{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import read_csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载数据集\n",
    "filename = 'pima-indians-diabetes.csv'\n",
    "dataset = read_csv(filename, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 显示数据实例个数、属性个数\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>88</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.134</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>543</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.158</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>125</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.232</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0    1   2   3    4     5      6   7  8\n",
       "0   6  148  72  35    0  33.6  0.627  50  1\n",
       "1   1   85  66  29    0  26.6  0.351  31  0\n",
       "2   8  183  64   0    0  23.3  0.672  32  1\n",
       "3   1   89  66  23   94  28.1  0.167  21  0\n",
       "4   0  137  40  35  168  43.1  2.288  33  1\n",
       "5   5  116  74   0    0  25.6  0.201  30  0\n",
       "6   3   78  50  32   88  31.0  0.248  26  1\n",
       "7  10  115   0   0    0  35.3  0.134  29  0\n",
       "8   2  197  70  45  543  30.5  0.158  53  1\n",
       "9   8  125  96   0    0   0.0  0.232  54  1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 前10个样本情况\n",
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0           1           2           3           4           5  \\\n",
       "count  768.000000  768.000000  768.000000  768.000000  768.000000  768.000000   \n",
       "mean     3.845052  120.894531   69.105469   20.536458   79.799479   31.992578   \n",
       "std      3.369578   31.972618   19.355807   15.952218  115.244002    7.884160   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      1.000000   99.000000   62.000000    0.000000    0.000000   27.300000   \n",
       "50%      3.000000  117.000000   72.000000   23.000000   30.500000   32.000000   \n",
       "75%      6.000000  140.250000   80.000000   32.000000  127.250000   36.600000   \n",
       "max     17.000000  199.000000  122.000000   99.000000  846.000000   67.100000   \n",
       "\n",
       "                6           7           8  \n",
       "count  768.000000  768.000000  768.000000  \n",
       "mean     0.471876   33.240885    0.348958  \n",
       "std      0.331329   11.760232    0.476951  \n",
       "min      0.078000   21.000000    0.000000  \n",
       "25%      0.243750   24.000000    0.000000  \n",
       "50%      0.372500   29.000000    0.000000  \n",
       "75%      0.626250   41.000000    1.000000  \n",
       "max      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 显示每个属性的统计概要（包括总数，均值，最小值，最大值以及一些百分比）\n",
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "# 线盒图\n",
    "dataset.plot(kind='box')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 柱状图\n",
    "dataset.hist()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 81 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 多变量散点图\n",
    "from pandas.plotting import scatter_matrix\n",
    "scatter_matrix(dataset)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From D:\\ProgramData\\Anaconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\ops\\nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From D:\\ProgramData\\Anaconda3\\envs\\deep\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27/150\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 149/150\n",
      "692/692 [==============================] - 0s 108us/step - loss: 0.5726 - accuracy: 0.7052\n",
      "Epoch 150/150\n",
      "692/692 [==============================] - 0s 110us/step - loss: 0.5721 - accuracy: 0.7052\n",
      "76/76 [==============================] - 0s 1ms/step\n",
      "0.7382946014404297\n"
     ]
    }
   ],
   "source": [
    "# MLP for Pima Indians Dataset with 10-fold cross validation via sklearn\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import numpy\n",
    "\n",
    "\n",
    "# Function to create model, required for KerasClassifier\n",
    "def create_model():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(12, input_dim=8, activation='relu'))\n",
    "    model.add(Dense(8, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "\n",
    "# fix random seed for reproducibility\n",
    "seed = 7\n",
    "numpy.random.seed(seed)\n",
    "# load pima indians dataset\n",
    "dataset = numpy.loadtxt(\"pima-indians-diabetes.csv\", delimiter=\",\")\n",
    "# split into input (X) and output (Y) variables\n",
    "X = dataset[:, 0:8]\n",
    "Y = dataset[:, 8]\n",
    "# create model\n",
    "model = KerasClassifier(build_fn=create_model,\n",
    "                        epochs=150,\n",
    "                        batch_size=10)\n",
    "# evaluate using 10-fold cross validation\n",
    "kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12/150\n",
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      "Epoch 103/150\n",
      "512/512 [==============================] - 0s 127us/step - loss: 0.5294 - accuracy: 0.7422\n",
      "Epoch 104/150\n",
      "512/512 [==============================] - 0s 136us/step - loss: 0.5368 - accuracy: 0.7422\n",
      "Epoch 105/150\n",
      "512/512 [==============================] - 0s 136us/step - loss: 0.5238 - accuracy: 0.7461\n",
      "Epoch 106/150\n",
      "512/512 [==============================] - 0s 142us/step - loss: 0.5383 - accuracy: 0.7500\n",
      "Epoch 107/150\n",
      "512/512 [==============================] - 0s 150us/step - loss: 0.5327 - accuracy: 0.7480\n",
      "Epoch 108/150\n",
      "512/512 [==============================] - 0s 144us/step - loss: 0.5332 - accuracy: 0.7402\n",
      "Epoch 109/150\n",
      "512/512 [==============================] - 0s 131us/step - loss: 0.5351 - accuracy: 0.7266\n",
      "Epoch 110/150\n",
      "512/512 [==============================] - 0s 142us/step - loss: 0.5179 - accuracy: 0.7441\n",
      "Epoch 111/150\n",
      "512/512 [==============================] - 0s 138us/step - loss: 0.5180 - accuracy: 0.7344\n",
      "Epoch 112/150\n",
      "512/512 [==============================] - 0s 144us/step - loss: 0.5211 - accuracy: 0.7441\n",
      "Epoch 113/150\n",
      "512/512 [==============================] - 0s 138us/step - loss: 0.5207 - accuracy: 0.7344\n",
      "Epoch 114/150\n",
      "512/512 [==============================] - 0s 144us/step - loss: 0.5361 - accuracy: 0.7500\n",
      "Epoch 115/150\n",
      "512/512 [==============================] - 0s 136us/step - loss: 0.5360 - accuracy: 0.7617\n",
      "Epoch 116/150\n",
      "512/512 [==============================] - 0s 140us/step - loss: 0.5204 - accuracy: 0.7500\n",
      "Epoch 117/150\n",
      "512/512 [==============================] - 0s 129us/step - loss: 0.5367 - accuracy: 0.7422\n",
      "Epoch 118/150\n",
      "512/512 [==============================] - 0s 142us/step - loss: 0.5284 - accuracy: 0.7207\n",
      "Epoch 119/150\n",
      "512/512 [==============================] - 0s 125us/step - loss: 0.5231 - accuracy: 0.7441\n",
      "Epoch 120/150\n",
      "512/512 [==============================] - 0s 136us/step - loss: 0.5099 - accuracy: 0.7617\n",
      "Epoch 121/150\n",
      "512/512 [==============================] - 0s 127us/step - loss: 0.5191 - accuracy: 0.7441\n",
      "Epoch 122/150\n",
      "512/512 [==============================] - 0s 136us/step - loss: 0.5149 - accuracy: 0.7441\n",
      "Epoch 123/150\n",
      "512/512 [==============================] - 0s 127us/step - loss: 0.5180 - accuracy: 0.7539\n",
      "Epoch 124/150\n",
      "512/512 [==============================] - 0s 131us/step - loss: 0.5159 - accuracy: 0.7637\n",
      "Epoch 125/150\n",
      "512/512 [==============================] - 0s 115us/step - loss: 0.5378 - accuracy: 0.7402\n",
      "Epoch 126/150\n",
      "512/512 [==============================] - 0s 115us/step - loss: 0.5164 - accuracy: 0.7402\n",
      "Epoch 127/150\n",
      "512/512 [==============================] - 0s 121us/step - loss: 0.5143 - accuracy: 0.7461\n",
      "Epoch 128/150\n",
      "512/512 [==============================] - 0s 127us/step - loss: 0.5118 - accuracy: 0.7441\n",
      "Epoch 129/150\n",
      "512/512 [==============================] - 0s 127us/step - loss: 0.5199 - accuracy: 0.7598\n",
      "Epoch 130/150\n",
      "512/512 [==============================] - 0s 134us/step - loss: 0.5107 - accuracy: 0.7402\n",
      "Epoch 131/150\n",
      "512/512 [==============================] - 0s 129us/step - loss: 0.5282 - accuracy: 0.7305\n",
      "Epoch 132/150\n",
      "512/512 [==============================] - 0s 129us/step - loss: 0.5221 - accuracy: 0.7402\n",
      "Epoch 133/150\n",
      "512/512 [==============================] - 0s 127us/step - loss: 0.5136 - accuracy: 0.7422\n",
      "Epoch 134/150\n",
      "512/512 [==============================] - 0s 131us/step - loss: 0.5101 - accuracy: 0.7461\n",
      "Epoch 135/150\n",
      "512/512 [==============================] - 0s 138us/step - loss: 0.5307 - accuracy: 0.7461\n",
      "Epoch 136/150\n",
      "512/512 [==============================] - 0s 166us/step - loss: 0.5331 - accuracy: 0.7441\n",
      "Epoch 137/150\n",
      "512/512 [==============================] - 0s 169us/step - loss: 0.5399 - accuracy: 0.7324\n",
      "Epoch 138/150\n",
      "512/512 [==============================] - 0s 132us/step - loss: 0.5151 - accuracy: 0.7520\n",
      "Epoch 139/150\n",
      "512/512 [==============================] - 0s 129us/step - loss: 0.5245 - accuracy: 0.7539\n",
      "Epoch 140/150\n",
      "512/512 [==============================] - 0s 143us/step - loss: 0.5216 - accuracy: 0.7559\n",
      "Epoch 141/150\n",
      "512/512 [==============================] - 0s 129us/step - loss: 0.5055 - accuracy: 0.7520\n",
      "Epoch 142/150\n",
      "512/512 [==============================] - 0s 140us/step - loss: 0.5210 - accuracy: 0.7480\n",
      "Epoch 143/150\n",
      "512/512 [==============================] - 0s 145us/step - loss: 0.5407 - accuracy: 0.7441\n",
      "Epoch 144/150\n",
      "512/512 [==============================] - 0s 153us/step - loss: 0.5128 - accuracy: 0.7461\n",
      "Epoch 145/150\n",
      "512/512 [==============================] - 0s 150us/step - loss: 0.5153 - accuracy: 0.7578\n",
      "Epoch 146/150\n",
      "512/512 [==============================] - 0s 148us/step - loss: 0.5050 - accuracy: 0.7559\n",
      "Epoch 147/150\n",
      "512/512 [==============================] - 0s 142us/step - loss: 0.5120 - accuracy: 0.7461\n",
      "Epoch 148/150\n",
      "512/512 [==============================] - 0s 136us/step - loss: 0.5034 - accuracy: 0.7715\n",
      "Epoch 149/150\n",
      "512/512 [==============================] - 0s 162us/step - loss: 0.5120 - accuracy: 0.7559\n",
      "Epoch 150/150\n",
      "512/512 [==============================] - 0s 169us/step - loss: 0.5069 - accuracy: 0.7559\n",
      "256/256 [==============================] - 0s 545us/step\n",
      "0.75\n"
     ]
    }
   ],
   "source": [
    "# 对数据进行3、5、7交叉验证，比较结果。\n",
    "# 3 交叉验证\n",
    "kfold = StratifiedKFold(n_splits=3, shuffle=True, random_state=seed)\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "614/614 [==============================] - 0s 586us/step - loss: 9.7508 - accuracy: 0.6515\n",
      "Epoch 2/150\n",
      "614/614 [==============================] - 0s 148us/step - loss: 1.2700 - accuracy: 0.5537\n",
      "Epoch 3/150\n",
      "614/614 [==============================] - 0s 159us/step - loss: 0.9208 - accuracy: 0.6010\n",
      "Epoch 4/150\n",
      "614/614 [==============================] - 0s 117us/step - loss: 0.8438 - accuracy: 0.6221\n",
      "Epoch 5/150\n",
      "614/614 [==============================] - 0s 109us/step - loss: 0.8136 - accuracy: 0.6189\n",
      "Epoch 6/150\n",
      "614/614 [==============================] - 0s 110us/step - loss: 0.7649 - accuracy: 0.6254\n",
      "Epoch 7/150\n",
      "614/614 [==============================] - 0s 107us/step - loss: 0.7514 - accuracy: 0.6368\n",
      "Epoch 8/150\n",
      "614/614 [==============================] - 0s 107us/step - loss: 0.7323 - accuracy: 0.6417\n",
      "Epoch 9/150\n",
      "614/614 [==============================] - 0s 106us/step - loss: 0.6980 - accuracy: 0.6482\n",
      "Epoch 10/150\n",
      "614/614 [==============================] - 0s 110us/step - loss: 0.6809 - accuracy: 0.6564\n",
      "Epoch 11/150\n",
      "614/614 [==============================] - 0s 114us/step - loss: 0.6630 - accuracy: 0.6775\n",
      "Epoch 12/150\n",
      "614/614 [==============================] - 0s 109us/step - loss: 0.6681 - accuracy: 0.6580\n",
      "Epoch 13/150\n",
      "614/614 [==============================] - 0s 109us/step - loss: 0.6478 - accuracy: 0.6547\n",
      "Epoch 14/150\n",
      "614/614 [==============================] - 0s 115us/step - loss: 0.6282 - accuracy: 0.6645\n",
      "Epoch 15/150\n",
      "614/614 [==============================] - 0s 115us/step - loss: 0.6272 - accuracy: 0.6694\n",
      "Epoch 16/150\n",
      "614/614 [==============================] - 0s 107us/step - loss: 0.6227 - accuracy: 0.6726\n",
      "Epoch 17/150\n",
      "614/614 [==============================] - 0s 114us/step - loss: 0.6216 - accuracy: 0.6678\n",
      "Epoch 18/150\n",
      "614/614 [==============================] - 0s 130us/step - loss: 0.6311 - accuracy: 0.6824\n",
      "Epoch 19/150\n",
      "614/614 [==============================] - 0s 135us/step - loss: 0.6086 - accuracy: 0.6726\n",
      "Epoch 20/150\n",
      "614/614 [==============================] - 0s 133us/step - loss: 0.6211 - accuracy: 0.6645\n",
      "Epoch 21/150\n",
      "614/614 [==============================] - 0s 127us/step - loss: 0.6164 - accuracy: 0.6824\n",
      "Epoch 22/150\n",
      "614/614 [==============================] - 0s 185us/step - loss: 0.6091 - accuracy: 0.6759\n",
      "Epoch 23/150\n",
      "614/614 [==============================] - 0s 177us/step - loss: 0.6113 - accuracy: 0.6906\n",
      "Epoch 24/150\n",
      "614/614 [==============================] - 0s 110us/step - loss: 0.5942 - accuracy: 0.6857\n",
      "Epoch 25/150\n",
      "614/614 [==============================] - 0s 109us/step - loss: 0.6103 - accuracy: 0.6857\n",
      "Epoch 26/150\n",
      "614/614 [==============================] - 0s 107us/step - loss: 0.5972 - accuracy: 0.7020\n",
      "Epoch 27/150\n",
      "614/614 [==============================] - 0s 110us/step - loss: 0.6145 - accuracy: 0.6694\n",
      "Epoch 28/150\n",
      "614/614 [==============================] - 0s 110us/step - loss: 0.6174 - accuracy: 0.6629\n",
      "Epoch 29/150\n",
      "614/614 [==============================] - 0s 132us/step - loss: 0.5819 - accuracy: 0.6938\n",
      "Epoch 30/150\n",
      "614/614 [==============================] - 0s 132us/step - loss: 0.5847 - accuracy: 0.7166\n",
      "Epoch 31/150\n",
      "614/614 [==============================] - 0s 132us/step - loss: 0.5976 - accuracy: 0.6840\n",
      "Epoch 32/150\n",
      "614/614 [==============================] - 0s 123us/step - loss: 0.5978 - accuracy: 0.6906\n",
      "Epoch 33/150\n",
      "614/614 [==============================] - 0s 115us/step - loss: 0.5984 - accuracy: 0.7068\n",
      "Epoch 34/150\n",
      "614/614 [==============================] - 0s 119us/step - loss: 0.6129 - accuracy: 0.6710\n",
      "Epoch 35/150\n",
      "614/614 [==============================] - 0s 119us/step - loss: 0.5702 - accuracy: 0.7085\n",
      "Epoch 36/150\n",
      "614/614 [==============================] - 0s 120us/step - loss: 0.5756 - accuracy: 0.7068\n",
      "Epoch 37/150\n",
      "614/614 [==============================] - 0s 112us/step - loss: 0.5755 - accuracy: 0.7117\n",
      "Epoch 38/150\n",
      "614/614 [==============================] - 0s 117us/step - loss: 0.5739 - accuracy: 0.7052\n",
      "Epoch 39/150\n",
      "614/614 [==============================] - 0s 148us/step - loss: 0.5830 - accuracy: 0.6971\n",
      "Epoch 40/150\n",
      "614/614 [==============================] - 0s 153us/step - loss: 0.5706 - accuracy: 0.7036\n",
      "Epoch 41/150\n",
      "614/614 [==============================] - 0s 114us/step - loss: 0.5721 - accuracy: 0.7215\n",
      "Epoch 42/150\n",
      "614/614 [==============================] - 0s 114us/step - loss: 0.5600 - accuracy: 0.7166\n",
      "Epoch 43/150\n",
      "614/614 [==============================] - 0s 110us/step - loss: 0.5757 - accuracy: 0.7101\n",
      "Epoch 44/150\n",
      "614/614 [==============================] - 0s 114us/step - loss: 0.5885 - accuracy: 0.7036\n",
      "Epoch 45/150\n",
      "614/614 [==============================] - 0s 107us/step - loss: 0.5629 - accuracy: 0.7166\n",
      "Epoch 46/150\n",
      "614/614 [==============================] - 0s 106us/step - loss: 0.5677 - accuracy: 0.7003\n",
      "Epoch 47/150\n",
      "614/614 [==============================] - 0s 112us/step - loss: 0.5993 - accuracy: 0.6857\n",
      "Epoch 48/150\n",
      "614/614 [==============================] - 0s 110us/step - loss: 0.5646 - accuracy: 0.7117\n",
      "Epoch 49/150\n",
      "614/614 [==============================] - 0s 109us/step - loss: 0.5670 - accuracy: 0.7134\n",
      "Epoch 50/150\n",
      "614/614 [==============================] - 0s 110us/step - loss: 0.5763 - accuracy: 0.6922\n",
      "Epoch 51/150\n",
      "614/614 [==============================] - 0s 112us/step - loss: 0.5565 - accuracy: 0.7231\n",
      "Epoch 52/150\n",
      "614/614 [==============================] - 0s 107us/step - loss: 0.6051 - accuracy: 0.6938\n",
      "Epoch 53/150\n",
      "614/614 [==============================] - 0s 110us/step - loss: 0.5629 - accuracy: 0.7166\n",
      "Epoch 54/150\n",
      "614/614 [==============================] - 0s 127us/step - loss: 0.5545 - accuracy: 0.7280\n",
      "Epoch 55/150\n",
      "614/614 [==============================] - 0s 120us/step - loss: 0.5671 - accuracy: 0.7020\n",
      "Epoch 56/150\n",
      "614/614 [==============================] - 0s 119us/step - loss: 0.5602 - accuracy: 0.7036\n",
      "Epoch 57/150\n",
      "614/614 [==============================] - 0s 120us/step - loss: 0.5540 - accuracy: 0.7296\n",
      "Epoch 58/150\n",
      "614/614 [==============================] - 0s 117us/step - loss: 0.5644 - accuracy: 0.7215\n",
      "Epoch 59/150\n",
      "614/614 [==============================] - 0s 117us/step - loss: 0.5573 - accuracy: 0.7231\n",
      "Epoch 60/150\n",
      "614/614 [==============================] - 0s 119us/step - loss: 0.5531 - accuracy: 0.7329\n",
      "Epoch 61/150\n",
      "614/614 [==============================] - 0s 119us/step - loss: 0.5651 - accuracy: 0.6857\n",
      "Epoch 62/150\n",
      "614/614 [==============================] - 0s 109us/step - loss: 0.5562 - accuracy: 0.7182\n",
      "Epoch 63/150\n",
      "614/614 [==============================] - 0s 107us/step - loss: 0.5561 - accuracy: 0.7264\n",
      "Epoch 64/150\n",
      "614/614 [==============================] - 0s 109us/step - loss: 0.5414 - accuracy: 0.7117\n",
      "Epoch 65/150\n",
      "614/614 [==============================] - 0s 107us/step - loss: 0.5551 - accuracy: 0.7199\n",
      "Epoch 66/150\n",
      "614/614 [==============================] - 0s 107us/step - loss: 0.5705 - accuracy: 0.7264\n",
      "Epoch 67/150\n",
      "614/614 [==============================] - 0s 112us/step - loss: 0.5500 - accuracy: 0.7296\n",
      "Epoch 68/150\n",
      "614/614 [==============================] - 0s 109us/step - loss: 0.5527 - accuracy: 0.7280\n",
      "Epoch 69/150\n",
      "614/614 [==============================] - 0s 110us/step - loss: 0.5366 - accuracy: 0.7231\n",
      "Epoch 70/150\n",
      "614/614 [==============================] - 0s 107us/step - loss: 0.5353 - accuracy: 0.7378\n",
      "Epoch 71/150\n",
      "614/614 [==============================] - 0s 109us/step - loss: 0.5372 - accuracy: 0.7313\n",
      "Epoch 72/150\n",
      "614/614 [==============================] - 0s 114us/step - loss: 0.5407 - accuracy: 0.7182\n",
      "Epoch 73/150\n",
      "614/614 [==============================] - 0s 112us/step - loss: 0.5395 - accuracy: 0.7459\n",
      "Epoch 74/150\n",
      "614/614 [==============================] - 0s 115us/step - loss: 0.5263 - accuracy: 0.7394\n",
      "Epoch 75/150\n",
      "614/614 [==============================] - 0s 111us/step - loss: 0.5392 - accuracy: 0.7296\n",
      "Epoch 76/150\n",
      "614/614 [==============================] - 0s 114us/step - loss: 0.5350 - accuracy: 0.7378\n",
      "Epoch 77/150\n",
      "614/614 [==============================] - 0s 115us/step - loss: 0.5482 - accuracy: 0.7166\n",
      "Epoch 78/150\n",
      "614/614 [==============================] - 0s 114us/step - loss: 0.5369 - accuracy: 0.7329\n",
      "Epoch 79/150\n",
      "614/614 [==============================] - 0s 114us/step - loss: 0.5342 - accuracy: 0.7443\n",
      "Epoch 80/150\n",
      "614/614 [==============================] - 0s 112us/step - loss: 0.5380 - accuracy: 0.7280\n",
      "Epoch 81/150\n",
      "614/614 [==============================] - 0s 115us/step - loss: 0.5234 - accuracy: 0.7476\n",
      "Epoch 82/150\n",
      "614/614 [==============================] - 0s 117us/step - loss: 0.5350 - accuracy: 0.7362\n",
      "Epoch 83/150\n",
      "614/614 [==============================] - 0s 107us/step - loss: 0.5302 - accuracy: 0.7492\n",
      "Epoch 84/150\n",
      "614/614 [==============================] - 0s 107us/step - loss: 0.5335 - accuracy: 0.7166\n",
      "Epoch 85/150\n",
      "614/614 [==============================] - 0s 109us/step - loss: 0.5377 - accuracy: 0.7378\n",
      "Epoch 86/150\n",
      "614/614 [==============================] - 0s 110us/step - loss: 0.5405 - accuracy: 0.7280\n",
      "Epoch 87/150\n",
      "614/614 [==============================] - 0s 117us/step - loss: 0.5490 - accuracy: 0.7215\n",
      "Epoch 88/150\n",
      "614/614 [==============================] - 0s 110us/step - loss: 0.5327 - accuracy: 0.7345\n",
      "Epoch 89/150\n",
      "614/614 [==============================] - 0s 109us/step - loss: 0.5379 - accuracy: 0.7215\n",
      "Epoch 90/150\n",
      "614/614 [==============================] - 0s 110us/step - loss: 0.5311 - accuracy: 0.7443\n",
      "Epoch 91/150\n",
      "614/614 [==============================] - 0s 110us/step - loss: 0.5253 - accuracy: 0.7557\n",
      "Epoch 92/150\n",
      "614/614 [==============================] - 0s 109us/step - loss: 0.5346 - accuracy: 0.7410\n",
      "Epoch 93/150\n",
      "614/614 [==============================] - 0s 136us/step - loss: 0.5228 - accuracy: 0.7508\n",
      "Epoch 94/150\n",
      "614/614 [==============================] - 0s 156us/step - loss: 0.5220 - accuracy: 0.7345\n",
      "Epoch 95/150\n",
      "614/614 [==============================] - 0s 154us/step - loss: 0.5379 - accuracy: 0.7215\n",
      "Epoch 96/150\n",
      "614/614 [==============================] - 0s 158us/step - loss: 0.5252 - accuracy: 0.7280\n",
      "Epoch 97/150\n",
      "614/614 [==============================] - 0s 145us/step - loss: 0.5358 - accuracy: 0.7362\n",
      "Epoch 98/150\n",
      "614/614 [==============================] - 0s 109us/step - loss: 0.5304 - accuracy: 0.7313\n",
      "Epoch 99/150\n",
      "614/614 [==============================] - 0s 109us/step - loss: 0.5252 - accuracy: 0.7394\n",
      "Epoch 100/150\n",
      "614/614 [==============================] - 0s 112us/step - loss: 0.5094 - accuracy: 0.7622\n",
      "Epoch 101/150\n",
      "614/614 [==============================] - 0s 110us/step - loss: 0.5352 - accuracy: 0.7362\n",
      "Epoch 102/150\n",
      "614/614 [==============================] - 0s 107us/step - loss: 0.5143 - accuracy: 0.7443\n",
      "Epoch 103/150\n",
      "614/614 [==============================] - 0s 107us/step - loss: 0.5198 - accuracy: 0.7459\n",
      "Epoch 104/150\n",
      "614/614 [==============================] - 0s 107us/step - loss: 0.5188 - accuracy: 0.7394\n",
      "Epoch 105/150\n",
      "614/614 [==============================] - 0s 138us/step - loss: 0.5167 - accuracy: 0.7476\n",
      "Epoch 106/150\n",
      "614/614 [==============================] - 0s 153us/step - loss: 0.5135 - accuracy: 0.7394\n",
      "Epoch 107/150\n",
      "614/614 [==============================] - 0s 158us/step - loss: 0.5101 - accuracy: 0.7541\n",
      "Epoch 108/150\n",
      "614/614 [==============================] - 0s 125us/step - loss: 0.5181 - accuracy: 0.7492\n",
      "Epoch 109/150\n",
      "614/614 [==============================] - 0s 115us/step - loss: 0.5255 - accuracy: 0.7459\n",
      "Epoch 110/150\n",
      "614/614 [==============================] - 0s 106us/step - loss: 0.5272 - accuracy: 0.7394\n",
      "Epoch 111/150\n",
      "614/614 [==============================] - 0s 109us/step - loss: 0.5196 - accuracy: 0.7443\n",
      "Epoch 112/150\n",
      "614/614 [==============================] - 0s 112us/step - loss: 0.5288 - accuracy: 0.7345\n",
      "Epoch 113/150\n",
      "614/614 [==============================] - 0s 107us/step - loss: 0.5147 - accuracy: 0.7443\n",
      "Epoch 114/150\n",
      "614/614 [==============================] - 0s 110us/step - loss: 0.5496 - accuracy: 0.7134\n",
      "Epoch 115/150\n",
      "614/614 [==============================] - 0s 109us/step - loss: 0.5154 - accuracy: 0.7524\n",
      "Epoch 116/150\n",
      "614/614 [==============================] - 0s 110us/step - loss: 0.5089 - accuracy: 0.7459\n",
      "Epoch 117/150\n",
      "614/614 [==============================] - 0s 119us/step - loss: 0.5153 - accuracy: 0.7606\n",
      "Epoch 118/150\n",
      "614/614 [==============================] - 0s 127us/step - loss: 0.5456 - accuracy: 0.7296\n",
      "Epoch 119/150\n",
      "614/614 [==============================] - 0s 119us/step - loss: 0.5127 - accuracy: 0.7459\n",
      "Epoch 120/150\n",
      "614/614 [==============================] - 0s 112us/step - loss: 0.5171 - accuracy: 0.7378\n",
      "Epoch 121/150\n",
      "614/614 [==============================] - 0s 110us/step - loss: 0.5124 - accuracy: 0.7476\n",
      "Epoch 122/150\n",
      "614/614 [==============================] - 0s 109us/step - loss: 0.5130 - accuracy: 0.7362\n",
      "Epoch 123/150\n",
      "614/614 [==============================] - 0s 114us/step - loss: 0.5139 - accuracy: 0.7378\n",
      "Epoch 124/150\n",
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     ]
    },
    {
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     ]
    },
    {
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     "text": [
      "Epoch 17/150\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 23/150\n",
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      "Epoch 49/150\n",
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      "Epoch 50/150\n",
      "615/615 [==============================] - 0s 135us/step - loss: 0.5662 - accuracy: 0.7236\n",
      "Epoch 51/150\n",
      "615/615 [==============================] - 0s 139us/step - loss: 0.5556 - accuracy: 0.7220\n",
      "Epoch 52/150\n",
      "615/615 [==============================] - 0s 141us/step - loss: 0.5579 - accuracy: 0.7252\n",
      "Epoch 53/150\n",
      "615/615 [==============================] - 0s 136us/step - loss: 0.5599 - accuracy: 0.7285\n",
      "Epoch 54/150\n",
      "615/615 [==============================] - 0s 141us/step - loss: 0.5633 - accuracy: 0.7138\n",
      "Epoch 55/150\n",
      "615/615 [==============================] - 0s 135us/step - loss: 0.5595 - accuracy: 0.7220\n",
      "Epoch 56/150\n",
      "615/615 [==============================] - 0s 141us/step - loss: 0.5539 - accuracy: 0.73500s - loss: 0.5624 - accuracy: 0.72\n",
      "Epoch 57/150\n",
      "615/615 [==============================] - 0s 135us/step - loss: 0.5557 - accuracy: 0.7268\n",
      "Epoch 58/150\n",
      "615/615 [==============================] - 0s 144us/step - loss: 0.5568 - accuracy: 0.7301\n",
      "Epoch 59/150\n",
      "615/615 [==============================] - 0s 146us/step - loss: 0.5656 - accuracy: 0.7285\n",
      "Epoch 60/150\n",
      "615/615 [==============================] - 0s 154us/step - loss: 0.5534 - accuracy: 0.7350\n",
      "Epoch 61/150\n",
      "615/615 [==============================] - 0s 146us/step - loss: 0.5613 - accuracy: 0.7187\n",
      "Epoch 62/150\n",
      "615/615 [==============================] - 0s 135us/step - loss: 0.5707 - accuracy: 0.7122\n",
      "Epoch 63/150\n",
      "615/615 [==============================] - 0s 135us/step - loss: 0.5646 - accuracy: 0.7236\n",
      "Epoch 64/150\n",
      "615/615 [==============================] - 0s 139us/step - loss: 0.5544 - accuracy: 0.7333\n",
      "Epoch 65/150\n",
      "615/615 [==============================] - 0s 143us/step - loss: 0.5518 - accuracy: 0.7285\n",
      "Epoch 66/150\n",
      "615/615 [==============================] - 0s 136us/step - loss: 0.5510 - accuracy: 0.7366\n",
      "Epoch 67/150\n",
      "615/615 [==============================] - 0s 136us/step - loss: 0.5491 - accuracy: 0.7236\n",
      "Epoch 68/150\n",
      "615/615 [==============================] - 0s 125us/step - loss: 0.5485 - accuracy: 0.7301\n",
      "Epoch 69/150\n",
      "615/615 [==============================] - 0s 131us/step - loss: 0.5511 - accuracy: 0.7350\n",
      "Epoch 70/150\n",
      "615/615 [==============================] - 0s 136us/step - loss: 0.5486 - accuracy: 0.7236\n",
      "Epoch 71/150\n",
      "615/615 [==============================] - 0s 139us/step - loss: 0.5477 - accuracy: 0.7350\n",
      "Epoch 72/150\n",
      "615/615 [==============================] - 0s 154us/step - loss: 0.5510 - accuracy: 0.7463\n",
      "Epoch 73/150\n",
      "615/615 [==============================] - 0s 152us/step - loss: 0.5657 - accuracy: 0.7089\n",
      "Epoch 74/150\n",
      "615/615 [==============================] - 0s 138us/step - loss: 0.5516 - accuracy: 0.7317\n",
      "Epoch 75/150\n",
      "615/615 [==============================] - 0s 151us/step - loss: 0.5453 - accuracy: 0.7382\n",
      "Epoch 76/150\n",
      "615/615 [==============================] - 0s 152us/step - loss: 0.5559 - accuracy: 0.7203\n",
      "Epoch 77/150\n",
      "615/615 [==============================] - 0s 156us/step - loss: 0.5454 - accuracy: 0.7431\n",
      "Epoch 78/150\n",
      "615/615 [==============================] - 0s 159us/step - loss: 0.5512 - accuracy: 0.7187\n",
      "Epoch 79/150\n",
      "615/615 [==============================] - 0s 154us/step - loss: 0.5413 - accuracy: 0.7317\n",
      "Epoch 80/150\n",
      "615/615 [==============================] - 0s 152us/step - loss: 0.5455 - accuracy: 0.7252\n",
      "Epoch 81/150\n",
      "615/615 [==============================] - 0s 161us/step - loss: 0.5493 - accuracy: 0.7301\n",
      "Epoch 82/150\n",
      "615/615 [==============================] - 0s 152us/step - loss: 0.5450 - accuracy: 0.7333\n",
      "Epoch 83/150\n",
