Example 14: Sequence-to-Scalar Prediction¶

Not all system identification tasks predict a full output sequence. Sometimes the target is a single scalar -- a physical parameter, a classification label, or a summary statistic. This example builds a custom data pipeline with HDF5Attrs for scalar targets and uses SeqAggregation to reduce RNN output to a scalar prediction.

Prerequisites¶

This example builds on Example 00 and Example 01. Make sure the library is installed:

uv sync --extra dev

Setup¶

In [1]:

from pathlib import Path

import torch.nn as nn
from torch.utils.data import DataLoader

from tsfast.tsdata import (
    WindowedDataset, HDF5Signals, HDF5Attrs, SourceEntry,
    DataLoaders, get_hdf_files, split_by_parent,
)
from tsfast.models.architectures.rnn import SimpleRNN
from tsfast.models._core.layers import SeqAggregation
from tsfast.models._core.scaling import ScaledModel, StandardScaler
from tsfast.training import Learner, fun_rmse

from pathlib import Path import torch.nn as nn from torch.utils.data import DataLoader from tsfast.tsdata import ( WindowedDataset, HDF5Signals, HDF5Attrs, SourceEntry, DataLoaders, get_hdf_files, split_by_parent, ) from tsfast.models.architectures.rnn import SimpleRNN from tsfast.models._core.layers import SeqAggregation from tsfast.models._core.scaling import ScaledModel, StandardScaler from tsfast.training import Learner, fun_rmse

The Task: Estimating Initial Conditions¶

We have spring-damper trajectories with different initial positions (x0) and velocities (v0). Given the opening segment of the time series [u, x, v] -- force input, position, and velocity -- can a neural network estimate what the initial conditions were?

This is a sequence-to-scalar regression problem: the input is a multi- channel time series and the output is a fixed-size vector [x0, v0].

Load the Dataset¶

The pinn_var_ic test data contains HDF5 files organized into train/, valid/, and test/ directories. Each file stores a 500-step spring-damper trajectory with the force input u, position x, and velocity v as datasets, plus scalar attributes like x0 (initial position) and v0 (initial velocity).

In [2]:

def _find_project_root(marker: str = "test_data") -> Path:
    """Walk up from script/notebook location to find the project root."""
    try:
        start = Path(__file__).resolve().parent
    except NameError:
        start = Path(".").resolve()
    p = start
    while p != p.parent:
        if (p / marker).is_dir():
            return p
        p = p.parent
    raise FileNotFoundError(f"Could not find '{marker}' directory above {start}")


_root = _find_project_root()
data_path = _root / "test_data" / "pinn_var_ic"

files = get_hdf_files(data_path)
print(f"Found {len(files)} HDF5 files")
for f in files[:5]:
    print(f"  {f.parent.name}/{f.name}")

def _find_project_root(marker: str = "test_data") -> Path: """Walk up from script/notebook location to find the project root.""" try: start = Path(__file__).resolve().parent except NameError: start = Path(".").resolve() p = start while p != p.parent: if (p / marker).is_dir(): return p p = p.parent raise FileNotFoundError(f"Could not find '{marker}' directory above {start}") _root = _find_project_root() data_path = _root / "test_data" / "pinn_var_ic" files = get_hdf_files(data_path) print(f"Found {len(files)} HDF5 files") for f in files[:5]: print(f" {f.parent.name}/{f.name}")

Found 34 HDF5 files
  test/x0_v0_chirp.h5
  test/x0_v0_sine_15hz.h5
  test/x1p5_v1_chirp.h5
  test/x1p5_v1_sine_15hz.h5
  test/x2_v3_chirp.h5

Building a Custom Data Pipeline¶

Unlike create_dls which creates sequence-to-sequence pipelines, here we need a custom pipeline with:

• Input: HDF5Signals reading [u, x, v] columns as a multi-channel time series
• Target: HDF5Attrs reading [x0, v0] from HDF5 file attributes

In [3]:

inputs_block = HDF5Signals(['u', 'x', 'v'])
targets_block = HDF5Attrs(['x0', 'v0'])

inputs_block = HDF5Signals(['u', 'x', 'v']) targets_block = HDF5Attrs(['x0', 'v0'])

Create SourceEntry objects from the discovered files. Each entry points to an HDF5 file with a default resampling factor of 1.0.

In [4]:

entries = [SourceEntry(path=str(f)) for f in files]
train_idx, valid_idx = split_by_parent(files)

train_entries = [entries[i] for i in train_idx]
valid_entries = [entries[i] for i in valid_idx]

entries = [SourceEntry(path=str(f)) for f in files] train_idx, valid_idx = split_by_parent(files) train_entries = [entries[i] for i in train_idx] valid_entries = [entries[i] for i in valid_idx]

Build WindowedDataset for train and validation splits. Using win_sz=100, stp_sz=500 gives one window per file: the first 100 timesteps of the trajectory. The window must start at the trajectory beginning -- that is where the initial condition is directly observable -- so the step size matches the file length (500) to prevent additional windows from later in the trajectory, whose [x0, v0] labels would not describe the window's own starting state.

In [5]:

train_ds = WindowedDataset(train_entries, inputs=inputs_block, targets=targets_block, win_sz=100, stp_sz=500)
valid_ds = WindowedDataset(valid_entries, inputs=inputs_block, targets=targets_block, win_sz=100, stp_sz=500)

print(f"Train samples: {len(train_ds)}")
print(f"Valid samples: {len(valid_ds)}")

train_ds = WindowedDataset(train_entries, inputs=inputs_block, targets=targets_block, win_sz=100, stp_sz=500) valid_ds = WindowedDataset(valid_entries, inputs=inputs_block, targets=targets_block, win_sz=100, stp_sz=500) print(f"Train samples: {len(train_ds)}") print(f"Valid samples: {len(valid_ds)}")

Train samples: 20
Valid samples: 6

Wrap in DataLoaders. We construct standard PyTorch DataLoaders and wrap them in DataLoaders for compatibility with the Learner.

In [6]:

train_dl = DataLoader(train_ds, batch_size=16, shuffle=True)
valid_dl = DataLoader(valid_ds, batch_size=16, shuffle=False)
dls = DataLoaders(train_dl, valid_dl)

train_dl = DataLoader(train_ds, batch_size=16, shuffle=True) valid_dl = DataLoader(valid_ds, batch_size=16, shuffle=False) dls = DataLoaders(train_dl, valid_dl)

Each component of the pipeline:

• HDF5Signals(['u', 'x', 'v']) -- extracts named datasets from each HDF5 file as a 3-channel time series with shape (seq_len, 3).
• HDF5Attrs(['x0', 'v0']) -- reads named attributes from HDF5 metadata as a scalar target vector of shape (2,).
• WindowedDataset -- combines the blocks and slices files into windows. With win_sz=100, stp_sz=500, each file produces exactly one sample covering the first 100 timesteps.
• split_by_parent -- uses the directory structure (train/, valid/) to split files into train and validation sets.

Modifying the Model for Scalar Output¶

A standard SimpleRNN produces a sequence output with shape (batch, seq_len, n_outputs). To predict scalars, we append SeqAggregation which selects the last timestep of the RNN output, reducing it to (batch, n_outputs).

We build the model manually with SimpleRNN because RNNLearner produces a pure sequence-to-sequence model with no way to append the aggregation layer. Normalization comes from ScaledModel.from_dls, which reads dls.norm_stats -- for a custom pipeline like this one, the stats are estimated automatically from the first training batches. ScaledModel wraps the RNN itself (its scalers broadcast over (batch, seq_len, features) tensors), and SeqAggregation follows it in the nn.Sequential: selecting the last timestep commutes with the per-feature output denormalization.

In [7]:

input_size = 3   # u, x, v
output_size = 2  # x0, v0
hidden_size = 40

rnn = SimpleRNN(input_size, output_size, hidden_size=hidden_size, rnn_type='gru')
model = nn.Sequential(
    ScaledModel.from_dls(rnn, dls, StandardScaler, StandardScaler),
    SeqAggregation(),
)

lrn = Learner(
    model,
    dls,
    loss_func=nn.L1Loss(),
    metrics=[fun_rmse],
    lr=1e-3,
)

input_size = 3 # u, x, v output_size = 2 # x0, v0 hidden_size = 40 rnn = SimpleRNN(input_size, output_size, hidden_size=hidden_size, rnn_type='gru') model = nn.Sequential( ScaledModel.from_dls(rnn, dls, StandardScaler, StandardScaler), SeqAggregation(), ) lrn = Learner( model, dls, loss_func=nn.L1Loss(), metrics=[fun_rmse], lr=1e-3, )

The model pipeline:

1. ScaledModel normalizes the 3-channel input, runs the wrapped SimpleRNN, and denormalizes the (batch, seq_len, 2) output.
2. SeqAggregation() selects the last timestep, yielding (batch, 2) -- one prediction for x0 and one for v0.

Train and Evaluate¶

With only 20 training trajectories, one batch covers most of an epoch, so a single epoch is just a couple of gradient steps. Training for 300 epochs still takes only seconds and brings the validation fun_rmse to roughly 0.1 -- the validation initial conditions are combinations never seen during training, so the model has to read them off the trajectory rather than memorize them.

In [8]:

lrn.fit_flat_cos(n_epoch=300, lr=1e-3)

lrn.fit_flat_cos(n_epoch=300, lr=1e-3)

Epoch 1/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 1/300: 100%|██████████| 2/2 [00:00<00:00, 14.55it/s, train=0.6738 | valid=0.5440 | fun_rmse=0.6691]

Epoch 1/300: 100%|██████████| 2/2 [00:00<00:00, 14.51it/s, train=0.6738 | valid=0.5440 | fun_rmse=0.6691]

Epoch 2/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 2/300: 100%|██████████| 2/2 [00:00<00:00, 206.35it/s, train=0.7124 | valid=0.5324 | fun_rmse=0.6547]

Epoch 2/300: 100%|██████████| 2/2 [00:00<00:00, 200.26it/s, train=0.7124 | valid=0.5324 | fun_rmse=0.6547]

Epoch 3/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 3/300: 100%|██████████| 2/2 [00:00<00:00, 143.40it/s, train=0.4756 | valid=0.5213 | fun_rmse=0.6417]

Epoch 3/300: 100%|██████████| 2/2 [00:00<00:00, 137.63it/s, train=0.4756 | valid=0.5213 | fun_rmse=0.6417]

Epoch 4/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 4/300: 100%|██████████| 2/2 [00:00<00:00, 118.64it/s, train=0.5902 | valid=0.5102 | fun_rmse=0.6296]

Epoch 4/300: 100%|██████████| 2/2 [00:00<00:00, 116.50it/s, train=0.5902 | valid=0.5102 | fun_rmse=0.6296]

Epoch 5/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 5/300: 100%|██████████| 2/2 [00:00<00:00, 160.28it/s, train=0.6557 | valid=0.4989 | fun_rmse=0.6177]

Epoch 5/300: 100%|██████████| 2/2 [00:00<00:00, 155.67it/s, train=0.6557 | valid=0.4989 | fun_rmse=0.6177]

Epoch 6/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 6/300: 100%|██████████| 2/2 [00:00<00:00, 205.57it/s, train=0.5576 | valid=0.4874 | fun_rmse=0.6059]

Epoch 6/300: 100%|██████████| 2/2 [00:00<00:00, 198.94it/s, train=0.5576 | valid=0.4874 | fun_rmse=0.6059]

Epoch 7/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 7/300: 100%|██████████| 2/2 [00:00<00:00, 170.61it/s, train=0.5845 | valid=0.4759 | fun_rmse=0.5943]

Epoch 7/300: 100%|██████████| 2/2 [00:00<00:00, 166.30it/s, train=0.5845 | valid=0.4759 | fun_rmse=0.5943]

Epoch 8/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 8/300: 100%|██████████| 2/2 [00:00<00:00, 169.37it/s, train=0.5782 | valid=0.4660 | fun_rmse=0.5823]

Epoch 8/300: 100%|██████████| 2/2 [00:00<00:00, 166.22it/s, train=0.5782 | valid=0.4660 | fun_rmse=0.5823]

Epoch 9/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 9/300: 100%|██████████| 2/2 [00:00<00:00, 190.71it/s, train=0.5754 | valid=0.4574 | fun_rmse=0.5704]

Epoch 9/300: 100%|██████████| 2/2 [00:00<00:00, 184.96it/s, train=0.5754 | valid=0.4574 | fun_rmse=0.5704]

Epoch 10/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 10/300: 100%|██████████| 2/2 [00:00<00:00, 94.55it/s, train=0.6378 | valid=0.4492 | fun_rmse=0.5586]

Epoch 10/300: 100%|██████████| 2/2 [00:00<00:00, 89.71it/s, train=0.6378 | valid=0.4492 | fun_rmse=0.5586]

Epoch 11/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 11/300: 100%|██████████| 2/2 [00:00<00:00, 81.81it/s, train=0.4018 | valid=0.4417 | fun_rmse=0.5467]

Epoch 11/300: 100%|██████████| 2/2 [00:00<00:00, 80.66it/s, train=0.4018 | valid=0.4417 | fun_rmse=0.5467]

Epoch 12/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 12/300: 100%|██████████| 2/2 [00:00<00:00, 56.88it/s, train=0.4463 | valid=0.4341 | fun_rmse=0.5357]

Epoch 12/300: 100%|██████████| 2/2 [00:00<00:00, 54.94it/s, train=0.4463 | valid=0.4341 | fun_rmse=0.5357]

Epoch 13/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 13/300: 100%|██████████| 2/2 [00:00<00:00, 51.09it/s, train=0.6322 | valid=0.4262 | fun_rmse=0.5247]

Epoch 13/300: 100%|██████████| 2/2 [00:00<00:00, 49.45it/s, train=0.6322 | valid=0.4262 | fun_rmse=0.5247]

Epoch 14/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 14/300: 100%|██████████| 2/2 [00:00<00:00, 65.21it/s, train=0.5689 | valid=0.4175 | fun_rmse=0.5122]

Epoch 14/300: 100%|██████████| 2/2 [00:00<00:00, 63.91it/s, train=0.5689 | valid=0.4175 | fun_rmse=0.5122]

Epoch 15/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 15/300: 100%|██████████| 2/2 [00:00<00:00, 81.30it/s, train=0.5850 | valid=0.4081 | fun_rmse=0.4988]

Epoch 15/300: 100%|██████████| 2/2 [00:00<00:00, 80.23it/s, train=0.5850 | valid=0.4081 | fun_rmse=0.4988]

Epoch 16/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 16/300: 100%|██████████| 2/2 [00:00<00:00, 155.26it/s, train=0.4899 | valid=0.3981 | fun_rmse=0.4845]

Epoch 16/300: 100%|██████████| 2/2 [00:00<00:00, 149.61it/s, train=0.4899 | valid=0.3981 | fun_rmse=0.4845]

Epoch 17/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 17/300: 100%|██████████| 2/2 [00:00<00:00, 49.19it/s, train=0.5454 | valid=0.3876 | fun_rmse=0.4697]

Epoch 17/300: 100%|██████████| 2/2 [00:00<00:00, 47.65it/s, train=0.5454 | valid=0.3876 | fun_rmse=0.4697]

Epoch 18/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 18/300: 100%|██████████| 2/2 [00:00<00:00, 49.00it/s, train=0.5296 | valid=0.3762 | fun_rmse=0.4536]

Epoch 18/300: 100%|██████████| 2/2 [00:00<00:00, 48.62it/s, train=0.5296 | valid=0.3762 | fun_rmse=0.4536]

Epoch 19/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 19/300: 100%|██████████| 2/2 [00:00<00:00, 64.73it/s, train=0.5061 | valid=0.3639 | fun_rmse=0.4366]

Epoch 19/300: 100%|██████████| 2/2 [00:00<00:00, 64.08it/s, train=0.5061 | valid=0.3639 | fun_rmse=0.4366]

Epoch 20/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 20/300: 100%|██████████| 2/2 [00:00<00:00, 94.77it/s, train=0.4523 | valid=0.3512 | fun_rmse=0.4191]

Epoch 20/300: 100%|██████████| 2/2 [00:00<00:00, 91.90it/s, train=0.4523 | valid=0.3512 | fun_rmse=0.4191]

Epoch 21/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 21/300: 100%|██████████| 2/2 [00:00<00:00, 73.52it/s, train=0.4936 | valid=0.3376 | fun_rmse=0.4008]

Epoch 21/300: 100%|██████████| 2/2 [00:00<00:00, 70.52it/s, train=0.4936 | valid=0.3376 | fun_rmse=0.4008]

Epoch 22/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 22/300: 100%|██████████| 2/2 [00:00<00:00, 46.20it/s, train=0.4761 | valid=0.3225 | fun_rmse=0.3810]

Epoch 22/300: 100%|██████████| 2/2 [00:00<00:00, 45.87it/s, train=0.4761 | valid=0.3225 | fun_rmse=0.3810]

Epoch 23/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 23/300: 100%|██████████| 2/2 [00:00<00:00, 106.68it/s, train=0.4121 | valid=0.3060 | fun_rmse=0.3604]

Epoch 23/300: 100%|██████████| 2/2 [00:00<00:00, 105.09it/s, train=0.4121 | valid=0.3060 | fun_rmse=0.3604]

Epoch 24/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 24/300: 100%|██████████| 2/2 [00:00<00:00, 181.10it/s, train=0.4426 | valid=0.2877 | fun_rmse=0.3391]

Epoch 24/300: 100%|██████████| 2/2 [00:00<00:00, 177.82it/s, train=0.4426 | valid=0.2877 | fun_rmse=0.3391]

Epoch 25/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 25/300: 100%|██████████| 2/2 [00:00<00:00, 148.38it/s, train=0.4326 | valid=0.2666 | fun_rmse=0.3168]

Epoch 25/300: 100%|██████████| 2/2 [00:00<00:00, 142.37it/s, train=0.4326 | valid=0.2666 | fun_rmse=0.3168]

Epoch 26/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 26/300: 100%|██████████| 2/2 [00:00<00:00, 58.37it/s, train=0.3574 | valid=0.2423 | fun_rmse=0.2962]

Epoch 26/300: 100%|██████████| 2/2 [00:00<00:00, 56.24it/s, train=0.3574 | valid=0.2423 | fun_rmse=0.2962]

Epoch 27/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 27/300: 100%|██████████| 2/2 [00:00<00:00, 62.99it/s, train=0.3990 | valid=0.2155 | fun_rmse=0.2797]

Epoch 27/300: 100%|██████████| 2/2 [00:00<00:00, 62.37it/s, train=0.3990 | valid=0.2155 | fun_rmse=0.2797]

Epoch 28/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 28/300: 100%|██████████| 2/2 [00:00<00:00, 52.30it/s, train=0.3844 | valid=0.2225 | fun_rmse=0.2722]

Epoch 28/300: 100%|██████████| 2/2 [00:00<00:00, 51.06it/s, train=0.3844 | valid=0.2225 | fun_rmse=0.2722]

Epoch 29/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 29/300: 100%|██████████| 2/2 [00:00<00:00, 45.67it/s, train=0.2710 | valid=0.2262 | fun_rmse=0.2737]

Epoch 29/300: 100%|██████████| 2/2 [00:00<00:00, 44.41it/s, train=0.2710 | valid=0.2262 | fun_rmse=0.2737]

Epoch 30/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 30/300: 100%|██████████| 2/2 [00:00<00:00, 71.66it/s, train=0.3178 | valid=0.2236 | fun_rmse=0.2772]

Epoch 30/300: 100%|██████████| 2/2 [00:00<00:00, 70.80it/s, train=0.3178 | valid=0.2236 | fun_rmse=0.2772]

Epoch 31/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 31/300: 100%|██████████| 2/2 [00:00<00:00, 154.29it/s, train=0.3791 | valid=0.2350 | fun_rmse=0.2821]

Epoch 31/300: 100%|██████████| 2/2 [00:00<00:00, 148.09it/s, train=0.3791 | valid=0.2350 | fun_rmse=0.2821]

Epoch 32/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 32/300: 100%|██████████| 2/2 [00:00<00:00, 113.94it/s, train=0.3032 | valid=0.2423 | fun_rmse=0.2826]

Epoch 32/300: 100%|██████████| 2/2 [00:00<00:00, 109.86it/s, train=0.3032 | valid=0.2423 | fun_rmse=0.2826]

Epoch 33/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 33/300: 100%|██████████| 2/2 [00:00<00:00, 102.08it/s, train=0.2284 | valid=0.2504 | fun_rmse=0.2829]

Epoch 33/300: 100%|██████████| 2/2 [00:00<00:00, 97.17it/s, train=0.2284 | valid=0.2504 | fun_rmse=0.2829] 

Epoch 34/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 34/300: 100%|██████████| 2/2 [00:00<00:00, 67.11it/s, train=0.3286 | valid=0.2560 | fun_rmse=0.2859]

Epoch 34/300: 100%|██████████| 2/2 [00:00<00:00, 64.96it/s, train=0.3286 | valid=0.2560 | fun_rmse=0.2859]

Epoch 35/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 35/300: 100%|██████████| 2/2 [00:00<00:00, 78.95it/s, train=0.2261 | valid=0.2512 | fun_rmse=0.2827]

Epoch 35/300: 100%|██████████| 2/2 [00:00<00:00, 76.13it/s, train=0.2261 | valid=0.2512 | fun_rmse=0.2827]

Epoch 36/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 36/300: 100%|██████████| 2/2 [00:00<00:00, 49.82it/s, train=0.3455 | valid=0.2351 | fun_rmse=0.2715]

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Epoch 37/300: 100%|██████████| 2/2 [00:00<00:00, 72.01it/s, train=0.2852 | valid=0.2204 | fun_rmse=0.2618]

Epoch 37/300: 100%|██████████| 2/2 [00:00<00:00, 68.89it/s, train=0.2852 | valid=0.2204 | fun_rmse=0.2618]

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Epoch 38/300: 100%|██████████| 2/2 [00:00<00:00, 74.92it/s, train=0.2844 | valid=0.2159 | fun_rmse=0.2553]

Epoch 38/300: 100%|██████████| 2/2 [00:00<00:00, 72.23it/s, train=0.2844 | valid=0.2159 | fun_rmse=0.2553]

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Epoch 39/300: 100%|██████████| 2/2 [00:00<00:00, 49.50it/s, train=0.2220 | valid=0.2151 | fun_rmse=0.2508]

Epoch 39/300: 100%|██████████| 2/2 [00:00<00:00, 47.39it/s, train=0.2220 | valid=0.2151 | fun_rmse=0.2508]

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Epoch 40/300: 100%|██████████| 2/2 [00:00<00:00, 94.17it/s, train=0.2317 | valid=0.2128 | fun_rmse=0.2476]

Epoch 40/300: 100%|██████████| 2/2 [00:00<00:00, 92.35it/s, train=0.2317 | valid=0.2128 | fun_rmse=0.2476]

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Epoch 41/300: 100%|██████████| 2/2 [00:00<00:00, 220.32it/s, train=0.2394 | valid=0.2116 | fun_rmse=0.2468]

Epoch 41/300: 100%|██████████| 2/2 [00:00<00:00, 211.19it/s, train=0.2394 | valid=0.2116 | fun_rmse=0.2468]

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Epoch 42/300: 100%|██████████| 2/2 [00:00<00:00, 193.73it/s, train=0.1739 | valid=0.2112 | fun_rmse=0.2486]

Epoch 42/300: 100%|██████████| 2/2 [00:00<00:00, 187.56it/s, train=0.1739 | valid=0.2112 | fun_rmse=0.2486]

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Epoch 43/300: 100%|██████████| 2/2 [00:00<00:00, 147.43it/s, train=0.2057 | valid=0.2169 | fun_rmse=0.2519]

Epoch 43/300: 100%|██████████| 2/2 [00:00<00:00, 143.68it/s, train=0.2057 | valid=0.2169 | fun_rmse=0.2519]

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Epoch 44/300: 100%|██████████| 2/2 [00:00<00:00, 145.14it/s, train=0.2300 | valid=0.2234 | fun_rmse=0.2554]

Epoch 44/300: 100%|██████████| 2/2 [00:00<00:00, 139.72it/s, train=0.2300 | valid=0.2234 | fun_rmse=0.2554]

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Epoch 45/300: 100%|██████████| 2/2 [00:00<00:00, 71.19it/s, train=0.2022 | valid=0.2248 | fun_rmse=0.2561]

Epoch 45/300: 100%|██████████| 2/2 [00:00<00:00, 68.47it/s, train=0.2022 | valid=0.2248 | fun_rmse=0.2561]

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Epoch 46/300: 100%|██████████| 2/2 [00:00<00:00, 71.60it/s, train=0.1996 | valid=0.2123 | fun_rmse=0.2465]

Epoch 46/300: 100%|██████████| 2/2 [00:00<00:00, 68.71it/s, train=0.1996 | valid=0.2123 | fun_rmse=0.2465]

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Epoch 47/300: 100%|██████████| 2/2 [00:00<00:00, 92.75it/s, train=0.2341 | valid=0.1959 | fun_rmse=0.2348]

Epoch 47/300: 100%|██████████| 2/2 [00:00<00:00, 89.38it/s, train=0.2341 | valid=0.1959 | fun_rmse=0.2348]

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Epoch 48/300: 100%|██████████| 2/2 [00:00<00:00, 75.87it/s, train=0.1824 | valid=0.1844 | fun_rmse=0.2244]

Epoch 48/300: 100%|██████████| 2/2 [00:00<00:00, 72.57it/s, train=0.1824 | valid=0.1844 | fun_rmse=0.2244]

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Epoch 49/300: 100%|██████████| 2/2 [00:00<00:00, 51.40it/s, train=0.2111 | valid=0.1782 | fun_rmse=0.2160]

Epoch 49/300: 100%|██████████| 2/2 [00:00<00:00, 49.84it/s, train=0.2111 | valid=0.1782 | fun_rmse=0.2160]

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Epoch 50/300: 100%|██████████| 2/2 [00:00<00:00, 71.48it/s, train=0.2187 | valid=0.1749 | fun_rmse=0.2091]

Epoch 50/300: 100%|██████████| 2/2 [00:00<00:00, 69.59it/s, train=0.2187 | valid=0.1749 | fun_rmse=0.2091]

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Epoch 51/300: 100%|██████████| 2/2 [00:00<00:00, 116.71it/s, train=0.1852 | valid=0.1788 | fun_rmse=0.2039]

Epoch 51/300: 100%|██████████| 2/2 [00:00<00:00, 114.53it/s, train=0.1852 | valid=0.1788 | fun_rmse=0.2039]

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Epoch 52/300: 100%|██████████| 2/2 [00:00<00:00, 100.71it/s, train=0.2100 | valid=0.1765 | fun_rmse=0.1956]

Epoch 52/300: 100%|██████████| 2/2 [00:00<00:00, 95.35it/s, train=0.2100 | valid=0.1765 | fun_rmse=0.1956] 

Epoch 53/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 53/300: 100%|██████████| 2/2 [00:00<00:00, 123.75it/s, train=0.1637 | valid=0.1689 | fun_rmse=0.1848]

Epoch 53/300: 100%|██████████| 2/2 [00:00<00:00, 119.27it/s, train=0.1637 | valid=0.1689 | fun_rmse=0.1848]

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Epoch 54/300: 100%|██████████| 2/2 [00:00<00:00, 103.22it/s, train=0.1707 | valid=0.1515 | fun_rmse=0.1667]

Epoch 54/300: 100%|██████████| 2/2 [00:00<00:00, 99.36it/s, train=0.1707 | valid=0.1515 | fun_rmse=0.1667] 

Epoch 55/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 55/300: 100%|██████████| 2/2 [00:00<00:00, 60.55it/s, train=0.1382 | valid=0.1273 | fun_rmse=0.1439]

Epoch 55/300: 100%|██████████| 2/2 [00:00<00:00, 58.76it/s, train=0.1382 | valid=0.1273 | fun_rmse=0.1439]

