From 0fe217f89c4b10c497017e21e1810d806c152b46 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Tue, 23 Jan 2018 11:54:24 +0000 Subject: [PATCH] Deploy to GitHub Pages: d43932c84653c58f3395f8c909cd882ea58d42e0 --- develop/doc/operators.json | 127 ++++++++++++++++++++++--------------- 1 file changed, 75 insertions(+), 52 deletions(-) diff --git a/develop/doc/operators.json b/develop/doc/operators.json index 79361fc1c56..511648b3f1f 100644 --- a/develop/doc/operators.json +++ b/develop/doc/operators.json @@ -1768,6 +1768,24 @@ "comment" : "(bool, default false) If true, output a scalar reduced along all dimensions.", "generated" : 0 } ] +},{ + "type" : "round", + "comment" : "\nRound Activation Operator.\n\n$out = [x]$\n\n", + "inputs" : [ + { + "name" : "X", + "comment" : "Input of Round operator", + "duplicable" : 0, + "intermediate" : 0 + } ], + "outputs" : [ + { + "name" : "Out", + "comment" : "Output of Round operator", + "duplicable" : 0, + "intermediate" : 0 + } ], + "attrs" : [ ] },{ "type" : "norm", "comment" : "\n \"Input shape: $(N, C, H, W)$\n Scale shape: $(C, 1)$\n Output shape: $(N, C, H, W)$\n Where\n forward\n $$\n [\\frac {x_{1}}{\\sqrt{\\sum{x_{i}^{2}}}} \\frac {x_{2}}{\\sqrt{\\sum{x_{i}^{2}}}} \\frac {x_{3}}{\\sqrt{\\sum{x_{i}^{2}}}} \\cdot \\cdot \\cdot \\frac {x_{n}}{\\sqrt{\\sum{x_{i}^{2}}}}]\n $$\n backward\n $$\n \\frac{\\frac{\\mathrm{d}L }{\\mathrm{d}y_{1}} - \\frac {x_{1}\\sum {\\frac{\\mathrm{d} L}{\\mathrm{d} y_{j}}}x_{j}}{\\sum x_{j}^{2}} }{\\sqrt{\\sum{x_{j}^{2}}}}\n $$\n ", @@ -3657,6 +3675,63 @@ "intermediate" : 0 } ], "attrs" : [ ] +},{ + "type" : "iou_similarity", + "comment" : "\nIOU Similarity Operator.\nComputes intersection-over-union (IOU) between two box lists.\n Box list 'X' should be a LoDTensor and 'Y' is a common Tensor,\n boxes in 'Y' are shared by all instance of the batched inputs of X.\n Given two boxes A and B, the calculation of IOU is as follows:\n\n$$\nIOU(A, B) = \n\\frac{area(A\\cap B)}{area(A)+area(B)-area(A\\cap B)}\n$$\n\n", + "inputs" : [ + { + "name" : "X", + "comment" : "(LoDTensor, default LoDTensor) Box list X is a 2-D LoDTensor with shape [N, 4] holds N boxes, each box is represented as [xmin, ymin, xmax, ymax], the shape of X is [N, 4]. [xmin, ymin] is the left top coordinate of the box if the input is image feature map, they are close to the origin of the coordinate system. [xmax, ymax] is the right bottom coordinate of the box. This tensor can contain LoD information to represent a batch of inputs. One instance of this batch can contain different numbers of entities.", + "duplicable" : 0, + "intermediate" : 0 + }, { + "name" : "Y", + "comment" : "(Tensor, default Tensor) Box list Y holds M boxes, each box is represented as [xmin, ymin, xmax, ymax], the shape of X is [N, 4]. [xmin, ymin] is the left top coordinate of the box if the input is image feature map, and [xmax, ymax] is the right bottom coordinate of the box.", + "duplicable" : 0, + "intermediate" : 0 + } ], + "outputs" : [ + { + "name" : "Out", + "comment" : "(LoDTensor, the lod is same as input X) The output of iou_similarity op, a tensor with shape [N, M] representing pairwise iou scores.", + "duplicable" : 0, + "intermediate" : 0 + } ], + "attrs" : [ ] +},{ + "type" : "conditional_block", + "comment" : "Conditional block operator\n\nRun the sub-block if X is not empty. Params is the other inputs and Out is the\noutputs of the sub-block.\n", + "inputs" : [ + { + "name" : "X", + "comment" : "The conditional variable of this operator. If X is empty, the whole sub-block will not be executed.", + "duplicable" : 1, + "intermediate" : 0 + }, { + "name" : "Params", + "comment" : "The input variables of the sub-block.", + "duplicable" : 1, + "intermediate" : 0 + } ], + "outputs" : [ + { + "name" : "Out", + "comment" : "The output variables of the sub-block.", + "duplicable" : 1, + "intermediate" : 0 + }, { + "name" : "Scope", + "comment" : "(std::vector) The step scope of conditional block. To unify the conditional block, rnn and while op, the type of scope is std::vector", + "duplicable" : 0, + "intermediate" : 0 + } ], + "attrs" : [ + { + "name" : "sub_block", + "type" : "block id", + "comment" : "The step block of conditional block operator", + "generated" : 0 + } ] },{ "type" : "rmsprop", "comment" : "\nRmsprop Optimizer. \n\n$$\nMeanSquareOut = decay * MeanSquare + (1 - decay) * Grad * Grad \\\\\nMomentOut = momentum * Moment +\n \\frac{LearningRate * Grad}{\\sqrt{MeanSquareOut + epsilon}} \\\\\nParamOut = Param - MomentOut\n$$\n\nThe original slides that proposed Rmsprop: Slide 29 of\nhttp://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n\n", @@ -4725,40 +4800,6 @@ "intermediate" : 0 } ], "attrs" : [ ] -},{ - "type" : "conditional_block", - "comment" : "Conditional block operator\n\nRun the sub-block if X is not empty. Params is the other inputs and Out is the\noutputs of the sub-block.\n", - "inputs" : [ - { - "name" : "X", - "comment" : "The conditional variable of this operator. If X is empty, the whole sub-block will not be executed.", - "duplicable" : 1, - "intermediate" : 0 - }, { - "name" : "Params", - "comment" : "The input variables of the sub-block.", - "duplicable" : 1, - "intermediate" : 0 - } ], - "outputs" : [ - { - "name" : "Out", - "comment" : "The output variables of the sub-block.", - "duplicable" : 1, - "intermediate" : 0 - }, { - "name" : "Scope", - "comment" : "(std::vector) The step scope of conditional block. To unify the conditional block, rnn and while op, the type of scope is std::vector", - "duplicable" : 0, - "intermediate" : 0 - } ], - "attrs" : [ - { - "name" : "sub_block", - "type" : "block id", - "comment" : "The step block of conditional block operator", - "generated" : 0 - } ] },{ "type" : "sum", "comment" : "\nSum operator.\n\nThis operators sums the input tensors. All the inputs can carry the\nLoD (Level of Details) information. However, the output only shares\nthe LoD information with the first input.\n", @@ -5769,22 +5810,4 @@ "comment" : "(float, default -0.5f) Learning Rate Power.", "generated" : 0 } ] -},{ - "type" : "round", - "comment" : "\nRound Activation Operator.\n\n$out = [x]$\n\n", - "inputs" : [ - { - "name" : "X", - "comment" : "Input of Round operator", - "duplicable" : 0, - "intermediate" : 0 - } ], - "outputs" : [ - { - "name" : "Out", - "comment" : "Output of Round operator", - "duplicable" : 0, - "intermediate" : 0 - } ], - "attrs" : [ ] }] -- GitLab