tensorboard

TensorBoard not working

由 亡梦爱人 提交于 2019-12-03 11:38:24
I'm able to use TensorFlow just fine. But I can't yet use TensorBoard at all. I'm following the instructions on tensorflow.org's Visualizing Learning page . When I run tensorboard --logdir=/tmp/mnist_logs --debug I get the following INFO:tensorflow:TensorBoard is in debug mode. INFO:tensorflow:Starting TensorBoard in directory /private/tmp/mnist_logs INFO:tensorflow:TensorBoard path_to_run is: {'/tmp/mnist_logs': None} INFO:tensorflow:Adding events from directory /tmp/mnist_logs INFO:tensorflow:Constructing EventAccumulator for /tmp/mnist_logs DEBUG:tensorflow:Opening a record reader pointing

How to run Tensorboard from python scipt in virtualenv?

由 蓝咒 提交于 2019-12-03 11:25:32
问题 Tensorboard should be started from commnad line like that: tensorboard --logdir=path I need to run it from code. Until now I use this: import os os.system('tensorboard --logdir=' + path) However tensorboard do not start because is not included in the system path. I use PyCharm with virtualenv on windows. I don't want to change system paths so the only option is to run it from virtualenv. How to do this? 回答1: Probably a bit late for an answer, but this is what worked for me in Python 3.6.2:

TensorBoard Distributions and Histograms with Keras and fit_generator

由 浪尽此生 提交于 2019-12-03 11:19:45
问题 I'm using Keras to train a CNN using the fit_generator function. It seems to be a known issue that TensorBoard doesn't show histograms and distributions in this setup. Did anybody figure out a way to make it work anyway? 回答1: There is no easy way to just plug it in with one line of code, you have to write your summaries by hand. Good news is that it's not difficult and you can use the TensorBoard callback code in Keras as a reference. (There is also a version 2 in preparation.) Basically,

TensorBoard: How to plot histogram for gradients?

由 时间秒杀一切 提交于 2019-12-03 09:38:35
问题 TensorBoard had the function to plot histograms of Tensors at session-time. I want a histogram for the gradients during training. tf.gradients(yvars,xvars) returns a list a gradients. However, tf.histogram_summary('name',Tensor) accepts only Tensors, not lists of Tensors. For the time being, I made a work-around. I flatten all Tensors to a column vector and concatenate them: for l in xrange(listlength): col_vec = tf.reshape(grads[l],[-1,1]) g = tf.concat(0,[g,col_vec]) grad_hist = tf

'tensorboard' is not recognized as an internal or external command,

由 无人久伴 提交于 2019-12-03 09:08:46
问题 Just started using Tensorflow, but I am not able to use tensorboard command on my cmd, it gives the error command C:\Users\tushar\PycharmProjects>tensorboard --logdir="NewTF" 'tensorboard' is not recognized as an internal or external command, operable program or batch file. I am using window 10 and have installed tensorboard library/ 回答1: I had the same problem for tensorflow 1.5.0 and windows10. Following tensor documentation ("Launching TensorBoard" section), you can try: python -m

Show more images in Tensorboard - Tensorflow object detection

由 拟墨画扇 提交于 2019-12-03 08:11:06
I am using Tensorflow's object detection framework . Training and evaluation jobs are going well, but in tensorboard I am only able to see 10 images for the evaluation job. Is there a way to increase this number to look at more images? I tried changing the config file: eval_config: { num_examples: 1000 max_evals: 50 } eval_input_reader: { tf_record_input_reader { input_path: "xxx/eval.record" } label_map_path: "xxx/label_map.pbtxt" shuffle: false num_readers: 1 } I thought the max_eval parameter would change this but it doesn't. This is the command i'm running for the evaluation job: python ..

TensorFlow image classification

由 空扰寡人 提交于 2019-12-03 07:18:39
问题 I am very new to TensorFlow. I am doing the image classification using my own training database. However, after I trained my own dataset, I have no idea on how to classify the input image. Here is my code for preparing my own dataset filenames = ['01.jpg', '02.jpg', '03.jpg', '04.jpg'] label = [0,1,1,1] filename_queue = tf.train.string_input_producer(filenames) reader = tf.WholeFileReader() filename, content = reader.read(filename_queue) image = tf.image.decode_jpeg(content, channels=3) image

Accessing Tensorboard on AWS

由 倖福魔咒の 提交于 2019-12-03 06:32:49
I'm trying to access Tensorboard on AWS. Here is my setting : Tensorboard : tensorboard --host 0.0.0.0 --logdir=train : Starting TensorBoard b'39' on port 6006 (You can navigate to http://172.31.18.170:6006 ) AWS Security groups (in): HTTPS TCP 443 0.0.0.0/0 Custom_TCP TCP 6006 0.0.0.0/0 However connecting to ec2-blabla.us-west-1.compute.amazonaws.com:6006 I can't see anything, I basically can't connect. Do you have any idea? You can use ssh tunneling technique. In your terminal you can write: ssh -i /path/to/your/AWS/key/file -NL 6006:localhost:6006 user@host where user and host are your aws

Meaning of Histogram on Tensorboard

由 こ雲淡風輕ζ 提交于 2019-12-03 05:59:32
问题 I am working on Google Tensorboard, and I'm feeling confused about the meaning of Histogram Plot. I read the tutorial, but it seems unclear to me. I really appreciate if anyone could help me figure out the meaning of each axis for Tensorboard Histogram Plot. Sample histogram from TensorBoard 回答1: I came across this question earlier, while also seeking information on how to interpret the histogram plots in TensorBoard. For me, the answer came from experiments of plotting known distributions.

How to extract and save images from tensorboard event summary?

由 試著忘記壹切 提交于 2019-12-03 05:52:55
Given a tensorflow event file, how can I extract images corresponding to a specific tag, and then save them to disk in a common format e.g. .png ? You could extract the images like so. The output format may depend on how the image is encoded in the summary, so the resulting write to disk may need to use another format besides .png import os import scipy.misc import tensorflow as tf def save_images_from_event(fn, tag, output_dir='./'): assert(os.path.isdir(output_dir)) image_str = tf.placeholder(tf.string) im_tf = tf.image.decode_image(image_str) sess = tf.InteractiveSession() with sess.as
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