def test_dropout_WITH_PROB_ZERO(self):
rnn = EncoderRNN(self.vocab_size, 10, 50, 16, dropout_p=0)
for param in rnn.parameters():
param.data.uniform_(-1, 1)
output1, _ = rnn(self.input_var, self.lengths)
output2, _ = rnn(self.input_var, self.lengths)
self.assertTrue(torch.equal(output1.data, output2.data))
def test_input_dropout_WITH_NON_ZERO_PROB(self):
rnn = EncoderRNN(self.vocab_size, 10, 50, 16, input_dropout_p=0.5)
for param in rnn.parameters():
param.data.uniform_(-1, 1)
equal = True
for _ in range(50):
output1, _ = rnn(self.input_var, self.lengths)
output2, _ = rnn(self.input_var, self.lengths)
if not torch.equal(output1.data, output2.data):
equal = False
break
self.assertFalse(equal)
def setUp(self):
test_path = os.path.dirname(os.path.realpath(__file__))
src = SourceField(batch_first=True)
tgt = TargetField(batch_first=True)
self.dataset = torchtext.data.TabularDataset(
path=os.path.join(test_path, 'data/eng-fra.txt'),
format='tsv',
fields=[('src', src), ('tgt', tgt)],
)
src.build_vocab(self.dataset)
tgt.build_vocab(self.dataset)
self.data_iterator = torchtext.data.BucketIterator(
dataset=self.dataset,
batch_size=64,
sort=False,
sort_within_batch=True,
sort_key=lambda x: len(x.src),
repeat=False)
encoder = EncoderRNN(len(src.vocab), 10, 10, 10, rnn_cell='lstm')
decoder = DecoderRNN(len(tgt.vocab),
10,
10,
tgt.sos_id,
tgt.eos_id,
rnn_cell='lstm')
self.seq2seq = Seq2seq(encoder, decoder)
for param in self.seq2seq.parameters():
param.data.uniform_(-0.08, 0.08)
def test_dropout_WITH_NON_ZERO_PROB(self):
# It's critical to set n_layer=2 here since dropout won't work
# when the RNN only has one layer according to pytorch's doc
rnn = EncoderRNN(self.vocab_size,
10,
50,
16,
n_layers=2,
dropout_p=0.5)
for param in rnn.parameters():
param.data.uniform_(-1, 1)
equal = True
for _ in range(50):
output1, _ = rnn(self.input_var, self.lengths)
output2, _ = rnn(self.input_var, self.lengths)
if not torch.equal(output1.data, output2.data):
equal = False
break
self.assertFalse(equal)
def setUp(self):
test_path = os.path.dirname(os.path.realpath(__file__))
src = SourceField()
tgt = TargetField()
self.dataset = torchtext.data.TabularDataset(
path=os.path.join(test_path, 'data/eng-fra.txt'), format='tsv',
fields=[('src', src), ('tgt', tgt)],
)
src.build_vocab(self.dataset)
tgt.build_vocab(self.dataset)
encoder = EncoderRNN(len(src.vocab), 10, 10, 10, rnn_cell='lstm')
decoder = DecoderRNN(len(tgt.vocab), 10, 10, tgt.sos_id, tgt.eos_id, rnn_cell='lstm')
self.seq2seq = Seq2seq(encoder, decoder)
for param in self.seq2seq.parameters():
param.data.uniform_(-0.08, 0.08)
def setUpClass(self):
test_path = os.path.dirname(os.path.realpath(__file__))
src = SourceField(batch_first=True)
trg = TargetField(batch_first=True)
dataset = torchtext.data.TabularDataset(
path=os.path.join(test_path, 'data/eng-fra.txt'),
format='tsv',
fields=[('src', src), ('trg', trg)],
)
src.build_vocab(dataset)
trg.build_vocab(dataset)
encoder = EncoderRNN(len(src.vocab), 5, 10, 10, rnn_cell='lstm')
decoder = DecoderRNN(len(trg.vocab),
10,
10,
trg.sos_id,
trg.eos_id,
rnn_cell='lstm')
seq2seq = Seq2seq(encoder, decoder)
self.predictor = Predictor(seq2seq, src.vocab, trg.vocab)