      "615/615 [==============================] - 0s 146us/step - loss: 0.5529 - accuracy: 0.7301\n",
      "Epoch 84/150\n",
      "615/615 [==============================] - 0s 139us/step - loss: 0.5465 - accuracy: 0.7398\n",
      "Epoch 85/150\n",
      "615/615 [==============================] - 0s 139us/step - loss: 0.5450 - accuracy: 0.7366\n",
      "Epoch 86/150\n",
      "615/615 [==============================] - 0s 143us/step - loss: 0.5488 - accuracy: 0.7431\n",
      "Epoch 87/150\n",
      "615/615 [==============================] - 0s 136us/step - loss: 0.5391 - accuracy: 0.7431\n",
      "Epoch 88/150\n",
      "615/615 [==============================] - 0s 139us/step - loss: 0.5468 - accuracy: 0.7236\n",
      "Epoch 89/150\n",
      "615/615 [==============================] - 0s 136us/step - loss: 0.5411 - accuracy: 0.7415\n",
      "Epoch 90/150\n",
      "615/615 [==============================] - 0s 141us/step - loss: 0.5388 - accuracy: 0.7317\n",
      "Epoch 91/150\n",
      "615/615 [==============================] - 0s 156us/step - loss: 0.5398 - accuracy: 0.7398\n",
      "Epoch 92/150\n",
      "615/615 [==============================] - 0s 151us/step - loss: 0.5395 - accuracy: 0.7447\n",
      "Epoch 93/150\n",
      "615/615 [==============================] - 0s 148us/step - loss: 0.5392 - accuracy: 0.7333\n",
      "Epoch 94/150\n",
      "615/615 [==============================] - 0s 151us/step - loss: 0.5367 - accuracy: 0.7236\n",
      "Epoch 95/150\n",
      "615/615 [==============================] - 0s 151us/step - loss: 0.5466 - accuracy: 0.7496\n",
      "Epoch 96/150\n",
      "615/615 [==============================] - 0s 151us/step - loss: 0.5451 - accuracy: 0.7171\n",
      "Epoch 97/150\n",
      "615/615 [==============================] - 0s 138us/step - loss: 0.5355 - accuracy: 0.7447\n",
      "Epoch 98/150\n",
      "615/615 [==============================] - 0s 170us/step - loss: 0.5392 - accuracy: 0.7398\n",
      "Epoch 99/150\n",
      "615/615 [==============================] - 0s 186us/step - loss: 0.5347 - accuracy: 0.7366\n",
      "Epoch 100/150\n",
      "615/615 [==============================] - 0s 159us/step - loss: 0.5393 - accuracy: 0.7203\n",
      "Epoch 101/150\n",
      "615/615 [==============================] - 0s 138us/step - loss: 0.5345 - accuracy: 0.7577\n",
      "Epoch 102/150\n",
      "615/615 [==============================] - 0s 135us/step - loss: 0.5391 - accuracy: 0.7301\n",
      "Epoch 103/150\n",
      "615/615 [==============================] - 0s 157us/step - loss: 0.5330 - accuracy: 0.7333\n",
      "Epoch 104/150\n",
      "615/615 [==============================] - 0s 180us/step - loss: 0.5393 - accuracy: 0.7415\n",
      "Epoch 105/150\n",
      "615/615 [==============================] - 0s 138us/step - loss: 0.5471 - accuracy: 0.7366\n",
      "Epoch 106/150\n",
      "615/615 [==============================] - 0s 133us/step - loss: 0.5354 - accuracy: 0.7301\n",
      "Epoch 107/150\n",
      "615/615 [==============================] - 0s 135us/step - loss: 0.5397 - accuracy: 0.7496\n",
      "Epoch 108/150\n",
      "615/615 [==============================] - 0s 130us/step - loss: 0.5322 - accuracy: 0.7593\n",
      "Epoch 109/150\n",
      "615/615 [==============================] - 0s 125us/step - loss: 0.5369 - accuracy: 0.7398\n",
      "Epoch 110/150\n",
      "615/615 [==============================] - 0s 126us/step - loss: 0.5466 - accuracy: 0.7382\n",
      "Epoch 111/150\n",
      "615/615 [==============================] - 0s 123us/step - loss: 0.5349 - accuracy: 0.7431\n",
      "Epoch 112/150\n",
      "615/615 [==============================] - 0s 131us/step - loss: 0.5359 - accuracy: 0.7447\n",
      "Epoch 113/150\n",
      "615/615 [==============================] - 0s 131us/step - loss: 0.5281 - accuracy: 0.7382\n",
      "Epoch 114/150\n",
      "615/615 [==============================] - 0s 135us/step - loss: 0.5339 - accuracy: 0.7496\n",
      "Epoch 115/150\n",
      "615/615 [==============================] - 0s 135us/step - loss: 0.5320 - accuracy: 0.7398\n",
      "Epoch 116/150\n",
      "615/615 [==============================] - 0s 144us/step - loss: 0.5291 - accuracy: 0.7415\n",
      "Epoch 117/150\n",
      "615/615 [==============================] - 0s 178us/step - loss: 0.5265 - accuracy: 0.7382\n",
      "Epoch 118/150\n",
      "615/615 [==============================] - 0s 143us/step - loss: 0.5349 - accuracy: 0.7415\n",
      "Epoch 119/150\n",
      "615/615 [==============================] - 0s 138us/step - loss: 0.5313 - accuracy: 0.7610\n",
      "Epoch 120/150\n",
      "615/615 [==============================] - 0s 139us/step - loss: 0.5293 - accuracy: 0.7398\n",
      "Epoch 121/150\n",
      "615/615 [==============================] - 0s 141us/step - loss: 0.5259 - accuracy: 0.7512\n",
      "Epoch 122/150\n",
      "615/615 [==============================] - 0s 139us/step - loss: 0.5222 - accuracy: 0.7480\n",
      "Epoch 123/150\n",
      "615/615 [==============================] - 0s 139us/step - loss: 0.5334 - accuracy: 0.7431\n",
      "Epoch 124/150\n",
      "615/615 [==============================] - 0s 146us/step - loss: 0.5301 - accuracy: 0.7333\n",
      "Epoch 125/150\n",
      "615/615 [==============================] - 0s 141us/step - loss: 0.5289 - accuracy: 0.7382\n",
      "Epoch 126/150\n",
      "615/615 [==============================] - 0s 143us/step - loss: 0.5247 - accuracy: 0.7593\n",
      "Epoch 127/150\n",
      "615/615 [==============================] - 0s 148us/step - loss: 0.5228 - accuracy: 0.75120s - loss: 0.5346 - accuracy: 0.74\n",
      "Epoch 128/150\n",
      "615/615 [==============================] - 0s 144us/step - loss: 0.5288 - accuracy: 0.7480\n",
      "Epoch 129/150\n",
      "615/615 [==============================] - 0s 144us/step - loss: 0.5319 - accuracy: 0.7398\n",
      "Epoch 130/150\n",
      "615/615 [==============================] - 0s 139us/step - loss: 0.5249 - accuracy: 0.7333\n",
      "Epoch 131/150\n",
      "615/615 [==============================] - 0s 131us/step - loss: 0.5245 - accuracy: 0.7415\n",
      "Epoch 132/150\n",
      "615/615 [==============================] - 0s 131us/step - loss: 0.5251 - accuracy: 0.7431\n",
      "Epoch 133/150\n",
      "615/615 [==============================] - 0s 138us/step - loss: 0.5239 - accuracy: 0.7463\n",
      "Epoch 134/150\n",
      "615/615 [==============================] - 0s 139us/step - loss: 0.5233 - accuracy: 0.7528\n",
      "Epoch 135/150\n",
      "615/615 [==============================] - 0s 143us/step - loss: 0.5283 - accuracy: 0.7480\n",
      "Epoch 136/150\n",
      "615/615 [==============================] - 0s 144us/step - loss: 0.5294 - accuracy: 0.7447\n",
      "Epoch 137/150\n",
      "615/615 [==============================] - 0s 144us/step - loss: 0.5231 - accuracy: 0.7512\n",
      "Epoch 138/150\n",
      "615/615 [==============================] - 0s 143us/step - loss: 0.5187 - accuracy: 0.7545\n",
      "Epoch 139/150\n",
      "615/615 [==============================] - 0s 144us/step - loss: 0.5197 - accuracy: 0.7496\n",
      "Epoch 140/150\n",
      "615/615 [==============================] - 0s 141us/step - loss: 0.5203 - accuracy: 0.7480\n",
      "Epoch 141/150\n",
      "615/615 [==============================] - 0s 144us/step - loss: 0.5305 - accuracy: 0.7382\n",
      "Epoch 142/150\n",
      "615/615 [==============================] - 0s 141us/step - loss: 0.5173 - accuracy: 0.7512\n",
      "Epoch 143/150\n",
      "615/615 [==============================] - 0s 146us/step - loss: 0.5217 - accuracy: 0.7463\n",
      "Epoch 144/150\n",
      "615/615 [==============================] - 0s 143us/step - loss: 0.5272 - accuracy: 0.7398\n",
      "Epoch 145/150\n",
      "615/615 [==============================] - 0s 144us/step - loss: 0.5162 - accuracy: 0.7561\n",
      "Epoch 146/150\n",
      "615/615 [==============================] - 0s 143us/step - loss: 0.5152 - accuracy: 0.7610\n",
      "Epoch 147/150\n",
      "615/615 [==============================] - 0s 139us/step - loss: 0.5296 - accuracy: 0.7593\n",
      "Epoch 148/150\n",
      "615/615 [==============================] - 0s 141us/step - loss: 0.5138 - accuracy: 0.7577\n",
      "Epoch 149/150\n",
      "615/615 [==============================] - 0s 143us/step - loss: 0.5176 - accuracy: 0.7350\n",
      "Epoch 150/150\n",
      "615/615 [==============================] - 0s 143us/step - loss: 0.5240 - accuracy: 0.7528\n",
      "153/153 [==============================] - 0s 971us/step\n",
      "0.7252864837646484\n"
     ]
    }
   ],
   "source": [
    "# 5 交叉验证\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed)\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "658/658 [==============================] - 0s 573us/step - loss: 4.1017 - accuracy: 0.3480\n",
      "Epoch 2/150\n",
      "658/658 [==============================] - 0s 146us/step - loss: 0.9249 - accuracy: 0.4362\n",
      "Epoch 3/150\n",
      "658/658 [==============================] - 0s 153us/step - loss: 0.7240 - accuracy: 0.5608\n",
      "Epoch 4/150\n",
      "658/658 [==============================] - 0s 152us/step - loss: 0.6877 - accuracy: 0.6292\n",
      "Epoch 5/150\n",
      "658/658 [==============================] - 0s 150us/step - loss: 0.6768 - accuracy: 0.6459\n",
      "Epoch 6/150\n",
      "658/658 [==============================] - 0s 149us/step - loss: 0.6691 - accuracy: 0.6535\n",
      "Epoch 7/150\n",
      "658/658 [==============================] - 0s 153us/step - loss: 0.6614 - accuracy: 0.6611\n",
      "Epoch 8/150\n",
      "658/658 [==============================] - 0s 146us/step - loss: 0.6508 - accuracy: 0.6626\n",
      "Epoch 9/150\n",
      "658/658 [==============================] - 0s 139us/step - loss: 0.6454 - accuracy: 0.6641\n",
      "Epoch 10/150\n",
      "658/658 [==============================] - 0s 141us/step - loss: 0.6401 - accuracy: 0.6687\n",
      "Epoch 11/150\n",
      "658/658 [==============================] - 0s 142us/step - loss: 0.6345 - accuracy: 0.6717\n",
      "Epoch 12/150\n",
      "658/658 [==============================] - 0s 144us/step - loss: 0.6309 - accuracy: 0.6869\n",
      "Epoch 13/150\n",
      "658/658 [==============================] - 0s 141us/step - loss: 0.6297 - accuracy: 0.6717\n",
      "Epoch 14/150\n",
      "658/658 [==============================] - 0s 144us/step - loss: 0.6246 - accuracy: 0.6991\n",
      "Epoch 15/150\n",
      "658/658 [==============================] - 0s 142us/step - loss: 0.6189 - accuracy: 0.6900\n",
      "Epoch 16/150\n",
      "658/658 [==============================] - 0s 142us/step - loss: 0.6213 - accuracy: 0.6793\n",
      "Epoch 17/150\n",
      "658/658 [==============================] - 0s 135us/step - loss: 0.6181 - accuracy: 0.6930\n",
      "Epoch 18/150\n",
      "658/658 [==============================] - 0s 133us/step - loss: 0.6137 - accuracy: 0.6991\n",
      "Epoch 19/150\n",
      "658/658 [==============================] - 0s 139us/step - loss: 0.6097 - accuracy: 0.7006\n",
      "Epoch 20/150\n",
      "658/658 [==============================] - 0s 142us/step - loss: 0.6059 - accuracy: 0.7173\n",
      "Epoch 21/150\n",
      "658/658 [==============================] - 0s 141us/step - loss: 0.6015 - accuracy: 0.7295\n",
      "Epoch 22/150\n",
      "658/658 [==============================] - 0s 144us/step - loss: 0.5970 - accuracy: 0.7356\n",
      "Epoch 23/150\n",
      "658/658 [==============================] - 0s 147us/step - loss: 0.5996 - accuracy: 0.6884\n",
      "Epoch 24/150\n",
      "658/658 [==============================] - 0s 150us/step - loss: 0.5950 - accuracy: 0.7143\n",
      "Epoch 25/150\n",
      "658/658 [==============================] - 0s 146us/step - loss: 0.5926 - accuracy: 0.7204\n",
      "Epoch 26/150\n",
      "658/658 [==============================] - 0s 141us/step - loss: 0.5882 - accuracy: 0.7280\n",
      "Epoch 27/150\n",
      "658/658 [==============================] - 0s 165us/step - loss: 0.5879 - accuracy: 0.7173\n",
      "Epoch 28/150\n",
      "658/658 [==============================] - 0s 170us/step - loss: 0.5836 - accuracy: 0.7280\n",
      "Epoch 29/150\n",
      "658/658 [==============================] - 0s 153us/step - loss: 0.5809 - accuracy: 0.7477\n",
      "Epoch 30/150\n",
      "658/658 [==============================] - 0s 149us/step - loss: 0.5802 - accuracy: 0.7280\n",
      "Epoch 31/150\n",
      "658/658 [==============================] - 0s 176us/step - loss: 0.5755 - accuracy: 0.7280\n",
      "Epoch 32/150\n",
      "658/658 [==============================] - 0s 191us/step - loss: 0.5753 - accuracy: 0.7356\n",
      "Epoch 33/150\n",
      "658/658 [==============================] - 0s 173us/step - loss: 0.5718 - accuracy: 0.73710s - loss: 0.5712 - accuracy: 0.73\n",
      "Epoch 34/150\n",
      "658/658 [==============================] - 0s 152us/step - loss: 0.5707 - accuracy: 0.7432\n",
      "Epoch 35/150\n",
      "658/658 [==============================] - 0s 186us/step - loss: 0.5714 - accuracy: 0.7249\n",
      "Epoch 36/150\n",
      "658/658 [==============================] - 0s 186us/step - loss: 0.5678 - accuracy: 0.7280\n",
      "Epoch 37/150\n",
      "658/658 [==============================] - 0s 167us/step - loss: 0.5628 - accuracy: 0.7371\n",
      "Epoch 38/150\n",
      "658/658 [==============================] - 0s 165us/step - loss: 0.5564 - accuracy: 0.7432\n",
      "Epoch 39/150\n",
      "658/658 [==============================] - 0s 150us/step - loss: 0.5602 - accuracy: 0.7538\n",
      "Epoch 40/150\n",
      "658/658 [==============================] - 0s 179us/step - loss: 0.5577 - accuracy: 0.7340\n",
      "Epoch 41/150\n",
      "658/658 [==============================] - 0s 182us/step - loss: 0.5556 - accuracy: 0.7325\n",
      "Epoch 42/150\n",
      "658/658 [==============================] - 0s 185us/step - loss: 0.5533 - accuracy: 0.7462\n",
      "Epoch 43/150\n",
      "658/658 [==============================] - 0s 165us/step - loss: 0.5522 - accuracy: 0.7432\n",
      "Epoch 44/150\n",
      "658/658 [==============================] - 0s 149us/step - loss: 0.5505 - accuracy: 0.7447\n",
      "Epoch 45/150\n",
      "658/658 [==============================] - 0s 176us/step - loss: 0.5492 - accuracy: 0.7219\n",
      "Epoch 46/150\n",
      "658/658 [==============================] - 0s 168us/step - loss: 0.5463 - accuracy: 0.7432\n",
      "Epoch 47/150\n",
      "658/658 [==============================] - 0s 173us/step - loss: 0.5456 - accuracy: 0.7447\n",
      "Epoch 48/150\n",
      "658/658 [==============================] - 0s 150us/step - loss: 0.5493 - accuracy: 0.7401\n",
      "Epoch 49/150\n",
      "658/658 [==============================] - 0s 141us/step - loss: 0.5457 - accuracy: 0.7401\n",
      "Epoch 50/150\n",
      "658/658 [==============================] - 0s 146us/step - loss: 0.5461 - accuracy: 0.7401\n",
      "Epoch 51/150\n",
      "658/658 [==============================] - 0s 149us/step - loss: 0.5435 - accuracy: 0.7447\n",
      "Epoch 52/150\n",
      "658/658 [==============================] - 0s 144us/step - loss: 0.5389 - accuracy: 0.7523\n",
      "Epoch 53/150\n",
      "658/658 [==============================] - 0s 150us/step - loss: 0.5421 - accuracy: 0.7447\n",
      "Epoch 54/150\n",
      "658/658 [==============================] - 0s 138us/step - loss: 0.5364 - accuracy: 0.7356\n",
      "Epoch 55/150\n",
