Note
Go to the end to download the full example code.
Mie-informed tandem neural network#
Here, we demonstrate how to train a design generator network capable to suggest core-shell particles with specific spectral response using PyMieDiff as differentiable forward-evaluator. The training pipeline follows the “Tandem” model:
target spectrum –> generator NN –> design –> Mie –> real spectrum
training loss is: MSE(target spec., real spec.)
author: O. Jackson, P. Wiecha, 06/2025
imports#
import time
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
import torch
from torch import nn
import pymiediff as pmd
setup optimiation target#
We setup the main configuration here: torch device, parameter limits and wavelengths
# torch compute device
device = "cpu"
# general config
N_samples = 25000
n_max = 4 # maximum Mie order fixed for performance
eps_env = torch.tensor(1.0, device=device)
lim_r = torch.as_tensor([40, 100], device=device)
lim_n_re = torch.as_tensor([1.5, 4.0], device=device)
lim_n_im = torch.as_tensor([0.0, 0.1], device=device)
wl0 = torch.linspace(400, 800, 40, device=device)
k0 = 2 * torch.pi / wl0
generate reference spectra#
we generate a large number of reference Mie spectra for existing particles, that will be used as design targets during training.
Note: this step could also be done without any physics knowledge, for example with artificial spectra (e.g. Lorentzians), or a scattering maximization loss.
# datagen: generate existing spectra (won't use the geometries for training)
r_c = torch.rand((N_samples), device=device) * torch.diff(lim_r)[0] + lim_r[0]
d_s = torch.rand((N_samples), device=device) * torch.diff(lim_r)[0] + lim_r[0]
r_s = r_c + d_s
n_re = torch.rand((N_samples, 2), device=device) * torch.diff(lim_n_re)[0] + lim_n_re[0]
n_im = torch.rand((N_samples, 2), device=device) * torch.diff(lim_n_im)[0] + lim_n_im[0]
n = n_re + 1j * n_im
# low-level API: permittivity required as spectra (for vectorization)
eps_c = torch.ones_like(k0).unsqueeze(0) * n[:, 0].unsqueeze(1) ** 2
eps_s = torch.ones_like(k0).unsqueeze(0) * n[:, 1].unsqueeze(1) ** 2
all_particles = pmd.multishell.cross_sections(
k0,
r_c=r_c,
eps_c=eps_c,
r_s=r_s,
eps_s=eps_s,
eps_env=eps_env,
n_max=n_max,
)
N_test = 128 # keep a few samples for testing
q_sca_target = all_particles["q_sca"][N_test:].to(dtype=torch.float32)
q_sca_target_test = all_particles["q_sca"][:N_test].to(dtype=torch.float32)
plt.plot(q_sca_target[30].detach().cpu().numpy()) # plot some test sample
[<matplotlib.lines.Line2D object at 0x7f06a0074590>]
Neural network classes / functions#
define the network model (simple MLP) and training loop
class FullyConnected(nn.Module):
def __init__(self, hidden_dim=1024):
super().__init__()
self.fc_in = nn.Linear(len(k0), hidden_dim)
self.relu1 = nn.ReLU()
self.fc_1 = nn.Linear(hidden_dim, hidden_dim)
self.relu2 = nn.ReLU()
self.fc_2 = nn.Linear(hidden_dim, hidden_dim)
self.relu3 = nn.ReLU()
self.fc_out = nn.Linear(hidden_dim, 6)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x = self.fc_in(x)
x = self.relu1(x)
x = self.fc_1(x)
x = self.relu2(x)
x = self.fc_2(x)
x = self.relu3(x)
x = self.fc_out(x)
x = self.sigmoid(x)
return x
def nn_pred_to_mie_geometry(pred):
# implicit normalization: multiply by user-defined limits
r_c = lim_r.max() * (pred[:, 0])
r_s = lim_r.max() * (pred[:, 0] + pred[:, 1])
n_c = lim_n_re.max() * pred[:, 2] + lim_n_im.max() * (1j * pred[:, 3])
n_s = lim_n_re.max() * pred[:, 4] + lim_n_im.max() * (1j * pred[:, 5])
eps_c = torch.ones_like(k0).unsqueeze(0) * n_c.unsqueeze(1) ** 2
eps_s = torch.ones_like(k0).unsqueeze(0) * n_s.unsqueeze(1) ** 2
return r_c, r_s, eps_c, eps_s
def train_loop(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset)
# Set the model to training mode - important for batch normalization and dropout layers
# Unnecessary in this situation but added for best practices
model.train()
prog_bar = tqdm(enumerate(dataloader), total=size // dataloader.batch_size)
for i_batch, X in prog_bar:
# model prediction: generate core-shell particles
pred = model(X)
# evaluate Mie
r_c, r_s, eps_c, eps_s = nn_pred_to_mie_geometry(pred)
res_mie = pmd.multishell.cross_sections(
k0,
r_c=r_c,
eps_c=eps_c,
r_s=r_s,
eps_s=eps_s,
eps_env=eps_env,
n_max=n_max,
)
q_sca_mie = res_mie["q_sca"].to(dtype=torch.float32)
# calc. loss
loss = loss_fn(q_sca_mie, X)
# Backpropagation
loss.backward()
optimizer.step()
optimizer.zero_grad()
# if i_batch % 100 == 0:
loss, current = loss.item(), i_batch * dataloader.batch_size + len(X)
prog_bar.set_description(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
training the Mie-informed network#
here we use some simple, manually optimized training schedule.
