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.farfield.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 0x7f2052fde850>]
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.farfield.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: 0.474073 [22272/24872]: 90%|████████▉ | 696/777 [00:30<00:03, 23.17it/s]
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loss: 0.293613 [23680/24872]: 90%|████████▉ | 696/777 [00:31<00:03, 23.17it/s]
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loss: 0.411463 [23744/24872]: 90%|████████▉ | 696/777 [00:32<00:03, 23.17it/s]
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loss: 0.416594 [23808/24872]: 90%|████████▉ | 696/777 [00:32<00:03, 23.17it/s]
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loss: 0.331621 [23904/24872]: 90%|████████▉ | 696/777 [00:32<00:03, 23.17it/s]
loss: 0.424663 [23936/24872]: 90%|████████▉ | 696/777 [00:32<00:03, 23.17it/s]
loss: 0.345341 [23968/24872]: 90%|████████▉ | 696/777 [00:32<00:03, 23.17it/s]
loss: 0.338926 [24000/24872]: 90%|████████▉ | 696/777 [00:32<00:03, 23.17it/s]
loss: 0.262642 [24032/24872]: 90%|████████▉ | 696/777 [00:32<00:03, 23.17it/s]
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loss: 0.293378 [24096/24872]: 90%|████████▉ | 696/777 [00:32<00:03, 23.17it/s]
loss: 0.339389 [24128/24872]: 90%|████████▉ | 696/777 [00:32<00:03, 23.17it/s]
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loss: 0.156832 [24224/24872]: 90%|████████▉ | 696/777 [00:32<00:03, 23.17it/s]
loss: 0.462913 [24256/24872]: 90%|████████▉ | 696/777 [00:32<00:03, 23.17it/s]
loss: 0.207359 [24288/24872]: 90%|████████▉ | 696/777 [00:32<00:03, 23.17it/s]
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loss: 0.343841 [24352/24872]: 90%|████████▉ | 696/777 [00:32<00:03, 23.17it/s]
loss: 0.285829 [24384/24872]: 90%|████████▉ | 696/777 [00:32<00:03, 23.17it/s]
loss: 0.383778 [24416/24872]: 90%|████████▉ | 696/777 [00:32<00:03, 23.17it/s]
loss: 0.326222 [24448/24872]: 90%|████████▉ | 696/777 [00:32<00:03, 23.17it/s]
loss: 0.301138 [24480/24872]: 90%|████████▉ | 696/777 [00:32<00:03, 23.17it/s]
loss: 0.229546 [24512/24872]: 90%|████████▉ | 696/777 [00:33<00:03, 23.17it/s]
loss: 0.294926 [24544/24872]: 90%|████████▉ | 696/777 [00:33<00:03, 23.17it/s]
loss: 0.275142 [24576/24872]: 90%|████████▉ | 696/777 [00:33<00:03, 23.17it/s]
loss: 0.294738 [24608/24872]: 90%|████████▉ | 696/777 [00:33<00:03, 23.17it/s]
loss: 0.353833 [24640/24872]: 90%|████████▉ | 696/777 [00:33<00:03, 23.17it/s]
loss: 0.305140 [24672/24872]: 90%|████████▉ | 696/777 [00:33<00:03, 23.17it/s]
loss: 0.352088 [24704/24872]: 90%|████████▉ | 696/777 [00:33<00:03, 23.17it/s]
loss: 0.254173 [24736/24872]: 90%|████████▉ | 696/777 [00:33<00:03, 23.17it/s]
loss: 0.389734 [24768/24872]: 90%|████████▉ | 696/777 [00:33<00:03, 23.17it/s]
loss: 0.332022 [24800/24872]: 90%|████████▉ | 696/777 [00:33<00:03, 23.17it/s]
loss: 0.321424 [24832/24872]: 90%|████████▉ | 696/777 [00:33<00:03, 23.17it/s]
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loss: 0.178564 [24872/24872]: 90%|████████▉ | 696/777 [00:33<00:03, 23.17it/s]
loss: 0.178564 [24872/24872]: : 778it [00:33, 23.21it/s]
Epoch 2, time=33.54s
0%| | 0/777 [00:00<?, ?it/s]
loss: 0.404187 [ 32/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.224965 [24608/24872]: 90%|█████████ | 702/777 [00:32<00:03, 23.39it/s]
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loss: 0.163905 [24872/24872]: : 778it [00:33, 23.40it/s]
Epoch 3, time=66.79s
0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.275088 [23424/24872]: 90%|█████████ | 701/777 [00:31<00:03, 23.35it/s]
loss: 0.206420 [23456/24872]: 90%|█████████ | 701/777 [00:31<00:03, 23.35it/s]
loss: 0.212096 [23488/24872]: 90%|█████████ | 701/777 [00:31<00:03, 23.35it/s]
loss: 0.213801 [23520/24872]: 90%|█████████ | 701/777 [00:31<00:03, 23.35it/s]
loss: 0.159702 [23552/24872]: 90%|█████████ | 701/777 [00:31<00:03, 23.35it/s]
loss: 0.258162 [23584/24872]: 90%|█████████ | 701/777 [00:31<00:03, 23.35it/s]
loss: 0.234310 [23616/24872]: 90%|█████████ | 701/777 [00:31<00:03, 23.35it/s]
loss: 0.387855 [23648/24872]: 90%|█████████ | 701/777 [00:31<00:03, 23.35it/s]
loss: 0.180971 [23680/24872]: 90%|█████████ | 701/777 [00:31<00:03, 23.35it/s]
loss: 0.208723 [23712/24872]: 90%|█████████ | 701/777 [00:31<00:03, 23.35it/s]
loss: 0.251756 [23744/24872]: 90%|█████████ | 701/777 [00:31<00:03, 23.35it/s]
loss: 0.246751 [23776/24872]: 90%|█████████ | 701/777 [00:31<00:03, 23.35it/s]
loss: 0.330614 [23808/24872]: 90%|█████████ | 701/777 [00:31<00:03, 23.35it/s]
loss: 0.204379 [23840/24872]: 90%|█████████ | 701/777 [00:31<00:03, 23.35it/s]
loss: 0.259910 [23872/24872]: 90%|█████████ | 701/777 [00:31<00:03, 23.35it/s]
loss: 0.243573 [23904/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.369093 [23936/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.245186 [23968/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.259659 [24000/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.225917 [24032/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.261260 [24064/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.181426 [24096/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.226778 [24128/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.186456 [24160/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.095960 [24192/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.117769 [24224/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.338371 [24256/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.160846 [24288/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.203330 [24320/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.296838 [24352/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.216351 [24384/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.282950 [24416/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.232048 [24448/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.258124 [24480/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.150086 [24512/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.200803 [24544/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.227350 [24576/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.245504 [24608/24872]: 90%|█████████ | 701/777 [00:32<00:03, 23.35it/s]
loss: 0.247224 [24640/24872]: 90%|█████████ | 701/777 [00:33<00:03, 23.35it/s]
loss: 0.200322 [24672/24872]: 90%|█████████ | 701/777 [00:33<00:03, 23.35it/s]
loss: 0.265170 [24704/24872]: 90%|█████████ | 701/777 [00:33<00:03, 23.35it/s]
loss: 0.164157 [24736/24872]: 90%|█████████ | 701/777 [00:33<00:03, 23.35it/s]
loss: 0.242595 [24768/24872]: 90%|█████████ | 701/777 [00:33<00:03, 23.35it/s]
loss: 0.221884 [24800/24872]: 90%|█████████ | 701/777 [00:33<00:03, 23.35it/s]
loss: 0.191837 [24832/24872]: 90%|█████████ | 701/777 [00:33<00:03, 23.35it/s]
loss: 0.215214 [24864/24872]: 90%|█████████ | 701/777 [00:33<00:03, 23.35it/s]
loss: 0.147651 [24872/24872]: 90%|█████████ | 701/777 [00:33<00:03, 23.35it/s]
loss: 0.147651 [24872/24872]: : 778it [00:33, 23.33it/s]
Epoch 4, time=100.14s
0%| | 0/777 [00:00<?, ?it/s]
loss: 0.225064 [ 32/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.136552 [ 64/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.227042 [22528/24872]: 91%|█████████ | 704/777 [00:30<00:03, 23.43it/s]
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loss: 0.233766 [22752/24872]: 91%|█████████ | 704/777 [00:30<00:03, 23.43it/s]
loss: 0.173770 [22784/24872]: 91%|█████████ | 704/777 [00:30<00:03, 23.43it/s]
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loss: 0.184712 [22848/24872]: 91%|█████████ | 704/777 [00:30<00:03, 23.43it/s]
loss: 0.126143 [22880/24872]: 91%|█████████ | 704/777 [00:30<00:03, 23.43it/s]
loss: 0.168810 [22912/24872]: 91%|█████████ | 704/777 [00:30<00:03, 23.43it/s]
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loss: 0.297863 [22976/24872]: 91%|█████████ | 704/777 [00:30<00:03, 23.43it/s]
loss: 0.218347 [23008/24872]: 91%|█████████ | 704/777 [00:30<00:03, 23.43it/s]
loss: 0.229356 [23040/24872]: 91%|█████████ | 704/777 [00:30<00:03, 23.43it/s]
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loss: 0.199016 [23104/24872]: 91%|█████████ | 704/777 [00:30<00:03, 23.43it/s]
loss: 0.313258 [23136/24872]: 91%|█████████ | 704/777 [00:30<00:03, 23.43it/s]
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loss: 0.229026 [23392/24872]: 91%|█████████ | 704/777 [00:31<00:03, 23.43it/s]
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loss: 0.205255 [23520/24872]: 91%|█████████ | 704/777 [00:31<00:03, 23.43it/s]
loss: 0.139436 [23552/24872]: 91%|█████████ | 704/777 [00:31<00:03, 23.43it/s]
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loss: 0.336249 [23648/24872]: 91%|█████████ | 704/777 [00:31<00:03, 23.43it/s]
loss: 0.173522 [23680/24872]: 91%|█████████ | 704/777 [00:31<00:03, 23.43it/s]
loss: 0.183959 [23712/24872]: 91%|█████████ | 704/777 [00:31<00:03, 23.43it/s]
loss: 0.268512 [23744/24872]: 91%|█████████ | 704/777 [00:31<00:03, 23.43it/s]
loss: 0.238595 [23776/24872]: 91%|█████████ | 704/777 [00:31<00:03, 23.43it/s]
loss: 0.322949 [23808/24872]: 91%|█████████ | 704/777 [00:31<00:03, 23.43it/s]
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loss: 0.218420 [23872/24872]: 91%|█████████ | 704/777 [00:31<00:03, 23.43it/s]
loss: 0.231957 [23904/24872]: 91%|█████████ | 704/777 [00:31<00:03, 23.43it/s]
loss: 0.320227 [23936/24872]: 91%|█████████ | 704/777 [00:31<00:03, 23.43it/s]
loss: 0.234192 [23968/24872]: 91%|█████████ | 704/777 [00:31<00:03, 23.43it/s]
loss: 0.214343 [24000/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.203002 [24032/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.250765 [24064/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
