In [48]:
# layerwisse loss
plt.figure()
plt.plot(np.arange(1,21,1), layer_loss_mean)
plt.xticks(np.arange(1,21,1))
plt.ylabel('GMSE')
plt.xlabel('number of unrolls/iterations')
plt.show()
import torch
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from src.models import *
from src.utils import *
from src.utils_data import *
import argparse
import time
import logging
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
cuda:0
graph_type = 'WS'
graph_size = 50
num_unroll = 20
logging.basicConfig(filename='logs/L2G_{}_m{}_x{}.log'.format(graph_type, graph_size, num_unroll),
filemode='w',
format='%(asctime)s - %(message)s',
datefmt='%d-%b-%y %H:%M:%S',
level=logging.INFO)
console = logging.StreamHandler()
console.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s | %(message)s', datefmt='%d-%b-%y %H:%M:%S')
console.setFormatter(formatter)
logging.getLogger().addHandler(console)
graph_type = 'WS'
edge_type = 'lognormal'
graph_size = 50
graph_hyper = {'k': 5,
'p': 0.2}
data = generate_WS_parallel(num_samples=8064,
num_signals=3000,
num_nodes=graph_size,
graph_hyper=graph_hyper,
weighted=edge_type,
weight_scale=True)
with open('data/dataset_{}_{}nodes.pickle'.format(graph_type, graph_size), 'wb') as handle:
pickle.dump(data, handle, protocol=4)
batch_size = 32
data_dir = 'data/dataset_{}_{}nodes.pickle'.format(graph_type, graph_size)
train_loader, val_loader, test_loader = data_loading(data_dir, batch_size=batch_size)
loading data at data/dataset_WS_50nodes.pickle successfully loading: train 6400, val 1600, test 64, batch 32
import matplotlib.pyplot as plt
import seaborn as sns
for _, W in test_loader:
eg = torch_sqaureform_to_matrix(W, device='cpu')
plt.figure()
sns.heatmap(eg[4])
plt.show()
<Figure size 640x480 with 2 Axes>
num_unroll = 20
graph_size = 50
n_hid = 32
n_latent = 16
n_nodeFeat = 1
n_graphFeat = 16
lr = 1e-02
lr_decay = 0.95
net = learn2graph(num_unroll, graph_size, n_hid,
n_latent, n_nodeFeat, n_graphFeat).to(device)
optimizer = optim.Adam(net.parameters(), lr=lr)
scheduler = lr_scheduler.ExponentialLR(optimizer, lr_decay)
logging.info(net)
04-Jun-21 11:31:29 | learn2graph(
(vae): TopoDiffVAE(
(enc): GraphEnc(
(conv1): GraphConvLayer()
(conv2): GraphConvLayer()
(fc1): Linear(in_features=64, out_features=32, bias=True)
(fc2): Linear(in_features=32, out_features=16, bias=True)
)
(f_mean): Sequential(
(0): Linear(in_features=16, out_features=16, bias=True)
)
(f_var): Sequential(
(0): Linear(in_features=16, out_features=16, bias=True)
)
(dec): Sequential(
(0): Linear(in_features=1241, out_features=1633, bias=True)
(1): Tanh()
(2): Linear(in_features=1633, out_features=1225, bias=True)
)
)
)
# Training:
n_epochs = 300
dur = []
epoch_train_gmse = []
epoch_val_gmse = []
for epoch in range(n_epochs):
train_unrolling_loss, train_vae_loss, train_kl_loss, train_gmse, val_gmse = [], [], [], [], []
t0 = time.time()
net.train()
for z, w_gt_batch in train_loader:
z = z.to(device)
w_gt_batch = w_gt_batch.to(device)
this_batch_size = w_gt_batch.size()[0]
optimizer.zero_grad()
w_list, vae_loss, vae_kl, _ = net.forward(z, w_gt_batch, threshold=1e-04, kl_hyper=1)
unrolling_loss = torch.mean(
torch.stack([acc_loss(w_list[i, :, :], w_gt_batch[i, :], dn=0.9) for i in range(batch_size)])
)
loss = unrolling_loss + vae_loss
loss.backward()
optimizer.step()
w_pred = w_list[:, num_unroll - 1, :]
gmse = gmse_loss_batch_mean(w_pred, w_gt_batch)
train_gmse.append(gmse.item())
train_unrolling_loss.append(unrolling_loss.item())
train_vae_loss.append(vae_loss.item())
train_kl_loss.append(vae_kl.item())
scheduler.step()
net.eval()
for z, w_gt_batch in val_loader:
z = z.to(device)
w_gt_batch = w_gt_batch.to(device)
w_list = net.validation(z, threshold=1e-04)
w_pred = torch.clamp(w_list[:, num_unroll - 1, :], min=0)
loss = gmse_loss_batch_mean(w_pred, w_gt_batch)
val_gmse.append(loss.item())
dur.append(time.time() - t0)
logging.info("Epoch {:04d} | lr: {:04.8f} | Time(s): {:.4f}".format(epoch + 1, scheduler.get_lr()[0], np.mean(dur)))
logging.info("== train Loss <unroll: {:04.4f} | vae : {:04.4f} | kl : {:04.4f}>".format(np.mean(train_unrolling_loss),
np.mean(train_vae_loss),
np.mean(train_kl_loss)))
logging.info("== gmse <train: {:04.4f} | val: {:04.4f}> ".format(np.mean(train_gmse), np.mean(val_gmse)))
epoch_train_gmse.append(np.mean(train_gmse))
epoch_val_gmse.append(np.mean(val_gmse))
04-Jun-21 11:31:59 | Epoch 0001 | lr: 0.00902500 | Time(s): 30.2504 04-Jun-21 11:31:59 | == train Loss <unroll: 293.2162 | vae : 1196304220.8786 | kl : 1196301420.3438> 04-Jun-21 11:31:59 | == gmse <train: 265.0365 | val: 0.6475> 04-Jun-21 11:32:29 | Epoch 0002 | lr: 0.00857375 | Time(s): 30.1442 04-Jun-21 11:32:29 | == train Loss <unroll: 33.6657 | vae : 3.2337 | kl : 0.0081> 04-Jun-21 11:32:29 | == gmse <train: 1.4905 | val: 0.5639> 04-Jun-21 11:32:59 | Epoch 0003 | lr: 0.00814506 | Time(s): 30.1208 04-Jun-21 11:32:59 | == train Loss <unroll: 26.2211 | vae : 3.7908 | kl : 0.0079> 04-Jun-21 11:32:59 | == gmse <train: 1.0757 | val: 0.6086> 04-Jun-21 11:33:29 | Epoch 0004 | lr: 0.00773781 | Time(s): 30.0819 04-Jun-21 11:33:29 | == train Loss <unroll: 21.8958 | vae : 4.5856 | kl : 0.0077> 04-Jun-21 11:33:29 | == gmse <train: 0.9735 | val: 0.6034> 04-Jun-21 11:33:59 | Epoch 0005 | lr: 0.00735092 | Time(s): 30.0788 04-Jun-21 11:33:59 | == train Loss <unroll: 18.7120 | vae : 6.3110 | kl : 0.0074> 04-Jun-21 11:33:59 | == gmse <train: 1.0008 | val: 0.7405> 04-Jun-21 11:34:29 | Epoch 0006 | lr: 0.00698337 | Time(s): 30.0895 04-Jun-21 11:34:29 | == train Loss <unroll: 16.9451 | vae : 10.1764 | kl : 0.0071> 04-Jun-21 11:34:29 | == gmse <train: 1.2590 | val: 0.9805> 04-Jun-21 11:34:59 | Epoch 0007 | lr: 0.00663420 | Time(s): 30.0516 04-Jun-21 11:34:59 | == train Loss <unroll: 15.2084 | vae : 17.3656 | kl : 0.0069> 04-Jun-21 11:34:59 | == gmse <train: 1.8049 | val: 1.7118> 04-Jun-21 11:35:29 | Epoch 0008 | lr: 0.00630249 | Time(s): 30.0421 04-Jun-21 11:35:29 | == train Loss <unroll: 14.1498 | vae : 25.8545 | kl : 0.0067> 04-Jun-21 11:35:29 | == gmse <train: 2.4321 | val: 2.0015> 04-Jun-21 11:35:59 | Epoch 0009 | lr: 0.00598737 | Time(s): 30.0294 04-Jun-21 11:35:59 | == train Loss <unroll: 13.2069 | vae : 31.5170 | kl : 0.0064> 04-Jun-21 11:35:59 | == gmse <train: 2.7835 | val: 2.7919> 04-Jun-21 11:36:29 | Epoch 0010 | lr: 0.00568800 | Time(s): 30.0215 04-Jun-21 11:36:29 | == train Loss <unroll: 12.3297 | vae : 34.9053 | kl : 0.0061> 04-Jun-21 11:36:29 | == gmse <train: 2.9200 | val: 2.1923> 04-Jun-21 11:36:59 | Epoch 0011 | lr: 0.00540360 | Time(s): 30.0136 04-Jun-21 11:36:59 | == train Loss <unroll: 11.5069 | vae : 37.0054 | kl : 0.0058> 04-Jun-21 11:36:59 | == gmse <train: 2.9357 | val: 2.7941> 04-Jun-21 11:37:29 | Epoch 0012 | lr: 0.00513342 | Time(s): 30.0085 04-Jun-21 11:37:29 | == train Loss <unroll: 10.3005 | vae : 36.3894 | kl : 0.0055> 04-Jun-21 11:37:29 | == gmse <train: 2.7597 | val: 2.2855> 04-Jun-21 11:37:59 | Epoch 0013 | lr: 0.00487675 | Time(s): 30.0025 04-Jun-21 11:37:59 | == train Loss <unroll: 10.5843 | vae : 35.7628 | kl : 0.0053> 04-Jun-21 11:37:59 | == gmse <train: 2.6650 | val: 2.3674> 04-Jun-21 11:38:29 | Epoch 0014 | lr: 0.00463291 | Time(s): 29.9983 04-Jun-21 11:38:29 | == train Loss <unroll: 9.3043 | vae : 33.5255 | kl : 0.0050> 04-Jun-21 11:38:29 | == gmse <train: 2.3839 | val: 2.1159> 04-Jun-21 11:38:59 | Epoch 0015 | lr: 0.00440127 | Time(s): 29.9948 04-Jun-21 11:38:59 | == train Loss <unroll: 7.4115 | vae : 30.9318 | kl : 0.0048> 04-Jun-21 11:38:59 | == gmse <train: 2.0796 | val: 1.9852> 04-Jun-21 11:39:29 | Epoch 0016 | lr: 0.00418120 | Time(s): 30.0102 04-Jun-21 11:39:29 | == train Loss <unroll: 6.6917 | vae : 28.7482 | kl : 0.0046> 04-Jun-21 11:39:29 | == gmse <train: 1.8720 | val: 1.7729> 04-Jun-21 11:39:59 | Epoch 0017 | lr: 0.00397214 | Time(s): 30.0162 04-Jun-21 11:39:59 | == train Loss <unroll: 6.1999 | vae : 26.6255 | kl : 0.0043> 04-Jun-21 11:39:59 | == gmse <train: 1.6912 | val: 1.6102> 04-Jun-21 11:40:29 | Epoch 0018 | lr: 0.00377354 | Time(s): 30.0134 04-Jun-21 11:40:29 | == train Loss <unroll: 5.8382 | vae : 25.1298 | kl : 0.0041> 04-Jun-21 11:40:29 | == gmse <train: 1.5562 | val: 1.4399> 04-Jun-21 11:40:59 | Epoch 0019 | lr: 0.00358486 | Time(s): 30.0029 04-Jun-21 11:40:59 | == train Loss <unroll: 5.4945 | vae : 22.9848 | kl : 0.0039> 04-Jun-21 11:40:59 | == gmse <train: 1.4046 | val: 1.3590> 04-Jun-21 11:41:29 | Epoch 0020 | lr: 0.00340562 | Time(s): 30.0090 04-Jun-21 11:41:59 | Epoch 0021 | lr: 0.00323534 | Time(s): 30.0042 04-Jun-21 11:41:59 | == train Loss <unroll: 4.9609 | vae : 19.6908 | kl : 0.0036> 04-Jun-21 11:41:59 | == gmse <train: 1.1857 | val: 