工业蒸汽量预测
转载
本项目使用人工神经网络完成蒸汽量回归预测，包括数据处理、异常值处理、相关性分析、模型构建、模型训练、模型预测等步骤。
工业蒸汽量预测
1. 引言
1.1 项目介绍
火力发电的基本原理是：燃料在燃烧时加热水生成蒸汽，蒸汽压力推动汽轮机旋转，然后汽轮机带动发电机旋转，产生电能。在这一系列的能量转化中，影响发电效率的核心是锅炉的燃烧效率，即燃料燃烧加热水产生高温高压蒸汽。锅炉的燃烧效率的影响因素很多，包括锅炉的可调参数，如燃烧给量，一二次风，引风，返料风，给水水量；以及锅炉的工况，比如锅炉床温、床压，炉膛温度、压力，过热器的温度等。本项目使用人工神经网络完成蒸汽量回归预测，包括数据处理、异常值处理、相关性分析、模型构建、模型训练、模型预测等步骤。1.2 数据集介绍
经脱敏后的锅炉传感器采集的数据（采集频率是分钟级别），根据锅炉的工况，预测产生的蒸汽量。
数据集各个字段以及数据类型如下所示：
数据集特征较多，需要筛选有用特征数据集最后一列为需要预测的目标值除最后一列外的数据列都是特征2. 环境准备
2.1 安装环境
In [ ]pip install missingno -q登录后复制
Note: you may need to restart the kernel to use updated packages.登录后复制
2.2 导入所需模块
In [1]import numpy as np import pandas as pd import matplotlib.pyplot as pltimport seaborn as snsimport missingno as msnofrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerfrom sklearn.pipeline import Pipelinefrom sklearn.linear_model import LogisticRegressionfrom sklearn.tree import DecisionTreeClassifierfrom sklearn.ensemble import GradientBoostingClassifierfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import GridSearchCVfrom sklearn.model_selection import cross_val_scorefrom sklearn.metrics import confusion_matrix, classification_report, accuracy_scorefrom sklearn import metricsfrom sklearn.metrics import roc_curve, auc, roc_auc_score登录后复制
3. 数据处理
3.1 读取数据
In [2]IndustrialSteam_train=pd.read_csv(r'data/data178496/zhengqi_train.txt',sep='\t')IndustrialSteam_test=pd.read_csv(r'data/data178496/zhengqi_test.txt',sep='\t')登录后复制 In [ ]
IndustrialSteam_train.head()登录后复制
V0 V1 V2 V3 V4 V5 V6 V7 V8 V9 ... \0 0.566 0.016 -0.143 0.407 0.452 -0.901 -1.812 -2.360 -0.436 -2.114 ... 1 0.968 0.437 0.066 0.566 0.194 -0.893 -1.566 -2.360 0.332 -2.114 ... 2 1.013 0.568 0.235 0.370 0.112 -0.797 -1.367 -2.360 0.396 -2.114 ... 3 0.733 0.368 0.283 0.165 0.599 -0.679 -1.200 -2.086 0.403 -2.114 ... 4 0.684 0.638 0.260 0.209 0.337 -0.454 -1.073 -2.086 0.314 -2.114 ... V29 V30 V31 V32 V33 V34 V35 V36 V37 target 0 0.136 0.109 -0.615 0.327 -4.627 -4.789 -5.101 -2.608 -3.508 0.175 1 -0.128 0.124 0.032 0.600 -0.843 0.160 0.364 -0.335 -0.730 0.676 2 -0.009 0.361 0.277 -0.116 -0.843 0.160 0.364 0.765 -0.589 0.633 3 0.015 0.417 0.279 0.603 -0.843 -0.065 0.364 0.333 -0.112 0.206 4 0.183 1.078 0.328 0.418 -0.843 -0.215 0.364 -0.280 -0.028 0.384 [5 rows x 39 columns]登录后复制
3.2 数据分析及可视化
3.2.1 缺失值检测
所有属性中有2888个非空值，因此没有丢失值。所有功能的数据类型都是float64，可以传入模型，无需进行数据类型转换。In [ ]IndustrialSteam_train.info()登录后复制
3.2.2 缺失值可视化
In [ ]missing_values = msno.bar(IndustrialSteam_train, figsize = (16,5),color = "#483D8B")登录后复制
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3.2.3 描述统计
In [ ]IndustrialSteam_train.describe().T登录后复制
count mean std min 25% 50% 75% maxV0 2888.0 0.123048 0.928031 -4.335 -0.29700 0.3590 0.72600 2.121V1 2888.0 0.056068 0.941515 -5.122 -0.22625 0.2725 0.59900 1.918V2 2888.0 0.289720 0.911236 -3.420 -0.31300 0.3860 0.91825 2.828V3 2888.0 -0.067790 0.970298 -3.956 -0.65225 -0.0445 0.62400 2.457V4 2888.0 0.012921 0.888377 -4.742 -0.38500 0.1100 0.55025 2.689V5 2888.0 -0.558565 0.517957 -2.182 -0.85300 -0.4660 -0.15400 0.489V6 2888.0 0.182892 0.918054 -4.576 -0.31000 0.3880 0.83125 1.895V7 2888.0 0.116155 0.955116 -5.048 -0.29500 0.3440 0.78225 1.918V8 2888.0 0.177856 0.895444 -4.692 -0.15900 0.3620 0.72600 2.245V9 2888.0 -0.169452 0.953813 -12.891 -0.39000 0.0420 0.04200 1.335V10 2888.0 0.034319 0.968272 -2.584 -0.42050 0.1570 0.61925 4.830V11 2888.0 -0.364465 0.858504 -3.160 -0.80325 -0.1120 0.24700 1.455V12 2888.0 0.023177 0.894092 -5.165 -0.41900 0.1230 0.61600 2.657V13 2888.0 0.195738 0.922757 -3.675 -0.39800 0.2895 0.86425 2.475V14 2888.0 0.016081 1.015585 -2.455 -0.66800 -0.1610 0.82975 2.558V15 2888.0 0.096146 1.033048 -2.903 -0.66225 -0.0005 0.73000 4.314V16 2888.0 0.113505 0.983128 -5.981 -0.30000 0.3060 0.77425 2.861V17 2888.0 -0.043458 0.655857 -2.224 -0.36600 0.1650 0.43000 2.023V18 2888.0 0.055034 0.953466 -3.582 -0.36750 0.0820 0.51325 4.441V19 2888.0 -0.114884 1.108859 -3.704 -0.98750 -0.0005 0.73725 3.431V20 2888.0 -0.186226 0.788511 -3.402 -0.67550 -0.1565 0.30400 3.525V21 2888.0 -0.056556 0.781471 -2.643 -0.51700 -0.0565 0.43150 2.259V22 2888.0 0.302893 0.639186 -1.375 -0.06300 0.2165 0.87200 2.018V23 2888.0 0.155978 0.978757 -5.542 0.09725 0.3380 0.36825 1.906V24 2888.0 -0.021813 1.033403 -1.344 -1.19100 0.0950 0.93125 2.423V25 2888.0 -0.051679 0.915957 -3.808 -0.55725 -0.0760 0.35600 7.284V26 2888.0 0.072092 0.889771 -5.131 -0.45200 0.0750 0.64425 2.980V27 2888.0 0.272407 0.270374 -1.164 0.15775 0.3250 0.44200 0.925V28 2888.0 0.137712 0.929899 -2.435 -0.45500 -0.4470 0.73000 4.671V29 2888.0 0.097648 1.061200 -2.912 -0.66400 -0.0230 0.74525 4.580V30 2888.0 0.055477 0.901934 -4.507 -0.28300 0.0535 0.48800 2.689V31 2888.0 0.127791 0.873028 -5.859 -0.17025 0.2995 0.63500 2.013V32 2888.0 0.020806 0.902584 -4.053 -0.40725 0.0390 0.55700 2.395V33 2888.0 0.007801 1.006995 -4.627 -0.49900 -0.0400 0.46200 5.465V34 2888.0 0.006715 1.003291 -4.789 -0.29000 0.1600 0.27300 5.110V35 2888.0 0.197764 0.985675 -5.695 -0.20250 0.3640 0.60200 2.324V36 2888.0 0.030658 0.970812 -2.608 -0.41300 0.1370 0.64425 5.238V37 2888.0 -0.130330 1.017196 -3.630 -0.79825 -0.1855 0.49525 3.000target 2888.0 0.126353 0.983966 -3.044 -0.35025 0.3130 0.79325 2.538登录后复制
