tf.raw_ops.QuantizeAndDequantizeV4
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Quantizes then dequantizes a tensor.
tf.raw_ops.QuantizeAndDequantizeV4(
input,
input_min,
input_max,
signed_input=True,
num_bits=8,
range_given=False,
round_mode='HALF_TO_EVEN',
narrow_range=False,
axis=-1,
name=None
)
This is almost identical to QuantizeAndDequantizeV2, except that it returns a
gradient of 1 for inputs that are within the quantization range, or 0 otherwise.
Args |
|---|
input
|
A Tensor. Must be one of the following types: bfloat16, half, float32, float64.
Tensor to quantize and then dequantize.
|
input_min
|
A Tensor. Must have the same type as input.
If range_given == True, this specifies the minimum input value that needs to
be represented, otherwise it is determined from the min value of the input
tensor.
|
input_max
|
A Tensor. Must have the same type as input.
If range_given == True, this specifies the maximum input value that needs to
be represented, otherwise it is determined from the max value of the input
tensor.
|
signed_input
|
An optional bool. Defaults to True.
Whether the quantization is signed or unsigned. (actually this parameter should
have been called signed_output)
|
num_bits
|
An optional int. Defaults to 8.
The bitwidth of the quantization.
|
range_given
|
An optional bool. Defaults to False.
Whether the range is given or should be determined from the input tensor.
|
round_mode
|
An optional string from: "HALF_TO_EVEN", "HALF_UP". Defaults to "HALF_TO_EVEN".
The 'round_mode' attribute controls which rounding tie-breaking algorithm is
used when rounding float values to their quantized equivalents. The following
rounding modes are currently supported:
- HALF_TO_EVEN: this is the default round_mode.
- HALF_UP: round towards positive. In this mode 7.5 rounds up to 8 and -7.5
rounds up to -7.
|
narrow_range
|
An optional bool. Defaults to False.
If True, then the absolute value of the quantized minimum value is the same as
the quantized maximum value, instead of 1 greater.
i.e. for 8 bit quantization, the minimum value is -127 instead of -128.
|
axis
|
An optional int. Defaults to -1.
If specified, this axis is treated as a channel or slice axis, and a separate
quantization range is used for each channel or slice along this axis.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor. Has the same type as input.
|
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf.raw_ops.QuantizeAndDequantizeV4\n\n\u003cbr /\u003e\n\nQuantizes then dequantizes a tensor.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.raw_ops.QuantizeAndDequantizeV4`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/QuantizeAndDequantizeV4)\n\n\u003cbr /\u003e\n\n tf.raw_ops.QuantizeAndDequantizeV4(\n input,\n input_min,\n input_max,\n signed_input=True,\n num_bits=8,\n range_given=False,\n round_mode='HALF_TO_EVEN',\n narrow_range=False,\n axis=-1,\n name=None\n )\n\nThis is almost identical to QuantizeAndDequantizeV2, except that it returns a\ngradient of 1 for inputs that are within the quantization range, or 0 otherwise.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `input` | A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`. Tensor to quantize and then dequantize. |\n| `input_min` | A `Tensor`. Must have the same type as `input`. If `range_given == True`, this specifies the minimum input value that needs to be represented, otherwise it is determined from the min value of the `input` tensor. |\n| `input_max` | A `Tensor`. Must have the same type as `input`. If `range_given == True`, this specifies the maximum input value that needs to be represented, otherwise it is determined from the max value of the `input` tensor. |\n| `signed_input` | An optional `bool`. Defaults to `True`. Whether the quantization is signed or unsigned. (actually this parameter should have been called **`signed_output`**) |\n| `num_bits` | An optional `int`. Defaults to `8`. The bitwidth of the quantization. |\n| `range_given` | An optional `bool`. Defaults to `False`. Whether the range is given or should be determined from the `input` tensor. |\n| `round_mode` | An optional `string` from: `\"HALF_TO_EVEN\", \"HALF_UP\"`. Defaults to `\"HALF_TO_EVEN\"`. The 'round_mode' attribute controls which rounding tie-breaking algorithm is used when rounding float values to their quantized equivalents. The following rounding modes are currently supported: \u003cbr /\u003e - HALF_TO_EVEN: this is the default round_mode. - HALF_UP: round towards positive. In this mode 7.5 rounds up to 8 and -7.5 rounds up to -7. |\n| `narrow_range` | An optional `bool`. Defaults to `False`. If True, then the absolute value of the quantized minimum value is the same as the quantized maximum value, instead of 1 greater. i.e. for 8 bit quantization, the minimum value is -127 instead of -128. |\n| `axis` | An optional `int`. Defaults to `-1`. If specified, this axis is treated as a channel or slice axis, and a separate quantization range is used for each channel or slice along this axis. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor`. Has the same type as `input`. ||\n\n\u003cbr /\u003e"]]