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loss: 0.208170 [24128/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.150018 [24160/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.099855 [24192/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.110312 [24224/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.307379 [24256/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.154112 [24288/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.211317 [24320/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.259524 [24352/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.208296 [24384/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.282749 [24416/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.236840 [24448/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.272361 [24480/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.158533 [24512/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.247574 [24544/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.186421 [24576/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.184364 [24608/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.284857 [24640/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.202164 [24672/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.228512 [24704/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.202060 [24736/24872]: 91%|█████████ | 704/777 [00:32<00:03, 23.43it/s]
loss: 0.304767 [24768/24872]: 91%|█████████ | 704/777 [00:33<00:03, 23.43it/s]
loss: 0.210959 [24800/24872]: 91%|█████████ | 704/777 [00:33<00:03, 23.43it/s]
loss: 0.197831 [24832/24872]: 91%|█████████ | 704/777 [00:33<00:03, 23.43it/s]
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loss: 0.165323 [24872/24872]: 91%|█████████ | 704/777 [00:33<00:03, 23.43it/s]
loss: 0.165323 [24872/24872]: : 778it [00:33, 23.44it/s]
Epoch 5, time=133.34s
0%| | 0/777 [00:00<?, ?it/s]
loss: 0.215962 [ 32/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.134436 [24872/24872]: 90%|████████▉ | 699/777 [00:33<00:03, 23.30it/s]
loss: 0.134436 [24872/24872]: : 778it [00:33, 23.34it/s]
-------------------------------
LR=0.0001, batch_size=64
-------------------------------
Epoch 1, time=166.67s
0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.148817 [24872/24872]: : 389it [00:22, 17.52it/s]
Epoch 2, time=188.88s
0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.124543 [24872/24872]: : 389it [00:22, 17.54it/s]
Epoch 3, time=211.06s
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loss: 0.116112 [24872/24872]: : 389it [00:22, 17.55it/s]
Epoch 4, time=233.22s
0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.111493 [24872/24872]: : 389it [00:22, 17.62it/s]
Epoch 5, time=255.30s
0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.120411 [24384/24872]: 0%| | 0/388 [00:21<?, ?it/s]
loss: 0.115055 [24448/24872]: 0%| | 0/388 [00:21<?, ?it/s]
loss: 0.113363 [24512/24872]: 0%| | 0/388 [00:21<?, ?it/s]
loss: 0.128628 [24576/24872]: 0%| | 0/388 [00:21<?, ?it/s]
loss: 0.116593 [24640/24872]: 0%| | 0/388 [00:22<?, ?it/s]
loss: 0.127630 [24704/24872]: 0%| | 0/388 [00:22<?, ?it/s]
loss: 0.129138 [24768/24872]: 0%| | 0/388 [00:22<?, ?it/s]
loss: 0.125214 [24832/24872]: 0%| | 0/388 [00:22<?, ?it/s]
loss: 0.101060 [24872/24872]: 0%| | 0/388 [00:22<?, ?it/s]
loss: 0.101060 [24872/24872]: : 389it [00:22, 17.49it/s]
-------------------------------
LR=0.0001, batch_size=128
-------------------------------
Epoch 1, time=277.54s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.135103 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.321957 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.229976 [ 384/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.187619 [ 512/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.154031 [ 640/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.206922 [ 768/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.181292 [ 896/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.150995 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.192712 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.200737 [ 1280/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.147492 [ 1408/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.145935 [ 1536/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.156060 [ 1664/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.107976 [ 1792/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.118166 [ 1920/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.154300 [ 2048/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.161464 [ 2176/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.129964 [ 2304/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.185968 [ 2432/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.121035 [ 2560/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.145747 [ 2688/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.129586 [ 2816/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.143524 [ 2944/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.129118 [ 3072/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.135740 [ 3200/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.112121 [ 3328/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.113046 [ 3456/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.128907 [ 3584/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.150142 [ 3712/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.132255 [ 3840/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.094338 [ 3968/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.143698 [ 4096/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.117093 [ 4224/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.099643 [ 4352/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.167877 [ 4480/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.103702 [ 4608/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.108158 [ 4736/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.134271 [ 4864/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.098747 [ 4992/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.142518 [ 5120/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.099173 [ 5248/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.127884 [ 5376/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.142005 [ 5504/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.149459 [ 5632/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.142788 [ 5760/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.134181 [ 5888/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.114715 [ 6016/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.147298 [ 6144/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.133585 [ 6272/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.132070 [ 6400/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.118629 [ 6528/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.127280 [ 6656/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.111195 [ 6784/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.143236 [ 6912/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.130231 [ 7040/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.140862 [ 7168/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.122563 [ 7296/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.098756 [ 7424/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.113051 [ 7552/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.131550 [ 7680/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.173952 [ 7808/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.114270 [ 7936/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.131563 [ 8064/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.103196 [ 8192/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.129438 [ 8320/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.119159 [ 8448/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.118657 [ 8576/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.122387 [ 8704/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.146886 [ 8832/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.120148 [ 8960/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.122219 [ 9088/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.117849 [ 9216/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.140454 [ 9344/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.120822 [ 9472/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.118732 [ 9600/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.126020 [ 9728/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.122008 [ 9856/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.097809 [ 9984/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.138266 [10112/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.115572 [10240/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.119649 [10368/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.099095 [10496/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.144325 [10624/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.110755 [10752/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.118294 [10880/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.125334 [11008/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.096729 [11136/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.096893 [11264/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.126278 [11392/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.108094 [11520/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.153788 [11648/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.107735 [11776/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.140412 [11904/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.112355 [12032/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.114622 [12160/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.117809 [12288/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.110851 [12416/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.129297 [12544/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.116626 [12672/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.121335 [12800/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.096234 [12928/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.151823 [13056/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.113965 [13184/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.124690 [13312/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.123452 [13440/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.122093 [13568/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.146955 [13696/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.118070 [13824/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.092420 [13952/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.113148 [14080/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.111540 [14208/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.141372 [14336/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.130369 [14464/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.112531 [14592/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.158012 [14720/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.154802 [14848/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.112596 [14976/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.134024 [15104/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.112954 [15232/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.141262 [15360/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.150711 [15488/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.140781 [15616/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.096757 [15744/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.129141 [15872/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.117516 [16000/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.095626 [16128/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.123065 [16256/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.130875 [16384/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.144697 [16512/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.152156 [16640/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.138795 [16768/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.138472 [16896/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.152871 [17024/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.117316 [17152/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.100221 [17280/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.133177 [17408/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.117454 [17536/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.171227 [17664/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.149270 [17792/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.104478 [17920/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.131374 [18048/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.145912 [18176/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.090308 [18304/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.109451 [18432/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.127162 [18560/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.106699 [18688/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.126348 [18816/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.114990 [18944/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.113924 [19072/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.092845 [19200/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.115676 [19328/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.111438 [19456/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.105450 [19584/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.115310 [19712/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.108173 [19840/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.121238 [19968/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.124288 [20096/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.101147 [20224/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.143865 [20352/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.096053 [20480/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.106864 [20608/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.145836 [20736/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.112516 [20864/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.118908 [20992/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.136474 [21120/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.127798 [21248/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.125084 [21376/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.134151 [21504/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.098046 [21632/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.119111 [21760/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.151327 [21888/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.140663 [22016/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.128907 [22144/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.126545 [22272/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.133641 [22400/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.169426 [22528/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.080970 [22656/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.096725 [22784/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.093444 [22912/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.133327 [23040/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.143793 [23168/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.079170 [23296/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.139908 [23424/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.098632 [23552/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.145354 [23680/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.147763 [23808/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.122602 [23936/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.130356 [24064/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.076087 [24192/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.108572 [24320/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.101167 [24448/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.105814 [24576/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.109110 [24704/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.119798 [24832/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.099133 [24872/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.099133 [24872/24872]: : 195it [00:16, 11.88it/s]
Epoch 2, time=293.96s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.127088 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.141200 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.137555 [ 384/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.124292 [ 512/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.124911 [ 640/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.139220 [ 768/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.163631 [ 896/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.140909 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.147022 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.136737 [ 1280/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.133885 [ 1408/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.131317 [ 1536/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.130436 [ 1664/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.100668 [ 1792/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.113499 [ 1920/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.137258 [ 2048/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.121962 [ 2176/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.115643 [ 2304/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.167726 [ 2432/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.114855 [ 2560/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.131904 [ 2688/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.120681 [ 2816/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.133159 [ 2944/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.124417 [ 3072/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.124081 [ 3200/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.104280 [ 3328/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.109182 [ 3456/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.114852 [ 3584/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.144865 [ 3712/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.128781 [ 3840/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.089445 [ 3968/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.141628 [ 4096/24872]: 0%| | 0/194 [00:02<?, ?it/s]
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loss: 0.097209 [22784/24872]: 0%| | 0/194 [00:14<?, ?it/s]
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loss: 0.127413 [24064/24872]: 0%| | 0/194 [00:15<?, ?it/s]
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loss: 0.101464 [24872/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.101464 [24872/24872]: : 195it [00:16, 11.91it/s]
Epoch 3, time=310.34s
0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.131286 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.131710 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.073516 [22656/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.089481 [22784/24872]: 0%| | 0/194 [00:15<?, ?it/s]
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loss: 0.122463 [23424/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.093852 [23552/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.129486 [23680/24872]: 0%| | 0/194 [00:15<?, ?it/s]
