TensorFlow+Keras深度学习人工智能实践应用/11-12 泰坦尼克数据集.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据集预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib.request\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url=\"http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic3.xls\"\n",
    "filepath=\"titanic3.xls\"\n",
    "if not os.path.isfile(filepath):\n",
    "    result=urllib.request.urlretrieve(url,filepath)\n",
    "    print('downloaded:',result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_df=pd.read_excel(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                            name     sex      age  sibsp  \\\n",
       "0       1         1   Allen, Miss. Elisabeth Walton  female  29.0000      0   \n",
       "1       1         1  Allison, Master. Hudson Trevor    male   0.9167      1   \n",
       "\n",
       "   parch  ticket      fare    cabin embarked boat  body  \\\n",
       "0      0   24160  211.3375       B5        S    2   NaN   \n",
       "1      2  113781  151.5500  C22 C26        S   11   NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "clos=['survived','name','pclass','sex','age','sibsp','parch','fare','embarked']\n",
    "all_df=all_df[clos]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived                            name  pclass     sex      age  sibsp  \\\n",
       "0         1   Allen, Miss. Elisabeth Walton       1  female  29.0000      0   \n",
       "1         1  Allison, Master. Hudson Trevor       1    male   0.9167      1   \n",
       "\n",
       "   parch      fare embarked  \n",
       "0      0  211.3375        S  \n",
       "1      2  151.5500        S  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = all_df.drop(['name'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived      0\n",
       "pclass        0\n",
       "sex           0\n",
       "age         263\n",
       "sibsp         0\n",
       "parch         0\n",
       "fare          1\n",
       "embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_mean=df['age'].mean()\n",
    "df['age']=df['age'].fillna(age_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "fare_mead=df['fare'].mean()\n",
    "df['fare']=df['fare'].fillna(fare_mead)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['sex']=df['sex'].map({'female':0,'male':1}).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_OneHot_df=pd.get_dummies(data=df,columns=['embarked'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass  sex      age  sibsp  parch      fare  embarked_C  \\\n",
       "0         1       1    0  29.0000      0      0  211.3375           0   \n",
       "1         1       1    1   0.9167      1      2  151.5500           0   \n",
       "\n",
       "   embarked_Q  embarked_S  \n",
       "0           0           1  \n",
       "1           0           1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_OneHot_df[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 10)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndarray=x_OneHot_df.values\n",
    "ndarray.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.    ,   1.    ,   0.    ,  29.    ,   0.    ,   0.    ,\n",
       "        211.3375,   0.    ,   0.    ,   1.    ],\n",
       "       [  1.    ,   1.    ,   1.    ,   0.9167,   1.    ,   2.    ,\n",
       "        151.55  ,   0.    ,   0.    ,   1.    ]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndarray[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "Label=ndarray[:,0]\n",
    "Features=ndarray[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Label[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.    ,   0.    ,  29.    ,   0.    ,   0.    , 211.3375,\n",
       "          0.    ,   0.    ,   1.    ],\n",
       "       [  1.    ,   1.    ,   0.9167,   1.    ,   2.    , 151.55  ,\n",
       "          0.    ,   0.    ,   1.    ]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Features[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "minmax_scale = preprocessing.MinMaxScaler(feature_range=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaledFeatures=minmax_scale.fit_transform(Features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 0.36116884, 0.        , 0.        ,\n",
       "        0.41250333, 0.        , 0.        , 1.        ],\n",
       "       [0.        , 1.        , 0.00939458, 0.125     , 0.22222222,\n",
       "        0.2958059 , 0.        , 0.        , 1.        ]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaledFeatures[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "msk=np.random.rand(len(all_df))<0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df=all_df[msk]\n",
    "test_df=all_df[~msk]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total: 1309 train: 1068 test: 241\n"
     ]
    }
   ],
   "source": [
    "print('total:',len(all_df),'train:',len(train_df),'test:',len(test_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PreprocessData(raw_df):\n",
    "    df=raw_df.drop(['name'],axis=1)\n",
    "    age_mean=df['age'].mean()\n",
    "    df['age']=df['age'].fillna(age_mean)\n",
    "    fare_mead=df['fare'].mean()\n",
    "    df['fare']=df['fare'].fillna(fare_mead)\n",
