There is nothing more behind it, it is a very basic loss function. What follows, 0-1 loss leads to estimating mode of the target distribution (as compared to L 1 loss for estimating median and L 2 loss for estimating mean). If 'gaussian' fitting is by least-squares, and if 'symmetric' a re-descending M estimator is used with Tukey's biweight function. Can be abbreviated. Fit the model or just extract the model frame. Can be abbreviated. Control parameters: see loess.control. Like the Python functions, the custom loss functions for R need to operate on tensor objects rather than R primitives. In order to perform these operations, you need to get a reference to the backend using backend. In my system configuration, this returns a reference to tensorflow. It also includes the following code snippet. Different loss functions. The aim of this paper is to study the impact of choosing a different loss function from a purely theoretical viewpoint. By introducing a convexity assumption – which is met by all loss functions commonly used in the literature, we show that different loss functions lead to different theoretical behaviors.
This is an loss function implementation of Keras version of frcnn.
source code
Bounding box regression
From the original frcnn paper, we get the region proposal network loss funcion that defined as: [ L({P_i},{t_i}) = frac{1}{N_{cls}} cdot sum_{i} {L_{cls}(P_i, P_i^*)} + lambda frac{1}{N_{reg}} cdot sum_{i} {P_i^* L_{reg}(t_i, t_i^*)} ]
So the second part of this equation is for regression loss (which is assocciated with the bounding box learning), and the loss function is a robust loss function(smooth L1) that defined in 'Fast R-CNN' paper. The implementation is showed above in the function 'rpn_loss_regr'. And its definiton is:
[ L_{loc}(t^u, v) = sum_{i in {x, y, w, h}}{smooth_{L1}(t_i^u-v_i)} ]
in which [ smooth_{L1}(x) = begin{cases} 0.5x^2 & {if |x| < 1 } |x| - 0.5 & {otherwise, } end{cases} ]
and here is an example how it's implemented in Keras, just help with the understanding:
ti is a vector representing the 4 parameterized coordinates of the predicted bounding box, and t0003i is that of the ground-truth box associated with a positive anchor.
For regression the four parameters are: [ t_x = frac{(x - x_a)}{w(a)} ][ t_x^_ = frac{(x^ _- x_a)}{w(a)} ][ t_y = frac{(y - y_a)}{h(a)} ][ t_y^_ = frac{(y^ _- y_a)}{h(a)} ][ t_w = log(frac{(w - w_a)}{w(a)}) ][ t_w^_ = log(frac{(w^ _- w_a)}{w(a)}) ][ t_h = log(frac{(h}{h(a)}) ][ t_h^_ = logfrac{h^_}{h(a)} ]
where x, y, w, and h denote the two coordinates of the box center, width, and height. Variables x, xa, and x0003 are for the predicted box, anchor box, and ground-truth box respectively (likewise for y;w; h). This can be thought of as bounding-box regression from an anchor box to a nearby ground-truth box.
Buffalo is a fast-paced and exciting game where you have to fill the screen with Buffalo symbols to win. This slot is a part of Xtra Reel Power feature of some Aristocrat games, and you have 1024 ways to win. Slots of vegas casino buffalo.
class loss function
So the second part of this equation is for regression loss (which is assocciated with the bounding box learning), and the loss function is a robust loss function(smooth L1) that defined in 'Fast R-CNN' paper. The implementation is showed above in the function 'rpn_loss_regr'. And its definiton is:
[ L_{loc}(t^u, v) = sum_{i in {x, y, w, h}}{smooth_{L1}(t_i^u-v_i)} ]
in which [ smooth_{L1}(x) = begin{cases} 0.5x^2 & {if |x| < 1 } |x| - 0.5 & {otherwise, } end{cases} ]
and here is an example how it's implemented in Keras, just help with the understanding:
ti is a vector representing the 4 parameterized coordinates of the predicted bounding box, and t0003i is that of the ground-truth box associated with a positive anchor.
For regression the four parameters are: [ t_x = frac{(x - x_a)}{w(a)} ][ t_x^_ = frac{(x^ _- x_a)}{w(a)} ][ t_y = frac{(y - y_a)}{h(a)} ][ t_y^_ = frac{(y^ _- y_a)}{h(a)} ][ t_w = log(frac{(w - w_a)}{w(a)}) ][ t_w^_ = log(frac{(w^ _- w_a)}{w(a)}) ][ t_h = log(frac{(h}{h(a)}) ][ t_h^_ = logfrac{h^_}{h(a)} ]
where x, y, w, and h denote the two coordinates of the box center, width, and height. Variables x, xa, and x0003 are for the predicted box, anchor box, and ground-truth box respectively (likewise for y;w; h). This can be thought of as bounding-box regression from an anchor box to a nearby ground-truth box.
Buffalo is a fast-paced and exciting game where you have to fill the screen with Buffalo symbols to win. This slot is a part of Xtra Reel Power feature of some Aristocrat games, and you have 1024 ways to win. Slots of vegas casino buffalo.
class loss function
The class loss funciton of the region proposal network(the first part of the equation) is rather easy to understand comparing with the regression part. As we know, the class loss function is to help decide the propoal region is an object or not (simple binary class), as usual it's computed by the (Softmax) over the K+1 outputs according to the the label 'Multi-task loss' in paper'Fast R-CNN'.
For training RPNs, a binary class label (of being an object or not) to each anchor. We assign a positive label to two kinds of anchors: (i) the anchor/anchors with the highest Intersectionover- Union (IoU) overlap with a ground-truth box, or (ii) an anchor that has an IoU overlap higher than 0.7 with any ground-truth box. The ground-truth label p0003i is 1 if the anchor is positive, and is 0 if the anchor is negative.
R Loss Function Calculator
other parameters
Morongo casino closure. And other parameters like how (lambda) being set is not actually associated with the loss function itself, and can be esaily find in the origianl paper and understand. so I just driectly quote from the paper:
'In our early implementation (as also in the released code), 0015 was set as 10, and the cls term in Eqn.(1) was normalized by the mini-batch size (i.e., Ncls = 256) and the reg term was normalized by the number of anchor locations (i.e., Nreg 0018 2; 400). Both cls and reg terms are roughly equally weighted in this way.'
R Glm Loss Function
Conclusion
So we have covered the RPN's loss fuction, and the then according to the achitechture, followed by a ROI pooling then the final classfication loss and the Bounding box regression loss which are essentially the same as the previous one. The slightly modification is that the final classification loss isn't binary, which need to be adapted.
R Xgboost Loss Function
Disability of the musculoskeletal system is primarily the inability, due to damage or infection in parts of the system, to perform the normal working movements of the body with normal excursion, strength, speed, coordination and endurance. It is essential that the examination on which ratings are based adequately portray the anatomical damage, and the functional loss, with respect to all these elements. The functional loss may be due to absence of part, or all, of the necessary bones, joints and muscles, or associated structures, or to deformity, adhesions, defective innervation, or other pathology, or it may be due to pain, supported by adequate pathology and evidenced by the visible behavior of the claimant undertaking the motion. Weakness is as important as limitation of motion, and a part which becomes painful on use must be regarded as seriously disabled. A little used part of the musculoskeletal system may be expected to show evidence of disuse, either through atrophy, the condition of the skin, absence of normal callosity or the like.
