Source code for tensorlayer.layers.dense.ternary_dense

#! /usr/bin/python
# -*- coding: utf-8 -*-

import tensorflow as tf
import tensorlayer as tl
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
from tensorlayer.layers.core import Layer
from tensorlayer.layers.utils import compute_alpha, ternary_operation

__all__ = [

[docs]class TernaryDense(Layer): """The :class:`TernaryDense` class is a ternary fully connected layer, which weights are either -1 or 1 or 0 while inference. Note that, the bias vector would not be tenaried. Parameters ---------- 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. in_channels: int The number of channels of the previous layer. If None, it will be automatically detected when the layer is forwarded for the first time. name : None or str A unique layer name. """ def __init__( self, n_units=100, act=None, use_gemm=False, W_init=tl.initializers.truncated_normal(stddev=0.05), b_init=tl.initializers.constant(value=0.0), in_channels=None, name=None, #'ternary_dense', ): super().__init__(name, act=act) self.n_units = n_units self.use_gemm = use_gemm self.W_init = W_init self.b_init = b_init self.in_channels = in_channels if self.in_channels is not None:, self.in_channels)) self._built = True "TernaryDense %s: %d %s" % (, n_units, self.act.__name__ if self.act is not None else 'No Activation') ) def __repr__(self): actstr = self.act.__name__ if self.act is not None else 'No Activation' s = ('{classname}(n_units={n_units}, ' + actstr) if self.in_channels is not None: s += ', in_channels=\'{in_channels}\'' if is not None: s += ', name=\'{name}\'' s += ')' return s.format(classname=self.__class__.__name__, **self.__dict__) def build(self, inputs_shape): if len(inputs_shape) != 2: raise Exception("The input dimension must be rank 2, please reshape or flatten it") if self.in_channels is None: self.in_channels = inputs_shape[1] if self.use_gemm: raise Exception("TODO. The current version use tf.matmul for inferencing.") n_in = inputs_shape[-1] self.W = self._get_weights(var_name="weights", shape=(n_in, self.n_units), init=self.W_init) if self.b_init is not None: self.b = self._get_weights(var_name="biases", shape=(self.n_units), init=self.b_init) def forward(self, inputs): # W = tl.act.sign(W) # dont update ... alpha = compute_alpha(self.W) W_ = ternary_operation(self.W) W_ = tf.multiply(alpha, W_) # W = tf.Variable(W) outputs = tf.matmul(inputs, W_) # self.outputs = xnor_gemm(self.inputs, W) # TODO if self.b_init is not None: outputs = tf.nn.bias_add(outputs, self.b, name='bias_add') if self.act: outputs = self.act(outputs) return outputs