    "    df['sex']=df['sex'].map({'female':0,'male':1}).astype(int)\n",
    "    x_OneHot_df=pd.get_dummies(data=df,columns=['embarked'])\n",
    "    \n",
    "    ndarray=x_OneHot_df.values\n",
    "    Label=ndarray[:,0]\n",
    "    Features=ndarray[:,1:]\n",
    "    \n",
    "    minmax_scale = preprocessing.MinMaxScaler(feature_range=(0,1))\n",
    "    scaledFeatures=minmax_scale.fit_transform(Features)\n",
    "    return scaledFeatures,Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_Features,train_Label=PreprocessData(train_df)\n",
    "test_Features,test_Label=PreprocessData(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.        , 0.        , 0.36116884, 0.        , 0.        ,\n",
       "         0.41250333, 0.        , 0.        , 1.        ],\n",
       "        [0.        , 0.        , 0.31106443, 0.125     , 0.22222222,\n",
       "         0.2958059 , 0.        , 0.        , 1.        ]]), array([1., 0.]))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_Features[:2],train_Label[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 搭建神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 40)                400       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 30)                1230      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 31        \n",
      "=================================================================\n",
      "Total params: 1,661\n",
      "Trainable params: 1,661\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model=Sequential()\n",
    "model.add(Dense(units=40,input_dim=9,kernel_initializer='uniform',activation='relu'))\n",
    "model.add(Dense(units=30,kernel_initializer='uniform',activation='relu'))\n",
    "model.add(Dense(units=1,kernel_initializer='uniform',activation='sigmoid'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 961 samples, validate on 107 samples\n",
      "Epoch 1/30\n",
      "961/961 [==============================] - 3s 3ms/step - loss: 0.6889 - acc: 0.5952 - val_loss: 0.6657 - val_acc: 0.7664\n",
      "Epoch 2/30\n",
      "961/961 [==============================] - 0s 104us/step - loss: 0.6620 - acc: 0.5983 - val_loss: 0.5709 - val_acc: 0.7664\n",
      "Epoch 3/30\n",
      "961/961 [==============================] - 0s 99us/step - loss: 0.6115 - acc: 0.6306 - val_loss: 0.5144 - val_acc: 0.7757\n",
      "Epoch 4/30\n",
      "961/961 [==============================] - 0s 99us/step - loss: 0.5635 - acc: 0.7648 - val_loss: 0.4710 - val_acc: 0.7850\n",
      "Epoch 5/30\n",
      "961/961 [==============================] - 0s 100us/step - loss: 0.5211 - acc: 0.7825 - val_loss: 0.4631 - val_acc: 0.7850\n",
      "Epoch 6/30\n",
      "961/961 [==============================] - 0s 98us/step - loss: 0.4988 - acc: 0.7784 - val_loss: 0.4559 - val_acc: 0.7850\n",
      "Epoch 7/30\n",
      "961/961 [==============================] - 0s 99us/step - loss: 0.4886 - acc: 0.7784 - val_loss: 0.4609 - val_acc: 0.7850\n",
      "Epoch 8/30\n",
      "961/961 [==============================] - 0s 100us/step - loss: 0.4857 - acc: 0.7794 - val_loss: 0.4517 - val_acc: 0.7850\n",
      "Epoch 9/30\n",
      "961/961 [==============================] - 0s 101us/step - loss: 0.4828 - acc: 0.7815 - val_loss: 0.4540 - val_acc: 0.7850\n",
      "Epoch 10/30\n",
      "961/961 [==============================] - 0s 101us/step - loss: 0.4779 - acc: 0.7898 - val_loss: 0.4550 - val_acc: 0.7850\n",
      "Epoch 11/30\n",
      "961/961 [==============================] - 0s 102us/step - loss: 0.4766 - acc: 0.7825 - val_loss: 0.4501 - val_acc: 0.8037\n",
      "Epoch 12/30\n",
      "961/961 [==============================] - 0s 101us/step - loss: 0.4717 - acc: 0.7898 - val_loss: 0.4443 - val_acc: 0.8037\n",
      "Epoch 13/30\n",
      "961/961 [==============================] - 0s 105us/step - loss: 0.4695 - acc: 0.7888 - val_loss: 0.4481 - val_acc: 0.8037\n",
      "Epoch 14/30\n",
      "961/961 [==============================] - 0s 109us/step - loss: 0.4715 - acc: 0.7825 - val_loss: 0.4426 - val_acc: 0.8224\n",
      "Epoch 15/30\n",
      "961/961 [==============================] - 0s 104us/step - loss: 0.4671 - acc: 0.7898 - val_loss: 0.4415 - val_acc: 0.8224\n",
      "Epoch 16/30\n",
      "961/961 [==============================] - 0s 102us/step - loss: 0.4664 - acc: 0.7971 - val_loss: 0.4403 - val_acc: 0.8224\n",
      "Epoch 17/30\n",
      "961/961 [==============================] - 0s 104us/step - loss: 0.4623 - acc: 0.8002 - val_loss: 0.4388 - val_acc: 0.8224\n",
      "Epoch 18/30\n",
      "961/961 [==============================] - 0s 120us/step - loss: 0.4608 - acc: 0.8012 - val_loss: 0.4389 - val_acc: 0.8224\n",
      "Epoch 19/30\n",
      "961/961 [==============================] - 0s 125us/step - loss: 0.4619 - acc: 0.8023 - val_loss: 0.4396 - val_acc: 0.8224\n",
      "Epoch 20/30\n",
      "961/961 [==============================] - 0s 120us/step - loss: 0.4617 - acc: 0.7950 - val_loss: 0.4481 - val_acc: 0.7944\n",
      "Epoch 21/30\n",
      "961/961 [==============================] - 0s 112us/step - loss: 0.4595 - acc: 0.8023 - val_loss: 0.4436 - val_acc: 0.8224\n",
      "Epoch 22/30\n",
