I want to get the weights between the last hidden layer and output layer from the deep neural network designed using matlab codes.
I have tried with net.LW but it is returning the weights between 2 hidden layers.
You can use net.LW to pass in two indices to get the weights between two layers.
net.LW{i,j}
Use numel(net.layers) to get the number of layers. This will be the index of the output layer. One less is the index of the last hidden layer.
j = numel(net.layers)
i = numel(net.layers) - 1
net.LW{i,j}
You can update with variables for your app.
MATLAB Answers Reference: https://www.mathworks.com/matlabcentral/answers/499607-set-a-specific-weight-for-a-connection-in-neural-networks
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I have designed a CNN in MATLAB with the next set of layers:
So, if I'm correct making my calculations, the output size of the last ReLU layer should be 7x7x32, which seems to be correct because that size contains 1568 values, and if we look at the Fully Connected Layer, we can see that the vector of weights has size 10x1568:
Now, I'm making a project where I'm coding the same CNN by hand. But when I have to code the Fully Connected Layer, I don't know how its vector of weights is related to the previous output. For example, I guess that a hypothetical output(1,1,1) value is connected to the weights Weights(:,1). But what about the others?.
My question therefore is, how should I loop through the output to match the weights from the first one (1) to the last one (1568)?.
I was reading this interesting article on convolutional neural networks. It showed this image, explaining that for every receptive field of 5x5 pixels/neurons, a value for a hidden value is calculated.
We can think of max-pooling as a way for the network to ask whether a given feature is found anywhere in a region of the image. It then throws away the exact positional information.
So max-pooling is applied.
With multiple convolutional layers, it looks something like this:
But my question is, this whole architecture could be build with perceptrons, right?
For every convolutional layer, one perceptron is needed, with layers:
input_size = 5x5;
hidden_size = 10; e.g.
output_size = 1;
Then for every receptive field in the original image, the 5x5 area is inputted into a perceptron to output the value of a neuron in the hidden layer. So basically doing this for every receptive field:
So the same perceptron is used 24x24 amount of times to construct the hidden layer, because:
is that we're going to use the same weights and bias for each of the 24×24 hidden neurons.
And this works for the hidden layer to the pooling layer as well, input_size = 2x2; output_size = 1;. And in the case of a max-pool layer, it's just a max() function on an array.
and then finally:
The final layer of connections in the network is a fully-connected
layer. That is, this layer connects every neuron from the max-pooled
layer to every one of the 10 output neurons.
which is a perceptron again.
So my final architecture looks like this:
-> 1 perceptron for every convolutional layer/feature map
-> run this perceptron for every receptive field to create feature map
-> 1 perceptron for every pooling layer
-> run this perceptron for every field in the feature map to create a pooling layer
-> finally input the values of the pooling layer in a regular ALL to ALL perceptron
Or am I overseeing something? Or is this already how they are programmed?
The answer very much depends on what exactly you call a Perceptron. Common options are:
Complete architecture. Then no, simply because it's by definition a different NN.
A model of a single neuron, specifically y = 1 if (w.x + b) > 0 else 0, where x is the input of the neuron, w and b are its trainable parameters and w.b denotes the dot product. Then yes, you can force a bunch of these perceptrons to share weights and call it a CNN. You'll find variants of this idea being used in binary neural networks.
A training algorithm, typically associated with the Perceptron architecture. This would make no sense to the question, because the learning algorithm is in principle orthogonal to the architecture. Though you cannot really use the Perceptron algorithm for anything with hidden layers, which would suggest no as the answer in this case.
Loss function associated with the original Perceptron. This notion of Peceptron is orthogonal to the problem at hand, you're loss function with a CNN is given by whatever you try to do with your whole model. You can eventually use it, but it is non-differentiable, so good luck :-)
A sidenote rant: You can see people refer to feed-forward, fully-connected NNs with hidden layers as "Multilayer Perceptrons" (MLPs). This is a misnomer, there are no Perceptrons in MLPs, see e.g. this discussion on Wikipedia -- unless you go explore some really weird ideas. It would make sense call these networks as Multilayer Linear Logistic Regression, because that's what they used to be composed of. Up till like 6 years ago.
