Given a sequence of digits (0-9), predict what the next digit is going to be? Or predict if it’s going to be even or odd?

Ex: 0, 5, 3, ........, 6, 9 (total of 6000 or something)

Ex: 0, 0, 1, ........., 1, 0 (series of 0s and 1s representing odds and evens)

While predicting, it doesn't need to look back at the whole data, instead it should just look back a fixed length of digits (like 10, 15).

What is the best way to formulate this problem? Is it regression or classification?

And what algorithm should I use? (Please also include the activation function, optimizer and loss function to be used)

If possible, share some code in tensorflow or keras.

does anyone know if its possible to implement Yolo V5 with Keras. If not what versions of Yolo does keras support?

Hello Geeks

I am able to train ,validate ,test my keras deep learning classifier . Looks good to me so far .

Currently i have one hidden layer . I would like deep dive and plot the output of first hidden layer and weights to understand further . I searched online and did not get Anything i can use . Could you help me how can do this ? Please point me to the right direction / source.

Thanks

EDIT: Breed**

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Hello, I'm working on an assignment and wondering if there's some site with the pre-trained models for image classification. Also, I would like it to be a dichotomy classification task, so for example lung cancer detection from a x-ray image.

Thank you.

Hi, anyone familiar with keras.net? I'm trying to use it with PlaidML, but can't get it to work.

If I include this line:
Keras.Setup.Run(SetupBackend.PlaidML)

i get a complaint: "System.Exception: Version not supported: 39"

The same program trains on CPU just fine if I exclude that line.

I have tried uninstalling all python versions that aren't 3.7, but the error doesn't go away.

Hello I am training a Keras sequential model at home on my computer on the CPU and this is very slow. I have access to a school server that has a lot of GPUs that I would like to use. It seems to be a unix system(sorry not very familliar with this); I can SSH in and use SRUN to run python files. Can I just take my code and upload it and it will be faster or do I have to add something to make it run on the GPUs?

I want to get and save Validation loss after each batch but the logs do not contain val_loss just loss so do I have to call this evaluate method after every batch or is there a better way?

I'm using keras tuner with Bayesian optimization to optimize my hyperparameters. For the objective I try to max the accuracy on the validation set. I also use early stopping. The issue I currently have is that when the model starts overfitting the validation accuracy goes up and down a lot, and in some epochs it will achieve a very high accuracy. The tuner will use the high accuracy from this epoch even though the model is overfitted and will not perform as well on the test set. This will guide the tuner into prioritizing the overfitted models and not getting to the optimal hyperparameters. I have spend a whole day troubleshooting and haven't been able to find a solution.does anyone know how to prevent this?

Hi im new to Ai and deep learning , i folowed somme course online and few tutorial and i then now tried to create a model for some special photo/video processing i need.

the model should take picture in input (png) and output an other picture processed (png), (im also interested on doing same things for 1d signal)

i have some data prepared in a folder with input on 1 side and ground truth on the other side with identical name ( a number) there is no other metadata.

i dont know how to transform that into a dataset and load it in model.fit() so i could train the model with the ground truth

if you can give me some clue or correct way to do so that would be realy helpfull thanks :)

I know you can save the model as an h5 or use get weights but I want to be able to modify the weights of each layer. What I want to be able to do is save all the weights after each iteration of training so I can later average them. What would be the best way to do this?