In [1]:
# As usual, a bit of setup
import time, os, json
import numpy as np
import matplotlib.pyplot as plt
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.rnn_layers import *
from cs231n.captioning_solver import CaptioningSolver
from cs231n.classifiers.rnn import CaptioningRNN
from cs231n.coco_utils import load_coco_data, sample_coco_minibatch, decode_captions
from cs231n.image_utils import image_from_url
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
# for auto-reloading external modules
# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython
%load_ext autoreload
%autoreload 2
def rel_error(x, y):
""" returns relative error """
return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))
In [2]:
# Load COCO data from disk; this returns a dictionary
# We'll work with dimensionality-reduced features for this notebook, but feel
# free to experiment with the original features by changing the flag below.
data = load_coco_data(pca_features=True)
# Print out all the keys and values from the data dictionary
for k, v in data.iteritems():
if type(v) == np.ndarray:
print k, type(v), v.shape, v.dtype
else:
print k, type(v), len(v)
idx_to_word <type 'list'> 1004
train_captions <type 'numpy.ndarray'> (400135, 17) int32
val_captions <type 'numpy.ndarray'> (195954, 17) int32
train_image_idxs <type 'numpy.ndarray'> (400135,) int32
val_features <type 'numpy.ndarray'> (40504, 512) float32
val_image_idxs <type 'numpy.ndarray'> (195954,) int32
train_features <type 'numpy.ndarray'> (82783, 512) float32
train_urls <type 'numpy.ndarray'> (82783,) |S63
val_urls <type 'numpy.ndarray'> (40504,) |S63
word_to_idx <type 'dict'> 1004
If you read recent papers, you'll see that many people use a variant on the vanialla RNN called Long-Short Term Memory (LSTM) RNNs. Vanilla RNNs can be tough to train on long sequences due to vanishing and exploding gradiants caused by repeated matrix multiplication. LSTMs solve this problem by replacing the simple update rule of the vanilla RNN with a gating mechanism as follows.
Similar to the vanilla RNN, at each timestep we receive an input $x_t\in\mathbb{R}^D$ and the previous hidden state $h_{t-1}\in\mathbb{R}^H$; the LSTM also maintains an $H$-dimensional cell state, so we also receive the previous cell state $c_{t-1}\in\mathbb{R}^H$. The learnable parameters of the LSTM are an input-to-hidden matrix $W_x\in\mathbb{R}^{4H\times D}$, a hidden-to-hidden matrix $W_h\in\mathbb{R}^{4H\times H}$ and a bias vector $b\in\mathbb{R}^{4H}$.
At each timestep we first compute an activation vector $a\in\mathbb{R}^{4H}$ as $a=W_xx_t + W_hh_{t-1}+b$. We then divide this into four vectors $a_i,a_f,a_o,a_g\in\mathbb{R}^H$ where $a_i$ consists of the first $H$ elements of $a$, $a_f$ is the next $H$ elements of $a$, etc. We then compute the input gate $g\in\mathbb{R}^H$, forget gate $f\in\mathbb{R}^H$, output gate $o\in\mathbb{R}^H$ and block input $g\in\mathbb{R}^H$ as
$$ \begin{align*} i = \sigma(a_i) \hspace{2pc} f = \sigma(a_f) \hspace{2pc} o = \sigma(a_o) \hspace{2pc} g = \tanh(a_g) \end{align*} $$where $\sigma$ is the sigmoid function and $\tanh$ is the hyperbolic tangent, both applied elementwise.
Finally we compute the next cell state $c_t$ and next hidden state $h_t$ as
$$ c_{t} = f\odot c_{t-1} + i\odot g \hspace{4pc} h_t = o\odot\tanh(c_t) $$where $\odot$ is the elementwise product of vectors.
In the rest of the notebook we will implement the LSTM update rule and apply it to the image captioning task.
Implement the forward pass for a single timestep of an LSTM in the lstm_step_forward function in the file cs231n/rnn_layers.py. This should be similar to the rnn_step_forward function that you implemented above, but using the LSTM update rule instead.
Once you are done, run the following to perform a simple test of your implementation. You should see errors around 1e-8 or less.
