Make TanH cleaner, more efficient, and possible to use in-place
Merge pull request #475 from jeffdonahue/tanh-fixes

Make TanH cleaner, more efficient, and possible to use in-place
padding for max pooling

Max pooling pads by -inf if the padding parameter is set.

Padding for pooling, like padding for convolution, can preserve the
dimensions of the bottom at the top. By setting the padding to
floor(kernel_size / 2) the top output is the "same" instead of the
"valid" part of the bottom input.
check the last pooling in padding and add padded max pooling test

[Adapted from a commit by @jeffdonahue by @shelhamer.]
Merge pull request #473 from shelhamer/pad-max-pooling

Padding for Max Pooling
Changed variable name: iscolor to is_color.
fix Makefile build dir link upgrade bug reported by @jamt9000
layers declare their names and number of input/output blobs, and don't
check top/bottom blob counts explicitly in SetUp; instead call base
Layer::SetUp.
move MemoryDataLayer decl. from vision_layers.hpp to data_layers.hpp
add fish bike example image
Merge pull request #479 from jeffdonahue/declare-layer-names-and-numblobs

Layers declare their types and number of bottom/top blobs
fix ArgMaxLayer bug in num bottom blobs decl. pointed out by @sguada
add DummyDataLayer
add DummyDataLayer tests
Merge pull request #480 from jeffdonahue/dummy-data-layer

Dummy data layer
lint dummy data layer
fix old detect.py default
pycaffe Detector crops with surrounding context

- caffe.Detector learned how to crop windows with context in the R-CNN
  style s.t. the bordero of the network input is a given amount of
  context.
- add --context_pad arg to detect.py for amount of context. Default is
  16, as in R-CNN.
make R-CNN the Caffe detection example
edit detection example, include R-CNN NMS