DCN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import *\n",
    "import tensorflow.keras.backend as K\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('criteo_sampled_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>I1</th>\n",
       "      <th>I2</th>\n",
       "      <th>I3</th>\n",
       "      <th>I4</th>\n",
       "      <th>I5</th>\n",
       "      <th>I6</th>\n",
       "      <th>I7</th>\n",
       "      <th>I8</th>\n",
       "      <th>I9</th>\n",
       "      <th>...</th>\n",
       "      <th>C17</th>\n",
       "      <th>C18</th>\n",
       "      <th>C19</th>\n",
       "      <th>C20</th>\n",
       "      <th>C21</th>\n",
       "      <th>C22</th>\n",
       "      <th>C23</th>\n",
       "      <th>C24</th>\n",
       "      <th>C25</th>\n",
       "      <th>C26</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>...</td>\n",
       "      <td>e5ba7672</td>\n",
       "      <td>f54016b9</td>\n",
       "      <td>21ddcdc9</td>\n",
       "      <td>b1252a9d</td>\n",
       "      <td>07b5194c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3a171ecb</td>\n",
       "      <td>c5c50484</td>\n",
       "      <td>e8b83407</td>\n",
       "      <td>9727dd16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>07c540c4</td>\n",
       "      <td>b04e4670</td>\n",
       "      <td>21ddcdc9</td>\n",
       "      <td>5840adea</td>\n",
       "      <td>60f6221e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3a171ecb</td>\n",
       "      <td>43f13e8b</td>\n",
       "      <td>e8b83407</td>\n",
       "      <td>731c3655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8efede7f</td>\n",
       "      <td>3412118d</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>e587c466</td>\n",
       "      <td>ad3062eb</td>\n",
       "      <td>3a171ecb</td>\n",
       "      <td>3b183c5c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>893</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4392.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1e88c74f</td>\n",
       "      <td>74ef3502</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6b3a5ca6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3a171ecb</td>\n",
       "      <td>9117a34a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1e88c74f</td>\n",
       "      <td>26b3c7a7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21c9516a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32c7478e</td>\n",
       "      <td>b34f3128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label   I1   I2    I3    I4      I5    I6    I7   I8     I9  ...       C17  \\\n",
       "0      0  1.0    1   5.0   0.0  1382.0   4.0  15.0  2.0  181.0  ...  e5ba7672   \n",
       "1      0  2.0    0  44.0   1.0   102.0   8.0   2.0  2.0    4.0  ...  07c540c4   \n",
       "2      0  2.0    0   1.0  14.0   767.0  89.0   4.0  2.0  245.0  ...  8efede7f   \n",
       "3      0  NaN  893   NaN   NaN  4392.0   NaN   0.0  0.0    0.0  ...  1e88c74f   \n",
       "4      0  3.0   -1   NaN   0.0     2.0   0.0   3.0  0.0    0.0  ...  1e88c74f   \n",
       "\n",
       "        C18       C19       C20       C21       C22       C23       C24  \\\n",
       "0  f54016b9  21ddcdc9  b1252a9d  07b5194c       NaN  3a171ecb  c5c50484   \n",
       "1  b04e4670  21ddcdc9  5840adea  60f6221e       NaN  3a171ecb  43f13e8b   \n",
       "2  3412118d       NaN       NaN  e587c466  ad3062eb  3a171ecb  3b183c5c   \n",
       "3  74ef3502       NaN       NaN  6b3a5ca6       NaN  3a171ecb  9117a34a   \n",
       "4  26b3c7a7       NaN       NaN  21c9516a       NaN  32c7478e  b34f3128   \n",
       "\n",
       "        C25       C26  \n",
       "0  e8b83407  9727dd16  \n",
       "1  e8b83407  731c3655  \n",
       "2       NaN       NaN  \n",
       "3       NaN       NaN  \n",
       "4       NaN       NaN  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = data.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义特征组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dense_feats = [f for f in cols if f[0] == \"I\"]\n",
    "sparse_feats = [f for f in cols if f[0] == \"C\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 处理dense特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_dense_feats(data, feats):\n",
    "    d = data.copy()\n",
    "    d = d[feats].fillna(0.0)\n",
    "    for f in feats:\n",
    "        d[f] = d[f].apply(lambda x: np.log(x+1) if x > -1 else -1)\n",
    "    \n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dense = process_dense_feats(data, dense_feats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 处理sparse特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_sparse_feats(data, feats):\n",
    "    d = data.copy()\n",
    "    d = d[feats].fillna(\"-1\")\n",
    "    for f in feats:\n",
    "        label_encoder = LabelEncoder()\n",
    "        d[f] = label_encoder.fit_transform(d[f])\n",
    "        \n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_sparse = process_sparse_feats(data, sparse_feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_data = pd.concat([data_dense, data_sparse], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_data['label'] = data['label']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型构建与训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 输入层"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dense特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dense_inputs = []\n",
    "for f in dense_feats:\n",
    "    _input = Input([1], name=f)\n",
    "    dense_inputs.append(_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor 'I1:0' shape=(None, 1) dtype=float32>,\n",
       " <tf.Tensor 'I2:0' shape=(None, 1) dtype=float32>,\n",
       " <tf.Tensor 'I3:0' shape=(None, 1) dtype=float32>,\n",
       " <tf.Tensor 'I4:0' shape=(None, 1) dtype=float32>,\n",
       " <tf.Tensor 'I5:0' shape=(None, 1) dtype=float32>,\n",
