From 6827312214c169cd85329a3bcb162416d882cc9c Mon Sep 17 00:00:00 2001 From: Travis CI Date: Tue, 12 Dec 2017 07:51:34 +0000 Subject: [PATCH] Deploy to GitHub Pages: d918ccded35d972eba42b07767972bc4c2dd0921 --- develop/doc/operators.json | 277 +++++++++++++++++++++---------------- 1 file changed, 155 insertions(+), 122 deletions(-) diff --git a/develop/doc/operators.json b/develop/doc/operators.json index e30ec042549..8f4e5b2640a 100644 --- a/develop/doc/operators.json +++ b/develop/doc/operators.json @@ -992,34 +992,6 @@ "comment" : "The small negative slope", "generated" : 0 } ] -},{ - "type" : "modified_huber_loss", - "comment" : "\nModified Huber Loss Operator.\n\nThis operator is used in binary classification problem. The shape of\ninput X and target Y are both [N, 1] and so is the shape of the output loss.\nSince target Y is not differentiable, calculating gradient for Y is illegal.\nThe formula of modified huber loss is:\n\n$$\nL(y, f(x)) = \n\\begin{cases}\n(\\max(0, 1 - yf(x)))^2, \\text{if} \\ yf(x) >= -1 \\\\\n -4yf(x), \\quad \\text{otherwise}\n\\end{cases}\n$$\n\nMake sure the values of target label Y are in {0, 1} here. This operator will\nscale values of Y to {-1, +1} when computing losses and gradients.\n\n", - "inputs" : [ - { - "name" : "X", - "comment" : "The input tensor of modified huber loss op. X is 2-D tensor with shape [batch_size, 1].", - "duplicable" : 0, - "intermediate" : 0 - }, { - "name" : "Y", - "comment" : "The target labels of modified huber loss op. The shape of Y is the same as X. Values of Y must be 0 or 1.", - "duplicable" : 0, - "intermediate" : 0 - } ], - "outputs" : [ - { - "name" : "IntermediateVal", - "comment" : "Variable to save intermediate result which will be reused in backward processing.", - "duplicable" : 0, - "intermediate" : 1 - }, { - "name" : "Out", - "comment" : "Classification loss for X.", - "duplicable" : 0, - "intermediate" : 0 - } ], - "attrs" : [ ] },{ "type" : "top_k", "comment" : "\nTop K operator\n\nIf the input is a vector (1d tensor), this operator finds the k largest \nentries in the vector and outputs their values and indices as vectors. \nThus values[j] is the j-th largest entry in input, and its index is indices[j].\n\nFor matrices, this operator computes the top k entries in each row. ", @@ -1408,35 +1380,6 @@ "intermediate" : 0 } ], "attrs" : [ ] -},{ - "type" : "elementwise_sub", - "comment" : "\nLimited Elementwise Sub Operator.\n\nThe equation is:\n\n$Out = X - Y$\n\nX is a tensor of any dimension and the dimensions of tensor Y must be smaller than\nor equal to the dimensions of X. \n\nThere are two cases for this operator:\n1. The shape of Y is same with X;\n2. The shape of Y is a subset of X.\n\nFor case 2:\nY will be broadcasted to match the shape of X and axis should be \nthe starting dimension index for broadcasting Y onto X.\n\nexample:\n shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\n\nBoth the input X and Y can carry the LoD (Level of Details) information,\nor not. But the output only shares the LoD information with input X.\n\n", - "inputs" : [ - { - "name" : "X", - "comment" : "(Tensor) The first input tensor of elementwise op", - "duplicable" : 0, - "intermediate" : 0 - }, { - "name" : "Y", - "comment" : "(Tensor) The second input tensor of elementwise op", - "duplicable" : 0, - "intermediate" : 0 - } ], - "outputs" : [ - { - "name" : "Out", - "comment" : "The output of elementwise op", - "duplicable" : 0, - "intermediate" : 0 - } ], - "attrs" : [ - { - "name" : "axis", - "type" : "int", - "comment" : "(int, default -1) The starting dimension index for broadcasting Y onto X", - "generated" : 0 - } ] },{ "type" : "reshape", "comment" : "\nReshape Operator.