Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 294950014
Change-Id: If6fc0222cf96b5c092d33b44c9215ffe9364875d
diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index cbf8567..beb4382 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -11520,7 +11520,7 @@
// element on that dimension. The dimension order is determined by the value of
// `data_format`, see above for details. Dilations in the batch and depth
// dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1 i:1 i:1 i:1}
func DepthwiseConv2dNativeBackpropFilterDilations(value []int64) DepthwiseConv2dNativeBackpropFilterAttr {
return func(m optionalAttr) {
m["dilations"] = value
@@ -11777,7 +11777,7 @@
//
// value: The cropped area of the image must have an aspect ratio =
// width / height within this range.
-// If not specified, defaults to {f:0.75 f:1.33}
+// If not specified, defaults to {f:0.75 f:1.33}
func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistortedBoundingBoxV2Attr {
return func(m optionalAttr) {
m["aspect_ratio_range"] = value
@@ -11788,7 +11788,7 @@
//
// value: The cropped area of the image must contain a fraction of the
// supplied image within this range.
-// If not specified, defaults to {f:0.05 f:1}
+// If not specified, defaults to {f:0.05 f:1}
func SampleDistortedBoundingBoxV2AreaRange(value []float32) SampleDistortedBoundingBoxV2Attr {
return func(m optionalAttr) {
m["area_range"] = value
@@ -11994,7 +11994,7 @@
//
// value: The cropped area of the image must have an aspect ratio =
// width / height within this range.
-// If not specified, defaults to {f:0.75 f:1.33}
+// If not specified, defaults to {f:0.75 f:1.33}
func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistortedBoundingBoxAttr {
return func(m optionalAttr) {
m["aspect_ratio_range"] = value
@@ -12005,7 +12005,7 @@
//
// value: The cropped area of the image must contain a fraction of the
// supplied image within this range.
-// If not specified, defaults to {f:0.05 f:1}
+// If not specified, defaults to {f:0.05 f:1}
func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr {
return func(m optionalAttr) {
m["area_range"] = value
@@ -18846,7 +18846,7 @@
// ImageSummaryBadColor sets the optional bad_color attribute to value.
//
// value: Color to use for pixels with non-finite values.
-// If not specified, defaults to {dtype:DT_UINT8 tensor_shape:{dim:{size:4}} int_val:255 int_val:0 int_val:0 int_val:255}
+// If not specified, defaults to {dtype:DT_UINT8 tensor_shape:{dim:{size:4}} int_val:255 int_val:0 int_val:0 int_val:255}
func ImageSummaryBadColor(value tf.Tensor) ImageSummaryAttr {
return func(m optionalAttr) {
m["bad_color"] = value
@@ -19986,7 +19986,7 @@
// filter element on that dimension. The dimension order is determined by the
// value of `data_format`, see above for details. Dilations in the batch and
// depth dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
func Conv3DBackpropFilterV2Dilations(value []int64) Conv3DBackpropFilterV2Attr {
return func(m optionalAttr) {
m["dilations"] = value
@@ -21238,7 +21238,7 @@
// element on that dimension. The dimension order is determined by the value of
// `data_format`, see above for details. Dilations in the batch and depth
// dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1 i:1 i:1 i:1}
func Conv2DBackpropInputDilations(value []int64) Conv2DBackpropInputAttr {
return func(m optionalAttr) {
m["dilations"] = value
@@ -21946,7 +21946,7 @@
// filter element on that dimension. The dimension order is determined by the
// value of `data_format`, see above for details. Dilations in the batch and
// depth dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1 i:1 i:1 i:1}
func Conv2DDilations(value []int64) Conv2DAttr {
return func(m optionalAttr) {
m["dilations"] = value
@@ -22142,7 +22142,7 @@
// QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations sets the optional dilations attribute to value.
//
// value: List of dilation values.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1 i:1 i:1 i:1}
func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr {
return func(m optionalAttr) {
m["dilations"] = value
@@ -22211,7 +22211,7 @@
// QuantizedDepthwiseConv2DWithBiasAndReluDilations sets the optional dilations attribute to value.
//
// value: List of dilation values.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1 i:1 i:1 i:1}
func QuantizedDepthwiseConv2DWithBiasAndReluDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAttr {
return func(m optionalAttr) {
m["dilations"] = value
@@ -22326,7 +22326,7 @@
// QuantizedDepthwiseConv2DWithBiasDilations sets the optional dilations attribute to value.
