{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2020-11-12T02:29:37.717688Z", "start_time": "2020-11-12T02:29:37.585423Z" } }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "np.random.seed(2020)\n", "import os\n", "import math\n", "import time" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2020-11-12T02:29:38.686498Z", "start_time": "2020-11-12T02:29:38.681483Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n", "D:\\Anaconda\\lib\\site-packages\\win_unicode_console\\__init__.py:31: RuntimeWarning: sys.stdin.encoding == 'cp936', whereas sys.stdout.encoding == 'UTF-8', readline hook consumer may assume they are the same\n", " readline_hook.enable(use_pyreadline=use_pyreadline)\n" ] } ], "source": [ "from sklearn.utils import shuffle\n", "from sklearn.metrics.regression import r2_score, mean_squared_error,mean_absolute_error\n", "import matplotlib.pyplot as plt\n", "import warnings\n", "from time import sleep\n", "from numpy import newaxis\n", "from keras.layers.core import Dense, Activation, Dropout\n", "from sklearn.metrics.regression import r2_score, mean_squared_error,mean_absolute_error\n", "from keras.models import Sequential\n", "import win_unicode_console#\n", "win_unicode_console.enable()#\n", "import matplotlib.pyplot as plt\n", "from sklearn.preprocessing import MinMaxScaler" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def build_model(layers):\n", " model = Sequential()\n", "\n", " model.add(Dense(\n", " input_dim=layers[0],output_dim=layers[1]))\n", " \n", " model.add(Dense(\n", " layers[2]))\n", " \n", " model.add(Activation(\"relu\"))\n", " \n", " model.add(Dense(\n", " layers[3]))\n", " \n", " model.add(Activation(\"relu\"))\n", " \n", " model.add(Dense(\n", " layers[4]))\n", " \n", " model.add(Dense(\n", " output_dim=layers[5]))\n", " \n", " model.add(Activation(\"tanh\"))\n", "\n", " start = time.time()\n", " model.compile(loss=\"mae\", optimizer=\"sgd\")\n", " \n", " return model" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2020-11-12T02:29:41.345468Z", "start_time": "2020-11-12T02:29:40.980371Z" } }, "outputs": [ { "data": { "text/html": [ "
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\n", "
" ], "text/plain": [ " GK fuhe TA TF T1A T2A W \\\n", "0 t28 334.858 1222.719 205.773 113.920278 226.345833 340.7500 \n", "1 t37 325.424 1216.441 208.136 115.638333 221.677222 338.6505 \n", "2 t46 328.218 1211.417 203.691 114.769444 219.778611 338.7840 \n", "3 t83 255.737 937.542 161.515 101.256389 165.234722 337.8665 \n", "4 t92 255.717 949.950 161.527 101.328889 168.152778 337.9570 \n", "5 t101 255.677 941.303 161.512 101.300833 167.727222 338.1345 \n", "6 t138 174.554 625.068 102.594 79.662222 91.868333 336.6955 \n", "7 t147 174.554 673.915 108.160 82.327222 103.232778 338.5260 \n", "8 t156 174.535 701.883 116.855 81.486389 119.375556 339.0945 \n", "9 t39 336.801 1245.758 207.513 112.505000 232.013056 342.8015 \n", "10 t48 330.557 1234.135 205.807 111.050278 232.468056 345.9140 \n", "11 t29 336.087 1266.167 199.155 118.137778 233.906111 350.2705 \n", "12 t38 328.238 1214.264 197.453 109.834167 229.244444 348.1335 \n", "13 t47 326.276 1211.542 196.423 109.827778 229.053611 348.2675 \n", "14 t93 264.616 1016.826 172.197 106.156111 180.305278 342.7230 \n", "15 t102 264.359 959.695 163.394 95.503611 171.353056 334.2165 \n", "16 t137 178.181 692.065 95.907 73.294167 118.773611 333.1745 \n", "17 t146 170.174 663.048 95.836 69.219444 114.490833 334.8800 \n", "18 t155 170.471 641.070 95.902 67.110833 109.118889 336.1345 \n", "\n", " M J \n", "0 1.6893 0 \n", "1 1.6893 0 \n", "2 1.6893 0 \n", "3 1.2783 0 \n", "4 1.2783 0 \n", "5 1.2783 0 \n", "6 1.2783 0 \n", "7 1.2783 0 \n", "8 1.2783 0 \n", "9 1.3180 0 \n", "10 1.3180 0 \n", "11 1.4975 0 \n", "12 1.4975 0 \n", "13 1.4975 0 \n", "14 1.4975 0 \n", "15 1.4975 0 \n", "16 1.0316 0 \n", "17 1.0316 0 \n", "18 1.0316 0 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "GK_all = pd.read_csv(\"F:/论文2/实验数据/工况信息表2_除基准工况.csv\")\n", "#GK_all = pd.read_csv(\"F:/三维可视化温度场/温度资料/现场数据组/工况4/工况信息表_除基准.csv\") # !!! 需要改动\n", "GK_all # 工况信息表" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2020-11-12T02:29:48.596037Z", "start_time": "2020-11-12T02:29:48.122592Z" } }, "outputs": [], "source": [ "# 添加总路径、坐标路径等信息\n", "os.chdir(\"F:/data/daqingshuju/shuzhimoni/fluent6gexianchangcanshu(162gegongkuang)/T-CO-NO-DPM-O2\") \n", "zb_index = pd.read_csv(\"F:/论文2/实验数据/xyzindex.csv\")\n", "name_list = list(GK_all[\"GK\"])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2020-11-12T02:08:03.756941Z", "start_time": "2020-11-12T02:08:03.752929Z" } }, "outputs": [], "source": [ "# 基准工况的信息\n", "jizhun_name = 't84'\n", "jizhun_info = np.array([260.395,969.185,168.928,97.85777778,172.6511111,339.412,1.4975])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2020-11-12T02:30:26.390802Z", "start_time": "2020-11-12T02:30:26.353703Z" } }, "outputs": [], "source": [ "# 定义生成边界数据的函数\n", "def Create_bianjie(list_gk, len_seq):\n", " Ne = [list_gk[0]] * len_seq\n", " TA = [list_gk[1]] * len_seq\n", " TF = [list_gk[2]] * len_seq\n", " T1A = [list_gk[3]] * len_seq\n", " T2A = [list_gk[4]] * len_seq\n", " Wen = [list_gk[5]] * len_seq\n", " Mo = [list_gk[6]] * len_seq\n", " Ne = pd.DataFrame(Ne)\n", " Ne.columns = [\"Ne\"]\n", " TA = pd.DataFrame(TA)\n", " TA.columns = [\"TA\"]\n", " TF = pd.DataFrame(TF)\n", " TF.columns = [\"TF\"]\n", " T1A = pd.DataFrame(T1A)\n", " T1A.columns = [\"T1A\"]\n", " T2A = pd.DataFrame(T2A)\n", " T2A.columns = [\"T2A\"]\n", " Wen = pd.DataFrame(Wen)\n", " Wen.columns = [\"Wen\"]\n", " Mo = pd.DataFrame(Mo)\n", " Mo.columns = [\"Mo\"]\n", "\n", " data = pd.concat([Ne, TA, TF, T1A, T2A, Wen, Mo], axis=1)\n", " return data " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2020-11-12T02:30:40.993980Z", "start_time": "2020-11-12T02:30:30.186787Z" } }, "outputs": [], "source": [ "#zb_index = pd.read_csv(\"F:/论文2/实验数据/xyzindex.csv\") # 只含坐标索引\n", "XYZ = pd.read_csv('t57', usecols=[1, 2, 3])\n", "xyz = XYZ.iloc[zb_index.values[:, 0]]\n", "xyz = pd.DataFrame(xyz.values)\n", "xyz.columns = ['x', 'y', 'z'] # 提取5万组随机坐标（具体数值和xyzindex.csv一致）" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2020-11-12T02:30:51.214242Z", "start_time": "2020-11-12T02:30:40.993980Z" } }, "outputs": [], "source": [ "# 基准工况的随机5万组温度\n", "T_jizun = pd.read_csv(jizhun_name, usecols=[7])#NO：usecols=[4]\n", "t_jizhun = T_jizun.iloc[zb_index.values[:, 0]] #\n", "t_jizhun = pd.DataFrame(t_jizhun.values)\n", "t_jizhun.columns = ['t_jizhun']" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2020-11-12T02:30:51.229404Z", "start_time": "2020-11-12T02:30:51.214242Z" } }, "outputs": [], "source": [ "# 定义生成训练和验证的数据集\n", "def create_dataM(traAval_names, traAval_info):\n", "\n", " dataM = pd.DataFrame() # 存放结果\n", " bj_jizhun = Create_bianjie(list_gk=jizhun_info, len_seq=50000) # 产生len_seq列基准信息\n", "\n", " for i in range(traAval_names.shape[0]): # traAval_names.shape[0]为除基准信息的余下工况数 \n", " bj_i = Create_bianjie(list_gk=traAval_info[i], len_seq=50000)\n", " Cha_i = bj_i - bj_jizhun # 第i个建模工况的边界差值\n", " O2_i = pd.read_csv(traAval_names[i], usecols=[7])\n", " o2_i = O2_i.iloc[zb_index.values[:, 0]]\n", " o2_i = pd.DataFrame(o2_i.values) #得到第i个工况对应的参数（温度/NO）对应的50000组数据\n", " #o2_i.columns=['t25']\n", " dataMi = pd.concat([\n", " o2_i, t_jizhun,\n", " pd.DataFrame(o2_i.values - t_jizhun.values), xyz, Cha_i\n", " ],\n", " axis=1) # 合并t25氧气、基准氧气，氧气差值、坐标、边界差值\n", " dataM = pd.concat([dataM, dataMi], axis=0) # 按行，上下合并\n", " dataM.columns = [\n", " 't57', 't_jizhun', 'Cha', 'x', 'y', 'z', 'Ne', 'TA', 'TF', 'T1A',\n", " 'T2A', 'Wen', 'Mo'\n", " ]\n", " return dataM" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2020-11-12T02:30:51.319685Z", "start_time": "2020-11-12T02:30:51.238424Z" } }, "outputs": [], "source": [ "# 定义生成测试的数据集\n", "def create_dataTe(test_name, test_info):\n", "\n", " bj_test = Create_bianjie(list_gk=test_info, len_seq=2776652) # 测试集的全部边界数据\n", " bj_jizhunA = Create_bianjie(list_gk=jizhun_info,\n", " len_seq=2776652) # 基准工况的全部边界数据\n", " bj = bj_test - bj_jizhunA\n", "\n", " O2_jA = pd.read_csv(jizhun_name, usecols=[7]) # 基准工况的全部温度\n", " O2_tA = pd.read_csv(test_name, usecols=[7]) # 测试工况的全部温度\n", "\n", " data_Te = pd.concat([O2_tA, O2_jA, O2_tA - O2_jA, XYZ, bj], axis=1)\n", " data_Te.columns = [\n", " 'test', 't_jizhun', 'Cha', 'x', 'y', 'z', 'Ne', 'TA', 'TF', 'T1A',\n", " 'T2A', 'Wen', 'Mo'\n", " ]\n", "\n", " return data_Te" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2020-11-12T02:30:51.395527Z", "start_time": "2020-11-12T02:30:51.321690Z" } }, "outputs": [], "source": [ "# 定义差值归一化的函数\n", "Maxmin = {\n", " 'Cha': [-2000, 2000],\n", " 'x': [-10, 10],\n", " 'y': [6, 65],\n", " 'z': [-30, 10],\n", " 'Ne': [-100, 200],\n", " 'TA': [-400, 700],\n", " 'TF': [-100, 200],\n", " 'T1A': [-50, 50],\n", " 'T2A': [-100, 200],\n", " 'Wen': [-50, 100],\n", " 'Mo': [-0.5,0.5],\n", "}\n", "\n", "\n", "def normalizeData(dataR, Maxmin, names):\n", " # resultN: 归一化的数据，dataR：待归一化的数据\n", " resultN = np.zeros(dataR.shape)\n", " for i in range(dataR.shape[1]):\n", " resultN[:, i] = (dataR.values[:, i] - Maxmin.get(names[i])[0]) / (\n", " Maxmin.get(names[i])[1] - Maxmin.get(names[i])[0])\n", " return resultN" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# 测试集的xyz、真实值、预测值、真实-预测 \n", "\n", "def save_data(data,name):\n", " path='F:\\\\论文2\\\\实验数据\\\\DNN\\\\第2类工况' \n", " path=os.path.join(path,name+'.txt')\n", " \n", " datasave=pd.concat([data,pd.DataFrame(data.iloc[:,0]-data.iloc[:,1]),dataTe[['x', 'y', 'z']]],axis=1)\n", " datasave.columns=[\"True\",\"Pre\",\"Cha\",\"X\",\"Y\",\"Z\"]\n", " \n", " datasave.to_csv(path,index=None)\n", "\n", "# 定义保存图片的函数\n", "def save_pig1(ytrue,ypre,name):\n", " \n", " Fig_compare=plt.figure(num=1,figsize=(10,6),dpi=92)\n", " # 创建子图\n", " ax1=Fig_compare.add_subplot(2,1,1)\n", " ax2=Fig_compare.add_subplot(2,1,2)\n", " \n", " ax1.set_ylabel('T',fontsize=12) \n", " ax1.plot(ytrue,linestyle='-',alpha=0.8,color='k',linewidth = 1) # 黑色代表真实值\n", " ax1.plot(ypre,linestyle='-',alpha=0.8,color='r',linewidth = 1) # 红色代表预测值 \n", " # ax1.legend(frameon=False,ncol=2) \n", " ax1.set_xticks([])\n", "\n", " ax2.set_xlabel('samples',fontsize=12)\n", " ax2.set_ylabel('Error',fontsize=12) \n", " ax2.plot(ytrue-ypre,linestyle='-',alpha=0.8,color='k',linewidth = 1) # 第二幅图代表误差曲线 \n", " # ax2.legend(frameon=False,ncol=1,loc='upper right') \n", " \n", " path='F:\\\\论文2\\\\实验数据\\\\DNN\\\\第2类工况' \n", " path=os.path.join(path,name+'Line'+'.png')\n", " plt.savefig(path,dpi=300,bbox_inches='tight',figsize=(12,8)) \n", " \n", " plt.close() \n", " \n", "def save_pig2(ytrue,ypre,name):\n", " \n", " Fig_compare=plt.figure(num=1,figsize=(10,8),dpi=92)\n", " # 创建子图\n", " ax1=Fig_compare.add_subplot(2,2,1)\n", " ax2=Fig_compare.add_subplot(2,2,2)\n", "\n", " ax1.set_ylabel(\"Compare\",fontsize=12) \n", " ax1.boxplot((ytrue,ypre),\n", " labels=('True','Predict'),sym='+',whis=1.5,showfliers = False,\n", " boxprops = {'color':'orangered','markeredgecolor':'pink'}\n", " ) \n", "\n", " ax2.set_ylabel(\"Error\",fontsize=12) \n", " ax2.boxplot((ytrue-ypre),\n", " sym='+',whis=1.5,showfliers = False,\n", " boxprops = {'color':'orangered','markeredgecolor':'pink'}\n", " ) \n", " \n", " path='F:\\\\论文2\\\\实验数据\\\\DNN\\\\第2类工况' \n", " path=os.path.join(path,name+'box'+'.png')\n", " plt.savefig(path,dpi=300,bbox_inches='tight',figsize=(12,8)) \n", " \n", " plt.close() " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "> Data Loaded. Compiling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Train on 567000 samples, validate on 63000 samples\n", "Epoch 1/30\n", "567000/567000 [==============================] - 7s 13us/step - loss: 0.0255 - val_loss: 0.0243\n", "Epoch 2/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0245 - val_loss: 0.0242\n", "Epoch 3/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0244 - val_loss: 0.0242\n", "Epoch 4/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0243 - val_loss: 0.0239\n", "Epoch 5/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0242 - val_loss: 0.0239\n", "Epoch 6/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0241 - val_loss: 0.0238\n", "Epoch 7/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0240 - val_loss: 0.0249\n", "Epoch 8/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0239 - val_loss: 0.0239\n", "Epoch 9/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0239 - val_loss: 0.0237\n", "Epoch 10/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0238 - val_loss: 0.0235\n", "Epoch 11/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0238 - val_loss: 0.0236\n", "Epoch 12/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0238 - val_loss: 0.0243\n", "Epoch 13/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0237 - val_loss: 0.0240\n", "Epoch 14/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0237 - val_loss: 0.0235\n", "Epoch 15/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0236 - val_loss: 0.0235\n", "Epoch 16/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0236 - val_loss: 0.0233\n", "Epoch 17/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0236 - val_loss: 0.0233\n", "Epoch 18/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0235 - val_loss: 0.0234\n", "Epoch 19/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0235 - val_loss: 0.0233\n", "Epoch 20/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0234 - val_loss: 0.0233\n", "Epoch 21/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0234 - val_loss: 0.0232\n", "Epoch 22/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0233 - val_loss: 0.0235\n", "Epoch 23/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0233 - val_loss: 0.0231\n", "Epoch 24/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0232 - val_loss: 0.0229\n", "Epoch 25/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0232 - val_loss: 0.0231\n", "Epoch 26/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0231 - val_loss: 0.0229\n", "Epoch 27/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0231 - val_loss: 0.0229\n", "Epoch 28/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0230 - val_loss: 0.0229\n", "Epoch 29/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0229 - val_loss: 0.0230\n", "Epoch 30/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0229 - val_loss: 0.0226\n", "最终测试结果 t28 MAE= 98.93857420299474\n", "R2: 0.89 RMSE: 159.69 MAE: 98.94\n", "建模时间 (s) : 164.89 \n", "测试时间 : 2.03 \n", "验证时间 : 20.38 \n", "> Data Loaded. Compiling...\n", "Train on 567000 samples, validate on 63000 samples\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0262 - val_loss: 0.0272\n", "Epoch 2/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0247 - val_loss: 0.0239\n", "Epoch 3/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0244 - val_loss: 0.0242\n", "Epoch 4/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0243 - val_loss: 0.0239\n", "Epoch 5/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0242 - val_loss: 0.0240\n", "Epoch 6/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0241 - val_loss: 0.0237\n", "Epoch 7/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0240\n", "Epoch 8/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0240 - val_loss: 0.0237\n", "Epoch 9/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0239 - val_loss: 0.0238\n", "Epoch 10/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0239 - val_loss: 0.0240\n", "Epoch 11/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0238 - val_loss: 0.0235\n", "Epoch 12/30\n", "567000/567000 [==============================] - 4s 8us/step - loss: 0.0238 - val_loss: 0.0236\n", "Epoch 13/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0237 - val_loss: 0.0236\n", "Epoch 14/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0235\n", "Epoch 15/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0236 - val_loss: 0.0239\n", "Epoch 16/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0236 - val_loss: 0.0245\n", "Epoch 17/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0235 - val_loss: 0.0235\n", "Epoch 18/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0235 - val_loss: 0.0237\n", "Epoch 19/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0234 - val_loss: 0.0231\n", "Epoch 20/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0234 - val_loss: 0.0231\n", "Epoch 21/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0234 - val_loss: 0.0232\n", "Epoch 22/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0233 - val_loss: 0.0233\n", "Epoch 23/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0233 - val_loss: 0.0229\n", "Epoch 24/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0232 - val_loss: 0.0229\n", "Epoch 25/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0232 - val_loss: 0.0228\n", "Epoch 26/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0232 - val_loss: 0.0230\n", "Epoch 27/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0231 - val_loss: 0.0232\n", "Epoch 28/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0231 - val_loss: 0.0233\n", "Epoch 29/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0230 - val_loss: 0.0230\n", "Epoch 30/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0230 - val_loss: 0.0230\n", "最终测试结果 t37 MAE= 104.02824084145341\n", "R2: 0.88 RMSE: 166.74 MAE: 104.03\n", "建模时间 (s) : 142.01 \n", "测试时间 : 1.60 \n", "验证时间 : 17.38 \n", "> Data Loaded. Compiling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Train on 567000 samples, validate on 63000 samples\n", "Epoch 1/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0255 - val_loss: 0.0244\n", "Epoch 2/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0244 - val_loss: 0.0251\n", "Epoch 3/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0242 - val_loss: 0.0240\n", "Epoch 4/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0241 - val_loss: 0.0238\n", "Epoch 5/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0241 - val_loss: 0.0239\n", "Epoch 6/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0240\n", "Epoch 