asked . 2021-05-12

Initialize a Neural Network from layers but do NOT train

I want to initialize a neural network from a set of layers, but I do not want to train it. Basically I just want to create it as a forward model, set the weights and biases randomly, and be able to evaluate it at given data point. The tutorials and documentation are not very clear on how to do this. For example from https://www.mathworks.com/help/nnet/examples/create-simple-deep-learning-network-for-classification.html they have

layers = [imageInputLayer([28 28 1])
          convolution2dLayer(5,20)
          reluLayer
          maxPooling2dLayer(2,'Stride',2)
          fullyConnectedLayer(10)
          softmaxLayer
          classificationLayer()];

But then they define the network through a training function:

convnet = trainNetwork(trainDigitData,layers,options);

I don't want this. I just want something along the lines of

convnet = network(layers);

Then I want to be able manually access all weights and biases, set them as I wish and be able to evaluate the network at a single or multiple input images. Is this possible? Thank you.

deep learning , matlab , simulink ,

Expert Answer

John Michell answered . 2024-12-20 18:29:52

Since

 trainedNet = trainNetwork(X, Y, layers, options)

 trains and returns a network, trainedNet,

Just use a random Y.

Then train the resulting net using your own algorithm.

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