Source code for akida.layers.dense1d

from akida.core import (Layer, LayerParams, LayerType)


[docs]class Dense1D(Layer): """Dense layer capable of working on 1D inputs. This is a simple dotproduct between an input of shape (1, 1, X) and a kernel of shape (X, F) to output a tensor of shape (1, 1, F). Being the result of a quantized operation, it is possible to apply some shifts to adjust the inputs/outputs scales to the equivalent operation performed on floats, while maintaining a limited usage of bits and performing the operations on integer values. The 1D Dense operation can be described as follows: >>> inputs = inputs << input_shift >>> prod = matmul(inputs, weights) >>> output = prod + (bias << bias_shift) >>> output = output * output_scale >> output_shift Inputs shape must be (1, 1, X), if not it's reshaped automatically at the beginning. Note that output values will be saturated on the range that can be represented with output_bits. Args: units (int): Positive integer, dimensionality of the output space. output_bits (int, optional): output bitwidth. Defaults to 8. buffer_bits (int, optional): buffer bitwidth. Defaults to 28. post_op_buffer_bits (int, optional): internal bitwidth for post operations. Defaults to 32. activation (bool, optional): apply a relu activation. Defaults to False. name (str, optional): name of the layer. Defaults to empty string. """ def __init__(self, units, output_bits=8, buffer_bits=28, post_op_buffer_bits=32, activation=False, name=""): try: params = LayerParams( LayerType.Dense2D, { "units": units, "output_bits": output_bits, "buffer_bits": buffer_bits, "post_op_buffer_bits": post_op_buffer_bits, "activation": activation, "collapse_dims": True, }) # 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