Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 314586235 Change-Id: I44923bbd5c38b6cc4b0aa3eee02453916f62d5a2
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
50c3196789
commit
59092da68c
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@ -12571,6 +12571,151 @@ func EncodePng(scope *Scope, image tf.Output, optional ...EncodePngAttr) (conten
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return op.Output(0)
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return op.Output(0)
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}
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}
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// Invert (flip) each bit of supported types; for example, type `uint8` value 01010101 becomes 10101010.
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//
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// Flip each bit of supported types. For example, type `int8` (decimal 2) binary 00000010 becomes (decimal -3) binary 11111101.
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// This operation is performed on each element of the tensor argument `x`.
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//
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// Example:
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// ```python
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// import tensorflow as tf
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// from tensorflow.python.ops import bitwise_ops
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//
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// # flip 2 (00000010) to -3 (11111101)
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// tf.assert_equal(-3, bitwise_ops.invert(2))
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//
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// dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
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// dtypes.uint8, dtypes.uint16, dtypes.uint32, dtypes.uint64]
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//
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// inputs = [0, 5, 3, 14]
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// for dtype in dtype_list:
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// # Because of issues with negative numbers, let's test this indirectly.
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// # 1. invert(a) and a = 0
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// # 2. invert(a) or a = invert(0)
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// input_tensor = tf.constant([0, 5, 3, 14], dtype=dtype)
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// not_a_and_a, not_a_or_a, not_0 = [bitwise_ops.bitwise_and(
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// input_tensor, bitwise_ops.invert(input_tensor)),
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// bitwise_ops.bitwise_or(
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// input_tensor, bitwise_ops.invert(input_tensor)),
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// bitwise_ops.invert(
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// tf.constant(0, dtype=dtype))]
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//
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// expected = tf.constant([0, 0, 0, 0], dtype=tf.float32)
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// tf.assert_equal(tf.cast(not_a_and_a, tf.float32), expected)
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//
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// expected = tf.cast([not_0] * 4, tf.float32)
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// tf.assert_equal(tf.cast(not_a_or_a, tf.float32), expected)
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//
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// # For unsigned dtypes let's also check the result directly.
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// if dtype.is_unsigned:
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// inverted = bitwise_ops.invert(input_tensor)
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// expected = tf.constant([dtype.max - x for x in inputs], dtype=tf.float32)
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// tf.assert_equal(tf.cast(inverted, tf.float32), tf.cast(expected, tf.float32))
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// ```
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func Invert(scope *Scope, x tf.Output) (y tf.Output) {
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if scope.Err() != nil {
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return
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}
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opspec := tf.OpSpec{
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Type: "Invert",
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Input: []tf.Input{
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x,
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},
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// DecodePngAttr is an optional argument to DecodePng.
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type DecodePngAttr func(optionalAttr)
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// DecodePngChannels sets the optional channels attribute to value.
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//
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// value: Number of color channels for the decoded image.
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// If not specified, defaults to 0
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func DecodePngChannels(value int64) DecodePngAttr {
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return func(m optionalAttr) {
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m["channels"] = value
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}
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}
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// DecodePngDtype sets the optional dtype attribute to value.
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// If not specified, defaults to DT_UINT8
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func DecodePngDtype(value tf.DataType) DecodePngAttr {
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return func(m optionalAttr) {
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m["dtype"] = value
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}
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}
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// Decode a PNG-encoded image to a uint8 or uint16 tensor.
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//
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// The attr `channels` indicates the desired number of color channels for the
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// decoded image.
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//
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// Accepted values are:
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//
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// * 0: Use the number of channels in the PNG-encoded image.
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// * 1: output a grayscale image.
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// * 3: output an RGB image.
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// * 4: output an RGBA image.
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//
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// If needed, the PNG-encoded image is transformed to match the requested number
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// of color channels.
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//
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// This op also supports decoding JPEGs and non-animated GIFs since the interface
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// is the same, though it is cleaner to use `tf.io.decode_image`.
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//
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// Arguments:
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// contents: 0-D. The PNG-encoded image.
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//
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// Returns 3-D with shape `[height, width, channels]`.
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func DecodePng(scope *Scope, contents tf.Output, optional ...DecodePngAttr) (image tf.Output) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{}
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for _, a := range optional {
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a(attrs)
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}
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opspec := tf.OpSpec{
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Type: "DecodePng",
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Input: []tf.Input{
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contents,
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// Adjust the saturation of one or more images.
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//
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// `images` is a tensor of at least 3 dimensions. The last dimension is
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// interpreted as channels, and must be three.
