<prompt> Qala marang-rang a methapo bakeng sa mohlala oa multilayer perceptron.nan  <response>: Instruction in English: Initialize a neural network for a multilayer perceptron model. . Response in English: Sure, here is some example code in Python using the Keras library to initialize a neural network for a multilayer perceptron model:pythonfrom keras.models import Sequentialfrom keras.layers import Dense Set the parameters for the modelinput_dim 10 Number of input featureshidden_dim1 32 Number of neurons in the first hidden layerhidden_dim2 64 Number of neurons in the second hidden layeroutput_dim 1 Number of output units Initialize the neural networkmodel Sequential Add a dense layer with ReLU activation functionmodel.addDensehidden_dim1, input_diminput_dim, activationrelu Add another dense layer with ReLU activation functionmodel.addDensehidden_dim2, activationrelu Add a dense output layer with a sigmoid activation functionmodel.addDenseoutput_dim, activationsigmoid Compile the modelmodel.compilelossbinary_crossentropy, optimizeradam, metricsaccuracy Summary of the model architecturemodel.summaryThis code creates a neural network with two hidden layers and one output layer. The hidden layers use the ReLU activation function, while the output layer uses a sigmoid function. The model is compiled using binary cross-entropy loss and the Adam optimizer, with accuracy as the evaluation metric. The model.summary function prints a summary of the model architecture. You can adjust the parameters and architecture as needed for your specific application. . Response in Sesotho: Ehlile, mona ke mohlala oa khoutu ho Python o sebelisang laeborari ea Keras ho qala marang-rang a neural bakeng sa mofuta oa multilayer perceptron: python ho tsoa ho keras.models import Sequential ho tsoa ho keras.layers import Dense Beha liparamente tsa model input_dim 10 Palo ea likarolo tsa ho kenya tse patiloeng_dim1 32 Palo ea li-neurone karolong ea pele e patiloeng e patiloeng_dim2 64 Palo ea li-neurone karolong ea bobeli e patiloeng ea output_dim 1 Palo ea likarolo tse hlahisoang Qala mokhoa oa neural network Sequential Eketsa lera le letenya le nang le mohlala oa tsebetso ea tsebetso ea ReLU.addDensehidden_dim1, input_diminput_dim, activationrelu Eketsa karolo e ngoe e teteaneng ka mohlala oa tsebetso ea ReLU activation.addDensehidden_dim2, activationrelu Kenya karolo e teteaneng ea tlhahiso e nang le mohlala oa sigmoid activation.addDenseoutput_dim, activationsigmoid Kopanya mohlala oa mohlala.compiletahlehelobinary_crossentropy, optimizeradam, metricsaccuracy. Kakaretso ea mohlala oa meralo ea meralo.kakaretso Khoutu ena e theha marang-rang a methapo e nang le likarolo tse peli tse patiloeng le karolo e le ngoe ea tlhahiso. Likarolo tse patiloeng li sebelisa tsebetso ea tsebetso ea ReLU, ha sekhahla sa tlhahiso se sebelisa mosebetsi oa sigmoid. Moetso ona o bokelloa ho sebelisoa tahlehelo ea binary cross-entropy le Adam optimizer, ka nepo joalo ka metric ea tlhahlobo. Mosebetsi oa mohlala.summary o hatisa kakaretso ea moetso oa meralo. O ka fetola liparamente le meaho kamoo ho hlokahalang bakeng sa tsebeliso ea hau e ikhethileng. 