3.2.4 样本特征分布可视化
In [ ]hist_plot = IndustrialSteam_train.hist(figsize = (20,20), color = "#483D8B")登录后复制
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3.2.5 相关性分析
用于查看特征与特征之间的相关性查看目标值与特征之间的相关性筛选出与目标值有较强相关性的特征In [ ]from pylab import mplfrom matplotlib.font_manager import FontPropertiesmyfont=FontProperties(fname=r'/usr/share/fonts/fangzheng/FZSYJW.TTF',size=12)sns.set(font=myfont.get_name())corr = IndustrialSteam_train.corr()# 调用热力图绘制相关性关系plt.figure(figsize=(25,25),dpi=150)sns.heatmap(corr, square=True, linewidths=0.1, annot=True)登录后复制
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3.2.6 筛选特征
以0.5为界限，同时在训练集和测试集中去除相关系数绝对值低于0.5的特征，确保被输入模型进行训练的特征与预测目标值有较强的相关性。
In [3]df_train = IndustrialSteam_train[['V0','V1','V3','V4','V8','V12','V16','V31','target']]df_test = IndustrialSteam_test[['V0','V1','V3','V4','V8','V12','V16','V31']]登录后复制
3.3 异常值处理
3.3.1 异常值检测
使用箱型图查看离群点（Outlier）对于离群点考虑使用异常值处理方法In [ ]plt.figure(figsize=(20,10))sns.boxenplot(data = df_train)plt.xticks(rotation=60)plt.show()登录后复制登录后复制
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3.3.2 异常值插补
与缺失值的插补一样，我们也可以插补异常值。我们可以在这种方法中使用均值、中值和零值来插补。由于我们进行了填补，而不是直接删除异常值。因此不会丢失数据。这里的中值是合适的，因为它不受异常值的影响。In [ ]#median imputationimport pandas as pdimport numpy as nptrain = df_trainsns.boxplot(train['V0'])plt.title("Box Plot before median imputation")plt.show()q1 = train['V0'].quantile(0.25)q3 = train['V0'].quantile(0.75)iqr = q3-q1Lower_tail = q1 - 1.5 * iqrUpper_tail = q3 + 1.5 * iqr# V0med = np.median(train['V0'])for i in train['V0']: if i > Upper_tail or i < Lower_tail: train['V0'] = train['V0'].replace(i, med)sns.boxplot(train['V0'])plt.title("Box Plot after median imputation")plt.show() # V1med = np.median(train['V1'])for i in train['V1']: if i > Upper_tail or i < Lower_tail: train['V1'] = train['V1'].replace(i, med)# V3med = np.median(train['V3'])for i in train['V3']: if i > Upper_tail or i < Lower_tail: train['V3'] = train['V3'].replace(i, med)# V4med = np.median(train['V4'])for i in train['V4']: if i > Upper_tail or i < Lower_tail: train['V4'] = train['V4'].replace(i, med)# V8med = np.median(train['V8'])for i in train['V8']: if i > Upper_tail or i < Lower_tail: train['V8'] = train['V8'].replace(i, med)# V12med = np.median(train['V12'])for i in train['V12']: if i > Upper_tail or i < Lower_tail: train['V12'] = train['V12'].replace(i, med)# V16med = np.median(train['V16'])for i in train['V16']: if i > Upper_tail or i < Lower_tail: train['V16'] = train['V16'].replace(i, med)# V31med = np.median(train['V31'])for i in train['V31']: if i > Upper_tail or i < Lower_tail: train['V31'] = train['V31'].replace(i, med)登录后复制 登录后复制登录后复制登录后复制
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.Try using .loc[row_indexer,col_indexer] = value insteadSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy登录后复制
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/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:27: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.Try using .loc[row_indexer,col_indexer] = value insteadSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:33: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.Try using .loc[row_indexer,col_indexer] = value insteadSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:39: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.Try using .loc[row_indexer,col_indexer] = value insteadSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:45: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.Try using .loc[row_indexer,col_indexer] = value insteadSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:51: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.Try using .loc[row_indexer,col_indexer] = value insteadSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:57: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.Try using .loc[row_indexer,col_indexer] = value insteadSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:63: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.Try using .loc[row_indexer,col_indexer] = value insteadSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy登录后复制
查看异常值插补后的数据分布
In [ ]plt.figure(figsize=(20,10))sns.boxenplot(data = df_train)plt.xticks(rotation=60)plt.show()登录后复制登录后复制
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3.4 数据预处理
3.4.1 拆分特征数据与目标值数据
选择'V0','V1','V3','V4','V8','V12','V16','V31'字段作为特征选择'target'字段作为目标值In [4]data_features = df_train.loc[df_train.index[:], ['V0','V1','V3','V4','V8','V12','V16','V31']]data_label = df_train['target']登录后复制
