Source code for tensorlayer.layers.dense.dropconnect

#! /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__ = [

[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 <>`__ """ 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.in_channels)) self._built = True "DropconnectDense %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) s += ', keep={keep}' 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") 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