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 0x7f5d621a5340>]
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: 0.399159 [22720/24872]: 0%| | 0/777 [00:27<?, ?it/s]
loss: 0.339568 [22752/24872]: 0%| | 0/777 [00:27<?, ?it/s]
loss: 0.343452 [22784/24872]: 0%| | 0/777 [00:28<?, ?it/s]
loss: 0.391304 [22816/24872]: 0%| | 0/777 [00:28<?, ?it/s]
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loss: 0.339989 [23616/24872]: 0%| | 0/777 [00:29<?, ?it/s]
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loss: 0.344899 [23808/24872]: 0%| | 0/777 [00:29<?, ?it/s]
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loss: 0.457034 [23872/24872]: 0%| | 0/777 [00:29<?, ?it/s]
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loss: 0.289430 [24320/24872]: 0%| | 0/777 [00:29<?, ?it/s]
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loss: 0.319808 [24384/24872]: 0%| | 0/777 [00:29<?, ?it/s]
loss: 0.311022 [24416/24872]: 0%| | 0/777 [00:29<?, ?it/s]
loss: 0.375248 [24448/24872]: 0%| | 0/777 [00:30<?, ?it/s]
loss: 0.375248 [24448/24872]: 98%|█████████▊| 764/777 [00:30<00:00, 25.46it/s]
loss: 0.350070 [24480/24872]: 98%|█████████▊| 764/777 [00:30<00:00, 25.46it/s]
loss: 0.268360 [24512/24872]: 98%|█████████▊| 764/777 [00:30<00:00, 25.46it/s]
loss: 0.315079 [24544/24872]: 98%|█████████▊| 764/777 [00:30<00:00, 25.46it/s]
loss: 0.262206 [24576/24872]: 98%|█████████▊| 764/777 [00:30<00:00, 25.46it/s]
loss: 0.436917 [24608/24872]: 98%|█████████▊| 764/777 [00:30<00:00, 25.46it/s]
loss: 0.294415 [24640/24872]: 98%|█████████▊| 764/777 [00:30<00:00, 25.46it/s]
loss: 0.242801 [24672/24872]: 98%|█████████▊| 764/777 [00:30<00:00, 25.46it/s]
loss: 0.342124 [24704/24872]: 98%|█████████▊| 764/777 [00:30<00:00, 25.46it/s]
loss: 0.479245 [24736/24872]: 98%|█████████▊| 764/777 [00:30<00:00, 25.46it/s]
loss: 0.214594 [24768/24872]: 98%|█████████▊| 764/777 [00:30<00:00, 25.46it/s]
loss: 0.261813 [24800/24872]: 98%|█████████▊| 764/777 [00:30<00:00, 25.46it/s]
loss: 0.429851 [24832/24872]: 98%|█████████▊| 764/777 [00:30<00:00, 25.46it/s]
loss: 0.491314 [24864/24872]: 98%|█████████▊| 764/777 [00:30<00:00, 25.46it/s]
loss: 0.479038 [24872/24872]: 98%|█████████▊| 764/777 [00:30<00:00, 25.46it/s]
loss: 0.479038 [24872/24872]: : 778it [00:30, 25.47it/s]
Epoch 2, time=30.54s
0%| | 0/777 [00:00<?, ?it/s]
loss: 0.333662 [ 32/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.349014 [ 64/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.360450 [ 96/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.276553 [ 128/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.290454 [ 160/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.302002 [ 192/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.314912 [ 224/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.277260 [ 256/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.400150 [ 288/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.258316 [ 320/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.418012 [ 352/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.295628 [ 384/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.358790 [ 416/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.321909 [ 448/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.337019 [ 480/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.173866 [ 512/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.311167 [ 544/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.272052 [ 576/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.329776 [ 608/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.290595 [ 640/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.267222 [ 672/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.382368 [ 704/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.192103 [ 736/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.386307 [ 768/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.294742 [ 800/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.310129 [ 832/24872]: 0%| | 0/777 [00:01<?, ?it/s]
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loss: 0.353763 [ 896/24872]: 0%| | 0/777 [00:01<?, ?it/s]
loss: 0.502205 [ 928/24872]: 0%| | 0/777 [00:01<?, ?it/s]
loss: 0.292156 [ 960/24872]: 0%| | 0/777 [00:01<?, ?it/s]
loss: 0.357291 [ 992/24872]: 0%| | 0/777 [00:01<?, ?it/s]
loss: 0.473111 [ 1024/24872]: 0%| | 0/777 [00:01<?, ?it/s]
loss: 0.427209 [ 1056/24872]: 0%| | 0/777 [00:01<?, ?it/s]
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loss: 0.356051 [ 1184/24872]: 0%| | 0/777 [00:01<?, ?it/s]
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loss: 0.324852 [ 1920/24872]: 0%| | 0/777 [00:02<?, ?it/s]
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loss: 0.305992 [ 2016/24872]: 0%| | 0/777 [00:02<?, ?it/s]
loss: 0.291732 [ 2048/24872]: 0%| | 0/777 [00:02<?, ?it/s]
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loss: 0.233857 [ 2848/24872]: 0%| | 0/777 [00:03<?, ?it/s]
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loss: 0.273528 [ 2944/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.501639 [ 2976/24872]: 0%| | 0/777 [00:03<?, ?it/s]
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loss: 0.322092 [ 3456/24872]: 0%| | 0/777 [00:04<?, ?it/s]
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loss: 0.223420 [22656/24872]: 0%| | 0/777 [00:27<?, ?it/s]
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loss: 0.343551 [22976/24872]: 0%| | 0/777 [00:27<?, ?it/s]
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loss: 0.259618 [23168/24872]: 0%| | 0/777 [00:27<?, ?it/s]
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loss: 0.206427 [24576/24872]: 0%| | 0/777 [00:29<?, ?it/s]
loss: 0.312141 [24608/24872]: 0%| | 0/777 [00:29<?, ?it/s]
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loss: 0.286549 [24704/24872]: 0%| | 0/777 [00:29<?, ?it/s]
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loss: 0.375678 [24864/24872]: 0%| | 0/777 [00:30<?, ?it/s]
loss: 0.375678 [24864/24872]: 100%|██████████| 777/777 [00:30<00:00, 25.88it/s]
loss: 0.362807 [24872/24872]: 100%|██████████| 777/777 [00:30<00:00, 25.88it/s]
loss: 0.362807 [24872/24872]: : 778it [00:30, 25.89it/s]
Epoch 3, time=60.59s
0%| | 0/777 [00:00<?, ?it/s]
loss: 0.301910 [ 32/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.274983 [ 416/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.296726 [24864/24872]: 100%|██████████| 777/777 [00:30<00:00, 25.87it/s]
loss: 0.290230 [24872/24872]: 100%|██████████| 777/777 [00:30<00:00, 25.87it/s]
loss: 0.290230 [24872/24872]: : 778it [00:30, 25.87it/s]
Epoch 4, time=90.66s
0%| | 0/777 [00:00<?, ?it/s]
loss: 0.264867 [ 32/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.282281 [ 64/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.211236 [ 128/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.292206 [ 224/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.211072 [ 256/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.282663 [ 288/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.235954 [ 320/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.183597 [ 384/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.287379 [ 416/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.219509 [ 448/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.136665 [ 512/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.222333 [ 608/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.207624 [ 640/24872]: 0%| | 0/777 [00:00<?, ?it/s]
loss: 0.210230 [ 672/24872]: 0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.252344 [ 1760/24872]: 0%| | 0/777 [00:02<?, ?it/s]
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loss: 0.239981 [ 1856/24872]: 0%| | 0/777 [00:02<?, ?it/s]
loss: 0.185196 [ 1888/24872]: 0%| | 0/777 [00:02<?, ?it/s]
loss: 0.246272 [ 1920/24872]: 0%| | 0/777 [00:02<?, ?it/s]
loss: 0.196535 [ 1952/24872]: 0%| | 0/777 [00:02<?, ?it/s]
loss: 0.253917 [ 1984/24872]: 0%| | 0/777 [00:02<?, ?it/s]
loss: 0.205874 [ 2016/24872]: 0%| | 0/777 [00:02<?, ?it/s]
loss: 0.197466 [ 2048/24872]: 0%| | 0/777 [00:02<?, ?it/s]
loss: 0.169948 [ 2080/24872]: 0%| | 0/777 [00:02<?, ?it/s]
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loss: 0.145614 [ 2400/24872]: 0%| | 0/777 [00:02<?, ?it/s]
loss: 0.223850 [ 2432/24872]: 0%| | 0/777 [00:02<?, ?it/s]
loss: 0.191394 [ 2464/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.340147 [ 2496/24872]: 0%| | 0/777 [00:03<?, ?it/s]
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loss: 0.231754 [ 2560/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.286213 [ 2592/24872]: 0%| | 0/777 [00:03<?, ?it/s]
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loss: 0.224047 [ 2656/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.273231 [ 2688/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.259427 [ 2720/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.177662 [ 2752/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.203200 [ 2784/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.172167 [ 2816/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.199427 [ 2848/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.167254 [ 2880/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.135075 [ 2912/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.202331 [ 2944/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.401421 [ 2976/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.226436 [ 3008/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.277694 [ 3040/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.225461 [ 3072/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.235317 [ 3104/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.265593 [ 3136/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.227834 [ 3168/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.169513 [ 3200/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.161557 [ 3232/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.207142 [ 3264/24872]: 0%| | 0/777 [00:03<?, ?it/s]