In [8]:
N, D, H = 3, 4, 5
x = np.linspace(-0.4, 1.2, num=N*D).reshape(N, D)
prev_h = np.linspace(-0.3, 0.7, num=N*H).reshape(N, H)
prev_c = np.linspace(-0.4, 0.9, num=N*H).reshape(N, H)
Wx = np.linspace(-2.1, 1.3, num=4*D*H).reshape(D, 4 * H)
Wh = np.linspace(-0.7, 2.2, num=4*H*H).reshape(H, 4 * H)
b = np.linspace(0.3, 0.7, num=4*H)
next_h, next_c, cache = lstm_step_forward(x, prev_h, prev_c, Wx, Wh, b)
expected_next_h = np.asarray([
[ 0.24635157, 0.28610883, 0.32240467, 0.35525807, 0.38474904],
[ 0.49223563, 0.55611431, 0.61507696, 0.66844003, 0.7159181 ],
[ 0.56735664, 0.66310127, 0.74419266, 0.80889665, 0.858299 ]])
expected_next_c = np.asarray([
[ 0.32986176, 0.39145139, 0.451556, 0.51014116, 0.56717407],
[ 0.66382255, 0.76674007, 0.87195994, 0.97902709, 1.08751345],
[ 0.74192008, 0.90592151, 1.07717006, 1.25120233, 1.42395676]])
print 'next_h error: ', rel_error(expected_next_h, next_h)
print 'next_c error: ', rel_error(expected_next_c, next_c)
next_h error: 5.70541304045e-09
next_c error: 5.81431230888e-09
In [10]:
N, D, H = 4, 5, 6
x = np.random.randn(N, D)
prev_h = np.random.randn(N, H)
prev_c = np.random.randn(N, H)
Wx = np.random.randn(D, 4 * H)
Wh = np.random.randn(H, 4 * H)
b = np.random.randn(4 * H)
next_h, next_c, cache = lstm_step_forward(x, prev_h, prev_c, Wx, Wh, b)
dnext_h = np.random.randn(*next_h.shape)
dnext_c = np.random.randn(*next_c.shape)
fx_h = lambda x: lstm_step_forward(x, prev_h, prev_c, Wx, Wh, b)[0]
fh_h = lambda h: lstm_step_forward(x, prev_h, prev_c, Wx, Wh, b)[0]
fc_h = lambda c: lstm_step_forward(x, prev_h, prev_c, Wx, Wh, b)[0]
fWx_h = lambda Wx: lstm_step_forward(x, prev_h, prev_c, Wx, Wh, b)[0]
fWh_h = lambda Wh: lstm_step_forward(x, prev_h, prev_c, Wx, Wh, b)[0]
fb_h = lambda b: lstm_step_forward(x, prev_h, prev_c, Wx, Wh, b)[0]
fx_c = lambda x: lstm_step_forward(x, prev_h, prev_c, Wx, Wh, b)[1]
fh_c = lambda h: lstm_step_forward(x, prev_h, prev_c, Wx, Wh, b)[1]
fc_c = lambda c: lstm_step_forward(x, prev_h, prev_c, Wx, Wh, b)[1]
fWx_c = lambda Wx: lstm_step_forward(x, prev_h, prev_c, Wx, Wh, b)[1]
fWh_c = lambda Wh: lstm_step_forward(x, prev_h, prev_c, Wx, Wh, b)[1]
fb_c = lambda b: lstm_step_forward(x, prev_h, prev_c, Wx, Wh, b)[1]
num_grad = eval_numerical_gradient_array
dx_num = num_grad(fx_h, x, dnext_h) + num_grad(fx_c, x, dnext_c)
dh_num = num_grad(fh_h, prev_h, dnext_h) + num_grad(fh_c, prev_h, dnext_c)
dc_num = num_grad(fc_h, prev_c, dnext_h) + num_grad(fc_c, prev_c, dnext_c)
dWx_num = num_grad(fWx_h, Wx, dnext_h) + num_grad(fWx_c, Wx, dnext_c)
dWh_num = num_grad(fWh_h, Wh, dnext_h) + num_grad(fWh_c, Wh, dnext_c)
db_num = num_grad(fb_h, b, dnext_h) + num_grad(fb_c, b, dnext_c)
dx, dh, dc, dWx, dWh, db = lstm_step_backward(dnext_h, dnext_c, cache)
print 'dx error: ', rel_error(dx_num, dx)
print 'dh error: ', rel_error(dh_num, dh)
print 'dc error: ', rel_error(dc_num, dc)
print 'dWx error: ', rel_error(dWx_num, dWx)
print 'dWh error: ', rel_error(dWh_num, dWh)
print 'db error: ', rel_error(db_num, db)
dx error: 4.45839572389e-10
dh error: 1.38987651018e-10
dc error: 3.74918879842e-11
dWx error: 1.65772018958e-09
dWh error: 2.22204898907e-09
db error: 8.19363645669e-09
In [12]:
N, D, H, T = 2, 5, 4, 3
x = np.linspace(-0.4, 0.6, num=N*T*D).reshape(N, T, D)
h0 = np.linspace(-0.4, 0.8, num=N*H).reshape(N, H)
Wx = np.linspace(-0.2, 0.9, num=4*D*H).reshape(D, 4 * H)
Wh = np.linspace(-0.3, 0.6, num=4*H*H).reshape(H, 4 * H)
b = np.linspace(0.2, 0.7, num=4*H)
h, cache = lstm_forward(x, h0, Wx, Wh, b)
expected_h = np.asarray([
[[ 0.01764008, 0.01823233, 0.01882671, 0.0194232 ],
[ 0.11287491, 0.12146228, 0.13018446, 0.13902939],
[ 0.31358768, 0.33338627, 0.35304453, 0.37250975]],
[[ 0.45767879, 0.4761092, 0.4936887, 0.51041945],
[ 0.6704845, 0.69350089, 0.71486014, 0.7346449 ],
[ 0.81733511, 0.83677871, 0.85403753, 0.86935314]]])
print 'h error: ', rel_error(expected_h, h)
h error: 8.61053745211e-08
In [28]:
from cs231n.rnn_layers import lstm_forward, lstm_backward
N, D, T, H = 2, 3, 10, 6
x = np.random.randn(N, T, D)
h0 = np.random.randn(N, H)
Wx = np.random.randn(D, 4 * H)
Wh = np.random.randn(H, 4 * H)
b = np.random.randn(4 * H)
out, cache = lstm_forward(x, h0, Wx, Wh, b)
dout = np.random.randn(*out.shape)
dx, dh0, dWx, dWh, db = lstm_backward(dout, cache)
fx = lambda x: lstm_forward(x, h0, Wx, Wh, b)[0]
fh0 = lambda h0: lstm_forward(x, h0, Wx, Wh, b)[0]
fWx = lambda Wx: lstm_forward(x, h0, Wx, Wh, b)[0]
fWh = lambda Wh: lstm_forward(x, h0, Wx, Wh, b)[0]
fb = lambda b: lstm_forward(x, h0, Wx, Wh, b)[0]
dx_num = eval_numerical_gradient_array(fx, x, dout)
dh0_num = eval_numerical_gradient_array(fh0, h0, dout)
dWx_num = eval_numerical_gradient_array(fWx, Wx, dout)
dWh_num = eval_numerical_gradient_array(fWh, Wh, dout)
db_num = eval_numerical_gradient_array(fb, b, dout)
print 'dx error: ', rel_error(dx_num, dx)
print 'dh0 error: ', rel_error(dh0_num, dh0)
print 'dWx error: ', rel_error(dWx_num, dWx)
print 'dWh error: ', rel_error(dWh_num, dWh)
print 'db error: ', rel_error(db_num, db)
dx error: 1.30950486388e-09
dh0 error: 2.94495469347e-09
dWx error: 9.07014083141e-10
dWh error: 7.85200111904e-08
db error: 8.94957204976e-10
Now that you have implemented an LSTM, update the implementation of the loss method of the CaptioningRNN class in the file cs231n/classifiers/rnn.py to handle the case where self.cell_type is lstm. This should require adding less than 10 lines of code.
