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Commit ccaf20fa authored by Marco Cristoforetti's avatar Marco Cristoforetti
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indexes valid and test datasets

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models/ixs_valid_test.txt 0 → 100644
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notebooks/.ipynb_checkpoints/model_training-checkpoint.ipynb
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......@@ -251,6 +251,15 @@
"ixs_test = ixs_valid_test[1::2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.savetxt('ixs_valid_test.txt', ixs_valid_test, fmt='%i')"
]
},
{
"cell_type": "code",
"execution_count": 45,
......
%% Cell type:code id: tags:
``` python
%matplotlib inline
import matplotlib.pyplot as plt
import sys; sys.path.append('../DST')
import os
from DST.config import data_path
import pandas as pd
import numpy as np
import seaborn as sns; sns.set(style="whitegrid", font_scale=1.3)
import torch
import torch.nn as nn
import time
import math
import torch.utils.data as utils_data
import torch.nn.functional as F
import datetime
```
%% Cell type:code id: tags:
``` python
torch.manual_seed(21894)
np.random.seed(21894)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
```
%% Cell type:code id: tags:
``` python
BEFORE = 12
AFTER = 12
```
%% Cell type:code id: tags:
``` python
dst_data = pd.read_pickle(os.path.join(data_path,'dst.pkl'))
dst_data['ora_round'] = dst_data.ora.apply(lambda x:int(x.split(':')[0]))
dati_agg = dst_data.groupby(['data','ora_round']).agg({
'BX': np.mean,
'BY': np.mean,
'BZ': np.mean,
'FLOW_SPEED': np.mean,
'PROTON_DENSITY': np.mean,
'TEMPERATURE': np.mean,
'PRESSION': np.mean,
'ELETTRIC': np.mean,
'y': np.mean})
dati_agg.reset_index(inplace=True)
dati_agg.sort_values(by = ['data','ora_round'],inplace=True)
dataset = dati_agg.drop(columns = ['data','ora_round']).values
dataset = torch.from_numpy(np.hstack([np.arange(len(dataset)).reshape([-1,1]),dataset]))
last_date_train = dati_agg[dati_agg.data <= datetime.datetime(2008,12,31)].index[-1]
len_valid_test = (len(dataset) - last_date_train)/2
last_date_train/len(dataset), len_valid_test/len(dataset)
data_in = dataset.unfold(0, BEFORE, 1).transpose(2,1)
data_out = dataset[BEFORE:].unfold(0, AFTER, 1).transpose(2,1)
data_in = data_in[:data_out.size(0)]
data_out = data_out[:,:,-1]
data_in.size(), data_out.size()
```
%%%% Output: execute_result
(torch.Size([261794, 12, 10]), torch.Size([261794, 12]))
%% Cell type:code id: tags:
``` python
where_not_nan_in = ~torch.isnan(data_in).any(2, keepdim=True).any(1, keepdim=True).reshape(-1)
data_in = data_in[where_not_nan_in]
data_out = data_out[where_not_nan_in]
```
%% Cell type:code id: tags:
``` python
where_not_nan_out = ~torch.isnan(data_out).any(1, keepdim=True).reshape(-1)
data_in = data_in[where_not_nan_out]
data_out = data_out[where_not_nan_out]
last_train = np.where(data_in[:,0,0] <= last_date_train)[0][-1] + 1
data_in = data_in[:, :, 1:]
#len_tr = int(data_in.size(0) * 0.6)
n_channels = data_in.size(2)
```
%% Cell type:code id: tags:
``` python
data_in.size(), data_out.size()
```
%%%% Output: execute_result
(torch.Size([186534, 12, 9]), torch.Size([186534, 12]))
%% Cell type:code id: tags:
``` python
class MinMaxScaler():
"""
Transform features by scaling each feature to a given range
Features in the last dim
The transformation is given by::
X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
X_scaled = X_std * (max - min) + min
where min, max = feature_range.
"""
def __init__(self, feature_range=(0,1)):
self.feature_range = feature_range
def fit(self, X):
X_size = X.size()
X = X.reshape(-1, X_size[-1])
data_min = X.min(axis=0).values
data_max = X.max(axis=0).values
data_range = data_max - data_min
self.scale_ = ((self.feature_range[1] - self.feature_range[0]) / data_range)
self.min_ = self.feature_range[0] - data_min * self.scale_
self.data_min_ = data_min
self.data_max_ = data_max
self.data_range_ = data_range
X = X.reshape(X_size)
return self
def transform(self, X):
X *= self.scale_
X += self.min_
return X
def inverse_transform(self, X):
X -= self.min_
X /= self.scale_
return X
```
%% Cell type:code id: tags:
``` python
mmScaler = MinMaxScaler((0.1, .9))
mmScaler.fit(data_in[:last_train])
data_in_scaled = data_in.clone()
data_in_scaled = mmScaler.transform(data_in_scaled)
```
%% Cell type:code id: tags:
``` python
mm_scaler_out = MinMaxScaler((0.1, .9))
mm_scaler_out.fit(data_in[:last_train, :, -1].reshape(-1, data_in.size(1), 1))
data_out_scaled = data_out.clone()
data_out_scaled = mm_scaler_out.transform(data_out_scaled)
```
%% Cell type:code id: tags:
``` python
class Dataset(utils_data.Dataset):
def __init__(self, dataset_in, dataset_out):
self.dataset_in = dataset_in
self.dataset_out = dataset_out
def __len__(self):
return self.dataset_in.size(0)
def __getitem__(self, idx):
din_src = self.dataset_in[idx]
dout = self.dataset_out[idx]
return din_src, dout
```
%% Cell type:code id: tags:
``` python
ixs_valid_test = np.arange(int(len_valid_test)) + last_train
np.random.shuffle(ixs_valid_test)
ixs_valid = ixs_valid_test[::2]
ixs_test = ixs_valid_test[1::2]
```
%% Cell type:code id: tags:
``` python
np.savetxt('ixs_valid_test.txt', ixs_valid_test, fmt='%i')
```
%% Cell type:code id: tags:
``` python
dst_min = data_out[:last_train].min(axis=1).values.flatten()
bins = [dst_min.min() - 10] + list(np.arange(-300, dst_min.max() + 10, 10))
h, b = np.histogram(dst_min, bins=bins)
if len(np.argwhere(h == 0)) > 0:
bins = np.delete(bins, np.argwhere(h == 0)[0] + 1)
h, b = np.histogram(dst_min, bins=bins)
w = h.max()/h
def fix_weight(dst_v):
pos = np.argwhere(np.abs(b - dst_v) == np.abs((b - dst_v)).min())[0,0]
if dst_v - b[pos] < 0:
pos = pos-1
return np.sqrt(w[pos]/h.max())
fix_weight_v = np.vectorize(fix_weight)
weights = fix_weight_v(dst_min)
sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, num_samples= len(dst_min))
BATCH_SIZE=256
dataset_tr = Dataset(data_in_scaled[:last_train], data_out_scaled[:last_train])
data_loader_tr = utils_data.DataLoader(dataset_tr, batch_size=BATCH_SIZE, num_workers = 4, shuffle=False, sampler = sampler)
dataset_val = Dataset(data_in_scaled[ixs_valid], data_out_scaled[ixs_valid])
data_loader_val = utils_data.DataLoader(dataset_val, batch_size=BATCH_SIZE, num_workers = 4,shuffle=True)
dataset_ts = Dataset(data_in_scaled[ixs_test], data_out_scaled[ixs_test])
data_loader_ts = utils_data.DataLoader(dataset_ts, batch_size=BATCH_SIZE, num_workers = 4,shuffle=True)
```
%% Cell type:code id: tags:
``` python
delta_var = mmScaler.transform(mmScaler.data_max_ - mmScaler.data_min_) * 0.01
```
%% Cell type:code id: tags:
``` python
batch = next(iter(data_loader_tr))
```
%% Cell type:code id: tags:
``` python
class DSTnet(nn.Module):
def __init__(self, nvars, nhidden_i, nhidden_o, n_out_i, before, after):
super().__init__()
self.nvars = nvars
self.nhidden_i = nhidden_i
self.nhidden_o = nhidden_o
self.before = before
self.after = after
self.n_out_i = n_out_i
self.lstm = nn.LSTM(self.nvars, self.n_out_i, self.nhidden_i, batch_first=True)
self.first_merged_layer = self.n_out_i * self.before
self.bn1 = nn.BatchNorm1d(num_features=self.first_merged_layer)
# self.bn1 = nn.LayerNorm(self.first_merged_layer)
self.linear_o_1 = nn.Linear(self.first_merged_layer, self.nhidden_o)
self.ln1 = nn.LayerNorm(self.nhidden_o )
self.linear_o_2 = nn.Linear(self.nhidden_o, self.nhidden_o)
self.linear_o_3 = nn.Linear(self.nhidden_o, self.nhidden_o // 2)
# self.bn2 = nn.BatchNorm1d(num_features=self.nhidden_o)
self.linear_o_4 = nn.Linear(self.nhidden_o // 2, self.after)
def init_hidden(self, batch_size):
hidden = torch.randn(self.nhidden_i, batch_size, self.n_out_i).to(device)
cell = torch.randn(self.nhidden_i, batch_size, self.n_out_i).to(device)
return (hidden, cell)
def forward(self, x0):
self.hidden = self.init_hidden(x0.size(0))
x = self.lstm(x0, self.hidden)[0].reshape(x0.shape[0], -1)
x = self.bn1(x)
# x = F.relu(x)
x = F.relu(self.linear_o_1(x))
# x = self.ln1(x)
x = F.dropout(x, 0.2, training=self.training)
x = F.relu(self.linear_o_2(x))
x = F.dropout(x, 0.2, training=self.training)
x = F.relu(self.linear_o_3(x))
x = F.dropout(x, 0.2, training=self.training)
x = self.linear_o_4(x)
return x
```
%% Cell type:code id: tags:
``` python
loss_f = nn.L1Loss()
loss_mse = nn.MSELoss()
nhidden_i = 2
nhidden_o = 128
n_out_i = 8
before = BEFORE
nvars = data_in_scaled.shape[-1]
dst_net = DSTnet(nvars, nhidden_i, nhidden_o, n_out_i, before, AFTER).to(device)
print(dst_net)
num_epochs = 10000
lr = 1e-5
optimizer = torch.optim.Adam(dst_net.parameters(), lr=lr, weight_decay=1e-5)
history_tr = np.zeros((num_epochs, 2))
history_valid = np.zeros((num_epochs, 2))
history_ts = np.zeros((num_epochs, 2))
for epoch in range(num_epochs):
# if epoch == 1400:
# lr = 1e-5
# optimizer = torch.optim.Adam(dst_net.parameters(), lr=lr)#, weight_decay=1e-5)
start_time = time.time()
for i, batch in enumerate(data_loader_tr):
# delta_batch0 = (( 1- 2 * torch.rand(batch[0].size()))*delta_var).float()
# delta_batch1 = (( 1- 2 * torch.rand(batch[1].size()))*delta_var[-1]).float()
x = (batch[0] + (( 1- 2 * torch.rand(batch[0].size())) * batch[0] * 0.001)).float().to(device) #+ delta_batch0
y = (batch[1] + (( 1- 2 * torch.rand(batch[1].size())) * batch[1] * 0.001)).float().to(device) #+ delta_batch1
optimizer.zero_grad()
dst_net.train()
outputs = dst_net(x)
# loss = loss_f(outputs*torch.tensor(np.arange(1, 13)), y*torch.tensor(np.arange(1, 13))) #+ torch.sqrt(loss_mse(outputs, y) + 0.0000001)# * (1 + torch.randn(y.shape).to(device) * 0.01))
loss = loss_f(outputs, y) #+ torch.sqrt(loss_mse(outputs, y) + 0.0000001)# * (1 + torch.randn(y.shape).to(device) * 0.01))
# loss = loss_mse(outputs, y)# * (1 + torch.randn(y.shape).to(device) * 0.01))
loss.backward()
optimizer.step()
dst_net.eval()
data_out_scaled_loss = mm_scaler_out.inverse_transform(data_out_scaled.clone())
outputs = dst_net(data_in_scaled[:last_train].to(device).float())
loss_tr = np.sqrt(loss_mse(mm_scaler_out.inverse_transform(outputs.cpu().clone()).to(device), data_out_scaled_loss[:last_train].to(device).float()).item())
loss_mae_tr = loss_f(mm_scaler_out.inverse_transform(outputs.cpu().clone()).to(device), data_out_scaled_loss[:last_train].to(device).float()).item()
outputs = dst_net(data_in_scaled[ixs_valid].to(device).float())
loss_valid = np.sqrt(loss_mse(mm_scaler_out.inverse_transform(outputs.cpu().clone()).to(device), data_out_scaled_loss[ixs_valid].to(device).float()).item())
loss_mae_valid = loss_f(mm_scaler_out.inverse_transform(outputs.cpu().clone()).to(device), data_out_scaled_loss[ixs_valid].to(device).float()).item()
outputs = dst_net(data_in_scaled[ixs_test].to(device).float())
loss_ts = np.sqrt(loss_mse(mm_scaler_out.inverse_transform(outputs.cpu().clone()).to(device), data_out_scaled_loss[ixs_test].to(device).float()).item())
loss_mae_ts = loss_f(mm_scaler_out.inverse_transform(outputs.cpu().clone()).to(device), data_out_scaled_loss[ixs_test].to(device).float()).item()
history_tr[epoch] = [loss_tr, loss_mae_tr]
history_valid[epoch] = [loss_valid, loss_mae_valid]
history_ts[epoch] = [loss_ts, loss_mae_ts]
epoch_time = time.time() - start_time
if (epoch % 10 == 0):
print('Epoch %d time = %.2f, tr_rmse = %0.5f, valid_rmse = %.5f, ts_rmse = %.5f, tr_mae = %0.5f, valid_mae = %.5f, ts_mae = %.5f' %
(epoch, epoch_time, loss_tr, loss_valid, loss_ts, loss_mae_tr, loss_mae_valid, loss_mae_ts))
torch.save(dst_net.state_dict(), '../models/dst_net_full.pth')
np.savetxt('../hist/history_tr_rmse_mae_full.txt', history_tr)
np.savetxt('../hist/history_valid_rmse_mae_full.txt', history_valid)
np.savetxt('../hist/history_ts_rmse_mae_full.txt', history_ts)
```
%%%% Output: stream
DSTnet(
(lstm): LSTM(9, 8, num_layers=2, batch_first=True)
(bn1): BatchNorm1d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(linear_o_1): Linear(in_features=96, out_features=64, bias=True)
(ln1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(linear_o_2): Linear(in_features=64, out_features=64, bias=True)
(linear_o_3): Linear(in_features=64, out_features=32, bias=True)
(linear_o_4): Linear(in_features=32, out_features=12, bias=True)
)
Epoch 0 time = 9.58, tr_rmse = 469.83637, valid_rmse = 475.11872, ts_rmse = 475.36345, tr_mae = 461.05780, valid_mae = 466.56754, ts_mae = 466.85388
Epoch 1 time = 10.01, tr_rmse = 388.58652, valid_rmse = 393.32884, ts_rmse = 393.88383, tr_mae = 360.97275, valid_mae = 366.07150, ts_mae = 366.71188
Epoch 2 time = 11.28, tr_rmse = 282.13619, valid_rmse = 286.54900, ts_rmse = 286.04732, tr_mae = 246.84175, valid_mae = 251.61148, ts_mae = 250.79152
Epoch 3 time = 11.71, tr_rmse = 212.87363, valid_rmse = 214.78504, ts_rmse = 214.61001, tr_mae = 180.41080, valid_mae = 182.33406, ts_mae = 182.10858
Epoch 4 time = 10.12, tr_rmse = 166.96828, valid_rmse = 167.52934, ts_rmse = 166.93450, tr_mae = 139.07254, valid_mae = 139.51793, ts_mae = 139.05449
Epoch 5 time = 10.23, tr_rmse = 140.29911, valid_rmse = 139.34353, ts_rmse = 138.90956, tr_mae = 115.83358, valid_mae = 114.99672, ts_mae = 114.60511
Epoch 6 time = 10.33, tr_rmse = 120.11670, valid_rmse = 117.13997, ts_rmse = 117.47479, tr_mae = 98.16451, valid_mae = 95.47210, ts_mae = 95.73440
Epoch 7 time = 10.31, tr_rmse = 107.94639, valid_rmse = 103.54335, ts_rmse = 104.31949, tr_mae = 87.92388, valid_mae = 83.86409, ts_mae = 84.50546
Epoch 8 time = 10.73, tr_rmse = 97.97863, valid_rmse = 93.65564, ts_rmse = 93.83395, tr_mae = 79.52438, valid_mae = 75.50884, ts_mae = 75.72380
Epoch 9 time = 11.80, tr_rmse = 88.08864, valid_rmse = 83.91973, ts_rmse = 84.07096, tr_mae = 71.08361, valid_mae = 67.13800, ts_mae = 67.23650
Epoch 10 time = 10.76, tr_rmse = 80.36456, valid_rmse = 76.11947, ts_rmse = 76.78473, tr_mae = 64.69331, valid_mae = 60.75425, ts_mae = 61.39462
Epoch 11 time = 10.88, tr_rmse = 72.85488, valid_rmse = 69.12700, ts_rmse = 68.93170, tr_mae = 58.58426, valid_mae = 55.14393, ts_mae = 54.92891
Epoch 12 time = 10.87, tr_rmse = 66.03329, valid_rmse = 62.42380, ts_rmse = 62.62510, tr_mae = 52.81767, valid_mae = 49.42125, ts_mae = 49.64627
Epoch 13 time = 10.50, tr_rmse = 61.55198, valid_rmse = 57.67189, ts_rmse = 57.33591, tr_mae = 49.39264, valid_mae = 46.03705, ts_mae = 45.57819
Epoch 14 time = 11.63, tr_rmse = 54.95930, valid_rmse = 51.38882, ts_rmse = 51.63277, tr_mae = 43.92029, valid_mae = 40.71434, ts_mae = 40.90249
Epoch 15 time = 12.06, tr_rmse = 49.49959, valid_rmse = 46.07767, ts_rmse = 46.29188, tr_mae = 39.43318, valid_mae = 36.51028, ts_mae = 36.73224
Epoch 16 time = 11.99, tr_rmse = 45.47639, valid_rmse = 42.51303, ts_rmse = 42.63423, tr_mae = 36.23988, valid_mae = 33.64177, ts_mae = 33.74437
Epoch 17 time = 10.14, tr_rmse = 41.53393, valid_rmse = 38.55350, ts_rmse = 38.22472, tr_mae = 33.14735, valid_mae = 30.51845, ts_mae = 30.38503
Epoch 18 time = 10.14, tr_rmse = 38.54800, valid_rmse = 35.39444, ts_rmse = 35.18512, tr_mae = 30.77516, valid_mae = 28.05162, ts_mae = 27.98601
Epoch 19 time = 10.21, tr_rmse = 34.60249, valid_rmse = 31.87325, ts_rmse = 31.92139, tr_mae = 27.43313, valid_mae = 25.12820, ts_mae = 25.14160
Epoch 20 time = 10.31, tr_rmse = 32.41131, valid_rmse = 28.78282, ts_rmse = 29.02951, tr_mae = 25.81217, valid_mae = 22.82328, ts_mae = 23.12247
Epoch 21 time = 10.10, tr_rmse = 29.63539, valid_rmse = 26.22939, ts_rmse = 26.28407, tr_mae = 23.49904, valid_mae = 20.76940, ts_mae = 20.76274
Epoch 22 time = 10.15, tr_rmse = 28.14579, valid_rmse = 24.74018, ts_rmse = 24.69780, tr_mae = 22.37327, valid_mae = 19.63754, ts_mae = 19.61604
Epoch 23 time = 10.14, tr_rmse = 25.84842, valid_rmse = 22.65259, ts_rmse = 22.41701, tr_mae = 20.45133, valid_mae = 17.93198, ts_mae = 17.75001
Epoch 24 time = 10.05, tr_rmse = 24.99609, valid_rmse = 21.44524, ts_rmse = 21.28371, tr_mae = 19.81792, valid_mae = 16.98862, ts_mae = 16.88686
Epoch 25 time = 10.28, tr_rmse = 24.22077, valid_rmse = 20.67378, ts_rmse = 20.61935, tr_mae = 19.29541, valid_mae = 16.43044, ts_mae = 16.43814
Epoch 26 time = 10.22, tr_rmse = 21.41277, valid_rmse = 18.25019, ts_rmse = 18.22687, tr_mae = 16.55845, valid_mae = 14.23280, ts_mae = 14.21476
Epoch 27 time = 10.03, tr_rmse = 20.52705, valid_rmse = 17.25853, ts_rmse = 17.11398, tr_mae = 15.79898, valid_mae = 13.37073, ts_mae = 13.31232
Epoch 28 time = 10.14, tr_rmse = 20.65998, valid_rmse = 17.21814, ts_rmse = 17.19531, tr_mae = 16.12646, valid_mae = 13.54704, ts_mae = 13.57142
Epoch 29 time = 10.20, tr_rmse = 20.19309, valid_rmse = 16.69780, ts_rmse = 16.61451, tr_mae = 15.72647, valid_mae = 13.06056, ts_mae = 13.08549
Epoch 30 time = 10.58, tr_rmse = 20.29426, valid_rmse = 16.83579, ts_rmse = 16.70910, tr_mae = 15.95925, valid_mae = 13.32516, ts_mae = 13.26896
Epoch 31 time = 10.47, tr_rmse = 20.42426, valid_rmse = 16.99970, ts_rmse = 16.94828, tr_mae = 16.21970, valid_mae = 13.60875, ts_mae = 13.59752
Epoch 32 time = 10.74, tr_rmse = 19.73991, valid_rmse = 16.32760, ts_rmse = 16.33337, tr_mae = 15.56311, valid_mae = 13.00600, ts_mae = 13.06388
Epoch 33 time = 10.57, tr_rmse = 19.63533, valid_rmse = 16.15513, ts_rmse = 16.05821, tr_mae = 15.52516, valid_mae = 12.87851, ts_mae = 12.84972
Epoch 34 time = 10.49, tr_rmse = 18.54266, valid_rmse = 15.20507, ts_rmse = 15.15259, tr_mae = 14.40320, valid_mae = 11.96353, ts_mae = 11.97522
Epoch 35 time = 10.34, tr_rmse = 19.29880, valid_rmse = 15.94948, ts_rmse = 15.93653, tr_mae = 15.38048, valid_mae = 12.87380, ts_mae = 12.90806
Epoch 36 time = 10.72, tr_rmse = 18.32653, valid_rmse = 14.98241, ts_rmse = 14.94959, tr_mae = 14.35635, valid_mae = 11.92258, ts_mae = 11.94039
Epoch 37 time = 10.43, tr_rmse = 17.21544, valid_rmse = 13.98859, ts_rmse = 13.89573, tr_mae = 13.09338, valid_mae = 10.89522, ts_mae = 10.84038
Epoch 38 time = 10.48, tr_rmse = 17.38045, valid_rmse = 14.06629, ts_rmse = 14.02992, tr_mae = 13.38677, valid_mae = 11.03890, ts_mae = 11.05629
Epoch 39 time = 10.22, tr_rmse = 17.14795, valid_rmse = 13.85358, ts_rmse = 13.78351, tr_mae = 13.16051, valid_mae = 10.87153, ts_mae = 10.84944
Epoch 40 time = 10.31, tr_rmse = 17.67979, valid_rmse = 14.47412, ts_rmse = 14.37751, tr_mae = 13.80511, valid_mae = 11.55238, ts_mae = 11.50688
Epoch 41 time = 11.34, tr_rmse = 17.77805, valid_rmse = 14.61537, ts_rmse = 14.54692, tr_mae = 14.00038, valid_mae = 11.77332, ts_mae = 11.76388
Epoch 42 time = 11.22, tr_rmse = 17.21191, valid_rmse = 14.02303, ts_rmse = 13.96267, tr_mae = 13.38353, valid_mae = 11.17042, ts_mae = 11.16596
