layers.py 文件源码

def __init__(self, incoming, filter_size,
                 init_std=5., W_logstd=None,
                 stride=1, pad=0,
                 nonlinearity=None,
                 convolution=conv1d_mc0, **kwargs):
        super(GaussianScan1DLayer, self).__init__(incoming, **kwargs)
        # convolution = conv1d_gpucorrmm_mc0
        # convolution = conv.conv1d_mc0
        # convolution = T.nnet.conv2d
        if nonlinearity is None:
            self.nonlinearity = nonlinearities.identity
        else:
            self.nonlinearity = nonlinearity

        self.filter_size = as_tuple(filter_size, 1)
        self.stride = as_tuple(stride, 1)
        self.convolution = convolution

        # if self.filter_size[0] % 2 == 0:
        #     raise NotImplementedError(
        #         'GaussianConv1dLayer requires odd filter size.')

        if pad == 'valid':
            self.pad = (0,)
        elif pad in ('full', 'same', 'strictsame'):
            self.pad = pad
        else:
            self.pad = as_tuple(pad, 1, int)

        if W_logstd is None:
            init_std = np.asarray(init_std, dtype=floatX)
            W_logstd = init.Constant(np.log(init_std))
        # print(W_std)
        # W_std = init.Constant(init_std),
        self.num_input_channels = self.input_shape[1]
        # self.num_filters = self.num_input_channels
        self.W_logstd = self.add_param(W_logstd,
                                       (self.num_input_channels,),
                                       name="W_logstd",
                                       regularizable=False)
        self.W = self.make_gaussian_filter()