tensorboard

问题 I wrote a CNN image classifier with tensorflow and use tensorboard to monitor the training. However when I stop and restore from a checkpoint, there are overlaps like: I followed the instruction on the Tensorboard README to write a SessionStatus.START message to the summary file, but it doesn't seem to work. This is my code: summary_writer.add_session_log(SessionLog(status=SessionLog.START),global_step=step) 回答1: i dont know if its an answer, but if you put the global_step variable into a

问题 When training an Object Detection DNN with Tensorflows Object Detection API it's Visualization Plattform Tensorboard plots a scalar named regularization_loss_1 What is this? I know what regularization is (to make the Network good at generalizing through various methods like dropout) But it is not clear to me what this displayed loss could be. Thanks! 回答1: TL;DR : it's just the additional loss generated by the regularization function. Add that to the network's loss and optimize over the sum of

问题 I am trying to output some summary scalars in an ML engine experiment at both train and eval time. tf.summary.scalar('loss', loss) is correctly outputting the summary scalars for both training and evaluation on the same plot in tensorboard. However, I am also trying to output other metrics at both train and eval time and they are only outputting at train time. The code immediately follows tf.summary.scalar('loss', loss) but does not appear to work. For example, the code as follows is only

问题 What is the best way to quickly see the updated graph in the most recent event file in an open TensorBoard session? Re-running my Python app results in a new log file being created with potentially new events/graph. However, TensorBoard does not seem to notice those differences, unless restarted. 回答1: It turns out that TensorBoard backend refreshes the logs every minute. This has been reported as a TensorFlow issue. The reload interval can be configured using the --reload_interval flag of the

问题 I am training an object detector and I ran the evaluation job. I see certain graphs in the tensorboard. What is DetectionBoxes_Recall/AR@10 vs AR@100 vs AR@100(medium) in the tensorflowboard as shown. And what is the difference between DetectionBoxes_Precision/mAP, mAP(large), mAP(medium), mAP(small), mAP(0.50IOU) and mAP(0.75IOU)? Please help I am very new to this thank you. 回答1: 'DetectionBoxes_Precision/mAP': mean average precision over classes averaged over IOU thresholds ranging from .5

问题 This article illustrates how to add Runtime statistics to Tensorboard: run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) run_metadata = tf.RunMetadata() summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True), options=run_options, run_metadata=run_metadata) train_writer.add_run_metadata(run_metadata, 'step%d' % i) train_writer.add_summary(summary, i) print('Adding run metadata for', i) which creates the following details in Tensorboard: This is fairly

问题 I presume it is some kind of moving average, but the valid range is between 0 and 1. 回答1: It is called exponential moving average, below is a code explanation how it is created. Assuming all the real scalar values are in a list called scalars the smoothing is applied as follows: def smooth(scalars: List[float], weight: float) -> List[float]: # Weight between 0 and 1 last = scalars[0] # First value in the plot (first timestep) smoothed = list() for point in scalars: smoothed_val = last *

问题 I've only seen a few questions that ask this, and none of them have an answer yet, so I thought I might as well try. I've been using gensim's word2vec model to create some vectors. I exported them into text, and tried importing it on tensorflow's live model of the embedding projector. One problem. It didn't work . It told me that the tensors were improperly formatted. So, being a beginner, I thought I would ask some people with more experience about possible solutions. Equivalent to my code:

问题 I've only seen a few questions that ask this, and none of them have an answer yet, so I thought I might as well try. I've been using gensim's word2vec model to create some vectors. I exported them into text, and tried importing it on tensorflow's live model of the embedding projector. One problem. It didn't work . It told me that the tensors were improperly formatted. So, being a beginner, I thought I would ask some people with more experience about possible solutions. Equivalent to my code:

问题 I installed TensorFlow on my MacBook Pro 10.12.5 from source code by steps described here. https://www.tensorflow.org/install/install_sources TensorFlow itself works well but I cannot run TensorBoard. It seems tensorboard is not installed properly. When I try running tensorboard --logdir=... it says -bash: tensorboard: command not found . And locate tensorboard returns empty. Do I need any additional step to install tensorboard? 回答1: If no other methods work then try this one. It may help you