//
// value: List of dilation values.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1 i:1 i:1 i:1}
func QuantizedDepthwiseConv2DWithBiasDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAttr {
return func(m optionalAttr) {
m["dilations"] = value
@@ -22385,7 +22385,7 @@
// QuantizedDepthwiseConv2DDilations sets the optional dilations attribute to value.
//
// value: List of dilation values.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1 i:1 i:1 i:1}
func QuantizedDepthwiseConv2DDilations(value []int64) QuantizedDepthwiseConv2DAttr {
return func(m optionalAttr) {
m["dilations"] = value
@@ -22559,7 +22559,7 @@
// QuantizedConv2DPerChannelDilations sets the optional dilations attribute to value.
//
// value: list of dilation values.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1 i:1 i:1 i:1}
func QuantizedConv2DPerChannelDilations(value []int64) QuantizedConv2DPerChannelAttr {
return func(m optionalAttr) {
m["dilations"] = value
@@ -22750,7 +22750,7 @@
// filter element on that dimension. The dimension order is determined by the
// value of `data_format`, see above for details. Dilations in the batch and
// depth dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
func Conv3DBackpropInputV2Dilations(value []int64) Conv3DBackpropInputV2Attr {
return func(m optionalAttr) {
m["dilations"] = value
@@ -25190,7 +25190,7 @@
type Conv3DBackpropFilterAttr func(optionalAttr)
// Conv3DBackpropFilterDilations sets the optional dilations attribute to value.
-// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
func Conv3DBackpropFilterDilations(value []int64) Conv3DBackpropFilterAttr {
return func(m optionalAttr) {
m["dilations"] = value
@@ -25229,313 +25229,6 @@
return op.Output(0)
}
-// Creates an all-zeros CSRSparseMatrix with shape `dense_shape`.
-//
-// Arguments:
-// dense_shape: The desired matrix shape.
-//
-//
-// Returns An empty CSR matrix with shape `dense_shape`.
-func SparseMatrixZeros(scope *Scope, dense_shape tf.Output, type_ tf.DataType) (sparse_matrix tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{"type": type_}
- opspec := tf.OpSpec{
- Type: "SparseMatrixZeros",
- Input: []tf.Input{
- dense_shape,
- },
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Forwards `data` to the output port determined by `pred`.
-//
-// If `pred` is true, the `data` input is forwarded to `output_true`. Otherwise,
-// the data goes to `output_false`.
-//
-// See also `RefSwitch` and `Merge`.
-//
-// Arguments:
-// data: The tensor to be forwarded to the appropriate output.
-// pred: A scalar that specifies which output port will receive data.
-//
-// Returns:
-// output_false: If `pred` is false, data will be forwarded to this output.
-// output_true: If `pred` is true, data will be forwarded to this output.
-func Switch(scope *Scope, data tf.Output, pred tf.Output) (output_false tf.Output, output_true tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "Switch",
- Input: []tf.Input{
- data, pred,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1)
-}
-
-// UnicodeEncodeAttr is an optional argument to UnicodeEncode.
-type UnicodeEncodeAttr func(optionalAttr)
-
-// UnicodeEncodeErrors sets the optional errors attribute to value.
-//
-// value: Error handling policy when there is invalid formatting found in the input.
-// The value of 'strict' will cause the operation to produce a InvalidArgument
-// error on any invalid input formatting. A value of 'replace' (the default) will
-// cause the operation to replace any invalid formatting in the input with the
-// `replacement_char` codepoint. A value of 'ignore' will cause the operation to
-// skip any invalid formatting in the input and produce no corresponding output
-// character.
-// If not specified, defaults to "replace"
-func UnicodeEncodeErrors(value string) UnicodeEncodeAttr {
- return func(m optionalAttr) {
- m["errors"] = value
- }
-}
-
-// UnicodeEncodeReplacementChar sets the optional replacement_char attribute to value.
-//
-// value: The replacement character codepoint to be used in place of any invalid
-// formatting in the input when `errors='replace'`. Any valid unicode codepoint may
-// be used. The default value is the default unicode replacement character is
-// 0xFFFD (U+65533).