loss: 0.250912 [ 3296/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.172953 [ 3328/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.199794 [ 3360/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.292983 [ 3392/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.216733 [ 3424/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.232260 [ 3456/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.277948 [ 3488/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.194704 [ 3520/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.201714 [ 3552/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.253998 [ 3584/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.228257 [ 3616/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.364167 [ 3648/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.214752 [ 3680/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.198279 [ 3712/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.239576 [ 3744/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.254938 [ 3776/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.125892 [ 3808/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.236944 [ 3840/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.181396 [ 3872/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.228906 [ 3904/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.220730 [ 3936/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.176664 [ 3968/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.219890 [ 4000/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.190414 [ 4032/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.316527 [ 4064/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.180220 [ 4096/24872]: 0%| | 0/777 [00:04<?, ?it/s]
loss: 0.152316 [ 4128/24872]: 0%| | 0/777 [00:05<?, ?it/s]
loss: 0.217070 [ 4160/24872]: 0%| | 0/777 [00:05<?, ?it/s]
loss: 0.191194 [ 4192/24872]: 0%| | 0/777 [00:05<?, ?it/s]
loss: 0.232117 [ 4224/24872]: 0%| | 0/777 [00:05<?, ?it/s]
loss: 0.187148 [ 4256/24872]: 0%| | 0/777 [00:05<?, ?it/s]
loss: 0.222778 [ 4288/24872]: 0%| | 0/777 [00:05<?, ?it/s]
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loss: 0.202939 [ 4416/24872]: 0%| | 0/777 [00:05<?, ?it/s]
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loss: 0.227595 [ 4480/24872]: 0%| | 0/777 [00:05<?, ?it/s]
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loss: 0.173707 [ 4544/24872]: 0%| | 0/777 [00:05<?, ?it/s]
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loss: 0.253505 [ 4960/24872]: 0%| | 0/777 [00:06<?, ?it/s]
loss: 0.208718 [ 4992/24872]: 0%| | 0/777 [00:06<?, ?it/s]
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loss: 0.284411 [24864/24872]: 0%| | 0/777 [00:30<?, ?it/s]
loss: 0.284411 [24864/24872]: 100%|██████████| 777/777 [00:30<00:00, 25.88it/s]
loss: 0.271566 [24872/24872]: 100%|██████████| 777/777 [00:30<00:00, 25.88it/s]
loss: 0.271566 [24872/24872]: : 778it [00:30, 25.89it/s]
Epoch 5, time=120.71s
0%| | 0/777 [00:00<?, ?it/s]
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loss: 0.159857 [24576/24872]: 0%| | 0/777 [00:29<?, ?it/s]
loss: 0.198994 [24608/24872]: 0%| | 0/777 [00:29<?, ?it/s]
loss: 0.169750 [24640/24872]: 0%| | 0/777 [00:29<?, ?it/s]
loss: 0.107735 [24672/24872]: 0%| | 0/777 [00:29<?, ?it/s]
loss: 0.182742 [24704/24872]: 0%| | 0/777 [00:29<?, ?it/s]
loss: 0.217402 [24736/24872]: 0%| | 0/777 [00:29<?, ?it/s]
loss: 0.131530 [24768/24872]: 0%| | 0/777 [00:29<?, ?it/s]
loss: 0.146410 [24800/24872]: 0%| | 0/777 [00:29<?, ?it/s]
loss: 0.196665 [24832/24872]: 0%| | 0/777 [00:30<?, ?it/s]
loss: 0.196665 [24832/24872]: 100%|█████████▉| 776/777 [00:30<00:00, 25.85it/s]
loss: 0.255172 [24864/24872]: 100%|█████████▉| 776/777 [00:30<00:00, 25.85it/s]
loss: 0.216973 [24872/24872]: 100%|█████████▉| 776/777 [00:30<00:00, 25.85it/s]
loss: 0.216973 [24872/24872]: : 778it [00:30, 25.86it/s]
-------------------------------
LR=0.0001, batch_size=64
-------------------------------
Epoch 1, time=150.80s
0%| | 0/388 [00:00<?, ?it/s]
loss: 0.224360 [ 64/24872]: 0%| | 0/388 [00:00<?, ?it/s]
loss: 0.323039 [ 128/24872]: 0%| | 0/388 [00:00<?, ?it/s]
loss: 0.190750 [ 192/24872]: 0%| | 0/388 [00:00<?, ?it/s]
loss: 0.225940 [ 256/24872]: 0%| | 0/388 [00:00<?, ?it/s]
loss: 0.279759 [ 320/24872]: 0%| | 0/388 [00:00<?, ?it/s]
loss: 0.207430 [ 384/24872]: 0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.189572 [ 512/24872]: 0%| | 0/388 [00:00<?, ?it/s]
loss: 0.198800 [ 576/24872]: 0%| | 0/388 [00:00<?, ?it/s]
loss: 0.212103 [ 640/24872]: 0%| | 0/388 [00:00<?, ?it/s]
loss: 0.199084 [ 704/24872]: 0%| | 0/388 [00:00<?, ?it/s]
loss: 0.143425 [ 768/24872]: 0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.201144 [ 896/24872]: 0%| | 0/388 [00:00<?, ?it/s]
loss: 0.278848 [ 960/24872]: 0%| | 0/388 [00:00<?, ?it/s]
loss: 0.287935 [ 1024/24872]: 0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.189680 [ 1152/24872]: 0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.190382 [ 1856/24872]: 0%| | 0/388 [00:01<?, ?it/s]
loss: 0.190233 [ 1920/24872]: 0%| | 0/388 [00:01<?, ?it/s]
loss: 0.186161 [ 1984/24872]: 0%| | 0/388 [00:01<?, ?it/s]
loss: 0.166626 [ 2048/24872]: 0%| | 0/388 [00:01<?, ?it/s]
loss: 0.137072 [ 2112/24872]: 0%| | 0/388 [00:01<?, ?it/s]
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loss: 0.143382 [ 2432/24872]: 0%| | 0/388 [00:01<?, ?it/s]
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loss: 0.121947 [ 8000/24872]: 0%| | 0/388 [00:06<?, ?it/s]
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loss: 0.241909 [24872/24872]: 0%| | 0/388 [00:19<?, ?it/s]
loss: 0.241909 [24872/24872]: : 389it [00:19, 19.68it/s]
Epoch 2, time=170.57s
0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.209816 [24872/24872]: : 389it [00:19, 19.67it/s]
Epoch 3, time=190.35s
0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.189865 [24872/24872]: : 389it [00:19, 19.61it/s]
Epoch 4, time=210.19s
0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.189853 [24872/24872]: : 389it [00:19, 19.47it/s]
Epoch 5, time=230.17s
0%| | 0/388 [00:00<?, ?it/s]
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loss: 0.153661 [24832/24872]: 0%| | 0/388 [00:19<?, ?it/s]
loss: 0.207047 [24872/24872]: 0%| | 0/388 [00:20<?, ?it/s]
loss: 0.207047 [24872/24872]: : 389it [00:20, 19.41it/s]
-------------------------------
LR=0.0001, batch_size=128
-------------------------------
Epoch 1, time=250.21s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.184328 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.526290 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.293090 [ 384/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.160216 [ 512/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.188940 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.105862 [21888/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.126078 [22016/24872]: 0%| | 0/194 [00:13<?, ?it/s]
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loss: 0.126110 [22656/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.128932 [22784/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.125451 [22912/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.145104 [23040/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.134471 [23168/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.126398 [23296/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.128858 [23424/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.115076 [23552/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.109419 [23680/24872]: 0%| | 0/194 [00:14<?, ?it/s]
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loss: 0.125291 [23936/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.124710 [24064/24872]: 0%| | 0/194 [00:14<?, ?it/s]
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loss: 0.182717 [24872/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.182717 [24872/24872]: : 195it [00:14, 13.16it/s]
Epoch 2, time=265.03s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.136163 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.112555 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.137353 [ 384/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.097423 [ 640/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.110444 [ 768/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.129257 [ 896/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.145044 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.118952 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.113159 [ 1280/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.115006 [ 1408/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.114365 [ 1664/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.118033 [ 1792/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.127632 [ 1920/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.130054 [ 2048/24872]: 0%| | 0/194 [00:01<?, ?it/s]
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loss: 0.130453 [19584/24872]: 0%| | 0/194 [00:11<?, ?it/s]
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loss: 0.110746 [19968/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.148076 [20096/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.119864 [20224/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.138704 [20352/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.160213 [20480/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.097861 [20608/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.123726 [20736/24872]: 0%| | 0/194 [00:12<?, ?it/s]
