"comment":"\nLimited Elementwise Sub Operator.\n\nThe equation is:\n\n.. math::\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\nFor example\n .. code-block:: python\n\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\nEither of the inputs X and Y or none can carry the LoD (Level of Details) information. However, 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":"rnn_memory_helper",
"comment":"",
...
...
@@ -2458,35 +2505,6 @@
"comment":"The target level 0 LoD from Attr().",
"generated":0
}]
},{
"type":"elementwise_sub",
"comment":"\nLimited Elementwise Sub Operator.\n\nThe equation is:\n\n.. math::\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\nFor example\n .. code-block:: python\n\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\nEither of the inputs X and Y or none can carry the LoD (Level of Details) information. However, 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":"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",
"comment":"\nReturns a list of places based on flags. The list will be used for parallel\nexecution.\n",
"inputs":[],
"outputs":[
{
"name":"Out",
"comment":"vector of Place",
"duplicable":0,
"intermediate":0
}],
"attrs":[
{
"name":"device_count",
"type":"int",
"comment":"device count",
"generated":0
},{
"name":"device_type",
"type":"string",
"comment":"device type",
"generated":0
}]
},{
"type":"read_from_array",
"comment":"\nReadFromArray Operator.\n\nRead a LoDTensor from 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$$T = A[i]$$\n\n",
"inputs":[
{
"name":"X",
"comment":"(TensorArray) the array will be read from.",
"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":"(LoDTensor) the tensor will be read from.",
"duplicable":0,
"intermediate":0
}],
"attrs":[]
},{
"type":"precision_recall",
"comment":"\nPrecision Recall Operator.\n\nWhen given Input(Indices) and Input(Labels), this operator can be used\nto compute various metrics including:\n1. macro average precision\n2. macro average recall\n3. macro f1 score\n4. micro average precision\n5. micro average recall\n6. micro f1 score\n\nTo compute the above metrics, we need to do statistics for true positives,\nfalse positives and false negatives. Here the count of true negatives is not\nnecessary, but counting it may provide potential usage and the cost is\ntrivial, so the operator also provides the count of true negatives.\n\nWe define state as a 2-D tensor with shape [class_number, 4]. Each row of a\nstate contains statistic variables for corresponding class. Layout of each row\nis: TP(true positives), FP(false positives), TN(true negatives),\nFN(false negatives). If Input(Weights) is provided, TP, FP, TN, FN will be\ncalculated by given weight instead of the instance count.\n\nThis operator also supports metrics computing for cross-batch situation. To\nachieve this, Input(StatesInfo) should be provided. State of current batch\ndata will be accumulated to Input(StatesInfo) and Output(AccumStatesInfo)\nis the accumulation state.\n\nOutput(BatchMetrics) is metrics of current batch data while\nOutput(AccumStatesInfo) is metrics of accumulation data.\n\n",
...
...
@@ -3261,6 +3343,35 @@
"comment":"(enum int, default sigmoid) The activation type used in update gate and reset gate.",
"generated":0
}]
},{
"type":"elementwise_max",
"comment":"\nLimited Elementwise Max Operator.\n\nThe equation is:\n\n.. math::\n Out = max(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\nFor example\n .. code-block:: python\n\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\nEither of the inputs X and Y or none can carry the LoD (Level of Details) information. However, 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 2-D tensor:\n\n [[1, 2, 3, 4]]\n\nOne dimension in the target shape can be set -1, representing that its\nsize is unknown. In this case, the real dimension will be infered from \nthe original shape of Input(X) and other dimensions in the target shape.\n",
...
...
@@ -3810,6 +3921,35 @@
"comment":"Expand times number for each dimension.",
"generated":0
}]
},{
"type":"elementwise_min",
"comment":"\nLimited Elementwise Max Operator.\n\nThe equation is:\n\n.. math::\n Out = min(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\nFor example\n .. code-block:: python\n\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\nEither of the inputs X and Y or none can carry the LoD (Level of Details) information. However, 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":"elementwise_div",
"comment":"\nLimited Elementwise Div Operator.\n\nThe equation is:\n\n.. math::\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\nFor example\n .. code-block:: python\n\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\nEither of the inputs X and Y or none can carry the LoD (Level of Details) information. However, the output only shares the LoD information with input X.\n\n",
...
...
@@ -4883,70 +5023,6 @@
"comment":"The number of thresholds to use when discretizing the roc curve.",
"generated":0
}]
},{
"type":"get_places",
"comment":"\nReturns a list of places based on flags. The list will be used for parallel\nexecution.\n",
"inputs":[],
"outputs":[
{
"name":"Out",
"comment":"vector of Place",
"duplicable":0,
"intermediate":0
}],
"attrs":[
{
"name":"device_count",
"type":"int",
"comment":"device count",
"generated":0
},{
"name":"device_type",
"type":"string",
"comment":"device type",
"generated":0
}]
},{
"type":"read_from_array",
"comment":"\nReadFromArray Operator.\n\nRead a LoDTensor from 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$$T = A[i]$$\n\n",
"inputs":[
{
"name":"X",
"comment":"(TensorArray) the array will be read from.",
"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":"(LoDTensor) the tensor will be read from.",
