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# Framework

## core:

Base classes

class `neurolab.core.``Layer`(ci, cn, co, property)

  Abstract Neural Layer class

  Parameters : | ci: int Number of inputs cn: int Number of neurons co: int Number of outputs property: dict propety: array shape example: {‘w: (10, 1), ‘b’: 10}

  `init`()

    Init Layer random values

  `step`(inp)

    Layer simulation step

class `neurolab.core.``Net`(inp_minmax, co, layers, connect, trainf, errorf)

  Neural Network class

  Parameters : | inp_minmax: minmax: list ci x 2 Range of input value co: int Number of output layers: list of Layer Network layers connect: list of list Connection scheme of layers* trainf: callable Train function errorf: callable Error function with derivative
    Connect format: | Example 1: for two-layers feed forwad network
        >>> connect = [[-1], # - layer 0 reseives the input network signal;
        ...            [0],  # - layer 1 reseives the output signal
        ...                  # from the layer 0;
        ...            [1]]  # - the network exit reseives the output
        ...                  # signal from the layer 1.
    Example 2: for two-layers Elman network with derivatives:
        >>> connect = [[-1, 0], # - layer 0 reseives the input network
        ...                     # signal and output signal from layer 0;
        ...            [0],     # - layer 1 reseives the output
        ...                     # signal from the layer 0;
        ...            [1]]     # - the network exit reseives the output
        ...                     # signals from the layer 1.

  `copy`()

    Copy network

  `init`()

    Iinitialization layers

  `reset`()

    Clear of deley

  `save`(fname)

    Save network on file

    Parameters : | fname: file name

  `sim`(input)

    Simulate a neural network

    Parameters : | input: array like array input vectors
    Returns : | outputs: array like array output vectors

  `step`(inp)

    Simulated step

    Parameters : | inp: array like Input vector
    Returns : | out: array Output vector

  `train`(*args, **kwargs)

    Train network see net.trainf.__doc__

class `neurolab.core.``Train`(epochf, epochs)

  Base train abstract class

  `error`(net, input, target, output=None)

    Only for train with teacher

class `neurolab.core.``Trainer`(Train, epochs=500, goal=0.01, show=100, **kwargs)

  Control of network training

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