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loss: 0.150027 [20992/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.101427 [21120/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.117258 [21248/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.106761 [21376/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.118527 [21504/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.117355 [21632/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.120786 [21760/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.123009 [21888/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.136268 [22016/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.104166 [22144/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.135294 [22272/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.124962 [22400/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.131824 [22528/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.131991 [22656/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.129898 [22784/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.134928 [22912/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.145737 [23040/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.135520 [23168/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.127886 [23296/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.131576 [23424/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.119867 [23552/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.103512 [23680/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.132680 [23808/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.119032 [23936/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.129379 [24064/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.120548 [24192/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.114199 [24320/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.133059 [24448/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.115061 [24576/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.100265 [24704/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.115475 [24832/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.187644 [24872/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.187644 [24872/24872]: : 195it [00:14, 13.18it/s]
Epoch 3, time=279.82s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.127454 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.107924 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.114203 [ 384/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.110405 [ 512/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.103475 [ 640/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.099587 [ 768/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.133570 [ 896/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.140484 [ 1024/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.121729 [ 1152/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.114575 [ 1280/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.099326 [ 1408/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.122761 [ 1536/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.117960 [ 1664/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.118854 [ 1792/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.147213 [ 1920/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.125292 [ 2048/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.131598 [ 2176/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.141197 [ 2304/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.129700 [ 2432/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.137518 [ 2560/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.142412 [ 2688/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.107809 [ 2816/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.098904 [ 2944/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.151156 [ 3072/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.153996 [ 3200/24872]: 0%| | 0/194 [00:01<?, ?it/s]
loss: 0.106629 [ 3328/24872]: 0%| | 0/194 [00:01<?, ?it/s]
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loss: 0.132825 [ 3584/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.142968 [ 3712/24872]: 0%| | 0/194 [00:02<?, ?it/s]
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loss: 0.110758 [ 4096/24872]: 0%| | 0/194 [00:02<?, ?it/s]
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loss: 0.104076 [ 4480/24872]: 0%| | 0/194 [00:02<?, ?it/s]
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loss: 0.130072 [ 4864/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.096254 [ 4992/24872]: 0%| | 0/194 [00:02<?, ?it/s]
loss: 0.101905 [ 5120/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.137175 [ 5248/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.140795 [ 5376/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.098070 [ 5504/24872]: 0%| | 0/194 [00:03<?, ?it/s]
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loss: 0.094833 [ 5888/24872]: 0%| | 0/194 [00:03<?, ?it/s]
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loss: 0.082207 [ 6528/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.136927 [ 6656/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.128588 [ 6784/24872]: 0%| | 0/194 [00:03<?, ?it/s]
loss: 0.104015 [ 6912/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.097484 [ 7040/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.105629 [ 7168/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.107498 [ 7296/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.110320 [ 7424/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.089532 [ 7552/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.105729 [ 7680/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.111550 [ 7808/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.123767 [ 7936/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.122321 [ 8064/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.116285 [ 8192/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.112473 [ 8320/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.084700 [ 8448/24872]: 0%| | 0/194 [00:04<?, ?it/s]
loss: 0.128193 [ 8576/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.123579 [ 8704/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.101357 [ 8832/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.124029 [ 8960/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.089408 [ 9088/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.124369 [ 9216/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.085964 [ 9344/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.113511 [ 9472/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.103410 [ 9600/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.118705 [ 9728/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.127077 [ 9856/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.094056 [ 9984/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.112528 [10112/24872]: 0%| | 0/194 [00:05<?, ?it/s]
loss: 0.089075 [10240/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.136947 [10368/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.114284 [10496/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.126036 [10624/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.085711 [10752/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.082802 [10880/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.114153 [11008/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.134858 [11136/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.102602 [11264/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.119595 [11392/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.100625 [11520/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.103556 [11648/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.117981 [11776/24872]: 0%| | 0/194 [00:06<?, ?it/s]
loss: 0.084352 [11904/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.119906 [12032/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.120694 [12160/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.123186 [12288/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.097208 [12416/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.103291 [12544/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.090087 [12672/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.143706 [12800/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.156374 [12928/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.095012 [13056/24872]: 0%| | 0/194 [00:07<?, ?it/s]
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loss: 0.088939 [13312/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.098326 [13440/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.116886 [13568/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.102835 [13696/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.084601 [13824/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.131861 [13952/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.117380 [14080/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.092674 [14208/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.136264 [14336/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.137236 [14464/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.104778 [14592/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.120080 [14720/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.123572 [14848/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.111836 [14976/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.101390 [15104/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.084468 [15232/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.101551 [15360/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.119681 [15488/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.107952 [15616/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.122716 [15744/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.111638 [15872/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.132821 [16000/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.124991 [16128/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.138371 [16256/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.108789 [16384/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.105815 [16512/24872]: 0%| | 0/194 [00:09<?, ?it/s]