model = FullyConnected().to(device)
confs = [
dict(bs=32, lr=1e-4, n_ep=5),
dict(bs=64, lr=1e-4, n_ep=5),
dict(bs=128, lr=1e-4, n_ep=6),
dict(bs=256, lr=1e-5, n_ep=6),
]
t_start = time.time()
for conf in confs:
learning_rate = conf["lr"]
batch_size = conf["bs"]
epochs = conf["n_ep"]
print("-------------------------------")
print(f"LR={learning_rate}, batch_size={batch_size}")
print("-------------------------------")
loss_fn = nn.MSELoss()
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
train_dataloader = torch.utils.data.DataLoader(q_sca_target, batch_size=batch_size)
for t in range(epochs):
print(f"Epoch {t+1}, time={time.time()-t_start:.2f}s")
train_loop(train_dataloader, model, loss_fn, optimizer)
print("Done!")
-------------------------------
LR=0.0001, batch_size=32
-------------------------------
Epoch 1, time=0.00s
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loss: 0.222680 [23616/24872]: 0%| | 0/777 [00:30<?, ?it/s]
loss: 0.222680 [23616/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.346880 [23648/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.344657 [23680/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.335473 [23712/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.355527 [23744/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.236474 [23776/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.371519 [23808/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.296287 [23840/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.409068 [23872/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.300034 [23904/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.475202 [23936/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.275134 [23968/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.267775 [24000/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.255639 [24032/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.307331 [24064/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.263582 [24096/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.423794 [24128/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.355937 [24160/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.299370 [24192/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.329409 [24224/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.259210 [24256/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.311036 [24288/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.360720 [24320/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.308461 [24352/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.201081 [24384/24872]: 95%|█████████▍| 738/777 [00:30<00:01, 24.57it/s]
loss: 0.433952 [24416/24872]: 95%|█████████▍| 738/777 [00:31<00:01, 24.57it/s]
loss: 0.278161 [24448/24872]: 95%|█████████▍| 738/777 [00:31<00:01, 24.57it/s]
loss: 0.335278 [24480/24872]: 95%|█████████▍| 738/777 [00:31<00:01, 24.57it/s]
loss: 0.338501 [24512/24872]: 95%|█████████▍| 738/777 [00:31<00:01, 24.57it/s]
loss: 0.292395 [24544/24872]: 95%|█████████▍| 738/777 [00:31<00:01, 24.57it/s]
loss: 0.299277 [24576/24872]: 95%|█████████▍| 738/777 [00:31<00:01, 24.57it/s]
loss: 0.290700 [24608/24872]: 95%|█████████▍| 738/777 [00:31<00:01, 24.57it/s]
loss: 0.435608 [24640/24872]: 95%|█████████▍| 738/777 [00:31<00:01, 24.57it/s]
loss: 0.245576 [24672/24872]: 95%|█████████▍| 738/777 [00:31<00:01, 24.57it/s]
loss: 0.245214 [24704/24872]: 95%|█████████▍| 738/777 [00:31<00:01, 24.57it/s]
loss: 0.423037 [24736/24872]: 95%|█████████▍| 738/777 [00:31<00:01, 24.57it/s]
loss: 0.309382 [24768/24872]: 95%|█████████▍| 738/777 [00:31<00:01, 24.57it/s]
loss: 0.285760 [24800/24872]: 95%|█████████▍| 738/777 [00:31<00:01, 24.57it/s]
loss: 0.316207 [24832/24872]: 95%|█████████▍| 738/777 [00:31<00:01, 24.57it/s]
loss: 0.251803 [24864/24872]: 95%|█████████▍| 738/777 [00:31<00:01, 24.57it/s]
loss: 0.266870 [24872/24872]: 95%|█████████▍| 738/777 [00:31<00:01, 24.57it/s]
loss: 0.266870 [24872/24872]: : 778it [00:31, 24.59it/s]
Epoch 2, time=31.64s
0%| | 0/777 [00:00<?, ?it/s]
loss: 0.283125 [ 32/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.411228 [ 64/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.380049 [ 224/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.389290 [ 256/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.464753 [ 288/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.328631 [ 320/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.236295 [ 416/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.287581 [ 448/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.438121 [ 512/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.255555 [23424/24872]: 94%|█████████▍| 732/777 [00:30<00:01, 24.39it/s]
loss: 0.350746 [23456/24872]: 94%|█████████▍| 732/777 [00:30<00:01, 24.39it/s]
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loss: 0.176218 [23616/24872]: 94%|█████████▍| 732/777 [00:30<00:01, 24.39it/s]
loss: 0.325020 [23648/24872]: 94%|█████████▍| 732/777 [00:30<00:01, 24.39it/s]
loss: 0.237580 [23680/24872]: 94%|█████████▍| 732/777 [00:30<00:01, 24.39it/s]
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loss: 0.283457 [23744/24872]: 94%|█████████▍| 732/777 [00:30<00:01, 24.39it/s]
loss: 0.198589 [23776/24872]: 94%|█████████▍| 732/777 [00:30<00:01, 24.39it/s]
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loss: 0.199334 [23840/24872]: 94%|█████████▍| 732/777 [00:30<00:01, 24.39it/s]
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loss: 0.192956 [24000/24872]: 94%|█████████▍| 732/777 [00:30<00:01, 24.39it/s]
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loss: 0.268430 [24480/24872]: 94%|█████████▍| 732/777 [00:31<00:01, 24.39it/s]
loss: 0.277551 [24512/24872]: 94%|█████████▍| 732/777 [00:31<00:01, 24.39it/s]
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loss: 0.218353 [24576/24872]: 94%|█████████▍| 732/777 [00:31<00:01, 24.39it/s]
loss: 0.221749 [24608/24872]: 94%|█████████▍| 732/777 [00:31<00:01, 24.39it/s]
loss: 0.338623 [24640/24872]: 94%|█████████▍| 732/777 [00:31<00:01, 24.39it/s]
loss: 0.194554 [24672/24872]: 94%|█████████▍| 732/777 [00:31<00:01, 24.39it/s]
loss: 0.208256 [24704/24872]: 94%|█████████▍| 732/777 [00:31<00:01, 24.39it/s]
loss: 0.293702 [24736/24872]: 94%|█████████▍| 732/777 [00:31<00:01, 24.39it/s]
loss: 0.249164 [24768/24872]: 94%|█████████▍| 732/777 [00:31<00:01, 24.39it/s]
loss: 0.220105 [24800/24872]: 94%|█████████▍| 732/777 [00:31<00:01, 24.39it/s]
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loss: 0.242622 [24872/24872]: 94%|█████████▍| 732/777 [00:31<00:01, 24.39it/s]
loss: 0.242622 [24872/24872]: : 778it [00:31, 24.41it/s]
Epoch 3, time=63.50s
0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.206104 [23904/24872]: 93%|█████████▎| 726/777 [00:30<00:02, 24.18it/s]
loss: 0.363155 [23936/24872]: 93%|█████████▎| 726/777 [00:30<00:02, 24.18it/s]
loss: 0.188020 [23968/24872]: 93%|█████████▎| 726/777 [00:30<00:02, 24.18it/s]
loss: 0.174132 [24000/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.172741 [24032/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.256323 [24064/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.166860 [24096/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.320422 [24128/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
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loss: 0.234046 [24224/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.168483 [24256/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.179039 [24288/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.278832 [24320/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.248030 [24352/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.154076 [24384/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.341630 [24416/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.198793 [24448/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.254220 [24480/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.240209 [24512/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.208710 [24544/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.198859 [24576/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.199115 [24608/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.303159 [24640/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.171682 [24672/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.189002 [24704/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.275190 [24736/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.234884 [24768/24872]: 93%|█████████▎| 726/777 [00:31<00:02, 24.18it/s]
loss: 0.189010 [24800/24872]: 93%|█████████▎| 726/777 [00:32<00:02, 24.18it/s]
loss: 0.254258 [24832/24872]: 93%|█████████▎| 726/777 [00:32<00:02, 24.18it/s]
loss: 0.170808 [24864/24872]: 93%|█████████▎| 726/777 [00:32<00:02, 24.18it/s]
loss: 0.207310 [24872/24872]: 93%|█████████▎| 726/777 [00:32<00:02, 24.18it/s]
loss: 0.207310 [24872/24872]: : 778it [00:32, 24.22it/s]
Epoch 4, time=95.63s
0%| | 0/777 [00:00<?, ?it/s]
loss: 0.190080 [ 32/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.341630 [ 288/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.310506 [23296/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.188899 [23328/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