Epoch 43 time = 11.88, tr_rmse = 17.55124, valid_rmse = 14.42488, ts_rmse = 14.35334, tr_mae = 13.82063, valid_mae = 11.62828, ts_mae = 11.63386
Epoch 44 time = 11.89, tr_rmse = 16.57793, valid_rmse = 13.49831, ts_rmse = 13.43744, tr_mae = 12.68515, valid_mae = 10.63555, ts_mae = 10.63021
Epoch 45 time = 12.33, tr_rmse = 15.78338, valid_rmse = 12.78805, ts_rmse = 12.68631, tr_mae = 11.78901, valid_mae = 9.89431, ts_mae = 9.88264
Epoch 46 time = 13.33, tr_rmse = 17.23601, valid_rmse = 14.28218, ts_rmse = 14.19937, tr_mae = 13.56428, valid_mae = 11.60134, ts_mae = 11.57264
Epoch 47 time = 15.47, tr_rmse = 16.13500, valid_rmse = 13.18999, ts_rmse = 13.06547, tr_mae = 12.26640, valid_mae = 10.38128, ts_mae = 10.32580
Epoch 48 time = 14.67, tr_rmse = 16.26168, valid_rmse = 13.38527, ts_rmse = 13.32936, tr_mae = 12.44717, valid_mae = 10.64614, ts_mae = 10.63803
Epoch 49 time = 13.45, tr_rmse = 16.17817, valid_rmse = 13.28476, ts_rmse = 13.23232, tr_mae = 12.38392, valid_mae = 10.53350, ts_mae = 10.55287
Epoch 50 time = 12.46, tr_rmse = 16.38148, valid_rmse = 13.47378, ts_rmse = 13.38205, tr_mae = 12.64094, valid_mae = 10.76516, ts_mae = 10.73328
Epoch 51 time = 12.26, tr_rmse = 15.56148, valid_rmse = 12.64202, ts_rmse = 12.52262, tr_mae = 11.65497, valid_mae = 9.82705, ts_mae = 9.79202
Epoch 52 time = 14.19, tr_rmse = 15.53018, valid_rmse = 12.64870, ts_rmse = 12.55463, tr_mae = 11.67029, valid_mae = 9.87795, ts_mae = 9.84212
Epoch 53 time = 12.87, tr_rmse = 15.28347, valid_rmse = 12.46730, ts_rmse = 12.38350, tr_mae = 11.38336, valid_mae = 9.68268, ts_mae = 9.67069
Epoch 54 time = 11.29, tr_rmse = 16.09112, valid_rmse = 13.29786, ts_rmse = 13.21833, tr_mae = 12.39709, valid_mae = 10.65072, ts_mae = 10.63649
Epoch 55 time = 10.59, tr_rmse = 15.83603, valid_rmse = 13.12799, ts_rmse = 13.04642, tr_mae = 12.12847, valid_mae = 10.48769, ts_mae = 10.47445
Epoch 56 time = 10.33, tr_rmse = 15.52105, valid_rmse = 12.81395, ts_rmse = 12.71112, tr_mae = 11.74423, valid_mae = 10.15618, ts_mae = 10.11368
Epoch 57 time = 10.42, tr_rmse = 16.41152, valid_rmse = 13.78769, ts_rmse = 13.70997, tr_mae = 12.85738, valid_mae = 11.27541, ts_mae = 11.25530
Epoch 58 time = 10.22, tr_rmse = 16.63460, valid_rmse = 14.08087, ts_rmse = 14.02129, tr_mae = 13.17562, valid_mae = 11.63457, ts_mae = 11.63199
Epoch 59 time = 10.13, tr_rmse = 16.16365, valid_rmse = 13.56861, ts_rmse = 13.52295, tr_mae = 12.60638, valid_mae = 11.06464, ts_mae = 11.06080
Epoch 60 time = 10.08, tr_rmse = 15.51564, valid_rmse = 12.89216, ts_rmse = 12.79591, tr_mae = 11.81635, valid_mae = 10.28886, ts_mae = 10.24864
Epoch 61 time = 9.86, tr_rmse = 15.91470, valid_rmse = 13.29360, ts_rmse = 13.22204, tr_mae = 12.33385, valid_mae = 10.77306, ts_mae = 10.74912
Epoch 62 time = 9.97, tr_rmse = 14.73767, valid_rmse = 12.04800, ts_rmse = 11.94883, tr_mae = 10.85858, valid_mae = 9.32158, ts_mae = 9.28417
Epoch 63 time = 10.08, tr_rmse = 15.05917, valid_rmse = 12.52876, ts_rmse = 12.40228, tr_mae = 11.29063, valid_mae = 9.90692, ts_mae = 9.85459
Epoch 64 time = 9.95, tr_rmse = 15.06442, valid_rmse = 12.44993, ts_rmse = 12.33474, tr_mae = 11.32299, valid_mae = 9.83320, ts_mae = 9.79063
Epoch 65 time = 10.02, tr_rmse = 15.61311, valid_rmse = 13.09232, ts_rmse = 13.02093, tr_mae = 12.03137, valid_mae = 10.58654, ts_mae = 10.57894
Epoch 66 time = 10.07, tr_rmse = 14.40380, valid_rmse = 11.71924, ts_rmse = 11.62332, tr_mae = 10.50385, valid_mae = 8.99472, ts_mae = 8.97009
Epoch 67 time = 10.05, tr_rmse = 14.52767, valid_rmse = 11.87821, ts_rmse = 11.78398, tr_mae = 10.68050, valid_mae = 9.18571, ts_mae = 9.16256
Epoch 68 time = 10.02, tr_rmse = 15.00845, valid_rmse = 12.55439, ts_rmse = 12.46748, tr_mae = 11.32056, valid_mae = 10.02200, ts_mae = 9.99311
Epoch 69 time = 10.33, tr_rmse = 14.44392, valid_rmse = 11.83655, ts_rmse = 11.75043, tr_mae = 10.57627, valid_mae = 9.14701, ts_mae = 9.12627
Epoch 70 time = 10.18, tr_rmse = 14.28779, valid_rmse = 11.66831, ts_rmse = 11.58814, tr_mae = 10.38579, valid_mae = 8.94792, ts_mae = 8.93932
Epoch 71 time = 11.14, tr_rmse = 15.31477, valid_rmse = 12.91881, ts_rmse = 12.85393, tr_mae = 11.74161, valid_mae = 10.47108, ts_mae = 10.46557
Epoch 72 time = 10.01, tr_rmse = 14.70426, valid_rmse = 12.21199, ts_rmse = 12.12925, tr_mae = 10.98379, valid_mae = 9.65113, ts_mae = 9.62532
Epoch 73 time = 10.07, tr_rmse = 13.99588, valid_rmse = 11.35286, ts_rmse = 11.24176, tr_mae = 10.04490, valid_mae = 8.59237, ts_mae = 8.56614
Epoch 74 time = 10.26, tr_rmse = 14.15444, valid_rmse = 11.56670, ts_rmse = 11.46455, tr_mae = 10.27206, valid_mae = 8.87231, ts_mae = 8.84424
Epoch 75 time = 9.92, tr_rmse = 14.01988, valid_rmse = 11.38251, ts_rmse = 11.26661, tr_mae = 10.09486, valid_mae = 8.65922, ts_mae = 8.62552
Epoch 76 time = 10.05, tr_rmse = 14.67822, valid_rmse = 12.12102, ts_rmse = 12.03409, tr_mae = 11.00063, valid_mae = 9.56788, ts_mae = 9.55477
Epoch 77 time = 9.90, tr_rmse = 13.68067, valid_rmse = 10.95839, ts_rmse = 10.84711, tr_mae = 9.65796, valid_mae = 8.15112, ts_mae = 8.11814
Epoch 78 time = 9.78, tr_rmse = 14.22561, valid_rmse = 11.56145, ts_rmse = 11.46000, tr_mae = 10.42153, valid_mae = 8.89960, ts_mae = 8.87085
Epoch 79 time = 9.64, tr_rmse = 14.30211, valid_rmse = 11.73217, ts_rmse = 11.63013, tr_mae = 10.53490, valid_mae = 9.11136, ts_mae = 9.08469
Epoch 80 time = 9.72, tr_rmse = 14.19549, valid_rmse = 11.63289, ts_rmse = 11.53184, tr_mae = 10.38946, valid_mae = 8.99306, ts_mae = 8.96507
Epoch 81 time = 9.64, tr_rmse = 14.44488, valid_rmse = 11.88758, ts_rmse = 11.81018, tr_mae = 10.74841, valid_mae = 9.32871, ts_mae = 9.31740
Epoch 82 time = 9.63, tr_rmse = 13.58662, valid_rmse = 10.87767, ts_rmse = 10.77203, tr_mae = 9.57060, valid_mae = 8.07753, ts_mae = 8.06174
Epoch 83 time = 9.68, tr_rmse = 13.42303, valid_rmse = 10.68689, ts_rmse = 10.56719, tr_mae = 9.34650, valid_mae = 7.82585, ts_mae = 7.79768
Epoch 84 time = 9.76, tr_rmse = 13.49247, valid_rmse = 10.79765, ts_rmse = 10.68637, tr_mae = 9.47515, valid_mae = 7.98611, ts_mae = 7.96827
Epoch 85 time = 9.55, tr_rmse = 13.89071, valid_rmse = 11.22091, ts_rmse = 11.10001, tr_mae = 10.01489, valid_mae = 8.50205, ts_mae = 8.46201
Epoch 86 time = 9.69, tr_rmse = 13.64691, valid_rmse = 10.97278, ts_rmse = 10.90439, tr_mae = 9.69617, valid_mae = 8.21510, ts_mae = 8.22004
Epoch 87 time = 9.72, tr_rmse = 13.17901, valid_rmse = 10.46408, ts_rmse = 10.35410, tr_mae = 9.03755, valid_mae = 7.57627, ts_mae = 7.56084
Epoch 88 time = 9.53, tr_rmse = 13.54532, valid_rmse = 10.84519, ts_rmse = 10.75348, tr_mae = 9.58779, valid_mae = 8.08651, ts_mae = 8.06878
Epoch 89 time = 9.61, tr_rmse = 13.30816, valid_rmse = 10.58069, ts_rmse = 10.48358, tr_mae = 9.22884, valid_mae = 7.73194, ts_mae = 7.71888
Epoch 90 time = 10.30, tr_rmse = 12.84662, valid_rmse = 10.07131, ts_rmse = 9.93971, tr_mae = 8.53808, valid_mae = 7.06664, ts_mae = 7.04660
Epoch 91 time = 9.63, tr_rmse = 13.02916, valid_rmse = 10.25566, ts_rmse = 10.14524, tr_mae = 8.83429, valid_mae = 7.32265, ts_mae = 7.31255
Epoch 92 time = 9.56, tr_rmse = 13.65271, valid_rmse = 11.02411, ts_rmse = 10.93602, tr_mae = 9.75074, valid_mae = 8.30927, ts_mae = 8.29930
Epoch 93 time = 9.86, tr_rmse = 12.95008, valid_rmse = 10.21134, ts_rmse = 10.07199, tr_mae = 8.74316, valid_mae = 7.27271, ts_mae = 7.23556
Epoch 94 time = 9.60, tr_rmse = 13.14386, valid_rmse = 10.44819, ts_rmse = 10.34299, tr_mae = 9.03990, valid_mae = 7.60458, ts_mae = 7.59260
Epoch 95 time = 9.60, tr_rmse = 12.78912, valid_rmse = 10.00238, ts_rmse = 9.88001, tr_mae = 8.50171, valid_mae = 7.00762, ts_mae = 6.98685
Epoch 96 time = 9.59, tr_rmse = 12.94203, valid_rmse = 10.17279, ts_rmse = 10.06654, tr_mae = 8.75254, valid_mae = 7.23814, ts_mae = 7.22357
Epoch 97 time = 9.57, tr_rmse = 12.59826, valid_rmse = 9.82026, ts_rmse = 9.68437, tr_mae = 8.22326, valid_mae = 6.74566, ts_mae = 6.73006
Epoch 98 time = 9.63, tr_rmse = 12.92355, valid_rmse = 10.16960, ts_rmse = 10.03940, tr_mae = 8.74053, valid_mae = 7.22504, ts_mae = 7.20040
Epoch 99 time = 9.64, tr_rmse = 12.63733, valid_rmse = 9.87658, ts_rmse = 9.75832, tr_mae = 8.28938, valid_mae = 6.83889, ts_mae = 6.82062
Epoch 100 time = 9.63, tr_rmse = 12.67251, valid_rmse = 9.92161, ts_rmse = 9.77713, tr_mae = 8.35471, valid_mae = 6.90170, ts_mae = 6.86750
Epoch 101 time = 9.50, tr_rmse = 12.90002, valid_rmse = 10.17578, ts_rmse = 10.04830, tr_mae = 8.72223, valid_mae = 7.25903, ts_mae = 7.22479
Epoch 102 time = 10.33, tr_rmse = 12.58596, valid_rmse = 9.82230, ts_rmse = 9.68495, tr_mae = 8.23168, valid_mae = 6.76074, ts_mae = 6.73515
Epoch 103 time = 11.60, tr_rmse = 12.55069, valid_rmse = 9.75375, ts_rmse = 9.63323, tr_mae = 8.17461, valid_mae = 6.66919, ts_mae = 6.65718
Epoch 104 time = 12.43, tr_rmse = 12.66751, valid_rmse = 9.92375, ts_rmse = 9.79058, tr_mae = 8.36662, valid_mae = 6.91178, ts_mae = 6.88959
Epoch 105 time = 9.97, tr_rmse = 12.44932, valid_rmse = 9.65303, ts_rmse = 9.52220, tr_mae = 8.02172, valid_mae = 6.54700, ts_mae = 6.52470
Epoch 106 time = 9.77, tr_rmse = 12.46710, valid_rmse = 9.66681, ts_rmse = 9.52531, tr_mae = 8.05451, valid_mae = 6.55822, ts_mae = 6.52735
Epoch 107 time = 9.66, tr_rmse = 12.71769, valid_rmse = 10.00135, ts_rmse = 9.86676, tr_mae = 8.46640, valid_mae = 7.02236, ts_mae = 6.99738
Epoch 108 time = 9.64, tr_rmse = 12.75950, valid_rmse = 10.03371, ts_rmse = 9.88442, tr_mae = 8.52509, valid_mae = 7.06267, ts_mae = 7.02889
Epoch 109 time = 9.75, tr_rmse = 12.70778, valid_rmse = 9.97790, ts_rmse = 9.84799, tr_mae = 8.45464, valid_mae = 7.00031, ts_mae = 6.97136
Epoch 110 time = 9.88, tr_rmse = 12.38902, valid_rmse = 9.62790, ts_rmse = 9.47578, tr_mae = 7.93482, valid_mae = 6.50208, ts_mae = 6.47624
Epoch 111 time = 10.64, tr_rmse = 12.67779, valid_rmse = 9.91986, ts_rmse = 9.80360, tr_mae = 8.41121, valid_mae = 6.91305, ts_mae = 6.89453
Epoch 112 time = 10.42, tr_rmse = 12.31152, valid_rmse = 9.53139, ts_rmse = 9.40533, tr_mae = 7.81515, valid_mae = 6.38153, ts_mae = 6.36694
Epoch 113 time = 10.18, tr_rmse = 12.20469, valid_rmse = 9.43003, ts_rmse = 9.28377, tr_mae = 7.63606, valid_mae = 6.22561, ts_mae = 6.20241
Epoch 114 time = 10.29, tr_rmse = 12.46466, valid_rmse = 9.70368, ts_rmse = 9.57696, tr_mae = 8.09414, valid_mae = 6.62006, ts_mae = 6.60201
Epoch 115 time = 10.11, tr_rmse = 12.35911, valid_rmse = 9.58442, ts_rmse = 9.44928, tr_mae = 7.91057, valid_mae = 6.45092, ts_mae = 6.43487
Epoch 116 time = 9.95, tr_rmse = 12.43472, valid_rmse = 9.67943, ts_rmse = 9.53903, tr_mae = 8.04232, valid_mae = 6.59204, ts_mae = 6.56901
Epoch 117 time = 9.93, tr_rmse = 12.59010, valid_rmse = 9.86404, ts_rmse = 9.72820, tr_mae = 8.29404, valid_mae = 6.85226, ts_mae = 6.83504
Epoch 118 time = 9.73, tr_rmse = 12.28011, valid_rmse = 9.52150, ts_rmse = 9.37112, tr_mae = 7.76807, valid_mae = 6.35469, ts_mae = 6.32748
Epoch 119 time = 9.67, tr_rmse = 12.26070, valid_rmse = 9.50103, ts_rmse = 9.36911, tr_mae = 7.76513, valid_mae = 6.33785, ts_mae = 6.32008
Epoch 120 time = 9.88, tr_rmse = 12.24698, valid_rmse = 9.47697, ts_rmse = 9.34078, tr_mae = 7.73763, valid_mae = 6.30484, ts_mae = 6.28843
Epoch 121 time = 9.63, tr_rmse = 12.33462, valid_rmse = 9.59934, ts_rmse = 9.46399, tr_mae = 7.89922, valid_mae = 6.47677, ts_mae = 6.46079
Epoch 122 time = 9.59, tr_rmse = 12.28515, valid_rmse = 9.51347, ts_rmse = 9.37951, tr_mae = 7.81837, valid_mae = 6.36412, ts_mae = 6.34477
Epoch 123 time = 9.76, tr_rmse = 12.07962, valid_rmse = 9.34540, ts_rmse = 9.18700, tr_mae = 7.41665, valid_mae = 6.06403, ts_mae = 6.04086
Epoch 124 time = 10.14, tr_rmse = 12.13731, valid_rmse = 9.37629, ts_rmse = 9.23750, tr_mae = 7.54123, valid_mae = 6.14802, ts_mae = 6.13302
Epoch 125 time = 10.17, tr_rmse = 12.12403, valid_rmse = 9.38473, ts_rmse = 9.23510, tr_mae = 7.53778, valid_mae = 6.14092, ts_mae = 6.11818
Epoch 126 time = 10.21, tr_rmse = 12.18409, valid_rmse = 9.43528, ts_rmse = 9.27855, tr_mae = 7.64273, valid_mae = 6.21341, ts_mae = 6.18222
Epoch 127 time = 10.11, tr_rmse = 12.23193, valid_rmse = 9.49594, ts_rmse = 9.34838, tr_mae = 7.74636, valid_mae = 6.33260, ts_mae = 6.30728
Epoch 128 time = 10.11, tr_rmse = 12.24432, valid_rmse = 9.49473, ts_rmse = 9.35764, tr_mae = 7.77105, valid_mae = 6.34558, ts_mae = 6.32702
Epoch 129 time = 10.33, tr_rmse = 12.17954, valid_rmse = 9.43895, ts_rmse = 9.29565, tr_mae = 7.64516, valid_mae = 6.23200, ts_mae = 6.20991
Epoch 130 time = 10.01, tr_rmse = 12.21154, valid_rmse = 9.46968, ts_rmse = 9.32772, tr_mae = 7.71866, valid_mae = 6.29693, ts_mae = 6.27550
Epoch 131 time = 9.89, tr_rmse = 12.02393, valid_rmse = 9.30178, ts_rmse = 9.14544, tr_mae = 7.34634, valid_mae = 6.01295, ts_mae = 5.99836
Epoch 132 time = 9.67, tr_rmse = 12.21697, valid_rmse = 9.47365, ts_rmse = 9.33209, tr_mae = 7.73759, valid_mae = 6.30941, ts_mae = 6.29013
Epoch 133 time = 9.53, tr_rmse = 12.11342, valid_rmse = 9.36954, ts_rmse = 9.21028, tr_mae = 7.53308, valid_mae = 6.14005, ts_mae = 6.11505
Epoch 134 time = 9.85, tr_rmse = 12.13826, valid_rmse = 9.39345, ts_rmse = 9.25181, tr_mae = 7.60142, valid_mae = 6.18174, ts_mae = 6.15985
Epoch 135 time = 9.59, tr_rmse = 12.16288, valid_rmse = 9.42688, ts_rmse = 9.27720, tr_mae = 7.64934, valid_mae = 6.23434, ts_mae = 6.20746
Epoch 136 time = 9.64, tr_rmse = 12.14524, valid_rmse = 9.40923, ts_rmse = 9.27187, tr_mae = 7.62366, valid_mae = 6.20490, ts_mae = 6.18589
Epoch 137 time = 9.96, tr_rmse = 12.09280, valid_rmse = 9.36022, ts_rmse = 9.20293, tr_mae = 7.51324, valid_mae = 6.12751, ts_mae = 6.10270
Epoch 138 time = 10.29, tr_rmse = 12.09668, valid_rmse = 9.35903, ts_rmse = 9.21267, tr_mae = 7.53575, valid_mae = 6.13177, ts_mae = 6.10882
Epoch 139 time = 10.61, tr_rmse = 12.02317, valid_rmse = 9.31265, ts_rmse = 9.16103, tr_mae = 7.39463, valid_mae = 6.03258, ts_mae = 6.01435
Epoch 140 time = 10.13, tr_rmse = 12.12939, valid_rmse = 9.38538, ts_rmse = 9.25536, tr_mae = 7.60557, valid_mae = 6.19602, ts_mae = 6.18018
Epoch 141 time = 9.98, tr_rmse = 12.27630, valid_rmse = 9.53153, ts_rmse = 9.39871, tr_mae = 7.86701, valid_mae = 6.40734, ts_mae = 6.37918
Epoch 142 time = 9.92, tr_rmse = 12.05217, valid_rmse = 9.30870, ts_rmse = 9.15616, tr_mae = 7.44782, valid_mae = 6.06440, ts_mae = 6.04647
Epoch 143 time = 9.93, tr_rmse = 12.06121, valid_rmse = 9.32761, ts_rmse = 9.18806, tr_mae = 7.47005, valid_mae = 6.09616, ts_mae = 6.08379
Epoch 144 time = 10.07, tr_rmse = 12.15252, valid_rmse = 9.40971, ts_rmse = 9.28042, tr_mae = 7.64656, valid_mae = 6.21536, ts_mae = 6.20234
Epoch 145 time = 9.82, tr_rmse = 12.04092, valid_rmse = 9.31385, ts_rmse = 9.15835, tr_mae = 7.44568, valid_mae = 6.06408, ts_mae = 6.04371
Epoch 146 time = 9.62, tr_rmse = 12.00922, valid_rmse = 9.29216, ts_rmse = 9.15069, tr_mae = 7.38351, valid_mae = 6.01544, ts_mae = 6.00239
Epoch 147 time = 9.57, tr_rmse = 12.02154, valid_rmse = 9.29804, ts_rmse = 9.15720, tr_mae = 7.42006, valid_mae = 6.04432, ts_mae = 6.03118
Epoch 148 time = 9.71, tr_rmse = 11.96063, valid_rmse = 9.26204, ts_rmse = 9.09052, tr_mae = 7.25539, valid_mae = 5.94576, ts_mae = 5.91728
Epoch 149 time = 9.76, tr_rmse = 11.98294, valid_rmse = 9.28031, ts_rmse = 9.11991, tr_mae = 7.33709, valid_mae = 6.00401, ts_mae = 5.97991
Epoch 150 time = 9.93, tr_rmse = 11.98201, valid_rmse = 9.26304, ts_rmse = 9.09726, tr_mae = 7.29550, valid_mae = 5.96803, ts_mae = 5.94490
Epoch 151 time = 10.36, tr_rmse = 11.95375, valid_rmse = 9.26240, ts_rmse = 9.10599, tr_mae = 7.28541, valid_mae = 5.96597, ts_mae = 5.93999
Epoch 152 time = 10.20, tr_rmse = 11.95320, valid_rmse = 9.26976, ts_rmse = 9.11181, tr_mae = 7.28612, valid_mae = 5.96873, ts_mae = 5.95088
Epoch 153 time = 11.14, tr_rmse = 11.94092, valid_rmse = 9.25315, ts_rmse = 9.09080, tr_mae = 7.23789, valid_mae = 5.93808, ts_mae = 5.91175
Epoch 154 time = 10.20, tr_rmse = 12.02068, valid_rmse = 9.31955, ts_rmse = 9.16298, tr_mae = 7.42571, valid_mae = 6.06238, ts_mae = 6.03997
Epoch 155 time = 9.98, tr_rmse = 11.94548, valid_rmse = 9.24708, ts_rmse = 9.08229, tr_mae = 7.27479, valid_mae = 5.94745, ts_mae = 5.92552
Epoch 156 time = 9.92, tr_rmse = 11.94377, valid_rmse = 9.27590, ts_rmse = 9.10089, tr_mae = 7.17972, valid_mae = 5.92313, ts_mae = 5.90295
Epoch 157 time = 9.92, tr_rmse = 11.93111, valid_rmse = 9.24700, ts_rmse = 9.08799, tr_mae = 7.24499, valid_mae = 5.94637, ts_mae = 5.92468
Epoch 158 time = 9.71, tr_rmse = 11.95140, valid_rmse = 9.24714, ts_rmse = 9.09890, tr_mae = 7.28302, valid_mae = 5.95412, ts_mae = 5.93894
Epoch 159 time = 9.84, tr_rmse = 12.06940, valid_rmse = 9.34891, ts_rmse = 9.20637, tr_mae = 7.54799, valid_mae = 6.13657, ts_mae = 6.11827
Epoch 160 time = 9.76, tr_rmse = 12.03176, valid_rmse = 9.30410, ts_rmse = 9.15052, tr_mae = 7.48819, valid_mae = 6.08559, ts_mae = 6.06591
Epoch 161 time = 9.60, tr_rmse = 12.00661, valid_rmse = 9.29226, ts_rmse = 9.15007, tr_mae = 7.43311, valid_mae = 6.04290, ts_mae = 6.02923
Epoch 162 time = 9.78, tr_rmse = 11.99979, valid_rmse = 9.28291, ts_rmse = 9.12949, tr_mae = 7.41862, valid_mae = 6.02945, ts_mae = 6.00485
Epoch 163 time = 10.27, tr_rmse = 11.92250, valid_rmse = 9.23770, ts_rmse = 9.08023, tr_mae = 7.24806, valid_mae = 5.92935, ts_mae = 5.90798
Epoch 164 time = 10.20, tr_rmse = 11.96087, valid_rmse = 9.26453, ts_rmse = 9.12817, tr_mae = 7.34646, valid_mae = 5.99501, ts_mae = 5.98024
Epoch 165 time = 10.21, tr_rmse = 11.96636, valid_rmse = 9.25312, ts_rmse = 9.09527, tr_mae = 7.37173, valid_mae = 6.00186, ts_mae = 5.97405
Epoch 166 time = 10.25, tr_rmse = 11.95451, valid_rmse = 9.25114, ts_rmse = 9.10794, tr_mae = 7.34201, valid_mae = 5.98468, ts_mae = 5.96936
Epoch 167 time = 10.05, tr_rmse = 11.93393, valid_rmse = 9.24919, ts_rmse = 9.09249, tr_mae = 7.30663, valid_mae = 5.97101, ts_mae = 5.94513
Epoch 168 time = 9.95, tr_rmse = 11.89580, valid_rmse = 9.24336, ts_rmse = 9.08408, tr_mae = 7.20925, valid_mae = 5.92660, ts_mae = 5.90493
Epoch 169 time = 9.85, tr_rmse = 12.01448, valid_rmse = 9.30519, ts_rmse = 9.14459, tr_mae = 7.46769, valid_mae = 6.06613, ts_mae = 6.03342
Epoch 170 time = 9.69, tr_rmse = 11.92328, valid_rmse = 9.24357, ts_rmse = 9.08134, tr_mae = 7.29060, valid_mae = 5.95392, ts_mae = 5.93193
Epoch 171 time = 9.87, tr_rmse = 11.97109, valid_rmse = 9.27559, ts_rmse = 9.12407, tr_mae = 7.40269, valid_mae = 6.02765, ts_mae = 6.00245
Epoch 172 time = 9.71, tr_rmse = 11.97110, valid_rmse = 9.27092, ts_rmse = 9.12137, tr_mae = 7.38728, valid_mae = 6.02429, ts_mae = 5.99801
