recurrent | recurring tasks | Job Scheduling library

You could try defining a custom GRU layer with a trainable variable for the states but not sure how the performance will be:

You can try using tf.random.normal:

To set this from the DB you need to have code like the following for the value

I tested this and it worked.

Since the parameter is a static parameter - which you can see when you hover the little icon next to the parameter

This means that this value is evaluated once, during creation and never again.

If you want to change during runtime you will need to call valve.set_portionSize(value) if you want to change the value during model execution.

See below

To access start and end node of a relationship, there is a slightly different syntax that you are using:

You can keep a depth counter as you recurse, and at each level, return the subdictionary that that has the largest associated counter:

The root cause for this behavior is not clear, however it seems it's related to the namespace. Pulling the docker image within another namespace works fine. Same works if one deploys the application in a new namespace.

Alternatively you can also delete the ns completely and then re-create it.

The matrix multiplications are correct, but they need to be performed at every timestep. And if return_sequences=False Keras returns the output of the last timestep of shape ( 1 , 4 ). So, we can create a for-loop which can perform the matrix multiplications for 3 timesteps.

Note: The input shape ( 3 , 2 ) denotes ( time_steps , num_features )

Here's the code to replicate the Keras RNN model,

As I understand it you are trying to identify all possible matches for the jumbled string in your list. You could sort the letters in the jumbled word and match the resulting list against sorted lists of the words in your data file.

First, I suggest you read Tensorflow's tutorial on time series forecasting. I played around a bit with your code and the data provided. The first important thing is that only the temperature column contains information. In the code below, I prepare the data so that X over a time window of 96 samples/steps and the next step is in Y. X is of dimension (n_samples, 96, 1) and Y of dimension (n_samples, ), I use only steps_backwards points for the past (and discarded the future for simplicity, without affecting the generality) I have tried different models (a simple Fully Connected or RNN + FC, etc.). I'm doing mean pooling (with the functional API rather than the sequential model definition approach) so that I have a single predicted value at the end.