experiments/STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Go: Update generated wrapper functions for TensorFlow ops.

Browse Source 
PiperOrigin-RevId: 158295169
This commit is contained in:
A. Unique TensorFlower 2017-06-07 11:38:51 -07:00 committed by TensorFlower Gardener
parent abe0877ef1
commit 492afc2e37
1 changed files with 26 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

26
tensorflow/go/op/wrappers.go
View File

@ -13463,6 +13463,11 @@ func Cross(scope *Scope, a tf.Output, b tf.Output) (product tf.Output) {
// to compute `input` is odd, it should be provided since it cannot be inferred // to compute `input` is odd, it should be provided since it cannot be inferred
// properly. // properly.
// //
// Along each axis `IRFFT2D` is computed on, if `fft_length` (or
// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the
// corresponding dimension of `input`, the dimension is cropped. If it is larger,
// the dimension is padded with zeros.
//
// Arguments: // Arguments:
// input: A complex64 tensor. // input: A complex64 tensor.
// fft_length: An int32 tensor of shape [2]. The FFT length for each dimension. // fft_length: An int32 tensor of shape [2]. The FFT length for each dimension.
@ -16691,6 +16696,10 @@ func Zeta(scope *Scope, x tf.Output, q tf.Output) (z tf.Output) {
// compute `input` is odd, it should be provided since it cannot be inferred // compute `input` is odd, it should be provided since it cannot be inferred
// properly. // properly.
// //
// Along the axis `IRFFT` is computed on, if `fft_length / 2 + 1` is smaller
// than the corresponding dimension of `input`, the dimension is cropped. If it is
// larger, the dimension is padded with zeros.
//
// Arguments: // Arguments:
// input: A complex64 tensor. // input: A complex64 tensor.
// fft_length: An int32 tensor of shape [1]. The FFT length. // fft_length: An int32 tensor of shape [1]. The FFT length.
@ -16874,6 +16883,10 @@ func AssignAddVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *
// `fft_length / 2 + 1` unique components of the FFT: the zero-frequency term, // `fft_length / 2 + 1` unique components of the FFT: the zero-frequency term,
// followed by the `fft_length / 2` positive-frequency terms. // followed by the `fft_length / 2` positive-frequency terms.
// //
// Along the axis `RFFT` is computed on, if `fft_length` is smaller than the
// corresponding dimension of `input`, the dimension is cropped. If it is larger,
// the dimension is padded with zeros.
//
// Arguments: // Arguments:
// input: A float32 tensor. // input: A float32 tensor.
// fft_length: An int32 tensor of shape [1]. The FFT length. // fft_length: An int32 tensor of shape [1]. The FFT length.
@ -17169,6 +17182,10 @@ func MaxPoolGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output,
// of `output`: the zero-frequency term, followed by the `fft_length / 2` // of `output`: the zero-frequency term, followed by the `fft_length / 2`
// positive-frequency terms. // positive-frequency terms.
// //
// Along each axis `RFFT3D` is computed on, if `fft_length` is smaller than the
// corresponding dimension of `input`, the dimension is cropped. If it is larger,
// the dimension is padded with zeros.
//
// Arguments: // Arguments:
// input: A float32 tensor. // input: A float32 tensor.
// fft_length: An int32 tensor of shape [3]. The FFT length for each dimension. // fft_length: An int32 tensor of shape [3]. The FFT length for each dimension.
@ -17514,6 +17531,11 @@ func StringSplit(scope *Scope, input tf.Output, delimiter tf.Output) (indices tf
// to compute `input` is odd, it should be provided since it cannot be inferred // to compute `input` is odd, it should be provided since it cannot be inferred
// properly. // properly.
// //
// Along each axis `IRFFT3D` is computed on, if `fft_length` (or
// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the
// corresponding dimension of `input`, the dimension is cropped. If it is larger,
// the dimension is padded with zeros.
//
// Arguments: // Arguments:
// input: A complex64 tensor. // input: A complex64 tensor.
// fft_length: An int32 tensor of shape [3]. The FFT length for each dimension. // fft_length: An int32 tensor of shape [3]. The FFT length for each dimension.
@ -18936,6 +18958,10 @@ func Erfc(scope *Scope, x tf.Output) (y tf.Output) {
// of `output`: the zero-frequency term, followed by the `fft_length / 2` // of `output`: the zero-frequency term, followed by the `fft_length / 2`
// positive-frequency terms. // positive-frequency terms.
// //
// Along each axis `RFFT2D` is computed on, if `fft_length` is smaller than the
// corresponding dimension of `input`, the dimension is cropped. If it is larger,
// the dimension is padded with zeros.
//
// Arguments: // Arguments:
// input: A float32 tensor. // input: A float32 tensor.
// fft_length: An int32 tensor of shape [2]. The FFT length for each dimension. // fft_length: An int32 tensor of shape [2]. The FFT length for each dimension.