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.
The ‘tensorflow’ package can be installed on Windows using the below line of code −
pip install tensorflow
Tensor is a data structure used in TensorFlow. It helps connect edges in a flow diagram. This flow diagram is known as the ‘Data flow graph’. Tensors are nothing but multidimensional array or a list.
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("The model is saved to HDF5 format") model.save('my_model.h5') print("The same model is recreated with same weights and optimizer") new_model = tf.keras.models.load_model('my_model.h5') print("The architecture of the model is observed") new_model.summary()
Code credit − https://www.tensorflow.org/tutorials/keras/save_and_load
The model newly created can be saved using the ‘save’ function.
It can be specifically saved to hdf5 format using the extension ‘h5’.
This model is loaded using the previous weights and optimizer.
The details about the new model is displayed on the console using the ‘summary’ method.