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//
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// The input image is considered in the RGB colorspace. Conceptually, the RGB
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// colors are first mapped into HSV. A scale is then applied all the saturation
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// values, and then remapped back to RGB colorspace.
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//
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// Arguments:
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// images: Images to adjust. At least 3-D.
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// scale: A float scale to add to the saturation.
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//
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// Returns The hue-adjusted image or images.
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func AdjustSaturation(scope *Scope, images tf.Output, scale tf.Output) (output tf.Output) {
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if scope.Err() != nil {
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return
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}
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opspec := tf.OpSpec{
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Type: "AdjustSaturation",
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Input: []tf.Input{
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images, scale,
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},
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// ExtractJpegShapeAttr is an optional argument to ExtractJpegShape.
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// ExtractJpegShapeAttr is an optional argument to ExtractJpegShape.
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type ExtractJpegShapeAttr func(optionalAttr)
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type ExtractJpegShapeAttr func(optionalAttr)
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@ -15370,123 +15515,6 @@ func TensorSummaryV2(scope *Scope, tag tf.Output, tensor tf.Output, serialized_s
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return op.Output(0)
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return op.Output(0)
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}
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}
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// Invert (flip) each bit of supported types; for example, type `uint8` value 01010101 becomes 10101010.
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//
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// Flip each bit of supported types. For example, type `int8` (decimal 2) binary 00000010 becomes (decimal -3) binary 11111101.
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// This operation is performed on each element of the tensor argument `x`.
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//
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// Example:
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// ```python
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// import tensorflow as tf
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// from tensorflow.python.ops import bitwise_ops
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//
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// # flip 2 (00000010) to -3 (11111101)
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// tf.assert_equal(-3, bitwise_ops.invert(2))
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//
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// dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
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// dtypes.uint8, dtypes.uint16, dtypes.uint32, dtypes.uint64]
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//
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// inputs = [0, 5, 3, 14]
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// for dtype in dtype_list:
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// # Because of issues with negative numbers, let's test this indirectly.
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// # 1. invert(a) and a = 0
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// # 2. invert(a) or a = invert(0)
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// input_tensor = tf.constant([0, 5, 3, 14], dtype=dtype)
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// not_a_and_a, not_a_or_a, not_0 = [bitwise_ops.bitwise_and(
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// input_tensor, bitwise_ops.invert(input_tensor)),
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// bitwise_ops.bitwise_or(
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// input_tensor, bitwise_ops.invert(input_tensor)),
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// bitwise_ops.invert(
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// tf.constant(0, dtype=dtype))]
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//
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// expected = tf.constant([0, 0, 0, 0], dtype=tf.float32)
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// tf.assert_equal(tf.cast(not_a_and_a, tf.float32), expected)
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//
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// expected = tf.cast([not_0] * 4, tf.float32)
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// tf.assert_equal(tf.cast(not_a_or_a, tf.float32), expected)
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//
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// # For unsigned dtypes let's also check the result directly.
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// if dtype.is_unsigned:
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// inverted = bitwise_ops.invert(input_tensor)
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// expected = tf.constant([dtype.max - x for x in inputs], dtype=tf.float32)
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// tf.assert_equal(tf.cast(inverted, tf.float32), tf.cast(expected, tf.float32))
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// ```
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func Invert(scope *Scope, x tf.Output) (y tf.Output) {
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if scope.Err() != nil {
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return
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}
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opspec := tf.OpSpec{
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Type: "Invert",
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Input: []tf.Input{
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x,
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},
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// DecodePngAttr is an optional argument to DecodePng.
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type DecodePngAttr func(optionalAttr)
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// DecodePngChannels sets the optional channels attribute to value.
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//
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// value: Number of color channels for the decoded image.
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// If not specified, defaults to 0
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func DecodePngChannels(value int64) DecodePngAttr {
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return func(m optionalAttr) {
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m["channels"] = value
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}
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}
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// DecodePngDtype sets the optional dtype attribute to value.
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// If not specified, defaults to DT_UINT8
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func DecodePngDtype(value tf.DataType) DecodePngAttr {
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return func(m optionalAttr) {
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m["dtype"] = value
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}
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}
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// Decode a PNG-encoded image to a uint8 or uint16 tensor.
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//
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// The attr `channels` indicates the desired number of color channels for the
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// decoded image.
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//
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// Accepted values are:
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//
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// * 0: Use the number of channels in the PNG-encoded image.
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// * 1: output a grayscale image.
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// * 3: output an RGB image.
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// * 4: output an RGBA image.
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//
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// If needed, the PNG-encoded image is transformed to match the requested number
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// of color channels.
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//
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// This op also supports decoding JPEGs and non-animated GIFs since the interface
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// is the same, though it is cleaner to use `tf.io.decode_image`.
