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Go: Update generated wrapper functions for TensorFlow ops.

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PiperOrigin-RevId: 238725068
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A. Unique TensorFlower 2019-03-15 15:48:17 -07:00 committed by TensorFlower Gardener
parent 4f086f4c0f
commit 623e667121
1 changed files with 27 additions and 0 deletions
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tensorflow/go/op/wrappers.go
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@ -126,6 +126,15 @@ func FakeQuantWithMinMaxVarsPerChannelNarrowRange(value bool) FakeQuantWithMinMa
// then de-quantized and output as floats in `[min; max]` interval. // then de-quantized and output as floats in `[min; max]` interval.
// `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive. // `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive.
// //
// Before quantization, `min` and `max` values are adjusted with the following
// logic.
// It is suggested to have `min <= 0 <= max`. If `0` is not in the range of values,
// the behavior can be unexpected:
// If `0 < min < max`: `min_adj = 0` and `max_adj = max - min`.
// If `min < max < 0`: `min_adj = min - max` and `max_adj = 0`.
// If `min <= 0 <= max`: `scale = (max - min) / (2^num_bits - 1) `,
// `min_adj = scale * round(min / scale)` and `max_adj = max + min_adj - min`.
//
// This operation has a gradient and thus allows for training `min` and `max` // This operation has a gradient and thus allows for training `min` and `max`
// values. // values.
func FakeQuantWithMinMaxVarsPerChannel(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsPerChannelAttr) (outputs tf.Output) { func FakeQuantWithMinMaxVarsPerChannel(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsPerChannelAttr) (outputs tf.Output) {
@ -307,6 +316,15 @@ func FakeQuantWithMinMaxArgsNarrowRange(value bool) FakeQuantWithMinMaxArgsAttr
// then de-quantized and output as floats in `[min; max]` interval. // then de-quantized and output as floats in `[min; max]` interval.
// `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive. // `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive.
// //
// Before quantization, `min` and `max` values are adjusted with the following
// logic.
// It is suggested to have `min <= 0 <= max`. If `0` is not in the range of values,
// the behavior can be unexpected:
// If `0 < min < max`: `min_adj = 0` and `max_adj = max - min`.
// If `min < max < 0`: `min_adj = min - max` and `max_adj = 0`.
// If `min <= 0 <= max`: `scale = (max - min) / (2^num_bits - 1) `,
// `min_adj = scale * round(min / scale)` and `max_adj = max + min_adj - min`.
//
// Quantization is called fake since the output is still in floating point. // Quantization is called fake since the output is still in floating point.
func FakeQuantWithMinMaxArgs(scope *Scope, inputs tf.Output, optional ...FakeQuantWithMinMaxArgsAttr) (outputs tf.Output) { func FakeQuantWithMinMaxArgs(scope *Scope, inputs tf.Output, optional ...FakeQuantWithMinMaxArgsAttr) (outputs tf.Output) {
if scope.Err() != nil { if scope.Err() != nil {
@ -20642,6 +20660,15 @@ func FakeQuantWithMinMaxVarsNarrowRange(value bool) FakeQuantWithMinMaxVarsAttr
// then de-quantized and output as floats in `[min; max]` interval. // then de-quantized and output as floats in `[min; max]` interval.
// `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive. // `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive.
// //
// Before quantization, `min` and `max` values are adjusted with the following
// logic.
// It is suggested to have `min <= 0 <= max`. If `0` is not in the range of values,
// the behavior can be unexpected:
// If `0 < min < max`: `min_adj = 0` and `max_adj = max - min`.
// If `min < max < 0`: `min_adj = min - max` and `max_adj = 0`.
// If `min <= 0 <= max`: `scale = (max - min) / (2^num_bits - 1) `,
// `min_adj = scale * round(min / scale)` and `max_adj = max + min_adj - min`.
//
// This operation has a gradient and thus allows for training `min` and `max` // This operation has a gradient and thus allows for training `min` and `max`
// values. // values.
func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsAttr) (outputs tf.Output) { func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsAttr) (outputs tf.Output) {