GraphSAGE 代码解析(二) - layers.py

原创文章～转载请注明出处哦。其他部分内容参见以下链接～
GraphSAGE 代码解析(一) - unsupervised_train.py
GraphSAGE 代码解析(三) - aggregators.py
GraphSAGE 代码解析(四) - models.py

 1 # global unique layer ID dictionary for layer name assignment
 2 _LAYER_UIDS = {}
 3
 4 def get_layer_uid(layer_name=''):
 5     """Helper function, assigns unique layer IDs."""
 6     if layer_name not in _LAYER_UIDS:
 7         _LAYER_UIDS[layer_name] = 1
 8         return 1
 9     else:
10         _LAYER_UIDS[layer_name] += 1
11         return _LAYER_UIDS[layer_name]

这里_LAYER_UIDS = {} 是记录layer及其出现次数的字典。

在 get_layer_uid()函数中，若layer_name从未出现过，如今出现了，则将_LAYER_UIDS[layer_name]设为1；否则累加。

作用： 在class Layer中，当未赋variable scope的name时，通过实例化Layer的次数来标定不同的layer_id.

例子：简化一下class Layer可以看出：

 class Layer():
     def __init__(self):
         layer = self.__class__.__name__
         name = layer + '_' + str(get_layer_uid(layer))
         print(name) 

 layer1 = Layer()
 layer2 = Layer()

 # Output:
 # Layer_1
 # Layer_2

2. class Layer

class Layer主要定义基本的层的API。

 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', 'model_size'}
         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() # "layer"
             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])

方法：

__init__(): 获取传入的name, logging, model_size参数。初始化实例变量name, vars{}, logging, sparse_inputs

_call(inputs): 定义层的计算图：获取input, 返回output.

__call__(inputs): 相当于_call()的装饰器，在实现列_call()基本功能后，丰富了其功能，这里主要通过tf.summary.histogram() 可以查看inputs与outputs分布情况的直方图。

_log_vars(): 记录所有变量。实现时主要将vars中的各个变量以直方图形式显示。

3. class Dense

Dense layer主要用于实现全连接层的基本功能。即为了最终得到 Relu(Wx + b)。

__init__(): 用于获取初始化成员变量。其中num_features_nonzero和featureless的作用目前还不清楚。

_call(): 用于实现并且返回Relu(Wx + b)

 class Dense(Layer):
     """Dense layer."""

     def __init__(self, input_dim, output_dim, dropout=0.,
                  act=tf.nn.relu, placeholders=None, bias=True, featureless=False,
                  sparse_inputs=False, **kwargs):
         super(Dense, self).__init__(**kwargs)

         self.dropout = dropout

         self.act = act
         self.featureless = featureless
         self.bias = bias
         self.input_dim = input_dim
         self.output_dim = output_dim

         # helper variable for sparse dropout
         self.sparse_inputs = sparse_inputs
         if sparse_inputs:
             self.num_features_nonzero = placeholders['num_features_nonzero']

         with tf.variable_scope(self.name + '_vars'):
             self.vars['weights'] = tf.get_variable('weights', shape=(input_dim, output_dim),
         dtype=tf.float32, initializer=tf.contrib.layers.xavier_initializer(),
         regularizer=tf.contrib.layers.l2_regularizer(FLAGS.weight_decay))
             if self.bias:
                 self.vars['bias'] = zeros([output_dim], name='bias')

         if self.logging:
             self._log_vars()

     def _call(self, inputs):
         x = inputs
         x = tf.nn.dropout(x, 1 - self.dropout)

         # transform
         output = tf.matmul(x, self.vars['weights'])

         # bias
         if self.bias:
             output += self.vars['bias']

         return self.act(output)