      "658/658 [==============================] - 0s 142us/step - loss: 0.5409 - accuracy: 0.7310\n",
      "Epoch 56/150\n",
      "658/658 [==============================] - 0s 144us/step - loss: 0.5348 - accuracy: 0.7584\n",
      "Epoch 57/150\n",
      "658/658 [==============================] - 0s 179us/step - loss: 0.5264 - accuracy: 0.7584\n",
      "Epoch 58/150\n",
      "658/658 [==============================] - 0s 186us/step - loss: 0.5344 - accuracy: 0.7386\n",
      "Epoch 59/150\n",
      "658/658 [==============================] - 0s 167us/step - loss: 0.5315 - accuracy: 0.7386\n",
      "Epoch 60/150\n",
      "658/658 [==============================] - 0s 150us/step - loss: 0.5236 - accuracy: 0.7584\n",
      "Epoch 61/150\n",
      "658/658 [==============================] - 0s 164us/step - loss: 0.5280 - accuracy: 0.7523\n",
      "Epoch 62/150\n",
      "658/658 [==============================] - 0s 126us/step - loss: 0.5268 - accuracy: 0.7523\n",
      "Epoch 63/150\n",
      "658/658 [==============================] - 0s 127us/step - loss: 0.5220 - accuracy: 0.7660\n",
      "Epoch 64/150\n",
      "658/658 [==============================] - 0s 132us/step - loss: 0.5202 - accuracy: 0.7553\n",
      "Epoch 65/150\n",
      "658/658 [==============================] - 0s 133us/step - loss: 0.5219 - accuracy: 0.7568\n",
      "Epoch 66/150\n",
      "658/658 [==============================] - 0s 130us/step - loss: 0.5270 - accuracy: 0.7538\n",
      "Epoch 67/150\n",
      "658/658 [==============================] - 0s 129us/step - loss: 0.5210 - accuracy: 0.7614\n",
      "Epoch 68/150\n",
      "658/658 [==============================] - 0s 149us/step - loss: 0.5284 - accuracy: 0.7447\n",
      "Epoch 69/150\n",
      "658/658 [==============================] - 0s 167us/step - loss: 0.5201 - accuracy: 0.7477\n",
      "Epoch 70/150\n",
      "658/658 [==============================] - 0s 132us/step - loss: 0.5169 - accuracy: 0.7553\n",
      "Epoch 71/150\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/150\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13/150\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19/150\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
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      "Epoch 45/150\n",
      "659/659 [==============================] - 0s 153us/step - loss: 0.5964 - accuracy: 0.7071\n",
      "Epoch 46/150\n",
      "659/659 [==============================] - 0s 153us/step - loss: 0.5919 - accuracy: 0.6980\n",
      "Epoch 47/150\n",
      "659/659 [==============================] - 0s 142us/step - loss: 0.7148 - accuracy: 0.6950\n",
      "Epoch 48/150\n",
      "659/659 [==============================] - 0s 163us/step - loss: 0.6340 - accuracy: 0.7026\n",
      "Epoch 49/150\n",
      "659/659 [==============================] - 0s 150us/step - loss: 0.6010 - accuracy: 0.6813\n",
      "Epoch 50/150\n",
      "659/659 [==============================] - 0s 153us/step - loss: 0.5518 - accuracy: 0.7420\n",
      "Epoch 51/150\n",
      "659/659 [==============================] - 0s 151us/step - loss: 0.6105 - accuracy: 0.7011\n",
      "Epoch 52/150\n",
      "659/659 [==============================] - 0s 156us/step - loss: 0.5515 - accuracy: 0.7269\n",
      "Epoch 53/150\n",
      "659/659 [==============================] - 0s 154us/step - loss: 0.5618 - accuracy: 0.7284\n",
      "Epoch 54/150\n",
      "659/659 [==============================] - 0s 156us/step - loss: 0.5638 - accuracy: 0.7026\n",
      "Epoch 55/150\n",
      "659/659 [==============================] - 0s 174us/step - loss: 0.5641 - accuracy: 0.7238\n",
      "Epoch 56/150\n",
      "659/659 [==============================] - 0s 192us/step - loss: 0.5824 - accuracy: 0.7132\n",
      "Epoch 57/150\n",
      "659/659 [==============================] - 0s 185us/step - loss: 0.5712 - accuracy: 0.7390\n",
      "Epoch 58/150\n",
      "659/659 [==============================] - 0s 194us/step - loss: 0.5582 - accuracy: 0.7299\n",
      "Epoch 59/150\n",
      "659/659 [==============================] - 0s 195us/step - loss: 0.5504 - accuracy: 0.7299\n",
      "Epoch 60/150\n",
      "659/659 [==============================] - 0s 203us/step - loss: 0.5627 - accuracy: 0.7253\n",
      "Epoch 61/150\n",
      "659/659 [==============================] - 0s 200us/step - loss: 0.5445 - accuracy: 0.7117\n",
      "Epoch 62/150\n",
      "659/659 [==============================] - 0s 191us/step - loss: 0.5674 - accuracy: 0.7162\n",
      "Epoch 63/150\n",
      "659/659 [==============================] - 0s 169us/step - loss: 0.5731 - accuracy: 0.7208\n",
      "Epoch 64/150\n",
      "659/659 [==============================] - 0s 177us/step - loss: 0.5797 - accuracy: 0.7360\n",
      "Epoch 65/150\n",
      "659/659 [==============================] - 0s 153us/step - loss: 0.5904 - accuracy: 0.7162\n",
      "Epoch 66/150\n",
      "659/659 [==============================] - 0s 197us/step - loss: 0.5385 - accuracy: 0.7314\n",
      "Epoch 67/150\n",
      "659/659 [==============================] - 0s 171us/step - loss: 0.5516 - accuracy: 0.7466\n",
      "Epoch 68/150\n",
      "659/659 [==============================] - 0s 171us/step - loss: 0.5552 - accuracy: 0.7360\n",
      "Epoch 69/150\n",
      "659/659 [==============================] - 0s 154us/step - loss: 0.5507 - accuracy: 0.7329\n",
      "Epoch 70/150\n",
      "659/659 [==============================] - 0s 154us/step - loss: 0.5812 - accuracy: 0.73440s - loss: 0.5519 - accuracy: 0.73\n",
      "Epoch 71/150\n",
      "659/659 [==============================] - 0s 154us/step - loss: 0.5702 - accuracy: 0.7162\n",
      "Epoch 72/150\n",
      "659/659 [==============================] - 0s 156us/step - loss: 0.6077 - accuracy: 0.7026\n",
      "Epoch 73/150\n",
      "659/659 [==============================] - 0s 159us/step - loss: 0.5347 - accuracy: 0.7405\n",
      "Epoch 74/150\n",
      "659/659 [==============================] - 0s 168us/step - loss: 0.5960 - accuracy: 0.7056\n",
      "Epoch 75/150\n",
      "659/659 [==============================] - 0s 150us/step - loss: 0.5488 - accuracy: 0.7360\n",
      "Epoch 76/150\n",
      "659/659 [==============================] - 0s 154us/step - loss: 0.5797 - accuracy: 0.7162\n",
      "Epoch 77/150\n",
      "659/659 [==============================] - 0s 153us/step - loss: 0.5640 - accuracy: 0.7542\n",
      "Epoch 78/150\n",
      "659/659 [==============================] - 0s 153us/step - loss: 0.6094 - accuracy: 0.7102\n",
      "Epoch 79/150\n",
      "659/659 [==============================] - 0s 153us/step - loss: 0.5526 - accuracy: 0.7238\n",
      "Epoch 80/150\n",
      "659/659 [==============================] - 0s 154us/step - loss: 0.5540 - accuracy: 0.7344\n",
      "Epoch 81/150\n",
      "659/659 [==============================] - 0s 157us/step - loss: 0.5728 - accuracy: 0.7223\n",
      "Epoch 82/150\n",
      "659/659 [==============================] - 0s 173us/step - loss: 0.5312 - accuracy: 0.7587\n",
      "Epoch 83/150\n",
      "659/659 [==============================] - 0s 156us/step - loss: 0.5573 - accuracy: 0.7284\n",
      "Epoch 84/150\n",
      "659/659 [==============================] - 0s 157us/step - loss: 0.5751 - accuracy: 0.7208\n",
      "Epoch 85/150\n",
      "659/659 [==============================] - 0s 173us/step - loss: 0.5380 - accuracy: 0.7314\n",
      "Epoch 86/150\n",
      "659/659 [==============================] - 0s 170us/step - loss: 0.5574 - accuracy: 0.7208\n",
      "Epoch 87/150\n",
      "659/659 [==============================] - 0s 154us/step - loss: 0.5176 - accuracy: 0.7496\n",
      "Epoch 88/150\n",
      "659/659 [==============================] - 0s 165us/step - loss: 0.5991 - accuracy: 0.7147\n",
      "Epoch 89/150\n",
      "659/659 [==============================] - 0s 182us/step - loss: 0.5783 - accuracy: 0.7178\n",
      "Epoch 90/150\n",
      "659/659 [==============================] - 0s 197us/step - loss: 0.5478 - accuracy: 0.7269\n",
      "Epoch 91/150\n",
      "659/659 [==============================] - 0s 156us/step - loss: 0.5313 - accuracy: 0.7466\n",
      "Epoch 92/150\n",
      "659/659 [==============================] - 0s 154us/step - loss: 0.6282 - accuracy: 0.7041\n",
      "Epoch 93/150\n",
      "659/659 [==============================] - 0s 159us/step - loss: 0.5970 - accuracy: 0.7071\n",
      "Epoch 94/150\n",
      "659/659 [==============================] - 0s 151us/step - loss: 0.6235 - accuracy: 0.7086\n",
      "Epoch 95/150\n",
      "659/659 [==============================] - 0s 153us/step - loss: 0.5416 - accuracy: 0.7436\n",
      "Epoch 96/150\n",
      "659/659 [==============================] - 0s 151us/step - loss: 0.5336 - accuracy: 0.7360\n",
      "Epoch 97/150\n",
      "659/659 [==============================] - 0s 150us/step - loss: 0.5663 - accuracy: 0.7299\n",
      "Epoch 98/150\n",
      "659/659 [==============================] - 0s 174us/step - loss: 0.5404 - accuracy: 0.7299\n",
      "Epoch 99/150\n",
      "659/659 [==============================] - 0s 176us/step - loss: 0.5281 - accuracy: 0.7572\n",
      "Epoch 100/150\n",
      "659/659 [==============================] - 0s 163us/step - loss: 0.5395 - accuracy: 0.7405\n",
      "Epoch 101/150\n",
      "659/659 [==============================] - 0s 154us/step - loss: 0.5438 - accuracy: 0.7208\n",
      "Epoch 102/150\n",
      "659/659 [==============================] - 0s 153us/step - loss: 0.5887 - accuracy: 0.7253\n",
      "Epoch 103/150\n",
      "659/659 [==============================] - 0s 162us/step - loss: 0.5322 - accuracy: 0.7390\n",
      "Epoch 104/150\n",
      "659/659 [==============================] - 0s 163us/step - loss: 0.5360 - accuracy: 0.7436\n",
      "Epoch 105/150\n",
      "659/659 [==============================] - 0s 153us/step - loss: 0.7068 - accuracy: 0.6844\n",
      "Epoch 106/150\n",
      "659/659 [==============================] - 0s 165us/step - loss: 0.5533 - accuracy: 0.7208\n",
      "Epoch 107/150\n",
      "659/659 [==============================] - 0s 173us/step - loss: 0.5553 - accuracy: 0.7344\n",
      "Epoch 108/150\n",
      "659/659 [==============================] - 0s 153us/step - loss: 0.5661 - accuracy: 0.7284\n",
      "Epoch 109/150\n",
      "659/659 [==============================] - 0s 153us/step - loss: 0.5466 - accuracy: 0.7466\n",
      "Epoch 110/150\n",
      "659/659 [==============================] - 0s 171us/step - loss: 0.5411 - accuracy: 0.7269\n",
      "Epoch 111/150\n",
      "659/659 [==============================] - 0s 166us/step - loss: 0.5905 - accuracy: 0.7071\n",
      "Epoch 112/150\n",
      "659/659 [==============================] - 0s 156us/step - loss: 0.5510 - accuracy: 0.7405\n",
      "Epoch 113/150\n",
      "659/659 [==============================] - 0s 171us/step - loss: 0.5282 - accuracy: 0.7557\n",
      "Epoch 114/150\n",
      "659/659 [==============================] - 0s 170us/step - loss: 0.5185 - accuracy: 0.7572\n",
      "Epoch 115/150\n",
      "659/659 [==============================] - 0s 150us/step - loss: 0.5246 - accuracy: 0.7436\n",
      "Epoch 116/150\n",
      "659/659 [==============================] - 0s 151us/step - loss: 0.5230 - accuracy: 0.7451\n",
      "Epoch 117/150\n",
      "659/659 [==============================] - 0s 151us/step - loss: 0.5165 - accuracy: 0.7572\n",
      "Epoch 118/150\n",
      "659/659 [==============================] - 0s 148us/step - loss: 0.5765 - accuracy: 0.7162\n",
      "Epoch 119/150\n",
      "659/659 [==============================] - 0s 142us/step - loss: 0.5289 - accuracy: 0.7527\n",
      "Epoch 120/150\n",
      "659/659 [==============================] - 0s 144us/step - loss: 0.5263 - accuracy: 0.7466\n",
      "Epoch 121/150\n",
      "659/659 [==============================] - 0s 151us/step - loss: 0.5342 - accuracy: 0.7238\n",
      "Epoch 122/150\n",
      "659/659 [==============================] - 0s 166us/step - loss: 0.5274 - accuracy: 0.7496\n",
      "Epoch 123/150\n",
      "659/659 [==============================] - 0s 171us/step - loss: 0.5223 - accuracy: 0.7466\n",
      "Epoch 124/150\n",
      "659/659 [==============================] - 0s 166us/step - loss: 0.5471 - accuracy: 0.7375\n",
      "Epoch 125/150\n",
      "659/659 [==============================] - 0s 179us/step - loss: 0.5130 - accuracy: 0.7299\n",
      "Epoch 126/150\n",
      "659/659 [==============================] - 0s 157us/step - loss: 0.5355 - accuracy: 0.7481\n",
      "Epoch 127/150\n",
      "659/659 [==============================] - 0s 156us/step - loss: 0.5927 - accuracy: 0.7405\n",
      "Epoch 128/150\n",
      "659/659 [==============================] - 0s 156us/step - loss: 0.5080 - accuracy: 0.7542\n",
      "Epoch 129/150\n",
      "659/659 [==============================] - 0s 148us/step - loss: 0.5429 - accuracy: 0.7496\n",
      "Epoch 130/150\n",
      "659/659 [==============================] - 0s 150us/step - loss: 0.5119 - accuracy: 0.7481\n",
      "Epoch 131/150\n",
      "659/659 [==============================] - 0s 151us/step - loss: 0.5669 - accuracy: 0.7041\n",
      "Epoch 132/150\n",
      "659/659 [==============================] - 0s 156us/step - loss: 0.5261 - accuracy: 0.7511\n",
      "Epoch 133/150\n",
      "659/659 [==============================] - 0s 151us/step - loss: 0.5260 - accuracy: 0.7587\n",
      "Epoch 134/150\n",
      "659/659 [==============================] - 0s 159us/step - loss: 0.5010 - accuracy: 0.7709\n",
      "Epoch 135/150\n",
      "659/659 [==============================] - 0s 157us/step - loss: 0.6188 - accuracy: 0.7117\n",
      "Epoch 136/150\n",
      "659/659 [==============================] - 0s 153us/step - loss: 0.5083 - accuracy: 0.7663\n",
      "Epoch 137/150\n",
      "659/659 [==============================] - 0s 151us/step - loss: 0.5473 - accuracy: 0.7284\n",
      "Epoch 138/150\n",
      "659/659 [==============================] - 0s 148us/step - loss: 0.5280 - accuracy: 0.7375\n",
      "Epoch 139/150\n",
      "659/659 [==============================] - 0s 147us/step - loss: 0.5413 - accuracy: 0.7451\n",
      "Epoch 140/150\n",
      "659/659 [==============================] - 0s 153us/step - loss: 0.5111 - accuracy: 0.7648\n",
      "Epoch 141/150\n",
      "659/659 [==============================] - 0s 150us/step - loss: 0.5121 - accuracy: 0.7587\n",
      "Epoch 142/150\n",
      "659/659 [==============================] - 0s 154us/step - loss: 0.5045 - accuracy: 0.7648\n",
      "Epoch 143/150\n",
      "659/659 [==============================] - 0s 153us/step - loss: 0.5292 - accuracy: 0.7527\n",
      "Epoch 144/150\n",
      "659/659 [==============================] - 0s 207us/step - loss: 0.5566 - accuracy: 0.7451\n",
      "Epoch 145/150\n",
      "659/659 [==============================] - 0s 147us/step - loss: 0.5712 - accuracy: 0.7132\n",
      "Epoch 146/150\n",
      "659/659 [==============================] - 0s 168us/step - loss: 0.5659 - accuracy: 0.7329\n",
      "Epoch 147/150\n",
      "659/659 [==============================] - 0s 180us/step - loss: 0.5246 - accuracy: 0.7314\n",
      "Epoch 148/150\n",
      "659/659 [==============================] - 0s 201us/step - loss: 0.5190 - accuracy: 0.7496\n",
      "Epoch 149/150\n",
      "659/659 [==============================] - 0s 177us/step - loss: 0.5475 - accuracy: 0.7405\n",
      "Epoch 150/150\n",
      "659/659 [==============================] - 0s 169us/step - loss: 0.5338 - accuracy: 0.7420\n",
      "109/109 [==============================] - 0s 2ms/step\n",
