diff options
| -rw-r--r-- | tensorflow/go/op/wrappers.go | 67 |
1 files changed, 2 insertions, 65 deletions
diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index 7cc11b6e19..93bd12c51d 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -601,8 +601,6 @@ func Copy(scope *Scope, input tf.Output, optional ...CopyAttr) (output tf.Output type QuantizeAndDequantizeAttr func(optionalAttr) // QuantizeAndDequantizeSignedInput sets the optional signed_input attribute to value. -// -// value: If the quantization is signed or unsigned. // If not specified, defaults to b:true func QuantizeAndDequantizeSignedInput(value bool) QuantizeAndDequantizeAttr { return func(m optionalAttr) { @@ -611,8 +609,6 @@ func QuantizeAndDequantizeSignedInput(value bool) QuantizeAndDequantizeAttr { } // QuantizeAndDequantizeNumBits sets the optional num_bits attribute to value. -// -// value: The bitwidth of the quantization. // If not specified, defaults to i:8 func QuantizeAndDequantizeNumBits(value int64) QuantizeAndDequantizeAttr { return func(m optionalAttr) { @@ -621,8 +617,6 @@ func QuantizeAndDequantizeNumBits(value int64) QuantizeAndDequantizeAttr { } // QuantizeAndDequantizeRangeGiven sets the optional range_given attribute to value. -// -// value: If the range is given or should be computed from the tensor. // If not specified, defaults to b:false func QuantizeAndDequantizeRangeGiven(value bool) QuantizeAndDequantizeAttr { return func(m optionalAttr) { @@ -631,8 +625,6 @@ func QuantizeAndDequantizeRangeGiven(value bool) QuantizeAndDequantizeAttr { } // QuantizeAndDequantizeInputMin sets the optional input_min attribute to value. -// -// value: If range is given, this is the min of the range. // If not specified, defaults to f:0 func QuantizeAndDequantizeInputMin(value float32) QuantizeAndDequantizeAttr { return func(m optionalAttr) { @@ -641,8 +633,6 @@ func QuantizeAndDequantizeInputMin(value float32) QuantizeAndDequantizeAttr { } // QuantizeAndDequantizeInputMax sets the optional input_max attribute to value. -// -// value: If range is given, this is the max of the range. // If not specified, defaults to f:0 func QuantizeAndDequantizeInputMax(value float32) QuantizeAndDequantizeAttr { return func(m optionalAttr) { @@ -650,62 +640,9 @@ func QuantizeAndDequantizeInputMax(value float32) QuantizeAndDequantizeAttr { } } -// Quantizes then dequantizes a tensor. -// -// DEPRECATED at GraphDef version 21: Replaced by QuantizeAndDequantizeV2 -// -// This op simulates the precision loss from the quantized forward pass by: -// 1. Quantizing the tensor to fixed point numbers, which should match the target -// quantization method when it is used in inference. -// 2. Dequantizing it back to floating point numbers for the following ops, most -// likely matmul. -// -// There are different ways to quantize. This version does not use the full range -// of the output type, choosing to elide the lowest possible value for symmetry -// (e.g., output range is -127 to 127, not -128 to 127 for signed 8 bit -// quantization), so that 0.0 maps to 0. -// -// To perform this op, we first find the range of values in our tensor. The range -// we use is always centered on 0, so we find m such that -// -// 1. m = max(abs(input_min), abs(input_max)) if range_given is true, -// 2. m = max(max(abs(min_elem(input)), abs(max_elem(input))) otherwise. -// -// Our input tensor range is then [-m, m]. -// -// Next, we choose our fixed-point quantization buckets, [min_fixed, max_fixed]. -// If signed_input is true, this is +// Use QuantizeAndDequantizeV2 instead. // -// [min_fixed, max_fixed ] = -// [-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1]. -// -// Otherwise, if signed_input is false, the fixed-point range is -// -// [min_fixed, max_fixed] = [0, (1 << num_bits) - 1]. -// -// From this we compute our scaling factor, s: -// -// s = (max_fixed - min_fixed) / (2 * m). -// -// Now we can quantize and dequantize the elements of our tensor. An element e -// is transformed into e': -// -// e' = (e * s).round_to_nearest() / s. -// -// Note that we have a different number of buckets in the signed vs. unsigned -// cases. For example, if num_bits == 8, we get 254 buckets in the signed case -// vs. 255 in the unsigned case. -// -// For example, suppose num_bits = 8 and m = 1. Then -// -// [min_fixed, max_fixed] = [-127, 127], and -// s = (127 + 127) / 2 = 127. -// -// Given the vector {-1, -0.5, 0, 0.3}, this is quantized to -// {-127, -63, 0, 38}, and dequantized to {-1, -63.0/127, 0, 38.0/127}. -// -// Arguments: -// input: Tensor to quantize and then dequantize. +// DEPRECATED at GraphDef version 22: Replaced by QuantizeAndDequantizeV2 func QuantizeAndDequantize(scope *Scope, input tf.Output, optional ...QuantizeAndDequantizeAttr) (output tf.Output) { if scope.Err() != nil { return |