在任意层上分割Keras模型 [英] Splitting a Keras model on an arbitrary layer

问题描述

我正在尝试创建一个在用户指定的图层上拆分Keras模型的函数.我有以下代码:

I'm trying to create a function that splits a Keras model on a user specified layer. I have the following code:

def return_split_models(model, layer):
    model_f, model_h = Sequential(), Sequential()
    for current_layer in range(0, layer+1):
        model_f.add(model.layers[current_layer])
    for current_layer in range(layer+1, len(model.layers)):
        model_h.add(model.layers[current_layer])
    return model_f, model_h

但是，当我们返回model_h并调用摘要时，我们将看到一个从未调用过该模型的ValueError.从其他帖子来看，这似乎与model_h的输入有关，但是我找不到能推广到任何指定层的示例.有人有指导吗?

However, when we return model_h and call a summary, we will see a ValueError that the model has never been called. From looking at other posts it seems like this has to do with the inputs for model_h, however I cannot find examples which generalize to any specified layer. Does anyone have any guidance?

推荐答案

您需要将InputLayer添加到model_h.

from keras.layers import InputLayer

def return_split_models(model, layer):
    model_f, model_h = Sequential(), Sequential()
    for current_layer in range(0, layer+1):
        model_f.add(model.layers[current_layer])
    # add input layer
    model_h.add(InputLayer(input_shape=model.layers[layer+1].input_shape[1:]))
    for current_layer in range(layer+1, len(model.layers)):
        model_h.add(model.layers[current_layer])
    return model_f, model_h

一个例子:

model = Sequential()
model.add(Dense(50,input_shape=(100,)))
model.add(Dense(40))
model.add(Dense(30))
model.add(Dense(20))
model.add(Dense(10))

model_f, model_h = return_split_models(model, 2)
print(model_f.summary())
print(model_h.summary())

# print
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 50)                5050      
_________________________________________________________________
dense_2 (Dense)              (None, 40)                2040      
_________________________________________________________________
dense_3 (Dense)              (None, 30)                1230      
=================================================================
Total params: 8,320
Trainable params: 8,320
Non-trainable params: 0
_________________________________________________________________
None
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_4 (Dense)              (None, 20)                620       
_________________________________________________________________
dense_5 (Dense)              (None, 10)                210       
=================================================================
Total params: 830
Trainable params: 830
Non-trainable params: 0
_________________________________________________________________
None

这篇关于在任意层上分割Keras模型的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！