blocks.py 文件源码

def dot_time_distributed_merge(model, layers, cos_norm=False):
    """ Merging two time series layers into one, producing a new time series that
    contains a dot-product scalar for each time step.

    If cos_norm=True, actually computes cosine similarity. """
    def batched_batched_dot(s):
        """ from (x,y,z)-shaped pair, produce (x,y)-shaped pair that replaces the z-vector pairs by their dot-products """
        import theano
        import theano.tensor as T
        return theano.scan(fn=lambda xm, ym: T.batched_dot(xm, ym),
                           outputs_info=None, sequences=s, non_sequences=None)[0]

    def batched_cos_sim(s):
        """ from (x,y,z)-shaped pair, produce (x,y)-shaped pair that replaces the z-vector pairs by their cosine similarities """
        import theano
        import theano.tensor as T
        return theano.scan(fn=lambda xm, ym: T.batched_dot(xm, ym) / T.sqrt(T.batched_dot(xm, xm) * T.batched_dot(ym, ym)),
                           outputs_info=None, sequences=s, non_sequences=None)[0]

    if cos_norm:
        lmb = batched_cos_sim
    else:
        lmb = batched_batched_dot

    return Lambda([model.nodes[l] for l in layers], lmb,
                       lambda s: (s[1][0], s[1][1]))