def DBN_JIT(train_features, train_labels, test_features, test_labels, hidden_units=[20, 12, 12], num_epochs_LR=200):
# training DBN model
#################################################################################################
starttime = time.time()
dbn_model = DBN(visible_units=train_features.shape[1],
hidden_units=hidden_units,
use_gpu=False)
dbn_model.train_static(train_features, train_labels, num_epochs=10)
# Finishing the training DBN model
# print('---------------------Finishing the training DBN model---------------------')
# using DBN model to construct features
DBN_train_features, _ = dbn_model.forward(train_features)
DBN_test_features, _ = dbn_model.forward(test_features)
DBN_train_features = DBN_train_features.numpy()
DBN_test_features = DBN_test_features.numpy()
train_features = np.hstack((train_features, DBN_train_features))
test_features = np.hstack((test_features, DBN_test_features))
if len(train_labels.shape) == 1:
num_classes = 1
else:
num_classes = train_labels.shape[1]
# lr_model = LR(input_size=hidden_units, num_classes=num_classes)
lr_model = LR(input_size=train_features.shape[1], num_classes=num_classes)
optimizer = torch.optim.Adam(lr_model.parameters(), lr=0.00001)
steps = 0
batches_test = mini_batches(X=test_features, Y=test_labels)
for epoch in range(1, num_epochs_LR + 1):
# building batches for training model
batches_train = mini_batches_update(X=train_features, Y=train_labels)
for batch in batches_train:
x_batch, y_batch = batch
x_batch, y_batch = torch.tensor(x_batch).float(), torch.tensor(y_batch).float()
optimizer.zero_grad()
predict = lr_model.forward(x_batch)
loss = nn.BCELoss()
loss = loss(predict, y_batch)
loss.backward()
optimizer.step()
# steps += 1
# if steps % 100 == 0:
# print('\rEpoch: {} step: {} - loss: {:.6f}'.format(epoch, steps, loss.item()))
endtime = time.time()
dtime = endtime - starttime
print("Train Time: %.8s s" % dtime) #显示到微秒
starttime = time.time()
y_pred, lables = lr_model.predict(data=batches_test)
endtime = time.time()
dtime = endtime - starttime
print("Eval Time: %.8s s" % dtime) #显示到微秒
return y_pred
