问题描述
我在 AI Platform 上使用 Tensorflow 训练了一个 DNN 模型。然后我在本地复制模型以仔细检查是否可以从相同的模型中获得预测。
gcloud ai-platform local predict --model-dir=/home/jupyter/end-to-end-ml/examples/e2e-ml-model-ex02/app/appbabyweight_trained/export/exporter/1615197796 --json-instances=inputs.json
预测是通过一些警告获得的。
If the signature defined in the model is not `serving_default` then you must specify it via --signature-name flag,otherwise the command may fail.
（指定签名名称时可以避免此警告：--signature-name predict）
将模型部署到 AI Platform 后，警告变为错误。服务签名名称必须为 serving_default，如以下错误消息所示：
{ "error": "Serving signature name: "serving_default" not found in signature def" }
使用此命令检查保存的模型后：
saved_model_cli show --dir /home/jupyter/end-to-end-ml/examples/e2e-ml-model-ex02/app/appbabyweight_trained2/output-dir/export/exporter/1615439076 --all
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:
signature_def['predict']:
The given SavedModel SignatureDef contains the following input(s):
inputs['gestation_weeks'] tensor_info:
dtype: DT_FLOAT
shape: (-1)
name: Placeholder_3:0
inputs['is_male'] tensor_info:
dtype: DT_STRING
shape: (-1)
name: Placeholder:0
inputs['mother_age'] tensor_info:
dtype: DT_FLOAT
shape: (-1)
name: Placeholder_1:0
inputs['plurality'] tensor_info:
dtype: DT_STRING
shape: (-1)
name: Placeholder_2:0
The given SavedModel SignatureDef contains the following output(s):
outputs['predictions'] tensor_info:
dtype: DT_FLOAT
shape: (-1,1)
name: dnn/logits/BiasAdd:0
Method name is: tensorflow/serving/predict
因此，我保存的模型的签名名称为 predict。
问题是：签名名称怎么改？
PS：下面是我定义 DNN 的方式：
# Define feature columns
def get_categorical(name,values):
return tf.feature_column.indicator_column(
tf.feature_column.categorical_column_with_vocabulary_list(name,values))
def get_cols():
# Define column types
return [\
get_categorical('is_male',['True','False','UnkNown']),tf.feature_column.numeric_column('mother_age'),get_categorical('plurality',['Single(1)','Twins(2)','Triplets(3)','Quadruplets(4)','Quintuplets(5)','Multiple(2+)']),tf.feature_column.numeric_column('gestation_weeks')
]
# Create serving input function to be able to serve predictions later using provided inputs
def serving_input_fn():
feature_placeholders = {
'is_male': tf.compat.v1.placeholder(tf.string,[None]),'mother_age': tf.compat.v1.placeholder(tf.float32,'plurality': tf.compat.v1.placeholder(tf.string,'gestation_weeks': tf.compat.v1.placeholder(tf.float32,[None])
}
features = {
key: tf.expand_dims(tensor,-1) for key,tensor in feature_placeholders.items()
}
return tf.estimator.export.ServingInputReceiver(features,feature_placeholders)
# Create estimator to train and evaluate
def train_and_evaluate(args):
EVAL_INTERVAL = 30
run_config = tf.estimator.runconfig(save_checkpoints_secs = EVAL_INTERVAL,keep_checkpoint_max = 3)
estimator = tf.estimator.DNNRegressor(
model_dir = args['output_dir'],feature_columns = get_cols(),hidden_units = args['nnsize'],config = run_config)
train_spec = tf.estimator.TrainSpec(
input_fn = read_dataset(args['train_data_path'],mode = tf.estimator.ModeKeys.TRAIN,batch_size =args['batch_size']),max_steps = TRAIN_STEPS)
exporter = tf.estimator.LatestExporter('exporter',serving_input_fn)
eval_spec = tf.estimator.EvalSpec(
input_fn = read_dataset(args['eval_data_path'],mode = tf.estimator.ModeKeys.EVAL,steps = args['eval_steps'],start_delay_secs = 60,# start evaluating after N seconds
throttle_secs = EVAL_INTERVAL,# evaluate every N seconds
exporters = exporter)
tf.estimator.train_and_evaluate(estimator,train_spec,eval_spec)
谢谢
解决方法
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