Deep learning models developed by PyTorch are used to predict the results of some complex problems like speech/ image recognition systems, Classification, and regression problems. The PyTorch library is used to develop these models in Python language. Now, let’s go through the concept of deep learning using PyTorch.
Different Machine learning and Deep learning models are built using the Python language. PyTorch is extensively used for the development of deep learning models.
What is Deep Learning?
Deep learning is a sub-branch of machine learning which is used to predict the result using provided dataset. Different layers in deep learning models are used for the prediction of provided data. The main and basic three layers of deep learning models are the Input, Hidden, and Output layers.
The Input layer is the first layer of deep learning models which is used to accept the data and help to process it as a training dataset. The middle layer is the Hidden layer which consists of some functions like Activation and Transfer functions. Both functions are used to process and extract features from the data. The Hidden layer is responsible for processing the data using different functions and passing it to the output layer.
The last layer of deep learning is the Output layer. This Output layer helps to convert this processed data from the hidden layer into the structured output form. The Output layer contains the result of the deep learning model.
These layers are the only basic structure of the deep learning model. Some variations are possible according to the change in the algorithms. Various algorithms are used to solve different problems in the deep learning domain.
Use of Deep Learning
Deep learning is a trending branch of machine learning used to solve different problems. These problems might be of different types like classification, regression, prediction, Image, and speech recognition problems. All these problems need some optimal solutions, and this is provided by deep learning algorithms. As mentioned above, all layers of deep learning algorithms/ Artificial Neural Network is used to solve these problems. So, let’s understand the PyTorch library used for deep learning algorithms.
PyTorch is a machine learning library used for the development of deep learning algorithms. There are various libraries available in the domain of machine learning and deep learning for development. TensorFlow is popular for development, but it is more static and complex than the PyTorch library. PyTorch provides dynamic nature to the programming and architecture of the overall programming model.
PyTorch is developed by ‘Meta‘ formerly Facebook. It has a similar syntax and functionality to the Python programming language, which makes it easy to use and integrate. Let’s see the details of the PyTorch library.
Comparison Between TensorFlow, Keras, and PyTorch
TensorFlow, Keras, and PyTorch all three are mainly used while developing any type of deep learning model. Their application is the same, but the overall structure and interface are different from each other. For example, TensorFlow is developed by Google and is very hard for beginners because we need to create a network-like structure while developing any model.
On the other hand, the Keras is very easy and supportive while developing the deep learning model. In the initial stages, the Keras was a separate model, but later it was merged into TensorFlow as a high-level API.
The PyTorch is very easy and user-friendly as compared to the other two models. All types of users, like beginners, intermediate, and advanced levels, can use this platform to execute the deep learning model easily. PyTorch was developed by Facebook to provide an easy platform for developing deep learning models.
Many advantages of PyTorch library include the easy to use, adaptive to any model, flexible, and adaptive to mobile systems too. This library helps to integrate with mobile and embedded systems. So, overall all three are used for development, but the process becomes easy and adaptive because of PyTorch.
Deep Learning Using PyTorch
To install the PyTorch library, we need Google Colab, which provides a platform to write code without any cost. It is free and provides GPU. The first step is to install all the libraries required to use PyTorch.
Step 1: Install Torch and Other Required Libraries
In step 1, we need to install the Torch, which is a basic library for deep learning. Other than this, we are also installing some basic libraries such as numpy, matplotlib, and torchvision.
import torch import torchvision import numpy as np import matplotlib.pyplot as plt EPOCH = 10 BATCH_SIZE= 64
Here, in lines 5 and 6, EPOCH and BATCH_SIZE are mentioned. These parameters are used to tell the number of samples we are passing through the deep learning network. After installing all the required libraries, we can start writing code for deep-learning models using PyTorch.
