nested_models.py

from keras.utils import plot_model
from keras.models import Model
from keras.layers import Input, Dense, concatenate

def create_inner_model():
    input=Input((10,))

    d1 = Dense(128)(input)
    d2 = Dense(64)(d1)

    return Model(inputs=input, outputs=d2)

inner = create_inner_model()

# share layers for model
i1 = Input((10,))
i2 = Input((10,))
out1 = inner(i1)
out2 = inner(i2)

# concat output
concat = concatenate([out1, out2])

# output binary classification
out = Dense(2, activation='softmax')(concat)

final=Model(inputs=[i1, i2], outputs=out)
plot_model(final, to_file='final_model.png')
plot_model(inner, to_file='inner_model.png')