"comment":"\nLog Activation Operator.\n\n$out = \\ln(x)$\n\nNatural logarithm of x.\n\n",
"inputs":[
{
"name":"X",
"comment":"Input of Log operator",
"duplicable":0,
"intermediate":0
}],
"outputs":[
{
"name":"Out",
"comment":"Output of Log operator",
"duplicable":0,
"intermediate":0
}],
"attrs":[]
},{
"type":"softmax",
"comment":"\nSoftmax Operator.\n\nThe input of the softmax operator is a 2-D tensor with shape N x K (N is the\nbatch_size, K is the dimension of input feature). The output tensor has the\nsame shape as the input tensor.\n\nFor each row of the input tensor, the softmax operator squashes the\nK-dimensional vector of arbitrary real values to a K-dimensional vector of real\nvalues in the range [0, 1] that add up to 1.\nIt computes the exponential of the given dimension and the sum of exponential\nvalues of all the other dimensions in the K-dimensional vector input.\nThen the ratio of the exponential of the given dimension and the sum of\nexponential values of all the other dimensions is the output of the softmax\noperator.\n\nFor each row $i$ and each column $j$ in Input(X), we have:\n $$Out[i, j] = \\frac{\\exp(X[i, j])}{\\sum_j(exp(X[i, j])}$$\n\n",
"comment":"\nLookup Table Operator.\n\nThis operator is used to perform lookups on the parameter W,\nthen concatenated into a dense tensor.\n\nThe input Ids can carry the LoD (Level of Details) information,\nor not. And the output only shares the LoD information with input Ids.\n\n",
...
...
@@ -2310,35 +2310,6 @@
"intermediate":0
}],
"attrs":[]
},{
"type":"lod_reset",
"comment":"LoDReset operator\n\nReset LoD of Input(X) into a new one specified by Input(TargetLoD) or\nAttr(target_lod), or set LoD for Input(X) if it doesn't have one.\nCurrently the lod_reset operator only supports the reset of level 0 LoD.\nAt least one of Input(TargetLoD) and Attr(target_lod) must be set,\nand if both of them are set, Input(TargetLoD) will be chosen as the\ntarget LoD.\n\nAn example:\nGiven a float LoDTensor X with shape (6, 1), its transpose form represents\n\n [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],\n\nwith LoD = [[0, 2, 5, 6]] and the three (transposed) sequences look like\n\n [1.0, 2.0], [3.0, 4.0, 5.0], [6.0].\n\nIf target LoD = [0, 4, 6], the lod_reset operator will reset the LoD and\nthe sequences that the LoDTensor Output(Out) contains becomes:\n\n [1.0, 2.0, 3.0, 4.0], [5.0, 6.0].\n\n",
"inputs":[
{
"name":"X",
"comment":"(LoDTensor) The input tensor of lod_reset operator.",
"duplicable":0,
"intermediate":0
},{
"name":"TargetLoD",
"comment":"(Tensor, optional) The target level 0 LoD from Input().",
"duplicable":0,
"intermediate":0
}],
"outputs":[
{
"name":"Out",
"comment":"(LoDTensor) The output tensor of lod_reset operator.",
"duplicable":0,
"intermediate":0
}],
"attrs":[
{
"name":"target_lod",
"type":"int array",
"comment":"The target level 0 LoD from Attr().",
"generated":0
}]
},{
"type":"logical_and",
"comment":"logical_and Operator\n\nIt operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean tensors.\nEach element of Out is calculated by $$Out = X \\&\\& Y$$\n",
...
...
@@ -2398,29 +2369,6 @@
"intermediate":0
}],
"attrs":[]
},{
"type":"write_to_array",
"comment":"\nWriteToArray Operator.\n\nThis operator writes a LoDTensor to a LoDTensor array.\n\nAssume $T$ is LoDTensor, $i$ is the subscript of the array, and $A$ is the array. The\nequation is\n\n$$A[i] = T$$\n\n",
"inputs":[
{
"name":"X",
"comment":"(LoDTensor) the tensor will be written to tensor array",
"duplicable":0,
"intermediate":0
},{
"name":"I",
"comment":"(Tensor) the subscript index in tensor array. The number of element should be 1",
"duplicable":0,
"intermediate":0
}],
"outputs":[
{
"name":"Out",
"comment":"(TensorArray) the tensor array will be written",
"comment":"(Tensor) The input tensor of norm operator. The format of input tensor is NCHW. Where N is batch size, C is the number of channels, H and W is the height and width of feature.",
"duplicable":0,
"intermediate":0
},{
"name":"Scale",
"comment":"(Tensor) The input tensor of norm operator. The format of input tensor is C * 1.",
"duplicable":0,
"intermediate":0
}],
"outputs":[
{
"name":"Out",
"comment":"(Tensor) The output tensor of norm operator.N * M.M = C * H * W",
"duplicable":0,
"intermediate":0
}],
"attrs":[
{
"name":"epsilon",
"type":"float",
"comment":"(float, default 1e-10) Constant for numerical stability.",
"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",
...
