#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorlayer.layers.core import Layer
from tensorlayer.layers.core import LayersConfig
from tensorlayer.layers.utils import quantize
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
__all__ = [
'BinaryDenseLayer',
]
[docs]class BinaryDenseLayer(Layer):
"""The :class:`BinaryDenseLayer` class is a binary fully connected layer, which weights are either -1 or 1 while inferencing.
Note that, the bias vector would not be binarized.
Parameters
----------
prev_layer : :class:`Layer`
Previous layer.
n_units : int
The number of units of this layer.
act : activation function
The activation function of this layer, usually set to ``tf.act.sign`` or apply :class:`SignLayer` after :class:`BatchNormLayer`.
use_gemm : boolean
If True, use gemm instead of ``tf.matmul`` for inference. (TODO).
W_init : initializer
The initializer for the weight matrix.
b_init : initializer or None
The initializer for the bias vector. If None, skip biases.
W_init_args : dictionary
The arguments for the weight matrix initializer.
b_init_args : dictionary
The arguments for the bias vector initializer.
name : a str
A unique layer name.
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self,
prev_layer,
n_units=100,
act=None,
use_gemm=False,
W_init=tf.truncated_normal_initializer(stddev=0.1),
b_init=tf.constant_initializer(value=0.0),
W_init_args=None,
b_init_args=None,
name='binary_dense',
):
super(BinaryDenseLayer, self
).__init__(prev_layer=prev_layer, act=act, W_init_args=W_init_args, b_init_args=b_init_args, name=name)
logging.info(
"BinaryDenseLayer %s: %d %s" %
(self.name, n_units, self.act.__name__ if self.act is not None else 'No Activation')
)
if self.inputs.get_shape().ndims != 2:
raise Exception("The input dimension must be rank 2, please reshape or flatten it")
if use_gemm:
raise Exception("TODO. The current version use tf.matmul for inferencing.")
n_in = int(self.inputs.get_shape()[-1])
self.n_units = n_units
with tf.variable_scope(name):
W = tf.get_variable(
name='W', shape=(n_in, n_units), initializer=W_init, dtype=LayersConfig.tf_dtype, **self.W_init_args
)
# W = tl.act.sign(W) # dont update ...
W = quantize(W)
# W = tf.Variable(W)
# print(W)
self.outputs = tf.matmul(self.inputs, W)
# self.outputs = xnor_gemm(self.inputs, W) # TODO
if b_init is not None:
try:
b = tf.get_variable(
name='b', shape=(n_units), initializer=b_init, dtype=LayersConfig.tf_dtype, **self.b_init_args
)
except Exception: # If initializer is a constant, do not specify shape.
b = tf.get_variable(name='b', initializer=b_init, dtype=LayersConfig.tf_dtype, **self.b_init_args)
self.outputs = tf.nn.bias_add(self.outputs, b, name='bias_add')
self.outputs = self._apply_activation(self.outputs)
self._add_layers(self.outputs)
if b_init is not None:
self._add_params([W, b])
else:
self._add_params(W)