【发布时间】：2020-02-21 21:50:26
【问题描述】：
我最近从同事那里收到了一个 keras 模型 (facenet_keras.h5)。该模型将输入 160 x 160 x 3 图像并输出 1 x 128 向量。我的工作是将这个给定模型转换为 iOS 项目的 coreML 模型。
我已使用 coremltools 尝试将模型转换为 mlmodel，但我不断收到消息 Keras layer '<class 'keras.layers.core.Lambda'>' not supported.
我已经包含了模型中的所有层。该模型非常重 (91 mb)。
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<keras.layers.merge.Concatenate at 0x15038b2b0>,
<keras.layers.convolutional.Conv2D at 0x15038b2e8>,
<keras.layers.core.Lambda at 0x15038b470>,
<keras.layers.core.Activation at 0x15038b4a8>,
<keras.layers.convolutional.Conv2D at 0x15038b4e0>,
<keras.layers.normalization.BatchNormalization at 0x15038b518>,
<keras.layers.core.Activation at 0x15038b6a0>,
<keras.layers.convolutional.Conv2D at 0x15038b7b8>,
<keras.layers.normalization.BatchNormalization at 0x15038b7f0>,
<keras.layers.core.Activation at 0x15038b978>,
<keras.layers.convolutional.Conv2D at 0x15038ba90>,
<keras.layers.convolutional.Conv2D at 0x15038bac8>,
<keras.layers.normalization.BatchNormalization at 0x15038bc50>,
<keras.layers.normalization.BatchNormalization at 0x15038bdd8>,
<keras.layers.core.Activation at 0x15038bef0>,
<keras.layers.core.Activation at 0x150373080>,
<keras.layers.merge.Concatenate at 0x1503730b8>,
<keras.layers.convolutional.Conv2D at 0x1503730f0>,
<keras.layers.core.Lambda at 0x150373278>,
<keras.layers.core.Activation at 0x1503732b0>,
<keras.layers.convolutional.Conv2D at 0x1503732e8>,
<keras.layers.normalization.BatchNormalization at 0x150373320>,
<keras.layers.core.Activation at 0x1503734a8>,
<keras.layers.convolutional.Conv2D at 0x1503735c0>,
<keras.layers.normalization.BatchNormalization at 0x1503735f8>,
<keras.layers.core.Activation at 0x150373780>,
<keras.layers.convolutional.Conv2D at 0x150373898>,
<keras.layers.convolutional.Conv2D at 0x1503738d0>,
<keras.layers.normalization.BatchNormalization at 0x150373a58>,
<keras.layers.normalization.BatchNormalization at 0x150373be0>,
<keras.layers.core.Activation at 0x150373cf8>,
<keras.layers.core.Activation at 0x150373e10>,
<keras.layers.merge.Concatenate at 0x150373e48>,
<keras.layers.convolutional.Conv2D at 0x150373e80>,
<keras.layers.core.Lambda at 0x15037f080>,
<keras.layers.core.Activation at 0x15037f0b8>,
<keras.layers.convolutional.Conv2D at 0x15037f0f0>,
<keras.layers.normalization.BatchNormalization at 0x15037f128>,
<keras.layers.core.Activation at 0x15037f2b0>,
<keras.layers.convolutional.Conv2D at 0x15037f3c8>,
<keras.layers.normalization.BatchNormalization at 0x15037f400>,
<keras.layers.core.Activation at 0x15037f588>,
<keras.layers.convolutional.Conv2D at 0x15037f6a0>,
<keras.layers.convolutional.Conv2D at 0x15037f6d8>,
<keras.layers.normalization.BatchNormalization at 0x15037f860>,
<keras.layers.normalization.BatchNormalization at 0x15037f9e8>,
<keras.layers.core.Activation at 0x15037fb00>,
<keras.layers.core.Activation at 0x15037fc18>,
<keras.layers.merge.Concatenate at 0x15037fc50>,
<keras.layers.convolutional.Conv2D at 0x15037fc88>,
<keras.layers.core.Lambda at 0x15037fcc0>,
<keras.layers.core.Activation at 0x15037fe80>,
<keras.layers.convolutional.Conv2D at 0x15037feb8>,
<keras.layers.normalization.BatchNormalization at 0x141c9e0b8>,
<keras.layers.core.Activation at 0x141c9e1d0>,
<keras.layers.convolutional.Conv2D at 0x141c9e208>,
<keras.layers.normalization.BatchNormalization at 0x141c9e390>,
<keras.layers.core.Activation at 0x141c9e4a8>,
<keras.layers.convolutional.Conv2D at 0x141c9e4e0>,
<keras.layers.convolutional.Conv2D at 0x141c9e668>,
<keras.layers.normalization.BatchNormalization at 0x141c9e7f0>,
<keras.layers.normalization.BatchNormalization at 0x141c9e908>,
<keras.layers.core.Activation at 0x141c9ea20>,
<keras.layers.core.Activation at 0x141c9ea58>,
<keras.layers.merge.Concatenate at 0x141c9ea90>,
<keras.layers.convolutional.Conv2D at 0x141c9eac8>,
<keras.layers.core.Lambda at 0x141c9ec50>,
<keras.layers.pooling.GlobalAveragePooling2D at 0x141c9ec88>,
<keras.layers.core.Dropout at 0x141c9ecc0>,
<keras.layers.core.Dense at 0x141c9ed30>,
<keras.layers.normalization.BatchNormalization at 0x141c9ed68>
有没有其他方法可以做到这一点？我自己对 coremltools 还是很陌生，所以任何帮助都将不胜感激。有没有办法我可以使用 add_custom_layers=True 和 custom_conversion_functions={}) 快速实现这个自定义层“keras.core.lambda”？
【问题讨论】：
-
你解决了这个问题吗？我对 LSTM 层有同样的问题
-
嗨，显然 Lambda 层和 LSTM 由 coremltools 支持。在我更新到新的操作系统和新的 CoreML 后，它恰好发生了转换。查看github.com/tf-coreml/tf-coreml。
-
@AneeshPrabu 您使用的是哪个版本？我正在使用最新的 4.0b3，它仍然给我同样的问题/错误 Lambda >>> ValueError: Keras layer '
' not supported.
标签： swift h5py tensorflow2.0 coreml coremltools