Does anyone know of any online resource that will allow me to plug in a neural network with provided input examples, initial weights, number of input nodes, hidden layer nodes, output nodes, expected output etc. And this resource will show the new generated weights. I want to be able to check my answers for my own neural network.
You could try checking out something Andrej Karpathy's ConvnetJS. You can change the network parameters on the spot and see the results.
For example, here are two screenshots with the default 5x5 filters on the first layer and then changing the parameter to a 10x10 filter on the first layer:
First Layer set to 5x5x8
First Layer set to 10x10x8
Related
The DeepFace paper from Facebook uses a Siamese network to learn a metric. They say that the DNN that extracts the 4096 dimensional face embedding has to be duplicated in a Siamese network, but both duplicates share weights. But if they share weights, every update to one of them will also change the other. So why do we need to duplicate them?
Why can't we just apply one DNN to two faces and then do backpropagation using the metric loss? Do they maybe mean this and just talk about duplicated networks for "better" understanding?
Quote from the paper:
We have also tested an end-to-end metric learning ap-
proach, known as Siamese network [8]: once learned, the
face recognition network (without the top layer) is repli-
cated twice (one for each input image) and the features are
used to directly predict whether the two input images be-
long to the same person. This is accomplished by: a) taking
the absolute difference between the features, followed by b)
a top fully connected layer that maps into a single logistic
unit (same/not same). The network has roughly the same
number of parameters as the original one, since much of it
is shared between the two replicas, but requires twice the
computation. Notice that in order to prevent overfitting on
the face verification task, we enable training for only the
two topmost layers.
Paper: https://research.fb.com/wp-content/uploads/2016/11/deepface-closing-the-gap-to-human-level-performance-in-face-verification.pdf
The short answer is that yes, I think that looking at the architecture of the network will help you understand what is going on. You have two networks that are "joined at the hip" i.e. sharing weights. That's what makes it a "Siamese network". The trick is that you want the two images you feed into the network to pass through the same embedding function. So to ensure that this happens both branches of the network need to share weights.
Then we combine the two embeddings into a metric loss (called "contrastive loss" in the image below). And we can back-propagate as normal, we just have two input branches available so that we can feed in two images at a time.
I think a picture is worth a thousand words. So check out how a Siamese network is constructed (at least conceptually) below.
The gradients depend on the activation values. So for each branch gradients will be different and final update could be based on some averaging to share the weights
I have implemented my own little NN with the back-propagation algorithm. What I do not understand at the moment is, if your hidden layer is fully connected with the input layer and fully connected with the output layer, aren't the weights for the nodes in the hidden layer updated equally for each hidden node?
Looking at the back propagation algorithm :
Back Propagation Algorithm Wikipedia
You can see weights update formula contains current weights values and also the outputs of nodes, then weight update differ for each connection between nodes.
And NEVER set the weights to 0. Either to any value. Always set them random.
Also Note this type of questions should be better asked on Data Science Stack Exchange site or Cross Validated Stack Exchange site
In a simple single-layer network, it is easy to calculate the target outputs of neurons, as they are identical to the target outputs of the network itself. However, in a multiple-layer network, I am not quite sure how to calculate the targets for each individual neuron in the hidden layers, because they do not necessarily have a direct connection to the final output and are most likely not given in the training data. How would one find these values?
I would not be surprised if I am missing something and am going about this incorrectly, but I would like to know nonetheless. Thanks in advance for any and all input.
Taken from this great guide on pg. 18:
Calculate the Errors for the hidden layer neurons. Unlike the output layer we can’t
calculate these directly (because we don’t have a Target), so we Back Propagate
them from the output layer (hence the name of the algorithm). This is done by
taking the Errors from the output neurons and running them back through the
weights to get the hidden layer errors.
Or in other words, you don't. You propagate the activations from the input to the output, calculate the error of the output, then backpropagate the error from the output back to the input (thus the name of the algorithm).
In the unfortunate case that the link I posted goes down, it can be found by Googling "backpropagation algorithm 3".
I would like to create a neural network with skip layer connections in MATLAB. Is there any way to modify "newff" function to allow for direct connections from input nodes to output layer?
In case you haven't found a solution for this yet, I had a similar problem. Note that if you have a look at the cell arrays net.IW and net.LW you can modify the adjacency matrices within these structures and essentially modify the weights of the connections in the neural networks any way you want, including connections from the input layer to any other layer (including the output) for example. You then just need to modify these matrices so that you'll get the skip-layer structure you desire.
For more information check this question out, it is similar to what you're asking.
I am trying to train a multilayer neural network in rapidminer.
The key to my problem is that I have three nodes on my output layer that I want predict:
(y1,y2,y3) = f(x1,x2,x3) with one hidden node.
As far as I understood, Rapidminer does not allow to assign a "special role" (e.g. label, id, prediction) to more than one variable.
I am assuming an output layer containing more than one node must be possible to model but how is this done?
Several posts recommended using the "setRole" operator - can you give me any hints on that if it is helpful?
Thank you,
Mat
You can create multiple labels as long as the types are of the form label1, label2 etc. Use the Generate Multi-Label Data operator to see an example.
Then use the Loop Labels operator and place the neural network operator within it. This will build as many models as there are labels.