Когда я обучаю свою модель функционального API tf.keras и обслуживаю ее с изображением докера tensorflow/serving, я получаю ошибку формы при вызове API.
Как я строю свою модель:
from tensorflow.keras.layers import Dense, DenseFeatures, Input
from tensorflow.keras.models import Model
from tensorflow import feature_column, string as tf_string
import os
feature_layer_inputs = {}
feature_columns = []
for header in ['categorical_one', 'categorical_two', 'categorical_three']:
feature_columns.append(feature_column.indicator_column(
feature_column.categorical_column_with_hash_bucket(header, hash_bucket_size=100)))
feature_layer_inputs[header] = Input(shape=(1,), name=header, dtype=tf_string)
fc_layer = DenseFeatures(feature_columns)
fc = fc_layer(feature_layer_inputs)
''' Feature Columns to Dense and merge with attention output '''
fc = Dense(300, activation='relu')(fc)
fc = Dense(324, activation='relu')(fc)
pred = Dense(num_classes, activation='softmax')(fc)
inputs = [v for v in feature_layer_inputs.values()]
model = Model(inputs=inputs, outputs=pred)
model.compile(...)
model.fit(...)
saved_model_path = "C:/Temp/saved_models/{}".format(int(time.time()))
os.mkdir(saved_model_path)
model.save(saved_model_path)
Как выглядит мое определение подписи обслуживания:
The given SavedModel SignatureDef contains the following input(s):
inputs['categorical_one'] tensor_info:
dtype: DT_STRING
shape: (-1, 1)
name: serving_default_categorical_one:0
inputs['categorical_two'] tensor_info:
dtype: DT_STRING
shape: (-1, 1)
name: serving_default_categorical_two:0
inputs['categorical_three'] tensor_info:
dtype: DT_STRING
shape: (-1, 1)
name: serving_default_categorical_three:0
The given SavedModel SignatureDef contains the following output(s):
outputs['dense'] tensor_info:
dtype: DT_FLOAT
shape: (-1, num_classes)
name: StatefulPartitionedCall:0
Method name is: tensorflow/serving/predict
Как я называю Serving API:
curl -d '{"instances": [ {"categorical_one": "ABC", "categorical_two": "DEF", "categorical_three": "GHI"} ] }' -X POST http://localhost:8501/v1/models/my-model-name/versions/1:predict
Сообщение об ошибке, которое я получаю:
{ "error": "Input to reshape is a tensor with 100 values, but the requested shape has 10000\n\t [[{{node StatefulPartitionedCall/StatefulPartitionedCall/model/dense_features/categorical_one_indicator/Reshape}}]]" }
Обратите внимание, что я опубликовал только важные части кода, а не каждую строку.
Любая идея приветствуется!