def build_model(hparams):
tf.reset_default_graph()
if hparams.model == 'fm':
model = fm.Model(hparams)
elif hparams.model == 'ffm':
model = ffm.Model(hparams)
elif hparams.model == 'nffm':
model = nffm.Model(hparams)
elif hparams.model == 'xdeepfm':
model = xdeepfm.Model(hparams)
elif hparams.model == 'deepFM':
model = deepFM.Model(hparams)
elif hparams.model == 'DCN':
model = DCN.Model(hparams)
elif hparams.model == 'AFM':
model = AFM.Model(hparams)
config_proto = tf.ConfigProto(log_device_placement=0,
allow_soft_placement=0)
config_proto.gpu_options.allow_growth = True
sess = tf.Session(config=config_proto)
sess.run(tf.global_variables_initializer())
model.set_Session(sess)
return model
def build_model(hparams):
tf.reset_default_graph()
if hparams.model == 'fm':
model = fm.Model(hparams)
elif hparams.model == 'ffm':
model = ffm.Model(hparams)
elif hparams.model == 'nffm':
model = nffm.Model(hparams)
elif hparams.model == 'xdeepfm':
model = xdeepfm.Model(hparams)
config_proto = tf.ConfigProto(log_device_placement=0,
allow_soft_placement=0)
config_proto.gpu_options.allow_growth = True
sess = tf.Session(config=config_proto)
sess.run(tf.global_variables_initializer())
# writer = tf.summary.FileWriter("D://DeepLearning//Tensorflow//ctrNet-tool-master//graph-1",sess.graph)
# writer.close()
model.set_Session(sess)
return model