tensorboard

问题 I have a couple of own tfrecord file made by myself. They are working perfectly in tf1, I used them in several projects. However if i want to use them in Tensorflow Object Detection API with tf2 (running the model_main_tf2.py script), I see the following in tensorboard: tensorboard images tab It totally masses up the images. (Running the /work/tfapi/research/object_detection/model_main.py script or even legacy_train and they looks fine) Is tf2 using different kind of encoding in tfrecords? Or

来源： https://stackoverflow.com/questions/63855334/why-does-tensorboard-not-show-all-metrics

来源： https://stackoverflow.com/questions/49127214/keras-how-to-output-learning-rate-onto-tensorboard

来源： https://stackoverflow.com/questions/43763858/change-images-slider-step-in-tensorboard

来源： https://stackoverflow.com/questions/63893714/tensorboard-hangs-when-visualizing-an-object-detection-graph

来源： https://stackoverflow.com/questions/62234889/tensorboard-got-stuck-in-namespace-hierarchy-finding-similar-subgraphs

来源： https://stackoverflow.com/questions/61549039/im-getting-an-error-of-modifiedtensorboard-object-has-no-attribute-write-lo

来源： https://stackoverflow.com/questions/56036922/how-to-kill-tensorboard-with-tensorflow2-jupyter-win

问题 I'm trying to implement smart reply https://www.tensorflow.org/lite/models/smart_reply/overview concept in Python. You can download tflite model file here https://storage.googleapis.com/download.tensorflow.org/models/tflite/smartreply_1.0_2017_11_01.zip. import numpy as np import tensorflow as tf interpreter = tf.lite.Interpreter(model_path="smartreply.tflite") interpreter.allocate_tensors() While using above code, i'm getting this error, Traceback (most recent call last): File "smart_reply

问题 I have a Tensorboard log file with 5 million samples. Tensorboard downsamples it when loading so that I don't run out of memory, but it's possible to override this behavior with --samples_per_plugin and load all of them. If I do this, I will run out of memory. Suppose I want to load the first 1000 samples without downsampling (e.g. if I'm interested in the details of what's happening to my network at the beginning of training). Is there a way to have Tensorboard load only a specified subset