ndf | Neural Unsigned Distance Fields (NDF) - Codebase | Machine Learning library

I have trained a model using the pix2pix pytorch implementation and would like to test it.

However when I test it I get the error

I have an ordered dictionary which has 4 keys and multiple values. I tried to create the dataframe like this

I have a .csv import from excel that has formula hangups that I am trying to remove. A simple version of the data is below.

I'm trying to do a sort by descending order using pandas in python with the percentage column, unfortunately, it's not comparing 1 digit with 2 digit floats.

This is my code:

I am working on a personal PySpark project for learning purposes and I have a peculiar problem.

I have a dataframe (df) with N columns, in which I want to subtract each column out of the next (e.g. col1 - col2, col2 - col3, ..., col(N+1) - colN) and save the resulting differences column in another dataframe.

I generate this df by parsing a JSON, saving to a pandas dataframe (schema: dates column, columns for each item) transposing the columns to rows (to have a single Items column and columns for each date) and then transforming it in a spark df. I do this because it seems that row by row operations in Spark are fairly difficult to implement.

I move the first column (the Items column) of the df to a new dataframe (ndf) so I am left with only the following schema (header is comprised of dates and the data is only integers):

I want to subtract the ints of column Date2 out of the ints from column Date1 (e.g. df.Date1 - df.Date2) and the resulting column of values (with the header of the larger column - Date1) to be saved/appended in the already existing ndf dataframe (the one in which I moved the column earlier). Then move on to subtract column Date2 and column Date3 (df.Date2 - df.Date3), and so on until column Date(N+1) - DateN, then stop.

The new Dataframe (ndf), created earlier from the Items column, would look like this:

Practically, I want to see the number with which each item has increased from one date to the next.

I was thinking that doing it in a for loop. Something like:

I have created this neural net:

I have a very large dataframe where only the first two columns are not bools. However everything is brought in as a string due to the source. The True/False fields do contain actual blanks (not nan) as well and are spelled out 'True' and 'False'

I'm trying to come up with a dynamic-ish way to do this without typing out or listing every column.

I created a database with 3 .mdf files and one .ndf file and a log file by mistake.

I am using DCGAN for synthesizing medical images. However, at the moment, Img_size is 64 which is too low resolution.

How can I change the generator and discriminator to make 512*512 high resolution?

Here is my code below.