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//
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// Arguments:
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// contents: 0-D. The PNG-encoded image.
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//
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// Returns 3-D with shape `[height, width, channels]`.
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func DecodePng(scope *Scope, contents tf.Output, optional ...DecodePngAttr) (image tf.Output) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{}
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for _, a := range optional {
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a(attrs)
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}
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opspec := tf.OpSpec{
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Type: "DecodePng",
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Input: []tf.Input{
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contents,
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// Computes the gradient for the sqrt of `x` wrt its input.
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// Computes the gradient for the sqrt of `x` wrt its input.
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//
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//
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// Specifically, `grad = dy * 0.5 / y`, where `y = sqrt(x)`, and `dy`
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// Specifically, `grad = dy * 0.5 / y`, where `y = sqrt(x)`, and `dy`
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@ -20380,34 +20408,6 @@ func TensorListPopBack(scope *Scope, input_handle tf.Output, element_shape tf.Ou
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return op.Output(0), op.Output(1)
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return op.Output(0), op.Output(1)
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}
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}
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// Adjust the saturation of one or more images.
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//
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// `images` is a tensor of at least 3 dimensions. The last dimension is
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// interpreted as channels, and must be three.
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//
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// The input image is considered in the RGB colorspace. Conceptually, the RGB
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// colors are first mapped into HSV. A scale is then applied all the saturation
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// values, and then remapped back to RGB colorspace.
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//
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// Arguments:
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// images: Images to adjust. At least 3-D.
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// scale: A float scale to add to the saturation.
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//
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// Returns The hue-adjusted image or images.
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func AdjustSaturation(scope *Scope, images tf.Output, scale tf.Output) (output tf.Output) {
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if scope.Err() != nil {
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return
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}
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opspec := tf.OpSpec{
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Type: "AdjustSaturation",
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Input: []tf.Input{
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images, scale,
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},
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// QueueDequeueManyV2Attr is an optional argument to QueueDequeueManyV2.
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// QueueDequeueManyV2Attr is an optional argument to QueueDequeueManyV2.
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type QueueDequeueManyV2Attr func(optionalAttr)
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type QueueDequeueManyV2Attr func(optionalAttr)
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@ -26421,72 +26421,45 @@ func Conv2DBackpropFilter(scope *Scope, input tf.Output, filter_sizes tf.Output,
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return op.Output(0)
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return op.Output(0)
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}
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}
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// ConfigureDistributedTPUAttr is an optional argument to ConfigureDistributedTPU.
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// Returns the truth value of x OR y element-wise.
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type ConfigureDistributedTPUAttr func(optionalAttr)
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// ConfigureDistributedTPUEmbeddingConfig sets the optional embedding_config attribute to value.
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//
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//
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// value: Reserved. Do not use.
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// *NOTE*: `LogicalOr` supports broadcasting. More about broadcasting
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// If not specified, defaults to ""
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// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
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func ConfigureDistributedTPUEmbeddingConfig(value string) ConfigureDistributedTPUAttr {
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func LogicalOr(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
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return func(m optionalAttr) {
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m["embedding_config"] = value
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}
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}
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// ConfigureDistributedTPUTpuEmbeddingConfig sets the optional tpu_embedding_config attribute to value.
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//
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// value: Serialized tensorflow.tpu.TPUEmbeddingConfiguration that
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// describes the embedding lookups of the program.
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// If not specified, defaults to ""
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func ConfigureDistributedTPUTpuEmbeddingConfig(value string) ConfigureDistributedTPUAttr {
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return func(m optionalAttr) {
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m["tpu_embedding_config"] = value
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}
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}
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// ConfigureDistributedTPUIsGlobalInit sets the optional is_global_init attribute to value.
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//
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// value: Reserved. Do not use.
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// If not specified, defaults to false
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func ConfigureDistributedTPUIsGlobalInit(value bool) ConfigureDistributedTPUAttr {
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return func(m optionalAttr) {
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m["is_global_init"] = value
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}
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}
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// ConfigureDistributedTPUEnableWholeMeshCompilations sets the optional enable_whole_mesh_compilations attribute to value.