...
@@ -3139,6 +3116,76 @@
"comment":"(int, default -1) The starting dimension index for broadcasting Y onto X",
"comment":"LoDReset operator\n\nReset LoD of Input(X) into a new one specified by Input(TargetLoD) or\nAttr(target_lod), or set LoD for Input(X) if it doesn't have one.\nCurrently the lod_reset operator only supports the reset of level 0 LoD.\nAt least one of Input(TargetLoD) and Attr(target_lod) must be set,\nand if both of them are set, Input(TargetLoD) will be chosen as the\ntarget LoD.\n\nAn example:\nGiven a float LoDTensor X with shape (6, 1), its transpose form represents\n\n [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],\n\nwith LoD = [[0, 2, 5, 6]] and the three (transposed) sequences look like\n\n [1.0, 2.0], [3.0, 4.0, 5.0], [6.0].\n\nIf target LoD = [0, 4, 6], the lod_reset operator will reset the LoD and\nthe sequences that the LoDTensor Output(Out) contains becomes:\n\n [1.0, 2.0, 3.0, 4.0], [5.0, 6.0].\n\n",
"inputs":[
{
"name":"X",
"comment":"(LoDTensor) The input tensor of lod_reset operator.",
"duplicable":0,
"intermediate":0
},{
"name":"TargetLoD",
"comment":"(Tensor, optional) The target level 0 LoD from Input().",
"duplicable":0,
"intermediate":0
}],
"outputs":[
{
"name":"Out",
"comment":"(LoDTensor) The output tensor of lod_reset operator.",
"duplicable":0,
"intermediate":0
}],
"attrs":[
{
"name":"target_lod",
"type":"int array",
"comment":"The target level 0 LoD from Attr().",
"generated":0
}]
},{
"type":"write_to_array",
"comment":"\nWriteToArray Operator.\n\nThis operator writes a LoDTensor to a LoDTensor array.\n\nAssume $T$ is LoDTensor, $i$ is the subscript of the array, and $A$ is the array. The\nequation is\n\n$$A[i] = T$$\n\n",
"inputs":[
{
"name":"X",
"comment":"(LoDTensor) the tensor will be written to tensor array",
"duplicable":0,
"intermediate":0
},{
"name":"I",
"comment":"(Tensor) the subscript index in tensor array. The number of element should be 1",
"duplicable":0,
"intermediate":0
}],
"outputs":[
{
"name":"Out",
"comment":"(TensorArray) the tensor array will be written",
"duplicable":0,
"intermediate":0
}],
"attrs":[]
},{
"type":"lod_array_length",
"comment":"\nLoDArrayLength Operator.\n\nThis operator obtains the length of lod tensor array:\n\n$$Out = len(X)$$\n\nNOTE: The output is a CPU Tensor since the control variable should be only in\nCPU and the length of LoDTensorArray should be used as control variables.\n\n",
...
...
@@ -5125,24 +5172,6 @@
"comment":"(float, default 1.0e-6) Constant for numerical stability",
"generated":0
}]
},{
"type":"log",
"comment":"\nLog Activation Operator.\n\n$out = \\ln(x)$\n\nNatural logarithm of x.\n\n",
"inputs":[
{
"name":"X",
"comment":"Input of Log operator",
"duplicable":0,
"intermediate":0
}],
"outputs":[
{
"name":"Out",
"comment":"Output of Log operator",
"duplicable":0,
"intermediate":0
}],
"attrs":[]
},{
"type":"nce",
"comment":"\nCompute and return the noise-contrastive estimation training loss.\nSee [Noise-contrastive estimation: A new estimation principle for unnormalized statistical models](http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf).\nBy default this operator uses a uniform distribution for sampling.\n",
