def modelTrain(x_data, y_data):
# now = datetime.utcnow().strftime("%Y%m%d")
# model_dir = "model/{}/".format(now)
model_dir = "Model/Regression/" + save_date + "/"
name = model_dir + gas_type + "_reg"
try:
if not (os.path.isdir(model_dir)):
os.makedirs(os.path.join(model_dir))
except OSError as e:
if e.errno != errno.EEXIST:
raise
start = timeit.default_timer()
before = 0
x_data = torch.Tensor(x_data).float()
y_data = torch.Tensor(y_data).float()
input = len(x_data[0])
hidden = 100
hidden2 = 500
hidden3 = 100
hidden4 = 300
hidden5 = 500
output = 1
epoch = 20000
learning_rate = 0.001
batch_size = 10
dnn = DNN(input,
output, [hidden, hidden2, hidden3, hidden4, hidden5],
softmax=False)
for step, loss in dnn.learn(x_data,
y_data,
epoch=epoch,
learning_rate=learning_rate,
batch_size=batch_size,
loss_func=MSELoss):
if step % 100 == 0:
gap = loss.tolist() - before
before = loss.tolist()
pick = timeit.default_timer()
time_gap = pick - start
print('step : %d, loss : %.10f, loss gap = %.10f, seconds = %d' %
(step, loss.tolist(), gap, time_gap))
if loss == 0:
gap = loss.tolist() - before
pick = timeit.default_timer()
time_gap = pick - start
break
print('step : %d, loss : %.10f, loss gap = %.10f, seconds = %d' %
(step, loss.tolist(), gap, time_gap))
save_model(dnn, name + '.pkl')
def modelTrain(x_data,y_data,name):
start = timeit.default_timer()
before = 0
x_data = torch.Tensor(x_data).float()
y_data = torch.Tensor(y_data).float()
input = len(x_data[0])
hidden = 200
hidden2 = 300
hidden3 = 300
hidden4 = 200
hidden5 = 100
output = 4
epoch = 10000
learning_rate = 0.01
batch_size = 10
dnn = DNN(input, output, [hidden,hidden2,hidden3,hidden4,hidden5],softmax=True)
for step, loss in dnn.learn(x_data, y_data, epoch=epoch, learning_rate=learning_rate, batch_size=batch_size, loss_func=MSELoss):
if step % 10 == 0:
gap = loss.tolist() - before
before = loss.tolist()
pick = timeit.default_timer()
time_gap = pick-start
print('step : %d, loss : %.10f, loss gap = %.10f, seconds = %d' %(step,loss.tolist(),gap,time_gap))
# if loss ==0 or gap==0:
# gap = loss.tolist() - before
# pick = timeit.default_timer()
# time_gap = pick - start
# print('step : %d, loss : %.10f, loss gap = %.10f, seconds = %d' % (step, loss.tolist(),gap,time_gap))
# break
print('step : %d, loss : %.10f, loss gap = %.10f, seconds = %d' % (step, loss.tolist(), gap, time_gap))
save_model(dnn, name+'.pkl')
from datetime import datetime
from datetime import datetime
from datetime import datetime