def __init__(self, data_dir, transform=None):
self.transform = transform
eng_sents, fra_sents = utils.read_langs(data_dir)
self.input_data = eng_sents + fra_sents
self.targets = np.concatenate(
(np.zeros(len(eng_sents), dtype=np.int64),
np.ones(len(fra_sents), dtype=np.int64)), 0)
self.vocab = utils.Vocab("all_data")
for sent in self.input_data:
self.vocab.add_sentence(sent)
self.input_idxs = [
utils.indexes_from_sentence(self.vocab, sent)
for sent in self.input_data
]
def test_read_lang_sents_reversed(self):
sents = utils.read_langs(self._data_dir, pairs=False, reverse=True)
english = [li.split("\t")[0] for li in self._text.split("\n")]
spanish = [li.split("\t")[1] for li in self._text.split("\n")]
self.assertEqual(sents, [spanish, english])
def test_read_lang_pairs_reversed(self):
pairs = utils.read_langs(self._data_dir, pairs=True, reverse=True)
english = [li.split("\t")[0] for li in self._text.split("\n")]
spanish = [li.split("\t")[1] for li in self._text.split("\n")]
self.assertEqual(pairs, [[sp, en] for en, sp in zip(english, spanish)])
def main():
input_lang, output_lang, pairs, data1, data2 = read_langs("eng", "fra", True)
input_tensor = [[input_lang.word2index[s] for s in es.split(' ')] for es in data1]
target_tensor = [[output_lang.word2index[s] for s in es.split(' ')] for es in data2]
max_length_inp, max_length_tar = max_length(input_tensor), max_length(target_tensor)
input_tensor = [pad_sequences(x, max_length_inp) for x in input_tensor]
target_tensor = [pad_sequences(x, max_length_tar) for x in target_tensor]
print(len(target_tensor))
input_tensor_train, input_tensor_val, target_tensor_train, target_tensor_val = train_test_split(input_tensor,
target_tensor,
test_size=0.2)
# Show length
print(len(input_tensor_train), len(target_tensor_train), len(input_tensor_val), len(target_tensor_val))
BUFFER_SIZE = len(input_tensor_train)
BATCH_SIZE = 64
N_BATCH = BUFFER_SIZE // BATCH_SIZE
embedding_dim = 256
units = 1024
vocab_inp_size = len(input_lang.word2index)
vocab_tar_size = len(output_lang.word2index)
train_dataset = MyData(input_tensor_train, target_tensor_train)
val_dataset = MyData(input_tensor_val, target_tensor_val)
dataset = DataLoader(train_dataset, batch_size=BATCH_SIZE,
drop_last=True,
shuffle=True)
device = torch.device("cpu")
encoder = Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)
decoder = Decoder(vocab_tar_size, embedding_dim, units, units, BATCH_SIZE)
encoder.to(device)
decoder.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(list(encoder.parameters()) + list(decoder.parameters()),
lr=0.001)
EPOCHS = 10
for epoch in range(EPOCHS):
start = time()
encoder.train()
decoder.train()
total_loss = 0
for (batch, (inp, targ, inp_len)) in enumerate(dataset):
loss = 0
xs, ys, lens = sort_batch(inp, targ, inp_len)
enc_output, enc_hidden = encoder(xs.to(device), lens, device)
dec_hidden = enc_hidden
dec_input = torch.tensor([[output_lang.word2index['<sos>']]] * BATCH_SIZE)
for t in range(1, ys.size(1)):
predictions, dec_hidden, _ = decoder(dec_input.to(device),
dec_hidden.to(device),
enc_output.to(device))
loss += loss_function(criterion, ys[:, t].to(device), predictions.to(device))
# loss += loss_
dec_input = ys[:, t].unsqueeze(1)
batch_loss = (loss / int(ys.size(1)))
total_loss += batch_loss
optimizer.zero_grad()
loss.backward()
### UPDATE MODEL PARAMETERS
optimizer.step()
if batch % 100 == 0:
print('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1,
batch,
batch_loss.detach().item()))
### TODO: Save checkpoint for model
print('Epoch {} Loss {:.4f}'.format(epoch + 1,
total_loss / N_BATCH))
print('Time taken for 1 epoch {} sec\n'.format(time.time() - start))