I would like to know what max pooling and mean pooling are for recurrent neural networks like LSTM while using them for sentiment analysis.
I think as far as I know we pooling is mostly used in convolution neural networks.
and it is a method of concentration of higher order matrix to lower order matrix which contains properties of inherent matrix...in pooling a matrix smaller size and is moved over the original matrix and max value or average value in smaller matrix is selected to form a new resultant matrix of further computation. link-https://machinelearningmastery.com/pooling-layers-for-convolutional-neural-networks/
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Supposed you have two convolutional neural networks implemented in matlab and composed by these layers:
imageInputLayer
ConvolutionalLayer
maxPoolinglayer
relulayer
softmaxlayer
fullyconnectedlayer
classification layer
Both of these networks have exactly same architecture.
I apply the same method of training for 2 networks with same hyperparameters.
Both of these networks have exactly same weights in their corresponding layers.
That is, both of these networks are a replica of each other.
Both of these networks are trained using exactly same training set and validation set without shuffle.
I am wondering:
Will the scores (training error and validation error) and trained weights be different for both?
Does it depend upon the method for training?
In short: Yes to both - because inital weights are usually initated using random numbers.
A tad less short: A neural network is simply an algorithm, if there is no noise (i.e. randomness) introduced in any function on the way, 2 networks will end up being completely the same.
I use Q-learning with neural network as approimator. And after several training iteration, weights acquire values in the range from 0 to 10. Can the weights take such values? Or does this indicate bad network parameters?
Weights can take those values. Especially when you're propagating a large number of iterations; the connections that need to be 'heavy', get 'heavier'.
There are plenty examples showing neural networks with weights larger than 1. Example.
Also, following this image, there is no such thing as weight limits:
legend
I am solving a classification problem. I train my unsupervised neural network for a set of entities (using skip-gram architecture).
The way I evaluate is to search k nearest neighbours for each point in validation data, from training data. I take weighted sum (weights based on distance) of labels of nearest neighbours and use that score of each point of validation data.
Observation - As I increase the number of epochs (model1 - 600 epochs, model 2- 1400 epochs and model 3 - 2000 epochs), my AUC improves at smaller values of k but saturates at the similar values.
What could be a possible explanation of this behaviour?
[Reposted from CrossValidated]
To cross check if imbalanced classes are an issue, try fitting a SVM model. If that gives a better classification(possible if your ANN is not very deep) it may be concluded that classes should be balanced first.
Also, try some kernel functions to check if this transformation makes data linearly separable?
I am making 8 x 8 tiles of Images and I want to train a RBF Neural Network in Matlab using those tiles as inputs. I understand that I can convert the matrix into a vector and use it. But is there a way to train them as matrices? (to preserve the locality) Or is there any other technique to solve this problem?
There is no way to use a matrix as an input to such a neural network, but anyway this won't change anything:
Assume you have any neural network with an image as input, one hidden layer, and the output layer. There will be one weight from every input pixel to every hidden unit. All weights are initialized randomly and then trained using backpropagation. The development of these weights does not depend on any local information - it only depends on the gradient of the output error with respect to the weight. Having a matrix input will therefore make no difference to having a vector input.
For example, you could make a vector out of the image, shuffle that vector in any way (as long as you do it the same way for all images) and the result would be (more or less, due to the random initialization) the same.
The way to handle local structures in the input data is using convolutional neural networks (CNN).
I have come up with a solution for a classification problem using neural networks. I have got the weight vectors for the same too. The data is 5 dimensional and there are 5 neurons in the hidden layer.
Suppose neuron 1 has input weights w11, w12, ...w15
I have to explain the physical interpretation of these weights...like a combination of these weights, what does it represent in the problem.Does any such interpretation exist or is that the neuron has no specific interpretation as such?
A single neuron will not give you any interpretation, but looking at a combination of couple of neuron can tell you which pattern in your data is captured by that set of neurons (assuming your data is complicated enough to have multiple patterns and yet not too complicated that there is too many connections in the network).
The weights corresponding to neuron 1, in your case w11...w15, are the weights that map the 5 input features to that neuron. The weights quantify the extent to which each feature will effect its respective neuron (which is representing some higher dimensional feature, in turn). Each neuron is a matrix representation of these weights, usually after having an activation function applied.
The mathematical formula that determines the value of the neuron matrix is matrix multiplication of the feature matrix and the weight matrix, and using the loss function, which is most basically the sum of the square of the difference between the output from the matrix multiplication and the actual label.Stochastic Gradient Descent is then used to adjust the weight matrix's values to minimize the loss function.