tensorboard

问题 I've trained my network and generated some training/validation losses which I saved via the following code example (example of training loss only, validation is perfectly equivalent): valid_summary_writer = tf.summary.create_file_writer("/path/to/logs/") with train_summary_writer.as_default(): tf.summary.scalar('Training Loss', data=epoch_loss, step=current_step) After training I would then like to view the loss curves using Tensorboard. However because I saved the loss curves under the names

问题 I've trained my network and generated some training/validation losses which I saved via the following code example (example of training loss only, validation is perfectly equivalent): valid_summary_writer = tf.summary.create_file_writer("/path/to/logs/") with train_summary_writer.as_default(): tf.summary.scalar('Training Loss', data=epoch_loss, step=current_step) After training I would then like to view the loss curves using Tensorboard. However because I saved the loss curves under the names

问题 I've trained my network and generated some training/validation losses which I saved via the following code example (example of training loss only, validation is perfectly equivalent): valid_summary_writer = tf.summary.create_file_writer("/path/to/logs/") with train_summary_writer.as_default(): tf.summary.scalar('Training Loss', data=epoch_loss, step=current_step) After training I would then like to view the loss curves using Tensorboard. However because I saved the loss curves under the names

问题 I'm using keras in my ML project with the TensorBoard callback. I have an image autoencoder and I want to visualize its progress in reconstructing some images. So I sub-classed the TensorBoard class as such: class Monitor(TensorBoard): def on_train_begin(self, logs=None): super().on_train_begin(logs) def on_epoch_begin(self, epoch, logs=None): # 1. Get the reconstructed images reconstructions = Autoencoder.predict(validation[0]) # 2. Generate a summary summary = tf.summary.image(

问题 I have a personal implementation of a RL algorithm that generates performance metrics every x time steps. That metric is simply a scalar, so I have an array of scalars that I want to display as a simple graph such as: I want to display it in real time in tensorboard like my above example. Thanks in advance 回答1: If you really want to use tensorboard you can start looking at tensorflow site and this datacamp tutorial on tensorboard. With tensorflow you can use summary.scalar to plot your custom

问题 ERROR: Failed to launch TensorBoard (exited with 1). Contents of stderr: Traceback (most recent call last): File "/home/arshad/anaconda3/bin/tensorboard", line 10, in sys.exit(run_main()) File "/home/arshad/anaconda3/lib/python3.7/site-packages/tensorboard/main.py", line 58, in run_main default.get_plugins() + default.get_dynamic_plugins(), File "/home/arshad/anaconda3/lib/python3.7/site-packages/tensorboard/default.py", line 110, in get_dynamic_plugins for entry_point in pkg_resources.iter

问题 I would like to shift all the step-values of a specific saved tensorboard summary run by a certain amount. For example: The results start at step 1.000.001 and I want the results to show a start at 1 again, shifting all the values by 1.000.000. How can I best do this? 回答1: I found the solution myself: You can just read it in again using a summary_iterator. and then for every event, save the values to a new summary. So in my case, I needed something like this: summary_writer = tf.summary

问题 I get the error below when I try launching tensorboard from my jupyter notebook . I am using a conda environment and using the env-specific kernel since tensorflow is only installed in my environment. What am I doing wrong? The error: %tensorboard --logdir logs ERROR: Failed to launch TensorBoard (exited with -6). Contents of stderr: [libprotobuf ERROR external/com_google_protobuf/src/google/protobuf/descriptor_database.cc:393] Invalid file descriptor data passed to EncodedDescriptorDatabase:

问题 I'm currently using Tensorboard using the below callback as outlined by this SO post as shown below. from keras.callbacks import ModelCheckpoint CHECKPOINT_FILE_PATH = '/{}_checkpoint.h5'.format(MODEL_NAME) checkpoint = ModelCheckpoint(CHECKPOINT_FILE_PATH, monitor='val_acc', verbose=1, save_best_only=True, mode='max', period=1) When I run Keras' dense net model, I get the following error. I haven't had any issues running Tensorboard in this manner with any of my other models, which makes

问题 I've been having problems with tensorboard probably due to a unclean exit in windows10. I use the below code to launch it in Jupyter: %load_ext tensorboard %tensorboard --logdir={dir} this is what I got: 'ERROR: Timed out waiting for TensorBoard to start. It may still be running as pid 24472.' and below code in cmd window to launch TB in Chrome: $ tensorboard --logdir {dir}' this is what I got: tensorboard_error I've done some research and tried the following things: -"taskkill /im