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
Except providing an answer to your question I will make the code more TF2.0-like. If you have any questions/need clarification, please post a comment down below. 1. Loading data I would advise to use Tensorflow Datasets library. There is absolutely no need to load data in numpy and transform it to tf.data.Dataset if one can … Read more
So, the following solution works well for me: import tensorflow as tf def make_image(tensor): “”” Convert an numpy representation image to Image protobuf. Copied from https://github.com/lanpa/tensorboard-pytorch/ “”” from PIL import Image height, width, channel = tensor.shape image = Image.fromarray(tensor) import io output = io.BytesIO() image.save(output, format=”PNG”) image_string = output.getvalue() output.close() return tf.Summary.Image(height=height, width=width, colorspace=channel, encoded_image_string=image_string) … Read more
Here’s a recipe I copied from one of Alex Mordvintsev deep dream notebook at some point from IPython.display import clear_output, Image, display, HTML import numpy as np def strip_consts(graph_def, max_const_size=32): “””Strip large constant values from graph_def.””” strip_def = tf.GraphDef() for n0 in graph_def.node: n = strip_def.node.add() n.MergeFrom(n0) if n.op == ‘Const’: tensor = n.attr[‘value’].tensor size … Read more
You can do [n.name for n in tf.get_default_graph().as_graph_def().node] Also, if you are prototyping in an IPython notebook, you can show the graph directly in notebook, see show_graph function in Alexander’s Deep Dream notebook
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:
It appears that the network hasn’t learned anything in the layers one to three. The last layer does change, so that means that there either may be something wrong with the gradients (if you’re tampering with them manually), you’re constraining learning to the last layer by optimizing only its weights or the last layer really … Read more
EDIT: You probably want to give the official %tensorboard magic a go, available from TensorFlow 1.13 onward. Prior to the existence of the %tensorboard magic, the standard way to achieve this was to proxy network traffic to the Colab VM using ngrok. A Colab example can be found here. These are the steps (the code … Read more