ResNet | ResNet Implementation Using Tensorflow | Machine Learning library

There's an unresolved issue in Tensorflow on this problem. According to the issue, you need to pass inputs of both outer model and inner model to get the output of inner model.

Since you are initializing a tensorflow (version < 2) session with tfc.InteractiveSession() you might need to initialize all values before running your session by calling:

IIUC, you should be able to do this directly the same way:

Adding a preprocessing layer after the Input layer is the same as adding it before the ResNet50 model,

This is the solution I've come up with. I assigned the layers that I wanted to keep as output to variables:

Your values to the constructor are not taken, because you are calling .from_pretrained(), which loads all values from the respective config file of the pretrained model (in your case, the corresponding config file can be viewed here). Even if some values might not be specified in the config, they will be primarily taken from the default values, instead of whatever you're passing before.

If you want to change attributes, you can do so after loading:

It seems like you need to create a custom Dataset.

You probably forgot to set padding=same. The default value is valid. Here is a working example:

Your problem in your new class GroupConv1D is that you store all your convolution modules in a regular python list self.conv_list instead of using nn Containers.
All methods that affect nn.Modules (e.g., .to(device), .eval(), etc.) are applied recursively to all relevant members of the "root" nn.Module.
However, how can pytorch tell which are the relevant members?
For this you have containers: they group together sub-modules, registers and parameters such that pytorch can recursively apply all relevant nn.Module's methods to them.

See, e.g., this answer.