\(VGG \) neural network In the previous posts we talked about \(LeNet-5 \) and AlexNet . Let’s now see one more example of a convolutional neural network called \(VGG-16 \) and \(VGG-19 \) network. In this network smaller filters are used, but the network was built to be deeper than convolutional neural networks we have seen in the previous posts. Architecture of \(VGG-16 \) Remarkable thing about the \(VGG-16 \) is that instead of having so many…
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\(AlexNet \) In the previous posts we talked about LeNet−5. Let’s now see one more example of a convolutional neural network. The second convolutional neural network that we are going to present is \(AlexNet \) neural network. An input to this neural network is \(227\times227\times3\). We have a color image as an input and that is why we have \(3 \) channels. \(AlexNet \) architecture Let’s explore the architecture of this convolutional neural network. \(Conv\enspace1\) First,…
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\(LeNet-5 \) 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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