      "Epoch  37 floating median sim range k accuracy 0.02549342105263158 \n",
      "Epoch  37 floating median range k accuracy 0.018914473684210526 \n",
      "Epoch  37 similar top k accuracy 0.009868421052631578 \n",
      "Epoch  37 top k accuracy 0.001644736842105263 \n",
      "Epoch  37 accuracy 0.0 \n",
      "Epoch  38 Batch    8/144   train_loss = 2.282\n",
      "Epoch  38 Batch   18/144   train_loss = 2.211\n",
      "Epoch  38 Batch   28/144   train_loss = 2.338\n",
      "Epoch  38 Batch   38/144   train_loss = 2.378\n",
      "Epoch  38 Batch   48/144   train_loss = 2.278\n",
      "Epoch  38 Batch   58/144   train_loss = 2.387\n",
      "Epoch  38 Batch   68/144   train_loss = 2.136\n",
      "Epoch  38 Batch   78/144   train_loss = 2.280\n",
      "Epoch  38 Batch   88/144   train_loss = 2.242\n",
      "Epoch  38 Batch   98/144   train_loss = 2.317\n",
      "Epoch  38 Batch  108/144   train_loss = 2.224\n",
      "Epoch  38 Batch  118/144   train_loss = 2.381\n",
      "Epoch  38 Batch  128/144   train_loss = 2.359\n",
      "Epoch  38 Batch  138/144   train_loss = 2.197\n",
      "Epoch  38 Batch    0/1   test_loss = 13.047\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 970\n",
      "min distance : 95\n",
      "平均距离 : 524.125\n",
      "距离中位数 : 504.0\n",
      "距离标准差 : 272.53666611118587\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 455.0625\n",
      "距离中位数 : 470.0\n",
      "距离标准差 : 290.549580267723\n",
      "Epoch  38 floating median sim range k accuracy 0.024839743589743588 \n",
      "Epoch  38 floating median range k accuracy 0.019230769230769232 \n",
      "Epoch  38 similar top k accuracy 0.010416666666666666 \n",
      "Epoch  38 top k accuracy 0.0016025641025641025 \n",
      "Epoch  38 accuracy 0.0 \n",
      "Epoch  39 Batch    4/144   train_loss = 2.290\n",
      "Epoch  39 Batch   14/144   train_loss = 2.413\n",
      "Epoch  39 Batch   24/144   train_loss = 2.181\n",
      "Epoch  39 Batch   34/144   train_loss = 2.354\n",
      "Epoch  39 Batch   44/144   train_loss = 2.178\n",
      "Epoch  39 Batch   54/144   train_loss = 2.172\n",
      "Epoch  39 Batch   64/144   train_loss = 2.273\n",
      "Epoch  39 Batch   74/144   train_loss = 2.183\n",
      "Epoch  39 Batch   84/144   train_loss = 2.184\n",
      "Epoch  39 Batch   94/144   train_loss = 2.139\n",
      "Epoch  39 Batch  104/144   train_loss = 2.223\n",
      "Epoch  39 Batch  114/144   train_loss = 2.268\n",
      "Epoch  39 Batch  124/144   train_loss = 2.214\n",
      "Epoch  39 Batch  134/144   train_loss = 2.398\n",
      "Epoch  39 Batch    0/1   test_loss = 13.135\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 971\n",
      "min distance : 107\n",
      "平均距离 : 523.53125\n",
      "距离中位数 : 513.0\n",
      "距离标准差 : 272.0889083065267\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 455.09375\n",
      "距离中位数 : 470.0\n",
      "距离标准差 : 290.6631812957009\n",
      "Epoch  39 floating median sim range k accuracy 0.02421875 \n",
      "Epoch  39 floating median range k accuracy 0.01875 \n",
      "Epoch  39 similar top k accuracy 0.0109375 \n",
      "Epoch  39 top k accuracy 0.0015625 \n",
      "Epoch  39 accuracy 0.0 \n",
      "Epoch  40 Batch    0/144   train_loss = 2.277\n",
      "Epoch  40 Batch   10/144   train_loss = 2.153\n",
      "Epoch  40 Batch   20/144   train_loss = 2.222\n",
      "Epoch  40 Batch   30/144   train_loss = 2.090\n",
      "Epoch  40 Batch   40/144   train_loss = 2.332\n",
      "Epoch  40 Batch   50/144   train_loss = 2.240\n",
      "Epoch  40 Batch   60/144   train_loss = 2.263\n",
      "Epoch  40 Batch   70/144   train_loss = 2.266\n",
      "Epoch  40 Batch   80/144   train_loss = 2.171\n",
      "Epoch  40 Batch   90/144   train_loss = 2.141\n",
      "Epoch  40 Batch  100/144   train_loss = 2.356\n",
      "Epoch  40 Batch  110/144   train_loss = 2.320\n",
      "Epoch  40 Batch  120/144   train_loss = 2.336\n",
      "Epoch  40 Batch  130/144   train_loss = 2.227\n",
      "Epoch  40 Batch  140/144   train_loss = 2.224\n",
      "Epoch  40 Batch    0/1   test_loss = 13.223\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 973\n",
      "min distance : 114\n",
      "平均距离 : 523.40625\n",
      "距离中位数 : 521.5\n",
      "距离标准差 : 272.48266680825316\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 455.1875\n",
      "距离中位数 : 470.5\n",
      "距离标准差 : 290.585340035849\n",
      "Epoch  40 floating median sim range k accuracy 0.023628048780487805 \n",
      "Epoch  40 floating median range k accuracy 0.019054878048780487 \n",
      "Epoch  40 similar top k accuracy 0.011432926829268292 \n",