      "0.7383295553071159\n"
     ]
    }
   ],
   "source": [
    "# 7 交叉验证\n",
    "kfold = StratifiedKFold(n_splits=7, shuffle=True, random_state=seed)\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "512/512 [==============================] - 1s 2ms/step - loss: 0.7170 - accuracy: 0.3887\n",
      "Epoch 2/150\n",
      "512/512 [==============================] - 0s 493us/step - loss: 0.6861 - accuracy: 0.6484\n",
      "Epoch 3/150\n",
      "512/512 [==============================] - 0s 483us/step - loss: 0.6854 - accuracy: 0.5996\n",
      "Epoch 4/150\n",
      "512/512 [==============================] - 0s 487us/step - loss: 0.6739 - accuracy: 0.6562\n",
      "Epoch 5/150\n",
      "512/512 [==============================] - 0s 489us/step - loss: 0.6679 - accuracy: 0.6660\n",
      "Epoch 6/150\n",
      "512/512 [==============================] - 0s 481us/step - loss: 0.6612 - accuracy: 0.6816\n",
      "Epoch 7/150\n",
      "512/512 [==============================] - 0s 487us/step - loss: 0.6594 - accuracy: 0.6543\n",
      "Epoch 8/150\n",
      "512/512 [==============================] - 0s 485us/step - loss: 0.6546 - accuracy: 0.6543\n",
      "Epoch 9/150\n",
      "512/512 [==============================] - 0s 462us/step - loss: 0.6500 - accuracy: 0.6543\n",
      "Epoch 10/150\n",
      "512/512 [==============================] - 0s 487us/step - loss: 0.6464 - accuracy: 0.6602\n",
      "Epoch 11/150\n",
      "512/512 [==============================] - 0s 487us/step - loss: 0.6422 - accuracy: 0.6582\n",
      "Epoch 12/150\n",
      "512/512 [==============================] - 0s 491us/step - loss: 0.6393 - accuracy: 0.6621\n",
      "Epoch 13/150\n",
      "512/512 [==============================] - 0s 495us/step - loss: 0.6372 - accuracy: 0.6621\n",
      "Epoch 14/150\n",
      "512/512 [==============================] - 0s 491us/step - loss: 0.6317 - accuracy: 0.6699\n",
      "Epoch 15/150\n",
      "512/512 [==============================] - 0s 506us/step - loss: 0.6325 - accuracy: 0.6660\n",
      "Epoch 16/150\n",
      "512/512 [==============================] - 0s 462us/step - loss: 0.6216 - accuracy: 0.6660\n",
      "Epoch 17/150\n",
      "512/512 [==============================] - 0s 487us/step - loss: 0.6206 - accuracy: 0.6719\n",
      "Epoch 18/150\n",
      "512/512 [==============================] - 0s 487us/step - loss: 0.6114 - accuracy: 0.6895\n",
      "Epoch 19/150\n",
      "512/512 [==============================] - 0s 530us/step - loss: 0.6078 - accuracy: 0.6855\n",
      "Epoch 20/150\n",
      "512/512 [==============================] - 0s 477us/step - loss: 0.5929 - accuracy: 0.6914\n",
      "Epoch 21/150\n",
      "512/512 [==============================] - 0s 487us/step - loss: 0.5885 - accuracy: 0.6914\n",
      "Epoch 22/150\n",
      "512/512 [==============================] - 0s 481us/step - loss: 0.5857 - accuracy: 0.7012\n",
      "Epoch 23/150\n",
      "512/512 [==============================] - 0s 499us/step - loss: 0.5861 - accuracy: 0.7051\n",
      "Epoch 24/150\n",
      "512/512 [==============================] - 0s 479us/step - loss: 0.5646 - accuracy: 0.7148\n",
      "Epoch 25/150\n",
      "512/512 [==============================] - 0s 473us/step - loss: 0.5722 - accuracy: 0.7168\n",
      "Epoch 26/150\n",
      "512/512 [==============================] - 0s 477us/step - loss: 0.5615 - accuracy: 0.7168\n",
      "Epoch 27/150\n",
      "512/512 [==============================] - 0s 479us/step - loss: 0.5678 - accuracy: 0.6992\n",
      "Epoch 28/150\n",
      "512/512 [==============================] - 0s 481us/step - loss: 0.5550 - accuracy: 0.7188\n",
      "Epoch 29/150\n",
      "512/512 [==============================] - 0s 473us/step - loss: 0.5499 - accuracy: 0.7227\n",
      "Epoch 30/150\n",
      "512/512 [==============================] - 0s 483us/step - loss: 0.5514 - accuracy: 0.7344\n",
      "Epoch 31/150\n",
      "512/512 [==============================] - 0s 479us/step - loss: 0.5472 - accuracy: 0.7168\n",
      "Epoch 32/150\n",
      "512/512 [==============================] - 0s 471us/step - loss: 0.5453 - accuracy: 0.7266\n",
      "Epoch 33/150\n",
      "512/512 [==============================] - 0s 456us/step - loss: 0.5430 - accuracy: 0.7246\n",
      "Epoch 34/150\n",
      "512/512 [==============================] - 0s 481us/step - loss: 0.5602 - accuracy: 0.7090\n",
      "Epoch 35/150\n",
      "512/512 [==============================] - 0s 473us/step - loss: 0.5458 - accuracy: 0.7344\n",
      "Epoch 36/150\n",
      "512/512 [==============================] - 0s 481us/step - loss: 0.5353 - accuracy: 0.7344\n",
      "Epoch 37/150\n",
      "512/512 [==============================] - 0s 487us/step - loss: 0.5359 - accuracy: 0.7227\n",
      "Epoch 38/150\n",
      "512/512 [==============================] - 0s 493us/step - loss: 0.5365 - accuracy: 0.7480\n",
      "Epoch 39/150\n",
      "512/512 [==============================] - 0s 491us/step - loss: 0.5349 - accuracy: 0.7402\n",
      "Epoch 40/150\n",
      "512/512 [==============================] - 0s 475us/step - loss: 0.5367 - accuracy: 0.7227\n",
      "Epoch 41/150\n",
      "512/512 [==============================] - 0s 475us/step - loss: 0.5369 - accuracy: 0.7383\n",
      "Epoch 42/150\n",
      "512/512 [==============================] - 0s 497us/step - loss: 0.5335 - accuracy: 0.7363\n",
      "Epoch 43/150\n",
      "512/512 [==============================] - 0s 505us/step - loss: 0.5361 - accuracy: 0.7363\n",
      "Epoch 44/150\n",
      "512/512 [==============================] - 0s 483us/step - loss: 0.5285 - accuracy: 0.7363\n",
      "Epoch 45/150\n",
      "512/512 [==============================] - 0s 489us/step - loss: 0.5311 - accuracy: 0.7207\n",
      "Epoch 46/150\n",
      "512/512 [==============================] - 0s 477us/step - loss: 0.5323 - accuracy: 0.7441\n",
      "Epoch 47/150\n",
      "512/512 [==============================] - 0s 485us/step - loss: 0.5293 - accuracy: 0.7422\n",
      "Epoch 48/150\n",
      "512/512 [==============================] - 0s 458us/step - loss: 0.5219 - accuracy: 0.7324\n",
      "Epoch 49/150\n",
      "512/512 [==============================] - 0s 464us/step - loss: 0.5194 - accuracy: 0.7480\n",
      "Epoch 50/150\n",
      "512/512 [==============================] - 0s 487us/step - loss: 0.5142 - accuracy: 0.7285\n",
      "Epoch 51/150\n",
      "512/512 [==============================] - 0s 491us/step - loss: 0.5170 - accuracy: 0.7344\n",
      "Epoch 52/150\n",
      "512/512 [==============================] - 0s 481us/step - loss: 0.5237 - accuracy: 0.7324\n",
      "Epoch 53/150\n",
      "512/512 [==============================] - 0s 491us/step - loss: 0.5187 - accuracy: 0.7344\n",
      "Epoch 54/150\n",
      "512/512 [==============================] - 0s 487us/step - loss: 0.5127 - accuracy: 0.7441\n",
      "Epoch 55/150\n",
      "512/512 [==============================] - 0s 501us/step - loss: 0.5139 - accuracy: 0.7461\n",
      "Epoch 56/150\n",
      "512/512 [==============================] - 0s 466us/step - loss: 0.5084 - accuracy: 0.7402\n",
      "Epoch 57/150\n",
      "512/512 [==============================] - 0s 477us/step - loss: 0.5063 - accuracy: 0.7520\n",
      "Epoch 58/150\n",
      "512/512 [==============================] - 0s 491us/step - loss: 0.5092 - accuracy: 0.7461\n",
      "Epoch 59/150\n",
      "512/512 [==============================] - 0s 499us/step - loss: 0.5122 - accuracy: 0.7246\n",
      "Epoch 60/150\n",
      "512/512 [==============================] - 0s 485us/step - loss: 0.4936 - accuracy: 0.7695\n",
      "Epoch 61/150\n",
      "512/512 [==============================] - 0s 487us/step - loss: 0.4929 - accuracy: 0.7539\n",
      "Epoch 62/150\n",
      "512/512 [==============================] - 0s 491us/step - loss: 0.4866 - accuracy: 0.7441\n",
      "Epoch 63/150\n",
      "512/512 [==============================] - 0s 489us/step - loss: 0.4999 - accuracy: 0.7715\n",
      "Epoch 64/150\n",
      "512/512 [==============================] - 0s 462us/step - loss: 0.4965 - accuracy: 0.7559\n",
      "Epoch 65/150\n",
      "512/512 [==============================] - 0s 481us/step - loss: 0.4897 - accuracy: 0.7637\n",
      "Epoch 66/150\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13/150\n",
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      "512/512 [==============================] - 0s 528us/step - loss: 0.4009 - accuracy: 0.8086\n",
      "Epoch 116/150\n",
      "512/512 [==============================] - 0s 528us/step - loss: 0.3953 - accuracy: 0.8164\n",
      "Epoch 117/150\n",
      "512/512 [==============================] - 0s 497us/step - loss: 0.4161 - accuracy: 0.8145\n",
      "Epoch 118/150\n",
      "512/512 [==============================] - 0s 538us/step - loss: 0.3822 - accuracy: 0.8262\n",
      "Epoch 119/150\n",
      "512/512 [==============================] - 0s 524us/step - loss: 0.3987 - accuracy: 0.8145\n",
      "Epoch 120/150\n",
      "512/512 [==============================] - 0s 536us/step - loss: 0.3967 - accuracy: 0.8223\n",
      "Epoch 121/150\n",
      "512/512 [==============================] - 0s 532us/step - loss: 0.3856 - accuracy: 0.8145\n",
      "Epoch 122/150\n",
      "512/512 [==============================] - 0s 538us/step - loss: 0.3968 - accuracy: 0.8125\n",
      "Epoch 123/150\n",
      "512/512 [==============================] - 0s 540us/step - loss: 0.3878 - accuracy: 0.8203\n",
      "Epoch 124/150\n",
      "512/512 [==============================] - 0s 505us/step - loss: 0.3786 - accuracy: 0.8281\n",
      "Epoch 125/150\n",
      "512/512 [==============================] - 0s 510us/step - loss: 0.3721 - accuracy: 0.8262\n",
      "Epoch 126/150\n",
      "512/512 [==============================] - 0s 532us/step - loss: 0.3636 - accuracy: 0.8418\n",
      "Epoch 127/150\n",
      "512/512 [==============================] - 0s 534us/step - loss: 0.3687 - accuracy: 0.8457\n",
      "Epoch 128/150\n",
      "512/512 [==============================] - 0s 530us/step - loss: 0.3754 - accuracy: 0.8125\n",
      "Epoch 129/150\n",
      "512/512 [==============================] - 0s 538us/step - loss: 0.3670 - accuracy: 0.8340\n",
      "Epoch 130/150\n",
      "512/512 [==============================] - 0s 538us/step - loss: 0.4580 - accuracy: 0.8066\n",
      "Epoch 131/150\n",
      "512/512 [==============================] - 0s 526us/step - loss: 0.4416 - accuracy: 0.7930\n",
      "Epoch 132/150\n",
      "512/512 [==============================] - 0s 508us/step - loss: 0.4274 - accuracy: 0.8086\n",
      "Epoch 133/150\n",
      "512/512 [==============================] - 0s 542us/step - loss: 0.4232 - accuracy: 0.7852\n",
      "Epoch 134/150\n",
      "512/512 [==============================] - 0s 542us/step - loss: 0.4189 - accuracy: 0.8008\n",
      "Epoch 135/150\n",
      "512/512 [==============================] - 0s 526us/step - loss: 0.3774 - accuracy: 0.8281\n",
      "Epoch 136/150\n",
      "512/512 [==============================] - 0s 534us/step - loss: 0.3677 - accuracy: 0.8262\n",
      "Epoch 137/150\n",
      "512/512 [==============================] - 0s 534us/step - loss: 0.3796 - accuracy: 0.8242\n",
      "Epoch 138/150\n",
      "512/512 [==============================] - 0s 524us/step - loss: 0.3825 - accuracy: 0.8301\n",
      "Epoch 139/150\n",
      "512/512 [==============================] - 0s 499us/step - loss: 0.3642 - accuracy: 0.8438\n",
      "Epoch 140/150\n",
      "512/512 [==============================] - 0s 538us/step - loss: 0.3635 - accuracy: 0.8438\n",
      "Epoch 141/150\n",
      "512/512 [==============================] - 0s 538us/step - loss: 0.3613 - accuracy: 0.8262\n",
      "Epoch 142/150\n",
      "512/512 [==============================] - 0s 534us/step - loss: 0.3681 - accuracy: 0.8281\n",
      "Epoch 143/150\n",
      "512/512 [==============================] - 0s 534us/step - loss: 0.3512 - accuracy: 0.8359\n",
      "Epoch 144/150\n",
      "512/512 [==============================] - 0s 542us/step - loss: 0.3441 - accuracy: 0.8496\n",
      "Epoch 145/150\n",
      "512/512 [==============================] - 0s 530us/step - loss: 0.3350 - accuracy: 0.8379\n",
      "Epoch 146/150\n",
      "512/512 [==============================] - 0s 499us/step - loss: 0.3868 - accuracy: 0.8242\n",
      "Epoch 147/150\n",
      "512/512 [==============================] - 0s 528us/step - loss: 0.3656 - accuracy: 0.8438\n",
      "Epoch 148/150\n",
      "512/512 [==============================] - 0s 532us/step - loss: 0.3643 - accuracy: 0.8242\n",
      "Epoch 149/150\n",
      "512/512 [==============================] - 0s 540us/step - loss: 0.3468 - accuracy: 0.8438\n",
      "Epoch 150/150\n",
      "512/512 [==============================] - 0s 542us/step - loss: 0.3534 - accuracy: 0.8438\n",
      "256/256 [==============================] - 1s 3ms/step\n",
      "0.7096354166666666\n"
     ]
    }
   ],
   "source": [
    "# 改变神经网络结构，（加深，加宽），看看什么结构对模型性能有较大影响。\n",
    "# 加深\n",
    "def create_model_1():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(12, input_dim=8, activation='relu'))\n",
    "    model.add(Dense(12, input_dim=12,activation='relu'))\n",
    "    model.add(Dense(12, input_dim=12,activation='relu'))\n",
    "    model.add(Dense(12,input_dim=12, activation='relu'))\n",
    "    model.add(Dense(12,input_dim=12, activation='relu'))\n",
    "    model.add(Dense(8, input_dim=12,activation='relu'))\n",
    "    model.add(Dense(8, input_dim=8,activation='relu'))\n",
    "    model.add(Dense(8, input_dim=8,activation='relu'))\n",
    "    model.add(Dense(8, input_dim=8,activation='relu'))\n",
    "    model.add(Dense(8, input_dim=8,activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'])\n",
    "    return model\n",
    "model = KerasClassifier(build_fn=create_model_1,\n",
    "                        epochs=150,\n",
    "                        batch_size=10)\n",
    "kfold = StratifiedKFold(n_splits=3, shuffle=True, random_state=seed)\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
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      "Epoch 2/150\n",
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      "Epoch 3/150\n",
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      "Epoch 4/150\n",
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      "Epoch 5/150\n",
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      "Epoch 6/150\n",
      "512/512 [==============================] - 0s 255us/step - loss: 0.6779 - accuracy: 0.65820s - loss: 0.6901 - accuracy: 0.\n",
      "Epoch 7/150\n",
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      "Epoch 8/150\n",
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      "Epoch 9/150\n",
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      "Epoch 10/150\n",
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      "Epoch 16/150\n",
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      "Epoch 17/150\n",
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      "Epoch 112/150\n",
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     ]
    },
    {
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      "Epoch 150/150\n",
      "512/512 [==============================] - 0s 193us/step - loss: 0.4463 - accuracy: 0.7715\n",
      "256/256 [==============================] - 0s 1ms/step\n",
      "Epoch 1/150\n",
      "512/512 [==============================] - 1s 1ms/step - loss: 2.2209 - accuracy: 0.5547\n",
