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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") model.load_weights(latest) 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 − https://www.tensorflow.org/tutorials/keras/save_and_load

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.

- Related Questions & Answers
- How can Keras be used to load weights from checkpoint and re-evaluate the model using Python?
- How can Tensorflow be used to load the flower dataset and model off the disk using Python?
- How can Tensorflow be used to train and compile the augmented model?
- How can Tensorflow be used to compare the linear model and the Convolutional model using Python?
- How can Tensorflow be used to visualize the results of the model?
- How can Tensorflow be used to compile the model using Python?
- How can Tensorflow be used to train the model using Python?
- How can Tensorflow be used with Estimators to optimize the model?
- How can Tensorflow and pre-trained model be used to compile the model using Python?
- How can Tensorflow be used to export the model so that it can be used later?
- How can Tensorflow be used to compile and fit the model using Python?
- After normalization, how can Tensorflow be used to train and build the model?
- How can Tensorflow be used to load the Illiad dataset using Python?
- How can Tensorflow be used to load the flower dataset and work with it?
- How can Tensorflow and pre-trained model be used to add classification head to the model?

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