Epoch 173 time = 9.92, tr_rmse = 11.91242, valid_rmse = 9.23236, ts_rmse = 9.05820, tr_mae = 7.26689, valid_mae = 5.93821, ts_mae = 5.91186
Epoch 174 time = 10.26, tr_rmse = 11.92822, valid_rmse = 9.22520, ts_rmse = 9.07796, tr_mae = 7.31747, valid_mae = 5.95477, ts_mae = 5.93551
Epoch 175 time = 10.33, tr_rmse = 11.88255, valid_rmse = 9.21310, ts_rmse = 9.04718, tr_mae = 7.20631, valid_mae = 5.90215, ts_mae = 5.88123
Epoch 176 time = 10.17, tr_rmse = 11.87675, valid_rmse = 9.22559, ts_rmse = 9.05423, tr_mae = 7.14677, valid_mae = 5.88592, ts_mae = 5.86035
Epoch 177 time = 10.14, tr_rmse = 11.88650, valid_rmse = 9.20524, ts_rmse = 9.04307, tr_mae = 7.23289, valid_mae = 5.90898, ts_mae = 5.88639
Epoch 178 time = 10.17, tr_rmse = 11.94746, valid_rmse = 9.25480, ts_rmse = 9.09992, tr_mae = 7.36934, valid_mae = 6.00055, ts_mae = 5.98023
Epoch 179 time = 9.98, tr_rmse = 11.86245, valid_rmse = 9.22126, ts_rmse = 9.04812, tr_mae = 7.17001, valid_mae = 5.89249, ts_mae = 5.86209
Epoch 180 time = 9.85, tr_rmse = 11.87409, valid_rmse = 9.21461, ts_rmse = 9.04575, tr_mae = 7.22949, valid_mae = 5.91262, ts_mae = 5.88749
Epoch 181 time = 9.98, tr_rmse = 11.88134, valid_rmse = 9.20783, ts_rmse = 9.03412, tr_mae = 7.23036, valid_mae = 5.90239, ts_mae = 5.87887
Epoch 182 time = 9.63, tr_rmse = 11.87552, valid_rmse = 9.31973, ts_rmse = 9.14347, tr_mae = 7.12477, valid_mae = 5.96495, ts_mae = 5.93619
Epoch 183 time = 9.73, tr_rmse = 11.88883, valid_rmse = 9.19873, ts_rmse = 9.03616, tr_mae = 7.22437, valid_mae = 5.90645, ts_mae = 5.88966
Epoch 184 time = 9.72, tr_rmse = 11.91378, valid_rmse = 9.23258, ts_rmse = 9.06552, tr_mae = 7.30647, valid_mae = 5.97188, ts_mae = 5.94648
Epoch 185 time = 9.66, tr_rmse = 11.87588, valid_rmse = 9.27383, ts_rmse = 9.08748, tr_mae = 7.11119, valid_mae = 5.91230, ts_mae = 5.87958
Epoch 186 time = 10.09, tr_rmse = 11.88798, valid_rmse = 9.20811, ts_rmse = 9.03456, tr_mae = 7.26477, valid_mae = 5.92447, ts_mae = 5.89436
Epoch 187 time = 10.27, tr_rmse = 11.84456, valid_rmse = 9.21985, ts_rmse = 9.03882, tr_mae = 7.13940, valid_mae = 5.88161, ts_mae = 5.84877
Epoch 188 time = 10.34, tr_rmse = 11.84135, valid_rmse = 9.19835, ts_rmse = 9.01326, tr_mae = 7.14339, valid_mae = 5.86605, ts_mae = 5.83192
Epoch 189 time = 10.11, tr_rmse = 11.84525, valid_rmse = 9.23149, ts_rmse = 9.05811, tr_mae = 7.10935, valid_mae = 5.88021, ts_mae = 5.85031
Epoch 190 time = 10.12, tr_rmse = 11.94332, valid_rmse = 9.29757, ts_rmse = 9.11061, tr_mae = 7.13404, valid_mae = 5.91587, ts_mae = 5.88781
Epoch 191 time = 9.94, tr_rmse = 11.87457, valid_rmse = 9.22200, ts_rmse = 9.05228, tr_mae = 7.23181, valid_mae = 5.92631, ts_mae = 5.89325
Epoch 192 time = 9.90, tr_rmse = 11.86250, valid_rmse = 9.18971, ts_rmse = 9.02317, tr_mae = 7.23591, valid_mae = 5.90139, ts_mae = 5.87672
Epoch 193 time = 9.81, tr_rmse = 11.84097, valid_rmse = 9.27201, ts_rmse = 9.08255, tr_mae = 7.09730, valid_mae = 5.90891, ts_mae = 5.87510
Epoch 194 time = 9.71, tr_rmse = 11.89535, valid_rmse = 9.22792, ts_rmse = 9.06222, tr_mae = 7.30328, valid_mae = 5.95544, ts_mae = 5.92563
Epoch 195 time = 9.68, tr_rmse = 11.83768, valid_rmse = 9.21425, ts_rmse = 9.04115, tr_mae = 7.10716, valid_mae = 5.86989, ts_mae = 5.84761
Epoch 196 time = 9.75, tr_rmse = 11.82915, valid_rmse = 9.19771, ts_rmse = 9.00730, tr_mae = 7.11883, valid_mae = 5.85419, ts_mae = 5.82227
Epoch 197 time = 9.86, tr_rmse = 11.86284, valid_rmse = 9.19667, ts_rmse = 9.02792, tr_mae = 7.24012, valid_mae = 5.90613, ts_mae = 5.87710
Epoch 198 time = 9.95, tr_rmse = 11.83923, valid_rmse = 9.18624, ts_rmse = 9.01938, tr_mae = 7.16725, valid_mae = 5.86767, ts_mae = 5.84152
Epoch 199 time = 10.14, tr_rmse = 11.83831, valid_rmse = 9.19354, ts_rmse = 9.01262, tr_mae = 7.18687, valid_mae = 5.88110, ts_mae = 5.84589
Epoch 200 time = 10.16, tr_rmse = 11.85885, valid_rmse = 9.19334, ts_rmse = 9.01663, tr_mae = 7.24259, valid_mae = 5.90721, ts_mae = 5.88097
Epoch 201 time = 10.18, tr_rmse = 11.92915, valid_rmse = 9.23669, ts_rmse = 9.06845, tr_mae = 7.37189, valid_mae = 6.01116, ts_mae = 5.97991
Epoch 202 time = 10.07, tr_rmse = 11.86387, valid_rmse = 9.24205, ts_rmse = 9.05623, tr_mae = 7.08191, valid_mae = 5.86115, ts_mae = 5.83404
Epoch 203 time = 10.01, tr_rmse = 11.88128, valid_rmse = 9.20540, ts_rmse = 9.03496, tr_mae = 7.30109, valid_mae = 5.94187, ts_mae = 5.90846
Epoch 204 time = 9.90, tr_rmse = 11.82509, valid_rmse = 9.16372, ts_rmse = 8.98825, tr_mae = 7.14794, valid_mae = 5.85317, ts_mae = 5.82624
Epoch 205 time = 9.79, tr_rmse = 11.84858, valid_rmse = 9.20607, ts_rmse = 9.02065, tr_mae = 7.22112, valid_mae = 5.90522, ts_mae = 5.86986
Epoch 206 time = 9.72, tr_rmse = 11.80826, valid_rmse = 9.17781, ts_rmse = 8.98572, tr_mae = 7.11373, valid_mae = 5.84084, ts_mae = 5.80887
Epoch 207 time = 9.70, tr_rmse = 11.81686, valid_rmse = 9.22000, ts_rmse = 9.03278, tr_mae = 7.12467, valid_mae = 5.88590, ts_mae = 5.84781
Epoch 208 time = 9.68, tr_rmse = 11.81497, valid_rmse = 9.20413, ts_rmse = 9.01627, tr_mae = 7.07578, valid_mae = 5.84877, ts_mae = 5.81205
Epoch 209 time = 9.80, tr_rmse = 11.86736, valid_rmse = 9.18941, ts_rmse = 9.02132, tr_mae = 7.27384, valid_mae = 5.92001, ts_mae = 5.89467
Epoch 210 time = 10.06, tr_rmse = 11.82237, valid_rmse = 9.23436, ts_rmse = 9.05197, tr_mae = 7.07685, valid_mae = 5.87367, ts_mae = 5.84502
Epoch 211 time = 10.73, tr_rmse = 11.80312, valid_rmse = 9.19685, ts_rmse = 9.00674, tr_mae = 7.12096, valid_mae = 5.85844, ts_mae = 5.82517
Epoch 212 time = 10.18, tr_rmse = 11.81738, valid_rmse = 9.16021, ts_rmse = 8.97825, tr_mae = 7.16430, valid_mae = 5.85686, ts_mae = 5.82566
Epoch 213 time = 10.33, tr_rmse = 11.81050, valid_rmse = 9.17086, ts_rmse = 8.98389, tr_mae = 7.16658, valid_mae = 5.86197, ts_mae = 5.82616
Epoch 214 time = 10.13, tr_rmse = 11.79881, valid_rmse = 9.16154, ts_rmse = 8.97778, tr_mae = 7.11266, valid_mae = 5.83430, ts_mae = 5.80172
Epoch 215 time = 9.89, tr_rmse = 11.80230, valid_rmse = 9.19327, ts_rmse = 9.00486, tr_mae = 7.08541, valid_mae = 5.84665, ts_mae = 5.81319
Epoch 216 time = 9.82, tr_rmse = 11.82477, valid_rmse = 9.26204, ts_rmse = 9.08005, tr_mae = 7.07727, valid_mae = 5.90339, ts_mae = 5.86797
Epoch 217 time = 9.87, tr_rmse = 11.90317, valid_rmse = 9.23293, ts_rmse = 9.06030, tr_mae = 7.34855, valid_mae = 5.97384, ts_mae = 5.94316
Epoch 218 time = 9.72, tr_rmse = 11.79810, valid_rmse = 9.17971, ts_rmse = 8.98953, tr_mae = 7.10364, valid_mae = 5.83990, ts_mae = 5.80359
Epoch 219 time = 9.78, tr_rmse = 11.79166, valid_rmse = 9.16976, ts_rmse = 8.98498, tr_mae = 7.11071, valid_mae = 5.83390, ts_mae = 5.80687
Epoch 220 time = 9.65, tr_rmse = 11.78979, valid_rmse = 9.17533, ts_rmse = 8.99086, tr_mae = 7.11345, valid_mae = 5.84433, ts_mae = 5.81026
Epoch 221 time = 9.62, tr_rmse = 11.80783, valid_rmse = 9.15762, ts_rmse = 8.97944, tr_mae = 7.17176, valid_mae = 5.86130, ts_mae = 5.83068
Epoch 222 time = 9.89, tr_rmse = 11.79197, valid_rmse = 9.15824, ts_rmse = 8.97256, tr_mae = 7.13716, valid_mae = 5.83950, ts_mae = 5.80769
Epoch 223 time = 10.15, tr_rmse = 11.78839, valid_rmse = 9.17133, ts_rmse = 8.98587, tr_mae = 7.13125, valid_mae = 5.85093, ts_mae = 5.81434
Epoch 224 time = 10.26, tr_rmse = 11.80114, valid_rmse = 9.15393, ts_rmse = 8.96476, tr_mae = 7.12675, valid_mae = 5.83838, ts_mae = 5.80369
Epoch 225 time = 11.08, tr_rmse = 11.80603, valid_rmse = 9.17958, ts_rmse = 8.98894, tr_mae = 7.16677, valid_mae = 5.87116, ts_mae = 5.83286
Epoch 226 time = 10.09, tr_rmse = 11.81781, valid_rmse = 9.21605, ts_rmse = 9.02238, tr_mae = 7.07457, valid_mae = 5.84315, ts_mae = 5.81150
Epoch 227 time = 9.92, tr_rmse = 11.79083, valid_rmse = 9.15234, ts_rmse = 8.96990, tr_mae = 7.16090, valid_mae = 5.85181, ts_mae = 5.82125
Epoch 228 time = 9.92, tr_rmse = 11.79166, valid_rmse = 9.23543, ts_rmse = 9.03391, tr_mae = 7.06433, valid_mae = 5.87290, ts_mae = 5.83601
Epoch 229 time = 9.90, tr_rmse = 11.79044, valid_rmse = 9.17452, ts_rmse = 8.97801, tr_mae = 7.13890, valid_mae = 5.85384, ts_mae = 5.81086
Epoch 230 time = 9.79, tr_rmse = 11.80582, valid_rmse = 9.15976, ts_rmse = 8.97070, tr_mae = 7.18560, valid_mae = 5.86910, ts_mae = 5.83126
Epoch 231 time = 9.85, tr_rmse = 11.79466, valid_rmse = 9.16309, ts_rmse = 8.97252, tr_mae = 7.17934, valid_mae = 5.86713, ts_mae = 5.82662
Epoch 232 time = 10.07, tr_rmse = 11.78108, valid_rmse = 9.21509, ts_rmse = 9.01985, tr_mae = 7.06689, valid_mae = 5.86407, ts_mae = 5.82627
Epoch 233 time = 9.67, tr_rmse = 11.77203, valid_rmse = 9.14827, ts_rmse = 8.96429, tr_mae = 7.11243, valid_mae = 5.83034, ts_mae = 5.79432
Epoch 234 time = 9.93, tr_rmse = 11.77145, valid_rmse = 9.14637, ts_rmse = 8.96191, tr_mae = 7.11801, valid_mae = 5.83577, ts_mae = 5.80333
Epoch 235 time = 10.16, tr_rmse = 11.76912, valid_rmse = 9.17674, ts_rmse = 8.98133, tr_mae = 7.06144, valid_mae = 5.82669, ts_mae = 5.79019
Epoch 236 time = 10.26, tr_rmse = 11.75961, valid_rmse = 9.16587, ts_rmse = 8.96385, tr_mae = 7.09041, valid_mae = 5.83061, ts_mae = 5.79037
Epoch 237 time = 10.18, tr_rmse = 11.76802, valid_rmse = 9.16457, ts_rmse = 8.95730, tr_mae = 7.09116, valid_mae = 5.82949, ts_mae = 5.78393
Epoch 238 time = 10.05, tr_rmse = 11.76838, valid_rmse = 9.16936, ts_rmse = 8.97172, tr_mae = 7.11443, valid_mae = 5.84533, ts_mae = 5.80533
Epoch 239 time = 9.93, tr_rmse = 11.76146, valid_rmse = 9.15032, ts_rmse = 8.96929, tr_mae = 7.10993, valid_mae = 5.83080, ts_mae = 5.79739
Epoch 240 time = 9.99, tr_rmse = 11.77892, valid_rmse = 9.16425, ts_rmse = 8.96659, tr_mae = 7.15868, valid_mae = 5.86172, ts_mae = 5.81751
Epoch 241 time = 10.35, tr_rmse = 11.75052, valid_rmse = 9.15911, ts_rmse = 8.96304, tr_mae = 7.09902, valid_mae = 5.83348, ts_mae = 5.79825
Epoch 242 time = 9.71, tr_rmse = 11.74653, valid_rmse = 9.15103, ts_rmse = 8.96552, tr_mae = 7.08035, valid_mae = 5.81923, ts_mae = 5.78341
Epoch 243 time = 9.82, tr_rmse = 11.78110, valid_rmse = 9.17649, ts_rmse = 8.99072, tr_mae = 7.16995, valid_mae = 5.87005, ts_mae = 5.83370
Epoch 244 time = 10.28, tr_rmse = 11.77787, valid_rmse = 9.17613, ts_rmse = 8.97783, tr_mae = 7.14170, valid_mae = 5.86498, ts_mae = 5.82106
Epoch 245 time = 9.72, tr_rmse = 11.77173, valid_rmse = 9.13716, ts_rmse = 8.95378, tr_mae = 7.15445, valid_mae = 5.84702, ts_mae = 5.81313
Epoch 246 time = 9.86, tr_rmse = 11.81869, valid_rmse = 9.18830, ts_rmse = 9.00555, tr_mae = 7.27003, valid_mae = 5.93502, ts_mae = 5.90188
Epoch 247 time = 10.37, tr_rmse = 11.76459, valid_rmse = 9.16187, ts_rmse = 8.97250, tr_mae = 7.12750, valid_mae = 5.84938, ts_mae = 5.81578
Epoch 248 time = 10.16, tr_rmse = 11.81257, valid_rmse = 9.18885, ts_rmse = 9.00226, tr_mae = 7.26875, valid_mae = 5.93548, ts_mae = 5.89742
Epoch 249 time = 10.44, tr_rmse = 11.77027, valid_rmse = 9.14672, ts_rmse = 8.95580, tr_mae = 7.17422, valid_mae = 5.86395, ts_mae = 5.82538
Epoch 250 time = 10.30, tr_rmse = 11.73947, valid_rmse = 9.14899, ts_rmse = 8.95315, tr_mae = 7.06319, valid_mae = 5.81401, ts_mae = 5.77824
Epoch 251 time = 10.07, tr_rmse = 11.75722, valid_rmse = 9.15113, ts_rmse = 8.95829, tr_mae = 7.12576, valid_mae = 5.84534, ts_mae = 5.80573
Epoch 252 time = 10.01, tr_rmse = 11.77384, valid_rmse = 9.15667, ts_rmse = 8.97659, tr_mae = 7.19560, valid_mae = 5.87548, ts_mae = 5.84240
Epoch 253 time = 10.14, tr_rmse = 11.77782, valid_rmse = 9.14728, ts_rmse = 8.96175, tr_mae = 7.19810, valid_mae = 5.87744, ts_mae = 5.84071
Epoch 254 time = 9.87, tr_rmse = 11.76770, valid_rmse = 9.15784, ts_rmse = 8.97070, tr_mae = 7.19357, valid_mae = 5.90183, ts_mae = 5.86578
Epoch 255 time = 9.87, tr_rmse = 11.74559, valid_rmse = 9.14752, ts_rmse = 8.96680, tr_mae = 7.10325, valid_mae = 5.83211, ts_mae = 5.79709
Epoch 256 time = 9.85, tr_rmse = 11.78722, valid_rmse = 9.21895, ts_rmse = 9.02315, tr_mae = 7.04445, valid_mae = 5.84254, ts_mae = 5.81146
Epoch 257 time = 9.62, tr_rmse = 11.73070, valid_rmse = 9.13710, ts_rmse = 8.95016, tr_mae = 7.10402, valid_mae = 5.83290, ts_mae = 5.79304
Epoch 258 time = 9.82, tr_rmse = 11.80076, valid_rmse = 9.17686, ts_rmse = 8.98830, tr_mae = 7.26565, valid_mae = 5.92930, ts_mae = 5.88742
Epoch 259 time = 9.99, tr_rmse = 11.73046, valid_rmse = 9.14051, ts_rmse = 8.95683, tr_mae = 7.07953, valid_mae = 5.82174, ts_mae = 5.79046
Epoch 260 time = 10.16, tr_rmse = 11.78609, valid_rmse = 9.17285, ts_rmse = 8.98359, tr_mae = 7.22291, valid_mae = 5.90643, ts_mae = 5.86948
Epoch 261 time = 10.19, tr_rmse = 11.75108, valid_rmse = 9.19353, ts_rmse = 8.99197, tr_mae = 7.02398, valid_mae = 5.81671, ts_mae = 5.78155
Epoch 262 time = 10.45, tr_rmse = 11.72230, valid_rmse = 9.13622, ts_rmse = 8.94034, tr_mae = 7.05811, valid_mae = 5.80696, ts_mae = 5.76865
Epoch 263 time = 9.98, tr_rmse = 11.72984, valid_rmse = 9.14182, ts_rmse = 8.94484, tr_mae = 7.11116, valid_mae = 5.83073, ts_mae = 5.79458
Epoch 264 time = 10.03, tr_rmse = 11.73023, valid_rmse = 9.14991, ts_rmse = 8.95021, tr_mae = 7.09274, valid_mae = 5.82769, ts_mae = 5.78570
Epoch 265 time = 9.93, tr_rmse = 11.73014, valid_rmse = 9.12612, ts_rmse = 8.94151, tr_mae = 7.11253, valid_mae = 5.83336, ts_mae = 5.79997
Epoch 266 time = 9.81, tr_rmse = 11.72354, valid_rmse = 9.17777, ts_rmse = 8.98196, tr_mae = 7.05459, valid_mae = 5.83963, ts_mae = 5.79851
Epoch 267 time = 9.79, tr_rmse = 11.72693, valid_rmse = 9.16454, ts_rmse = 8.96751, tr_mae = 7.07021, valid_mae = 5.83612, ts_mae = 5.79574
Epoch 268 time = 9.69, tr_rmse = 11.72356, valid_rmse = 9.22286, ts_rmse = 9.01990, tr_mae = 7.04285, valid_mae = 5.86626, ts_mae = 5.81656
Epoch 269 time = 9.68, tr_rmse = 11.71624, valid_rmse = 9.15036, ts_rmse = 8.94995, tr_mae = 7.06144, valid_mae = 5.81993, ts_mae = 5.77844
Epoch 270 time = 9.71, tr_rmse = 11.73166, valid_rmse = 9.12990, ts_rmse = 8.93355, tr_mae = 7.12414, valid_mae = 5.83550, ts_mae = 5.79583
Epoch 271 time = 9.88, tr_rmse = 11.73215, valid_rmse = 9.13704, ts_rmse = 8.93064, tr_mae = 7.06820, valid_mae = 5.80657, ts_mae = 5.77106
Epoch 272 time = 10.19, tr_rmse = 11.71502, valid_rmse = 9.15378, ts_rmse = 8.94589, tr_mae = 7.02481, valid_mae = 5.79787, ts_mae = 5.75824
Epoch 273 time = 10.35, tr_rmse = 11.79026, valid_rmse = 9.16884, ts_rmse = 8.98626, tr_mae = 7.26837, valid_mae = 5.93061, ts_mae = 5.89448
Epoch 274 time = 10.26, tr_rmse = 11.70327, valid_rmse = 9.14011, ts_rmse = 8.93248, tr_mae = 7.03380, valid_mae = 5.79601, ts_mae = 5.75508
Epoch 275 time = 10.00, tr_rmse = 11.80393, valid_rmse = 9.18795, ts_rmse = 9.00815, tr_mae = 7.31290, valid_mae = 5.95872, ts_mae = 5.92280
Epoch 276 time = 9.96, tr_rmse = 11.74232, valid_rmse = 9.16866, ts_rmse = 8.96614, tr_mae = 7.02149, valid_mae = 5.79642, ts_mae = 5.75963
Epoch 277 time = 9.93, tr_rmse = 11.71182, valid_rmse = 9.14527, ts_rmse = 8.94195, tr_mae = 7.07927, valid_mae = 5.82573, ts_mae = 5.78217
Epoch 278 time = 9.86, tr_rmse = 11.70496, valid_rmse = 9.13463, ts_rmse = 8.94380, tr_mae = 7.10489, valid_mae = 5.83181, ts_mae = 5.79439
Epoch 279 time = 9.76, tr_rmse = 11.71876, valid_rmse = 9.12114, ts_rmse = 8.92689, tr_mae = 7.12604, valid_mae = 5.84383, ts_mae = 5.80748
Epoch 280 time = 9.76, tr_rmse = 11.71844, valid_rmse = 9.14172, ts_rmse = 8.94364, tr_mae = 7.11267, valid_mae = 5.84207, ts_mae = 5.80396
Epoch 281 time = 9.54, tr_rmse = 11.71301, valid_rmse = 9.12416, ts_rmse = 8.93648, tr_mae = 7.13884, valid_mae = 5.84798, ts_mae = 5.81348
Epoch 282 time = 9.85, tr_rmse = 11.70648, valid_rmse = 9.14033, ts_rmse = 8.94755, tr_mae = 7.08012, valid_mae = 5.82333, ts_mae = 5.78616
Epoch 283 time = 10.12, tr_rmse = 11.75006, valid_rmse = 9.15722, ts_rmse = 8.97008, tr_mae = 7.20739, valid_mae = 5.89443, ts_mae = 5.85865
Epoch 284 time = 10.25, tr_rmse = 11.69680, valid_rmse = 9.15533, ts_rmse = 8.95744, tr_mae = 7.02334, valid_mae = 5.80577, ts_mae = 5.76649
Epoch 285 time = 10.18, tr_rmse = 11.72731, valid_rmse = 9.14127, ts_rmse = 8.95151, tr_mae = 7.18118, valid_mae = 5.87736, ts_mae = 5.83460
Epoch 286 time = 10.20, tr_rmse = 11.69487, valid_rmse = 9.11612, ts_rmse = 8.92580, tr_mae = 7.10238, valid_mae = 5.82995, ts_mae = 5.79141
Epoch 287 time = 9.92, tr_rmse = 11.70697, valid_rmse = 9.13613, ts_rmse = 8.95328, tr_mae = 7.12039, valid_mae = 5.84137, ts_mae = 5.80824
Epoch 288 time = 9.85, tr_rmse = 11.70605, valid_rmse = 9.14925, ts_rmse = 8.93784, tr_mae = 7.02240, valid_mae = 5.79223, ts_mae = 5.75661
Epoch 289 time = 9.97, tr_rmse = 11.68761, valid_rmse = 9.13814, ts_rmse = 8.94306, tr_mae = 7.07348, valid_mae = 5.82607, ts_mae = 5.78712
Epoch 290 time = 9.78, tr_rmse = 11.71507, valid_rmse = 9.14569, ts_rmse = 8.94124, tr_mae = 7.13738, valid_mae = 5.86106, ts_mae = 5.81798
Epoch 291 time = 9.72, tr_rmse = 11.67557, valid_rmse = 9.14375, ts_rmse = 8.93616, tr_mae = 7.02582, valid_mae = 5.80623, ts_mae = 5.76181
Epoch 292 time = 9.81, tr_rmse = 11.70772, valid_rmse = 9.20870, ts_rmse = 8.99781, tr_mae = 7.01272, valid_mae = 5.84674, ts_mae = 5.80796
Epoch 293 time = 9.75, tr_rmse = 11.70678, valid_rmse = 9.12092, ts_rmse = 8.92818, tr_mae = 7.14028, valid_mae = 5.85338, ts_mae = 5.81126
Epoch 294 time = 10.43, tr_rmse = 11.66192, valid_rmse = 9.13645, ts_rmse = 8.92976, tr_mae = 7.01513, valid_mae = 5.79819, ts_mae = 5.75570
Epoch 295 time = 10.23, tr_rmse = 11.74085, valid_rmse = 9.14413, ts_rmse = 8.96079, tr_mae = 7.18919, valid_mae = 5.87866, ts_mae = 5.84128
Epoch 296 time = 10.16, tr_rmse = 11.68670, valid_rmse = 9.11235, ts_rmse = 8.91582, tr_mae = 7.10929, valid_mae = 5.83283, ts_mae = 5.78909
Epoch 297 time = 10.09, tr_rmse = 11.68740, valid_rmse = 9.18549, ts_rmse = 8.97517, tr_mae = 7.00089, valid_mae = 5.83096, ts_mae = 5.78569
Epoch 298 time = 10.01, tr_rmse = 11.68648, valid_rmse = 9.13017, ts_rmse = 8.93182, tr_mae = 7.09697, valid_mae = 5.83352, ts_mae = 5.78901
Epoch 299 time = 9.89, tr_rmse = 11.66788, valid_rmse = 9.13758, ts_rmse = 8.93408, tr_mae = 7.02694, valid_mae = 5.80981, ts_mae = 5.76587
Epoch 300 time = 9.91, tr_rmse = 11.67946, valid_rmse = 9.12655, ts_rmse = 8.92285, tr_mae = 7.08905, valid_mae = 5.83028, ts_mae = 5.78256
Epoch 301 time = 9.79, tr_rmse = 11.68633, valid_rmse = 9.11656, ts_rmse = 8.91961, tr_mae = 7.12458, valid_mae = 5.84116, ts_mae = 5.79934
Epoch 302 time = 9.74, tr_rmse = 11.66888, valid_rmse = 9.12743, ts_rmse = 8.92229, tr_mae = 7.05239, valid_mae = 5.81383, ts_mae = 5.77167
Epoch 303 time = 10.57, tr_rmse = 11.67279, valid_rmse = 9.11347, ts_rmse = 8.90616, tr_mae = 7.05019, valid_mae = 5.79842, ts_mae = 5.75914
