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from initializations import *
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import tensorflow as tf
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flags = tf.app.flags
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FLAGS = flags.FLAGS
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# global unique layer ID dictionary for layer name assignment
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_LAYER_UIDS = {}
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def get_layer_uid(layer_name=''):
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"""Helper function, assigns unique layer IDs
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分配唯一的层ID
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"""
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if layer_name not in _LAYER_UIDS:
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_LAYER_UIDS[layer_name] = 1
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return 1
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else:
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_LAYER_UIDS[layer_name] += 1
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return _LAYER_UIDS[layer_name]
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def dropout_sparse(x, keep_prob, num_nonzero_elems):
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"""
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Dropout for sparse tensors. Currently fails for very large sparse tensors (>1M elements)
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num_nonzero_elems: 稀疏矩阵中的非零元素个数
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keep_prob:
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x: input
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"""
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noise_shape = [num_nonzero_elems]
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random_tensor = keep_prob
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random_tensor += tf.random_uniform(noise_shape)
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dropout_mask = tf.cast(tf.floor(random_tensor), dtype=tf.bool)
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pre_out = tf.sparse_retain(x, dropout_mask)
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return pre_out * (1. / keep_prob)
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class Layer(object):
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"""Base layer class. Defines basic API for all layer objects.
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# Properties
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name: String, defines the variable scope of the layer.
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# Methods
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_call(inputs): Defines computation graph of layer
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(i.e. takes input, returns output)
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__call__(inputs): Wrapper for _call()
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"""
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def __init__(self, **kwargs):
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allowed_kwargs = {'name', 'logging'}
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for kwarg in kwargs.keys():
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assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg
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name = kwargs.get('name')
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if not name:
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layer = self.__class__.__name__.lower()
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name = layer + '_' + str(get_layer_uid(layer))
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self.name = name
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self.vars = {}
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logging = kwargs.get('logging', False)
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self.logging = logging
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self.issparse = False
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def _call(self, inputs):
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return inputs
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def __call__(self, inputs):
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with tf.name_scope(self.name):
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outputs = self._call(inputs)
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return outputs
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class GraphConvolution(Layer):
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"""Basic graph convolution layer for undirected graph without edge labels."""
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def __init__(self, input_dim, output_dim, adj, dropout=0., act=tf.nn.relu, **kwargs):
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super(GraphConvolution, self).__init__(**kwargs)
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with tf.variable_scope(self.name + '_vars'):
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self.vars['weights'] = weight_variable_glorot(input_dim, output_dim, name="weights")
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self.dropout = dropout
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self.adj = adj
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self.act = act
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def _call(self, inputs):
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x = inputs
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x = tf.nn.dropout(x, 1 - self.dropout)
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x = tf.matmul(x, self.vars['weights'])
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x = tf.sparse_tensor_dense_matmul(self.adj, x)
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outputs = self.act(x)
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return outputs
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class GraphConvolutionSparse(Layer):
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"""
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Graph convolution layer for sparse inputs.
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多了一个features_nonzero
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"""
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def __init__(self, input_dim, output_dim, adj, features_nonzero, dropout=0., act=tf.nn.relu, **kwargs):
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super(GraphConvolutionSparse, self).__init__(**kwargs)
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with tf.variable_scope(self.name + '_vars'):
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self.vars['weights'] = weight_variable_glorot(input_dim, output_dim, name="weights")
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self.dropout = dropout
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self.adj = adj
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self.act = act
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self.issparse = True
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self.features_nonzero = features_nonzero
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def _call(self, inputs):
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x = inputs
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x = dropout_sparse(x, 1 - self.dropout, self.features_nonzero)
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x = tf.sparse_tensor_dense_matmul(x, self.vars['weights'])
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x = tf.sparse_tensor_dense_matmul(self.adj, x)
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outputs = self.act(x)
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return outputs
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class InnerProductDecoder(Layer):
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"""Decoder model layer for link prediction."""
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def __init__(self, input_dim, dropout=0., act=tf.nn.sigmoid, **kwargs):
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super(InnerProductDecoder, self).__init__(**kwargs)
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self.dropout = dropout
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self.act = act
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def _call(self, inputs):
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"""
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这个decoder部分实际上就只是input的转置再乘input
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"""
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inputs = tf.nn.dropout(inputs, 1 - self.dropout)
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x = tf.transpose(inputs)
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x = tf.matmul(inputs, x)
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x = tf.reshape(x, [-1])
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outputs = self.act(x)
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return outputs
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class GraphConvolution_z2g(Layer):
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"""Basic graph convolution layer for undirected graph without edge labels."""
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def __init__(self, input_dim, output_dim, adj, dropout=0., act=tf.nn.relu, **kwargs):
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super(GraphConvolution, self).__init__(**kwargs)
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with tf.variable_scope(self.name + '_vars'):
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self.vars['weights'] = weight_variable_glorot(input_dim, output_dim, name="weights")
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self.dropout = dropout
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self.adj = adj
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self.act = act
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def _call(self, inputs):
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x = inputs
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x = tf.nn.dropout(x, 1 - self.dropout)
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x = tf.matmul(x, self.vars['weights'])
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x = tf.sparse_tensor_dense_matmul(self.adj, x)
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outputs = self.act(x)
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return outputs
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def _call(self, inputs):
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x = inputs
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x = dropout_sparse(x, 1 - self.dropout, self.features_nonzero)
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x = tf.sparse_tensor_dense_matmul(x, self.vars['weights'])
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x = tf.sparse_tensor_dense_matmul(self.adj, x)
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outputs = self.act(x)
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return outputs
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