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// If not specified, defaults to false
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|
||||||
func ConfigureDistributedTPUEnableWholeMeshCompilations(value bool) ConfigureDistributedTPUAttr {
|
|
||||||
return func(m optionalAttr) {
|
|
||||||
m["enable_whole_mesh_compilations"] = value
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// ConfigureDistributedTPUCompilationFailureClosesChips sets the optional compilation_failure_closes_chips attribute to value.
|
|
||||||
// If not specified, defaults to true
|
|
||||||
func ConfigureDistributedTPUCompilationFailureClosesChips(value bool) ConfigureDistributedTPUAttr {
|
|
||||||
return func(m optionalAttr) {
|
|
||||||
m["compilation_failure_closes_chips"] = value
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// Sets up the centralized structures for a distributed TPU system.
|
|
||||||
//
|
|
||||||
// Returns A serialized tensorflow.tpu.TopologyProto that describes the TPU
|
|
||||||
// topology.
|
|
||||||
func ConfigureDistributedTPU(scope *Scope, optional ...ConfigureDistributedTPUAttr) (topology tf.Output) {
|
|
||||||
if scope.Err() != nil {
|
if scope.Err() != nil {
|
||||||
return
|
return
|
||||||
}
|
}
|
||||||
attrs := map[string]interface{}{}
|
opspec := tf.OpSpec{
|
||||||
for _, a := range optional {
|
Type: "LogicalOr",
|
||||||
a(attrs)
|
Input: []tf.Input{
|
||||||
|
x, y,
|
||||||
|
},
|
||||||
|
}
|
||||||
|
op := scope.AddOperation(opspec)
|
||||||
|
return op.Output(0)
|
||||||
|
}
|
||||||
|
|
||||||
|
// Adds `bias` to `value`.
|
||||||
|
//
|
||||||
|
// This is a deprecated version of BiasAdd and will be soon removed.
|
||||||
|
//
|
||||||
|
// This is a special case of `tf.add` where `bias` is restricted to be 1-D.
|
||||||
|
// Broadcasting is supported, so `value` may have any number of dimensions.
|
||||||
|
//
|
||||||
|
// Arguments:
|
||||||
|
// value: Any number of dimensions.
|
||||||
|
// bias: 1-D with size the last dimension of `value`.
|
||||||
|
//
|
||||||
|
// Returns Broadcasted sum of `value` and `bias`.
|
||||||
|
func BiasAddV1(scope *Scope, value tf.Output, bias tf.Output) (output tf.Output) {
|
||||||
|
if scope.Err() != nil {
|
||||||
|
return
|
||||||
}
|
}
|
||||||
opspec := tf.OpSpec{
|
opspec := tf.OpSpec{
|
||||||
Type: "ConfigureDistributedTPU",
|
Type: "BiasAddV1",
|
||||||
|
Input: []tf.Input{
|
||||||
Attrs: attrs,
|
value, bias,
|
||||||
|
},
|
||||||
}
|
}
|
||||||
op := scope.AddOperation(opspec)
|
op := scope.AddOperation(opspec)
|
||||||
return op.Output(0)
|
return op.Output(0)
|
||||||
|
|
@ -29339,21 +29312,6 @@ func RandomPoissonV2(scope *Scope, shape tf.Output, rate tf.Output, optional ...
|
||||||
return op.Output(0)
|
return op.Output(0)
|
||||||
}
|
}
|
||||||
|
|
||||||
// Computes the derivative of a Gamma random sample w.r.t. `alpha`.
|
|
||||||
func RandomGammaGrad(scope *Scope, alpha tf.Output, sample tf.Output) (output tf.Output) {
|
|
||||||
if scope.Err() != nil {
|
|
||||||
return
|
|
||||||
}
|
|
||||||
opspec := tf.OpSpec{
|
|
||||||
Type: "RandomGammaGrad",
|
|
||||||
Input: []tf.Input{
|
|
||||||
alpha, sample,
|
|
||||||
},
|
|
||||||
}
|
|
||||||
op := scope.AddOperation(opspec)
|
|
||||||
return op.Output(0)
|
|
||||||
}
|
|
||||||
|
|
||||||
// Creates a dataset that takes a Bernoulli sample of the contents of another dataset.
|
// Creates a dataset that takes a Bernoulli sample of the contents of another dataset.
|
||||||
//
|
//
|
||||||
// There is no transformation in the `tf.data` Python API for creating this dataset.
|
// There is no transformation in the `tf.data` Python API for creating this dataset.
|
||||||
|
|
@ -34779,50 +34737,6 @@ func SparseSliceGrad(scope *Scope, backprop_val_grad tf.Output, input_indices tf
|
||||||
return op.Output(0)
|
return op.Output(0)
|
||||||
}
|
}
|
||||||
|
|
||||||
// Returns the truth value of x OR y element-wise.
