Source code for tensorlayerx.nn.layers.linear.binary_linear

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

import tensorlayerx as tlx
from tensorlayerx import logging
from tensorlayerx.nn.core import Module

__all__ = [

[docs]class BinaryLinear(Module): """The :class:`BinaryLinear` 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 ---------- out_features : 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:`Sign` after :class:`BatchNorm`. 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_features: 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. Examples -------- >>> net = tlx.nn.Input([10, 784], name='input') >>> net = tlx.nn.BinaryLinear(out_features=800, act=tlx.ReLU, name='BinaryLinear1')(net) >>> output shape :(10, 800) >>> net = tlx.nn.BinaryLinear(out_features=10, name='BinaryLineart')(net) >>> output shape : (10, 10) """ def __init__( self, out_features=100, act=None, use_gemm=False, W_init='truncated_normal', b_init='constant', in_features=None, name=None, ): super().__init__(name, act=act) self.out_features = out_features 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_features = in_features if self.in_features is not None:, self.in_features)) self._built = True "BinaryDense %s: %d %s" % (, out_features, self.act.__class__.__name__ if self.act is not None else 'No Activation') ) def __repr__(self): actstr = self.act.__class__.__name__ if self.act is not None else 'No Activation' s = ('{classname}(out_features={out_features}, ' + actstr) if self.in_features is not None: s += ', in_features=\'{in_features}\'' 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_features is None: self.in_features = inputs_shape[1] if self.use_gemm: raise Exception("TODO. The current version use tf.matmul for inferencing.") n_in = inputs_shape[-1] self.weights = self._get_weights("weights", shape=(n_in, self.out_features), init=self.W_init) self.biases = None if self.b_init is not None: self.biases = self._get_weights("biases", shape=(self.out_features), init=self.b_init) self.bias_add = tlx.ops.BiasAdd() self.binary_dense = tlx.ops.BinaryDense(self.weights, self.biases) def forward(self, inputs): if self._forward_state == False: if self._built == False: self._built = True self._forward_state = True outputs = self.binary_dense(inputs) if self.act: outputs = self.act(outputs) if not self._nodes_fixed and self._build_graph: self._add_node(inputs, outputs) self._nodes_fixed = True return outputs