NOTE: This question was first posted in cross-validated website but I was instructed to move it off that website as it was not a good fit.I am new in implementation of machine learning, neural network 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 vectorsMy 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 it is listed in above. Lets assume I can initialize the wights (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 if I get to know how-to implement such? I would be happy to be directed to any post or link which has already discussed this matter.Thanks,Payam
How to implement patternet in python as it is in matlab?
machine learning;python;neural network;scikit learn;matlab