      "Epoch 2/150\n",
      "512/512 [==============================] - 0s 232us/step - loss: 1.2274 - accuracy: 0.5879\n",
      "Epoch 3/150\n",
      "512/512 [==============================] - 0s 238us/step - loss: 1.0018 - accuracy: 0.6074\n",
      "Epoch 4/150\n",
      "512/512 [==============================] - 0s 244us/step - loss: 0.9897 - accuracy: 0.6055\n",
      "Epoch 5/150\n",
      "512/512 [==============================] - 0s 232us/step - loss: 0.8458 - accuracy: 0.6152\n",
      "Epoch 6/150\n",
      "512/512 [==============================] - 0s 220us/step - loss: 0.8012 - accuracy: 0.6172\n",
      "Epoch 7/150\n",
      "512/512 [==============================] - 0s 247us/step - loss: 0.7471 - accuracy: 0.6543\n",
      "Epoch 8/150\n",
      "512/512 [==============================] - 0s 273us/step - loss: 0.7441 - accuracy: 0.6484\n",
      "Epoch 9/150\n",
      "512/512 [==============================] - 0s 267us/step - loss: 0.6984 - accuracy: 0.6719\n",
      "Epoch 10/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.6844 - accuracy: 0.6602\n",
      "Epoch 11/150\n",
      "512/512 [==============================] - 0s 253us/step - loss: 0.7442 - accuracy: 0.6348\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12/150\n",
      "512/512 [==============================] - 0s 249us/step - loss: 0.9244 - accuracy: 0.6484\n",
      "Epoch 13/150\n",
      "512/512 [==============================] - 0s 261us/step - loss: 0.7824 - accuracy: 0.6582\n",
      "Epoch 14/150\n",
      "512/512 [==============================] - 0s 247us/step - loss: 0.7572 - accuracy: 0.6406\n",
      "Epoch 15/150\n",
      "512/512 [==============================] - 0s 224us/step - loss: 0.7100 - accuracy: 0.6484\n",
      "Epoch 16/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.6705 - accuracy: 0.6602\n",
      "Epoch 17/150\n",
      "512/512 [==============================] - 0s 205us/step - loss: 0.6149 - accuracy: 0.6973\n",
      "Epoch 18/150\n",
      "512/512 [==============================] - 0s 232us/step - loss: 0.6338 - accuracy: 0.7051\n",
      "Epoch 19/150\n",
      "512/512 [==============================] - 0s 224us/step - loss: 0.6182 - accuracy: 0.6895\n",
      "Epoch 20/150\n",
      "512/512 [==============================] - 0s 224us/step - loss: 0.6223 - accuracy: 0.7148\n",
      "Epoch 21/150\n",
      "512/512 [==============================] - 0s 216us/step - loss: 0.6620 - accuracy: 0.6641\n",
      "Epoch 22/150\n",
      "512/512 [==============================] - 0s 206us/step - loss: 0.7229 - accuracy: 0.6387\n",
      "Epoch 23/150\n",
      "512/512 [==============================] - 0s 216us/step - loss: 0.6368 - accuracy: 0.6738\n",
      "Epoch 24/150\n",
      "512/512 [==============================] - 0s 243us/step - loss: 0.6353 - accuracy: 0.6660\n",
      "Epoch 25/150\n",
      "512/512 [==============================] - 0s 259us/step - loss: 0.6382 - accuracy: 0.6699\n",
      "Epoch 26/150\n",
      "512/512 [==============================] - 0s 214us/step - loss: 0.6608 - accuracy: 0.6836\n",
      "Epoch 27/150\n",
      "512/512 [==============================] - 0s 205us/step - loss: 0.6366 - accuracy: 0.7129\n",
      "Epoch 28/150\n",
      "512/512 [==============================] - 0s 203us/step - loss: 0.6588 - accuracy: 0.6953\n",
      "Epoch 29/150\n",
      "512/512 [==============================] - 0s 220us/step - loss: 0.6006 - accuracy: 0.7031\n",
      "Epoch 30/150\n",
      "512/512 [==============================] - 0s 218us/step - loss: 0.6216 - accuracy: 0.7129\n",
      "Epoch 31/150\n",
      "512/512 [==============================] - 0s 226us/step - loss: 0.6290 - accuracy: 0.6953\n",
      "Epoch 32/150\n",
      "512/512 [==============================] - 0s 255us/step - loss: 0.6161 - accuracy: 0.6914\n",
      "Epoch 33/150\n",
      "512/512 [==============================] - 0s 296us/step - loss: 0.5944 - accuracy: 0.7188\n",
      "Epoch 34/150\n",
      "512/512 [==============================] - 0s 277us/step - loss: 0.6661 - accuracy: 0.6680\n",
      "Epoch 35/150\n",
      "512/512 [==============================] - 0s 286us/step - loss: 0.6456 - accuracy: 0.7090\n",
      "Epoch 36/150\n",
      "512/512 [==============================] - 0s 277us/step - loss: 0.5785 - accuracy: 0.7109\n",
      "Epoch 37/150\n",
      "512/512 [==============================] - 0s 279us/step - loss: 0.5737 - accuracy: 0.7266\n",
      "Epoch 38/150\n",
      "512/512 [==============================] - 0s 230us/step - loss: 0.6419 - accuracy: 0.6719\n",
      "Epoch 39/150\n",
      "512/512 [==============================] - 0s 222us/step - loss: 0.6514 - accuracy: 0.6934\n",
      "Epoch 40/150\n",
      "512/512 [==============================] - 0s 222us/step - loss: 0.5852 - accuracy: 0.7227\n",
      "Epoch 41/150\n",
      "512/512 [==============================] - 0s 214us/step - loss: 0.6087 - accuracy: 0.7031\n",
      "Epoch 42/150\n",
      "512/512 [==============================] - 0s 203us/step - loss: 0.6160 - accuracy: 0.7148\n",
      "Epoch 43/150\n",
      "512/512 [==============================] - 0s 232us/step - loss: 0.6001 - accuracy: 0.7207\n",
      "Epoch 44/150\n",
      "512/512 [==============================] - 0s 238us/step - loss: 0.5830 - accuracy: 0.7070\n",
      "Epoch 45/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.6069 - accuracy: 0.7090\n",
      "Epoch 46/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.5961 - accuracy: 0.7148\n",
      "Epoch 47/150\n",
      "512/512 [==============================] - 0s 232us/step - loss: 0.5927 - accuracy: 0.6875\n",
      "Epoch 48/150\n",
      "512/512 [==============================] - 0s 228us/step - loss: 0.5735 - accuracy: 0.7441\n",
      "Epoch 49/150\n",
      "512/512 [==============================] - 0s 228us/step - loss: 0.5973 - accuracy: 0.7031\n",
      "Epoch 50/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.7097 - accuracy: 0.6699\n",
      "Epoch 51/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.5486 - accuracy: 0.7480\n",
      "Epoch 52/150\n",
      "512/512 [==============================] - 0s 296us/step - loss: 0.5609 - accuracy: 0.7383\n",
      "Epoch 53/150\n",
      "512/512 [==============================] - 0s 275us/step - loss: 0.5486 - accuracy: 0.7520\n",
      "Epoch 54/150\n",
      "512/512 [==============================] - 0s 240us/step - loss: 0.5674 - accuracy: 0.7109\n",
      "Epoch 55/150\n",
      "512/512 [==============================] - 0s 220us/step - loss: 0.6174 - accuracy: 0.6836\n",
      "Epoch 56/150\n",
      "512/512 [==============================] - 0s 220us/step - loss: 0.5295 - accuracy: 0.7559\n",
      "Epoch 57/150\n",
      "512/512 [==============================] - 0s 279us/step - loss: 0.5357 - accuracy: 0.7480\n",
      "Epoch 58/150\n",
      "512/512 [==============================] - 0s 259us/step - loss: 0.5583 - accuracy: 0.7402\n",
      "Epoch 59/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.5369 - accuracy: 0.7539\n",
      "Epoch 60/150\n",
      "512/512 [==============================] - 0s 236us/step - loss: 0.5530 - accuracy: 0.7305\n",
      "Epoch 61/150\n",
      "512/512 [==============================] - 0s 236us/step - loss: 0.6392 - accuracy: 0.7012\n",
      "Epoch 62/150\n",
      "512/512 [==============================] - 0s 232us/step - loss: 0.5605 - accuracy: 0.72660s - loss: 0.5553 - accuracy: 0.73\n",
      "Epoch 63/150\n",
      "512/512 [==============================] - 0s 230us/step - loss: 0.5570 - accuracy: 0.7266\n",
      "Epoch 64/150\n",
      "512/512 [==============================] - 0s 220us/step - loss: 0.5831 - accuracy: 0.7168\n",
      "Epoch 65/150\n",
      "512/512 [==============================] - 0s 261us/step - loss: 0.5588 - accuracy: 0.7344\n",
      "Epoch 66/150\n",
      "512/512 [==============================] - 0s 269us/step - loss: 0.5350 - accuracy: 0.7461\n",
      "Epoch 67/150\n",
      "512/512 [==============================] - 0s 240us/step - loss: 0.5719 - accuracy: 0.7207\n",
      "Epoch 68/150\n",
      "512/512 [==============================] - 0s 214us/step - loss: 0.5696 - accuracy: 0.7207\n",
      "Epoch 69/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.5659 - accuracy: 0.7305\n",
      "Epoch 70/150\n",
      "512/512 [==============================] - 0s 201us/step - loss: 0.5302 - accuracy: 0.7207\n",
      "Epoch 71/150\n",
      "512/512 [==============================] - 0s 222us/step - loss: 0.5406 - accuracy: 0.7324\n",
      "Epoch 72/150\n",
      "512/512 [==============================] - 0s 282us/step - loss: 0.5284 - accuracy: 0.7461\n",
      "Epoch 73/150\n",
      "512/512 [==============================] - 0s 280us/step - loss: 0.5867 - accuracy: 0.7305\n",
      "Epoch 74/150\n",
      "512/512 [==============================] - 0s 282us/step - loss: 0.5892 - accuracy: 0.7148\n",
      "Epoch 75/150\n",
      "512/512 [==============================] - 0s 249us/step - loss: 0.5489 - accuracy: 0.7344\n",
      "Epoch 76/150\n",
      "512/512 [==============================] - 0s 228us/step - loss: 0.6166 - accuracy: 0.7070\n",
      "Epoch 77/150\n",
      "512/512 [==============================] - 0s 230us/step - loss: 0.6373 - accuracy: 0.7246\n",
      "Epoch 78/150\n",
      "512/512 [==============================] - 0s 236us/step - loss: 0.5686 - accuracy: 0.7148\n",
      "Epoch 79/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.5615 - accuracy: 0.7168\n",
      "Epoch 80/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.6057 - accuracy: 0.7090\n",
      "Epoch 81/150\n",
      "512/512 [==============================] - 0s 236us/step - loss: 0.5916 - accuracy: 0.7148\n",
      "Epoch 82/150\n",
      "512/512 [==============================] - 0s 228us/step - loss: 0.5510 - accuracy: 0.7402\n",
      "Epoch 83/150\n",
      "512/512 [==============================] - 0s 230us/step - loss: 0.5006 - accuracy: 0.7656\n",
      "Epoch 84/150\n",
      "512/512 [==============================] - 0s 232us/step - loss: 0.5113 - accuracy: 0.7441\n",
      "Epoch 85/150\n",
      "512/512 [==============================] - 0s 230us/step - loss: 0.5818 - accuracy: 0.7324\n",
      "Epoch 86/150\n",
      "512/512 [==============================] - 0s 230us/step - loss: 0.5094 - accuracy: 0.7676\n",
      "Epoch 87/150\n",
      "512/512 [==============================] - 0s 243us/step - loss: 0.5271 - accuracy: 0.7461\n",
      "Epoch 88/150\n",
      "512/512 [==============================] - 0s 243us/step - loss: 0.4906 - accuracy: 0.7793\n",
      "Epoch 89/150\n",
      "512/512 [==============================] - 0s 216us/step - loss: 0.7345 - accuracy: 0.6914\n",
      "Epoch 90/150\n",
      "512/512 [==============================] - 0s 206us/step - loss: 0.6193 - accuracy: 0.7129\n",
      "Epoch 91/150\n",
      "512/512 [==============================] - 0s 218us/step - loss: 0.5568 - accuracy: 0.7344\n",
      "Epoch 92/150\n",
      "512/512 [==============================] - 0s 218us/step - loss: 0.5118 - accuracy: 0.7578\n",
      "Epoch 93/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.5046 - accuracy: 0.7461\n",
      "Epoch 94/150\n",
      "512/512 [==============================] - 0s 257us/step - loss: 0.5111 - accuracy: 0.7559\n",
      "Epoch 95/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.4977 - accuracy: 0.7715\n",
      "Epoch 96/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.5046 - accuracy: 0.7480\n",
      "Epoch 97/150\n",
      "512/512 [==============================] - 0s 238us/step - loss: 0.5073 - accuracy: 0.7598\n",
      "Epoch 98/150\n",
      "512/512 [==============================] - 0s 224us/step - loss: 0.5278 - accuracy: 0.7539\n",
      "Epoch 99/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.5183 - accuracy: 0.7598\n",
      "Epoch 100/150\n",
      "512/512 [==============================] - 0s 232us/step - loss: 0.4954 - accuracy: 0.7637\n",
      "Epoch 101/150\n",
      "512/512 [==============================] - 0s 232us/step - loss: 0.4983 - accuracy: 0.7754\n",
      "Epoch 102/150\n",
      "512/512 [==============================] - 0s 240us/step - loss: 0.4987 - accuracy: 0.7734\n",
      "Epoch 103/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.5313 - accuracy: 0.7422\n",
      "Epoch 104/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.5272 - accuracy: 0.7539\n",
      "Epoch 105/150\n",
      "512/512 [==============================] - 0s 224us/step - loss: 0.5053 - accuracy: 0.7539\n",
      "Epoch 106/150\n",
      "512/512 [==============================] - 0s 216us/step - loss: 0.5448 - accuracy: 0.7617\n",
      "Epoch 107/150\n",
      "512/512 [==============================] - 0s 222us/step - loss: 0.5501 - accuracy: 0.7383\n",
      "Epoch 108/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.5371 - accuracy: 0.7480\n",
      "Epoch 109/150\n",
      "512/512 [==============================] - 0s 236us/step - loss: 0.5448 - accuracy: 0.7324\n",
      "Epoch 110/150\n",
      "512/512 [==============================] - 0s 245us/step - loss: 0.5130 - accuracy: 0.7617\n",
      "Epoch 111/150\n",
      "512/512 [==============================] - 0s 236us/step - loss: 0.5276 - accuracy: 0.7461\n",
      "Epoch 112/150\n",
      "512/512 [==============================] - 0s 236us/step - loss: 0.5311 - accuracy: 0.7324\n",
      "Epoch 113/150\n",
      "512/512 [==============================] - 0s 236us/step - loss: 0.5107 - accuracy: 0.7500\n",
      "Epoch 114/150\n",
      "512/512 [==============================] - 0s 240us/step - loss: 0.4932 - accuracy: 0.7676\n",
      "Epoch 115/150\n",
      "512/512 [==============================] - 0s 238us/step - loss: 0.5323 - accuracy: 0.7617\n",
      "Epoch 116/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.5222 - accuracy: 0.7363\n",
      "Epoch 117/150\n",
      "512/512 [==============================] - 0s 242us/step - loss: 0.5232 - accuracy: 0.7578\n",
      "Epoch 118/150\n",
      "512/512 [==============================] - 0s 271us/step - loss: 0.4962 - accuracy: 0.7656\n",
      "Epoch 119/150\n",
      "512/512 [==============================] - 0s 238us/step - loss: 0.4980 - accuracy: 0.7910\n",
      "Epoch 120/150\n",
      "512/512 [==============================] - 0s 228us/step - loss: 0.5095 - accuracy: 0.7695\n",
      "Epoch 121/150\n",
      "512/512 [==============================] - 0s 224us/step - loss: 0.4802 - accuracy: 0.7676\n",
      "Epoch 122/150\n",
      "512/512 [==============================] - 0s 216us/step - loss: 0.5424 - accuracy: 0.7500\n",
      "Epoch 123/150\n",
      "512/512 [==============================] - 0s 226us/step - loss: 0.5226 - accuracy: 0.7676\n",
      "Epoch 124/150\n",
      "512/512 [==============================] - 0s 236us/step - loss: 0.4870 - accuracy: 0.7539\n",
      "Epoch 125/150\n",
      "512/512 [==============================] - 0s 238us/step - loss: 0.4942 - accuracy: 0.7422\n",
      "Epoch 126/150\n",
      "512/512 [==============================] - 0s 245us/step - loss: 0.4951 - accuracy: 0.7734\n",
      "Epoch 127/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.5084 - accuracy: 0.7793\n",
      "Epoch 128/150\n",
      "512/512 [==============================] - 0s 240us/step - loss: 0.4967 - accuracy: 0.7578\n",
      "Epoch 129/150\n",
      "512/512 [==============================] - 0s 242us/step - loss: 0.4803 - accuracy: 0.7676\n",
      "Epoch 130/150\n",