Once you have done so, run the following to check your implementation. You should see a difference of less than 1e-10.
In [30]:
N, D, W, H = 10, 20, 30, 40
word_to_idx = {'<NULL>': 0, 'cat': 2, 'dog': 3}
V = len(word_to_idx)
T = 13
model = CaptioningRNN(word_to_idx,
input_dim=D,
wordvec_dim=W,
hidden_dim=H,
cell_type='lstm',
dtype=np.float64)
# Set all model parameters to fixed values
for k, v in model.params.iteritems():
model.params[k] = np.linspace(-1.4, 1.3, num=v.size).reshape(*v.shape)
features = np.linspace(-0.5, 1.7, num=N*D).reshape(N, D)
captions = (np.arange(N * T) % V).reshape(N, T)
loss, grads = model.loss(features, captions)
expected_loss = 9.82445935443
print 'loss: ', loss
print 'expected loss: ', expected_loss
print 'difference: ', abs(loss - expected_loss)
loss: 9.82445935443
expected loss: 9.82445935443
difference: 2.26840768391e-12
In [31]:
small_data = load_coco_data(max_train=50)
small_lstm_model = CaptioningRNN(
cell_type='lstm',
word_to_idx=data['word_to_idx'],
input_dim=data['train_features'].shape[1],
hidden_dim=512,
wordvec_dim=256,
dtype=np.float32,
)
small_lstm_solver = CaptioningSolver(small_lstm_model, small_data,
update_rule='adam',
num_epochs=50,
batch_size=25,
optim_config={
'learning_rate': 5e-3,
},
lr_decay=0.995,
verbose=True, print_every=10,
)
small_lstm_solver.train()
# Plot the training losses
plt.plot(small_lstm_solver.loss_history)
plt.xlabel('Iteration')
plt.ylabel('Loss')
plt.title('Training loss history')
plt.show()
(Iteration 1 / 100) loss: 72.956343
(Iteration 11 / 100) loss: 40.014447
(Iteration 21 / 100) loss: 21.908289
(Iteration 31 / 100) loss: 11.110767
(Iteration 41 / 100) loss: 4.216231
(Iteration 51 / 100) loss: 1.805185
(Iteration 61 / 100) loss: 0.883786
(Iteration 71 / 100) loss: 0.340748
(Iteration 81 / 100) loss: 0.154977
(Iteration 91 / 100) loss: 0.089899
In [35]:
for split in ['train', 'val']:
minibatch = sample_coco_minibatch(small_data, split=split, batch_size=2)
gt_captions, features, urls = minibatch
gt_captions = decode_captions(gt_captions, data['idx_to_word'])
sample_captions = small_lstm_model.sample(features)
sample_captions = decode_captions(sample_captions, data['idx_to_word'])
for gt_caption, sample_caption, url in zip(gt_captions, sample_captions, urls):
plt.imshow(image_from_url(url))
plt.title('%s\n%s\nGT:%s' % (split, sample_caption, gt_caption))
plt.axis('off')
plt.show()
Using the pieces you have implemented in this and the previous notebook, try to train a captioning model that gives decent qualitative results (better than the random garbage you saw with the overfit models) when sampling on the validation set. You can subsample the training set if you want; we just want to see samples on the validatation set that are better than random.
Don't spend too much time on this part; we don't have any explicit accuracy thresholds you need to meet.
In [59]:
import sys
#_ORG_STDOUT = sys.stdout
#_ORG_STDERR = sys.stderr
class captoutput:
def __init__(self):
self.log = open("log.txt", "w")
self.term = _ORG_STDOUT
self.terr = _ORG_STDERR
def __enter__(self):
sys.stdout = self
sys.stderr = self
return self
def __exit__(self):
sys.stdout = _ORG_STDOUT
sys.stderr = _ORG_STDERR
self.close()
def write(self, msg):
self.term.write(msg)
self.log.write(msg)
self.log.flush()
def close(self):
if self.log:
self.log.close()
def read(self):
with open("log.txt") as f:
txt = f.read()
return txt
#sys.stdout = captout()
#small_data = load_coco_data(max_train=50000)
#small_lstm_model = CaptioningRNN(
# cell_type='lstm',
# word_to_idx=data['word_to_idx'],
# input_dim=data['train_features'].shape[1],
# hidden_dim=1024,
# wordvec_dim=512,
# dtype=np.float32,
# )
#small_lstm_solver = CaptioningSolver(small_lstm_model, small_data,
# update_rule='adam',
# num_epochs=50,
# batch_size=50,
# optim_config={
# 'learning_rate': 5e-3,
# },
# lr_decay=0.9,
# verbose=True, print_every=10,
# )
#small_lstm_solver.train()
#since i was running the code on inek's i needed to close the browser therefore i saved output to a log file, i used the above
#parameters and code to train the model
f = open("log.txt", "r").read()
print f
(Iteration 1 / 50000) loss: 30.926130
(Iteration 11 / 50000) loss: 29.070883
(Iteration 21 / 50000) loss: 31.601230
(Iteration 31 / 50000) loss: 29.873266
(Iteration 41 / 50000) loss: 27.608146
(Iteration 51 / 50000) loss: 30.443934
