[FIXED] How to save TextVectorization to disk in tensorflow?

Issue

I have trained a TextVectorization layer (see below), and I want to save it to disk, so that I can reload it next time? I have tried pickle and joblib.dump(). It does not work.

from tensorflow.keras.layers.experimental.preprocessing import TextVectorization 

text_dataset = tf.data.Dataset.from_tensor_slices(text_clean) 
    
vectorizer = TextVectorization(max_tokens=100000, output_mode='tf-idf',ngrams=None)
    
vectorizer.adapt(text_dataset.batch(1024))

The generated error is the following:

InvalidArgumentError: Cannot convert a Tensor of dtype resource to a NumPy array

How can I save it?

Solution

Instead of pickling the object, pickle the configuration and weights. Later unpickle it and use configuration to create the object and load the saved weights. Official docs here.

Code

text_dataset = tf.data.Dataset.from_tensor_slices([
                                                   "this is some clean text", 
                                                   "some more text", 
                                                   "even some more text"]) 
# Fit a TextVectorization layer
vectorizer = TextVectorization(max_tokens=10, output_mode='tf-idf',ngrams=None)    
vectorizer.adapt(text_dataset.batch(1024))

# Vector for word "this"
print (vectorizer("this"))

# Pickle the config and weights
pickle.dump({'config': vectorizer.get_config(),
             'weights': vectorizer.get_weights()}
            , open("tv_layer.pkl", "wb"))

print ("*"*10)
# Later you can unpickle and use 
# `config` to create object and 
# `weights` to load the trained weights. 

from_disk = pickle.load(open("tv_layer.pkl", "rb"))
new_v = TextVectorization.from_config(from_disk['config'])
# You have to call `adapt` with some dummy data (BUG in Keras)
new_v.adapt(tf.data.Dataset.from_tensor_slices(["xyz"]))
new_v.set_weights(from_disk['weights'])

# Lets see the Vector for word "this"
print (new_v("this"))

Output:

tf.Tensor(
[[0.         0.         0.         0.         0.91629076 0.
  0.         0.         0.         0.        ]], shape=(1, 10), dtype=float32)
**********
tf.Tensor(
[[0.         0.         0.         0.         0.91629076 0.
  0.         0.         0.         0.        ]], shape=(1, 10), dtype=float32)

Answered By - mujjiga