为什么LSTM模型在多个模型运行中产生不同的预测?

我使用长期短期记忆(LSTM)来生成预测。我注意到,每次运行LSTM模型时,它都会使用相同的数据生成稍微不同的预测。我想知道为什么会这样,如果我做错了什么?

谢谢您

from numpy import array
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import TimeDistributed
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D

# split a univariate sequence into samples
def split_sequence(sequence, n_steps):
    X, y = list(), list()
    for i in range(len(sequence)):
        # find the end of this pattern
        end_ix = i + n_steps
        # check if we are beyond the sequence
        if end_ix > len(sequence)-1:
            break
        # gather input and output parts of the pattern
        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]
        X.append(seq_x)
        y.append(seq_y)
    return array(X), array(y)

def LSTM_Model(Data, N_Steps, Epochs):
    # define input sequence
    raw_seq = Data

    # choose a number of time steps
    n_steps_og = N_Steps

    # split into samples
    X, y = split_sequence(raw_seq, n_steps_og)

    # reshape from [samples, timesteps] into [samples, subsequences, timesteps, features]
    n_features = 1
    n_seq = 2
    n_steps = 2
    X = X.reshape((X.shape[0], n_seq, n_steps, n_features))


    # define model
    model = Sequential()
    model.add(TimeDistributed(Conv1D(filters=64, kernel_size=1, activation='relu'), input_shape=(None, n_steps, n_features)))
    model.add(TimeDistributed(MaxPooling1D(pool_size=2)))
    model.add(TimeDistributed(Flatten()))
    model.add(LSTM(50, activation='relu'))
    model.add(Dense(1))
    model.compile(optimizer='adam', loss='mse')


   # fit model
   model.fit(X, y, epochs=Epochs, verbose=2)
   #Create Forcasting data
   #Now take the last 4 days of the Model data for the forcast
   Forcast_data = Data[len(new_data) - n_steps_og:]

   # demonstrate prediction
   x_input = array(Forcast_data)
   x_input = x_input.reshape((1, n_seq, n_steps, n_features))
   yhat = float(model.predict(x_input, verbose=0))
   return(yhat)