\n\nReshape Input(X) into the shape specified by Attr(shape).\n\nAn example:\nGiven a 2-D tensor X with 2 rows and 2 columns\n\n [[1, 2], [3, 4]]\n\nand target shape = [1, 4], the reshape operator will transform\nthe tensor X into a 1-D tensor:\n\n [1, 2, 3, 4]\n\n", @@ -3317,6 +3260,119 @@ "intermediate" : 0 } ], "attrs" : [ ] +},{ + "type" : "fill", + "comment" : "Fill operator\n\nFill an tensor with `value` and `shape`. The type of the tensor is specify by\n`dtype`.\n", + "inputs" : [ ], + "outputs" : [ + { + "name" : "Out", + "comment" : "(LoDTensor) The output tensor.", + "duplicable" : 0, + "intermediate" : 0 + } ], + "attrs" : [ + { + "name" : "value", + "type" : "float array", + "comment" : "The float values of tensor, which are flatten in row major", + "generated" : 0 + }, { + "name" : "shape", + "type" : "int array", + "comment" : "The shape of output tensor", + "generated" : 0 + }, { + "name" : "dtype", + "type" : "int", + "comment" : "The data type of output tensor, Default is float", + "generated" : 0 + }, { + "name" : "force_cpu", + "type" : "bool", + "comment" : "Whether the output tensor must be at CPU memory or not. Default is false.", + "generated" : 0 + } ] +},{ + "type" : "sigmoid_cross_entropy_with_logits", + "comment" : "\nSigmoidCrossEntropyWithLogits Operator.\n\nThis measures the element-wise probability error in classification tasks\nin which each class is independent. This can be thought of as predicting labels\nfor a data-point, where labels are not mutually exclusive.\nFor example, a news article can be about politics, technology or sports\nat the same time or none of these.\n\nThe logistic loss is given as follows:\n\n $$loss = -Labels * \\log(\\sigma(X)) - (1 - Labels) * \\log(1 - \\sigma(X))$$\n\nWe know that $$\\sigma(X) = (1 / (1 + \\exp(-X)))$$. By substituting this we get:\n\n $$loss = X - X * Labels + \\log(1 + \\exp(-X))$$\n\nFor stability and to prevent overflow of $$\\exp(-X)$$ when X < 0,\nwe reformulate the loss as follows:\n\n $$loss = \\max(X, 0) - X * Labels + \\log(1 + \\exp(-|X|))$$\n\nBoth the input `X` and `Labels` can carry the LoD (Level of Details) information.\nHowever the output only shares the LoD with input `X`.\n\n", + "inputs" : [ + { + "name" : "X", + "comment" : "(Tensor, default Tensor), a 2-D tensor with shape N x D, where N is the batch size and D is the number of classes. This input is a tensor of logits computed by the previous operator. Logits are unscaled log probabilities given as log(p/(1-p)).", + "duplicable" : 0, + "intermediate" : 0 + }, { + "name" : "Label", + "comment" : "(Tensor, default Tensor), a 2-D tensor of the same type and shape as X. This input is a tensor of probabalistic labels for each logit", + "duplicable" : 0, + "intermediate" : 0 + } ], + "outputs" : [ + { + "name" : "Out", + "comment" : "(Tensor, default Tensor), a 2-D tensor with shape N x D of elementwise logistic losses.", + "duplicable" : 0, + "intermediate" : 0 + } ], + "attrs" : [ ] +},{ + "type" : "modified_huber_loss", + "comment" : "\nModified Huber Loss Operator.