Class BinaryConv2d

tensorlayer/layers/convolution/binary_conv.py:15–159

The :class:`BinaryConv2d` class is a 2D binary CNN layer, which weights are either -1 or 1 while inference. Note that, the bias vector would not be binarized. Parameters ---------- n_filter : int The number of filters. filter_size : tuple of int The filter

class BinaryConv2d(Layer):
    """
    The :class:`BinaryConv2d` class is a 2D binary CNN layer, which weights are either -1 or 1 while inference.

    Note that, the bias vector would not be binarized.

    Parameters
    ----------
    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 inference.
        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, 100, 100, 32], name='input')
    >>> binaryconv2d = tl.layers.QuanConv2d(
    ...     n_filter=64, filter_size=(3, 3), strides=(2, 2), act=tf.nn.relu, in_channels=32, name='binaryconv2d'
    ... )(net)
    >>> print(binaryconv2d)
    >>> output shape : (8, 50, 50, 64)

    """

    def __init__(
        self,
        n_filter=32,
        filter_size=(3, 3),
        strides=(1, 1),
        act=None,
        padding='SAME',
        use_gemm=False,
        data_format="channels_last",
        dilation_rate=(1, 1),