Keras functional API and TensorFlow Hub

Question

I'm trying to use a Universal Sentence Encoder from TF Hub as a keras layer in a functional way. I would like to use hub.KerasLayer with Keras Functional API, but i'm not sure how to achieve that, so far I've only seen exmaples of hub.KerasLayer with the Sequential API

import tensorflow_hub as hub
import tensorflow as tf
from tensorflow.keras import layers
import tf_sentencepiece


use_url = 'https://tfhub.dev/google/universal-sentence-encoder-multilingual-large/1'

english_sentences = ["dog", "Puppies are nice.", "I enjoy taking long walks along the beach with my dog."]
english_sentences = np.array(english_sentences, dtype=object)[:, np.newaxis]


seq = layers.Input(shape=(None, ), name='sentence', dtype=tf.string)
module = hub.KerasLayer(hub.Module(use_url))(seq)
model = tf.keras.models.Model(inputs=[seq], outputs=[module])
model.summary()

x = model.predict(english_sentences)
print(x)

the code above runs into this error when passing the input layer to the embedding: TypeError: Can't convert 'inputs': Shape TensorShape([Dimension(None), Dimension(None)]) is incompatible with TensorShape([Dimension(None)])

Is it possible to use hub.KerasLayer with keras functional API in TensorFlow 1.x? if it can be done, how?

Answer 1

Try This

sentence_encoding_layer = hub.KerasLayer("https://tfhub.dev/google/universal-sentence-encoder/4",
                                         trainable=False,
                                         input_shape = [],
                                         dtype = tf.string,
                                         name = 'U.S.E')

inputs = tf.keras.layers.Input(shape = (), dtype = 'string',name = 'input_layer')

x = sentence_encoding_layer(inputs)
x = tf.keras.layers.Dense(64,activation = 'relu')(x)

outputs = tf.keras.layers.Dense(1,activation = 'sigmoid',name = 'output_layer')(x)

model = tf.keras.Model(inputs,outputs,name = 'Transfer_learning_USE')
model.summary()

model.predict([sentence])