\n\nThis operator is used in binary classification problem. The shape of\ninput X and target Y are both [N, 1] and so is the shape of the output loss.\nSince target Y is not differentiable, calculating gradient for Y is illegal.\nThe formula of modified huber loss is:\n\n$$\nL(y, f(x)) = \n\\begin{cases}\n(\\max(0, 1 - yf(x)))^2, \\text{if} \\ yf(x) >= -1 \\\\\n -4yf(x), \\quad \\text{otherwise}\n\\end{cases}\n$$\n\nMake sure the values of target label Y are in {0, 1} here. This operator will\nscale values of Y to {-1, +1} when computing losses and gradients.\n\n", + "inputs" : [ + { + "name" : "X", + "comment" : "The input tensor of modified huber loss op. X is 2-D tensor with shape [batch_size, 1].", + "duplicable" : 0, + "intermediate" : 0 + }, { + "name" : "Y", + "comment" : "The target labels of modified huber loss op. The shape of Y is the same as X. Values of Y must be 0 or 1.", + "duplicable" : 0, + "intermediate" : 0 + } ], + "outputs" : [ + { + "name" : "IntermediateVal", + "comment" : "Variable to save intermediate result which will be reused in backward processing.", + "duplicable" : 0, + "intermediate" : 1 + }, { + "name" : "Out", + "comment" : "Classification loss for X.", + "duplicable" : 0, + "intermediate" : 0 + } ], + "attrs" : [ ] +},{ + "type" : "elementwise_sub", + "comment" : "\nLimited Elementwise Sub Operator.\n\nThe equation is:\n\n$Out = X - Y$\n\nX is a tensor of any dimension and the dimensions of tensor Y must be smaller than\nor equal to the dimensions of X. \n\nThere are two cases for this operator:\n1. The shape of Y is same with X;\n2. The shape of Y is a subset of X.\n\nFor case 2:\nY will be broadcasted to match the shape of X and axis should be \nthe starting dimension index for broadcasting Y onto X.\n\nexample:\n shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\n\nBoth the input X and Y can carry the LoD (Level of Details) information,\nor not. But the output only shares the LoD information with input X.\n\n", + "inputs" : [ + { + "name" : "X", + "comment" : "(Tensor) The first input tensor of elementwise op", + "duplicable" : 0, + "intermediate" : 0 + }, { + "name" : "Y", + "comment" : "(Tensor) The second input tensor of elementwise op", + "duplicable" : 0, + "intermediate" : 0 + } ], + "outputs" : [ + { + "name" : "Out", + "comment" : "The output of elementwise op", + "duplicable" : 0, + "intermediate" : 0 + } ], + "attrs" : [ + { + "name" : "axis", + "type" : "int", + "comment" : "(int, default -1) The starting dimension index for broadcasting Y onto X", + "generated" : 0 + } ] },{ "type" : "reduce_mean", "comment" : "\n{ReduceOp} Operator.\n\nThis operator computes the mean of input tensor along the given dimension. \nThe result tensor has 1 fewer dimension than the input unless keep_dim is true.\n\n", @@ -3629,6 +3685,48 @@ "intermediate" : 0 } ], "attrs" : [ ] +},{ + "type" : "tanh", + "comment" : "\nTanh Activation Operator.\n\n$$y = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\n\n", + "inputs" : [ + { + "name" : "X", + "comment" : "Input of Tanh operator", + "duplicable" : 0, + "intermediate" : 0 + } ], + "outputs" : [ + { + "name" : "Y", + "comment" : "Output of Tanh operator", + "duplicable" : 0, + "intermediate" : 0 + } ], + "attrs" : [ ] +},{ + "type" : "feed", + "comment" : "\nFeed Operator.\n\nIt should not be configured by users directly.