      "Epoch  40 top k accuracy 0.001524390243902439 \n",
      "Epoch  40 accuracy 0.0 \n",
      "Epoch  41 Batch    6/144   train_loss = 2.295\n",
      "Epoch  41 Batch   16/144   train_loss = 2.316\n",
      "Epoch  41 Batch   26/144   train_loss = 2.194\n",
      "Epoch  41 Batch   36/144   train_loss = 2.151\n",
      "Epoch  41 Batch   46/144   train_loss = 2.182\n",
      "Epoch  41 Batch   56/144   train_loss = 2.305\n",
      "Epoch  41 Batch   66/144   train_loss = 2.170\n",
      "Epoch  41 Batch   76/144   train_loss = 2.273\n",
      "Epoch  41 Batch   86/144   train_loss = 2.057\n",
      "Epoch  41 Batch   96/144   train_loss = 2.236\n",
      "Epoch  41 Batch  106/144   train_loss = 2.166\n",
      "Epoch  41 Batch  116/144   train_loss = 2.350\n",
      "Epoch  41 Batch  126/144   train_loss = 2.180\n",
      "Epoch  41 Batch  136/144   train_loss = 2.216\n",
      "Epoch  41 Batch    0/1   test_loss = 13.288\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 975\n",
      "min distance : 115\n",
      "平均距离 : 523.1875\n",
      "距离中位数 : 521.0\n",
      "距离标准差 : 273.4469141236558\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 455.28125\n",
      "距离中位数 : 471.5\n",
      "距离标准差 : 290.6477931250081\n",
      "Epoch  41 floating median sim range k accuracy 0.023065476190476192 \n",
      "Epoch  41 floating median range k accuracy 0.018601190476190476 \n",
      "Epoch  41 similar top k accuracy 0.011904761904761904 \n",
      "Epoch  41 top k accuracy 0.001488095238095238 \n",
      "Epoch  41 accuracy 0.0 \n",
      "Epoch  42 Batch    2/144   train_loss = 2.102\n",
      "Epoch  42 Batch   12/144   train_loss = 2.190\n",
      "Epoch  42 Batch   22/144   train_loss = 2.371\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  42 Batch   32/144   train_loss = 2.236\n",
      "Epoch  42 Batch   42/144   train_loss = 2.191\n",
      "Epoch  42 Batch   52/144   train_loss = 2.273\n",
      "Epoch  42 Batch   62/144   train_loss = 2.287\n",
      "Epoch  42 Batch   72/144   train_loss = 2.226\n",
      "Epoch  42 Batch   82/144   train_loss = 2.307\n",
      "Epoch  42 Batch   92/144   train_loss = 2.192\n",
      "Epoch  42 Batch  102/144   train_loss = 2.237\n",
      "Epoch  42 Batch  112/144   train_loss = 2.073\n",
      "Epoch  42 Batch  122/144   train_loss = 2.297\n",
      "Epoch  42 Batch  132/144   train_loss = 2.448\n",
      "Epoch  42 Batch  142/144   train_loss = 2.239\n",
      "Epoch  42 Batch    0/1   test_loss = 13.368\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 976\n",
      "min distance : 116\n",
      "平均距离 : 522.5\n",
      "距离中位数 : 510.0\n",
      "距离标准差 : 273.765068078453\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 455.1875\n",
      "距离中位数 : 471.0\n",
      "距离标准差 : 290.535006227735\n",
      "Epoch  42 floating median sim range k accuracy 0.02252906976744186 \n",
      "Epoch  42 floating median range k accuracy 0.018168604651162792 \n",
      "Epoch  42 similar top k accuracy 0.012354651162790697 \n",
      "Epoch  42 top k accuracy 0.0014534883720930232 \n",
      "Epoch  42 accuracy 0.0 \n",
      "Epoch  43 Batch    8/144   train_loss = 2.253\n",
      "Epoch  43 Batch   18/144   train_loss = 2.182\n",
      "Epoch  43 Batch   28/144   train_loss = 2.308\n",
      "Epoch  43 Batch   38/144   train_loss = 2.350\n",
      "Epoch  43 Batch   48/144   train_loss = 2.246\n",
      "Epoch  43 Batch   58/144   train_loss = 2.350\n",
      "Epoch  43 Batch   68/144   train_loss = 2.105\n",
      "Epoch  43 Batch   78/144   train_loss = 2.249\n",
      "Epoch  43 Batch   88/144   train_loss = 2.214\n",
      "Epoch  43 Batch   98/144   train_loss = 2.284\n",
      "Epoch  43 Batch  108/144   train_loss = 2.191\n",
      "Epoch  43 Batch  118/144   train_loss = 2.352\n",
      "Epoch  43 Batch  128/144   train_loss = 2.331\n",
      "Epoch  43 Batch  138/144   train_loss = 2.162\n",
      "Epoch  43 Batch    0/1   test_loss = 13.461\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 977\n",
      "min distance : 114\n",
      "平均距离 : 521.53125\n",
      "距离中位数 : 508.0\n",
      "距离标准差 : 273.11467193001096\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 455.125\n",
      "距离中位数 : 471.0\n",
      "距离标准差 : 290.56687590811174\n",
      "Epoch  43 floating median sim range k accuracy 0.022017045454545456 \n",
      "Epoch  43 floating median range k accuracy 0.01775568181818182 \n",
      "Epoch  43 similar top k accuracy 0.01278409090909091 \n",
      "Epoch  43 top k accuracy 0.0014204545454545455 \n",
      "Epoch  43 accuracy 0.0 \n",
