bachelor-thesis | Text Classification using Concept Maps
kandi X-RAY | bachelor-thesis Summary
kandi X-RAY | bachelor-thesis Summary
Text Classification using Concept Maps
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QUESTION
I recently started working with thingsboard in context of my bachelor-thesis. Right know I am trying out different functionalities in order to check which requirements thingsboard already satisfies and which have to be implemented by me.
Within that process I am stuck at the requirement that thingsboard dashboard should give live information about the connectivity status of a registered device (connected, disconnected, active, inactive).
To solve that I've tried out editing the rule chain used by the device profile assigned to the device of interest. Specifically I worked with the message type switch
and tried out various action nodes connected to it via ConnectEvent, DisconnectEvent, ActiveEvent and InactiveEvent. What I would like to have is the connectivity status of the device to be stored in an SERVER_SCOPE
attribute and then being displayed in the entity widget. All I've achieved is to generate an alarm when disconnected and cleared when (re)connected. I know there is a server side attribute called active
that changes true
immediately when device connects but it only turns false
after the inactivityTimeout
expired.
I also checked the ThingsBoard Device Connectivity Status page, the Create Alarm when the Device is offline guide and took long researches but couldn't get it to work.
Do you guys have any solutions or tips for me?
Kind regard.
...ANSWER
Answered 2021-Dec-01 at 15:28With the help of @JacksonB I managed to solve my problem.
What did I do?
Create a blue Transform-script node.
Change message type to
POST_ATTRIBUTES_REQUEST
and add desired attributes. Here's my code for that:var newType = "POST_ATTRIBUTES_REQUEST"
msg.connectivity = "connected"
return {msg: msg, metadata: metadata, msgType: newType};
Than just connect this to an post attribute node in the rule chain like shown in the image. Link to screenshot of rule chain
No I am able to instantly see if a device is connected or disconnected by displaying the value of that attribute on my dashboard.
QUESTION
For my Bachelor-Thesis I have to work with conda for ArcGIS Pro but I am a bloody rookie. When I try anything like installing new packages or even just cloning an environment I get an Error back. E.g.:
...ANSWER
Answered 2020-Jul-01 at 01:36It looks like you may have triggered Offline Mode in Anaconda Navigator (see Offline Mode documentation), which can toggle based on whether or not an internet connection is detected.. It can also be manually toggled from the command line, e.g., you could disable it with:
QUESTION
This question is related to Teacher-Student System: Training Student with Top-k Hypotheses List
I want to configure a teacher-student system, where a teacher seq2seq model generates a top-k list of hypotheses, which are used to train a student seq2seq model.
I select the top-k hypotheses list from the teacher’s ChoiceLayer
(or output layer) by:
ANSWER
Answered 2020-Jun-13 at 12:26This might actually be a bug. Via register_as_extern_data
, I'm not exactly sure that the logic of translate_to_common_search_beam
is correct. I think the code currently expects that a target never has a beam.
So, to answer your question: I think you are doing it already correct (so we can close this StackOverflow question).
You should open a GitHub issue about this (and then link it here). It would be good to come up with a simple test case:
- I.e. where there is some beam (you don't even need RecLayer for that, just a single ChoiceLayer would be enough I think),
- then
register_as_extern_data
on that, - and then some other layer which uses this target in some way (e.g. just with
"loss": "ce"
).
Probably this will cause exactly your problem.
QUESTION
I want to configure a teacher-student system, where a teacher seq2seq model generates a top-k list of hypotheses, which are used to train a student seq2seq model.
My plan to implement this, is to batch the teacher hypotheses, meaning that the teacher outputs a tensor with batch axis length of k * B, where B is the input batch axis length. The output batch tensor, now contains k hypotheses for each sequence in the input batch tensor, sorted by position of the associated input sequence in the input batch.
This tensor is set as the student’s training target. However, the student’s batch tensor still has a batch axis length of B, so I utilize tf.repeat
to repeat the sequences in the output tensor of the student’s encoder k times, before feeding that tensor into the student’s decoder.
For debugging purposes I made the simplification to repeat the single best hypothesis of the teacher, for now, before I’m going to implement the top-k list selection.
Here is a summary of my config file:
...ANSWER
Answered 2020-Jun-12 at 11:06It does not only validate the batch length. It will collapse the batch and time (it has used flatten_with_seq_len_mask
, see code of Loss.init
and that function) and then calculate the loss on that flattened tensor. So also the seq length need to match. This might be a problem but I'm not sure. As you have the same target also for the rec layer itself, it should have the same seq length in training.
You can debug this by carefully checking the output of debug_print_layer_output_template
, i.e. check the Data
(batch-shape-meta) output, if the axes are all correct as you expect them to be.
(debug_print_layer_output_template
can and should always be enabled. It will not make it slower.)
You can also temporarily enable debug_print_layer_output_shape
, which will really print the shape of all tensors. That way you can verify how it looks like.
Your usage of ReinterpretDataLayer
looks very wrong. You should never ever explicitly set the axes by integer (like "set_axes": {"B": 1, "T": 0}
). Why are you doing this at all? This could be the reason why it is messed up in the end.
Your repeat
function is not very generic. You are using hard coded axes integers there as well. You should never do that. Instead, you would write sth like:
QUESTION
While trying to loading my trained Keras model for further inference with
...ANSWER
Answered 2020-Mar-02 at 07:34You can try with compile = False
parameter in your load_model call. This will remove any metadata related to optimizers and loss function and since you have a reinitialize function and if you don't need to train it from where you stopped last time, that won't be a problem I guess.
As you said it works with compile = False, I think the problem might be in your custom functions for loss/optimizer etc, I can't pin point what exactly is the problem, you can try tf. instead of tf.keras.backend. if you want.
QUESTION
Following is my code to train an U-Net. It is mostly normal Keras code with my own loss functions and metrics, which is not important for the error. To avoid overfitting I tried to add a BatchNormalization layer after every convolution layer, however, I keep getting a very strange error.
...ANSWER
Answered 2020-Jan-27 at 15:22Just replace
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