diff --git a/tensorflow/core/ops/compat/ops_history.v0.pbtxt b/tensorflow/core/ops/compat/ops_history.v0.pbtxt
index 56956b246cb..b471d96f8fc 100644
--- a/tensorflow/core/ops/compat/ops_history.v0.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v0.pbtxt
@@ -383,6 +383,47 @@ op {
}
}
}
+op {
+ name: "AdjustContrast"
+ input_arg {
+ name: "images"
+ type_attr: "T"
+ }
+ input_arg {
+ name: "contrast_factor"
+ type: DT_FLOAT
+ }
+ input_arg {
+ name: "min_value"
+ type: DT_FLOAT
+ }
+ input_arg {
+ name: "max_value"
+ type: DT_FLOAT
+ }
+ output_arg {
+ name: "output"
+ type: DT_FLOAT
+ }
+ attr {
+ name: "T"
+ type: "type"
+ allowed_values {
+ list {
+ type: DT_UINT8
+ type: DT_INT8
+ type: DT_INT16
+ type: DT_INT32
+ type: DT_INT64
+ type: DT_FLOAT
+ type: DT_DOUBLE
+ }
+ }
+ }
+ deprecation {
+ version: 2
+ }
+}
op {
name: "AdjustContrastv2"
input_arg {
@@ -3656,6 +3697,66 @@ op {
type: "bool"
}
}
+op {
+ name: "BatchNormWithGlobalNormalization"
+ input_arg {
+ name: "t"
+ type_attr: "T"
+ }
+ input_arg {
+ name: "m"
+ type_attr: "T"
+ }
+ input_arg {
+ name: "v"
+ type_attr: "T"
+ }
+ input_arg {
+ name: "beta"
+ type_attr: "T"
+ }
+ input_arg {
+ name: "gamma"
+ type_attr: "T"
+ }
+ output_arg {
+ name: "result"
+ 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
+ }
+ }
+ }
+ attr {
+ name: "variance_epsilon"
+ type: "float"
+ }
+ attr {
+ name: "scale_after_normalization"
+ type: "bool"
+ }
+ deprecation {
+ version: 9
+ }
+}
op {
name: "BatchNormWithGlobalNormalizationGrad"
input_arg {
@@ -3942,6 +4043,82 @@ op {
type: "bool"
}
}
+op {
+ name: "BatchNormWithGlobalNormalizationGrad"
+ input_arg {
+ name: "t"
+ type_attr: "T"
+ }
+ input_arg {
+ name: "m"
+ type_attr: "T"
+ }
+ input_arg {
+ name: "v"
+ type_attr: "T"
+ }
+ input_arg {
+ name: "gamma"
+ type_attr: "T"
+ }
+ input_arg {
+ name: "backprop"
+ type_attr: "T"
+ }
+ output_arg {
+ name: "dx"
+ type_attr: "T"
+ }
+ output_arg {
+ name: "dm"
+ type_attr: "T"
+ }
+ output_arg {
+ name: "dv"
+ type_attr: "T"
+ }
+ output_arg {
+ name: "db"
+ type_attr: "T"
+ }
+ output_arg {
+ name: "dg"
+ 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
+ }
+ }
+ }
+ attr {
+ name: "variance_epsilon"
+ type: "float"
+ }
+ attr {
+ name: "scale_after_normalization"
+ type: "bool"
+ }
+ deprecation {
+ version: 9
+ }
+}
op {
name: "BatchSelfAdjointEig"
input_arg {
@@ -11855,6 +12032,54 @@ op {
}
is_stateful: true
}
+op {
+ name: "RandomCrop"
+ input_arg {
+ name: "image"
+ type_attr: "T"
+ }
+ input_arg {
+ name: "size"
+ type: DT_INT64
+ }
+ output_arg {
+ name: "output"
+ type_attr: "T"
+ }
+ attr {
+ name: "T"
+ type: "type"
+ allowed_values {
+ list {
+ type: DT_UINT8
+ type: DT_INT8
+ type: DT_INT16
+ type: DT_INT32
+ type: DT_INT64
+ type: DT_FLOAT
+ type: DT_DOUBLE
+ }
+ }
+ }
+ attr {
+ name: "seed"
+ type: "int"
+ default_value {
+ i: 0
+ }
+ }
+ attr {
+ name: "seed2"
+ type: "int"
+ default_value {
+ i: 0
+ }
+ }
+ deprecation {
+ version: 8
+ }
+ is_stateful: true
+}
op {
name: "RandomShuffle"
input_arg {
@@ -18823,6 +19048,28 @@ op {
type: "type"
}
}
+op {
+ name: "TileGrad"
+ input_arg {
+ name: "input"
+ type_attr: "T"
+ }
+ input_arg {
+ name: "multiples"
