How can Keras be used with a pre-trained model using Python?

Tensorflow is a machine learning framework that is provided by Google. It is an open-source framework used in conjunction with Python to implement algorithms, deep learning applications, and much more. It is used in research and for production purposes.

Keras means ‘horn’ in Greek. Keras was developed as a part of the research for the project ONEIROS (Open ended Neuro-Electronic Intelligent Robot Operating System). Keras is a deep learning API, which is written in Python. It is a high-level API that has a productive interface that helps solve machine learning problems.

It runs on top of Tensorflow framework. It was built to help experiment in a quick manner. It provides essential abstractions and building blocks that are essential in developing and encapsulating machine learning solutions.

It is highly scalable and comes with cross-platform abilities. This means Keras can be run on TPU or clusters of GPUs. Keras models can also be exported to run in a web browser or a mobile phone as well.

Keras is already present within the Tensorflow package. It can be accessed using the below line of code.

import tensorflow
from tensorflow import keras

We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook. Following is the code snippet −


print("A convolutional model with pre-trained weights is loaded")
base_model = keras.applications.Xception(
print("This model is freezed")
base_model.trainable = False
print("A sequential model is used to add a trainable classifier on top of the base")
model = keras.Sequential([
print("Compile the model")
print("Fit the model to the test data")

Code credit −


A convolutional model with pre-trained weights is loaded
Downloading data from [==============================] - 1s 0us/step
This model is freezed
A sequential model is used to add a trainable classifier on top of the base
Compile the model
Fit the model to the test data


  • A sequential model stack can be used, along with the help of a pre-trained model to initialize classification layers.

  • Once this model has been built, it is compiled.

  • Once the compilation is complete, this model can be fit to the training data.

Updated on: 18-Jan-2021


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