Explanation For neural networks, we usually use loss to assess how well the network has learned to classify the input image (or other tasks). The loss term is usually a scalar value. In order to update the parameters of the network, we need to calculate the gradient of loss w.r.t to the parameters, which is … Read more
Ok I think this is about the simplest way to explain it all. Your example is 1 image, size 2×2, with 1 channel. You have 1 filter, with size 1×1, and 1 channel (size is height x width x channels x number of filters). For this simple case the resulting 2×2, 1 channel image (size … Read more
Units: The amount of “neurons”, or “cells”, or whatever the layer has inside it. It’s a property of each layer, and yes, it’s related to the output shape (as we will see later). In your picture, except for the input layer, which is conceptually different from other layers, you have: Hidden layer 1: 4 units … Read more