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loss: 0.196140 [23424/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.287855 [23456/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.215361 [23488/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.239329 [23520/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.196779 [23552/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.237473 [23584/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.149353 [23616/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.282091 [23648/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.201593 [23680/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.224037 [23712/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.244668 [23744/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.174341 [23776/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.223451 [23808/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.160269 [23840/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.208183 [23872/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.195317 [23904/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.344156 [23936/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.175776 [23968/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.159474 [24000/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.144877 [24032/24872]: 94%|█████████▎| 728/777 [00:30<00:02, 24.24it/s]
loss: 0.231458 [24064/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.153034 [24096/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.287441 [24128/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.243038 [24160/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.172962 [24192/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.211541 [24224/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.166176 [24256/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.164050 [24288/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.258868 [24320/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.240365 [24352/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.149504 [24384/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.317467 [24416/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.180037 [24448/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.244772 [24480/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.215650 [24512/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.212149 [24544/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.181679 [24576/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.174152 [24608/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.267714 [24640/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.160238 [24672/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.171379 [24704/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.263279 [24736/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.213443 [24768/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.174841 [24800/24872]: 94%|█████████▎| 728/777 [00:31<00:02, 24.24it/s]
loss: 0.252641 [24832/24872]: 94%|█████████▎| 728/777 [00:32<00:02, 24.24it/s]
loss: 0.149507 [24864/24872]: 94%|█████████▎| 728/777 [00:32<00:02, 24.24it/s]
loss: 0.217583 [24872/24872]: 94%|█████████▎| 728/777 [00:32<00:02, 24.24it/s]
loss: 0.217583 [24872/24872]: : 778it [00:32, 24.24it/s]
Epoch 5, time=127.73s
0%| | 0/777 [00:00<?, ?it/s]
loss: 0.184487 [ 32/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.268028 [ 64/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.142595 [23776/24872]: 96%|█████████▌| 743/777 [00:30<00:01, 24.75it/s]
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loss: 0.173422 [24576/24872]: 96%|█████████▌| 743/777 [00:31<00:01, 24.75it/s]
loss: 0.150845 [24608/24872]: 96%|█████████▌| 743/777 [00:31<00:01, 24.75it/s]
loss: 0.233674 [24640/24872]: 96%|█████████▌| 743/777 [00:31<00:01, 24.75it/s]
loss: 0.140672 [24672/24872]: 96%|█████████▌| 743/777 [00:31<00:01, 24.75it/s]
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loss: 0.189219 [24768/24872]: 96%|█████████▌| 743/777 [00:31<00:01, 24.75it/s]
loss: 0.156036 [24800/24872]: 96%|█████████▌| 743/777 [00:31<00:01, 24.75it/s]
loss: 0.237722 [24832/24872]: 96%|█████████▌| 743/777 [00:31<00:01, 24.75it/s]
loss: 0.141117 [24864/24872]: 96%|█████████▌| 743/777 [00:31<00:01, 24.75it/s]
loss: 0.199975 [24872/24872]: 96%|█████████▌| 743/777 [00:31<00:01, 24.75it/s]
loss: 0.199975 [24872/24872]: : 778it [00:31, 24.77it/s]
-------------------------------
LR=0.0001, batch_size=64
-------------------------------
Epoch 1, time=159.13s
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loss: 0.136429 [24872/24872]: : 389it [00:20, 19.44it/s]
Epoch 2, time=179.15s
0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.110952 [24872/24872]: 0%| | 0/388 [00:19<?, ?it/s]
loss: 0.110952 [24872/24872]: : 389it [00:19, 19.71it/s]
Epoch 3, time=198.88s
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loss: 0.114273 [24872/24872]: 0%| | 0/388 [00:19<?, ?it/s]
loss: 0.114273 [24872/24872]: : 389it [00:19, 19.77it/s]
Epoch 4, time=218.56s
0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.114209 [24872/24872]: : 389it [00:19, 19.53it/s]
Epoch 5, time=238.48s
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loss: 0.095943 [24872/24872]: 0%| | 0/388 [00:19<?, ?it/s]
loss: 0.095943 [24872/24872]: : 389it [00:19, 19.86it/s]
-------------------------------
LR=0.0001, batch_size=128
-------------------------------
Epoch 1, time=258.07s
0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.096561 [22528/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.115507 [22656/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.122155 [22784/24872]: 0%| | 0/194 [00:13<?, ?it/s]
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loss: 0.126761 [23296/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.110471 [23424/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.148485 [23552/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.128029 [23680/24872]: 0%| | 0/194 [00:13<?, ?it/s]
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loss: 0.135596 [23936/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.107224 [24064/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.131007 [24192/24872]: 0%| | 0/194 [00:13<?, ?it/s]
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loss: 0.086235 [24872/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.086235 [24872/24872]: : 195it [00:14, 13.53it/s]
Epoch 2, time=272.48s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.134623 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.119232 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.136399 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.102779 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.109022 [22656/24872]: 0%| | 0/194 [00:13<?, ?it/s]
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loss: 0.095744 [23040/24872]: 0%| | 0/194 [00:13<?, ?it/s]
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loss: 0.125804 [23296/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.109478 [23424/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.143524 [23552/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.122485 [23680/24872]: 0%| | 0/194 [00:13<?, ?it/s]
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loss: 0.130316 [23936/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.096271 [24064/24872]: 0%| | 0/194 [00:14<?, ?it/s]
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loss: 0.082755 [24872/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.082755 [24872/24872]: : 195it [00:14, 13.41it/s]
Epoch 3, time=287.02s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.131183 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.115411 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.112330 [ 768/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.135175 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.092074 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.120357 [ 1792/24872]: 0%| | 0/194 [00:01<?, ?it/s]
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loss: 0.102128 [19200/24872]: 0%| | 0/194 [00:11<?, ?it/s]
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loss: 0.096218 [22528/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.111220 [22656/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.134113 [22784/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.112398 [22912/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.094525 [23040/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.124890 [23168/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.125654 [23296/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.104904 [23424/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.143613 [23552/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.123411 [23680/24872]: 0%| | 0/194 [00:13<?, ?it/s]
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loss: 0.126238 [23936/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.095436 [24064/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.125939 [24192/24872]: 0%| | 0/194 [00:13<?, ?it/s]
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loss: 0.120026 [24448/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.115811 [24576/24872]: 0%| | 0/194 [00:14<?, ?it/s]
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loss: 0.148531 [24832/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.079670 [24872/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.079670 [24872/24872]: : 195it [00:14, 13.62it/s]
Epoch 4, time=301.34s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.131819 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.110132 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.112239 [ 512/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.108992 [ 768/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.146473 [ 896/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.131443 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.099937 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.115626 [ 1280/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.115748 [ 1792/24872]: 0%| | 0/194 [00:01<?, ?it/s]
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loss: 0.108496 [20480/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.104927 [20608/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.100431 [20736/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.104987 [20864/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.090293 [20992/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.114833 [21120/24872]: 0%| | 0/194 [00:12<?, ?it/s]