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loss: 0.112702 [23936/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.121834 [24064/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.071237 [24192/24872]: 0%| | 0/194 [00:15<?, ?it/s]
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loss: 0.094382 [24872/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.094382 [24872/24872]: : 195it [00:16, 11.87it/s]
Epoch 4, time=326.77s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.115482 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.144683 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.125854 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.135503 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.124014 [ 1664/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.093733 [ 1792/24872]: 0%| | 0/194 [00:01<?, ?it/s]
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loss: 0.083130 [22656/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.090427 [22784/24872]: 0%| | 0/194 [00:15<?, ?it/s]
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loss: 0.119329 [23424/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.094846 [23552/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.135145 [23680/24872]: 0%| | 0/194 [00:15<?, ?it/s]
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loss: 0.116604 [23936/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.122716 [24064/24872]: 0%| | 0/194 [00:15<?, ?it/s]
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loss: 0.093095 [24872/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.093095 [24872/24872]: : 195it [00:16, 11.85it/s]
Epoch 5, time=343.23s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.135984 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.128137 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.131316 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.120182 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.089624 [ 1792/24872]: 0%| | 0/194 [00:01<?, ?it/s]
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loss: 0.132646 [21504/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.095964 [21632/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.115399 [21760/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.132165 [21888/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.116633 [22016/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.113660 [22144/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.108729 [22272/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.110341 [22400/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.150427 [22528/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.075423 [22656/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.084903 [22784/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.084419 [22912/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.123150 [23040/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.127589 [23168/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.080810 [23296/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.117638 [23424/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.092276 [23552/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.130594 [23680/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.133716 [23808/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.117592 [23936/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.113938 [24064/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.073992 [24192/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.109925 [24320/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.091012 [24448/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.110299 [24576/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.104093 [24704/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.122265 [24832/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.111512 [24872/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.111512 [24872/24872]: : 195it [00:16, 11.83it/s]
Epoch 6, time=359.71s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.112614 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.151051 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.123797 [ 384/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.116394 [ 512/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.101415 [ 640/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.121237 [ 768/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.131907 [ 896/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.129855 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.118213 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.119219 [ 1280/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.115996 [ 1408/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.106437 [ 1536/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.117904 [ 1664/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.097768 [ 1792/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.085648 [ 1920/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.127223 [ 2048/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.110564 [ 2176/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.097328 [ 2304/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.148503 [ 2432/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.106677 [ 2560/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.113639 [ 2688/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.118133 [ 2816/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.119934 [ 2944/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.113868 [ 3072/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.107213 [ 3200/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.102406 [ 3328/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.093730 [ 3456/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.096802 [ 3584/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.138280 [ 3712/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.112403 [ 3840/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.080992 [ 3968/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.133848 [ 4096/24872]: 0%| | 0/194 [00:02<?, ?it/s]
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loss: 0.156670 [ 4480/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.089928 [ 4608/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.093648 [ 4736/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.103966 [ 4864/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.085654 [ 4992/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.121495 [ 5120/24872]: 0%| | 0/194 [00:03<?, ?it/s]
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loss: 0.118050 [ 5376/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.128985 [ 5504/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.129457 [ 5632/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.118591 [ 5760/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.113647 [ 5888/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.103646 [ 6016/24872]: 0%| | 0/194 [00:03<?, ?it/s]
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loss: 0.113741 [ 6272/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.107247 [ 6400/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.096015 [ 6528/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.116269 [ 6656/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.098891 [ 6784/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.127646 [ 6912/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.109426 [ 7040/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.110737 [ 7168/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.099353 [ 7296/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.077775 [ 7424/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.094458 [ 7552/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.115419 [ 7680/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.130187 [ 7808/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.094735 [ 7936/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.125467 [ 8064/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.079398 [ 8192/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.106829 [ 8320/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.113897 [ 8448/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.104033 [ 8576/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.107701 [ 8704/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.135369 [ 8832/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.108716 [ 8960/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.105990 [ 9088/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.111708 [ 9216/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.134618 [ 9344/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.098323 [ 9472/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.109131 [ 9600/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.101998 [ 9728/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.122315 [ 9856/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.080655 [ 9984/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.130575 [10112/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.104963 [10240/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.098897 [10368/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.083755 [10496/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.134214 [10624/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.103977 [10752/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.106544 [10880/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.108929 [11008/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.087149 [11136/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.088213 [11264/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.119119 [11392/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.087727 [11520/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.133344 [11648/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.085568 [11776/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.134618 [11904/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.110622 [12032/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.098544 [12160/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.101300 [12288/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.111231 [12416/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.097279 [12544/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.109546 [12672/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.093816 [12800/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.092787 [12928/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.148867 [13056/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.095717 [13184/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.122655 [13312/24872]: 0%| | 0/194 [00:08<?, ?it/s]
