# -*- encoding:utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
import logging
import tensorflow as tf
from easy_rec.python.utils.activation import get_activation
if tf.__version__ >= '2.0':
tf = tf.compat.v1
[docs]class DNN:
[docs] def __init__(self,
dnn_config,
l2_reg,
name='dnn',
is_training=False,
last_layer_no_activation=False,
last_layer_no_batch_norm=False):
"""Initializes a `DNN` Layer.
Args:
dnn_config: instance of easy_rec.python.protos.dnn_pb2.DNN
l2_reg: l2 regularizer
name: scope of the DNN, so that the parameters could be separated from other dnns
is_training: train phase or not, impact batch_norm and dropout
last_layer_no_activation: in last layer, use or not use activation
last_layer_no_batch_norm: in last layer, use or not use batch norm
"""
self._config = dnn_config
self._l2_reg = l2_reg
self._name = name
self._is_training = is_training
logging.info('dnn activation function = %s' % self._config.activation)
self.activation = get_activation(
self._config.activation, training=is_training)
self._last_layer_no_activation = last_layer_no_activation
self._last_layer_no_batch_norm = last_layer_no_batch_norm
@property
def hidden_units(self):
return self._config.hidden_units
@property
def dropout_ratio(self):
return self._config.dropout_ratio
def __call__(self, deep_fea, hidden_layer_feature_output=False):
hidden_units_len = len(self.hidden_units)
if hidden_units_len == 1 and self.hidden_units[0] == 0:
return deep_fea
hidden_feature_dict = {}
for i, unit in enumerate(self.hidden_units):
deep_fea = tf.layers.dense(
inputs=deep_fea,
units=unit,
kernel_regularizer=self._l2_reg,
activation=None,
name='%s/dnn_%d' % (self._name, i))
if self._config.use_bn and ((i + 1 < hidden_units_len) or
not self._last_layer_no_batch_norm):
deep_fea = tf.layers.batch_normalization(
deep_fea,
training=self._is_training,
trainable=True,
name='%s/dnn_%d/bn' % (self._name, i))
if (i + 1 < hidden_units_len) or not self._last_layer_no_activation:
deep_fea = self.activation(
deep_fea, name='%s/dnn_%d/act' % (self._name, i))
if len(self.dropout_ratio) > 0 and self._is_training:
assert self.dropout_ratio[
i] < 1, 'invalid dropout_ratio: %.3f' % self.dropout_ratio[i]
deep_fea = tf.nn.dropout(
deep_fea,
keep_prob=1 - self.dropout_ratio[i],
name='%s/%d/dropout' % (self._name, i))
if hidden_layer_feature_output:
hidden_feature_dict['hidden_layer' + str(i)] = deep_fea
if (i + 1 == hidden_units_len):
hidden_feature_dict['hidden_layer_end'] = deep_fea
return hidden_feature_dict
else:
return deep_fea