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loss: 0.119553 [21376/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.142086 [21504/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.114770 [21632/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.087989 [21760/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.096026 [21888/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.064134 [22016/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.109899 [22144/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.105860 [22272/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.116549 [22400/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.088718 [22528/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.102508 [22656/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.116250 [22784/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.103099 [22912/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.090342 [23040/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.118584 [23168/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.113713 [23296/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.098214 [23424/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.134687 [23552/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.116145 [23680/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.104334 [23808/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.122931 [23936/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.088084 [24064/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.124307 [24192/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.085473 [24320/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.123072 [24448/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.109664 [24576/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.119419 [24704/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.134288 [24832/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.093022 [24872/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.093022 [24872/24872]: : 195it [00:14, 13.65it/s]
Epoch 5, time=315.63s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.137590 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.119256 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.137168 [ 384/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.114021 [ 512/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.134218 [ 640/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.123077 [ 768/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.144491 [ 896/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.148835 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.093064 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.129541 [ 1280/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.131895 [ 1408/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.125880 [ 1536/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.119533 [ 1664/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.113792 [ 1792/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.120089 [ 1920/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.105405 [ 2048/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.148101 [ 2176/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.108919 [ 2304/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.123825 [ 2432/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.138459 [ 2560/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.105741 [ 2688/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.127338 [ 2816/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.089622 [ 2944/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.137716 [ 3072/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.118430 [ 3200/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.091397 [ 3328/24872]: 0%| | 0/194 [00:01<?, ?it/s]
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loss: 0.125403 [ 3584/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.099156 [ 3712/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.110578 [ 3840/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.114320 [ 3968/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.131220 [ 4096/24872]: 0%| | 0/194 [00:02<?, ?it/s]
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loss: 0.120542 [ 4480/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.103373 [ 4608/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.115529 [ 4736/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.109406 [ 4864/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.120603 [ 4992/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.087242 [ 5120/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.132065 [ 5248/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.147542 [ 5376/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.096116 [ 5504/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.118912 [ 5632/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.127992 [ 5760/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.122630 [ 5888/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.080941 [ 6016/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.131834 [ 6144/24872]: 0%| | 0/194 [00:03<?, ?it/s]
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loss: 0.115440 [ 6400/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.099619 [ 6528/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.127709 [ 6656/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.124440 [ 6784/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.113790 [ 6912/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.120603 [ 7040/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.092718 [ 7168/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.113159 [ 7296/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.132649 [ 7424/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.116827 [ 7552/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.108842 [ 7680/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.134836 [ 7808/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.124693 [ 7936/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.103361 [ 8064/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.143659 [ 8192/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.101109 [ 8320/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.110589 [ 8448/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.134380 [ 8576/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.168423 [ 8704/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.122220 [ 8832/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.086950 [ 8960/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.124604 [ 9088/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.099307 [ 9216/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.106536 [ 9344/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.138139 [ 9472/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.095416 [ 9600/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.109421 [ 9728/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.116491 [ 9856/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.134610 [ 9984/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.129069 [10112/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.135629 [10240/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.106665 [10368/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.139741 [10496/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.130227 [10624/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.106398 [10752/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.127370 [10880/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.116084 [11008/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.157410 [11136/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.151888 [11264/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.113793 [11392/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.161509 [11520/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.136447 [11648/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.150747 [11776/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.118696 [11904/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.129229 [12032/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.177383 [12160/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.128321 [12288/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.161420 [12416/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.146295 [12544/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.149435 [12672/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.166109 [12800/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.112277 [12928/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.131646 [13056/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.129287 [13184/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.119797 [13312/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.112212 [13440/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.134179 [13568/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.140154 [13696/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.141359 [13824/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.117427 [13952/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.113662 [14080/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.120130 [14208/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.124672 [14336/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.103514 [14464/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.125431 [14592/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.141355 [14720/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.137648 [14848/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.151379 [14976/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.109059 [15104/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.111422 [15232/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.094234 [15360/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.118125 [15488/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.101204 [15616/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.110946 [15744/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.105649 [15872/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.104658 [16000/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.093996 [16128/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.139824 [16256/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.140329 [16384/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.110740 [16512/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.128654 [16640/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.113116 [16768/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.112224 [16896/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.095240 [17024/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.106570 [17152/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.100255 [17280/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.139633 [17408/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.132981 [17536/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.132696 [17664/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.126255 [17792/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.122774 [17920/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.088238 [18048/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.126808 [18176/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.124274 [18304/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.133747 [18432/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.090333 [18560/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.108478 [18688/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.096918 [18816/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.089130 [18944/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.111404 [19072/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.093377 [19200/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.119005 [19328/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.097407 [19456/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.115518 [19584/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.151030 [19712/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.117003 [19840/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.109576 [19968/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.114894 [20096/24872]: 0%| | 0/194 [00:11<?, ?it/s]
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loss: 0.116169 [20352/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.108952 [20480/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.108094 [20608/24872]: 0%| | 0/194 [00:11<?, ?it/s]
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loss: 0.098410 [21888/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.073564 [22016/24872]: 0%| | 0/194 [00:12<?, ?it/s]
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loss: 0.091619 [22528/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.108238 [22656/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.120878 [22784/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.105933 [22912/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.093504 [23040/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.123811 [23168/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.114011 [23296/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.103896 [23424/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.135402 [23552/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.120973 [23680/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.110659 [23808/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.129478 [23936/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.095601 [24064/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.121487 [24192/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.101734 [24320/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.116658 [24448/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.121540 [24576/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.110265 [24704/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.149018 [24832/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.085656 [24872/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.085656 [24872/24872]: : 195it [00:14, 13.69it/s]
Epoch 6, time=329.87s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.134137 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.110612 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.139018 [ 384/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.115517 [ 512/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.119020 [ 640/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.113061 [ 768/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.138482 [ 896/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.128189 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.100002 [22656/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.113477 [22784/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.102238 [22912/24872]: 0%| | 0/194 [00:13<?, ?it/s]
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loss: 0.109461 [23552/24872]: 0%| | 0/194 [00:13<?, ?it/s]
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loss: 0.123766 [24192/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.096302 [24320/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.106290 [24448/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.101002 [24576/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.111388 [24704/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.118918 [24832/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.089259 [24872/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.089259 [24872/24872]: : 195it [00:14, 13.73it/s]
-------------------------------
LR=1e-05, batch_size=256
-------------------------------
Epoch 1, time=344.07s
0%| | 0/97 [00:00<?, ?it/s]
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loss: 0.110070 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.096328 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.125296 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
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loss: 0.091854 [13568/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.101819 [13824/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.092054 [14080/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.106429 [14336/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.096450 [14592/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.116510 [14848/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.107026 [15104/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.087298 [15360/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.091552 [15616/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.098521 [15872/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.082908 [16128/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.121091 [16384/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.099262 [16640/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.097785 [16896/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.088848 [17152/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.091694 [17408/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.130108 [17664/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.095430 [17920/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.093790 [18176/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.113521 [18432/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.083267 [18688/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.083952 [18944/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.088877 [19200/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.094408 [19456/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.105448 [19712/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.098053 [19968/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.094364 [20224/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.102520 [20480/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.089437 [20736/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.089354 [20992/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.101166 [21248/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.111342 [21504/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.092791 [21760/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.071775 [22016/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.096323 [22272/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.097537 [22528/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.093437 [22784/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.086087 [23040/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.099953 [23296/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.089185 [23552/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.102087 [23808/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.095477 [24064/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.100683 [24320/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.095420 [24576/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.104947 [24832/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.080749 [24872/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.080749 [24872/24872]: : 98it [00:10, 9.13it/s]