7/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0245\n", "Epoch 8/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0239 - val_loss: 0.0237\n", "Epoch 9/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0239 - val_loss: 0.0239\n", "Epoch 10/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0239 - val_loss: 0.0241\n", "Epoch 11/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0238 - val_loss: 0.0236\n", "Epoch 12/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0238 - val_loss: 0.0239\n", "Epoch 13/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0238 - val_loss: 0.0236\n", "Epoch 14/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0236\n", "Epoch 15/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0236\n", "Epoch 16/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0235\n", "Epoch 17/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0237\n", "Epoch 18/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0236\n", "Epoch 19/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0236 - val_loss: 0.0235\n", "Epoch 20/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0236 - val_loss: 0.0242\n", "Epoch 21/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0236 - val_loss: 0.0236\n", "Epoch 22/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0236 - val_loss: 0.0236\n", "Epoch 23/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0236 - val_loss: 0.0238\n", "Epoch 24/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0236 - val_loss: 0.0237\n", "Epoch 25/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0235 - val_loss: 0.0234\n", "Epoch 26/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0235 - val_loss: 0.0234\n", "Epoch 27/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0235 - val_loss: 0.0233\n", "Epoch 28/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0235 - val_loss: 0.0233\n", "Epoch 29/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0235 - val_loss: 0.0235\n", "Epoch 30/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0234 - val_loss: 0.0233\n", "最终测试结果 t46 MAE= 106.06908761625668\n", "R2: 0.88 RMSE: 167.04 MAE: 106.07\n", "建模时间 (s) : 157.06 \n", "测试时间 : 2.37 \n", "验证时间 : 22.98 \n", "> Data Loaded. Compiling...\n", "Train on 567000 samples, validate on 63000 samples\n", "Epoch 1/30\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "567000/567000 [==============================] - 5s 9us/step - loss: 0.0261 - val_loss: 0.0247\n", "Epoch 2/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0249 - val_loss: 0.0254\n", "Epoch 3/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0248 - val_loss: 0.0246\n", "Epoch 4/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0247 - val_loss: 0.0250\n", "Epoch 5/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0246 - val_loss: 0.0243\n", "Epoch 6/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0246 - val_loss: 0.0254\n", "Epoch 7/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0245 - val_loss: 0.0246\n", "Epoch 8/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0245 - val_loss: 0.0241\n", "Epoch 9/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0245 - val_loss: 0.0243\n", "Epoch 10/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0244 - val_loss: 0.0241\n", "Epoch 11/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0244 - val_loss: 0.0243\n", "Epoch 12/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0244 - val_loss: 0.0240\n", "Epoch 13/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0244 - val_loss: 0.0241\n", "Epoch 14/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0243 - val_loss: 0.0241\n", "Epoch 15/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0243 - val_loss: 0.0247\n", "Epoch 16/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0243 - val_loss: 0.0241\n", "Epoch 17/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0243 - val_loss: 0.0240\n", "Epoch 18/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0243 - val_loss: 0.0240\n", "Epoch 19/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0242 - val_loss: 0.0239\n", "Epoch 20/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0242 - val_loss: 0.0241\n", "Epoch 21/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0242 - val_loss: 0.0241\n", "Epoch 22/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0242 - val_loss: 0.0241\n", "Epoch 23/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0242 - val_loss: 0.0239\n", "Epoch 24/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0241 - val_loss: 0.0239\n", "Epoch 25/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0241 - val_loss: 0.0241\n", "Epoch 26/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0241 - val_loss: 0.0246\n", "Epoch 27/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0241 - val_loss: 0.0241\n", "Epoch 28/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0241 - val_loss: 0.0238\n", "Epoch 29/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0240\n", "Epoch 30/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0246\n", "最终测试结果 t83 MAE= 91.59523294567123\n", "R2: 0.88 RMSE: 139.22 MAE: 91.60\n", "建模时间 (s) : 148.74 \n", "测试时间 : 1.62 \n", "验证时间 : 16.42 \n", "> Data Loaded. Compiling...\n", "Train on 567000 samples, validate on 63000 samples\n", "Epoch 1/30\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "567000/567000 [==============================] - 5s 9us/step - loss: 0.0260 - val_loss: 0.0244\n", "Epoch 2/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0248 - val_loss: 0.0262\n", "Epoch 3/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0245 - val_loss: 0.0245\n", "Epoch 4/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0244 - val_loss: 0.0253\n", "Epoch 5/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0243 - val_loss: 0.0240\n", "Epoch 6/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0242 - val_loss: 0.0239\n", "Epoch 7/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0241 - val_loss: 0.0239\n", "Epoch 8/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0241 - val_loss: 0.0238\n", "Epoch 9/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0240\n", "Epoch 10/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0240\n", "Epoch 11/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0239 - val_loss: 0.0239\n", "Epoch 12/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0239 - val_loss: 0.0237\n", "Epoch 13/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0239 - val_loss: 0.0237\n", "Epoch 14/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0238 - val_loss: 0.0236\n", "Epoch 15/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0238 - val_loss: 0.0236\n", "Epoch 16/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0238 - val_loss: 0.0237\n", "Epoch 17/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0238 - val_loss: 0.0236\n", "Epoch 18/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0235\n", "Epoch 19/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0237 - val_loss: 0.0237\n", "Epoch 20/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0236 - val_loss: 0.0238\n", "Epoch 21/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0236 - val_loss: 0.0235\n", "Epoch 22/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0236 - val_loss: 0.0233\n", "Epoch 23/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0235 - val_loss: 0.0240\n", "Epoch 24/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0235 - val_loss: 0.0234\n", "Epoch 25/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0235 - val_loss: 0.0232\n", "Epoch 26/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0234 - val_loss: 0.0232\n", "Epoch 27/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0234 - val_loss: 0.0233\n", "Epoch 28/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0234 - val_loss: 0.0233\n", "Epoch 29/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0234 - val_loss: 0.0231\n", "Epoch 30/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0233 - val_loss: 0.0234\n", "最终测试结果 t92 MAE= 91.68638689659612\n", "R2: 0.89 RMSE: 140.76 MAE: 91.69\n", "建模时间 (s) : 156.28 \n", "测试时间 : 1.56 \n", "验证时间 : 16.72 \n", "> Data Loaded. Compiling...