Compare backprop gradients with numerical estimates.
Run the net forward and backward calculating gradients.
Run the net forward returning cell array of activations.
Run the network backward returning a struct array of gradients.
Calculate ∂J(z,y)/∂z where J is the softmax cost, y is the desired
output and z is the linear output of the final layer.
Return the softmax cost J(z,y) where y is the desired output and z is
the linear output of the final layer.
Return a version of the network where all parameters are double
precision.
Return a version of the network where all parameters are in gpuArrays.
Calculate logsumexp of each column of the input preventing
over/underflow.
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Call forward in small batches and merge the results.
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