Note
Go to the end to download the full example code.
Mie-informed tandem neural network#
Here, we demonstrate how to train a design generator network capable to suggest core-shell particles with specific spectral response using PyMieDiff as differentiable forward-evaluator. The training pipeline follows the “Tandem” model:
target spectrum –> generator NN –> design –> Mie –> real spectrum
training loss is: MSE(target spec., real spec.)
author: O. Jackson, P. Wiecha, 06/2025
imports#
import time
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
import torch
from torch import nn
import pymiediff as pmd
setup optimiation target#
We setup the main configuration here: torch device, parameter limits and wavelengths
# torch compute device
device = "cpu"
# general config
N_samples = 25000
n_max = 4 # maximum Mie order fixed for performance
eps_env = torch.tensor(1.0, device=device)
lim_r = torch.as_tensor([40, 100], device=device)
lim_n_re = torch.as_tensor([1.5, 4.0], device=device)
lim_n_im = torch.as_tensor([0.0, 0.1], device=device)
wl0 = torch.linspace(400, 800, 40, device=device)
k0 = 2 * torch.pi / wl0
generate reference spectra#
we generate a large number of reference Mie spectra for existing particles, that will be used as design targets during training.
Note: this step could also be done without any physics knowledge, for example with artificial spectra (e.g. Lorentzians), or a scattering maximization loss.
# datagen: generate existing spectra (won't use the geometries for training)
r_c = torch.rand((N_samples), device=device) * torch.diff(lim_r)[0] + lim_r[0]
d_s = torch.rand((N_samples), device=device) * torch.diff(lim_r)[0] + lim_r[0]
r_s = r_c + d_s
n_re = torch.rand((N_samples, 2), device=device) * torch.diff(lim_n_re)[0] + lim_n_re[0]
n_im = torch.rand((N_samples, 2), device=device) * torch.diff(lim_n_im)[0] + lim_n_im[0]
n = n_re + 1j * n_im
# low-level API: permittivity required as spectra (for vectorization)
eps_c = torch.ones_like(k0).unsqueeze(0) * n[:, 0].unsqueeze(1) ** 2
eps_s = torch.ones_like(k0).unsqueeze(0) * n[:, 1].unsqueeze(1) ** 2
all_particles = pmd.multishell.cross_sections(
k0,
r_c=r_c,
eps_c=eps_c,
r_s=r_s,
eps_s=eps_s,
eps_env=eps_env,
n_max=n_max,
)
N_test = 128 # keep a few samples for testing
q_sca_target = all_particles["q_sca"][N_test:].to(dtype=torch.float32)
q_sca_target_test = all_particles["q_sca"][:N_test].to(dtype=torch.float32)
plt.plot(q_sca_target[30].detach().cpu().numpy()) # plot some test sample
[<matplotlib.lines.Line2D object at 0x7f4abc60f680>]
Neural network classes / functions#
define the network model (simple MLP) and training loop
class FullyConnected(nn.Module):
def __init__(self, hidden_dim=1024):
super().__init__()
self.fc_in = nn.Linear(len(k0), hidden_dim)
self.relu1 = nn.ReLU()
self.fc_1 = nn.Linear(hidden_dim, hidden_dim)
self.relu2 = nn.ReLU()
self.fc_2 = nn.Linear(hidden_dim, hidden_dim)
self.relu3 = nn.ReLU()
self.fc_out = nn.Linear(hidden_dim, 6)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x = self.fc_in(x)
x = self.relu1(x)
x = self.fc_1(x)
x = self.relu2(x)
x = self.fc_2(x)
x = self.relu3(x)
x = self.fc_out(x)
x = self.sigmoid(x)
return x
def nn_pred_to_mie_geometry(pred):
# implicit normalization: multiply by user-defined limits
r_c = lim_r.max() * (pred[:, 0])
r_s = lim_r.max() * (pred[:, 0] + pred[:, 1])
n_c = lim_n_re.max() * pred[:, 2] + lim_n_im.max() * (1j * pred[:, 3])
n_s = lim_n_re.max() * pred[:, 4] + lim_n_im.max() * (1j * pred[:, 5])
eps_c = torch.ones_like(k0).unsqueeze(0) * n_c.unsqueeze(1) ** 2
eps_s = torch.ones_like(k0).unsqueeze(0) * n_s.unsqueeze(1) ** 2
return r_c, r_s, eps_c, eps_s
def train_loop(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset)
# Set the model to training mode - important for batch normalization and dropout layers
# Unnecessary in this situation but added for best practices
model.train()
prog_bar = tqdm(enumerate(dataloader), total=size // dataloader.batch_size)
for i_batch, X in prog_bar:
# model prediction: generate core-shell particles
pred = model(X)
# evaluate Mie
r_c, r_s, eps_c, eps_s = nn_pred_to_mie_geometry(pred)
res_mie = pmd.multishell.cross_sections(
k0,
r_c=r_c,
eps_c=eps_c,
r_s=r_s,
eps_s=eps_s,
eps_env=eps_env,
n_max=n_max,
)
q_sca_mie = res_mie["q_sca"].to(dtype=torch.float32)
# calc. loss
loss = loss_fn(q_sca_mie, X)
# Backpropagation
loss.backward()
optimizer.step()
optimizer.zero_grad()
# if i_batch % 100 == 0:
loss, current = loss.item(), i_batch * dataloader.batch_size + len(X)
prog_bar.set_description(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
training the Mie-informed network#
here we use some simple, manually optimized training schedule.