Epoch 304 time = 9.75, tr_rmse = 11.67730, valid_rmse = 9.13821, ts_rmse = 8.91721, tr_mae = 7.03685, valid_mae = 5.81701, ts_mae = 5.76522
Epoch 305 time = 9.85, tr_rmse = 11.73014, valid_rmse = 9.15189, ts_rmse = 8.95870, tr_mae = 7.22170, valid_mae = 5.91165, ts_mae = 5.87053
Epoch 306 time = 10.15, tr_rmse = 11.71194, valid_rmse = 9.15786, ts_rmse = 8.95059, tr_mae = 7.15303, valid_mae = 5.87518, ts_mae = 5.82505
Epoch 307 time = 10.36, tr_rmse = 11.65742, valid_rmse = 9.11307, ts_rmse = 8.89646, tr_mae = 7.03748, valid_mae = 5.79814, ts_mae = 5.74972
Epoch 308 time = 10.26, tr_rmse = 11.67783, valid_rmse = 9.12096, ts_rmse = 8.92333, tr_mae = 7.10512, valid_mae = 5.83550, ts_mae = 5.79083
Epoch 309 time = 10.29, tr_rmse = 11.72600, valid_rmse = 9.14332, ts_rmse = 8.94245, tr_mae = 7.20016, valid_mae = 5.89429, ts_mae = 5.84909
Epoch 310 time = 10.07, tr_rmse = 11.66108, valid_rmse = 9.13132, ts_rmse = 8.92313, tr_mae = 7.04284, valid_mae = 5.80979, ts_mae = 5.76343
Epoch 311 time = 9.99, tr_rmse = 11.70143, valid_rmse = 9.12679, ts_rmse = 8.91974, tr_mae = 7.17843, valid_mae = 5.87934, ts_mae = 5.83161
Epoch 312 time = 9.95, tr_rmse = 11.69062, valid_rmse = 9.21617, ts_rmse = 9.00518, tr_mae = 6.98759, valid_mae = 5.84729, ts_mae = 5.80588
Epoch 313 time = 11.29, tr_rmse = 11.65654, valid_rmse = 9.11938, ts_rmse = 8.91377, tr_mae = 7.02624, valid_mae = 5.80139, ts_mae = 5.75584
Epoch 314 time = 9.81, tr_rmse = 11.64697, valid_rmse = 9.14172, ts_rmse = 8.94594, tr_mae = 6.99312, valid_mae = 5.80137, ts_mae = 5.76200
Epoch 315 time = 9.87, tr_rmse = 11.64405, valid_rmse = 9.11519, ts_rmse = 8.91144, tr_mae = 7.00968, valid_mae = 5.78977, ts_mae = 5.74419
Epoch 316 time = 9.73, tr_rmse = 11.65735, valid_rmse = 9.16019, ts_rmse = 8.94851, tr_mae = 6.98131, valid_mae = 5.80462, ts_mae = 5.76097
Epoch 317 time = 9.76, tr_rmse = 11.71324, valid_rmse = 9.14418, ts_rmse = 8.94169, tr_mae = 7.18592, valid_mae = 5.88887, ts_mae = 5.83857
Epoch 318 time = 10.02, tr_rmse = 11.66830, valid_rmse = 9.12007, ts_rmse = 8.91270, tr_mae = 7.06745, valid_mae = 5.81667, ts_mae = 5.77247
Epoch 319 time = 10.29, tr_rmse = 11.66161, valid_rmse = 9.15901, ts_rmse = 8.96298, tr_mae = 7.00146, valid_mae = 5.81963, ts_mae = 5.77829
Epoch 320 time = 10.25, tr_rmse = 11.66493, valid_rmse = 9.11102, ts_rmse = 8.90328, tr_mae = 7.09813, valid_mae = 5.83706, ts_mae = 5.79523
Epoch 321 time = 10.16, tr_rmse = 11.65093, valid_rmse = 9.10470, ts_rmse = 8.90077, tr_mae = 7.02073, valid_mae = 5.79142, ts_mae = 5.74819
Epoch 322 time = 10.21, tr_rmse = 11.64512, valid_rmse = 9.13918, ts_rmse = 8.91721, tr_mae = 7.01152, valid_mae = 5.80558, ts_mae = 5.75232
Epoch 323 time = 10.06, tr_rmse = 11.66082, valid_rmse = 9.15965, ts_rmse = 8.95020, tr_mae = 6.98812, valid_mae = 5.80971, ts_mae = 5.76696
Epoch 324 time = 9.91, tr_rmse = 11.65474, valid_rmse = 9.20591, ts_rmse = 8.97869, tr_mae = 6.99403, valid_mae = 5.85043, ts_mae = 5.79530
Epoch 325 time = 10.04, tr_rmse = 11.64274, valid_rmse = 9.10672, ts_rmse = 8.89985, tr_mae = 7.05703, valid_mae = 5.80918, ts_mae = 5.76442
Epoch 326 time = 9.84, tr_rmse = 11.65158, valid_rmse = 9.16229, ts_rmse = 8.95919, tr_mae = 7.02358, valid_mae = 5.83089, ts_mae = 5.78826
Epoch 327 time = 9.85, tr_rmse = 11.65996, valid_rmse = 9.14991, ts_rmse = 8.93763, tr_mae = 7.00751, valid_mae = 5.81285, ts_mae = 5.76815
Epoch 328 time = 9.79, tr_rmse = 11.64173, valid_rmse = 9.11536, ts_rmse = 8.91975, tr_mae = 7.03250, valid_mae = 5.80407, ts_mae = 5.76339
Epoch 329 time = 9.67, tr_rmse = 11.67022, valid_rmse = 9.17325, ts_rmse = 8.98022, tr_mae = 7.04314, valid_mae = 5.85065, ts_mae = 5.80749
Epoch 330 time = 9.74, tr_rmse = 11.65733, valid_rmse = 9.11193, ts_rmse = 8.90355, tr_mae = 7.09529, valid_mae = 5.82976, ts_mae = 5.78411
Epoch 331 time = 9.90, tr_rmse = 11.64769, valid_rmse = 9.15153, ts_rmse = 8.94871, tr_mae = 6.99012, valid_mae = 5.80826, ts_mae = 5.76539
Epoch 332 time = 10.05, tr_rmse = 11.72356, valid_rmse = 9.14997, ts_rmse = 8.96570, tr_mae = 7.22130, valid_mae = 5.90794, ts_mae = 5.86584
Epoch 333 time = 10.36, tr_rmse = 11.68466, valid_rmse = 9.11727, ts_rmse = 8.90990, tr_mae = 7.15400, valid_mae = 5.86252, ts_mae = 5.81554
Epoch 334 time = 10.15, tr_rmse = 11.66963, valid_rmse = 9.14461, ts_rmse = 8.94441, tr_mae = 7.08941, valid_mae = 5.84077, ts_mae = 5.79250
Epoch 335 time = 10.32, tr_rmse = 11.63924, valid_rmse = 9.11139, ts_rmse = 8.90389, tr_mae = 7.05495, valid_mae = 5.81257, ts_mae = 5.76666
Epoch 336 time = 10.14, tr_rmse = 11.70307, valid_rmse = 9.14397, ts_rmse = 8.95345, tr_mae = 7.18276, valid_mae = 5.88620, ts_mae = 5.83780
Epoch 337 time = 10.16, tr_rmse = 11.64092, valid_rmse = 9.11941, ts_rmse = 8.91214, tr_mae = 7.00824, valid_mae = 5.79649, ts_mae = 5.75091
Epoch 338 time = 9.84, tr_rmse = 11.67561, valid_rmse = 9.18823, ts_rmse = 8.97623, tr_mae = 6.97726, valid_mae = 5.82340, ts_mae = 5.77999
Epoch 339 time = 9.87, tr_rmse = 11.62891, valid_rmse = 9.13637, ts_rmse = 8.90922, tr_mae = 6.98737, valid_mae = 5.79101, ts_mae = 5.73989
Epoch 340 time = 9.90, tr_rmse = 11.64153, valid_rmse = 9.12322, ts_rmse = 8.92964, tr_mae = 7.06852, valid_mae = 5.82446, ts_mae = 5.78204
Epoch 341 time = 9.78, tr_rmse = 11.62220, valid_rmse = 9.11143, ts_rmse = 8.90467, tr_mae = 7.00835, valid_mae = 5.78735, ts_mae = 5.74484
Epoch 342 time = 9.63, tr_rmse = 11.63116, valid_rmse = 9.11429, ts_rmse = 8.91351, tr_mae = 7.01777, valid_mae = 5.79510, ts_mae = 5.74923
Epoch 343 time = 9.72, tr_rmse = 11.62674, valid_rmse = 9.16556, ts_rmse = 8.94949, tr_mae = 7.00547, valid_mae = 5.82949, ts_mae = 5.77579
Epoch 344 time = 9.91, tr_rmse = 11.63801, valid_rmse = 9.13357, ts_rmse = 8.94074, tr_mae = 7.05784, valid_mae = 5.82305, ts_mae = 5.78090
Epoch 345 time = 11.04, tr_rmse = 11.62158, valid_rmse = 9.09472, ts_rmse = 8.88956, tr_mae = 6.99329, valid_mae = 5.77626, ts_mae = 5.73329
Epoch 346 time = 10.29, tr_rmse = 11.73508, valid_rmse = 9.16250, ts_rmse = 8.97159, tr_mae = 7.26054, valid_mae = 5.93731, ts_mae = 5.89172
Epoch 347 time = 10.44, tr_rmse = 11.61254, valid_rmse = 9.12382, ts_rmse = 8.90630, tr_mae = 6.99150, valid_mae = 5.79365, ts_mae = 5.73837
Epoch 348 time = 10.18, tr_rmse = 11.61316, valid_rmse = 9.12059, ts_rmse = 8.90235, tr_mae = 7.00908, valid_mae = 5.79733, ts_mae = 5.74357
Epoch 349 time = 10.01, tr_rmse = 11.63782, valid_rmse = 9.19445, ts_rmse = 8.98794, tr_mae = 6.99986, valid_mae = 5.85318, ts_mae = 5.80249
Epoch 350 time = 10.03, tr_rmse = 11.63597, valid_rmse = 9.13568, ts_rmse = 8.91943, tr_mae = 7.06425, valid_mae = 5.82446, ts_mae = 5.77047
Epoch 351 time = 9.97, tr_rmse = 11.60788, valid_rmse = 9.12071, ts_rmse = 8.91761, tr_mae = 6.99428, valid_mae = 5.78865, ts_mae = 5.74412
Epoch 352 time = 9.87, tr_rmse = 11.62250, valid_rmse = 9.11639, ts_rmse = 8.92351, tr_mae = 7.00297, valid_mae = 5.78825, ts_mae = 5.74822
Epoch 353 time = 9.77, tr_rmse = 11.61830, valid_rmse = 9.14309, ts_rmse = 8.93485, tr_mae = 6.99883, valid_mae = 5.80911, ts_mae = 5.76145
Epoch 354 time = 9.52, tr_rmse = 11.60637, valid_rmse = 9.10001, ts_rmse = 8.88574, tr_mae = 6.99510, valid_mae = 5.77656, ts_mae = 5.72947
Epoch 355 time = 10.32, tr_rmse = 11.61219, valid_rmse = 9.11781, ts_rmse = 8.92091, tr_mae = 6.99658, valid_mae = 5.79693, ts_mae = 5.75238
Epoch 356 time = 10.06, tr_rmse = 11.60429, valid_rmse = 9.14490, ts_rmse = 8.95110, tr_mae = 6.99732, valid_mae = 5.81707, ts_mae = 5.77099
Epoch 357 time = 10.30, tr_rmse = 11.59966, valid_rmse = 9.13700, ts_rmse = 8.92636, tr_mae = 6.97458, valid_mae = 5.79384, ts_mae = 5.74697
Epoch 358 time = 10.26, tr_rmse = 11.60331, valid_rmse = 9.09249, ts_rmse = 8.90257, tr_mae = 7.02485, valid_mae = 5.79202, ts_mae = 5.75205
Epoch 359 time = 10.19, tr_rmse = 11.60498, valid_rmse = 9.12691, ts_rmse = 8.90914, tr_mae = 6.97200, valid_mae = 5.78238, ts_mae = 5.73192
Epoch 360 time = 10.08, tr_rmse = 11.64920, valid_rmse = 9.14510, ts_rmse = 8.95098, tr_mae = 7.10330, valid_mae = 5.84775, ts_mae = 5.80240
Epoch 361 time = 10.19, tr_rmse = 11.67283, valid_rmse = 9.13117, ts_rmse = 8.95670, tr_mae = 7.15367, valid_mae = 5.86911, ts_mae = 5.82311
Epoch 362 time = 9.95, tr_rmse = 11.61620, valid_rmse = 9.11837, ts_rmse = 8.91115, tr_mae = 7.04744, valid_mae = 5.81391, ts_mae = 5.76509
Epoch 363 time = 9.99, tr_rmse = 11.60914, valid_rmse = 9.09927, ts_rmse = 8.90572, tr_mae = 7.04601, valid_mae = 5.80139, ts_mae = 5.75608
Epoch 364 time = 9.93, tr_rmse = 11.59973, valid_rmse = 9.12116, ts_rmse = 8.90586, tr_mae = 6.98742, valid_mae = 5.78771, ts_mae = 5.73846
Epoch 365 time = 9.69, tr_rmse = 11.61782, valid_rmse = 9.12681, ts_rmse = 8.92908, tr_mae = 7.04828, valid_mae = 5.81816, ts_mae = 5.76872
Epoch 366 time = 11.37, tr_rmse = 11.60660, valid_rmse = 9.10156, ts_rmse = 8.90219, tr_mae = 7.03442, valid_mae = 5.79652, ts_mae = 5.75117
Epoch 367 time = 11.39, tr_rmse = 11.62511, valid_rmse = 9.14811, ts_rmse = 8.95199, tr_mae = 7.03533, valid_mae = 5.82992, ts_mae = 5.77870
Epoch 368 time = 10.54, tr_rmse = 11.60309, valid_rmse = 9.09472, ts_rmse = 8.89800, tr_mae = 7.03277, valid_mae = 5.79722, ts_mae = 5.75231
Epoch 369 time = 10.78, tr_rmse = 11.64773, valid_rmse = 9.15399, ts_rmse = 8.96218, tr_mae = 7.08016, valid_mae = 5.84245, ts_mae = 5.79252
Epoch 370 time = 10.90, tr_rmse = 11.64174, valid_rmse = 9.12072, ts_rmse = 8.92969, tr_mae = 7.08602, valid_mae = 5.82883, ts_mae = 5.77774
Epoch 371 time = 11.51, tr_rmse = 11.58940, valid_rmse = 9.08690, ts_rmse = 8.89475, tr_mae = 6.99022, valid_mae = 5.77266, ts_mae = 5.72955
Epoch 372 time = 14.32, tr_rmse = 11.66345, valid_rmse = 9.12259, ts_rmse = 8.94343, tr_mae = 7.16885, valid_mae = 5.87074, ts_mae = 5.82651
Epoch 373 time = 12.52, tr_rmse = 11.59399, valid_rmse = 9.08642, ts_rmse = 8.88831, tr_mae = 7.03117, valid_mae = 5.79115, ts_mae = 5.74712
Epoch 374 time = 14.24, tr_rmse = 11.60183, valid_rmse = 9.13371, ts_rmse = 8.93763, tr_mae = 7.00778, valid_mae = 5.81443, ts_mae = 5.76414
Epoch 375 time = 12.47, tr_rmse = 11.58708, valid_rmse = 9.11789, ts_rmse = 8.90871, tr_mae = 6.97218, valid_mae = 5.77928, ts_mae = 5.73658
Epoch 376 time = 11.46, tr_rmse = 11.61762, valid_rmse = 9.19828, ts_rmse = 8.99767, tr_mae = 6.98947, valid_mae = 5.86163, ts_mae = 5.80808
Epoch 377 time = 11.29, tr_rmse = 11.58487, valid_rmse = 9.12182, ts_rmse = 8.91004, tr_mae = 6.97428, valid_mae = 5.78748, ts_mae = 5.73594
Epoch 378 time = 11.19, tr_rmse = 11.57604, valid_rmse = 9.07869, ts_rmse = 8.87690, tr_mae = 7.00407, valid_mae = 5.77715, ts_mae = 5.73373
Epoch 379 time = 11.28, tr_rmse = 11.58069, valid_rmse = 9.11015, ts_rmse = 8.89862, tr_mae = 6.99905, valid_mae = 5.78936, ts_mae = 5.73829
Epoch 380 time = 11.09, tr_rmse = 11.62475, valid_rmse = 9.17438, ts_rmse = 8.95516, tr_mae = 6.97236, valid_mae = 5.82065, ts_mae = 5.76983
Epoch 381 time = 10.55, tr_rmse = 11.58400, valid_rmse = 9.12749, ts_rmse = 8.91437, tr_mae = 6.96351, valid_mae = 5.78524, ts_mae = 5.73730
Epoch 382 time = 10.50, tr_rmse = 11.59269, valid_rmse = 9.08617, ts_rmse = 8.90498, tr_mae = 7.06289, valid_mae = 5.81268, ts_mae = 5.76759
Epoch 383 time = 10.60, tr_rmse = 11.61432, valid_rmse = 9.10702, ts_rmse = 8.93079, tr_mae = 7.07174, valid_mae = 5.82024, ts_mae = 5.77541
Epoch 384 time = 10.74, tr_rmse = 11.58798, valid_rmse = 9.12528, ts_rmse = 8.92435, tr_mae = 6.96088, valid_mae = 5.78343, ts_mae = 5.73615
Epoch 385 time = 11.20, tr_rmse = 11.60105, valid_rmse = 9.14060, ts_rmse = 8.94376, tr_mae = 7.04677, valid_mae = 5.83385, ts_mae = 5.78304
Epoch 386 time = 11.65, tr_rmse = 11.57512, valid_rmse = 9.09201, ts_rmse = 8.90648, tr_mae = 6.99978, valid_mae = 5.78216, ts_mae = 5.74040
Epoch 387 time = 11.30, tr_rmse = 11.57994, valid_rmse = 9.10749, ts_rmse = 8.91088, tr_mae = 6.97776, valid_mae = 5.78366, ts_mae = 5.73372
Epoch 388 time = 11.34, tr_rmse = 11.61029, valid_rmse = 9.20682, ts_rmse = 8.99296, tr_mae = 6.96532, valid_mae = 5.85544, ts_mae = 5.80021
Epoch 389 time = 10.97, tr_rmse = 11.56845, valid_rmse = 9.09158, ts_rmse = 8.87982, tr_mae = 6.98634, valid_mae = 5.77395, ts_mae = 5.72354
Epoch 390 time = 10.75, tr_rmse = 11.56432, valid_rmse = 9.07254, ts_rmse = 8.88381, tr_mae = 6.99743, valid_mae = 5.77232, ts_mae = 5.73373
Epoch 391 time = 12.03, tr_rmse = 11.57096, valid_rmse = 9.11113, ts_rmse = 8.90015, tr_mae = 6.96548, valid_mae = 5.77928, ts_mae = 5.72784
Epoch 392 time = 11.12, tr_rmse = 11.56896, valid_rmse = 9.08221, ts_rmse = 8.87262, tr_mae = 7.02144, valid_mae = 5.79007, ts_mae = 5.73741
Epoch 393 time = 11.67, tr_rmse = 11.57111, valid_rmse = 9.08619, ts_rmse = 8.89895, tr_mae = 7.02125, valid_mae = 5.78923, ts_mae = 5.74777
Epoch 394 time = 11.22, tr_rmse = 11.55719, valid_rmse = 9.09749, ts_rmse = 8.89835, tr_mae = 6.99011, valid_mae = 5.78102, ts_mae = 5.73263
Epoch 395 time = 12.40, tr_rmse = 11.64022, valid_rmse = 9.12859, ts_rmse = 8.94007, tr_mae = 7.22637, valid_mae = 5.93725, ts_mae = 5.89365
Epoch 396 time = 11.07, tr_rmse = 11.60647, valid_rmse = 9.09737, ts_rmse = 8.92651, tr_mae = 7.08862, valid_mae = 5.82346, ts_mae = 5.77935
Epoch 397 time = 10.93, tr_rmse = 11.57358, valid_rmse = 9.09470, ts_rmse = 8.90988, tr_mae = 7.00113, valid_mae = 5.78009, ts_mae = 5.73656
Epoch 398 time = 11.55, tr_rmse = 11.56106, valid_rmse = 9.08252, ts_rmse = 8.88775, tr_mae = 6.99482, valid_mae = 5.77609, ts_mae = 5.72807
Epoch 399 time = 14.18, tr_rmse = 11.59602, valid_rmse = 9.21584, ts_rmse = 9.01448, tr_mae = 6.96296, valid_mae = 5.86078, ts_mae = 5.81031
Epoch 400 time = 12.72, tr_rmse = 11.56065, valid_rmse = 9.08293, ts_rmse = 8.91120, tr_mae = 6.99534, valid_mae = 5.78094, ts_mae = 5.73949
Epoch 401 time = 11.31, tr_rmse = 11.58209, valid_rmse = 9.10751, ts_rmse = 8.93677, tr_mae = 7.03965, valid_mae = 5.80702, ts_mae = 5.76790
Epoch 402 time = 10.89, tr_rmse = 11.56422, valid_rmse = 9.11846, ts_rmse = 8.92860, tr_mae = 6.98464, valid_mae = 5.80181, ts_mae = 5.75043
Epoch 403 time = 10.83, tr_rmse = 11.56557, valid_rmse = 9.11483, ts_rmse = 8.91433, tr_mae = 6.97800, valid_mae = 5.78518, ts_mae = 5.73537
Epoch 404 time = 10.80, tr_rmse = 11.55746, valid_rmse = 9.06090, ts_rmse = 8.88553, tr_mae = 7.03165, valid_mae = 5.78856, ts_mae = 5.74572
Epoch 405 time = 11.08, tr_rmse = 11.58410, valid_rmse = 9.10731, ts_rmse = 8.92278, tr_mae = 7.05905, valid_mae = 5.81948, ts_mae = 5.77103
Epoch 406 time = 10.15, tr_rmse = 11.55347, valid_rmse = 9.12514, ts_rmse = 8.93761, tr_mae = 6.95808, valid_mae = 5.79070, ts_mae = 5.74520
Epoch 407 time = 9.96, tr_rmse = 11.56875, valid_rmse = 9.14350, ts_rmse = 8.95514, tr_mae = 6.97868, valid_mae = 5.81390, ts_mae = 5.76151
Epoch 408 time = 9.94, tr_rmse = 11.56955, valid_rmse = 9.08532, ts_rmse = 8.90998, tr_mae = 7.04306, valid_mae = 5.80024, ts_mae = 5.75836
Epoch 409 time = 9.95, tr_rmse = 11.56891, valid_rmse = 9.10711, ts_rmse = 8.91467, tr_mae = 7.01423, valid_mae = 5.80416, ts_mae = 5.74956
Epoch 410 time = 9.96, tr_rmse = 11.53694, valid_rmse = 9.06980, ts_rmse = 8.88129, tr_mae = 6.99217, valid_mae = 5.77098, ts_mae = 5.72859
Epoch 411 time = 10.20, tr_rmse = 11.55798, valid_rmse = 9.07372, ts_rmse = 8.87282, tr_mae = 6.99655, valid_mae = 5.77374, ts_mae = 5.72231
Epoch 412 time = 9.93, tr_rmse = 11.54878, valid_rmse = 9.06272, ts_rmse = 8.87999, tr_mae = 7.00977, valid_mae = 5.77950, ts_mae = 5.73503
Epoch 413 time = 9.98, tr_rmse = 11.55972, valid_rmse = 9.10757, ts_rmse = 8.92924, tr_mae = 7.00018, valid_mae = 5.79749, ts_mae = 5.75401
Epoch 414 time = 10.56, tr_rmse = 11.61993, valid_rmse = 9.10381, ts_rmse = 8.94597, tr_mae = 7.16075, valid_mae = 5.86395, ts_mae = 5.82455
Epoch 415 time = 10.58, tr_rmse = 11.54527, valid_rmse = 9.11755, ts_rmse = 8.93098, tr_mae = 6.97140, valid_mae = 5.78924, ts_mae = 5.74271
Epoch 416 time = 10.28, tr_rmse = 11.55443, valid_rmse = 9.10258, ts_rmse = 8.92358, tr_mae = 6.99447, valid_mae = 5.79522, ts_mae = 5.74772
Epoch 417 time = 10.33, tr_rmse = 11.55140, valid_rmse = 9.08002, ts_rmse = 8.89349, tr_mae = 7.03159, valid_mae = 5.79364, ts_mae = 5.74496
Epoch 418 time = 10.25, tr_rmse = 11.54852, valid_rmse = 9.07297, ts_rmse = 8.90516, tr_mae = 7.00739, valid_mae = 5.78112, ts_mae = 5.74256
Epoch 419 time = 10.09, tr_rmse = 11.57266, valid_rmse = 9.16012, ts_rmse = 8.95855, tr_mae = 6.97074, valid_mae = 5.82309, ts_mae = 5.76090
Epoch 420 time = 9.95, tr_rmse = 11.54715, valid_rmse = 9.06937, ts_rmse = 8.88687, tr_mae = 7.01475, valid_mae = 5.78085, ts_mae = 5.73335
Epoch 421 time = 9.96, tr_rmse = 11.54472, valid_rmse = 9.08849, ts_rmse = 8.90760, tr_mae = 7.00107, valid_mae = 5.78444, ts_mae = 5.73819
Epoch 422 time = 10.00, tr_rmse = 11.55200, valid_rmse = 9.10293, ts_rmse = 8.91820, tr_mae = 6.98664, valid_mae = 5.78724, ts_mae = 5.73604
Epoch 423 time = 9.95, tr_rmse = 11.54836, valid_rmse = 9.08232, ts_rmse = 8.87762, tr_mae = 6.97583, valid_mae = 5.76487, ts_mae = 5.71150
Epoch 424 time = 9.92, tr_rmse = 11.56578, valid_rmse = 9.14334, ts_rmse = 8.94480, tr_mae = 6.95630, valid_mae = 5.81592, ts_mae = 5.76483
Epoch 425 time = 9.96, tr_rmse = 11.54268, valid_rmse = 9.09754, ts_rmse = 8.91410, tr_mae = 6.98757, valid_mae = 5.78859, ts_mae = 5.74142
Epoch 426 time = 9.97, tr_rmse = 11.55215, valid_rmse = 9.07502, ts_rmse = 8.90204, tr_mae = 7.03499, valid_mae = 5.79321, ts_mae = 5.74959
Epoch 427 time = 10.02, tr_rmse = 11.54055, valid_rmse = 9.07330, ts_rmse = 8.90225, tr_mae = 6.99203, valid_mae = 5.77038, ts_mae = 5.73209
Epoch 428 time = 10.04, tr_rmse = 11.56176, valid_rmse = 9.13251, ts_rmse = 8.93598, tr_mae = 6.94204, valid_mae = 5.77512, ts_mae = 5.72781
Epoch 429 time = 10.28, tr_rmse = 11.52427, valid_rmse = 9.07034, ts_rmse = 8.87631, tr_mae = 6.94847, valid_mae = 5.75419, ts_mae = 5.70584
Epoch 430 time = 10.09, tr_rmse = 11.57299, valid_rmse = 9.11058, ts_rmse = 8.94649, tr_mae = 7.05667, valid_mae = 5.81737, ts_mae = 5.77203
Epoch 431 time = 10.08, tr_rmse = 11.55348, valid_rmse = 9.17225, ts_rmse = 8.98311, tr_mae = 6.95692, valid_mae = 5.83426, ts_mae = 5.78272
Epoch 432 time = 10.15, tr_rmse = 11.53248, valid_rmse = 9.09983, ts_rmse = 8.92240, tr_mae = 6.98447, valid_mae = 5.79033, ts_mae = 5.73736
Epoch 433 time = 10.02, tr_rmse = 11.51622, valid_rmse = 9.05400, ts_rmse = 8.87430, tr_mae = 6.99462, valid_mae = 5.77166, ts_mae = 5.72485
Epoch 434 time = 9.98, tr_rmse = 11.53498, valid_rmse = 9.07923, ts_rmse = 8.88721, tr_mae = 6.97028, valid_mae = 5.76661, ts_mae = 5.71571
