class SeqTransDec(Block): def __init__(self, *layers, **kw): """ first two layers must be embedding layers. Final softmax is added automatically""" assert ("smodim" in kw and "outdim" in kw) smodim = kw["smodim"] outdim = kw["outdim"] del kw["smodim"] del kw["outdim"] super(SeqTransDec, self).__init__(**kw) self.inpemb = layers[0] self.outemb = layers[1] self.block = RecStack(*(layers[2:] + (Lin(indim=smodim, dim=outdim), Softmax()))) def apply(self, inpseq, outseq, maskseq=None): # embed with the two embedding layers emb = self._get_emb(inpseq, outseq) res = self.block(emb) ret = SeqTransducer.applymask(res, maskseq=maskseq) return ret def _get_emb(self, inpseq, outseq): iemb = self.inpemb(inpseq) # (batsize, seqlen, inpembdim) oemb = self.outemb(outseq) # (batsize, seqlen, outembdim) emb = T.concatenate([iemb, oemb], axis=iemb.ndim - 1) # (batsize, seqlen, inpembdim+outembdim) return emb def rec(self, inpa, inpb, *states): emb = self._get_emb(inpa, inpb) return self.block.rec(emb, *states) def get_init_info(self, initstates): return self.block.get_init_info(initstates)
class SeqTransDec(Block): def __init__(self, *layers, **kw): """ first two layers must be embedding layers. Final softmax is added automatically""" assert("smodim" in kw and "outdim" in kw) smodim = kw["smodim"] outdim = kw["outdim"] del kw["smodim"]; del kw["outdim"] super(SeqTransDec, self).__init__(**kw) self.inpemb = layers[0] self.outemb = layers[1] self.block = RecStack(*(layers[2:] + (Lin(indim=smodim, dim=outdim), Softmax()))) def apply(self, inpseq, outseq, maskseq=None): # embed with the two embedding layers emb = self._get_emb(inpseq, outseq) res = self.block(emb) ret = SeqTransducer.applymask(res, maskseq=maskseq) return ret def _get_emb(self, inpseq, outseq): iemb = self.inpemb(inpseq) # (batsize, seqlen, inpembdim) oemb = self.outemb(outseq) # (batsize, seqlen, outembdim) emb = T.concatenate([iemb, oemb], axis=iemb.ndim-1) # (batsize, seqlen, inpembdim+outembdim) return emb def rec(self, inpa, inpb, *states): emb = self._get_emb(inpa, inpb) return self.block.rec(emb, *states) def get_init_info(self, initstates): return self.block.get_init_info(initstates)
def __init__(self, *layers, **kw): """ first two layers must be embedding layers. Final softmax is added automatically""" assert ("smodim" in kw and "outdim" in kw) smodim = kw["smodim"] outdim = kw["outdim"] del kw["smodim"] del kw["outdim"] super(SeqTransDec, self).__init__(**kw) self.inpemb = layers[0] self.outemb = layers[1] self.block = RecStack(*(layers[2:] + (Lin(indim=smodim, dim=outdim), Softmax())))
def __init__(self, wordembdim=50, entembdim=200, innerdim=200, attdim=100, outdim=1e4, numwords=4e5, **kw): super(FBSeqSimpEncDecAtt, self).__init__(**kw) self.indim = wordembdim self.outdim = outdim self.wordembdim = wordembdim self.encinnerdim = innerdim self.decinnerdim = innerdim self.entembdim = entembdim self.wordencoder = WordEmbed(indim=numwords, outdim=self.wordembdim, trainfrac=1.0) self.rnn = RecStack( self.wordencoder, GRU(dim=self.wordembdim, innerdim=self.encinnerdim)) attgen = LinearGateAttentionGenerator(indim=self.encinnerdim + self.decinnerdim, attdim=attdim) attcon = WeightedSumAttCon() self.dec = SeqDecoder([ VectorEmbed(indim=self.outdim, dim=self.entembdim), GRU(dim=self.entembdim, innerdim=self.decinnerdim) ], attention=Attention(attgen, attcon), outconcat=True, inconcat=False, innerdim=self.encinnerdim + self.decinnerdim)
def stack(*layers, **kw): rec = False for layer in layers: if isinstance(layer, RNUBase): rec = True break if rec is True: return RecStack(*layers, **kw) else: return BlockStack(*layers, **kw)
def __init__(self, *layers, **kw): """ first two layers must be embedding layers. Final softmax is added automatically""" assert("smodim" in kw and "outdim" in kw) smodim = kw["smodim"] outdim = kw["outdim"] del kw["smodim"]; del kw["outdim"] super(SeqTransDec, self).__init__(**kw) self.inpemb = layers[0] self.outemb = layers[1] self.block = RecStack(*(layers[2:] + (Lin(indim=smodim, dim=outdim), Softmax())))