      "512/512 [==============================] - 0s 238us/step - loss: 0.5478 - accuracy: 0.7539\n",
      "Epoch 131/150\n",
      "512/512 [==============================] - 0s 240us/step - loss: 0.5093 - accuracy: 0.7676\n",
      "Epoch 132/150\n",
      "512/512 [==============================] - 0s 230us/step - loss: 0.4982 - accuracy: 0.7598\n",
      "Epoch 133/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.5993 - accuracy: 0.7012\n",
      "Epoch 134/150\n",
      "512/512 [==============================] - 0s 243us/step - loss: 0.4924 - accuracy: 0.7598\n",
      "Epoch 135/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.4846 - accuracy: 0.7598\n",
      "Epoch 136/150\n",
      "512/512 [==============================] - 0s 236us/step - loss: 0.5020 - accuracy: 0.7559\n",
      "Epoch 137/150\n",
      "512/512 [==============================] - 0s 218us/step - loss: 0.4989 - accuracy: 0.7695\n",
      "Epoch 138/150\n",
      "512/512 [==============================] - 0s 216us/step - loss: 0.4916 - accuracy: 0.7617\n",
      "Epoch 139/150\n",
      "512/512 [==============================] - 0s 222us/step - loss: 0.4979 - accuracy: 0.7773\n",
      "Epoch 140/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.4715 - accuracy: 0.7715\n",
      "Epoch 141/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.5549 - accuracy: 0.7441\n",
      "Epoch 142/150\n",
      "512/512 [==============================] - 0s 249us/step - loss: 0.5208 - accuracy: 0.7578\n",
      "Epoch 143/150\n",
      "512/512 [==============================] - 0s 240us/step - loss: 0.5056 - accuracy: 0.7520\n",
      "Epoch 144/150\n",
      "512/512 [==============================] - 0s 238us/step - loss: 0.4689 - accuracy: 0.7910\n",
      "Epoch 145/150\n",
      "512/512 [==============================] - 0s 240us/step - loss: 0.5343 - accuracy: 0.7402\n",
      "Epoch 146/150\n",
      "512/512 [==============================] - 0s 236us/step - loss: 0.4677 - accuracy: 0.7812\n",
      "Epoch 147/150\n",
      "512/512 [==============================] - 0s 234us/step - loss: 0.5886 - accuracy: 0.7129\n",
      "Epoch 148/150\n",
      "512/512 [==============================] - 0s 245us/step - loss: 0.4881 - accuracy: 0.8027\n",
      "Epoch 149/150\n",
      "512/512 [==============================] - 0s 238us/step - loss: 0.5804 - accuracy: 0.7227\n",
      "Epoch 150/150\n",
      "512/512 [==============================] - 0s 236us/step - loss: 0.5368 - accuracy: 0.7461\n",
      "256/256 [==============================] - 0s 1ms/step\n",
      "0.7057291666666666\n"
     ]
    }
   ],
   "source": [
    "# 改变神经网络结构，（加深，加宽），看看什么结构对模型性能有较大影响。\n",
    "# 加宽\n",
    "def create_model_2():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(24, input_dim=8, activation='relu'))\n",
    "    model.add(Dense(16, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'])\n",
    "    return model\n",
    "model = KerasClassifier(build_fn=create_model_2,\n",
    "                        epochs=150,\n",
    "                        batch_size=10)\n",
    "kfold = StratifiedKFold(n_splits=3, shuffle=True, random_state=seed)\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "512/512 [==============================] - 1s 2ms/step - loss: 10.2267 - accuracy: 0.4414\n",
      "Epoch 2/150\n",
      "512/512 [==============================] - 0s 401us/step - loss: 1.4275 - accuracy: 0.5723\n",
      "Epoch 3/150\n",
      "512/512 [==============================] - 0s 409us/step - loss: 1.1507 - accuracy: 0.5840\n",
      "Epoch 4/150\n",
      "512/512 [==============================] - 0s 397us/step - loss: 1.0179 - accuracy: 0.5723\n",
      "Epoch 5/150\n",
      "512/512 [==============================] - 0s 364us/step - loss: 0.8948 - accuracy: 0.6094\n",
      "Epoch 6/150\n",
      "512/512 [==============================] - 0s 403us/step - loss: 0.8406 - accuracy: 0.5996\n",
      "Epoch 7/150\n",
      "512/512 [==============================] - 0s 411us/step - loss: 0.8130 - accuracy: 0.6152\n",
      "Epoch 8/150\n",
      "512/512 [==============================] - 0s 405us/step - loss: 0.7915 - accuracy: 0.6055\n",
      "Epoch 9/150\n",
      "512/512 [==============================] - 0s 413us/step - loss: 0.7304 - accuracy: 0.6309\n",
      "Epoch 10/150\n",
      "512/512 [==============================] - 0s 407us/step - loss: 0.7604 - accuracy: 0.6328\n",
      "Epoch 11/150\n",
      "512/512 [==============================] - 0s 407us/step - loss: 0.6743 - accuracy: 0.6445\n",
      "Epoch 12/150\n",
      "512/512 [==============================] - 0s 411us/step - loss: 0.7093 - accuracy: 0.6504\n",
      "Epoch 13/150\n",
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      "Epoch 14/150\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epoch 64/150\n",
      "512/512 [==============================] - 0s 364us/step - loss: 0.5398 - accuracy: 0.7285\n",
      "Epoch 65/150\n",
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      "Epoch 67/150\n",
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      "Epoch 68/150\n",
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      "Epoch 69/150\n",
      "512/512 [==============================] - 0s 347us/step - loss: 0.6204 - accuracy: 0.6914\n",
      "Epoch 70/150\n",
      "512/512 [==============================] - 0s 355us/step - loss: 0.5407 - accuracy: 0.7168\n",
      "Epoch 71/150\n",
      "512/512 [==============================] - 0s 360us/step - loss: 0.5505 - accuracy: 0.7266\n",
      "Epoch 72/150\n",
      "512/512 [==============================] - 0s 349us/step - loss: 0.6014 - accuracy: 0.6934\n",
      "Epoch 73/150\n",
      "512/512 [==============================] - 0s 355us/step - loss: 0.5979 - accuracy: 0.6992\n",
      "Epoch 74/150\n",
      "512/512 [==============================] - 0s 395us/step - loss: 0.5873 - accuracy: 0.7324\n",
      "Epoch 75/150\n",
      "512/512 [==============================] - 0s 415us/step - loss: 0.5577 - accuracy: 0.7383\n",
      "Epoch 76/150\n",
      "512/512 [==============================] - 0s 364us/step - loss: 0.5619 - accuracy: 0.6953\n",
      "Epoch 77/150\n",
      "512/512 [==============================] - 0s 355us/step - loss: 0.5697 - accuracy: 0.7383\n",
      "Epoch 78/150\n",
      "512/512 [==============================] - 0s 378us/step - loss: 0.7010 - accuracy: 0.6777\n",
      "Epoch 79/150\n",
      "512/512 [==============================] - 0s 356us/step - loss: 0.5189 - accuracy: 0.7402\n",
      "Epoch 80/150\n",
      "512/512 [==============================] - 0s 356us/step - loss: 0.5604 - accuracy: 0.7305\n",
      "Epoch 81/150\n",
      "512/512 [==============================] - 0s 356us/step - loss: 0.5493 - accuracy: 0.7305\n",
      "Epoch 82/150\n",
      "512/512 [==============================] - 0s 360us/step - loss: 0.5551 - accuracy: 0.7480\n",
      "Epoch 83/150\n",
      "512/512 [==============================] - 0s 353us/step - loss: 0.5105 - accuracy: 0.7578\n",
      "Epoch 84/150\n",
      "512/512 [==============================] - 0s 353us/step - loss: 0.5696 - accuracy: 0.7324\n",
      "Epoch 85/150\n",
      "512/512 [==============================] - 0s 354us/step - loss: 0.5515 - accuracy: 0.7246\n",
      "Epoch 86/150\n",
      "512/512 [==============================] - 0s 351us/step - loss: 0.5150 - accuracy: 0.74800s - loss: 0.5053 - accuracy: 0.75\n",
      "Epoch 87/150\n",
      "512/512 [==============================] - 0s 444us/step - loss: 0.5187 - accuracy: 0.7344\n",
      "Epoch 88/150\n",
      "512/512 [==============================] - 0s 399us/step - loss: 0.5357 - accuracy: 0.7422\n",
      "Epoch 89/150\n",
      "512/512 [==============================] - 0s 429us/step - loss: 0.5101 - accuracy: 0.7559\n",
      "Epoch 90/150\n",
      "512/512 [==============================] - 0s 405us/step - loss: 0.5397 - accuracy: 0.7266\n",
      "Epoch 91/150\n",
      "512/512 [==============================] - 0s 411us/step - loss: 0.5568 - accuracy: 0.7285\n",
      "Epoch 92/150\n",
      "512/512 [==============================] - 0s 374us/step - loss: 0.5207 - accuracy: 0.7539\n",
      "Epoch 93/150\n",
      "512/512 [==============================] - 0s 374us/step - loss: 0.5445 - accuracy: 0.7520\n",
      "Epoch 94/150\n",
      "512/512 [==============================] - 0s 378us/step - loss: 0.5438 - accuracy: 0.7441\n",
      "Epoch 95/150\n",
      "512/512 [==============================] - 0s 442us/step - loss: 0.5584 - accuracy: 0.7402\n",
      "Epoch 96/150\n",
      "512/512 [==============================] - 0s 475us/step - loss: 0.5075 - accuracy: 0.7773\n",
      "Epoch 97/150\n",
      "512/512 [==============================] - 0s 372us/step - loss: 0.5176 - accuracy: 0.7441\n",
      "Epoch 98/150\n",
      "512/512 [==============================] - 0s 397us/step - loss: 0.6083 - accuracy: 0.7051\n",
      "Epoch 99/150\n",
      "512/512 [==============================] - 0s 360us/step - loss: 0.5436 - accuracy: 0.7402\n",
      "Epoch 100/150\n",
      "512/512 [==============================] - 0s 360us/step - loss: 0.5166 - accuracy: 0.7539\n",
      "Epoch 101/150\n",
      "512/512 [==============================] - 0s 364us/step - loss: 0.5076 - accuracy: 0.7559\n",
      "Epoch 102/150\n",
      "512/512 [==============================] - 0s 360us/step - loss: 0.5695 - accuracy: 0.7402\n",
      "Epoch 103/150\n",
      "512/512 [==============================] - 0s 358us/step - loss: 0.5161 - accuracy: 0.7383\n",
      "Epoch 104/150\n",
      "512/512 [==============================] - 0s 405us/step - loss: 0.5191 - accuracy: 0.7363\n",
      "Epoch 105/150\n",
      "512/512 [==============================] - 0s 401us/step - loss: 0.5441 - accuracy: 0.7266\n",
      "Epoch 106/150\n",
      "512/512 [==============================] - 0s 452us/step - loss: 0.5056 - accuracy: 0.7539\n",
      "Epoch 107/150\n",
      "512/512 [==============================] - 0s 419us/step - loss: 0.6572 - accuracy: 0.6895\n",
      "Epoch 108/150\n",
      "512/512 [==============================] - 0s 436us/step - loss: 0.5735 - accuracy: 0.7383\n",
      "Epoch 109/150\n",
      "512/512 [==============================] - 0s 432us/step - loss: 0.5534 - accuracy: 0.7285\n",
      "Epoch 110/150\n",
      "512/512 [==============================] - 0s 425us/step - loss: 0.5053 - accuracy: 0.7656\n",
      "Epoch 111/150\n",
      "512/512 [==============================] - 0s 399us/step - loss: 0.5429 - accuracy: 0.7480\n",
      "Epoch 112/150\n",
      "512/512 [==============================] - 0s 374us/step - loss: 0.5088 - accuracy: 0.7559\n",
      "Epoch 113/150\n",
      "512/512 [==============================] - 0s 392us/step - loss: 0.5075 - accuracy: 0.7461\n",
      "Epoch 114/150\n",
      "512/512 [==============================] - 0s 415us/step - loss: 0.5510 - accuracy: 0.7344\n",
      "Epoch 115/150\n",
      "512/512 [==============================] - 0s 411us/step - loss: 0.5493 - accuracy: 0.7480\n",
      "Epoch 116/150\n",
      "512/512 [==============================] - 0s 366us/step - loss: 0.5815 - accuracy: 0.6992\n",
      "Epoch 117/150\n",
      "512/512 [==============================] - 0s 353us/step - loss: 0.5677 - accuracy: 0.7402\n",
      "Epoch 118/150\n",
      "512/512 [==============================] - 0s 432us/step - loss: 0.5062 - accuracy: 0.7695\n",
      "Epoch 119/150\n",
      "512/512 [==============================] - 0s 448us/step - loss: 0.5252 - accuracy: 0.7598\n",
      "Epoch 120/150\n",
      "512/512 [==============================] - 0s 454us/step - loss: 0.5761 - accuracy: 0.7305\n",
      "Epoch 121/150\n",
      "512/512 [==============================] - 0s 411us/step - loss: 0.5128 - accuracy: 0.72460s - loss: 0.5166 - accuracy: \n",
      "Epoch 122/150\n",
      "512/512 [==============================] - 0s 456us/step - loss: 0.6392 - accuracy: 0.7168\n",
      "Epoch 123/150\n",
      "512/512 [==============================] - 0s 413us/step - loss: 0.4956 - accuracy: 0.7656\n",
      "Epoch 124/150\n",
      "512/512 [==============================] - 0s 450us/step - loss: 0.5569 - accuracy: 0.7520\n",
      "Epoch 125/150\n",
      "512/512 [==============================] - 0s 590us/step - loss: 0.5443 - accuracy: 0.7266\n",
      "Epoch 126/150\n",
      "512/512 [==============================] - 0s 501us/step - loss: 0.5014 - accuracy: 0.7539\n",
      "Epoch 127/150\n",
      "512/512 [==============================] - 0s 415us/step - loss: 0.5374 - accuracy: 0.7520\n",
      "Epoch 128/150\n",
      "512/512 [==============================] - 0s 485us/step - loss: 0.4834 - accuracy: 0.7773\n",
      "Epoch 129/150\n",
      "512/512 [==============================] - 0s 487us/step - loss: 0.5055 - accuracy: 0.7480\n",
      "Epoch 130/150\n",
      "512/512 [==============================] - 0s 405us/step - loss: 0.5079 - accuracy: 0.7461\n",
      "Epoch 131/150\n",
      "512/512 [==============================] - 0s 440us/step - loss: 0.5362 - accuracy: 0.7480\n",
      "Epoch 132/150\n",
      "512/512 [==============================] - 0s 397us/step - loss: 0.4927 - accuracy: 0.7734\n",
      "Epoch 133/150\n",
      "512/512 [==============================] - 0s 394us/step - loss: 0.5140 - accuracy: 0.7578\n",
      "Epoch 134/150\n",
      "512/512 [==============================] - 0s 407us/step - loss: 0.5662 - accuracy: 0.7363\n",
      "Epoch 135/150\n",
      "512/512 [==============================] - 0s 397us/step - loss: 0.6099 - accuracy: 0.7012\n",
      "Epoch 136/150\n",
      "512/512 [==============================] - 0s 491us/step - loss: 0.4887 - accuracy: 0.7598\n",
      "Epoch 137/150\n",
      "512/512 [==============================] - 0s 501us/step - loss: 0.5654 - accuracy: 0.7188\n",
      "Epoch 138/150\n",
      "512/512 [==============================] - 0s 444us/step - loss: 0.5022 - accuracy: 0.7773\n",
      "Epoch 139/150\n",
      "512/512 [==============================] - 0s 395us/step - loss: 0.5063 - accuracy: 0.7461\n",
      "Epoch 140/150\n",
      "512/512 [==============================] - 0s 419us/step - loss: 0.4852 - accuracy: 0.7812\n",
      "Epoch 141/150\n",
      "512/512 [==============================] - 0s 399us/step - loss: 0.5683 - accuracy: 0.7266\n",
      "Epoch 142/150\n",
      "512/512 [==============================] - 0s 393us/step - loss: 0.5181 - accuracy: 0.7715\n",
      "Epoch 143/150\n",
      "512/512 [==============================] - 0s 446us/step - loss: 0.4860 - accuracy: 0.7715\n",
      "Epoch 144/150\n",
      "512/512 [==============================] - 0s 407us/step - loss: 0.4916 - accuracy: 0.7734\n",
      "Epoch 145/150\n",
      "512/512 [==============================] - 0s 413us/step - loss: 0.5876 - accuracy: 0.7285\n",
      "Epoch 146/150\n",
      "512/512 [==============================] - 0s 380us/step - loss: 0.4830 - accuracy: 0.7695\n",
      "Epoch 147/150\n",
      "512/512 [==============================] - 0s 403us/step - loss: 0.5655 - accuracy: 0.7305\n",
      "Epoch 148/150\n",
      "512/512 [==============================] - 0s 485us/step - loss: 0.5199 - accuracy: 0.7715\n",
      "Epoch 149/150\n",
      "512/512 [==============================] - 0s 444us/step - loss: 0.5001 - accuracy: 0.7578\n",
      "Epoch 150/150\n",
      "512/512 [==============================] - 0s 429us/step - loss: 0.4888 - accuracy: 0.7715\n",
      "256/256 [==============================] - 1s 3ms/step\n",
      "0.7018229166666666\n"
     ]
    }