Epoch 435 time = 10.12, tr_rmse = 11.56259, valid_rmse = 9.07612, ts_rmse = 8.90671, tr_mae = 7.06113, valid_mae = 5.80030, ts_mae = 5.75949
Epoch 436 time = 10.01, tr_rmse = 11.56371, valid_rmse = 9.06734, ts_rmse = 8.90860, tr_mae = 7.05190, valid_mae = 5.79514, ts_mae = 5.75305
Epoch 437 time = 10.05, tr_rmse = 11.56021, valid_rmse = 9.15960, ts_rmse = 8.96592, tr_mae = 6.94833, valid_mae = 5.81482, ts_mae = 5.76439
Epoch 438 time = 9.99, tr_rmse = 11.55762, valid_rmse = 9.07363, ts_rmse = 8.91374, tr_mae = 7.07455, valid_mae = 5.80894, ts_mae = 5.76607
Epoch 439 time = 10.00, tr_rmse = 11.51694, valid_rmse = 9.05464, ts_rmse = 8.87417, tr_mae = 6.99610, valid_mae = 5.76882, ts_mae = 5.72370
Epoch 440 time = 9.98, tr_rmse = 11.52522, valid_rmse = 9.06258, ts_rmse = 8.88271, tr_mae = 7.02773, valid_mae = 5.78745, ts_mae = 5.74077
Epoch 441 time = 10.01, tr_rmse = 11.53125, valid_rmse = 9.07261, ts_rmse = 8.90521, tr_mae = 7.03626, valid_mae = 5.79579, ts_mae = 5.74623
Epoch 442 time = 9.95, tr_rmse = 11.54047, valid_rmse = 9.11507, ts_rmse = 8.93359, tr_mae = 7.00177, valid_mae = 5.79922, ts_mae = 5.74871
Epoch 443 time = 9.82, tr_rmse = 11.53218, valid_rmse = 9.05381, ts_rmse = 8.87712, tr_mae = 6.99153, valid_mae = 5.76137, ts_mae = 5.71688
Epoch 444 time = 9.88, tr_rmse = 11.55138, valid_rmse = 9.13432, ts_rmse = 8.94754, tr_mae = 6.94366, valid_mae = 5.78546, ts_mae = 5.73612
Epoch 445 time = 10.05, tr_rmse = 11.52961, valid_rmse = 9.14743, ts_rmse = 8.94244, tr_mae = 6.93462, valid_mae = 5.80382, ts_mae = 5.75146
Epoch 446 time = 10.73, tr_rmse = 11.52585, valid_rmse = 9.12754, ts_rmse = 8.95051, tr_mae = 6.97233, valid_mae = 5.80617, ts_mae = 5.75387
Epoch 447 time = 10.37, tr_rmse = 11.50645, valid_rmse = 9.07918, ts_rmse = 8.89777, tr_mae = 6.96248, valid_mae = 5.77783, ts_mae = 5.72141
Epoch 448 time = 10.13, tr_rmse = 11.54404, valid_rmse = 9.15617, ts_rmse = 8.96324, tr_mae = 6.93352, valid_mae = 5.80888, ts_mae = 5.76199
Epoch 449 time = 10.07, tr_rmse = 11.51997, valid_rmse = 9.10126, ts_rmse = 8.92056, tr_mae = 6.94079, valid_mae = 5.77966, ts_mae = 5.72751
Epoch 450 time = 10.01, tr_rmse = 11.50479, valid_rmse = 9.07365, ts_rmse = 8.89522, tr_mae = 6.97760, valid_mae = 5.77264, ts_mae = 5.72791
Epoch 451 time = 9.88, tr_rmse = 11.53828, valid_rmse = 9.14021, ts_rmse = 8.97261, tr_mae = 6.96691, valid_mae = 5.81817, ts_mae = 5.76816
Epoch 452 time = 9.91, tr_rmse = 11.52201, valid_rmse = 9.08947, ts_rmse = 8.90104, tr_mae = 6.93882, valid_mae = 5.76391, ts_mae = 5.71656
Epoch 453 time = 9.96, tr_rmse = 11.48868, valid_rmse = 9.08300, ts_rmse = 8.89423, tr_mae = 6.93796, valid_mae = 5.76839, ts_mae = 5.71520
Epoch 454 time = 9.89, tr_rmse = 11.50903, valid_rmse = 9.09706, ts_rmse = 8.90277, tr_mae = 6.92072, valid_mae = 5.76005, ts_mae = 5.71312
Epoch 455 time = 9.96, tr_rmse = 11.57293, valid_rmse = 9.08832, ts_rmse = 8.92192, tr_mae = 7.11080, valid_mae = 5.83094, ts_mae = 5.78500
Epoch 456 time = 9.82, tr_rmse = 11.52132, valid_rmse = 9.05844, ts_rmse = 8.89521, tr_mae = 7.01418, valid_mae = 5.77827, ts_mae = 5.73614
Epoch 457 time = 10.04, tr_rmse = 11.51261, valid_rmse = 9.08273, ts_rmse = 8.90728, tr_mae = 6.94333, valid_mae = 5.76583, ts_mae = 5.72144
Epoch 458 time = 10.76, tr_rmse = 11.54825, valid_rmse = 9.09324, ts_rmse = 8.93115, tr_mae = 7.02842, valid_mae = 5.79944, ts_mae = 5.75221
Epoch 459 time = 10.10, tr_rmse = 11.52317, valid_rmse = 9.11108, ts_rmse = 8.94689, tr_mae = 6.96231, valid_mae = 5.79647, ts_mae = 5.74863
Epoch 460 time = 10.02, tr_rmse = 11.55128, valid_rmse = 9.10215, ts_rmse = 8.93011, tr_mae = 7.05737, valid_mae = 5.81168, ts_mae = 5.76628
Epoch 461 time = 10.03, tr_rmse = 11.53679, valid_rmse = 9.14893, ts_rmse = 8.95553, tr_mae = 6.92432, valid_mae = 5.80405, ts_mae = 5.74742
Epoch 462 time = 9.88, tr_rmse = 11.53137, valid_rmse = 9.14797, ts_rmse = 8.96908, tr_mae = 6.94734, valid_mae = 5.81572, ts_mae = 5.76560
Epoch 463 time = 10.02, tr_rmse = 11.59019, valid_rmse = 9.13835, ts_rmse = 8.98326, tr_mae = 7.08736, valid_mae = 5.83940, ts_mae = 5.79302
Epoch 464 time = 10.30, tr_rmse = 11.53681, valid_rmse = 9.15117, ts_rmse = 8.96970, tr_mae = 6.93235, valid_mae = 5.80163, ts_mae = 5.75045
Epoch 465 time = 9.81, tr_rmse = 11.53375, valid_rmse = 9.12756, ts_rmse = 8.94531, tr_mae = 6.98968, valid_mae = 5.80590, ts_mae = 5.75284
Epoch 466 time = 9.80, tr_rmse = 11.51414, valid_rmse = 9.10183, ts_rmse = 8.93793, tr_mae = 6.97849, valid_mae = 5.78393, ts_mae = 5.73417
Epoch 467 time = 9.90, tr_rmse = 11.54028, valid_rmse = 9.08465, ts_rmse = 8.91298, tr_mae = 7.05642, valid_mae = 5.80669, ts_mae = 5.75595
Epoch 468 time = 9.90, tr_rmse = 11.53910, valid_rmse = 9.11801, ts_rmse = 8.95253, tr_mae = 7.01193, valid_mae = 5.80341, ts_mae = 5.75310
Epoch 469 time = 9.84, tr_rmse = 11.55686, valid_rmse = 9.11415, ts_rmse = 8.95174, tr_mae = 7.05679, valid_mae = 5.81270, ts_mae = 5.76753
Epoch 470 time = 12.37, tr_rmse = 11.50016, valid_rmse = 9.06381, ts_rmse = 8.89815, tr_mae = 6.99552, valid_mae = 5.77272, ts_mae = 5.72541
Epoch 471 time = 10.28, tr_rmse = 11.50142, valid_rmse = 9.05945, ts_rmse = 8.89468, tr_mae = 6.98295, valid_mae = 5.76418, ts_mae = 5.71887
Epoch 472 time = 11.37, tr_rmse = 11.51416, valid_rmse = 9.14902, ts_rmse = 8.97109, tr_mae = 6.92760, valid_mae = 5.80730, ts_mae = 5.75795
Epoch 473 time = 11.10, tr_rmse = 11.57685, valid_rmse = 9.10754, ts_rmse = 8.94786, tr_mae = 7.13039, valid_mae = 5.85051, ts_mae = 5.80362
Epoch 474 time = 11.38, tr_rmse = 11.55021, valid_rmse = 9.21998, ts_rmse = 9.03010, tr_mae = 6.94139, valid_mae = 5.86445, ts_mae = 5.81362
Epoch 475 time = 11.23, tr_rmse = 11.53562, valid_rmse = 9.13785, ts_rmse = 8.95780, tr_mae = 6.93532, valid_mae = 5.78807, ts_mae = 5.74182
Epoch 476 time = 10.79, tr_rmse = 11.54773, valid_rmse = 9.10645, ts_rmse = 8.95858, tr_mae = 7.05545, valid_mae = 5.81359, ts_mae = 5.76995
Epoch 477 time = 11.40, tr_rmse = 11.53072, valid_rmse = 9.11324, ts_rmse = 8.94562, tr_mae = 6.96452, valid_mae = 5.78782, ts_mae = 5.73936
Epoch 478 time = 9.92, tr_rmse = 11.49202, valid_rmse = 9.04427, ts_rmse = 8.87238, tr_mae = 6.95566, valid_mae = 5.74822, ts_mae = 5.70224
Epoch 479 time = 9.96, tr_rmse = 11.49439, valid_rmse = 9.10278, ts_rmse = 8.94243, tr_mae = 6.93632, valid_mae = 5.77601, ts_mae = 5.72989
Epoch 480 time = 10.42, tr_rmse = 11.50256, valid_rmse = 9.15609, ts_rmse = 8.97816, tr_mae = 6.93190, valid_mae = 5.81211, ts_mae = 5.76043
Epoch 481 time = 10.28, tr_rmse = 11.50888, valid_rmse = 9.15371, ts_rmse = 8.96821, tr_mae = 6.93789, valid_mae = 5.81260, ts_mae = 5.75491
Epoch 482 time = 10.26, tr_rmse = 11.51814, valid_rmse = 9.14122, ts_rmse = 8.95666, tr_mae = 6.95613, valid_mae = 5.80250, ts_mae = 5.74758
Epoch 483 time = 10.26, tr_rmse = 11.49796, valid_rmse = 9.05702, ts_rmse = 8.88706, tr_mae = 6.99531, valid_mae = 5.77182, ts_mae = 5.72551
Epoch 484 time = 10.45, tr_rmse = 11.54349, valid_rmse = 9.11820, ts_rmse = 8.96319, tr_mae = 7.05282, valid_mae = 5.82127, ts_mae = 5.77228
Epoch 485 time = 11.01, tr_rmse = 11.51899, valid_rmse = 9.10570, ts_rmse = 8.94737, tr_mae = 7.00139, valid_mae = 5.79387, ts_mae = 5.75018
Epoch 486 time = 12.42, tr_rmse = 11.49744, valid_rmse = 9.03344, ts_rmse = 8.85723, tr_mae = 7.00595, valid_mae = 5.76874, ts_mae = 5.72222
Epoch 487 time = 10.67, tr_rmse = 11.50855, valid_rmse = 9.09508, ts_rmse = 8.92663, tr_mae = 6.99368, valid_mae = 5.78810, ts_mae = 5.73341
Epoch 488 time = 10.76, tr_rmse = 11.48999, valid_rmse = 9.08221, ts_rmse = 8.90832, tr_mae = 6.96128, valid_mae = 5.76403, ts_mae = 5.71530
Epoch 489 time = 10.24, tr_rmse = 11.53612, valid_rmse = 9.09346, ts_rmse = 8.92953, tr_mae = 7.05112, valid_mae = 5.79973, ts_mae = 5.75331
Epoch 490 time = 10.17, tr_rmse = 11.54504, valid_rmse = 9.13523, ts_rmse = 8.98001, tr_mae = 7.03173, valid_mae = 5.81966, ts_mae = 5.77296
Epoch 491 time = 10.82, tr_rmse = 11.52385, valid_rmse = 9.16513, ts_rmse = 8.99429, tr_mae = 6.92570, valid_mae = 5.80949, ts_mae = 5.76463
Epoch 492 time = 11.13, tr_rmse = 11.48495, valid_rmse = 9.08084, ts_rmse = 8.92053, tr_mae = 6.95696, valid_mae = 5.76766, ts_mae = 5.72461
Epoch 493 time = 10.87, tr_rmse = 11.49542, valid_rmse = 9.13482, ts_rmse = 8.96021, tr_mae = 6.91927, valid_mae = 5.78481, ts_mae = 5.73755
Epoch 494 time = 11.08, tr_rmse = 11.48503, valid_rmse = 9.06856, ts_rmse = 8.87199, tr_mae = 6.97844, valid_mae = 5.77024, ts_mae = 5.70904
Epoch 495 time = 10.95, tr_rmse = 11.51078, valid_rmse = 9.06766, ts_rmse = 8.91761, tr_mae = 7.02634, valid_mae = 5.78856, ts_mae = 5.74497
Epoch 496 time = 11.00, tr_rmse = 11.51120, valid_rmse = 9.08197, ts_rmse = 8.91701, tr_mae = 7.02033, valid_mae = 5.78809, ts_mae = 5.73682
Epoch 497 time = 10.72, tr_rmse = 11.50802, valid_rmse = 9.07785, ts_rmse = 8.92076, tr_mae = 7.00157, valid_mae = 5.77855, ts_mae = 5.73279
Epoch 498 time = 11.28, tr_rmse = 11.47514, valid_rmse = 9.05457, ts_rmse = 8.90489, tr_mae = 6.97459, valid_mae = 5.76368, ts_mae = 5.71987
Epoch 499 time = 10.55, tr_rmse = 11.47407, valid_rmse = 9.10811, ts_rmse = 8.93017, tr_mae = 6.91098, valid_mae = 5.77407, ts_mae = 5.72312
Epoch 500 time = 10.72, tr_rmse = 11.48019, valid_rmse = 9.06698, ts_rmse = 8.89999, tr_mae = 6.98463, valid_mae = 5.77064, ts_mae = 5.72380
Epoch 501 time = 10.63, tr_rmse = 11.51511, valid_rmse = 9.18406, ts_rmse = 9.01645, tr_mae = 6.94419, valid_mae = 5.84293, ts_mae = 5.79235
Epoch 502 time = 10.23, tr_rmse = 11.49904, valid_rmse = 9.11410, ts_rmse = 8.94205, tr_mae = 6.98058, valid_mae = 5.78950, ts_mae = 5.73339
Epoch 503 time = 9.97, tr_rmse = 11.49992, valid_rmse = 9.08845, ts_rmse = 8.93180, tr_mae = 7.00282, valid_mae = 5.78366, ts_mae = 5.73717
Epoch 504 time = 10.01, tr_rmse = 11.48277, valid_rmse = 9.07495, ts_rmse = 8.91542, tr_mae = 6.98692, valid_mae = 5.77430, ts_mae = 5.72493
Epoch 505 time = 10.36, tr_rmse = 11.50328, valid_rmse = 9.13745, ts_rmse = 8.97183, tr_mae = 6.94428, valid_mae = 5.79923, ts_mae = 5.74984
Epoch 506 time = 10.59, tr_rmse = 11.46396, valid_rmse = 9.09733, ts_rmse = 8.92990, tr_mae = 6.90472, valid_mae = 5.76755, ts_mae = 5.71965
Epoch 507 time = 10.65, tr_rmse = 11.46511, valid_rmse = 9.09473, ts_rmse = 8.92671, tr_mae = 6.90839, valid_mae = 5.76548, ts_mae = 5.71986
Epoch 508 time = 10.69, tr_rmse = 11.51998, valid_rmse = 9.10378, ts_rmse = 8.94910, tr_mae = 7.05225, valid_mae = 5.80980, ts_mae = 5.75475
Epoch 509 time = 10.50, tr_rmse = 11.50564, valid_rmse = 9.07372, ts_rmse = 8.92258, tr_mae = 7.03297, valid_mae = 5.79057, ts_mae = 5.74820
Epoch 510 time = 11.07, tr_rmse = 11.49493, valid_rmse = 9.08058, ts_rmse = 8.92364, tr_mae = 7.00196, valid_mae = 5.78525, ts_mae = 5.73371
Epoch 511 time = 11.09, tr_rmse = 11.50483, valid_rmse = 9.09950, ts_rmse = 8.92552, tr_mae = 6.93104, valid_mae = 5.76273, ts_mae = 5.71908
Epoch 512 time = 11.94, tr_rmse = 11.45531, valid_rmse = 9.07946, ts_rmse = 8.91165, tr_mae = 6.92608, valid_mae = 5.75338, ts_mae = 5.70528
Epoch 513 time = 10.22, tr_rmse = 11.48489, valid_rmse = 9.11252, ts_rmse = 8.94841, tr_mae = 6.93238, valid_mae = 5.77046, ts_mae = 5.72651
Epoch 514 time = 10.07, tr_rmse = 11.50275, valid_rmse = 9.07553, ts_rmse = 8.92614, tr_mae = 7.04305, valid_mae = 5.79836, ts_mae = 5.75074
Epoch 515 time = 10.17, tr_rmse = 11.51894, valid_rmse = 9.10206, ts_rmse = 8.93804, tr_mae = 7.07202, valid_mae = 5.81730, ts_mae = 5.76724
Epoch 516 time = 10.58, tr_rmse = 11.46800, valid_rmse = 9.08140, ts_rmse = 8.92094, tr_mae = 6.93209, valid_mae = 5.75978, ts_mae = 5.71178
Epoch 517 time = 10.53, tr_rmse = 11.51993, valid_rmse = 9.09443, ts_rmse = 8.93004, tr_mae = 7.04300, valid_mae = 5.80088, ts_mae = 5.75054
Epoch 518 time = 10.65, tr_rmse = 11.49589, valid_rmse = 9.12466, ts_rmse = 8.97036, tr_mae = 6.97687, valid_mae = 5.79813, ts_mae = 5.74435
Epoch 519 time = 10.44, tr_rmse = 11.46360, valid_rmse = 9.07331, ts_rmse = 8.91871, tr_mae = 6.92846, valid_mae = 5.75713, ts_mae = 5.71255
Epoch 520 time = 10.57, tr_rmse = 11.49312, valid_rmse = 9.19187, ts_rmse = 9.02933, tr_mae = 6.93010, valid_mae = 5.85131, ts_mae = 5.80241
Epoch 521 time = 10.70, tr_rmse = 11.48878, valid_rmse = 9.09875, ts_rmse = 8.92711, tr_mae = 6.96240, valid_mae = 5.77075, ts_mae = 5.71427
Epoch 522 time = 10.64, tr_rmse = 11.47591, valid_rmse = 9.07451, ts_rmse = 8.91770, tr_mae = 6.98700, valid_mae = 5.77282, ts_mae = 5.72475
Epoch 523 time = 10.64, tr_rmse = 11.50631, valid_rmse = 9.12487, ts_rmse = 8.95083, tr_mae = 7.00473, valid_mae = 5.80799, ts_mae = 5.75271
Epoch 524 time = 10.39, tr_rmse = 11.44311, valid_rmse = 9.06564, ts_rmse = 8.88793, tr_mae = 6.92156, valid_mae = 5.74461, ts_mae = 5.69273
Epoch 525 time = 10.75, tr_rmse = 11.46499, valid_rmse = 9.05991, ts_rmse = 8.91849, tr_mae = 6.98056, valid_mae = 5.76736, ts_mae = 5.72461
Epoch 526 time = 10.68, tr_rmse = 11.46696, valid_rmse = 9.08043, ts_rmse = 8.91457, tr_mae = 6.93939, valid_mae = 5.76283, ts_mae = 5.70991
Epoch 527 time = 10.05, tr_rmse = 11.44879, valid_rmse = 9.03688, ts_rmse = 8.87894, tr_mae = 6.92202, valid_mae = 5.73328, ts_mae = 5.69254
Epoch 528 time = 10.02, tr_rmse = 11.48192, valid_rmse = 9.12036, ts_rmse = 8.96594, tr_mae = 6.92520, valid_mae = 5.77903, ts_mae = 5.73163
Epoch 529 time = 9.96, tr_rmse = 11.48721, valid_rmse = 9.09519, ts_rmse = 8.93207, tr_mae = 7.00964, valid_mae = 5.78968, ts_mae = 5.74143
Epoch 530 time = 9.97, tr_rmse = 11.44447, valid_rmse = 9.07613, ts_rmse = 8.90220, tr_mae = 6.89951, valid_mae = 5.74910, ts_mae = 5.69894
Epoch 531 time = 9.98, tr_rmse = 11.43485, valid_rmse = 9.05776, ts_rmse = 8.91426, tr_mae = 6.91537, valid_mae = 5.74008, ts_mae = 5.70222
Epoch 532 time = 10.07, tr_rmse = 11.46551, valid_rmse = 9.07569, ts_rmse = 8.92037, tr_mae = 6.94383, valid_mae = 5.75631, ts_mae = 5.70956
Epoch 533 time = 10.40, tr_rmse = 11.46672, valid_rmse = 9.06770, ts_rmse = 8.90877, tr_mae = 6.99181, valid_mae = 5.77048, ts_mae = 5.72456
Epoch 534 time = 10.88, tr_rmse = 11.47315, valid_rmse = 9.12321, ts_rmse = 8.97373, tr_mae = 6.93260, valid_mae = 5.78706, ts_mae = 5.73825
Epoch 535 time = 10.90, tr_rmse = 11.63434, valid_rmse = 9.17745, ts_rmse = 9.02990, tr_mae = 7.26815, valid_mae = 5.94257, ts_mae = 5.89426
Epoch 536 time = 10.74, tr_rmse = 11.47390, valid_rmse = 9.08887, ts_rmse = 8.93306, tr_mae = 6.97784, valid_mae = 5.77402, ts_mae = 5.72324
Epoch 537 time = 10.92, tr_rmse = 11.49091, valid_rmse = 9.09548, ts_rmse = 8.95396, tr_mae = 7.00900, valid_mae = 5.78397, ts_mae = 5.74142
Epoch 538 time = 10.48, tr_rmse = 11.46464, valid_rmse = 9.09118, ts_rmse = 8.92964, tr_mae = 6.96572, valid_mae = 5.77054, ts_mae = 5.72069
Epoch 539 time = 10.38, tr_rmse = 11.43855, valid_rmse = 9.05069, ts_rmse = 8.89081, tr_mae = 6.94980, valid_mae = 5.75258, ts_mae = 5.70305
Epoch 540 time = 9.93, tr_rmse = 11.47437, valid_rmse = 9.13123, ts_rmse = 8.97898, tr_mae = 6.92507, valid_mae = 5.78423, ts_mae = 5.73684
Epoch 541 time = 10.28, tr_rmse = 11.44979, valid_rmse = 9.06895, ts_rmse = 8.90646, tr_mae = 6.96528, valid_mae = 5.76316, ts_mae = 5.71335
Epoch 542 time = 10.16, tr_rmse = 11.44940, valid_rmse = 9.08277, ts_rmse = 8.92877, tr_mae = 6.92456, valid_mae = 5.75757, ts_mae = 5.71299
Epoch 543 time = 10.62, tr_rmse = 11.52503, valid_rmse = 9.09490, ts_rmse = 8.95456, tr_mae = 7.09562, valid_mae = 5.82450, ts_mae = 5.78196
Epoch 544 time = 10.95, tr_rmse = 11.46833, valid_rmse = 9.13569, ts_rmse = 8.98983, tr_mae = 6.93403, valid_mae = 5.80207, ts_mae = 5.74978
Epoch 545 time = 11.14, tr_rmse = 11.41832, valid_rmse = 9.04878, ts_rmse = 8.87690, tr_mae = 6.92607, valid_mae = 5.74344, ts_mae = 5.69303
Epoch 546 time = 10.98, tr_rmse = 11.43990, valid_rmse = 9.08991, ts_rmse = 8.93429, tr_mae = 6.90384, valid_mae = 5.76197, ts_mae = 5.71357
%%%% Output: error
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-114-2113d1b2072d> in <module>
35 optimizer.zero_grad()
36 dst_net.train()
---> 37 outputs = dst_net(x)
38 # loss = loss_f(outputs*torch.tensor(np.arange(1, 13)), y*torch.tensor(np.arange(1, 13))) #+ torch.sqrt(loss_mse(outputs, y) + 0.0000001)# * (1 + torch.randn(y.shape).to(device) * 0.01))
39 loss = loss_f(outputs, y) #+ torch.sqrt(loss_mse(outputs, y) + 0.0000001)# * (1 + torch.randn(y.shape).to(device) * 0.01))
~/miniconda3/envs/dst/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
539 result = self._slow_forward(*input, **kwargs)
540 else:
--> 541 result = self.forward(*input, **kwargs)
542 for hook in self._forward_hooks.values():
543 hook_result = hook(self, input, result)
<ipython-input-111-0123f353083b> in forward(self, x0)
31 self.hidden = self.init_hidden(x0.size(0))
32
---> 33 x = self.lstm(x0, self.hidden)[0].reshape(x0.shape[0], -1)
34 x = self.bn1(x)
35 # x = F.relu(x)
~/miniconda3/envs/dst/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
539 result = self._slow_forward(*input, **kwargs)
540 else:
--> 541 result = self.forward(*input, **kwargs)
542 for hook in self._forward_hooks.values():
543 hook_result = hook(self, input, result)
~/miniconda3/envs/dst/lib/python3.7/site-packages/torch/nn/modules/rnn.py in forward(self, input, hx)
562 return self.forward_packed(input, hx)
563 else:
--> 564 return self.forward_tensor(input, hx)
565
566
~/miniconda3/envs/dst/lib/python3.7/site-packages/torch/nn/modules/rnn.py in forward_tensor(self, input, hx)
541 unsorted_indices = None
542
--> 543 output, hidden = self.forward_impl(input, hx, batch_sizes, max_batch_size, sorted_indices)
544
545 return output, self.permute_hidden(hidden, unsorted_indices)
~/miniconda3/envs/dst/lib/python3.7/site-packages/torch/nn/modules/rnn.py in forward_impl(self, input, hx, batch_sizes, max_batch_size, sorted_indices)
524 if batch_sizes is None:
525 result = _VF.lstm(input, hx, self._get_flat_weights(), self.bias, self.num_layers,
--> 526 self.dropout, self.training, self.bidirectional, self.batch_first)
527 else:
528 result = _VF.lstm(input, batch_sizes, hx, self._get_flat_weights(), self.bias,
KeyboardInterrupt:
%% Cell type:code id: tags:
``` python
plt.figure(figsize=(12,8))
plt.plot(history_tr[:,0], alpha=0.5, label='train')
plt.plot(history_valid[:,0], alpha=0.5, label='validation')
plt.plot(history_ts[:,0], alpha=0.5, label='test')
plt.xlim([0,len(history_ts[history_ts[:,0]>0])]);
plt.ylim([8,13]);
plt.legend()
plt.xlabel('epoch')
plt.ylabel('loss')
plt.tick_params(labelright=True)
```
%%%% Output: display_data
%% Cell type:code id: tags:
``` python
```
......
