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tf.quantization.quantize_and_dequantize_v2

Quantizes then dequantizes a tensor.

Updates the gradient definition for quantization that is outside the range to be 0.To simulate the V1 the behavior of tf.quantization.quantize_and_dequantize(...) use tf.grad_pass_through(tf.quantization.quantize_and_dequantize_v2)(...).

Example usage:

def getQuantizeOp(input):
    input_tensor = tf.placeholder(tf.float32, shape=[4, 4])
    net = tf.quantization.quantize_and_dequantize(input,
                                                  input_min=min_threshold,
                                                  input_max=max_threshold,
                                                  range_given=True)

To simulate v1 behavior:

def testDecomposeQuantizeDequantize(self):
    def f(input_tensor):
      return tf.quantization.quantize_and_dequantize_v2(input_tensor,
                                                        input_min = 5.0,
                                                        input_max= -10.0,
                                                        range_given=True)
    input_tensor = tf.placeholder(tf.float32, shape=[4, 4])
    net = tf.grad_pass_through(f)(input_tensor)

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.

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