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Python 3.x model.summary()和plot_model()显示tensorflow.keras中构建的模型中的任何内容_Python 3.x_Tensorflow_Keras_Deep Learning_Tensorflow2.0 - Fatal编程技术网

Python 3.x model.summary()和plot_model()显示tensorflow.keras中构建的模型中的任何内容

Python 3.x model.summary()和plot_model()显示tensorflow.keras中构建的模型中的任何内容,python-3.x,tensorflow,keras,deep-learning,tensorflow2.0,Python 3.x,Tensorflow,Keras,Deep Learning,Tensorflow2.0,我正在测试一些东西,包括动态地构建一个FCNN网络。想法是根据给定的列表构建层数和神经元数,虚拟代码为: neurons = [10,20,30] # First Dense has 10 neuron, 2nd has 20 and third has 30 inputs = keras.Input(shape=(1024,)) x = Dense(10,activation='relu')(inputs) for n in neurons: x = Dense(n,activatio

我正在测试一些东西,包括动态地构建一个
FCNN
网络。想法是根据给定的列表构建层数和神经元数,虚拟代码为:

neurons = [10,20,30] # First Dense has 10 neuron, 2nd has 20 and third has 30

inputs = keras.Input(shape=(1024,))
x = Dense(10,activation='relu')(inputs)

for n in neurons:
  x = Dense(n,activation='relu')(x)

out = Dense(1,activation='sigmoid')(x)
model = Model(inputs,out)
model.summary()
keras.utils.plot_model(model,'model.png')
for layer in model.layers:
  print(layer.name)

令我惊讶的是,它什么也没有显示。我甚至再次编译并运行了这些函数,结果什么也没有显示

model.summary
始终显示可训练和不可训练参数的数量,但不显示模型结构和层名称。为什么会这样?或者这正常吗?

关于
model.summary()
,不要一次将
tf 2.x
和单机版混用。如果我在
tf2.x
中运行您的模型,我会得到预期的结果

from tensorflow.keras.layers import *
from tensorflow.keras import Model 
from tensorflow import keras 

# your code ...
model.summary()
Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 1024)]            0         
_________________________________________________________________
dense (Dense)                (None, 10)                10250     
_________________________________________________________________
dense_1 (Dense)              (None, 10)                110       
_________________________________________________________________
dense_2 (Dense)              (None, 20)                220       
_________________________________________________________________
dense_3 (Dense)              (None, 30)                630       
_________________________________________________________________
dense_4 (Dense)              (None, 1)                 31        
=================================================================
Total params: 11,241
Trainable params: 11,241
Non-trainable params: 0
_________________________________
关于打印模型,打印模型时可以使用两个选项。以下是一个例子:

keras.utils.plot_model(model, show_dtype=True, 
                       show_layer_names=True, show_shapes=True,  
                       to_file='model.png')

哦!我的错!如果意外使用
keras.model
。谢谢你的帮助。很高兴帮助你