|
|
||||||
//
|
|
||||||
// *NOTE*: `LogicalOr` supports broadcasting. More about broadcasting
|
|
||||||
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
|
|
||||||
func LogicalOr(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
|
|
||||||
if scope.Err() != nil {
|
|
||||||
return
|
|
||||||
}
|
|
||||||
opspec := tf.OpSpec{
|
|
||||||
Type: "LogicalOr",
|
|
||||||
Input: []tf.Input{
|
|
||||||
x, y,
|
|
||||||
},
|
|
||||||
}
|
|
||||||
op := scope.AddOperation(opspec)
|
|
||||||
return op.Output(0)
|
|
||||||
}
|
|
||||||
|
|
||||||
// Adds `bias` to `value`.
|
|
||||||
//
|
|
||||||
// This is a deprecated version of BiasAdd and will be soon removed.
|
|
||||||
//
|
|
||||||
// This is a special case of `tf.add` where `bias` is restricted to be 1-D.
|
|
||||||
// Broadcasting is supported, so `value` may have any number of dimensions.
|
|
||||||
//
|
|
||||||
// Arguments:
|
|
||||||
// value: Any number of dimensions.
|
|
||||||
// bias: 1-D with size the last dimension of `value`.
|
|
||||||
//
|
|
||||||
// Returns Broadcasted sum of `value` and `bias`.
|
|
||||||
func BiasAddV1(scope *Scope, value tf.Output, bias tf.Output) (output tf.Output) {
|
|
||||||
if scope.Err() != nil {
|
|
||||||
return
|
|
||||||
}
|
|
||||||
opspec := tf.OpSpec{
|
|
||||||
Type: "BiasAddV1",
|
|
||||||
Input: []tf.Input{
|
|
||||||
value, bias,
|
|
||||||
},
|
|
||||||
}
|
|
||||||
op := scope.AddOperation(opspec)
|
|
||||||
return op.Output(0)
|
|
||||||
}
|
|
||||||
|
|
||||||
// Generates sparse cross from a list of sparse and dense tensors.
|
// Generates sparse cross from a list of sparse and dense tensors.
|
||||||
//
|
//
|
||||||
// The op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each
|
// The op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each
|
||||||
|
|
@ -38331,6 +38245,139 @@ func RecvTPUEmbeddingActivations(scope *Scope, num_outputs int64, config string)
|
||||||
return outputs
|
return outputs
|
||||||
}
|
}
|
||||||
|
|
||||||
|
// Computes the derivative of a Gamma random sample w.r.t. `alpha`.
|
||||||
|
func RandomGammaGrad(scope *Scope, alpha tf.Output, sample tf.Output) (output tf.Output) {
|
||||||
|
if scope.Err() != nil {
|
||||||
|
return
|
||||||
|
}
|
||||||
|
opspec := tf.OpSpec{
|
||||||
|
Type: "RandomGammaGrad",
|
||||||
|
Input: []tf.Input{
|
||||||
|
alpha, sample,
|
||||||
|
},
|
||||||
|
}
|
||||||
|
op := scope.AddOperation(opspec)
|
||||||
|
return op.Output(0)
|
||||||
|
}
|
||||||
|
|
||||||
|
// LRNGradAttr is an optional argument to LRNGrad.
|
||||||
|
type LRNGradAttr func(optionalAttr)
|
||||||
|
|
||||||
|
// LRNGradDepthRadius sets the optional depth_radius attribute to value.
|
||||||
|
//
|
||||||
|
// value: A depth radius.
|
||||||
|
// If not specified, defaults to 5
|
||||||
|
func LRNGradDepthRadius(value int64) LRNGradAttr {
|
||||||
|
return func(m optionalAttr) {
|
||||||
|
m["depth_radius"] = value
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// LRNGradBias sets the optional bias attribute to value.
|
||||||
|
//
|
||||||
|
// value: An offset (usually > 0 to avoid dividing by 0).
|
||||||
|
// If not specified, defaults to 1
|
||||||
|
func LRNGradBias(value float32) LRNGradAttr {
|
||||||
|
return func(m optionalAttr) {
|
||||||
|
m["bias"] = value
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// LRNGradAlpha sets the optional alpha attribute to value.
|
||||||
|
//
|
||||||
|
// value: A scale factor, usually positive.
|
||||||
|
// If not specified, defaults to 1
|
||||||
|
func LRNGradAlpha(value float32) LRNGradAttr {
|
||||||
|
return func(m optionalAttr) {
|
||||||
|
m["alpha"] = value
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// LRNGradBeta sets the optional beta attribute to value.
|
||||||
|
//
|
||||||
|
// value: An exponent.
|
||||||
|
// If not specified, defaults to 0.5
|
||||||
|
func LRNGradBeta(value float32) LRNGradAttr {
|
||||||
|
return func(m optionalAttr) {
|
||||||
|
m["beta"] = value
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Gradients for Local Response Normalization.
