def objective(batch_size, hidden_size, learning_rate):
batch_size = int(batch_size)
hidden_size = int(hidden_size)
TEXT, vocab_size, word_embeddings, train_iter, valid_iter, test_iter = load_patents.load_dataset(
batch_size, cache_data=False)
output_size = 2
embedding_length = 300
weights = word_embeddings
model = LSTMClassifier(batch_size, output_size, hidden_size, vocab_size,
embedding_length, word_embeddings)
#model = AttentionModel(batch_size, output_size, hidden_size, vocab_size, embedding_length, weights)
#model = RNN(batch_size, output_size, hidden_size, vocab_size, embedding_length, weights)
#model = RCNN(batch_size, output_size, hidden_size, vocab_size, embedding_length, weights)
#model = SelfAttention(batch_size, output_size, hidden_size, vocab_size, embedding_length, weights)
loss_fn = F.cross_entropy
for epoch in range(10):
#(model, train_iter, epoch, batch_size, learning_rate)
train_loss, train_acc = train_model(model, train_iter, epoch,
batch_size, learning_rate)
val_loss, val_acc = eval_model(model, valid_iter, batch_size)
test_loss, test_acc = eval_model(model, test_iter, batch_size)
print(f'Test Loss: {test_loss:.3f}, Test Acc: {test_acc:.2f}%')
return test_acc
def run_best_model(args):
learning_rate = args.lr
batch_size = args.batch_size
hidden_size = args.hidden_size
epochs = args.epochs
cache_data = args.cache_data
output_size = 2
embedding_length = 300
TEXT, vocab_size, word_embeddings, train_iter, valid_iter, test_iter = load_patents.load_dataset(
batch_size, cache_data=cache_data)
weights = word_embeddings
model = LSTMClassifier(batch_size, output_size, hidden_size, vocab_size,
embedding_length, word_embeddings)
acc_list = []
val_list = []
for epoch in range(epochs):
#(model, train_iter, epoch, batch_size, learning_rate)
train_loss, train_acc = train_model(model, train_iter, epoch,
batch_size, learning_rate)
val_loss, val_acc = eval_model(model, valid_iter, batch_size)
print(
f'EPOCH {epoch} -- Train/Val Loss: {train_loss:.3f}/{val_loss:.3f}, Train/Val Acc: {train_acc:.2f}%/{val_acc:.2f}%'
)
acc_list.append(train_acc)
val_list.append(val_acc)
plt.plot(acc_list, label="train")
plt.plot(val_list, label="val")
plt.savefig('acc_graph.png')
test_loss, test_acc = eval_model(model, test_iter, batch_size)
print("performance of model:")
print(f'Test Loss: {test_loss:.3f}, Test Acc: {test_acc:.2f}%')
def train_and_val(args):
TEXT, vocab_size, word_embeddings, train_iter, valid_iter, test_iter = load_patents.load_dataset(
)
learning_rate = args.lr
batch_size = args.batch_size
output_size = 2
hidden_size = 256
embedding_length = 300
weights = word_embeddings #I think ????????
if args.arch == 'LSTM':
model = LSTMClassifier(batch_size, output_size, hidden_size,
vocab_size, embedding_length, word_embeddings)
elif args.arch == 'RNN':
model = AttentionModel(batch_size, output_size, hidden_size,
vocab_size, embedding_length, weights)
elif args.arch == 'RNN':
model = RNN(batch_size, output_size, hidden_size, vocab_size,
embedding_length, weights)
elif args.arch == 'RNN':
model = CNN(batch_size, output_size, in_channels, out_channels,
kernel_heights, stride, padding, keep_probab, vocab_size,
embedding_length, weights)
elif args.arch == 'RNN':
model = RCNN(batch_size, output_size, hidden_size, vocab_size,
embedding_length, weights)
elif args.arch == 'selfAttn':
model = SelfAttention(batch_size, output_size, hidden_size, vocab_size,
embedding_length, weights)
loss_fn = F.cross_entropy
for epoch in range(10):
train_loss, train_acc = train_model(model, train_iter, loss_fn, epoch)
val_loss, val_acc = eval_model(model, valid_iter, loss_fn)
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}%'
)
test_loss, test_acc = eval_model(model, test_iter, loss_fn)
print(f'Test Loss: {test_loss:.3f}, Test Acc: {test_acc:.2f}%')
ts = time.time()
timestamp = time.ctime(ts)
with open("./results.out", "w+") as f:
f.write(timestamp + '\n')
f.write(f'Test Loss: {test_loss:.3f}, Test Acc: {test_acc:.2f}%\n')
# for arg in args:
# #f.write(arg.name + ':')
# f.write(str(arg) + ', ')
f.write("Epochs: " + str(args.epochs))
f.write("\n")
def run_best_model(args):
learning_rate = args.lr
batch_size = args.batch_size
hidden_size = args.hidden_size
epochs = args.epochs
output_size = 2
embedding_length = 300
TEXT, vocab_size, word_embeddings, train_iter, valid_iter, test_iter = load_patents.load_dataset(
batch_size)
weights = word_embeddings
model = LSTMClassifier(batch_size, output_size, hidden_size, vocab_size,
embedding_length, word_embeddings)
for epoch in range(epochs):
#(model, train_iter, epoch, batch_size, learning_rate)
train_loss, train_acc = train_model(model, train_iter, epoch,
batch_size, learning_rate)
val_loss, val_acc = eval_model(model, valid_iter, batch_size)
test_loss, test_acc = eval_model(model, test_iter, batch_size)
print("performance of model:")
print(f'Test Loss: {test_loss:.3f}, Test Acc: {test_acc:.2f}%')