将模型另存为 H5 或 SavedModel 时出现 TensorFlow Hub 错误

Question

I want to use this TF Hub asset:https://tfhub.dev/google/imagenet/resnet_v1_50/feature_vector/3

Versions:

Version:  1.15.0-dev20190726
Eager mode:  False
Hub version:  0.5.0
GPU is available

Code

feature_extractor_url = "https://tfhub.dev/google/imagenet/resnet_v1_50/feature_vector/3"
feature_extractor_layer = hub.KerasLayer(module,
                                         input_shape=(HEIGHT, WIDTH, CHANNELS))

I get:

ValueError: Importing a SavedModel with tf.saved_model.load requires a 'tags=' argument if there is more than one MetaGraph. Got 'tags=None', but there are 2 MetaGraphs in the SavedModel with tag sets [[], ['train']]. Pass a 'tags=' argument to load this SavedModel.

I tried:

module = hub.Module("https://tfhub.dev/google/imagenet/resnet_v1_50/feature_vector/3",
                    tags={"train"})
feature_extractor_layer = hub.KerasLayer(module, 
                                         input_shape=(HEIGHT, WIDTH, CHANNELS))

But when I try to save the model I get:

tf.keras.experimental.export_saved_model(model, tf_model_path)
# model.save(h5_model_path) # Same error 

NotImplementedError: Can only generate a valid config for `hub.KerasLayer(handle, ...)`that uses a string `handle`.
Got `type(handle)`: <class 'tensorflow_hub.module.Module'>

Tutorial here

Answer 1

It's been a while, but assuming you have migrated to the TF2, this can easily be accomplished with the most recent model version as follows:

import tensorflow as tf
import tensorflow_hub as hub

num_classes=10 # For example
m = tf.keras.Sequential([
    hub.KerasLayer("https://tfhub.dev/google/imagenet/resnet_v1_50/feature_vector/5", trainable=True)
    tf.keras.layers.Dense(num_classes, activation='softmax')
])
m.build([None, 224, 224, 3])  # Batch input shape.

# train as needed

m.save("/some/output/path")

Please update this question if that doesn't work for you. I believe your issue arose from mixing hub.Module with hub.KerasLayer . The model version you were using was in TF1 Hub format, so within TF1 it is meant to be used exclusively with hub.Module , and not mixed with hub.KerasLayer . Within TF2, hub.KerasLayer can load TF1 Hub format models directly from their URL for composition in larger models, but they cannot be fine-tuned.

Please refer to this compatibility guide for more information

Answer 2

您应该使用tf.keras.models.save_model(model,'NeuralNetworkModel')您将在一个文件夹中获得保存的模型，该文件夹可以稍后在您的顺序网络中使用