model = FullyConnected().to(device)
confs = [
dict(bs=32, lr=1e-4, n_ep=5),
dict(bs=64, lr=1e-4, n_ep=5),
dict(bs=128, lr=1e-4, n_ep=6),
dict(bs=256, lr=1e-5, n_ep=6),
]
t_start = time.time()
for conf in confs:
learning_rate = conf["lr"]
batch_size = conf["bs"]
epochs = conf["n_ep"]
print("-------------------------------")
print(f"LR={learning_rate}, batch_size={batch_size}")
print("-------------------------------")
loss_fn = nn.MSELoss()
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
train_dataloader = torch.utils.data.DataLoader(q_sca_target, batch_size=batch_size)
for t in range(epochs):
print(f"Epoch {t+1}, time={time.time()-t_start:.2f}s")
train_loop(train_dataloader, model, loss_fn, optimizer)
print("Done!")
-------------------------------
LR=0.0001, batch_size=32
-------------------------------
Epoch 1, time=0.00s
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loss: 0.276461 [24288/24872]: 0%| | 0/777 [00:23<?, ?it/s]
loss: 0.247414 [24320/24872]: 0%| | 0/777 [00:23<?, ?it/s]
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loss: 0.256958 [24480/24872]: 0%| | 0/777 [00:23<?, ?it/s]
loss: 0.396443 [24512/24872]: 0%| | 0/777 [00:23<?, ?it/s]
loss: 0.241695 [24544/24872]: 0%| | 0/777 [00:23<?, ?it/s]
loss: 0.258353 [24576/24872]: 0%| | 0/777 [00:23<?, ?it/s]
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loss: 0.279682 [24640/24872]: 0%| | 0/777 [00:23<?, ?it/s]
loss: 0.263939 [24672/24872]: 0%| | 0/777 [00:23<?, ?it/s]
loss: 0.284468 [24704/24872]: 0%| | 0/777 [00:23<?, ?it/s]
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loss: 0.449649 [24872/24872]: 0%| | 0/777 [00:23<?, ?it/s]
loss: 0.449649 [24872/24872]: : 778it [00:23, 32.84it/s]
Epoch 2, time=23.69s
0%| | 0/777 [00:00<?, ?it/s]
loss: 0.321199 [ 32/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.379899 [ 64/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.285944 [ 96/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.354536 [ 160/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.266818 [ 192/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.262643 [ 224/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.296268 [ 256/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.277587 [ 288/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.247391 [ 320/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.420611 [ 352/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.409803 [ 384/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.213102 [ 416/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.363554 [ 448/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.395119 [ 480/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.273261 [ 512/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.276058 [ 544/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.207460 [23584/24872]: 0%| | 0/777 [00:25<?, ?it/s]
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loss: 0.271044 [23680/24872]: 0%| | 0/777 [00:25<?, ?it/s]
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loss: 0.288213 [23808/24872]: 0%| | 0/777 [00:26<?, ?it/s]
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loss: 0.213311 [24288/24872]: 0%| | 0/777 [00:26<?, ?it/s]
loss: 0.212418 [24320/24872]: 0%| | 0/777 [00:26<?, ?it/s]
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loss: 0.265550 [24384/24872]: 0%| | 0/777 [00:26<?, ?it/s]
loss: 0.332891 [24416/24872]: 0%| | 0/777 [00:26<?, ?it/s]
loss: 0.299463 [24448/24872]: 0%| | 0/777 [00:26<?, ?it/s]
loss: 0.212673 [24480/24872]: 0%| | 0/777 [00:26<?, ?it/s]
loss: 0.336086 [24512/24872]: 0%| | 0/777 [00:26<?, ?it/s]
loss: 0.222766 [24544/24872]: 0%| | 0/777 [00:26<?, ?it/s]
loss: 0.223325 [24576/24872]: 0%| | 0/777 [00:26<?, ?it/s]
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loss: 0.246372 [24640/24872]: 0%| | 0/777 [00:26<?, ?it/s]
loss: 0.219327 [24672/24872]: 0%| | 0/777 [00:27<?, ?it/s]
loss: 0.244044 [24704/24872]: 0%| | 0/777 [00:27<?, ?it/s]
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loss: 0.366332 [24872/24872]: 0%| | 0/777 [00:27<?, ?it/s]
loss: 0.366332 [24872/24872]: : 778it [00:27, 28.53it/s]
Epoch 3, time=50.96s
0%| | 0/777 [00:00<?, ?it/s]
loss: 0.217217 [ 32/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.279478 [ 64/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.197558 [ 288/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.211068 [ 320/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.185930 [ 416/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.290058 [ 448/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.303471 [24872/24872]: : 778it [00:28, 27.12it/s]
Epoch 4, time=79.65s
0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.204730 [24544/24872]: 0%| | 0/777 [00:27<?, ?it/s]
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loss: 0.274584 [24872/24872]: : 778it [00:27, 27.87it/s]
Epoch 5, time=107.57s
0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.171082 [24704/24872]: 0%| | 0/777 [00:26<?, ?it/s]
loss: 0.166982 [24736/24872]: 0%| | 0/777 [00:26<?, ?it/s]
loss: 0.192164 [24768/24872]: 0%| | 0/777 [00:27<?, ?it/s]
loss: 0.222834 [24800/24872]: 0%| | 0/777 [00:27<?, ?it/s]
loss: 0.199489 [24832/24872]: 0%| | 0/777 [00:27<?, ?it/s]
loss: 0.204046 [24864/24872]: 0%| | 0/777 [00:27<?, ?it/s]
loss: 0.233870 [24872/24872]: 0%| | 0/777 [00:27<?, ?it/s]
loss: 0.233870 [24872/24872]: : 778it [00:27, 28.64it/s]
-------------------------------
LR=0.0001, batch_size=64
-------------------------------
Epoch 1, time=134.74s
0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.215248 [ 1920/24872]: 0%| | 0/388 [00:01<?, ?it/s]
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loss: 0.182333 [24872/24872]: 0%| | 0/388 [00:16<?, ?it/s]
loss: 0.182333 [24872/24872]: : 389it [00:16, 24.30it/s]
Epoch 2, time=150.75s
0%| | 0/388 [00:00<?, ?it/s]
loss: 0.148205 [ 64/24872]: 0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.173768 [24872/24872]: : 389it [00:16, 24.25it/s]
Epoch 3, time=166.79s
0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.174007 [24872/24872]: : 389it [00:17, 21.76it/s]
Epoch 4, time=184.67s
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loss: 0.139772 [24872/24872]: : 389it [00:18, 20.87it/s]
Epoch 5, time=203.30s
0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.141802 [23744/24872]: 0%| | 0/388 [00:17<?, ?it/s]
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loss: 0.202555 [24192/24872]: 0%| | 0/388 [00:17<?, ?it/s]
loss: 0.119424 [24256/24872]: 0%| | 0/388 [00:17<?, ?it/s]
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loss: 0.175097 [24384/24872]: 0%| | 0/388 [00:17<?, ?it/s]
loss: 0.175692 [24448/24872]: 0%| | 0/388 [00:17<?, ?it/s]
loss: 0.164534 [24512/24872]: 0%| | 0/388 [00:17<?, ?it/s]
loss: 0.090774 [24576/24872]: 0%| | 0/388 [00:17<?, ?it/s]
loss: 0.158462 [24640/24872]: 0%| | 0/388 [00:17<?, ?it/s]
loss: 0.109507 [24704/24872]: 0%| | 0/388 [00:18<?, ?it/s]
loss: 0.108750 [24768/24872]: 0%| | 0/388 [00:18<?, ?it/s]
loss: 0.150023 [24832/24872]: 0%| | 0/388 [00:18<?, ?it/s]
loss: 0.137341 [24872/24872]: 0%| | 0/388 [00:18<?, ?it/s]
loss: 0.137341 [24872/24872]: : 389it [00:18, 21.41it/s]
-------------------------------
LR=0.0001, batch_size=128
-------------------------------
Epoch 1, time=221.47s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.102065 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.331610 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.199787 [ 384/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.224357 [ 512/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.164470 [ 640/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.182419 [ 768/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.213347 [ 896/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.148940 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.149916 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.172707 [ 1280/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.147339 [ 1792/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.141879 [ 1920/24872]: 0%| | 0/194 [00:01<?, ?it/s]
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loss: 0.107025 [22656/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.116072 [22784/24872]: 0%| | 0/194 [00:13<?, ?it/s]
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loss: 0.122303 [23296/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.098751 [23424/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.134937 [23552/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.110529 [23680/24872]: 0%| | 0/194 [00:13<?, ?it/s]
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loss: 0.147237 [23936/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.121447 [24064/24872]: 0%| | 0/194 [00:14<?, ?it/s]
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loss: 0.116204 [24872/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.116204 [24872/24872]: : 195it [00:14, 13.38it/s]
Epoch 2, time=236.04s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.113300 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.097131 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.099611 [ 384/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.092595 [ 512/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.105183 [ 768/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.126430 [ 896/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.116845 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.107345 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.133689 [ 1664/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.115599 [ 1792/24872]: 0%| | 0/194 [00:01<?, ?it/s]
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loss: 0.108340 [21248/24872]: 0%| | 0/194 [00:12<?, ?it/s]