Epoch 56/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 56/300: 100%|██████████| 2/2 [00:00<00:00, 78.05it/s, train=0.1293 | valid=0.1063 | fun_rmse=0.1254]

Epoch 56/300: 100%|██████████| 2/2 [00:00<00:00, 75.96it/s, train=0.1293 | valid=0.1063 | fun_rmse=0.1254]

Epoch 57/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 57/300: 100%|██████████| 2/2 [00:00<00:00, 69.81it/s, train=0.1270 | valid=0.0899 | fun_rmse=0.1108]

Epoch 57/300: 100%|██████████| 2/2 [00:00<00:00, 68.39it/s, train=0.1270 | valid=0.0899 | fun_rmse=0.1108]

Epoch 58/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 58/300: 100%|██████████| 2/2 [00:00<00:00, 48.78it/s, train=0.1408 | valid=0.0802 | fun_rmse=0.0980]

Epoch 58/300: 100%|██████████| 2/2 [00:00<00:00, 47.24it/s, train=0.1408 | valid=0.0802 | fun_rmse=0.0980]

Epoch 59/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 59/300: 100%|██████████| 2/2 [00:00<00:00, 85.59it/s, train=0.1277 | valid=0.0769 | fun_rmse=0.0883]

Epoch 59/300: 100%|██████████| 2/2 [00:00<00:00, 82.39it/s, train=0.1277 | valid=0.0769 | fun_rmse=0.0883]

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Epoch 60/300: 100%|██████████| 2/2 [00:00<00:00, 117.10it/s, train=0.1091 | valid=0.0667 | fun_rmse=0.0796]

Epoch 60/300: 100%|██████████| 2/2 [00:00<00:00, 115.17it/s, train=0.1091 | valid=0.0667 | fun_rmse=0.0796]

Epoch 61/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 61/300: 100%|██████████| 2/2 [00:00<00:00, 206.18it/s, train=0.1200 | valid=0.0570 | fun_rmse=0.0695]

Epoch 61/300: 100%|██████████| 2/2 [00:00<00:00, 198.50it/s, train=0.1200 | valid=0.0570 | fun_rmse=0.0695]

Epoch 62/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 62/300: 100%|██████████| 2/2 [00:00<00:00, 85.66it/s, train=0.0982 | valid=0.0555 | fun_rmse=0.0675]

Epoch 62/300: 100%|██████████| 2/2 [00:00<00:00, 81.30it/s, train=0.0982 | valid=0.0555 | fun_rmse=0.0675]

Epoch 63/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 63/300: 100%|██████████| 2/2 [00:00<00:00, 84.15it/s, train=0.0950 | valid=0.0611 | fun_rmse=0.0814]

Epoch 63/300: 100%|██████████| 2/2 [00:00<00:00, 82.17it/s, train=0.0950 | valid=0.0611 | fun_rmse=0.0814]

Epoch 64/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 64/300: 100%|██████████| 2/2 [00:00<00:00, 64.13it/s, train=0.1054 | valid=0.0652 | fun_rmse=0.0943]

Epoch 64/300: 100%|██████████| 2/2 [00:00<00:00, 61.60it/s, train=0.1054 | valid=0.0652 | fun_rmse=0.0943]

Epoch 65/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 65/300: 100%|██████████| 2/2 [00:00<00:00, 90.07it/s, train=0.1062 | valid=0.0730 | fun_rmse=0.1008]

Epoch 65/300: 100%|██████████| 2/2 [00:00<00:00, 85.19it/s, train=0.1062 | valid=0.0730 | fun_rmse=0.1008]

Epoch 66/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 66/300: 100%|██████████| 2/2 [00:00<00:00, 77.04it/s, train=0.0799 | valid=0.0931 | fun_rmse=0.1112]

Epoch 66/300: 100%|██████████| 2/2 [00:00<00:00, 73.81it/s, train=0.0799 | valid=0.0931 | fun_rmse=0.1112]

Epoch 67/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 67/300: 100%|██████████| 2/2 [00:00<00:00, 66.15it/s, train=0.0712 | valid=0.1022 | fun_rmse=0.1157]

Epoch 67/300: 100%|██████████| 2/2 [00:00<00:00, 63.57it/s, train=0.0712 | valid=0.1022 | fun_rmse=0.1157]

Epoch 68/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 68/300: 100%|██████████| 2/2 [00:00<00:00, 104.46it/s, train=0.0919 | valid=0.0995 | fun_rmse=0.1124]

Epoch 68/300: 100%|██████████| 2/2 [00:00<00:00, 102.71it/s, train=0.0919 | valid=0.0995 | fun_rmse=0.1124]

Epoch 69/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 69/300: 100%|██████████| 2/2 [00:00<00:00, 86.93it/s, train=0.0765 | valid=0.0978 | fun_rmse=0.1085]

Epoch 69/300: 100%|██████████| 2/2 [00:00<00:00, 84.40it/s, train=0.0765 | valid=0.0978 | fun_rmse=0.1085]

Epoch 70/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 70/300: 100%|██████████| 2/2 [00:00<00:00, 116.33it/s, train=0.0897 | valid=0.0889 | fun_rmse=0.1019]

Epoch 70/300: 100%|██████████| 2/2 [00:00<00:00, 110.82it/s, train=0.0897 | valid=0.0889 | fun_rmse=0.1019]

Epoch 71/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 71/300: 100%|██████████| 2/2 [00:00<00:00, 80.42it/s, train=0.0952 | valid=0.0703 | fun_rmse=0.0897]

Epoch 71/300: 100%|██████████| 2/2 [00:00<00:00, 76.68it/s, train=0.0952 | valid=0.0703 | fun_rmse=0.0897]

Epoch 72/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 72/300: 100%|██████████| 2/2 [00:00<00:00, 82.49it/s, train=0.0675 | valid=0.0606 | fun_rmse=0.0824]

Epoch 72/300: 100%|██████████| 2/2 [00:00<00:00, 80.11it/s, train=0.0675 | valid=0.0606 | fun_rmse=0.0824]

Epoch 73/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 73/300: 100%|██████████| 2/2 [00:00<00:00, 62.64it/s, train=0.0669 | valid=0.0627 | fun_rmse=0.0849]

Epoch 73/300: 100%|██████████| 2/2 [00:00<00:00, 62.10it/s, train=0.0669 | valid=0.0627 | fun_rmse=0.0849]

Epoch 74/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 74/300: 100%|██████████| 2/2 [00:00<00:00, 74.65it/s, train=0.0662 | valid=0.0760 | fun_rmse=0.0969]

Epoch 74/300: 100%|██████████| 2/2 [00:00<00:00, 73.89it/s, train=0.0662 | valid=0.0760 | fun_rmse=0.0969]

Epoch 75/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 75/300: 100%|██████████| 2/2 [00:00<00:00, 62.97it/s, train=0.0824 | valid=0.0935 | fun_rmse=0.1123]

Epoch 75/300: 100%|██████████| 2/2 [00:00<00:00, 61.08it/s, train=0.0824 | valid=0.0935 | fun_rmse=0.1123]

Epoch 76/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 76/300: 100%|██████████| 2/2 [00:00<00:00, 79.60it/s, train=0.0711 | valid=0.0993 | fun_rmse=0.1182]

Epoch 76/300: 100%|██████████| 2/2 [00:00<00:00, 76.93it/s, train=0.0711 | valid=0.0993 | fun_rmse=0.1182]

Epoch 77/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 77/300: 100%|██████████| 2/2 [00:00<00:00, 79.87it/s, train=0.0975 | valid=0.0879 | fun_rmse=0.1086]

Epoch 77/300: 100%|██████████| 2/2 [00:00<00:00, 78.62it/s, train=0.0975 | valid=0.0879 | fun_rmse=0.1086]

Epoch 78/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 78/300: 100%|██████████| 2/2 [00:00<00:00, 165.63it/s, train=0.0559 | valid=0.0762 | fun_rmse=0.0993]

Epoch 78/300: 100%|██████████| 2/2 [00:00<00:00, 161.86it/s, train=0.0559 | valid=0.0762 | fun_rmse=0.0993]

Epoch 79/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 79/300: 100%|██████████| 2/2 [00:00<00:00, 168.10it/s, train=0.0639 | valid=0.0728 | fun_rmse=0.0968]

Epoch 79/300: 100%|██████████| 2/2 [00:00<00:00, 164.16it/s, train=0.0639 | valid=0.0728 | fun_rmse=0.0968]

Epoch 80/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 80/300: 100%|██████████| 2/2 [00:00<00:00, 228.26it/s, train=0.0566 | valid=0.0762 | fun_rmse=0.0976]

Epoch 80/300: 100%|██████████| 2/2 [00:00<00:00, 220.71it/s, train=0.0566 | valid=0.0762 | fun_rmse=0.0976]

Epoch 81/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 81/300: 100%|██████████| 2/2 [00:00<00:00, 227.44it/s, train=0.0641 | valid=0.0732 | fun_rmse=0.0946]

Epoch 81/300: 100%|██████████| 2/2 [00:00<00:00, 220.35it/s, train=0.0641 | valid=0.0732 | fun_rmse=0.0946]

Epoch 82/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 82/300: 100%|██████████| 2/2 [00:00<00:00, 169.75it/s, train=0.0555 | valid=0.0684 | fun_rmse=0.0876]

Epoch 82/300: 100%|██████████| 2/2 [00:00<00:00, 166.05it/s, train=0.0555 | valid=0.0684 | fun_rmse=0.0876]

Epoch 83/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 83/300: 100%|██████████| 2/2 [00:00<00:00, 186.29it/s, train=0.0679 | valid=0.0748 | fun_rmse=0.0931]

Epoch 83/300: 100%|██████████| 2/2 [00:00<00:00, 178.71it/s, train=0.0679 | valid=0.0748 | fun_rmse=0.0931]

Epoch 84/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 84/300: 100%|██████████| 2/2 [00:00<00:00, 77.59it/s, train=0.0619 | valid=0.0872 | fun_rmse=0.1064]

Epoch 84/300: 100%|██████████| 2/2 [00:00<00:00, 73.36it/s, train=0.0619 | valid=0.0872 | fun_rmse=0.1064]

Epoch 85/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 85/300: 100%|██████████| 2/2 [00:00<00:00, 54.54it/s, train=0.0848 | valid=0.0915 | fun_rmse=0.1138]

Epoch 85/300: 100%|██████████| 2/2 [00:00<00:00, 54.07it/s, train=0.0848 | valid=0.0915 | fun_rmse=0.1138]

Epoch 86/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 86/300: 100%|██████████| 2/2 [00:00<00:00, 80.92it/s, train=0.0728 | valid=0.1017 | fun_rmse=0.1227]

Epoch 86/300: 100%|██████████| 2/2 [00:00<00:00, 77.27it/s, train=0.0728 | valid=0.1017 | fun_rmse=0.1227]

Epoch 87/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 87/300: 100%|██████████| 2/2 [00:00<00:00, 61.23it/s, train=0.0926 | valid=0.1051 | fun_rmse=0.1259]

Epoch 87/300: 100%|██████████| 2/2 [00:00<00:00, 59.21it/s, train=0.0926 | valid=0.1051 | fun_rmse=0.1259]

Epoch 88/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 88/300: 100%|██████████| 2/2 [00:00<00:00, 51.77it/s, train=0.0641 | valid=0.0915 | fun_rmse=0.1132]

Epoch 88/300: 100%|██████████| 2/2 [00:00<00:00, 50.68it/s, train=0.0641 | valid=0.0915 | fun_rmse=0.1132]

Epoch 89/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 89/300: 100%|██████████| 2/2 [00:00<00:00, 72.12it/s, train=0.0649 | valid=0.0709 | fun_rmse=0.0937]

Epoch 89/300: 100%|██████████| 2/2 [00:00<00:00, 71.25it/s, train=0.0649 | valid=0.0709 | fun_rmse=0.0937]

Epoch 90/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 90/300: 100%|██████████| 2/2 [00:00<00:00, 230.65it/s, train=0.0720 | valid=0.0598 | fun_rmse=0.0830]

Epoch 90/300: 100%|██████████| 2/2 [00:00<00:00, 221.71it/s, train=0.0720 | valid=0.0598 | fun_rmse=0.0830]

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Epoch 91/300: 100%|██████████| 2/2 [00:00<00:00, 99.14it/s, train=0.0646 | valid=0.0664 | fun_rmse=0.0901]

Epoch 91/300: 100%|██████████| 2/2 [00:00<00:00, 95.23it/s, train=0.0646 | valid=0.0664 | fun_rmse=0.0901]

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Epoch 92/300: 100%|██████████| 2/2 [00:00<00:00, 46.50it/s, train=0.0667 | valid=0.0800 | fun_rmse=0.1077]

Epoch 92/300: 100%|██████████| 2/2 [00:00<00:00, 46.15it/s, train=0.0667 | valid=0.0800 | fun_rmse=0.1077]

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Epoch 93/300: 100%|██████████| 2/2 [00:00<00:00, 61.10it/s, train=0.0815 | valid=0.0975 | fun_rmse=0.1226]

Epoch 93/300: 100%|██████████| 2/2 [00:00<00:00, 58.63it/s, train=0.0815 | valid=0.0975 | fun_rmse=0.1226]

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Epoch 94/300: 100%|██████████| 2/2 [00:00<00:00, 152.27it/s, train=0.0686 | valid=0.1073 | fun_rmse=0.1283]

Epoch 94/300: 100%|██████████| 2/2 [00:00<00:00, 147.02it/s, train=0.0686 | valid=0.1073 | fun_rmse=0.1283]

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Epoch 95/300: 100%|██████████| 2/2 [00:00<00:00, 99.60it/s, train=0.0860 | valid=0.1026 | fun_rmse=0.1233]

Epoch 95/300: 100%|██████████| 2/2 [00:00<00:00, 97.54it/s, train=0.0860 | valid=0.1026 | fun_rmse=0.1233]

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Epoch 96/300: 100%|██████████| 2/2 [00:00<00:00, 51.36it/s, train=0.0784 | valid=0.0926 | fun_rmse=0.1169]

