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keras tensorboard: plot train and validation scalars in a same figure

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问题 So I am using tensorboard within keras. In tensorflow one could use two different summarywriters for train and validation scalars so that tensorboard could plot them in a same figure. Something like the figure in TensorBoard - Plot training and validation losses on the same graph? Is there a way to do this in keras? Thanks. 回答1: To handle the validation logs with a separate writer, you can write a custom callback that wraps around the original TensorBoard methods. import os import tensorflow

TensorBoard - Plot training and validation losses on the same graph?

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问题 Is there a way to plot both the training losses and validation losses on the same graph? It's easy to have two separate scalar summaries for each of them individually, but this puts them on separate graphs. If both are displayed in the same graph it's much easier to see the gap between them and whether or not they have begin to diverge due to overfitting. Is there a built in way to do this? If not, a work around way? Thank you much! 回答1: The work-around I have been doing is to use two

In Tensorflow, get the names of all the Tensors in a graph

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I am creating neural nets with Tensorflow and skflow ; for some reason I want to get the values of some inner tensors for a given input, so I am using myClassifier.get_layer_value(input, "tensorName") , myClassifier being a skflow.estimators.TensorFlowEstimator . However, I find it difficult to find the correct syntax of the tensor name, even knowing its name (and I'm getting confused between operation and tensors), so I'm using tensorboard to plot the graph and look for the name. Is there a way to enumerate all the tensors in a graph without using tensorboard? You can do [n.name for n in tf

Can I use TensorBoard with Google Colab?

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Is there any way to use TensorBoard when training a TensorFlow model on Google Colab? Joppe Geluykens 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 snippets represent cells of type "code" in colab): Get TensorBoard running in the background. Inspired by this answer . LOG_DIR = '/tmp/log' get_ipython().system_raw( 'tensorboard -

Understanding TensorBoard (weight) histograms

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问题 It is really straightforward to see and understand the scalar values in TensorBoard. However, it\'s not clear how to understand histogram graphs. For example, they are the histograms of my network weights. (After fixing a bug thanks to sunside) What is the best way to interpret these? Layer 1 weights look mostly flat, what does this mean? I added the network construction code here. X = tf.placeholder(tf.float32, [None, input_size], name=\"input_x\") x_image = tf.reshape(X, [-1, 6, 10, 1]) tf

Simple way to visualize a TensorFlow graph in Jupyter?

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问题 The official way to visualize a TensorFlow graph is with TensorBoard, but sometimes I just want a quick look at the graph when I\'m working in Jupyter. Is there a quick solution, ideally based on TensorFlow tools, or standard SciPy packages (like matplotlib), but if necessary based on 3rd party libraries? 回答1: TensorFlow 2.0 now supports TensorBoard in Jupyter via magic commands (e.g %tensorboard --logdir logs/train ). Here's a link to tutorials and examples. [EDITS 1, 2] As @MiniQuark

In Tensorflow, get the names of all the Tensors in a graph

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问题 I am creating neural nets with Tensorflow and skflow ; for some reason I want to get the values of some inner tensors for a given input, so I am using myClassifier.get_layer_value(input, \"tensorName\") , myClassifier being a skflow.estimators.TensorFlowEstimator . However, I find it difficult to find the correct syntax of the tensor name, even knowing its name (and I\'m getting confused between operation and tensors), so I\'m using tensorboard to plot the graph and look for the name. Is

Can I use TensorBoard with Google Colab?

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问题 Is there any way to use TensorBoard when training a TensorFlow model on Google Colab? 回答1: 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 snippets represent cells of type "code" in colab): Get TensorBoard running in the