Implementing PatterNet in Python as it is in MATLAB

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 in 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 it is listed in 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.