|
||||||
|
//
|
||||||
|
// Arguments:
|
||||||
|
// input_grads: 4-D with shape `[batch, height, width, channels]`.
|
||||||
|
// input_image: 4-D with shape `[batch, height, width, channels]`.
|
||||||
|
// output_image: 4-D with shape `[batch, height, width, channels]`.
|
||||||
|
//
|
||||||
|
// Returns The gradients for LRN.
|
||||||
|
func LRNGrad(scope *Scope, input_grads tf.Output, input_image tf.Output, output_image tf.Output, optional ...LRNGradAttr) (output tf.Output) {
|
||||||
|
if scope.Err() != nil {
|
||||||
|
return
|
||||||
|
}
|
||||||
|
attrs := map[string]interface{}{}
|
||||||
|
for _, a := range optional {
|
||||||
|
a(attrs)
|
||||||
|
}
|
||||||
|
opspec := tf.OpSpec{
|
||||||
|
Type: "LRNGrad",
|
||||||
|
Input: []tf.Input{
|
||||||
|
input_grads, input_image, output_image,
|
||||||
|
},
|
||||||
|
Attrs: attrs,
|
||||||
|
}
|
||||||
|
op := scope.AddOperation(opspec)
|
||||||
|
return op.Output(0)
|
||||||
|
}
|
||||||
|
|
||||||
|
// PrelinearizeAttr is an optional argument to Prelinearize.
|
||||||
|
type PrelinearizeAttr func(optionalAttr)
|
||||||
|
|
||||||
|
// PrelinearizeShape sets the optional shape attribute to value.
|
||||||
|
//
|
||||||
|
// value: The shape of the tensor.
|
||||||
|
// If not specified, defaults to <>
|
||||||
|
func PrelinearizeShape(value tf.Shape) PrelinearizeAttr {
|
||||||
|
return func(m optionalAttr) {
|
||||||
|
m["shape"] = value
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// PrelinearizeLayout sets the optional layout attribute to value.
|
||||||
|
//
|
||||||
|
// value: A vector holding the requested layout in minor-to-major sequence. If a layout
|
||||||
|
// attribute is passed but its values are all -1 the layout will be computed by
|
||||||
|
// the infeed operation.
|
||||||
|
// If not specified, defaults to <>
|
||||||
|
func PrelinearizeLayout(value []int64) PrelinearizeAttr {
|
||||||
|
return func(m optionalAttr) {
|
||||||
|
m["layout"] = value
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// An op which linearizes one Tensor value to an opaque variant tensor.
|
||||||
|
//
|
||||||
|
// Arguments:
|
||||||
|
// input: A tensor that will be linearized.
|
||||||
|
func Prelinearize(scope *Scope, input tf.Output, optional ...PrelinearizeAttr) (output tf.Output) {
|
||||||
|
if scope.Err() != nil {
|
||||||
|
return
|
||||||
|
}
|
||||||
|
attrs := map[string]interface{}{}
|
||||||
|
for _, a := range optional {
|
||||||
|
a(attrs)
|
||||||
|
}
|
||||||
|
opspec := tf.OpSpec{
|
||||||
|
Type: "Prelinearize",
|
||||||
|
Input: []tf.Input{
|
||||||
|
input,
|
||||||
|
},
|
||||||
|
Attrs: attrs,
|
||||||
|
}
|
||||||
|
op := scope.AddOperation(opspec)
|
||||||
|
return op.Output(0)
|
||||||
|
}
|
||||||
|
|
||||||
// Computes the sparse Cholesky decomposition of `input`.
|
// Computes the sparse Cholesky decomposition of `input`.
|
||||||
//
|
//
|
||||||
// Computes the Sparse Cholesky decomposition of a sparse matrix, with the given
|
// Computes the Sparse Cholesky decomposition of a sparse matrix, with the given
|
||||||
|
|
@ -48648,6 +48695,77 @@ func RetrieveTPUEmbeddingMomentumParameters(scope *Scope, num_shards int64, shar
|
||||||
return op.Output(0), op.Output(1)
|
return op.Output(0), op.Output(1)
|
||||||
}
|
}
|
||||||
|
|
||||||
|
// ConfigureDistributedTPUAttr is an optional argument to ConfigureDistributedTPU.
|
||||||
|
type ConfigureDistributedTPUAttr func(optionalAttr)
|
||||||
|
|
||||||
|
// ConfigureDistributedTPUEmbeddingConfig sets the optional embedding_config attribute to value.
|
||||||
|
//
|
||||||
|
// value: Reserved. Do not use.
