Hi there!
I need to develop a NN for the classification of signal points and my question is how to prepare training data for that if I have the next data and structures.
I have thousands of signals on a hard drive in csv format, I can use them for training.
csv is read into a separate dataframe for each signal (input instance data). Points can be classified only within dataframe  to classify points you need to know all the signal (One point can’t be classified separately  only full set of data carries the information about possible point classes  e.g. there are probabilities of distributions of points of class 1 along the xaxis).
I have thousands of such separate dataframes  all points of them are labeled.
Each file has about 10 000 rows, and one row holds data for one point of a signal y(x):
i  number of current point,
x  coordinate value for current point i
y  value of a signal at x  y(x)
delta_y  error value for current point x
point_class  for simplification it’s a binarytype variable  0/1 (label to train and classify)
(0  for all points that are from class 0, and 1 for another class of points).
So for inputs  it’s possible to pass 30 000 values, and classify each of 10 000 points of input signal defining a class for each point if current signal  so we have 10 000 outputs.
The task for neural network is to get the arrays x[], y[], delta_y[] as input and classify each point of input signal (point_class column).
In this case output of a NN must have the same size as one input signal  10 000 outputs to characterize 10 000 input points of the signal. All outputs can be interpreted as probability that point has class 1  value can be 0…1.
So for training I have inputs : x[], y[], delta_y[
]  (10 000 elements each) 
(we are giving y(x), delta_y(x), x
to the input of neural network)
and it must calculate the outputs:  p[]: [0, 0, 0, 1, ... , 1, 0, 0]
 (10 000 elements), so it can be plotted like p(x).
 How to handle all the files into one dataframe or it is possible to create a training dataset just using separate input csvs as and others as outputs  for each training case? (input1.csv → output1.csv)
Maybe someone can provide examples similar to this task?

Which layer types to use in this case as outputs?

Is it a good idea to have such a large network (input number
If any additional information is needed  I can provide anything for understanding and processing.