tensorflow2 sparse input name missing in saved model - tensorflow

Question

tensorflow 2.3
ubuntu16.04
python=3.7.7
When using tf.keras.Input with 'sparse=True', the input tensor info names are unreadable in serving signatures, such as args_0, args_0_1, args_0_2. As a result, it is very hard to distinguish when multiple sparse inputs are used in one model.
Source code / logs
def make_parse_example_test(serialized_example):
# 解析的字典格式
data_dict_test = { # 解析example
'label': tf.io.FixedLenFeature([1], tf.float32),
'features': tf.io.FixedLenFeature([38], tf.float32),
# 'emb_arr': tf.io.FixedLenFeature([100],tf.float32),
'scid_index': tf.io.VarLenFeature(tf.int64),
}
features = tf.io.parse_single_example(serialized_example, features=data_dict_test)
label = features.pop('label')
return features,label
def batch_input(file_dir,batchsize):
# 判断是否是文件目录,创建文件流
if os.path.isdir(file_dir):
files = os.listdir(file_dir)
filenamequeues = list(map(lambda x: file_dir+x, files))
else:
filenamequeues = [file_dir]
print(filenamequeues)
dataset = tf.data.TFRecordDataset(filenamequeues)
# dataset = dataset.batch(batchsize)
dataset = dataset.map(make_parse_example_test,num_parallel_calls=4)
dataset = dataset.batch(batchsize)
# 读入数据，对数据进行混洗(shuffle)、分批batch
dataset = dataset.prefetch(-1)
return dataset
class EmbeddingLayer(tf.keras.layers.Layer):
def __init__(self, input_dim, output_dim):
super(EmbeddingLayer, self).__init__(trainable=True)
self.params = tf.Variable(tf.random.truncated_normal([input_dim, output_dim]), trainable=True)
def call(self,inputs):
param = tf.nn.safe_embedding_lookup_sparse(self.params,inputs)
return param
def get_config(self):
return super(EmbeddingLayer, self).get_config()
def models():
feature1 = tf.keras.layers.Input(shape=[38], name='features', dtype=tf.float32)
scid_index = tf.keras.layers.Input(shape=[None], name='scid_index', dtype=tf.int64,sparse=True)
scid_em = EmbeddingLayer(780000,50)(scid_index)
feature_all = tf.keras.layers.concatenate([
feature1,scid_em
])
h1 = tf.keras.layers.Dense(256, activation=leaky_relu,name='h1')(feature_all)
h2 = tf.keras.layers.Dense(256, activation=leaky_relu, name="h2")(h1)
h3 = tf.keras.layers.Dense(1, activation=leaky_relu, name="h3")(h2)
output = tf.keras.layers.Activation(activation="sigmoid")(h3)
out = tf.keras.models.Model(
inputs=[feature1,scid_index],
outputs=[output]
)
return out
train = batch_input('./part-r-00001',512)
model = models()
model.compile(loss="binary_crossentropy", optimizer="adam", metrics=[tf.keras.metrics.AUC(),
tf.keras.metrics.Precision(),
tf.keras.metrics.Recall(),
tf.keras.metrics.BinaryAccuracy()])
model.fit(train, epochs=1,verbose=1,class_weight={0:1,1:2},steps_per_epoch=5)
print(model.input_names)
model.save("./model3test")
[examine exported_model]
$ saved_model_cli show --dir ./model3test --all
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:
signature_def['__saved_model_init_op']:
The given SavedModel SignatureDef contains the following input(s):
The given SavedModel SignatureDef contains the following output(s):
outputs['__saved_model_init_op'] tensor_info:
dtype: DT_INVALID
shape: unknown_rank
name: NoOp
Method name is:
signature_def['serving_default']:
The given SavedModel SignatureDef contains the following input(s):
inputs['args_0'] tensor_info:
dtype: DT_INT64
shape: (-1, 2)
name: serving_default_args_0:0
inputs['args_0_1'] tensor_info:
dtype: DT_INT64
shape: (-1)
name: serving_default_args_0_1:0
inputs['args_0_2'] tensor_info:
dtype: DT_INT64
shape: (2)
name: serving_default_args_0_2:0
The given SavedModel SignatureDef contains the following output(s):
outputs['label'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 1)
name: StatefulPartitionedCall_18:0
Method name is: tensorflow/serving/predict