Fix the condition prefetch_needs_rand in the ImageDataLayer
Initialize the RNG generator with an orthogonally newed Generator
Merge pull request #508 from kloudkl/ImageDataLayer-RNG-core-dump

Image data layer rng core dump
Added multi_label param to window layer to produce multiple binary labels per class
Added Multi_label_accuracy_layer it computes the average accuracy, for now it just compute the overal mean accuracy and cross entropy loss

Conflicts:
	src/caffe/proto/caffe.proto

Conflicts:
	src/caffe/proto/caffe.proto
Changed MultiLabelAccurcay to assume binary [0,1] labels

Computing sign is not good for binary [0,1]
Split accuracy into positive and negative
Now multi-labels are -1,0,1 with 0 means ignore, also MultiLayerAccuracy compute the loss and can be used in training
Added initial test for MultiLabelAccuracy adapted from CrossEntropy
LOG(INFO) and take sign of random labels to make them -1 and 1

Correct count to be an int for computing the sign and fixed the range of targets in the setup
Added logs to SigmoidCrossEntropyLossReference and simplify formula
Fixed the loss in MultiLabelAccuracyLayer to consider negative labels as 0
Added zeros to the labels using code from dropout

Changed the default value for threshold to 0, added none, half and all zeros tests
Remove LOGs now all test pass
# This is a combination of 2 commits.
# The first commit's message is:

Added MultiLabelAccuracyParameter with positive and negative weights

Conflicts:
	src/caffe/proto/caffe.proto

# This is the 2nd commit message:

Added positive and negative weights, change Accuracy to Sensitivity and
Specificity
Added MultiLabelAccuracyParameter with positive and negative weights

Conflicts:
	src/caffe/proto/caffe.proto

Added positive and negative weights, change Accuracy to Sensitivity and
Specificity
Added check that loss should be positive or 0 to multi_label_accuracy test
Return mean loss in Forward Test
Initialize top_labels with 0, added one_vs_all to params, if true positive for one class is negative for the others
Replace Likehood ratio by Harmonic Mean of Sens and Spec