load_model Exception

[kyocheng]

Hi, I'm looking for a solution to my issue, and I tried several suggestions with no success. I hope someone can help me with this.

this is my code

import os
import sys
print(sys.path)
#import keras
import numpy as np

from sklearn.datasets import load_files
from keras.models import load_model
import tensorflow as tf
import autokeras as ak
from datetime import datetime
# dataset = keras.utils.get_file(
#     fname="aclImdb.tar.gz",
#     origin="http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz",
#     extract=True,
# )

# set path to dataset
IMDB_DATADIR = "/root/blazehttp/cmdi_single"

classes = ["pos", "neg"]
train_data = load_files(
    os.path.join(IMDB_DATADIR, "train"), shuffle=True, categories=classes
)
test_data = load_files(
    os.path.join(IMDB_DATADIR, "test"), shuffle=False, categories=classes
)

x_train = np.array(train_data.data)[:10]
y_train = np.array(train_data.target)[:10]
x_test = np.array(test_data.data)[:20]
y_test = np.array(test_data.target)[:20]

print(x_train.shape)  # (25000,)
print(y_train.shape)  # (25000, 1)
y_train = y_train.reshape(-1, 1)
print(y_train.shape)  # (25000, 1)
print("x_train[0][:50]:",x_train[0][:50])  # this film was just brilliant casting



clf = ak.TextClassifier(
    overwrite=False, max_trials=1,directory="autokeras_model_cmd",project_name="autokeras_project_cmd"
)  # It only tries 1 model as a quick demo.

clf.fit(x_train, y_train,epochs=1,  batch_size=2) # Added validation data for monitoring performance)


print("start test:")
predicted_y = clf.predict(x_test)

print("predicted_y:",predicted_y)
print("x_test:",x_test)
print("y_test:",y_test)

model = clf.export_model()

print(type(model))  # <class 'tensorflow.python.keras.engine.training.Model'>

#时间字符串
now = datetime.now()
formatted_str_ = now.strftime("%Y-%m-%d-%H-%M-%S")
file_name = "model_autokeras.keras" #"model_autokeras_" + formatted_str_ + ".keras"
print("keras_model.summary:",file_name)
model.summary()
print("model.save:",file_name)
model.save(file_name)  # 修复方式：确保文件扩展名为.keras

loaded_model = load_model(
    file_name,custom_objects=ak.CUSTOM_OBJECTS
)

 Non-trainable params: 0 (0.00 B)
model.save: model_autokeras_2024-10-10-10-11-44.keras
Traceback (most recent call last):
  File "/root/test/automl/cmdi-test.py", line 73, in <module>
    loaded_model = load_model(
  File "/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_api.py", line 189, in load_model
    return saving_lib.load_model(
  File "/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py", line 365, in load_model
    return _load_model_from_fileobj(
  File "/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py", line 442, in _load_model_from_fileobj
    model = _model_from_config(
  File "/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py", line 431, in _model_from_config
    model = deserialize_keras_object(
  File "/usr/local/lib/python3.10/dist-packages/keras/src/saving/serialization_lib.py", line 718, in deserialize_keras_object
    instance = cls.from_config(inner_config)
  File "/usr/local/lib/python3.10/dist-packages/keras/src/models/model.py", line 526, in from_config
    return functional_from_config(
  File "/usr/local/lib/python3.10/dist-packages/keras/src/models/functional.py", line 518, in functional_from_config
    process_node(layer, node_data)
  File "/usr/local/lib/python3.10/dist-packages/keras/src/models/functional.py", line 465, in process_node
    layer(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 122, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/usr/local/lib/python3.10/dist-packages/keras_nlp/src/utils/tensor_utils.py", line 74, in wrapper
    x = fn(self, x, y=y, sample_weight=sample_weight, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/keras_nlp/src/models/text_classifier_preprocessor.py", line 110, in call
    x = tuple(self.tokenizer(segment) for segment in x)
  File "/usr/local/lib/python3.10/dist-packages/keras_nlp/src/models/text_classifier_preprocessor.py", line 110, in <genexpr>
    x = tuple(self.tokenizer(segment) for segment in x)
  File "/usr/local/lib/python3.10/dist-packages/keras_nlp/src/utils/tensor_utils.py", line 62, in wrapper
    x = fn(self, x, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/keras_nlp/src/tokenizers/tokenizer.py", line 205, in call
    return self.tokenize(inputs, *args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/keras_nlp/src/utils/tensor_utils.py", line 62, in wrapper
    x = fn(self, x, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/keras_nlp/src/tokenizers/word_piece_tokenizer.py", line 475, in tokenize
    self._check_vocabulary()
  File "/usr/local/lib/python3.10/dist-packages/keras_nlp/src/tokenizers/word_piece_tokenizer.py", line 468, in _check_vocabulary
    raise ValueError(
ValueError: Exception encountered when calling BertTextClassifierPreprocessor.call().

Could not automatically infer the output shape / dtype of 'bert_text_classifier_preprocessor' (of type BertTextClassifierPreprocessor). Either the `BertTextClassifierPreprocessor.call()` method is incorrect, or you need to implement the `BertTextClassifierPreprocessor.compute_output_spec() / compute_output_shape()` method. Error encountered:

Exception encountered when calling BertTokenizer.call().

No vocabulary has been set for WordPieceTokenizer. Make sure to pass a `vocabulary` argument when creating the layer.

Arguments received by BertTokenizer.call():
  ? inputs=tf.Tensor(shape=(None,), dtype=string)
  ? args=<class 'inspect._empty'>
  ? training=None
  ? kwargs=<class 'inspect._empty'>

Arguments received by BertTextClassifierPreprocessor.call():
  ? args=('<KerasTensor shape=(None,),\

Setup Details
Include the details about the versions of:

Python 3.10.15
autokeras 2.0.0
keras 3.6.0
keras-nlp 0.15.1
keras-tuner 1.4.7
numpy 1.26.4
tensorflow 2.17.0
tensorflow-io-gcs-filesystem 0.37.1
tensorflow-text 2.17.0