(Iteration 61 / 50000) loss: 29.725397
(Iteration 71 / 50000) loss: 28.025682
(Iteration 81 / 50000) loss: 26.722375
(Iteration 91 / 50000) loss: 29.231495
(Iteration 101 / 50000) loss: 30.085486
(Iteration 111 / 50000) loss: 25.889995
(Iteration 121 / 50000) loss: 31.518549
(Iteration 131 / 50000) loss: 31.410766
(Iteration 141 / 50000) loss: 30.248458
(Iteration 151 / 50000) loss: 28.827061
(Iteration 161 / 50000) loss: 28.923514
(Iteration 171 / 50000) loss: 29.634317
(Iteration 181 / 50000) loss: 26.052727
(Iteration 191 / 50000) loss: 25.890425
(Iteration 201 / 50000) loss: 29.870180
(Iteration 211 / 50000) loss: 30.890177
(Iteration 221 / 50000) loss: 26.539171
(Iteration 231 / 50000) loss: 25.099992
(Iteration 241 / 50000) loss: 26.520576
(Iteration 251 / 50000) loss: 29.033133
(Iteration 261 / 50000) loss: 32.032609
(Iteration 271 / 50000) loss: 28.158833
(Iteration 281 / 50000) loss: 25.275156
(Iteration 291 / 50000) loss: 30.612113
(Iteration 301 / 50000) loss: 26.956531
(Iteration 311 / 50000) loss: 26.891888
(Iteration 321 / 50000) loss: 28.420599
(Iteration 331 / 50000) loss: 25.966178
(Iteration 341 / 50000) loss: 28.184189
(Iteration 351 / 50000) loss: 28.036208
(Iteration 361 / 50000) loss: 28.988733
(Iteration 371 / 50000) loss: 28.628232
(Iteration 381 / 50000) loss: 26.383788
(Iteration 391 / 50000) loss: 29.830352
(Iteration 401 / 50000) loss: 28.160849
(Iteration 411 / 50000) loss: 29.472551
(Iteration 421 / 50000) loss: 31.039493
(Iteration 431 / 50000) loss: 26.597537
(Iteration 441 / 50000) loss: 28.445439
(Iteration 451 / 50000) loss: 28.499469
(Iteration 461 / 50000) loss: 28.026059
(Iteration 471 / 50000) loss: 29.247035
(Iteration 481 / 50000) loss: 27.027281
(Iteration 491 / 50000) loss: 28.537066
(Iteration 501 / 50000) loss: 28.267088
(Iteration 511 / 50000) loss: 27.308803
(Iteration 521 / 50000) loss: 28.137453
(Iteration 531 / 50000) loss: 26.030754
(Iteration 541 / 50000) loss: 28.392395
(Iteration 551 / 50000) loss: 28.725845
(Iteration 561 / 50000) loss: 30.046796
(Iteration 571 / 50000) loss: 27.490267
(Iteration 581 / 50000) loss: 26.665169
(Iteration 591 / 50000) loss: 26.497946
(Iteration 601 / 50000) loss: 27.382327
(Iteration 611 / 50000) loss: 24.913609
(Iteration 621 / 50000) loss: 24.369487
(Iteration 631 / 50000) loss: 28.561450
(Iteration 641 / 50000) loss: 26.873792
(Iteration 651 / 50000) loss: 27.210855
(Iteration 661 / 50000) loss: 27.908226
(Iteration 671 / 50000) loss: 30.072569
(Iteration 681 / 50000) loss: 27.672405
(Iteration 691 / 50000) loss: 29.239565
(Iteration 701 / 50000) loss: 26.657840
(Iteration 711 / 50000) loss: 27.731789
(Iteration 721 / 50000) loss: 28.808451
(Iteration 731 / 50000) loss: 29.337360
(Iteration 741 / 50000) loss: 27.986443
(Iteration 751 / 50000) loss: 25.670206
(Iteration 761 / 50000) loss: 26.954642
(Iteration 771 / 50000) loss: 27.588657
(Iteration 781 / 50000) loss: 28.300060
(Iteration 791 / 50000) loss: 25.665245
(Iteration 801 / 50000) loss: 27.945385
(Iteration 811 / 50000) loss: 26.916274
(Iteration 821 / 50000) loss: 24.603606
(Iteration 831 / 50000) loss: 25.905304
(Iteration 841 / 50000) loss: 26.024455
(Iteration 851 / 50000) loss: 27.914536
(Iteration 861 / 50000) loss: 25.036607
(Iteration 871 / 50000) loss: 26.765515
(Iteration 881 / 50000) loss: 27.184556
(Iteration 891 / 50000) loss: 27.591900
(Iteration 901 / 50000) loss: 27.441229
(Iteration 911 / 50000) loss: 27.533784
(Iteration 921 / 50000) loss: 25.674196
(Iteration 931 / 50000) loss: 27.915254
(Iteration 941 / 50000) loss: 25.970476
(Iteration 951 / 50000) loss: 28.177879
(Iteration 961 / 50000) loss: 25.382486
(Iteration 971 / 50000) loss: 26.630886
(Iteration 981 / 50000) loss: 25.006498
(Iteration 991 / 50000) loss: 27.183955
(Iteration 1001 / 50000) loss: 26.024022
(Iteration 1011 / 50000) loss: 26.643973
(Iteration 1021 / 50000) loss: 27.984407
(Iteration 1031 / 50000) loss: 25.092191
(Iteration 1041 / 50000) loss: 28.229451
(Iteration 1051 / 50000) loss: 25.982922
(Iteration 1061 / 50000) loss: 25.856620
(Iteration 1071 / 50000) loss: 26.886829
(Iteration 1081 / 50000) loss: 28.565694
(Iteration 1091 / 50000) loss: 25.785249
(Iteration 1101 / 50000) loss: 26.577723
(Iteration 1111 / 50000) loss: 26.714301
(Iteration 1121 / 50000) loss: 26.308523
(Iteration 1131 / 50000) loss: 24.979574
