Keras-还原特定时间戳的LSTM隐藏状态

LSTM - Making predictions on partial sequence ). 如前一个问题所述,我已经训练了 有状态的

[Feature 1,Feature 2, .... ,Feature 3][Label 1]
[Feature 1,Feature 2, .... ,Feature 3][Label 2]
...
[Feature 1,Feature 2, .... ,Feature 3][Label 100]

型号代码:

def build_model(num_samples, num_features, is_training):
    model = Sequential()
    opt = optimizers.Adam(lr=0.0005, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0001)

    batch_size = None if is_training else 1
    stateful = False if is_training else True
    first_lstm = LSTM(32, batch_input_shape=(batch_size, num_samples, num_features),  return_sequences=True,
                      activation='tanh', stateful=stateful)

    model.add(first_lstm)
    model.add(LeakyReLU())
    model.add(Dropout(0.2))
    model.add(LSTM(16, return_sequences=True, activation='tanh', stateful=stateful))
    model.add(Dropout(0.2))
    model.add(LeakyReLU())
    model.add(LSTM(8, return_sequences=True, activation='tanh', stateful=stateful))
    model.add(LeakyReLU())
    model.add(Dense(1, activation='sigmoid'))

    if is_training:
        model.compile(loss='binary_crossentropy', optimizer=opt,
                      metrics=['accuracy', f1])
    return model

当预测时,模型是 无国籍的

[Feature 1,Feature 2, .... ,Feature 10][Label 1] -> (model) -> probability

打电话 model.reset_states() 模型加工完一批100个样品后。模型运行良好,效果良好。

多源

我的问题:

当我测试我的模型时,我可以控制样本的顺序,我可以确保样本来自同一个来源。i、 e前100个样本都来自源1,然后在调用 模型。重置状态()

但是,在我的生产环境中,示例以异步方式到达,例如:

先从源1采集3个样本,然后从源2采集2个样本

我的问题是: