tensorboard

I was running a very long training (reinforcement learning with 20M steps) and writing summary every 10k steps. In between step 4M and 6M, I saw 2 peaks in my TensorBoard scalar chart for game score, then I let it run and went to sleep. In the morning, it was running at about step 12M, but the peaks between step 4M and 6M that I saw earlier disappeared from the chart. I tried to zoom in and found out that TensorBoard (randomly?) skipped some of the data points. I also tried to export the data but some data point including the peaks are also missing in the exported .csv. I looked for answers

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? 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 TensorBoard callback code in Keras as a reference: https://github.com/fchollet/keras/blob/master/keras/callbacks.py#L537 Basically, write a function e.g. write_summaries(model) and call it whenever you want to write your

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/ I had the same problem for tensorflow 1.5.0 and windows10. Following tensor documentation ("Launching TensorBoard" section) , you can try: python -m tensorboard.main --logdir=[PATH_TO_LOGDIR] Now tensorboard is working properly for me. I also had the same sort of

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 = tf.cast(image, tf.float32) resized_image = tf.image.resize_images(image, 224, 224) image_batch ,

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 marc_alain 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. So, the conventional normal distribution with mean = 0 and sigma = 1 can be produced in TensorFlow

Where can I find documentation about the Embedding Projector included in Tensorboard? Here , there are some references to it, but there's no step-by-step example/tutorial on how to use it. As far as I am aware this is the only documentation about embedding visualization on the TensorFlow website. Though the code snippet might not be very instructive for the first time users, so here is an example usage: import os import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data LOG_DIR = 'logs' mnist = input_data.read_data_sets('MNIST_data') images = tf.Variable(mnist.test

In tensorboard, I would like to superimpose 2 plots on the same graph (training and validation losses of a neural network). I can see 2 separate plots, but not one plot with 2 superimposed curves. Otherwise, I get one plot in zigzag. How can I do? It is possible to superimpose two plots in Tensorboard. You'll have to satisfy both of the following: Create two separate tf.train.SummaryWriter objects such that it outputs in two folders. Create two summaries (e.g. tf.scalar_summary ) with the same name . For example to plot training and validation loss: # Before training train_summary = tf.scalar

I'm using the Keras TensorBoard callback. I would like to run a grid search and visualize the results of each single model in the tensor board. The problem is that all results of the different runs are merged together and the loss plot is a mess like this: How can I rename each run to have something similar to this: Here the code of the grid search: df = pd.read_csv('data/prepared_example.csv') df = time_series.create_index(df, datetime_index='DATE', other_index_list=['ITEM', 'AREA']) target = ['D'] attributes = ['S', 'C', 'D-10','D-9', 'D-8', 'D-7', 'D-6', 'D-5', 'D-4', 'D-3', 'D-2', 'D-1']

I was using tensorflows script optimize_for_inderence.py on the ssd_mobilenet_v1_coco model with following command: python -m tensorflow.python.tools.optimize_for_inference \ --input /path/to/frozen_inference_graph.pb \ --output /path/to/optimized_inference_graph.pb \ --input_names=image_tensor \ --output_names=detection_boxes,detection_scores,num_detections,detection_classes It worked without errors, but if i want to use the created Model .pb file for Tensorboard or for Inference it gives me following error: ValueError: graph_def is invalid at node u'ToFloat': Input tensor 'image_tensor:0'