#! /usr/bin/python
# -*- coding: utf-8 -*-
import numbers
import tensorflow as tf
import tensorlayer as tl
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
from tensorlayer.layers.core import Layer
__all__ = [
'DropconnectDense',
]
[docs]class DropconnectDense(Layer):
"""
The :class:`DropconnectDense` class is :class:`Dense` with DropConnect
behaviour which randomly removes connections between this layer and the previous
layer according to a keeping probability.
Parameters
----------
keep : float
The keeping probability.
The lower the probability it is, the more activations are set to zero.
n_units : int
The number of units of this layer.
act : activation function
The activation function of this layer.
W_init : weights initializer
The initializer for the weight matrix.
b_init : biases initializer
The initializer for the bias vector.
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 : str
A unique layer name.
Examples
--------
>>> net = tl.layers.Input([None, 784], name='input')
>>> net = tl.layers.DropconnectDense(keep=0.8,
... n_units=800, act=tf.nn.relu, name='relu1')(net)
>>> net = tl.layers.DropconnectDense(keep=0.5,
... n_units=800, act=tf.nn.relu, name='relu2')(net)
>>> net = tl.layers.DropconnectDense(keep=0.5,
... n_units=10, name='output')(net)
References
----------
- `Wan, L. (2013). Regularization of neural networks using dropconnect <http://machinelearning.wustl.edu/mlpapers/papers/icml2013_wan13>`__
"""
def __init__(
self,
keep=0.5,
n_units=100,
act=None,
W_init=tl.initializers.truncated_normal(stddev=0.05),
b_init=tl.initializers.constant(value=0.0),
in_channels=None,
name=None, # 'dropconnect',
):
super().__init__(name, act=act)
if isinstance(keep, numbers.Real) and not (keep > 0 and keep <= 1):
raise ValueError("keep must be a scalar tensor or a float in the " "range (0, 1], got %g" % keep)
self.keep = keep
self.n_units = n_units
self.W_init = W_init
self.b_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(
"DropconnectDense %s: %d %s" %
(self.name, 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)
s += ', keep={keep}'
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")
if self.in_channels is None:
self.in_channels = inputs_shape[1]
n_in = inputs_shape[-1]
self.W = self._get_weights("weights", shape=(n_in, self.n_units), init=self.W_init)
if self.b_init:
self.b = self._get_weights("biases", shape=(self.n_units), init=self.b_init)
def forward(self, inputs):
W_dropcon = tf.nn.dropout(self.W, 1 - (self.keep))
outputs = tf.matmul(inputs, W_dropcon)
if self.b_init:
outputs = tf.nn.bias_add(outputs, self.b, name='bias_add')
if self.act:
outputs = self.act(outputs)
return outputs