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loss: 0.121169 [21504/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.125920 [21632/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.085901 [21760/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.099464 [21888/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.127047 [22016/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.119078 [22144/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.098591 [22272/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.097534 [22400/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.107731 [22528/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.108190 [22656/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.109038 [22784/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.109390 [22912/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.122641 [23040/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.121321 [23168/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.116998 [23296/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.095769 [23424/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.126060 [23552/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.106698 [23680/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.102107 [23808/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.136256 [23936/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.120582 [24064/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.153293 [24192/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.096142 [24320/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.148625 [24448/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.113061 [24576/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.120934 [24704/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.111921 [24832/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.110355 [24872/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.110355 [24872/24872]: : 195it [00:14, 13.11it/s]
Epoch 3, time=250.92s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.096954 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.097683 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.088089 [ 384/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.090789 [ 512/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.092663 [ 640/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.098677 [ 768/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.118046 [ 896/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.108044 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.104584 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.091824 [ 1280/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.138349 [ 1408/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.150077 [ 1536/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.125488 [ 1664/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.099935 [ 1792/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.116337 [ 1920/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.088149 [ 2048/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.107045 [ 2176/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.086189 [ 2304/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.134689 [ 2432/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.097793 [ 2560/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.097264 [ 2688/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.106150 [ 2816/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.113722 [ 2944/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.098498 [ 3072/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.079425 [ 3200/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.117383 [ 3328/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.141105 [ 3456/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.099665 [ 3584/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.101788 [ 3712/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.099607 [ 3840/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.086038 [ 3968/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.130832 [ 4096/24872]: 0%| | 0/194 [00:02<?, ?it/s]
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loss: 0.100601 [ 4352/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.108675 [ 4480/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.120181 [ 4608/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.130298 [ 4736/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.127507 [ 4864/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.128794 [ 4992/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.107807 [ 5120/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.120907 [ 5248/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.103085 [ 5376/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.085756 [ 5504/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.120299 [ 5632/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.120598 [ 5760/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.107209 [ 5888/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.106201 [ 6016/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.102384 [ 6144/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.105438 [ 6272/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.123218 [ 6400/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.079734 [ 6528/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.085241 [ 6656/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.120605 [ 6784/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.110558 [ 6912/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.087797 [ 7040/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.102607 [ 7168/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.127310 [ 7296/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.096252 [ 7424/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.090855 [ 7552/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.112359 [ 7680/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.109705 [ 7808/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.103731 [ 7936/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.102587 [ 8064/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.095477 [ 8192/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.073549 [ 8320/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.095778 [ 8448/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.129901 [ 8576/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.118475 [ 8704/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.105934 [ 8832/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.093563 [ 8960/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.109036 [ 9088/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.115007 [ 9216/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.107032 [ 9344/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.132382 [ 9472/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.123087 [ 9600/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.097870 [ 9728/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.103403 [ 9856/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.121053 [ 9984/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.091537 [10112/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.094137 [10240/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.136060 [10368/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.122306 [10496/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.115561 [10624/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.113689 [10752/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.143878 [10880/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.104914 [11008/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.121381 [11136/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.121250 [11264/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.102094 [11392/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.110667 [11520/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.114743 [11648/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.112390 [11776/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.111826 [11904/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.115566 [12032/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.137346 [12160/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.118588 [12288/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.131477 [12416/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.094410 [12544/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.106617 [12672/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.118228 [12800/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.129073 [12928/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.127779 [13056/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.125362 [13184/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.137749 [13312/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.124674 [13440/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.090625 [13568/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.108676 [13696/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.134558 [13824/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.120446 [13952/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.105750 [14080/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.099327 [14208/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.141880 [14336/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.120728 [14464/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.095018 [14592/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.111279 [14720/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.143282 [14848/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.113757 [14976/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.112724 [15104/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.118307 [15232/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.113055 [15360/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.125545 [15488/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.099214 [15616/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.143000 [15744/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.137094 [15872/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.119216 [16000/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.109737 [16128/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.133208 [16256/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.124988 [16384/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.124992 [16512/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.123697 [16640/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.134832 [16768/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.119484 [16896/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.117999 [17024/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.140140 [17152/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.135267 [17280/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.140554 [17408/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.113962 [17536/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.129595 [17664/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.123681 [17792/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.120864 [17920/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.113784 [18048/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.106794 [18176/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.090865 [18304/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.098164 [18432/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.116752 [18560/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.123765 [18688/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.120764 [18816/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.114788 [18944/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.128041 [19072/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.096651 [19200/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.062914 [19328/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.113016 [19456/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.083551 [19584/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.145876 [19712/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.101768 [19840/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.134391 [19968/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.102175 [20096/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.138737 [20224/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.132121 [20352/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.095730 [20480/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.110845 [20608/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.130918 [20736/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.092338 [20864/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.109891 [20992/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.110863 [21120/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.125519 [21248/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.141726 [21376/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.122058 [21504/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.122348 [21632/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.092399 [21760/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.087095 [21888/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.128538 [22016/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.111202 [22144/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.096908 [22272/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.100415 [22400/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.101731 [22528/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.117081 [22656/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.107138 [22784/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.108404 [22912/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.115160 [23040/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.120700 [23168/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.114867 [23296/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.094403 [23424/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.120703 [23552/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.102733 [23680/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.104479 [23808/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.137061 [23936/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.120804 [24064/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.153125 [24192/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.089586 [24320/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.149014 [24448/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.113225 [24576/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.124275 [24704/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.110244 [24832/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.123045 [24872/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.123045 [24872/24872]: : 195it [00:14, 13.12it/s]
Epoch 4, time=265.78s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.096756 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.107352 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.085886 [ 384/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.098765 [ 512/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.093755 [ 640/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.098198 [ 768/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.121494 [ 896/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.106525 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.110500 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.092605 [ 1280/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.157458 [ 1408/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.161342 [ 1536/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.140835 [ 1664/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.116468 [ 1792/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.118900 [ 1920/24872]: 0%| | 0/194 [00:01<?, ?it/s]
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loss: 0.095406 [22656/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.113040 [22784/24872]: 0%| | 0/194 [00:13<?, ?it/s]
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loss: 0.120483 [23296/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.095758 [23424/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.123434 [23552/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.106879 [23680/24872]: 0%| | 0/194 [00:14<?, ?it/s]
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loss: 0.116124 [24872/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.116124 [24872/24872]: : 195it [00:14, 13.18it/s]
Epoch 5, time=280.58s
0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.100378 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.086979 [23424/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.115487 [23552/24872]: 0%| | 0/194 [00:14<?, ?it/s]
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loss: 0.108950 [24872/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.108950 [24872/24872]: : 195it [00:14, 13.12it/s]
Epoch 6, time=295.44s
0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.121029 [13824/24872]: 0%| | 0/194 [00:08<?, ?it/s]
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loss: 0.091448 [14080/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.096151 [14208/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.101617 [14336/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.114470 [14464/24872]: 0%| | 0/194 [00:08<?, ?it/s]
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loss: 0.098557 [14720/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.115370 [14848/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.092038 [14976/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.106428 [15104/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.101074 [15232/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.107940 [15360/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.114180 [15488/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.084886 [15616/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.128017 [15744/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.109341 [15872/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.103263 [16000/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.097838 [16128/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.126072 [16256/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.093179 [16384/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.108076 [16512/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.104418 [16640/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.109279 [16768/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.092774 [16896/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.113791 [17024/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.099427 [17152/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.103176 [17280/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.119504 [17408/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.094728 [17536/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.107139 [17664/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.116493 [17792/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.102550 [17920/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.104084 [18048/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.083705 [18176/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.080213 [18304/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.087474 [18432/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.095421 [18560/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.099598 [18688/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.100993 [18816/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.105371 [18944/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.112542 [19072/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.085394 [19200/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.055284 [19328/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.100460 [19456/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.077749 [19584/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.142974 [19712/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.095143 [19840/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.126884 [19968/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.097993 [20096/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.119902 [20224/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.121859 [20352/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.091260 [20480/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.095418 [20608/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.121835 [20736/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.092546 [20864/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.099164 [20992/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.103987 [21120/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.107209 [21248/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.129023 [21376/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.107403 [21504/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.112041 [21632/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.079336 [21760/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.100868 [21888/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.122640 [22016/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.107803 [22144/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.095178 [22272/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.086386 [22400/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.103029 [22528/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.099973 [22656/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.105806 [22784/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.098933 [22912/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.112793 [23040/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.113056 [23168/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.108124 [23296/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.086306 [23424/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.118229 [23552/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.096134 [23680/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.097471 [23808/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.144805 [23936/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.108549 [24064/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.134784 [24192/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.083706 [24320/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.120986 [24448/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.107619 [24576/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.111443 [24704/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.108050 [24832/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.096211 [24872/24872]: 0%| | 0/194 [00:15<?, ?it/s]
loss: 0.096211 [24872/24872]: : 195it [00:15, 12.93it/s]
-------------------------------
LR=1e-05, batch_size=256
-------------------------------
Epoch 1, time=310.52s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.089531 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.080566 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.087837 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.098700 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.086621 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.132619 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.093953 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.090036 [ 2048/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.083601 [ 2304/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.098985 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.089251 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.089454 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.081735 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.101179 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.083114 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.090757 [ 4096/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.095073 [ 4352/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.101214 [ 4608/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.104909 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.103619 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.098726 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.085319 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.097417 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.084072 [ 6144/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.094260 [ 6400/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.068555 [ 6656/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.094457 [ 6912/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.076570 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.095289 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.094266 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.091407 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.082417 [ 8192/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.071220 [ 8448/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.107388 [ 8704/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.085514 [ 8960/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.095110 [ 9216/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.099748 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.088590 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.098378 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.079351 [10240/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.103312 [10496/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.096230 [10752/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.104445 [11008/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.095297 [11264/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.088344 [11520/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.091265 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.089725 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.094400 [12288/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.087975 [12544/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.089455 [12800/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.103856 [13056/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.099465 [13312/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.082870 [13568/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.095147 [13824/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.091638 [14080/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.084969 [14336/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.072866 [14592/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.095324 [14848/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.086139 [15104/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.087362 [15360/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.087141 [15616/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.108400 [15872/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.091377 [16128/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.100842 [16384/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.094304 [16640/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.092906 [16896/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.096426 [17152/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.097028 [17408/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.091846 [17664/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.097510 [17920/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.083967 [18176/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.072332 [18432/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.090885 [18688/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.096331 [18944/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.096293 [19200/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.073133 [19456/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.101423 [19712/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.103651 [19968/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.099003 [20224/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.094583 [20480/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.104035 [20736/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.082254 [20992/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.088935 [21248/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.111791 [21504/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.084730 [21760/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.088914 [22016/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.087958 [22272/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.082574 [22528/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.089447 [22784/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.097325 [23040/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.100521 [23296/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.089764 [23552/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.089145 [23808/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.112996 [24064/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.099458 [24320/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.102711 [24576/24872]: 0%| | 0/97 [00:12<?, ?it/s]
loss: 0.096511 [24832/24872]: 0%| | 0/97 [00:12<?, ?it/s]
loss: 0.090339 [24872/24872]: 0%| | 0/97 [00:12<?, ?it/s]
loss: 0.090339 [24872/24872]: : 98it [00:12, 8.04it/s]
Epoch 2, time=322.72s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.080289 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.075157 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.081295 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.093834 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.082590 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.124369 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.089112 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.085681 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.079811 [ 2304/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.096992 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.086928 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.087736 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.079691 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.100332 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.079591 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.087027 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.092707 [ 4352/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.098841 [ 4608/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.102053 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.101797 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.095074 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.083650 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.095563 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.082914 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.091946 [ 6400/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.066604 [ 6656/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.092647 [ 6912/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.075515 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.092690 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.091810 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.088756 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.078893 [ 8192/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.069752 [ 8448/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.105047 [ 8704/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.083537 [ 8960/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.092386 [ 9216/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.096536 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.087097 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.097027 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.077617 [10240/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.102766 [10496/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.093030 [10752/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.101340 [11008/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.094216 [11264/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.086612 [11520/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.089876 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.087471 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.091888 [12288/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.086811 [12544/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.086981 [12800/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.101705 [13056/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.097653 [13312/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.081341 [13568/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.093794 [13824/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.089500 [14080/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.082983 [14336/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.071204 [14592/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.093491 [14848/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.085107 [15104/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.086458 [15360/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.086136 [15616/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.105209 [15872/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.089930 [16128/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.099382 [16384/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.092220 [16640/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.091427 [16896/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.094799 [17152/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.095650 [17408/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.090177 [17664/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.096456 [17920/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.082110 [18176/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.070924 [18432/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.089456 [18688/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.094371 [18944/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.093514 [19200/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.071641 [19456/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.100476 [19712/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.101802 [19968/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.097002 [20224/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.093705 [20480/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.099813 [20736/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.080827 [20992/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.087365 [21248/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.111225 [21504/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.083654 [21760/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.088192 [22016/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.087151 [22272/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.081216 [22528/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.088710 [22784/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.096713 [23040/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.100248 [23296/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.089415 [23552/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.088779 [23808/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.112072 [24064/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.098627 [24320/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.101509 [24576/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.095638 [24832/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.089338 [24872/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.089338 [24872/24872]: : 98it [00:11, 8.32it/s]
Epoch 3, time=334.50s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.079654 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.074132 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.080430 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.092950 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.081459 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.122081 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.087814 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.084755 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.078478 [ 2304/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.095789 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.085809 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.086632 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.078527 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.098992 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.078453 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.086187 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.091771 [ 4352/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.098055 [ 4608/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.100601 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.100977 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.094121 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.082597 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.094610 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.082401 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.090766 [ 6400/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.065793 [ 6656/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.091871 [ 6912/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.074930 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.091055 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.090710 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.088654 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.077853 [ 8192/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.068994 [ 8448/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.103736 [ 8704/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.082687 [ 8960/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.091376 [ 9216/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.094826 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.086516 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.095379 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.076911 [10240/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.102096 [10496/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.091891 [10752/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.100147 [11008/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.093447 [11264/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.085836 [11520/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.088894 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.086644 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.090584 [12288/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.086233 [12544/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.085542 [12800/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.100648 [13056/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.096761 [13312/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.079998 [13568/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.092775 [13824/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.088011 [14080/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.082052 [14336/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.070282 [14592/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.092825 [14848/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.084246 [15104/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.085916 [15360/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.085491 [15616/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.103636 [15872/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.089016 [16128/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.098404 [16384/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.091072 [16640/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.090612 [16896/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.093710 [17152/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.094771 [17408/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.089355 [17664/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.095645 [17920/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.080992 [18176/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.070169 [18432/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.088404 [18688/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.093167 [18944/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.090870 [19200/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.070892 [19456/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.099711 [19712/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.100405 [19968/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.095829 [20224/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.093181 [20480/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.097923 [20736/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.079594 [20992/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.086494 [21248/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.110798 [21504/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.082819 [21760/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.087578 [22016/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.086356 [22272/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.080052 [22528/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.087925 [22784/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.096119 [23040/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.099561 [23296/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.088850 [23552/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.088274 [23808/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.111136 [24064/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.097855 [24320/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.100635 [24576/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.094934 [24832/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.088525 [24872/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.088525 [24872/24872]: : 98it [00:11, 8.27it/s]