\n\n", + "inputs" : [ + { + "name" : "X", + "comment" : "The input of feed op", + "duplicable" : 0, + "intermediate" : 0 + } ], + "outputs" : [ + { + "name" : "Out", + "comment" : "The output of feed op", + "duplicable" : 0, + "intermediate" : 0 + } ], + "attrs" : [ + { + "name" : "col", + "type" : "int", + "comment" : "(int) The column of feed", + "generated" : 0 + } ] },{ "type" : "rnn_memory_helper", "comment" : "", @@ -4810,71 +4908,6 @@ "intermediate" : 0 } ], "attrs" : [ ] -},{ - "type" : "sigmoid_cross_entropy_with_logits", - "comment" : "\nSigmoidCrossEntropyWithLogits Operator.\n\nThis measures the element-wise probability error in classification tasks\nin which each class is independent. This can be thought of as predicting labels\nfor a data-point, where labels are not mutually exclusive.\nFor example, a news article can be about politics, technology or sports\nat the same time or none of these.\n\nThe logistic loss is given as follows:\n\n $$loss = -Labels * \\log(\\sigma(X)) - (1 - Labels) * \\log(1 - \\sigma(X))$$\n\nWe know that $$\\sigma(X) = (1 / (1 + \\exp(-X)))$$. By substituting this we get:\n\n $$loss = X - X * Labels + \\log(1 + \\exp(-X))$$\n\nFor stability and to prevent overflow of $$\\exp(-X)$$ when X < 0,\nwe reformulate the loss as follows:\n\n $$loss = \\max(X, 0) - X * Labels + \\log(1 + \\exp(-|X|))$$\n\nBoth the input `X` and `Labels` can carry the LoD (Level of Details) information.\nHowever the output only shares the LoD with input `X`.\n\n", - "inputs" : [ - { - "name" : "X", - "comment" : "(Tensor, default Tensor), a 2-D tensor with shape N x D, where N is the batch size and D is the number of classes. This input is a tensor of logits computed by the previous operator. Logits are unscaled log probabilities given as log(p/(1-p)).", - "duplicable" : 0, - "intermediate" : 0 - }, { - "name" : "Label", - "comment" : "(Tensor, default Tensor), a 2-D tensor of the same type and shape as X. This input is a tensor of probabalistic labels for each logit", - "duplicable" : 0, - "intermediate" : 0 - } ], - "outputs" : [ - { - "name" : "Out", - "comment" : "(Tensor, default Tensor), a 2-D tensor with shape N x D of elementwise logistic losses.", - "duplicable" : 0, - "intermediate" : 0 - } ], - "attrs" : [ ] -},{ - "type" : "feed", - "comment" : "\nFeed Operator.\n\nIt should not be configured by users directly.\n\n", - "inputs" : [ - { - "name" : "X", - "comment" : "The input of feed op", - "duplicable" : 0, - "intermediate" : 0 - } ], - "outputs" : [ - { - "name" : "Out", - "comment" : "The output of feed op", - "duplicable" : 0, - "intermediate" : 0 - } ], - "attrs" : [ - { - "name" : "col", - "type" : "int", - "comment" : "(int) The column of feed", - "generated" : 0 - } ] -},{ - "type" : "tanh", - "comment" : "\nTanh Activation Operator.\n\n$$y = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\n\n", - "inputs" : [ - { - "name" : "X", - "comment" : "Input of Tanh operator", - "duplicable" : 0, - "intermediate" : 0 - } ], - "outputs" : [ - { - "name" : "Y", - "comment" : "Output of Tanh operator", - "duplicable" : 0, - "intermediate" : 0 - } ], - "attrs" : [ ] },{ "type" : "accuracy", "comment" : "\nAccuracy Operator. \n\nIt will print accuracy rate for classification.\nThe accuracy is calculated as follows:\n\n$$accuracy = \\frac{NumOfCorrectPredicts}{NumOfAllSamples}$$\n\nBoth the input Out and Label can carry the LoD (Level of Details)\ninformation, or not. But the output only shares the LoD information \nwith the input Out(Inference).\n\n", -- GitLab