Wight | Communicate with PhantomJS in Perl | Test Automation library

One way to do it is to use the Graphics API. This has a FillPolygon method that takes a list of points and a brush. To use the source bitmap you would use the TextureBrush. Put this together and you would end up with something like this:

It's difficult to know exactly how to refactor without being on your end, but something I can point out is that you have several CSS selectors that effectively do the same thing:

You do need a for loop, but you can use list comprehension to make it cleaner. Also, python standard library has a very nice statistics module that you can use for the calculation of the mean. As extra note, please, do not use list as a variable name, it can be confused with the type list.

Here's a sample using a stored procedure to read the source table and add rows to a target table. You can see in the code that there are constants to define the source and target tables.

Limitation: Right now the code expects the postcode, households, and subsequent "property" + "property fastest down" columns to be in well-ordered ordinal position. If that will not be the case, then there needs to be a loop to read the column names to find where these columns are in ordinal positions.

Usage Note: The JavaScript is constructing a custom JSON. Since Snowflake doesn't like single-row inserts, it's constructing a JSON array and flattening out the array to insert 1000 rows at a time. There's a constant that sets the row buffer. If the JSON gets over 16MB it will fail, so that may need to be adjusted downward if that happens.

Recommendation: If you want to productionize this, you may want to create a stream table off your source table. It will make it much easier to process only the new rows using this SP. https://snowflake.pavlik.us/index.php/2020/01/12/snowflake-streams-made-simple. The code would need to be modified to ignore the final three metadata columns and then run a DML to nowhere (insert into target_table from stream_table where 1 = 0) to advance the stream.

Here's the SP code to fill the target table to get started.

From the comment section for the benefit of the community.

This is not specific to focal loss, all keras loss functions take y_true and y_pred, you do not need to worry where those parameters are coming from, they are fed by keras automatically.

1-2 MB is huge, you need to reduce the size to at least 200KB and even less for small screens

gsub is vectorized so you don't need lapply (unless you are wanting to apply the function to multiple columns). In regex, * is a quantifier indicating 0 or more of the thing that comes before it, so the pattern "Finland*" matches "Finlan" followed by any number of ds.

Instead, let's replace the first : and anything after it .* with an empty string. (. in regex means "any character", so .* is "any number of any characters".)

Based on your query, it seems that the class number won't be different in Dataset2. At the same time, you also don't want to use image net weight. So, in that case, you don't need to map or store the weight (as described below). Just load the model and weight and train on Dataset2. Freeze the all trained layer from Dataset1 and train the last layer on Dataset2; really straight forward.

In my below response, though you're not needed the full information, I am keeping that anyway for future reference.

Here is a small demonstration of what you probably need. Hope it gives you some insight. Here we will train the CIRFAR data set which has 10 classes and try to use it for transfer learning with on different data set which probably has different input sizes and a different number of classes.

Call reset_index as below. Replace