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loss: 0.114929 [13568/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.142226 [13696/24872]: 0%| | 0/194 [00:09<?, ?it/s]
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loss: 0.110762 [13952/24872]: 0%| | 0/194 [00:09<?, ?it/s]
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loss: 0.133536 [15104/24872]: 0%| | 0/194 [00:09<?, ?it/s]
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loss: 0.115895 [15360/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.152743 [15488/24872]: 0%| | 0/194 [00:10<?, ?it/s]
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loss: 0.086575 [15744/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.121166 [15872/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.110939 [16000/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.088159 [16128/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.103482 [16256/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.112855 [16384/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.122954 [16512/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.140929 [16640/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.113824 [16768/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.126029 [16896/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.130452 [17024/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.110590 [17152/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.086723 [17280/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.109352 [17408/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.101764 [17536/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.138443 [17664/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.139048 [17792/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.084551 [17920/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.113246 [18048/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.153265 [18176/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.072396 [18304/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.099416 [18432/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.105226 [18560/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.088648 [18688/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.099920 [18816/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.103490 [18944/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.103307 [19072/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.077590 [19200/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.093775 [19328/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.091811 [19456/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.081388 [19584/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.103663 [19712/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.085748 [19840/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.105019 [19968/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.113398 [20096/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.079944 [20224/24872]: 0%| | 0/194 [00:13<?, ?it/s]
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loss: 0.088029 [20864/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.088811 [20992/24872]: 0%| | 0/194 [00:13<?, ?it/s]
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loss: 0.107962 [21760/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.141233 [21888/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.105010 [22016/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.108928 [22144/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.114662 [22272/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.103149 [22400/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.138827 [22528/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.076665 [22656/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.086757 [22784/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.081258 [22912/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.120326 [23040/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.114432 [23168/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.073790 [23296/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.114386 [23424/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.084926 [23552/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.130714 [23680/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.131563 [23808/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.117874 [23936/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.111914 [24064/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.076596 [24192/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.113624 [24320/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.086416 [24448/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.115562 [24576/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.106703 [24704/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.110491 [24832/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.095379 [24872/24872]: 0%| | 0/194 [00:16<?, ?it/s]
loss: 0.095379 [24872/24872]: : 195it [00:16, 11.85it/s]
-------------------------------
LR=1e-05, batch_size=256
-------------------------------
Epoch 1, time=376.16s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.112657 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.107971 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.101506 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.108027 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.114396 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.100568 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.096691 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.092114 [ 2048/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.093056 [ 2304/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.120716 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.103184 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.107511 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.095672 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.085505 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.112139 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.093192 [ 4096/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.078020 [ 4352/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.112541 [ 4608/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.095606 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.091945 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.090856 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.115465 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.109417 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.109315 [ 6144/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.101016 [ 6400/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.094011 [ 6656/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.105549 [ 6912/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.103654 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.081958 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.096639 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.106678 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.088477 [ 8192/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.097680 [ 8448/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.095653 [ 8704/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.119726 [ 8960/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.092152 [ 9216/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.104932 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.095256 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.088017 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.101753 [10240/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.087794 [10496/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.110123 [10752/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.096305 [11008/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.077289 [11264/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.094520 [11520/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.097824 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.109740 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.095577 [12288/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.091647 [12544/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.086681 [12800/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.096699 [13056/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.091751 [13312/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.084704 [13568/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.102956 [13824/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.080058 [14080/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.095639 [14336/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.098426 [14592/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.117832 [14848/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.097480 [15104/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.088544 [15360/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.113282 [15616/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.092652 [15872/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.084007 [16128/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.098087 [16384/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.113525 [16640/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.104181 [16896/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.110269 [17152/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.085347 [17408/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.109109 [17664/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.103992 [17920/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.121398 [18176/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.074626 [18432/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.089627 [18688/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.090340 [18944/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.081105 [19200/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.086651 [19456/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.085800 [19712/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.086774 [19968/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.088039 [20224/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.094978 [20480/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.104850 [20736/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.084735 [20992/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.113286 [21248/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.109647 [21504/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.088397 [21760/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.108713 [22016/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.100162 [22272/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.109163 [22528/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.068858 [22784/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.084093 [23040/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.082324 [23296/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.090278 [23552/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.117197 [23808/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.102644 [24064/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.075780 [24320/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.085093 [24576/24872]: 0%| | 0/97 [00:12<?, ?it/s]
loss: 0.089627 [24832/24872]: 0%| | 0/97 [00:12<?, ?it/s]
loss: 0.077942 [24872/24872]: 0%| | 0/97 [00:12<?, ?it/s]
loss: 0.077942 [24872/24872]: : 98it [00:12, 7.99it/s]
Epoch 2, time=388.43s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.098276 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.098225 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.095949 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.101868 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.104763 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.093533 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.090559 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.086880 [ 2048/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.088917 [ 2304/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.118116 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.100200 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.105986 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.092538 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.082997 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.107080 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.090431 [ 4096/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.075836 [ 4352/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.110823 [ 4608/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.092404 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.089388 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.087525 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.113149 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.107447 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.106404 [ 6144/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.097540 [ 6400/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.089465 [ 6656/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.103651 [ 6912/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.099489 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.079627 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.094793 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.104957 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.086384 [ 8192/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.096332 [ 8448/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.094666 [ 8704/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.117849 [ 8960/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.090261 [ 9216/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.101845 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.093668 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.086401 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.100306 [10240/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.084925 [10496/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.108753 [10752/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.095120 [11008/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.076254 [11264/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.092893 [11520/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.096284 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.108196 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.092698 [12288/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.088842 [12544/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.084736 [12800/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.095886 [13056/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.089934 [13312/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.082736 [13568/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.101976 [13824/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.079801 [14080/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.094488 [14336/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.097156 [14592/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.115967 [14848/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.096360 [15104/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.087538 [15360/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.110757 [15616/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.091656 [15872/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.082977 [16128/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.097009 [16384/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.112168 [16640/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.102328 [16896/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.108078 [17152/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.084368 [17408/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.107993 [17664/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.102799 [17920/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.119084 [18176/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.073746 [18432/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.088996 [18688/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.089009 [18944/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.079954 [19200/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.085054 [19456/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.084674 [19712/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.086000 [19968/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.086574 [20224/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.094326 [20480/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.104387 [20736/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.083687 [20992/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.112401 [21248/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.108340 [21504/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.088148 [21760/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.105667 [22016/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.099235 [22272/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.108621 [22528/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.068308 [22784/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.083186 [23040/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.080116 [23296/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.089757 [23552/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.115488 [23808/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.099705 [24064/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.074788 [24320/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.084149 [24576/24872]: 0%| | 0/97 [00:12<?, ?it/s]
loss: 0.088968 [24832/24872]: 0%| | 0/97 [00:12<?, ?it/s]
loss: 0.076861 [24872/24872]: 0%| | 0/97 [00:12<?, ?it/s]
loss: 0.076861 [24872/24872]: : 98it [00:12, 8.04it/s]
