110 lines
3.6 KiB
Python
110 lines
3.6 KiB
Python
#! /usr/bin/python
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# -*- coding: utf-8 -*-
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import tensorlayer as tl
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from tensorlayer import logging
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from tensorlayer.layers.core import Module
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from tensorlayer.layers.utils import compute_alpha, ternary_operation
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__all__ = [
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'TernaryDense',
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]
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class TernaryDense(Module):
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"""The :class:`TernaryDense` class is a ternary fully connected layer, which weights are either -1 or 1 or 0 while inference.
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# TODO The TernaryDense only supports TensorFlow backend.
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Note that, the bias vector would not be tenaried.
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Parameters
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----------
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n_units : int
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The number of units of this layer.
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act : activation function
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The activation function of this layer, usually set to ``tf.act.sign`` or apply :class:`SignLayer` after :class:`BatchNormLayer`.
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use_gemm : boolean
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If True, use gemm instead of ``tf.matmul`` for inference. (TODO).
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W_init : initializer
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The initializer for the weight matrix.
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b_init : initializer or None
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The initializer for the bias vector. If None, skip biases.
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in_channels: int
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The number of channels of the previous layer.
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If None, it will be automatically detected when the layer is forwarded for the first time.
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name : None or str
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A unique layer name.
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"""
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def __init__(
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self,
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n_units=100,
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act=None,
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use_gemm=False,
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W_init=tl.initializers.truncated_normal(stddev=0.05),
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b_init=tl.initializers.constant(value=0.0),
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in_channels=None,
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name=None, #'ternary_dense',
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):
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super().__init__(name, act=act)
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self.n_units = n_units
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self.use_gemm = use_gemm
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self.W_init = W_init
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self.b_init = b_init
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self.in_channels = in_channels
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if self.in_channels is not None:
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self.build((None, self.in_channels))
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self._built = True
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logging.info(
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"TernaryDense %s: %d %s" %
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(self.name, n_units, self.act.__name__ if self.act is not None else 'No Activation')
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)
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def __repr__(self):
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actstr = self.act.__name__ if self.act is not None else 'No Activation'
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s = ('{classname}(n_units={n_units}, ' + actstr)
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if self.in_channels is not None:
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s += ', in_channels=\'{in_channels}\''
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if self.name is not None:
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s += ', name=\'{name}\''
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s += ')'
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return s.format(classname=self.__class__.__name__, **self.__dict__)
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def build(self, inputs_shape):
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if len(inputs_shape) != 2:
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raise Exception("The input dimension must be rank 2, please reshape or flatten it")
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if self.in_channels is None:
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self.in_channels = inputs_shape[1]
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if self.use_gemm:
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raise Exception("TODO. The current version use tf.matmul for inferencing.")
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n_in = inputs_shape[-1]
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self.W = self._get_weights(var_name="weights", shape=(n_in, self.n_units), init=self.W_init)
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if self.b_init is not None:
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self.b = self._get_weights(var_name="biases", shape=(self.n_units), init=self.b_init)
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def forward(self, inputs):
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if self._forward_state == False:
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if self._built == False:
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self.build(tl.get_tensor_shape(inputs))
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self._built = True
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self._forward_state = True
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alpha = compute_alpha(self.W)
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W_ = ternary_operation(self.W)
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W_ = tl.ops.multiply(alpha, W_)
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outputs = tl.ops.matmul(inputs, W_)
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if self.b_init is not None:
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outputs = tl.ops.bias_add(outputs, self.b, name='bias_add')
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if self.act:
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outputs = self.act(outputs)
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return outputs
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