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: pymiediff backend, torch device, parameter limits and wavelengths
# pymiediff backend to use and torch compute device
backend = "torch"
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.coreshell.cross_sections(
k0,
r_c=r_c,
eps_c=eps_c,
r_s=r_s,
eps_s=eps_s,
eps_env=eps_env,
backend=backend,
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 0x7ff455aee1b0>]
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.coreshell.cross_sections(
k0,
r_c=r_c,
eps_c=eps_c,
r_s=r_s,
eps_s=eps_s,
eps_env=eps_env,
backend=backend,
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: 0.249040 [24416/24872]: 0%| | 0/777 [00:25<?, ?it/s]
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loss: 0.335610 [24480/24872]: 0%| | 0/777 [00:25<?, ?it/s]
loss: 0.259211 [24512/24872]: 0%| | 0/777 [00:25<?, ?it/s]
loss: 0.301738 [24544/24872]: 0%| | 0/777 [00:25<?, ?it/s]
loss: 0.290498 [24576/24872]: 0%| | 0/777 [00:25<?, ?it/s]
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loss: 0.362023 [24640/24872]: 0%| | 0/777 [00:25<?, ?it/s]
loss: 0.334413 [24672/24872]: 0%| | 0/777 [00:25<?, ?it/s]
loss: 0.233532 [24704/24872]: 0%| | 0/777 [00:25<?, ?it/s]
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loss: 0.451986 [24872/24872]: 0%| | 0/777 [00:25<?, ?it/s]
loss: 0.451986 [24872/24872]: : 778it [00:25, 30.20it/s]
Epoch 2, time=25.79s
0%| | 0/777 [00:00<?, ?it/s]
loss: 0.303133 [ 32/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.151487 [ 64/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.285219 [ 128/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.288636 [ 192/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.343785 [ 224/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.235235 [ 256/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.413842 [ 288/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.475065 [ 320/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.434514 [ 352/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.236320 [ 384/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.311150 [ 416/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.398568 [ 448/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.273421 [ 480/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.349302 [ 512/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.283959 [ 544/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.301722 [ 576/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.451115 [24320/24872]: 0%| | 0/777 [00:24<?, ?it/s]
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loss: 0.225022 [24480/24872]: 0%| | 0/777 [00:25<?, ?it/s]
loss: 0.205182 [24512/24872]: 0%| | 0/777 [00:25<?, ?it/s]
loss: 0.245083 [24544/24872]: 0%| | 0/777 [00:25<?, ?it/s]
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loss: 0.277316 [24640/24872]: 0%| | 0/777 [00:25<?, ?it/s]
loss: 0.265430 [24672/24872]: 0%| | 0/777 [00:25<?, ?it/s]
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loss: 0.366332 [24872/24872]: 0%| | 0/777 [00:25<?, ?it/s]
loss: 0.366332 [24872/24872]: : 778it [00:25, 30.43it/s]
Epoch 3, time=51.36s
0%| | 0/777 [00:00<?, ?it/s]
loss: 0.259917 [ 32/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.133448 [ 64/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.198882 [ 256/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.343592 [ 288/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.261486 [ 416/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.324468 [24872/24872]: : 778it [00:25, 30.21it/s]
Epoch 4, time=77.11s
0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.201145 [24512/24872]: 0%| | 0/777 [00:25<?, ?it/s]
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loss: 0.250099 [24640/24872]: 0%| | 0/777 [00:25<?, ?it/s]
loss: 0.252220 [24672/24872]: 0%| | 0/777 [00:25<?, ?it/s]
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loss: 0.198486 [24768/24872]: 0%| | 0/777 [00:25<?, ?it/s]
loss: 0.250616 [24800/24872]: 0%| | 0/777 [00:26<?, ?it/s]
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loss: 0.295531 [24872/24872]: 0%| | 0/777 [00:26<?, ?it/s]
loss: 0.295531 [24872/24872]: : 778it [00:26, 29.80it/s]
Epoch 5, time=103.22s
0%| | 0/777 [00:00<?, ?it/s]
loss: 0.247508 [ 32/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.138430 [24512/24872]: 0%| | 0/777 [00:26<?, ?it/s]
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loss: 0.279357 [24872/24872]: 0%| | 0/777 [00:26<?, ?it/s]
loss: 0.279357 [24872/24872]: : 778it [00:26, 29.45it/s]
-------------------------------
LR=0.0001, batch_size=64
-------------------------------
Epoch 1, time=129.64s
0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.192798 [24872/24872]: 0%| | 0/388 [00:17<?, ?it/s]
loss: 0.192798 [24872/24872]: : 389it [00:17, 22.73it/s]
Epoch 2, time=146.75s
0%| | 0/388 [00:00<?, ?it/s]
loss: 0.153127 [ 64/24872]: 0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.173006 [24872/24872]: : 389it [00:16, 23.36it/s]
Epoch 3, time=163.41s
0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.161859 [24872/24872]: : 389it [00:16, 23.66it/s]
Epoch 4, time=179.85s
0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.191032 [24872/24872]: : 389it [00:16, 23.62it/s]
Epoch 5, time=196.32s
0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.102616 [23744/24872]: 0%| | 0/388 [00:15<?, ?it/s]
loss: 0.139525 [23808/24872]: 0%| | 0/388 [00:15<?, ?it/s]
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loss: 0.102359 [24128/24872]: 0%| | 0/388 [00:15<?, ?it/s]
loss: 0.154934 [24192/24872]: 0%| | 0/388 [00:15<?, ?it/s]
loss: 0.141856 [24256/24872]: 0%| | 0/388 [00:15<?, ?it/s]
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loss: 0.114953 [24384/24872]: 0%| | 0/388 [00:15<?, ?it/s]
loss: 0.154392 [24448/24872]: 0%| | 0/388 [00:15<?, ?it/s]
loss: 0.117111 [24512/24872]: 0%| | 0/388 [00:15<?, ?it/s]
loss: 0.173359 [24576/24872]: 0%| | 0/388 [00:15<?, ?it/s]
loss: 0.179067 [24640/24872]: 0%| | 0/388 [00:15<?, ?it/s]
loss: 0.104396 [24704/24872]: 0%| | 0/388 [00:16<?, ?it/s]
loss: 0.112175 [24768/24872]: 0%| | 0/388 [00:16<?, ?it/s]
loss: 0.137737 [24832/24872]: 0%| | 0/388 [00:16<?, ?it/s]
loss: 0.159709 [24872/24872]: 0%| | 0/388 [00:16<?, ?it/s]
loss: 0.159709 [24872/24872]: : 389it [00:16, 24.10it/s]
-------------------------------
LR=0.0001, batch_size=128
-------------------------------
Epoch 1, time=212.47s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.122156 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.532525 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.230023 [ 384/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.260358 [ 512/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.371063 [ 640/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.264400 [ 768/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.167511 [ 896/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.172567 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.258811 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.223883 [ 1280/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.155479 [ 1920/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.135087 [ 4096/24872]: 0%| | 0/194 [00:01<?, ?it/s]
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loss: 0.114984 [ 5888/24872]: 0%| | 0/194 [00:02<?, ?it/s]
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loss: 0.095610 [20096/24872]: 0%| | 0/194 [00:09<?, ?it/s]
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loss: 0.126820 [22656/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.117499 [22784/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.096184 [22912/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.083021 [23040/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.124367 [23168/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.152178 [23296/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.132283 [23424/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.127272 [23552/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.119083 [23680/24872]: 0%| | 0/194 [00:11<?, ?it/s]
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loss: 0.112712 [23936/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.114094 [24064/24872]: 0%| | 0/194 [00:11<?, ?it/s]
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loss: 0.156620 [24320/24872]: 0%| | 0/194 [00:11<?, ?it/s]
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loss: 0.147257 [24872/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.147257 [24872/24872]: : 195it [00:11, 16.28it/s]
Epoch 2, time=224.44s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.107305 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.114779 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.148364 [ 384/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.112922 [ 512/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.098234 [ 640/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.131202 [ 768/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.138700 [ 896/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.101445 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.136815 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.099743 [ 1280/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.125671 [ 1408/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.097025 [ 1536/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.129602 [ 1664/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.095975 [ 1792/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.089459 [ 1920/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.102970 [ 2176/24872]: 0%| | 0/194 [00:01<?, ?it/s]
