Below is the Python code for making an ensemble model. All the inputs are the same for all three models. But what if the models have different input shapes due to different window size, such as LSTM models. So the input shapes for Model A would be (window_size_A, features) and for Model B would be (window_size_B, features). The window sizes are different but the number of features are the same. As such, due to the different window size, the training data of the same dataset is split differently for each model such that the X_train.shape for model A: (train_data_A, window_size_A, output) And for Model B: (train_data_B, window_size_B, output). Note the training data is from the same dataset but the length is different due to the different window size. How would you make an ensemble of these models?
def get_model(): inputs = keras.Input(shape=(128,)) outputs = layers.Dense(1)(inputs) return keras.Model(inputs, outputs) model1 = get_model() model2 = get_model() model3 = get_model() inputs = keras.Input(shape=(128,)) y1 = model1(inputs) y2 = model2(inputs) y3 = model3(inputs) outputs = layers.average([y1, y2, y3]) ensemble_model = keras.Model(inputs=inputs, outputs=outputs)