notebooks/model_training.ipynb
View file @ ccaf20fa
......@@ -251,6 +251,15 @@
"ixs_test = ixs_valid_test[1::2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.savetxt('ixs_valid_test.txt', ixs_valid_test, fmt='%i')"
]
},
{
"cell_type": "code",
"execution_count": 45,
......
%% Cell type:code id: tags:
``` python
%matplotlib inline
import matplotlib.pyplot as plt
import sys; sys.path.append('../DST')
import os
from DST.config import data_path
import pandas as pd
import numpy as np
import seaborn as sns; sns.set(style="whitegrid", font_scale=1.3)
import torch
import torch.nn as nn
import time
import math
import torch.utils.data as utils_data
import torch.nn.functional as F
import datetime
```
%% Cell type:code id: tags:
``` python
torch.manual_seed(21894)
np.random.seed(21894)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
```
%% Cell type:code id: tags:
``` python
BEFORE = 12
AFTER = 12
```
%% Cell type:code id: tags:
``` python
dst_data = pd.read_pickle(os.path.join(data_path,'dst.pkl'))
dst_data['ora_round'] = dst_data.ora.apply(lambda x:int(x.split(':')[0]))
dati_agg = dst_data.groupby(['data','ora_round']).agg({
'BX': np.mean,
'BY': np.mean,
'BZ': np.mean,
'FLOW_SPEED': np.mean,
'PROTON_DENSITY': np.mean,
'TEMPERATURE': np.mean,
'PRESSION': np.mean,
'ELETTRIC': np.mean,
'y': np.mean})
dati_agg.reset_index(inplace=True)
dati_agg.sort_values(by = ['data','ora_round'],inplace=True)
dataset = dati_agg.drop(columns = ['data','ora_round']).values
dataset = torch.from_numpy(np.hstack([np.arange(len(dataset)).reshape([-1,1]),dataset]))
last_date_train = dati_agg[dati_agg.data <= datetime.datetime(2008,12,31)].index[-1]
len_valid_test = (len(dataset) - last_date_train)/2
last_date_train/len(dataset), len_valid_test/len(dataset)
data_in = dataset.unfold(0, BEFORE, 1).transpose(2,1)
data_out = dataset[BEFORE:].unfold(0, AFTER, 1).transpose(2,1)
data_in = data_in[:data_out.size(0)]
data_out = data_out[:,:,-1]
data_in.size(), data_out.size()
```
%%%% Output: execute_result
(torch.Size([261794, 12, 10]), torch.Size([261794, 12]))
%% Cell type:code id: tags:
``` python
where_not_nan_in = ~torch.isnan(data_in).any(2, keepdim=True).any(1, keepdim=True).reshape(-1)
data_in = data_in[where_not_nan_in]
data_out = data_out[where_not_nan_in]
```
%% Cell type:code id: tags:
``` python
where_not_nan_out = ~torch.isnan(data_out).any(1, keepdim=True).reshape(-1)
data_in = data_in[where_not_nan_out]
data_out = data_out[where_not_nan_out]
last_train = np.where(data_in[:,0,0] <= last_date_train)[0][-1] + 1
data_in = data_in[:, :, 1:]
#len_tr = int(data_in.size(0) * 0.6)
n_channels = data_in.size(2)
```
%% Cell type:code id: tags:
``` python
data_in.size(), data_out.size()
```
%%%% Output: execute_result
(torch.Size([186534, 12, 9]), torch.Size([186534, 12]))
%% Cell type:code id: tags:
``` python
class MinMaxScaler():
"""
Transform features by scaling each feature to a given range
Features in the last dim
The transformation is given by::
X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
X_scaled = X_std * (max - min) + min
where min, max = feature_range.
"""
def __init__(self, feature_range=(0,1)):
self.feature_range = feature_range
def fit(self, X):
X_size = X.size()
X = X.reshape(-1, X_size[-1])
data_min = X.min(axis=0).values
data_max = X.max(axis=0).values
data_range = data_max - data_min
self.scale_ = ((self.feature_range[1] - self.feature_range[0]) / data_range)
self.min_ = self.feature_range[0] - data_min * self.scale_
self.data_min_ = data_min
self.data_max_ = data_max
self.data_range_ = data_range
X = X.reshape(X_size)
return self
def transform(self, X):
X *= self.scale_
X += self.min_
return X
def inverse_transform(self, X):
X -= self.min_
X /= self.scale_
return X
```
%% Cell type:code id: tags:
``` python
mmScaler = MinMaxScaler((0.1, .9))
mmScaler.fit(data_in[:last_train])
data_in_scaled = data_in.clone()
data_in_scaled = mmScaler.transform(data_in_scaled)
```
%% Cell type:code id: tags:
``` python
mm_scaler_out = MinMaxScaler((0.1, .9))
mm_scaler_out.fit(data_in[:last_train, :, -1].reshape(-1, data_in.size(1), 1))
data_out_scaled = data_out.clone()
data_out_scaled = mm_scaler_out.transform(data_out_scaled)
```
%% Cell type:code id: tags:
``` python
class Dataset(utils_data.Dataset):
def __init__(self, dataset_in, dataset_out):
self.dataset_in = dataset_in
self.dataset_out = dataset_out
def __len__(self):
return self.dataset_in.size(0)
def __getitem__(self, idx):
din_src = self.dataset_in[idx]
dout = self.dataset_out[idx]
return din_src, dout
```
%% Cell type:code id: tags:
``` python
ixs_valid_test = np.arange(int(len_valid_test)) + last_train
np.random.shuffle(ixs_valid_test)
ixs_valid = ixs_valid_test[::2]
ixs_test = ixs_valid_test[1::2]
```
%% Cell type:code id: tags:
``` python
np.savetxt('ixs_valid_test.txt', ixs_valid_test, fmt='%i')
```
%% Cell type:code id: tags:
``` python
dst_min = data_out[:last_train].min(axis=1).values.flatten()
bins = [dst_min.min() - 10] + list(np.arange(-300, dst_min.max() + 10, 10))
h, b = np.histogram(dst_min, bins=bins)
if len(np.argwhere(h == 0)) > 0:
bins = np.delete(bins, np.argwhere(h == 0)[0] + 1)
h, b = np.histogram(dst_min, bins=bins)
w = h.max()/h
def fix_weight(dst_v):
pos = np.argwhere(np.abs(b - dst_v) == np.abs((b - dst_v)).min())[0,0]
if dst_v - b[pos] < 0:
pos = pos-1
return np.sqrt(w[pos]/h.max())
fix_weight_v = np.vectorize(fix_weight)
weights = fix_weight_v(dst_min)
sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, num_samples= len(dst_min))
BATCH_SIZE=256
dataset_tr = Dataset(data_in_scaled[:last_train], data_out_scaled[:last_train])
data_loader_tr = utils_data.DataLoader(dataset_tr, batch_size=BATCH_SIZE, num_workers = 4, shuffle=False, sampler = sampler)
dataset_val = Dataset(data_in_scaled[ixs_valid], data_out_scaled[ixs_valid])
data_loader_val = utils_data.DataLoader(dataset_val, batch_size=BATCH_SIZE, num_workers = 4,shuffle=True)
dataset_ts = Dataset(data_in_scaled[ixs_test], data_out_scaled[ixs_test])
data_loader_ts = utils_data.DataLoader(dataset_ts, batch_size=BATCH_SIZE, num_workers = 4,shuffle=True)
```
%% Cell type:code id: tags:
``` python
delta_var = mmScaler.transform(mmScaler.data_max_ - mmScaler.data_min_) * 0.01
```
%% Cell type:code id: tags:
``` python
batch = next(iter(data_loader_tr))
```
%% Cell type:code id: tags:
``` python
class DSTnet(nn.Module):
def __init__(self, nvars, nhidden_i, nhidden_o, n_out_i, before, after):
super().__init__()
self.nvars = nvars
self.nhidden_i = nhidden_i
self.nhidden_o = nhidden_o
self.before = before
self.after = after
self.n_out_i = n_out_i
self.lstm = nn.LSTM(self.nvars, self.n_out_i, self.nhidden_i, batch_first=True)
self.first_merged_layer = self.n_out_i * self.before
self.bn1 = nn.BatchNorm1d(num_features=self.first_merged_layer)
# self.bn1 = nn.LayerNorm(self.first_merged_layer)
self.linear_o_1 = nn.Linear(self.first_merged_layer, self.nhidden_o)
self.ln1 = nn.LayerNorm(self.nhidden_o )
self.linear_o_2 = nn.Linear(self.nhidden_o, self.nhidden_o)
self.linear_o_3 = nn.Linear(self.nhidden_o, self.nhidden_o // 2)
# self.bn2 = nn.BatchNorm1d(num_features=self.nhidden_o)
self.linear_o_4 = nn.Linear(self.nhidden_o // 2, self.after)
def init_hidden(self, batch_size):
hidden = torch.randn(self.nhidden_i, batch_size, self.n_out_i).to(device)
cell = torch.randn(self.nhidden_i, batch_size, self.n_out_i).to(device)
return (hidden, cell)
def forward(self, x0):
self.hidden = self.init_hidden(x0.size(0))
x = self.lstm(x0, self.hidden)[0].reshape(x0.shape[0], -1)
x = self.bn1(x)
# x = F.relu(x)
x = F.relu(self.linear_o_1(x))
# x = self.ln1(x)
x = F.dropout(x, 0.2, training=self.training)
x = F.relu(self.linear_o_2(x))
x = F.dropout(x, 0.2, training=self.training)
x = F.relu(self.linear_o_3(x))
x = F.dropout(x, 0.2, training=self.training)
x = self.linear_o_4(x)
return x
```
%% Cell type:code id: tags:
``` python
loss_f = nn.L1Loss()
loss_mse = nn.MSELoss()
nhidden_i = 2
nhidden_o = 128
n_out_i = 8
before = BEFORE
nvars = data_in_scaled.shape[-1]
dst_net = DSTnet(nvars, nhidden_i, nhidden_o, n_out_i, before, AFTER).to(device)
print(dst_net)
num_epochs = 10000
lr = 1e-5
optimizer = torch.optim.Adam(dst_net.parameters(), lr=lr, weight_decay=1e-5)
history_tr = np.zeros((num_epochs, 2))
history_valid = np.zeros((num_epochs, 2))
history_ts = np.zeros((num_epochs, 2))
for epoch in range(num_epochs):
# if epoch == 1400:
# lr = 1e-5
# optimizer = torch.optim.Adam(dst_net.parameters(), lr=lr)#, weight_decay=1e-5)
start_time = time.time()
for i, batch in enumerate(data_loader_tr):
# delta_batch0 = (( 1- 2 * torch.rand(batch[0].size()))*delta_var).float()
# delta_batch1 = (( 1- 2 * torch.rand(batch[1].size()))*delta_var[-1]).float()
x = (batch[0] + (( 1- 2 * torch.rand(batch[0].size())) * batch[0] * 0.001)).float().to(device) #+ delta_batch0
y = (batch[1] + (( 1- 2 * torch.rand(batch[1].size())) * batch[1] * 0.001)).float().to(device) #+ delta_batch1
optimizer.zero_grad()
dst_net.train()
outputs = dst_net(x)
# loss = loss_f(outputs*torch.tensor(np.arange(1, 13)), y*torch.tensor(np.arange(1, 13))) #+ torch.sqrt(loss_mse(outputs, y) + 0.0000001)# * (1 + torch.randn(y.shape).to(device) * 0.01))
loss = loss_f(outputs, y) #+ torch.sqrt(loss_mse(outputs, y) + 0.0000001)# * (1 + torch.randn(y.shape).to(device) * 0.01))
# loss = loss_mse(outputs, y)# * (1 + torch.randn(y.shape).to(device) * 0.01))
loss.backward()
optimizer.step()
dst_net.eval()
data_out_scaled_loss = mm_scaler_out.inverse_transform(data_out_scaled.clone())
outputs = dst_net(data_in_scaled[:last_train].to(device).float())
loss_tr = np.sqrt(loss_mse(mm_scaler_out.inverse_transform(outputs.cpu().clone()).to(device), data_out_scaled_loss[:last_train].to(device).float()).item())
loss_mae_tr = loss_f(mm_scaler_out.inverse_transform(outputs.cpu().clone()).to(device), data_out_scaled_loss[:last_train].to(device).float()).item()
outputs = dst_net(data_in_scaled[ixs_valid].to(device).float())
loss_valid = np.sqrt(loss_mse(mm_scaler_out.inverse_transform(outputs.cpu().clone()).to(device), data_out_scaled_loss[ixs_valid].to(device).float()).item())
loss_mae_valid = loss_f(mm_scaler_out.inverse_transform(outputs.cpu().clone()).to(device), data_out_scaled_loss[ixs_valid].to(device).float()).item()
outputs = dst_net(data_in_scaled[ixs_test].to(device).float())
loss_ts = np.sqrt(loss_mse(mm_scaler_out.inverse_transform(outputs.cpu().clone()).to(device), data_out_scaled_loss[ixs_test].to(device).float()).item())
loss_mae_ts = loss_f(mm_scaler_out.inverse_transform(outputs.cpu().clone()).to(device), data_out_scaled_loss[ixs_test].to(device).float()).item()
history_tr[epoch] = [loss_tr, loss_mae_tr]
history_valid[epoch] = [loss_valid, loss_mae_valid]
history_ts[epoch] = [loss_ts, loss_mae_ts]
epoch_time = time.time() - start_time
if (epoch % 10 == 0):
print('Epoch %d time = %.2f, tr_rmse = %0.5f, valid_rmse = %.5f, ts_rmse = %.5f, tr_mae = %0.5f, valid_mae = %.5f, ts_mae = %.5f' %
(epoch, epoch_time, loss_tr, loss_valid, loss_ts, loss_mae_tr, loss_mae_valid, loss_mae_ts))
torch.save(dst_net.state_dict(), '../models/dst_net_full.pth')
np.savetxt('../hist/history_tr_rmse_mae_full.txt', history_tr)
np.savetxt('../hist/history_valid_rmse_mae_full.txt', history_valid)
np.savetxt('../hist/history_ts_rmse_mae_full.txt', history_ts)
```
%%%% Output: stream
DSTnet(
(lstm): LSTM(9, 8, num_layers=2, batch_first=True)
(bn1): BatchNorm1d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(linear_o_1): Linear(in_features=96, out_features=64, bias=True)
(ln1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(linear_o_2): Linear(in_features=64, out_features=64, bias=True)
(linear_o_3): Linear(in_features=64, out_features=32, bias=True)
(linear_o_4): Linear(in_features=32, out_features=12, bias=True)
)
Epoch 0 time = 9.58, tr_rmse = 469.83637, valid_rmse = 475.11872, ts_rmse = 475.36345, tr_mae = 461.05780, valid_mae = 466.56754, ts_mae = 466.85388
Epoch 1 time = 10.01, tr_rmse = 388.58652, valid_rmse = 393.32884, ts_rmse = 393.88383, tr_mae = 360.97275, valid_mae = 366.07150, ts_mae = 366.71188
Epoch 2 time = 11.28, tr_rmse = 282.13619, valid_rmse = 286.54900, ts_rmse = 286.04732, tr_mae = 246.84175, valid_mae = 251.61148, ts_mae = 250.79152
Epoch 3 time = 11.71, tr_rmse = 212.87363, valid_rmse = 214.78504, ts_rmse = 214.61001, tr_mae = 180.41080, valid_mae = 182.33406, ts_mae = 182.10858
Epoch 4 time = 10.12, tr_rmse = 166.96828, valid_rmse = 167.52934, ts_rmse = 166.93450, tr_mae = 139.07254, valid_mae = 139.51793, ts_mae = 139.05449
Epoch 5 time = 10.23, tr_rmse = 140.29911, valid_rmse = 139.34353, ts_rmse = 138.90956, tr_mae = 115.83358, valid_mae = 114.99672, ts_mae = 114.60511
Epoch 6 time = 10.33, tr_rmse = 120.11670, valid_rmse = 117.13997, ts_rmse = 117.47479, tr_mae = 98.16451, valid_mae = 95.47210, ts_mae = 95.73440
Epoch 7 time = 10.31, tr_rmse = 107.94639, valid_rmse = 103.54335, ts_rmse = 104.31949, tr_mae = 87.92388, valid_mae = 83.86409, ts_mae = 84.50546
Epoch 8 time = 10.73, tr_rmse = 97.97863, valid_rmse = 93.65564, ts_rmse = 93.83395, tr_mae = 79.52438, valid_mae = 75.50884, ts_mae = 75.72380
Epoch 9 time = 11.80, tr_rmse = 88.08864, valid_rmse = 83.91973, ts_rmse = 84.07096, tr_mae = 71.08361, valid_mae = 67.13800, ts_mae = 67.23650
Epoch 10 time = 10.76, tr_rmse = 80.36456, valid_rmse = 76.11947, ts_rmse = 76.78473, tr_mae = 64.69331, valid_mae = 60.75425, ts_mae = 61.39462
Epoch 11 time = 10.88, tr_rmse = 72.85488, valid_rmse = 69.12700, ts_rmse = 68.93170, tr_mae = 58.58426, valid_mae = 55.14393, ts_mae = 54.92891
Epoch 12 time = 10.87, tr_rmse = 66.03329, valid_rmse = 62.42380, ts_rmse = 62.62510, tr_mae = 52.81767, valid_mae = 49.42125, ts_mae = 49.64627
Epoch 13 time = 10.50, tr_rmse = 61.55198, valid_rmse = 57.67189, ts_rmse = 57.33591, tr_mae = 49.39264, valid_mae = 46.03705, ts_mae = 45.57819
Epoch 14 time = 11.63, tr_rmse = 54.95930, valid_rmse = 51.38882, ts_rmse = 51.63277, tr_mae = 43.92029, valid_mae = 40.71434, ts_mae = 40.90249
Epoch 15 time = 12.06, tr_rmse = 49.49959, valid_rmse = 46.07767, ts_rmse = 46.29188, tr_mae = 39.43318, valid_mae = 36.51028, ts_mae = 36.73224
Epoch 16 time = 11.99, tr_rmse = 45.47639, valid_rmse = 42.51303, ts_rmse = 42.63423, tr_mae = 36.23988, valid_mae = 33.64177, ts_mae = 33.74437
Epoch 17 time = 10.14, tr_rmse = 41.53393, valid_rmse = 38.55350, ts_rmse = 38.22472, tr_mae = 33.14735, valid_mae = 30.51845, ts_mae = 30.38503
Epoch 18 time = 10.14, tr_rmse = 38.54800, valid_rmse = 35.39444, ts_rmse = 35.18512, tr_mae = 30.77516, valid_mae = 28.05162, ts_mae = 27.98601
Epoch 19 time = 10.21, tr_rmse = 34.60249, valid_rmse = 31.87325, ts_rmse = 31.92139, tr_mae = 27.43313, valid_mae = 25.12820, ts_mae = 25.14160
Epoch 20 time = 10.31, tr_rmse = 32.41131, valid_rmse = 28.78282, ts_rmse = 29.02951, tr_mae = 25.81217, valid_mae = 22.82328, ts_mae = 23.12247
Epoch 21 time = 10.10, tr_rmse = 29.63539, valid_rmse = 26.22939, ts_rmse = 26.28407, tr_mae = 23.49904, valid_mae = 20.76940, ts_mae = 20.76274
Epoch 22 time = 10.15, tr_rmse = 28.14579, valid_rmse = 24.74018, ts_rmse = 24.69780, tr_mae = 22.37327, valid_mae = 19.63754, ts_mae = 19.61604
Epoch 23 time = 10.14, tr_rmse = 25.84842, valid_rmse = 22.65259, ts_rmse = 22.41701, tr_mae = 20.45133, valid_mae = 17.93198, ts_mae = 17.75001
Epoch 24 time = 10.05, tr_rmse = 24.99609, valid_rmse = 21.44524, ts_rmse = 21.28371, tr_mae = 19.81792, valid_mae = 16.98862, ts_mae = 16.88686
Epoch 25 time = 10.28, tr_rmse = 24.22077, valid_rmse = 20.67378, ts_rmse = 20.61935, tr_mae = 19.29541, valid_mae = 16.43044, ts_mae = 16.43814
Epoch 26 time = 10.22, tr_rmse = 21.41277, valid_rmse = 18.25019, ts_rmse = 18.22687, tr_mae = 16.55845, valid_mae = 14.23280, ts_mae = 14.21476
Epoch 27 time = 10.03, tr_rmse = 20.52705, valid_rmse = 17.25853, ts_rmse = 17.11398, tr_mae = 15.79898, valid_mae = 13.37073, ts_mae = 13.31232
Epoch 28 time = 10.14, tr_rmse = 20.65998, valid_rmse = 17.21814, ts_rmse = 17.19531, tr_mae = 16.12646, valid_mae = 13.54704, ts_mae = 13.57142
Epoch 29 time = 10.20, tr_rmse = 20.19309, valid_rmse = 16.69780, ts_rmse = 16.61451, tr_mae = 15.72647, valid_mae = 13.06056, ts_mae = 13.08549
Epoch 30 time = 10.58, tr_rmse = 20.29426, valid_rmse = 16.83579, ts_rmse = 16.70910, tr_mae = 15.95925, valid_mae = 13.32516, ts_mae = 13.26896
Epoch 31 time = 10.47, tr_rmse = 20.42426, valid_rmse = 16.99970, ts_rmse = 16.94828, tr_mae = 16.21970, valid_mae = 13.60875, ts_mae = 13.59752
