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336 lines
12 KiB
336 lines
12 KiB
import numpy as np |
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import tensorflow as tf |
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class Model(object): |
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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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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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name = self.__class__.__name__.lower() |
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self.name = name |
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logging = kwargs.get('logging', False) |
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self.logging = logging |
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self.vars = {} |
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def _build(self): |
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raise NotImplementedError |
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def build(self): |
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""" Wrapper for _build() """ |
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with tf.variable_scope(self.name): |
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self._build() |
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variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name) |
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self.vars = {var.name: var for var in variables} |
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def fit(self): |
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pass |
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def predict(self): |
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pass |
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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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""" |
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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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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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def weight_variable_glorot(input_dim, output_dim, name=""): |
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"""Create a weight variable with Glorot & Bengio (AISTATS 2010) |
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initialization. |
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""" |
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init_range = np.sqrt(6.0 / (input_dim + output_dim)) |
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initial = tf.random_uniform([input_dim, output_dim], minval=-init_range, |
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maxval=init_range, dtype=tf.float32) |
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return tf.Variable(initial, name=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: The number of non-zero elements in the sparse matrix |
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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 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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def gaussian_noise_layer(input_layer, std): |
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noise = tf.random_normal(shape=tf.shape(input_layer), mean=0.0, stddev=std, dtype=tf.float32) |
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return input_layer + noise |
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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 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 GCN(Model): |
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def __init__(self, placeholders, num_features, features_nonzero, settings, **kwargs): |
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super(GCN, self).__init__(**kwargs) |
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""" |
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inputs: Input features |
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input_dim: dimensionality |
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feature_nonzero:Non-zero feature number |
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adj: adjacency matrix |
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dropout:dropout |
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""" |
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self.inputs = placeholders['features'] |
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self.input_dim = num_features |
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self.features_nonzero = features_nonzero |
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self.adj = placeholders['adj'] |
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self.dropout = placeholders['dropout'] |
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self.settings = settings |
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def construct(self, inputs=None, hidden=None, reuse=False): |
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if inputs == None: |
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inputs = self.inputs |
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with tf.variable_scope('Encoder', reuse=reuse): |
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self.hidden1 = GraphConvolutionSparse(input_dim=self.input_dim, |
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output_dim=self.settings.hidden1, |
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adj=self.adj, |
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features_nonzero=self.features_nonzero, |
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act=tf.nn.relu, |
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dropout=self.dropout, |
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logging=self.logging, |
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name='e_dense_1')(inputs) |
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self.noise = gaussian_noise_layer(self.hidden1, 0.1) |
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if hidden == None: |
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hidden = self.hidden1 |
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self.embeddings = GraphConvolution(input_dim=self.settings.hidden1, |
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output_dim=self.settings.hidden2, |
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adj=self.adj, |
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act=lambda x: x, |
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dropout=self.dropout, |
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logging=self.logging, |
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name='e_dense_2')(hidden) |
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self.z_mean = self.embeddings |
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self.reconstructions = InnerProductDecoder(input_dim=self.settings.hidden2, |
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act=lambda x: x, |
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logging=self.logging)(self.embeddings) |
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return self.z_mean, self.reconstructions |
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def dense(x, n1, n2, name): |
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""" |
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Used to create a dense layer. |
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:param x: input tensor to the dense layer |
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:param n1: no. of input neurons |
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:param n2: no. of output neurons |