   ],
   "source": [
    "# 改变神经网络结构，（加深，加宽），看看什么结构对模型性能有较大影响。\n",
    "# 加宽\n",
    "def create_model_2():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(24, input_dim=8, activation='relu'))\n",
    "    model.add(Dense(16, input_dim=24, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "\n",
    "model = KerasClassifier(build_fn=create_model_2, epochs=150, batch_size=10)\n",
    "kfold = StratifiedKFold(n_splits=3, shuffle=True, random_state=seed)\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "512/512 [==============================] - 2s 3ms/step - loss: 0.6805 - accuracy: 0.6367\n",
      "Epoch 2/150\n",
      "512/512 [==============================] - 0s 576us/step - loss: 0.6584 - accuracy: 0.6523\n",
      "Epoch 3/150\n",
      "512/512 [==============================] - 0s 610us/step - loss: 0.6513 - accuracy: 0.6523\n",
      "Epoch 4/150\n",
      "512/512 [==============================] - 0s 584us/step - loss: 0.6455 - accuracy: 0.6523\n",
      "Epoch 5/150\n",
      "512/512 [==============================] - 0s 579us/step - loss: 0.6398 - accuracy: 0.6523\n",
      "Epoch 6/150\n",
      "512/512 [==============================] - 0s 577us/step - loss: 0.6291 - accuracy: 0.6523\n",
      "Epoch 7/150\n",
      "512/512 [==============================] - 0s 526us/step - loss: 0.6307 - accuracy: 0.6523\n",
      "Epoch 8/150\n",
      "512/512 [==============================] - 0s 561us/step - loss: 0.6249 - accuracy: 0.6523\n",
      "Epoch 9/150\n",
      "512/512 [==============================] - 0s 582us/step - loss: 0.6198 - accuracy: 0.6523\n",
      "Epoch 10/150\n",
      "512/512 [==============================] - 0s 596us/step - loss: 0.6168 - accuracy: 0.6523\n",
      "Epoch 11/150\n",
      "512/512 [==============================] - 0s 635us/step - loss: 0.6087 - accuracy: 0.6523\n",
      "Epoch 12/150\n",
      "512/512 [==============================] - 0s 632us/step - loss: 0.6119 - accuracy: 0.6523\n",
      "Epoch 13/150\n",
      "512/512 [==============================] - 0s 588us/step - loss: 0.6028 - accuracy: 0.6523\n",
      "Epoch 14/150\n",
      "512/512 [==============================] - 0s 579us/step - loss: 0.6032 - accuracy: 0.6523\n",
      "Epoch 15/150\n",
      "512/512 [==============================] - 0s 592us/step - loss: 0.5931 - accuracy: 0.6680\n",
      "Epoch 16/150\n",
      "512/512 [==============================] - 0s 588us/step - loss: 0.5914 - accuracy: 0.6973\n",
      "Epoch 17/150\n",
      "512/512 [==============================] - 0s 606us/step - loss: 0.5867 - accuracy: 0.6914\n",
      "Epoch 18/150\n",
      "512/512 [==============================] - 0s 580us/step - loss: 0.6141 - accuracy: 0.6855\n",
      "Epoch 19/150\n",
      "512/512 [==============================] - 0s 625us/step - loss: 0.5875 - accuracy: 0.6914\n",
      "Epoch 20/150\n",
      "512/512 [==============================] - 0s 608us/step - loss: 0.6134 - accuracy: 0.6367\n",
      "Epoch 21/150\n",
      "512/512 [==============================] - 0s 629us/step - loss: 0.5956 - accuracy: 0.6895\n",
      "Epoch 22/150\n",
      "512/512 [==============================] - 0s 645us/step - loss: 0.5806 - accuracy: 0.7246\n",
      "Epoch 23/150\n",
      "512/512 [==============================] - 0s 575us/step - loss: 0.5725 - accuracy: 0.6973\n",
      "Epoch 24/150\n",
      "512/512 [==============================] - 0s 577us/step - loss: 0.5733 - accuracy: 0.7168\n",
      "Epoch 25/150\n",
      "512/512 [==============================] - 0s 573us/step - loss: 0.5508 - accuracy: 0.7422\n",
      "Epoch 26/150\n",
      "512/512 [==============================] - 0s 543us/step - loss: 0.6049 - accuracy: 0.6855\n",
      "Epoch 27/150\n",
      "512/512 [==============================] - 0s 621us/step - loss: 0.5575 - accuracy: 0.7500\n",
      "Epoch 28/150\n",
      "512/512 [==============================] - 0s 641us/step - loss: 0.5435 - accuracy: 0.7402\n",
      "Epoch 29/150\n",
      "512/512 [==============================] - 0s 588us/step - loss: 0.5516 - accuracy: 0.7344\n",
      "Epoch 30/150\n",
      "512/512 [==============================] - 0s 584us/step - loss: 0.5469 - accuracy: 0.7363\n",
      "Epoch 31/150\n",
      "512/512 [==============================] - 0s 596us/step - loss: 0.5665 - accuracy: 0.6973\n",
      "Epoch 32/150\n",
      "512/512 [==============================] - 0s 582us/step - loss: 0.5374 - accuracy: 0.7207\n",
      "Epoch 33/150\n",
      "512/512 [==============================] - 0s 545us/step - loss: 0.5364 - accuracy: 0.7441\n",
      "Epoch 34/150\n",
      "512/512 [==============================] - 0s 641us/step - loss: 0.5232 - accuracy: 0.7539\n",
      "Epoch 35/150\n",
      "512/512 [==============================] - 0s 577us/step - loss: 0.5177 - accuracy: 0.7441\n",
      "Epoch 36/150\n",
      "512/512 [==============================] - 0s 643us/step - loss: 0.5435 - accuracy: 0.7285\n",
      "Epoch 37/150\n",
      "512/512 [==============================] - 0s 580us/step - loss: 0.5249 - accuracy: 0.7539\n",
      "Epoch 38/150\n",
      "512/512 [==============================] - 0s 612us/step - loss: 0.5283 - accuracy: 0.7441\n",
      "Epoch 39/150\n",
      "512/512 [==============================] - 0s 590us/step - loss: 0.4969 - accuracy: 0.7676\n",
      "Epoch 40/150\n",
      "512/512 [==============================] - 0s 596us/step - loss: 0.4840 - accuracy: 0.7695\n",
      "Epoch 41/150\n",
      "512/512 [==============================] - 0s 586us/step - loss: 0.5000 - accuracy: 0.7422\n",
      "Epoch 42/150\n",
      "512/512 [==============================] - 0s 579us/step - loss: 0.5037 - accuracy: 0.7520\n",
      "Epoch 43/150\n",
      "512/512 [==============================] - 0s 627us/step - loss: 0.5108 - accuracy: 0.7441\n",
      "Epoch 44/150\n",
      "512/512 [==============================] - 0s 604us/step - loss: 0.5106 - accuracy: 0.7383\n",
      "Epoch 45/150\n",
      "512/512 [==============================] - 0s 573us/step - loss: 0.5277 - accuracy: 0.7109\n",
      "Epoch 46/150\n",
      "512/512 [==============================] - ETA: 0s - loss: 0.5003 - accuracy: 0.74 - 0s 573us/step - loss: 0.4925 - accuracy: 0.7539\n",
      "Epoch 47/150\n",
      "512/512 [==============================] - 0s 592us/step - loss: 0.4881 - accuracy: 0.7578\n",
      "Epoch 48/150\n",
      "512/512 [==============================] - 0s 596us/step - loss: 0.4726 - accuracy: 0.7773\n",
      "Epoch 49/150\n",
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      "Epoch 50/150\n",
      "512/512 [==============================] - 0s 575us/step - loss: 0.4749 - accuracy: 0.7656\n",
      "Epoch 51/150\n",
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      "Epoch 52/150\n",
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      "Epoch 53/150\n",
      "512/512 [==============================] - 0s 571us/step - loss: 0.4633 - accuracy: 0.7793\n",
      "Epoch 54/150\n",
      "512/512 [==============================] - 0s 588us/step - loss: 0.4885 - accuracy: 0.7715\n",
      "Epoch 55/150\n",
      "512/512 [==============================] - 0s 577us/step - loss: 0.4709 - accuracy: 0.7578\n",
      "Epoch 56/150\n",
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      "Epoch 57/150\n",
      "512/512 [==============================] - 0s 557us/step - loss: 0.4506 - accuracy: 0.7949\n",
      "Epoch 58/150\n",
      "512/512 [==============================] - 0s 594us/step - loss: 0.4551 - accuracy: 0.78120s - loss: 0.4839 - accuracy: \n",
      "Epoch 59/150\n",
      "512/512 [==============================] - 0s 516us/step - loss: 0.4442 - accuracy: 0.7773\n",
      "Epoch 60/150\n",
      "512/512 [==============================] - 0s 567us/step - loss: 0.4689 - accuracy: 0.7734\n",
      "Epoch 61/150\n",
      "512/512 [==============================] - 0s 557us/step - loss: 0.4650 - accuracy: 0.7812\n",
      "Epoch 62/150\n",
      "512/512 [==============================] - ETA: 0s - loss: 0.4641 - accuracy: 0.77 - 0s 571us/step - loss: 0.4651 - accuracy: 0.7715\n",
      "Epoch 63/150\n",
      "512/512 [==============================] - 0s 561us/step - loss: 0.4507 - accuracy: 0.7754\n",
      "Epoch 64/150\n",
      "512/512 [==============================] - 0s 563us/step - loss: 0.4598 - accuracy: 0.7637\n",
      "Epoch 65/150\n",
      "512/512 [==============================] - 0s 575us/step - loss: 0.4412 - accuracy: 0.7715\n",
      "Epoch 66/150\n",
      "512/512 [==============================] - 0s 547us/step - loss: 0.4606 - accuracy: 0.7676\n",
      "Epoch 67/150\n",
      "512/512 [==============================] - 0s 584us/step - loss: 0.4630 - accuracy: 0.7656\n",
      "Epoch 68/150\n",
      "512/512 [==============================] - 0s 584us/step - loss: 0.4491 - accuracy: 0.7656\n",
      "Epoch 69/150\n",
      "512/512 [==============================] - 0s 582us/step - loss: 0.4240 - accuracy: 0.7988\n",
      "Epoch 70/150\n",
      "512/512 [==============================] - 0s 580us/step - loss: 0.4846 - accuracy: 0.7676\n",
      "Epoch 71/150\n",
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      "Epoch 72/150\n",
      "512/512 [==============================] - 0s 586us/step - loss: 0.4862 - accuracy: 0.7656\n",
      "Epoch 73/150\n",
      "512/512 [==============================] - 0s 575us/step - loss: 0.4420 - accuracy: 0.7812\n",
      "Epoch 74/150\n",
      "512/512 [==============================] - 0s 586us/step - loss: 0.4681 - accuracy: 0.7695\n",
      "Epoch 75/150\n",
      "512/512 [==============================] - 0s 580us/step - loss: 0.4449 - accuracy: 0.7793\n",
      "Epoch 76/150\n",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/150\n",
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      "512/512 [==============================] - 0s 604us/step - loss: 0.4487 - accuracy: 0.7910\n",
      "Epoch 125/150\n",
      "512/512 [==============================] - 0s 598us/step - loss: 0.4300 - accuracy: 0.8164\n",
      "Epoch 126/150\n",
      "512/512 [==============================] - 0s 604us/step - loss: 0.4285 - accuracy: 0.8105\n",
      "Epoch 127/150\n",
      "512/512 [==============================] - 0s 614us/step - loss: 0.4666 - accuracy: 0.7812\n",
      "Epoch 128/150\n",
      "512/512 [==============================] - 0s 614us/step - loss: 0.4500 - accuracy: 0.7891\n",
      "Epoch 129/150\n",
      "512/512 [==============================] - 0s 608us/step - loss: 0.4536 - accuracy: 0.7871\n",
      "Epoch 130/150\n",
      "512/512 [==============================] - 0s 635us/step - loss: 0.4290 - accuracy: 0.8086\n",
      "Epoch 131/150\n",
      "512/512 [==============================] - 0s 653us/step - loss: 0.4746 - accuracy: 0.7676\n",
      "Epoch 132/150\n",
      "512/512 [==============================] - 0s 627us/step - loss: 0.4305 - accuracy: 0.8145\n",
      "Epoch 133/150\n",
      "512/512 [==============================] - 0s 643us/step - loss: 0.4965 - accuracy: 0.7480\n",
      "Epoch 134/150\n",
      "512/512 [==============================] - 0s 845us/step - loss: 0.4249 - accuracy: 0.8125\n",
      "Epoch 135/150\n",
      "512/512 [==============================] - 0s 688us/step - loss: 0.4561 - accuracy: 0.7793\n",
      "Epoch 136/150\n",
      "512/512 [==============================] - 0s 596us/step - loss: 0.4458 - accuracy: 0.7969\n",
      "Epoch 137/150\n",
      "512/512 [==============================] - 0s 641us/step - loss: 0.4194 - accuracy: 0.8184\n",
      "Epoch 138/150\n",
      "512/512 [==============================] - 0s 600us/step - loss: 0.4187 - accuracy: 0.8223\n",
      "Epoch 139/150\n",
      "512/512 [==============================] - 0s 639us/step - loss: 0.4127 - accuracy: 0.8262\n",
      "Epoch 140/150\n",
      "512/512 [==============================] - 0s 639us/step - loss: 0.4013 - accuracy: 0.8340\n",
      "Epoch 141/150\n",
      "512/512 [==============================] - 0s 588us/step - loss: 0.4275 - accuracy: 0.8164\n",
      "Epoch 142/150\n",
      "512/512 [==============================] - 0s 608us/step - loss: 0.4394 - accuracy: 0.8066\n",
      "Epoch 143/150\n",
      "512/512 [==============================] - 0s 604us/step - loss: 0.4262 - accuracy: 0.8066\n",
      "Epoch 144/150\n",
      "512/512 [==============================] - 0s 596us/step - loss: 0.4114 - accuracy: 0.8242\n",
      "Epoch 145/150\n",
      "512/512 [==============================] - 0s 791us/step - loss: 0.4104 - accuracy: 0.8242\n",
      "Epoch 146/150\n",
      "512/512 [==============================] - 0s 730us/step - loss: 0.4080 - accuracy: 0.8164\n",
      "Epoch 147/150\n",
      "512/512 [==============================] - 0s 610us/step - loss: 0.4436 - accuracy: 0.7930\n",
      "Epoch 148/150\n",
      "512/512 [==============================] - 0s 682us/step - loss: 0.4229 - accuracy: 0.8105\n",
      "Epoch 149/150\n",
      "512/512 [==============================] - 0s 649us/step - loss: 0.4303 - accuracy: 0.8008\n",
      "Epoch 150/150\n",
      "512/512 [==============================] - 0s 637us/step - loss: 0.4321 - accuracy: 0.8066\n",
      "256/256 [==============================] - 1s 4ms/step\n",
      "0.7174479166666666\n"
     ]
    }
   ],
   "source": [
    "# 改变神经网络结构，（加深，加宽），看看什么结构对模型性能有较大影响。\n",
    "# 加宽 加深\n",
    "def create_model_2():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(24, input_dim=8, activation='relu'))\n",
    "    model.add(Dense(24, input_dim=24, activation='relu'))\n",
    "    model.add(Dense(24, input_dim=24, activation='relu'))\n",
    "    model.add(Dense(24, input_dim=24, activation='relu'))\n",
    "    model.add(Dense(24, input_dim=24, activation='relu'))\n",
    "    model.add(Dense(12, input_dim=24, activation='relu'))\n",
    "    model.add(Dense(12, input_dim=12, activation='relu'))\n",
    "    model.add(Dense(12, input_dim=12, activation='relu'))\n",
    "    model.add(Dense(12, input_dim=12, activation='relu'))\n",
    "    model.add(Dense(12, input_dim=12, activation='relu'))\n",
    "    model.add(Dense(12, input_dim=12, activation='relu'))\n",
    "    model.add(Dense(8, input_dim=12, activation='relu'))\n",
    "    model.add(Dense(8, input_dim=12, activation='relu'))\n",
    "    model.add(Dense(8, input_dim=12, activation='relu'))\n",
    "    model.add(Dense(8, input_dim=12, activation='relu'))\n",
    "    model.add(Dense(8, input_dim=12, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "\n",
    "model = KerasClassifier(build_fn=create_model_2, epochs=150, batch_size=10)\n",
    "kfold = StratifiedKFold(n_splits=3, shuffle=True, random_state=seed)\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_accuracy', 'loss', 'accuracy'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# 可视化训练过程（损失函数关系图，精确度关系图）\n",
    "import matplotlib.pyplot as plt\n",
    "# Fit the model\n",
    "model = KerasClassifier(build_fn=create_model,\n",
    "                        epochs=150,\n",
    "                        batch_size=10)\n",
    "history = model.fit(X,\n",
    "                    Y,\n",
    "                    validation_split=0.33,\n",
    "                    epochs=150,\n",
    "                    batch_size=10,\n",
    "                    verbose=0)\n",
    "# list all data in history\n",
    "print(history.history.keys())\n",
    "\n",
    "# summarize history for accuracy\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "deep",
   "language": "python",
   "name": "deep"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}