Machine learning Inception-v3迁移学习&x27；冻结'；层

我使用了Inception-v3网络进行迁移学习。网络中的前172层被“冻结”。但是Inception-v3网络只有48层。“额外”层从何而来

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非常感谢

实际上，初始模块要复杂一些。它没有一个分支。之后，这些多个分支被连接成单个分支。如果列出keras中的所有图层，则会得到以下结果：

[ 1 ]  <keras.engine.topology.InputLayer object at 0x7f94c6b95750>
[ 2 ]  <keras.layers.convolutional.Conv2D object at 0x7f94c6577610>
[ 3 ]  <keras.layers.normalization.BatchNormalization object at 0x7f94c65778d0>
[ 4 ]  <keras.layers.core.Activation object at 0x7f94c6577a90>
[ 5 ]  <keras.layers.convolutional.Conv2D object at 0x7f94c6900f50>
[ 6 ]  <keras.layers.normalization.BatchNormalization object at 0x7f94c6878bd0>
[ 7 ]  <keras.layers.core.Activation object at 0x7f94c690e750>
[ 8 ]  <keras.layers.convolutional.Conv2D object at 0x7f94c68d1e10>
[ 9 ]  <keras.layers.normalization.BatchNormalization object at 0x7f94c68d1f10>
[ 10 ]  <keras.layers.core.Activation object at 0x7f94c6652b90>
[ 11 ]  <keras.layers.pooling.MaxPooling2D object at 0x7f94c6319390>
[ 12 ]  <keras.layers.convolutional.Conv2D object at 0x7f94c6309490>
[ 13 ]  <keras.layers.normalization.BatchNormalization object at 0x7f94c62cea50>
[ 14 ]  <keras.layers.core.Activation object at 0x7f94c628b890>
[ 15 ]  <keras.layers.convolutional.Conv2D object at 0x7f94c62a4490>
[ 16 ]  <keras.layers.normalization.BatchNormalization object at 0x7f94c62b7750>
[ 17 ]  <keras.layers.core.Activation object at 0x7f94c626ac50>
[ 18 ]  <keras.layers.pooling.MaxPooling2D object at 0x7f94c6226a10>
[ 19 ]  <keras.layers.convolutional.Conv2D object at 0x7f94c6064850>
[ 20 ]  <keras.layers.normalization.BatchNormalization object at 0x7f94c6076b10>
[ 21 ]  <keras.layers.core.Activation object at 0x7f94c602bf10>
[ 22 ]  <keras.layers.convolutional.Conv2D object at 0x7f94c6146ad0>
[ 23 ]  <keras.layers.convolutional.Conv2D object at 0x7f94c5ff7710>
[ 24 ]  <keras.layers.normalization.BatchNormalization object at 0x7f94c6155d90>
[ 25 ]  <keras.layers.normalization.BatchNormalization object at 0x7f94c5f889d0>
[ 26 ]  <keras.layers.core.Activation object at 0x7f94c6119e90>
[ 27 ]  <keras.layers.core.Activation object at 0x7f94c5fbaed0>
[ 28 ]  <keras.layers.pooling.AveragePooling2D object at 0x7f94c5e88850>
[ 29 ]  <keras.layers.convolutional.Conv2D object at 0x7f94c62329d0>
[ 30 ]  <keras.layers.convolutional.Conv2D object at 0x7f94c60d4990>
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[ 42 ]  <keras.layers.convolutional.Conv2D object at 0x7f94c5c4f990>
[ 43 ]  <keras.layers.normalization.BatchNormalization object at 0x7f94c5c4f590>
[ 44 ]  <keras.layers.core.Activation object at 0x7f94c5c22310>
[ 45 ]  <keras.layers.convolutional.Conv2D object at 0x7f94c5db9910>
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[ 93 ]  <keras.layers.core.Activation object at 0x7f94c5484fd0>
[ 94 ]  <keras.layers.convolutional.Conv2D object at 0x7f94c561dd90>
[ 95 ]  <keras.layers.convolutional.Conv2D object at 0x7f94c5441750>
[ 96 ]  <keras.layers.normalization.BatchNormalization object at 0x7f94c567fd50>
[ 97 ]  <keras.layers.normalization.BatchNormalization object at 0x7f94c5452910>
[ 98 ]  <keras.layers.core.Activation object at 0x7f94c5581e50>
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[ 100 ]  <keras.layers.pooling.MaxPooling2D object at 0x7f94c53c6a50>
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[ 119 ]  <keras.layers.core.Activation object at 0x7f94c4f924d0>
[ 120 ]  <keras.layers.pooling.AveragePooling2D object at 0x7f94c4ed3f50>
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