I have an LSTM network and I use it to predict the load. I want to get the confidence interval for the prediction. I am not sure that I can get that or not. I have tried and search in in the different platform, however, I could not find the solution. Here is my simple model.
import pandas as pd pd.options.mode.chained_assignment = None # default='warn' import numpy as np
import tensorflow as tf from datetime import datetime from tensorflow import keras from keras.models import Sequential from keras.layers import LSTM, Dense, Dropout from keras.layers.recurrent import LSTM from matplotlib import pyplot as plt from sklearn.preprocessing import StandardScaler X_train = np.random.rand(10,5,2) Y_train = np.random.rand(10,2) X_test = np.random.rand(3, 5, 2) model = Sequential() model.add(LSTM(64, activation='relu', input_shape=(X_train.shape, X_train.shape), return_sequences=True)) model.add(LSTM(32, activation='relu', return_sequences=False)) model.add(Dropout(0.2)) model.add(Dense(Y_train.shape, kernel_regularizer='l2')) opt = keras.optimizers.Adam(learning_rate=0.001) model.compile(optimizer=opt, loss='mse') history = model.fit(X_train, Y_train, epochs=1, batch_size=200, validation_split=0.1,verbose=1) prediction = model.predict(X_test)