代码拉取完成，页面将自动刷新
Commits on Oct 15, 2018，提交的SHA-1 hash值（提交ID）：114c4c45b1cb6144b2540f92a2b357c3f445e98e
只保留执行所需要的代码及文件，其他已删除。
config/network.yaml配置文件，data/dnn/infer.userid.txt、res/infer.userid.txt等数据文件由用户从开源链接下载导入
请参照昇腾社区CANN商用版文档先使用迁移工具进行NPU自动迁移：https://www.hiascend.com/document/detail/zh/canncommercial/700/modeldev/tfmigr1/tfmigr1_000009.html
1、修改IO/iterator.py，把第30~41行
_fm_feat_indices, _fm_feat_values,
_fm_feat_shape, _labels, _dnn_feat_indices,
_dnn_feat_values, _dnn_feat_weights, _dnn_feat_shape = iterator.get_next()
self.initializer = iterator.initializer
self.fm_feat_indices = _fm_feat_indices
self.fm_feat_values = _fm_feat_values
self.fm_feat_shape = _fm_feat_shape
self.labels = _labels
self.dnn_feat_indices = _dnn_feat_indices
self.dnn_feat_values = _dnn_feat_values
self.dnn_feat_weights = _dnn_feat_weights
self.dnn_feat_shape = _dnn_feat_shape
改为：
batch = iterator.get_next()
self.initializer = iterator.initializer
self.fm_feat_indices = batch.get('fm_feat_indices')
self.fm_feat_values = batch.get('fm_feat_values')
self.fm_feat_shape = batch.get('fm_feat_shape')
self.labels = batch.get('labels')
self.dnn_feat_indices = batch.get('dnn_feat_indices')
self.dnn_feat_values = batch.get('dnn_feat_values')
self.dnn_feat_weights = batch.get('dnn_feat_weights')
self.dnn_feat_shape = batch.get('dnn_feat_shape')
第63~65行
return fm_feat_indices, fm_feat_values,
fm_feat_shape, labels, dnn_feat_indices,
dnn_feat_values, dnn_feat_weights, dnn_feat_shape
改为：
return {
'fm_feat_indices': fm_feat_indices, 'fm_feat_values': fm_feat_values, 'fm_feat_shape': fm_feat_shape,
'labels': labels, 'dnn_feat_indices': dnn_feat_indices, 'dnn_feat_values': dnn_feat_values,
'dnn_feat_weights': dnn_feat_weights, 'dnn_feat_shape': dnn_feat_shape
}
2、修改src/base_model.py。把embedding初始化值设成tf.zeros_initializer()，把84行
return tf.truncated_normal_initializer(stddev=hparams.init_value)
改为（为了对比CPU，xDeepFM源代码这里也要一起修改）：
return tf.zeros_initializer()
更新自动改图模式下生成新数据集中batch的label记录，把188~189行
def eval(self, sess):
return sess.run([self.loss, self.data_loss, self.pred, self.iterator.labels], \
改为：
def eval(self, sess, eval_label):
return sess.run([self.loss, self.data_loss, self.pred, eval_label], \
3、修改src/exDeepFM.py。在第6行添加
from mx_rec.core.embedding import create_table
from mx_rec.core.embedding import sparse_lookup
把40~43行
w_fm_nn_input_orgin = tf.nn.embedding_lookup_sparse(self.embedding,
fm_sparse_index,
fm_sparse_weight,
combiner="sum")
改为：
dense_indices = tf.sparse.to_dense(fm_sparse_index, default_value=0)
dense_weights = tf.sparse.to_dense(fm_sparse_weight, default_value=0)
sparse_hashtable = create_table(key_dtype=tf.int32,
dim=tf.TensorShape([hparams.dim]),
name='sparse_embeddings_table',
emb_initializer=tf.zeros_initializer(),
device_vocabulary_size=hparams.FEATURE_COUNT,
host_vocabulary_size=0
)
embedded_values = sparse_lookup(sparse_hashtable,
dense_indices,
is_train=True,
name="sparse_embeddings",
modify_graph=True)
w_fm_nn_input_orgin = tf.reduce_sum(embedded_values * tf.expand_dims(dense_weights, axis=-1), axis=1)
4、修改main.py。在第176行添加
# init
from mx_rec.util.initialize import init
init(use_dynamic=True,
use_dynamic_expansion=False)
5、修改train.py。把第35~57行
graph = tf.Graph()
with graph.as_default():
# feed train file name, valid file name, or test file name
filenames = tf.placeholder(tf.string, shape=[None])
#src_dataset = tf.contrib.data.TFRecordDataset(filenames)
src_dataset = tf.data.TFRecordDataset(filenames)
if hparams.data_format == 'ffm':
batch_input = FfmIterator(src_dataset)
elif hparams.data_format == 'din':
batch_input = DinIterator(src_dataset)
elif hparams.data_format == 'cccfnet':
batch_input = CCCFNetIterator(src_dataset)
else:
raise ValueError("not support {0} format data".format(hparams.data_format))
# build model
model = model_creator(
hparams,
iterator=batch_input,
scope=scope)
return TrainModel(
graph=graph,
改为：
# feed train file name, valid file name, or test file name
filenames = tf.placeholder(tf.string, shape=[None])
# src_dataset = tf.contrib.data.TFRecordDataset(filenames)
src_dataset = tf.data.TFRecordDataset(filenames)
if hparams.data_format == 'ffm':
batch_input = FfmIterator(src_dataset)
elif hparams.data_format == 'din':
batch_input = DinIterator(src_dataset)