I want to build a neural network to learn a set of standard feature vectors. The set is thus of shape (N,100), where N is the number of samples. However, set of labels is of shape (Nx18) (e.g. each "label" is another array of 18 elements). I'm quite new to keras and neural nets, and I only know how to deal with the label when it is one dimensional (e.g. 0 or 1 in binary classification). How do I can deal with multi-dimensional output?
Thanks!
Maybe I don't completely understand the question but the simplest way would be to have an output layer with 18 neuron. Each neuron output one value, i.e. the output will be a vector of 18 values.
One possible way of doing this would be a feed-forward neural network with on hidden layer, e.g. containing 100 neurons. You will need the Dense layer in Keras for this.
nb_hidden = 100
model = Sequential()
model.add(Dense(input_dim = 100, output_dim = nb_hidden)
model.add(Dense(output_dim = 18, activation = 'softmax')
model.compile(loss='categorical_crossentropy', optimizer='adadelta')
Consider varying the number of hidden layers, the general network topology (e.g. include a Dropout layer) and the activation functions until you get a good result.
I am using Neural Network (NN) wizard in MATLAB for some implementations. Also i can use code-based version of NN in MATLAB which is available after construction NN by wizard (It is clear!).
When we provide our NN with MATLAB, it is a fully connected input-hidden layer. For example, if you have 4 inputs in the input layer and 2 neurons in the hidden layer, we have fully connected relation between 4 inputs and 2 neurons in hidden layer. I am going to manipulate this connections. For example, disconnect 3rd input connection to 1st neuron and 2nd input connection to 2nd neuron in hidden layer. How is it possible by the MATLAB?
Thank you in advance for any guidance.
I read completely documentation of NN in MATLAB. With the following command we can access to each connection and change their weights and bias so that the desired connection gets off duty!
For a NN with one hidden layer:
Network.IW{1,1} = The matrix of Input weights to Hidden layer.
Network.LW{2,1} = The matrix of Hidden layer weights to Output layer.
Network.b{1,1} = The matrix of bias between Input to Hidden layer.
Network.b{2,1} = The matrix of bias between Hidden layer to Output.
Then, we can set 0 to those connections (both weights and bias) between Input and Hidden layer as our desired. With this type of configuration, we can re-construct the neural network infrastructure.
If you want to randomize this switching off and on, of some nodes, you can also use the dropoutLayer in Matlab. This works best for deep NNs.
https://in.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.dropoutlayer.html
I have trained a 3-layer (input, hidden and output) feedforward neural network in Matlab. After training, I would like to simulate the trained network with an input test vector and obtain the response of the neurons of the hidden layer (not the final output layer). How can I go about doing this?
Additionally, after training a neural network, is it possible to "cut away" the final output layer and make the current hidden layer as the new output layer (for any future use)?
Extra-info: I'm building an autoencoder network.
The trained weights for a trained network are available in the net.LW property. You can use these weights to get the hidden layer outputs
From Matlab Documentation
nnproperty.net_LW
Neural network LW property.
NET.LW
This property defines the weight matrices of weights going to layers
from other layers. It is always an Nl x Nl cell array, where Nl is the
number of network layers (net.numLayers).
The weight matrix for the weight going to the ith layer from the jth
layer (or a null matrix []) is located at net.LW{i,j} if
net.layerConnect(i,j) is 1 (or 0).
The weight matrix has as many rows as the size of the layer it goes to
(net.layers{i}.size). It has as many columns as the product of the size
of the layer it comes from with the number of delays associated with the
weight:
net.layers{j}.size * length(net.layerWeights{i,j}.delays)
Addition to using input and layer weights and biases, you may add a output connect from desired layer (after training the network). I found it possible and easy but I didn't exam the correctness of it.