Epoch 3, time=400.62s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.097483 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.097280 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.094773 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.100796 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.103097 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.092651 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.089715 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.086125 [ 2048/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.087845 [ 2304/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.116692 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.099544 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.105142 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.091692 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.081978 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.105725 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.089465 [ 4096/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.075047 [ 4352/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.109899 [ 4608/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.090958 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.088561 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.086980 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.112047 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.106370 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.105152 [ 6144/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.096132 [ 6400/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.087978 [ 6656/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.102826 [ 6912/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.097877 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.078879 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.093693 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.103678 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.085361 [ 8192/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.095629 [ 8448/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.093584 [ 8704/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.117193 [ 8960/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.089304 [ 9216/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.100784 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.092569 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.085587 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.099684 [10240/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.083826 [10496/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.107976 [10752/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.094618 [11008/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.075624 [11264/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.092034 [11520/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.095470 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.106617 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.090684 [12288/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.087619 [12544/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.083120 [12800/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.095129 [13056/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.088622 [13312/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.081740 [13568/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.101517 [13824/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.079522 [14080/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.093595 [14336/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.096164 [14592/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.114855 [14848/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.095822 [15104/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.086622 [15360/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.109273 [15616/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.090797 [15872/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.082344 [16128/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.096245 [16384/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.111215 [16640/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.101128 [16896/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.106861 [17152/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.083748 [17408/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.107169 [17664/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.101865 [17920/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.117628 [18176/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.073186 [18432/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.088375 [18688/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.088009 [18944/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.079037 [19200/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.083812 [19456/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.082916 [19712/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.085393 [19968/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.085577 [20224/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.093884 [20480/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.103612 [20736/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.082612 [20992/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.111720 [21248/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.107456 [21504/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.087639 [21760/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.104481 [22016/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.098644 [22272/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.107964 [22528/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.067819 [22784/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.082536 [23040/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.078984 [23296/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.089259 [23552/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.114014 [23808/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.098009 [24064/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.074127 [24320/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.083437 [24576/24872]: 0%| | 0/97 [00:12<?, ?it/s]
loss: 0.087836 [24832/24872]: 0%| | 0/97 [00:12<?, ?it/s]
loss: 0.076112 [24872/24872]: 0%| | 0/97 [00:12<?, ?it/s]
loss: 0.076112 [24872/24872]: : 98it [00:12, 8.01it/s]
Epoch 4, time=412.86s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.096653 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.096121 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.093393 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.099648 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.101898 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.091980 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.088971 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.086038 [ 2048/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.087124 [ 2304/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.115477 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.098776 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.104122 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.091036 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.081142 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.104706 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.088871 [ 4096/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.074585 [ 4352/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.109115 [ 4608/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.089982 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.088036 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.086438 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.111143 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.105399 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.104373 [ 6144/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.095160 [ 6400/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.086923 [ 6656/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.102228 [ 6912/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.096801 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.078208 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.092870 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.102461 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.084664 [ 8192/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.094850 [ 8448/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.092736 [ 8704/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.116458 [ 8960/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.088360 [ 9216/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.100137 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.091757 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.084914 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.099132 [10240/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.082867 [10496/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.107290 [10752/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.093656 [11008/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.075032 [11264/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.091386 [11520/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.094826 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.105723 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.089218 [12288/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.086796 [12544/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.082068 [12800/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.094279 [13056/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.087556 [13312/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.080939 [13568/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.101067 [13824/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.078938 [14080/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.092854 [14336/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.095329 [14592/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.114025 [14848/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.095378 [15104/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.085766 [15360/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.108255 [15616/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.090065 [15872/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.081734 [16128/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.095559 [16384/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.110365 [16640/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.100200 [16896/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.105961 [17152/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.083164 [17408/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.106527 [17664/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.101089 [17920/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.116352 [18176/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.072679 [18432/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.087764 [18688/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.087108 [18944/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.078309 [19200/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.082803 [19456/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.081565 [19712/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.084845 [19968/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.084885 [20224/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.093522 [20480/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.102858 [20736/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.081758 [20992/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.111109 [21248/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.106757 [21504/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.087106 [21760/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.103900 [22016/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.098233 [22272/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.106951 [22528/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.067425 [22784/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.081706 [23040/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.078498 [23296/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.088907 [23552/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.113081 [23808/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.096720 [24064/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.073777 [24320/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.082967 [24576/24872]: 0%| | 0/97 [00:12<?, ?it/s]
loss: 0.087265 [24832/24872]: 0%| | 0/97 [00:12<?, ?it/s]
loss: 0.075601 [24872/24872]: 0%| | 0/97 [00:12<?, ?it/s]
loss: 0.075601 [24872/24872]: : 98it [00:12, 8.05it/s]
Epoch 5, time=425.04s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.096313 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.095463 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.092881 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.098686 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.100793 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.091576 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.088430 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.085338 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.086437 [ 2304/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.114603 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.098087 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.103576 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.090376 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.080568 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.103775 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.088422 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.074140 [ 4352/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.108590 [ 4608/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.089247 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.087350 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.085984 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.110297 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.104722 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.103548 [ 6144/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.094308 [ 6400/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.086079 [ 6656/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.101661 [ 6912/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.095651 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.077624 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.092191 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.101538 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.084113 [ 8192/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.094347 [ 8448/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.091998 [ 8704/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.115901 [ 8960/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.087513 [ 9216/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.099490 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.091131 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.084301 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.098665 [10240/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.082070 [10496/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.106833 [10752/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.092845 [11008/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.074593 [11264/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.090857 [11520/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.094257 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.104809 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.088154 [12288/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.086048 [12544/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.081251 [12800/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.093574 [13056/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.086624 [13312/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.080274 [13568/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.100592 [13824/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.078468 [14080/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.092211 [14336/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.094674 [14592/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.113292 [14848/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.094841 [15104/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.085031 [15360/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.107464 [15616/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.089532 [15872/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.081166 [16128/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.094906 [16384/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.109732 [16640/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.099434 [16896/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.105199 [17152/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.082578 [17408/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.105921 [17664/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.100521 [17920/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.115227 [18176/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.072234 [18432/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.087185 [18688/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.086305 [18944/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.077681 [19200/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.082042 [19456/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.080371 [19712/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.084334 [19968/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.084240 [20224/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.093140 [20480/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.102108 [20736/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.080938 [20992/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.110528 [21248/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.106072 [21504/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.086561 [21760/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.103313 [22016/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.097747 [22272/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.106198 [22528/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.066991 [22784/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.081127 [23040/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.077782 [23296/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.088407 [23552/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.112427 [23808/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.096003 [24064/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.073273 [24320/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.082522 [24576/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.086475 [24832/24872]: 0%| | 0/97 [00:12<?, ?it/s]
loss: 0.075057 [24872/24872]: 0%| | 0/97 [00:12<?, ?it/s]
loss: 0.075057 [24872/24872]: : 98it [00:12, 8.05it/s]
Epoch 6, time=437.21s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.095569 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
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loss: 0.074690 [24872/24872]: 0%| | 0/97 [00:12<?, ?it/s]
loss: 0.074690 [24872/24872]: : 98it [00:12, 8.09it/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.farfield.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: (7 minutes 35.560 seconds)
Estimated memory usage: 3012 MB