3.4.2 划分数据集
按照0.8：0.2的比例划分训练集与测试集In [5]from sklearn.model_selection import train_test_split# 数据集划分x_train, x_test, y_train, y_test = train_test_split(data_features, data_label, test_size=0.2, random_state=6)print("训练集的特征值：\n", x_train.shape)print("测试集的标签值：\n", y_test.shape)print("The length of original data X is:", data_features.shape[0])print("The length of train Data is:", x_train.shape[0])print("The length of test Data is:", x_test.shape[0])登录后复制 训练集的特征值： (2310, 8)测试集的标签值： (578,)The length of original data X is: 2888The length of train Data is: 2310The length of test Data is: 578登录后复制
3.4.3 重置索引
对于数据集索引进行重置，确保所有数据都从第0条开始排序
In [6]x_train=x_train.reset_index(drop=True)x_test=x_test.reset_index(drop=True)y_train=y_train.reset_index(drop=True)y_test=y_test.reset_index(drop=True)登录后复制
3.4.4 转换数组
对重置完成的各个数据集，将数据转换成矩阵形式供后续使用。
In [7]x_train=np.array(x_train)x_test=np.array(x_test)y_train=np.array(y_train)y_test=np.array(y_test)登录后复制 In [ ]
y_train登录后复制
array([-0.987, 1.142, -2.555, ..., -0.42 , 1.024, 1.046])登录后复制 In [8]
y_train = np.array(y_train)y_train = y_train.reshape(-1,1)y_test = np.array(y_test)y_test = y_test.reshape(-1,1)登录后复制
3. 5 归一化（标准化）
对训练集与测试集进行归一化或者标准化处理本处我们选择标准化In [ ]from sklearn.preprocessing import MinMaxScalerfrom sklearn.preprocessing import StandardScaler# 1. 实例化一个转换器类transfer = StandardScaler()# 2. 标准化x_train = transfer.fit_transform(x_train)x_test = transfer.fit_transform(x_test)y_train = transfer.fit_transform(y_train)y_test = transfer.fit_transform(y_test)# df_test_x = transfer.fit_transform(df_test)登录后复制 In [ ]
x_train[0]登录后复制
array([-0.187, -0.33 , -1.523, 0.6 , -0.437, 0.389, -0.626, -0.057])登录后复制
3.6 设置随机数种子
In [ ]import randomimport paddleseed = 666# 设置随机种子 固定结果def set_seed(seed): np.random.seed(seed) random.seed(seed) paddle.seed(seed)set_seed(seed)登录后复制
4. 模型构建
搭建全连接神经网络
8节点输入1输出节点In [9]import paddleimport paddle.nn as nn# 定义动态图class Classification(paddle.nn.Layer): def __init__(self): super(Classification, self).__init__() self.fc1 = paddle.nn.Linear(8, 1) # 网络的前向计算函数 def forward(self, inputs): pred = self.fc1(inputs) return pred登录后复制
5. 可视化损失函数
In [11]train_nums = []train_costs = []def draw_train_process(iters,train_costs): title="training cost" plt.title(title, fontsize=24) plt.xlabel("iter", fontsize=14) plt.ylabel("cost", fontsize=14) plt.plot(iters, train_costs,color='red',label='training cost') plt.grid() plt.show()登录后复制 6. 自定义损失函数
In [20]import paddleimport paddle.nn.functional as Fclass kl_loss(paddle.nn.Layer): def __init__(self): super(kl_loss, self).__init__() def forward(self, p, q, label): ce_loss = 0.5 * (F.mse_loss(p, label=label)) + F.mse_loss(q, label=label) kl_loss = self.compute_kl_loss(p, q) # carefully choose hyper-parameters loss = ce_loss + 0.3 * kl_loss return loss def compute_kl_loss(self, p, q): p_loss = F.kl_div(F.log_softmax(p, axis=-1), F.softmax(q, axis=-1), reduction='none') q_loss = F.kl_div(F.log_softmax(q, axis=-1), F.softmax(p, axis=-1), reduction='none') # You can choose whether to use function "sum" and "mean" depending on your task p_loss = p_loss.sum() q_loss = q_loss.sum() loss = (p_loss + q_loss) / 2 return loss登录后复制
7. 模型训练
7.1 开启训练
超参数设定如下：
BATCH_SIZE = 16EPOCH_NUM = 20learning_rate=0.0002 损失函数：kl_lossIn [33]import paddle.nn.functional as Fimport paddley_preds = []labels_list = []BATCH_SIZE = 16train_data = x_traintrain_data_y = y_traintest_data = x_testtest_data_y = y_testcompute_kl_loss = kl_loss()CET_loss = paddle.nn.CrossEntropyLoss()def train(model): print('start training ... ') # 开启模型训练模式 model.train() EPOCH_NUM = 20 train_num = 0 scheduler = paddle.optimizer.lr.CosineAnnealingDecay(learning_rate=0.0002, T_max=int(train_data.shape[0]/BATCH_SIZE*EPOCH_NUM), verbose=False) optimizer = paddle.optimizer.Adam(learning_rate=scheduler, parameters=model.parameters()) for epoch_id in range(EPOCH_NUM): # 在每轮迭代开始之前，将训练数据的顺序随机的打乱 np.random.shuffle(train_data) # 将训练数据进行拆分，每个batch包含8条数据 mini_batches = [np.append(train_data[k: k+BATCH_SIZE], train_data_y[k: k+BATCH_SIZE], axis = 1) for k in range(0, len(train_data), BATCH_SIZE)] for batch_id, data in enumerate(mini_batches): features_np = np.array(data[:, :8], np.float32) labels_np = np.array(data[:, -1:], np.float32) features = paddle.to_tensor(features_np) labels = paddle.to_tensor(labels_np) #前向计算 #y_pred = model(features) y_pred1 = model(features) y_pred2 = model(features) cost = compute_kl_loss(y_pred1, y_pred2, label=labels) # cost = CET_loss(y_pred, labels) #cost = F.mse_loss(y_pred, label=labels) train_cost = cost.numpy()[0] #反向传播 cost.backward() #最小化loss，更新参数 optimizer.step() # 清除梯度 optimizer.clear_grad() if batch_id % 500 == 0 and epoch_id % 1 == 0: print("Pass:%d,Cost:%0.5f"%(epoch_id, train_cost)) train_num = train_num + BATCH_SIZE train_nums.append(train_num) train_costs.append(train_cost)model = Classification()train(model)登录后复制 start training ... Pass:0,Cost:2.43391Pass:1,Cost:3.92359Pass:2,Cost:2.98257Pass:3,Cost:2.93184Pass:4,Cost:2.18770Pass:5,Cost:3.19956Pass:6,Cost:4.07202Pass:7,Cost:2.55369Pass:8,Cost:3.19636Pass:9,Cost:3.43391Pass:10,Cost:2.27505Pass:11,Cost:1.95374Pass:12,Cost:2.40070Pass:13,Cost:3.80006Pass:14,Cost:2.00660Pass:15,Cost:3.59392Pass:16,Cost:2.63512Pass:17,Cost:2.65104Pass:18,Cost:2.91626Pass:19,Cost:2.96661登录后复制
