Class QuanConv2dWithBN

tensorlayer/layers/convolution/quan_conv_bn.py:18–234

The :class:`QuanConv2dWithBN` class is a quantized convolutional layer with BN, which weights are 'bitW' bits and the output of the previous layer are 'bitA' bits while inferencing. Note that, the bias vector would keep the same. Parameters ---------- n_filter : int The

class QuanConv2dWithBN(Layer):
    """The :class:`QuanConv2dWithBN` class is a quantized convolutional layer with BN, which weights are 'bitW' bits and the output of the previous layer
    are 'bitA' bits while inferencing.

    Note that, the bias vector would keep the same.

    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.
    padding : str
        The padding algorithm type: "SAME" or "VALID".
    act : activation function
        The activation function of this layer.
    decay : float
        A decay factor for `ExponentialMovingAverage`.
        Suggest to use a large value for large dataset.
    epsilon : float
        Eplison.
    is_train : boolean
        Is being used for training or inference.
    beta_init : initializer or None
        The initializer for initializing beta, if None, skip beta.
        Usually you should not skip beta unless you know what happened.
    gamma_init : initializer or None
        The initializer for initializing gamma, if None, skip gamma.
    bitW : int
        The bits of this layer's parameter
    bitA : int
        The bits of the output of previous layer
    use_gemm : boolean
        If True, use gemm instead of ``tf.matmul`` for inferencing. (TODO).
    W_init : initializer
        The initializer for the the weight matrix.
    W_init_args : dictionary
        The arguments for the weight matrix initializer.
    data_format : str
        "NHWC" or "NCHW", default is "NHWC".
    dilation_rate : tuple of int
        Specifying the dilation rate to use for dilated convolution.
    in_channels : int
        The number of in channels.
    name : str
        A unique layer name.

    Examples
    ---------
    >>> import tensorlayer as tl
    >>> net = tl.layers.Input([50, 256, 256, 3])
    >>> layer = tl.layers.QuanConv2dWithBN(n_filter=64, filter_size=(5,5),strides=(1,1),padding='SAME',name='qcnnbn1')
    >>> print(layer)
    >>> net = tl.layers.QuanConv2dWithBN(n_filter=64, filter_size=(5,5),strides=(1,1),padding='SAME',name='qcnnbn1')(net)
    >>> print(net)