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Keras模型中Resnet50的中间输出

tf.keras deep-learning keras python

1

user2458922 Maria · 技术社区 · 4 年前

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()

//培训//完成模型拟合(..)

现在如何仅从层输出?

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)

收到错误

AttributeError: 层resnet50具有多个入站节点,因此 “层输出”的概念定义不清。使用 get_output_at(node_index) 相反添加代码添加Markdown

0 回复 | 直到 4 年前

1

user11530462 user11530462 4 年前

请使用重试 tf.keras 以导入模型和图层。

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

然后运行相同的程序:

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)

输出:

resnet50
(1, 2048)
(2048,)