GCNLayer.py

# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
_LAYER_UIDS = {}


def uniform(shape, scale=0.05, name=None):
    """Uniform init."""
    initial = tf.random_uniform(shape, minval=-scale, maxval=scale, dtype=tf.float32)
    return tf.Variable(initial, name=name)
def zeros(shape, name=None):
    """All zeros."""
    initial = tf.zeros(shape, dtype=tf.float32)
    return tf.Variable(initial, name=name)
def ones(shape, name=None):
    """All ones."""
    initial = tf.ones(shape, dtype=tf.float32)
    return tf.Variable(initial, name=name)
def glorot(shape, name=None):
    """Glorot & Bengio (AISTATS 2010) init."""
    init_range = np.sqrt(6.0/(shape[0]+shape[1]))
    initial = tf.random_uniform(shape, minval=-init_range, maxval=init_range, dtype=tf.float32)
    return tf.Variable(initial, name=name)
def weight_variable(shape, name):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial, name = name)
def bias_variable(shape, name):
    initial = tf.constant(0.01, shape=shape)
    return tf.Variable(initial, name = name)
def get_layer_uid(layer_name=''):
    """Helper function, assigns unique layer IDs."""
    if layer_name not in _LAYER_UIDS:
        _LAYER_UIDS[layer_name] = 1
        return 1
    else:
        _LAYER_UIDS[layer_name] += 1
        return _LAYER_UIDS[layer_name]


def sparse_dropout(x, keep_prob, noise_shape):
    """Dropout for sparse tensors."""
    random_tensor = keep_prob
    random_tensor += tf.random_uniform(noise_shape)
    dropout_mask = tf.cast(tf.floor(random_tensor), dtype=tf.bool)
    pre_out = tf.sparse_retain(x, dropout_mask)
    return pre_out * (1./keep_prob)


def dot(x, y, sparse=False):
    """Wrapper for tf.matmul (sparse vs dense)."""
    if sparse:
        res = tf.sparse_tensor_dense_matmul(x, y)
    else:
        res = tf.matmul(x, y)
    return res

    


  
class Layer(object):
    """Base layer class. Defines basic API for all layer objects.
    Implementation inspired by keras (http://keras.io).

    # Properties
        name: String, defines the variable scope of the layer.
        logging: Boolean, switches Tensorflow histogram logging on/off

    # Methods
        _call(inputs): Defines computation graph of layer
            (i.e. takes input, returns output)
        __call__(inputs): Wrapper for _call()
        _log_vars(): Log all variables
    """

    def __init__(self, **kwargs):
        allowed_kwargs = {'name', 'logging'}
        for kwarg in kwargs.keys():
            assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg
        name = kwargs.get('name')
        if not name:
            layer = self.__class__.__name__.lower()
            name = layer + '_' + str(get_layer_uid(layer))
        self.name = name
        self.vars = {}
        logging = kwargs.get('logging', False)
        self.logging = logging
        self.sparse_inputs = False

    def _call(self, inputs):
        return inputs

    def __call__(self, inputs):
        with tf.name_scope(self.name):
            if self.logging and not self.sparse_inputs:
                tf.summary.histogram(self.name + '/inputs', inputs)
            outputs = self._call(inputs)
            if self.logging:
                tf.summary.histogram(self.name + '/outputs', outputs)
            return outputs

    def _log_vars(self):
        for var in self.vars:
            tf.summary.histogram(self.name + '/vars/' + var, self.vars[var])
      
class GraphConvolution(Layer):
    """Graph convolution layer."""
    def __init__(self, input_dim, output_dim, support,  act=tf.nn.softplus, bias=False,
                 isnorm=False, isSparse = False,   **kwargs):
        super(GraphConvolution, self).__init__(**kwargs)
        self.act = act
        self.support = support#DAD
        self.bias = bias
        self.isnorm = isnorm
        self.isSparse = isSparse
        # helper variable for sparse dropout

        with tf.variable_scope(self.name + '_vars'):
            for i in range(1):
                self.vars['weights_' + str(i)] = uniform([input_dim, output_dim],
                                                        name='weights_' + str(i))
            if self.bias:
                self.vars['bias'] = uniform([output_dim], name='bias')

        if self.logging:
            self._log_vars()

    def _call(self, inputs):
        x = inputs
        supports = list()
        pre_sup = dot(x, self.vars['weights_' + str(0)], sparse = self.isSparse)
        support = dot( self.support, pre_sup, sparse=False )

        
        supports.append(support)
        output = tf.add_n(supports)

        # bias
        if self.bias:
            output += self.vars['bias']
        if self.isnorm==True:
            output = tf.nn.l2_normalize(output, dim=0)
        return self.act(output)