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I can't find anyone who explains to a layman how to load an onnx model into a python script, then use that model to make a prediction when fed an image. All I could find were these lines of code:

I am trying to schedule a data-quality monitoring job in AWS SageMaker by following steps mentioned in this AWS documentation page. I have enabled data-capture for my endpoint. Then, trained a baseline on my training csv file and statistics and constraints are available in S3 like this:

I am trying to build a graphical audio spectrum analyzer on Linux. I run an FFT function on each buffer of PCM samples/frames fed to the audio hardware so I can see which frequencies are the most prevalent in the audio output. Everything works, except the results from the FFT function only allocate a few array elements (bins) to the lower and mid frequencies. I understand that audio is logarithmic, and the FFT works with linear data. But with so little allocation to low/mid frequencies, I'm not sure how I can separate things cleanly to show the frequency distribution graphically. I have tried with window sizes of 256 up to 1024 bytes, and while the larger windows give more resolution in the low/mid range, it's still not that much. I am also applying a Hann function to each chunk of data to smooth out the window boundaries.

For example, I test using a mono audio file that plays tones at 120, 440, 1000, 5000, 15000 and 20000 Hz. These should be somewhat evenly distributed throughout the spectrum when interpreting them logarithmically. However, since FFTW works linearly, with a 256 element or 1024 element array only about 10% of the return array actually holds values up to about 5 kHz. The remainder of the array from FFTW contains frequencies above 10-15 kHz.

Here's roughly the result I'm after:

But this is what I'm actually getting:

Again, I understand this is probably working as designed, but I still need a way to get more resolution in the bottom and mids so I can separate the frequencies better.

What can I do to make this work?

I'm having some issues saving a trained TensorFlow model, where I have a StringLookup layer and I'm required to use TFRecods as input for training. A minimal example to reproduce the issue:

First I define the training data

I am working on certain stock-related projects where I have had a task to scrape all data on a daily basis for the last 5 years. i.e from 2016 to date. I particularly thought of using selenium because I can use crawler and bot to scrape the data based on the date. So I used the use of button click with selenium and now I want the same data that is displayed by the selenium browser to be fed by scrappy. This is the website I am working on right now. I have written the following code inside scrappy spider.

I've converted a Keras model for use with OpenVino. The original Keras model used sigmoid to return scores ranging from 0 to 1 for binary classification. After converting the model for use with OpenVino, the scores are all near 0.99 for both classes but seem slightly lower for one of the classes.

For example, test1.jpg and test2.jpg (from opposite classes) yield scores of 0.00320357 and 0.9999, respectively.

With OpenVino, the same images yield scores of 0.9998982 and 0.9962392, respectively.

Edit* One suspicion is that the input array is still accepted by the OpenVino model but is somehow changed in shape or "scrambled" and therefore is never a match for class one? In other words, if you fed it random noise, the score would also always be 0.9999. Maybe I'd have to somehow get the OpenVino model to accept the original shape (1,180,180,3) instead of (1,3,180,180) so I don't have to force the input into a different shape than the one the original model accepted? That's weird though because I specified the shape when making the xml and bin for openvino:

I was trying to make a simple function in the parent element that updates a variable when it gets an emit from the child element. I've been stuck on it for 2 hours and I'm fed up because I know that the answer will be so simple.

Code in question:

Child element:

I am trying to run a command within go with the following lines of code.

I'm receiving some data over serial of variable names and values in char. The variable name gets stored in an array pointed to by the char*. I'm trying to compare the char array data I received, to several other char arrays so I can determine which variable I have received data for.

How can I convert the char* to a char array, so I can compare it to other arrays, for example by using the strcmp function?

Basically the serial data gets fed into an array and processed by this function: