neural | Authentication library

I would suggest you to use conda (Ananconda/Miniconda) to create a separate environment and install tensorflow-gpu, cudnn and cudatoolkit. Miniconda has a much smaller footprint than Anaconda. I would suggest you to install Miniconda if you do not have conda already.

If you've two input of such layer, then you can simply initialize your weights something like as follows

I can't run your code so I created something similar and I get this error when pre_norms has values NaN.

I get pre_norms with NaN because predictors has columns No_Model,Technique which have strings and predictors-predictors.mean()/predictors.std()) convert them to NaN

Solution could be removing columns No_Model,Technique but this create empty data - so it is useless.

I don't know you full code but you should check what you have in variables and if you have NaN then you have wrong calculations.

It seems that you want to understand that how could you use your dataset and apply LSTMs over it to get some meaningful out of your data.

Now here you can reframe your data set to create more features from your present data set for eg.

Features That could be derived out of Data

1. Take Out day of the month (which day is it 1-31)
2. Week of the month (which week of month it is 1-4)
3. Day of the week (Monday - Saturday)
4. what is the time ( you can have any of the value out of 188)

Features that could be added from opensource data

1. What is the wheather of the day
2. Is there any holiday nearby(days remaining for next holiday/function etc.)

Now let's Assume for each row you have K features in your data and you have a target that you have to predict which is what is the availability of parking. P(#parking_space|X)

Now just just keep your timesteps as a variable while creating your model and reshape your data from X.shape-->(Examples, Features) to the format X.shape-->(examples,Timesteps,Features). You can use below code and define your own look_back

Here your architecture will be many to many with Tx=Ty

Here’s a ‘tidyverse’ solution:

Example with dummy data:

Since the column does not have a field supplied, I'd recommend either supplying a field or colID to the column. This would be the simplest approach without having to use any API calls to move the column:

383 on the log is not the batch size. It's the number of steps which is data_size / batch_size.

The problem that training does not work properly is probably because of very low or high learning rate. Try adjusting the learning rate.

Turns out it's just a problem with the input type.

Doing something like this will work: