use Blob directly instead of shared_ptr for EltwiseLayer::max_idx_

This is in keeping with #742.
add abstract Layer::Reshape, and document the new method protocol
don't reallocate blobs when shrinking memory use

This allows nets to be reshaped very quickly (essentially for free) as
long as sufficient memory has been allocated. Calling Blob::Reshape in
order to free up memory becomes impossible; however, this is not a
normal use case (and deleting blobs does free memory).
enable reshaping in the forward pass

Note that calling Reshape when no reshape is necessary should be
effectively a no-op, so this is not a performance regression.
separate setTensor4dDesc from createTensor4dDesc

This will make it possible to add reshaping to cuDNN layers.
separate setConvolutionDesc from createConvolutionDesc
split off Reshape for data layers
split off Reshape for loss layers
split off Reshape for neuron layers
split off Reshape for common layers
split off Reshape for vision layers

Note that we are dropping some checks from LRN layer. However, these
checks are fairly redundant; something is very wrong if these layers
are producing top blobs that are different sizes than their inputs, and
tests are the right place to catch that. The thing that really should be
checked (that isn't) is that that local_size needs to be odd; this will
be added in a future commit.
call Reshape in Layer::SetUp

Strictly speaking, Reshape doesn't need to be called until the first
Forward call; however, much existing code (especially tests) assumes
that top blobs will be set up in SetUp, so we may as well do it there.
default LayerSetUp to no-op instead of NOT_IMPLEMENTED

Now that top blobs are set up in Layer::Reshape, it's Reshape that is
mandatory, and simple layers often don't need to implement LayerSetUp.
Reshape is (already) declared abstract, so not implementing it is a
compile-time error.
test net reshaping
include Reshape in caffe time

Since we are now calling Reshape in the Forward pass, it's only fair to
include it when timing. Reshape calls should normally be four or so
orders of magnitude faster than Forward calls; this change also makes it
easy to notice a mistake that causes something slow to happen in
Reshape.
add Net::Reshape for only reshaping

Note that it is not normally necessary to call this function when using
reshapable nets, but sometimes it can be useful to compute the sizes of
intermediate layers without waiting for the forward pass.
[pycaffe] expose Net::Reshape
[docs] clarify the use of Blob::Reshape a bit
check that LRN's local_size is odd as the current implementation requires
Merge pull request #594 from longjon/layer-reshaping

On-the-fly net resizing, without reallocation (where possible)