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我正在尝试在 medium 上遵循这个RNN 教程，并在进行过程中对其进行重构。当我运行我的代码时，它似乎可以工作，但是当我尝试打印出current state变量以查看神经网络内部发生了什么时，我得到了所有1s。这是预期的行为吗？状态是否由于某种原因没有更新？据我了解，current state应该包含所有批次的隐藏层中的最新值，所以它绝对不应该都是1s。任何帮助将不胜感激。

def __train_minibatch__(self, batch_num, sess, current_state):
    """
    Trains one minibatch.

    :type batch_num: int
    :param batch_num: the current batch number.

    :type sess: tensorflow Session
    :param sess: the session during which training occurs.

    :type current_state: numpy matrix (array of arrays)
    :param current_state: the current hidden state

    :type return: (float, numpy matrix)
    :param return: (the calculated loss for this minibatch, the updated hidden state)
    """
    start_index = batch_num * self.settings.truncate
    end_index = start_index + self.settings.truncate

    batch_x = self.x_train_batches[:, start_index:end_index]
    batch_y = self.y_train_batches[:, start_index:end_index]
    total_loss, train_step, current_state, predictions_series = sess.run(
        [self.total_loss_fun, self.train_step_fun, self.current_state, self.predictions_series],
        feed_dict={
            self.batch_x_placeholder:batch_x, 
            self.batch_y_placeholder:batch_y, 
            self.hidden_state:current_state
        })
    return total_loss, current_state, predictions_series
# End of __train_minibatch__()

def __train_epoch__(self, epoch_num, sess, current_state, loss_list):
    """
    Trains one full epoch.

    :type epoch_num: int
    :param epoch_num: the number of the current epoch.

    :type sess: tensorflow Session
    :param sess: the session during training occurs.

    :type current_state: numpy matrix
    :param current_state: the current hidden state.

    :type loss_list: list of floats
    :param loss_list: holds the losses incurred during training.

    :type return: (float, numpy matrix)
    :param return: (the latest incurred lost, the latest hidden state)
    """
    self.logger.info("Starting epoch: %d" % (epoch_num))

    for batch_num in range(self.num_batches):
        # Debug log outside of function to reduce number of arguments.
        self.logger.debug("Training minibatch : ", batch_num, " | ", "epoch : ", epoch_num + 1)
        total_loss, current_state, predictions_series = self.__train_minibatch__(batch_num, sess, current_state)
        loss_list.append(total_loss)
    # End of batch training

    self.logger.info("Finished epoch: %d | loss: %f" % (epoch_num, total_loss))
    return total_loss, current_state, predictions_series
# End of __train_epoch__()

def train(self):
    """
    Trains the given model on the given dataset, and saves the losses incurred
    at the end of each epoch to a plot image.
    """
    self.logger.info("Started training the model.")
    self.__unstack_variables__()
    self.__create_functions__()
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        loss_list = []

        current_state = np.zeros((self.settings.batch_size, self.settings.hidden_size), dtype=float)
        for epoch_idx in range(1, self.settings.epochs + 1):
            total_loss, current_state, predictions_series = self.__train_epoch__(epoch_idx, sess, current_state, loss_list)
            print("Shape: ", current_state.shape, " | Current output: ", current_state)
            # End of epoch training

    self.logger.info("Finished training the model. Final loss: %f" % total_loss)
    self.__plot__(loss_list)
    self.generate_output()
# End of train()

更新

在完成教程的第二部分并使用内置的 RNN api 后，问题就消失了，这意味着我使用current_state变量的方式有问题，或者更改了 tensorflow API 导致了一些古怪的事情发生（我不过，我很确定是前者）。如果有人有明确的答案，我会留下这个问题。

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1 回答 1

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首先，您应该确保“它似乎可以工作”是正确的，并且您的测试错误确实越来越低。

我有一个假设是最后一批被破坏了，因为数据的长度total_series_length / batch_size不是truncated_backprop_length. （我没有检查它是否确实发生了它用零填充。教程中的代码太旧，无法在我的 tf 版本上运行，我们没有你的代码。）最后一个小批量，最后只有零可以导致最终current_state收敛到所有的。在任何其他小批量current_state上都不会是全部。

您可以尝试在current_state每次运行时sess.run打印__train_minibatch__. 或者也许只是每 1000 个小批量打印一次。

于 2017-08-18T02:36:25.377 回答