print(
"Epoch {0} finished. Total Training Loss: {1}, Driver Loss: {2}, Risk Loss: {3}"
.format(epoch, train_total_loss, train_driver_loss,
train_risk_loss))
path = os.path.join(save_path, str(epoch))
os.makedirs(path)
model.save_parameters(path)
bar = progressbar.ProgressBar()
valid_LB = 0.0
val_LB1 = 0.0
val_LB2 = 0.0
for batch in bar(val_batch_order):
batch_end = min(model.batch_size * (batch + 1), x_valid.shape[0])
batch_start = model.batch_size * batch
l1, l2, l3 = model.likelihood(batch_start, batch_end)
val_total_loss += (l1 + l2 + l3) * (batch_end - batch_start)
val_driver_loss += l2 * (batch_end - batch_start)
val_risk_loss += l3 * (batch_end - batch_start)
val_total_loss /= x_valid.shape[0]
val_driver_loss /= x_valid.shape[0]
val_risk_loss /= x_valid.shape[0]
print("Val loss: {}, Val driver loss: {}, Val Risk Loss: {}".format(
val_total_loss, val_driver_loss, val_risk_loss))
###Classification
h_train = []
h_val = []
for i in range(x_train.shape[0] // model.batch_size + 1):
if os.path.isfile(path + "params.pkl"):
print "Restarting from earlier saved parameters!"
model.load_parameters(path)
LB_list = np.load(path + "LB_list.npy")
epoch = len(LB_list)
if __name__ == "__main__":
print "iterating"
while epoch < n_epochs:
epoch += 1
start = time.time()
np.random.shuffle(batch_order)
LB = 0.
for batch in batch_order:
batch_LB = model.update(batch, epoch)
LB += batch_LB
LB /= len(batch_order)
LB_list = np.append(LB_list, LB)
print "Epoch {0} finished. LB: {1}, time: {2}".format(epoch, LB, time.time() - start)
np.save(path + "LB_list.npy", LB_list)
#model.save_parameters(path)
valid_LB = model.likelihood(x_valid)
print "LB on validation set: {0}".format(valid_LB)
if os.path.isfile(path + "params.pkl"):
print "Restarting from earlier saved parameters!"
model.load_parameters(path)
LB_list = np.load(path + "LB_list.npy")
epoch = len(LB_list)
if __name__ == "__main__":
print "iterating"
while epoch < n_epochs:
epoch += 1
start = time.time()
np.random.shuffle(batch_order)
LB = 0.
for batch in batch_order:
batch_LB = model.update(batch, epoch)
LB += batch_LB
LB /= len(batch_order)
LB_list = np.append(LB_list, LB)
print "Epoch {0} finished. LB: {1}, time: {2}".format(
epoch, LB,
time.time() - start)
np.save(path + "LB_list.npy", LB_list)
#model.save_parameters(path)
valid_LB = model.likelihood(x_valid)
print "LB on validation set: {0}".format(valid_LB)