In the middle of the desert you can say anything you want
_keras_historyAttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute '_keras_history disappears if we dont’t use eager execution inside the metric, fine if we use it inside the model. That is tf.config.experimental_run_functions_eagerly(False) inside metrics.py solves this, but model.run_eagerly=True is fine.
https://github.com/tensorflow/addons/pull/377 re output_masks and it being blocked
tensorflow - What is the difference between tf.keras and tf.python.keras? - Stack Overflow
_ does the magic. Can be used in expressions too.
self.xxxSome kind of ML language switcher that trains on my input – I write something in L1, delete, write same keystrokes on L2 => training instance. Also based on window class and time maybe?
“Could not find valid device for node.” while eagerly executing. - means wrong input type.
Mixing keras and tf.keras is bad karma; only the same one should be used and it should be tf.keras.
AttributeError:’Tensor’ object has no attribute ‘_keras_history’ – inneka.com
Everything should be done by:
When you are using tf operation – you are getting tf tensor object which doesn’t have history field. When you use keras functions you will get keras.tensors.
I should read this sometime: Breakpoints - Help | IntelliJ IDEA
I should create a better ym that supports copying markdown links that have |s in them. Using Add ability to yank inline by jgkamat · Pull Request #4651 · qutebrowser/qutebrowser · GitHub most probably.
tf.boolean_mask | TensorFlow Core r2.0 is something similar to what I do with tensor*mask, but it removes the rows where the condition is not fulfilled.
Keras custom metrics raises error when update_state returns an op. · Issue #30711 · tensorflow/tensorflow · GitHub - forget about returning ops in custom metrics, internal Google TPU issue thing. It’s supposed not to work. Error was:
TypeError: To be compatible with tf.contrib.eager.defun, Python functions must return zero or more Tensors; in compilation of <function Function._defun_with_scope.
.wrapped_fn at 0xb34ec5d08>, found return value of type <class ’tensorflow.python.framework.ops.Operation’>, which is not a Tensor.
tf.assign_add - TensorFlow Python - W3cubDocs - is this another place to read readable TF documentation?
model.run_eagerly=True is not enough – when creating a custom Metric, as mentioned in metrics.py, tf.config.experimental_run_functions_eagerly(True) is also needed.
As added bonus - if this is not enabled, Intellij Idea debugging also doesn’t work. As in the breakpoints get ignored.
I really should resurrect my link DB.
Sandeep Aparajit: Tutorial: Conditional Random Field (CRF) is a nice 108-page presentation spanning basic probability theory and flowing to Bayes, marginals, CRF etc etc, very very self-contained.
Generative VS Discriminative Models - Prathap Manohar Joshi - Medium
Overview — ELI5 0.9.0 documentation “.. is a Python package which helps to debug machine learning classifiers and explain their predictions.”
If I * a tensor by another tensor I get a per element multiplication. I keep forgetting this for some reason
I can even edit EagerTensors by right click -> Edit value! Quite a weird UI but still nice
Edited my “someday” report:
report.sd.filter=status:pending sprint:s sprint.isnt:srv
sprint:s seems to catch srv too, which I don’t want. Not anymore. Also Taskwarrior - FAQ is the list of such modifiers.
Attribute modifiers make filters more precise. Supported modifiers are:
Modifiers Example Equivalent Meaning
---------------- ----------------- ------------------- -------------------------
due:today due = today Fuzzy match
not due.not:today due != today Fuzzy non-match
before, below due.before:today due < tomorrow Exact date comparison
after, above due.after:today due > tomorrow Exact date comparison
none project.none: project == '' Empty
any project.any: project !== '' Not empty
is, equals project.is:x project == x Exact match
isnt project.isnt:x project !== x Exact non-match
has, contains desc.has:Hello desc ~ Hello Pattern match
hasnt, desc.hasnt:Hello desc !~ Hello Pattern non-match
startswith, left desc.left:Hel desc ~ '^Hel' Beginning match
endswith, right desc.right:llo desc ~ 'llo$' End match
word desc.word:Hello desc ~ '\bHello\b' Boundaried word match
noword desc.noword:Hello desc !~ '\bHello\b' Boundaried word non-match
In intellij idea you can set more options for each breakpoint after right-clicking on it; especially “disable until breakpoint X is hit”, where X can be disabled.
.. is not there by default all the time; the hard-to-find answer for this is adding model.run_eagerly=True after model.compile().
Of course, the following also works:
[x[1][1]['mycast'] for x in dataset.enumerate(5).__iter__()]
… add what you tell it to add, even if you’ve use tf.one_hot() on the data before. Then you get weird zeros in the result of the one hot encoding.
Ausstattung für die erste eigene Wohnung - Checkliste is a nice checklist :)