conv.py 文件源码

def __init__(self, incoming, num_filters, filter_size, stride=1,
                 pad=0, untie_biases=False,
                 W=init.GlorotUniform(), b=init.Constant(0.),
                 nonlinearity=nonlinearities.rectify, flip_filters=True,
                 convolution=conv.conv1d_mc0, **kwargs):
        if isinstance(incoming, tuple):
            input_shape = incoming
        else:
            input_shape = incoming.output_shape

        # Retrieve the supplied name, if it exists; otherwise use ''
        if 'name' in kwargs:
            basename = kwargs['name'] + '.'
            # Create a separate version of kwargs for the contained layers
            # which does not include 'name'
            layer_kwargs = dict((key, arg) for key, arg in kwargs.items() if key != 'name')
        else:
            basename = ''
            layer_kwargs = kwargs
        self.conv1d = Conv1DLayer(InputLayer((None,) + input_shape[2:]), num_filters, filter_size, stride, pad,
                                  untie_biases, W, b, nonlinearity, flip_filters, convolution, name=basename + "conv1d",
                                  **layer_kwargs)
        self.W = self.conv1d.W
        self.b = self.conv1d.b
        super(ConvTimeStep1DLayer, self).__init__(incoming, **kwargs)