What is use of clone_model functions in keras why is it used?

Recipe Objective

What is the 'clone_model' function in Keras? Why is it used?

A clone mode is a model that reproduces the behavior of the original model, where we can input new tensors, with new weights to the layers. The cloned models behave differently if we customize the cloned model or we use the custom clone_function.

Hands-On Guide to the Art of Tuning Locality Sensitive Hashing in Python

Step 1- Importing Libraries

import numpy as np import keras from keras.models import Sequential from keras.layers import UpSampling2D

Step 2- Cloning.

We will make a model to clone.

# define input data X = np.array([10, 6, 3, 20]) # show input data for context print(X) # reshape input data into one sample a sample with a channel X = X.reshape((1, 2, 2, 1)) # define model model = Sequential() model.add(UpSampling2D(input_shape=(2, 2, 1))) model.summary()

[10  6  3 20]
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
up_sampling2d (UpSampling2D) (None, 4, 4, 1)           0         
=================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0
_________________________________________________________________

Step-3 we will clone the model.

#cloning of model model_copy= keras.models.clone_model(model) model_copy.build((None, 5)) # replace 10 with number of variables in input layer model_copy.compile(optimizer='adam', loss='categorical_crossentropy') model_copy.summary()

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
up_sampling2d (UpSampling2D) (None, 4, 4, 1)           0         
=================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0
_________________________________________________________________