相关疑难解决方法(0)

我正在使用张量流中的双向动态RNN进行文本标记.在加工输入维度后,我尝试运行一个Session.这是blstm设置部分:

fw_lstm_cell = BasicLSTMCell(LSTM_DIMS)
bw_lstm_cell = BasicLSTMCell(LSTM_DIMS)

(fw_outputs, bw_outputs), _ = bidirectional_dynamic_rnn(fw_lstm_cell,
                                                        bw_lstm_cell,
                                                        x_place,
                                                        sequence_length=SEQLEN,
                                                        dtype='float32')

这是运行部分:

  with tf.Graph().as_default():
    # Placehoder Settings
    x_place, y_place = set_placeholder(BATCH_SIZE, EM_DIMS, MAXLEN)

    # BLSTM Model Building
    hlogits = tf_kcpt.build_blstm(x_place)

    # Compute loss
    loss = tf_kcpt.get_loss(log_likelihood)

    # Training
    train_op = tf_kcpt.training(loss)

    # load Eval method
    eval_correct = tf_kcpt.evaluation(logits, y_place)

    # Session Setting & Init
    init = tf.global_variables_initializer()
    sess = tf.Session()
    sess.run(init)

    # tensor summary setting
    summary = tf.summary.merge_all()
    summary_writer = tf.summary.FileWriter(LOG_DIR, sess.graph)

    # Save
    saver …

I am applying transfer-learning on a pre-trained network using the GPU version of keras. I don't understand how to define the parameters max_queue_size, workers, and use_multiprocessing. If I change these parameters (primarily to speed-up learning), I am unsure whether all data is still seen per epoch.

max_queue_size:

• maximum size of the internal training queue which is used to "precache" samples from the generator

• Question: Does this refer to how many batches are prepared on CPU? How …