(Iteration 1141 / 50000) loss: 26.756139
(Iteration 1151 / 50000) loss: 27.159430
(Iteration 1161 / 50000) loss: 27.680463
(Iteration 1171 / 50000) loss: 28.321832
(Iteration 1181 / 50000) loss: 28.386754
(Iteration 1191 / 50000) loss: 26.758713
(Iteration 1201 / 50000) loss: 24.822881
(Iteration 1211 / 50000) loss: 26.673262
(Iteration 1221 / 50000) loss: 27.468768
(Iteration 1231 / 50000) loss: 27.799777
(Iteration 1241 / 50000) loss: 24.179218
(Iteration 1251 / 50000) loss: 27.470215
(Iteration 1261 / 50000) loss: 25.755874
(Iteration 1271 / 50000) loss: 26.982653
(Iteration 1281 / 50000) loss: 24.013282
(Iteration 1291 / 50000) loss: 26.606301
(Iteration 1301 / 50000) loss: 25.415308
(Iteration 1311 / 50000) loss: 26.289749
(Iteration 1321 / 50000) loss: 27.766659
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(Iteration 1411 / 50000) loss: 25.867241
(Iteration 1421 / 50000) loss: 25.458545
(Iteration 1431 / 50000) loss: 24.838994
(Iteration 1441 / 50000) loss: 24.477856
(Iteration 1451 / 50000) loss: 25.537385
(Iteration 1461 / 50000) loss: 27.251884
(Iteration 1471 / 50000) loss: 24.997199
(Iteration 1481 / 50000) loss: 27.669329
(Iteration 1491 / 50000) loss: 26.619078
(Iteration 1501 / 50000) loss: 25.738802
(Iteration 1511 / 50000) loss: 24.673971
(Iteration 1521 / 50000) loss: 24.857616
(Iteration 1531 / 50000) loss: 24.361794
(Iteration 1541 / 50000) loss: 25.411605
(Iteration 1551 / 50000) loss: 26.095347
(Iteration 1561 / 50000) loss: 23.375312
(Iteration 1571 / 50000) loss: 24.075270
(Iteration 1581 / 50000) loss: 24.527163
(Iteration 1591 / 50000) loss: 24.456808
(Iteration 1601 / 50000) loss: 24.247041
(Iteration 1611 / 50000) loss: 28.687336
(Iteration 1621 / 50000) loss: 24.767607
(Iteration 1631 / 50000) loss: 28.151576
(Iteration 1641 / 50000) loss: 27.305339
(Iteration 1651 / 50000) loss: 22.927375
(Iteration 1661 / 50000) loss: 27.530474
(Iteration 1671 / 50000) loss: 24.480313
(Iteration 1681 / 50000) loss: 26.066825
(Iteration 1691 / 50000) loss: 23.923833
(Iteration 1701 / 50000) loss: 24.993270
(Iteration 1711 / 50000) loss: 24.477478
(Iteration 1721 / 50000) loss: 25.712409
(Iteration 1731 / 50000) loss: 24.306951
(Iteration 1741 / 50000) loss: 24.279497
(Iteration 1751 / 50000) loss: 24.842832
(Iteration 1761 / 50000) loss: 25.763138
(Iteration 1771 / 50000) loss: 26.093069
(Iteration 1781 / 50000) loss: 23.821324
(Iteration 1791 / 50000) loss: 25.322239
(Iteration 1801 / 50000) loss: 25.254542
(Iteration 1811 / 50000) loss: 22.329390
(Iteration 1821 / 50000) loss: 24.645844
(Iteration 1831 / 50000) loss: 24.832529
(Iteration 1841 / 50000) loss: 27.098495
(Iteration 1851 / 50000) loss: 24.578657
(Iteration 1861 / 50000) loss: 27.554647
(Iteration 1871 / 50000) loss: 25.025150
(Iteration 1881 / 50000) loss: 26.359313
(Iteration 1891 / 50000) loss: 27.151641
(Iteration 1901 / 50000) loss: 25.834320
(Iteration 1911 / 50000) loss: 24.473711
(Iteration 1921 / 50000) loss: 26.830604
(Iteration 1931 / 50000) loss: 25.845421
(Iteration 1941 / 50000) loss: 23.343691
(Iteration 1951 / 50000) loss: 24.279959
(Iteration 1961 / 50000) loss: 25.305406
(Iteration 1971 / 50000) loss: 27.338967
(Iteration 1981 / 50000) loss: 24.652411
(Iteration 1991 / 50000) loss: 26.458899
(Iteration 2001 / 50000) loss: 25.585148
(Iteration 2011 / 50000) loss: 25.224274
(Iteration 2021 / 50000) loss: 24.797197
(Iteration 2031 / 50000) loss: 24.398065
(Iteration 2041 / 50000) loss: 26.874232
(Iteration 2051 / 50000) loss: 26.659299
(Iteration 2061 / 50000) loss: 23.651646
(Iteration 2071 / 50000) loss: 26.124512
(Iteration 2081 / 50000) loss: 24.804420
(Iteration 2091 / 50000) loss: 25.319467
(Iteration 2101 / 50000) loss: 23.849383
(Iteration 2111 / 50000) loss: 26.783550
(Iteration 2121 / 50000) loss: 26.316597
(Iteration 2131 / 50000) loss: 22.223642
(Iteration 2141 / 50000) loss: 24.126290
(Iteration 2151 / 50000) loss: 25.360958
(Iteration 2161 / 50000) loss: 23.360438
(Iteration 2171 / 50000) loss: 24.314161
(Iteration 2181 / 50000) loss: 25.381773
(Iteration 2191 / 50000) loss: 25.409461
(Iteration 2201 / 50000) loss: 26.419749
(Iteration 2211 / 50000) loss: 23.301151
(Iteration 2221 / 50000) loss: 22.845281
(Iteration 2231 / 50000) loss: 26.860357
(Iteration 2241 / 50000) loss: 22.708815
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(Iteration 2271 / 50000) loss: 27.078018
