tensorboard

问题 How to detect source of vanishing gradients in pytorch? By vanishing gradients, I mean then the training loss doesn't go down below some value, even on limited sets of data. I am trying to train some network, and I have the above problem, in which I can't even get the network to over fit, but can't understand the source of the problem. I've spent a long time googling this, and only found ways to prevent over fitting, but nothing about under fitting, or specifically, vanishing gradients. What

问题 How to detect source of vanishing gradients in pytorch? By vanishing gradients, I mean then the training loss doesn't go down below some value, even on limited sets of data. I am trying to train some network, and I have the above problem, in which I can't even get the network to over fit, but can't understand the source of the problem. I've spent a long time googling this, and only found ways to prevent over fitting, but nothing about under fitting, or specifically, vanishing gradients. What

问题 I use Matplotlib to create custom t-SNE embedding plots at each epoch during trainging. I would like the plots to be displayed on Tensorboard in a slider format, like this MNST example: But instead each batch of plots is displayed as separate summaries per epoch, which is really hard to review later. See below: It appears to be creating multiple image summaries with the same name, so appending _X suffix instead of overwriting or adding to slider like I want. Similarly, when I use the family

问题 Tensorboard version: 2.3.0 before I install profile-plugin on Tensorboard: so I followed that order: pip install -U tensorboard-plugin-profile > Successfully installed tensorboard-plugin-profile-2.3.0 and the next time, still find a Profile button in tensorboard I tried to use this URL http://localhost:6006/#profile to open profile but it shows No dashboards are active for the current data set. 回答1: Problem Analysis Your problem sounds like you are using a virtual environment . Probably, you

问题 Tensorboard version: 2.3.0 before I install profile-plugin on Tensorboard: so I followed that order: pip install -U tensorboard-plugin-profile > Successfully installed tensorboard-plugin-profile-2.3.0 and the next time, still find a Profile button in tensorboard I tried to use this URL http://localhost:6006/#profile to open profile but it shows No dashboards are active for the current data set. 回答1: Problem Analysis Your problem sounds like you are using a virtual environment . Probably, you

问题 I've been using the balloon.py example script in the Matterport Mask R-CNN repo [https://github.com/matterport/Mask_RCNN/blob/master/samples/balloon/balloon.py] for learning how to implement tensorboard to monitor the training process. The training itself is going fine, but I've completely failed to implement tensorboard. So far I've added: # create Tensorboard logdir = os.path.join("logs", datetime.datetime.now().strftime("%Y%m%d-%H%M%S")) tensorboard_callback = tf.keras.callbacks

问题 I would like to be able plot the training loss per batch and the average validation loss for the validation set on the same plot in Tensorboard. I ran into this issue when my validation set was too large to fit into memory so required batching and the use of tf.metrics update ops. This question could apply to any Tensorflow metrics you wanted to appear on the same graph in Tensorboard. I am able to plot these two graphs separately (see here) plot the validation-loss-per-validation-batch on

问题 I would like to be able plot the training loss per batch and the average validation loss for the validation set on the same plot in Tensorboard. I ran into this issue when my validation set was too large to fit into memory so required batching and the use of tf.metrics update ops. This question could apply to any Tensorflow metrics you wanted to appear on the same graph in Tensorboard. I am able to plot these two graphs separately (see here) plot the validation-loss-per-validation-batch on

问题 I'm using Tensorflow Object Detection API for detection and localization of one class object in images. For these purposes I use pre-trained faster_rcnn_resnet50_coco_2018_01_28 model. I want to detect under/overfitting after training of the model. I see training loss, but after evaluating Tensorboard only shows mAP and Precision metrics and no loss. Is this possible to plot a validation loss on Tensorboard too? 回答1: There is validation loss. Assuming you're using the latest API, the curve

问题 I'm using Tensorflow Object Detection API for detection and localization of one class object in images. For these purposes I use pre-trained faster_rcnn_resnet50_coco_2018_01_28 model. I want to detect under/overfitting after training of the model. I see training loss, but after evaluating Tensorboard only shows mAP and Precision metrics and no loss. Is this possible to plot a validation loss on Tensorboard too? 回答1: There is validation loss. Assuming you're using the latest API, the curve