如何创建一个模型来培训所有用户？

Question

我的数据集包含1000个用户的10天数据。我正在为每个用户培训和测试数据，以提高预测准确性。在下面的代码中，每个用户都有自己的模型。如何为所有用户创建全局模型（一个模型）。每个用户有96个测试数据，重新培训用于培训。由于位置点是分类的，因此实现了一种热编码来对位置点进行编码。

list = list_users[:100]
with open("accuracy_Lstm.csv","w") as f:
    f.write('user,LSTM \n')
    for user in list:
        user_data = newdataframe[newdataframe.user==user]
        encoded=encoding(user_data) #One hot encoding
        X_train = []
        y_train = []
        for i in range(1, len(encoded)-96):
            X_train.append(encoded[i-1])
            y_train.append(encoded[i])
        X_train, y_train = np.array(X_train), np.array(y_train)
        X_test = encoded[-192:-96,:]
        X_true = encoded[-96:,:]
        X_trainL=X_train.reshape(X_train.shape[0],1,X_train.shape[1])
        time_steps = 1
    #Lstm
        model = Sequential()
        model.add(LSTM(X_train.shape[1], input_shape=(time_steps,X_train.shape[1]), activation='relu'))
        model.add(Dense(X_train.shape[1]))
        model.compile(loss='mse', optimizer='adam')
        model.fit(X_trainL, y_train, batch_size=96, epochs=100, verbose =1)
        model.summary()

        X_testL=X_test.reshape(X_test.shape[0],1,X_test.shape[1])

        pedL =one_hot_decode(model.predict(X_testL))
        true=one_hot_decode(X_true)
        try:
            accuracy = ((sum(x == y for x, y in zip(pedL, true)))/(len(pedL)))*100
        except ZeroDivisionError:
            accuracy = 0
        f.write(' %d,  %f \n'%(user, accuracy))

如何为所有用户创建一个模型？