Class Conv2d

tensorlayer/layers/convolution/simplified_conv.py:148–278

Simplified version of :class:`Conv2dLayer`. 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.

class Conv2d(Layer):
    """Simplified version of :class:`Conv2dLayer`.

    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.
    dilation_rate : tuple of int
        Specifying the dilation rate to use for dilated convolution.
    act : activation function
        The activation function of this layer.
    padding : str
        The padding algorithm type: "SAME" or "VALID".
    data_format : str
        "channels_last" (NHWC, default) or "channels_first" (NCHW).
    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, 400, 400, 3], name='input')
    >>> conv2d = tl.layers.Conv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), b_init=None, in_channels=3, name='conv2d_1')
    >>> print(conv2d)
    >>> tensor = tl.layers.Conv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), act=tf.nn.relu, name='conv2d_2')(net)
    >>> print(tensor)

    """

    def __init__(
        self,
        n_filter=32,
        filter_size=(3, 3),
        strides=(1, 1),
        act=None,
        padding='SAME',
        data_format='channels_last',
        dilation_rate=(1, 1),
        W_init=tl.initializers.truncated_normal(stddev=0.02),
        b_init=tl.initializers.constant(value=0.0),
        in_channels=None,
        name=None  # 'conv2d',
    ):
        super().__init__(name, act=act)
        self.n_filter = n_filter
        self.filter_size = filter_size