def __init__(self, embedder, *layers, **kw): """ layers must have an embedding layers first, final softmax layer is added automatically""" assert ("smodim" in kw and "outdim" in kw) self.embedder = embedder smodim = kw["smodim"] outdim = kw["outdim"] del kw["smodim"] del kw["outdim"] super(SeqTransducer, self).__init__(**kw) self.block = RecStack(*(layers + (Lin(indim=smodim, dim=outdim), Softmax())))
def test_recappl(self): batsize = 100 self.dims = [50, 20, 30, 40] recstack = RecStack(*[ GRU(dim=self.dims[i], innerdim=self.dims[i + 1]) for i in range(len(self.dims) - 1) ]) mu = RecPredictor(recstack).init(batsize) for i in range(3): inpval = np.random.random((batsize, 50)).astype("float32") outpvals = mu.feed(inpval) self.assertEqual(outpvals.shape, (batsize, self.dims[-1]))
def init(self): #MEMORY: encodes how entity is written + custom entity embeddings wencpg = WordEncoderPlusGlove(numchars=self.numchars, numwords=self.numwords, encdim=self.wordencdim, embdim=self.wordembdim, embtrainfrac=0.0, glovepath=self.glovepath) self.memenco = SeqEncoder( wencpg, GRU(dim=self.wordembdim + self.wordencdim, innerdim=self.encinnerdim)) entemb = VectorEmbed(indim=self.outdim, dim=self.entembdim) self.mempayload = ConcatBlock(entemb, self.memenco) self.memblock = MemoryBlock(self.mempayload, self.memdata, indim=self.outdim, outdim=self.encinnerdim + self.entembdim) #ENCODER: uses the same language encoder as memory #wencpg2 = WordEncoderPlusGlove(numchars=self.numchars, numwords=self.numwords, encdim=self.wordencdim, embdim=self.wordembdim, embtrainfrac=0.0, glovepath=glovepath) self.enc = RecStack( wencpg, GRU(dim=self.wordembdim + self.wordencdim, innerdim=self.encinnerdim)) #ATTENTION attgen = LinearGateAttentionGenerator(indim=self.encinnerdim + self.decinnerdim, innerdim=self.attdim) attcon = WeightedSumAttCon() #DECODER #entemb2 = VectorEmbed(indim=self.outdim, dim=self.entembdim) self.softmaxoutblock = stack( self.memaddr(self.memblock, indim=self.decinnerdim + self.encinnerdim, memdim=self.memblock.outdim, attdim=self.attdim), Softmax()) self.dec = SeqDecoder([ self.memblock, GRU(dim=self.entembdim + self.encinnerdim, innerdim=self.decinnerdim) ], outconcat=True, inconcat=False, attention=Attention(attgen, attcon), innerdim=self.decinnerdim + self.encinnerdim, softmaxoutblock=self.softmaxoutblock)
def __init__(self, wordembdim=50, wordencdim=50, entembdim=200, innerdim=200, attdim=100, outdim=1e4, numwords=4e5, numchars=128, glovepath=None, **kw): super(FBSeqCompEncDecAtt, self).__init__(**kw) self.indim = wordembdim + wordencdim self.outdim = outdim self.wordembdim = wordembdim self.wordencdim = wordencdim self.encinnerdim = innerdim self.entembdim = entembdim self.decinnerdim = innerdim self.wordencoder = WordEncoderPlusGlove(numchars=numchars, numwords=numwords, encdim=self.wordencdim, embdim=self.wordembdim, embtrainfrac=0.0, glovepath=glovepath) self.rnn = RecStack( self.wordencoder, GRU(dim=wordembdim + wordencdim, innerdim=self.encinnerdim)) attgen = LinearGateAttentionGenerator(indim=self.encinnerdim + self.decinnerdim, innerdim=attdim) attcon = WeightedSumAttCon() self.dec = SeqDecoder([ VectorEmbed(indim=self.outdim, dim=self.entembdim), GRU(dim=self.entembdim, innerdim=self.decinnerdim) ], attention=Attention(attgen, attcon), outconcat=True, inconcat=False, innerdim=self.encinnerdim + self.decinnerdim)