-// If not specified, defaults to 65533
-func UnicodeEncodeReplacementChar(value int64) UnicodeEncodeAttr {
- return func(m optionalAttr) {
- m["replacement_char"] = value
- }
-}
-
-// Encode a tensor of ints into unicode strings.
-//
-// Returns a vector of strings, where `output[i]` is constructed by encoding the
-// Unicode codepoints in `input_values[input_splits[i]:input_splits[i+1]]`
-// using `output_encoding`.
-//
-// ---
-//
-// Example:
-//
-// ```
-// input_values = [72, 101, 108, 108, 111, 87, 111, 114, 108, 100]
-// input_splits = [0, 5, 10]
-// output_encoding = 'UTF-8'
-//
-// output = ['Hello', 'World']
-// ```
-//
-// Arguments:
-// input_values: A 1D tensor containing the unicode codepoints that should be encoded.
-// input_splits: A 1D tensor specifying how the unicode codepoints should be split into strings.
-// In particular, `output[i]` is constructed by encoding the codepoints in the
-// slice `input_values[input_splits[i]:input_splits[i+1]]`.
-// output_encoding: Unicode encoding of the output strings. Valid encodings are: `"UTF-8",
-// "UTF-16-BE", and "UTF-32-BE"`.
-//
-// Returns The 1-D Tensor of strings encoded from the provided unicode codepoints.
-func UnicodeEncode(scope *Scope, input_values tf.Output, input_splits tf.Output, output_encoding string, optional ...UnicodeEncodeAttr) (output tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{"output_encoding": output_encoding}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "UnicodeEncode",
- Input: []tf.Input{
- input_values, input_splits,
- },
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingFTRLParametersGradAccumDebug.
-type RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr func(optionalAttr)
-
-// RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableId sets the optional table_id attribute to value.
-// If not specified, defaults to -1
-//
-// REQUIRES: value >= -1
-func RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr {
- return func(m optionalAttr) {
- m["table_id"] = value
- }
-}
-
-// RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableName sets the optional table_name attribute to value.
-// If not specified, defaults to ""
-func RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr {
- return func(m optionalAttr) {
- m["table_name"] = value
- }
-}
-
-// RetrieveTPUEmbeddingFTRLParametersGradAccumDebugConfig sets the optional config attribute to value.
-// If not specified, defaults to ""
-func RetrieveTPUEmbeddingFTRLParametersGradAccumDebugConfig(value string) RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr {
- return func(m optionalAttr) {
- m["config"] = value
- }
-}
-
-// Retrieve FTRL embedding parameters with debug support.
-//
-// An op that retrieves optimization parameters from embedding to host
-// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
-// the correct embedding table configuration. For example, this op is
-// used to retrieve updated parameters before saving a checkpoint.
-//
-// Returns:
-// parameters: Parameter parameters updated by the FTRL optimization algorithm.
-// accumulators: Parameter accumulators updated by the FTRL optimization algorithm.
-// linears: Parameter linears updated by the FTRL optimization algorithm.
-// gradient_accumulators: Parameter gradient_accumulators updated by the FTRL optimization algorithm.
-func RetrieveTPUEmbeddingFTRLParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr) (parameters tf.Output, accumulators tf.Output, linears tf.Output, gradient_accumulators tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "RetrieveTPUEmbeddingFTRLParametersGradAccumDebug",
-
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2), op.Output(3)
-}
-
-// DepthwiseConv2dNativeAttr is an optional argument to DepthwiseConv2dNative.
-type DepthwiseConv2dNativeAttr func(optionalAttr)
-
-// DepthwiseConv2dNativeDataFormat sets the optional data_format attribute to value.
-//
-// value: Specify the data format of the input and output data. With the
-// default format "NHWC", the data is stored in the order of:
-// [batch, height, width, channels].
-// Alternatively, the format could be "NCHW", the data storage order of:
-// [batch, channels, height, width].
-// If not specified, defaults to "NHWC"
-func DepthwiseConv2dNativeDataFormat(value string) DepthwiseConv2dNativeAttr {
- return func(m optionalAttr) {
- m["data_format"] = value
- }
-}
-
-// DepthwiseConv2dNativeDilations sets the optional dilations attribute to value.
