import tensorflow as tf
from antk.core import config
from antk.core import generic_model
[docs]def dnn_concat(data, configfile,
layers=[16, 8],
activation='tanhlecun',
initrange=1e-3,
bn=True,
keep_prob=.95,
concat_size=24,
uembed=32,
iembed=32,
learnrate=.00001,
verbose=True,
epochs=10,
maxbadcount=20,
mb=2000,
eval_rate=500):
with tf.name_scope('ant_graph'):
ant = config.AntGraph(configfile,
data=data.dev.features,
marker='-',
graph_name='dnn_concat',
variable_bindings={'layers': layers,
'activation': activation,
'initrange': initrange,
'bn': bn,
'keep_prob': keep_prob,
'concat_size': concat_size,
'uembed': uembed,
'iembed': iembed,
})
y = ant.tensor_out
y_ = tf.placeholder("float", [None, None], name='Target')
ant.placeholderdict['ratings'] = y_ # put the new placeholder in the graph for training
with tf.name_scope('objective'):
objective = tf.reduce_sum(tf.square(y_ - y))
with tf.name_scope('dev_rmse'):
dev_rmse = tf.sqrt(tf.div(tf.reduce_sum(tf.square(y - y_)), data.dev.num_examples))
with tf.name_scope('training'):
model = generic_model.Model(objective, ant.placeholderdict,
mb=mb,
learnrate=learnrate,
verbose=verbose,
maxbadcount=maxbadcount,
epochs=epochs,
evaluate=dev_rmse,
predictions=y,
model_name='dnn_concat',
make_histograms=False,
save=False,
tensorboard=False)
model.train(data.train, dev=data.dev, eval_schedule=eval_rate)
return model
