Source code for tensorlayer.layers.dense.binary_dense

#! /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__ = [

[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) "BinaryDenseLayer %s: %d %s" % (, 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)