\n", "Train on 567000 samples, validate on 63000 samples\n", "Epoch 1/30\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "567000/567000 [==============================] - 4s 6us/step - loss: 0.0267 - val_loss: 0.0270\n", "Epoch 2/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0250 - val_loss: 0.0245\n", "Epoch 3/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0247 - val_loss: 0.0245\n", "Epoch 4/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0246 - val_loss: 0.0252\n", "Epoch 5/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0245 - val_loss: 0.0244\n", "Epoch 6/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0244 - val_loss: 0.0245\n", "Epoch 7/30\n", "567000/567000 [==============================] - 10s 17us/step - loss: 0.0243 - val_loss: 0.0247\n", "Epoch 8/30\n", "567000/567000 [==============================] - 8s 14us/step - loss: 0.0243 - val_loss: 0.0243\n", "Epoch 9/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0242 - val_loss: 0.0242\n", "Epoch 10/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0242 - val_loss: 0.0244\n", "Epoch 11/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0241 - val_loss: 0.0250\n", "Epoch 12/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0241 - val_loss: 0.0240\n", "Epoch 13/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0241 - val_loss: 0.0240\n", "Epoch 14/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0241 - val_loss: 0.0240\n", "Epoch 15/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0240 - val_loss: 0.0239\n", "Epoch 16/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0240 - val_loss: 0.0239\n", "Epoch 17/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0240 - val_loss: 0.0241\n", "Epoch 18/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0240\n", "Epoch 19/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0239 - val_loss: 0.0239\n", "Epoch 20/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0239 - val_loss: 0.0239\n", "Epoch 21/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0239 - val_loss: 0.0246\n", "Epoch 22/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0239 - val_loss: 0.0242\n", "Epoch 23/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0238 - val_loss: 0.0242\n", "Epoch 24/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0238 - val_loss: 0.0238\n", "Epoch 25/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0238 - val_loss: 0.0237\n", "Epoch 26/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0238 - val_loss: 0.0237\n", "Epoch 27/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0238 - val_loss: 0.0238\n", "Epoch 28/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0237 - val_loss: 0.0244\n", "Epoch 29/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0237 - val_loss: 0.0242\n", "Epoch 30/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0237 - val_loss: 0.0237\n", "最终测试结果 t101 MAE= 89.62759365271195\n", "R2: 0.89 RMSE: 138.13 MAE: 89.63\n", "建模时间 (s) : 173.58 \n", "测试时间 : 1.74 \n", "验证时间 : 17.44 \n", "> Data Loaded. Compiling...\n", "Train on 567000 samples, validate on 63000 samples\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0258 - val_loss: 0.0248\n", "Epoch 2/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0246 - val_loss: 0.0244\n", "Epoch 3/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0243 - val_loss: 0.0241\n", "Epoch 4/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0242 - val_loss: 0.0252\n", "Epoch 5/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0241 - val_loss: 0.0239\n", "Epoch 6/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0240 - val_loss: 0.0238\n", "Epoch 7/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0240 - val_loss: 0.0238\n", "Epoch 8/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0239 - val_loss: 0.0240\n", "Epoch 9/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0238 - val_loss: 0.0236\n", "Epoch 10/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0238 - val_loss: 0.0236\n", "Epoch 11/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0237 - val_loss: 0.0239\n", "Epoch 12/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0237 - val_loss: 0.0247\n", "Epoch 13/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0236 - val_loss: 0.0235\n", "Epoch 14/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0236 - val_loss: 0.0234\n", "Epoch 15/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0235 - val_loss: 0.0235\n", "Epoch 16/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0235 - val_loss: 0.0233\n", "Epoch 17/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0234 - val_loss: 0.0234\n", "Epoch 18/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0234 - val_loss: 0.0233\n", "Epoch 19/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0234 - val_loss: 0.0239\n", "Epoch 20/30\n", "567000/567000 [==============================] - 7s 12us/step - loss: 0.0233 - val_loss: 0.0235\n", "Epoch 21/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0233 - val_loss: 0.0231\n", "Epoch 22/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0233 - val_loss: 0.0231\n", "Epoch 23/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0233 - val_loss: 0.0231\n", "Epoch 24/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0232 - val_loss: 0.0234\n", "Epoch 25/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0232 - val_loss: 0.0230\n", "Epoch 26/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0232 - val_loss: 0.0231\n", "Epoch 27/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0231 - val_loss: 0.0230\n", "Epoch 28/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0231 - val_loss: 0.0229\n", "Epoch 29/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0231 - val_loss: 0.0232\n", "Epoch 30/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0231 - val_loss: 0.0229\n", "最终测试结果 t138 MAE= 101.70958472996048\n", "R2: 0.83 RMSE: 159.46 MAE: 101.71\n", "建模时间 (s) : 174.84 \n", "测试时间 : 1.86 \n", "验证时间 : 18.61 \n", "> Data Loaded. Compiling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Train on 567000 samples, validate on 63000 samples\n", "Epoch 1/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0273 - val_loss: 0.0244\n", "Epoch 2/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0248 - val_loss: 0.0246\n", "Epoch 3/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0246 - val_loss: 0.0244\n", "Epoch 4/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0244 - val_loss: 0.0241\n", "Epoch 5/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0243 - val_loss: 0.0244\n", "Epoch 6/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0242 - val_loss: 0.0244\n", "Epoch 7/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0241 - val_loss: 0.0267\n", "Epoch 8/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0241 - val_loss: 0.0247\n", "Epoch 9/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0239\n", "Epoch 10/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0240 - val_loss: 0.0238\n", "Epoch 11/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0239 - val_loss: 0.0247\n", "Epoch 12/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0239 - val_loss: 0.0242\n", "Epoch 13/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0238 - val_loss: 0.0237\n", "Epoch 14/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0238 - val_loss: 0.0237\n", "Epoch 15/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0238 - val_loss: 0.0237\n", "Epoch 16/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0237 - val_loss: 0.0240\n", "Epoch 17/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0237 - val_loss: 0.0236\n", "Epoch 18/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0236 - val_loss: 0.0241\n", "Epoch 19/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0236 - val_loss: 0.0250\n", "Epoch 20/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0235 - val_loss: 0.0234\n", "Epoch 21/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0235 - val_loss: 0.0235\n", "Epoch 22/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0235 - val_loss: 0.0237\n", "Epoch 23/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0235 - val_loss: 0.0241\n", "Epoch 24/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0234 - val_loss: 0.0241\n", "Epoch 25/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0234 - val_loss: 0.0235\n", "Epoch 26/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0234 - val_loss: 0.0246\n", "Epoch 27/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0234 - val_loss: 0.0236\n", "Epoch 28/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0233 - val_loss: 0.0236\n", "Epoch 29/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0233 - val_loss: 0.0231\n", "Epoch 30/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0233 - val_loss: 0.0243\n", "最终测试结果 t147 MAE= 105.17144762157076\n", "R2: 0.83 RMSE: 160.37 MAE: 105.17\n", "建模时间 (s) : 148.97 \n", "测试时间 : 1.94 \n", "验证时间 : 16.63 \n", "> Data Loaded. Compiling...