Epoch 96/300: 100%|██████████| 2/2 [00:00<00:00, 50.31it/s, train=0.0784 | valid=0.0926 | fun_rmse=0.1169]

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Epoch 97/300: 100%|██████████| 2/2 [00:00<00:00, 68.58it/s, train=0.0604 | valid=0.0896 | fun_rmse=0.1117]

Epoch 97/300: 100%|██████████| 2/2 [00:00<00:00, 67.41it/s, train=0.0604 | valid=0.0896 | fun_rmse=0.1117]

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Epoch 98/300: 100%|██████████| 2/2 [00:00<00:00, 113.44it/s, train=0.0694 | valid=0.0880 | fun_rmse=0.1068]

Epoch 98/300: 100%|██████████| 2/2 [00:00<00:00, 110.77it/s, train=0.0694 | valid=0.0880 | fun_rmse=0.1068]

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Epoch 99/300: 100%|██████████| 2/2 [00:00<00:00, 150.73it/s, train=0.0584 | valid=0.0875 | fun_rmse=0.1065]

Epoch 99/300: 100%|██████████| 2/2 [00:00<00:00, 146.33it/s, train=0.0584 | valid=0.0875 | fun_rmse=0.1065]

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Epoch 100/300: 100%|██████████| 2/2 [00:00<00:00, 197.43it/s, train=0.0518 | valid=0.0910 | fun_rmse=0.1083]

Epoch 100/300: 100%|██████████| 2/2 [00:00<00:00, 191.18it/s, train=0.0518 | valid=0.0910 | fun_rmse=0.1083]

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Epoch 101/300: 100%|██████████| 2/2 [00:00<00:00, 148.57it/s, train=0.0835 | valid=0.0943 | fun_rmse=0.1098]

Epoch 101/300: 100%|██████████| 2/2 [00:00<00:00, 143.93it/s, train=0.0835 | valid=0.0943 | fun_rmse=0.1098]

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Epoch 102/300: 100%|██████████| 2/2 [00:00<00:00, 108.39it/s, train=0.0773 | valid=0.0987 | fun_rmse=0.1143]

Epoch 102/300: 100%|██████████| 2/2 [00:00<00:00, 103.31it/s, train=0.0773 | valid=0.0987 | fun_rmse=0.1143]

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Epoch 103/300: 100%|██████████| 2/2 [00:00<00:00, 98.28it/s, train=0.0645 | valid=0.1050 | fun_rmse=0.1213]

Epoch 103/300: 100%|██████████| 2/2 [00:00<00:00, 94.72it/s, train=0.0645 | valid=0.1050 | fun_rmse=0.1213]

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Epoch 104/300: 100%|██████████| 2/2 [00:00<00:00, 59.03it/s, train=0.0757 | valid=0.1017 | fun_rmse=0.1204]

Epoch 104/300: 100%|██████████| 2/2 [00:00<00:00, 56.89it/s, train=0.0757 | valid=0.1017 | fun_rmse=0.1204]

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Epoch 105/300: 100%|██████████| 2/2 [00:00<00:00, 37.27it/s, train=0.0891 | valid=0.0886 | fun_rmse=0.1154]

Epoch 105/300: 100%|██████████| 2/2 [00:00<00:00, 36.19it/s, train=0.0891 | valid=0.0886 | fun_rmse=0.1154]

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Epoch 106/300: 100%|██████████| 2/2 [00:00<00:00, 72.69it/s, train=0.0531 | valid=0.0860 | fun_rmse=0.1126]

Epoch 106/300: 100%|██████████| 2/2 [00:00<00:00, 69.50it/s, train=0.0531 | valid=0.0860 | fun_rmse=0.1126]

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Epoch 107/300: 100%|██████████| 2/2 [00:00<00:00, 84.83it/s, train=0.0665 | valid=0.0854 | fun_rmse=0.1068]

Epoch 107/300: 100%|██████████| 2/2 [00:00<00:00, 83.26it/s, train=0.0665 | valid=0.0854 | fun_rmse=0.1068]

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Epoch 108/300: 100%|██████████| 2/2 [00:00<00:00, 101.89it/s, train=0.0587 | valid=0.0791 | fun_rmse=0.1015]

Epoch 108/300: 100%|██████████| 2/2 [00:00<00:00, 99.74it/s, train=0.0587 | valid=0.0791 | fun_rmse=0.1015] 

Epoch 109/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 109/300: 100%|██████████| 2/2 [00:00<00:00, 204.30it/s, train=0.0550 | valid=0.0758 | fun_rmse=0.1041]

Epoch 109/300: 100%|██████████| 2/2 [00:00<00:00, 197.38it/s, train=0.0550 | valid=0.0758 | fun_rmse=0.1041]

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Epoch 110/300: 100%|██████████| 2/2 [00:00<00:00, 128.84it/s, train=0.0805 | valid=0.0819 | fun_rmse=0.1101]

Epoch 110/300: 100%|██████████| 2/2 [00:00<00:00, 126.65it/s, train=0.0805 | valid=0.0819 | fun_rmse=0.1101]

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Epoch 111/300: 100%|██████████| 2/2 [00:00<00:00, 154.45it/s, train=0.0704 | valid=0.0858 | fun_rmse=0.1126]

Epoch 111/300: 100%|██████████| 2/2 [00:00<00:00, 149.10it/s, train=0.0704 | valid=0.0858 | fun_rmse=0.1126]

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Epoch 112/300: 100%|██████████| 2/2 [00:00<00:00, 141.98it/s, train=0.0600 | valid=0.0863 | fun_rmse=0.1100]

Epoch 112/300: 100%|██████████| 2/2 [00:00<00:00, 138.80it/s, train=0.0600 | valid=0.0863 | fun_rmse=0.1100]

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Epoch 113/300: 100%|██████████| 2/2 [00:00<00:00, 62.69it/s, train=0.0579 | valid=0.0931 | fun_rmse=0.1127]

Epoch 113/300: 100%|██████████| 2/2 [00:00<00:00, 62.17it/s, train=0.0579 | valid=0.0931 | fun_rmse=0.1127]

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Epoch 114/300: 100%|██████████| 2/2 [00:00<00:00, 43.34it/s, train=0.0480 | valid=0.1064 | fun_rmse=0.1268]

Epoch 114/300: 100%|██████████| 2/2 [00:00<00:00, 42.22it/s, train=0.0480 | valid=0.1064 | fun_rmse=0.1268]

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Epoch 115/300: 100%|██████████| 2/2 [00:00<00:00, 55.84it/s, train=0.0575 | valid=0.1023 | fun_rmse=0.1270]

Epoch 115/300: 100%|██████████| 2/2 [00:00<00:00, 53.45it/s, train=0.0575 | valid=0.1023 | fun_rmse=0.1270]

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Epoch 116/300: 100%|██████████| 2/2 [00:00<00:00, 55.96it/s, train=0.0733 | valid=0.0835 | fun_rmse=0.1105]

Epoch 116/300: 100%|██████████| 2/2 [00:00<00:00, 55.40it/s, train=0.0733 | valid=0.0835 | fun_rmse=0.1105]

Epoch 117/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 117/300: 100%|██████████| 2/2 [00:00<00:00, 170.54it/s, train=0.0635 | valid=0.0740 | fun_rmse=0.1003]

Epoch 117/300: 100%|██████████| 2/2 [00:00<00:00, 166.18it/s, train=0.0635 | valid=0.0740 | fun_rmse=0.1003]

Epoch 118/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 118/300: 100%|██████████| 2/2 [00:00<00:00, 185.84it/s, train=0.0787 | valid=0.0778 | fun_rmse=0.1012]

Epoch 118/300: 100%|██████████| 2/2 [00:00<00:00, 181.04it/s, train=0.0787 | valid=0.0778 | fun_rmse=0.1012]

Epoch 119/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 119/300: 100%|██████████| 2/2 [00:00<00:00, 223.67it/s, train=0.0809 | valid=0.0837 | fun_rmse=0.1073]

Epoch 119/300: 100%|██████████| 2/2 [00:00<00:00, 216.66it/s, train=0.0809 | valid=0.0837 | fun_rmse=0.1073]

Epoch 120/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 120/300: 100%|██████████| 2/2 [00:00<00:00, 190.87it/s, train=0.0633 | valid=0.0908 | fun_rmse=0.1181]

Epoch 120/300: 100%|██████████| 2/2 [00:00<00:00, 185.47it/s, train=0.0633 | valid=0.0908 | fun_rmse=0.1181]

Epoch 121/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 121/300: 100%|██████████| 2/2 [00:00<00:00, 192.52it/s, train=0.0718 | valid=0.0988 | fun_rmse=0.1265]

Epoch 121/300: 100%|██████████| 2/2 [00:00<00:00, 185.13it/s, train=0.0718 | valid=0.0988 | fun_rmse=0.1265]

Epoch 122/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 122/300: 100%|██████████| 2/2 [00:00<00:00, 137.01it/s, train=0.0768 | valid=0.1074 | fun_rmse=0.1304]

Epoch 122/300: 100%|██████████| 2/2 [00:00<00:00, 131.25it/s, train=0.0768 | valid=0.1074 | fun_rmse=0.1304]

Epoch 123/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 123/300: 100%|██████████| 2/2 [00:00<00:00, 124.77it/s, train=0.0757 | valid=0.1126 | fun_rmse=0.1303]

Epoch 123/300: 100%|██████████| 2/2 [00:00<00:00, 119.28it/s, train=0.0757 | valid=0.1126 | fun_rmse=0.1303]

Epoch 124/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 124/300: 100%|██████████| 2/2 [00:00<00:00, 109.04it/s, train=0.0617 | valid=0.1102 | fun_rmse=0.1259]

Epoch 124/300: 100%|██████████| 2/2 [00:00<00:00, 104.53it/s, train=0.0617 | valid=0.1102 | fun_rmse=0.1259]

Epoch 125/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 125/300: 100%|██████████| 2/2 [00:00<00:00, 61.75it/s, train=0.0801 | valid=0.1011 | fun_rmse=0.1185]

Epoch 125/300: 100%|██████████| 2/2 [00:00<00:00, 59.24it/s, train=0.0801 | valid=0.1011 | fun_rmse=0.1185]

Epoch 126/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 126/300: 100%|██████████| 2/2 [00:00<00:00, 81.40it/s, train=0.0695 | valid=0.0854 | fun_rmse=0.1089]

Epoch 126/300: 100%|██████████| 2/2 [00:00<00:00, 78.44it/s, train=0.0695 | valid=0.0854 | fun_rmse=0.1089]

Epoch 127/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 127/300: 100%|██████████| 2/2 [00:00<00:00, 62.12it/s, train=0.0612 | valid=0.0760 | fun_rmse=0.1067]

Epoch 127/300: 100%|██████████| 2/2 [00:00<00:00, 59.11it/s, train=0.0612 | valid=0.0760 | fun_rmse=0.1067]

Epoch 128/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 128/300: 100%|██████████| 2/2 [00:00<00:00, 59.17it/s, train=0.0598 | valid=0.0771 | fun_rmse=0.1123]

Epoch 128/300: 100%|██████████| 2/2 [00:00<00:00, 57.41it/s, train=0.0598 | valid=0.0771 | fun_rmse=0.1123]

Epoch 129/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 129/300: 100%|██████████| 2/2 [00:00<00:00, 65.45it/s, train=0.0748 | valid=0.0803 | fun_rmse=0.1189]

Epoch 129/300: 100%|██████████| 2/2 [00:00<00:00, 64.71it/s, train=0.0748 | valid=0.0803 | fun_rmse=0.1189]

Epoch 130/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 130/300: 100%|██████████| 2/2 [00:00<00:00, 198.18it/s, train=0.0629 | valid=0.0858 | fun_rmse=0.1227]

Epoch 130/300: 100%|██████████| 2/2 [00:00<00:00, 194.17it/s, train=0.0629 | valid=0.0858 | fun_rmse=0.1227]

Epoch 131/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 131/300: 100%|██████████| 2/2 [00:00<00:00, 107.60it/s, train=0.0466 | valid=0.0991 | fun_rmse=0.1283]

Epoch 131/300: 100%|██████████| 2/2 [00:00<00:00, 106.03it/s, train=0.0466 | valid=0.0991 | fun_rmse=0.1283]

Epoch 132/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 132/300: 100%|██████████| 2/2 [00:00<00:00, 65.93it/s, train=0.0673 | valid=0.1096 | fun_rmse=0.1334]

Epoch 132/300: 100%|██████████| 2/2 [00:00<00:00, 65.44it/s, train=0.0673 | valid=0.1096 | fun_rmse=0.1334]

Epoch 133/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 133/300: 100%|██████████| 2/2 [00:00<00:00, 69.67it/s, train=0.0596 | valid=0.1084 | fun_rmse=0.1284]

Epoch 133/300: 100%|██████████| 2/2 [00:00<00:00, 66.96it/s, train=0.0596 | valid=0.1084 | fun_rmse=0.1284]

Epoch 134/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 134/300: 100%|██████████| 2/2 [00:00<00:00, 55.13it/s, train=0.0625 | valid=0.0936 | fun_rmse=0.1131]

Epoch 134/300: 100%|██████████| 2/2 [00:00<00:00, 52.97it/s, train=0.0625 | valid=0.0936 | fun_rmse=0.1131]

Epoch 135/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 135/300: 100%|██████████| 2/2 [00:00<00:00, 46.45it/s, train=0.0660 | valid=0.0794 | fun_rmse=0.1016]

Epoch 135/300: 100%|██████████| 2/2 [00:00<00:00, 45.10it/s, train=0.0660 | valid=0.0794 | fun_rmse=0.1016]

Epoch 136/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 136/300: 100%|██████████| 2/2 [00:00<00:00, 69.07it/s, train=0.0493 | valid=0.0742 | fun_rmse=0.0998]

Epoch 136/300: 100%|██████████| 2/2 [00:00<00:00, 68.37it/s, train=0.0493 | valid=0.0742 | fun_rmse=0.0998]