elif hparams.data_format == 'cccfnet':
batch_input = CCCFNetIterator(src_dataset)
else:
raise ValueError("not support {0} format data".format(hparams.data_format))
# build model
model = model_creator(
hparams,
iterator=batch_input,
scope=scope)
return TrainModel(
graph=tf.get_default_graph(),
把第68~73行
load_sess.run(load_model.iterator.initializer, feed_dict={load_model.filenames: [filename]})
preds = []
labels = []
while True:
try:
_, _, step_pred, step_labels = load_model.model.eval(load_sess)
改为：
from mx_rec.util.initialize import ConfigInitializer
eval_label = ConfigInitializer.get_instance().train_params_config.get_target_batch(True).get("labels")
initializer = ConfigInitializer.get_instance().train_params_config.get_initializer(True)
load_sess.run(initializer, feed_dict={load_model.filenames: [filename]})
preds = []
labels = []
while True:
try:
_, _, step_pred, step_labels = load_model.model.eval(load_sess, eval_label)
在第223行添加
from mx_rec.graph.modifier import modify_graph_and_start_emb_cache
modify_graph_and_start_emb_cache(dump_graph=True)
把第239行
train_sess.run(train_model.iterator.initializer, feed_dict={train_model.filenames: [hparams.train_file_cache]})
改为：
from mx_rec.util.initialize import ConfigInitializer
initializer = ConfigInitializer.get_instance().train_params_config.get_initializer(True)
train_sess.run(initializer, feed_dict={train_model.filenames: [hparams.train_file_cache]})
6、为了适配Rec SDK运行环境，添加了run.sh。
1、修改utils/util.py。把第63行
config = yaml.load(f)
改为（为了xDeepFM源代码在CPU上能跑通，这里也要一起修改）：
config = yaml.safe_load(f)
2、由于去掉了无关代码src/CIN.py，修改main.py适配。把第156~158行
'opnn', 'fm', 'lr', 'din', 'cccfnet', 'deepcross', 'exDeepFM', "cross", "CIN"]:
raise ValueError(
"model type must be cccfnet, deepFM, deepWide, dnn, ipnn, opnn, fm, lr, din, deepcross, exDeepFM, cross, CIN but you set is {0}".format(
改为：
'opnn', 'fm', 'lr', 'din', 'cccfnet', 'deepcross', 'exDeepFM', "cross"]:
raise ValueError(
"model type must be cccfnet, deepFM, deepWide, dnn, ipnn, opnn, fm, lr, din, deepcross, exDeepFM, "
"cross, but you set is {0}".format(config['model']['model_type']))
修改train.py适配。删除第21行代码
from src.CIN import CINModel
删除第210~212行代码
elif hparams.model_type == 'CIN':
print("run extreme cin model!")
model_creator = CINModel
bash run.sh main.py 10.10.10.10
其中，10.10.10.10为服务器IP，请替换成对应服务器IP。
1、CPU:
step 1 , total_loss: 0.6931, data_loss: 0.6931
step 2 , total_loss: 0.6905, data_loss: 0.6905
finish one epoch!
at epoch 0 train info: loss:0.6918214857578278 eval info: auc:0.4867, logloss:0.6865 test info: auc:0.4867, logloss:0.6865
at epoch 0 , train time: 0.6 eval time: 0.3
step 1 , total_loss: 0.6845, data_loss: 0.6845
step 2 , total_loss: 0.6818, data_loss: 0.6818
finish one epoch!
at epoch 1 train info: loss:0.6831814646720886 eval info: auc:0.485, logloss:0.6801 test info: auc:0.485, logloss:0.6801
at epoch 1 , train time: 0.2 eval time: 0.1
step 1 , total_loss: 0.6766, data_loss: 0.6766
step 2 , total_loss: 0.6732, data_loss: 0.6732
finish one epoch!
at epoch 2 train info: loss:0.6748818755149841 eval info: auc:0.4832, logloss:0.6738 test info: auc:0.4832, logloss:0.6738
at epoch 2 , train time: 0.1 eval time: 0.1
2、Rec SDK:
[1,0]<stdout>:step 1 , total_loss: 0.6931, data_loss: 0.6931
[1,0]<stdout>:step 2 , total_loss: 0.6905, data_loss: 0.6905
[1,0]<stdout>:finish one epoch!
[1,0]<stdout>:at epoch 0 train info: loss:0.6918215453624725 eval info: auc:0.4867, logloss:0.6865 test info: auc:0.4867, logloss:0.6865
[1,0]<stdout>:at epoch 0 , train time: 15.9 eval time: 3.1
[1,0]<stdout>:step 1 , total_loss: 0.6845, data_loss: 0.6845
[1,0]<stdout>:step 2 , total_loss: 0.6818, data_loss: 0.6818
[1,0]<stdout>:finish one epoch!
[1,0]<stdout>:at epoch 1 train info: loss:0.6831814646720886 eval info: auc:0.485, logloss:0.6801 test info: auc:0.485, logloss:0.6801
[1,0]<stdout>:at epoch 1 , train time: 7.8 eval time: 0.7
[1,0]<stdout>:step 1 , total_loss: 0.6766, data_loss: 0.6766
[1,0]<stdout>:step 2 , total_loss: 0.6732, data_loss: 0.6732
[1,0]<stdout>:finish one epoch!
[1,0]<stdout>:at epoch 2 train info: loss:0.6748818457126617 eval info: auc:0.4832, logloss:0.6738 test info: auc:0.4832, logloss:0.6738
[1,0]<stdout>:at epoch 2 , train time: 0.5 eval time: 0.7
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