# build symbolic expression to add bias and apply activation function, i.e. produce neural net layer output
# A few words on ``dimshuffle`` :
# ``dimshuffle`` is a powerful tool in reshaping a tensor;
# what it allows you to do is to shuffle dimension around
# but also to insert new ones along which the tensor will be
# broadcastable;
# dimshuffle('x', 2, 'x', 0, 1)
# This will work on 3d tensors with no broadcastable
# dimensions. The first dimension will be broadcastable,
# then we will have the third dimension of the input tensor as
# the second of the resulting tensor, etc. If the tensor has
# shape (20, 30, 40), the resulting tensor will have dimensions
# (1, 40, 1, 20, 30). (AxBxC tensor is mapped to 1xCx1xAxB tensor)
# More examples:
# dimshuffle('x') -> make a 0d (scalar) into a 1d vector
# dimshuffle(0, 1) -> identity
# dimshuffle(1, 0) -> inverts the first and second dimensions
# dimshuffle('x', 0) -> make a row out of a 1d vector (N to 1xN)
# dimshuffle(0, 'x') -> make a column out of a 1d vector (N to Nx1)
# dimshuffle(2, 0, 1) -> AxBxC to CxAxB
# dimshuffle(0, 'x', 1) -> AxB to Ax1xB
# dimshuffle(1, 'x', 0) -> AxB to Bx1xA
shape a = (20, 30, 40)
a.dimshuffle('x', 2, 'x', 0, 1)
20 is '0', 30 is '1', 40 is '2'
Bacause dimshuffle('x') makes a 0d (scalar), dimshuffle('x', 2, 'x', 0, 1) makes vector 'a' into array(1, 40, 1, 20, 30)
