Hello, I have the problem, that my LSTM Model does noth predict the correct output.
It does not predict the correct training output, although it was trained with this output…nor does it predict the correct test data output.
Can anyone help?
You can find the data here:
And here is my code (but don’t know if that helps, since something seems to be really wrong ?!):
import pandas as pd
import tensorflow as tf
import pandas as pd
from tensorflow.keras import layers
from keras.models import Sequential
import numpy as np
from keras.layers import LSTM
from keras.layers import Dense
df_svt = pd.read_csv(“D:/datas.csv”, sep=";")
df_svt = df_svt.iloc[6980:]
df_svt = df_svt.iloc[:8158]
df_y = pd.DataFrame(df_svt.y)
df_y_train = df_y.iloc[:6501]
df_y_test = df_y.iloc[6501:8158]
y_train = df_y_train.to_numpy()
y_test = df_y_test.to_numpy()
df_svt = df_svt.drop(columns=[“y”])
df_x_train = df_svt.iloc[:6501]
df_x_test = df_svt.iloc[6501:8158]
x_train = df_x_train.to_numpy()
x_test = df_x_test.to_numpy()
train_X = x_train.reshape(6501,1,7)
test_X = x_test.reshape(1657,1,7)
train_Y = y_train.reshape(6501,1)
test_Y = y_test.reshape(1657,1)
model = tf.keras.Sequential()
model.add(LSTM(units=70,recurrent_activation=“sigmoid”, return_sequences=True , input_shape=(train_X.shape[1],train_X.shape[2]) ))
model.add(LSTM(units=70, recurrent_activation=“sigmoid”,return_sequences=True))
model.add(LSTM(units=30, recurrent_activation=“sigmoid”, return_sequences=False,activation=“softmax” ))
model.add(Dense(units=1))
model.compile(optimizer=“adam”,loss=“mean_squared_error”, metrics=“accuracy”)
model.fit(train_X, train_Y, epochs=200, verbose=1)
model.summary()
train = model.predict(train_X)
predict = model.predict(test_X)
print(train)
print(predict)