7.2 训练过程可视化
In [41]import matplotlibimport matplotlib.pyplot as pltimport warningswarnings.filterwarnings('ignore')%matplotlib inlinedraw_train_process(train_nums, train_costs)登录后复制 登录后复制登录后复制登录后复制
8. 模型预测
使用测试集数据进行预测对预测后的数据进行形状转换方便后续可视化In [36]train_data = x_traintrain_data_y = y_traintest_data = x_testtest_data_y = y_testdef predict(model): print('start evaluating ... ') model.eval() outputs = [] mini_batches = [np.append(test_data[k: k+BATCH_SIZE], test_data_y[k: k+BATCH_SIZE], axis = 1) for k in range(0, len(test_data), BATCH_SIZE)] for data in mini_batches: features_np = np.array(data[:, :8], np.float32) features = paddle.to_tensor(features_np) pred = model(features) out = paddle.argmax(pred, axis=1) outputs.extend(out.numpy()) return outputsoutputs = predict(model)登录后复制 start evaluating ...登录后复制 In [38]
predict_result = []for infer_feature in test_data: infer_feature = infer_feature.reshape(1, 8) infer_feature = paddle.to_tensor(np.array(infer_feature, dtype='float32')) result = model(infer_feature) predict_result.append(result)print(predict_result)登录后复制
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stop_gradient=False, [[0.39858559]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.31886303]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.29493463]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.00735997]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.42210022]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.06257382]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.76618671]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.59000134]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.81734991]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.48811778]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.50484604]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.17658579]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.15134256]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.22699168]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.11169553]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.96924913]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.01308975]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.80368865]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.40497336]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.16192749]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.14131603]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[1.22107267]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.39507094]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.03073066]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.27309778]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.00708114]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.57821071]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.33658504]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.11668704]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.66753232]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.97967565]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.16306505]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.05496188]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.51948881]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.67708051]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.05163373]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.00239483]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.14013566]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.34191847]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.25270984]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.11080459]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.48523274]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.19357768]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.17594978]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.07142785]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.20822111]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.08473505]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.15897757]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.13883314]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.34538117]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