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Implementing a convolutional neural network (CNN) in PyTorch

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by sneha@openweaver.com dot icon Updated: Jul 20, 2023

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The convolutional neural network is a type of deep learning model. It helps to design for analyzing visual data such as images and videos. The human brain's organization and functioning of the visual cortex inspires it. This is achieved using convolutional layers, which apply convolution operations to input images.  

 

There is a different type of layers used in convolutional neural networks (CNN):  

Convolutional Layers:  

Convolutional layers are the fundamental building blocks of CNN. These layers perform the convolution operation by applying learnable filters to the input. Each filter is small and slides across the input volume. It computes dot products at every spatial location.  

Pooling Layers:  

They help degrade samples of the feature maps generated by the convolutional layers. They help reduce the spatial dimensions of the data while retaining important information. Max pooling selects the most value within a local neighbourhood.  

Fully Connected Layers:  

Fully connected layers, known as dense layers, are used in the final stages of CNNs. These layers connect each neuron in one layer to each neuron in the next layer.  

Dropout Layers:  

Dropout is a regularization technique used in CNNs to prevent overfitting. Overfitting occurs when the network memorizes the training data instead of generalizing it. A dropout layer sets a fraction of input units to zero during each training iteration. Dropout reduces the interdependencies between neurons. It prevents them from relying too much on specific set features.  

 

A Convolutional Neural Network is a powerful deep-learning model. It helps in image recognition and processing tasks. The structure and functioning of the human visual cortex inspire it. CNNs contain convolutional, pooling, and connected layers. It works together to learn and extract features from input images. 


Here is an example of implementing a convolutional neural network (CNN) in PyTorch.



Fig1: Preview of Output when the code is run in IDE.

Code


In this solution, we will implement a convolutional neural network (CNN) in PyTorch

Performing Convolution (NOT cross-correlation) in pytorch

Lines of Code : 42dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import torch
import torch.nn.functional as F
import torch.autograd as autograd
import numpy as np

#A vertical edge detection filter. 
#Because this filter is not symmetric, for correct convolution the filter must be flipped before element-wise multiplication
filters = autograd.Variable(torch.FloatTensor([[[[-1, 1]]]]))

#A test image of a square
inputs = autograd.Variable(torch.FloatTensor([[[[0,0,0,0,0,0,0], [0, 0, 1, 1, 1, 0, 0], 
                                             [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0],
                                            [0,0,0,0,0,0,0]]]]))
print(F.conv2d(inputs, filters))

Variable containing:
(0 ,0 ,.,.) = 
  0  0  0  0  0  0
  0  1  0  0 -1  0
  0  1  0  0 -1  0
  0  1  0  0 -1  0
  0  0  0  0  0  0
[torch.FloatTensor of size 1x1x5x6]

def flip_tensor(t):
    flipped = t.numpy().copy()

    for i in range(len(filters.size())):
        flipped = np.flip(flipped,i) #Reverse given tensor on dimention i
    return torch.from_numpy(flipped.copy())

print(F.conv2d(inputs, autograd.Variable(flip_tensor(filters.data))))

Variable containing:
(0 ,0 ,.,.) = 
  0  0  0  0  0  0
  0 -1  0  0  1  0
  0 -1  0  0  1  0
  0 -1  0  0  1  0
  0  0  0  0  0  0
[torch.FloatTensor of size 1x1x5x6]

Instructions


  1. Install Jupyter Notebook on your computer.
  2. Open terminal and install the required libraries with following commands.
  3. Install PyTorch - pip install pytorch.
  4. Install Numpy - pip install numpy.
  5. Copy the snippet using the 'copy' button and paste it into that file.
  6. Make sure to remove the output written in the code (the parts which state 'variable containing' are output). You can also refer to Fig1 added above to understand better.
  7. Run the file using run button.


I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.


I found this code snippet by searching for "Implementing a convolutional neural network in PyTorch" in kandi. You can try any such use case!

Dependent Libraries

pytorch-tutorialby yunjey

Python doticonstar image 26754 doticonVersion:Currentdoticon
License: Permissive (MIT)

PyTorch Tutorial for Deep Learning Researchers

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            pytorch-tutorialby yunjey

            Python doticon star image 26754 doticonVersion:Currentdoticon License: Permissive (MIT)

            PyTorch Tutorial for Deep Learning Researchers
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                      numpyby numpy

                      Python doticonstar image 23755 doticonVersion:v1.25.0rc1doticon
                      License: Permissive (BSD-3-Clause)

                      The fundamental package for scientific computing with Python.

