How can I use trained data of ANN with softmax as the output layer? Is it possible with Matlab inbuilt neural network tool (nnstart)?
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I am reading a lot of articles about neural networks and I found very different information. I understand that the supervised neural network can be also regression and classification. In both cases I can use the sigmoid function but what is the difference?
A single-layer neural network is essentially the same thing as linear regression. That's because of how neural networks work: Each input gets weighted with a weight factor to produce an output, and the weight factors are iteratively chosen in such a way that the error (the discrepancy between the outputs produced by the model and the correct output that should be produced for a given input) is minimised. Linear regression does the same thing. But in a neural network, you can stack several of such layers on top of each other.
Classification is a potential, but by far not the only, use case for neural networks. Conversely, there are classification algorithms that don't use neural networks (e.g. K-nearest neighbours). The sigmoid function is often used as an activation function for the last layer in a classifier neural network.
My new question is about activation function for the output layer in regression model in Neural Network
SO: Which is better? Linear activation function or NO activation function?
I'm trying to understand the relationship between a simple Perceptron and a neural network one gets when using the keras Sequence class.
I learned that the neural network perceptron looks as such:
Each "node" in the first layer is one of the features of a sample x_1, x_2,...,x_n
Could somebody explain the jump to the neural network I find in the Keras package below?
Since the input layer has four nodes, does that mean that network consists of four of the perceptron networks?
There is seem to be misunderstanding on what a perceptron is. A perceptron is a single unit that multiplies the inputs with weights, sums them up and applies an activation function:
Now the diagrams you have are called multi-layer perceptrons (MLP) and consist of a stack of perceptrons organised in layers, wiki. In Keras, there is no explicit notion of a perceptron but of a layer of perceptrons implemented as a Dense layer because the layers are densely connected, ie every output is connected to every input between layers. The second diagram would correspond to:
model = Sequential()
model.add(Dense(4, activation='sigmoid', input_dim=3))
model.add(Dense(4, activation='sigmoid'))
model.add(Dense(1, activation='sigmoid'))
assuming you have sigmoid activation. In this case, the input layer is implicit by specifying the input_dim=3 and the final layer would be the output layer.
I set up the network using the softmax function, and I noticed that after training the network, the output layer activation function just changes to logsig. What could be the reason for that?
My data is scaled between 0 and 1.
I am using the neural network toolbox that Matlab provides. I trained a NARX neural network for time series problems. I am trying to predict future values using the inputs I am giving to the neural network.
I am able to see the error graphs and the response for the testing and validation samples, but how do I test new samples? How can I make a prediction using the trained neural network? I could not find any documentation.
This was my attempt
>> net(input2')
ans =
[917.9814]
But no matter what the inputs are, I am getting the exact same output always....