tensorboard

问题 I created a few summary ops throughout my graph like so: tf.summary.scalar('cross_entropy', cross_entropy) tf.summary.scalar('accuracy', accuracy) and of course merged and got a writer: sess = tf.InteractiveSession() summaries = tf.summary.merge_all() train_writer = tf.summary.FileWriter(TENSORBOARD_TRAINING_DIR, sess.graph) tf.global_variables_initializer().run() and I write these in each training iteration: summary, acc = sess.run([summaries, accuracy], feed_dict={...}) train_writer.add

问题 Is there any way that I can log the validaton loss and accuracy to tensorboard when using tf-slim? When I was using keras, the following code can do this for me: model.fit_generator(generator=train_gen(), validation_data=valid_gen(),...) Then the model will evaluate the validation loss and accuracy after each epoch, which is very convenient. But how to achieve this using tf-slim? The following steps are using primitive tensorflow, which is not what I want: with tf.Session() as sess: for step

问题 Running TensorBoard r0.9 results in graph visualizations as expected but all events and histograms that successfully displayed in r0.8 are not. Has r0.9 introduced a change to the command line that should be used to launch TensorBoard, or to the code needed to generate events and histograms for TensorBoard to display? Note that neither new summaries and histograms written with recent runs using r0.9 TensorFlow, nor existing ones written (and displayed) in the past, are displayed. Graphs

问题 Running TensorBoard r0.9 results in graph visualizations as expected but all events and histograms that successfully displayed in r0.8 are not. Has r0.9 introduced a change to the command line that should be used to launch TensorBoard, or to the code needed to generate events and histograms for TensorBoard to display? Note that neither new summaries and histograms written with recent runs using r0.9 TensorFlow, nor existing ones written (and displayed) in the past, are displayed. Graphs

问题 I see tensorflow support AWS s3 file system (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/platform/s3) but I am unable to use the S3 path with tensorboard. I tried latest nightly 0.4.0rc3 but no luck. I built locally also and made sure Do you wish to build TensorFlow with Amazon S3 File System support? [Y/n]: set to YES but still i don't see tensorboard --logdir=s3://bucket/path working at all. Am I missing something here? 回答1: If you start a tensorboard by using AWS

问题 I am using Tensorflow to build up the Neural Network, and I would like to show training results on the Tensorboard. So far everything works fine. But I have a question on "event file" for the Tensorboard. I notice that every time when I run my python script, it generates different event files. And when I run my local server using $ python /usr/local/lib/python2.7/dist-packages/tensorflow/tensorboard/tensorboard.py --logdir=/home/project/tmp/ , it shows up error if there are more than 1 event

问题 I'm attempting to visualize Google's Inception v3 model using TensorBoard in TensorFlow 0.7.1 and am unable to do so. The TensorBoard Graph tab stalls with the statement Data : Reading graph.pbtxt I downloaded an un-Tarred the inception v3 model. The graph protobuffer is in /tmp/imagenet/classify_image_graph_def.pb . Here's my code to dump the model: import os import os.path import tensorflow as tf from tensorflow.python.platform import gfile INCEPTION_LOG_DIR = '/tmp/inception_v3_log' if not

问题 I am not able to connect tensorboard to my Google Cloud Platform as I am facing the following error: Command that I am running: gcloud auth application-default login tensorboard --logdir=gs://mybucket_which_contains_train_and_eval_directories Stacktrace: Exception in thread Reloader: Traceback (most recent call last): File "c:\python\python35\lib\threading.py", line 914, in _bootstrap_inner self.run() File "c:\python\python35\lib\threading.py", line 862, in run self._target(*self._args, *

问题 I am using Tensorflow DNNRegressor Estimator model for making a neural network. But calling estimator.train() function is giving output as follows: I.e. my loss function is varying a lot with every step. But as far as I know, my loss function should decrease with no of iterations. Also, find the attached screenshot for Tensorboard Visualisation for loss function: The doubts I'm not able to figure out are: Whether it is overall loss function value (combined loss for every step processed till

问题 I am using Tensorflow DNNRegressor Estimator model for making a neural network. But calling estimator.train() function is giving output as follows: I.e. my loss function is varying a lot with every step. But as far as I know, my loss function should decrease with no of iterations. Also, find the attached screenshot for Tensorboard Visualisation for loss function: The doubts I'm not able to figure out are: Whether it is overall loss function value (combined loss for every step processed till