Weights Matrix Final Fully Connected Layer
Conceptually, a neural network layer is often written like y = W*x where * is matrix multiplication, x is an input vector and y an output vector. If x has 2000 units and y 4800, then indeed W should have size (4800, 2000), i.e. 4800 rows and 2000 columns.
However, in implementations we usually work on a batch of inputs X. Say X is (b, 2000) where b is your batch size. We don't want to transform each element of X individually by doing W*x as above since this would be inefficient.
Instead we would like to transform all inputs at the same time. This can be done via Y = X*W.T where W.T is the transpose of W. You can work out that this essentially applies W*x to each row of X (i.e. each input). Y is then a (b, 4800) matrix containing all transformed inputs.
In Tensorflow, the weight matrix is simply saved in this transposed state, since it is usually the form that is needed anyway. Thus, we have a matrix with shape (2000, 4800) (the shape of W.T).
My question is, I think, too simple, but it's giving me headaches. I think I'm missing either something conceptually in Neural Networks or Tensorflow is returning some wrong layer. 
I have a network in which last layer outputs 4800 units. The penultimate layer has 2000 units. I expect my weight matrix for last layer to have the shape (4800, 2000) but when I print out the shape in Tensorflow I see (2000, 4800). Please can someone confirm which shape of weight matrix the last layer should have? Depending on the answer, I can further debug the issue. Thanks.