def __init__(self, inpvocsize=None, inpembdim=None, inpemb=None, inpencinnerdim=None, bidir=False, maskid=None, dropout=False, rnu=GRU, inpencoder=None, memvocsize=None, memembdim=None, memembmat=None, memencinnerdim=None, memencoder=None, inp_att_dist=CosineDistance(), mem_att_dist=CosineDistance(), inp_attention=None, mem_attention=None, coredims=None, corernu=GRU, core=None, explicit_interface=False, scalaraggdim=None, write_value_dim=None, nsteps=100, posvecdim=None, mem_pos_repr=None, inp_pos_repr=None, inp_addr_extractor=None, mem_addr_extractor=None, write_addr_extractor=None, write_addr_generator=None, write_addr_dist=CosineDistance(), write_value_generator=None, write_value_extractor=None, mem_erase_generator=None, mem_change_generator=None, memsampler=None, memsamplemethod=None, memsampletemp=0.3, **kw): # INPUT ENCODING if inpencoder is None: inpencoder = SeqEncoder.RNN(indim=inpvocsize, inpembdim=inpembdim, inpemb=inpemb, innerdim=inpencinnerdim, bidir=bidir, maskid=maskid, dropout_in=dropout, dropout_h=dropout, rnu=rnu).all_outputs() lastinpdim = inpencinnerdim if not issequence( inpencinnerdim) else inpencinnerdim[-1] else: lastinpdim = inpencoder.block.layers[-1].innerdim # MEMORY ENCODING if memembmat is None: memembmat = param((memvocsize, memembdim), name="memembmat").glorotuniform() if memencoder is None: memencoder = SeqEncoder.RNN(inpemb=False, innerdim=memencinnerdim, bidir=bidir, dropout_in=dropout, dropout_h=dropout, rnu=rnu, inpembdim=memembdim).all_outputs() lastmemdim = memencinnerdim if not issequence( memencinnerdim) else memencinnerdim[-1] else: lastmemdim = memencoder.block.layers[-1].innerdim # POSITION VECTORS if posvecdim is not None and inp_pos_repr is None: inp_pos_repr = RNNWithoutInput(posvecdim, dropout=dropout) if posvecdim is not None and mem_pos_repr is None: mem_pos_repr = RNNWithoutInput(posvecdim, dropout=dropout) xtra_dim = posvecdim if posvecdim is not None else 0 # CORE RNN - THE THINKER if core is None: corelayers, _ = MakeRNU.fromdims( [lastinpdim + lastmemdim + xtra_dim * 2] + coredims, rnu=corernu, dropout_in=dropout, dropout_h=dropout, param_init_states=True) core = RecStack(*corelayers) lastcoredim = core.get_statespec()[-1][0][1][0] # ATTENTIONS if mem_attention is None: mem_attention = Attention(mem_att_dist) if inp_attention is None: inp_attention = Attention(inp_att_dist) if write_addr_generator is None: write_addr_generator = AttGen(write_addr_dist) # WRITE VALUE if write_value_generator is None: write_value_generator = WriteValGenerator(write_value_dim, memvocsize, dropout=dropout) # MEMORY SAMPLER if memsampler is not None: assert (memsamplemethod is None) if memsamplemethod is not None: assert (memsampler is None) memsampler = GumbelSoftmax(temperature=memsampletemp) ################ STATE INTERFACES ################# if not explicit_interface: if inp_addr_extractor is None: inp_addr_extractor = Forward(lastcoredim, lastinpdim + xtra_dim, dropout=dropout) if mem_addr_extractor is None: inp_addr_extractor = Forward(lastcoredim, lastmemdim + xtra_dim, dropout=dropout) # WRITE INTERFACE if write_addr_extractor is None: write_addr_extractor = Forward(lastcoredim, lastmemdim + xtra_dim, dropout=dropout) if write_value_extractor is None: write_value_extractor = Forward(lastcoredim, write_value_dim, dropout=dropout) # MEM UPDATE INTERFACE if mem_erase_generator is None: mem_erase_generator = StateToScalar(lastcoredim, scalaraggdim) if mem_change_generator is None: mem_change_generator = StateToScalar(lastcoredim, scalaraggdim) else: inp_addr_extractor, mem_addr_extractor, write_addr_extractor, \ write_value_extractor, mem_erase_generator, mem_change_generator = \ make_vector_slicers(0, lastinpdim + xtra_dim, lastmemdim + xtra_dim, lastmemdim + xtra_dim, write_value_dim, 1, 1) super(SimpleBulkNN, self).__init__(inpencoder=inpencoder, memembmat=memembmat, memencoder=memencoder, inp_attention=inp_attention, mem_attention=mem_attention, core=core, memsampler=memsampler, nsteps=nsteps, inp_addr_extractor=inp_addr_extractor, mem_addr_extractor=mem_addr_extractor, write_addr_extractor=write_addr_extractor, write_addr_generator=write_addr_generator, mem_erase_generator=mem_erase_generator, mem_change_generator=mem_change_generator, write_value_generator=write_value_generator, write_value_extractor=write_value_extractor, inp_pos_repr=inp_pos_repr, mem_pos_repr=mem_pos_repr, **kw)