Epoch 32 time = 10.74, tr_rmse = 19.73991, valid_rmse = 16.32760, ts_rmse = 16.33337, tr_mae = 15.56311, valid_mae = 13.00600, ts_mae = 13.06388
Epoch 33 time = 10.57, tr_rmse = 19.63533, valid_rmse = 16.15513, ts_rmse = 16.05821, tr_mae = 15.52516, valid_mae = 12.87851, ts_mae = 12.84972
Epoch 34 time = 10.49, tr_rmse = 18.54266, valid_rmse = 15.20507, ts_rmse = 15.15259, tr_mae = 14.40320, valid_mae = 11.96353, ts_mae = 11.97522
Epoch 35 time = 10.34, tr_rmse = 19.29880, valid_rmse = 15.94948, ts_rmse = 15.93653, tr_mae = 15.38048, valid_mae = 12.87380, ts_mae = 12.90806
Epoch 36 time = 10.72, tr_rmse = 18.32653, valid_rmse = 14.98241, ts_rmse = 14.94959, tr_mae = 14.35635, valid_mae = 11.92258, ts_mae = 11.94039
Epoch 37 time = 10.43, tr_rmse = 17.21544, valid_rmse = 13.98859, ts_rmse = 13.89573, tr_mae = 13.09338, valid_mae = 10.89522, ts_mae = 10.84038
Epoch 38 time = 10.48, tr_rmse = 17.38045, valid_rmse = 14.06629, ts_rmse = 14.02992, tr_mae = 13.38677, valid_mae = 11.03890, ts_mae = 11.05629
Epoch 39 time = 10.22, tr_rmse = 17.14795, valid_rmse = 13.85358, ts_rmse = 13.78351, tr_mae = 13.16051, valid_mae = 10.87153, ts_mae = 10.84944
Epoch 40 time = 10.31, tr_rmse = 17.67979, valid_rmse = 14.47412, ts_rmse = 14.37751, tr_mae = 13.80511, valid_mae = 11.55238, ts_mae = 11.50688
Epoch 41 time = 11.34, tr_rmse = 17.77805, valid_rmse = 14.61537, ts_rmse = 14.54692, tr_mae = 14.00038, valid_mae = 11.77332, ts_mae = 11.76388
Epoch 42 time = 11.22, tr_rmse = 17.21191, valid_rmse = 14.02303, ts_rmse = 13.96267, tr_mae = 13.38353, valid_mae = 11.17042, ts_mae = 11.16596
Epoch 43 time = 11.88, tr_rmse = 17.55124, valid_rmse = 14.42488, ts_rmse = 14.35334, tr_mae = 13.82063, valid_mae = 11.62828, ts_mae = 11.63386
Epoch 44 time = 11.89, tr_rmse = 16.57793, valid_rmse = 13.49831, ts_rmse = 13.43744, tr_mae = 12.68515, valid_mae = 10.63555, ts_mae = 10.63021
Epoch 45 time = 12.33, tr_rmse = 15.78338, valid_rmse = 12.78805, ts_rmse = 12.68631, tr_mae = 11.78901, valid_mae = 9.89431, ts_mae = 9.88264
Epoch 46 time = 13.33, tr_rmse = 17.23601, valid_rmse = 14.28218, ts_rmse = 14.19937, tr_mae = 13.56428, valid_mae = 11.60134, ts_mae = 11.57264
Epoch 47 time = 15.47, tr_rmse = 16.13500, valid_rmse = 13.18999, ts_rmse = 13.06547, tr_mae = 12.26640, valid_mae = 10.38128, ts_mae = 10.32580
Epoch 48 time = 14.67, tr_rmse = 16.26168, valid_rmse = 13.38527, ts_rmse = 13.32936, tr_mae = 12.44717, valid_mae = 10.64614, ts_mae = 10.63803
Epoch 49 time = 13.45, tr_rmse = 16.17817, valid_rmse = 13.28476, ts_rmse = 13.23232, tr_mae = 12.38392, valid_mae = 10.53350, ts_mae = 10.55287
Epoch 50 time = 12.46, tr_rmse = 16.38148, valid_rmse = 13.47378, ts_rmse = 13.38205, tr_mae = 12.64094, valid_mae = 10.76516, ts_mae = 10.73328
Epoch 51 time = 12.26, tr_rmse = 15.56148, valid_rmse = 12.64202, ts_rmse = 12.52262, tr_mae = 11.65497, valid_mae = 9.82705, ts_mae = 9.79202
Epoch 52 time = 14.19, tr_rmse = 15.53018, valid_rmse = 12.64870, ts_rmse = 12.55463, tr_mae = 11.67029, valid_mae = 9.87795, ts_mae = 9.84212
Epoch 53 time = 12.87, tr_rmse = 15.28347, valid_rmse = 12.46730, ts_rmse = 12.38350, tr_mae = 11.38336, valid_mae = 9.68268, ts_mae = 9.67069
Epoch 54 time = 11.29, tr_rmse = 16.09112, valid_rmse = 13.29786, ts_rmse = 13.21833, tr_mae = 12.39709, valid_mae = 10.65072, ts_mae = 10.63649
Epoch 55 time = 10.59, tr_rmse = 15.83603, valid_rmse = 13.12799, ts_rmse = 13.04642, tr_mae = 12.12847, valid_mae = 10.48769, ts_mae = 10.47445
Epoch 56 time = 10.33, tr_rmse = 15.52105, valid_rmse = 12.81395, ts_rmse = 12.71112, tr_mae = 11.74423, valid_mae = 10.15618, ts_mae = 10.11368
Epoch 57 time = 10.42, tr_rmse = 16.41152, valid_rmse = 13.78769, ts_rmse = 13.70997, tr_mae = 12.85738, valid_mae = 11.27541, ts_mae = 11.25530
Epoch 58 time = 10.22, tr_rmse = 16.63460, valid_rmse = 14.08087, ts_rmse = 14.02129, tr_mae = 13.17562, valid_mae = 11.63457, ts_mae = 11.63199
Epoch 59 time = 10.13, tr_rmse = 16.16365, valid_rmse = 13.56861, ts_rmse = 13.52295, tr_mae = 12.60638, valid_mae = 11.06464, ts_mae = 11.06080
Epoch 60 time = 10.08, tr_rmse = 15.51564, valid_rmse = 12.89216, ts_rmse = 12.79591, tr_mae = 11.81635, valid_mae = 10.28886, ts_mae = 10.24864
Epoch 61 time = 9.86, tr_rmse = 15.91470, valid_rmse = 13.29360, ts_rmse = 13.22204, tr_mae = 12.33385, valid_mae = 10.77306, ts_mae = 10.74912
Epoch 62 time = 9.97, tr_rmse = 14.73767, valid_rmse = 12.04800, ts_rmse = 11.94883, tr_mae = 10.85858, valid_mae = 9.32158, ts_mae = 9.28417
Epoch 63 time = 10.08, tr_rmse = 15.05917, valid_rmse = 12.52876, ts_rmse = 12.40228, tr_mae = 11.29063, valid_mae = 9.90692, ts_mae = 9.85459
Epoch 64 time = 9.95, tr_rmse = 15.06442, valid_rmse = 12.44993, ts_rmse = 12.33474, tr_mae = 11.32299, valid_mae = 9.83320, ts_mae = 9.79063
Epoch 65 time = 10.02, tr_rmse = 15.61311, valid_rmse = 13.09232, ts_rmse = 13.02093, tr_mae = 12.03137, valid_mae = 10.58654, ts_mae = 10.57894
Epoch 66 time = 10.07, tr_rmse = 14.40380, valid_rmse = 11.71924, ts_rmse = 11.62332, tr_mae = 10.50385, valid_mae = 8.99472, ts_mae = 8.97009
Epoch 67 time = 10.05, tr_rmse = 14.52767, valid_rmse = 11.87821, ts_rmse = 11.78398, tr_mae = 10.68050, valid_mae = 9.18571, ts_mae = 9.16256
Epoch 68 time = 10.02, tr_rmse = 15.00845, valid_rmse = 12.55439, ts_rmse = 12.46748, tr_mae = 11.32056, valid_mae = 10.02200, ts_mae = 9.99311
Epoch 69 time = 10.33, tr_rmse = 14.44392, valid_rmse = 11.83655, ts_rmse = 11.75043, tr_mae = 10.57627, valid_mae = 9.14701, ts_mae = 9.12627
Epoch 70 time = 10.18, tr_rmse = 14.28779, valid_rmse = 11.66831, ts_rmse = 11.58814, tr_mae = 10.38579, valid_mae = 8.94792, ts_mae = 8.93932
Epoch 71 time = 11.14, tr_rmse = 15.31477, valid_rmse = 12.91881, ts_rmse = 12.85393, tr_mae = 11.74161, valid_mae = 10.47108, ts_mae = 10.46557
Epoch 72 time = 10.01, tr_rmse = 14.70426, valid_rmse = 12.21199, ts_rmse = 12.12925, tr_mae = 10.98379, valid_mae = 9.65113, ts_mae = 9.62532
Epoch 73 time = 10.07, tr_rmse = 13.99588, valid_rmse = 11.35286, ts_rmse = 11.24176, tr_mae = 10.04490, valid_mae = 8.59237, ts_mae = 8.56614
Epoch 74 time = 10.26, tr_rmse = 14.15444, valid_rmse = 11.56670, ts_rmse = 11.46455, tr_mae = 10.27206, valid_mae = 8.87231, ts_mae = 8.84424
Epoch 75 time = 9.92, tr_rmse = 14.01988, valid_rmse = 11.38251, ts_rmse = 11.26661, tr_mae = 10.09486, valid_mae = 8.65922, ts_mae = 8.62552
Epoch 76 time = 10.05, tr_rmse = 14.67822, valid_rmse = 12.12102, ts_rmse = 12.03409, tr_mae = 11.00063, valid_mae = 9.56788, ts_mae = 9.55477
Epoch 77 time = 9.90, tr_rmse = 13.68067, valid_rmse = 10.95839, ts_rmse = 10.84711, tr_mae = 9.65796, valid_mae = 8.15112, ts_mae = 8.11814
Epoch 78 time = 9.78, tr_rmse = 14.22561, valid_rmse = 11.56145, ts_rmse = 11.46000, tr_mae = 10.42153, valid_mae = 8.89960, ts_mae = 8.87085
Epoch 79 time = 9.64, tr_rmse = 14.30211, valid_rmse = 11.73217, ts_rmse = 11.63013, tr_mae = 10.53490, valid_mae = 9.11136, ts_mae = 9.08469
Epoch 80 time = 9.72, tr_rmse = 14.19549, valid_rmse = 11.63289, ts_rmse = 11.53184, tr_mae = 10.38946, valid_mae = 8.99306, ts_mae = 8.96507
Epoch 81 time = 9.64, tr_rmse = 14.44488, valid_rmse = 11.88758, ts_rmse = 11.81018, tr_mae = 10.74841, valid_mae = 9.32871, ts_mae = 9.31740
Epoch 82 time = 9.63, tr_rmse = 13.58662, valid_rmse = 10.87767, ts_rmse = 10.77203, tr_mae = 9.57060, valid_mae = 8.07753, ts_mae = 8.06174
Epoch 83 time = 9.68, tr_rmse = 13.42303, valid_rmse = 10.68689, ts_rmse = 10.56719, tr_mae = 9.34650, valid_mae = 7.82585, ts_mae = 7.79768
Epoch 84 time = 9.76, tr_rmse = 13.49247, valid_rmse = 10.79765, ts_rmse = 10.68637, tr_mae = 9.47515, valid_mae = 7.98611, ts_mae = 7.96827
Epoch 85 time = 9.55, tr_rmse = 13.89071, valid_rmse = 11.22091, ts_rmse = 11.10001, tr_mae = 10.01489, valid_mae = 8.50205, ts_mae = 8.46201
Epoch 86 time = 9.69, tr_rmse = 13.64691, valid_rmse = 10.97278, ts_rmse = 10.90439, tr_mae = 9.69617, valid_mae = 8.21510, ts_mae = 8.22004
Epoch 87 time = 9.72, tr_rmse = 13.17901, valid_rmse = 10.46408, ts_rmse = 10.35410, tr_mae = 9.03755, valid_mae = 7.57627, ts_mae = 7.56084
Epoch 88 time = 9.53, tr_rmse = 13.54532, valid_rmse = 10.84519, ts_rmse = 10.75348, tr_mae = 9.58779, valid_mae = 8.08651, ts_mae = 8.06878
Epoch 89 time = 9.61, tr_rmse = 13.30816, valid_rmse = 10.58069, ts_rmse = 10.48358, tr_mae = 9.22884, valid_mae = 7.73194, ts_mae = 7.71888
Epoch 90 time = 10.30, tr_rmse = 12.84662, valid_rmse = 10.07131, ts_rmse = 9.93971, tr_mae = 8.53808, valid_mae = 7.06664, ts_mae = 7.04660
Epoch 91 time = 9.63, tr_rmse = 13.02916, valid_rmse = 10.25566, ts_rmse = 10.14524, tr_mae = 8.83429, valid_mae = 7.32265, ts_mae = 7.31255
Epoch 92 time = 9.56, tr_rmse = 13.65271, valid_rmse = 11.02411, ts_rmse = 10.93602, tr_mae = 9.75074, valid_mae = 8.30927, ts_mae = 8.29930
Epoch 93 time = 9.86, tr_rmse = 12.95008, valid_rmse = 10.21134, ts_rmse = 10.07199, tr_mae = 8.74316, valid_mae = 7.27271, ts_mae = 7.23556
Epoch 94 time = 9.60, tr_rmse = 13.14386, valid_rmse = 10.44819, ts_rmse = 10.34299, tr_mae = 9.03990, valid_mae = 7.60458, ts_mae = 7.59260
Epoch 95 time = 9.60, tr_rmse = 12.78912, valid_rmse = 10.00238, ts_rmse = 9.88001, tr_mae = 8.50171, valid_mae = 7.00762, ts_mae = 6.98685
Epoch 96 time = 9.59, tr_rmse = 12.94203, valid_rmse = 10.17279, ts_rmse = 10.06654, tr_mae = 8.75254, valid_mae = 7.23814, ts_mae = 7.22357
Epoch 97 time = 9.57, tr_rmse = 12.59826, valid_rmse = 9.82026, ts_rmse = 9.68437, tr_mae = 8.22326, valid_mae = 6.74566, ts_mae = 6.73006
Epoch 98 time = 9.63, tr_rmse = 12.92355, valid_rmse = 10.16960, ts_rmse = 10.03940, tr_mae = 8.74053, valid_mae = 7.22504, ts_mae = 7.20040
Epoch 99 time = 9.64, tr_rmse = 12.63733, valid_rmse = 9.87658, ts_rmse = 9.75832, tr_mae = 8.28938, valid_mae = 6.83889, ts_mae = 6.82062
Epoch 100 time = 9.63, tr_rmse = 12.67251, valid_rmse = 9.92161, ts_rmse = 9.77713, tr_mae = 8.35471, valid_mae = 6.90170, ts_mae = 6.86750
Epoch 101 time = 9.50, tr_rmse = 12.90002, valid_rmse = 10.17578, ts_rmse = 10.04830, tr_mae = 8.72223, valid_mae = 7.25903, ts_mae = 7.22479
Epoch 102 time = 10.33, tr_rmse = 12.58596, valid_rmse = 9.82230, ts_rmse = 9.68495, tr_mae = 8.23168, valid_mae = 6.76074, ts_mae = 6.73515
Epoch 103 time = 11.60, tr_rmse = 12.55069, valid_rmse = 9.75375, ts_rmse = 9.63323, tr_mae = 8.17461, valid_mae = 6.66919, ts_mae = 6.65718
Epoch 104 time = 12.43, tr_rmse = 12.66751, valid_rmse = 9.92375, ts_rmse = 9.79058, tr_mae = 8.36662, valid_mae = 6.91178, ts_mae = 6.88959
Epoch 105 time = 9.97, tr_rmse = 12.44932, valid_rmse = 9.65303, ts_rmse = 9.52220, tr_mae = 8.02172, valid_mae = 6.54700, ts_mae = 6.52470
Epoch 106 time = 9.77, tr_rmse = 12.46710, valid_rmse = 9.66681, ts_rmse = 9.52531, tr_mae = 8.05451, valid_mae = 6.55822, ts_mae = 6.52735
Epoch 107 time = 9.66, tr_rmse = 12.71769, valid_rmse = 10.00135, ts_rmse = 9.86676, tr_mae = 8.46640, valid_mae = 7.02236, ts_mae = 6.99738
Epoch 108 time = 9.64, tr_rmse = 12.75950, valid_rmse = 10.03371, ts_rmse = 9.88442, tr_mae = 8.52509, valid_mae = 7.06267, ts_mae = 7.02889
Epoch 109 time = 9.75, tr_rmse = 12.70778, valid_rmse = 9.97790, ts_rmse = 9.84799, tr_mae = 8.45464, valid_mae = 7.00031, ts_mae = 6.97136
Epoch 110 time = 9.88, tr_rmse = 12.38902, valid_rmse = 9.62790, ts_rmse = 9.47578, tr_mae = 7.93482, valid_mae = 6.50208, ts_mae = 6.47624
Epoch 111 time = 10.64, tr_rmse = 12.67779, valid_rmse = 9.91986, ts_rmse = 9.80360, tr_mae = 8.41121, valid_mae = 6.91305, ts_mae = 6.89453
Epoch 112 time = 10.42, tr_rmse = 12.31152, valid_rmse = 9.53139, ts_rmse = 9.40533, tr_mae = 7.81515, valid_mae = 6.38153, ts_mae = 6.36694
Epoch 113 time = 10.18, tr_rmse = 12.20469, valid_rmse = 9.43003, ts_rmse = 9.28377, tr_mae = 7.63606, valid_mae = 6.22561, ts_mae = 6.20241
Epoch 114 time = 10.29, tr_rmse = 12.46466, valid_rmse = 9.70368, ts_rmse = 9.57696, tr_mae = 8.09414, valid_mae = 6.62006, ts_mae = 6.60201
Epoch 115 time = 10.11, tr_rmse = 12.35911, valid_rmse = 9.58442, ts_rmse = 9.44928, tr_mae = 7.91057, valid_mae = 6.45092, ts_mae = 6.43487
Epoch 116 time = 9.95, tr_rmse = 12.43472, valid_rmse = 9.67943, ts_rmse = 9.53903, tr_mae = 8.04232, valid_mae = 6.59204, ts_mae = 6.56901
Epoch 117 time = 9.93, tr_rmse = 12.59010, valid_rmse = 9.86404, ts_rmse = 9.72820, tr_mae = 8.29404, valid_mae = 6.85226, ts_mae = 6.83504
Epoch 118 time = 9.73, tr_rmse = 12.28011, valid_rmse = 9.52150, ts_rmse = 9.37112, tr_mae = 7.76807, valid_mae = 6.35469, ts_mae = 6.32748
Epoch 119 time = 9.67, tr_rmse = 12.26070, valid_rmse = 9.50103, ts_rmse = 9.36911, tr_mae = 7.76513, valid_mae = 6.33785, ts_mae = 6.32008
Epoch 120 time = 9.88, tr_rmse = 12.24698, valid_rmse = 9.47697, ts_rmse = 9.34078, tr_mae = 7.73763, valid_mae = 6.30484, ts_mae = 6.28843
Epoch 121 time = 9.63, tr_rmse = 12.33462, valid_rmse = 9.59934, ts_rmse = 9.46399, tr_mae = 7.89922, valid_mae = 6.47677, ts_mae = 6.46079
Epoch 122 time = 9.59, tr_rmse = 12.28515, valid_rmse = 9.51347, ts_rmse = 9.37951, tr_mae = 7.81837, valid_mae = 6.36412, ts_mae = 6.34477
Epoch 123 time = 9.76, tr_rmse = 12.07962, valid_rmse = 9.34540, ts_rmse = 9.18700, tr_mae = 7.41665, valid_mae = 6.06403, ts_mae = 6.04086
Epoch 124 time = 10.14, tr_rmse = 12.13731, valid_rmse = 9.37629, ts_rmse = 9.23750, tr_mae = 7.54123, valid_mae = 6.14802, ts_mae = 6.13302
Epoch 125 time = 10.17, tr_rmse = 12.12403, valid_rmse = 9.38473, ts_rmse = 9.23510, tr_mae = 7.53778, valid_mae = 6.14092, ts_mae = 6.11818
Epoch 126 time = 10.21, tr_rmse = 12.18409, valid_rmse = 9.43528, ts_rmse = 9.27855, tr_mae = 7.64273, valid_mae = 6.21341, ts_mae = 6.18222
Epoch 127 time = 10.11, tr_rmse = 12.23193, valid_rmse = 9.49594, ts_rmse = 9.34838, tr_mae = 7.74636, valid_mae = 6.33260, ts_mae = 6.30728
Epoch 128 time = 10.11, tr_rmse = 12.24432, valid_rmse = 9.49473, ts_rmse = 9.35764, tr_mae = 7.77105, valid_mae = 6.34558, ts_mae = 6.32702
Epoch 129 time = 10.33, tr_rmse = 12.17954, valid_rmse = 9.43895, ts_rmse = 9.29565, tr_mae = 7.64516, valid_mae = 6.23200, ts_mae = 6.20991
Epoch 130 time = 10.01, tr_rmse = 12.21154, valid_rmse = 9.46968, ts_rmse = 9.32772, tr_mae = 7.71866, valid_mae = 6.29693, ts_mae = 6.27550
Epoch 131 time = 9.89, tr_rmse = 12.02393, valid_rmse = 9.30178, ts_rmse = 9.14544, tr_mae = 7.34634, valid_mae = 6.01295, ts_mae = 5.99836
Epoch 132 time = 9.67, tr_rmse = 12.21697, valid_rmse = 9.47365, ts_rmse = 9.33209, tr_mae = 7.73759, valid_mae = 6.30941, ts_mae = 6.29013
Epoch 133 time = 9.53, tr_rmse = 12.11342, valid_rmse = 9.36954, ts_rmse = 9.21028, tr_mae = 7.53308, valid_mae = 6.14005, ts_mae = 6.11505
Epoch 134 time = 9.85, tr_rmse = 12.13826, valid_rmse = 9.39345, ts_rmse = 9.25181, tr_mae = 7.60142, valid_mae = 6.18174, ts_mae = 6.15985
Epoch 135 time = 9.59, tr_rmse = 12.16288, valid_rmse = 9.42688, ts_rmse = 9.27720, tr_mae = 7.64934, valid_mae = 6.23434, ts_mae = 6.20746
Epoch 136 time = 9.64, tr_rmse = 12.14524, valid_rmse = 9.40923, ts_rmse = 9.27187, tr_mae = 7.62366, valid_mae = 6.20490, ts_mae = 6.18589
Epoch 137 time = 9.96, tr_rmse = 12.09280, valid_rmse = 9.36022, ts_rmse = 9.20293, tr_mae = 7.51324, valid_mae = 6.12751, ts_mae = 6.10270
Epoch 138 time = 10.29, tr_rmse = 12.09668, valid_rmse = 9.35903, ts_rmse = 9.21267, tr_mae = 7.53575, valid_mae = 6.13177, ts_mae = 6.10882
Epoch 139 time = 10.61, tr_rmse = 12.02317, valid_rmse = 9.31265, ts_rmse = 9.16103, tr_mae = 7.39463, valid_mae = 6.03258, ts_mae = 6.01435
Epoch 140 time = 10.13, tr_rmse = 12.12939, valid_rmse = 9.38538, ts_rmse = 9.25536, tr_mae = 7.60557, valid_mae = 6.19602, ts_mae = 6.18018
Epoch 141 time = 9.98, tr_rmse = 12.27630, valid_rmse = 9.53153, ts_rmse = 9.39871, tr_mae = 7.86701, valid_mae = 6.40734, ts_mae = 6.37918
Epoch 142 time = 9.92, tr_rmse = 12.05217, valid_rmse = 9.30870, ts_rmse = 9.15616, tr_mae = 7.44782, valid_mae = 6.06440, ts_mae = 6.04647
Epoch 143 time = 9.93, tr_rmse = 12.06121, valid_rmse = 9.32761, ts_rmse = 9.18806, tr_mae = 7.47005, valid_mae = 6.09616, ts_mae = 6.08379
Epoch 144 time = 10.07, tr_rmse = 12.15252, valid_rmse = 9.40971, ts_rmse = 9.28042, tr_mae = 7.64656, valid_mae = 6.21536, ts_mae = 6.20234
Epoch 145 time = 9.82, tr_rmse = 12.04092, valid_rmse = 9.31385, ts_rmse = 9.15835, tr_mae = 7.44568, valid_mae = 6.06408, ts_mae = 6.04371
Epoch 146 time = 9.62, tr_rmse = 12.00922, valid_rmse = 9.29216, ts_rmse = 9.15069, tr_mae = 7.38351, valid_mae = 6.01544, ts_mae = 6.00239