|
||||||
|
// If not specified, defaults to ""
|
||||||
|
func ConfigureDistributedTPUEmbeddingConfig(value string) ConfigureDistributedTPUAttr {
|
||||||
|
return func(m optionalAttr) {
|
||||||
|
m["embedding_config"] = value
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// ConfigureDistributedTPUTpuEmbeddingConfig sets the optional tpu_embedding_config attribute to value.
|
||||||
|
//
|
||||||
|
// value: Serialized tensorflow.tpu.TPUEmbeddingConfiguration that
|
||||||
|
// describes the embedding lookups of the program.
|
||||||
|
// If not specified, defaults to ""
|
||||||
|
func ConfigureDistributedTPUTpuEmbeddingConfig(value string) ConfigureDistributedTPUAttr {
|
||||||
|
return func(m optionalAttr) {
|
||||||
|
m["tpu_embedding_config"] = value
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// ConfigureDistributedTPUIsGlobalInit sets the optional is_global_init attribute to value.
|
||||||
|
//
|
||||||
|
// value: Reserved. Do not use.
|
||||||
|
// If not specified, defaults to false
|
||||||
|
func ConfigureDistributedTPUIsGlobalInit(value bool) ConfigureDistributedTPUAttr {
|
||||||
|
return func(m optionalAttr) {
|
||||||
|
m["is_global_init"] = value
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// ConfigureDistributedTPUEnableWholeMeshCompilations sets the optional enable_whole_mesh_compilations attribute to value.
|
||||||
|
// If not specified, defaults to false
|
||||||
|
func ConfigureDistributedTPUEnableWholeMeshCompilations(value bool) ConfigureDistributedTPUAttr {
|
||||||
|
return func(m optionalAttr) {
|
||||||
|
m["enable_whole_mesh_compilations"] = value
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// ConfigureDistributedTPUCompilationFailureClosesChips sets the optional compilation_failure_closes_chips attribute to value.
|
||||||
|
// If not specified, defaults to true
|
||||||
|
func ConfigureDistributedTPUCompilationFailureClosesChips(value bool) ConfigureDistributedTPUAttr {
|
||||||
|
return func(m optionalAttr) {
|
||||||
|
m["compilation_failure_closes_chips"] = value
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Sets up the centralized structures for a distributed TPU system.
|
||||||
|
//
|
||||||
|
// Returns A serialized tensorflow.tpu.TopologyProto that describes the TPU
|
||||||
|
// topology.
|
||||||
|
func ConfigureDistributedTPU(scope *Scope, optional ...ConfigureDistributedTPUAttr) (topology tf.Output) {
|
||||||
|
if scope.Err() != nil {
|
||||||
|
return
|
||||||
|
}
|
||||||
|
attrs := map[string]interface{}{}
|
||||||
|
for _, a := range optional {
|
||||||
|
a(attrs)
|
||||||
|
}
|
||||||
|
opspec := tf.OpSpec{
|
||||||
|
Type: "ConfigureDistributedTPU",
|
||||||
|
|
||||||
|
Attrs: attrs,
|
||||||
|
}
|
||||||
|
op := scope.AddOperation(opspec)
|
||||||
|
return op.Output(0)
|
||||||
|
}
|
||||||
|
|
||||||
// Combines (nests of) input elements into a dataset of (nests of) windows.
|
// Combines (nests of) input elements into a dataset of (nests of) windows.
|
||||||
//
|
//
|
||||||
// A "window" is a finite dataset of flat elements of size `size` (or possibly
|
// A "window" is a finite dataset of flat elements of size `size` (or possibly
|
||||||
|
|
@ -48908,124 +49026,6 @@ func RetrieveTPUEmbeddingFTRLParameters(scope *Scope, num_shards int64, shard_id
|
||||||
return op.Output(0), op.Output(1), op.Output(2)
|
return op.Output(0), op.Output(1), op.Output(2)
|
||||||
}
|
}
|
||||||
|
|
||||||
// LRNGradAttr is an optional argument to LRNGrad.
|
|
||||||
type LRNGradAttr func(optionalAttr)
|
|
||||||
|
|
||||||
// LRNGradDepthRadius sets the optional depth_radius attribute to value.
|
|
||||||
//
|
|
||||||
// value: A depth radius.
|
|
||||||
// If not specified, defaults to 5
|
|
||||||
func LRNGradDepthRadius(value int64) LRNGradAttr {
|
|
||||||
return func(m optionalAttr) {
|
|
||||||
m["depth_radius"] = value
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// LRNGradBias sets the optional bias attribute to value.
|
|
||||||
//
|
|
||||||
// value: An offset (usually > 0 to avoid dividing by 0).