-//
-// value: 1-D tensor of length 4. The dilation factor for each dimension of
-// `input`. If set to k > 1, there will be k-1 skipped cells between each filter
-// element on that dimension. The dimension order is determined by the value of
-// `data_format`, see above for details. Dilations in the batch and depth
-// dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
-func DepthwiseConv2dNativeDilations(value []int64) DepthwiseConv2dNativeAttr {
- return func(m optionalAttr) {
- m["dilations"] = value
- }
-}
-
-// Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
-//
-// Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
-// and a filter / kernel tensor of shape
-// `[filter_height, filter_width, in_channels, channel_multiplier]`, containing
-// `in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies
-// a different filter to each input channel (expanding from 1 channel to
-// `channel_multiplier` channels for each), then concatenates the results
-// together. Thus, the output has `in_channels * channel_multiplier` channels.
-//
-// ```
-// for k in 0..in_channels-1
-// for q in 0..channel_multiplier-1
-// output[b, i, j, k * channel_multiplier + q] =
-// sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
-// filter[di, dj, k, q]
-// ```
-//
-// Must have `strides[0] = strides[3] = 1`. For the most common case of the same
-// horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
-//
-// Arguments:
-//
-//
-// strides: 1-D of length 4. The stride of the sliding window for each dimension
-// of `input`.
-// padding: The type of padding algorithm to use.
-func DepthwiseConv2dNative(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeAttr) (output tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{"strides": strides, "padding": padding}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "DepthwiseConv2dNative",
- Input: []tf.Input{
- input, filter,
- },
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Conv3DBackpropInputAttr is an optional argument to Conv3DBackpropInput.
-type Conv3DBackpropInputAttr func(optionalAttr)
-
-// Conv3DBackpropInputDilations sets the optional dilations attribute to value.
-// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
-func Conv3DBackpropInputDilations(value []int64) Conv3DBackpropInputAttr {
- return func(m optionalAttr) {
- m["dilations"] = value
- }
-}
-
-// Computes the gradients of 3-D convolution with respect to the input.
-//
-// DEPRECATED at GraphDef version 10: Use Conv3DBackpropInputV2
-//
-// Arguments:
-// input: Shape `[batch, depth, rows, cols, in_channels]`.
-// filter: Shape `[depth, rows, cols, in_channels, out_channels]`.
-// `in_channels` must match between `input` and `filter`.
-// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
-// out_channels]`.
-// strides: 1-D tensor of length 5. The stride of the sliding window for each
-// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-// padding: The type of padding algorithm to use.
-func Conv3DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropInputAttr) (output tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{"strides": strides, "padding": padding}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "Conv3DBackpropInput",
- Input: []tf.Input{
- input, filter, out_backprop,
- },
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
// Compute the lower regularized incomplete Gamma function `P(a, x)`.
//
// The lower regularized incomplete Gamma function is defined as:
@@ -25829,7 +25522,7 @@
// element on that dimension. The dimension order is determined by the value of
// `data_format`, see above for details. Dilations in the batch and depth
// dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1 i:1 i:1 i:1}
func DepthwiseConv2dNativeBackpropInputDilations(value []int64) DepthwiseConv2dNativeBackpropInputAttr {
return func(m optionalAttr) {
m["dilations"] = value
@@ -25875,6 +25568,313 @@
return op.Output(0)
}
+// Conv3DBackpropInputAttr is an optional argument to Conv3DBackpropInput.
+type Conv3DBackpropInputAttr func(optionalAttr)
+
+// Conv3DBackpropInputDilations sets the optional dilations attribute to value.
+// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
+func Conv3DBackpropInputDilations(value []int64) Conv3DBackpropInputAttr {
+ return func(m optionalAttr) {
+ m["dilations"] = value
+ }
+}
+
+// Computes the gradients of 3-D convolution with respect to the input.
+//
+// DEPRECATED at GraphDef version 10: Use Conv3DBackpropInputV2
+//
+// Arguments:
+// input: Shape `[batch, depth, rows, cols, in_channels]`.
+// filter: Shape `[depth, rows, cols, in_channels, out_channels]`.
+// `in_channels` must match between `input` and `filter`.
+// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
+// out_channels]`.
+// strides: 1-D tensor of length 5. The stride of the sliding window for each
+// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+// padding: The type of padding algorithm to use.
+func Conv3DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropInputAttr) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"strides": strides, "padding": padding}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "Conv3DBackpropInput",
+ Input: []tf.Input{
+ input, filter, out_backprop,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Creates an all-zeros CSRSparseMatrix with shape `dense_shape`.
