Hello,
I am trying to use Binary Cross Entropy Loss (BCE loss) for Simese network.
I have two inputs for BCE loss function:

output (input_dy) →  tensor of size [4] , output of neural network
true_labels (y_true)  →  tensor of size [4],  target (true value)

For BCE loss, the input parameters must be of the dimension:

output (input_dy)  → [Batch_size, no. of classes]
true_labels (y_true) → [Batch_size]

The following diagram explains the query:

Capture784×240 3.4 KB

I need a function in python using pytorch to convert the dy matrix to a 2D matrix with the output probabilities that sum to 1. [To note: dy should be iterated through length of it, as it is the output of the network for every input ]
Further a 2D array must be represented into one hot encoding, which will be true_labels (that will represent Binary classes with 0 & 1)
I need both output matrix and true_labels matrix for BCE Loss with following dimensions:

output dimension → [4, 2]
true_labels → [4]

Any help is most appreciated!
Thank you in advance. @ptrblck