我们使用Keras序列模型来创建和运行预测天气变量的模型然而，模型的预测结果与输入值存在相同的问题，我们减少了隐藏的人数，降低了学习率，并应用了提前停止。但问题还没解决我不知道怎么解决

import numpy as np
import tensorflow as tf

import pandas as pd

from keras.models import Sequential
from keras.layers import Embedding, LSTM, GRU, Dense, Dropout
from keras.models import load_model
from keras.optimizers import Adam
from keras.preprocessing import sequence
from keras.callbacks import EarlyStopping

from sklearn.model_selection import train_test_split

from sklearn import preprocessing

df1 = pd.DataFrame({'T' : df['기온(°C)'],
                    'WS' : df['풍속(m/s)'],
                    'RH' : df['습도(%)'],
                    'P' : df['해면기압(hPa)'],
                    'VS' : df['시정(10m)'],
                    'TD' : df['이슬점온도(°C)']})

wfm.add(Dense(12, input_dim = 6, activation = 'tanh'))
wfm.add(Dense(12, activation = 'tanh'))
wfm.add(Dense(6, activation = 'relu'))
wfm.add(Dense(6, activation = 'relu'))
wfm.add(Dense(6, activation = 'relu'))
wfm.add(Dense(6, activation = 'relu'))
wfm.add(Dense(6, activation = 'relu'))
wfm.add(Dense(6, activation = 'relu'))

wfm.compile(loss = 'mean_squared_logarithmic_error', optimizer = tf.keras.optimizers.Adam(learning_rate = 0.001))

wfm.fit(x_train, y_train, epochs = 100, batch_size = 10,
        validation_data = (x_val, y_val))

def predict(data):
  result = wfm.predict(data)

  result = scaler.inverse_transform(data)

  print("1시간 뒤 기상변수 예측")
  print("기온 : ", result[0,0], "degreeC")
  print("풍속 : ", result[0,1], "m/s")
  print("습도 : ", result[0,2], "%")
  print("기압 : ", result[0,3], "hPa")
  print("시정 : ", result[0,4]*10, "m")
  print("이슬점 온도 : ", result[0,5], "degreeC")

  n = pd.DataFrame({'T' : [35.2],
                    'WS' : [0.8],
                    'RH' : [48],
                    'P' : [1004.3],
                    'VS' : [1963],
                    'TD' : [22.5]})

  now = (n - df1.min())/(df1.max() - df1.min())

  predict(now)

字符串
这就是结果

1/1 [==============================] - 0s 129ms/step
1시간 뒤 기상변수 예측
기온 :  35.2 degreeC
풍속 :  0.8 m/s
습도 :  48.0 %
기압 :  1004.3 hPa
시정 :  19629.999999999996 m
이슬점 온도 :  22.50000000000001 degreeC

型
使用MinMaxScaler将df 1标准化为scikit-learn