How can Tensorflow be used to test, reset the model and load the latest checkpoint?

PythonServer Side ProgrammingProgrammingTensorflow

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. It has optimization techniques that help in performing complicated mathematical operations quickly. This is because it uses NumPy and multi−dimensional arrays. These multi−dimensional arrays are also known as ‘tensors’. The framework supports working with deep neural network. It is highly scalable, and comes with many popular datasets.

The ‘tensorflow’ package can be installed on Windows using the below line of code −

pip install tensorflow

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 −


print("A new model instance is created")
model = create_model()
print("The previously saved weights are loaded")
print("The model is being re−evaluated")
loss, acc = model.evaluate(test_images, test_labels, verbose=2)
print("This is the restored model, with accuracy: {:5.3f}%".format(100 * acc))

Code credit −


A new model instance is created
The previously saved weights are loaded
The model is being re-evaluated
32/32 - 0s - loss: 0.4828 - sparse_categorical_accuracy: 0.8770
This is the restored model, with accuracy:87.700%


  • Again, a new model of the instance is created using the ‘create_model’ method.

  • The previously saved weights are loaded to this instance using the ‘load_weights’ method.

  • This new model is evaluated using the ‘evaluate’ method.

  • Its accuracy and loss during training is determined.

  • These values are displayed on the console.

Updated on 20-Jan-2021 13:37:47