      "961/961 [==============================] - 0s 113us/step - loss: 0.4617 - acc: 0.7992 - val_loss: 0.4485 - val_acc: 0.8224\n",
      "Epoch 23/30\n",
      "961/961 [==============================] - 0s 108us/step - loss: 0.4610 - acc: 0.7981 - val_loss: 0.4423 - val_acc: 0.8224\n",
      "Epoch 24/30\n",
      "961/961 [==============================] - 0s 109us/step - loss: 0.4577 - acc: 0.7981 - val_loss: 0.4396 - val_acc: 0.8224\n",
      "Epoch 25/30\n",
      "961/961 [==============================] - 0s 102us/step - loss: 0.4571 - acc: 0.7992 - val_loss: 0.4410 - val_acc: 0.8224\n",
      "Epoch 26/30\n",
      "961/961 [==============================] - 0s 105us/step - loss: 0.4563 - acc: 0.8075 - val_loss: 0.4444 - val_acc: 0.8224\n",
      "Epoch 27/30\n",
      "961/961 [==============================] - 0s 97us/step - loss: 0.4652 - acc: 0.7960 - val_loss: 0.4416 - val_acc: 0.8224\n",
      "Epoch 28/30\n",
      "961/961 [==============================] - 0s 101us/step - loss: 0.4547 - acc: 0.8075 - val_loss: 0.4437 - val_acc: 0.8224\n",
      "Epoch 29/30\n",
      "961/961 [==============================] - 0s 100us/step - loss: 0.4543 - acc: 0.8065 - val_loss: 0.4402 - val_acc: 0.8131\n",
      "Epoch 30/30\n",
      "961/961 [==============================] - 0s 100us/step - loss: 0.4533 - acc: 0.8065 - val_loss: 0.4416 - val_acc: 0.8224\n"
     ]
    }
   ],
   "source": [
    "train_history=model.fit(x=train_Features,y=train_Label,validation_split=0.1,epochs=30,batch_size=30,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def show_train_history(train_history,train,validation):\n",
    "    plt.plot(train_history.history[train])\n",
    "    plt.plot(train_history.history[validation])\n",
    "    plt.title('Train History')\n",
    "    plt.ylabel(train)\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.legend(['train','validation'],loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_train_history(train_history,'acc','val_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_train_history(train_history,'loss','val_loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "241/241 [==============================] - 0s 95us/step\n",
      "\n",
      "accuracy= 0.8049792533593554\n"
     ]
    }
   ],
   "source": [
    "scores=model.evaluate(test_Features,test_Label)\n",
    "print()\n",
    "print('accuracy=',scores[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jack和Rose的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "Jack=pd.Series([0,'Jack',3,'male',23,1,0,5.0000,'S'])\n",
    "Rose=pd.Series([1,'Rose',1,'female',20,1,0,100.0000,'S'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "JR_df=pd.DataFrame([list(Jack),list(Rose)],columns=['survived','name','pclass','sex','age','sibsp','parch','fare','embarked'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_df=pd.concat([all_df,JR_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_features,Label=PreprocessData(all_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_probability=model.predict(all_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 53,
     "metadata": {},
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   "source": [
    "all_probability[:10]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd=all_df\n",
    "pd.insert(len(all_df.columns),'probability',all_probability)"
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   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
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       "   survived  name  pclass     sex   age  sibsp  parch   fare embarked  \\\n",
       "0         0  Jack       3    male  23.0      1      0    5.0        S   \n",
       "1         1  Rose       1  female  20.0      1      0  100.0        S   \n",
       "\n",
       "   probability  \n",
       "0     0.151098  \n",
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   "execution_count": 56,
   "metadata": {},
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       "     survived                                             name  pclass  \\\n",
       "2           0                     Allison, Miss. Helen Loraine       1   \n",
       "4           0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)       1   \n",
       "105         0                         Evans, Miss. Edith Corse       1   \n",
       "169         0                       Isham, Miss. Ann Elizabeth       1   \n",
       "286         0           Straus, Mrs. Isidor (Rosalie Ida Blun)       1   \n",
       "\n",
       "        sex   age  sibsp  parch      fare embarked  probability  \n",
       "2    female   2.0      1      2  151.5500        S     0.970936  \n",
       "4    female  25.0      1      2  151.5500        S     0.967703  \n",
       "105  female  36.0      0      0   31.6792        C     0.981394  \n",
       "169  female  50.0      0      0   28.7125        C     0.976603  \n",
       "286  female  63.0      1      0  221.7792        S     0.955001  "
      ]
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     "execution_count": 56,
     "metadata": {},
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    "pd[(pd['survived']==0)&(pd['probability']>0.9)]"
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