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How can Keras be used to save the entire 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 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 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
Example
print("A new model instance is created")
model = create_model()
print("The model is fit to the training data")
model.fit(train_images, train_labels, epochs=5)
print("The model is saved")
!mkdir -p saved_model
model.save('saved_model/my_model')
ls saved_modelCode credit −https://www.tensorflow.org/tutorials/keras/save_and_load
Output
Explanation
The new model is created using the ‘create_model’ method.
This new model is fit to the training data.
A new directory is created to store the new model.
Once the fitting is done, it is saved using the ‘save’ method.
The path to the saved model is displayed on the console.
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