Python Code for Ensemble Model for Image Classification

Model averaging is an ensemble technique where multiple sub-models contribute equally to a combined prediction. In this video, you will discover how to develop a stacked generalization ensemble for deep learning neural networks.
A model averaging ensemble combines the predictions from multiple trained models. A limitation of this approach is that each model contributes the same amount to the ensemble prediction, regardless of how well the model performed.
Link to download the dataset:
https://www.kaggle.com/datasets/yasserhessein/the-kvasir-dataset

Link for Implementing Pre-trained Models for Image Classification:
https://www.youtube.com/watch?v=Fxy6WTnUIww&t=181s

Python Code to Combine Pre-Trained Models:
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.layers import Input, Average
model_1 = load_model('/content/drive/MyDrive/model-01-0.9044.hdf5')
model_1 = Model(inputs=model_1.inputs,
outputs=model_1.outputs,
name='name_of_model_1')
model_2 = load_model('/content/drive/MyDrive/model-04-0.8156.hdf5')
model_2 = Model(inputs=model_2.inputs,
outputs=model_2.outputs,
name='name_of_model_2')
models = [model_1, model_2]
model_input = Input(shape=(224, 224, 3))
model_outputs = [model(model_input) for model in models]
ensemble_output = Average()(model_outputs)
ensemble_model = Model(inputs=model_input, outputs=ensemble_output, name='ensemble')