(Iteration 2281 / 50000) loss: 23.728591
(Iteration 2291 / 50000) loss: 25.745536
(Iteration 2301 / 50000) loss: 23.837006
(Iteration 2311 / 50000) loss: 25.076898
(Iteration 2321 / 50000) loss: 25.812691
(Iteration 2331 / 50000) loss: 23.390736
(Iteration 2341 / 50000) loss: 25.914371
(Iteration 2351 / 50000) loss: 23.761057
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(Iteration 2371 / 50000) loss: 23.646581
(Iteration 2381 / 50000) loss: 23.945801
(Iteration 2391 / 50000) loss: 22.543917
(Iteration 2401 / 50000) loss: 26.254549
(Iteration 2411 / 50000) loss: 22.362316
(Iteration 2421 / 50000) loss: 24.884465
(Iteration 2431 / 50000) loss: 24.279306
(Iteration 2441 / 50000) loss: 24.574530
(Iteration 2451 / 50000) loss: 23.463663
(Iteration 2461 / 50000) loss: 22.730254
(Iteration 2471 / 50000) loss: 23.302722
(Iteration 2481 / 50000) loss: 24.171579
(Iteration 2491 / 50000) loss: 24.601159
(Iteration 2501 / 50000) loss: 24.165054
(Iteration 2511 / 50000) loss: 23.695381
(Iteration 2521 / 50000) loss: 24.982344
(Iteration 2531 / 50000) loss: 24.425312
(Iteration 2541 / 50000) loss: 26.731578
(Iteration 2551 / 50000) loss: 23.954384
(Iteration 2561 / 50000) loss: 24.427643
(Iteration 2571 / 50000) loss: 24.588089
(Iteration 2581 / 50000) loss: 23.936148
(Iteration 2591 / 50000) loss: 22.354957
(Iteration 2601 / 50000) loss: 24.542692
(Iteration 2611 / 50000) loss: 25.578933
(Iteration 2621 / 50000) loss: 24.296249
(Iteration 2631 / 50000) loss: 23.703376
(Iteration 2641 / 50000) loss: 26.210923
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(Iteration 2711 / 50000) loss: 24.762456
(Iteration 2721 / 50000) loss: 22.780127
(Iteration 2731 / 50000) loss: 23.651029
(Iteration 2741 / 50000) loss: 25.895968
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(Iteration 2761 / 50000) loss: 24.886435
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(Iteration 2801 / 50000) loss: 22.082031
(Iteration 2811 / 50000) loss: 24.194906
(Iteration 2821 / 50000) loss: 23.336834
(Iteration 2831 / 50000) loss: 23.891726
(Iteration 2841 / 50000) loss: 27.685216
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(Iteration 2911 / 50000) loss: 22.805850
(Iteration 2921 / 50000) loss: 23.912635
(Iteration 2931 / 50000) loss: 21.466595
(Iteration 2941 / 50000) loss: 24.921710
(Iteration 2951 / 50000) loss: 23.675006
(Iteration 2961 / 50000) loss: 24.766163
(Iteration 2971 / 50000) loss: 24.936274
(Iteration 2981 / 50000) loss: 23.098093
(Iteration 2991 / 50000) loss: 24.959830
(Iteration 3001 / 50000) loss: 23.327401
(Iteration 3011 / 50000) loss: 22.307848
(Iteration 3021 / 50000) loss: 22.051121
(Iteration 3031 / 50000) loss: 22.449622
(Iteration 3041 / 50000) loss: 21.915756
(Iteration 3051 / 50000) loss: 24.541494
(Iteration 3061 / 50000) loss: 23.232721
(Iteration 3071 / 50000) loss: 25.892722
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(Iteration 3091 / 50000) loss: 25.303866
(Iteration 3101 / 50000) loss: 22.044903
(Iteration 3111 / 50000) loss: 26.075894
(Iteration 3121 / 50000) loss: 22.790129
(Iteration 3131 / 50000) loss: 21.991797
(Iteration 3141 / 50000) loss: 21.137556
(Iteration 3151 / 50000) loss: 23.080023
(Iteration 3161 / 50000) loss: 21.932215
(Iteration 3171 / 50000) loss: 25.339084
(Iteration 3181 / 50000) loss: 22.291852
(Iteration 3191 / 50000) loss: 23.349550
(Iteration 3201 / 50000) loss: 24.089402
(Iteration 3211 / 50000) loss: 22.934421
(Iteration 3221 / 50000) loss: 24.127132
(Iteration 3231 / 50000) loss: 23.999292
(Iteration 3241 / 50000) loss: 22.552732
(Iteration 3251 / 50000) loss: 25.116679
(Iteration 3261 / 50000) loss: 24.207387
(Iteration 3271 / 50000) loss: 23.830072
(Iteration 3281 / 50000) loss: 22.193077
(Iteration 3291 / 50000) loss: 23.385983
(Iteration 3301 / 50000) loss: 23.764899
(Iteration 3311 / 50000) loss: 23.861217
(Iteration 3321 / 50000) loss: 21.944560
(Iteration 3331 / 50000) loss: 23.885232
(Iteration 3341 / 50000) loss: 23.969034
(Iteration 3351 / 50000) loss: 23.810024
(Iteration 3361 / 50000) loss: 21.777030
(Iteration 3371 / 50000) loss: 22.824446
(Iteration 3381 / 50000) loss: 24.401014
(Iteration 3391 / 50000) loss: 23.145499
(Iteration 3401 / 50000) loss: 21.841794
(Iteration 3411 / 50000) loss: 26.163799
(Iteration 3421 / 50000) loss: 22.254940
(Iteration 3431 / 50000) loss: 21.864860
