[GitHub] [incubator-mxnet] mouryarishik opened a new issue #15193: How to calculate gradients for the intermediate outputs using symbol?

[GitBox <gi...@apache.org>]

mouryarishik opened a new issue #15193: How to calculate gradients for the intermediate outputs using symbol?
URL: https://github.com/apache/incubator-mxnet/issues/15193
 
 
   Is it possible to compute gradients of intermediate values of a computation?
   For example:-
   ```
   a = mx.sym.var(name = 'a', shape = (1, 1), dtype = 'float64')
   b = mx.sym.var(name = 'b', shape = (1, 1), dtype = 'float64')
   
   c = mx.sym.broadcast_add(a, b, name = 'c')
   d = mx.sym.make_loss(mx.sym.broadcast_add(c, b, name = 'd'))
   
   bind = d.simple_bind(ctx = mx.cpu(1))
   bind.forward(a = mx.nd.ones((1, 1)), b = mx.nd.ones((1, 1)), grad_req = {'a': 'write', 'b':'write', 'c':'write'})
   outputs = bind.outputs[0]
   # print(outputs)
   bind.backward()
   print(bind.grad_dict)
   #prints 
     # {'a': 
     # [[1.]]
     # <NDArray 1x1 @cpu(1)>, 
     # 'b': 
     # [[2.]]
     # <NDArray 1x1 @cpu(1)>}
   ```
   Here bind.grad_dict doesn’t provide gradients of c even though I’ve written 'c':'write' in grad_req .
   So how can I get the gradients of “d with respect to c” as well?

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