You can use TensorBoard’s Python classes or script to extract the data: How can I export data from TensorBoard? If you’d like to export data to visualize elsewhere (e.g. iPython Notebook), that’s possible too. You can directly depend on the underlying classes that TensorBoard uses for loading data: python/summary/event_accumulator.py (for loading data from a single … Read more
It is quite easy to do if you have the image in a memory buffer. Below, I show an example, where a pyplot is saved to a buffer and then converted to a TF image representation which is then sent to an image summary. import io import matplotlib.pyplot as plt import tensorflow as tf def … Read more
There are several different ways you could achieve this, but you’re on the right track with creating different tf.summary.scalar() nodes. Since you must explicitly call SummaryWriter.add_summary() each time you want to log a quantity to the event file, the simplest approach is probably to fetch the appropriate summary node each time you want to get … Read more
This is a known bug which has been patched: https://github.com/pytorch/pytorch/pull/69904 You can either use the nightly-release of PyTorch, or otherwise downgrade setup tools to setuptools version 59.5.0: pip install setuptools==59.5.0
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 … Read more
UPDATE For newer TF versions (tensorflow>=1.14.0 & tensorflow != 2.0.0a0 – newer than TF2.0-alpha) load the extension like this %load_ext tensorboard OLD ANSWER The extension needs to be loaded first: %load_ext tensorboard.notebook %tensorboard –logdir {logs_base_dir}
ORIGINAL ANSWER It is called exponential moving average, below is a code explanation how it is created. Assuming all the real scalar values are in a list called scalars the smoothing is applied as follows: def smooth(scalars: List[float], weight: float) -> List[float]: # Weight between 0 and 1 last = scalars[0] # First value in … Read more
The work-around I have been doing is to use two SummaryWriter with different log dir for training set and cross-validation set respectively. And you will see something like this:
The steps to install Tensorflow are here: https://www.tensorflow.org/install/ For example, on Linux for CPU-only (no GPU), you would type this command: pip install -U pip pip install tensorflow Since TensorFlow depends on TensorBoard, running the following command should not be necessary: pip install tensorboard
In fact there is an option to change the default port … tensorboard –logdir=/tmp –port=8008