Epoch 2, time=354.81s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.099365 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.102729 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.091872 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.119069 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.085731 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.109031 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.089242 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.090156 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.105308 [ 2304/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.096462 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.095333 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.098911 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.091997 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.099408 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.085358 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.108740 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.105579 [ 4352/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.094042 [ 4608/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.091163 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.089644 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.124077 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.093196 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.099393 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.086619 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.088522 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.100332 [ 6656/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.098775 [ 6912/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.088652 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.091481 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.094267 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.108011 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.109504 [ 8192/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.090827 [ 8448/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.108515 [ 8704/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.087284 [ 8960/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.082089 [ 9216/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.105112 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.084911 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.099163 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.103898 [10240/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.093089 [10496/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.097299 [10752/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.091984 [11008/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.119832 [11264/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.089681 [11520/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.101378 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.094047 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.115870 [12288/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.109771 [12544/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.111430 [12800/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.078878 [13056/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.087858 [13312/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.090621 [13568/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.100615 [13824/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.090473 [14080/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.104374 [14336/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.095820 [14592/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.114463 [14848/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.104053 [15104/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.086163 [15360/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.090467 [15616/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.095957 [15872/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.081628 [16128/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.119585 [16384/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.099024 [16640/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.097244 [16896/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.086191 [17152/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.090041 [17408/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.128586 [17664/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.094702 [17920/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.092230 [18176/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.111931 [18432/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.082786 [18688/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.083355 [18944/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.087008 [19200/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.092766 [19456/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.104114 [19712/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.097143 [19968/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.092933 [20224/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.101929 [20480/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.088154 [20736/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.088293 [20992/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.099140 [21248/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.108494 [21504/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.092092 [21760/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.070918 [22016/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.093733 [22272/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.097884 [22528/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.092840 [22784/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.084954 [23040/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.099314 [23296/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.086647 [23552/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.100788 [23808/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.094540 [24064/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.099071 [24320/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.094659 [24576/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.103678 [24832/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.076256 [24872/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.076256 [24872/24872]: : 98it [00:10, 9.24it/s]
Epoch 3, time=365.42s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.097903 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.100975 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.091634 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.118348 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.085420 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.108674 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.088207 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.089395 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.103751 [ 2304/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.095408 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.094225 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.098036 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.090465 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.098950 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.084106 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.107932 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.104078 [ 4352/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.092926 [ 4608/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.089826 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.087559 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.119803 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.092072 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.098110 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.086777 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.087275 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.099715 [ 6656/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.097095 [ 6912/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.087362 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.090250 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.092651 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.106994 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.105575 [ 8192/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.090338 [ 8448/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.106818 [ 8704/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.086032 [ 8960/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.080827 [ 9216/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.104259 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.084417 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.099634 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.103203 [10240/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.091669 [10496/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.096017 [10752/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.090259 [11008/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.118311 [11264/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.088468 [11520/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.100874 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.093695 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.114312 [12288/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.108672 [12544/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.110520 [12800/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.078232 [13056/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.086505 [13312/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.089885 [13568/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.098923 [13824/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.089586 [14080/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.103009 [14336/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.095002 [14592/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.113360 [14848/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.103081 [15104/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.085742 [15360/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.089554 [15616/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.094092 [15872/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.080923 [16128/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.118585 [16384/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.098411 [16640/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.096008 [16896/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.084820 [17152/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.089128 [17408/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.127455 [17664/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.093837 [17920/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.091139 [18176/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.111029 [18432/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.082238 [18688/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.082443 [18944/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.086037 [19200/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.091983 [19456/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.103495 [19712/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.096391 [19968/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.091825 [20224/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.101543 [20480/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.087350 [20736/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.087586 [20992/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.098138 [21248/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.107019 [21504/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.091729 [21760/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.070401 [22016/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.092185 [22272/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.097738 [22528/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.092322 [22784/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.084367 [23040/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.098724 [23296/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.085788 [23552/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.099621 [23808/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.093984 [24064/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.098130 [24320/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.094082 [24576/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.102679 [24832/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.073965 [24872/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.073965 [24872/24872]: : 98it [00:10, 9.24it/s]
Epoch 4, time=376.03s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.096944 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.100035 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.091391 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.117738 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.085046 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.107875 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.087481 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.088621 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.102622 [ 2304/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.094618 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.093594 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.097431 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.089465 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.098550 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.083253 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.107129 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.103200 [ 4352/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.092341 [ 4608/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.088627 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.086424 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.117105 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.091068 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.097228 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.086750 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.086498 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.099034 [ 6656/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.096116 [ 6912/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.086519 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.089315 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.091577 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.106267 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.103288 [ 8192/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.089913 [ 8448/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.105429 [ 8704/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.085231 [ 8960/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.080073 [ 9216/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.103210 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.083904 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.098909 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.102616 [10240/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.090466 [10496/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.094952 [10752/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.089243 [11008/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.116736 [11264/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.087621 [11520/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.100220 [11776/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.093355 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.113054 [12288/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.107750 [12544/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.109552 [12800/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.077617 [13056/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.085676 [13312/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.089203 [13568/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.097441 [13824/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.088834 [14080/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.101996 [14336/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.094255 [14592/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.112549 [14848/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.102225 [15104/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.085369 [15360/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.088696 [15616/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.092888 [15872/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.080331 [16128/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.117677 [16384/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.097649 [16640/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.094941 [16896/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.083989 [17152/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.088446 [17408/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.126576 [17664/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.093110 [17920/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.090247 [18176/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.110163 [18432/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.081742 [18688/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.081740 [18944/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.085282 [19200/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.091344 [19456/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.103042 [19712/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.095586 [19968/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.090978 [20224/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.101211 [20480/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.086608 [20736/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.086958 [20992/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.097337 [21248/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.105897 [21504/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.091322 [21760/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.070003 [22016/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.091042 [22272/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.097342 [22528/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.091823 [22784/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.083667 [23040/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.097962 [23296/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.085032 [23552/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.098688 [23808/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.093460 [24064/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.097352 [24320/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.093560 [24576/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.101821 [24832/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.071918 [24872/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.071918 [24872/24872]: : 98it [00:10, 9.27it/s]
