def test_vector_out(self):
decdim = 50
outvocsize = 17
outemb = IdxToOneHot(outvocsize)
outembdim = outvocsize
decrnus = [GRU(dim=outvocsize, innerdim=decdim)]
dec = SeqDecoder([outemb]+decrnus, innerdim=decdim*2, outconcat=True, inconcat=False)
ctxdata = np.random.random((1, decdim)).astype("float32")
seqdata = np.asarray([[2, 3, 4]])
pred = dec.predict(ctxdata, seqdata)
self.assertEqual(pred.shape, (1, 3, outvocsize))
dec = SeqDecoder([outemb]+decrnus, innerdim=decdim*2, softmaxoutblock=False, outconcat=True, inconcat=False)
pred = dec.predict(ctxdata, seqdata)
self.assertEqual(pred.shape, (1, 3, decdim*2))
class TestAttentionRNNDecoder(TestCase):
def setUp(self):
vocsize = 10
innerdim = 50
encdim = 30
seqlen = 5
batsize = 77
self.att = Attention(AttGen(BilinearDistance(innerdim, encdim)),
WeightedSumAttCon())
self.decwatt = SeqDecoder(
[IdxToOneHot(vocsize), GRU(dim=vocsize+encdim, innerdim=innerdim)],
inconcat=True,
attention=self.att,
innerdim=innerdim
)
self.decwoatt = SeqDecoder(
[IdxToOneHot(vocsize), GRU(dim=vocsize+encdim, innerdim=innerdim)],
inconcat=True,
innerdim=innerdim
)
self.attdata = np.random.random((batsize, seqlen, encdim)).astype("float32")
self.data = np.random.random((batsize, encdim)).astype("float32")
self.seqdata = np.random.randint(0, vocsize, (batsize, seqlen))
self.predshape = (batsize, seqlen, vocsize)
def test_shape(self):
pred = self.decwatt.predict(self.attdata, self.seqdata)
self.assertEqual(pred.shape, self.predshape)
def test_shape_wo_att(self):
pred = self.decwoatt.predict(self.data, self.seqdata)
self.assertEqual(pred.shape, self.predshape)
def test_attentiongenerator_param_in_allparams(self):
inps, outps = self.decwatt.autobuild(self.attdata, self.seqdata)
allparams = outps[0].allparams
self.assertIn(self.att.attentiongenerator.dist.W, allparams)
def test_attentiongenerator_param_not_in_params_of_dec_wo_att(self):
_, outps = self.decwoatt.autobuild(self.data, self.seqdata)
allparams = outps[0].allparams
self.assertNotIn(self.att.attentiongenerator.dist.W, allparams)
def test_vector_out(self):
decdim = 50
outvocsize = 17
outemb = IdxToOneHot(outvocsize)
outembdim = outvocsize
decrnus = [GRU(dim=outvocsize, innerdim=decdim)]
dec = SeqDecoder([outemb] + decrnus,
innerdim=decdim * 2,
outconcat=True,
inconcat=False)
ctxdata = np.random.random((2, decdim)).astype("float32")
seqdata = np.asarray([[2, 3, 4], [2, 3, 4]])
pred = dec.predict(ctxdata, seqdata)
self.assertEqual(pred.shape, (2, 3, outvocsize))
dec = SeqDecoder([outemb] + decrnus,
innerdim=decdim * 2,
softmaxoutblock=False,
outconcat=True,
inconcat=False)
pred = dec.predict(ctxdata, seqdata)
self.assertEqual(pred.shape, (2, 3, decdim * 2))
class TestSimpleRNNDecoder(TestCase):
def setUp(self):
self.batsize = 70
self.vocsize = 27
self.encdim = 44
self.hdim = 33
self.statedim = 50
self.seqlen = 30
self.encodings_data = np.random.random((self.batsize, self.encdim)).astype("float32")
self.sequence_data = np.random.randint(0, self.vocsize, (self.batsize, self.seqlen))
self.dec = SeqDecoder(
[IdxToOneHot(self.vocsize), GRU(dim=self.vocsize+self.encdim, innerdim=self.hdim), LSTM(dim=self.hdim, innerdim=self.statedim)],
inconcat=True,
innerdim=self.statedim
)
def test_rnndecoder_output_shape(self):
outvals = self.dec.predict(self.encodings_data, self.sequence_data)
self.assertEqual(outvals.shape, (self.batsize, self.seqlen, self.vocsize))
class TestSimpleRNNDecoder(TestCase):
def setUp(self):
self.batsize = 70
self.vocsize = 27
self.encdim = 44
self.hdim = 33
self.statedim = 50
self.seqlen = 30
self.encodings_data = np.random.random(
(self.batsize, self.encdim)).astype("float32")
self.sequence_data = np.random.randint(0, self.vocsize,
(self.batsize, self.seqlen))
self.dec = SeqDecoder([
IdxToOneHot(self.vocsize),
GRU(dim=self.vocsize + self.encdim, innerdim=self.hdim),
LSTM(dim=self.hdim, innerdim=self.statedim)
],
inconcat=True,
innerdim=self.statedim)
def test_rnndecoder_output_shape(self):
outvals = self.dec.predict(self.encodings_data, self.sequence_data)
self.assertEqual(outvals.shape,
(self.batsize, self.seqlen, self.vocsize))
class TestAttentionRNNDecoder(TestCase):
def setUp(self):
vocsize = 10
innerdim = 50
encdim = 30
seqlen = 5
batsize = 77
self.att = Attention(LinearSumAttentionGenerator(indim=innerdim + encdim), WeightedSumAttCon())
self.decwatt = SeqDecoder(
[IdxToOneHot(vocsize), GRU(dim=vocsize+encdim, innerdim=innerdim)],
inconcat=True,
attention=self.att,
innerdim=innerdim
)
self.decwoatt = SeqDecoder(
[IdxToOneHot(vocsize), GRU(dim=vocsize+encdim, innerdim=innerdim)],
inconcat=True,
innerdim=innerdim
)
self.attdata = np.random.random((batsize, seqlen, encdim)).astype("float32")
self.data = np.random.random((batsize, encdim)).astype("float32")
self.seqdata = np.random.randint(0, vocsize, (batsize, seqlen))
self.predshape = (batsize, seqlen, vocsize)
def test_shape(self):
pred = self.decwatt.predict(self.attdata, self.seqdata)
self.assertEqual(pred.shape, self.predshape)
def test_shape_wo_att(self):
pred = self.decwoatt.predict(self.data, self.seqdata)
self.assertEqual(pred.shape, self.predshape)
def test_attentiongenerator_param_in_allparams(self):
self.decwatt.predict(self.attdata, self.seqdata)
allparams = self.decwatt.output.allparams
self.assertIn(self.att.attentiongenerator.W, allparams)
def test_attentiongenerator_param_not_in_params_of_dec_wo_att(self):
self.decwoatt.predict(self.data, self.seqdata)
allparams = self.decwoatt.output.allparams
self.assertNotIn(self.att.attentiongenerator.W, allparams)