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loss: 0.103318 [16768/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.092949 [16896/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.108847 [17024/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.113051 [17152/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.127353 [17280/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.130167 [17408/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.088372 [17536/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.115943 [17664/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.141706 [17792/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.132049 [17920/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.092417 [18048/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.078811 [18176/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.133313 [18304/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.118456 [18432/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.091289 [18560/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.100692 [18688/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.122515 [18816/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.128696 [18944/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.123612 [19072/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.120009 [19200/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.115667 [19328/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.113877 [19456/24872]: 0%| | 0/194 [00:11<?, ?it/s]
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loss: 0.095416 [19712/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.093971 [19840/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.111484 [19968/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.134231 [20096/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.101494 [20224/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.123209 [20352/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.147810 [20480/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.099690 [20608/24872]: 0%| | 0/194 [00:12<?, ?it/s]
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loss: 0.108435 [21888/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.129035 [22016/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.110704 [22144/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.121451 [22272/24872]: 0%| | 0/194 [00:13<?, ?it/s]
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loss: 0.120357 [22528/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.122530 [22656/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.128165 [22784/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.122141 [22912/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.147010 [23040/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.134809 [23168/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.118553 [23296/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.122137 [23424/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.121055 [23552/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.103856 [23680/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.121974 [23808/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.120406 [23936/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.122261 [24064/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.120461 [24192/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.105582 [24320/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.122582 [24448/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.114443 [24576/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.094994 [24704/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.110063 [24832/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.162960 [24872/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.162960 [24872/24872]: : 195it [00:14, 13.30it/s]
Epoch 4, time=294.49s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.110463 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.105584 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.113670 [22656/24872]: 0%| | 0/194 [00:13<?, ?it/s]
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loss: 0.108110 [23296/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.118967 [23424/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.112195 [23552/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.096003 [23680/24872]: 0%| | 0/194 [00:14<?, ?it/s]
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loss: 0.144752 [24872/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.144752 [24872/24872]: : 195it [00:14, 13.21it/s]
Epoch 5, time=309.25s
0%| | 0/194 [00:00<?, ?it/s]
loss: 0.100315 [ 128/24872]: 0%| | 0/194 [00:00<?, ?it/s]
loss: 0.096467 [ 256/24872]: 0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.124772 [24872/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.124772 [24872/24872]: : 195it [00:14, 13.33it/s]
Epoch 6, time=323.88s
0%| | 0/194 [00:00<?, ?it/s]
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loss: 0.081969 [11904/24872]: 0%| | 0/194 [00:06<?, ?it/s]
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loss: 0.095736 [12160/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.119570 [12288/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.089514 [12416/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.101947 [12544/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.084746 [12672/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.127046 [12800/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.136411 [12928/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.100458 [13056/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.069464 [13184/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.104177 [13312/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.092611 [13440/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.114082 [13568/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.086004 [13696/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.074878 [13824/24872]: 0%| | 0/194 [00:07<?, ?it/s]
loss: 0.116946 [13952/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.109377 [14080/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.100364 [14208/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.135354 [14336/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.120729 [14464/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.099814 [14592/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.104518 [14720/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.102323 [14848/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.104081 [14976/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.086855 [15104/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.068980 [15232/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.082469 [15360/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.092838 [15488/24872]: 0%| | 0/194 [00:08<?, ?it/s]
loss: 0.093016 [15616/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.117154 [15744/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.094054 [15872/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.120533 [16000/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.106119 [16128/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.116145 [16256/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.099848 [16384/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.112625 [16512/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.092130 [16640/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.104050 [16768/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.088098 [16896/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.095386 [17024/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.097598 [17152/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.121560 [17280/24872]: 0%| | 0/194 [00:09<?, ?it/s]
loss: 0.103962 [17408/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.076838 [17536/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.105788 [17664/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.117811 [17792/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.122153 [17920/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.084114 [18048/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.066695 [18176/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.125007 [18304/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.091585 [18432/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.093625 [18560/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.091357 [18688/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.112079 [18816/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.119787 [18944/24872]: 0%| | 0/194 [00:10<?, ?it/s]
loss: 0.121412 [19072/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.111179 [19200/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.098577 [19328/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.113987 [19456/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.091505 [19584/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.079279 [19712/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.073405 [19840/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.100941 [19968/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.106398 [20096/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.103247 [20224/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.128538 [20352/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.141076 [20480/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.084928 [20608/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.095284 [20736/24872]: 0%| | 0/194 [00:11<?, ?it/s]
loss: 0.111475 [20864/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.104325 [20992/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.091193 [21120/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.110282 [21248/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.075233 [21376/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.106533 [21504/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.095245 [21632/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.123990 [21760/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.096269 [21888/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.130149 [22016/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.103049 [22144/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.102684 [22272/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.110286 [22400/24872]: 0%| | 0/194 [00:12<?, ?it/s]