\n", "Train on 567000 samples, validate on 63000 samples\n", "Epoch 1/30\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "567000/567000 [==============================] - 4s 7us/step - loss: 0.0269 - val_loss: 0.0244\n", "Epoch 2/30\n", "567000/567000 [==============================] - 7s 12us/step - loss: 0.0249 - val_loss: 0.0247\n", "Epoch 3/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0246 - val_loss: 0.0243\n", "Epoch 4/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0245 - val_loss: 0.0243\n", "Epoch 5/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0244 - val_loss: 0.0241\n", "Epoch 6/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0243 - val_loss: 0.0242\n", "Epoch 7/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0242 - val_loss: 0.0241\n", "Epoch 8/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0241 - val_loss: 0.0239\n", "Epoch 9/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0241 - val_loss: 0.0239\n", "Epoch 10/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0238\n", "Epoch 11/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0237\n", "Epoch 12/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0239 - val_loss: 0.0237\n", "Epoch 13/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0238 - val_loss: 0.0237\n", "Epoch 14/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0238 - val_loss: 0.0241\n", "Epoch 15/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0239\n", "Epoch 16/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0240\n", "Epoch 17/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0234\n", "Epoch 18/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0236 - val_loss: 0.0234\n", "Epoch 19/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0236 - val_loss: 0.0241\n", "Epoch 20/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0236 - val_loss: 0.0237\n", "Epoch 21/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0236 - val_loss: 0.0235\n", "Epoch 22/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0235 - val_loss: 0.0233\n", "Epoch 23/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0235 - val_loss: 0.0235\n", "Epoch 24/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0235 - val_loss: 0.0232\n", "Epoch 25/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0234 - val_loss: 0.0232\n", "Epoch 26/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0234 - val_loss: 0.0238\n", "Epoch 27/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0234 - val_loss: 0.0231\n", "Epoch 28/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0233 - val_loss: 0.0233\n", "Epoch 29/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0233 - val_loss: 0.0231\n", "Epoch 30/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0233 - val_loss: 0.0232\n", "最终测试结果 t156 MAE= 108.11548666080624\n", "R2: 0.81 RMSE: 166.08 MAE: 108.12\n", "建模时间 (s) : 159.58 \n", "测试时间 : 1.82 \n", "验证时间 : 16.39 \n", "> Data Loaded. Compiling...\n", "Train on 567000 samples, validate on 63000 samples\n", "Epoch 1/30\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "567000/567000 [==============================] - 4s 6us/step - loss: 0.0266 - val_loss: 0.0247\n", "Epoch 2/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0246 - val_loss: 0.0248\n", "Epoch 3/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0243 - val_loss: 0.0253\n", "Epoch 4/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0242 - val_loss: 0.0240\n", "Epoch 5/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0241 - val_loss: 0.0237\n", "Epoch 6/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0238\n", "Epoch 7/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0239 - val_loss: 0.0256\n", "Epoch 8/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0238 - val_loss: 0.0237\n", "Epoch 9/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0238 - val_loss: 0.0235\n", "Epoch 10/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0235\n", "Epoch 11/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0239\n", "Epoch 12/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0236 - val_loss: 0.0234\n", "Epoch 13/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0236 - val_loss: 0.0235\n", "Epoch 14/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0235 - val_loss: 0.0235\n", "Epoch 15/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0235 - val_loss: 0.0238\n", "Epoch 16/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0234 - val_loss: 0.0234\n", "Epoch 17/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0234 - val_loss: 0.0233\n", "Epoch 18/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0233 - val_loss: 0.0232\n", "Epoch 19/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0233 - val_loss: 0.0231\n", "Epoch 20/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0233 - val_loss: 0.0234\n", "Epoch 21/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0233 - val_loss: 0.0232\n", "Epoch 22/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0232 - val_loss: 0.0230\n", "Epoch 23/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0232 - val_loss: 0.0244\n", "Epoch 24/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0231 - val_loss: 0.0229\n", "Epoch 25/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0231 - val_loss: 0.0231\n", "Epoch 26/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0231 - val_loss: 0.0229\n", "Epoch 27/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0230 - val_loss: 0.0228\n", "Epoch 28/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0230 - val_loss: 0.0229\n", "Epoch 29/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0230 - val_loss: 0.0232\n", "Epoch 30/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0229 - val_loss: 0.0228\n", "最终测试结果 t39 MAE= 123.01923113285177\n", "R2: 0.83 RMSE: 194.07 MAE: 123.02\n", "建模时间 (s) : 159.57 \n", "测试时间 : 1.76 \n", "验证时间 : 16.49 \n", "> Data Loaded. Compiling...\n", "Train on 567000 samples, validate on 63000 samples\n", "Epoch 1/30\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "567000/567000 [==============================] - 4s 6us/step - loss: 0.0260 - val_loss: 0.0239\n", "Epoch 2/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0244 - val_loss: 0.0247\n", "Epoch 3/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0242 - val_loss: 0.0236\n", "Epoch 4/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0240 - val_loss: 0.0235\n", "Epoch 5/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0240 - val_loss: 0.0235\n", "Epoch 6/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0239 - val_loss: 0.0234\n", "Epoch 7/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0238 - val_loss: 0.0241\n", "Epoch 8/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0238 - val_loss: 0.0241\n", "Epoch 9/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0237 - val_loss: 0.0233\n", "Epoch 10/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0232\n", "Epoch 11/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0237 - val_loss: 0.0237\n", "Epoch 12/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0236 - val_loss: 0.0232\n", "Epoch 13/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0236 - val_loss: 0.0234\n", "Epoch 14/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0235 - val_loss: 0.0232\n", "Epoch 15/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0235 - val_loss: 0.0231\n", "Epoch 16/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0234 - val_loss: 0.0230\n", "Epoch 17/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0234 - val_loss: 0.0231\n", "Epoch 18/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0234 - val_loss: 0.0230\n", "Epoch 19/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0234 - val_loss: 0.0237\n", "Epoch 20/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0233 - val_loss: 0.0230\n", "Epoch 21/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0233 - val_loss: 0.0231\n", "Epoch 22/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0233 - val_loss: 0.0229\n", "Epoch 23/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0233 - val_loss: 0.0228\n", "Epoch 24/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0233 - val_loss: 0.0229\n", "Epoch 25/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0232 - val_loss: 0.0228\n", "Epoch 26/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0232 - val_loss: 0.0243\n", "Epoch 27/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0232 - val_loss: 0.0227\n", "Epoch 28/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0232 - val_loss: 0.0227\n", "Epoch 29/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0231 - val_loss: 0.0232\n", "Epoch 30/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0231 - val_loss: 0.0229\n", "最终测试结果 t48 MAE= 125.2823947834236\n", "R2: 0.82 RMSE: 200.18 MAE: 125.28\n", "建模时间 (s) : 162.62 \n", "测试时间 : 2.26 \n", "验证时间 : 19.88 \n", "> Data Loaded. Compiling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Train on 567000 samples, validate on 63000 samples\n", "Epoch 1/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0268 - val_loss: 0.0249\n", "Epoch 2/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0253 - val_loss: 0.0247\n", "Epoch 3/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0250 - val_loss: 0.0247\n", "Epoch 4/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0249 - val_loss: 0.0248\n", "Epoch 5/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0248 - val_loss: 0.0247\n", "Epoch 6/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0247 - val_loss: 0.0247\n", "Epoch 7/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0246 - val_loss: 0.0244\n", "Epoch 8/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0245 - val_loss: 0.0243\n", "Epoch 9/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0244 - val_loss: 0.0248\n", "Epoch 