+//
+// Arguments:
+// dense_shape: The desired matrix shape.
+//
+//
+// Returns An empty CSR matrix with shape `dense_shape`.
+func SparseMatrixZeros(scope *Scope, dense_shape tf.Output, type_ tf.DataType) (sparse_matrix tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"type": type_}
+ opspec := tf.OpSpec{
+ Type: "SparseMatrixZeros",
+ Input: []tf.Input{
+ dense_shape,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Forwards `data` to the output port determined by `pred`.
+//
+// If `pred` is true, the `data` input is forwarded to `output_true`. Otherwise,
+// the data goes to `output_false`.
+//
+// See also `RefSwitch` and `Merge`.
+//
+// Arguments:
+// data: The tensor to be forwarded to the appropriate output.
+// pred: A scalar that specifies which output port will receive data.
+//
+// Returns:
+// output_false: If `pred` is false, data will be forwarded to this output.
+// output_true: If `pred` is true, data will be forwarded to this output.
+func Switch(scope *Scope, data tf.Output, pred tf.Output) (output_false tf.Output, output_true tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "Switch",
+ Input: []tf.Input{
+ data, pred,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1)
+}
+
+// UnicodeEncodeAttr is an optional argument to UnicodeEncode.
+type UnicodeEncodeAttr func(optionalAttr)
+
+// UnicodeEncodeErrors sets the optional errors attribute to value.
+//
+// value: Error handling policy when there is invalid formatting found in the input.
+// The value of 'strict' will cause the operation to produce a InvalidArgument
+// error on any invalid input formatting. A value of 'replace' (the default) will
+// cause the operation to replace any invalid formatting in the input with the
+// `replacement_char` codepoint. A value of 'ignore' will cause the operation to
+// skip any invalid formatting in the input and produce no corresponding output
+// character.
+// If not specified, defaults to "replace"
+func UnicodeEncodeErrors(value string) UnicodeEncodeAttr {
+ return func(m optionalAttr) {
+ m["errors"] = value
+ }
+}
+
+// UnicodeEncodeReplacementChar sets the optional replacement_char attribute to value.
+//
+// value: The replacement character codepoint to be used in place of any invalid
+// formatting in the input when `errors='replace'`. Any valid unicode codepoint may
+// be used. The default value is the default unicode replacement character is
+// 0xFFFD (U+65533).
+// If not specified, defaults to 65533
+func UnicodeEncodeReplacementChar(value int64) UnicodeEncodeAttr {
+ return func(m optionalAttr) {
+ m["replacement_char"] = value
+ }
+}
+
+// Encode a tensor of ints into unicode strings.
+//
+// Returns a vector of strings, where `output[i]` is constructed by encoding the
+// Unicode codepoints in `input_values[input_splits[i]:input_splits[i+1]]`
+// using `output_encoding`.
+//
+// ---
+//
+// Example:
+//
+// ```
+// input_values = [72, 101, 108, 108, 111, 87, 111, 114, 108, 100]
+// input_splits = [0, 5, 10]
+// output_encoding = 'UTF-8'
+//
+// output = ['Hello', 'World']
+// ```
+//
+// Arguments:
+// input_values: A 1D tensor containing the unicode codepoints that should be encoded.
+// input_splits: A 1D tensor specifying how the unicode codepoints should be split into strings.
+// In particular, `output[i]` is constructed by encoding the codepoints in the
+// slice `input_values[input_splits[i]:input_splits[i+1]]`.
+// output_encoding: Unicode encoding of the output strings. Valid encodings are: `"UTF-8",
+// "UTF-16-BE", and "UTF-32-BE"`.
+//
+// Returns The 1-D Tensor of strings encoded from the provided unicode codepoints.
+func UnicodeEncode(scope *Scope, input_values tf.Output, input_splits tf.Output, output_encoding string, optional ...UnicodeEncodeAttr) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"output_encoding": output_encoding}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "UnicodeEncode",
+ Input: []tf.Input{
+ input_values, input_splits,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingFTRLParametersGradAccumDebug.
+type RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr func(optionalAttr)
+
+// RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableId sets the optional table_id attribute to value.
