week4/1-3文本数据建模流程.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1-3 文本数据建模流程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 数据集描述\n",
    "imdb数据集的目标是根据电影评论的文本内容预测评论的感情标签\n",
    "* 训练集有20000条电影评论文本\n",
    "* 测试集有5000条电影评论文本\n",
    "* 一般的处理步骤包括了：构建词典、编码转换、序列填充、构建数据管道\n",
    "* 中文的文本处理还包括了中文的文本分割\n",
    "\n",
    "一般TensorFlow之中的文本数据处理方式有两种\n",
    "* tf.keras.preprocessing中的Tokenizer词典构建工具和tf.keras.util.Squence构建文本数据生成器管道\n",
    "* tf.data.TextLineDataset搭配tf.keras.layers.experimental.preprocessing.TextVectorization预处理层"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 数据导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import models, layers, preprocessing, optimizers, losses, metrics\n",
    "from tensorflow.keras.layers.experimental.preprocessing import TextVectorization\n",
    "import re, string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[b'the', b'and', b'a', b'of', b'to', b'is', b'in', b'it', b'i', b'this', b'that', b'was', b'as', b'for', b'with', b'movie', b'but', b'film', b'on', b'not', b'you', b'his', b'are', b'have', b'be', b'he', b'one', b'its', b'at', b'all', b'by', b'an', b'they', b'from', b'who', b'so', b'like', b'her', b'just', b'or', b'about', b'has', b'if', b'out', b'some', b'there', b'what', b'good', b'more', b'when', b'very', b'she', b'even', b'my', b'no', b'would', b'up', b'time', b'only', b'which', b'story', b'really', b'their', b'were', b'had', b'see', b'can', b'me', b'than', b'we', b'much', b'well', b'get', b'been', b'will', b'into', b'people', b'also', b'other', b'do', b'bad', b'because', b'great', b'first', b'how', b'him', b'most', b'dont', b'made', b'then', b'them', b'films', b'movies', b'way', b'make', b'could', b'too', b'any', b'after', b'characters']\n",
      "text:  tf.Tensor([b\"I only saw this recently but had been aware of it for a number of years and have always been intrigued by its title. It now belongs to me as one of my very favourite films. It is hard to describe the incredible subject matter the Maysles discovered but everything in it works wonderfully. It has so many memorable images and moments where you feel you are encroaching on a very private world. I fell in love with this film and with the characters in it. It is as though the filmmakers have cast a spell of the audience and drawn us into the strange world of the eccentric Beales, a true aristocratic family. It has a tangible atmosphere and I found myself wishing I could be there away from it all, cooking my corn on the cob at my bedside table. It has an air of sadness that permeates throughout. A fall from greatness for this once esteemed family. The money had gone but their airs and graces remained, as well as their beauty. It drew me in from the first frame and long after the film finished I found myself wondering about their fate. Wondering that if I took a walk along East Hampton beach I might still hear Old Edie's voice in the night and see the silhouette of Little Edie dancing in the window behind the thick hanging creeper. Unforgettable.\"], shape=(1,), dtype=string)\n",
      "tf.Tensor(\n",
      "[[ 10  60 207  11 994  18  66  75   1   5   9  15   4 585   5 149   3  25\n",
      "  204  75   1  32  29 424   9 147   1   6  69  14  28   5  55  52   1  93\n",
      "    9   7 262   6   1   2   1 854 542   2   1   1  18 278   8   9 481   1\n",
      "    9  43  37 104 875   1   3 376 112  22 230  22  24   1  20   4  52   1\n",
      "  182  10   1   8 114  16  11  19   3  16   2 101   8   9   9   7  14 148\n",
      "    2 870  25 176   4   1   5   2 311   3   1 167  77   2 697 182   5   2\n",
      "    1   1   4 272   1 215   9  43   4   1 832   3  10 246 524   1  10  97\n",
      "   26  47 240  35   9  31   1  55   1  20   2   1  30  55   1   1   9  43\n",
      "   33 979   5   1  12   1 444   4 776  35   1  15  11 270   1 215   2 269\n",
      "   66 814  18  64   1   3   1   1  14  73  14  64 918   9   1  69   8  35\n",
      "    2  85   1   3 208 100   2  19   1  10 246 524   1  42  64   1   1  12\n",
      "   44  10]], shape=(1, 200), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "train_data_path = '../data/imdb/train.csv'\n",
    "test_data_path = '../data/imdb/test.csv'\n",
    "max_words = 1000  # 词典大小\n",
    "max_len = 200  # 每个sample保留200次词\n",
    "batch_size = 20\n",
    "\n",
    "# 将标签和文本分割开\n",
    "def split_line(line):\n",
    "    arr = tf.strings.split(line, '\\t')\n",
    "    label = tf.expand_dims(tf.cast(tf.strings.to_number(arr[0]), tf.int32), axis = 0)\n",
    "    text = tf.expand_dims(arr[1], axis = 0)\n",
    "    return (text, label)\n",
    "\n",
    "# 加载数据集\n",
    "def load_dataset(file_path):\n",
    "    return tf.data.TextLineDataset(filenames = [file_path]) \\\n",
    "                .map(split_line, num_parallel_calls = tf.data.experimental.AUTOTUNE) \\\n",
    "                .shuffle(buffer_size = 1000) \\\n",
    "                .batch(batch_size) \\\n",
    "                .prefetch(tf.data.experimental.AUTOTUNE)\n",
    "\n",
    "ds_train = load_dataset(train_data_path)\n",
    "ds_test = load_dataset(test_data_path)\n",
    "\n",
    "# 构建词典\n",
    "def clean_text(text):\n",
    "    lowercase = tf.strings.lower(text)\n",
    "    stripped_html = tf.strings.regex_replace(lowercase, '<br />', ' ')\n",
    "    cleaned_punctuation = tf.strings.regex_replace(stripped_html, '[%s]' % re.escape(string.punctuation), '')\n",
    "    return cleaned_punctuation\n",
    "\n",
    "vectorize_layer = TextVectorization(standardize = clean_text, split = 'whitespace',\n",
    "                                    max_tokens = max_words - 1, output_mode = 'int',\n",
    "                                   output_sequence_length = max_len)\n",
    "\n",
    "ds_text = ds_train.map(lambda text, label: text)\n",