10/30\n", "567000/567000 [==============================] - 4s 8us/step - loss: 0.0244 - val_loss: 0.0243\n", "Epoch 11/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0244 - val_loss: 0.0245\n", "Epoch 12/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0243 - val_loss: 0.0241\n", "Epoch 13/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0243 - val_loss: 0.0240\n", "Epoch 14/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0242 - val_loss: 0.0241\n", "Epoch 15/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0242 - val_loss: 0.0239\n", "Epoch 16/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0242 - val_loss: 0.0240\n", "Epoch 17/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0241 - val_loss: 0.0247\n", "Epoch 18/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0241 - val_loss: 0.0251\n", "Epoch 19/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0241 - val_loss: 0.0240\n", "Epoch 20/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0241 - val_loss: 0.0245\n", "Epoch 21/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0239\n", "Epoch 22/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0240 - val_loss: 0.0244\n", "Epoch 23/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0239 - val_loss: 0.0237\n", "Epoch 24/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0239 - val_loss: 0.0247\n", "Epoch 25/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0239 - val_loss: 0.0235\n", "Epoch 26/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0238 - val_loss: 0.0235\n", "Epoch 27/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0238 - val_loss: 0.0235\n", "Epoch 28/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0237 - val_loss: 0.0237\n", "Epoch 29/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0237 - val_loss: 0.0240\n", "Epoch 30/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0235\n", "最终测试结果 t29 MAE= 70.36164308040253\n", "R2: 0.94 RMSE: 112.54 MAE: 70.36\n", "建模时间 (s) : 144.97 \n", "测试时间 : 1.83 \n", "验证时间 : 17.81 \n", "> Data Loaded. Compiling...\n", "Train on 567000 samples, validate on 63000 samples\n", "Epoch 1/30\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "567000/567000 [==============================] - 5s 9us/step - loss: 0.0267 - val_loss: 0.0255\n", "Epoch 2/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0252 - val_loss: 0.0248\n", "Epoch 3/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0249 - val_loss: 0.0249\n", "Epoch 4/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0248 - val_loss: 0.0246\n", "Epoch 5/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0248 - val_loss: 0.0246\n", "Epoch 6/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0247 - val_loss: 0.0245\n", "Epoch 7/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0246 - val_loss: 0.0251\n", "Epoch 8/30\n", "567000/567000 [==============================] - 5s 8us/step - loss: 0.0246 - val_loss: 0.0244\n", "Epoch 9/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0245 - val_loss: 0.0244\n", "Epoch 10/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0244 - val_loss: 0.0243\n", "Epoch 11/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0244 - val_loss: 0.0244\n", "Epoch 12/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0243 - val_loss: 0.0243\n", "Epoch 13/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0243 - val_loss: 0.0241\n", "Epoch 14/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0242 - val_loss: 0.0242\n", "Epoch 15/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0242 - val_loss: 0.0241\n", "Epoch 16/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0241 - val_loss: 0.0240\n", "Epoch 17/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0241 - val_loss: 0.0243\n", "Epoch 18/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0240 - val_loss: 0.0238\n", "Epoch 19/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0240 - val_loss: 0.0239\n", "Epoch 20/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0240 - val_loss: 0.0238\n", "Epoch 21/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0239 - val_loss: 0.0238\n", "Epoch 22/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0239 - val_loss: 0.0237\n", "Epoch 23/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0239 - val_loss: 0.0243\n", "Epoch 24/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0239 - val_loss: 0.0238\n", "Epoch 25/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0238 - val_loss: 0.0246\n", "Epoch 26/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0238 - val_loss: 0.0235\n", "Epoch 27/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0238 - val_loss: 0.0239\n", "Epoch 28/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0236\n", "Epoch 29/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0234\n", "Epoch 30/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0237\n", "最终测试结果 t38 MAE= 75.7154856352181\n", "R2: 0.93 RMSE: 120.99 MAE: 75.72\n", "建模时间 (s) : 163.55 \n", "测试时间 : 1.94 \n", "验证时间 : 18.27 \n", "> Data Loaded. Compiling...\n", "Train on 567000 samples, validate on 63000 samples\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0276 - val_loss: 0.0261\n", "Epoch 2/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0252 - val_loss: 0.0251\n", "Epoch 3/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0249 - val_loss: 0.0249\n", "Epoch 4/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0248 - val_loss: 0.0248\n", "Epoch 5/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0247 - val_loss: 0.0250\n", "Epoch 6/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0246 - val_loss: 0.0245\n", "Epoch 7/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0245 - val_loss: 0.0248\n", "Epoch 8/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0245 - val_loss: 0.0247\n", "Epoch 9/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0244 - val_loss: 0.0252\n", "Epoch 10/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0244 - val_loss: 0.0244\n", "Epoch 11/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0243 - val_loss: 0.0244\n", "Epoch 12/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0243 - val_loss: 0.0243\n", "Epoch 13/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0243 - val_loss: 0.0243\n", "Epoch 14/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0242 - val_loss: 0.0244\n", "Epoch 15/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0242 - val_loss: 0.0243\n", "Epoch 16/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0242 - val_loss: 0.0242\n", "Epoch 17/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0241 - val_loss: 0.0245\n", "Epoch 18/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0241 - val_loss: 0.0248\n", "Epoch 19/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0241 - val_loss: 0.0241\n", "Epoch 20/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0240\n", "Epoch 21/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0242\n", "Epoch 22/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0248\n", "Epoch 23/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0239\n", "Epoch 24/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0239\n", "Epoch 25/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0239 - val_loss: 0.0244\n", "Epoch 26/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0239 - val_loss: 0.0240\n", "Epoch 27/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0239 - val_loss: 0.0240\n", "Epoch 28/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0238 - val_loss: 0.0238\n", "Epoch 29/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0238 - val_loss: 0.0248\n", "Epoch 30/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0238 - val_loss: 0.0246\n", "最终测试结果 t47 MAE= 77.57815817095944\n", "R2: 0.93 RMSE: 121.25 MAE: 77.58\n", "建模时间 (s) : 153.53 \n", "测试时间 : 1.61 \n", "验证时间 : 17.16 \n", "> Data Loaded. Compiling...