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                                numpyby numpy

                                Python doticon star image 23755 doticonVersion:v1.25.0rc1doticon License: Permissive (BSD-3-Clause)

                                The fundamental package for scientific computing with Python.
                                Support
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                                          If you do not have PyTorch that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the PyTorch page in kandi.


                                          You can search for any dependent library on kandi like PyTorch / numpy.

                                          Environment Tested


                                          I tested this solution in the following versions. Be mindful of changes when working with other versions.

                                          1. The solution is created in Python3.9.6
                                          2. The solution is tested on PyTorch 2.0.0 version.


                                          Using this solution, we are able to implement a convolutional neural network (CNN) in PyTorch.



                                          This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us to implement a convolutional neural network (CNN) in PyTorch.

                                          Support


                                          1. For any support on kandi solution kits, please use the chat
                                          2. For further learning resources, visit the Open Weaver Community learning page.

                                          FAQ:  

                                          1. What is the purpose of deep learning models when using CNN?  

                                          The deep learning models, when using CNNs, leverage deep learning techniques. It solves complex problems and extracts meaningful insights from data.  

                                           

                                          Deep learning models are designed to learn hierarchical representations from raw input data. It is without the need for explicit feature engineering. Py Torch is a popular deep-learning framework. It provides a flexible platform for building, training, and deploying deep learning models.  

                                           

                                          2. What optimizer should I use for training images when using a CNN in Py Torch?  

                                          When training images, a used optimizer is the stochastic gradient descent optimizer. SGD is a popular choice for training deep learning models. It includes CNNs, due to its simplicity and effectiveness.  

                                           

                                          It performs well in practice and has been adopted for training CNNs on image datasets. Depending on the specific requirements, consider using other optimizers.  

                                           

                                          3. How does data augmentation help improve the performance of a CNN trained on input images?  

                                          Data augmentation helps improve the performance of a CNN trained on input images. It does so by expanding the available training dataset through various transformations. Data augmentation introduces extra variations into the training data by applying random transformations. 

                                           

                                          There are several benefits for CNN raining in Py Torch.  

                                          • Increased Robustness  
                                          • Improved Generalization  
                                          • Increased Dataset Size  
                                          • Balancing Class Distribution.  

                                           

                                          4. Are there any methods for preprocessing input images before training them on a CNN?  

                                          There are common methods for preprocessing input images before training them on CNN. Preprocessing is an essential step. It helps standardize the input data. It improves the effectiveness of the training process.  

                                           

                                          There are a few used preprocessing techniques used in the Convolutional Neural Network:  

                                          • Resize  
                                          • Normalization  
                                          • Data Augmentation  
                                          • Grayscale Conversion  
                                          • To Tensor Conversion.  

                                           

                                          5. Is there any way to reduce overfitting when training a complex CNN?  

                                          There are techniques to reduce overfitting when training a complex CNN. Overfitting happens if a model performs well on the training data. But it needs to generalize to unseen data.  

                                          Reduce Model Complexity:  

                                          If your CNN is complex, it may have too many parameters relative to the size of the training dataset. It makes it prone to overfitting. Consider reducing the number of layers and decreasing the number of neurons. It employs regularization techniques.  

                                          Regularization Techniques - Dropout:  

                                          Dropout is a popular regularization technique. It is where selected neurons are "dropped out" during training. It will let the network learn about robust features. In Py Torch, you can add dropout layers using nn Dropout for 2D convolutions.  

                                          Data Augmentation:  

                                          As discussed earlier, data augmentation introduces variations to the training dataset. It helps the model generalize better. Applying rotation, scaling, flipping, and translation to the input images can reduce overfitting.  

                                          Early Stopping:  

                                          Monitor the model's performance on a validation set during training and stop training. It is when the validation performance starts to degrade. This prevents the model from over-optimizing the training data. It allows you to capture the best-performing model.  

                                          Cross-Validation:  

                                          Instead of relying on a single train-validation split, consider employing cross-validation. This involves splitting the dataset into many folds and training the model. It trains the model on different combinations of train and validation sets. By averaging the results, you can estimate the model's performance. It reduces overfitting.  

                                          Increase Training Data:  

                                          Acquiring more training data can help mitigate overfitting. A larger dataset provides a broader representation of the underlying patterns. It reduces the risk of the model memorizing specific examples.  

                                          Early Layer Freezing and Fine-tuning:  

                                          Start by training the network with frozen layers except for the last few layers. This allows the initial layers to learn features before fine-tuning and fine-tuning the entire network. 

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