1.1361> 04-Jun-21 11:43:59 | Epoch 0025 | lr: 0.00263520 | Time(s): 30.0006 04-Jun-21 11:43:59 | == train Loss <unroll: 4.2220 | vae : 14.7988 | kl : 0.0031> 04-Jun-21 11:43:59 | == gmse <train: 0.9279 | val: 0.8702> 04-Jun-21 11:44:29 | Epoch 0026 | lr: 0.00250344 | Time(s): 30.0009 04-Jun-21 11:44:29 | == train Loss <unroll: 4.0915 | vae : 13.9958 | kl : 0.0030> 04-Jun-21 11:44:29 | == gmse <train: 0.8916 | val: 0.8445> 04-Jun-21 11:44:59 | Epoch 0027 | lr: 0.00237827 | Time(s): 30.0001 04-Jun-21 11:44:59 | == train Loss <unroll: 3.9761 | vae : 13.0488 | kl : 0.0029> 04-Jun-21 11:44:59 | == gmse <train: 0.8567 | val: 0.7697> 04-Jun-21 11:45:29 | Epoch 0028 | lr: 0.00225936 | Time(s): 29.9996 04-Jun-21 11:45:29 | == train Loss <unroll: 3.8760 | vae : 12.2816 | kl : 0.0028> 04-Jun-21 11:45:29 | == gmse <train: 0.8293 | val: 0.8027> 04-Jun-21 11:45:59 | Epoch 0029 | lr: 0.00214639 | Time(s): 29.9998 04-Jun-21 11:45:59 | == train Loss <unroll: 3.7911 | vae : 11.4998 | kl : 0.0028> 04-Jun-21 11:45:59 | == gmse <train: 0.8043 | val: 0.7803> 04-Jun-21 11:46:29 | Epoch 0030 | lr: 0.00203907 | Time(s): 29.9947 04-Jun-21 11:46:29 | == train Loss <unroll: 3.7155 | vae : 10.9565 | kl : 0.0027> 04-Jun-21 11:46:29 | == gmse <train: 0.7859 | val: 0.7094> 04-Jun-21 11:46:59 | Epoch 0031 | lr: 0.00193711 | Time(s): 29.9947 04-Jun-21 11:46:59 | == train Loss <unroll: 3.6469 | vae : 10.1929 | kl : 0.0026> 04-Jun-21 11:46:59 | == gmse <train: 0.7642 | val: 0.7283> 04-Jun-21 11:47:29 | Epoch 0032 | lr: 0.00184026 | Time(s): 29.9960 04-Jun-21 11:47:29 | == train Loss <unroll: 7.0184 | vae : 9.6117 | kl : 0.0026> 04-Jun-21 11:47:29 | == gmse <train: 0.7704 | val: 1.7540> 04-Jun-21 11:47:59 | Epoch 0033 | lr: 0.00174825 | Time(s): 29.9984 04-Jun-21 11:47:59 | == train Loss <unroll: 20.6907 | vae : 9.2357 | kl : 0.0025> 04-Jun-21 11:47:59 | == gmse <train: 0.8554 | val: 0.7062> 04-Jun-21 11:48:29 | Epoch 0034 | lr: 0.00166083 | Time(s): 30.0035 04-Jun-21 11:48:29 | == train Loss <unroll: 4.7404 | vae : 8.6370 | kl : 0.0025> 04-Jun-21 11:48:29 | == gmse <train: 0.7623 | val: 0.7184> 04-Jun-21 11:48:59 | Epoch 0035 | lr: 0.00157779 | Time(s): 30.0078 04-Jun-21 11:48:59 | == train Loss <unroll: 4.3245 | vae : 8.2726 | kl : 0.0025> 04-Jun-21 11:48:59 | == gmse <train: 0.7528 | val: 0.6855> 04-Jun-21 11:49:29 | Epoch 0036 | lr: 0.00149890 | Time(s): 30.0126 04-Jun-21 11:49:29 | == train Loss <unroll: 4.1100 | vae : 7.8362 | kl : 0.0024> 04-Jun-21 11:49:29 | == gmse <train: 0.7381 | val: 0.6750> 04-Jun-21 11:50:00 | Epoch 0037 | lr: 0.00142396 | Time(s): 30.0177 04-Jun-21 11:50:00 | == train Loss <unroll: 3.9832 | vae : 7.5042 | kl : 0.0024> 04-Jun-21 11:50:00 | == gmse <train: 0.7230 | val: 0.6936> 04-Jun-21 11:50:30 | Epoch 0038 | lr: 0.00135276 | Time(s): 30.0219 04-Jun-21 11:50:30 | == train Loss <unroll: 3.8961 | vae : 7.1082 | kl : 0.0024> 04-Jun-21 11:50:30 | == gmse <train: 0.7063 | val: 0.6852> 04-Jun-21 11:51:00 | Epoch 0039 | lr: 0.00128512 | Time(s): 30.0268 04-Jun-21 11:51:00 | == train Loss <unroll: 3.8305 | vae : 6.8298 | kl : 0.0023> 04-Jun-21 11:51:00 | == gmse <train: 0.6940 | val: 0.6655> 04-Jun-21 11:51:30 | Epoch 0040 | lr: 0.00122087 | Time(s): 30.0295 04-Jun-21 11:51:30 | == train Loss <unroll: 3.7721 | vae : 6.5951 | kl : 0.0023> 04-Jun-21 11:51:30 | == gmse <train: 0.6847 | val: 0.6727> 04-Jun-21 11:52:00 | Epoch 0041 | lr: 0.00115982 | Time(s): 30.0321 04-Jun-21 11:52:00 | == train Loss <unroll: 3.6602 | vae : 6.2149 | kl : 0.0023> 04-Jun-21 11:52:00 | == gmse <train: 0.6904 | val: 0.6760> 04-Jun-21 11:52:30 | Epoch 0042 | lr: 0.00110183 | Time(s): 30.0349 04-Jun-21 11:52:30 | == train Loss <unroll: 3.4517 | vae : 5.9919 | kl : 0.0023> 04-Jun-21 11:52:30 | == gmse <train: 0.6976 | val: 0.6829> 04-Jun-21 11:53:01 | Epoch 0043 | lr: 0.00104674 | Time(s): 30.0372 04-Jun-21 11:53:01 | == train Loss <unroll: 3.2872 | vae : 5.8009 | kl : 0.0023> 04-Jun-21 11:53:01 | == gmse <train: 0.6881 | val: 0.6640> 04-Jun-21 11:53:31 | Epoch 0044 | lr: 0.00099440 | Time(s): 30.0397 04-Jun-21 11:53:31 | == train Loss <unroll: 3.1547 | vae : 5.6249 | kl : 0.0022> 04-Jun-21 11:53:31 | == gmse <train: 0.6772 | val: 0.6343> 04-Jun-21 11:54:01 | Epoch 0045 | lr: 0.00094468 | Time(s): 30.0436 04-Jun-21 11:54:01 | == train Loss <unroll: 3.0435 | vae : 5.3756 | kl : 0.0022> 04-Jun-21 11:54:01 | == gmse <train: 0.6629 | val: 0.6243> 04-Jun-21 11:54:31 | Epoch 0046 | lr: 0.00089745 | Time(s): 30.0489 04-Jun-21 11:54:31 | == train Loss <unroll: 2.9509 | vae : 5.1993 | kl : 0.0022> 04-Jun-21 11:54:31 | == gmse <train: 0.6502 | val: 0.6423> 04-Jun-21 11:55:01 | Epoch 0047 | lr: 0.00085258 | Time(s): 30.0447 04-Jun-21 11:55:01 | == train Loss <unroll: 2.8681 | vae : 4.9743 | kl : 0.0022> 04-Jun-21 11:55:01 | == gmse <train: 0.6356 | val: 0.6313> 04-Jun-21 11:55:31 | Epoch 0048 | lr: 0.00080995 | Time(s): 30.0401 04-Jun-21 11:55:31 | == train Loss <unroll: 2.7836 | vae : 4.8383 | kl : 0.0022> 04-Jun-21 11:55:31 | == gmse <train: 0.6189 | val: 0.5397> 04-Jun-21 11:56:01 | Epoch 0049 | lr: 0.00076945 | Time(s): 30.0354 04-Jun-21 11:56:01 | == train Loss <unroll: 2.3708 | vae : 4.7017 | kl : 0.0022> 04-Jun-21 11:56:01 | == gmse <train: 0.4968 | val: 0.3841> 04-Jun-21 11:56:30 | Epoch 0050 | lr: 0.00073098 | Time(s): 30.0313 04-Jun-21 11:56:30 | == train Loss <unroll: 1.9740 | vae : 4.5385 | kl : 0.0022> 04-Jun-21 11:56:30 | == gmse <train: 0.4250 | val: 0.3257> 04-Jun-21 11:57:00 | Epoch 0051 | lr: 0.00069443 | Time(s): 30.0300 04-Jun-21 11:57:00 | == train Loss <unroll: 1.8043 | vae : 4.3938 | kl : 0.0022> 04-Jun-21 11:57:00 | == gmse <train: 0.4028 | val: 0.3111> 04-Jun-21 11:57:30 | Epoch 0052 | lr: 0.00065971 | Time(s): 30.0290 04-Jun-21 11:57:30 | == train Loss <unroll: 1.7029 | vae : 4.2768 | kl : 0.0021> 04-Jun-21 11:57:30 | == gmse <train: 0.3879 | val: 0.2813> 04-Jun-21 11:58:00 | Epoch 0053 | lr: 0.00062672 | Time(s): 30.0280 04-Jun-21 11:58:00 | == train Loss <unroll: 1.6347 | vae : 4.1660 | kl : 0.0021> 04-Jun-21 11:58:00 | == gmse <train: 0.3727 | val: 0.2669> 04-Jun-21 11:58:30 | Epoch 0054 | lr: 0.00059539 | Time(s): 30.0272 04-Jun-21 11:58:30 | == train Loss <unroll: 1.5844 | vae : 4.0195 | kl : 0.0021> 04-Jun-21 11:58:30 | == gmse <train: 0.3561 | val: 0.2620> 04-Jun-21 11:59:00 | Epoch 0055 | lr: 0.00056562 | Time(s): 30.0263 04-Jun-21 11:59:00 | == train Loss <unroll: 1.5515 | vae : 3.9638 | kl : 0.0021> 04-Jun-21 11:59:00 | == gmse <train: 0.3443 | val: 0.2622> 04-Jun-21 11:59:30 | Epoch 0056 | lr: 0.00053734 | Time(s): 30.0254 04-Jun-21 11:59:30 | == train Loss <unroll: 1.5185 | vae : 3.8498 | kl : 0.0021> 04-Jun-21 11:59:30 | == gmse <train: 0.3304 | val: 0.2466> 04-Jun-21 12:00:00 | Epoch 0057 | lr: 0.00051047 | Time(s): 30.0233 04-Jun-21 12:00:00 | == train Loss <unroll: 1.5533 | vae : 3.7897 | kl : 0.0021> 04-Jun-21 12:00:00 | == gmse <train: 0.3221 | val: 0.2461> 04-Jun-21 12:00:30 | Epoch 0058 | lr: 0.00048495 | Time(s): 30.0200 04-Jun-21 12:00:30 | == train Loss <unroll: 1.4784 | vae : 3.6997 | kl : 0.0021> 04-Jun-21 12:00:30 | == gmse <train: 0.3074 | val: 0.2395> 04-Jun-21 12:01:00 | Epoch 0059 | lr: 0.00046070 | Time(s): 30.0170 04-Jun-21 12:01:00 | == train Loss <unroll: 1.4643 | vae : 3.6381 | kl : 0.0021> 04-Jun-21 12:01:00 | == gmse <train: 0.2979 | val: 0.2219> 04-Jun-21 12:01:30 | Epoch 0060 | lr: 0.00043766 | Time(s): 30.0139 04-Jun-21 12:01:30 | == train Loss <unroll: 1.4423 | vae : 3.5477 | kl : 0.0021> 04-Jun-21 12:01:30 | == gmse <train: 0.2867 | val: 0.2281> 04-Jun-21 12:02:00 | Epoch 0061 | lr: 0.00041578 | Time(s): 30.0129 04-Jun-21 12:02:00 | == train Loss <unroll: 1.4285 | vae : 3.4965 | kl : 0.0021> 04-Jun-21 12:02:00 | == gmse <train: 0.2777 | val: 0.2140> 04-Jun-21 12:02:30 | Epoch 0062 | lr: 0.00039499 | Time(s): 30.0142 04-Jun-21 12:02:30 | == train Loss <unroll: 1.4200 | vae : 3.4277 | kl : 0.0020> 04-Jun-21 12:02:30 | == gmse <train: 0.2687 | val: 0.2120> 04-Jun-21 12:03:00 | Epoch 0063 | lr: 0.00037524 | Time(s): 30.0154 04-Jun-21 12:03:00 | == train Loss <unroll: 1.4163 | vae : 3.3852 | kl : 0.0020> 04-Jun-21 12:03:00 | == gmse <train: 0.2613 | val: 0.2103> 04-Jun-21 12:03:30 | Epoch 0064 | lr: 0.00035648 | Time(s): 30.0125 04-Jun-21 12:03:30 | == train Loss <unroll: 1.3896 | vae : 3.3181 | kl : 0.0020> 04-Jun-21 12:03:30 | == gmse <train: 0.2505 | val: 0.1959> 04-Jun-21 12:04:00 | Epoch 0065 | lr: 0.00033866 | Time(s): 30.0123 04-Jun-21 12:04:00 | == train Loss <unroll: 1.3911 | vae : 3.2583 | kl : 0.0020> 04-Jun-21 12:04:00 | == gmse <train: 0.2434 | val: 0.1943> 04-Jun-21 12:04:30 | Epoch 0066 | lr: 0.00032172 | Time(s): 30.0130 04-Jun-21 