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loss: 0.114767 [21248/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.102066 [21376/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.102520 [21504/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.102217 [21632/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.076849 [21760/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.099550 [21888/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.098687 [22016/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.124170 [22144/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.101147 [22272/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.096961 [22400/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.099713 [22528/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.117695 [22656/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.106618 [22784/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.088248 [22912/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.070960 [23040/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.119358 [23168/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.134639 [23296/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.126058 [23424/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.126277 [23552/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.114280 [23680/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.096110 [23808/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.111149 [23936/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.125303 [24064/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.100773 [24192/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.146991 [24320/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.115098 [24448/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.090025 [24576/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.096830 [24704/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.095789 [24832/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.146779 [24872/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.146779 [24872/24872]: : 195it [00:12, 16.24it/s]
Epoch 3, time=236.46s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.095773 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.116752 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.155022 [ 384/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.100028 [ 512/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.095155 [ 640/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.123847 [ 768/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.113446 [ 896/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.107440 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.128190 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.099410 [ 1280/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.113363 [ 1408/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.092703 [ 1536/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.129406 [ 1664/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.097643 [ 1792/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.081637 [ 1920/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.105902 [ 2048/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.100972 [ 2176/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.100209 [ 2304/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.091705 [ 2432/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.086306 [ 2560/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.108733 [ 2688/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.092021 [ 2816/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.114824 [ 2944/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.101971 [ 3072/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.100559 [ 3200/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.104831 [ 3328/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.111304 [ 3456/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.093903 [ 3584/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.128623 [ 3712/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.124233 [ 3840/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.103535 [ 3968/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.122377 [ 4096/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.115306 [ 4224/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.095817 [ 4352/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.120287 [ 4480/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.085213 [ 4608/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.100989 [ 4736/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.117907 [ 4864/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.094395 [ 4992/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.105650 [ 5120/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.127287 [ 5248/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.138750 [ 5376/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.123998 [ 5504/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.110407 [ 5632/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.113664 [ 5760/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.099429 [ 5888/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.098197 [ 6016/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.091191 [ 6144/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.105902 [ 6272/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.120664 [ 6400/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.141437 [ 6528/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.126964 [ 6656/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.076101 [ 6784/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.116484 [ 6912/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.094742 [ 7040/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.112411 [ 7168/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.089831 [ 7296/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.100991 [ 7424/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.155167 [ 7552/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.133941 [ 7680/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.119206 [ 7808/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.105543 [ 7936/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.101784 [ 8064/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.096085 [ 8192/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.133418 [ 8320/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.106210 [ 8448/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.093895 [ 8576/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.147552 [ 8704/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.087243 [ 8832/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.156310 [ 8960/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.121189 [ 9088/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.115764 [ 9216/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.124492 [ 9344/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.126963 [ 9472/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.124432 [ 9600/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.114537 [ 9728/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.108429 [ 9856/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.109656 [ 9984/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.120960 [10112/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.121768 [10240/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.129859 [10368/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.103853 [10496/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.094527 [10624/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.132651 [10752/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.083706 [10880/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.106801 [11008/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.090775 [11136/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.123774 [11264/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.082434 [11392/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.124011 [11520/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.092589 [11648/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.115287 [11776/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.100992 [11904/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.110120 [12032/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.102789 [12160/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.122817 [12288/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.078775 [12416/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.114761 [12544/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.154039 [12672/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.117400 [12800/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.125357 [12928/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.104627 [13056/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.117017 [13184/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.079760 [13312/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.135770 [13440/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.120972 [13568/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.126105 [13696/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.086944 [13824/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.134212 [13952/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.095726 [14080/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.096567 [14208/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.077348 [14336/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.089476 [14464/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.089030 [14592/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.094709 [14720/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.096561 [14848/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.088089 [14976/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.102665 [15104/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.117732 [15232/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.116001 [15360/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.092786 [15488/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.120790 [15616/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.097581 [15744/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.098279 [15872/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.081567 [16000/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.095743 [16128/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.110129 [16256/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.094847 [16384/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.127687 [16512/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.131729 [16640/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.108397 [16768/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.116477 [16896/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.106595 [17024/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.111949 [17152/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.117197 [17280/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.119948 [17408/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.123975 [17536/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.100788 [17664/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.096331 [17792/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.100552 [17920/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.101883 [18048/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.102852 [18176/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.089266 [18304/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.108225 [18432/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.092448 [18560/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.105987 [18688/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.085369 [18816/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.084743 [18944/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.087964 [19072/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.081903 [19200/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.121035 [19328/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.105449 [19456/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.109307 [19584/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.117960 [19712/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.084615 [19840/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.102304 [19968/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.082229 [20096/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.088304 [20224/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.111533 [20352/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.080760 [20480/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.115795 [20608/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.109970 [20736/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.137034 [20864/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.104878 [20992/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.131528 [21120/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.111493 [21248/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.100254 [21376/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.099310 [21504/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.098161 [21632/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.073454 [21760/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.097872 [21888/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.100735 [22016/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.126354 [22144/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.102897 [22272/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.087911 [22400/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.099909 [22528/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.109609 [22656/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.106766 [22784/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.083233 [22912/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.069284 [23040/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.113453 [23168/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.135038 [23296/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.122645 [23424/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.117060 [23552/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.108473 [23680/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.085530 [23808/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.108587 [23936/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.107182 [24064/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.098679 [24192/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.136856 [24320/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.127295 [24448/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.093150 [24576/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.111618 [24704/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.142508 [24832/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.132718 [24872/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.132718 [24872/24872]: : 195it [00:11, 16.54it/s]
Epoch 4, time=248.24s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.135374 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.111067 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.174731 [ 384/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.131826 [ 512/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.089872 [ 640/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.151521 [ 768/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.130673 [ 896/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.117038 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.143231 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.110184 [ 1280/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.121925 [ 1408/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.091830 [ 1536/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.140403 [ 1664/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.093813 [ 1792/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.099321 [ 1920/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.110981 [22656/24872]: 0%| | 0/194 [00:10<?, ?it/s]
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loss: 0.133073 [23296/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.121845 [23424/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.117922 [23552/24872]: 0%| | 0/194 [00:11<?, ?it/s]
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loss: 0.125390 [24872/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.125390 [24872/24872]: : 195it [00:11, 16.51it/s]
Epoch 5, time=260.06s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.086734 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.113330 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.119240 [24872/24872]: : 195it [00:11, 16.58it/s]
Epoch 6, time=271.82s
0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.093604 [14464/24872]: 0%| | 0/194 [00:06<?, ?it/s]
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loss: 0.092463 [14848/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.088297 [14976/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.099423 [15104/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.116228 [15232/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.104428 [15360/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.100301 [15488/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.099587 [15616/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.095550 [15744/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.089489 [15872/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.074197 [16000/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.093647 [16128/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.103075 [16256/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.090960 [16384/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.119221 [16512/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.121708 [16640/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.097291 [16768/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.107872 [16896/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.097154 [17024/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.094398 [17152/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.096166 [17280/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.110396 [17408/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.114369 [17536/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.095184 [17664/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.092425 [17792/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.084882 [17920/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.103864 [18048/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.100141 [18176/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.082516 [18304/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.116720 [18432/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.090227 [18560/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.107126 [18688/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.070657 [18816/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.080097 [18944/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.096103 [19072/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.078128 [19200/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.111681 [19328/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.102159 [19456/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.100453 [19584/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.119626 [19712/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.078265 [19840/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.095018 [19968/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.089357 [20096/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.086828 [20224/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.121732 [20352/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.073293 [20480/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.116543 [20608/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.109598 [20736/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.126783 [20864/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.093351 [20992/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.109621 [21120/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.115976 [21248/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.103990 [21376/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.100838 [21504/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.105953 [21632/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.070661 [21760/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.092944 [21888/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.093128 [22016/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.110312 [22144/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.085797 [22272/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.083524 [22400/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.086886 [22528/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.100244 [22656/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.088413 [22784/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.083506 [22912/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.067924 [23040/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.122870 [23168/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.124282 [23296/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.113686 [23424/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.110884 [23552/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.096449 [23680/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.084182 [23808/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.100223 [23936/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.108465 [24064/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.092804 [24192/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.123724 [24320/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.100985 [24448/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.084884 [24576/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.084692 [24704/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.085637 [24832/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.112173 [24872/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.112173 [24872/24872]: : 195it [00:11, 16.73it/s]