.07930353]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.35454777]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.58794606]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.33299047]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.25655201]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.10445303]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.04779747]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.10455710]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.16646618]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.31524831]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.04964443]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.58060354]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.02770084]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.83897114]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.02168062]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.35580465]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.39009991]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.13909817]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.08075938]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.18590415]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.56150711]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.18525685]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.01324606]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.07201144]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.28585029]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.90698087]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.20296726]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.39485294]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.48382780]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.18443805]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.38270891]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.79779136]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.19123754]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[1.21670949]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.01447338]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.12670536]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.01824123]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.14000210]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.16719995]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.18032512]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.18801895]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.20721790]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.75916696]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.21391419]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.22957066]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.35765213]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.57816088]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.53739858]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.08023025]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.23322487]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.27490732]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.11255787]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.90063202]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.21748936]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.46920335]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-1.03563023]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.07641967]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.28591272]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.60917199]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.27543157]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.30377823]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.01602978]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.36365208]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.63090140]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.06347218]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.05933932]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.70915890]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.72446680]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.96640921]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.29156765]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.35821834]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.42687842]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.43299255]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.41873679]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.24981216]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.69104838]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.04902317]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.06218195]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.31550786]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.45310614]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.17188393]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.12393215]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.57967651]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.20886803]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.14294177]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.49980709]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.28482857]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.00266491]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.56916320]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.31185475]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.71436346]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.66652203]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.32024388]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.45304009]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.32588464]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.16148154]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.42767087]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.60954678]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.85734975]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.55337751]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.25894463]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.32260633]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.03098416]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.13747558]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.09666431]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.45293269]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.19382098]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.40163389]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.42662674]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.05361977]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.38799116]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.02736823]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.02932636]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.10339427]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.46115249]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.36047184]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.33328989]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.11792254]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.06596407]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.10048011]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.21324658]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.11020529]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.08897623]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.17561601]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.11536156]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.93352878]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.35112262]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.22222342]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.04151958]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.39091966]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.44056484]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.50789940]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-1.01449597]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.64409053]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.03456168]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.40700445]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.16290851]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[-0.12790632]]), Tensor(shape=[1, 1], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[0.41777393]])]登录后复制 In [39]
predict_result = np.array(predict_result)predict_result_new = predict_result.reshape(-1,1)test_data_y_new = test_data_y登录后复制
9. 可视化预测值与真实值
In [40]# 绘制预测与真值结果plt.figure(figsize=(12,5), dpi=80)plt.plot(test_data_y_new[:100], label="True value")plt.plot(predict_result_new[:100], label="Pred value")plt.xlabel("Sample",fontproperties = 'Times New Roman', size = 18)plt.ylabel("Value",fontproperties = 'Times New Roman', size = 18)plt.legend(loc='best')plt.yticks(fontproperties = 'Times New Roman', size = 18)plt.xticks(fontproperties = 'Times New Roman', size = 18)plt.title("True VS Pred",fontproperties = 'Times New Roman', size = 18)plt.legend(loc="best")plt.show()登录后复制 登录后复制
10. 总结
本项目搭建人工神经网络实现了蒸汽量回归预测。做了数据处理部分，包括异常值处理、相关性分析等。在今后，可以考虑使用其他网络模型进行预测，例如卷积网络。另外，可以尝试进一步优化超参数来优化模型。免责声明
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