Hi all,

I am trying to develop a simple network using the `mx.symbol.linalg_gemm2`

function. However I cannot get the training of the model to work due to the impossibility of incorrectly inferring the symbol shapes. I am using the R API.

The code follows:

```
NFEAT = 795
nDest 780
batchSz = 150
DL = customArrayIter(X.model, data.shape=c(NFEAT, 150), label=Y.model, batch.size=1)
X = mx.symbol.Variable('data')
A = mx.symbol.Variable('A')
B = mx.symbol.Variable('B')
CC = mx.symbol.linalg_gemm2(X, A, name='CC')
Yhat = mx.symbol.linalg_gemm2(B, CC, name='Yhat')
out = mx.symbol.SoftmaxActivation(Yhat, name='out')
loss = mx.symbol.LinearRegressionOutput(Yhat, name='loss')
```

To note that using the function `mx.symbol.infer.shape`

as follows:

```
shps=mx.symbol.infer.shape(loss, data=c(NFEAT, 150), A=c(nDest, NFEAT), B=c(150, 1))
```

The shapes appear to be correctly inferred.

Can someone advise me on hoe to specify the shapes in the mx.mdoel.FeedForward.create function?

Thanks a lot!