-------------------------------
LR=1e-05, batch_size=256
-------------------------------
Epoch 1, time=283.48s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.094213 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.116586 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.090711 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.098942 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.091897 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.087287 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.097673 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.083586 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.092909 [ 2304/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.079073 [ 2560/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.096799 [ 2816/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.084060 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.090736 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.086189 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.109929 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.099515 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.103230 [ 4352/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.085613 [ 4608/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.098835 [ 4864/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.078482 [ 5120/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.106432 [ 5376/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.093882 [ 5632/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.097403 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.076952 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.095374 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.091011 [ 6656/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.074658 [ 6912/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.084081 [ 7168/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.076612 [ 7424/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.108627 [ 7680/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.084431 [ 7936/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.076183 [ 8192/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.095250 [ 8448/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.085050 [ 8704/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.072636 [ 8960/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.088514 [ 9216/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.087395 [ 9472/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.074884 [ 9728/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.080439 [ 9984/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.087580 [10240/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.090822 [10496/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.084174 [10752/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.074893 [11008/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.089407 [11264/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.080884 [11520/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.080058 [11776/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.081448 [12032/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.093950 [12288/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.073966 [12544/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.108944 [12800/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.090052 [13056/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.081634 [13312/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.107067 [13568/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.086885 [13824/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.093123 [14080/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.069636 [14336/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.069092 [14592/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.078140 [14848/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.079016 [15104/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.098032 [15360/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.084350 [15616/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.084317 [15872/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.076037 [16128/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.088324 [16384/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.109893 [16640/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.093443 [16896/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.087997 [17152/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.097372 [17408/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.097424 [17664/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.083162 [17920/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.085789 [18176/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.085701 [18432/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.083752 [18688/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.070727 [18944/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.074059 [19200/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.097433 [19456/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.089015 [19712/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.074259 [19968/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.078170 [20224/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.077103 [20480/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.092410 [20736/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.096658 [20992/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.091319 [21248/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.085325 [21504/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.073085 [21760/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.082501 [22016/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.093628 [22272/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.079266 [22528/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.084848 [22784/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.063658 [23040/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.106770 [23296/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.104110 [23552/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.078660 [23808/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.092491 [24064/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.094260 [24320/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.080091 [24576/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.073125 [24832/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.106891 [24872/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.106891 [24872/24872]: : 98it [00:08, 11.51it/s]
Epoch 2, time=291.99s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.082805 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.101750 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.084893 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.090809 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.086020 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.082086 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.089244 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.080428 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.088275 [ 2304/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.076363 [ 2560/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.093737 [ 2816/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.079251 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.088477 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.083839 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.105294 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.097829 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.099408 [ 4352/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.083745 [ 4608/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.092101 [ 4864/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.075847 [ 5120/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.105517 [ 5376/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.091249 [ 5632/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.093576 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.075257 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.092287 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.088650 [ 6656/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.072178 [ 6912/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.079838 [ 7168/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.073444 [ 7424/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.098744 [ 7680/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.082209 [ 7936/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.074963 [ 8192/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.091284 [ 8448/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.084904 [ 8704/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.074025 [ 8960/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.085706 [ 9216/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.086985 [ 9472/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.080180 [ 9728/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.078058 [ 9984/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.086914 [10240/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.091526 [10496/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.086341 [10752/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.072348 [11008/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.090982 [11264/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.080346 [11520/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.078043 [11776/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.080256 [12032/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.096111 [12288/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.072834 [12544/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.108325 [12800/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.088881 [13056/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.080755 [13312/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.111474 [13568/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.085013 [13824/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.092690 [14080/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.068797 [14336/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.066769 [14592/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.077365 [14848/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.077262 [15104/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.096980 [15360/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.081922 [15616/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.079205 [15872/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.075253 [16128/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.085663 [16384/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.105200 [16640/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.092458 [16896/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.083709 [17152/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.093082 [17408/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.092877 [17664/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.080572 [17920/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.084242 [18176/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.087011 [18432/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.082381 [18688/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.068107 [18944/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.071558 [19200/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.095729 [19456/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.087071 [19712/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.073448 [19968/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.078611 [20224/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.076077 [20480/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.091027 [20736/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.095250 [20992/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.090685 [21248/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.083847 [21504/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.071178 [21760/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.081360 [22016/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.091122 [22272/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.078179 [22528/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.083583 [22784/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.063274 [23040/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.105656 [23296/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.104004 [23552/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.077727 [23808/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.091738 [24064/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.092727 [24320/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.078961 [24576/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.071645 [24832/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.103781 [24872/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.103781 [24872/24872]: : 98it [00:08, 11.41it/s]
Epoch 3, time=300.59s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.080721 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.100496 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.083580 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.090124 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.085342 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.080981 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.086950 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.079358 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.085183 [ 2304/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.074113 [ 2560/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.091179 [ 2816/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.076848 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.086388 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.082872 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.102404 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.096539 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.094900 [ 4352/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.082795 [ 4608/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.086604 [ 4864/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.075205 [ 5120/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.104727 [ 5376/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.089960 [ 5632/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.087702 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.074787 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.088691 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.087954 [ 6656/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.071115 [ 6912/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.078559 [ 7168/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.072509 [ 7424/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.099391 [ 7680/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.080933 [ 7936/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.074169 [ 8192/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.089439 [ 8448/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.082660 [ 8704/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.070691 [ 8960/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.084248 [ 9216/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.086013 [ 9472/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.080274 [ 9728/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.077178 [ 9984/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.086887 [10240/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.091201 [10496/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.090802 [10752/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.071140 [11008/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.087468 [11264/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.079865 [11520/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.076824 [11776/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.080708 [12032/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.095564 [12288/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.071748 [12544/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.109829 [12800/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.088006 [13056/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.080252 [13312/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.112197 [13568/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.084423 [13824/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.092777 [14080/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.068101 [14336/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.065700 [14592/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.076886 [14848/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.076262 [15104/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.097174 [15360/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.081189 [15616/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.077630 [15872/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.074660 [16128/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.084855 [16384/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.103580 [16640/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.092566 [16896/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.082691 [17152/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.092215 [17408/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.090832 [17664/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.079401 [17920/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.084631 [18176/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.085921 [18432/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.081711 [18688/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.066162 [18944/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.071086 [19200/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.094446 [19456/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.085940 [19712/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.073759 [19968/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.079514 [20224/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.075561 [20480/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.090293 [20736/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.094390 [20992/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.090522 [21248/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.083047 [21504/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.069979 [21760/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.080729 [22016/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.089907 [22272/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.077353 [22528/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.083033 [22784/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.064046 [23040/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.105281 [23296/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.102955 [23552/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.077064 [23808/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.091703 [24064/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.091981 [24320/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.077447 [24576/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.070873 [24832/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.101979 [24872/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.101979 [24872/24872]: : 98it [00:08, 11.26it/s]
