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 was developed as a part of 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 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 to remove layers −
print("Removing layers using the pop function") model.pop() print("The current number of layers in the model after eliminating one layer") print(len(model.layers))
Code credit − https://www.tensorflow.org/guide/keras/sequential_model
Removing layers using the pop function The current number of layers in the model after eliminating one layer 2
The ‘pop’ function can be called by associating the name of the model with the function using dot operator.
Once this is done, the length of the layers can be checked.
This will help confirm that one of the layers has actually been deleted.