from __future__ import print_function
import tensorflow as tf
from antk.core import config
from antk.core import generic_model
from antk.core import node_ops
[docs]def dssm(data, configfile,
layers=[10,10,10],
bn=True,
keep_prob=.95,
act='tanhlecun',
initrange=1,
kfactors=10,
lamb=.1,
mb=500,
learnrate=0.0001,
verbose=True,
maxbadcount=10,
epochs=100,
model_name='dssm',
random_seed=500,
eval_rate=500):
datadict = data.user.features.copy()
datadict.update(data.item.features)
configdatadict = data.dev.features.copy()
configdatadict.update(datadict)
with tf.name_scope('ant_graph'):
ant = config.AntGraph(configfile,
data=configdatadict,
marker='-',
graph_name='basic_mf',
variable_bindings={'initrange': initrange, 'kfactors': kfactors})
y_ = tf.placeholder("float", [None, None], name='Target')
ant.placeholderdict['ratings'] = y_
with tf.name_scope('objective'):
if type(ant.tensor_out) is list:
objective = tf.reduce_sum(tf.square(y_ - ant.tensor_out[0]))
for i in range(1, len(ant.tensor_out)):
objective += tf.reduce_sum(tf.square(y_ - ant.tensor_out[i]))
objective += (lamb*tf.reduce_sum(tf.square(ant.tensordict['huser'])) +
lamb*tf.reduce_sum(tf.square(ant.tensordict['hitem'])) +
lamb*tf.reduce_sum(tf.square(ant.tensordict['ubias'])) +
lamb*tf.reduce_sum(tf.square(ant.tensordict['ibias'])))
with tf.name_scope('dev_rmse'):
dev_rmse = node_ops.rmse(ant.tensor_out[0], y_)
model = generic_model.Model(objective, ant.placeholderdict,
mb=mb,
learnrate=learnrate,
verbose=verbose,
maxbadcount=maxbadcount,
epochs=epochs,
evaluate=dev_rmse,
predictions=ant.tensor_out[0],
model_name='dssm',
random_seed=random_seed)
model.train(data.train, dev=data.dev, supplement=datadict, eval_schedule=eval_rate)
return model