python-notebooks | AI Platform Notebooks API_ : is a managed service | Build Tool library

Solved using this API modification in Sequential.cs:

As of April 19. 2020 it is not possible with the .NET Wrapper as documented in this issue on the GitHub page for Keras.NET

Solved the issue parsing manually the attribute '.str' of 'line0' into an array of ints.

It seems the batch_size should be 20 not 32.

Since you have steps_per_epoch = 100, it will execute next() on train generator 100 times before going to next epoch.

Now, in train_generator the batch_size is 32, so it can generate 2000/32 number of batches, given that you have 2000 number of training samples. And that is approximate 62.

So on 63th time executing next() on train_generator will give nothing and it will tell Your input ran out of data;

Ideally,

The issue is that when you draw a plot in base graphics draw directly on a device. The line of your code grob= plot(r_fit$unique.death.times, pred[1,], type = "l", col = "red") creates a NULL object (unlike ggplot which would return a plot object).

You can make the plot directly in ggplot (there are a few ways of doing this but I've done a simple example bolow) and convert it with ggplotly:

After I watched a few videos on recursion and saw a few code examples, I tried, manually stepped through the code and somehow managed to do it with recursion. This solution is independent on the nestedness of the bookmarks, therefore a generalized solution for everyone.

Note: all the bookmarks were in the Bookmarks Toolbar in Firefox. This is highlighted in the generate_md function. You can tackle with it there. If I improve the answer later, I will make it more general.

That's a known issue, please refer to https://github.com/rstudio/keras/issues/1090

One of the solutions that sometimes works is to wrap original generator from R with keras:::as_generator.function function:

So upon closer look at the distill article, in footnote[9]

Images were optimized for 2560 steps in a color-decorrelated fourier-transformed space, using Adam at a learning rate of 0.05. We used each of following transformations in the given order at each step of the optimization: • Padding the input by 16 pixels to avoid edge artifacts • Jittering by up to 16 pixels • Scaling by a factor randomly selected from this list: 1, 0.975, 1.025, 0.95, 1.05 • Rotating by an angle randomly selected from this list; in degrees: -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5 • Jittering a second time by up to 8 pixels • Cropping the padding

Yes, you can create an SSH tunnel to connect to the Jupyter Notebook web interface using PUTTY on windows. Before proceeding, make sure that the Jupyter Notebook instance is up and running on the server. Just follow the below instructions:

1. Download the latest version of PUTTY
2. Open PUTTY and enter the server URL or IP address as the hostname
3. Now, go to SSH on the bottom of the left pane to expand the menu and then click on Tunnels
4. Enter the port number which you want to use to access Jupyter on your local machine. Choose 8000 or greater (ie 8001, 8002, etc.) to avoid ports used by other services, and set the destination as localhost:8888 where :8888 is the number of the port that Jupyter Notebook is running on. Now click the Add button, and the ports should appear in the Forwarded ports list.
5. Finally, click the Open button to connect to the server via SSH and tunnel the desired ports. Navigate to http://localhost:8000 (or whatever port you chose) in a web browser to connect to Jupyter Notebook running on the server.