      "Epoch  44 Batch    4/144   train_loss = 2.265\n",
      "Epoch  44 Batch   14/144   train_loss = 2.390\n",
      "Epoch  44 Batch   24/144   train_loss = 2.155\n",
      "Epoch  44 Batch   34/144   train_loss = 2.331\n",
      "Epoch  44 Batch   44/144   train_loss = 2.155\n",
      "Epoch  44 Batch   54/144   train_loss = 2.144\n",
      "Epoch  44 Batch   64/144   train_loss = 2.248\n",
      "Epoch  44 Batch   74/144   train_loss = 2.157\n",
      "Epoch  44 Batch   84/144   train_loss = 2.153\n",
      "Epoch  44 Batch   94/144   train_loss = 2.117\n",
      "Epoch  44 Batch  104/144   train_loss = 2.197\n",
      "Epoch  44 Batch  114/144   train_loss = 2.245\n",
      "Epoch  44 Batch  124/144   train_loss = 2.192\n",
      "Epoch  44 Batch  134/144   train_loss = 2.379\n",
      "Epoch  44 Batch    0/1   test_loss = 13.537\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 977\n",
      "min distance : 112\n",
      "平均距离 : 520.125\n",
      "距离中位数 : 506.0\n",
      "距离标准差 : 274.396582294678\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 455.0625\n",
      "距离中位数 : 471.0\n",
      "距离标准差 : 290.63442861049685\n",
      "Epoch  44 floating median sim range k accuracy 0.021527777777777778 \n",
      "Epoch  44 floating median range k accuracy 0.017361111111111112 \n",
      "Epoch  44 similar top k accuracy 0.013194444444444444 \n",
      "Epoch  44 top k accuracy 0.001388888888888889 \n",
      "Epoch  44 accuracy 0.0 \n",
      "Epoch  45 Batch    0/144   train_loss = 2.242\n",
      "Epoch  45 Batch   10/144   train_loss = 2.128\n",
      "Epoch  45 Batch   20/144   train_loss = 2.202\n",
      "Epoch  45 Batch   30/144   train_loss = 2.068\n",
      "Epoch  45 Batch   40/144   train_loss = 2.306\n",
      "Epoch  45 Batch   50/144   train_loss = 2.216\n",
      "Epoch  45 Batch   60/144   train_loss = 2.237\n",
      "Epoch  45 Batch   70/144   train_loss = 2.243\n",
      "Epoch  45 Batch   80/144   train_loss = 2.153\n",
      "Epoch  45 Batch   90/144   train_loss = 2.117\n",
      "Epoch  45 Batch  100/144   train_loss = 2.342\n",
      "Epoch  45 Batch  110/144   train_loss = 2.288\n",
      "Epoch  45 Batch  120/144   train_loss = 2.316\n",
      "Epoch  45 Batch  130/144   train_loss = 2.208\n",
      "Epoch  45 Batch  140/144   train_loss = 2.204\n",
      "Epoch  45 Batch    0/1   test_loss = 13.629\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 979\n",
      "min distance : 111\n",
      "平均距离 : 520.53125\n",
      "距离中位数 : 511.0\n",
      "距离标准差 : 275.37860124460923\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 455.21875\n",
      "距离中位数 : 470.5\n",
      "距离标准差 : 290.45833762940515\n",
      "Epoch  45 floating median sim range k accuracy 0.021059782608695652 \n",
      "Epoch  45 floating median range k accuracy 0.016983695652173912 \n",
      "Epoch  45 similar top k accuracy 0.01358695652173913 \n",
      "Epoch  45 top k accuracy 0.001358695652173913 \n",
      "Epoch  45 accuracy 0.0 \n",
      "Epoch  46 Batch    6/144   train_loss = 2.270\n",
      "Epoch  46 Batch   16/144   train_loss = 2.294\n",
      "Epoch  46 Batch   26/144   train_loss = 2.176\n",
      "Epoch  46 Batch   36/144   train_loss = 2.131\n",
      "Epoch  46 Batch   46/144   train_loss = 2.153\n",
      "Epoch  46 Batch   56/144   train_loss = 2.286\n",
      "Epoch  46 Batch   66/144   train_loss = 2.145\n",
      "Epoch  46 Batch   76/144   train_loss = 2.257\n",
      "Epoch  46 Batch   86/144   train_loss = 2.037\n",
      "Epoch  46 Batch   96/144   train_loss = 2.218\n",
      "Epoch  46 Batch  106/144   train_loss = 2.150\n",
      "Epoch  46 Batch  116/144   train_loss = 2.329\n",
      "Epoch  46 Batch  126/144   train_loss = 2.157\n",
      "Epoch  46 Batch  136/144   train_loss = 2.196\n",
      "Epoch  46 Batch    0/1   test_loss = 13.727\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 980\n",
      "min distance : 111\n",
      "平均距离 : 520.59375\n",
      "距离中位数 : 507.5\n",
      "距离标准差 : 275.6684896953903\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 455.09375\n",
      "距离中位数 : 470.5\n",
      "距离标准差 : 290.41160007984786\n",
      "Epoch  46 floating median sim range k accuracy 0.020611702127659573 \n",
      "Epoch  46 floating median range k accuracy 0.01795212765957447 \n",
      "Epoch  46 similar top k accuracy 0.013962765957446808 \n",
      "Epoch  46 top k accuracy 0.0013297872340425532 \n",
      "Epoch  46 accuracy 0.0 \n",
      "Epoch  47 Batch    2/144   train_loss = 2.083\n",
      "Epoch  47 Batch   12/144   train_loss = 2.170\n",
      "Epoch  47 Batch   22/144   train_loss = 2.351\n",
      "Epoch  47 Batch   32/144   train_loss = 2.218\n",
      "Epoch  47 Batch   42/144   train_loss = 2.164\n",
      "Epoch  47 Batch   52/144   train_loss = 2.253\n",
      "Epoch  47 Batch   62/144   train_loss = 2.273\n",
      "Epoch  47 Batch   72/144   train_loss = 2.204\n",
      "Epoch  47 Batch   82/144   train_loss = 2.285\n",
      "Epoch  47 Batch   92/144   train_loss = 2.166\n",
      "Epoch  47 Batch  102/144   train_loss = 2.217\n",
      "Epoch  47 Batch  112/144   train_loss = 2.058\n",
      "Epoch  47 Batch  122/144   train_loss = 2.282\n",
      "Epoch  47 Batch  132/144   train_loss = 2.430\n",
      "Epoch  47 Batch  142/144   train_loss = 2.207\n",
      "Epoch  47 Batch    0/1   test_loss = 13.778\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 984\n",
      "min distance : 110\n",
      "平均距离 : 518.46875\n",
      "距离中位数 : 502.0\n",
      "距离标准差 : 275.28337494922846\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 455.15625\n",
      "距离中位数 : 470.0\n",
      "距离标准差 : 290.38445522434137\n",
      "Epoch  47 floating median sim range k accuracy 0.020182291666666668 \n",
      "Epoch  47 floating median range k accuracy 0.018880208333333332 \n",
      "Epoch  47 similar top k accuracy 0.014322916666666666 \n",
      "Epoch  47 top k accuracy 0.0013020833333333333 \n",
      "Epoch  47 accuracy 0.0 \n",
      "Epoch  48 Batch    8/144   train_loss = 2.232\n",
      "Epoch  48 Batch   18/144   train_loss = 2.163\n",
      "Epoch  48 Batch   28/144   train_loss = 2.291\n",
      "Epoch  48 Batch   38/144   train_loss = 2.331\n",
      "Epoch  48 Batch   48/144   train_loss = 2.233\n",
      "Epoch  48 Batch   58/144   train_loss = 2.332\n",
      "Epoch  48 Batch   68/144   train_loss = 2.087\n",
      "Epoch  48 Batch   78/144   train_loss = 2.229\n",
      "Epoch  48 Batch   88/144   train_loss = 2.200\n",
      "Epoch  48 Batch   98/144   train_loss = 2.264\n",
      "Epoch  48 Batch  108/144   train_loss = 2.175\n",
      "Epoch  48 Batch  118/144   train_loss = 2.336\n",
      "Epoch  48 Batch  128/144   train_loss = 2.312\n",
      "Epoch  48 Batch  138/144   train_loss = 2.146\n",
      "Epoch  48 Batch    0/1   test_loss = 13.868\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 988\n",
      "min distance : 107\n",
      "平均距离 : 519.71875\n",
      "距离中位数 : 517.0\n",
      "距离标准差 : 276.2162009159446\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 455.03125\n",
      "距离中位数 : 470.0\n",
      "距离标准差 : 290.3609267333287\n",
      "Epoch  48 floating median sim range k accuracy 0.019770408163265307 \n",
      "Epoch  48 floating median range k accuracy 0.018494897959183673 \n",
      "Epoch  48 similar top k accuracy 0.014668367346938776 \n",
      "Epoch  48 top k accuracy 0.0012755102040816326 \n",
      "Epoch  48 accuracy 0.0 \n",
      "Epoch  49 Batch    4/144   train_loss = 2.246\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  49 Batch   14/144   train_loss = 2.370\n",
      "Epoch  49 Batch   24/144   train_loss = 2.133\n",
      "Epoch  49 Batch   34/144   train_loss = 2.309\n",
      "Epoch  49 Batch   44/144   train_loss = 2.138\n",
      "Epoch  49 Batch   54/144   train_loss = 2.131\n",
      "Epoch  49 Batch   64/144   train_loss = 2.231\n",
      "Epoch  49 Batch   74/144   train_loss = 2.143\n",
      "Epoch  49 Batch   84/144   train_loss = 2.135\n",
      "Epoch  49 Batch   94/144   train_loss = 2.102\n",
      "Epoch  49 Batch  104/144   train_loss = 2.179\n",
      "Epoch  49 Batch  114/144   train_loss = 2.224\n",
      "Epoch  49 Batch  124/144   train_loss = 2.175\n",
      "Epoch  49 Batch  134/144   train_loss = 2.370\n",
      "Epoch  49 Batch    0/1   test_loss = 13.961\n",
      "真实值在预测值中的距离数据：\n",
      "max distance : 990\n",
      "min distance : 105\n",
      "平均距离 : 521.125\n",
      "距离中位数 : 516.0\n",
      "距离标准差 : 277.6904470359036\n",
      "真实值在预测值相似向量中的距离数据：\n",
      "max distance : 998\n",
      "min distance : 4\n",
      "平均距离 : 454.96875\n",
      "距离中位数 : 469.5\n",
      "距离标准差 : 290.3773928415184\n",
      "Epoch  49 floating median sim range k accuracy 0.019375 \n",
      "Epoch  49 floating median range k accuracy 0.018125 \n",
      "Epoch  49 similar top k accuracy 0.015 \n",
      "Epoch  49 top k accuracy 0.00125 \n",
      "Epoch  49 accuracy 0.0 \n",
      "Model Trained and Saved\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "batches = get_batches(int_text[:-(batch_size+1)], batch_size, seq_length)\n",
    "test_batches = get_batches(int_text[-(batch_size+1):], batch_size, seq_length)\n",
    "top_k = 10\n",
    "topk_acc_list = []\n",
