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
As Fabrizio says, TensorBoard is a great tool for visualizing the contents of your summary logs. However, if you want to perform a custom analysis, you can use tf.train.summary_iterator() function to loop over all of the tf.Event and tf.Summary protocol buffers in the log: for summary in tf.train.summary_iterator(“/path/to/log/file”): # Perform custom processing in here. UPDATE … Read more
Note: The solution you’ve posted (erase TensorBoard’s log files and kill the process) will work, but it isn’t preferred, because it destroys historical information about your training. Instead, you can have each new training job write to a new subdirectory (of your top-level log directory). Then, TensorBoard will consider each job a new “run” and … Read more
In addition to TensorBoard scanning subdirectories (so you can pass a directory containing the directories with your runs), you can also pass multiple directories to TensorBoard explicitly and give custom names (example taken from the –help output): tensorboard –logdir=name1:/path/to/logs/1,name2:/path/to/logs/2 More information can be found at the TensorBoard documentation. In recent versions of TensorBoard, aliasing this … 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
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
answer moved from edit to self-answer as requested I just played around with this a little bit, and it seems that if one combines tf.control_dependencies with tf.record_summaries_every_n_global_steps it behaves as expected and the summary only gets recorded every nth step. But if they are run together within a session, such as session.run([train, summs]), the summaries … Read more
keras.callbacks.TensorBoard(log_dir=”./Graph”, histogram_freq=0, write_graph=True, write_images=True) This line creates a Callback Tensorboard object, you should capture that object and give it to the fit function of your model. tbCallBack = keras.callbacks.TensorBoard(log_dir=”./Graph”, histogram_freq=0, write_graph=True, write_images=True) … model.fit(…inputs and parameters…, callbacks=[tbCallBack]) This way you gave your callback object to the function. It will be run during the training and … Read more
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
Here is what I do to avoid the issues of making the remote server accept your local external IP: when I ssh into the machine, I use the option -L to transfer the port 6006 of the remote server into the port 16006 of my machine (for instance): ssh -L 16006:127.0.0.1:6006 olivier@my_server_ip What it does … Read more