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update ASNE method (IEEE TKDE2018)

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Chengbin HOU 2019-04-15 11:44:09 +01:00 committed by GitHub
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1 changed files with 7 additions and 5 deletions
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src/libnrl/asne.py
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@ -68,20 +68,22 @@ class ASNE(BaseEstimator, TransformerMixin):
self.attr_embed = tf.matmul(self.train_data_attr, self.weights['attr_embeddings']) # batch_size * attr_dim self.attr_embed = tf.matmul(self.train_data_attr, self.weights['attr_embeddings']) # batch_size * attr_dim
self.embed_layer = tf.concat([self.id_embed, self.alpha * self.attr_embed], 1) # batch_size * (id_dim + attr_dim) #an error due to old tf! self.embed_layer = tf.concat([self.id_embed, self.alpha * self.attr_embed], 1) # batch_size * (id_dim + attr_dim) #an error due to old tf!
'''
## can add hidden_layers component here!---------------------------------- ## can add hidden_layers component here!----------------------------------
#0) no hidden layer #0) no hidden layer
#1) 128 #1) 128
#2) 256+128 ##--------paper stated it used two hidden layers with softsign #2) 256+128
#3) 512+256+128 #3) 512+256+128
# Note: according to the Fig 5 in paper https://ieeexplore.ieee.org/abstract/document/8326519
# here we follow 2) i.e. 256 softsign + 128 softsign
len_h1_in = self.id_embedding_size + self.attr_embedding_size len_h1_in = self.id_embedding_size + self.attr_embedding_size
len_h1_out = 256 #or self.id_embedding_size + self.attr_embedding_size # if only add h1 len_h1_out = 256 # 256 softsign
len_h2_in = len_h1_out len_h2_in = len_h1_out
len_h2_out = self.id_embedding_size + self.attr_embedding_size len_h2_out = self.id_embedding_size + self.attr_embedding_size # 128 softsign i.e. dim of embeddings
self.h1 = add_layer(inputs=self.embed_layer, in_size=len_h1_in, out_size=len_h1_out, activation_function=tf.nn.softsign) self.h1 = add_layer(inputs=self.embed_layer, in_size=len_h1_in, out_size=len_h1_out, activation_function=tf.nn.softsign)
self.h2 = add_layer(inputs=self.h1, in_size=len_h2_in, out_size=len_h2_out, activation_function=tf.nn.softsign) self.h2 = add_layer(inputs=self.h1, in_size=len_h2_in, out_size=len_h2_out, activation_function=tf.nn.softsign)
## ------------------------------------------------------------------------- ## -------------------------------------------------------------------------
'''
# Compute the loss, using a sample of the negative labels each time. # Compute the loss, using a sample of the negative labels each time.
self.loss = tf.reduce_mean(tf.nn.sampled_softmax_loss(weights=self.weights['out_embeddings'], biases=self.weights['biases'], # if one needs to change layers self.loss = tf.reduce_mean(tf.nn.sampled_softmax_loss(weights=self.weights['out_embeddings'], biases=self.weights['biases'], # if one needs to change layers