I am training a convolutional neural network on colored images and am trying to understand how and when the parameters of the neural network get updated. Suppose I have 1000 images. While creating the network, libraries like Keras take the dimensions of one image as the input layer dimensions (suppose 28 x 28 x 3) here. Does this mean that at a time only one image is passed through the network? The explanation here (http://cs231n.github.io/convolutional-networks/) also suggests that the CNN takes one image at a time. If this is indeed true, what happens to the network after this image is passed? Do parameters get updated after each image is passed?
As mentioned here (https://stackoverflow.com/questions/4752626/epoch-vs-iteration-when-training-neural-networks) the parameters of the neural network are updated after a batch of images is passed through it and not after every sample/image. How are multiple images even passed to a CNN? By how I mean in a schematic diagram, how would that look?
When we are talking about parameter update, does it mean only the weights or does that also imply learning rate, decay etc. ?