tree_ws_counts.config

dotproduct x_dot_y()
-all_user dnn([$kfactors,$kfactors,$kfactors], activation='tanh',bn=True,keep_prob=None)
--tanh_user tf.nn.tanh()
---merge_user concat($kfactors)
----huser lookup(dataname='user', initrange=$initrange, shape=[None, $kfactors])
----hage dnn([$kfactors,$kfactors,$kfactors],activation='tanh',bn=True,keep_prob=None)
-----agelookup embedding()
------age placeholder(tf.float32)
------user placeholder(tf.int32)
----hsex dnn([$kfactors,$kfactors,$kfactors],activation='tanh',bn=True,keep_prob=None)
-----sexlookup embedding()
------sex_weights weights('tnorm', [3, $kfactors])
------sexes embedding()
-------sex placeholder(tf.int32)
-------user placeholder(tf.int32)
-all_item dnn([$kfactors,$kfactors,$kfactors], activation='tanh',bn=True,keep_prob=None)
--tanh_item tf.nn.tanh()
---merge_item concat($kfactors)
----hitem lookup(dataname='item', initrange=$initrange, shape=[None, $kfactors])
----hgenre dnn([$kfactors,$kfactors,$kfactors],activation='tanh',bn=True,keep_prob=None)
-----genrelookup embedding()
------genre placeholder(tf.float32)
------item placeholder(tf.int32)
----hcounts dnn([$kfactors,$kfactors,$kfactors],activation='tanh',bn=True,keep_prob=None)
-----countslookup embedding()
------docterm placeholder(tf.float32)
------item placeholder(tf.int32)
-ibias lookup(dataname='item', shape=[None, 1], initrange=$initrange)
-ubias lookup(dataname='user', shape=[None, 1], initrange=$initrange)