- Trending Categories
- Data Structure
- Operating System
- C Programming
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
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( weights='imagenet', include_top=False, pooling='avg') 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([ base_model, layers.Dense(1000), ]) print("Compile the model") print("Fit the model to the test data") model.compile(...) model.fit(...)
Code credit − https://www.tensorflow.org/guide/keras/sequential_model
A convolutional model with pre-trained weights is loaded Downloading data from https://storage.googleapis.com/tensorflow/kerasapplications/xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h583689472/83683744 [==============================] - 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.
- How can Tensorflow and pre-trained model be used to create base model from pre-trained convnets?
- How can Tensorflow and pre-trained model be used to compile the model using Python?
- How can a customized model be pre-trained?
- How can Tensorflow and pre-trained model be used to continue training the model using Python?
- How can Tensorflow be used with pre-trained model to rescale pixel values?
- How can Tensorflow and pre-trained model be used to visualize the data using Python?
- How can Tensorflow be used to extract features with the help of pre-trained model using Python?
- How can Tensorflow and pre-trained model be used for feature extraction?
- How can Tensorflow and pre-trained model be used for fine tuning?
- How can Tensorflow used with the pre-trained model to compile the model?
- How can Keras be used to plot the model using Python?
- How can Keras be used to train the model using Python?
- How can Keras be used to evaluate the model using Python?
- How can Tensorflow and pre-trained model be used for evaluation and prediction of data using Python?
- How can Tensorflow and pre-trained model be used to add classification head to the model?