Epoch 5, time=386.60s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.096075 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.099436 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.091104 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.117138 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.084944 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.107370 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.086825 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.087701 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.101554 [ 2304/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.093927 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.093098 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.096849 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.088273 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.098090 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.082350 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.106082 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.102607 [ 4352/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.091970 [ 4608/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.087776 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.085331 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.114442 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.090099 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.096319 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.087087 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.085837 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.098321 [ 6656/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.095241 [ 6912/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.085733 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.088486 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.090431 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.105673 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.102312 [ 8192/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.089527 [ 8448/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.104702 [ 8704/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.084354 [ 8960/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.079498 [ 9216/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.102426 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.083563 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.099138 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.102357 [10240/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.089408 [10496/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.093885 [10752/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.088347 [11008/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.115643 [11264/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.086998 [11520/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.099831 [11776/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.092865 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.111811 [12288/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.106983 [12544/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.108721 [12800/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.077117 [13056/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.084928 [13312/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.088745 [13568/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.096323 [13824/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.088213 [14080/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.100953 [14336/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.093628 [14592/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.111750 [14848/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.101891 [15104/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.085024 [15360/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.088056 [15616/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.091668 [15872/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.079752 [16128/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.116878 [16384/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.096961 [16640/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.093744 [16896/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.083289 [17152/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.087681 [17408/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.125328 [17664/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.092389 [17920/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.089361 [18176/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.109311 [18432/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.081184 [18688/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.080959 [18944/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.084505 [19200/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.090808 [19456/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.102603 [19712/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.094908 [19968/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.090159 [20224/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.100965 [20480/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.085992 [20736/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.086364 [20992/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.096794 [21248/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.105008 [21504/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.090827 [21760/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.069599 [22016/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.090073 [22272/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.096966 [22528/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.091365 [22784/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.083004 [23040/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.097206 [23296/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.084298 [23552/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.097826 [23808/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.092910 [24064/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.096586 [24320/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.093064 [24576/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.101091 [24832/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.070741 [24872/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.070741 [24872/24872]: : 98it [00:10, 9.29it/s]
Epoch 6, time=397.15s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.095327 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.098963 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.090715 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.116529 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.084623 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.106807 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.086247 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.086931 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.100634 [ 2304/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.093270 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.092625 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.096370 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.087511 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.097617 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.081669 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.105266 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.102177 [ 4352/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.091567 [ 4608/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.087249 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.084559 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.112894 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.089299 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.095508 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.086823 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.085252 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.097734 [ 6656/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.094712 [ 6912/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.085097 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.087822 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.089554 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.105078 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.101668 [ 8192/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.089224 [ 8448/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.103895 [ 8704/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.083969 [ 8960/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.078987 [ 9216/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.101514 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.083216 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
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loss: 0.069638 [24872/24872]: : 98it [00:10, 9.25it/s]
Done!
test the network#
Do some qualitative tests: Let the trained network predict some particle geometries and compare their Mie spectra with the traget spectrum.
# pick a few of the training samples for testing.
# Note: Ideally tests should be done on separate samples!
sca_test = q_sca_target_test
pred = model(sca_test)
# evaluate Mie
r_c_test, r_s_test, eps_c_test, eps_s_test = nn_pred_to_mie_geometry(pred)
res_mie = pmd.multishell.cross_sections(
k0,
r_c=r_c_test,
eps_c=eps_c_test,
r_s=r_s_test,
eps_s=eps_s_test,
eps_env=eps_env,
n_max=n_max,
)
# plot
i_plot = np.random.randint(len(sca_test), size=4)
plt.figure(figsize=(12, 10))
for i_n, i in enumerate(i_plot):
plt.subplot(2, 2, i_n + 1)
plt.plot(
wl0.detach().cpu().numpy(),
sca_test[i].detach().cpu().numpy(),
label="reference",
)
plt.plot(
wl0.detach().cpu().numpy(),
res_mie["q_sca"][i].detach().cpu().numpy(),
label="predicted particle",
)
plt.legend()
plt.xlabel("wavelength (nm)")
plt.ylabel("scat. efficiency")
plt.show()
Total running time of the script: (6 minutes 55.181 seconds)
Estimated memory usage: 873 MB