+// If not specified, defaults to -1
+//
+// REQUIRES: value >= -1
+func RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr {
+ return func(m optionalAttr) {
+ m["table_id"] = value
+ }
+}
+
+// RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableName sets the optional table_name attribute to value.
+// If not specified, defaults to ""
+func RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr {
+ return func(m optionalAttr) {
+ m["table_name"] = value
+ }
+}
+
+// RetrieveTPUEmbeddingFTRLParametersGradAccumDebugConfig sets the optional config attribute to value.
+// If not specified, defaults to ""
+func RetrieveTPUEmbeddingFTRLParametersGradAccumDebugConfig(value string) RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr {
+ return func(m optionalAttr) {
+ m["config"] = value
+ }
+}
+
+// Retrieve FTRL embedding parameters with debug support.
+//
+// An op that retrieves optimization parameters from embedding to host
+// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+// the correct embedding table configuration. For example, this op is
+// used to retrieve updated parameters before saving a checkpoint.
+//
+// Returns:
+// parameters: Parameter parameters updated by the FTRL optimization algorithm.
+// accumulators: Parameter accumulators updated by the FTRL optimization algorithm.
+// linears: Parameter linears updated by the FTRL optimization algorithm.
+// gradient_accumulators: Parameter gradient_accumulators updated by the FTRL optimization algorithm.
+func RetrieveTPUEmbeddingFTRLParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr) (parameters tf.Output, accumulators tf.Output, linears tf.Output, gradient_accumulators tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "RetrieveTPUEmbeddingFTRLParametersGradAccumDebug",
+
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1), op.Output(2), op.Output(3)
+}
+
+// DepthwiseConv2dNativeAttr is an optional argument to DepthwiseConv2dNative.
+type DepthwiseConv2dNativeAttr func(optionalAttr)
+
+// DepthwiseConv2dNativeDataFormat sets the optional data_format attribute to value.
+//
+// value: Specify the data format of the input and output data. With the
+// default format "NHWC", the data is stored in the order of:
+// [batch, height, width, channels].
+// Alternatively, the format could be "NCHW", the data storage order of:
+// [batch, channels, height, width].
+// If not specified, defaults to "NHWC"
+func DepthwiseConv2dNativeDataFormat(value string) DepthwiseConv2dNativeAttr {
+ return func(m optionalAttr) {
+ m["data_format"] = value
+ }
+}
+
+// DepthwiseConv2dNativeDilations sets the optional dilations attribute to value.
+//
+// value: 1-D tensor of length 4. The dilation factor for each dimension of
+// `input`. If set to k > 1, there will be k-1 skipped cells between each filter
+// element on that dimension. The dimension order is determined by the value of
+// `data_format`, see above for details. Dilations in the batch and depth
+// dimensions must be 1.
+// If not specified, defaults to {i:1 i:1 i:1 i:1}
+func DepthwiseConv2dNativeDilations(value []int64) DepthwiseConv2dNativeAttr {
+ return func(m optionalAttr) {
+ m["dilations"] = value
+ }
+}
+
+// Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
+//
+// Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
+// and a filter / kernel tensor of shape
+// `[filter_height, filter_width, in_channels, channel_multiplier]`, containing
+// `in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies
+// a different filter to each input channel (expanding from 1 channel to
+// `channel_multiplier` channels for each), then concatenates the results
+// together. Thus, the output has `in_channels * channel_multiplier` channels.
+//
+// ```
+// for k in 0..in_channels-1
+// for q in 0..channel_multiplier-1
+// output[b, i, j, k * channel_multiplier + q] =
+// sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
+// filter[di, dj, k, q]
+// ```
+//
+// Must have `strides[0] = strides[3] = 1`. For the most common case of the same
+// horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
+//
+// Arguments:
+//
+//
+// strides: 1-D of length 4. The stride of the sliding window for each dimension
+// of `input`.
+// padding: The type of padding algorithm to use.
+func DepthwiseConv2dNative(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeAttr) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"strides": strides, "padding": padding}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "DepthwiseConv2dNative",
+ Input: []tf.Input{
+ input, filter,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// Updates the table to associates keys with values.
//
// The tensor `keys` must be of the same type as the keys of the table.