    "topk_acc = 0\n",
    "sim_topk_acc_list = []\n",
    "sim_topk_acc = 0\n",
    "\n",
    "range_k = 5\n",
    "floating_median_idx = 0\n",
    "floating_median_acc_range_k = 0\n",
    "floating_median_acc_range_k_list = []\n",
    "\n",
    "floating_median_sim_idx = 0\n",
    "floating_median_sim_acc_range_k = 0\n",
    "floating_median_sim_acc_range_k_list = []\n",
    "\n",
    "losses = {'train':[], 'test':[]}\n",
    "accuracies = {'accuracy':[], 'topk':[], 'sim_topk':[], 'floating_median_acc_range_k':[], 'floating_median_sim_acc_range_k':[]}\n",
    "\n",
    "with tf.Session(graph=train_graph) as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    saver = tf.train.Saver()\n",
    "    for epoch_i in range(epochs):\n",
    "        state = sess.run(initial_state, {input_text: batches[0][0]})\n",
    "\n",
    "        #训练的迭代，保存训练损失\n",
    "        for batch_i, (x, y) in enumerate(batches):\n",
    "            feed = {\n",
    "                input_text: x,\n",
    "                targets: y,\n",
    "                initial_state: state,\n",
    "                lr: learning_rate}\n",
    "            train_loss, state, _ = sess.run([cost, final_state, train_op], feed)  #\n",
    "            losses['train'].append(train_loss)\n",
    "            \n",
    "            # Show every <show_every_n_batches> batches\n",
    "            if (epoch_i * len(batches) + batch_i) % show_every_n_batches == 0:\n",
    "                print('Epoch {:>3} Batch {:>4}/{}   train_loss = {:.3f}'.format(\n",
    "                    epoch_i,\n",
    "                    batch_i,\n",
    "                    len(batches),\n",
    "                    train_loss))\n",
    "                \n",
    "        #使用测试数据的迭代\n",
    "        acc_list = []\n",
    "        prev_state = sess.run(initial_state, {input_text: np.array([[1]])})#test_batches[0][0]\n",
    "        for batch_i, (x, y) in enumerate(test_batches):\n",
    "            # Get Prediction\n",
    "            test_loss, acc, probabilities, prev_state = sess.run(\n",
    "                [cost, accuracy, probs, final_state],\n",
    "                {input_text: x, \n",
    "                 targets: y,\n",
    "                 initial_state: prev_state})  #\n",
    "            \n",
    "            #保存测试损失和准确率\n",
    "            acc_list.append(acc)\n",
    "            losses['test'].append(test_loss)\n",
    "            accuracies['accuracy'].append(acc)\n",
    "\n",
    "            print('Epoch {:>3} Batch {:>4}/{}   test_loss = {:.3f}'.format(\n",
    "                epoch_i,\n",
    "                batch_i,\n",
    "                len(test_batches),\n",
    "                test_loss))\n",
    "    \n",
    "            #利用嵌入矩阵和生成的预测计算得到相似度矩阵sim\n",
    "            valid_embedding = tf.nn.embedding_lookup(normalized_embedding, np.squeeze(probabilities.argmax(2)))\n",
    "            similarity = tf.matmul(valid_embedding, tf.transpose(normalized_embedding))\n",
    "            sim = similarity.eval()\n",
    "            \n",
    "            #保存预测结果的Top K准确率和与预测结果距离最近的Top K准确率\n",
    "            topk_acc = 0\n",
    "            sim_topk_acc = 0\n",
    "            for ii in range(len(probabilities)):\n",
    "\n",
    "                nearest = (-sim[ii, :]).argsort()[0:top_k]\n",
    "                if y[ii] in nearest:\n",
    "                    sim_topk_acc += 1\n",
    "\n",
    "                if y[ii] in (-probabilities[ii]).argsort()[0][0:top_k]:\n",
    "                    topk_acc += 1\n",
    "\n",
    "            topk_acc = topk_acc / len(y)\n",
    "            topk_acc_list.append(topk_acc)\n",
    "            accuracies['topk'].append(topk_acc)\n",
    "            \n",
    "            sim_topk_acc = sim_topk_acc / len(y)\n",
    "            sim_topk_acc_list.append(sim_topk_acc)\n",
    "            accuracies['sim_topk'].append(sim_topk_acc)\n",
    "\n",
    "            #计算真实值在预测值中的距离数据\n",
    "            realInSim_distance_list = []\n",
    "            realInPredict_distance_list = []\n",
    "            for ii in range(len(probabilities)):\n",
    "                sim_nearest = (-sim[ii, :]).argsort()\n",
    "                idx = list(sim_nearest).index(y[ii])\n",
    "                realInSim_distance_list.append(idx)\n",
    "                \n",
    "                nearest = (-probabilities[ii]).argsort()[0]\n",
    "                idx = list(nearest).index(y[ii])\n",
    "                realInPredict_distance_list.append(idx)\n",
    "                \n",
    "            print('真实值在预测值中的距离数据：')\n",