(Iteration 3441 / 50000) loss: 23.768853
(Iteration 3451 / 50000) loss: 24.968298
(Iteration 3461 / 50000) loss: 24.056934
(Iteration 3471 / 50000) loss: 22.874120
(Iteration 3481 / 50000) loss: 22.894880
(Iteration 3491 / 50000) loss: 23.559703
(Iteration 3501 / 50000) loss: 23.033933
(Iteration 3511 / 50000) loss: 23.657632
(Iteration 3521 / 50000) loss: 23.856937
(Iteration 3531 / 50000) loss: 23.384962
(Iteration 3541 / 50000) loss: 21.987550
(Iteration 3551 / 50000) loss: 22.710152
(Iteration 3561 / 50000) loss: 23.430265
(Iteration 3571 / 50000) loss: 23.663510
(Iteration 3581 / 50000) loss: 24.506699
(Iteration 3591 / 50000) loss: 23.738003
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(Iteration 3611 / 50000) loss: 24.530472
(Iteration 3621 / 50000) loss: 22.202798
(Iteration 3631 / 50000) loss: 23.368526
(Iteration 3641 / 50000) loss: 23.624008
(Iteration 3651 / 50000) loss: 24.721385
(Iteration 3661 / 50000) loss: 23.869856
(Iteration 3671 / 50000) loss: 23.239123
(Iteration 3681 / 50000) loss: 23.872734
(Iteration 3691 / 50000) loss: 23.989828
(Iteration 3701 / 50000) loss: 23.775158
(Iteration 3711 / 50000) loss: 22.803934
(Iteration 3721 / 50000) loss: 23.450899
(Iteration 3731 / 50000) loss: 23.090121
(Iteration 3741 / 50000) loss: 23.528472
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(Iteration 3811 / 50000) loss: 21.290426
(Iteration 3821 / 50000) loss: 20.935243
(Iteration 3831 / 50000) loss: 23.536577
(Iteration 3841 / 50000) loss: 22.614619
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(Iteration 3921 / 50000) loss: 23.821671
(Iteration 3931 / 50000) loss: 24.241799
(Iteration 3941 / 50000) loss: 24.474328
(Iteration 3951 / 50000) loss: 21.721901
(Iteration 3961 / 50000) loss: 21.267580
(Iteration 3971 / 50000) loss: 22.757693
(Iteration 3981 / 50000) loss: 21.907911
(Iteration 3991 / 50000) loss: 23.688277
(Iteration 4001 / 50000) loss: 23.423222
(Iteration 4011 / 50000) loss: 21.877247
(Iteration 4021 / 50000) loss: 25.059861
(Iteration 4031 / 50000) loss: 22.185143
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(Iteration 4111 / 50000) loss: 22.746074
(Iteration 4121 / 50000) loss: 22.987963
(Iteration 4131 / 50000) loss: 20.781571
(Iteration 4141 / 50000) loss: 20.549921
(Iteration 4151 / 50000) loss: 21.667560
(Iteration 4161 / 50000) loss: 21.244800
(Iteration 4171 / 50000) loss: 20.716015
(Iteration 4181 / 50000) loss: 21.321830
(Iteration 4191 / 50000) loss: 24.955916
(Iteration 4201 / 50000) loss: 22.125517
(Iteration 4211 / 50000) loss: 23.026855
(Iteration 4221 / 50000) loss: 23.666508
(Iteration 4231 / 50000) loss: 23.258541
(Iteration 4241 / 50000) loss: 22.655983
(Iteration 4251 / 50000) loss: 23.579580
(Iteration 4261 / 50000) loss: 21.378548
(Iteration 4271 / 50000) loss: 23.572377
(Iteration 4281 / 50000) loss: 20.492207
(Iteration 4291 / 50000) loss: 22.133580
(Iteration 4301 / 50000) loss: 23.079507
(Iteration 4311 / 50000) loss: 21.813561
(Iteration 4321 / 50000) loss: 20.518320
(Iteration 4331 / 50000) loss: 22.883881
(Iteration 4341 / 50000) loss: 22.623029
(Iteration 4351 / 50000) loss: 22.362846
(Iteration 4361 / 50000) loss: 20.420900
(Iteration 4371 / 50000) loss: 22.954308
(Iteration 4381 / 50000) loss: 23.025488
(Iteration 4391 / 50000) loss: 21.448610
(Iteration 4401 / 50000) loss: 22.017987
(Iteration 4411 / 50000) loss: 22.531510
(Iteration 4421 / 50000) loss: 20.150642
(Iteration 4431 / 50000) loss: 23.603983
(Iteration 4441 / 50000) loss: 20.515706
(Iteration 4451 / 50000) loss: 24.454975
(Iteration 4461 / 50000) loss: 21.788702
(Iteration 4471 / 50000) loss: 20.693378
(Iteration 4481 / 50000) loss: 22.060716
(Iteration 4491 / 50000) loss: 21.888109
(Iteration 4501 / 50000) loss: 19.995999
(Iteration 4511 / 50000) loss: 24.263554
(Iteration 4521 / 50000) loss: 23.219617
(Iteration 4531 / 50000) loss: 23.395078
(Iteration 4541 / 50000) loss: 24.380893
(Iteration 4551 / 50000) loss: 22.017857
(Iteration 4561 / 50000) loss: 21.049995
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(Iteration 4581 / 50000) loss: 21.117909
(Iteration 4591 / 50000) loss: 19.933161
(Iteration 4601 / 50000) loss: 19.572107
(Iteration 4611 / 50000) loss: 22.303534
(Iteration 4621 / 50000) loss: 22.622785
(Iteration 4631 / 50000) loss: 22.511456
(Iteration 4641 / 50000) loss: 20.556505
(Iteration 4651 / 50000) loss: 21.558934
