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Automated rollback of change 126349886

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Change: 126374056
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Zongheng Yang 2016-06-30 19:29:45 -08:00 committed by TensorFlower Gardener
parent 1c96204d91
commit 5187b6c0be
2 changed files with 0 additions and 238 deletions
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109
tensorflow/core/ops/compat/ops_history.v0.pbtxt
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@ -23173,115 +23173,6 @@ op {
}
}
}
op {
name: "SparseSparseMaximum"
input_arg {
name: "a_indices"
type: DT_INT64
}
input_arg {
name: "a_values"
type_attr: "T"
}
input_arg {
name: "a_shape"
type: DT_INT64
}
input_arg {
name: "b_indices"
type: DT_INT64
}
input_arg {
name: "b_values"
type_attr: "T"
}
input_arg {
name: "b_shape"
type: DT_INT64
}
output_arg {
name: "output_indices"
type: DT_INT64
}
output_arg {
name: "output_values"
type_attr: "T"
}
attr {
name: "T"
type: "type"
allowed_values {
list {
type: DT_FLOAT
type: DT_DOUBLE
type: DT_INT32
type: DT_INT64
type: DT_UINT8
type: DT_INT16
type: DT_INT8
type: DT_UINT16
type: DT_HALF
}
}
}
}
op {
name: "SparseSparseMinimum"
input_arg {
name: "a_indices"
type: DT_INT64
}
input_arg {
name: "a_values"
type_attr: "T"
}
input_arg {
name: "a_shape"
type: DT_INT64
}
input_arg {
name: "b_indices"
type: DT_INT64
}
input_arg {
name: "b_values"
type_attr: "T"
}
input_arg {
name: "b_shape"
type: DT_INT64
}
output_arg {
name: "output_indices"
type: DT_INT64
}
output_arg {
name: "output_values"
type_attr: "T"
}
attr {
name: "T"
type: "type"
allowed_values {
list {
type: DT_FLOAT
type: DT_DOUBLE
type: DT_INT64
type: DT_INT32
type: DT_UINT8
type: DT_UINT16
type: DT_INT16
type: DT_INT8
type: DT_COMPLEX64
type: DT_COMPLEX128
type: DT_QINT8
type: DT_QUINT8
type: DT_QINT32
type: DT_HALF
}
}
}
}
op {
name: "SparseSplit"
input_arg {

129
tensorflow/core/ops/ops.pbtxt
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@ -13696,135 +13696,6 @@ op {
summary: "Computes softmax cross entropy cost and gradients to backpropagate."
description: "Unlike `SoftmaxCrossEntropyWithLogits`, this operation does not accept\na matrix of label probabilities, but rather a single label per row\nof features. This label is considered to have probability 1.0 for the\ngiven row.\n\nInputs are the logits, not probabilities."
}
op {
name: "SparseSparseMaximum"
input_arg {
name: "a_indices"
description: "2-D. `N x R` matrix with the indices of non-empty values in a\nSparseTensor, in the canonical lexicographic ordering."
type: DT_INT64
}
input_arg {
name: "a_values"
description: "1-D. `N` non-empty values corresponding to `a_indices`."
type_attr: "T"
}
input_arg {
name: "a_shape"
description: "1-D. Shape of the input SparseTensor."
type: DT_INT64
}
input_arg {
name: "b_indices"
description: "counterpart to `a_indices` for the other operand."
type: DT_INT64
}
input_arg {
name: "b_values"
description: "counterpart to `a_values` for the other operand; must be of the same dtype."
type_attr: "T"
}
input_arg {
name: "b_shape"
description: "counterpart to `a_shape` for the other operand; the two shapes must be equal."
type: DT_INT64
}
output_arg {
name: "output_indices"
description: "2-D. The indices of the output SparseTensor."
type: DT_INT64
}
output_arg {
name: "output_values"
description: "1-D. The values of the output SparseTensor."
type_attr: "T"
}
attr {
name: "T"
type: "type"
allowed_values {
list {
type: DT_FLOAT
type: DT_DOUBLE
type: DT_INT32
type: DT_INT64
type: DT_UINT8
type: DT_INT16
type: DT_INT8
type: DT_UINT16
type: DT_HALF
}
}
}
summary: "Returns the element-wise max of two SparseTensors."
description: "Assumes the two SparseTensors have the same shape, i.e., no broadcasting."
}
op {
name: "SparseSparseMinimum"
input_arg {
name: "a_indices"
description: "2-D. `N x R` matrix with the indices of non-empty values in a\nSparseTensor, in the canonical lexicographic ordering."
type: DT_INT64
}
input_arg {
name: "a_values"
description: "1-D. `N` non-empty values corresponding to `a_indices`."
type_attr: "T"
}
input_arg {
name: "a_shape"
description: "1-D. Shape of the input SparseTensor."
type: DT_INT64
}
input_arg {
name: "b_indices"
description: "counterpart to `a_indices` for the other operand."
type: DT_INT64
}
input_arg {
name: "b_values"
description: "counterpart to `a_values` for the other operand; must be of the same dtype."
type_attr: "T"
}
input_arg {
name: "b_shape"
description: "counterpart to `a_shape` for the other operand; the two shapes must be equal."
type: DT_INT64
}
output_arg {
name: "output_indices"
description: "2-D. The indices of the output SparseTensor."
type: DT_INT64
}
output_arg {
name: "output_values"
description: "1-D. The values of the output SparseTensor."
type_attr: "T"
}
attr {
name: "T"
type: "type"
allowed_values {
list {
type: DT_FLOAT
type: DT_DOUBLE
type: DT_INT64
type: DT_INT32
type: DT_UINT8
type: DT_UINT16
type: DT_INT16
type: DT_INT8
type: DT_COMPLEX64
type: DT_COMPLEX128
type: DT_QINT8
type: DT_QUINT8
type: DT_QINT32
type: DT_HALF
}
}
}
summary: "Returns the element-wise min of two SparseTensors."
description: "Assumes the two SparseTensors have the same shape, i.e., no broadcasting."
}
op {
name: "SparseSplit"
input_arg {