@@ -26452,7 +26452,7 @@
// filter element on that dimension. The dimension order is determined by the
// value of `data_format`, see above for details. Dilations in the batch and
// depth dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1 i:1 i:1 i:1}
func QuantizedConv2DDilations(value []int64) QuantizedConv2DAttr {
return func(m optionalAttr) {
m["dilations"] = value
@@ -27517,7 +27517,7 @@
// filter element on that dimension. The dimension order is determined by the
// value of `data_format`, see above for details. Dilations in the batch and
// depth dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
func Conv3DDilations(value []int64) Conv3DAttr {
return func(m optionalAttr) {
m["dilations"] = value
@@ -34689,34 +34689,6 @@
return op.Output(0)
}
-// Inverse 2D fast Fourier transform.
-//
-// Computes the inverse 2-dimensional discrete Fourier transform over the
-// inner-most 2 dimensions of `input`.
-//
-// Arguments:
-// input: A complex tensor.
-//
-// Returns A complex tensor of the same shape as `input`. The inner-most 2
-// dimensions of `input` are replaced with their inverse 2D Fourier transform.
-//
-// @compatibility(numpy)
-// Equivalent to np.fft.ifft2
-// @end_compatibility
-func IFFT2D(scope *Scope, input tf.Output) (output tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "IFFT2D",
- Input: []tf.Input{
- input,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
// RetrieveTPUEmbeddingProximalAdagradParametersAttr is an optional argument to RetrieveTPUEmbeddingProximalAdagradParameters.
type RetrieveTPUEmbeddingProximalAdagradParametersAttr func(optionalAttr)
@@ -34849,6 +34821,34 @@
return op.Output(0)
}
+// Inverse 2D fast Fourier transform.
+//
+// Computes the inverse 2-dimensional discrete Fourier transform over the
+// inner-most 2 dimensions of `input`.
+//
+// Arguments:
+// input: A complex tensor.
+//
+// Returns A complex tensor of the same shape as `input`. The inner-most 2
+// dimensions of `input` are replaced with their inverse 2D Fourier transform.
+//
+// @compatibility(numpy)
+// Equivalent to np.fft.ifft2
+// @end_compatibility
+func IFFT2D(scope *Scope, input tf.Output) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "IFFT2D",
+ Input: []tf.Input{
+ input,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// Concatenates a list of `SparseTensor` along the specified dimension.
//
// Concatenation is with respect to the dense versions of these sparse tensors.
@@ -39977,6 +39977,106 @@
return scope.AddOperation(opspec)
}
+// RetrieveTPUEmbeddingMomentumParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingMomentumParametersGradAccumDebug.
+type RetrieveTPUEmbeddingMomentumParametersGradAccumDebugAttr func(optionalAttr)
+
+// RetrieveTPUEmbeddingMomentumParametersGradAccumDebugTableId sets the optional table_id attribute to value.
+// If not specified, defaults to -1
+//
+// REQUIRES: value >= -1
+func RetrieveTPUEmbeddingMomentumParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingMomentumParametersGradAccumDebugAttr {
+ return func(m optionalAttr) {
+ m["table_id"] = value
+ }
+}
+
+// RetrieveTPUEmbeddingMomentumParametersGradAccumDebugTableName sets the optional table_name attribute to value.
+// If not specified, defaults to ""
+func RetrieveTPUEmbeddingMomentumParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingMomentumParametersGradAccumDebugAttr {
+ return func(m optionalAttr) {
+ m["table_name"] = value
+ }
+}
+
+// RetrieveTPUEmbeddingMomentumParametersGradAccumDebugConfig sets the optional config attribute to value.
+// If not specified, defaults to ""
+func RetrieveTPUEmbeddingMomentumParametersGradAccumDebugConfig(value string) RetrieveTPUEmbeddingMomentumParametersGradAccumDebugAttr {
+ return func(m optionalAttr) {
+ m["config"] = value
+ }
+}
+
+// Retrieve Momentum embedding parameters with debug support.
+//
+// An op that retrieves optimization parameters from embedding to host
+// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+// the correct embedding table configuration. For example, this op is
+// used to retrieve updated parameters before saving a checkpoint.
+//
+// Returns:
+// parameters: Parameter parameters updated by the Momentum optimization algorithm.
+// momenta: Parameter momenta updated by the Momentum optimization algorithm.
+// gradient_accumulators: Parameter gradient_accumulators updated by the Momentum optimization algorithm.
+func RetrieveTPUEmbeddingMomentumParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingMomentumParametersGradAccumDebugAttr) (parameters tf.Output, momenta tf.Output, gradient_accumulators tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "RetrieveTPUEmbeddingMomentumParametersGradAccumDebug",
+
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1), op.Output(2)
+}
+
+// StatelessRandomUniformFullIntAttr is an optional argument to StatelessRandomUniformFullInt.
+type StatelessRandomUniformFullIntAttr func(optionalAttr)
+
+// StatelessRandomUniformFullIntDtype sets the optional dtype attribute to value.
+//
+// value: The type of the output.
+// If not specified, defaults to DT_UINT64
+func StatelessRandomUniformFullIntDtype(value tf.DataType) StatelessRandomUniformFullIntAttr {
+ return func(m optionalAttr) {
+ m["dtype"] = value
+ }
+}
+
+// Outputs deterministic pseudorandom random integers from a uniform distribution.
+//
+// The generated values are uniform integers covering the whole range of `dtype`.
+//
+// The outputs are a deterministic function of `shape` and `seed`.
+//
+// Arguments:
+// shape: The shape of the output tensor.
+// seed: 2 seeds (shape [2]).
+//
+// Returns Random values with specified shape.
+func StatelessRandomUniformFullInt(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomUniformFullIntAttr) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "StatelessRandomUniformFullInt",
+ Input: []tf.Input{
+ shape, seed,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// Shuts down a running distributed TPU system.
//
// The op returns an error if no system is running.
@@ -41254,63 +41354,6 @@
return op.Output(0)
}
-// RetrieveTPUEmbeddingMomentumParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingMomentumParametersGradAccumDebug.
-type RetrieveTPUEmbeddingMomentumParametersGradAccumDebugAttr func(optionalAttr)
-
-// RetrieveTPUEmbeddingMomentumParametersGradAccumDebugTableId sets the optional table_id attribute to value.
-// If not specified, defaults to -1
-//
-// REQUIRES: value >= -1
-func RetrieveTPUEmbeddingMomentumParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingMomentumParametersGradAccumDebugAttr {
- return func(m optionalAttr) {
- m["table_id"] = value
- }
-}
-
-// RetrieveTPUEmbeddingMomentumParametersGradAccumDebugTableName sets the optional table_name attribute to value.
-// If not specified, defaults to ""
-func RetrieveTPUEmbeddingMomentumParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingMomentumParametersGradAccumDebugAttr {
- return func(m optionalAttr) {
- m["table_name"] = value
- }
-}
-
-// RetrieveTPUEmbeddingMomentumParametersGradAccumDebugConfig sets the optional config attribute to value.
-// If not specified, defaults to ""
-func RetrieveTPUEmbeddingMomentumParametersGradAccumDebugConfig(value string) RetrieveTPUEmbeddingMomentumParametersGradAccumDebugAttr {
- return func(m optionalAttr) {
- m["config"] = value
- }
-}
-
-// Retrieve Momentum embedding parameters with debug support.
-//
-// An op that retrieves optimization parameters from embedding to host
-// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
-// the correct embedding table configuration. For example, this op is
-// used to retrieve updated parameters before saving a checkpoint.
-//
-// Returns:
-// parameters: Parameter parameters updated by the Momentum optimization algorithm.
-// momenta: Parameter momenta updated by the Momentum optimization algorithm.
-// gradient_accumulators: Parameter gradient_accumulators updated by the Momentum optimization algorithm.
-func RetrieveTPUEmbeddingMomentumParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingMomentumParametersGradAccumDebugAttr) (parameters tf.Output, momenta tf.Output, gradient_accumulators tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "RetrieveTPUEmbeddingMomentumParametersGradAccumDebug",
-
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0), op.Output(1), op.Output(2)
-}
-
// MaxPoolGradGradV2Attr is an optional argument to MaxPoolGradGradV2.
type MaxPoolGradGradV2Attr func(optionalAttr)
@@ -45333,7 +45376,7 @@
// element on that dimension. The dimension order is determined by the value of
// `data_format`, see above for details. Dilations in the batch and depth
// dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1 i:1 i:1 i:1}
func Conv2DBackpropFilterDilations(value []int64) Conv2DBackpropFilterAttr {
return func(m optionalAttr) {
m["dilations"] = value