Epoch 4, time=346.35s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.079140 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.073481 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.079753 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.092215 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.080737 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.120562 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.086745 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.084033 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.077624 [ 2304/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.094881 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.084992 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.085897 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.077661 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.098081 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.077657 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.085523 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.091087 [ 4352/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.097174 [ 4608/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.099332 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.100281 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.093397 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.081712 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.093509 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.081952 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.089764 [ 6400/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.064928 [ 6656/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.091209 [ 6912/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.074356 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.089855 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.089820 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.088225 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.077113 [ 8192/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.068483 [ 8448/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.102507 [ 8704/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.081888 [ 8960/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.090718 [ 9216/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.093473 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.085943 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.094021 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.076334 [10240/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.101331 [10496/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.090941 [10752/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.099322 [11008/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.092836 [11264/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.085138 [11520/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.088066 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.085904 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.089323 [12288/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.085732 [12544/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.084259 [12800/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.099550 [13056/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.096084 [13312/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.078646 [13568/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.091869 [13824/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.086721 [14080/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.081251 [14336/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.069430 [14592/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.092256 [14848/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.083455 [15104/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.085413 [15360/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.085008 [15616/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.102491 [15872/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.088195 [16128/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.097655 [16384/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.090038 [16640/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.089806 [16896/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.092781 [17152/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.094247 [17408/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.088622 [17664/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.094957 [17920/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.080148 [18176/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.069572 [18432/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.087473 [18688/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.092206 [18944/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.088905 [19200/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.070290 [19456/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.099024 [19712/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.099456 [19968/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.094832 [20224/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.092716 [20480/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.096651 [20736/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.078737 [20992/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.085891 [21248/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.110470 [21504/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.082122 [21760/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.086850 [22016/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.085483 [22272/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.079352 [22528/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.087372 [22784/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.095543 [23040/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.098858 [23296/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.088332 [23552/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.087827 [23808/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.110131 [24064/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.097124 [24320/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.099924 [24576/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.094286 [24832/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.087859 [24872/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.087859 [24872/24872]: : 98it [00:11, 8.33it/s]
Epoch 5, time=358.12s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.078780 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.072808 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.079060 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.091505 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.080177 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.119312 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.085882 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.083382 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.076963 [ 2304/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.094100 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.084196 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.085251 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.076900 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.097281 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.076956 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.084978 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.090568 [ 4352/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.096347 [ 4608/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.098295 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.099645 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.092773 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.080933 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.092638 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.081524 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.088738 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.064154 [ 6656/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.090618 [ 6912/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.073823 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.088822 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.089050 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.087572 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.076483 [ 8192/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.067972 [ 8448/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.101483 [ 8704/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.081209 [ 8960/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.090100 [ 9216/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.092403 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.085348 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.092866 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.075831 [10240/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.100600 [10496/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.090083 [10752/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.098713 [11008/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.092347 [11264/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.084614 [11520/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.087298 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.085253 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.088228 [12288/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.085262 [12544/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.083218 [12800/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.098637 [13056/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.095458 [13312/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.077553 [13568/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.091097 [13824/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.085672 [14080/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.080653 [14336/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.068578 [14592/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.091701 [14848/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.082767 [15104/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.084840 [15360/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.084506 [15616/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.101560 [15872/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.087510 [16128/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.096923 [16384/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.089071 [16640/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.089065 [16896/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.091920 [17152/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.093746 [17408/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.087872 [17664/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.094340 [17920/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.079415 [18176/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.069052 [18432/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.086641 [18688/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.091478 [18944/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.087617 [19200/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.069728 [19456/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.098403 [19712/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.098766 [19968/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.093935 [20224/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.092200 [20480/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.095678 [20736/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.078199 [20992/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.085232 [21248/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.110137 [21504/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.081369 [21760/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.086100 [22016/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.084651 [22272/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.078832 [22528/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.086770 [22784/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.094885 [23040/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.098241 [23296/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.087817 [23552/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.087365 [23808/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.109118 [24064/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.096498 [24320/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.099261 [24576/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.093759 [24832/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.087381 [24872/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.087381 [24872/24872]: : 98it [00:11, 8.31it/s]
Epoch 6, time=369.92s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.078452 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.072155 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.078461 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.090898 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.079598 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.118251 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.085082 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.082746 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.076417 [ 2304/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.093533 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.083478 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.084608 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.076236 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.096626 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.076328 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.084499 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.090119 [ 4352/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.095599 [ 4608/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.097335 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.099037 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.092240 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.080225 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.091825 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.081125 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.087822 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.063461 [ 6656/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.090096 [ 6912/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.073393 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.087990 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.088287 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.086887 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.075972 [ 8192/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.067468 [ 8448/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.100483 [ 8704/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.080593 [ 8960/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.089511 [ 9216/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.091402 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.084761 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.092085 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.075344 [10240/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.099838 [10496/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.089234 [10752/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.098111 [11008/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.091917 [11264/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.084080 [11520/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.086605 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.084672 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.087138 [12288/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.084844 [12544/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.082191 [12800/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.097774 [13056/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.094895 [13312/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.076668 [13568/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.090499 [13824/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.084766 [14080/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.080130 [14336/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.067746 [14592/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.091207 [14848/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.082158 [15104/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.084164 [15360/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.084055 [15616/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.100673 [15872/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.086934 [16128/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.096299 [16384/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.088251 [16640/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.088436 [16896/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.091153 [17152/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.093236 [17408/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.087231 [17664/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.093798 [17920/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.078804 [18176/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.068519 [18432/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.085910 [18688/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.090783 [18944/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.086663 [19200/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.069221 [19456/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.097780 [19712/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.098182 [19968/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.093180 [20224/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.091663 [20480/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.094394 [20736/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.077677 [20992/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.084683 [21248/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.109861 [21504/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.080627 [21760/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.085309 [22016/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.084034 [22272/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.078475 [22528/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.086366 [22784/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.094485 [23040/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.097704 [23296/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.087260 [23552/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.086962 [23808/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.108112 [24064/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.095904 [24320/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.098742 [24576/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.093334 [24832/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.086912 [24872/24872]: 0%| | 0/97 [00:11<?, ?it/s]
loss: 0.086912 [24872/24872]: : 98it [00:11, 8.37it/s]
Done!
test the network#
Do some qualitative tests: Let the trained network predict some particle geometries and compare their Mie spectra with the traget spectrum.
# pick a few of the training samples for testing.
# Note: Ideally tests should be done on separate samples!
sca_test = q_sca_target_test
pred = model(sca_test)
# evaluate Mie
r_c_test, r_s_test, eps_c_test, eps_s_test = nn_pred_to_mie_geometry(pred)
res_mie = pmd.multishell.cross_sections(
k0,
r_c=r_c_test,
eps_c=eps_c_test,
r_s=r_s_test,
eps_s=eps_s_test,
eps_env=eps_env,
n_max=n_max,
)
# plot
i_plot = np.random.randint(len(sca_test), size=4)
plt.figure(figsize=(12, 10))
for i_n, i in enumerate(i_plot):
plt.subplot(2, 2, i_n + 1)
plt.plot(
wl0.detach().cpu().numpy(),
sca_test[i].detach().cpu().numpy(),
label="reference",
)
plt.plot(
wl0.detach().cpu().numpy(),
res_mie["q_sca"][i].detach().cpu().numpy(),
label="predicted particle",
)
plt.legend()
plt.xlabel("wavelength (nm)")
plt.ylabel("scat. efficiency")
plt.show()
Total running time of the script: (6 minutes 27.181 seconds)
Estimated memory usage: 2013 MB