loss: 0.100130 [22528/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.119766 [22656/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.127693 [22784/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.119007 [22912/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.130231 [23040/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.112852 [23168/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.096060 [23296/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.139485 [23424/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.109489 [23552/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.095780 [23680/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.094318 [23808/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.108704 [23936/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.105757 [24064/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.110573 [24192/24872]: 0%| | 0/194 [00:13<?, ?it/s]
loss: 0.113355 [24320/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.111766 [24448/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.105013 [24576/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.081332 [24704/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.096849 [24832/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.139450 [24872/24872]: 0%| | 0/194 [00:14<?, ?it/s]
loss: 0.139450 [24872/24872]: : 195it [00:14, 13.54it/s]
-------------------------------
LR=1e-05, batch_size=256
-------------------------------
Epoch 1, time=338.28s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.093246 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.095403 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.077396 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.110306 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.098825 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.089099 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.088410 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.102336 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.102642 [ 2304/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.099597 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.094959 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.100008 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.100530 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.100087 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.098867 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.094118 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.086679 [ 4352/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.093098 [ 4608/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.102306 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.081787 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.121646 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.095188 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.091741 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.098806 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.105582 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.087629 [ 6656/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.093413 [ 6912/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.079745 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.087189 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.083366 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.098129 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.098485 [ 8192/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.085324 [ 8448/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.104890 [ 8704/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.093260 [ 8960/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.087556 [ 9216/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.095259 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.088047 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.095193 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.081033 [10240/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.098194 [10496/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.086099 [10752/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.080787 [11008/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.108619 [11264/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.094372 [11520/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.094655 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.072060 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.099571 [12288/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.079788 [12544/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.094921 [12800/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.106443 [13056/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.069765 [13312/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.086552 [13568/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.071451 [13824/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.100383 [14080/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.101025 [14336/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.094490 [14592/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.090526 [14848/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.083783 [15104/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.068100 [15360/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.085527 [15616/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.096502 [15872/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.105394 [16128/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.092955 [16384/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.092187 [16640/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.083260 [16896/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.086154 [17152/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.104153 [17408/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.079289 [17664/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.111564 [17920/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.068870 [18176/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.097638 [18432/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.080310 [18688/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.104357 [18944/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.107523 [19200/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.094145 [19456/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.074020 [19712/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.072680 [19968/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.086833 [20224/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.116504 [20480/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.075384 [20736/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.096887 [20992/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.086657 [21248/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.076445 [21504/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.082927 [21760/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.092022 [22016/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.081189 [22272/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.092954 [22528/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.101148 [22784/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.110285 [23040/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.083493 [23296/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.101128 [23552/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.076803 [23808/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.092983 [24064/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.095587 [24320/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.093730 [24576/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.078168 [24832/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.111018 [24872/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.111018 [24872/24872]: : 98it [00:10, 9.10it/s]
Epoch 2, time=349.05s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.081773 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.084935 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.072697 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.100892 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.088550 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.085162 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.082653 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.096416 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.096993 [ 2304/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.094264 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.091900 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.095201 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.099384 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.093551 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.096738 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.091124 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.083353 [ 4352/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.088897 [ 4608/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.099826 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.077990 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.109934 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.090507 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.090140 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.096968 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.102796 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.084254 [ 6656/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.089231 [ 6912/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.076609 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.085326 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.081692 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.093930 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.097064 [ 8192/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.088516 [ 8448/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.101595 [ 8704/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.090388 [ 8960/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.083882 [ 9216/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.092616 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.084721 