addendum | Automatically exported from code.google.com/p/addendum | Build Tool library

I want to design and train a neural network for the automatic recognition of the edges, in some microscopic images. I am using Keras for a start, I may consider PyTorch later.

The structure of the images is rather simple, with some dark areas, and some clear areas, relatively easy to distinguish, and the task is to select the pixels of the contour between dark and clear areas. The transition between dark and clear is gradual, so my result is not a single line of edge pixels, but rather a 10 or 15 pixels wide "ribbon" at the edge.

I have manually annotated 200-something images, so for each image I have another image, of the same size, where the pixels of the contours are black, and all the other pixels are white.

I have seen many tutorials on how to design, compile and fit a model (a neural network), and then how to test it, using the manually annotated data.

However, most of the tutorials work on problems of classification, where the number of neurons in the output layer is the number of categories.

My problem is not a problem of classification, and ideally my output should be an image of the same size of the input.

So, here is my question:

What is the best way to design the output layer? Is a layer with a number of neurons equal to the number of pixels the best idea? Or this is a waste, and there is a more efficient way?

Addendum

1. The images are "easy", but it is still difficult to find the contour pixels, so I believe that it is worth using the machine learning approach.
2. The transition between dark and clear is a little gradual, so my result is not a single line of pixels on the edge, but rather a band, a 10 or 15 wide ribbon of edge pixels. Since I am after a ribbon of pixels, my categories should be "edge" and "not-edge". If I use the categories "dark pixels" and "clear pixels", and then numerically find the pixels between the two areas I do not get the "ribbon" result, which I need.

I’m trying to use PyMC3 Minibatch ADVI for Bayesian Regression. The pm.fit function throws the following error and I’m not sure how to fix it.

It says that the ‘str’ object has no attribute ‘type’. What is any ‘str’ object from the error message here? I’ve mapped float tensors for more_replacements to the best of what I know.

I have a list ("years") of data frames, where each data frame is named by year:

I have modified d3noob's d3.js version 4

https://bl.ocks.org/d3noob/43a860bc0024792f8803bba8ca0d5ecd

derivative of Mike Bostock's collapsible tree

https://bl.ocks.org/mbostock/4339083

adding mouse-based pan and zoom, and loading the JSON data from an external file.

https://jsfiddle.net/vstuart/acx5zfgn/31/

• [updated, with solution] https://jsfiddle.net/vstuart/acx5zfgn/62/

Although not shown in that fiddle, in my HTML frontend (based on the AjaxSolr framework) I have hyperlinks corresponding to Apache Solr facets that when clicked update the web page (via an updated Solr request)

All of that is working well.

However, I also want to be able to have the corresponding d3.js node expand, when I click those facet hyperlinks.

Any suggestions would be greatly appreciated.

Addendum. For those interested in the answer, I updated my JSFiddle, HTML code (below) and GitHub Gist -- all at two places: search that code for "per answer at" to view the additions.

To verify the code additions, I dumped my updated the HTML content in my Gist to a new index.html file, which runs as expected in Firefox v88.0 running on localhost (no webserver needed).

Single-page code

• gist: https://gist.github.com/victoriastuart/996f568be10bb843b1f810c01b4dfa51

Here's my problem, I am trying to download excel xlsx files from a WP site, if I type

the url I assigned to a variable in my code, called stock, directly in browser, Firefox downloads it perfectly.

I'm trying to do this with Python so I've made a script using requests and then Pandas for processing and manipulation.

However even though the file seems to download it returns an error, I tried using both open and with open as suggested on similar problems I've found here, but in my case it returns an error 'ValueError: Seek of closed file', I attempted several variations to the code, with no result, the outcome was always the error.

Here is my code

I have a Make target called test. It looks like this:

I have a short function, where if anything goes wrong at any part of the process, I can get the data from previous parts of the process and write them out for future investigation. For example:

I am trying to access the username attribute of a Django User object from a Profile object that is related by a OneToOneField.

I am fetching and processing images with nodejs, using node-fetch and canvas. So far, things have been working well. I have a series of image urls, and I fetch them all in parallel using Promise.all: