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What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?
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.
TensorFlow is used in research and for production purposes and 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. It uses GPU computation and automates the management of resources. It comes with multitude of machine learning libraries, and is well-supported and documented.
The framework has the ability to run deep neural network models, train them, and create applications that predict relevant characteristics of the respective datasets. 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. They can be identified using three main attributes:
- Rank: It tells about the dimensionality of the tensor. It can be understood as the order of the tensor or the number of dimensions in the tensor that has been defined.
- Type: It tells about the data type associated with the elements of the Tensor. It can be a one dimensional, two dimensional or n dimensional tensor.
- Shape: It is the number of rows and columns together.
Keras means ‘horn’ in Greek. 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. 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
Above, we imported Keras.
- What is Keras with respect to Tensorflow?
- What is Feed-Forward Neural Networks?
- What are Neural Networks?
- How can Tensorflow be used to work with tf.data API and tokenizer?
- How does linear regression work with Tensorflow in Python?
- How can Tensorflow be used to load the flower dataset and work with it?
- How can Tensorflow be used to work with character substring in Python?
- What are the applications for Neural Networks?
- What is segmentation with respect to text data in Tensorflow?
- How can Tensorflow and Tensorflow text be used to tokenize string data?
- What are uncide scripts with respect to Tensorflow and Python?
- What are the advantages and disadvantages of Artificial Neural Networks?
- How can Tensorflow be used with Estimators to create feature columns and input functions?
- How can Tensorflow be used to download the flower dataset using keras sequential API?
- How can Tensorflow be used to explore the flower dataset using keras sequential API?