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:

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