12:04:30 | == train Loss <unroll: 1.3654 | vae : 3.2095 | kl : 0.0020> 04-Jun-21 12:04:30 | == gmse <train: 0.2338 | val: 0.1859> 04-Jun-21 12:05:00 | Epoch 0067 | lr: 0.00030564 | Time(s): 30.0120 04-Jun-21 12:05:00 | == train Loss <unroll: 1.3541 | vae : 3.1683 | kl : 0.0020> 04-Jun-21 12:05:00 | == gmse <train: 0.2263 | val: 0.1884> 04-Jun-21 12:05:30 | Epoch 0068 | lr: 0.00029035 | Time(s): 30.0113 04-Jun-21 12:05:30 | == train Loss <unroll: 1.3439 | vae : 3.1279 | kl : 0.0020> 04-Jun-21 12:05:30 | == gmse <train: 0.2187 | val: 0.1827> 04-Jun-21 12:06:00 | Epoch 0069 | lr: 0.00027584 | Time(s): 30.0136 04-Jun-21 12:06:00 | == train Loss <unroll: 1.3322 | vae : 3.0768 | kl : 0.0020> 04-Jun-21 12:06:00 | == gmse <train: 0.2109 | val: 0.1728> 04-Jun-21 12:06:30 | Epoch 0070 | lr: 0.00026205 | Time(s): 30.0106 04-Jun-21 12:06:30 | == train Loss <unroll: 1.3262 | vae : 3.0408 | kl : 0.0019> 04-Jun-21 12:06:30 | == gmse <train: 0.2043 | val: 0.1689> 04-Jun-21 12:06:59 | Epoch 0071 | lr: 0.00024894 | Time(s): 30.0063 04-Jun-21 12:06:59 | == train Loss <unroll: 1.3127 | vae : 3.0093 | kl : 0.0019> 04-Jun-21 12:06:59 | == gmse <train: 0.1967 | val: 0.1648> 04-Jun-21 12:07:29 | Epoch 0072 | lr: 0.00023650 | Time(s): 30.0041 04-Jun-21 12:07:29 | == train Loss <unroll: 1.3006 | vae : 2.9677 | kl : 0.0019> 04-Jun-21 12:07:29 | == gmse <train: 0.1895 | val: 0.1623> 04-Jun-21 12:07:59 | Epoch 0073 | lr: 0.00022467 | Time(s): 30.0016 04-Jun-21 12:07:59 | == train Loss <unroll: 1.3032 | vae : 2.9389 | kl : 0.0019> 04-Jun-21 12:07:59 | == gmse <train: 0.1841 | val: 0.1672> 04-Jun-21 12:08:29 | Epoch 0074 | lr: 0.00021344 | Time(s): 29.9998 04-Jun-21 12:08:29 | == train Loss <unroll: 1.2813 | vae : 2.9143 | kl : 0.0019> 04-Jun-21 12:08:29 | == gmse <train: 0.1762 | val: 0.1467> 04-Jun-21 12:08:59 | Epoch 0075 | lr: 0.00020277 | Time(s): 30.0010 04-Jun-21 12:08:59 | == train Loss <unroll: 1.2737 | vae : 2.8776 | kl : 0.0018> 04-Jun-21 12:08:59 | == gmse <train: 0.1699 | val: 0.1431> 04-Jun-21 12:09:29 | Epoch 0076 | lr: 0.00019263 | Time(s): 30.0029 04-Jun-21 12:09:29 | == train Loss <unroll: 1.2579 | vae : 2.8505 | kl : 0.0018> 04-Jun-21 12:09:29 | == gmse <train: 0.1633 | val: 0.1365> 04-Jun-21 12:09:59 | Epoch 0077 | lr: 0.00018300 | Time(s): 30.0053 04-Jun-21 12:09:59 | == train Loss <unroll: 1.2475 | vae : 2.8240 | kl : 0.0018> 04-Jun-21 12:09:59 | == gmse <train: 0.1578 | val: 0.1372> 04-Jun-21 12:10:30 | Epoch 0078 | lr: 0.00017385 | Time(s): 30.0075 04-Jun-21 12:10:30 | == train Loss <unroll: 1.2327 | vae : 2.7982 | kl : 0.0018> 04-Jun-21 12:10:30 | == gmse <train: 0.1525 | val: 0.1288> 04-Jun-21 12:11:00 | Epoch 0079 | lr: 0.00016515 | Time(s): 30.0093 04-Jun-21 12:11:00 | == train Loss <unroll: 1.2225 | vae : 2.7792 | kl : 0.0018> 04-Jun-21 12:11:00 | == gmse <train: 0.1483 | val: 0.1241> 04-Jun-21 12:11:30 | Epoch 0080 | lr: 0.00015690 | Time(s): 30.0112 04-Jun-21 12:11:30 | == train Loss <unroll: 1.2134 | vae : 2.7498 | kl : 0.0017> 04-Jun-21 12:11:30 | == gmse <train: 0.1443 | val: 0.1251> 04-Jun-21 12:12:00 | Epoch 0081 | lr: 0.00014905 | Time(s): 30.0118 04-Jun-21 12:12:00 | == train Loss <unroll: 1.2081 | vae : 2.7312 | kl : 0.0017> 04-Jun-21 12:12:00 | == gmse <train: 0.1412 | val: 0.1243> 04-Jun-21 12:12:30 | Epoch 0082 | lr: 0.00014160 | Time(s): 30.0119 04-Jun-21 12:12:30 | == train Loss <unroll: 1.2020 | vae : 2.7037 | kl : 0.0017> 04-Jun-21 12:12:30 | == gmse <train: 0.1381 | val: 0.1207> 04-Jun-21 12:13:00 | Epoch 0083 | lr: 0.00013452 | Time(s): 30.0114 04-Jun-21 12:13:00 | == train Loss <unroll: 1.1971 | vae : 2.6928 | kl : 0.0017> 04-Jun-21 12:13:00 | == gmse <train: 0.1355 | val: 0.1180> 04-Jun-21 12:13:30 | Epoch 0084 | lr: 0.00012779 | Time(s): 30.0108 04-Jun-21 12:13:30 | == train Loss <unroll: 1.1922 | vae : 2.6739 | kl : 0.0016> 04-Jun-21 12:13:30 | == gmse <train: 0.1332 | val: 0.1129> 04-Jun-21 12:14:00 | Epoch 0085 | lr: 0.00012140 | Time(s): 30.0104 04-Jun-21 12:14:00 | == train Loss <unroll: 1.1888 | vae : 2.6510 | kl : 0.0016> 04-Jun-21 12:14:00 | == gmse <train: 0.1312 | val: 0.1126> 04-Jun-21 12:14:30 | Epoch 0086 | lr: 0.00011533 | Time(s): 30.0101 04-Jun-21 12:14:30 | == train Loss <unroll: 1.1845 | vae : 2.6350 | kl : 0.0016> 04-Jun-21 12:14:30 | == gmse <train: 0.1293 | val: 0.1135> 04-Jun-21 12:15:00 | Epoch 0087 | lr: 0.00010957 | Time(s): 30.0100 04-Jun-21 12:15:00 | == train Loss <unroll: 1.1814 | vae : 2.6201 | kl : 0.0016> 04-Jun-21 12:15:00 | == gmse <train: 0.1278 | val: 0.1113> 04-Jun-21 12:15:30 | Epoch 0088 | lr: 0.00010409 | Time(s): 30.0096 04-Jun-21 12:15:30 | == train Loss <unroll: 1.1765 | vae : 2.6022 | kl : 0.0015> 04-Jun-21 12:15:30 | == gmse <train: 0.1260 | val: 0.1082> 04-Jun-21 12:16:00 | Epoch 0089 | lr: 0.00009888 | Time(s): 30.0089 04-Jun-21 12:16:00 | == train Loss <unroll: 1.1727 | vae : 2.5938 | kl : 0.0015> 04-Jun-21 12:16:00 | == gmse <train: 0.1244 | val: 0.1082> 04-Jun-21 12:16:30 | Epoch 0090 | lr: 0.00009394 | Time(s): 30.0074 04-Jun-21 12:16:30 | == train Loss <unroll: 1.1694 | vae : 2.5678 | kl : 0.0015> 04-Jun-21 12:16:30 | == gmse <train: 0.1232 | val: 0.1055> 04-Jun-21 12:17:00 | Epoch 0091 | lr: 0.00008924 | Time(s): 30.0078 04-Jun-21 12:17:00 | == train Loss <unroll: 1.1654 | vae : 2.5636 | kl : 0.0014> 04-Jun-21 12:17:00 | == gmse <train: 0.1218 | val: 0.1043> 04-Jun-21 12:17:30 | Epoch 0092 | lr: 0.00008478 | Time(s): 30.0071 04-Jun-21 12:17:30 | == train Loss <unroll: 1.1617 | vae : 2.5476 | kl : 0.0014> 04-Jun-21 12:17:30 | == gmse <train: 0.1206 | val: 0.1050> 04-Jun-21 12:18:00 | Epoch 0093 | lr: 0.00008054 | Time(s): 30.0071 04-Jun-21 12:18:00 | == train Loss <unroll: 1.1591 | vae : 2.5370 | kl : 0.0014> 04-Jun-21 12:18:00 | == gmse <train: 0.1196 | val: 0.1016> 04-Jun-21 12:18:30 | Epoch 0094 | lr: 0.00007651 | Time(s): 30.0066 04-Jun-21 12:18:30 | == train Loss <unroll: 1.1547 | vae : 2.5164 | kl : 0.0013> 04-Jun-21 12:18:30 | == gmse <train: 0.1183 | val: 0.1021> 04-Jun-21 12:19:00 | Epoch 0095 | lr: 0.00007269 | Time(s): 30.0066 04-Jun-21 12:19:00 | == train Loss <unroll: 1.1517 | vae : 2.5150 | kl : 0.0013> 04-Jun-21 12:19:00 | == gmse <train: 0.1173 | val: 0.1021> 04-Jun-21 12:19:30 | Epoch 0096 | lr: 0.00006905 | Time(s): 30.0068 04-Jun-21 12:19:30 | == train Loss <unroll: 1.1481 | vae : 2.5019 | kl : 0.0013> 04-Jun-21 12:19:30 | == gmse <train: 0.1162 | val: 0.1011> 04-Jun-21 12:20:00 | Epoch 0097 | lr: 0.00006560 | Time(s): 30.0067 04-Jun-21 12:20:00 | == train Loss <unroll: 1.1448 | vae : 2.4968 | kl : 0.0012> 04-Jun-21 12:20:00 | == gmse <train: 0.1153 | val: 0.1011> 04-Jun-21 12:20:30 | Epoch 0098 | lr: 0.00006232 | Time(s): 30.0069 04-Jun-21 12:20:30 | == train Loss <unroll: 1.1415 | vae : 2.4850 | kl : 0.0012> 04-Jun-21 12:20:30 | == gmse <train: 0.1142 | val: 0.1006> 04-Jun-21 12:21:00 | Epoch 0099 | lr: 0.00005921 | Time(s): 30.0079 04-Jun-21 12:21:00 | == train Loss <unroll: 1.1387 | vae : 2.4743 | kl : 0.0012> 04-Jun-21 12:21:00 | == gmse <train: 0.1133 | val: 0.1008> 04-Jun-21 12:21:30 | Epoch 0100 | lr: 0.00005625 | Time(s): 30.0088 04-Jun-21 12:21:30 | == train Loss <unroll: 1.1360 | vae : 2.4644 | kl : 0.0011> 04-Jun-21 12:21:30 | == gmse <train: 0.1125 | val: 0.1003> 04-Jun-21 12:22:00 | Epoch 0101 | lr: 0.00005343 | Time(s): 30.0098 04-Jun-21 12:22:00 | == train Loss <unroll: 1.1331 | vae : 2.4573 | kl : 0.0011> 04-Jun-21 12:22:00 | == gmse <train: 0.1116 | val: 0.0981> 04-Jun-21 12:22:30 | Epoch 0102 | lr: 0.00005076 | Time(s): 30.0109 04-Jun-21 12:22:30 | == train Loss <unroll: 1.1308 | vae : 2.4463 | kl : 0.0011> 04-Jun-21 12:22:30 | == gmse <train: 0.1109 | val: 0.0990> 04-Jun-21 12:23:00 | Epoch 0103 | lr: 0.00004822 | Time(s): 30.0118 04-Jun-21 12:23:00 | == train Loss <unroll: 1.1285 | vae : 2.4371 | kl : 0.0010> 04-Jun-21 12:23:00 | == gmse <train: 0.1101 | val: 0.0987> 04-Jun-21 12:23:30 | Epoch 0104 | lr: 0.00004581 | Time(s): 30.0129 04-Jun-21 12:23:30 | == train Loss <unroll: 1.1263 | vae : 2.4284 | kl : 0.0010> 04-Jun-21 12:23:30 | == gmse <train: 0.1094 | val: 0.0975> 04-Jun-21 12:24:01 | Epoch 0105 | lr: 0.00004352 | Time(s): 30.0148 04-Jun-21 12:24:01 | == train Loss <unroll: 1.1248 | vae : 2.4226 | kl : 0.0009> 04-Jun-21 12:24:01 | == gmse <train: 0.1089 | val: 0.0971> 04-Jun-21 12:24:31 | Epoch 0106 | lr: 0.00004135 | Time(s): 30.0155 04-Jun-21 12:24:31 | == train Loss <unroll: 1.1230 | vae : 2.4139 | kl : 0.0009> 04-Jun-21 12:24:31 | == gmse <train: 0.1083 | val: 0.0974> 04-Jun-21 12:25:01 | Epoch 0107 | lr: 0.00003928 | Time(s): 30.0160 04-Jun-21 12:25:01 | == train Loss <unroll: 1.1216 | vae : 2.4050 | kl : 0.0009> 04-Jun-21 12:25:01 | == gmse <train: 0.1078 | val: 0.0985> 04-Jun-21 12:25:31 | Epoch 0108 | lr: 0.00003731 | Time(s): 30.0166 04-Jun-21 12:25:31 | == train Loss <unroll: 1.1203 | vae : 2.4004 | kl : 0.0008> 04-Jun-21 12:25:31 | == gmse <train: 0.1073 | val: 0.0978> 04-Jun-21 12:26:01 | Epoch 0109 | lr: 0.00003545 | Time(s): 30.0173 04-Jun-21 12:26:01 | == train Loss <unroll: 1.1197 | vae : 2.3981 | kl : 0.0008> 04-Jun-21 12:26:01 | == gmse <train: 0.1071 | val: 0.0984> 04-Jun-21 12:26:31 | Epoch 0110 | lr: 0.00003368 | Time(s): 30.0179 04-Jun-21 12:26:31 | == train Loss <unroll: 1.1182 | vae : 2.3901 | kl : 0.0007> 04-Jun-21 12:26:31 | == gmse <train: 0.1065 | val: 0.0970> 04-Jun-21 12:27:01 | Epoch 0111 | lr: 0.00003199 | Time(s): 30.0188 04-Jun-21 12:27:01 | == train Loss <unroll: 1.1173 | vae : 2.3868 | kl : 0.0007> 04-Jun-21 12:27:01 | == gmse <train: 0.1062 | val: 0.0979> 04-Jun-21 12:27:31 | Epoch 0112 | lr: 0.00003039 | Time(s): 30.0195 04-Jun-21 12:27:31 | == train Loss <unroll: 1.1166 | vae : 2.3754 | kl : 0.0007> 04-Jun-21 12:27:31 | == gmse <train: 0.1059 | val: 0.0950> 04-Jun-21 12:28:01 | Epoch 0113 | lr: 0.00002887 | Time(s): 30.0204 04-Jun-21 12:28:01 | == train Loss <unroll: 1.1159 | vae : 2.3729 | kl : 0.0006> 04-Jun-21 12:28:01 | == gmse <train: 0.1056 | val: 0.0964> 04-Jun-21 12:28:32 | Epoch 0114 | lr: 0.00002743 | Time(s): 30.0211 04-Jun-21 12:28:32 | == train Loss <unroll: 1.1155 | vae : 2.3686 | kl : 0.0006> 04-Jun-21 12:28:32 | == gmse <train: 0.1054 | val: 0.0970> 04-Jun-21 12:29:02 | Epoch 0115 | lr: 0.00002606 | Time(s): 30.0218 04-Jun-21 12:29:02 | == train Loss <unroll: 1.1146 | vae : 2.3608 | kl : 0.0006> 04-Jun-21 12:29:02 | == gmse <train: 0.1050 | val: 0.0980> 04-Jun-21 12:29:32 | Epoch 0116 | lr: 0.00002475 | Time(s): 30.0225 04-Jun-21 12:29:32 | == train Loss <unroll: 1.1142 | vae : 2.3559 | kl : 0.0005> 04-Jun-21 12:29:32 | == gmse <train: 0.1048 | val: 0.0962> 04-Jun-21 12:30:02 | Epoch 0117 | lr: 0.00002352 | Time(s): 30.0236 04-Jun-21 12:30:02 | == train Loss <unroll: 1.1138 | vae : 2.3519 | kl : 0.0005> 04-Jun-21 12:30:02 | == gmse <train: 0.1047 | val: 0.0956> 04-Jun-21 12:30:32 | Epoch 0118 | lr: 0.00002234 | Time(s): 30.0242 04-Jun-21 12:30:32 | == train Loss <unroll: 1.1133 | vae : 2.3476 | kl : 0.0005> 04-Jun-21 12:30:32 | == gmse <train: 0.1044 | val: 0.0968> 04-Jun-21 12:31:02 | Epoch 0119 | lr: 0.00002122 | Time(s): 30.0250 04-Jun-21 12:31:02 | == train Loss <unroll: 1.1132 | vae : 2.3435 | kl : 0.0004> 04-Jun-21 12:31:02 | == gmse <train: 0.1043 | val: 0.0941> 04-Jun-21 12:31:32 | Epoch 0120 | lr: 0.00002016 | Time(s): 30.0257 04-Jun-21 12:31:32 | == train Loss <unroll: 1.1125 | vae : 2.3376 | kl : 0.0004> 04-Jun-21 12:31:32 | == gmse <train: 0.1040 | val: 0.0965> 04-Jun-21 12:32:02 | Epoch 0121 | lr: 0.00001915 | Time(s): 30.0263 04-Jun-21 12:32:02 | == train Loss <unroll: 1.1126 | vae : 2.3372 | kl : 0.0004> 04-Jun-21 12:32:02 | == gmse <train: 0.1040 | val: 0.0953> 04-Jun-21 12:32:32 | Epoch 0122 | lr: 0.00001820 | Time(s): 30.0270 04-Jun-21 12:32:32 | == train Loss <unroll: 1.1117 | vae : 2.3319 | kl : 0.0004> 04-Jun-21 12:32:32 | == gmse <train: 0.1037 | val: 0.0948> 04-Jun-21 12:33:02 | Epoch 0123 | lr: 0.00001729 | Time(s): 30.0273 04-Jun-21 12:33:02 | == train Loss <unroll: 1.1115 | vae : 2.3275 | kl : 0.0003> 04-Jun-21 12:33:02 | == gmse <train: 0.1036 | val: 0.0956> 04-Jun-21 12:33:32 | Epoch 0124 | lr: 0.00001642 | Time(s): 30.0261 04-Jun-21 12:33:32 | == train Loss <unroll: 1.1113 | vae : 2.3238 | kl : 0.0003> 04-Jun-21 12:33:32 | == gmse <train: 0.1034 | val: 0.0964> 04-Jun-21 12:34:02 | Epoch 0125 | lr: 0.00001560 | Time(s): 30.0265 04-Jun-21 12:34:02 | == train Loss <unroll: 1.1111 | vae : 2.3209 | kl : 0.0003> 04-Jun-21 12:34:02 | == gmse <train: 0.1033 | val: 0.0941> 04-Jun-21 12:34:32 | Epoch 0126 | lr: 0.00001482 | Time(s): 30.0260 04-Jun-21 12:34:32 | == train Loss <unroll: 1.1107 | vae : 2.3185 | kl : 0.0003> 04-Jun-21 12:34:32 | == gmse <train: 0.1031 | val: 0.0953> 04-Jun-21 12:35:02 | Epoch 0127 | lr: 0.00001408 | Time(s): 30.0260 04-Jun-21 12:35:02 | == train Loss <unroll: 1.1106 | vae : 2.3155 | kl : 0.0002> 04-Jun-21 12:35:02 | == gmse <train: 0.1031 | val: 0.0939> 04-Jun-21 12:35:32 | Epoch 0128 | lr: 0.00001338 | Time(s): 30.0251 04-Jun-21 12:35:32 | == train Loss <unroll: 1.1103 | vae : 2.3131 | kl : 0.0002> 04-Jun-21 12:35:32 | == gmse <train: 0.1030 | val: 0.0944> 04-Jun-21 12:36:02 | Epoch 0129 | lr: 0.00001271 | Time(s): 30.0235 04-Jun-21 12:36:02 | == train Loss <unroll: 1.1102 | vae : 2.3096 | kl : 0.0002> 04-Jun-21 12:36:02 | == gmse <train: 0.1029 | val: 0.0964> 04-Jun-21 12:36:32 | Epoch 0130 | lr: 0.00001207 | Time(s): 30.0213 04-Jun-21 12:36:32 | == train Loss <unroll: 1.1099 | vae : 2.3066 | kl : 0.0002> 04-Jun-21 12:36:32 | == gmse <train: 0.1028 | val: 0.0956> 04-Jun-21 12:37:02 | Epoch 0131 | lr: 0.00001147 | Time(s): 30.0192 04-Jun-21 12:37:02 | == train Loss <unroll: 1.1097 | vae : 2.3027 | kl : 0.0002> 04-Jun-21 12:37:02 | == gmse <train: 0.1027 | val: 0.0950> 04-Jun-21 12:37:32 | Epoch 0132 | lr: 0.00001090 | Time(s): 30.0196 04-Jun-21 12:37:32 | == train Loss <unroll: 1.1095 | vae : 2.3002 | kl : 0.0002> 04-Jun-21 12:37:32 | == gmse <train: 0.1026 | val: 0.0952> 04-Jun-21 12:38:02 | Epoch 0133 | lr: 0.00001035 | Time(s): 30.0200 04-Jun-21 12:38:02 | == train Loss <unroll: 1.1095 | vae : 2.2988 | kl : 0.0001> 04-Jun-21 12:38:02 | == gmse <train: 0.1026 | val: 0.0947> 04-Jun-21 12:38:32 | Epoch 0134 | lr: 0.00000983 | Time(s): 30.0210 04-Jun-21 12:38:32 | == train Loss <unroll: 1.1092 | vae : 2.2960 | kl : 0.0001> 04-Jun-21 12:38:32 | == gmse <train: 0.1024 | val: 0.0941> 04-Jun-21 12:39:02 | Epoch 0135 | lr: 0.00000934 | Time(s): 30.0219 04-Jun-21 12:39:02 | == train Loss <unroll: 1.1092 | vae : 2.2941 | kl : 0.0001> 04-Jun-21 12:39:02 | == gmse <train: 0.1024 | val: 0.0943> 04-Jun-21 12:39:32 | Epoch 0136 | lr: 0.00000887 | Time(s): 30.0211 04-Jun-21 12:39:32 | == train Loss <unroll: 1.1089 | vae : 2.2916 | kl : 0.0001> 04-Jun-21 12:39:32 | == gmse <train: 0.1023 | val: 0.0945> 04-Jun-21 12:40:02 | Epoch 0137 | lr: 0.00000843 | Time(s): 30.0214 04-Jun-21 12:40:02 | == train Loss <unroll: 1.1088 | vae : 2.2902 | kl : 0.0001> 04-Jun-21 12:40:02 | == gmse <train: 0.1022 | val: 0.0937> 04-Jun-21 12:40:32 | Epoch 0138 | lr: 0.00000801 | Time(s): 30.0210 04-Jun-21 12:40:32 | == train Loss <unroll: 1.1088 | vae : 2.2877 | kl : 0.0001> 04-Jun-21 12:40:32 | == gmse <train: 0.1022 | val: 0.0948> 04-Jun-21 12:41:02 | Epoch 0139 | lr: 0.00000761 | Time(s): 30.0213 04-Jun-21 12:41:02 | == train Loss <unroll: 1.1086 | vae : 2.2852 | kl : 0.0001> 04-Jun-21 12:41:02 | == gmse <train: 0.1021 | val: 0.0953> 04-Jun-21 12:41:32 | Epoch 0140 | lr: 0.00000723 | Time(s): 30.0215 04-Jun-21 12:41:32 | == train Loss <unroll: 1.1085 | vae : 2.2845 | kl : 0.0001> 04-Jun-21 12:41:32 | == gmse <train: 0.1021 | val: 0.0948> 04-Jun-21 12:42:02 | Epoch 0141 | lr: 0.00000687 | Time(s): 30.0220 04-Jun-21 12:42:02 | == train Loss <unroll: 1.1084 | vae : 2.2816 | kl : 0.0001> 04-Jun-21 12:42:02 | == gmse <train: 0.1021 | val: 0.0955> 04-Jun-21 12:42:32 | Epoch 0142 | lr: 0.00000652 | Time(s): 30.0221 04-Jun-21 12:42:32 | == train Loss <unroll: 1.1082 | vae : 2.2812 | kl : 0.0001> 04-Jun-21 12:42:32 | == gmse <train: 0.1020 | val: 0.0935> 04-Jun-21 12:43:02 | Epoch 0143 | lr: 0.00000620 | Time(s): 30.0223 04-Jun-21 12:43:02 | == train Loss <unroll: 1.1082 | vae : 2.2789 | kl : 0.0001> 04-Jun-21 12:43:02 | == gmse <train: 0.1019 | val: 0.0944> 04-Jun-21 12:43:32 | Epoch 0144 | lr: 0.00000589 | Time(s): 30.0214 04-Jun-21 12:43:32 | == train Loss <unroll: 1.1081 | vae : 2.2773 | kl : 0.0001> 04-Jun-21 12:43:32 | == gmse <train: 0.1019 | val: 0.0949> 04-Jun-21 12:44:02 | Epoch 0145 | lr: 0.00000559 | Time(s): 30.0206 04-Jun-21 12:44:02 | == train Loss <unroll: 1.1080 | vae : 2.2763 | kl : 0.0000> 04-Jun-21 12:44:02 | == gmse <train: 0.1018 | val: 0.0938> 04-Jun-21 12:44:32 | Epoch 0146 | lr: 0.00000531 | Time(s): 30.0206 04-Jun-21 12:44:32 | == train Loss <unroll: 1.1079 | vae : 2.2741 | kl : 0.0000> 04-Jun-21 12:44:32 | == gmse <train: 0.1018 | val: 0.0944> 04-Jun-21 12:45:02 | Epoch 0147 | lr: 0.00000505 | Time(s): 30.0215 04-Jun-21 12:45:02 | == train Loss <unroll: 1.1078 | vae : 2.2731 | kl : 0.0000> 04-Jun-21 12:45:02 | == gmse <train: 0.1017 | val: 0.0947> 04-Jun-21 12:45:32 | Epoch 0148 | lr: 0.00000480 | Time(s): 30.0220 04-Jun-21 12:45:32 | == train Loss <unroll: 1.1077 | vae : 2.2715 | kl : 0.0000> 04-Jun-21 12:45:32 | == gmse <train: 0.1017 | val: 0.0946> 04-Jun-21 12:46:03 | Epoch 0149 | lr: 0.00000456 | Time(s): 30.0232 04-Jun-21 12:46:03 | == train Loss <unroll: 1.1076 | vae : 2.2701 | kl : 0.0000> 04-Jun-21 12:46:03 | == gmse <train: 0.1017 | val: 0.0943> 04-Jun-21 12:46:33 | Epoch 0150 | lr: 0.00000433 | Time(s): 30.0246 04-Jun-21 12:46:33 | == train Loss <unroll: 1.1076 | vae : 2.2693 | kl : 0.0000> 04-Jun-21 12:46:33 | == gmse <train: 0.1017 | val: 0.0943> 04-Jun-21 12:47:03 | Epoch 0151 | lr: 0.00000411 | Time(s): 30.0254 04-Jun-21 12:47:03 | == train Loss <unroll: 1.1076 | vae : 2.2678 | kl : 0.0000> 04-Jun-21 12:47:03 | == gmse <train: 0.1016 | val: 0.0941> 04-Jun-21 12:47:33 | Epoch 0152 | lr: 0.00000391 | Time(s): 30.0255 04-Jun-21 12:47:33 | == train Loss <unroll: 1.1075 | vae : 2.2667 | kl : 0.0000> 04-Jun-21 12:47:33 | == gmse <train: 0.1016 | val: 0.0934> 04-Jun-21 12:48:03 | Epoch 0153 | lr: 0.00000371 | Time(s): 30.0259 04-Jun-21 12:48:03 | == train Loss <unroll: 1.1074 | vae : 2.2660 | kl : 0.0000> 04-Jun-21 12:48:03 | == gmse <train: 0.1016 | val: 0.0946> 04-Jun-21 12:48:33 | Epoch 0154 | lr: 0.00000352 | Time(s): 30.0255 04-Jun-21 12:48:33 | == train Loss <unroll: 1.1074 | vae : 2.2651 | kl : 0.0000> 04-Jun-21 12:48:33 | == gmse <train: 0.1016 | val: 0.0945> 04-Jun-21 12:49:03 | Epoch 0155 | lr: 0.00000335 | Time(s): 30.0253 04-Jun-21 12:49:03 | == train Loss <unroll: 1.1074 | vae : 2.2634 | kl : 0.0000> 04-Jun-21 12:49:03 | == gmse <train: 0.1015 | val: 0.0943> 04-Jun-21 12:49:33 | Epoch 0156 | lr: 0.00000318 | Time(s): 30.0242 04-Jun-21 12:49:33 | == train Loss <unroll: 1.1073 | vae : 2.2633 | kl : 0.0000> 04-Jun-21 12:49:33 | == gmse <train: 0.1015 | val: 0.0938> 04-Jun-21 12:50:03 | Epoch 0157 | lr: 0.00000302 | Time(s): 30.0232 04-Jun-21 12:50:03 | == train Loss <unroll: 1.1072 | vae : 2.2618 | kl : 0.0000> 04-Jun-21 12:50:03 | == gmse <train: 0.1015 | val: 0.0941> 04-Jun-21 12:50:33 | Epoch 0158 | lr: 0.00000287 | Time(s): 30.0221 04-Jun-21 12:50:33 | == train Loss <unroll: 1.1072 | vae : 2.2611 | kl : 0.0000> 04-Jun-21 12:50:33 | == gmse <train: 0.1015 | val: 0.0942> 04-Jun-21 12:51:03 | Epoch 0159 | lr: 0.00000273 | Time(s): 30.0233 04-Jun-21 12:51:03 | == train Loss <unroll: 1.1071 | vae : 2.2599 | kl : 0.0000> 04-Jun-21 12:51:03 | == gmse <train: 0.1014 | val: 0.0941> 04-Jun-21 12:51:33 | Epoch 0160 | lr: 0.00000259 | Time(s): 30.0244 04-Jun-21 12:51:33 | == train Loss <unroll: 1.1071 | vae : 2.2596 | kl : 0.0000> 04-Jun-21 12:51:33 | == gmse <train: 0.1014 | val: 0.0944> 04-Jun-21 12:52:03 | Epoch 0161 | lr: 0.00000246 | Time(s): 30.0254 04-Jun-21 12:52:03 | == train Loss <unroll: 1.1071 | vae : 2.2584 | kl : 0.0000> 04-Jun-21 12:52:03 | == gmse <train: 0.1014 | val: 0.0935> 04-Jun-21 12:52:34 | Epoch 0162 | lr: 0.00000234 | Time(s): 30.0264 04-Jun-21 12:52:34 | == train Loss <unroll: 1.1070 | vae : 2.2577 | kl : 0.0000> 04-Jun-21 12:52:34 | == gmse <train: 0.1014 | val: 0.0934> 04-Jun-21 12:53:04 | Epoch 0163 | lr: 0.00000222 | Time(s): 30.0274 04-Jun-21 12:53:04 | == train Loss <unroll: 1.1070 | vae : 2.2568 | kl : 0.0000> 04-Jun-21 12:53:04 | == gmse <train: 0.1014 | val: 0.0947> 04-Jun-21 12:53:34 | Epoch 0164 | lr: 0.00000211 | Time(s): 30.0282 04-Jun-21 12:53:34 | == train Loss <unroll: 1.1070 | vae : 2.2563 | kl : 0.0000> 04-Jun-21 12:53:34 | == gmse <train: 0.1014 | val: 0.0940> 04-Jun-21 12:54:04 | Epoch 0165 | lr: 0.00000201 | Time(s): 30.0288 04-Jun-21 12:54:04 | == train Loss <unroll: 1.1069 | vae : 2.2557 | kl : 0.0000> 04-Jun-21 12:54:04 | == gmse <train: 0.1013 | val: 0.0936> 04-Jun-21 12:54:34 | Epoch 0166 | lr: 0.00000190 | Time(s): 30.0301 04-Jun-21 12:54:34 | == train Loss <unroll: 1.1069 | vae : 2.2554 | kl : 0.0000> 04-Jun-21 12:54:34 | == gmse <train: 0.1013 | val: 0.0935> 04-Jun-21 12:55:04 | Epoch 0167 | lr: 0.00000181 | Time(s): 30.0305 04-Jun-21 12:55:04 | == train Loss <unroll: 1.1069 | vae : 2.2545 | kl : 0.0000> 04-Jun-21 12:55:04 | == gmse <train: 0.1013 | val: 0.0936> 04-Jun-21 12:55:34 | Epoch 0168 | lr: 0.00000172 | Time(s): 30.0298 04-Jun-21 12:55:34 | == train Loss <unroll: 1.1068 | vae : 2.2542 | kl : 0.0000> 04-Jun-21 12:55:34 | == gmse <train: 0.1013 | val: 0.0948> 04-Jun-21 12:56:04 | Epoch 0169 | lr: 0.00000163 | Time(s): 30.0303 04-Jun-21 12:56:04 | == train Loss <unroll: 1.1068 | vae : 2.2535 | kl : 0.0000> 04-Jun-21 12:56:04 | == gmse <train: 0.1013 | val: 0.0942> 04-Jun-21 12:56:34 | Epoch 0170 | lr: 0.00000155 | Time(s): 30.0302 04-Jun-21 12:56:34 | == train Loss <unroll: 1.1068 | vae : 2.2530 | kl : 0.0000> 04-Jun-21 12:56:34 | == gmse <train: 0.1013 | val: 0.0937> 04-Jun-21 12:57:04 | Epoch 0171 | lr: 0.00000147 | Time(s): 30.0304 04-Jun-21 12:57:04 | == train Loss <unroll: 1.1067 | vae : 2.2524 | kl : 0.0000> 04-Jun-21 12:57:04 | == gmse <train: 0.1013 | val: 0.0945> 04-Jun-21 12:57:35 | Epoch 0172 | lr: 0.00000140 | Time(s): 30.0306 04-Jun-21 12:57:35 | == train Loss <unroll: 1.1068 | vae : 2.2521 | kl : 0.0000> 04-Jun-21 12:57:35 | == gmse <train: 0.1013 | val: 0.0944> 04-Jun-21 12:58:05 | Epoch 0173 | lr: 0.00000133 | Time(s): 30.0307 04-Jun-21 12:58:05 | == train Loss <unroll: 1.1067 | vae : 2.2517 | kl : 0.0000> 04-Jun-21 12:58:05 | == gmse <train: 0.1012 | val: 0.0935> 04-Jun-21 12:58:35 | Epoch 0174 | lr: 0.00000126 | Time(s): 30.0309 04-Jun-21 12:58:35 | == train Loss <unroll: 1.1067 | vae : 2.2511 | kl : 0.0000> 04-Jun-21 12:58:35 | == gmse <train: 0.1012 | val: 0.0937> 04-Jun-21 12:59:05 | Epoch 0175 | lr: 0.00000120 | Time(s): 30.0311 04-Jun-21 12:59:05 | == train Loss <unroll: 1.1067 | vae : 2.2507 | kl : 0.0000> 04-Jun-21 12:59:05 | == gmse <train: 0.1012 | val: 0.0935> 04-Jun-21 12:59:35 | Epoch 0176 | lr: 0.00000114 | Time(s): 30.0312 04-Jun-21 12:59:35 | == train Loss <unroll: 1.1067 | vae : 2.2502 | kl : 0.0000> 04-Jun-21 12:59:35 | == gmse <train: 0.1012 | val: 0.0938> 04-Jun-21 13:00:05 | Epoch 0177 | lr: 0.00000108 | Time(s): 30.0318 04-Jun-21 13:00:05 | == train Loss <unroll: 1.1066 | vae : 2.2499 | kl : 0.0000> 04-Jun-21 13:00:05 | == gmse <train: 0.1012 | val: 0.0941> 04-Jun-21 13:00:35 | Epoch 0178 | lr: 0.00000103 | Time(s): 30.0327 04-Jun-21 13:00:35 | == train Loss <unroll: 1.1066 | vae : 2.2494 | kl : 0.0000> 04-Jun-21 13:00:35 | == gmse <train: 0.1012 | val: 0.0939> 04-Jun-21 13:01:05 | Epoch 0179 | lr: 0.00000098 | Time(s): 30.0337 04-Jun-21 13:01:05 | == train Loss <unroll: 1.1066 | vae : 2.2493 | kl : 0.0000> 04-Jun-21 13:01:05 | == gmse <train: 0.1012 | val: 0.0942> 04-Jun-21 13:01:36 | Epoch 0180 | lr: 0.00000093 | Time(s): 30.0346 04-Jun-21 13:01:36 | == train Loss <unroll: 1.1066 | vae : 2.2489 | kl : 0.0000> 04-Jun-21 13:01:36 | == gmse <train: 0.1012 | val: 0.0938> 04-Jun-21 13:02:06 | Epoch 0181 | lr: 0.00000088 | Time(s): 30.0355 04-Jun-21 13:02:06 | == train Loss <unroll: 1.1066 | vae : 2.2486 | kl : 0.0000> 04-Jun-21 13:02:06 | == gmse <train: 0.1012 | val: 0.0940> 04-Jun-21 13:02:36 | Epoch 0182 | lr: 0.00000084 | Time(s): 30.0364 04-Jun-21 13:02:36 | == train Loss <unroll: 1.1066 | vae : 2.2483 | kl : 0.0000> 04-Jun-21 13:02:36 | == gmse <train: 0.1012 | val: 0.0940> 04-Jun-21 13:03:06 | Epoch 0183 | lr: 0.00000080 | Time(s): 30.0366 04-Jun-21 13:03:06 | == train Loss <unroll: 1.1066 | vae : 2.2480 | kl : 0.0000> 04-Jun-21 13:03:06 | == gmse <train: 0.1012 | val: 0.0937> 04-Jun-21 13:03:36 | Epoch 0184 | lr: 0.00000076 | Time(s): 30.0365 04-Jun-21 13:03:36 | == train Loss <unroll: 1.1065 | vae : 2.2475 | kl : 0.0000> 04-Jun-21 13:03:36 | == gmse <train: 0.1012 | val: 0.0936> 04-Jun-21 13:04:06 | Epoch 0185 | lr: 0.00000072 | Time(s): 30.0369 04-Jun-21 13:04:06 | == train Loss <unroll: 1.1065 | vae : 2.2475 | kl : 0.0000> 04-Jun-21 13:04:06 | == gmse <train: 0.1012 | val: 0.0938> 04-Jun-21 13:04:36 | Epoch 0186 | lr: 0.00000068 | Time(s): 30.0370 04-Jun-21 13:04:36 | == train Loss <unroll: 1.1065 | vae : 2.2473 | kl : 0.0000> 04-Jun-21 13:04:36 | == gmse <train: 0.1012 | val: 0.0940> 04-Jun-21 13:05:06 | Epoch 0187 | lr: 0.00000065 | Time(s): 30.0368 04-Jun-21 13:05:06 | == train Loss <unroll: 1.1065 | vae : 2.2471 | kl : 0.0000> 04-Jun-21 13:05:06 | == gmse <train: 0.1012 | val: 0.0935> 04-Jun-21 13:05:36 | Epoch 0188 | lr: 0.00000062 | Time(s): 30.0368 04-Jun-21 13:05:36 | == train Loss <unroll: 1.1065 | vae : 2.2468 | kl : 0.0000> 04-Jun-21 13:05:36 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:06:06 | Epoch 0189 | lr: 0.00000059 | Time(s): 30.0373 04-Jun-21 13:06:06 | == train Loss <unroll: 1.1065 | vae : 2.2465 | kl : 0.0000> 04-Jun-21 13:06:06 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:06:36 | Epoch 0190 | lr: 0.00000056 | Time(s): 30.0376 04-Jun-21 13:06:36 | == train Loss <unroll: 1.1065 | vae : 2.2463 | kl : 0.0000> 04-Jun-21 13:06:36 | == gmse <train: 0.1011 | val: 0.0936> 04-Jun-21 13:07:07 | Epoch 0191 | lr: 0.00000053 | Time(s): 30.0377 04-Jun-21 13:07:07 | == train Loss <unroll: 1.1065 | vae : 2.2461 | kl : 0.0000> 04-Jun-21 13:07:07 | == gmse <train: 0.1011 | val: 0.0941> 04-Jun-21 13:07:37 | Epoch 0192 | lr: 0.00000050 | Time(s): 30.0374 04-Jun-21 13:07:37 | == train Loss <unroll: 1.1065 | vae : 2.2460 | kl : 0.0000> 04-Jun-21 13:07:37 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:08:06 | Epoch 0193 | lr: 0.00000048 | Time(s): 30.0369 04-Jun-21 13:08:06 | == train Loss <unroll: 1.1064 | vae : 2.2459 | kl : 0.0000> 04-Jun-21 13:08:06 | == gmse <train: 0.1011 | val: 0.0940> 04-Jun-21 13:08:36 | Epoch 0194 | lr: 0.00000045 | Time(s): 30.0365 04-Jun-21 13:08:36 | == train Loss <unroll: 1.1065 | vae : 2.2456 | kl : 0.0000> 04-Jun-21 13:08:36 | == gmse <train: 0.1011 | val: 0.0935> 04-Jun-21 13:09:06 | Epoch 0195 | lr: 0.00000043 | Time(s): 30.0360 04-Jun-21 13:09:06 | == train Loss <unroll: 1.1064 | vae : 2.2454 | kl : 0.0000> 04-Jun-21 13:09:06 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:09:36 | Epoch 0196 | lr: 0.00000041 | Time(s): 30.0350 04-Jun-21 13:09:36 | == train Loss <unroll: 1.1064 | vae : 2.2453 | kl : 0.0000> 04-Jun-21 13:09:36 | == gmse <train: 0.1011 | val: 0.0940> 04-Jun-21 13:10:06 | Epoch 0197 | lr: 0.00000039 | Time(s): 30.0347 04-Jun-21 13:10:06 | == train Loss <unroll: 1.1064 | vae : 2.2451 | kl : 0.0000> 04-Jun-21 13:10:06 | == gmse <train: 0.1011 | val: 0.0936> 04-Jun-21 13:10:36 | Epoch 0198 | lr: 0.00000037 | Time(s): 30.0343 04-Jun-21 13:10:36 | == train Loss <unroll: 1.1064 | vae : 2.2450 | kl : 0.0000> 04-Jun-21 13:10:36 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:11:06 | Epoch 0199 | lr: 0.00000035 | Time(s): 30.0335 04-Jun-21 13:11:06 | == train Loss <unroll: 1.1064 | vae : 2.2448 | kl : 0.0000> 04-Jun-21 13:11:06 | == gmse <train: 0.1011 | val: 0.0939> 04-Jun-21 13:11:36 | Epoch 0200 | lr: 0.00000033 | Time(s): 30.0326 04-Jun-21 13:11:36 | == train Loss <unroll: 1.1064 | vae : 2.2448 | kl : 0.0000> 04-Jun-21 13:11:36 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:12:06 | Epoch 0201 | lr: 0.00000032 | Time(s): 30.0319 04-Jun-21 13:12:06 | == train Loss <unroll: 1.1064 | vae : 2.2446 | kl : 0.0000> 04-Jun-21 13:12:06 | == gmse <train: 0.1011 | val: 0.0939> 04-Jun-21 13:12:36 | Epoch 0202 | lr: 0.00000030 | Time(s): 30.0310 04-Jun-21 13:12:36 | == train Loss <unroll: 1.1064 | vae : 2.2444 | kl : 0.0000> 04-Jun-21 13:12:36 | == gmse <train: 0.1011 | val: 0.0939> 04-Jun-21 13:13:05 | Epoch 0203 | lr: 0.00000029 | Time(s): 30.0300 04-Jun-21 13:13:05 | == train Loss <unroll: 1.1064 | vae : 2.2444 | kl : 0.0000> 04-Jun-21 13:13:05 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:13:35 | Epoch 0204 | lr: 0.00000027 | Time(s): 30.0290 04-Jun-21 13:13:35 | == train Loss <unroll: 1.1064 | vae : 2.2443 | kl : 0.0000> 04-Jun-21 13:13:35 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:14:05 | Epoch 0205 | lr: 0.00000026 | Time(s): 30.0281 04-Jun-21 13:14:05 | == train Loss <unroll: 1.1064 | vae : 2.2443 | kl : 0.0000> 04-Jun-21 13:14:05 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:14:35 | Epoch 0206 | lr: 0.00000024 | Time(s): 30.0270 04-Jun-21 13:14:35 | == train Loss <unroll: 1.1064 | vae : 2.2441 | kl : 0.0000> 04-Jun-21 13:14:35 | == gmse <train: 0.1011 | val: 0.0941> 04-Jun-21 13:15:05 | Epoch 0207 | lr: 0.00000023 | Time(s): 30.0265 04-Jun-21 13:15:05 | == train Loss <unroll: 1.1064 | vae : 2.2440 | kl : 0.0000> 04-Jun-21 13:15:05 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:15:35 | Epoch 0208 | lr: 0.00000022 | Time(s): 30.0263 04-Jun-21 13:15:35 | == train Loss <unroll: 1.1064 | vae : 2.2439 | kl : 0.0000> 04-Jun-21 13:15:35 | == gmse <train: 0.1011 | val: 0.0936> 04-Jun-21 13:16:05 | Epoch 0209 | lr: 0.00000021 | Time(s): 30.0255 04-Jun-21 13:16:05 | == train Loss <unroll: 1.1064 | vae : 2.2438 | kl : 0.0000> 04-Jun-21 13:16:05 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:16:35 | Epoch 0210 | lr: 0.00000020 | Time(s): 30.0246 04-Jun-21 13:16:35 | == train Loss <unroll: 1.1064 | vae : 2.2437 | kl : 0.0000> 04-Jun-21 13:16:35 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:17:04 | Epoch 0211 | lr: 0.00000019 | Time(s): 30.0239 04-Jun-21 13:17:04 | == train Loss <unroll: 1.1064 | vae : 2.2436 | kl : 0.0000> 04-Jun-21 13:17:04 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:17:34 | Epoch 0212 | lr: 0.00000018 | Time(s): 30.0238 04-Jun-21 13:17:34 | == train Loss <unroll: 1.1064 | vae : 2.2436 | kl : 0.0000> 04-Jun-21 13:17:34 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:18:04 | Epoch 0213 | lr: 0.00000017 | Time(s): 30.0232 04-Jun-21 13:18:04 | == train Loss <unroll: 1.1064 | vae : 2.2435 | kl : 0.0000> 04-Jun-21 13:18:04 | == gmse <train: 0.1011 | val: 0.0940> 04-Jun-21 13:18:34 | Epoch 0214 | lr: 0.00000016 | Time(s): 30.0218 04-Jun-21 13:18:34 | == train Loss <unroll: 1.1064 | vae : 2.2435 | kl : 0.0000> 04-Jun-21 13:18:34 | == gmse <train: 0.1011 | val: 0.0939> 04-Jun-21 13:19:04 | Epoch 0215 | lr: 0.00000015 | Time(s): 30.0209 04-Jun-21 13:19:04 | == train Loss <unroll: 1.1064 | vae : 2.2434 | kl : 0.0000> 04-Jun-21 13:19:04 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:19:34 | Epoch 0216 | lr: 0.00000015 | Time(s): 30.0200 04-Jun-21 13:19:34 | == train Loss <unroll: 1.1063 | vae : 2.2433 | kl : 0.0000> 04-Jun-21 13:19:34 | == gmse <train: 0.1011 | val: 0.0936> 04-Jun-21 13:20:03 | Epoch 0217 | lr: 0.00000014 | Time(s): 30.0187 04-Jun-21 13:20:03 | == train Loss <unroll: 1.1064 | vae : 2.2433 | kl : 0.0000> 04-Jun-21 13:20:03 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:20:33 | Epoch 0218 | lr: 0.00000013 | Time(s): 30.0173 04-Jun-21 13:20:33 | == train Loss <unroll: 1.1063 | vae : 2.2432 | kl : 0.0000> 04-Jun-21 13:20:33 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:21:04 | Epoch 0219 | lr: 0.00000013 | Time(s): 30.0186 04-Jun-21 13:21:04 | == train Loss <unroll: 1.1063 | vae : 2.2431 | kl : 0.0000> 04-Jun-21 13:21:04 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:21:33 | Epoch 0220 | lr: 0.00000012 | Time(s): 30.0184 04-Jun-21 13:21:33 | == train Loss <unroll: 1.1063 | vae : 2.2431 | kl : 0.0000> 04-Jun-21 13:21:33 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:22:03 | Epoch 0221 | lr: 0.00000011 | Time(s): 30.0180 04-Jun-21 13:22:03 | == train Loss <unroll: 1.1063 | vae : 2.2431 | kl : 0.0000> 04-Jun-21 13:22:03 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:22:33 | Epoch 0222 | lr: 0.00000011 | Time(s): 30.0178 04-Jun-21 13:22:33 | == train Loss <unroll: 1.1063 | vae : 2.2430 | kl : 0.0000> 04-Jun-21 13:22:33 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:23:03 | Epoch 0223 | lr: 0.00000010 | Time(s): 30.0176 04-Jun-21 13:23:03 | == train Loss <unroll: 1.1063 | vae : 2.2430 | kl : 0.0000> 04-Jun-21 13:23:03 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:23:33 | Epoch 0224 | lr: 0.00000010 | Time(s): 30.0176 04-Jun-21 13:23:33 | == train Loss <unroll: 1.1063 | vae : 2.2429 | kl : 0.0000> 04-Jun-21 13:23:33 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:24:04 | Epoch 0225 | lr: 0.00000009 | Time(s): 30.0183 04-Jun-21 13:24:04 | == train Loss <unroll: 1.1063 | vae : 2.2428 | kl : 0.0000> 04-Jun-21 13:24:04 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:24:34 | Epoch 0226 | lr: 0.00000009 | Time(s): 30.0190 04-Jun-21 13:24:34 | == train Loss <unroll: 1.1063 | vae : 2.2429 | kl : 0.0000> 04-Jun-21 13:24:34 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:25:04 | Epoch 0227 | lr: 0.00000008 | Time(s): 30.0199 04-Jun-21 13:25:04 | == train Loss <unroll: 1.1063 | vae : 2.2428 | kl : 0.0000> 04-Jun-21 13:25:04 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:25:34 | Epoch 0228 | lr: 0.00000008 | Time(s): 30.0205 04-Jun-21 13:25:34 | == train Loss <unroll: 1.1063 | vae : 2.2428 | kl : 0.0000> 04-Jun-21 13:25:34 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:26:04 | Epoch 0229 | lr: 0.00000008 | Time(s): 30.0210 04-Jun-21 13:26:04 | == train Loss <unroll: 1.1063 | vae : 2.2427 | kl : 0.0000> 04-Jun-21 13:26:04 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:26:34 | Epoch 0230 | lr: 0.00000007 | Time(s): 30.0219 04-Jun-21 13:26:34 | == train Loss <unroll: 1.1063 | vae : 2.2427 | kl : 0.0000> 04-Jun-21 13:26:34 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:27:05 | Epoch 0231 | lr: 0.00000007 | Time(s): 30.0221 04-Jun-21 13:27:05 | == train Loss <unroll: 1.1063 | vae : 2.2427 | kl : 0.0000> 04-Jun-21 13:27:05 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:27:35 | Epoch 0232 | lr: 0.00000006 | Time(s): 30.0219 04-Jun-21 13:27:35 | == train Loss <unroll: 1.1063 | vae : 2.2427 | kl : 0.0000> 04-Jun-21 13:27:35 | == gmse <train: 0.1011 | val: 0.0936> 04-Jun-21 13:28:05 | Epoch 0233 | lr: 0.00000006 | Time(s): 30.0218 04-Jun-21 13:28:05 | == train Loss <unroll: 1.1063 | vae : 2.2426 | kl : 0.0000> 04-Jun-21 13:28:05 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:28:35 | Epoch 0234 | lr: 0.00000006 | Time(s): 30.0218 04-Jun-21 13:28:35 | == train Loss <unroll: 1.1063 | vae : 2.2426 | kl : 0.0000> 04-Jun-21 13:28:35 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:29:05 | Epoch 0235 | lr: 0.00000006 | Time(s): 30.0218 04-Jun-21 13:29:05 | == train Loss <unroll: 1.1063 | vae : 2.2425 | kl : 0.0000> 04-Jun-21 13:29:05 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:29:35 | Epoch 0236 | lr: 0.00000005 | Time(s): 30.0218 04-Jun-21 13:29:35 | == train Loss <unroll: 1.1063 | vae : 2.2426 | kl : 0.0000> 04-Jun-21 13:29:35 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:30:05 | Epoch 0237 | lr: 0.00000005 | Time(s): 30.0226 04-Jun-21 13:30:05 | == train Loss <unroll: 1.1063 | vae : 2.2425 | kl : 0.0000> 04-Jun-21 13:30:05 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:30:35 | Epoch 0238 | lr: 0.00000005 | Time(s): 30.0232 04-Jun-21 13:30:35 | == train Loss <unroll: 1.1063 | vae : 2.2425 | kl : 0.0000> 04-Jun-21 13:30:35 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:31:05 | Epoch 0239 | lr: 0.00000005 | Time(s): 30.0240 04-Jun-21 13:31:05 | == train Loss <unroll: 1.1063 | vae : 2.2425 | kl : 0.0000> 04-Jun-21 13:31:05 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:31:35 | Epoch 0240 | lr: 0.00000004 | Time(s): 30.0237 04-Jun-21 13:31:35 | == train Loss <unroll: 1.1063 | vae : 2.2425 | kl : 0.0000> 04-Jun-21 13:31:35 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:32:05 | Epoch 0241 | lr: 0.00000004 | Time(s): 30.0236 04-Jun-21 13:32:05 | == train Loss <unroll: 1.1063 | vae : 2.2424 | kl : 0.0000> 04-Jun-21 13:32:05 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:32:35 | Epoch 0242 | lr: 0.00000004 | Time(s): 30.0234 04-Jun-21 13:32:35 | == train Loss <unroll: 1.1063 | vae : 2.2424 | kl : 0.0000> 04-Jun-21 13:32:35 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:33:05 | Epoch 0243 | lr: 0.00000004 | Time(s): 30.0232 04-Jun-21 13:33:05 | == train Loss <unroll: 1.1063 | vae : 2.2424 | kl : 0.0000> 04-Jun-21 13:33:05 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:33:35 | Epoch 0244 | lr: 0.00000003 | Time(s): 30.0231 04-Jun-21 13:33:35 | == train Loss <unroll: 1.1063 | vae : 2.2424 | kl : 0.0000> 04-Jun-21 13:33:35 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:34:05 | Epoch 0245 | lr: 0.00000003 | Time(s): 30.0234 04-Jun-21 13:34:05 | == train Loss <unroll: 1.1063 | vae : 2.2423 | kl : 0.0000> 04-Jun-21 13:34:05 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:34:35 | Epoch 0246 | lr: 0.00000003 | Time(s): 30.0238 04-Jun-21 13:34:35 | == train Loss <unroll: 1.1063 | vae : 2.2423 | kl : 0.0000> 04-Jun-21 13:34:35 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:35:05 | Epoch 0247 | lr: 0.00000003 | Time(s): 30.0240 04-Jun-21 13:35:05 | == train Loss <unroll: 1.1063 | vae : 2.2423 | kl : 0.0000> 04-Jun-21 13:35:05 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:35:36 | Epoch 0248 | lr: 0.00000003 | Time(s): 30.0243 04-Jun-21 13:35:36 | == train Loss <unroll: 1.1063 | vae : 2.2423 | kl : 0.0000> 04-Jun-21 13:35:36 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:36:06 | Epoch 0249 | lr: 0.00000003 | Time(s): 30.0248 04-Jun-21 13:36:06 | == train Loss <unroll: 1.1063 | vae : 2.2423 | kl : 0.0000> 04-Jun-21 13:36:06 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:36:36 | Epoch 0250 | lr: 0.00000003 | Time(s): 30.0256 04-Jun-21 13:36:36 | == train Loss <unroll: 1.1063 | vae : 2.2423 | kl : 0.0000> 04-Jun-21 13:36:36 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:37:06 | Epoch 0251 | lr: 0.00000002 | Time(s): 30.0255 04-Jun-21 13:37:06 | == train Loss <unroll: 1.1063 | vae : 2.2422 | kl : 0.0000> 04-Jun-21 13:37:06 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:37:36 | Epoch 0252 | lr: 0.00000002 | Time(s): 30.0255 04-Jun-21 13:37:36 | == train Loss <unroll: 1.1063 | vae : 2.2422 | kl : 0.0000> 04-Jun-21 13:37:36 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:38:06 | Epoch 0253 | lr: 0.00000002 | Time(s): 30.0254 04-Jun-21 13:38:06 | == train Loss <unroll: 1.1063 | vae : 2.2422 | kl : 0.0000> 04-Jun-21 13:38:06 | == gmse <train: 0.1011 | val: 0.0937> 04-Jun-21 13:38:36 | Epoch 0254 | lr: 0.00000002 | Time(s): 30.0253 04-Jun-21 13:38:36 | == train Loss <unroll: 1.1063 | vae : 2.2422 | kl : 0.0000> 04-Jun-21 13:38:36 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:39:06 | Epoch 0255 | lr: 0.00000002 | Time(s): 30.0260 04-Jun-21 13:39:06 | == train Loss <unroll: 1.1063 | vae : 2.2422 | kl : 0.0000> 04-Jun-21 13:39:06 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:39:36 | Epoch 0256 | lr: 0.00000002 | Time(s): 30.0268 04-Jun-21 13:39:36 | == train Loss <unroll: 1.1063 | vae : 2.2422 | kl : 0.0000> 04-Jun-21 13:39:36 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:40:07 | Epoch 0257 | lr: 0.00000002 | Time(s): 30.0275 04-Jun-21 13:40:07 | == train Loss <unroll: 1.1063 | vae : 2.2422 | kl : 0.0000> 04-Jun-21 13:40:07 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:40:37 | Epoch 0258 | lr: 0.00000002 | Time(s): 30.0282 04-Jun-21 13:40:37 | == train Loss <unroll: 1.1063 | vae : 2.2422 | kl : 0.0000> 04-Jun-21 13:40:37 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:41:07 | Epoch 0259 | lr: 0.00000002 | Time(s): 30.0286 04-Jun-21 13:41:07 | == train Loss <unroll: 1.1063 | vae : 2.2422 | kl : 0.0000> 04-Jun-21 13:41:07 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:41:37 | Epoch 0260 | lr: 0.00000002 | Time(s): 30.0291 04-Jun-21 13:41:37 | == train Loss <unroll: 1.1063 | vae : 2.2422 | kl : 0.0000> 04-Jun-21 13:41:37 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:42:07 | Epoch 0261 | lr: 0.00000001 | Time(s): 30.0289 04-Jun-21 13:42:07 | == train Loss <unroll: 1.1063 | vae : 2.2421 | kl : 0.0000> 04-Jun-21 13:42:07 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:42:37 | Epoch 0262 | lr: 0.00000001 | Time(s): 30.0290 04-Jun-21 13:42:37 | == train Loss <unroll: 1.1063 | vae : 2.2422 | kl : 0.0000> 04-Jun-21 13:42:37 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:43:07 | Epoch 0263 | lr: 0.00000001 | Time(s): 30.0297 04-Jun-21 13:43:07 | == train Loss <unroll: 1.1063 | vae : 2.2421 | kl : 0.0000> 04-Jun-21 13:43:07 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:43:37 | Epoch 0264 | lr: 0.00000001 | Time(s): 30.0300 04-Jun-21 13:43:37 | == train Loss <unroll: 1.1063 | vae : 2.2421 | kl : 0.0000> 04-Jun-21 13:43:37 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:44:08 | Epoch 0265 | lr: 0.00000001 | Time(s): 30.0307 04-Jun-21 13:44:08 | == train Loss <unroll: 1.1063 | vae : 2.2421 | kl : 0.0000> 04-Jun-21 13:44:08 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:44:38 | Epoch 0266 | lr: 0.00000001 | Time(s): 30.0317 04-Jun-21 13:44:38 | == train Loss <unroll: 1.1063 | vae : 2.2421 | kl : 0.0000> 04-Jun-21 13:44:38 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:45:08 | Epoch 0267 | lr: 0.00000001 | Time(s): 30.0319 04-Jun-21 13:45:08 | == train Loss <unroll: 1.1063 | vae : 2.2421 | kl : 0.0000> 04-Jun-21 13:45:08 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:45:38 | Epoch 0268 | lr: 0.00000001 | Time(s): 30.0320 04-Jun-21 13:45:38 | == train Loss <unroll: 1.1063 | vae : 2.2421 | kl : 0.0000> 04-Jun-21 13:45:38 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:46:08 | Epoch 0269 | lr: 0.00000001 | Time(s): 30.0322 04-Jun-21 13:46:08 | == train Loss <unroll: 1.1063 | vae : 2.2421 | kl : 0.0000> 04-Jun-21 13:46:08 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:46:38 | Epoch 0270 | lr: 0.00000001 | Time(s): 30.0326 04-Jun-21 13:46:38 | == train Loss <unroll: 1.1063 | vae : 2.2421 | kl : 0.0000> 04-Jun-21 13:46:38 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:47:08 | Epoch 0271 | lr: 0.00000001 | Time(s): 30.0327 04-Jun-21 13:47:08 | == train Loss <unroll: 1.1063 | vae : 2.2421 | kl : 0.0000> 04-Jun-21 13:47:08 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:47:39 | Epoch 0272 | lr: 0.00000001 | Time(s): 30.0329 04-Jun-21 13:47:39 | == train Loss <unroll: 1.1063 | vae : 2.2421 | kl : 0.0000> 04-Jun-21 13:47:39 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:48:09 | Epoch 0273 | lr: 0.00000001 | Time(s): 30.0328 04-Jun-21 13:48:09 | == train Loss <unroll: 1.1063 | vae : 2.2421 | kl : 0.0000> 04-Jun-21 13:48:09 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:48:39 | Epoch 0274 | lr: 0.00000001 | Time(s): 30.0331 04-Jun-21 13:48:39 | == train Loss <unroll: 1.1063 | vae : 2.2421 | kl : 0.0000> 04-Jun-21 13:48:39 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:49:09 | Epoch 0275 | lr: 0.00000001 | Time(s): 30.0335 04-Jun-21 13:49:09 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:49:09 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:49:39 | Epoch 0276 | lr: 0.00000001 | Time(s): 30.0338 04-Jun-21 13:49:39 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:49:39 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:50:09 | Epoch 0277 | lr: 0.00000001 | Time(s): 30.0340 04-Jun-21 13:50:09 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:50:09 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:50:39 | Epoch 0278 | lr: 0.00000001 | Time(s): 30.0345 04-Jun-21 13:50:39 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:50:39 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:51:09 | Epoch 0279 | lr: 0.00000001 | Time(s): 30.0346 04-Jun-21 13:51:09 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:51:09 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:51:39 | Epoch 0280 | lr: 0.00000001 | Time(s): 30.0346 04-Jun-21 13:51:39 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:51:39 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:52:09 | Epoch 0281 | lr: 0.00000001 | Time(s): 30.0346 04-Jun-21 13:52:09 | == train Loss <unroll: 1.1063 | vae : 2.2421 | kl : 0.0000> 04-Jun-21 13:52:09 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:52:39 | Epoch 0282 | lr: 0.00000000 | Time(s): 30.0346 04-Jun-21 13:52:39 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:52:39 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:53:09 | Epoch 0283 | lr: 0.00000000 | Time(s): 30.0347 04-Jun-21 13:53:09 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:53:09 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:53:40 | Epoch 0284 | lr: 0.00000000 | Time(s): 30.0354 04-Jun-21 13:53:40 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:53:40 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:54:10 | Epoch 0285 | lr: 0.00000000 | Time(s): 30.0357 04-Jun-21 13:54:10 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:54:10 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:54:40 | Epoch 0286 | lr: 0.00000000 | Time(s): 30.0360 04-Jun-21 13:54:40 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:54:40 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:55:10 | Epoch 0287 | lr: 0.00000000 | Time(s): 30.0363 04-Jun-21 13:55:10 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:55:10 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:55:40 | Epoch 0288 | lr: 0.00000000 | Time(s): 30.0366 04-Jun-21 13:55:40 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:55:40 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:56:10 | Epoch 0289 | lr: 0.00000000 | Time(s): 30.0369 04-Jun-21 13:56:10 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:56:10 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:56:40 | Epoch 0290 | lr: 0.00000000 | Time(s): 30.0372 04-Jun-21 13:56:40 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:56:40 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:57:10 | Epoch 0291 | lr: 0.00000000 | Time(s): 30.0373 04-Jun-21 13:57:10 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:57:10 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:57:41 | Epoch 0292 | lr: 0.00000000 | Time(s): 30.0375 04-Jun-21 13:57:41 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:57:41 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:58:11 | Epoch 0293 | lr: 0.00000000 | Time(s): 30.0376 04-Jun-21 13:58:11 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:58:11 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:58:41 | Epoch 0294 | lr: 0.00000000 | Time(s): 30.0377 04-Jun-21 13:58:41 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:58:41 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:59:11 | Epoch 0295 | lr: 0.00000000 | Time(s): 30.0378 04-Jun-21 13:59:11 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:59:11 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 13:59:41 | Epoch 0296 | lr: 0.00000000 | Time(s): 30.0377 04-Jun-21 13:59:41 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 13:59:41 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 14:00:11 | Epoch 0297 | lr: 0.00000000 | Time(s): 30.0382 04-Jun-21 14:00:11 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 14:00:11 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 14:00:41 | Epoch 0298 | lr: 0.00000000 | Time(s): 30.0381 04-Jun-21 14:00:41 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 14:00:41 | == gmse <train: 0.1011 | val: 0.0938> 04-Jun-21 14:01:11 | Epoch 0299 | lr: 0.00000000 | Time(s): 30.0379 04-Jun-21 14:01:11 | == train Loss <unroll: 1.1063 | vae : 2.2420 | kl : 0.0000> 04-Jun-21 14:01:11 | == gmse <train: 0.1011 | val: 0.0938>
# save trained model
save_path = 'saved_model/L2G_{}{}_unroll{}.pt'.format(graph_type,
graph_size,
num_unroll)
torch.save({'net_state_dict': net.state_dict(),
'optimiser_state_dict': optimizer.state_dict()
}, save_path)
logging.info('model saved at: {}'.format(save_path))
04-Jun-21 14:34:17 | model saved at: saved_model/L2G_WS50_unroll20.pt
for z, w_gt_batch in test_loader:
test_loss = []
z = z.to(device)
w_gt_batch = w_gt_batch.to(device)
this_batch_size = w_gt_batch.size()[0]
adj_batch = w_gt_batch.clone()
adj_batch[adj_batch > 0] = 1
w_list = net.validation(z, threshold=1e-04)
w_pred = torch.clamp(w_list[:, num_unroll - 1, :], min=0)
loss_mean = gmse_loss_batch_mean(w_pred, w_gt_batch)
loss_pred = gmse_loss_batch(w_pred, w_gt_batch)
layer_loss_batch = torch.stack([layerwise_gmse_loss(w_list[i, :, :], w_gt_batch[i, :]) for i in range(batch_size)])
loss_all_data = loss_pred.detach().cpu().numpy()
final_pred_loss, final_pred_loss_ci, _, _ = mean_confidence_interval(loss_all_data, 0.95)
logging.info('GMSE: {} +- {}'.format(final_pred_loss, final_pred_loss_ci))
aps_auc = binary_metrics_batch(adj_batch, w_pred, device)
logging.info('aps: {} +- {}'.format(aps_auc['aps_mean'], aps_auc['aps_ci']))
logging.info('auc: {} +- {}'.format(aps_auc['auc_mean'], aps_auc['auc_ci']))
layer_loss_mean = [mean_confidence_interval(layer_loss_batch[:,i].detach().cpu().numpy(), confidence=0.95)[0] for i in range(num_unroll)]
layer_loss_mean_ci = [mean_confidence_interval(layer_loss_batch[:,i].detach().cpu().numpy(), confidence=0.95)[1] for i in range(num_unroll)]
logging.info('layerwise test loss :{}'.format(layer_loss_mean))
04-Jun-21 14:40:11 | GMSE: 0.09289076179265976 +- 0.0029431590516546023 04-Jun-21 14:40:11 | aps: 0.9963036197528777 +- 0.0007225115197087517 04-Jun-21 14:40:11 | auc: 0.9992655555555555 +- 0.00039366530730706285 04-Jun-21 14:40:11 | layerwise test loss :[1.4559345, 0.89995754, 0.23471525, 0.14071073, 0.1402622, 0.17302305, 0.14294592, 0.12140663, 0.15670082, 0.14045528, 0.10375355, 0.0888529, 0.09007615, 0.0938451, 0.08667662, 0.086669095, 0.086669095, 0.086669095, 0.08666909, 0.10030652]
# layerwisse loss
plt.figure()
plt.plot(np.arange(1,21,1), layer_loss_mean)
plt.xticks(np.arange(1,21,1))
plt.ylabel('GMSE')
plt.xlabel('number of unrolls/iterations')
plt.show()
result = {
'epoch_train_gmse': epoch_train_gmse,
'epoch_val_gmse': epoch_train_gmse,
'pred_gmse_mean': final_pred_loss,
'pred_gmse_mean_ci': final_pred_loss_ci,
'auc_mean': aps_auc['auc_mean'],
'auc_ci': aps_auc['auc_ci'],
'aps_mean': aps_auc['aps_mean'],
'aps_ci': aps_auc['aps_ci'],
'layerwise_gmse_mean': layer_loss_mean,
'layerwise_gmse_mean_ci ': layer_loss_mean_ci
}
result_path = 'saved_results/L2G_{}{}_unroll{}.pt'.format(graph_type,
graph_size,
num_unroll)
with open(result_path, 'wb') as handle:
pickle.dump(result, handle, protocol=4)
logging.info('results saved at: {}'.format(result_path))
04-Jun-21 14:40:16 | results saved at: saved_results/L2G_WS50_unroll20.pt
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.spatial.distance import squareform
idx = 50
W = squareform(w_pred[idx,:].detach().cpu().numpy())
W[W <= 1e-01] = 0
plt.figure()
sns.heatmap(W, cmap = 'pink_r', vmax = 0.305)
plt.title('prediction')
plt.show()
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.spatial.distance import squareform
plt.figure()
sns.heatmap(squareform(w_gt_batch[idx,:].detach().cpu().numpy()), cmap = 'pink_r', vmin = 0, vmax = 0.305)
plt.title('groundtruth')
plt.show()