\n", "Train on 567000 samples, validate on 63000 samples\n", "Epoch 1/30\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "567000/567000 [==============================] - 4s 7us/step - loss: 0.0273 - val_loss: 0.0276\n", "Epoch 2/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0259 - val_loss: 0.0253\n", "Epoch 3/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0257 - val_loss: 0.0253\n", "Epoch 4/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0255 - val_loss: 0.0252\n", "Epoch 5/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0254 - val_loss: 0.0250\n", "Epoch 6/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0253 - val_loss: 0.0248\n", "Epoch 7/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0252 - val_loss: 0.0246\n", "Epoch 8/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0251 - val_loss: 0.0259\n", "Epoch 9/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0250 - val_loss: 0.0246\n", "Epoch 10/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0249 - val_loss: 0.0244\n", "Epoch 11/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0249 - val_loss: 0.0244\n", "Epoch 12/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0248 - val_loss: 0.0243\n", "Epoch 13/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0248 - val_loss: 0.0244\n", "Epoch 14/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0247 - val_loss: 0.0250\n", "Epoch 15/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0247 - val_loss: 0.0242\n", "Epoch 16/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0246 - val_loss: 0.0250\n", "Epoch 17/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0246 - val_loss: 0.0248\n", "Epoch 18/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0246 - val_loss: 0.0242\n", "Epoch 19/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0245 - val_loss: 0.0246\n", "Epoch 20/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0245 - val_loss: 0.0240\n", "Epoch 21/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0245 - val_loss: 0.0252\n", "Epoch 22/30\n", "567000/567000 [==============================] - 7s 12us/step - loss: 0.0244 - val_loss: 0.0242\n", "Epoch 23/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0244 - val_loss: 0.0240\n", "Epoch 24/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0244 - val_loss: 0.0240\n", "Epoch 25/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0243 - val_loss: 0.0244\n", "Epoch 26/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0243 - val_loss: 0.0238\n", "Epoch 27/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0243 - val_loss: 0.0238\n", "Epoch 28/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0243 - val_loss: 0.0238\n", "Epoch 29/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0242 - val_loss: 0.0241\n", "Epoch 30/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0242 - val_loss: 0.0240\n", "最终测试结果 t93 MAE= 26.853556622300168\n", "R2: 0.99 RMSE: 50.72 MAE: 26.85\n", "建模时间 (s) : 159.96 \n", "测试时间 : 1.86 \n", "验证时间 : 17.95 \n", "> Data Loaded. Compiling...\n", "Train on 567000 samples, validate on 63000 samples\n", "Epoch 1/30\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "567000/567000 [==============================] - 4s 6us/step - loss: 0.0268 - val_loss: 0.0254\n", "Epoch 2/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0253 - val_loss: 0.0254\n", "Epoch 3/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0251 - val_loss: 0.0252\n", "Epoch 4/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0250 - val_loss: 0.0250\n", "Epoch 5/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0248 - val_loss: 0.0251\n", "Epoch 6/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0248 - val_loss: 0.0247\n", "Epoch 7/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0247 - val_loss: 0.0246\n", "Epoch 8/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0246 - val_loss: 0.0246\n", "Epoch 9/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0246 - val_loss: 0.0244\n", "Epoch 10/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0245 - val_loss: 0.0245\n", "Epoch 11/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0244 - val_loss: 0.0244\n", "Epoch 12/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0244 - val_loss: 0.0260\n", "Epoch 13/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0244 - val_loss: 0.0242\n", "Epoch 14/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0243 - val_loss: 0.0241\n", "Epoch 15/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0243 - val_loss: 0.0247\n", "Epoch 16/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0242 - val_loss: 0.0243\n", "Epoch 17/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0242 - val_loss: 0.0258\n", "Epoch 18/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0241 - val_loss: 0.0240\n", "Epoch 19/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0240 - val_loss: 0.0240\n", "Epoch 20/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0240\n", "Epoch 21/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0240 - val_loss: 0.0247\n", "Epoch 22/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0239 - val_loss: 0.0243\n", "Epoch 23/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0239 - val_loss: 0.0237\n", "Epoch 24/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0239 - val_loss: 0.0242\n", "Epoch 25/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0238 - val_loss: 0.0242\n", "Epoch 26/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0238 - val_loss: 0.0241\n", "Epoch 27/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0237 - val_loss: 0.0240\n", "Epoch 28/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0244\n", "Epoch 29/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0236 - val_loss: 0.0235\n", "Epoch 30/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0236 - val_loss: 0.0245\n", "最终测试结果 t102 MAE= 46.25705486930832\n", "R2: 0.97 RMSE: 76.45 MAE: 46.26\n", "建模时间 (s) : 161.28 \n", "测试时间 : 1.71 \n", "验证时间 : 18.44 \n", "> Data Loaded. Compiling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Train on 567000 samples, validate on 63000 samples\n", "Epoch 1/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0267 - val_loss: 0.0257\n", "Epoch 2/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0245 - val_loss: 0.0260\n", "Epoch 3/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0241 - val_loss: 0.0237\n", "Epoch 4/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0239 - val_loss: 0.0235\n", "Epoch 5/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0238 - val_loss: 0.0234\n", "Epoch 6/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0237 - val_loss: 0.0234\n", "Epoch 7/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0236 - val_loss: 0.0235\n", "Epoch 8/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0236 - val_loss: 0.0235\n", "Epoch 9/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0235 - val_loss: 0.0234\n", "Epoch 10/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0235 - val_loss: 0.0232\n", "Epoch 11/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0234 - val_loss: 0.0233\n", "Epoch 12/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0234 - val_loss: 0.0232\n", "Epoch 13/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0233 - val_loss: 0.0231\n", "Epoch 14/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0233 - val_loss: 0.0232\n", "Epoch 15/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0233 - val_loss: 0.0231\n", "Epoch 16/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0233 - val_loss: 0.0233\n", "Epoch 17/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0232 - val_loss: 0.0232\n", "Epoch 18/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0232 - val_loss: 0.0232\n", "Epoch 19/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0232 - val_loss: 0.0231\n", "Epoch 20/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0231 - val_loss: 0.0230\n", "Epoch 21/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0231 - val_loss: 0.0234\n", "Epoch 22/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0231 - val_loss: 0.0228\n", "Epoch 23/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0231 - val_loss: 0.0228\n", "Epoch 24/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0231 - val_loss: 0.0228\n", "Epoch 25/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0230 - val_loss: 0.0229\n", "Epoch 26/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0230 - val_loss: 0.0229\n", "Epoch 27/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0230 - val_loss: 0.0229\n", "Epoch 28/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0230 - val_loss: 0.0227\n", "Epoch 29/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0229 - val_loss: 0.0228\n", "Epoch 30/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0229 - val_loss: 0.0244\n", "最终测试结果 t137 MAE= 141.52858167237963\n", "R2: 0.70 RMSE: 210.71 MAE: 141.53\n", "建模时间 (s) : 164.03 \n", "测试时间 : 2.02 \n", "验证时间 : 17.81 \n", "> Data Loaded. Compiling...