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TensorFlow provides a tf.summary.tensor_summary() function that appears to be a multidimensional variant of tf.summary.scalar():
tf.summary.tensor_summary(name, tensor, summary_description=None, collections=None) I thought it could be useful for summarizing inferred probabilities per class ... somewhat like
op_summary = tf.summary.tensor_summary('classes', some_tensor) # ... summary = sess.run(op_summary) writer.add_summary(summary) However it appears that TensorBoard doesn't provide a way to display these summaries at all. How are they meant to be used?
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The .summary () function in tensorflow.js is used to print a text summary in favor of the sequential model’s layers.
Tensorflow.js is an open-source library that is developed by Google for running machine learning models as well as deep learning neural networks in the browser or node environment. The .summary () function in tensorflow.js is used to print a text summary in favor of the sequential model’s layers.
The tf.LayersModel is a class used for training, inference, and evaluation of layers model in tensorflow.js. It contains methods for training, evaluation, prediction, and for saving of layers model purposes. So in this post, we are going to know about the model.summary () function.
I cannot get it to work either. It seems like that feature is still under development. See this video from the TensorFlow Dev Summit that states that the tensor_summary is still under development (starting at 9:17): https://youtu.be/eBbEDRsCmv4?t=9m17s. It will probably be better defined and examples should be provided in the future.
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