\(LeNet-5 \) The goal of \(LeNet-5 \) was to recognize handwritten digits. So, it takes as an input \(32\times32\times1 \) image. It is a grayscale image, thus the number of channels is \(1 \). Here is a picture of it’s arhitecture. \(LeNet-5 \) architecture In the first step we use \(6\enspace5 \times 5\) filters with a stride \(s=1 \) and \(no\enspace padding\). Therefore we end up with a \(28 \times 28 \times 6 \) volume.…
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Last time we saw we are Convolutions very useful when we include them in our neural networks. In this post we will give just an overview of the 11 Convolutional Neural Network architectures. Convolutional Neural Netwok Academic Paper Authors Python experiments LeNet-5 ” Gradient-based Learning Applied to Document Recognition “ Yann LeChun, Leon Bottou, Youshua Bengaio, Patric Haffner AlexNet “ImageNet Classification with Deep Convolutional Neural Networks” Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton …
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Why convolutions ? In this post we will talk about why convolutions or convolutional neural networks work so well in a computer vision. Convolutions are very useful when we include them in our neural networks. There are two main advantages of \(Convolutional \) layers over \(Fully\enspace connected\) layers: parameter sharing and sparsity of connections. We can illustrate an example. Let’s say that we have a \(32\times32\times3\) dimensional image. This actually comes from the example from the previous…
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Pooling layers Apart from convolutional layers, \(ConvNets \) often use pooling layers to reduce the image size. Hence, this layer speeds up the computation and this also makes some of the features they detect a bit more robust. Let’s go through an example of pooling, and then we’ll talk about why we might want to apply them. There are two types of pooling: \(Max \enspace pooling \) \(Average \enspace pooling \) \(Max \enspace pooling \) Suppose…
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A simple Convolutional Neural Network – A ConvNet In the last post we saw the building blocks of a single convolutional layer in a \(ConvNet \). Now let’s go through a concrete example of a simple convolutional neural network. Let’s say we have an image and we want to do an image classification or image recognition. We want to take an image \(X \) as input and decide if this is a cat or not a…
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