Epoch 4, time=309.30s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.079267 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.098893 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.083140 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.089850 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.086182 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.080758 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.084613 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.077202 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.081525 [ 2304/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.072634 [ 2560/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.086762 [ 2816/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.076436 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.085035 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.082315 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.101225 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.094473 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.092112 [ 4352/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.082011 [ 4608/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.085008 [ 4864/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.074412 [ 5120/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.099987 [ 5376/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.089791 [ 5632/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.085194 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.074496 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.086186 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.088266 [ 6656/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.070604 [ 6912/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.078278 [ 7168/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.072087 [ 7424/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.100721 [ 7680/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.080249 [ 7936/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.073933 [ 8192/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.088682 [ 8448/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.080909 [ 8704/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.068724 [ 8960/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.083760 [ 9216/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.085372 [ 9472/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.080210 [ 9728/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.076461 [ 9984/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.087224 [10240/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.090346 [10496/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.093405 [10752/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.070437 [11008/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.085374 [11264/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.079516 [11520/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.076098 [11776/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.081031 [12032/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.094233 [12288/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.071156 [12544/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.112222 [12800/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.087513 [13056/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.079966 [13312/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.108995 [13568/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.084605 [13824/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.093326 [14080/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.067628 [14336/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.065080 [14592/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.076385 [14848/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.075470 [15104/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.098633 [15360/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.080826 [15616/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.076340 [15872/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.074086 [16128/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.084416 [16384/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.102783 [16640/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.093899 [16896/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.082481 [17152/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.091577 [17408/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.089705 [17664/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.078465 [17920/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.085937 [18176/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.083810 [18432/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.081145 [18688/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.064432 [18944/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.071778 [19200/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.093398 [19456/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.085308 [19712/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.074306 [19968/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.079209 [20224/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.075086 [20480/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.089645 [20736/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.093787 [20992/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.090537 [21248/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.082255 [21504/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.068499 [21760/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.080005 [22016/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.088790 [22272/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.076506 [22528/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.082495 [22784/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.064621 [23040/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.104798 [23296/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.101409 [23552/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.076478 [23808/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.092327 [24064/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.092872 [24320/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.077058 [24576/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.070568 [24832/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.101072 [24872/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.101072 [24872/24872]: : 98it [00:08, 11.24it/s]
Epoch 5, time=318.02s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.078784 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.098791 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.081490 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.088289 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.085420 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.079472 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.083717 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.075143 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.078780 [ 2304/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.070884 [ 2560/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.084611 [ 2816/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.075588 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.083793 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.081463 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.099244 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.092508 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.089386 [ 4352/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.080700 [ 4608/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.083627 [ 4864/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.073758 [ 5120/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.096869 [ 5376/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.088522 [ 5632/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.083411 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.073582 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.084750 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.087760 [ 6656/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.069685 [ 6912/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.077658 [ 7168/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.071114 [ 7424/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.100236 [ 7680/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.079620 [ 7936/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.073347 [ 8192/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.087920 [ 8448/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.079876 [ 8704/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.067395 [ 8960/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.083220 [ 9216/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.084438 [ 9472/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.079512 [ 9728/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.076189 [ 9984/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.086984 [10240/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.089412 [10496/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.093171 [10752/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.069701 [11008/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.084410 [11264/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.078874 [11520/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.075257 [11776/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.080646 [12032/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.093012 [12288/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.070566 [12544/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.111865 [12800/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.086537 [13056/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.079540 [13312/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.107472 [13568/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.083933 [13824/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.092945 [14080/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.067196 [14336/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.064374 [14592/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.075735 [14848/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.074984 [15104/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.098305 [15360/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.080056 [15616/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.075088 [15872/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.073740 [16128/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.083704 [16384/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.102047 [16640/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.093668 [16896/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.081837 [17152/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.091042 [17408/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.089022 [17664/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.077224 [17920/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.086085 [18176/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.082645 [18432/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.080524 [18688/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.063452 [18944/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.071610 [19200/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.092826 [19456/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.084497 [19712/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.073584 [19968/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.078745 [20224/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.074558 [20480/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.089126 [20736/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.093276 [20992/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.090214 [21248/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.081436 [21504/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.067355 [21760/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.079273 [22016/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.087659 [22272/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.075767 [22528/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.082192 [22784/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.064333 [23040/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.104009 [23296/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.100599 [23552/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.075846 [23808/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.092407 [24064/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.091440 [24320/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.075919 [24576/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.070175 [24832/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.100423 [24872/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.100423 [24872/24872]: : 98it [00:08, 11.33it/s]
Epoch 6, time=326.67s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.078181 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.097793 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.080580 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.087432 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.084689 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.078568 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.082771 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.074167 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.077487 [ 2304/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.070069 [ 2560/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.083533 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.075839 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.083339 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.080968 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.099202 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.091588 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.089384 [ 4352/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.079473 [ 4608/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.083145 [ 4864/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.073589 [ 5120/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.094968 [ 5376/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.088257 [ 5632/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.082600 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.073406 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.084197 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.087514 [ 6656/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.069199 [ 6912/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.077134 [ 7168/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.071067 [ 7424/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.099126 [ 7680/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.079061 [ 7936/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.073193 [ 8192/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.087081 [ 8448/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.079429 [ 8704/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.066909 [ 8960/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.082242 [ 9216/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.083784 [ 9472/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.079159 [ 9728/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.075567 [ 9984/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.086876 [10240/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.088948 [10496/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.092778 [10752/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.069180 [11008/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.083891 [11264/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.078392 [11520/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.074676 [11776/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.080393 [12032/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.092405 [12288/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.069990 [12544/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.111550 [12800/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.085647 [13056/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.078889 [13312/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.106112 [13568/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.083363 [13824/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.092432 [14080/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.066444 [14336/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.063787 [14592/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.075055 [14848/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.074396 [15104/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.097767 [15360/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.079408 [15616/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.074024 [15872/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.073348 [16128/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.083116 [16384/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.101377 [16640/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.093296 [16896/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.081314 [17152/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.090512 [17408/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.088511 [17664/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.076117 [17920/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.086020 [18176/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.081778 [18432/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.079907 [18688/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.062807 [18944/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.071391 [19200/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.092179 [19456/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.083607 [19712/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.072706 [19968/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.078236 [20224/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.073965 [20480/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.088561 [20736/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.092750 [20992/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.089845 [21248/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.080725 [21504/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.066760 [21760/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.078606 [22016/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.086789 [22272/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.075057 [22528/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.081731 [22784/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.064044 [23040/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.103373 [23296/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.099864 [23552/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.075277 [23808/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.092325 [24064/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.089697 [24320/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.075146 [24576/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.070050 [24832/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.099753 [24872/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.099753 [24872/24872]: : 98it [00:08, 11.35it/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.coreshell.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: (5 minutes 41.364 seconds)
Estimated memory usage: 5356 MB