Merge branch 'master' into DeepCrowd

* master: (21 commits)
  Update docs for ND blobs (BVLC#1970) and layer type is a string (BVLC#1694)
  Add ReshapeParameter axis and num_axes to reshape only a particular span of the input shape
  basic tests (Forward, Gradient) for ReshapeLayer
  ReshapeLayer fixups for ND blobs
  Added a Reshape layer for copying-free modification of blob dimensions.
  Spatial Pyramid Pooling Layer
  remove bogus implementation of SigmoidCrossEntropyLossLayer::Forward_gpu
  remove superfluous empty destructors
  [pycaffe] use bp::object instead of PyObject* for self in Python layer
  python: PEP8; changed docstring documentation style to NumPyDoc style
  This imports the wrong io module in Python 3.
  check that count_ does not overflow in Blob::Reshape
  Modify for better readability regarding temporary bufffer for backward computation
  Fix redundancy of parameter backward computation
  Added support for original implementation, using (margin - d^2), through the legacy_version parameter.
  added epsilon to prevent possible division by zero in gradient calculation
  Fixed contrastive loss layer to be the same as proposed in Hadsell et al 2006
  remove spurious net.hpp includes
  always call Layer::Reshape in Layer::Forward
  Increment iter_ before snapshotting, remove +1 logic -- fixes final snapshot being off by one
  ...

docs/tutorial/data.md

@@ -10,15 +10,15 @@ New input types are supported by developing a new data layer -- the rest of the
 
 This data layer definition
 
-    layers {
+    layer {
       name: "mnist"
-      # DATA layer loads leveldb or lmdb storage DBs for high-throughput.
-      type: DATA
+      # Data layer loads leveldb or lmdb storage DBs for high-throughput.
+      type: "Data"
       # the 1st top is the data itself: the name is only convention
       top: "data"
       # the 2nd top is the ground truth: the name is only convention
       top: "label"
-      # the DATA layer configuration
+      # the Data layer configuration
       data_param {
         # path to the DB
         source: "examples/mnist/mnist_train_lmdb"
@@ -46,9 +46,9 @@ The (data, label) pairing is a convenience for classification models.
 
 **Transformations**: data preprocessing is parametrized by transformation messages within the data layer definition.
 
-    layers {
+    layer {
       name: "data"
-      type: DATA
+      type: "Data"
       [...]
       transform_param {
         scale: 0.1