NOTE: This question was first posted in Cross Validated Stack Exchange but I was instructed to move it off that website as it was not a good fit.
I am new to the implementation of machine learning, neural networks in python. I am trying to implement patternet in python as it is in MATLAB; more specifically to keep the network configuration settings. Below is what I get when I use paternet(10):
Neural Network name: 'Pattern Recognition Neural Network' userdata: (your custom info) dimensions: numInputs: 1 numLayers: 2 numOutputs: 1 numInputDelays: 0 numLayerDelays: 0 numFeedbackDelays: 0 numWeightElements: 230 sampleTime: 1 connections: biasConnect: [1; 1] inputConnect: [1; 0] layerConnect: [0 0; 1 0] outputConnect: [0 1] subobjects: input: Equivalent to inputs{1} output: Equivalent to outputs{2} inputs: {1x1 cell array of 1 input} layers: {2x1 cell array of 2 layers} outputs: {1x2 cell array of 1 output} biases: {2x1 cell array of 2 biases} inputWeights: {2x1 cell array of 1 weight} layerWeights: {2x2 cell array of 1 weight} functions: adaptFcn: 'adaptwb' adaptParam: (none) derivFcn: 'defaultderiv' divideFcn: 'divideind' divideParam: .trainInd, .valInd, .testInd divideMode: 'sample' initFcn: 'initlay' performFcn: 'crossentropy' performParam: .regularization, .normalization plotFcns: {'plotperform', plottrainstate, ploterrhist, plotconfusion, plotroc} plotParams: {1x5 cell array of 5 params} trainFcn: 'trainscg' trainParam: .showWindow, .showCommandLine, .show, .epochs, .time, .goal, .min_grad, .max_fail, .sigma, .lambda weight and bias values: IW: {2x1 cell} containing 1 input weight matrix LW: {2x2 cell} containing 1 layer weight matrix b: {2x1 cell} containing 2 bias vectors My intention is not to create the exact net data structure, I would like to mainly implement the activation functions to different layers of my network as is listed above. Let's assume I can initialize the weights (network parameters).
I followed this post Getting Started with Deep Learning and Python as well as Multi-layer Perceptron to start. In the latter link, I learned that I can implement MLP by utilizing MLPclassifier from sklearn.neural_network library:
MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08, hidden_layer_sizes=(5, 2), learning_rate='constant', learning_rate_init=0.001, max_iter=200, momentum=0.9, nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False, warm_start=False) However, its setting is not exactly the one that I am trying to have. Reading MLPclassifier document page did not help me further.
I would appreciate it if I get to know how to implement it as such? I would be happy to be directed to any post or link which has already discussed this matter.