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.091982 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.079919 [10240/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.094600 [10496/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.084941 [10752/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.078594 [11008/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.103668 [11264/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.090911 [11520/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.091761 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.073481 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.097066 [12288/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.079707 [12544/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.093041 [12800/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.103344 [13056/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.073561 [13312/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.083032 [13568/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.070500 [13824/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.097100 [14080/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.098167 [14336/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.092353 [14592/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.087861 [14848/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.084190 [15104/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.065841 [15360/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.085030 [15616/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.092771 [15872/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.105051 [16128/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.090103 [16384/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.092786 [16640/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.079388 [16896/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.084696 [17152/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.102136 [17408/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.078569 [17664/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.109616 [17920/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.067808 [18176/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.096736 [18432/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.079910 [18688/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.104034 [18944/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.105627 [19200/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.092250 [19456/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.072201 [19712/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.072766 [19968/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.084983 [20224/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.114553 [20480/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.073938 [20736/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.096590 [20992/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.085335 [21248/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.074211 [21504/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.081502 [21760/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.090958 [22016/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.080468 [22272/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.091595 [22528/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.099271 [22784/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.109512 [23040/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.082638 [23296/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.100232 [23552/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.074942 [23808/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.091660 [24064/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.094227 [24320/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.092979 [24576/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.076757 [24832/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.107665 [24872/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.107665 [24872/24872]: : 98it [00:10, 9.20it/s]
Epoch 3, time=359.70s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.081535 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.083804 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.071962 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.100061 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.087641 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.084171 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.081502 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.095900 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.095609 [ 2304/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.092941 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.090780 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.092975 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.098579 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.092315 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.095029 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.089750 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.082477 [ 4352/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.087644 [ 4608/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.098522 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.077229 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.108390 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.089936 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.089186 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.096317 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.101679 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.082743 [ 6656/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.087111 [ 6912/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.075565 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.084020 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.080596 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.092142 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.095659 [ 8192/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.087555 [ 8448/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.100400 [ 8704/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.089562 [ 8960/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.082639 [ 9216/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.091041 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.083878 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.090270 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.079171 [10240/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.093683 [10496/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.084416 [10752/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.077772 [11008/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.102112 [11264/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.089163 [11520/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.090948 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.072801 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.096711 [12288/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.079108 [12544/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.092005 [12800/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.101917 [13056/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.073365 [13312/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.081628 [13568/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.069828 [13824/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.095954 [14080/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.097262 [14336/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.090736 [14592/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.086486 [14848/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.082993 [15104/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.064777 [15360/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.083627 [15616/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.091386 [15872/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.103803 [16128/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.088908 [16384/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.091903 [16640/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.078476 [16896/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.083701 [17152/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.101380 [17408/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.078278 [17664/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.108581 [17920/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.066948 [18176/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.095845 [18432/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.079232 [18688/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.102615 [18944/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.104397 [19200/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.091168 [19456/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.071242 [19712/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.072496 [19968/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.083991 [20224/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.113449 [20480/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.072797 [20736/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.095948 [20992/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.084366 [21248/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.072987 [21504/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.080919 [21760/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.090461 [22016/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.079811 [22272/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.090501 [22528/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.098395 [22784/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.108802 [23040/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.081808 [23296/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.099641 [23552/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.073628 [23808/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.090679 [24064/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.093272 [24320/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.092006 [24576/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.075784 [24832/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.105596 [24872/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.105596 [24872/24872]: : 98it [00:10, 9.16it/s]
