无法保存自定义子类模型

灵感来自 tf.keras.Model subclassing
我可以训练它并获得成功的结果,但是 .
我用python3.6和tensorflow v1.10(或v1.9)

import tensorflow as tf
from tensorflow.keras.datasets import mnist


class Classifier(tf.keras.Model):
    def __init__(self):
        super().__init__(name="custom_model")

        self.batch_norm1 = tf.layers.BatchNormalization()
        self.conv1 = tf.layers.Conv2D(32, (7, 7))
        self.pool1 = tf.layers.MaxPooling2D((2, 2), (2, 2))

        self.batch_norm2 = tf.layers.BatchNormalization()
        self.conv2 = tf.layers.Conv2D(64, (5, 5))
        self.pool2 = tf.layers.MaxPooling2D((2, 2), (2, 2))

    def call(self, inputs, training=None, mask=None):
        x = self.batch_norm1(inputs)
        x = self.conv1(x)
        x = tf.nn.relu(x)
        x = self.pool1(x)

        x = self.batch_norm2(x)
        x = self.conv2(x)
        x = tf.nn.relu(x)
        x = self.pool2(x)

        return x


if __name__ == '__main__':
    (x_train, y_train), (x_test, y_test) = mnist.load_data()

    x_train = x_train.reshape(*x_train.shape, 1)[:1000]
    y_train = y_train.reshape(*y_train.shape, 1)[:1000]

    x_test = x_test.reshape(*x_test.shape, 1)
    y_test = y_test.reshape(*y_test.shape, 1)

    y_train = tf.keras.utils.to_categorical(y_train)
    y_test = tf.keras.utils.to_categorical(y_test)

    model = Classifier()

    inputs = tf.keras.Input((28, 28, 1))

    x = model(inputs)
    x = tf.keras.layers.Flatten()(x)
    x = tf.keras.layers.Dense(10, activation="sigmoid")(x)

    model = tf.keras.Model(inputs=inputs, outputs=x)
    model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
    model.fit(x_train, y_train, epochs=1, shuffle=True)

    model.save("./my_model")

错误消息:

1000/1000 [==============================] - 1s 1ms/step - loss: 4.6037 - acc: 0.7025
Traceback (most recent call last):
  File "/home/user/Data/test/python/mnist/mnist_run.py", line 62, in <module>
    model.save("./my_model")
  File "/home/user/miniconda3/envs/ml3.6/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py", line 1278, in save
    save_model(self, filepath, overwrite, include_optimizer)
  File "/home/user/miniconda3/envs/ml3.6/lib/python3.6/site-packages/tensorflow/python/keras/engine/saving.py", line 101, in save_model
    'config': model.get_config()
  File "/home/user/miniconda3/envs/ml3.6/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py", line 1049, in get_config
    layer_config = layer.get_config()
  File "/home/user/miniconda3/envs/ml3.6/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py", line 1028, in get_config
    raise NotImplementedError
NotImplementedError

Process finished with exit code 1

我查了一下错误线,发现 获取配置 自我是图形网络

有人处理这个问题吗?

更新1:

找到注释(用于模型保存)
文件:keras/engine/network.py

#子类网络不可序列化
#(除非序列化是由
#子类网络的作者)。

我想知道,为什么他们不在 documentation (例如:“使用没有保存能力的子类!”)

发现于 keras documentation

在子类模型中,模型的拓扑被定义为Python代码
(而不是静态的图层图)。也就是说模特
无法检查或序列化拓扑。因此,以下
方法和属性不可用于子类模型:

model.to_yaml()和model.to_json()
model.get_config()和model.save()。

所以,没有办法通过使用子类来保存模型。
Model.save_weights()