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I have a bme688 sensor (from Pimoroni) connected to a RPI ZERO. I have the PAHO MQTT library so I can send the data to the broker.

If you see code below, in the very last line, I have a "time.sleep(5)". The code below works perfectly well. It takes the readings and sends them via the MQTT. The problem I have is that if I change the time from 5 seconds to 300 seconds (10 minutes), the MQTT does not seem to send the data. The RPI ZERO has the raspbian desktop installed so I ran it using Thonny to see if I get an error but everything works fine with the 300 second delay... but it does not send the data across to the broker.

Any thoughts?

I am using MediaStore to fetch the absoluteImagePaths and feed it to Glide in adapter class. currently I am using MediaStore.MediaColumns.DATA to get the path. But, Recently google deprecated that api and may not work in near future. So, what is the replacement for this?

I have been trying to figure this out all day, as I would like to add an image depending on the outcome of the emotion may detect. Just wanted to add some some images but I'm still new to this. Can anyone help me with this one to.

btw here's my code:

Suppose you have a neural network with 2 layers A and B. A gets the network input. A and B are consecutive (A's output is fed into B as input). Both A and B output predictions (prediction1 and prediction2) Picture of the described architecture You calculate a loss (loss1) directly after the first layer (A) with a target (target1). You also calculate a loss after the second layer (loss2) with its own target (target2).

Does it make sense to use the sum of loss1 and loss2 as the error function and back propagate this loss through the entire network? If so, why is it "allowed" to back propagate loss1 through B even though it has nothing to do with it?

This question is related to this question https://datascience.stackexchange.com/questions/37022/intuition-importance-of-intermediate-supervision-in-deep-learning but it does not answer my question sufficiently. In my case, A and B are unrelated modules. In the aforementioned question, A and B would be identical. The targets would be the same, too.

(Additional information) The reason why I'm asking is that I'm trying to understand LCNN (https://github.com/zhou13/lcnn) from this paper. LCNN is made up of an Hourglass backbone, which then gets fed into MultiTask Learner (creates loss1), which in turn gets fed into a LineVectorizer Module (loss2). Both loss1 and loss2 are then summed up here and then back propagated through the entire network here.

Even though I've visited several deep learning lectures, I didn't know this was "allowed" or makes sense to do. I would have expected to use two loss.backward(), one for each loss. Or is the pytorch computational graph doing something magical here? LCNN converges and outperforms other neural networks which try to solve the same task.

I am getting this error which I am not sure what it means:

Type mismatch. Required: session.Expression[Boolean], found: Validation[CheckBuilder[JsonPathCheckType, JsonNode, String] with SaveAs[JsonPathCheckType, JsonNode, String]]

I am trying to retrieve a value called "title" from an API response body for a user's created assessment and save it as "titleSession". Because not every user that is fed into the simulation will have associated created assessments I am trying to make the saving of "titleSession" only occur if there already exists a "title", hence the doIf:

I'm trying to zip a massive directory with images that will be fed into a deep learning system. This is incredibly time consuming, so I would like to stop prematurely the zipping proccess with Ctrl + C and zip the directory in different "batches".

Currently I'm using zip -r9v folder.zip folder, and I've seen that the option -u allows to update changed files and add new ones.

I'm worried about some file or the zip itself ending up corrupted if I terminate the process with Ctrl + C. From this answer I understand that the cp can be terminated safely, and this other answer suggests that gzip is also safe.

Putting it all together: Is it safe to end prematurely the zip command? Is the -u option viable for zipping in different batches?

I have modified VGG16 in pytorch to insert things like BN and dropout within the feature extractor. By chance I now noticed something strange when I changed the definition of the forward method from:

I am trying to introduce dynamic workflows into my landscape that involves multiple steps of different model inference where the output from one model gets fed into another model.Currently we have few Celery workers spread across hosts to manage the inference chain. As the complexity increase, we are attempting to build workflows on the fly. For that purpose, I got a dynamic DAG setup with Celeryexecutor working. Now, is there a way I can retain the current Celery setup and route airflow driven tasks to the same workers? I do understand that the setup in these workers should have access to the DAG folders and environment same as the airflow server. I want to know how the celery worker need to be started in these servers so that airflow can route the same tasks that used to be done by the manual workflow from a python application. If I start the workers using command "airflow celery worker", I cannot access my application tasks. If I start celery the way it is currently ie "celery -A proj", airflow has nothing to do with it. Looking for ideas to make it work.