I am training a simple sequential model for NLP in keras. Here is the model architecture:

I am implementing a multi-class classification machine learning model with Keras for NLP. I’m not sure whether this context is useful, but due to class imbalances, I created a class_weight array to be implemented in the model.fit() function as follows:

File “C:UsersuserAppDataLocalProgramsPythonPython311Libsite-packageskerassrcopsoperation.py”, line 21, in init
raise ValueError(
ValueError: Argument name must be a string and cannot contain character /. Received: name=conv1/7x7_s2 (of type <class ‘str’>)

here is my code.

The question is complete in the title. For the context I am training an alignment model using a CTC loss. In this case the CTC loss requires the as target value the sequence of labels (without label duration). Additionally I’d like to use a metric that evaluates the probability distribution of the model using the sequence of labels including repetitions (label durations). So the model has one output but tow different target values.

my work requires me to record the intermediate states of a Keras model. As shown below, I need to extract the states between layers. I wonder if Keras comes with something that I can use to dump this?

I’m making a model with Keras that tries to predict if a person has Parkinson’s disease based on a drawing of a spiral using this dataset. However, my model is limited to a max train accuracy of 72.22% and a max test accuracy of 66.67% for some reason no matter how many more epochs or layers I add.

I am using keras for an image recognition Sequential CNN model. I have about 300,000 images 424×424, but only the inner 168×168 is relevant.

I wrote a simple custom loss function in keras (below here) which uses a for loop .
This function is very stupid, I could avoid the for loop but I wrote it as exercise for me.
I get an exception unless I use model.run_eagerly=True.
I don’t understand why I get the exception and why I get it only when model.run_eagerly is False.