GraphSAGE 代码解析(三) - aggregators.py
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GraphSAGE 代码解析(一) - unsupervised_train.py
1. class MeanAggregator(Layer):
该类主要用于实现
1. __init__()
__init_() 用于获取并初始化成员变量 dropout, bias(False), act(ReLu), concat(False), input_dim, output_dim, name(Variable scopr)
用glorot()方法初始化节点v的权值矩阵 vars['self_weights'] 和邻居节点均值u的权值矩阵 vars['neigh_weights']
用零向量初始化vars['bias']。(见inits.py: zeros(shape))
若logging为True,则调用 layers.py 中 class Layer()的成员函数_log_vars(), 生成vars中各个变量的直方图。
glorot()
其中，glorot() 在inits.py中定义，用于权值初始化。(from .inits import glorot)
均匀分布初始化方法，又称Xavier均匀初始化，参数从 [-limit, limit] 的均匀分布产生，其中limit为 sqrt(6 / (fan_in + fan_out))。fan_in为权值张量的输入单元数，fan_out是权重张量的输出单元数。该函数返回 [fan_in, fan_out]大小的Variable。
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)
2. _call(inputs)
class MeanAggregator(Layer) 中的 _call(inputs) 函数是对父类class Layer(object)方法_call(inputs)的重写。
用于实现最上方的迭代更新式子。
在layer.py 中定义的 class Layer(object)中，执行特殊函数def __call__(inputs) 时有： outputs = self._call(inputs)调用_call(inputs) 方法，也即在这里调用子类MeanAggregator(Layer)中的_call(inputs)方法。
tf.nn.dropout(x, keep_prob, noise_shape=None, seed=None, name=None)
With probability keep_prob, outputs the input element scaled up by 1 / keep_prob, otherwise outputs 0. The scaling is so that the expected sum is unchanged.
注意：输出的非0元素是原来的 “1/keep_prob” 倍，以保证总和不变。
tf.add_n(inputs, name=None)
Adds all input tensors element-wise. Args:
inputs: A list of Tensor or IndexedSlices objects, each with same shape and type.
name: A name for the operation (optional).
Returns:
A Tensor of same shape and type as the elements of inputs. Raises:
ValueError: If inputs don't all have same shape and dtype or the shape cannot be inferred.
output = tf.concat([from_self, from_neighs], axis=1)
这里注意在concat后其维数变为之前的2倍。
3. class MeanAggregator(Layer) 代码
class MeanAggregator(Layer):
"""
Aggregates via mean followed by matmul and non-linearity.
""" def __init__(self, input_dim, output_dim, neigh_input_dim=None,
dropout=0., bias=False, act=tf.nn.relu,
name=None, concat=False, **kwargs):
super(MeanAggregator, self).__init__(**kwargs) self.dropout = dropout
self.bias = bias
self.act = act
self.concat = concat if neigh_input_dim is None:
neigh_input_dim = input_dim if name is not None:
name = '/' + name
else:
name = '' with tf.variable_scope(self.name + name + '_vars'):
self.vars['neigh_weights'] = glorot([neigh_input_dim, output_dim],
name='neigh_weights')
self.vars['self_weights'] = glorot([input_dim, output_dim],
name='self_weights')
if self.bias:
self.vars['bias'] = zeros([self.output_dim], name='bias') if self.logging:
self._log_vars() self.input_dim = input_dim
self.output_dim = output_dim def _call(self, inputs):
self_vecs, neigh_vecs = inputs neigh_vecs = tf.nn.dropout(neigh_vecs, 1-self.dropout)
self_vecs = tf.nn.dropout(self_vecs, 1-self.dropout)
neigh_means = tf.reduce_mean(neigh_vecs, axis=1) # [nodes] x [out_dim]
from_neighs = tf.matmul(neigh_means, self.vars['neigh_weights']) from_self = tf.matmul(self_vecs, self.vars["self_weights"]) if not self.concat:
output = tf.add_n([from_self, from_neighs])
else:
output = tf.concat([from_self, from_neighs], axis=1) # bias
if self.bias:
output += self.vars['bias'] return self.act(output)
2. class GCNAggregator(Layer)
这里__init__()与MeanAggregator基本相同，在_call()的实现中略有不同。
def _call(self, inputs):
self_vecs, neigh_vecs = inputs neigh_vecs = tf.nn.dropout(neigh_vecs, 1-self.dropout)
self_vecs = tf.nn.dropout(self_vecs, 1-self.dropout)
means = tf.reduce_mean(tf.concat([neigh_vecs,
tf.expand_dims(self_vecs, axis=1)], axis=1), axis=1) # [nodes] x [out_dim]
output = tf.matmul(means, self.vars['weights']) # bias
if self.bias:
output += self.vars['bias'] return self.act(output)
其中对means求解时，
1. 先将self_vecs行列转换(tf.expand_dims(self_vecs, axis=1)),
2. 之后self_vecs的行数与neigh_vecs行数相同时，将二者concat, 即相当于在原先的neigh_vecs矩阵后面新增一列self_vecs的转置
3. 最后将得到的矩阵每行求均值，即得means.
之后means与权值矩阵vars['weights']求内积，并加上vars['bias'], 最终将该值带入激活函数(ReLu)。
下面举个例子简单说明(例子中省略了点乘W的操作)：
import tensorflow as tf neigh_vecs = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
self_vecs = [2, 3, 4] means = tf.reduce_mean(tf.concat([neigh_vecs,
tf.expand_dims(self_vecs, axis=1)], axis=1), axis=1) print(tf.shape(self_vecs)) print(tf.expand_dims(self_vecs, axis=0))
# Tensor("ExpandDims_1:0", shape=(1, 3), dtype=int32) print(tf.expand_dims(self_vecs, axis=1))
# Tensor("ExpandDims_2:0", shape=(3, 1), dtype=int32) sess = tf.Session()
print(sess.run(tf.expand_dims(self_vecs, axis=1)))
# [[2]
# [3]
# [4]] print(sess.run(tf.concat([neigh_vecs,
tf.expand_dims(self_vecs, axis=1)], axis=1)))
# [[1 2 3 2]
# [4 5 6 3]
# [7 8 9 4]] print(means)
# Tensor("Mean:0", shape=(3,), dtype=int32) print(sess.run(tf.reduce_mean(tf.concat([neigh_vecs,
tf.expand_dims(self_vecs, axis=1)], axis=1), axis=1)))
# [2 4 7] # [[1 2 3 2] = 8 // 4 = 2
# [4 5 6 3] = 18 // 4 = 4
# [7 8 9 4]] = 28 // 4 = 7 bias = [1]
output = means + bias
print(sess.run(output))
# [3 5 8]
# [2 + 1, 4 + 1, 7 + 1] = [3, 5, 8]
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