       " <tf.Tensor 'I6:0' shape=(None, 1) dtype=float32>,\n",
       " <tf.Tensor 'I7:0' shape=(None, 1) dtype=float32>,\n",
       " <tf.Tensor 'I8:0' shape=(None, 1) dtype=float32>,\n",
       " <tf.Tensor 'I9:0' shape=(None, 1) dtype=float32>,\n",
       " <tf.Tensor 'I10:0' shape=(None, 1) dtype=float32>,\n",
       " <tf.Tensor 'I11:0' shape=(None, 1) dtype=float32>,\n",
       " <tf.Tensor 'I12:0' shape=(None, 1) dtype=float32>,\n",
       " <tf.Tensor 'I13:0' shape=(None, 1) dtype=float32>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dense_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat_dense_inputs = Concatenate(axis=1)(dense_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'concatenate/Identity:0' shape=(None, 13) dtype=float32>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat_dense_inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sparse特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_inputs = []\n",
    "for f in sparse_feats:\n",
    "    _input = Input([1], name=f)\n",
    "    sparse_inputs.append(_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_kd_embed = []\n",
    "for _input in sparse_inputs:\n",
    "    f = _input.name.split(':')[0]\n",
    "    voc_size = total_data[f].nunique()\n",
    "    _embed = Flatten()(Embedding(voc_size, k, embeddings_regularizer=tf.keras.regularizers.l2(0.7))(_input))\n",
    "    sparse_kd_embed.append(_embed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor 'flatten/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_1/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_2/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_3/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_4/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_5/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_6/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_7/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_8/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_9/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_10/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_11/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_12/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_13/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_14/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_15/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_16/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_17/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_18/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_19/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_20/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_21/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_22/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_23/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_24/Identity:0' shape=(None, 8) dtype=float32>,\n",
       " <tf.Tensor 'flatten_25/Identity:0' shape=(None, 8) dtype=float32>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_kd_embed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat_sparse_inputs = Concatenate(axis=1)(sparse_kd_embed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'concatenate_1/Identity:0' shape=(None, 208) dtype=float32>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat_sparse_inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 所有输入embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_inputs = Concatenate(axis=1)([concat_sparse_inputs, concat_dense_inputs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'concatenate_2/Identity:0' shape=(None, 221) dtype=float32>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embed_inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_layer(x0, xl):\n",
    "    \"\"\"\n",
    "    实现一层cross layer\n",
    "    @param x0: 特征embeddings\n",
    "    @param xl: 第l层的输出结果\n",
    "    \"\"\"\n",
    "    # 1.获取xl层的embedding size\n",
    "    embed_dim = xl.shape[-1]\n",
    "    # 2.初始化当前层的W和b\n",
    "    w = tf.Variable(tf.random.truncated_normal(shape=(embed_dim,), stddev=0.01))\n",
    "    b = tf.Variable(tf.zeros(shape=(embed_dim,)))\n",
    "    # 3.计算feature crossing\n",
    "    x1_T = tf.reshape(xl, [-1, 1, embed_dim])\n",
    "    x_lw = tf.tensordot(x1_T, w, axes=1)\n",
    "    cross = x0 * x_lw \n",
    "    return cross + b + xl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_cross_layer(x0, num_layer=3):\n",
    "    \"\"\"\n",
    "    构建多层cross layer\n",
    "    @param x0: 所有特征的embeddings\n",
    "    @param num_layers: cross net的层数\n",
    "    \"\"\"\n",
    "    # 初始化xl为x0\n",
    "    xl = x0\n",
    "    # 构建多层cross net\n",
    "    for i in range(num_layer):\n",
    "        xl = cross_layer(x0, xl)\n",
    "    \n",
    "    return xl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cross net\n",
    "cross_layer_output = build_cross_layer(embed_inputs, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'add_5:0' shape=(None, 221) dtype=float32>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_layer_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DNN部分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc_layer = Dropout(0.5)(Dense(128, activation='relu')(embed_inputs))\n",
    "fc_layer = Dropout(0.3)(Dense(128, activation='relu')(fc_layer))\n",
