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How can Keras be used to manually save the weights 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 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 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 −
Example
print("The weights are saved")
model.save_weights('./checkpoints/my_checkpoint')
print("A new model instance is created")
model = create_model()
print("Restore the weights of the old model")
model.load_weights('./checkpoints/my_checkpoint')
print("The model is being evaluated")
loss, acc = model.evaluate(test_images, test_labels, verbose=2)
print("Restored model, accuracy: {:5.2f}%".format(100 * acc))Code credit − https://www.tensorflow.org/tutorials/keras/save_and_load
Output
Explanation
The weights for the new model are saved using the ‘save_weights’ method.
Another new model is created using the ‘create_model’ method.
The weights of the old model are restored.
The new model is associated with the old weights and evaluated.
This new model is evaluated using the ‘evaluate’ method.
Its accuracy and loss during training is determined.
These values are displayed on the console.
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