8.1.1.8.1.1. `blueoil.quantizations.binary`¶

8.1.1.8.1.1.1. Module Contents¶

8.1.1.8.1.1.1.1. Functions¶

`binary_channel_wise_mean_scaling_quantizer`(backward=None, dtype=tf.float32)	Binary channel wise mean scaling quantizer.
`binary_mean_scaling_quantizer`(backward=None, dtype=tf.float32)	Binary mean scaling quantizer.

blueoil.quantizations.binary.binary_channel_wise_mean_scaling_quantizer(backward=None, dtype=tf.float32)¶

Binary channel wise mean scaling quantizer.

This quantization creates a binary channel wise mean scaling quantizer. If backward is provided, this backward will be used in backpropagation.

This method is varient of XNOR-Net 1 weight quantization, the difference from XNOR-Net 1 is backward function.

op_type is BinaryChannelWiseMeanScalingQuantizer.

Forward is:

\[\begin{split}\begin{align} \bar{\mathbf{x}} & = \frac{1}{n}||\mathbf{X}||_{\ell1} & \text{$\bar{\mathbf{x}}$ is a $c$-channels vector} \\ & & \text{$n$ is number of elements in each channel of $\mathbf{X}$} \\\\ \mathbf{Y} & = \text{sign}\big(\mathbf{X}\big) \times \bar{\mathbf{x}} &\\ \end{align}\end{split}\]

Default backward is:

\[\frac{\partial Loss}{\partial \mathbf{X}} = \frac{\partial Loss}{\partial \mathbf{Y}}\]

Parameters

backward (callable) – Be used in backpropagation.
dtype (tf.DType) – Define the data type of args of forward and backward.

Returns

forward function (grad_func defined).

Return type

callable

Reference:

1(1,2): XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

blueoil.quantizations.binary.binary_mean_scaling_quantizer(backward=None, dtype=tf.float32)¶

Binary mean scaling quantizer.

This quantization creates a binary mean scaling quantizer. If backward is provided, this backward will be used in backpropagation.

This method is DoReFa-Net 2 weight quantization.

op_type is BinaryMeanScalingQuantizer.