    "fc_layer_output = Dropout(0.1)(Dense(128, activation='relu')(fc_layer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'dropout_2/Identity:0' shape=(None, 128) dtype=float32>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fc_layer_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 输出结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'add_5:0' shape=(None, 221) dtype=float32>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_layer_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'dropout_2/Identity:0' shape=(None, 128) dtype=float32>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fc_layer_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stack_layer = Concatenate()([cross_layer_output, fc_layer_output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_layer = Dense(1, activation='sigmoid', use_bias=True)(stack_layer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 编译模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(dense_inputs+sparse_inputs, output_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "C1 (InputLayer)                 [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C2 (InputLayer)                 [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C3 (InputLayer)                 [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C4 (InputLayer)                 [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C5 (InputLayer)                 [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C6 (InputLayer)                 [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C7 (InputLayer)                 [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C8 (InputLayer)                 [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C9 (InputLayer)                 [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C10 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C11 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C12 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C13 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C14 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C15 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C16 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C17 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C18 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C19 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C20 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C21 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C22 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C23 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C24 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C25 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "C26 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding (Embedding)           (None, 1, 8)         8720        C1[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, 1, 8)         4216        C2[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "embedding_2 (Embedding)         (None, 1, 8)         1660632     C3[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "embedding_3 (Embedding)         (None, 1, 8)         687288      C4[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "embedding_4 (Embedding)         (None, 1, 8)         1968        C5[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "embedding_5 (Embedding)         (None, 1, 8)         112         C6[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "embedding_6 (Embedding)         (None, 1, 8)         82864       C7[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "embedding_7 (Embedding)         (None, 1, 8)         4096        C8[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "embedding_8 (Embedding)         (None, 1, 8)         24          C9[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "embedding_9 (Embedding)         (None, 1, 8)         201432      C10[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "embedding_10 (Embedding)        (None, 1, 8)         36448       C11[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "embedding_11 (Embedding)        (None, 1, 8)         1417232     C12[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "embedding_12 (Embedding)        (None, 1, 8)         24328       C13[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "embedding_13 (Embedding)        (None, 1, 8)         208         C14[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "embedding_14 (Embedding)        (None, 1, 8)         65080       C15[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "embedding_15 (Embedding)        (None, 1, 8)         1112024     C16[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "embedding_16 (Embedding)        (None, 1, 8)         80          C17[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "embedding_17 (Embedding)        (None, 1, 8)         28912       C18[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "embedding_18 (Embedding)        (None, 1, 8)         13992       C19[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "embedding_19 (Embedding)        (None, 1, 8)         32          C20[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "embedding_20 (Embedding)        (None, 1, 8)         1282816     C21[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "embedding_21 (Embedding)        (None, 1, 8)         120         C22[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "embedding_22 (Embedding)        (None, 1, 8)         120         C23[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "embedding_23 (Embedding)        (None, 1, 8)         256768      C24[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "embedding_24 (Embedding)        (None, 1, 8)         512         C25[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "embedding_25 (Embedding)        (None, 1, 8)         195448      C26[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 8)            0           embedding[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 8)            0           embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)             (None, 8)            0           embedding_2[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_3 (Flatten)             (None, 8)            0           embedding_3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_4 (Flatten)             (None, 8)            0           embedding_4[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_5 (Flatten)             (None, 8)            0           embedding_5[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_6 (Flatten)             (None, 8)            0           embedding_6[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_7 (Flatten)             (None, 8)            0           embedding_7[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_8 (Flatten)             (None, 8)            0           embedding_8[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_9 (Flatten)             (None, 8)            0           embedding_9[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_10 (Flatten)            (None, 8)            0           embedding_10[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_11 (Flatten)            (None, 8)            0           embedding_11[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_12 (Flatten)            (None, 8)            0           embedding_12[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_13 (Flatten)            (None, 8)            0           embedding_13[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_14 (Flatten)            (None, 8)            0           embedding_14[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_15 (Flatten)            (None, 8)            0           embedding_15[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_16 (Flatten)            (None, 8)            0           embedding_16[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_17 (Flatten)            (None, 8)            0           embedding_17[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_18 (Flatten)            (None, 8)            0           embedding_18[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_19 (Flatten)            (None, 8)            0           embedding_19[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_20 (Flatten)            (None, 8)            0           embedding_20[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_21 (Flatten)            (None, 8)            0           embedding_21[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_22 (Flatten)            (None, 8)            0           embedding_22[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_23 (Flatten)            (None, 8)            0           embedding_23[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_24 (Flatten)            (None, 8)            0           embedding_24[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_25 (Flatten)            (None, 8)            0           embedding_25[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "I1 (InputLayer)                 [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "I2 (InputLayer)                 [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "I3 (InputLayer)                 [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "I4 (InputLayer)                 [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "I5 (InputLayer)                 [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "I6 (InputLayer)                 [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "I7 (InputLayer)                 [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "I8 (InputLayer)                 [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "I9 (InputLayer)                 [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "I10 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "I11 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "I12 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "I13 (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 208)          0           flatten[0][0]                    \n",
      "                                                                 flatten_1[0][0]                  \n",
      "                                                                 flatten_2[0][0]                  \n",
      "                                                                 flatten_3[0][0]                  \n",
      "                                                                 flatten_4[0][0]                  \n",
      "                                                                 flatten_5[0][0]                  \n",
      "                                                                 flatten_6[0][0]                  \n",
      "                                                                 flatten_7[0][0]                  \n",
      "                                                                 flatten_8[0][0]                  \n",
      "                                                                 flatten_9[0][0]                  \n",
      "                                                                 flatten_10[0][0]                 \n",
      "                                                                 flatten_11[0][0]                 \n",
      "                                                                 flatten_12[0][0]                 \n",
      "                                                                 flatten_13[0][0]                 \n",
      "                                                                 flatten_14[0][0]                 \n",
      "                                                                 flatten_15[0][0]                 \n",
      "                                                                 flatten_16[0][0]                 \n",
      "                                                                 flatten_17[0][0]                 \n",
      "                                                                 flatten_18[0][0]                 \n",
      "                                                                 flatten_19[0][0]                 \n",
      "                                                                 flatten_20[0][0]                 \n",
      "                                                                 flatten_21[0][0]                 \n",
      "                                                                 flatten_22[0][0]                 \n",
