diff options
author | A. Unique TensorFlower <gardener@tensorflow.org> | 2017-05-24 14:40:05 -0700 |
---|---|---|
committer | TensorFlower Gardener <gardener@tensorflow.org> | 2017-05-24 14:43:49 -0700 |
commit | 6250204a85445d76de75b9963681c5957a656ed0 (patch) | |
tree | 64ee632b8c4fbf4a5718f4308ba15adba04e14d2 | |
parent | c5076bcea990d5963623af05235c8eb2bf1d7cec (diff) |
Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 157039326
-rw-r--r-- | tensorflow/go/op/wrappers.go | 198 |
1 files changed, 121 insertions, 77 deletions
diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index 1ecab3e8e2..60d08d3c10 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -2326,83 +2326,6 @@ func ParallelConcat(scope *Scope, values []tf.Output, shape tf.Shape) (output tf return op.Output(0) } -// MfccAttr is an optional argument to Mfcc. -type MfccAttr func(optionalAttr) - -// MfccUpperFrequencyLimit sets the optional upper_frequency_limit attribute to value. -// -// value: The highest frequency to use when calculating the -// ceptstrum. -// If not specified, defaults to 4000 -func MfccUpperFrequencyLimit(value float32) MfccAttr { - return func(m optionalAttr) { - m["upper_frequency_limit"] = value - } -} - -// MfccLowerFrequencyLimit sets the optional lower_frequency_limit attribute to value. -// -// value: The lowest frequency to use when calculating the -// ceptstrum. -// If not specified, defaults to 20 -func MfccLowerFrequencyLimit(value float32) MfccAttr { - return func(m optionalAttr) { - m["lower_frequency_limit"] = value - } -} - -// MfccFilterbankChannelCount sets the optional filterbank_channel_count attribute to value. -// -// value: Resolution of the Mel bank used internally. -// If not specified, defaults to 40 -func MfccFilterbankChannelCount(value int64) MfccAttr { - return func(m optionalAttr) { - m["filterbank_channel_count"] = value - } -} - -// MfccDctCoefficientCount sets the optional dct_coefficient_count attribute to value. -// -// value: How many output channels to produce per time slice. -// If not specified, defaults to 13 -func MfccDctCoefficientCount(value int64) MfccAttr { - return func(m optionalAttr) { - m["dct_coefficient_count"] = value - } -} - -// Transforms a spectrogram into a form that's useful for speech recognition. -// -// Mel Frequency Cepstral Coefficients are a way of representing audio data that's -// been effective as an input feature for machine learning. They are created by -// taking the spectrum of a spectrogram (a 'cepstrum'), and discarding some of the -// higher frequencies that are less significant to the human ear. They have a long -// history in the speech recognition world, and https://en.wikipedia.org/wiki/Mel-frequency_cepstrum -// is a good resource to learn more. -// -// Arguments: -// spectrogram: Typically produced by the Spectrogram op, with magnitude_squared -// set to true. -// sample_rate: How many samples per second the source audio used. -func Mfcc(scope *Scope, spectrogram tf.Output, sample_rate tf.Output, optional ...MfccAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Mfcc", - Input: []tf.Input{ - spectrogram, sample_rate, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // UniqueAttr is an optional argument to Unique. type UniqueAttr func(optionalAttr) @@ -19411,6 +19334,127 @@ func Atan(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } +// MfccAttr is an optional argument to Mfcc. +type MfccAttr func(optionalAttr) + +// MfccUpperFrequencyLimit sets the optional upper_frequency_limit attribute to value. +// +// value: The highest frequency to use when calculating the +// ceptstrum. +// If not specified, defaults to 4000 +func MfccUpperFrequencyLimit(value float32) MfccAttr { + return func(m optionalAttr) { + m["upper_frequency_limit"] = value + } +} + +// MfccLowerFrequencyLimit sets the optional lower_frequency_limit attribute to value. +// +// value: The lowest frequency to use when calculating the +// ceptstrum. +// If not specified, defaults to 20 +func MfccLowerFrequencyLimit(value float32) MfccAttr { + return func(m optionalAttr) { + m["lower_frequency_limit"] = value + } +} + +// MfccFilterbankChannelCount sets the optional filterbank_channel_count attribute to value. +// +// value: Resolution of the Mel bank used internally. +// If not specified, defaults to 40 +func MfccFilterbankChannelCount(value int64) MfccAttr { + return func(m optionalAttr) { + m["filterbank_channel_count"] = value + } +} + +// MfccDctCoefficientCount sets the optional dct_coefficient_count attribute to value. +// +// value: How many output channels to produce per time slice. +// If not specified, defaults to 13 +func MfccDctCoefficientCount(value int64) MfccAttr { + return func(m optionalAttr) { + m["dct_coefficient_count"] = value + } +} + +// Transforms a spectrogram into a form that's useful for speech recognition. +// +// Mel Frequency Cepstral Coefficients are a way of representing audio data that's +// been effective as an input feature for machine learning. They are created by +// taking the spectrum of a spectrogram (a 'cepstrum'), and discarding some of the +// higher frequencies that are less significant to the human ear. They have a long +// history in the speech recognition world, and https://en.wikipedia.org/wiki/Mel-frequency_cepstrum +// is a good resource to learn more. +// +// Arguments: +// spectrogram: Typically produced by the Spectrogram op, with magnitude_squared +// set to true. +// sample_rate: How many samples per second the source audio used. +func Mfcc(scope *Scope, spectrogram tf.Output, sample_rate tf.Output, optional ...MfccAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Mfcc", + Input: []tf.Input{ + spectrogram, sample_rate, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QuantizedAddAttr is an optional argument to QuantizedAdd. +type QuantizedAddAttr func(optionalAttr) + +// QuantizedAddToutput sets the optional Toutput attribute to value. +// If not specified, defaults to DT_QINT32 +func QuantizedAddToutput(value tf.DataType) QuantizedAddAttr { + return func(m optionalAttr) { + m["Toutput"] = value + } +} + +// Returns x + y element-wise, working on quantized buffers. +// +// Arguments: +// +// +// min_x: The float value that the lowest quantized `x` value represents. +// max_x: The float value that the highest quantized `x` value represents. +// min_y: The float value that the lowest quantized `y` value represents. +// max_y: The float value that the highest quantized `y` value represents. +// +// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents. +// +// *NOTE*: `QuantizedAdd` supports limited forms of broadcasting. More about +// broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func QuantizedAdd(scope *Scope, x tf.Output, y tf.Output, min_x tf.Output, max_x tf.Output, min_y tf.Output, max_y tf.Output, optional ...QuantizedAddAttr) (z tf.Output, min_z tf.Output, max_z tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedAdd", + Input: []tf.Input{ + x, y, min_x, max_x, min_y, max_y, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + // ResourceSparseApplyAdadeltaAttr is an optional argument to ResourceSparseApplyAdadelta. type ResourceSparseApplyAdadeltaAttr func(optionalAttr) |