Tensorflow Hub Error When Saving Model As H5 Or Savedmodel

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 i

Solution 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

Solution 2:

You should use tf.keras.models.save_model(model,'NeuralNetworkModel') You will get saved model in a folder that can be used later in your sequential nework