TensorFlow Tutorial – Introduction to TensorFlow
Machine learning is the process of training the system so that the system can make predictions as humans. The process of machine learning is a lengthy process with complex and difficult model-building. The Google developers team went for an easy framework- TensorFlow.
In this Tensorflow tutorial article, we will learn more about TensorFlow.
TensorFlow is an open-source library for implementing machine learning. Google discovered TensorFlow for the purpose of machine learning. It provides a simple approach to solve complex computations of machine learning with the help of graphs.
TensorFlow Introduction
The TensorFlow is a combination of tensor and flow, which indicates tensor as nodes in the graph and their flow with the help of edges. The graph contains the nodes that represent operations, and the edges represent tensors.
Tensors are the representation of multi-dimensional arrays or vectors. It is the combination of digits, representing the data in its code form. The representation of tensors is in the form of edges.
The data storage is done in vectors that have a shape, which also defines the dimensionality of a matrix. The tensors represent scalars and vectors. A tensor is an object having three unique properties: Name, shape, and dtype.
Graphs are an important element of TensorFlow. It provides the visualization for the operations and every movement of nodes in the model. Every computation is an abstract representation of calculation taken out by the model.
Graphs are mainly made using TensorBoard. TensorBoard is a library of TensorFlow which deals with the visualization. It provides images, histograms, audio for a better understanding of the model. It can run on CPU as well as GPU or any operating system.
Features of TensorFlow
Based on its multiple functionalities, the library provides many features such as
1. It can run on any operating system for e.g., Windows, IoS, MacOs, Linux, etc.
2. It is an open-source platform.
3. It provides an API that helps to build machine learning models easily.
4. It provides integration with other libraries such as Keras, Pandas, NumPy for manipulation of data.
5. It can run with a variety of languages such as C, C++, JS, C#, Java, Python, R, Ruby.
6. It is scalable.
7. It provides the tool that eases modeling, i.e., TensorBoard, which helps to visualize the data.
8. It provides various functions as optimizers, loss, and accuracy to improve the learning of a system.
Why is TensorFlow popular?
With its wide and open accessibility, it is easily available to all the users. It provides services that are important for machine learning. The library has proper documentation for each and every functionality of the platform. It has API support for all the deep and machine learning algorithms, such as CNN and RNN.
It supports the graphical visualization, which helps to view each operation. Visualization also helps to debug the code in an easy way. With the advancing features, it brings most of the users to implement their use case.
Types of Tensors
There are three types of tensors, namely: constants, variables, and placeholders.
● Constants: It represents the node that stores only one value, which does not change with time. The parameters in sync with constant are value, name= const, shape= None, dtype= None. Its denotation is tf.constant.
● Variables: It represents the different values at each run; hence it is known as a variable. On the execution, it prints the most current value. The denotation is tf.variable.
● Placeholders: The creation of nodes for these tensors is done, but the data is fed later on. It is done when inputs depend on data taken later on from an external entity. Hence, this tensor requires the only data type and shape given in the definition initially.
The visualization of the graphs is done using an interface TensorBoard. TensorBoard is a collection of web services that help in debugging, eager execution, and a dynamic runtime. It’s a tool that eases the debugging with its functionality and is the sole method of Visualization.
TensorFlow API
TensorFlow has various interface supports to execute the machine learning algorithms. Its API allows desktop, android, or any other server using the distribution among CPUs and GPUs. The categorization of Application Programming Interface (API) is done in two divisions:
● Low-level API
● High-level API
Low-level API provides full control over the programming and the models. TensorFlow Core is the low-level API. TensorFlow provides supports to many programming APIs such as C, C++, Python, C#, R, Ruby, etc.
High-level API is built for the easy performance of tasks using tf.contrib.learn. It is built over the low-level AP (TensorFlow Core) which helps maintain consistency and reduces redundancy.
Any program based on the TensorFlow Core can be considered as a combination of two parts:
- Develop a graph of different TensorFlow nodes, which is also known as a Computational graph.
- Execute the graph. It helps to analyze the nodes and operations in a graph. It is run using a session that manages the state and control of nodes at runtime.
Process of TensorFlow
The process of TensorFlow model building usually follows these steps:
1. Define the model – Tune the model according to the user’s requirements.
2. Define a loss function – Set the function to a value so that maximum accuracy can be achieved by optimization.
3. Get training data – Get the data for training and set apart the 20-40% data for testing.
4. Run the training data and use an “optimizer” to adjust the variables to fit the model – Run the model on data, and on each iteration(epoch), it optimizes the learning of a model.
Summary
So we have seen introduction to TensorFlow in this TensorFlow tutorial. Google’s invention for machine learning TensorFlow is an open-source framework. This library allows to create, execute the algorithms which are user-friendly and suitable for a system.
It allows eager execution through the visualization. It is provisioned with the help of TensorBoard and eases the calculations, thus reducing the complexity of computations.