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of the parameter vectors.

learning rate.

shenanigans in a separate process.

just dlib exceptions.

Concat layer

Added getter for trainer::train_one_step_calls

dimensions of its inputs rather than always outputting a tensor that has the
dimensions of its immediate predecessors.

find_max_box_constrained().  Now the bounds can be empty for some variables.

caused by num_computational_layers being wrong when tax layers were placed as
the first layer.  These visit functions being wrong also caused multi-GPU
support to not work on such networks.

std::async() since std::async creates new threads with each invocation, which
in turn causes objects with thread_local storage duration to be reconstructed
each time.  This is problematic because CUDA context objects for cublas and
cudnn get reconstructed over and over, slowing things down and generally using
more resources than should be used.