ValueError:应在输入列表上调用合并层.Tensorflow Keras [英] ValueError: A merge layer should be called on a list of inputs. Tensorflow Keras
问题描述
我目前正在尝试使用MobileNetV2的前50层.因此,我想提取这些图层并创建一个新模型.
I am currently trying to use the first 50 layers of the MobileNetV2. Therefore, I want to extract those layers and create a new model.
我以为我可以调用每个层,但是"block_2_add"层会导致错误,我不明白为什么.
I thought I could just call every layer, but the "block_2_add" layer causes an error and I don't understand why.
import tensorflow as tf
from keras.models import Model
mobile_net=tf.keras.applications.mobilenet_v2.MobileNetV2(input_shape=(224,224,3), alpha=0.5, include_top=False, weights='imagenet')
inputs = Input(shape=(224, 224, 3))
x=mobile_net.layers[1](inputs)
for layer in mobile_net.layers[2:50]:
x=layer(x)
{'name': 'block_2_add', 'trainable': True, 'dtype': 'float32'}
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-77-5873b9344fa3> in <module>()
3 for layer in mobile_net.layers[2:50]:
4 print(layer.get_config())
----> 5 x=layer(x)
6
7 for layer in mobile_net.layers[:50]:
1 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/merge.py in call(self, inputs)
119 def call(self, inputs):
120 if not isinstance(inputs, list):
--> 121 raise ValueError('A merge layer should be called on a list of inputs.')
122 if self._reshape_required:
123 reshaped_inputs = []
ValueError: A merge layer should be called on a list of inputs.
推荐答案
我的猜测是MobileNetV2不是顺序模型,即层图不是线性的.如果您只需要模型的输出而不是任何中间层的输出,我认为下面的代码就可以完成这项工作(即使您似乎想在输出之前计算最后一层,结果仍然应该是您想要的):
My guess is that the MobileNetV2 is not a sequential model, i.e. the layers graph is not linear. If you want just the output of the model and not any intermediate layer outputs, I think following code should do the job (even though it seems that you want to compute the last layer before output, the result still should be what you want):
import tensorflow as tf
from keras.models import Model
mobile_net=tf.keras.applications.mobilenet_v2.MobileNetV2(input_shape=(224,224,3), alpha=0.5, include_top=False, weights='imagenet')
inputs = Input(shape=(224, 224, 3))
output = mobile_net(inputs)
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