    "            print('max distance : {}'.format(max(realInPredict_distance_list)))\n",
    "            print('min distance : {}'.format(min(realInPredict_distance_list)))\n",
    "            print('平均距离 : {}'.format(np.mean(realInPredict_distance_list)))\n",
    "            print('距离中位数 : {}'.format(np.median(realInPredict_distance_list)))\n",
    "            print('距离标准差 : {}'.format(np.std(realInPredict_distance_list)))\n",
    "            \n",
    "            print('真实值在预测值相似向量中的距离数据：')\n",
    "            print('max distance : {}'.format(max(realInSim_distance_list)))\n",
    "            print('min distance : {}'.format(min(realInSim_distance_list)))\n",
    "            print('平均距离 : {}'.format(np.mean(realInSim_distance_list)))\n",
    "            print('距离中位数 : {}'.format(np.median(realInSim_distance_list)))\n",
    "            print('距离标准差 : {}'.format(np.std(realInSim_distance_list)))\n",
    "#             sns.distplot(realInPredict_distance_list, rug=True)  #, hist=False\n",
    "            #plt.hist(np.log(realInPredict_distance_list), bins=50, color='steelblue', normed=True )\n",
    "\n",
    "            #计算以距离中位数为中心，范围K为半径的准确率\n",
    "            floating_median_sim_idx = int(np.median(realInSim_distance_list))\n",
    "            floating_median_sim_acc_range_k = 0\n",
    "        \n",
    "            floating_median_idx = int(np.median(realInPredict_distance_list))\n",
    "            floating_median_acc_range_k = 0\n",
    "            for ii in range(len(probabilities)):\n",
    "                nearest_floating_median = (-probabilities[ii]).argsort()[0][floating_median_idx - range_k:floating_median_idx + range_k]\n",
    "                if y[ii] in nearest_floating_median:\n",
    "                    floating_median_acc_range_k += 1\n",
    "                    \n",
    "                nearest_floating_median_sim = (-sim[ii, :]).argsort()[floating_median_sim_idx - range_k:floating_median_sim_idx + range_k]\n",
    "                if y[ii] in nearest_floating_median_sim:\n",
    "                    floating_median_sim_acc_range_k += 1\n",
    "                    \n",
    "            floating_median_acc_range_k = floating_median_acc_range_k / len(y)\n",
    "            floating_median_acc_range_k_list.append(floating_median_acc_range_k)\n",
    "            accuracies['floating_median_acc_range_k'].append(floating_median_acc_range_k)\n",
    "            \n",
    "            floating_median_sim_acc_range_k = floating_median_sim_acc_range_k / len(y)\n",
    "            floating_median_sim_acc_range_k_list.append(floating_median_sim_acc_range_k)\n",
    "            accuracies['floating_median_sim_acc_range_k'].append(floating_median_sim_acc_range_k)\n",
    "            \n",
    "        print('Epoch {:>3} floating median sim range k accuracy {} '.format(epoch_i, np.mean(floating_median_sim_acc_range_k_list)))#:.3f\n",
    "        print('Epoch {:>3} floating median range k accuracy {} '.format(epoch_i, np.mean(floating_median_acc_range_k_list)))#:.3f\n",
    "        print('Epoch {:>3} similar top k accuracy {} '.format(epoch_i, np.mean(sim_topk_acc_list)))#:.3f\n",
    "        print('Epoch {:>3} top k accuracy {} '.format(epoch_i, np.mean(topk_acc_list)))#:.3f\n",
    "        print('Epoch {:>3} accuracy {} '.format(epoch_i, np.mean(acc_list)))#:.3f\n",
    "        \n",
    "    # Save Model\n",
    "    saver.save(sess, save_dir)  #, global_step=epoch_i\n",
    "    print('Model Trained and Saved')\n",
    "    embed_mat = sess.run(normalized_embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 真实值在预测值中的距离图表"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "真实值在预测值相似向量中的距离数据：\n",
    "max distance : 965\n",
    "min distance : 20\n",
    "平均距离 : 472.71875\n",
    "距离中位数 : 562.0\n",
    "距离标准差 : 311.20714909596387"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2efa42e8>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 400
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(realInSim_distance_list, rug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "真实值在预测值中的距离数据：\n",
    "max distance : 946\n",
    "min distance : 7\n",
    "平均距离 : 556.65625\n",
    "距离中位数 : 570.0\n",
    "距离标准差 : 274.8418419490335"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c422b22e8>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 393
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(realInPredict_distance_list, rug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从最后一次的距离图表来看，模型训练的还不够好，从0到1000的距离都有可能，也就是没有学到规律。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 显示训练Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 364