    "vectorize_layer.adapt(ds_text)\n",
    "print(vectorize_layer.get_vocabulary()[0:100])\n",
    "for text in ds_text.unbatch().take(1):\n",
    "    print(\"text: \", text)\n",
    "    print(vectorize_layer(tf.expand_dims(text, 0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建单词数据集\n",
    "ds_train = ds_train.map(lambda text, label: (vectorize_layer(text), label)).take(100) # 减少数据量\n",
    "ds_test = ds_test.map(lambda text, label: (vectorize_layer(text), label)).take(100)  # 减少数据量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 200)]             0         \n",
      "_________________________________________________________________\n",
      "embedding (Embedding)        (None, 200, 7)            7000      \n",
      "_________________________________________________________________\n",
      "lstm (LSTM)                  (None, 16)                1536      \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 32)                544       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 9,113\n",
      "Trainable params: 9,113\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 构建模型, 模型简单就不用类封装了\n",
    "tf.keras.backend.clear_session()\n",
    "x_input = layers.Input(shape = (max_len, ))\n",
    "x = layers.Embedding(max_words, output_dim = 7, input_length = max_len)(x_input)  # output:(None, max_len, 7)\n",
    "x = layers.LSTM(16)(x)  # output: (None, 16)\n",
    "x = layers.Dense(32)(x)\n",
    "x = layers.Dense(1, activation = 'sigmoid')(x)  # output: (None, 1)\n",
    "model = tf.keras.Model(inputs = [x_input], outputs = [x])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "100/100 [==============================] - 16s 160ms/step - loss: 0.6933 - accuracy: 0.5005 - val_loss: 0.6950 - val_accuracy: 0.4780\n",
      "Epoch 2/20\n",
      "100/100 [==============================] - 14s 144ms/step - loss: 0.6930 - accuracy: 0.4970 - val_loss: 0.6941 - val_accuracy: 0.4870\n",
      "Epoch 3/20\n",
      "100/100 [==============================] - 15s 147ms/step - loss: 0.6915 - accuracy: 0.5110 - val_loss: 0.6923 - val_accuracy: 0.5375\n",
      "Epoch 4/20\n",
      "100/100 [==============================] - 15s 147ms/step - loss: 0.6799 - accuracy: 0.5745 - val_loss: 0.6932 - val_accuracy: 0.5225\n",
      "Epoch 5/20\n",
      "100/100 [==============================] - 15s 147ms/step - loss: 0.6555 - accuracy: 0.6030 - val_loss: 0.6964 - val_accuracy: 0.5360\n",
      "Epoch 6/20\n",
      "100/100 [==============================] - 15s 146ms/step - loss: 0.6064 - accuracy: 0.6190 - val_loss: 0.7318 - val_accuracy: 0.5170\n",
      "Epoch 7/20\n",
      "100/100 [==============================] - 15s 149ms/step - loss: 0.5771 - accuracy: 0.6305 - val_loss: 0.7769 - val_accuracy: 0.5175\n",
      "Epoch 8/20\n",
      "100/100 [==============================] - 15s 147ms/step - loss: 0.5594 - accuracy: 0.6410 - val_loss: 0.7609 - val_accuracy: 0.5185\n",
      "Epoch 9/20\n",
      "100/100 [==============================] - 15s 148ms/step - loss: 0.5363 - accuracy: 0.6720 - val_loss: 0.8460 - val_accuracy: 0.5330\n",
      "Epoch 10/20\n",
      "100/100 [==============================] - 15s 151ms/step - loss: 0.5198 - accuracy: 0.6520 - val_loss: 0.7977 - val_accuracy: 0.5420\n",
      "Epoch 11/20\n",
      "100/100 [==============================] - 15s 151ms/step - loss: 0.5025 - accuracy: 0.6970 - val_loss: 0.8650 - val_accuracy: 0.5090\n",
      "Epoch 12/20\n",
      "100/100 [==============================] - 15s 148ms/step - loss: 0.5019 - accuracy: 0.6955 - val_loss: 0.8866 - val_accuracy: 0.5215\n",
      "Epoch 13/20\n",
      " 25/100 [======>.......................] - ETA: 8:45 - loss: 0.4689 - accuracy: 0.6920"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-d0de031e9ae7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.001\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbinary_crossentropy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m    846\u001b[0m                 batch_size=batch_size):\n\u001b[1;32m    847\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 848\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    849\u001b[0m               \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    850\u001b[0m               \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    578\u001b[0m         \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    609\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 611\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    612\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2418\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2419\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2420\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2422\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1663\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[1;32m   1664\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1665\u001b[0;31m         self.captured_inputs)\n\u001b[0m\u001b[1;32m   1666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1667\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1744\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1745\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1747\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1748\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    596\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    599\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss = tf.keras.losses.binary_crossentropy, metrics=['accuracy'])\n",
    "model.fit(ds_train, epochs = 20, validation_data = ds_test)  # 训练时间较长, 先停一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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