      "                                                                 flatten_23[0][0]                 \n",
      "                                                                 flatten_24[0][0]                 \n",
      "                                                                 flatten_25[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 13)           0           I1[0][0]                         \n",
      "                                                                 I2[0][0]                         \n",
      "                                                                 I3[0][0]                         \n",
      "                                                                 I4[0][0]                         \n",
      "                                                                 I5[0][0]                         \n",
      "                                                                 I6[0][0]                         \n",
      "                                                                 I7[0][0]                         \n",
      "                                                                 I8[0][0]                         \n",
      "                                                                 I9[0][0]                         \n",
      "                                                                 I10[0][0]                        \n",
      "                                                                 I11[0][0]                        \n",
      "                                                                 I12[0][0]                        \n",
      "                                                                 I13[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 221)          0           concatenate_1[0][0]              \n",
      "                                                                 concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Reshape (TensorFlow [(None, 1, 221)]     0           concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot/Shape (Te [(3,)]               0           tf_op_layer_Reshape[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot/GatherV2  [(2,)]               0           tf_op_layer_Tensordot/Shape[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot/GatherV2_ [(1,)]               0           tf_op_layer_Tensordot/Shape[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot/Prod (Ten [()]                 0           tf_op_layer_Tensordot/GatherV2[0]\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot/Prod_1 (T [()]                 0           tf_op_layer_Tensordot/GatherV2_1[\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot/transpose [(None, 1, 221)]     0           tf_op_layer_Reshape[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot/stack (Te [(2,)]               0           tf_op_layer_Tensordot/Prod[0][0] \n",
      "                                                                 tf_op_layer_Tensordot/Prod_1[0][0\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot/Reshape ( [(None, None)]       0           tf_op_layer_Tensordot/transpose[0\n",
      "                                                                 tf_op_layer_Tensordot/stack[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot/MatMul (T [(None, 1)]          0           tf_op_layer_Tensordot/Reshape[0][\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot/concat (T [(2,)]               0           tf_op_layer_Tensordot/GatherV2[0]\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot (TensorFl [(None, 1)]          0           tf_op_layer_Tensordot/MatMul[0][0\n",
      "                                                                 tf_op_layer_Tensordot/concat[0][0\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_mul (TensorFlowOpLa [(None, 221)]        0           concatenate_2[0][0]              \n",
      "                                                                 tf_op_layer_Tensordot[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add (TensorFlowOpLa [(None, 221)]        0           tf_op_layer_mul[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_1 (TensorFlowOp [(None, 221)]        0           tf_op_layer_add[0][0]            \n",
      "                                                                 concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Reshape_1 (TensorFl [(None, 1, 221)]     0           tf_op_layer_add_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_1/Shape ( [(3,)]               0           tf_op_layer_Reshape_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_1/GatherV [(2,)]               0           tf_op_layer_Tensordot_1/Shape[0][\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_1/GatherV [(1,)]               0           tf_op_layer_Tensordot_1/Shape[0][\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_1/Prod (T [()]                 0           tf_op_layer_Tensordot_1/GatherV2[\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_1/Prod_1  [()]                 0           tf_op_layer_Tensordot_1/GatherV2_\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_1/transpo [(None, 1, 221)]     0           tf_op_layer_Reshape_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_1/stack ( [(2,)]               0           tf_op_layer_Tensordot_1/Prod[0][0\n",
      "                                                                 tf_op_layer_Tensordot_1/Prod_1[0]\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_1/Reshape [(None, None)]       0           tf_op_layer_Tensordot_1/transpose\n",
      "                                                                 tf_op_layer_Tensordot_1/stack[0][\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_1/MatMul  [(None, 1)]          0           tf_op_layer_Tensordot_1/Reshape[0\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_1/concat  [(2,)]               0           tf_op_layer_Tensordot_1/GatherV2[\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_1 (Tensor [(None, 1)]          0           tf_op_layer_Tensordot_1/MatMul[0]\n",
