Meaning of Histogram on Tensorboard

Question

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

Answer 1

When plotting histograms, we put the bin limits on the x-axis and the count on the y-axis. However, the whole point of histogram is to show how a tensor changes over times. Hence, as you may have already guessed, the depth axis (z-axis) containing the numbers 100 and 300, shows the epoch numbers.

The default histogram mode is Offset mode. Here the histogram for each epoch is offset in the z-axis by a certain value (to fit all epochs in the graph). This is like seeing all histograms places one after the other, from one corner of the ceiling of the room (from the mid point of the front ceiling edge to be precise).

In the Overlay mode, the z-axis is collapsed, and the histograms become transparent, so you can move and hover over to highlight the one corresponding to a particular epoch. This is more like the front view of the Offset mode, with only outlines of histograms.

As explained in the documentation here:

tf.summary.histogram takes an arbitrarily sized and shaped Tensor, and compresses it into a histogram data structure consisting of many bins with widths and counts. For example, let's say we want to organize the numbers [0.5, 1.1, 1.3, 2.2, 2.9, 2.99] into bins. We could make three bins:

• a bin containing everything from 0 to 1 (it would contain one element, 0.5),
• a bin containing everything from 1-2 (it would contain two elements, 1.1 and 1.3),
• a bin containing everything from 2-3 (it would contain three elements: 2.2, 2.9 and 2.99).

TensorFlow uses a similar approach to create bins, but unlike in our example, it doesn't create integer bins. For large, sparse datasets, that might result in many thousands of bins. Instead, the bins are exponentially distributed, with many bins close to 0 and comparatively few bins for very large numbers. However, visualizing exponentially-distributed bins is tricky; if height is used to encode count, then wider bins take more space, even if they have the same number of elements. Conversely, encoding count in the area makes height comparisons impossible. Instead, the histograms resample the data into uniform bins. This can lead to unfortunate artifacts in some cases.

Please read the documentation further to get the full knowledge of plots displayed in the histogram tab.