Epoch 137/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 137/300: 100%|██████████| 2/2 [00:00<00:00, 83.58it/s, train=0.0691 | valid=0.0792 | fun_rmse=0.1055]

Epoch 137/300: 100%|██████████| 2/2 [00:00<00:00, 82.49it/s, train=0.0691 | valid=0.0792 | fun_rmse=0.1055]

Epoch 138/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 138/300: 100%|██████████| 2/2 [00:00<00:00, 191.45it/s, train=0.0520 | valid=0.0825 | fun_rmse=0.1111]

Epoch 138/300: 100%|██████████| 2/2 [00:00<00:00, 184.37it/s, train=0.0520 | valid=0.0825 | fun_rmse=0.1111]

Epoch 139/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 139/300: 100%|██████████| 2/2 [00:00<00:00, 174.94it/s, train=0.0496 | valid=0.0808 | fun_rmse=0.1118]

Epoch 139/300: 100%|██████████| 2/2 [00:00<00:00, 170.20it/s, train=0.0496 | valid=0.0808 | fun_rmse=0.1118]

Epoch 140/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 140/300: 100%|██████████| 2/2 [00:00<00:00, 103.61it/s, train=0.0563 | valid=0.0874 | fun_rmse=0.1157]

Epoch 140/300: 100%|██████████| 2/2 [00:00<00:00, 100.83it/s, train=0.0563 | valid=0.0874 | fun_rmse=0.1157]

Epoch 141/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 141/300: 100%|██████████| 2/2 [00:00<00:00, 54.23it/s, train=0.0529 | valid=0.0992 | fun_rmse=0.1233]

Epoch 141/300: 100%|██████████| 2/2 [00:00<00:00, 52.84it/s, train=0.0529 | valid=0.0992 | fun_rmse=0.1233]

Epoch 142/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 142/300: 100%|██████████| 2/2 [00:00<00:00, 66.91it/s, train=0.0558 | valid=0.0981 | fun_rmse=0.1192]

Epoch 142/300: 100%|██████████| 2/2 [00:00<00:00, 64.55it/s, train=0.0558 | valid=0.0981 | fun_rmse=0.1192]

Epoch 143/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 143/300: 100%|██████████| 2/2 [00:00<00:00, 56.69it/s, train=0.0449 | valid=0.0905 | fun_rmse=0.1129]

Epoch 143/300: 100%|██████████| 2/2 [00:00<00:00, 54.57it/s, train=0.0449 | valid=0.0905 | fun_rmse=0.1129]

Epoch 144/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 144/300: 100%|██████████| 2/2 [00:00<00:00, 70.12it/s, train=0.0639 | valid=0.0936 | fun_rmse=0.1192]

Epoch 144/300: 100%|██████████| 2/2 [00:00<00:00, 69.53it/s, train=0.0639 | valid=0.0936 | fun_rmse=0.1192]

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Epoch 145/300: 100%|██████████| 2/2 [00:00<00:00, 64.10it/s, train=0.0585 | valid=0.1034 | fun_rmse=0.1294]

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Epoch 146/300: 100%|██████████| 2/2 [00:00<00:00, 114.39it/s, train=0.0566 | valid=0.1081 | fun_rmse=0.1359]

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Epoch 147/300: 100%|██████████| 2/2 [00:00<00:00, 130.34it/s, train=0.0535 | valid=0.1100 | fun_rmse=0.1380]

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Epoch 148/300: 100%|██████████| 2/2 [00:00<00:00, 182.50it/s, train=0.0617 | valid=0.1074 | fun_rmse=0.1343]

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Epoch 149/300: 100%|██████████| 2/2 [00:00<00:00, 93.52it/s, train=0.0684 | valid=0.1011 | fun_rmse=0.1258]

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Epoch 150/300: 100%|██████████| 2/2 [00:00<00:00, 73.73it/s, train=0.0513 | valid=0.0930 | fun_rmse=0.1182]

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Epoch 151/300: 100%|██████████| 2/2 [00:00<00:00, 41.61it/s, train=0.0517 | valid=0.0936 | fun_rmse=0.1194]

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Epoch 152/300: 100%|██████████| 2/2 [00:00<00:00, 68.79it/s, train=0.0487 | valid=0.0966 | fun_rmse=0.1203]

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Epoch 153/300: 100%|██████████| 2/2 [00:00<00:00, 77.38it/s, train=0.0548 | valid=0.0956 | fun_rmse=0.1195]

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Epoch 154/300: 100%|██████████| 2/2 [00:00<00:00, 57.47it/s, train=0.0488 | valid=0.0962 | fun_rmse=0.1241]

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Epoch 155/300: 100%|██████████| 2/2 [00:00<00:00, 122.94it/s, train=0.0592 | valid=0.1025 | fun_rmse=0.1322]

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Epoch 156/300: 100%|██████████| 2/2 [00:00<00:00, 159.79it/s, train=0.0584 | valid=0.1066 | fun_rmse=0.1364]

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Epoch 157/300: 100%|██████████| 2/2 [00:00<00:00, 177.38it/s, train=0.0584 | valid=0.1087 | fun_rmse=0.1382]

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Epoch 158/300: 100%|██████████| 2/2 [00:00<00:00, 141.85it/s, train=0.0617 | valid=0.1099 | fun_rmse=0.1395]

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Epoch 159/300: 100%|██████████| 2/2 [00:00<00:00, 69.86it/s, train=0.0529 | valid=0.1095 | fun_rmse=0.1382]

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Epoch 160/300: 100%|██████████| 2/2 [00:00<00:00, 50.95it/s, train=0.0595 | valid=0.1077 | fun_rmse=0.1346]

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Epoch 161/300: 100%|██████████| 2/2 [00:00<00:00, 46.54it/s, train=0.0437 | valid=0.1052 | fun_rmse=0.1325]

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Epoch 162/300: 100%|██████████| 2/2 [00:00<00:00, 39.49it/s, train=0.0507 | valid=0.1010 | fun_rmse=0.1300]

Epoch 162/300: 100%|██████████| 2/2 [00:00<00:00, 38.42it/s, train=0.0507 | valid=0.1010 | fun_rmse=0.1300]

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Epoch 163/300: 100%|██████████| 2/2 [00:00<00:00, 83.79it/s, train=0.0609 | valid=0.1011 | fun_rmse=0.1309]

Epoch 163/300: 100%|██████████| 2/2 [00:00<00:00, 79.51it/s, train=0.0609 | valid=0.1011 | fun_rmse=0.1309]

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Epoch 164/300: 100%|██████████| 2/2 [00:00<00:00, 129.06it/s, train=0.0454 | valid=0.1085 | fun_rmse=0.1363]

Epoch 164/300: 100%|██████████| 2/2 [00:00<00:00, 126.76it/s, train=0.0454 | valid=0.1085 | fun_rmse=0.1363]

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Epoch 165/300: 100%|██████████| 2/2 [00:00<00:00, 148.67it/s, train=0.0608 | valid=0.1181 | fun_rmse=0.1429]

Epoch 165/300: 100%|██████████| 2/2 [00:00<00:00, 143.63it/s, train=0.0608 | valid=0.1181 | fun_rmse=0.1429]

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Epoch 166/300: 100%|██████████| 2/2 [00:00<00:00, 80.45it/s, train=0.0579 | valid=0.1234 | fun_rmse=0.1483]

Epoch 166/300: 100%|██████████| 2/2 [00:00<00:00, 77.12it/s, train=0.0579 | valid=0.1234 | fun_rmse=0.1483]

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Epoch 167/300: 100%|██████████| 2/2 [00:00<00:00, 62.77it/s, train=0.0667 | valid=0.1211 | fun_rmse=0.1468]

Epoch 167/300: 100%|██████████| 2/2 [00:00<00:00, 59.77it/s, train=0.0667 | valid=0.1211 | fun_rmse=0.1468]

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Epoch 168/300: 100%|██████████| 2/2 [00:00<00:00, 53.14it/s, train=0.0561 | valid=0.1168 | fun_rmse=0.1440]

Epoch 168/300: 100%|██████████| 2/2 [00:00<00:00, 51.49it/s, train=0.0561 | valid=0.1168 | fun_rmse=0.1440]

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Epoch 169/300: 100%|██████████| 2/2 [00:00<00:00, 47.11it/s, train=0.0494 | valid=0.1132 | fun_rmse=0.1394]

Epoch 169/300: 100%|██████████| 2/2 [00:00<00:00, 45.84it/s, train=0.0494 | valid=0.1132 | fun_rmse=0.1394]

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Epoch 170/300: 100%|██████████| 2/2 [00:00<00:00, 81.84it/s, train=0.0507 | valid=0.1107 | fun_rmse=0.1357]

Epoch 170/300: 100%|██████████| 2/2 [00:00<00:00, 78.66it/s, train=0.0507 | valid=0.1107 | fun_rmse=0.1357]

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Epoch 171/300: 100%|██████████| 2/2 [00:00<00:00, 90.06it/s, train=0.0459 | valid=0.1110 | fun_rmse=0.1352]

Epoch 171/300: 100%|██████████| 2/2 [00:00<00:00, 87.58it/s, train=0.0459 | valid=0.1110 | fun_rmse=0.1352]

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Epoch 172/300: 100%|██████████| 2/2 [00:00<00:00, 72.89it/s, train=0.0462 | valid=0.1140 | fun_rmse=0.1377]

Epoch 172/300: 100%|██████████| 2/2 [00:00<00:00, 71.29it/s, train=0.0462 | valid=0.1140 | fun_rmse=0.1377]

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Epoch 173/300: 100%|██████████| 2/2 [00:00<00:00, 65.26it/s, train=0.0500 | valid=0.1136 | fun_rmse=0.1419]

Epoch 173/300: 100%|██████████| 2/2 [00:00<00:00, 62.57it/s, train=0.0500 | valid=0.1136 | fun_rmse=0.1419]

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Epoch 174/300: 100%|██████████| 2/2 [00:00<00:00, 79.90it/s, train=0.0429 | valid=0.1220 | fun_rmse=0.1486]

Epoch 174/300: 100%|██████████| 2/2 [00:00<00:00, 77.83it/s, train=0.0429 | valid=0.1220 | fun_rmse=0.1486]

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Epoch 175/300: 100%|██████████| 2/2 [00:00<00:00, 70.71it/s, train=0.0464 | valid=0.1168 | fun_rmse=0.1467]

Epoch 175/300: 100%|██████████| 2/2 [00:00<00:00, 68.01it/s, train=0.0464 | valid=0.1168 | fun_rmse=0.1467]

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Epoch 176/300: 100%|██████████| 2/2 [00:00<00:00, 119.12it/s, train=0.0457 | valid=0.1066 | fun_rmse=0.1402]

Epoch 176/300: 100%|██████████| 2/2 [00:00<00:00, 115.40it/s, train=0.0457 | valid=0.1066 | fun_rmse=0.1402]

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Epoch 177/300: 100%|██████████| 2/2 [00:00<00:00, 43.10it/s, train=0.0453 | valid=0.1043 | fun_rmse=0.1354]

Epoch 177/300: 100%|██████████| 2/2 [00:00<00:00, 42.80it/s, train=0.0453 | valid=0.1043 | fun_rmse=0.1354]

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Epoch 178/300: 100%|██████████| 2/2 [00:00<00:00, 65.13it/s, train=0.0454 | valid=0.1089 | fun_rmse=0.1355]

Epoch 178/300: 100%|██████████| 2/2 [00:00<00:00, 64.09it/s, train=0.0454 | valid=0.1089 | fun_rmse=0.1355]

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Epoch 179/300: 100%|██████████| 2/2 [00:00<00:00, 55.91it/s, train=0.0468 | valid=0.1159 | fun_rmse=0.1399]

Epoch 179/300: 100%|██████████| 2/2 [00:00<00:00, 53.23it/s, train=0.0468 | valid=0.1159 | fun_rmse=0.1399]

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Epoch 180/300: 100%|██████████| 2/2 [00:00<00:00, 100.65it/s, train=0.0504 | valid=0.1214 | fun_rmse=0.1465]

Epoch 180/300: 100%|██████████| 2/2 [00:00<00:00, 99.01it/s, train=0.0504 | valid=0.1214 | fun_rmse=0.1465] 

Epoch 181/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 181/300: 100%|██████████| 2/2 [00:00<00:00, 101.35it/s, train=0.0456 | valid=0.1213 | fun_rmse=0.1527]

Epoch 181/300: 100%|██████████| 2/2 [00:00<00:00, 98.95it/s, train=0.0456 | valid=0.1213 | fun_rmse=0.1527] 

Epoch 182/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 182/300: 100%|██████████| 2/2 [00:00<00:00, 71.07it/s, train=0.0473 | valid=0.1202 | fun_rmse=0.1522]

Epoch 182/300: 100%|██████████| 2/2 [00:00<00:00, 68.69it/s, train=0.0473 | valid=0.1202 | fun_rmse=0.1522]

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Epoch 183/300: 100%|██████████| 2/2 [00:00<00:00, 74.61it/s, train=0.0475 | valid=0.1125 | fun_rmse=0.1471]

Epoch 183/300: 100%|██████████| 2/2 [00:00<00:00, 72.37it/s, train=0.0475 | valid=0.1125 | fun_rmse=0.1471]

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Epoch 184/300: 100%|██████████| 2/2 [00:00<00:00, 72.24it/s, train=0.0418 | valid=0.1096 | fun_rmse=0.1443]

Epoch 184/300: 100%|██████████| 2/2 [00:00<00:00, 69.56it/s, train=0.0418 | valid=0.1096 | fun_rmse=0.1443]

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Epoch 185/300: 100%|██████████| 2/2 [00:00<00:00, 64.47it/s, train=0.0462 | valid=0.1094 | fun_rmse=0.1424]

Epoch 185/300: 100%|██████████| 2/2 [00:00<00:00, 62.45it/s, train=0.0462 | valid=0.1094 | fun_rmse=0.1424]

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Epoch 186/300: 100%|██████████| 2/2 [00:00<00:00, 76.52it/s, train=0.0522 | valid=0.1092 | fun_rmse=0.1401]