Step 2: Loading of The Data
For 2nd step, we need to load the data required to form a model. The dataset may be in the form of numbers, images, audio signals, etc. This data needs to be loaded properly before using or applying the algorithm. This data is used to collect features from them to train the model. For example, We can use Python libraries to load different types of datasets like CSV files, categorial data, etc. In the same way, PyTorch is used to include customized datasets using Dataset Class.
This PyTorch library also provides some functions to understand the dataset which is provided to train the models. For example, __len()__ function is used to calculate the length of the provided dataset.
Step 3: Preprocessing of The Data
The next step for the formation of a deep learning model is to preprocess the dataset which is loaded. Pre-processing is nothing but the conversion of data from a 2D to a 1D model. If the dataset consists of images, then it’ll update all the images according to the vector sizes. Vector sizes are user-defined, like 20*20, 28*28, etc. To deal with the multidimensional arrays, the PyTorch library provides the ‘tensor’. This tensor is mentioned as a ‘GPU tensor’ , ‘CPU tensor’. There are nine types of ‘GPU tensor’ and nine types of ‘CPU tensor’.
Step 4: Define the Model
The next step in this process is to define the model. There is ‘torch.nn’ package in the PyTorch library which is used to include different functionalities which are used to define the deep learning model. Neural networks are defined using this torch.nn package. We can use different functions like linear, sigmoid, Sequential, SoftMax, activation functions, etc.
Step 5: Loss Function and Optimizer
The next important step to define any deep learning model is to mention the Loss function and Optimizer. 20 different loss functions are provided by PyTorch package for the deep learning model. The nn package is used to include all types of loss functions. For example, mean square loss, maximum likelihood, raw scores, etc.
The PyTorch library provides the torch.optim module to use the optimizer function in the deep learning algorithm.
Step 6: Train the Model
After defining the models, loss functions, and optimizers, we need to train the model to make predictions and get results. A training loop needs to be defined to train any model. The calling of the training loop helps to use the optimizer again, which helps to train the model correctly. ‘EPOCHS’ is used to define the loop number of the deep learning model.
Step 7: Test the model
After completing all six steps, we can test the model using our data. Testing is important part of making predictions and getting results. The evaluation loop needs to be defined again to test the model in the PyTorch package.
Practical Implementations Of PyTorch Projects
There are different areas where we can use PyTorch for model development. There are different models in the PyTorch library, like VGG, ResNet, and custom models, which are used for the classification of images. These image classification models are used for dividing the dataset or an image into different categories.
The most popular application of the PyTorch library is object detection. Object detection helps to detect the object from the image or live video. This is very trending in the field of autonomous vehicles in advanced technologies for Visually impaired people. In simple words, every deep learning model which is related to classification, detection, and transfer learning is easily developed using this PyTorch library.
Frequently Asked Questions and Discussion About PyTorch
Define PyTorch and its use.
Ans: PyTorch is a library available in Python used for the development of deep learning algorithms. The PyTorch library provides a platform for users to develop a model using a graphical approach. It is a very easy and compatible model for development.
What is the difference between different libraries available to implement deep learning algorithms?
Ans: There are different libraries, like TensorFlow, Keras, PyTorch, etc., used to develop the deep learning algorithm. For beginners, TensorFlow is a little difficult to understand due to the framework, but PyTorch is very easy and adaptive. Keras is now considered a part of TensorFlow as a high-level API. So, for beginners, PyTorch is a very good choice for developing deep learning algorithms.
Which types of models are developed using PyTorch in the deep learning domain?
Ans: There are different models which are developed using PyTorch. For example, Image recognition models, Object detection models, Reinforcement learning, etc. All these models can be implemented using PyTorch.
This article defines the process of executing a deep learning model using PyTorch in 7 steps. After executing these seven steps, we can develop any module to make predictions. The base of deep learning is a neural network, which is well executed using PyTorch in Python. This article also highlights some points, like the difference between TensorFlow, Keras, and PyTorch. Some use, and practical implementations of PyTorch are also I Hope you will enjoy this article!