# -*- encoding:utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
import tensorflow as tf
from easy_rec.python.layers import dnn
from easy_rec.python.model.rank_model import RankModel
from easy_rec.python.protos.dcn_pb2 import DCN as DCNConfig # NOQA
if tf.__version__ >= '2.0':
tf = tf.compat.v1
[docs]class DCN(RankModel):
[docs] def __init__(self,
model_config,
feature_configs,
features,
labels=None,
is_training=False):
super(DCN, self).__init__(model_config, feature_configs, features, labels,
is_training)
assert self._model_config.WhichOneof('model') == 'dcn', \
'invalid model config: %s' % self._model_config.WhichOneof('model')
self._model_config = self._model_config.dcn
assert isinstance(self._model_config, DCNConfig)
self._features, _ = self._input_layer(self._feature_dict, 'all')
def _cross_net(self, tensor, num_cross_layers):
x = x0 = tensor
input_dim = tensor.shape[-1]
for i in range(num_cross_layers):
name = 'cross_layer_%s' % i
w = tf.get_variable(
name=name + '_w',
dtype=tf.float32,
shape=(input_dim),
)
b = tf.get_variable(name=name + '_b', dtype=tf.float32, shape=(input_dim))
xw = tf.reduce_sum(x * w, axis=1, keepdims=True) # (B, 1)
x = tf.math.add(tf.math.add(x0 * xw, b), x)
return x
[docs] def build_predict_graph(self):
tower_fea_arr = []
# deep tower
deep_tower_config = self._model_config.deep_tower
dnn_layer = dnn.DNN(deep_tower_config.dnn, self._l2_reg, 'dnn',
self._is_training)
deep_tensor = dnn_layer(self._features)
tower_fea_arr.append(deep_tensor)
# cross tower
cross_tower_config = self._model_config.cross_tower
num_cross_layers = cross_tower_config.cross_num
cross_tensor = self._cross_net(self._features, num_cross_layers)
tower_fea_arr.append(cross_tensor)
# final tower
all_fea = tf.concat(tower_fea_arr, axis=1)
final_dnn_layer = dnn.DNN(self._model_config.final_dnn, self._l2_reg,
'final_dnn', self._is_training)
all_fea = final_dnn_layer(all_fea)
output = tf.layers.dense(all_fea, self._num_class, name='output')
self._add_to_prediction_dict(output)
return self._prediction_dict