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GraphSAGE 代码解析(一) - unsupervised_train.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：
1 class Layer(): 2 def __init__(self): 3 layer = self.__class__.__name__ 4 name = layer + '_' + str(get_layer_uid(layer)) 5 print(name) 6 7 layer1 = Layer() 8 layer2 = Layer() 9 10 # Output: 11 # Layer_1 12 # Layer_2
2. class Layer
class Layer主要定义基本的层的API。
1 class Layer(object): 2 """Base layer class. Defines basic API for all layer objects. 3 Implementation inspired by keras (http://keras.io). 4 # Properties 5 name: String, defines the variable scope of the layer. 6 logging: Boolean, switches Tensorflow histogram logging on/off 7 8 # Methods 9 _call(inputs): Defines computation graph of layer 10 (i.e. takes input, returns output) 11 __call__(inputs): Wrapper for _call() 12 _log_vars(): Log all variables 13 """ 14 15 def __init__(self, **kwargs): 16 allowed_kwargs = {'name', 'logging', 'model_size'} 17 for kwarg in kwargs.keys(): 18 assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg 19 name = kwargs.get('name') 20 if not name: 21 layer = self.__class__.__name__.lower() # "layer" 22 name = layer + '_' + str(get_layer_uid(layer)) 23 self.name = name 24 self.vars = {} 25 logging = kwargs.get('logging', False) 26 self.logging = logging 27 self.sparse_inputs = False 28 29 def _call(self, inputs): 30 return inputs 31 32 def __call__(self, inputs): 33 with tf.name_scope(self.name): 34 if self.logging and not self.sparse_inputs: 35 tf.summary.histogram(self.name + '/inputs', inputs) 36 outputs = self._call(inputs) 37 if self.logging: 38 tf.summary.histogram(self.name + '/outputs', outputs) 39 return outputs 40 41 def _log_vars(self): 42 for var in self.vars: 43 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)
1 class Dense(Layer): 2 """Dense layer.""" 3 4 def __init__(self, input_dim, output_dim, dropout=0., 5 act=tf.nn.relu, placeholders=None, bias=True, featureless=False, 6 sparse_inputs=False, **kwargs): 7 super(Dense, self).__init__(**kwargs) 8 9 self.dropout = dropout 10 11 self.act = act 12 self.featureless = featureless 13 self.bias = bias 14 self.input_dim = input_dim 15 self.output_dim = output_dim 16 17 # helper variable for sparse dropout 18 self.sparse_inputs = sparse_inputs 19 if sparse_inputs: 20 self.num_features_nonzero = placeholders['num_features_nonzero'] 21 22 with tf.variable_scope(self.name + '_vars'): 23 self.vars['weights'] = tf.get_variable('weights', shape=(input_dim, output_dim), 24 dtype=tf.float32, initializer=tf.contrib.layers.xavier_initializer(), 25 regularizer=tf.contrib.layers.l2_regularizer(FLAGS.weight_decay)) 26 if self.bias: 27 self.vars['bias'] = zeros([output_dim], name='bias') 28 29 if self.logging: 30 self._log_vars() 31 32 def _call(self, inputs): 33 x = inputs 34 x = tf.nn.dropout(x, 1 - self.dropout) 35 36 # transform 37 output = tf.matmul(x, self.vars['weights']) 38 39 # bias 40 if self.bias: 41 output += self.vars['bias'] 42 43 return self.act(output)