TensorFlow 2.1

Entrenamiento y guardado de un modelo

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf

mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(256, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    # Name the output layer output, which is used to obtain the result during model inference.
    tf.keras.layers.Dense(10, activation='softmax', name="output")
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=10)

tf.keras.models.save_model(model, "./mnist")

Código de inferencia

En el archivo de código de inferencia de modelo customize_service.py, agregue una clase de modelo hijo. Esta clase de modelo hijo hereda las propiedades de su clase de modelo padre. Para obtener más información sobre las instrucciones de importación de diferentes tipos de clases de modelo padre, consulte Tabla 1.

import logging
import threading

import numpy as np
import tensorflow as tf
from PIL import Image

from model_service.tfserving_model_service import TfServingBaseService

logger = logging.getLogger()
logger.setLevel(logging.INFO)


class MnistService(TfServingBaseService):

    def __init__(self, model_name, model_path):
        self.model_name = model_name
        self.model_path = model_path
        self.model = None
        self.predict = None

       # The label file can be loaded here and used in the post-processing function.
        # Directories for storing the label.txt file on OBS and in the model package

        # with open(os.path.join(self.model_path, 'label.txt')) as f:
        #     self.label = json.load(f)
        # Load the model in saved_model format in non-blocking mode to prevent blocking timeout.
        thread = threading.Thread(target=self.load_model)
        thread.start()

    def load_model(self):
        # Load the model in saved_model format.
        self.model = tf.saved_model.load(self.model_path)

        signature_defs = self.model.signatures.keys()

        signature = []
        # only one signature allowed
        for signature_def in signature_defs:
            signature.append(signature_def)

        if len(signature) == 1:
            model_signature = signature[0]
        else:
            logging.warning("signatures more than one, use serving_default signature from %s", signature)
            model_signature = tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY

        self.predict = self.model.signatures[model_signature]

    def _preprocess(self, data):
        images = []
        for k, v in data.items():
            for file_name, file_content in v.items():
                image1 = Image.open(file_content)
                image1 = np.array(image1, dtype=np.float32)
                image1.resize((28, 28, 1))
                images.append(image1)

        images = tf.convert_to_tensor(images, dtype=tf.dtypes.float32)
        preprocessed_data = images

        return preprocessed_data

    def _inference(self, data):

        return self.predict(data)

    def _postprocess(self, data):

        return {
            "result": int(data["output"].numpy()[0].argmax())
        }