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Build MLP from scratch only using Numpy. Able to perform simple supervised learning problems in any dimension and with variable layer configurations. It can also be used for Auto-Encoder problems.

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MLP

Implementation of a Multi-layer perceptron in python only using the numpy library.

Example for solving the XOR-Problem

inputs = np.array([[0.0, 0.0,], [0.0,1.0],[1.0,0.0],[1.0,1.0]])
outputs = np.array([[-1.0],[1.0],[1.0],[-1.0]])
mlp = create_network([1,4,20,1], tanh)
mlp.train(inputs, outputs, learning_rate = 0.1, epochs = 500 )

Example for Auto-Encoder

inputs, outputs = np.eye(8), np.eye(8)
mlp = create_network([8,3,8], sgn)
mlp.train(inputs, outputs, learning_rate = 0.1, epochs = 4000 )

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Build MLP from scratch only using Numpy. Able to perform simple supervised learning problems in any dimension and with variable layer configurations. It can also be used for Auto-Encoder problems.

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