nait 2.0.1

Easy neural networks

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Nait

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Neural network module

Made to be easy and fast

Network - class

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Syntax

Network()

train - method

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Syntax

train(x, y, layer_size=4, layers=1, activation_function="linear", generate_network=True, learning_rate=0.01, optimizer="randomize", sample_size=10, epochs=100, exit_increase=0.1, backup=None)

Arguments

• x list --- training inputs

• y list --- training outputs

• layer_size integer --- number of neurons in hidden layers

• layers integer --- if the time when logged should be displayed in the message

• activation_function string --- function applied to the output of each layer (linear / relu / step / sigmoid / leaky_relu)

• generate_network boolean --- if the program should generate a new network or try to train the existing one

• learning_rate float --- how fast the network learns (faster learning may result in lower precision)

• optimizer string --- which optimizer to use (randomize / backpropagate) - 'backpropagate' optimizer is highly unstable and needs normalized inputs

• sample_size int --- how many network samples in each batch - only for 'randomize' optimizer

• epochs integer --- number of epochs the network should train for

• exit_increase float --- exits if the loss gets _ over the lowest loss - only for 'backpropagate' optimizer

• backup string --- file to backup network while training (if left blank or set to None the network will not backup)

predict - method

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Syntax

predict(input)

Arguments

• input list --- input

save - method

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Syntax

save(file)

Arguments

• file string --- file to save network to

load - method

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Syntax

load(file)

Arguments

• file string --- file to load network from

evaluate - method

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Syntax

evaluate(x, y)

Arguments

• x list --- testing inputs

• y list --- testing outputs

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Nait v2.0.1 - Change Log

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• Complete re-write

• Added backpropagate optimizer

• Changed training display

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