Epoch 147 time = 9.57, tr_rmse = 12.02154, valid_rmse = 9.29804, ts_rmse = 9.15720, tr_mae = 7.42006, valid_mae = 6.04432, ts_mae = 6.03118
Epoch 148 time = 9.71, tr_rmse = 11.96063, valid_rmse = 9.26204, ts_rmse = 9.09052, tr_mae = 7.25539, valid_mae = 5.94576, ts_mae = 5.91728
Epoch 149 time = 9.76, tr_rmse = 11.98294, valid_rmse = 9.28031, ts_rmse = 9.11991, tr_mae = 7.33709, valid_mae = 6.00401, ts_mae = 5.97991
Epoch 150 time = 9.93, tr_rmse = 11.98201, valid_rmse = 9.26304, ts_rmse = 9.09726, tr_mae = 7.29550, valid_mae = 5.96803, ts_mae = 5.94490
Epoch 151 time = 10.36, tr_rmse = 11.95375, valid_rmse = 9.26240, ts_rmse = 9.10599, tr_mae = 7.28541, valid_mae = 5.96597, ts_mae = 5.93999
Epoch 152 time = 10.20, tr_rmse = 11.95320, valid_rmse = 9.26976, ts_rmse = 9.11181, tr_mae = 7.28612, valid_mae = 5.96873, ts_mae = 5.95088
Epoch 153 time = 11.14, tr_rmse = 11.94092, valid_rmse = 9.25315, ts_rmse = 9.09080, tr_mae = 7.23789, valid_mae = 5.93808, ts_mae = 5.91175
Epoch 154 time = 10.20, tr_rmse = 12.02068, valid_rmse = 9.31955, ts_rmse = 9.16298, tr_mae = 7.42571, valid_mae = 6.06238, ts_mae = 6.03997
Epoch 155 time = 9.98, tr_rmse = 11.94548, valid_rmse = 9.24708, ts_rmse = 9.08229, tr_mae = 7.27479, valid_mae = 5.94745, ts_mae = 5.92552
Epoch 156 time = 9.92, tr_rmse = 11.94377, valid_rmse = 9.27590, ts_rmse = 9.10089, tr_mae = 7.17972, valid_mae = 5.92313, ts_mae = 5.90295
Epoch 157 time = 9.92, tr_rmse = 11.93111, valid_rmse = 9.24700, ts_rmse = 9.08799, tr_mae = 7.24499, valid_mae = 5.94637, ts_mae = 5.92468
Epoch 158 time = 9.71, tr_rmse = 11.95140, valid_rmse = 9.24714, ts_rmse = 9.09890, tr_mae = 7.28302, valid_mae = 5.95412, ts_mae = 5.93894
Epoch 159 time = 9.84, tr_rmse = 12.06940, valid_rmse = 9.34891, ts_rmse = 9.20637, tr_mae = 7.54799, valid_mae = 6.13657, ts_mae = 6.11827
Epoch 160 time = 9.76, tr_rmse = 12.03176, valid_rmse = 9.30410, ts_rmse = 9.15052, tr_mae = 7.48819, valid_mae = 6.08559, ts_mae = 6.06591
Epoch 161 time = 9.60, tr_rmse = 12.00661, valid_rmse = 9.29226, ts_rmse = 9.15007, tr_mae = 7.43311, valid_mae = 6.04290, ts_mae = 6.02923
Epoch 162 time = 9.78, tr_rmse = 11.99979, valid_rmse = 9.28291, ts_rmse = 9.12949, tr_mae = 7.41862, valid_mae = 6.02945, ts_mae = 6.00485
Epoch 163 time = 10.27, tr_rmse = 11.92250, valid_rmse = 9.23770, ts_rmse = 9.08023, tr_mae = 7.24806, valid_mae = 5.92935, ts_mae = 5.90798
Epoch 164 time = 10.20, tr_rmse = 11.96087, valid_rmse = 9.26453, ts_rmse = 9.12817, tr_mae = 7.34646, valid_mae = 5.99501, ts_mae = 5.98024
Epoch 165 time = 10.21, tr_rmse = 11.96636, valid_rmse = 9.25312, ts_rmse = 9.09527, tr_mae = 7.37173, valid_mae = 6.00186, ts_mae = 5.97405
Epoch 166 time = 10.25, tr_rmse = 11.95451, valid_rmse = 9.25114, ts_rmse = 9.10794, tr_mae = 7.34201, valid_mae = 5.98468, ts_mae = 5.96936
Epoch 167 time = 10.05, tr_rmse = 11.93393, valid_rmse = 9.24919, ts_rmse = 9.09249, tr_mae = 7.30663, valid_mae = 5.97101, ts_mae = 5.94513
Epoch 168 time = 9.95, tr_rmse = 11.89580, valid_rmse = 9.24336, ts_rmse = 9.08408, tr_mae = 7.20925, valid_mae = 5.92660, ts_mae = 5.90493
Epoch 169 time = 9.85, tr_rmse = 12.01448, valid_rmse = 9.30519, ts_rmse = 9.14459, tr_mae = 7.46769, valid_mae = 6.06613, ts_mae = 6.03342
Epoch 170 time = 9.69, tr_rmse = 11.92328, valid_rmse = 9.24357, ts_rmse = 9.08134, tr_mae = 7.29060, valid_mae = 5.95392, ts_mae = 5.93193
Epoch 171 time = 9.87, tr_rmse = 11.97109, valid_rmse = 9.27559, ts_rmse = 9.12407, tr_mae = 7.40269, valid_mae = 6.02765, ts_mae = 6.00245
Epoch 172 time = 9.71, tr_rmse = 11.97110, valid_rmse = 9.27092, ts_rmse = 9.12137, tr_mae = 7.38728, valid_mae = 6.02429, ts_mae = 5.99801
Epoch 173 time = 9.92, tr_rmse = 11.91242, valid_rmse = 9.23236, ts_rmse = 9.05820, tr_mae = 7.26689, valid_mae = 5.93821, ts_mae = 5.91186
Epoch 174 time = 10.26, tr_rmse = 11.92822, valid_rmse = 9.22520, ts_rmse = 9.07796, tr_mae = 7.31747, valid_mae = 5.95477, ts_mae = 5.93551
Epoch 175 time = 10.33, tr_rmse = 11.88255, valid_rmse = 9.21310, ts_rmse = 9.04718, tr_mae = 7.20631, valid_mae = 5.90215, ts_mae = 5.88123
Epoch 176 time = 10.17, tr_rmse = 11.87675, valid_rmse = 9.22559, ts_rmse = 9.05423, tr_mae = 7.14677, valid_mae = 5.88592, ts_mae = 5.86035
Epoch 177 time = 10.14, tr_rmse = 11.88650, valid_rmse = 9.20524, ts_rmse = 9.04307, tr_mae = 7.23289, valid_mae = 5.90898, ts_mae = 5.88639
Epoch 178 time = 10.17, tr_rmse = 11.94746, valid_rmse = 9.25480, ts_rmse = 9.09992, tr_mae = 7.36934, valid_mae = 6.00055, ts_mae = 5.98023
Epoch 179 time = 9.98, tr_rmse = 11.86245, valid_rmse = 9.22126, ts_rmse = 9.04812, tr_mae = 7.17001, valid_mae = 5.89249, ts_mae = 5.86209
Epoch 180 time = 9.85, tr_rmse = 11.87409, valid_rmse = 9.21461, ts_rmse = 9.04575, tr_mae = 7.22949, valid_mae = 5.91262, ts_mae = 5.88749
Epoch 181 time = 9.98, tr_rmse = 11.88134, valid_rmse = 9.20783, ts_rmse = 9.03412, tr_mae = 7.23036, valid_mae = 5.90239, ts_mae = 5.87887
Epoch 182 time = 9.63, tr_rmse = 11.87552, valid_rmse = 9.31973, ts_rmse = 9.14347, tr_mae = 7.12477, valid_mae = 5.96495, ts_mae = 5.93619
Epoch 183 time = 9.73, tr_rmse = 11.88883, valid_rmse = 9.19873, ts_rmse = 9.03616, tr_mae = 7.22437, valid_mae = 5.90645, ts_mae = 5.88966
Epoch 184 time = 9.72, tr_rmse = 11.91378, valid_rmse = 9.23258, ts_rmse = 9.06552, tr_mae = 7.30647, valid_mae = 5.97188, ts_mae = 5.94648
Epoch 185 time = 9.66, tr_rmse = 11.87588, valid_rmse = 9.27383, ts_rmse = 9.08748, tr_mae = 7.11119, valid_mae = 5.91230, ts_mae = 5.87958
Epoch 186 time = 10.09, tr_rmse = 11.88798, valid_rmse = 9.20811, ts_rmse = 9.03456, tr_mae = 7.26477, valid_mae = 5.92447, ts_mae = 5.89436
Epoch 187 time = 10.27, tr_rmse = 11.84456, valid_rmse = 9.21985, ts_rmse = 9.03882, tr_mae = 7.13940, valid_mae = 5.88161, ts_mae = 5.84877
Epoch 188 time = 10.34, tr_rmse = 11.84135, valid_rmse = 9.19835, ts_rmse = 9.01326, tr_mae = 7.14339, valid_mae = 5.86605, ts_mae = 5.83192
Epoch 189 time = 10.11, tr_rmse = 11.84525, valid_rmse = 9.23149, ts_rmse = 9.05811, tr_mae = 7.10935, valid_mae = 5.88021, ts_mae = 5.85031
Epoch 190 time = 10.12, tr_rmse = 11.94332, valid_rmse = 9.29757, ts_rmse = 9.11061, tr_mae = 7.13404, valid_mae = 5.91587, ts_mae = 5.88781
Epoch 191 time = 9.94, tr_rmse = 11.87457, valid_rmse = 9.22200, ts_rmse = 9.05228, tr_mae = 7.23181, valid_mae = 5.92631, ts_mae = 5.89325
Epoch 192 time = 9.90, tr_rmse = 11.86250, valid_rmse = 9.18971, ts_rmse = 9.02317, tr_mae = 7.23591, valid_mae = 5.90139, ts_mae = 5.87672
Epoch 193 time = 9.81, tr_rmse = 11.84097, valid_rmse = 9.27201, ts_rmse = 9.08255, tr_mae = 7.09730, valid_mae = 5.90891, ts_mae = 5.87510
Epoch 194 time = 9.71, tr_rmse = 11.89535, valid_rmse = 9.22792, ts_rmse = 9.06222, tr_mae = 7.30328, valid_mae = 5.95544, ts_mae = 5.92563
Epoch 195 time = 9.68, tr_rmse = 11.83768, valid_rmse = 9.21425, ts_rmse = 9.04115, tr_mae = 7.10716, valid_mae = 5.86989, ts_mae = 5.84761
Epoch 196 time = 9.75, tr_rmse = 11.82915, valid_rmse = 9.19771, ts_rmse = 9.00730, tr_mae = 7.11883, valid_mae = 5.85419, ts_mae = 5.82227
Epoch 197 time = 9.86, tr_rmse = 11.86284, valid_rmse = 9.19667, ts_rmse = 9.02792, tr_mae = 7.24012, valid_mae = 5.90613, ts_mae = 5.87710
Epoch 198 time = 9.95, tr_rmse = 11.83923, valid_rmse = 9.18624, ts_rmse = 9.01938, tr_mae = 7.16725, valid_mae = 5.86767, ts_mae = 5.84152
Epoch 199 time = 10.14, tr_rmse = 11.83831, valid_rmse = 9.19354, ts_rmse = 9.01262, tr_mae = 7.18687, valid_mae = 5.88110, ts_mae = 5.84589
Epoch 200 time = 10.16, tr_rmse = 11.85885, valid_rmse = 9.19334, ts_rmse = 9.01663, tr_mae = 7.24259, valid_mae = 5.90721, ts_mae = 5.88097
Epoch 201 time = 10.18, tr_rmse = 11.92915, valid_rmse = 9.23669, ts_rmse = 9.06845, tr_mae = 7.37189, valid_mae = 6.01116, ts_mae = 5.97991
Epoch 202 time = 10.07, tr_rmse = 11.86387, valid_rmse = 9.24205, ts_rmse = 9.05623, tr_mae = 7.08191, valid_mae = 5.86115, ts_mae = 5.83404
Epoch 203 time = 10.01, tr_rmse = 11.88128, valid_rmse = 9.20540, ts_rmse = 9.03496, tr_mae = 7.30109, valid_mae = 5.94187, ts_mae = 5.90846
Epoch 204 time = 9.90, tr_rmse = 11.82509, valid_rmse = 9.16372, ts_rmse = 8.98825, tr_mae = 7.14794, valid_mae = 5.85317, ts_mae = 5.82624
Epoch 205 time = 9.79, tr_rmse = 11.84858, valid_rmse = 9.20607, ts_rmse = 9.02065, tr_mae = 7.22112, valid_mae = 5.90522, ts_mae = 5.86986
Epoch 206 time = 9.72, tr_rmse = 11.80826, valid_rmse = 9.17781, ts_rmse = 8.98572, tr_mae = 7.11373, valid_mae = 5.84084, ts_mae = 5.80887
Epoch 207 time = 9.70, tr_rmse = 11.81686, valid_rmse = 9.22000, ts_rmse = 9.03278, tr_mae = 7.12467, valid_mae = 5.88590, ts_mae = 5.84781
Epoch 208 time = 9.68, tr_rmse = 11.81497, valid_rmse = 9.20413, ts_rmse = 9.01627, tr_mae = 7.07578, valid_mae = 5.84877, ts_mae = 5.81205
Epoch 209 time = 9.80, tr_rmse = 11.86736, valid_rmse = 9.18941, ts_rmse = 9.02132, tr_mae = 7.27384, valid_mae = 5.92001, ts_mae = 5.89467
Epoch 210 time = 10.06, tr_rmse = 11.82237, valid_rmse = 9.23436, ts_rmse = 9.05197, tr_mae = 7.07685, valid_mae = 5.87367, ts_mae = 5.84502
Epoch 211 time = 10.73, tr_rmse = 11.80312, valid_rmse = 9.19685, ts_rmse = 9.00674, tr_mae = 7.12096, valid_mae = 5.85844, ts_mae = 5.82517
Epoch 212 time = 10.18, tr_rmse = 11.81738, valid_rmse = 9.16021, ts_rmse = 8.97825, tr_mae = 7.16430, valid_mae = 5.85686, ts_mae = 5.82566
Epoch 213 time = 10.33, tr_rmse = 11.81050, valid_rmse = 9.17086, ts_rmse = 8.98389, tr_mae = 7.16658, valid_mae = 5.86197, ts_mae = 5.82616
Epoch 214 time = 10.13, tr_rmse = 11.79881, valid_rmse = 9.16154, ts_rmse = 8.97778, tr_mae = 7.11266, valid_mae = 5.83430, ts_mae = 5.80172
Epoch 215 time = 9.89, tr_rmse = 11.80230, valid_rmse = 9.19327, ts_rmse = 9.00486, tr_mae = 7.08541, valid_mae = 5.84665, ts_mae = 5.81319
Epoch 216 time = 9.82, tr_rmse = 11.82477, valid_rmse = 9.26204, ts_rmse = 9.08005, tr_mae = 7.07727, valid_mae = 5.90339, ts_mae = 5.86797
Epoch 217 time = 9.87, tr_rmse = 11.90317, valid_rmse = 9.23293, ts_rmse = 9.06030, tr_mae = 7.34855, valid_mae = 5.97384, ts_mae = 5.94316
Epoch 218 time = 9.72, tr_rmse = 11.79810, valid_rmse = 9.17971, ts_rmse = 8.98953, tr_mae = 7.10364, valid_mae = 5.83990, ts_mae = 5.80359
Epoch 219 time = 9.78, tr_rmse = 11.79166, valid_rmse = 9.16976, ts_rmse = 8.98498, tr_mae = 7.11071, valid_mae = 5.83390, ts_mae = 5.80687
Epoch 220 time = 9.65, tr_rmse = 11.78979, valid_rmse = 9.17533, ts_rmse = 8.99086, tr_mae = 7.11345, valid_mae = 5.84433, ts_mae = 5.81026
Epoch 221 time = 9.62, tr_rmse = 11.80783, valid_rmse = 9.15762, ts_rmse = 8.97944, tr_mae = 7.17176, valid_mae = 5.86130, ts_mae = 5.83068
Epoch 222 time = 9.89, tr_rmse = 11.79197, valid_rmse = 9.15824, ts_rmse = 8.97256, tr_mae = 7.13716, valid_mae = 5.83950, ts_mae = 5.80769
Epoch 223 time = 10.15, tr_rmse = 11.78839, valid_rmse = 9.17133, ts_rmse = 8.98587, tr_mae = 7.13125, valid_mae = 5.85093, ts_mae = 5.81434
Epoch 224 time = 10.26, tr_rmse = 11.80114, valid_rmse = 9.15393, ts_rmse = 8.96476, tr_mae = 7.12675, valid_mae = 5.83838, ts_mae = 5.80369
Epoch 225 time = 11.08, tr_rmse = 11.80603, valid_rmse = 9.17958, ts_rmse = 8.98894, tr_mae = 7.16677, valid_mae = 5.87116, ts_mae = 5.83286
Epoch 226 time = 10.09, tr_rmse = 11.81781, valid_rmse = 9.21605, ts_rmse = 9.02238, tr_mae = 7.07457, valid_mae = 5.84315, ts_mae = 5.81150
Epoch 227 time = 9.92, tr_rmse = 11.79083, valid_rmse = 9.15234, ts_rmse = 8.96990, tr_mae = 7.16090, valid_mae = 5.85181, ts_mae = 5.82125
Epoch 228 time = 9.92, tr_rmse = 11.79166, valid_rmse = 9.23543, ts_rmse = 9.03391, tr_mae = 7.06433, valid_mae = 5.87290, ts_mae = 5.83601
Epoch 229 time = 9.90, tr_rmse = 11.79044, valid_rmse = 9.17452, ts_rmse = 8.97801, tr_mae = 7.13890, valid_mae = 5.85384, ts_mae = 5.81086
Epoch 230 time = 9.79, tr_rmse = 11.80582, valid_rmse = 9.15976, ts_rmse = 8.97070, tr_mae = 7.18560, valid_mae = 5.86910, ts_mae = 5.83126
Epoch 231 time = 9.85, tr_rmse = 11.79466, valid_rmse = 9.16309, ts_rmse = 8.97252, tr_mae = 7.17934, valid_mae = 5.86713, ts_mae = 5.82662
Epoch 232 time = 10.07, tr_rmse = 11.78108, valid_rmse = 9.21509, ts_rmse = 9.01985, tr_mae = 7.06689, valid_mae = 5.86407, ts_mae = 5.82627
Epoch 233 time = 9.67, tr_rmse = 11.77203, valid_rmse = 9.14827, ts_rmse = 8.96429, tr_mae = 7.11243, valid_mae = 5.83034, ts_mae = 5.79432
Epoch 234 time = 9.93, tr_rmse = 11.77145, valid_rmse = 9.14637, ts_rmse = 8.96191, tr_mae = 7.11801, valid_mae = 5.83577, ts_mae = 5.80333
Epoch 235 time = 10.16, tr_rmse = 11.76912, valid_rmse = 9.17674, ts_rmse = 8.98133, tr_mae = 7.06144, valid_mae = 5.82669, ts_mae = 5.79019
Epoch 236 time = 10.26, tr_rmse = 11.75961, valid_rmse = 9.16587, ts_rmse = 8.96385, tr_mae = 7.09041, valid_mae = 5.83061, ts_mae = 5.79037
Epoch 237 time = 10.18, tr_rmse = 11.76802, valid_rmse = 9.16457, ts_rmse = 8.95730, tr_mae = 7.09116, valid_mae = 5.82949, ts_mae = 5.78393
Epoch 238 time = 10.05, tr_rmse = 11.76838, valid_rmse = 9.16936, ts_rmse = 8.97172, tr_mae = 7.11443, valid_mae = 5.84533, ts_mae = 5.80533
Epoch 239 time = 9.93, tr_rmse = 11.76146, valid_rmse = 9.15032, ts_rmse = 8.96929, tr_mae = 7.10993, valid_mae = 5.83080, ts_mae = 5.79739
Epoch 240 time = 9.99, tr_rmse = 11.77892, valid_rmse = 9.16425, ts_rmse = 8.96659, tr_mae = 7.15868, valid_mae = 5.86172, ts_mae = 5.81751
Epoch 241 time = 10.35, tr_rmse = 11.75052, valid_rmse = 9.15911, ts_rmse = 8.96304, tr_mae = 7.09902, valid_mae = 5.83348, ts_mae = 5.79825
Epoch 242 time = 9.71, tr_rmse = 11.74653, valid_rmse = 9.15103, ts_rmse = 8.96552, tr_mae = 7.08035, valid_mae = 5.81923, ts_mae = 5.78341
Epoch 243 time = 9.82, tr_rmse = 11.78110, valid_rmse = 9.17649, ts_rmse = 8.99072, tr_mae = 7.16995, valid_mae = 5.87005, ts_mae = 5.83370
Epoch 244 time = 10.28, tr_rmse = 11.77787, valid_rmse = 9.17613, ts_rmse = 8.97783, tr_mae = 7.14170, valid_mae = 5.86498, ts_mae = 5.82106
Epoch 245 time = 9.72, tr_rmse = 11.77173, valid_rmse = 9.13716, ts_rmse = 8.95378, tr_mae = 7.15445, valid_mae = 5.84702, ts_mae = 5.81313
Epoch 246 time = 9.86, tr_rmse = 11.81869, valid_rmse = 9.18830, ts_rmse = 9.00555, tr_mae = 7.27003, valid_mae = 5.93502, ts_mae = 5.90188
Epoch 247 time = 10.37, tr_rmse = 11.76459, valid_rmse = 9.16187, ts_rmse = 8.97250, tr_mae = 7.12750, valid_mae = 5.84938, ts_mae = 5.81578
Epoch 248 time = 10.16, tr_rmse = 11.81257, valid_rmse = 9.18885, ts_rmse = 9.00226, tr_mae = 7.26875, valid_mae = 5.93548, ts_mae = 5.89742
Epoch 249 time = 10.44, tr_rmse = 11.77027, valid_rmse = 9.14672, ts_rmse = 8.95580, tr_mae = 7.17422, valid_mae = 5.86395, ts_mae = 5.82538
Epoch 250 time = 10.30, tr_rmse = 11.73947, valid_rmse = 9.14899, ts_rmse = 8.95315, tr_mae = 7.06319, valid_mae = 5.81401, ts_mae = 5.77824
Epoch 251 time = 10.07, tr_rmse = 11.75722, valid_rmse = 9.15113, ts_rmse = 8.95829, tr_mae = 7.12576, valid_mae = 5.84534, ts_mae = 5.80573
Epoch 252 time = 10.01, tr_rmse = 11.77384, valid_rmse = 9.15667, ts_rmse = 8.97659, tr_mae = 7.19560, valid_mae = 5.87548, ts_mae = 5.84240
Epoch 253 time = 10.14, tr_rmse = 11.77782, valid_rmse = 9.14728, ts_rmse = 8.96175, tr_mae = 7.19810, valid_mae = 5.87744, ts_mae = 5.84071
Epoch 254 time = 9.87, tr_rmse = 11.76770, valid_rmse = 9.15784, ts_rmse = 8.97070, tr_mae = 7.19357, valid_mae = 5.90183, ts_mae = 5.86578
Epoch 255 time = 9.87, tr_rmse = 11.74559, valid_rmse = 9.14752, ts_rmse = 8.96680, tr_mae = 7.10325, valid_mae = 5.83211, ts_mae = 5.79709
Epoch 256 time = 9.85, tr_rmse = 11.78722, valid_rmse = 9.21895, ts_rmse = 9.02315, tr_mae = 7.04445, valid_mae = 5.84254, ts_mae = 5.81146
Epoch 257 time = 9.62, tr_rmse = 11.73070, valid_rmse = 9.13710, ts_rmse = 8.95016, tr_mae = 7.10402, valid_mae = 5.83290, ts_mae = 5.79304
Epoch 258 time = 9.82, tr_rmse = 11.80076, valid_rmse = 9.17686, ts_rmse = 8.98830, tr_mae = 7.26565, valid_mae = 5.92930, ts_mae = 5.88742
Epoch 259 time = 9.99, tr_rmse = 11.73046, valid_rmse = 9.14051, ts_rmse = 8.95683, tr_mae = 7.07953, valid_mae = 5.82174, ts_mae = 5.79046
Epoch 260 time = 10.16, tr_rmse = 11.78609, valid_rmse = 9.17285, ts_rmse = 8.98359, tr_mae = 7.22291, valid_mae = 5.90643, ts_mae = 5.86948
Epoch 261 time = 10.19, tr_rmse = 11.75108, valid_rmse = 9.19353, ts_rmse = 8.99197, tr_mae = 7.02398, valid_mae = 5.81671, ts_mae = 5.78155
Epoch 262 time = 10.45, tr_rmse = 11.72230, valid_rmse = 9.13622, ts_rmse = 8.94034, tr_mae = 7.05811, valid_mae = 5.80696, ts_mae = 5.76865