Epoch 4, time=370.41s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.080823 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.082912 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.071619 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.098795 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.086884 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.083496 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.080685 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.095119 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.094816 [ 2304/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.092065 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.089973 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.091509 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.097909 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.091316 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.093824 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.088761 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.081723 [ 4352/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.086894 [ 4608/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.097497 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.076550 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.107417 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.089476 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.088424 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.095688 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.100698 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.081640 [ 6656/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.085701 [ 6912/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.074831 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.082999 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.079965 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.090851 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.094454 [ 8192/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.086742 [ 8448/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.099308 [ 8704/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.088856 [ 8960/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.081708 [ 9216/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.089805 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.083062 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.089107 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.078437 [10240/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.092912 [10496/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.083921 [10752/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.077041 [11008/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.100980 [11264/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.087946 [11520/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.090290 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.072051 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.096400 [12288/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.078486 [12544/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.091209 [12800/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.100863 [13056/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.072851 [13312/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.080695 [13568/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.069269 [13824/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.095017 [14080/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.096559 [14336/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.089565 [14592/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.085410 [14848/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.082031 [15104/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.064035 [15360/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.082536 [15616/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.089980 [15872/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.102642 [16128/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.087982 [16384/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.091072 [16640/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.077777 [16896/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.082832 [17152/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.100728 [17408/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.077938 [17664/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.107698 [17920/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.066177 [18176/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.095027 [18432/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.078535 [18688/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.101366 [18944/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.103506 [19200/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.090394 [19456/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.070520 [19712/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.072179 [19968/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.083196 [20224/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.112464 [20480/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.071855 [20736/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.095327 [20992/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.083139 [21248/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.072245 [21504/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.080300 [21760/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.089943 [22016/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.079159 [22272/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.089572 [22528/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.097742 [22784/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.108024 [23040/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.081005 [23296/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.099073 [23552/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.072694 [23808/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.089809 [24064/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.092429 [24320/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.091219 [24576/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.074965 [24832/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.103916 [24872/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.103916 [24872/24872]: : 98it [00:10, 9.19it/s]
Epoch 5, time=381.07s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.080048 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.081974 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.071051 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.097718 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.086225 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.082693 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.079635 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.094266 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.094154 [ 2304/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.091112 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.089120 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.090262 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.097440 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.090126 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.092622 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.087873 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.080819 [ 4352/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.085866 [ 4608/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.096633 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.075907 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.106556 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.088993 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.087647 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.095013 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.099803 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.080794 [ 6656/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.084640 [ 6912/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.074247 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.082201 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.079445 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.089848 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.093391 [ 8192/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.085995 [ 8448/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.098285 [ 8704/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.088277 [ 8960/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.080807 [ 9216/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.088740 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.082239 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.088140 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.077904 [10240/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.092231 [10496/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.083449 [10752/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.076414 [11008/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.099969 [11264/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.086906 [11520/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.089649 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.071506 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.096183 [12288/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.078016 [12544/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.090468 [12800/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.100036 [13056/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.072381 [13312/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.079947 [13568/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.068752 [13824/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.094263 [14080/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.095950 [14336/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.088448 [14592/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.084424 [14848/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.081295 [15104/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.063316 [15360/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.081676 [15616/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.088741 [15872/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.101596 [16128/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.087291 [16384/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.090336 [16640/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.077266 [16896/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.082098 [17152/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.100182 [17408/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.077597 [17664/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.107090 [17920/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.065672 [18176/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.094299 [18432/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.077956 [18688/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.100463 [18944/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.102748 [19200/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.089718 [19456/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.069943 [19712/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.071905 [19968/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.082536 [20224/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.111569 [20480/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.071138 [20736/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.094794 [20992/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.082571 [21248/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.071611 [21504/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.079793 [21760/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.089467 [22016/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.078618 [22272/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.088844 [22528/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.097197 [22784/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.107277 [23040/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.080328 [23296/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.098461 [23552/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.071920 [23808/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.089052 [24064/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.091577 [24320/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.090448 [24576/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.074290 [24832/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.102464 [24872/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.102464 [24872/24872]: : 98it [00:10, 9.20it/s]
Epoch 6, time=391.72s
0%| | 0/97 [00:00<?, ?it/s]
loss: 0.079305 [ 256/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.081145 [ 512/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.070559 [ 768/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.097067 [ 1024/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.085621 [ 1280/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.081999 [ 1536/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.078786 [ 1792/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.093513 [ 2048/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.093606 [ 2304/24872]: 0%| | 0/97 [00:00<?, ?it/s]
loss: 0.090366 [ 2560/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.088306 [ 2816/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.089146 [ 3072/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.097037 [ 3328/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.089082 [ 3584/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.091470 [ 3840/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.087060 [ 4096/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.080023 [ 4352/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.085012 [ 4608/24872]: 0%| | 0/97 [00:01<?, ?it/s]
loss: 0.095842 [ 4864/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.075338 [ 5120/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.105782 [ 5376/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.088537 [ 5632/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.086963 [ 5888/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.094393 [ 6144/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.098932 [ 6400/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.080049 [ 6656/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.083821 [ 6912/24872]: 0%| | 0/97 [00:02<?, ?it/s]
loss: 0.073752 [ 7168/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.081451 [ 7424/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.079013 [ 7680/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.089045 [ 7936/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.092371 [ 8192/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.085237 [ 8448/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.097353 [ 8704/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.087748 [ 8960/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.080081 [ 9216/24872]: 0%| | 0/97 [00:03<?, ?it/s]
loss: 0.087917 [ 9472/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.081514 [ 9728/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.087382 [ 9984/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.077421 [10240/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.091625 [10496/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.082925 [10752/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.075822 [11008/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.099131 [11264/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.086214 [11520/24872]: 0%| | 0/97 [00:04<?, ?it/s]
loss: 0.089061 [11776/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.070794 [12032/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.095763 [12288/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.077394 [12544/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.089741 [12800/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.099352 [13056/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.071813 [13312/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.079237 [13568/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.068202 [13824/24872]: 0%| | 0/97 [00:05<?, ?it/s]
loss: 0.093478 [14080/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.095339 [14336/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.087405 [14592/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.083576 [14848/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.080654 [15104/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.062654 [15360/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.080958 [15616/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.087790 [15872/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.100734 [16128/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.086683 [16384/24872]: 0%| | 0/97 [00:06<?, ?it/s]
loss: 0.089742 [16640/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.076707 [16896/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.081455 [17152/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.099535 [17408/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.077151 [17664/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.106418 [17920/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.065126 [18176/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.093560 [18432/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.077453 [18688/24872]: 0%| | 0/97 [00:07<?, ?it/s]
loss: 0.099754 [18944/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.102015 [19200/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.089064 [19456/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.069401 [19712/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.071635 [19968/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.081856 [20224/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.110678 [20480/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.070477 [20736/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.094229 [20992/24872]: 0%| | 0/97 [00:08<?, ?it/s]
loss: 0.080556 [21248/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.070920 [21504/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.079379 [21760/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.089015 [22016/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.078206 [22272/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.088392 [22528/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.096822 [22784/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.106387 [23040/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.079841 [23296/24872]: 0%| | 0/97 [00:09<?, ?it/s]
loss: 0.097949 [23552/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.071224 [23808/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.088309 [24064/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.090664 [24320/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.089607 [24576/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.073694 [24832/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.101283 [24872/24872]: 0%| | 0/97 [00:10<?, ?it/s]
loss: 0.101283 [24872/24872]: : 98it [00:10, 9.20it/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 49.999 seconds)
Estimated memory usage: 675 MB