      "                                                                 tf_op_layer_Tensordot_1/concat[0]\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_mul_1 (TensorFlowOp [(None, 221)]        0           concatenate_2[0][0]              \n",
      "                                                                 tf_op_layer_Tensordot_1[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_2 (TensorFlowOp [(None, 221)]        0           tf_op_layer_mul_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_3 (TensorFlowOp [(None, 221)]        0           tf_op_layer_add_2[0][0]          \n",
      "                                                                 tf_op_layer_add_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Reshape_2 (TensorFl [(None, 1, 221)]     0           tf_op_layer_add_3[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_2/Shape ( [(3,)]               0           tf_op_layer_Reshape_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_2/GatherV [(2,)]               0           tf_op_layer_Tensordot_2/Shape[0][\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_2/GatherV [(1,)]               0           tf_op_layer_Tensordot_2/Shape[0][\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_2/Prod (T [()]                 0           tf_op_layer_Tensordot_2/GatherV2[\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_2/Prod_1  [()]                 0           tf_op_layer_Tensordot_2/GatherV2_\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_2/transpo [(None, 1, 221)]     0           tf_op_layer_Reshape_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_2/stack ( [(2,)]               0           tf_op_layer_Tensordot_2/Prod[0][0\n",
      "                                                                 tf_op_layer_Tensordot_2/Prod_1[0]\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_2/Reshape [(None, None)]       0           tf_op_layer_Tensordot_2/transpose\n",
      "                                                                 tf_op_layer_Tensordot_2/stack[0][\n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 128)          28416       concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_2/MatMul  [(None, 1)]          0           tf_op_layer_Tensordot_2/Reshape[0\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_2/concat  [(2,)]               0           tf_op_layer_Tensordot_2/GatherV2[\n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 128)          0           dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Tensordot_2 (Tensor [(None, 1)]          0           tf_op_layer_Tensordot_2/MatMul[0]\n",
      "                                                                 tf_op_layer_Tensordot_2/concat[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 128)          16512       dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_mul_2 (TensorFlowOp [(None, 221)]        0           concatenate_2[0][0]              \n",
      "                                                                 tf_op_layer_Tensordot_2[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 128)          0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_4 (TensorFlowOp [(None, 221)]        0           tf_op_layer_mul_2[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 128)          16512       dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_5 (TensorFlowOp [(None, 221)]        0           tf_op_layer_add_4[0][0]          \n",
      "                                                                 tf_op_layer_add_3[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 128)          0           dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 349)          0           tf_op_layer_add_5[0][0]          \n",
      "                                                                 dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 1)            350         concatenate_3[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 7,147,262\n",
      "Trainable params: 7,147,262\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"adam\", \n",
    "              loss=\"binary_crossentropy\", \n",
    "              metrics=[\"binary_crossentropy\", tf.keras.metrics.AUC(name='auc')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = total_data.loc[:500000-1]\n",
    "valid_data = total_data.loc[500000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dense_x = [train_data[f].values for f in dense_feats]\n",
    "train_sparse_x = [train_data[f].values for f in sparse_feats]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_label = [train_data['label'].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dense_x = [valid_data[f].values for f in dense_feats]\n",
    "val_sparse_x = [valid_data[f].values for f in sparse_feats]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_label = [valid_data['label'].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 500000 samples, validate on 100000 samples\n",
      "Epoch 1/5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\framework\\indexed_slices.py:424: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n",
      "F:\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\framework\\indexed_slices.py:424: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "276352/500000 [===============>..............] - ETA: 2:16 - loss: 27.2252 - binary_crossentropy: 0.5143 - auc: 0.7090"
     ]
    }
   ],
   "source": [
    "model.fit(train_dense_x+train_sparse_x, \n",
    "          train_label, epochs=5, batch_size=128,\n",
    "          validation_data=(val_dense_x+val_sparse_x, val_label)\n",
    "         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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