hdf5 | library aims to provide an abstraction of the hdf5 file | Performance Testing library

I am working with .tif images. I try to read a .tif image in order to access it later on pixel level and read some values. The error that I get when using Pillow is this:

I have this code to write chunks of arrays to HDF5 and I want to add a timestamp attribute to each chunk or another field with a datetime

While I try to install TensorFlow I get this error :

I have the files: main.cpp, tools.cpp, tools.h, integrator.cpp, and integrator.h.

I have tried to link HDF5 to this code (it compiles/links just fine without hdf5 stuff).

Here's what I am using to compile:

I have a simple 2 layer Tensorflow model that I am trying to train on a dataset of equal-sized stereo audio files to tell me if the sound is coming more from the left side or the right side. This means the input is an array of 3072 by 2 arrays and the output is an array of 1's and 0's to represent left and right.

The problem is that when I run the program, it fails at model.fit() with an invalid argument error.

Code:

I am training a TensorFlow RNN model using LSTM layers to determine if sound is coming more from the right or left in a stereo audio signal. The model training goes smoothly, then, once it is done training, I get an Invalid Argument Error as shown below. Does anyone know what could be causing this? I have tried fixing it using the solution to a similar question found here, but to no avail.

I do not understand why it is expecting a tensor of shape [32,2]. Did I define that somewhere I am unaware of?

Here is my code:

I have this code to write an array to Hdf5 with HDF5Sharp. But the problem is that I need the data to be written in chunks of 1 x 100 x 500 instead of 100k x 100 x 500 and I cannot figure out how to do it.

I am working on a pretty dynamic C++ program which allows the user to define their own data structures which are then serialized in an output HDF5 data file. Instead of requiring the user to define a new HDF5 data type, I am "splitting" their data structures into HDF5 subgroups in which I store the different member variable data sets. I am interested in labeling the HDF5 group that has the subgroup members with the type of the data structure that was written to it so that future users of the data file will have more knowledge about how to use the data contained within it.

All of this context gets me to my question in the title. How reliable are demangled names? The crux of the issue could be summarized with the following example (using boost to demangle as an example, not a necessity). If I use

I run association rules using efficient_apriori package in Python. I need to save results. My idea is:

1. convert rules into pandas df
2. save df to hdf5.

Problem is that I have 4 bilions of rules. And following code crashes on signal 9: SIGKILL: