class ConvolutionalNN():
def __init__(self, embedding, batch_size):
TEXT, vocab_size, word_embeddings, self.train_iter, self.valid_iter, self.test_iter = load_dataset.load(
embedding=embedding, batch_size=batch_size)
self.embedding = embedding
output_size = 10
in_channel = 1
out_channel = 16
kernel_heights = [3, 5, 7]
keep_probab = 0
stride = 1
padding = [1, 2, 3]
embedding_length = 300
self.model = CNN(batch_size, output_size, in_channel, out_channel,
kernel_heights, stride, padding, keep_probab,
vocab_size, embedding_length, word_embeddings)
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
self.model.parameters()),
weight_decay=0.0005,
lr=0.0001)
loss_fn = F.cross_entropy
self.training_handler = TrainingHandler(optimizer, loss_fn, batch_size)
def train(self, numberOfEpochs):
patience_threshold = 3
patience = patience_threshold
min_valid_loss = np.Inf
for epoch in range(numberOfEpochs):
if torch.cuda.is_available():
torch.cuda.empty_cache()
train_loss, train_acc = self.training_handler.train_model(
self.model, self.train_iter, epoch)
val_loss, val_acc = self.training_handler.eval_model(
self.model, self.valid_iter)
print(
f'Epoch: {epoch+1:02}, Train Loss: {train_loss:.3f}, Train Acc: {train_acc:.2f}%, Val. Loss: {val_loss:3f}, Val. Acc: {val_acc:.2f}%'
)
output_handler.outputFileHandler.write(
f'Epoch: {epoch+1:02}, Train Loss: {train_loss:.3f}, Train Acc: {train_acc:.2f}%, Val. Loss: {val_loss:3f}, Val. Acc: {val_acc:.2f}%'
)
patience -= 1
if val_loss < min_valid_loss and abs(min_valid_loss -
val_loss) > 0.005:
patience = patience_threshold
torch.save(self.model, "./saved_models/cnn-" + self.embedding)
min_valid_loss = val_loss
if patience == 0:
break
def test(self):
self.model = torch.load("./saved_models/cnn-" + self.embedding)
test_loss, test_acc = self.training_handler.eval_model(
self.model, self.test_iter)
return test_loss, test_acc
class LongShortTermMemoryAttention():
def __init__(self, embedding, batch_size):
TEXT, vocab_size, word_embeddings, self.train_iter, self.valid_iter, self.test_iter = load_dataset.load(
embedding=embedding, batch_size=batch_size)
self.embedding = embedding
output_size = 10
hidden_size = 256
embedding_length = 300
self.model = AttentionModel(batch_size, output_size, hidden_size,
vocab_size, embedding_length,
word_embeddings)
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
self.model.parameters()),
weight_decay=0.0005,
lr=0.0001)
loss_fn = F.cross_entropy
self.training_handler = TrainingHandler(optimizer, loss_fn, batch_size)
def train(self, numberOfEpochs):
patience_threshold = 3
patience = patience_threshold
min_valid_loss = np.Inf
for epoch in range(numberOfEpochs):
if torch.cuda.is_available():
torch.cuda.empty_cache()
train_loss, train_acc = self.training_handler.train_model(
self.model, self.train_iter, epoch)
val_loss, val_acc = self.training_handler.eval_model(
self.model, self.valid_iter)
print(
f'Epoch: {epoch+1:02}, Train Loss: {train_loss:.3f}, Train Acc: {train_acc:.2f}%, Val. Loss: {val_loss:3f}, Val. Acc: {val_acc:.2f}%'
)
output_handler.outputFileHandler.write(
f'Epoch: {epoch+1:02}, Train Loss: {train_loss:.3f}, Train Acc: {train_acc:.2f}%, Val. Loss: {val_loss:3f}, Val. Acc: {val_acc:.2f}%'
)
patience -= 1
if val_loss < min_valid_loss and abs(min_valid_loss -
val_loss) > 0.005:
patience = patience_threshold
torch.save(self.model,
"./saved_models/lstm-attn-" + self.embedding)
min_valid_loss = val_loss
if patience == 0:
break
def test(self):
self.model = torch.load("./saved_models/lstm-attn-" + self.embedding)
test_loss, test_acc = self.training_handler.eval_model(
self.model, self.test_iter)
return test_loss, test_acc
