Non-negative weight matrix where each row lies on a simplex

Hi I need to implement a fully connected linear layer in symbolic mode, where the weights are restricted to lie on a simplex, i.e,. each row of the weights vector is a probability distribution (the entries of each row are all non-negative and they sum to one). One way that I could think of imposing this restriction is to use softmax on weights as follows:

y =, mx.sym.transpose(mx.sym.softmax(W))),

where x is the input symbol and y is the output symbol, and W is the weight matrix.

However, it’s not clear to me how to tell mxnet that the W is not a symbol, but a parameter matrix that needs to be learned during training.

Can anyone give me pointers on how to set W in the above equation to be a parameter matrix?