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses['train'], label='Training loss')\n",
    "plt.legend()\n",
    "_ = plt.ylim()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 显示测试Loss\n",
    "测试损失始终没有降下去。。。，epochs高一点的话会出现下降-上升-下降的波浪形曲线。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 370
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses['test'], label='Test loss')\n",
    "plt.legend()\n",
    "_ = plt.ylim()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 显示准确率\n",
    " - 测试准确率\n",
    " - Top K准确率\n",
    " - 相似度Top K准确率\n",
    " - 浮动距离中位数Range K准确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 380
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(accuracies['accuracy'], label='Accuracy')\n",
    "plt.plot(accuracies['topk'], label='Top K')\n",
    "plt.plot(accuracies['sim_topk'], label='Similar Top K')\n",
    "plt.plot(accuracies['floating_median_acc_range_k'], label='Floating Median Range K Acc')\n",
    "plt.plot(accuracies['floating_median_sim_acc_range_k'], label='Floating Median Sim Range K Acc')\n",
    "plt.legend()\n",
    "_ = plt.ylim()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 显示预测结果和实际开奖结果\n",
    "感觉从趋势上看起来还行，实际结果是一个都没有猜中 ：P\n",
    "\n",
    "有的简直错的离谱，南辕北辙"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 383
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for batch_i, (x, y) in enumerate(test_batches):\n",
    "    plt.plot(y, label='Targets')\n",
    "    plt.plot(np.squeeze(probabilities.argmax(2)), label='Prediction')\n",
    "    plt.legend()\n",
    "    _ = plt.ylim()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 保存参数\n",
    "保存 `seq_length` 和 `save_dir` 在生成预测时使用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump((seq_length, save_dir), open('params.p', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "_, vocab_to_int, int_to_vocab = pickle.load(open('preprocess.p', mode='rb'))\n",
    "\n",
    "seq_length, load_dir = pickle.load(open('params.p', mode='rb'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实现生成预测函数\n",
    "### 获取 Tensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_tensors(loaded_graph):\n",
    "    inputs = loaded_graph.get_tensor_by_name(\"input:0\")\n",
    "    initial_state = loaded_graph.get_tensor_by_name(\"initial_state:0\")\n",
    "    final_state = loaded_graph.get_tensor_by_name(\"final_state:0\")\n",
    "    probs = loaded_graph.get_tensor_by_name(\"probs:0\")\n",
    "    return inputs, initial_state, final_state, probs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 选择号码\n",
    "实现 `pick_word()` 函数从概率向量 `probabilities`或相似度向量`sim`中选择号码。\n",
    "\n",
    "pred_mode是选择预测的种类：\n",
    "- sim：从相似度向量Top K中选号。\n",
    "- median：从浮动距离中位数（相似度向量）Range K中选号。\n",
    "- topk：从概率向量Top K中选号。\n",
    "- max：从概率向量中选择最大概率的号码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pick_word(probabilities, sim, int_to_vocab, top_n = 5, pred_mode = 'sim'):\n",
    "\n",
    "    vocab_size = len(int_to_vocab)\n",
    "\n",
    "    if pred_mode == 'sim':\n",
    "        p = np.squeeze(sim)\n",
    "        p[np.argsort(p)[:-top_n]] = 0\n",
    "        p = p / np.sum(p)\n",
    "        c = np.random.choice(vocab_size, 1, p=p)[0]\n",
    "        return int_to_vocab[c]\n",
    "    elif pred_mode == 'median':\n",
    "        p = np.squeeze(sim)\n",
    "        p[np.argsort(p)[:floating_median_sim_idx - top_n]] = 0\n",
    "        p[np.argsort(p)[floating_median_sim_idx + top_n:]] = 0\n",
    "        p = np.abs(p) / np.sum(np.abs(p))\n",
    "        c = np.random.choice(vocab_size, 1, p=p)[0]\n",
    "        return int_to_vocab[c]\n",
    "    elif pred_mode == 'topk':\n",
    "        p = np.squeeze(probabilities)\n",
    "        p[np.argsort(p)[:-top_n]] = 0\n",
    "        p = p / np.sum(p)\n",
    "        c = np.random.choice(vocab_size, 1, p=p)[0]\n",
    "        return int_to_vocab[c]\n",
    "    elif pred_mode == 'max':\n",
    "        return int_to_vocab[probabilities.argmax()]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成彩票号码\n",
    "开始进行预测彩票了。\n",
    " - `gen_length` 作为你想生成多少期的号码。\n",
    " - `prime_word` 是前一期号码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./save\n",
      "913 909 997 006 278 455 016 260 349 606 271 992 049 957 970 723 571 412\n"
     ]
    }
   ],
   "source": [
    "gen_length = 17\n",
    "prime_word = [\"623\", \"891\", \"262\", \"761\", \"900\", \"598\", \"306\", \"580\", \"243\", \"202\"]\n",
    "\n",
    "loaded_graph = tf.Graph() \n",
    "with tf.Session(graph=loaded_graph) as sess:\n",
