local.py 文件源码

def call(self, x, mask=None):
        stride = self.subsample_length
        output_length, feature_dim, nb_filter = self.W_shape

        xs = []
        for i in range(output_length):
            slice_length = slice(i * stride, i * stride + self.filter_length)
            xs.append(K.reshape(x[:, slice_length, :], (1, -1, feature_dim)))
        x_aggregate = K.concatenate(xs, axis=0)
        # (output_length, batch_size, nb_filter)
        output = K.batch_dot(x_aggregate, self.W)
        output = K.permute_dimensions(output, (1, 0, 2))

        if self.bias:
            output += K.reshape(self.b, (1, output_length, nb_filter))

        output = self.activation(output)
        return output