无法向保存的 Keras 模型添加图层.“模型"对象没有属性“添加" [英] Cannot add layers to saved Keras Model. 'Model' object has no attribute 'add'

问题描述

我使用 model.save() 保存了一个模型.我正在尝试重新加载模型并添加几层并调整一些超参数，但是，它引发了 AttributeError.

I have a saved a model using model.save(). I'm trying to reload the model and add a few layers and tune some hyper-parameters, however, it throws the AttributeError.

使用 load_model() 加载模型.

我想我不明白如何将图层添加到保存的图层.如果有人能在这里指导我，那就太好了.我是深度学习和使用 keras 的新手，所以我的要求可能很愚蠢.

I guess I'm missing understanding how to add layers to saved layers. If someone can guide me here, it will be great. I'm a novice to deep learning and using keras, so probably my request would be silly.

片段:

prev_model = load_model('final_model.h5') # loading the previously saved model.

prev_model.add(Dense(256,activation='relu'))
prev_model.add(Dropout(0.5))
prev_model.add(Dense(1,activation='sigmoid'))

model = Model(inputs=prev_model.input, outputs=prev_model(prev_model.output))

以及它抛出的错误:

Traceback (most recent call last):
  File "image_classifier_3.py", line 39, in <module>
    prev_model.add(Dense(256,activation='relu'))
AttributeError: 'Model' object has no attribute 'add'

我知道添加层适用于新的 Sequential() 模型，但我们如何添加到现有保存的模型中?

I know adding layers works on new Sequential() model, but how do we add to existing saved models?

推荐答案

add 方法仅存在于 顺序模型 (顺序class)，这是一个更强大但更复杂的函数模型的简单接口(Model 类).load_model 将始终返回一个 Model 实例，这是最通用的类​​.

The add method is present only in sequential models (Sequential class), which is a simpler interface to the more powerful but complicated functional model (Model class). load_model will always return a Model instance, which is the most generic class.

您可以查看示例以了解如何组合不同的模型，但最终的想法是，Model 的行为与任何其他层非常相似.所以你应该能够做到:

You can look at the example to see how you can compose different models, but the idea is that, in the end, a Model behaves pretty much like any other layer. So you should be able to do:

prev_model = load_model('final_model.h5') # loading the previously saved model.

new_model = Sequential()
new_model.add(prev_model)
new_model.add(Dense(256,activation='relu'))
new_model.add(Dropout(0.5))
new_model.add(Dense(1,activation='sigmoid'))

new_model.compile(...)

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