tf.quantization.quantize_and_dequantize

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Quantizes then dequantizes a tensor.

tf.quantization.quantize_and_dequantize(
    input, input_min, input_max, signed_input=True, num_bits=8, range_given=False,
    round_mode='HALF_TO_EVEN', name=None, narrow_range=False, axis=None
)

Args:

• input: A Tensor to quantize and dequantize.
• input_min: If range_given=True, the minimum input value, that needs to be represented in the quantized representation. If axis is specified, this should be a vector of minimum values for each slice along axis.
• input_max: If range_given=True, the maximum input value that needs to be represented in the quantized representation. If axis is specified, this should be a vector of maximum values for each slice along axis.
• signed_input: True if the quantization is signed or unsigned.
• num_bits: The bitwidth of the quantization.
• range_given: If true use input_min and input_max for the range of the input, otherwise determine min and max from the input Tensor.
• round_mode: Rounding mode when rounding from float values to quantized ones. one of ['HALF_TO_EVEN', 'HALF_UP']
• name: Optional name for the operation.
• narrow_range: 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: Integer. If specified, refers to a dimension of the input tensor, such that quantization will be per slice along that dimension.

Returns:

A Tensor. Each element is the result of quantizing and dequantizing the corresponding element of input.