Epoch 186/300: 100%|██████████| 2/2 [00:00<00:00, 74.33it/s, train=0.0522 | valid=0.1092 | fun_rmse=0.1401]

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Epoch 187/300: 100%|██████████| 2/2 [00:00<00:00, 87.73it/s, train=0.0402 | valid=0.1085 | fun_rmse=0.1380]

Epoch 187/300: 100%|██████████| 2/2 [00:00<00:00, 84.38it/s, train=0.0402 | valid=0.1085 | fun_rmse=0.1380]

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Epoch 188/300: 100%|██████████| 2/2 [00:00<00:00, 102.67it/s, train=0.0512 | valid=0.1103 | fun_rmse=0.1376]

Epoch 188/300: 100%|██████████| 2/2 [00:00<00:00, 101.15it/s, train=0.0512 | valid=0.1103 | fun_rmse=0.1376]

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Epoch 189/300: 100%|██████████| 2/2 [00:00<00:00, 175.52it/s, train=0.0488 | valid=0.1182 | fun_rmse=0.1437]

Epoch 189/300: 100%|██████████| 2/2 [00:00<00:00, 171.78it/s, train=0.0488 | valid=0.1182 | fun_rmse=0.1437]

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Epoch 190/300: 100%|██████████| 2/2 [00:00<00:00, 184.76it/s, train=0.0541 | valid=0.1236 | fun_rmse=0.1502]

Epoch 190/300: 100%|██████████| 2/2 [00:00<00:00, 179.72it/s, train=0.0541 | valid=0.1236 | fun_rmse=0.1502]

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Epoch 191/300: 100%|██████████| 2/2 [00:00<00:00, 162.90it/s, train=0.0496 | valid=0.1255 | fun_rmse=0.1530]

Epoch 191/300: 100%|██████████| 2/2 [00:00<00:00, 158.75it/s, train=0.0496 | valid=0.1255 | fun_rmse=0.1530]

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Epoch 192/300: 100%|██████████| 2/2 [00:00<00:00, 180.69it/s, train=0.0487 | valid=0.1263 | fun_rmse=0.1564]

Epoch 192/300: 100%|██████████| 2/2 [00:00<00:00, 175.58it/s, train=0.0487 | valid=0.1263 | fun_rmse=0.1564]

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Epoch 193/300: 100%|██████████| 2/2 [00:00<00:00, 122.83it/s, train=0.0526 | valid=0.1301 | fun_rmse=0.1619]

Epoch 193/300: 100%|██████████| 2/2 [00:00<00:00, 118.12it/s, train=0.0526 | valid=0.1301 | fun_rmse=0.1619]

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Epoch 194/300: 100%|██████████| 2/2 [00:00<00:00, 59.91it/s, train=0.0462 | valid=0.1300 | fun_rmse=0.1643]

Epoch 194/300: 100%|██████████| 2/2 [00:00<00:00, 58.60it/s, train=0.0462 | valid=0.1300 | fun_rmse=0.1643]

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Epoch 195/300: 100%|██████████| 2/2 [00:00<00:00, 73.45it/s, train=0.0396 | valid=0.1288 | fun_rmse=0.1629]

Epoch 195/300: 100%|██████████| 2/2 [00:00<00:00, 72.60it/s, train=0.0396 | valid=0.1288 | fun_rmse=0.1629]

Epoch 196/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 196/300: 100%|██████████| 2/2 [00:00<00:00, 61.81it/s, train=0.0401 | valid=0.1205 | fun_rmse=0.1568]

Epoch 196/300: 100%|██████████| 2/2 [00:00<00:00, 60.45it/s, train=0.0401 | valid=0.1205 | fun_rmse=0.1568]

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Epoch 197/300: 100%|██████████| 2/2 [00:00<00:00, 66.76it/s, train=0.0478 | valid=0.1157 | fun_rmse=0.1511]

Epoch 197/300: 100%|██████████| 2/2 [00:00<00:00, 65.06it/s, train=0.0478 | valid=0.1157 | fun_rmse=0.1511]

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Epoch 198/300: 100%|██████████| 2/2 [00:00<00:00, 72.48it/s, train=0.0415 | valid=0.1145 | fun_rmse=0.1500]

Epoch 198/300: 100%|██████████| 2/2 [00:00<00:00, 70.83it/s, train=0.0415 | valid=0.1145 | fun_rmse=0.1500]

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Epoch 199/300: 100%|██████████| 2/2 [00:00<00:00, 152.70it/s, train=0.0434 | valid=0.1203 | fun_rmse=0.1542]

Epoch 199/300: 100%|██████████| 2/2 [00:00<00:00, 147.62it/s, train=0.0434 | valid=0.1203 | fun_rmse=0.1542]

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Epoch 200/300: 100%|██████████| 2/2 [00:00<00:00, 87.45it/s, train=0.0413 | valid=0.1286 | fun_rmse=0.1618]

Epoch 200/300: 100%|██████████| 2/2 [00:00<00:00, 86.05it/s, train=0.0413 | valid=0.1286 | fun_rmse=0.1618]

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Epoch 201/300: 100%|██████████| 2/2 [00:00<00:00, 144.52it/s, train=0.0322 | valid=0.1350 | fun_rmse=0.1675]

Epoch 201/300: 100%|██████████| 2/2 [00:00<00:00, 140.21it/s, train=0.0322 | valid=0.1350 | fun_rmse=0.1675]

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Epoch 202/300: 100%|██████████| 2/2 [00:00<00:00, 98.27it/s, train=0.0350 | valid=0.1345 | fun_rmse=0.1661]

Epoch 202/300: 100%|██████████| 2/2 [00:00<00:00, 95.85it/s, train=0.0350 | valid=0.1345 | fun_rmse=0.1661]

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Epoch 203/300: 100%|██████████| 2/2 [00:00<00:00, 64.40it/s, train=0.0397 | valid=0.1315 | fun_rmse=0.1595]

Epoch 203/300: 100%|██████████| 2/2 [00:00<00:00, 62.13it/s, train=0.0397 | valid=0.1315 | fun_rmse=0.1595]

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Epoch 204/300: 100%|██████████| 2/2 [00:00<00:00, 51.23it/s, train=0.0499 | valid=0.1240 | fun_rmse=0.1537]

Epoch 204/300: 100%|██████████| 2/2 [00:00<00:00, 49.38it/s, train=0.0499 | valid=0.1240 | fun_rmse=0.1537]

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Epoch 205/300: 100%|██████████| 2/2 [00:00<00:00, 81.32it/s, train=0.0357 | valid=0.1163 | fun_rmse=0.1508]

Epoch 205/300: 100%|██████████| 2/2 [00:00<00:00, 77.27it/s, train=0.0357 | valid=0.1163 | fun_rmse=0.1508]

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Epoch 206/300: 100%|██████████| 2/2 [00:00<00:00, 48.70it/s, train=0.0375 | valid=0.1155 | fun_rmse=0.1527]

Epoch 206/300: 100%|██████████| 2/2 [00:00<00:00, 48.39it/s, train=0.0375 | valid=0.1155 | fun_rmse=0.1527]

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Epoch 207/300: 100%|██████████| 2/2 [00:00<00:00, 74.25it/s, train=0.0465 | valid=0.1272 | fun_rmse=0.1602]

Epoch 207/300: 100%|██████████| 2/2 [00:00<00:00, 72.40it/s, train=0.0465 | valid=0.1272 | fun_rmse=0.1602]

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Epoch 208/300: 100%|██████████| 2/2 [00:00<00:00, 79.74it/s, train=0.0365 | valid=0.1374 | fun_rmse=0.1676]

Epoch 208/300: 100%|██████████| 2/2 [00:00<00:00, 78.56it/s, train=0.0365 | valid=0.1374 | fun_rmse=0.1676]

Epoch 209/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 209/300: 100%|██████████| 2/2 [00:00<00:00, 174.96it/s, train=0.0339 | valid=0.1404 | fun_rmse=0.1726]

Epoch 209/300: 100%|██████████| 2/2 [00:00<00:00, 170.99it/s, train=0.0339 | valid=0.1404 | fun_rmse=0.1726]

Epoch 210/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 210/300: 100%|██████████| 2/2 [00:00<00:00, 178.12it/s, train=0.0517 | valid=0.1423 | fun_rmse=0.1736]

Epoch 210/300: 100%|██████████| 2/2 [00:00<00:00, 170.90it/s, train=0.0517 | valid=0.1423 | fun_rmse=0.1736]

Epoch 211/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 211/300: 100%|██████████| 2/2 [00:00<00:00, 111.13it/s, train=0.0483 | valid=0.1467 | fun_rmse=0.1745]

Epoch 211/300: 100%|██████████| 2/2 [00:00<00:00, 108.89it/s, train=0.0483 | valid=0.1467 | fun_rmse=0.1745]

Epoch 212/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 212/300: 100%|██████████| 2/2 [00:00<00:00, 147.61it/s, train=0.0496 | valid=0.1526 | fun_rmse=0.1785]

Epoch 212/300: 100%|██████████| 2/2 [00:00<00:00, 141.14it/s, train=0.0496 | valid=0.1526 | fun_rmse=0.1785]

Epoch 213/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 213/300: 100%|██████████| 2/2 [00:00<00:00, 63.32it/s, train=0.0541 | valid=0.1552 | fun_rmse=0.1764]

Epoch 213/300: 100%|██████████| 2/2 [00:00<00:00, 60.90it/s, train=0.0541 | valid=0.1552 | fun_rmse=0.1764]

Epoch 214/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 214/300: 100%|██████████| 2/2 [00:00<00:00, 51.92it/s, train=0.0506 | valid=0.1480 | fun_rmse=0.1735]

Epoch 214/300: 100%|██████████| 2/2 [00:00<00:00, 50.33it/s, train=0.0506 | valid=0.1480 | fun_rmse=0.1735]

Epoch 215/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 215/300: 100%|██████████| 2/2 [00:00<00:00, 47.40it/s, train=0.0393 | valid=0.1373 | fun_rmse=0.1682]

Epoch 215/300: 100%|██████████| 2/2 [00:00<00:00, 46.47it/s, train=0.0393 | valid=0.1373 | fun_rmse=0.1682]

Epoch 216/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 216/300: 100%|██████████| 2/2 [00:00<00:00, 69.69it/s, train=0.0609 | valid=0.1302 | fun_rmse=0.1570]

Epoch 216/300: 100%|██████████| 2/2 [00:00<00:00, 67.88it/s, train=0.0609 | valid=0.1302 | fun_rmse=0.1570]

Epoch 217/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 217/300: 100%|██████████| 2/2 [00:00<00:00, 131.24it/s, train=0.0423 | valid=0.1241 | fun_rmse=0.1550]

Epoch 217/300: 100%|██████████| 2/2 [00:00<00:00, 128.53it/s, train=0.0423 | valid=0.1241 | fun_rmse=0.1550]

Epoch 218/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 218/300: 100%|██████████| 2/2 [00:00<00:00, 116.77it/s, train=0.0494 | valid=0.1164 | fun_rmse=0.1507]

Epoch 218/300: 100%|██████████| 2/2 [00:00<00:00, 112.88it/s, train=0.0494 | valid=0.1164 | fun_rmse=0.1507]

Epoch 219/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 219/300: 100%|██████████| 2/2 [00:00<00:00, 71.17it/s, train=0.0435 | valid=0.1107 | fun_rmse=0.1492]

Epoch 219/300: 100%|██████████| 2/2 [00:00<00:00, 68.52it/s, train=0.0435 | valid=0.1107 | fun_rmse=0.1492]

Epoch 220/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 220/300: 100%|██████████| 2/2 [00:00<00:00, 46.59it/s, train=0.0364 | valid=0.1192 | fun_rmse=0.1554]

Epoch 220/300: 100%|██████████| 2/2 [00:00<00:00, 45.28it/s, train=0.0364 | valid=0.1192 | fun_rmse=0.1554]

Epoch 221/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 221/300: 100%|██████████| 2/2 [00:00<00:00, 67.83it/s, train=0.0385 | valid=0.1204 | fun_rmse=0.1571]

Epoch 221/300: 100%|██████████| 2/2 [00:00<00:00, 67.13it/s, train=0.0385 | valid=0.1204 | fun_rmse=0.1571]

Epoch 222/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 222/300: 100%|██████████| 2/2 [00:00<00:00, 85.18it/s, train=0.0454 | valid=0.1273 | fun_rmse=0.1598]

Epoch 222/300: 100%|██████████| 2/2 [00:00<00:00, 81.73it/s, train=0.0454 | valid=0.1273 | fun_rmse=0.1598]

Epoch 223/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 223/300: 100%|██████████| 2/2 [00:00<00:00, 63.31it/s, train=0.0379 | valid=0.1281 | fun_rmse=0.1572]

Epoch 223/300: 100%|██████████| 2/2 [00:00<00:00, 61.34it/s, train=0.0379 | valid=0.1281 | fun_rmse=0.1572]

Epoch 224/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 224/300: 100%|██████████| 2/2 [00:00<00:00, 67.11it/s, train=0.0436 | valid=0.1297 | fun_rmse=0.1555]

Epoch 224/300: 100%|██████████| 2/2 [00:00<00:00, 66.61it/s, train=0.0436 | valid=0.1297 | fun_rmse=0.1555]

Epoch 225/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 225/300: 100%|██████████| 2/2 [00:00<00:00, 74.54it/s, train=0.0537 | valid=0.1309 | fun_rmse=0.1602]

Epoch 225/300: 100%|██████████| 2/2 [00:00<00:00, 73.66it/s, train=0.0537 | valid=0.1309 | fun_rmse=0.1602]

Epoch 226/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 226/300: 100%|██████████| 2/2 [00:00<00:00, 164.69it/s, train=0.0470 | valid=0.1396 | fun_rmse=0.1662]

Epoch 226/300: 100%|██████████| 2/2 [00:00<00:00, 160.90it/s, train=0.0470 | valid=0.1396 | fun_rmse=0.1662]

Epoch 227/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 227/300: 100%|██████████| 2/2 [00:00<00:00, 196.17it/s, train=0.0471 | valid=0.1360 | fun_rmse=0.1662]

