"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<float>), 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<float>), 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<float>), 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",
"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<float>), 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<float>), 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<float>), 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",
"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",
