我正在尝试将 Keras 模型保存在 H5 文件中。 Keras 模型有一个 自定义层
当我尝试 恢复模型 时,出现以下错误:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-5-0fbff9b56a9d> in <module>()
      1 model.save('model.h5')
      2 del model
----> 3 model = tf.keras.models.load_model('model.h5')

8 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/generic_utils.py in class_and_config_for_serialized_keras_object(config, module_objects, custom_objects, printable_module_name)
    319   cls = get_registered_object(class_name, custom_objects, module_objects)
    320   if cls is None:
--> 321     raise ValueError('Unknown ' + printable_module_name + ': ' + class_name)
    322
    323   cls_config = config['config']

ValueError: Unknown layer: CustomLayer

你能告诉我我应该如何保存和加载所有自定义 Keras 层的权重吗? (另外,保存时没有警告,是否可以从我已经保存但现在无法加载的 H5 文件中加载模型?)

这是此错误的最小工作代码示例 (MCVE),以及完整的扩展消息:Google Colab Notebook

为了完整起见,这是我用来制作自定义图层的代码。get_configfrom_config 都工作正常。

class CustomLayer(tf.keras.layers.Layer):
    def __init__(self, k, name=None):
        super(CustomLayer, self).__init__(name=name)
        self.k = k

    def get_config(self):
        return {'k': self.k}

    def call(self, input):
        return tf.multiply(input, 2)

model = tf.keras.models.Sequential([
    tf.keras.Input(name='input_layer', shape=(10,)),
    CustomLayer(10, name='custom_layer'),
    tf.keras.layers.Dense(1, activation='sigmoid', name='output_layer')
])
model.save('model.h5')
model = tf.keras.models.load_model('model.h5')

最佳答案

更正编号 1 是使用 Custom_Objectsloading Saved Model 即替换代码,

new_model = tf.keras.models.load_model('model.h5')


new_model = tf.keras.models.load_model('model.h5', custom_objects={'CustomLayer': CustomLayer})

由于我们使用 Custom Layersbuild Model 和之前的 Saving it,我们应该使用 0x251812141 它,而我们应该使用 0x251812141 和 0x2518121431 而 13321313135

更正数2是在Custom Layer的Custom Objects函数中添加Loading,如
def __init__(self, k, name=None, **kwargs):
        super(CustomLayer, self).__init__(name=name)
        self.k = k
        super(CustomLayer, self).__init__(**kwargs)

完整的工作代码如下所示:
import tensorflow as tf

class CustomLayer(tf.keras.layers.Layer):
    def __init__(self, k, name=None, **kwargs):
        super(CustomLayer, self).__init__(name=name)
        self.k = k
        super(CustomLayer, self).__init__(**kwargs)


    def get_config(self):
        config = super(CustomLayer, self).get_config()
        config.update({"k": self.k})
        return config

    def call(self, input):
        return tf.multiply(input, 2)

model = tf.keras.models.Sequential([
    tf.keras.Input(name='input_layer', shape=(10,)),
    CustomLayer(10, name='custom_layer'),
    tf.keras.layers.Dense(1, activation='sigmoid', name='output_layer')
])
tf.keras.models.save_model(model, 'model.h5')
new_model = tf.keras.models.load_model('model.h5', custom_objects={'CustomLayer': CustomLayer})

print(new_model.summary())

上述代码的输出如下所示:
WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
custom_layer_1 (CustomLayer) (None, 10)                0
_________________________________________________________________
output_layer (Dense)         (None, 1)                 11
=================================================================
Total params: 11
Trainable params: 11
Non-trainable params: 0

希望这可以帮助。快乐学习!

关于python - 使用自定义层保存 Keras 模型,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/62280161/

10-14 14:12
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