padded.py 文件源码

def __init__(self, incoming, num_filters, filter_size, stride=(1, 1),
                 pad=0, untie_biases=False, W=init.GlorotUniform(),
                 b=init.Constant(0.), nonlinearity=nonlinearities.rectify,
                 flip_filters=True, convolution=theano.tensor.nnet.conv2d,
                 centered=True, **kwargs):
        """A padded convolutional layer

        Note
        ----
        If used in place of a :class:``lasagne.layers.Conv2DLayer`` be
        sure to specify `flag_filters=False`, which is the default for
        that layer

        Parameters
        ----------
        incoming : lasagne.layers.Layer
            The input layer
        num_filters : int
            The number of filters or kernels of the convolution
        filter_size : int or iterable of int
            The size of the filters
        stride : int or iterable of int
            The stride or subsampling of the convolution
        pad :  int, iterable of int, ``full``, ``same`` or ``valid``
            **Ignored!** Kept for compatibility with the
            :class:``lasagne.layers.Conv2DLayer``
        untie_biases : bool
            See :class:``lasagne.layers.Conv2DLayer``
        W : Theano shared variable, expression, numpy array or callable
            See :class:``lasagne.layers.Conv2DLayer``
        b : Theano shared variable, expression, numpy array, callable or None
            See :class:``lasagne.layers.Conv2DLayer``
        nonlinearity : callable or None
            See :class:``lasagne.layers.Conv2DLayer``
        flip_filters : bool
            See :class:``lasagne.layers.Conv2DLayer``
        convolution : callable
            See :class:``lasagne.layers.Conv2DLayer``
        centered : bool
            If True, the padding will be added on both sides. If False
            the zero padding will be applied on the upper left side.
        **kwargs
            Any additional keyword arguments are passed to the
            :class:``lasagne.layers.Layer`` superclass
        """
        self.centered = centered
        if pad not in [0, (0, 0), [0, 0]]:
            warnings.warn('The specified padding will be ignored',
                          RuntimeWarning)
        super(PaddedConv2DLayer, self).__init__(incoming, num_filters,
                                                filter_size, stride, pad,
                                                untie_biases, W, b,
                                                nonlinearity, flip_filters,
                                                **kwargs)
        if self.input_shape[2:] != (None, None):
            warnings.warn('This Layer should only be used when the size of '
                          'the image is not known', RuntimeWarning)