How to merge two different models in Keras

Overview

In the Keras, the user can design a model in two ways. Each method has its own use cases. These methods are:

  • Sequential API: We use this method when the objective is to write the code in a linear fashion. This method makes debugging easier because of its readability.
  • Functional API: It follows a non-linear topology for designing models. As a user, we have the flexibilty to join different layers of different networks.

We use the functional APIs usually when we’re working with more than two models simultaneously.

In this shot, we’ll discuss how a user can merge two separate models from a built in keras function; keras.layers.concatenate()

It is defined as follows:

merged_layer= keras.layers.concatenate(inputs, axix, name="")

  • inputs: The layers of two models at which we want to merge these models.

  • axis: The axis along which we want to concatenate the two layers.

  • name: The name of concatenated/merged layer.

Let’s look at the steps to merge the two models.

  • For this, we’ll require the following libraries:
import numpy as np
import tensorflow as tf
from tensorflow import keras
from keras import layers
from keras.layers import Input, Dense, concatenate
from keras.models import Model
from keras.utils import plot_model
  • Model A and B are the sample models that we want to merge.

Note that functional API is used in this job. However, a sequential API (using the add() function) would also have served the purpose.

# Model A
a_ip_img = Input(shape=(32,32,1), name="Input_a")
al_1 = Dense(64, activation = "relu",name ="a_layer_1")(a_ip_img)
al_2 = Dense(128, activation="relu",name ="a_layer_2")(al_1)
al_3 = Dense(64, activation="relu",name ="a_layer_3")(al_2)
al_4 = Dense(32, activation="sigmoid",name ="a_output_layer")(al_3)
#Model B
b_ip_img = Input(shape=(32,32,1), name="Input_b")
bl_1 = Dense(64, activation="relu",name ="b_layer_1")(b_ip_img)
bl_2 = Dense(32, activation = "sigmoid",name ="b_output_layer")(bl_1)
  • Now that we have our models, we can concatenate them using the concatenate() function.
#Merging model A and B
a_b = concatenate([al_4,bl_2],name="concatenated_layer")
  • We can opt to make further layers after the concatenated layer or call this layer output_layer. Here, another dense layer has been added before model generation.
#Merging model A and B
a_b = concatenate([al_4,bl_2],name="concatenated_layer")
#Final Layer
output_layer = Dense(16, activation = "sigmoid", name = "output_layer")(a_b)
#Model Definition 
merged = Model(inputs=[(a_ip_img,b_ip_img)],outputs=[output_layer], name = "merged model")

We need to be careful when initializing the final model using the keras.Model() function. We must mention the inputs of both models in the inputs field.

  • We can analyze the resulting model using keras.utils or keras.Model.summary().
#Model Details
merged.summary()
keras.utils.plot_model(merged, "output/architecture.png", show_shapes=True)

The final is attached below. We can also run this and play around with different inputs and parameters.

import numpy as np
import tensorflow as tf
from tensorflow import keras
from keras import layers
from tensorflow.keras.layers import Input, Dense, concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.utils import plot_model
# Model A
a_ip_img = Input(shape=(32,32,1), name="Input_a")
al_1 = Dense(64, activation = "relu",name ="a_layer_1")(a_ip_img)
al_2 = Dense(128, activation="relu",name ="a_layer_2")(al_1)
al_3 = Dense(64, activation="relu",name ="a_layer_3")(al_2)
al_4 = Dense(32, activation="sigmoid",name ="a_output_layer")(al_3)
#Model B
b_ip_img = Input(shape=(32,32,1), name="Input_b")
bl_1 = Dense(64, activation="relu",name ="b_layer_1")(b_ip_img)
bl_2 = Dense(32, activation = "sigmoid",name ="b_output_layer")(bl_1)
#Merging model A and B
a_b = concatenate([al_4,bl_2],name="concatenated_layer")
#Final Layer
output_layer = Dense(16, activation = "sigmoid", name = "output_layer")(a_b)
#Model Definition 
merged = Model(inputs=[(a_ip_img,b_ip_img)],outputs=[output_layer], name = "merged model")
#Model Details
merged.summary()
keras.utils.plot_model(merged, "output/architecture.png", show_shapes=True)

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