Source code for akida.layers.fully_connected

from akida.core import Layer, LayerType, LayerParams

[docs]class FullyConnected(Layer): """This represents a Dense or Linear neural layer. The FullyConnected layer accepts 1-bit, 2-bit or 4-bit input tensors. The FullyConnected can be configured with 1-bit, 2-bit or 4-bit weights. It multiplies the inputs by its internal unit weights, returning a 4D tensor of values whose first dimension is the number of samples and the last dimension represents the number of units. It can optionally apply a step-wise ReLU activation to its outputs. Args: units (int): number of units. name (str, optional): name of the layer. Defaults to empty string. weights_bits (int, optional): number of bits used to quantize weights. Defaults to 1. activation (bool, optional): enable or disable activation function. Defaults to True. act_bits (int, optional): number of bits used to quantize the neuron response. Defaults to 1. """ def __init__(self, units, name="", weights_bits=1, activation=True, act_bits=1): try: params = LayerParams( LayerType.FullyConnected, { "units": units, "weights_bits": weights_bits, "activation": activation, "act_bits": act_bits }) # Call parent constructor to initialize C++ bindings # Note that we invoke directly __init__ instead of using super, as # specified in pybind documentation Layer.__init__(self, params, name) except BaseException: self = None raise