Epoch 227/300: 100%|██████████| 2/2 [00:00<00:00, 190.60it/s, train=0.0471 | valid=0.1360 | fun_rmse=0.1662]

Epoch 228/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 228/300: 100%|██████████| 2/2 [00:00<00:00, 210.70it/s, train=0.0311 | valid=0.1242 | fun_rmse=0.1641]

Epoch 228/300: 100%|██████████| 2/2 [00:00<00:00, 205.28it/s, train=0.0311 | valid=0.1242 | fun_rmse=0.1641]

Epoch 229/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 229/300: 100%|██████████| 2/2 [00:00<00:00, 180.10it/s, train=0.0539 | valid=0.1236 | fun_rmse=0.1641]

Epoch 229/300: 100%|██████████| 2/2 [00:00<00:00, 176.68it/s, train=0.0539 | valid=0.1236 | fun_rmse=0.1641]

Epoch 230/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 230/300: 100%|██████████| 2/2 [00:00<00:00, 228.83it/s, train=0.0343 | valid=0.1283 | fun_rmse=0.1658]

Epoch 230/300: 100%|██████████| 2/2 [00:00<00:00, 223.52it/s, train=0.0343 | valid=0.1283 | fun_rmse=0.1658]

Epoch 231/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 231/300: 100%|██████████| 2/2 [00:00<00:00, 150.57it/s, train=0.0400 | valid=0.1341 | fun_rmse=0.1688]

Epoch 231/300: 100%|██████████| 2/2 [00:00<00:00, 147.51it/s, train=0.0400 | valid=0.1341 | fun_rmse=0.1688]

Epoch 232/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 232/300: 100%|██████████| 2/2 [00:00<00:00, 94.43it/s, train=0.0391 | valid=0.1368 | fun_rmse=0.1692]

Epoch 232/300: 100%|██████████| 2/2 [00:00<00:00, 90.53it/s, train=0.0391 | valid=0.1368 | fun_rmse=0.1692]

Epoch 233/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 233/300: 100%|██████████| 2/2 [00:00<00:00, 66.69it/s, train=0.0322 | valid=0.1381 | fun_rmse=0.1675]

Epoch 233/300: 100%|██████████| 2/2 [00:00<00:00, 62.59it/s, train=0.0322 | valid=0.1381 | fun_rmse=0.1675]

Epoch 234/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 234/300: 100%|██████████| 2/2 [00:00<00:00, 69.08it/s, train=0.0276 | valid=0.1311 | fun_rmse=0.1627]

Epoch 234/300: 100%|██████████| 2/2 [00:00<00:00, 68.25it/s, train=0.0276 | valid=0.1311 | fun_rmse=0.1627]

Epoch 235/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 235/300: 100%|██████████| 2/2 [00:00<00:00, 75.38it/s, train=0.0313 | valid=0.1246 | fun_rmse=0.1570]

Epoch 235/300: 100%|██████████| 2/2 [00:00<00:00, 72.06it/s, train=0.0313 | valid=0.1246 | fun_rmse=0.1570]

Epoch 236/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 236/300: 100%|██████████| 2/2 [00:00<00:00, 72.18it/s, train=0.0480 | valid=0.1229 | fun_rmse=0.1550]

Epoch 236/300: 100%|██████████| 2/2 [00:00<00:00, 69.65it/s, train=0.0480 | valid=0.1229 | fun_rmse=0.1550]

Epoch 237/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 237/300: 100%|██████████| 2/2 [00:00<00:00, 73.38it/s, train=0.0365 | valid=0.1234 | fun_rmse=0.1559]

Epoch 237/300: 100%|██████████| 2/2 [00:00<00:00, 71.04it/s, train=0.0365 | valid=0.1234 | fun_rmse=0.1559]

Epoch 238/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 238/300: 100%|██████████| 2/2 [00:00<00:00, 136.44it/s, train=0.0351 | valid=0.1254 | fun_rmse=0.1548]

Epoch 238/300: 100%|██████████| 2/2 [00:00<00:00, 132.99it/s, train=0.0351 | valid=0.1254 | fun_rmse=0.1548]

Epoch 239/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 239/300: 100%|██████████| 2/2 [00:00<00:00, 152.10it/s, train=0.0366 | valid=0.1299 | fun_rmse=0.1594]

Epoch 239/300: 100%|██████████| 2/2 [00:00<00:00, 145.68it/s, train=0.0366 | valid=0.1299 | fun_rmse=0.1594]

Epoch 240/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 240/300: 100%|██████████| 2/2 [00:00<00:00, 93.56it/s, train=0.0443 | valid=0.1328 | fun_rmse=0.1633]

Epoch 240/300: 100%|██████████| 2/2 [00:00<00:00, 91.71it/s, train=0.0443 | valid=0.1328 | fun_rmse=0.1633]

Epoch 241/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 241/300: 100%|██████████| 2/2 [00:00<00:00, 101.45it/s, train=0.0385 | valid=0.1335 | fun_rmse=0.1668]

Epoch 241/300: 100%|██████████| 2/2 [00:00<00:00, 98.07it/s, train=0.0385 | valid=0.1335 | fun_rmse=0.1668] 

Epoch 242/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 242/300: 100%|██████████| 2/2 [00:00<00:00, 78.58it/s, train=0.0452 | valid=0.1331 | fun_rmse=0.1704]

Epoch 242/300: 100%|██████████| 2/2 [00:00<00:00, 74.37it/s, train=0.0452 | valid=0.1331 | fun_rmse=0.1704]

Epoch 243/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 243/300: 100%|██████████| 2/2 [00:00<00:00, 73.30it/s, train=0.0305 | valid=0.1369 | fun_rmse=0.1722]

Epoch 243/300: 100%|██████████| 2/2 [00:00<00:00, 70.45it/s, train=0.0305 | valid=0.1369 | fun_rmse=0.1722]

Epoch 244/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 244/300: 100%|██████████| 2/2 [00:00<00:00, 60.60it/s, train=0.0420 | valid=0.1412 | fun_rmse=0.1741]

Epoch 244/300: 100%|██████████| 2/2 [00:00<00:00, 58.31it/s, train=0.0420 | valid=0.1412 | fun_rmse=0.1741]

Epoch 245/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 245/300: 100%|██████████| 2/2 [00:00<00:00, 53.83it/s, train=0.0427 | valid=0.1422 | fun_rmse=0.1746]

Epoch 245/300: 100%|██████████| 2/2 [00:00<00:00, 51.92it/s, train=0.0427 | valid=0.1422 | fun_rmse=0.1746]

Epoch 246/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 246/300: 100%|██████████| 2/2 [00:00<00:00, 101.37it/s, train=0.0328 | valid=0.1456 | fun_rmse=0.1750]

Epoch 246/300: 100%|██████████| 2/2 [00:00<00:00, 99.98it/s, train=0.0328 | valid=0.1456 | fun_rmse=0.1750] 

Epoch 247/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 247/300: 100%|██████████| 2/2 [00:00<00:00, 172.42it/s, train=0.0492 | valid=0.1434 | fun_rmse=0.1726]

Epoch 247/300: 100%|██████████| 2/2 [00:00<00:00, 168.61it/s, train=0.0492 | valid=0.1434 | fun_rmse=0.1726]

Epoch 248/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 248/300: 100%|██████████| 2/2 [00:00<00:00, 181.43it/s, train=0.0408 | valid=0.1411 | fun_rmse=0.1694]

Epoch 248/300: 100%|██████████| 2/2 [00:00<00:00, 177.43it/s, train=0.0408 | valid=0.1411 | fun_rmse=0.1694]

Epoch 249/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 249/300: 100%|██████████| 2/2 [00:00<00:00, 136.73it/s, train=0.0467 | valid=0.1391 | fun_rmse=0.1674]

Epoch 249/300: 100%|██████████| 2/2 [00:00<00:00, 132.88it/s, train=0.0467 | valid=0.1391 | fun_rmse=0.1674]

Epoch 250/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 250/300: 100%|██████████| 2/2 [00:00<00:00, 111.43it/s, train=0.0484 | valid=0.1382 | fun_rmse=0.1694]

Epoch 250/300: 100%|██████████| 2/2 [00:00<00:00, 108.41it/s, train=0.0484 | valid=0.1382 | fun_rmse=0.1694]

Epoch 251/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 251/300: 100%|██████████| 2/2 [00:00<00:00, 66.10it/s, train=0.0439 | valid=0.1314 | fun_rmse=0.1648]

Epoch 251/300: 100%|██████████| 2/2 [00:00<00:00, 63.93it/s, train=0.0439 | valid=0.1314 | fun_rmse=0.1648]

Epoch 252/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 252/300: 100%|██████████| 2/2 [00:00<00:00, 89.28it/s, train=0.0406 | valid=0.1205 | fun_rmse=0.1575]

Epoch 252/300: 100%|██████████| 2/2 [00:00<00:00, 86.20it/s, train=0.0406 | valid=0.1205 | fun_rmse=0.1575]

Epoch 253/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 253/300: 100%|██████████| 2/2 [00:00<00:00, 114.44it/s, train=0.0264 | valid=0.1224 | fun_rmse=0.1541]

Epoch 253/300: 100%|██████████| 2/2 [00:00<00:00, 112.42it/s, train=0.0264 | valid=0.1224 | fun_rmse=0.1541]

Epoch 254/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 254/300: 100%|██████████| 2/2 [00:00<00:00, 254.23it/s, train=0.0330 | valid=0.1231 | fun_rmse=0.1554]

Epoch 254/300: 100%|██████████| 2/2 [00:00<00:00, 244.74it/s, train=0.0330 | valid=0.1231 | fun_rmse=0.1554]

Epoch 255/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 255/300: 100%|██████████| 2/2 [00:00<00:00, 155.84it/s, train=0.0317 | valid=0.1244 | fun_rmse=0.1608]

Epoch 255/300: 100%|██████████| 2/2 [00:00<00:00, 151.26it/s, train=0.0317 | valid=0.1244 | fun_rmse=0.1608]

Epoch 256/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 256/300: 100%|██████████| 2/2 [00:00<00:00, 163.70it/s, train=0.0308 | valid=0.1304 | fun_rmse=0.1663]

Epoch 256/300: 100%|██████████| 2/2 [00:00<00:00, 159.23it/s, train=0.0308 | valid=0.1304 | fun_rmse=0.1663]

Epoch 257/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 257/300: 100%|██████████| 2/2 [00:00<00:00, 186.40it/s, train=0.0346 | valid=0.1301 | fun_rmse=0.1662]

Epoch 257/300: 100%|██████████| 2/2 [00:00<00:00, 180.01it/s, train=0.0346 | valid=0.1301 | fun_rmse=0.1662]

Epoch 258/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 258/300: 100%|██████████| 2/2 [00:00<00:00, 138.05it/s, train=0.0287 | valid=0.1333 | fun_rmse=0.1654]

Epoch 258/300: 100%|██████████| 2/2 [00:00<00:00, 132.06it/s, train=0.0287 | valid=0.1333 | fun_rmse=0.1654]

Epoch 259/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 259/300: 100%|██████████| 2/2 [00:00<00:00, 92.36it/s, train=0.0357 | valid=0.1334 | fun_rmse=0.1655]

Epoch 259/300: 100%|██████████| 2/2 [00:00<00:00, 88.21it/s, train=0.0357 | valid=0.1334 | fun_rmse=0.1655]

Epoch 260/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 260/300: 100%|██████████| 2/2 [00:00<00:00, 107.60it/s, train=0.0290 | valid=0.1345 | fun_rmse=0.1678]

Epoch 260/300: 100%|██████████| 2/2 [00:00<00:00, 103.92it/s, train=0.0290 | valid=0.1345 | fun_rmse=0.1678]

Epoch 261/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 261/300: 100%|██████████| 2/2 [00:00<00:00, 159.23it/s, train=0.0335 | valid=0.1351 | fun_rmse=0.1695]

Epoch 261/300: 100%|██████████| 2/2 [00:00<00:00, 155.99it/s, train=0.0335 | valid=0.1351 | fun_rmse=0.1695]

Epoch 262/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 262/300: 100%|██████████| 2/2 [00:00<00:00, 190.48it/s, train=0.0441 | valid=0.1360 | fun_rmse=0.1687]

Epoch 262/300: 100%|██████████| 2/2 [00:00<00:00, 184.94it/s, train=0.0441 | valid=0.1360 | fun_rmse=0.1687]

Epoch 263/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 263/300: 100%|██████████| 2/2 [00:00<00:00, 149.12it/s, train=0.0333 | valid=0.1367 | fun_rmse=0.1674]

Epoch 263/300: 100%|██████████| 2/2 [00:00<00:00, 144.97it/s, train=0.0333 | valid=0.1367 | fun_rmse=0.1674]

Epoch 264/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 264/300: 100%|██████████| 2/2 [00:00<00:00, 139.54it/s, train=0.0379 | valid=0.1356 | fun_rmse=0.1660]

Epoch 264/300: 100%|██████████| 2/2 [00:00<00:00, 136.55it/s, train=0.0379 | valid=0.1356 | fun_rmse=0.1660]

Epoch 265/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 265/300: 100%|██████████| 2/2 [00:00<00:00, 132.66it/s, train=0.0248 | valid=0.1329 | fun_rmse=0.1638]

Epoch 265/300: 100%|██████████| 2/2 [00:00<00:00, 129.56it/s, train=0.0248 | valid=0.1329 | fun_rmse=0.1638]

Epoch 266/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 266/300: 100%|██████████| 2/2 [00:00<00:00, 68.23it/s, train=0.0222 | valid=0.1309 | fun_rmse=0.1619]

Epoch 266/300: 100%|██████████| 2/2 [00:00<00:00, 66.94it/s, train=0.0222 | valid=0.1309 | fun_rmse=0.1619]

Epoch 267/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 267/300: 100%|██████████| 2/2 [00:00<00:00, 72.01it/s, train=0.0346 | valid=0.1301 | fun_rmse=0.1606]

Epoch 267/300: 100%|██████████| 2/2 [00:00<00:00, 68.92it/s, train=0.0346 | valid=0.1301 | fun_rmse=0.1606]

Epoch 268/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 268/300: 100%|██████████| 2/2 [00:00<00:00, 65.91it/s, train=0.0247 | valid=0.1301 | fun_rmse=0.1604]

Epoch 268/300: 100%|██████████| 2/2 [00:00<00:00, 63.60it/s, train=0.0247 | valid=0.1301 | fun_rmse=0.1604]

Epoch 269/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 269/300: 100%|██████████| 2/2 [00:00<00:00, 57.28it/s, train=0.0349 | valid=0.1302 | fun_rmse=0.1611]

Epoch 269/300: 100%|██████████| 2/2 [00:00<00:00, 55.88it/s, train=0.0349 | valid=0.1302 | fun_rmse=0.1611]

Epoch 270/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 270/300: 100%|██████████| 2/2 [00:00<00:00, 52.66it/s, train=0.0194 | valid=0.1283 | fun_rmse=0.1610]

Epoch 270/300: 100%|██████████| 2/2 [00:00<00:00, 52.08it/s, train=0.0194 | valid=0.1283 | fun_rmse=0.1610]

Epoch 271/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 271/300: 100%|██████████| 2/2 [00:00<00:00, 161.35it/s, train=0.0324 | valid=0.1276 | fun_rmse=0.1610]

Epoch 271/300: 100%|██████████| 2/2 [00:00<00:00, 157.33it/s, train=0.0324 | valid=0.1276 | fun_rmse=0.1610]

Epoch 272/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 272/300: 100%|██████████| 2/2 [00:00<00:00, 207.32it/s, train=0.0305 | valid=0.1297 | fun_rmse=0.1623]

Epoch 272/300: 100%|██████████| 2/2 [00:00<00:00, 200.30it/s, train=0.0305 | valid=0.1297 | fun_rmse=0.1623]

Epoch 273/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 273/300: 100%|██████████| 2/2 [00:00<00:00, 218.14it/s, train=0.0223 | valid=0.1306 | fun_rmse=0.1626]

Epoch 273/300: 100%|██████████| 2/2 [00:00<00:00, 210.13it/s, train=0.0223 | valid=0.1306 | fun_rmse=0.1626]

Epoch 274/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 274/300: 100%|██████████| 2/2 [00:00<00:00, 192.80it/s, train=0.0250 | valid=0.1303 | fun_rmse=0.1625]

Epoch 274/300: 100%|██████████| 2/2 [00:00<00:00, 184.93it/s, train=0.0250 | valid=0.1303 | fun_rmse=0.1625]

Epoch 275/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 275/300: 100%|██████████| 2/2 [00:00<00:00, 182.42it/s, train=0.0269 | valid=0.1302 | fun_rmse=0.1626]

Epoch 275/300: 100%|██████████| 2/2 [00:00<00:00, 176.47it/s, train=0.0269 | valid=0.1302 | fun_rmse=0.1626]

Epoch 276/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 276/300: 100%|██████████| 2/2 [00:00<00:00, 127.09it/s, train=0.0305 | valid=0.1302 | fun_rmse=0.1624]

Epoch 276/300: 100%|██████████| 2/2 [00:00<00:00, 123.18it/s, train=0.0305 | valid=0.1302 | fun_rmse=0.1624]

Epoch 277/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 277/300: 100%|██████████| 2/2 [00:00<00:00, 87.00it/s, train=0.0240 | valid=0.1314 | fun_rmse=0.1631]

Epoch 277/300: 100%|██████████| 2/2 [00:00<00:00, 79.84it/s, train=0.0240 | valid=0.1314 | fun_rmse=0.1631]

Epoch 278/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 278/300: 100%|██████████| 2/2 [00:00<00:00, 44.47it/s, train=0.0210 | valid=0.1332 | fun_rmse=0.1643]

Epoch 278/300: 100%|██████████| 2/2 [00:00<00:00, 42.76it/s, train=0.0210 | valid=0.1332 | fun_rmse=0.1643]

Epoch 279/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 279/300: 100%|██████████| 2/2 [00:00<00:00, 52.78it/s, train=0.0372 | valid=0.1345 | fun_rmse=0.1651]

Epoch 279/300: 100%|██████████| 2/2 [00:00<00:00, 51.16it/s, train=0.0372 | valid=0.1345 | fun_rmse=0.1651]

Epoch 280/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 280/300: 100%|██████████| 2/2 [00:00<00:00, 60.33it/s, train=0.0197 | valid=0.1342 | fun_rmse=0.1648]

Epoch 280/300: 100%|██████████| 2/2 [00:00<00:00, 59.79it/s, train=0.0197 | valid=0.1342 | fun_rmse=0.1648]

Epoch 281/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 281/300: 100%|██████████| 2/2 [00:00<00:00, 76.52it/s, train=0.0370 | valid=0.1327 | fun_rmse=0.1643]

Epoch 281/300: 100%|██████████| 2/2 [00:00<00:00, 73.94it/s, train=0.0370 | valid=0.1327 | fun_rmse=0.1643]

Epoch 282/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 282/300: 100%|██████████| 2/2 [00:00<00:00, 140.22it/s, train=0.0348 | valid=0.1317 | fun_rmse=0.1639]

Epoch 282/300: 100%|██████████| 2/2 [00:00<00:00, 138.10it/s, train=0.0348 | valid=0.1317 | fun_rmse=0.1639]

Epoch 283/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 283/300: 100%|██████████| 2/2 [00:00<00:00, 181.57it/s, train=0.0375 | valid=0.1320 | fun_rmse=0.1643]

Epoch 283/300: 100%|██████████| 2/2 [00:00<00:00, 178.52it/s, train=0.0375 | valid=0.1320 | fun_rmse=0.1643]

Epoch 284/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 284/300: 100%|██████████| 2/2 [00:00<00:00, 142.72it/s, train=0.0268 | valid=0.1333 | fun_rmse=0.1649]

Epoch 284/300: 100%|██████████| 2/2 [00:00<00:00, 139.28it/s, train=0.0268 | valid=0.1333 | fun_rmse=0.1649]

Epoch 285/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 285/300: 100%|██████████| 2/2 [00:00<00:00, 119.85it/s, train=0.0291 | valid=0.1343 | fun_rmse=0.1653]

Epoch 285/300: 100%|██████████| 2/2 [00:00<00:00, 113.33it/s, train=0.0291 | valid=0.1343 | fun_rmse=0.1653]

Epoch 286/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 286/300: 100%|██████████| 2/2 [00:00<00:00, 43.78it/s, train=0.0187 | valid=0.1345 | fun_rmse=0.1652]

Epoch 286/300: 100%|██████████| 2/2 [00:00<00:00, 43.35it/s, train=0.0187 | valid=0.1345 | fun_rmse=0.1652]

Epoch 287/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 287/300: 100%|██████████| 2/2 [00:00<00:00, 47.64it/s, train=0.0241 | valid=0.1342 | fun_rmse=0.1648]

Epoch 287/300: 100%|██████████| 2/2 [00:00<00:00, 45.40it/s, train=0.0241 | valid=0.1342 | fun_rmse=0.1648]

Epoch 288/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 288/300: 100%|██████████| 2/2 [00:00<00:00, 73.62it/s, train=0.0305 | valid=0.1334 | fun_rmse=0.1641]

Epoch 288/300: 100%|██████████| 2/2 [00:00<00:00, 71.03it/s, train=0.0305 | valid=0.1334 | fun_rmse=0.1641]

Epoch 289/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 289/300: 100%|██████████| 2/2 [00:00<00:00, 63.34it/s, train=0.0213 | valid=0.1325 | fun_rmse=0.1634]

Epoch 289/300: 100%|██████████| 2/2 [00:00<00:00, 59.88it/s, train=0.0213 | valid=0.1325 | fun_rmse=0.1634]

Epoch 290/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 290/300: 100%|██████████| 2/2 [00:00<00:00, 55.74it/s, train=0.0311 | valid=0.1320 | fun_rmse=0.1631]

Epoch 290/300: 100%|██████████| 2/2 [00:00<00:00, 55.29it/s, train=0.0311 | valid=0.1320 | fun_rmse=0.1631]

Epoch 291/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 291/300: 100%|██████████| 2/2 [00:00<00:00, 190.40it/s, train=0.0293 | valid=0.1318 | fun_rmse=0.1630]

Epoch 291/300: 100%|██████████| 2/2 [00:00<00:00, 185.06it/s, train=0.0293 | valid=0.1318 | fun_rmse=0.1630]

Epoch 292/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 292/300: 100%|██████████| 2/2 [00:00<00:00, 109.68it/s, train=0.0254 | valid=0.1318 | fun_rmse=0.1631]

Epoch 292/300: 100%|██████████| 2/2 [00:00<00:00, 105.18it/s, train=0.0254 | valid=0.1318 | fun_rmse=0.1631]

Epoch 293/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 293/300: 100%|██████████| 2/2 [00:00<00:00, 60.53it/s, train=0.0219 | valid=0.1319 | fun_rmse=0.1632]

Epoch 293/300: 100%|██████████| 2/2 [00:00<00:00, 59.84it/s, train=0.0219 | valid=0.1319 | fun_rmse=0.1632]

Epoch 294/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 294/300: 100%|██████████| 2/2 [00:00<00:00, 55.64it/s, train=0.0351 | valid=0.1321 | fun_rmse=0.1635]

Epoch 294/300: 100%|██████████| 2/2 [00:00<00:00, 52.96it/s, train=0.0351 | valid=0.1321 | fun_rmse=0.1635]

Epoch 295/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 295/300: 100%|██████████| 2/2 [00:00<00:00, 57.24it/s, train=0.0210 | valid=0.1322 | fun_rmse=0.1636]

Epoch 295/300: 100%|██████████| 2/2 [00:00<00:00, 55.44it/s, train=0.0210 | valid=0.1322 | fun_rmse=0.1636]

Epoch 296/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 296/300: 100%|██████████| 2/2 [00:00<00:00, 91.07it/s, train=0.0234 | valid=0.1323 | fun_rmse=0.1638]

Epoch 296/300: 100%|██████████| 2/2 [00:00<00:00, 88.35it/s, train=0.0234 | valid=0.1323 | fun_rmse=0.1638]

Epoch 297/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 297/300: 100%|██████████| 2/2 [00:00<00:00, 97.23it/s, train=0.0283 | valid=0.1324 | fun_rmse=0.1638]

Epoch 297/300: 100%|██████████| 2/2 [00:00<00:00, 95.96it/s, train=0.0283 | valid=0.1324 | fun_rmse=0.1638]

Epoch 298/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 298/300: 100%|██████████| 2/2 [00:00<00:00, 103.19it/s, train=0.0334 | valid=0.1324 | fun_rmse=0.1638]

Epoch 298/300: 100%|██████████| 2/2 [00:00<00:00, 101.67it/s, train=0.0334 | valid=0.1324 | fun_rmse=0.1638]

Epoch 299/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 299/300: 100%|██████████| 2/2 [00:00<00:00, 143.15it/s, train=0.0311 | valid=0.1325 | fun_rmse=0.1639]

Epoch 299/300: 100%|██████████| 2/2 [00:00<00:00, 138.30it/s, train=0.0311 | valid=0.1325 | fun_rmse=0.1639]

Epoch 300/300:   0%|          | 0/2 [00:00<?, ?it/s]

Epoch 300/300: 100%|██████████| 2/2 [00:00<00:00, 99.06it/s, train=0.0234 | valid=0.1325 | fun_rmse=0.1639]

Epoch 300/300: 100%|██████████| 2/2 [00:00<00:00, 96.77it/s, train=0.0234 | valid=0.1325 | fun_rmse=0.1639]

Inspect Predictions¶

After training, we extract predictions and targets from the validation set to compare them side by side.

In [9]:

preds, targs = lrn.get_preds()
print(f"Predictions shape: {preds.shape}")  # (n_samples, 2)
print(f"Targets shape:     {targs.shape}")  # (n_samples, 2)

for i in range(min(5, len(preds))):
    print(
        f"  Pred: x0={preds[i, 0]:.3f}, v0={preds[i, 1]:.3f}  |  "
        f"True: x0={targs[i, 0]:.3f}, v0={targs[i, 1]:.3f}"
    )

preds, targs = lrn.get_preds() print(f"Predictions shape: {preds.shape}") # (n_samples, 2) print(f"Targets shape: {targs.shape}") # (n_samples, 2) for i in range(min(5, len(preds))): print( f" Pred: x0={preds[i, 0]:.3f}, v0={preds[i, 1]:.3f} | " f"True: x0={targs[i, 0]:.3f}, v0={targs[i, 1]:.3f}" )

Predictions shape: torch.Size([6, 2])
Targets shape:     torch.Size([6, 2])
  Pred: x0=0.040, v0=-1.074  |  True: x0=0.000, v0=-1.000
  Pred: x0=-0.201, v0=-0.928  |  True: x0=0.000, v0=-1.000
  Pred: x0=0.820, v0=-0.246  |  True: x0=0.500, v0=0.000
  Pred: x0=0.745, v0=-0.162  |  True: x0=0.500, v0=0.000
  Pred: x0=-0.485, v0=1.019  |  True: x0=-0.500, v0=1.000

Key Takeaways¶

• Sequence-to-scalar tasks predict a single value (or vector) from a time series -- useful for parameter estimation, classification, and condition monitoring.
• HDF5Attrs reads scalar targets from HDF5 file attributes, complementing HDF5Signals for the input.
• SeqAggregation reduces RNN sequence output to a scalar by selecting the last timestep. It is a standard nn.Module that can be appended to any sequence model via nn.Sequential.
• Custom pipelines combine WindowedDataset, HDF5Signals, HDF5Attrs, and split_by_parent for flexible task definitions beyond the standard create_dls workflow.
• Normalization still applies: ScaledModel.from_dls attaches input and output scalers to a hand-built model, using dls.norm_stats estimated from training batches.
• The same RNN architectures work for both sequence-to-sequence and sequence-to-scalar problems -- only the output reduction changes.