    "    loader = tf.train.import_meta_graph(load_dir + '.meta')\n",
    "    loader.restore(sess, load_dir)\n",
    "\n",
    "    input_text, initial_state, final_state, probs = get_tensors(loaded_graph)  \n",
    "    normalized_embedding = loaded_graph.get_tensor_by_name(\"truediv:0\")\n",
    "\n",
    "    gen_sentences = []  \n",
    "    prev_state = sess.run(initial_state, {input_text: np.array([[1]])})\n",
    "    \n",
    "    x = np.zeros((1, 1))\n",
    "    for word in prime_word:\n",
    "        x[0,0] = vocab_to_int[word]\n",
    "        probabilities, prev_state = sess.run(\n",
    "            [probs, final_state],\n",
    "            {input_text: x, initial_state: prev_state})\n",
    "\n",
    "    valid_embedding = tf.nn.embedding_lookup(normalized_embedding, probabilities.argmax())\n",
    "    valid_embedding = tf.reshape(valid_embedding, (1, embed_dim))\n",
    "\n",
    "    similarity = tf.matmul(valid_embedding, tf.transpose(normalized_embedding))\n",
    "    sim = similarity.eval()\n",
    "\n",
    "    pred_word = pick_word(probabilities, sim, int_to_vocab, 5, 'topk')  # median  topk  max  sim\n",
    "    gen_sentences.append(pred_word)\n",
    "    \n",
    "    for n in range(gen_length):\n",
    "        x[0,0] = pred_word\n",
    "\n",
    "        probabilities, prev_state = sess.run(\n",
    "            [probs, final_state],\n",
    "            {input_text: x, initial_state: prev_state})\n",
    "        \n",
    "        valid_embedding = tf.nn.embedding_lookup(normalized_embedding, probabilities.argmax())\n",
    "        valid_embedding = tf.reshape(valid_embedding, (1, embed_dim))\n",
    "        similarity = tf.matmul(valid_embedding, tf.transpose(normalized_embedding))\n",
    "        sim = similarity.eval()\n",
    "        \n",
    "        pred_word = pick_word(probabilities, sim, int_to_vocab, 5, 'topk')  # median  topk  max  sim\n",
    "\n",
    "        gen_sentences.append(pred_word)\n",
    "    \n",
    "    cp_script = ' '.join(gen_sentences)\n",
    "    cp_script = cp_script.replace('\\n ', '\\n')\n",
    "    cp_script = cp_script.replace('( ', '(')\n",
    "        \n",
    "    print(cp_script)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 结论\n",
    "\n",
    "先从数据上说，训练的最后打印出的准确率如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Epoch  49 floating median sim range k accuracy 0.01125 \n",
    "\n",
    "Epoch  49 floating median range k accuracy 0.02875 \n",
    "\n",
    "Epoch  49 similar top k accuracy 0.0 \n",
    "\n",
    "Epoch  49 top k accuracy 0.004375 \n",
    "\n",
    "Epoch  49 accuracy 0.0 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正常的开奖概率是1‰。\n",
    "\n",
    "准确率和相似度向量top k一样都是0，一个都没猜中。。。\n",
    "\n",
    "top k是4.3‰左右，但因为是top 10，所以实际上是0.43‰左右。\n",
    "\n",
    "浮动中位数准确率在11.25‰~28.75‰之间，但由于这个范围range 10，所以实际上是1.125~2.875‰之间。\n",
    "\n",
    "真没比正常开奖概率好多少。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从训练结果打印出的准确率，和往期开奖的相互之间的距离图都可以看得出来，想进行彩票预测实际上是不可行的。在排列三如此简单的、排列组合只有1000(样本空间已经足够小了)的等概率事件上进行预测都如此的困难，这也印证了数学的奇妙之处。都说了彩票是等概率，那么出任何一种号码都是有可能的，没有规律可言。惊不惊喜？意不意外？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 新的思路\n",
    "既然不能准确的预测，唯一能给我们提供思路的就是学习器学到的趋势，来看看下面的代码。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- int_sentences：里面保存着上面生成的若干期号码\n",
    "- val_data：是最新几期的开奖号码，作为validate数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "int_sentences = [int(words) for words in gen_sentences]\n",
    "int_sentences = int_sentences[1:]\n",
    "\n",
    "val_data = [[103],[883],[939],[36],[435],[173],[572],[828],[509],[723],[145],[621],[535],[385],[98],[321],[427]]\n",
    "\n",
    "plt.plot(int_sentences, label='History')\n",
    "plt.plot(val_data, label='val_data')\n",
    "plt.legend()\n",
    "_ = plt.ylim()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看得出来，虽然每期预测的号码不对，但是下一期号码的大概范围以及若干期号码的变化趋势学习的还可以，剩下的就要靠运气了：）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "今天的分享就到这里，大家洗洗睡吧 ：）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 913 909 997 006 278 455 016 260 349 606 271 992 049 957 970 723 571 412"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 383
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "int_sentences = [int(words) for words in gen_sentences]\n",
    "int_sentences = int_sentences[1:]\n",
    "\n",
    "val_data = [[103],[883],[939],[36],[435],[173],[572],[828],[509],[723],[145],[621],[535],[385],[98],[321],[427]]\n",
    "plt.plot(int_sentences, label='History')\n",
    "plt.plot(val_data, label='val_data')\n",
    "plt.legend()\n",
    "_ = plt.ylim()"
   ]
  }
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