Accessing Input Layer data in Tensorflow/Keras

Question

I am trying to replicate a neural network for depth estimation. The original authors have taken a pre-trained network and added between the fully connected layer and the convolutional layer a 'Superpixel Pooling Layer'. In this layer, the convolutional feature maps are upsampled and the features per superpixel are averaged.

My problem is that in order to successfully achieve this, I need to calculate the superpixels per image. How can I access the data being used by keras/tensorflow during batch processing to perform SLIC oversegmentation?

I considered splitting the tasks and working by pieces i.e. feed the images into the convolutional network. Process the outputs separately and then feed them into a fully connected layer. However, this makes further training of the network impossible.

Answer 1

At the time it seems to be impossible to actually access the data within the symbolic tensor. It also seems unlikely that such functionality will be added in the future since in the Tensorflow page it says:

A Tensor object is a symbolic handle to the result of an operation, but does not actually hold the values of the operation's output.

Keras allows for the creation of personalized layers. However, these are limited by the available backend operations. As such, it is simply not possible to access the batch data.