|
|
||||||
// If not specified, defaults to 1
|
|
||||||
func LRNGradBias(value float32) LRNGradAttr {
|
|
||||||
return func(m optionalAttr) {
|
|
||||||
m["bias"] = value
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// LRNGradAlpha sets the optional alpha attribute to value.
|
|
||||||
//
|
|
||||||
// value: A scale factor, usually positive.
|
|
||||||
// If not specified, defaults to 1
|
|
||||||
func LRNGradAlpha(value float32) LRNGradAttr {
|
|
||||||
return func(m optionalAttr) {
|
|
||||||
m["alpha"] = value
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// LRNGradBeta sets the optional beta attribute to value.
|
|
||||||
//
|
|
||||||
// value: An exponent.
|
|
||||||
// If not specified, defaults to 0.5
|
|
||||||
func LRNGradBeta(value float32) LRNGradAttr {
|
|
||||||
return func(m optionalAttr) {
|
|
||||||
m["beta"] = value
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// Gradients for Local Response Normalization.
|
|
||||||
//
|
|
||||||
// Arguments:
|
|
||||||
// input_grads: 4-D with shape `[batch, height, width, channels]`.
|
|
||||||
// input_image: 4-D with shape `[batch, height, width, channels]`.
|
|
||||||
// output_image: 4-D with shape `[batch, height, width, channels]`.
|
|
||||||
//
|
|
||||||
// Returns The gradients for LRN.
|
|
||||||
func LRNGrad(scope *Scope, input_grads tf.Output, input_image tf.Output, output_image tf.Output, optional ...LRNGradAttr) (output tf.Output) {
|
|
||||||
if scope.Err() != nil {
|
|
||||||
return
|
|
||||||
}
|
|
||||||
attrs := map[string]interface{}{}
|
|
||||||
for _, a := range optional {
|
|
||||||
a(attrs)
|
|
||||||
}
|
|
||||||
opspec := tf.OpSpec{
|
|
||||||
Type: "LRNGrad",
|
|
||||||
Input: []tf.Input{
|
|
||||||
input_grads, input_image, output_image,
|
|
||||||
},
|
|
||||||
Attrs: attrs,
|
|
||||||
}
|
|
||||||
op := scope.AddOperation(opspec)
|
|
||||||
return op.Output(0)
|
|
||||||
}
|
|
||||||
|
|
||||||
// PrelinearizeAttr is an optional argument to Prelinearize.
|
|
||||||
type PrelinearizeAttr func(optionalAttr)
|
|
||||||
|
|
||||||
// PrelinearizeShape sets the optional shape attribute to value.
|
|
||||||
//
|
|
||||||
// value: The shape of the tensor.
|
|
||||||
// If not specified, defaults to <>
|
|
||||||
func PrelinearizeShape(value tf.Shape) PrelinearizeAttr {
|
|
||||||
return func(m optionalAttr) {
|
|
||||||
m["shape"] = value
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// PrelinearizeLayout sets the optional layout attribute to value.
|
|
||||||
//
|
|
||||||
// value: A vector holding the requested layout in minor-to-major sequence. If a layout
|
|
||||||
// attribute is passed but its values are all -1 the layout will be computed by
|
|
||||||
// the infeed operation.
|
|
||||||
// If not specified, defaults to <>
|
|
||||||
func PrelinearizeLayout(value []int64) PrelinearizeAttr {
|
|
||||||
return func(m optionalAttr) {
|
|
||||||
m["layout"] = value
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// An op which linearizes one Tensor value to an opaque variant tensor.
|
|
||||||
//
|
|
||||||
// Arguments:
|
|
||||||
// input: A tensor that will be linearized.
|
|
||||||
func Prelinearize(scope *Scope, input tf.Output, optional ...PrelinearizeAttr) (output tf.Output) {
|
|
||||||
if scope.Err() != nil {
|
|
||||||
return
|
|
||||||
}
|
|
||||||
attrs := map[string]interface{}{}
|
|
||||||
for _, a := range optional {
|
|
||||||
a(attrs)
|
|
||||||
}
|
|
||||||
opspec := tf.OpSpec{
|
|
||||||
Type: "Prelinearize",
|
|
||||||
Input: []tf.Input{
|
|
||||||
input,
|
|
||||||
},
|
|
||||||
Attrs: attrs,
|
|
||||||
}
|
|
||||||
op := scope.AddOperation(opspec)
|
|
||||||
return op.Output(0)
|
|
||||||
}
|
|
||||||
|
|
||||||
// Returns the result of a TPU compilation.
|
// Returns the result of a TPU compilation.
|
||||||
//
|
//
|
||||||
// This operation returns the result of a TPU compilation as a serialized
|
// This operation returns the result of a TPU compilation as a serialized
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue