tensorboard

问题 I Want to see it Tensorflow Graph and Weight using Tensorflow in Jupyter Notebook . But It is not working. I use Jupyter notebook on the remote server. %tensorboard --logdir logs %load_ext tensorboard it say it is too long to response at static ip. How can I solve it?! 回答1: Reading this Github issue, you can find that specifying the host manually when launching Tensorboard apparently does the trick. Instead of %tensorboard --logdir {logs_base_dir} Run %tensorboard --logdir {logs_base_dir} -

问题 I'm new in tensorflow and i follow this tutorial to learn about this framework. Now i'm trying to visualize my graph using Tensorboard but but i get a tensorboard blank page without any result. The code that i use to visualize the graph is: from __future__ import print_function import tensorflow as tf import numpy as np def add_layer(inputs, in_size, out_size, n_layer, activation_function=None): # add one more layer and return the output of this layer layer_name = 'layer%s' % n_layer with tf

问题 My X_test are 128x128x3 images and my Y_test are 512x512x3 images. I want to show, after each epoch, how the input (X_test) looked, how the expected output (Y_test) looked, but also how the actual output looked . So far, I've only figured out how to add the first 2 in Tensorboard. Here is the code that calls the Callback: model.fit(X_train, Y_train, epochs=epochs, verbose=2, shuffle=False, validation_data=(X_test, Y_test), batch_size=batch_size, callbacks=get_callbacks()) Here is the Callback

问题 My X_test are 128x128x3 images and my Y_test are 512x512x3 images. I want to show, after each epoch, how the input (X_test) looked, how the expected output (Y_test) looked, but also how the actual output looked . So far, I've only figured out how to add the first 2 in Tensorboard. Here is the code that calls the Callback: model.fit(X_train, Y_train, epochs=epochs, verbose=2, shuffle=False, validation_data=(X_test, Y_test), batch_size=batch_size, callbacks=get_callbacks()) Here is the Callback

问题 I am trying to run tensorboard: tensorboard --logdir=runs . I have also tried: tensorboard --logdir=runs --host=127.0.0.1 . I am running the command from the terminal from within the the directory, which contains the runs folder. I get the following error: [libprotobuf FATAL external/com_google_protobuf/src/google/protobuf/descriptor.cc:1367] CHECK failed: GeneratedDatabase()->Add(encoded_file_descriptor, size): libc++abi.dylib: terminating with uncaught exception of type google::protobuf:

问题 Hi~ I want to visualize the NN in Eager mode in tf1.15 (can not switch to 2.0.0). And the implementation is based on low-level API of Tensorflow 1.15. I want to use the tensorboard to visualize it. I write a log tracing code but get the error: WARNING:tensorflow: The TensorFlow contrib module will not be included in TensorFlow 2.0. For more information, please see: * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md * https://github.com/tensorflow/addons *

问题 This question is about TensorFlow (and TensorBoard) version 2.2rc3, but I have experienced the same issue with 2.1. It is a continuation of the question 'Messed up TensorBoard graphs due to Python operations'. Consider the following code: from datetime import datetime import tensorflow as tf from tensorflow import keras inputs = keras.layers.Input(shape=(784, )) outputs = tf.zeros([32, 10], tf.float32) for i in range(0, 3): x = keras.layers.Dense(32, activation='relu', name='Model/Block' +

问题 Tensorflow version : 1.11.0 I am trying to use TensorBoard with Tensorflow keras model for projector visualisation. I am getting AttributeError: Layer features has no inbound nodes. I am not sure why I get this error in below simple code. I indeed google the error but I could not find right solution to fix it. from os import makedirs from os.path import exists, join import tensorflow as tf mnist = tf.keras.datasets.mnist import numpy as np (x_train, y_train),(x_test, y_test) = mnist.load_data

问题 This question is about TensorFlow (and TensorBoard) version 2.2rc3, but I have experienced the same issue with 2.1. Consider the following weird code: from datetime import datetime import tensorflow as tf from tensorflow import keras inputs = keras.layers.Input(shape=(784, )) x1 = keras.layers.Dense(32, activation='relu', name='Model/Block1/relu')(inputs) x1 = keras.layers.Dropout(0.2, name='Model/Block1/dropout')(x1) x1 = keras.layers.Dense(10, activation='softmax', name='Model/Block1

问题 I'm trying to add TensorBoard functionality to this SageMaker example: https://github.com/awslabs/amazon-sagemaker-examples/blob/master/hyperparameter_tuning/keras_bring_your_own/hpo_bring_your_own_keras_container.ipynb The issue is that SageMaker's Estimator.fit() does not seem to support Keras models compiled with callbacks. Now from this git issue post it was described that what I need to do for TensorBoard functionality is, "You need your code inside the container to save checkpoints to