neural-network- how to choose the best model
I am using nftool to train my data (90 piece of data), and my training algorithm is "Bayesian Regularization" I am going to use different neurons to compare and find which one is the best model. Is there any appropriate step I need to follow? Do I need to retrain again and again until getting the good performance of model for each neurons?
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what is standard readable csv format for neural networks in matlab?
I have this table in csv format: feature table
how can I use it as neural networks input in matlab?
i wanna define a convolutional APP decoder that decoder QPSK complex signal using matlab function, to be used in IDMA
in fact, I wanna decode QPSK complex signal
hConEnc=comm.ConvolutionalEncoder('TerminationMethod','Truncated','TrellisStructure',poly2trellis(3,[5 7])); hAppDec = comm.APPDecoder(... 'TerminationMethod','Truncated','TrellisStructure',poly2trellis(3,[5 7]), ... 'Algorithm','True APP',.... 'CodedBitLLROutputPort',true);
Error using comm.APPDecoder/step Complexity mismatch with input 2; expected real, got complex. Error in G_NOMA_IDMA_SAMSON_ZITHA>CBCReceiever (line 297) [outSoftData outLLR]= step(hAppDec,ini,transpose(tmp));
MATLAB: Webcam and other Support packages are not working
webcamin Matlab 2014b to acquire images. But today when I used
webcamin matlab script, it shows an error
Error using webcam (line 13) MATLAB Support Package for Webcams has not been installed. Open Support Package Installer to install the Webcam Support Package.
I already installed Webcam Support Package and its location is
To change my support directory I used
matlabshared.supportpkg.getSupportPackageRootbut it shows error
Undefined variable "matlabshared" or class "matlabshared.supportpkg.getSupportPackageRoot".
When I tried to reinstall webcam support package, it shows error that already you have
R2014b\usbwebcamsfirst make empty that. I have many other support packages and so don't want to reinstall every package again. How should I solve this problem?
Word Embedding - GloVe Tensorflow
Can anyone explain how to map word into vector space ?
I am trying to implement GloVe Model. After building a co-occurrence matrix i am stopped because i don't know what can i do next ?
Please answer me as quick as possible.
Custom (convolutional) connections between two Keras layers
I am looking for a possibility to define custom interconnections between two Keras layers. I want to mimic a convolutional behavior with a custom and varying number of inputs. The following simplified example, sketched below, illustrates my needs. Inputs 0, 1, and 2 shall be combined into a single cell. Input 3 shall be considered alone and 4 and 5 shall be combined as well. In the example, the input groups (0, 1, 2), (3), and (4, 5) are always combined in one neuron. A further step would be a combination in several neurons (e.g. inputs 0, 1, and 2 into two hidden layer neurons).
X Output layer / | \ X X X Hidden layer /|\ | / \ X X X X X X Input layer 0 1 2 3 4 5
I did not find a straight-forward solution to this problem in the Keras documentation or maybe I am looking at the wrong places. Convolutional layers are always expecting a fixed number of input values. This problems seems not to complex to me. I did not provide any code, because there is nothing worth sharing yet. However, I will update the question with code when I find a working solution.
Maybe some background for this problem. I split up categorical values into hot vectors. For instance, a categorical values with three manifestations 'a', 'b', 'c' into (1, 0, 0), (0, 1, 0), and (0, 0, 1). These are fed into a neural network alongside other values. Leading to inputs (1, 0, 0, X, X, X), (0, 1, 0, X, X, X), and (0, 0, 1, X, X, X) for the above example network (X for an arbitrary value). When I have a fully connected layer now, the network looses the information that the inputs 0, 1, and 2 actually originated from the same variable and should be considered together. With the architecture above I want to ensure that the network considers these values together before correlating them with other variables. I hope this make sense, if not please let me know why.
Update: The answer supplied a good code example.