\n", "Train on 567000 samples, validate on 63000 samples\n", "Epoch 1/30\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "567000/567000 [==============================] - 5s 9us/step - loss: 0.0256 - val_loss: 0.0246\n", "Epoch 2/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0244 - val_loss: 0.0237\n", "Epoch 3/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0242 - val_loss: 0.0237\n", "Epoch 4/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0241 - val_loss: 0.0244\n", "Epoch 5/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0240 - val_loss: 0.0236\n", "Epoch 6/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0239 - val_loss: 0.0240\n", "Epoch 7/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0238 - val_loss: 0.0237\n", "Epoch 8/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0238 - val_loss: 0.0233\n", "Epoch 9/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0237 - val_loss: 0.0234\n", "Epoch 10/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0237 - val_loss: 0.0232\n", "Epoch 11/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0236 - val_loss: 0.0232\n", "Epoch 12/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0236 - val_loss: 0.0233\n", "Epoch 13/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0235 - val_loss: 0.0231\n", "Epoch 14/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0235 - val_loss: 0.0236\n", "Epoch 15/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0235 - val_loss: 0.0231\n", "Epoch 16/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0234 - val_loss: 0.0238\n", "Epoch 17/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0234 - val_loss: 0.0229\n", "Epoch 18/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0234 - val_loss: 0.0229\n", "Epoch 19/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0233 - val_loss: 0.0231\n", "Epoch 20/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0233 - val_loss: 0.0229\n", "Epoch 21/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0233 - val_loss: 0.0228\n", "Epoch 22/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0232 - val_loss: 0.0228\n", "Epoch 23/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0232 - val_loss: 0.0230\n", "Epoch 24/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0232 - val_loss: 0.0227\n", "Epoch 25/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0232 - val_loss: 0.0227\n", "Epoch 26/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0231 - val_loss: 0.0230\n", "Epoch 27/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0231 - val_loss: 0.0226\n", "Epoch 28/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0231 - val_loss: 0.0228\n", "Epoch 29/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0230 - val_loss: 0.0228\n", "Epoch 30/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0230 - val_loss: 0.0225\n", "最终测试结果 t146 MAE= 129.11913454352376\n", "R2: 0.72 RMSE: 200.66 MAE: 129.12\n", "建模时间 (s) : 167.88 \n", "测试时间 : 1.72 \n", "验证时间 : 16.94 \n", "> Data Loaded. Compiling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=10, units=30)`\n", " \"\"\"\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1)`\n", "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Train on 567000 samples, validate on 63000 samples\n", "Epoch 1/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0262 - val_loss: 0.0242\n", "Epoch 2/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0245 - val_loss: 0.0239\n", "Epoch 3/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0242 - val_loss: 0.0238\n", "Epoch 4/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0241 - val_loss: 0.0238\n", "Epoch 5/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0240 - val_loss: 0.0237\n", "Epoch 6/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0240 - val_loss: 0.0237\n", "Epoch 7/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0239 - val_loss: 0.0237\n", "Epoch 8/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0239 - val_loss: 0.0237\n", "Epoch 9/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0239 - val_loss: 0.0236\n", "Epoch 10/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0238 - val_loss: 0.0237\n", "Epoch 11/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0238 - val_loss: 0.0235\n", "Epoch 12/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0238 - val_loss: 0.0237\n", "Epoch 13/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0238 - val_loss: 0.0237\n", "Epoch 14/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0237 - val_loss: 0.0239\n", "Epoch 15/30\n", "567000/567000 [==============================] - 7s 12us/step - loss: 0.0237 - val_loss: 0.0237\n", "Epoch 16/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0237 - val_loss: 0.0234\n", "Epoch 17/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0237 - val_loss: 0.0241\n", "Epoch 18/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0236 - val_loss: 0.0234\n", "Epoch 19/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0236 - val_loss: 0.0235\n", "Epoch 20/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0236 - val_loss: 0.0234\n", "Epoch 21/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0236 - val_loss: 0.0237\n", "Epoch 22/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0236 - val_loss: 0.0238\n", "Epoch 23/30\n", "567000/567000 [==============================] - 6s 11us/step - loss: 0.0235 - val_loss: 0.0233\n", "Epoch 24/30\n", "567000/567000 [==============================] - 5s 9us/step - loss: 0.0235 - val_loss: 0.0233\n", "Epoch 25/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0235 - val_loss: 0.0232\n", "Epoch 26/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0235 - val_loss: 0.0240\n", "Epoch 27/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0235 - val_loss: 0.0232\n", "Epoch 28/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0235 - val_loss: 0.0233\n", "Epoch 29/30\n", "567000/567000 [==============================] - 6s 10us/step - loss: 0.0234 - val_loss: 0.0234\n", "Epoch 30/30\n", "567000/567000 [==============================] - 5s 10us/step - loss: 0.0234 - val_loss: 0.0238\n", "最终测试结果 t155 MAE= 127.45773548213042\n", "R2: 0.73 RMSE: 198.24 MAE: 127.46\n", "建模时间 (s) : 177.24 \n", "测试时间 : 1.73 \n", "验证时间 : 18.44 \n" ] } ], "source": [ "Ymax = Maxmin.get('Cha')[1]\n", "Ymin = Maxmin.get('Cha')[0]\n", "\n", "for i in range(19):#range（）具体数值根据含工况数定\n", "\n", " # 依次提取name作为测试集\n", " test_name = name_list[i] #得到测试工况名\n", " test_info = GK_all.values[i, 1:] #得到测试工况对应的边界条件\n", "\n", " # 剩余变量作为训练和验证\n", " trainAval = GK_all.drop(i).values #得到除测试工况外所有其它工况名及对应的边界条件\n", " traAval_names = trainAval[:, 0] #得到除测试工况外所有其它工况名\n", " traAval_info = trainAval[:, 1:] #得到除测试工况外所有其它工况对应的边界条件\n", "\n", " # 根据各数据对应的边界条件，生成不同长度的建模数据(包含真实、基准、差值建模数据)\n", " dataM = create_dataM(traAval_names=traAval_names, #30000*（除测试工况外所有其它工况数行）*13列\n", " traAval_info=traAval_info)\n", " dataTe = create_dataTe(test_name=test_name, test_info=test_info) #2776652行*13列\n", "\n", " # 选取数据，去除前两列，只让差值参与建模，统一的标准进行归一化\n", " dataMs = dataM.iloc[:, 2:] # 实际输入，只含差值\n", " dataTes = dataTe.iloc[:, 2:]\n", "\n", " dataMsN = normalizeData(dataR=dataMs, Maxmin=Maxmin,\n", " names=dataMs.columns) # 归一化的数据\n", " dataTesN = normalizeData(dataR=dataTes,\n", " Maxmin=Maxmin,\n", " names=dataTes.columns)\n", "\n", " #数据划分训练、验证、测试，建立ELM模型\n", " split_num = int(0.7 * dataMsN.shape[0])\n", " np.random.shuffle(dataMsN)\n", "\n", " X_train = dataMsN[:split_num, 1:]\n", " Y_train = dataMsN[:split_num, 0]\n", "\n", " X_val = dataMsN[split_num:, 1:]\n", " Y_val = dataMsN[split_num:, 0]\n", "\n", " X_test = dataTesN[:, 1:]\n", " Y_test = dataTesN[:, 0]\n", "\n", " \n", "\n", " print('> Data Loaded. Compiling...')\n", " global_start_time = time.time()\n", " epochs=30 \n", " model = build_model([10, 30, 30, 30, 30, 1])\n", " model.fit(X_train,Y_train,batch_size=100,nb_epoch=epochs,validation_split=0.1)\n", "\n", "\n", " a=time.time() - global_start_time\n", "\n", " \n", " # 验证\n", " \n", " b=time.time()\n", " Y_pred = model.predict(X_val) #归一化后的验证值\n", " c=time.time()-b\n", " Yp = Y_pred * (Ymax - Ymin) + Ymin #反归一化\n", "\n", "\n", " \n", " # 评价结果采用MAE，预测差值反归一化后，加到基准上和测试进行对比\n", " d=time.time()\n", " YptestN = model.predict(X_test)\n", " e=time.time()-d\n", " Yptest = YptestN * (Ymax - Ymin) + Ymin # 反归一化的差值输出\n", " \n", " # 将偏差加基准后的负值点强制归零\n", " \n", " X_val = dataMsN[split_num:, 1:]\n", " Y_val = dataMsN[split_num:, 0]\n", "\n", " X_test = dataTesN[:, 1:]\n", " Y_test = dataTesN[:, 0]\n", " \n", " \n", " resultA=pd.concat([dataTe.iloc[:,0],pd.DataFrame(dataTe.values[:,1]+Yptest[:,0])],axis=1) \n", " resultA.columns=['True','Pre']\n", " prediction=resultA.values[:,1]\n", " index_0=resultA[resultA['Pre']<0].index\n", " for indexi in index_0:\n", " prediction[indexi]=0 \n", " resultL=pd.concat([pd.DataFrame(resultA.values[:,0]),pd.DataFrame(prediction)],axis=1)\n", " resultL.columns=['True','Pre']\n", " \n", " print(\n", " \"最终测试结果\", test_name, \"MAE=\",\n", " mean_absolute_error(y_true=resultL.values[:,0],\n", " y_pred=resultL.values[:,1]))\n", " print('R2: %0.2f RMSE: %0.2f MAE: %0.2f' % (r2_score(resultL.values[:,0],resultL.values[:,1]),math.sqrt(mean_squared_error(resultL.values[:,0],resultL.values[:,1])),mean_absolute_error(resultL.values[:,0], resultL.values[:,1])))\n", " print('建模时间 (s) : %0.2f '% a )\n", " print('测试时间 : %0.2f '%c )\n", " print('验证时间 : %0.2f '%e )\n", " save_data(data=resultL,name=test_name)\n", " save_pig1(ypre=resultL.values[:,1],ytrue=resultL.values[:,0],name=test_name)\n", " save_pig2(ypre=resultL.values[:,1],ytrue=resultL.values[:,0],name=test_name)\n", " \n", "# 保存预测结果\n", "# save_data(data=resultL,name=test_name)\n", "# mae_list.append(mean_absolute_error(y_true=resultL.values[:,0],y_pred=resultL.values[:,1]))\n", "#test_name_list.append(test_name) " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "oldHeight": 199, "position": { "height": "221px", "left": "915px", "right": "20px", "top": "1px", "width": "446px" }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "varInspector_section_display": "block", "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }