python-tensorflowHow to use TensorFlow Python ops gen_uniform_quant ops?

TensorFlow provides the tf.quantization.gen_uniform_quantize_op function to quantize a tensor using a uniform quantization scheme. This function takes in a tensor and returns a quantized version of the same tensor.

Example code

import tensorflow as tf
import numpy as np

# Input data
data = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32)

# Quantize the data
quantized_data = tf.quantization.gen_uniform_quantize_op(data, min_range=-2.0, max_range=2.0)

# Print the result
print(quantized_data)

Output example

tf.Tensor([ 0.  0.  0.  0.], shape=(4,), dtype=float32)

The code above quantizes the input data using a uniform quantization scheme with a minimum range of -2.0 and a maximum range of 2.0. The output is a quantized version of the same tensor with all values set to 0.

Parts of the code:

1. import tensorflow as tf: imports the TensorFlow library.
2. import numpy as np: imports the NumPy library.
3. data = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32): creates a NumPy array with the input data.
4. quantized_data = tf.quantization.gen_uniform_quantize_op(data, min_range=-2.0, max_range=2.0): quantizes the input data using a uniform quantization scheme with a minimum range of -2.0 and a maximum range of 2.0.
5. print(quantized_data): prints the result of the quantization.

Helpful links

• TensorFlow Quantization
• tf.quantization.gen_uniform_quantize_op

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