假设来自 Keras Model 的 Re.net50 的中间 Output

Question

import keras
print(keras.__version__)
#2.3.0

from keras.models import Sequential
from keras.layers import Input, Dense,TimeDistributed
from keras.models import Model

model = Sequential()
resnet = ResNet50(include_top = False, pooling = 'avg', weights = 'imagenet')
model.add(resnet)

model.add(Dense(10, activation = 'relu'))
model.add(Dense(6, activation = 'sigmoid'))
model.summary()

// 训练 // model.fit(..) 完成

现在如何从图层中提取 output？

model.layers[0]._name='resnet50'
print(model.layers[0].name) # prints resnet50

layer_output = model.get_layer("resnet50").output
intermediate_model = Model(inputs=[model.input, resnet.input], outputs=[layer_output])
result = intermediate_model.predict([x, x])

print(result.shape)
print(result[0].shape)

出错了

AttributeError：层 re.net50 有多个入站节点，因此“层输出”的概念定义不明确。 请改用get_output_at(node_index) 。 添加代码添加 Markdown

Answer 1

请再次尝试使用tf.keras导入 model 和图层。

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, Dense,TimeDistributed
from tensorflow.keras.models import Model

然后运行相同的：

model.layers[0]._name='resnet50'
print(model.layers[0].name) # prints resnet50

layer_output = model.get_layer("resnet50").output
intermediate_model = Model(inputs=[model.input, resnet.input], outputs=[layer_output])

x = tf.ones((1, 250, 250, 3))
result = intermediate_model.predict([x, x])

print(result.shape)
print(result[0].shape)

Output：

resnet50
(1, 2048)
(2048,)