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:param name: name of the entire dense layer.i.e, variable scope name. |
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:return: tensor with shape [batch_size, n2] |
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""" |
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with tf.variable_scope(name, reuse=None): |
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# np.random.seed(1) |
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tf.set_random_seed(1) |
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weights = tf.get_variable("weights", shape=[n1, n2], |
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initializer=tf.random_normal_initializer(mean=0., stddev=0.01)) |
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bias = tf.get_variable("bias", shape=[n2], initializer=tf.constant_initializer(0.0)) |
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out = tf.add(tf.matmul(x, weights), bias, name='matmul') |
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return out |
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class Discriminator(Model): |
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def __init__(self, settings, **kwargs): |
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super(Discriminator, self).__init__(**kwargs) |
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self.act = tf.nn.relu |
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self.settings = settings |
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def construct(self, inputs, reuse=False): |
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with tf.variable_scope('Discriminator'): |
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if reuse: |
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tf.get_variable_scope().reuse_variables() |
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tf.set_random_seed(1) |
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dc_den1 = tf.nn.relu(dense(inputs, self.settings.hidden2, self.settings.hidden3, name='dc_den1')) |
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dc_den2 = tf.nn.relu(dense(dc_den1, self.settings.hidden3, self.settings.hidden1, name='dc_den2')) |
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output = dense(dc_den2, self.settings.hidden1, 1, name='dc_output') |
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return output |
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class D_graph(Model): |
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def __init__(self, num_features, **kwargs): |
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super(D_graph, self).__init__(**kwargs) |
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self.act = tf.nn.relu |
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self.num_features = num_features |
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def construct(self, inputs, reuse=False): |
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# input是一张Graph的adj,把每一列当成一个通道,所以input的通道数是num_nodes |
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with tf.variable_scope('D_Graph'): |
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if reuse: |
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tf.get_variable_scope().reuse_variables() |
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# np.random.seed(1) |
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# tf.set_random_seed(1) |
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dc_den1 = tf.nn.relu(dense(inputs, self.num_features, 512, name='GD_den1')) # (bs,num_nodes,512) |
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dc_den2 = tf.nn.relu(dense(dc_den1, 512, 128, name='GD_den2')) # (bs, num_nodes, 128) |
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output = dense(dc_den2, 128, 1, name='GD_output') # (bs,num_nodes,1) |
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return output |
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class Generator_z2g(Model): |
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def __init__(self, placeholders, num_features, features_nonzero, settings, **kwargs): |
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super(Generator_z2g, self).__init__(**kwargs) |
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""" |
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inputs:输入 |
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input_dim:feature的数量,即input的维度? |
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feature_nonzero:非0的特征 |
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adj:邻接矩阵 |
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dropout:dropout |
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""" |
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self.inputs = placeholders['real_distribution'] |
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self.input_dim = num_features |
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self.features_nonzero = features_nonzero |
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self.adj = placeholders['adj'] |
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self.dropout = placeholders['dropout'] |
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self.settings = settings |
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def construct(self, inputs=None, reuse=False): |
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if inputs == None: |
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inputs = self.inputs |
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with tf.variable_scope('Decoder', reuse=reuse): |
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self.hidden1 = GraphConvolution(input_dim=self.settings.hidden2, |
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output_dim=self.settings.hidden1, |
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adj=self.adj, |
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act=tf.nn.relu, |
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dropout=self.dropout, |
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logging=self.logging, |
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name='GG_dense_1')(inputs) |
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self.embeddings = GraphConvolution(input_dim=self.settings.hidden1, |
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output_dim=self.input_dim, |
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adj=self.adj, |
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act=lambda x: x, |
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dropout=self.dropout, |
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logging=self.logging, |
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name='GG_dense_2')(self.hidden1) |
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self.z_mean = self.embeddings |
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return self.z_mean, self.hidden1 |
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class BGAN(object): |
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def __init__(self, placeholders, num_features, num_nodes, features_nonzero, settings): |
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self.discriminator = Discriminator(settings) |
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self.D_Graph = D_graph(num_features) |
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self.d_real = self.discriminator.construct(placeholders['real_distribution']) |
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self.GD_real = self.D_Graph.construct(placeholders['features_dense']) |
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self.ae_model = GCN(placeholders, num_features, features_nonzero, settings) |
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self.model_z2g = Generator_z2g(placeholders, num_features, features_nonzero, settings)
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