Issue replicating AutoKeras StructuredDataClassifier

[NullCodex]

I have a model that I generated using AutoKeras and I want to replicate the model so that I can construct it with keras tuner to do further hyperparameter tuning. But I am running into issues replicating the model. The model summary of the autokeras model is:

Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 11)]              0         
_________________________________________________________________
multi_category_encoding (Mul (None, 11)                0         
_________________________________________________________________
normalization (Normalization (None, 11)                23        
_________________________________________________________________
dense (Dense)                (None, 16)                192       
_________________________________________________________________
re_lu (ReLU)                 (None, 16)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 32)                544       
_________________________________________________________________
re_lu_1 (ReLU)               (None, 32)                0         
_________________________________________________________________
dense_2 (Dense)              (None, 3)                 99        
_________________________________________________________________
classification_head_1 (Softm (None, 3)                 0         
=================================================================
Total params: 858
Trainable params: 835
Non-trainable params: 23

Layer config

{'batch_input_shape': (None, 11), 'dtype': 'string', 'sparse': False, 'ragged': False, 'name': 'input_1'}
{'name': 'multi_category_encoding', 'trainable': True, 'dtype': 'float32', 'encoding': ListWrapper(['int', 'int', 'int', 'int', 'int', 'int', 'int', 'int', 'int', 'int', 'int'])}
{'name': 'normalization', 'trainable': True, 'dtype': 'float32', 'axis': (-1,)}
{'name': 'dense', 'trainable': True, 'dtype': 'float32', 'units': 16, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}
{'name': 're_lu', 'trainable': True, 'dtype': 'float32', 'max_value': None, 'negative_slope': array(0., dtype=float32), 'threshold': array(0., dtype=float32)}
{'name': 'dense_1', 'trainable': True, 'dtype': 'float32', 'units': 32, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}
{'name': 're_lu_1', 'trainable': True, 'dtype': 'float32', 'max_value': None, 'negative_slope': array(0., dtype=float32), 'threshold': array(0., dtype=float32)}
{'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units': 3, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}
{'name': 'classification_head_1', 'trainable': True, 'dtype': 'float32', 'axis': -1}

My training data is a dataframe that's converted to string type with both numerical and categorical data. Since the output is softmax i used LabelBinarizer to convert the target classes.

To make sure the model was replicated properly, i used keras.clone_model. The optimizer and loss function follows from the original model. I was able to find the values using model.optimizer and model.loss to create a copy of the model and try training it myself. But when I tried to train it myself the accuracy does not improve despite hitting 500 epochs.

Is there something that I am missing when it comes to training the model from scratch?