![]() ![]() Firstly, the MNIST data can't be used as it is for the LeNet5 architecture.Shuffle = True) Loading and Transforming the Data Using torchvision, we will load the dataset as this will allow us to perform any pre-processing steps easily. # Device will determine whether to run the training on GPU or CPU.ĭevice = vice('cuda' if _available() else 'cpu') Importing the libraries Loading the Dataset # Define relevant variables for the ML task Let's start by importing the required libraries and defining some variables (hyperparameters and device are also detailed to help the package determine whether to train on GPU or CPU): # Load in relevant libraries, and alias where appropriate You can see some of the samples of images below: Source: Importing the Libraries The images are greyscale, all with a size of 28x28, and is composed of 60,000 training images and 10,000 testing images. The MNIST dataset contains images of handwritten numerical digits. Let's start by loading and analyzing the data. Finally, we have the output layer which has 10 output neurons, since the MNIST data have 10 classes for each of the represented 10 numerical digits. Then comes the first fully connected layer, with 84 neurons. This reduces the output feature map to 5x5x16.Īfter this, a convolutional layer of size 5x5 with 120 filters is applied to flatten the feature map to 120 values. Same filter size (5x5) with 16 filters is now applied to the output followed by a pooling layer. After this, pooling is applied to decrease the feature map by half, i.e, 14x14圆. This will reduce the width and height of the image while increasing the depth (number of channels). The first convolutional layer has a filter size of 5x5 with 6 such filters. After this, we start with our convolutional layers So the input image should contain just one channel. LeNet5 accepts as input a greyscale image of 32x32, indicating that the architecture is not suitable for RGB images (multiple channels). Let's now understand the architecture of LeNet5 as shown in the figure below: LeNet5 Architecture (Source: )Īs the name indicates, LeNet5 has 5 layers with two convolutional and three fully connected layers. In the paper, the LeNet5 was used for the recognition of handwritten characters. You can read the original paper here: Gradient-Based Learning Applied to Document Recognition. It was proposed by Yann LeCun and others in 1998. LeNet5 is one of the earliest Convolutional Neural Networks (CNNs). Finally, we will see how the model performs on the unseen test data. Using PyTorch, we will build our LeNet5 from scratch and train it on our data. We will then load and analyze our dataset, MNIST, using the provided class from torchvision. We will start by exploring the architecture of LeNet5. We are building this CNN from scratch in PyTorch, and will also see how it performs on a real-world dataset. In this article, we will be building one of the earliest Convolutional Neural Networks ever introduced, LeNet5 ( paper). As a follow-up to my previous post, we will continue writing convolutional neural networks from scratch in PyTorch by building some of the classic CNNs and see them in action on a dataset. ![]()
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