Class DorefaConv2d

tensorlayer/layers/convolution/dorefa_conv.py:15–169

The :class:`DorefaConv2d` class is a 2D quantized convolutional layer, which weights are 'bitW' bits and the output of the previous layer are 'bitA' bits while inferencing. Note that, the bias vector would not be binarized. Parameters ---------- bitW : int The bits of t

class DorefaConv2d(Layer):
    """The :class:`DorefaConv2d` class is a 2D quantized convolutional layer, which weights are 'bitW' bits and the output of the previous layer
    are 'bitA' bits while inferencing.

    Note that, the bias vector would not be binarized.

    Parameters
    ----------
    bitW : int
        The bits of this layer's parameter
    bitA : int
        The bits of the output of previous layer
    n_filter : int
        The number of filters.
    filter_size : tuple of int
        The filter size (height, width).
    strides : tuple of int
        The sliding window strides of corresponding input dimensions.
        It must be in the same order as the ``shape`` parameter.
    act : activation function
        The activation function of this layer.
    padding : str
        The padding algorithm type: "SAME" or "VALID".
    use_gemm : boolean
        If True, use gemm instead of ``tf.matmul`` for inferencing.
        TODO: support gemm
    data_format : str
        "channels_last" (NHWC, default) or "channels_first" (NCHW).
    dilation_rate : tuple of int
        Specifying the dilation rate to use for dilated convolution.
    W_init : initializer
        The initializer for the the weight matrix.
    b_init : initializer or None
        The initializer for the the bias vector. If None, skip biases.
    in_channels : int
        The number of in channels.
    name : None or str
        A unique layer name.

    Examples
    ---------
    With TensorLayer

    >>> net = tl.layers.Input([8, 12, 12, 32], name='input')
    >>> dorefaconv2d = tl.layers.QuanConv2d(
    ...     n_filter=32, filter_size=(5, 5), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='dorefaconv2d'
    ... )(net)
    >>> print(dorefaconv2d)
    >>> output shape : (8, 12, 12, 32)

    """

    def __init__(
        self,
        bitW=1,
        bitA=3,
        n_filter=32,
        filter_size=(3, 3),