#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorlayerx as tlx
from tensorlayerx import logging
from tensorlayerx.nn.core import Module
__all__ = [
'TernaryDense',
]
[docs]class TernaryDense(Module):
"""The :class:`TernaryDense` class is a ternary fully connected layer, which weights are either -1 or 1 or 0 while inference.
# TODO The TernaryDense only supports TensorFlow backend.
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 or str
The initializer for the weight matrix.
b_init : initializer or None or str
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='truncated_normal',
b_init='constant',
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 = self.str_to_init(W_init)
self.b_init = self.str_to_init(b_init)
self.in_channels = in_channels
if self.in_channels is not None:
self.build((None, self.in_channels))
self._built = True
logging.info(
"TernaryDense %s: %d %s" %
(self.name, n_units, self.act.__class__.__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 self.name 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)
self.b = None
if self.b_init is not None:
self.b = self._get_weights(var_name="biases", shape=(self.n_units), init=self.b_init)
self.ternary_dense = tlx.ops.TernaryDense(self.W, self.b)
def forward(self, inputs):
if self._forward_state == False:
if self._built == False:
self.build(tlx.get_tensor_shape(inputs))
self._built = True
self._forward_state = True
outputs = self.ternary_dense(inputs)
if self.act:
outputs = self.act(outputs)
return outputs