+ type: DT_INT32
+ }
+ output_arg {
+ name: "output"
+ type_attr: "T"
+ }
+ attr {
+ name: "T"
+ type: "type"
+ }
+ deprecation {
+ version: 3
+ }
+}
op {
name: "TopK"
input_arg {
@@ -19031,6 +19278,53 @@ op {
}
}
}
+op {
+ name: "TopK"
+ input_arg {
+ name: "input"
+ type_attr: "T"
+ }
+ output_arg {
+ name: "values"
+ type_attr: "T"
+ }
+ output_arg {
+ name: "indices"
+ type: DT_INT32
+ }
+ attr {
+ name: "k"
+ type: "int"
+ has_minimum: true
+ }
+ attr {
+ name: "sorted"
+ type: "bool"
+ default_value {
+ b: true
+ }
+ }
+ 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
+ }
+ }
+ }
+ deprecation {
+ version: 7
+ }
+}
op {
name: "TopKV2"
input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index fca9fa69b4c..da0039e2e0a 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -154,6 +154,10 @@ op {
}
}
summary: "Deprecated. Disallowed in GraphDef version >= 2."
+ deprecation {
+ version: 2
+ explanation: "Use AdjustContrastv2 instead"
+ }
}
op {
name: "AdjustContrastv2"
@@ -1681,6 +1685,10 @@ op {
}
summary: "Batch normalization."
description: "This op is deprecated. Prefer `tf.nn.batch_normalization`."
+ deprecation {
+ version: 9
+ explanation: "Use tf.nn.batch_normalization()"
+ }
}
op {
name: "BatchNormWithGlobalNormalizationGrad"
@@ -1768,6 +1776,10 @@ op {
}
summary: "Gradients for batch normalization."
description: "This op is deprecated. See `tf.nn.batch_normalization`."
+ deprecation {
+ version: 9
+ explanation: "Use tf.nn.batch_normalization()"
+ }
}
op {
name: "BatchSelfAdjointEig"
@@ -7370,6 +7382,10 @@ op {
}
summary: "Randomly crop `image`."
description: "`size` is a 1-D int64 tensor with 2 elements representing the crop height and\nwidth. The values must be non negative.\n\nThis Op picks a random location in `image` and crops a `height` by `width`\nrectangle from that location. The random location is picked so the cropped\narea will fit inside the original image."
+ deprecation {
+ version: 8
+ explanation: "Random crop is now pure Python"
+ }
is_stateful: true
}
op {
@@ -11925,6 +11941,10 @@ op {
}
summary: "Returns the gradient of `Tile`."
description: "Since `Tile` takes an input and repeats the input `multiples` times\nalong each dimension, `TileGrad` takes in `multiples` and aggregates\neach repeated tile of `input` into `output`."
+ deprecation {
+ version: 3
+ explanation: "TileGrad has been replaced with reduce_sum"
+ }
}
op {
name: "TopK"
@@ -11976,6 +11996,10 @@ op {
}
summary: "Finds values and indices of the `k` largest elements for the last dimension."
description: "If the input is a vector (rank-1), finds the `k` largest entries in the vector\nand outputs their values and indices as vectors. Thus `values[j]` is the\n`j`-th largest entry in `input`, and its index is `indices[j]`.\n\nFor matrices (resp. higher rank input), computes the top `k` entries in each\nrow (resp. vector along the last dimension). Thus,\n\n values.shape = indices.shape = input.shape[:-1] + [k]\n\nIf two elements are equal, the lower-index element appears first.\n\nIf `k` varies dynamically, use `TopKV2` below."
+ deprecation {
+ version: 7
+ explanation: "Use TopKV2 instead"
+ }
}
op {
name: "TopKV2"