(Iteration 4661 / 50000) loss: 22.516404
(Iteration 4671 / 50000) loss: 21.034696
(Iteration 4681 / 50000) loss: 20.580145
(Iteration 4691 / 50000) loss: 20.135264
(Iteration 4701 / 50000) loss: 21.448252
(Iteration 4711 / 50000) loss: 20.945050
(Iteration 4721 / 50000) loss: 20.739266
(Iteration 4731 / 50000) loss: 20.322136
(Iteration 4741 / 50000) loss: 21.766342
(Iteration 4751 / 50000) loss: 23.080450
(Iteration 4761 / 50000) loss: 21.744860
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(Iteration 4811 / 50000) loss: 24.129633
(Iteration 4821 / 50000) loss: 20.855473
(Iteration 4831 / 50000) loss: 21.967207
(Iteration 4841 / 50000) loss: 23.447107
(Iteration 4851 / 50000) loss: 22.908405
(Iteration 4861 / 50000) loss: 23.698209
(Iteration 4871 / 50000) loss: 21.725997
(Iteration 4881 / 50000) loss: 22.803424
(Iteration 4891 / 50000) loss: 21.340950
(Iteration 4901 / 50000) loss: 19.163235
(Iteration 4911 / 50000) loss: 21.648118
(Iteration 4921 / 50000) loss: 20.779833
(Iteration 4931 / 50000) loss: 19.850648
(Iteration 4941 / 50000) loss: 20.017689
(Iteration 4951 / 50000) loss: 23.025248
(Iteration 4961 / 50000) loss: 18.760954
(Iteration 4971 / 50000) loss: 20.558591
(Iteration 4981 / 50000) loss: 20.973140
(Iteration 4991 / 50000) loss: 23.179926
(Iteration 5001 / 50000) loss: 22.141270
(Iteration 5011 / 50000) loss: 22.088409
(Iteration 5021 / 50000) loss: 21.480088
(Iteration 5031 / 50000) loss: 22.493216
(Iteration 5041 / 50000) loss: 21.813057
(Iteration 5051 / 50000) loss: 21.558678
(Iteration 5061 / 50000) loss: 23.058744
(Iteration 5071 / 50000) loss: 20.503597
(Iteration 5081 / 50000) loss: 21.208037
(Iteration 5091 / 50000) loss: 20.081319
(Iteration 5101 / 50000) loss: 19.694733
(Iteration 5111 / 50000) loss: 21.458480
(Iteration 5121 / 50000) loss: 19.122882
(Iteration 5131 / 50000) loss: 20.435460
(Iteration 5141 / 50000) loss: 21.769694
(Iteration 5151 / 50000) loss: 20.719976
(Iteration 5161 / 50000) loss: 21.668327
(Iteration 5171 / 50000) loss: 22.763358
(Iteration 5181 / 50000) loss: 21.596523
(Iteration 5191 / 50000) loss: 21.937386
(Iteration 5201 / 50000) loss: 20.595539
(Iteration 5211 / 50000) loss: 22.730250
(Iteration 5221 / 50000) loss: 24.919865
(Iteration 5231 / 50000) loss: 20.519277
(Iteration 5241 / 50000) loss: 19.540004
(Iteration 5251 / 50000) loss: 24.039228
(Iteration 5261 / 50000) loss: 19.218905
(Iteration 5271 / 50000) loss: 19.091521
(Iteration 5281 / 50000) loss: 19.688470
(Iteration 5291 / 50000) loss: 22.045766
(Iteration 5301 / 50000) loss: 19.029959
(Iteration 5311 / 50000) loss: 20.702372
(Iteration 5321 / 50000) loss: 21.596280
(Iteration 5331 / 50000) loss: 20.278536
(Iteration 5341 / 50000) loss: 19.349478
(Iteration 5351 / 50000) loss: 21.524768
(Iteration 5361 / 50000) loss: 22.438924
(Iteration 5371 / 50000) loss: 21.619846
(Iteration 5381 / 50000) loss: 22.978886
(Iteration 5391 / 50000) loss: 20.310543
(Iteration 5401 / 50000) loss: 21.671787
(Iteration 5411 / 50000) loss: 20.772511
(Iteration 5421 / 50000) loss: 22.205400
(Iteration 5431 / 50000) loss: 18.629825
(Iteration 5441 / 50000) loss: 21.192941
(Iteration 5451 / 50000) loss: 20.808159
(Iteration 5461 / 50000) loss: 20.586499
(Iteration 5471 / 50000) loss: 21.543697
(Iteration 5481 / 50000) loss: 20.760599
(Iteration 5491 / 50000) loss: 19.343566
(Iteration 5501 / 50000) loss: 19.398574
(Iteration 5511 / 50000) loss: 23.755869
(Iteration 5521 / 50000) loss: 21.682787
(Iteration 5531 / 50000) loss: 20.033302
(Iteration 5541 / 50000) loss: 20.916389
(Iteration 5551 / 50000) loss: 19.878234
(Iteration 5561 / 50000) loss: 20.440956
(Iteration 5571 / 50000) loss: 19.826960
(Iteration 5581 / 50000) loss: 18.158928
(Iteration 5591 / 50000) loss: 22.680598
(Iteration 5601 / 50000) loss: 21.702390
(Iteration 5611 / 50000) loss: 22.075435
(Iteration 5621 / 50000) loss: 19.761415
(Iteration 5631 / 50000) loss: 21.261737
(Iteration 5641 / 50000) loss: 23.292195
(Iteration 5651 / 50000) loss: 20.432820
(Iteration 5661 / 50000) loss: 20.586842
(Iteration 5671 / 50000) loss: 21.023676
(Iteration 5681 / 50000) loss: 20.935793
(Iteration 5691 / 50000) loss: 22.283069
(Iteration 5701 / 50000) loss: 20.519489
(Iteration 5711 / 50000) loss: 20.835395
(Iteration 5721 / 50000) loss: 20.373061
(Iteration 5731 / 50000) loss: 19.190660
(Iteration 5741 / 50000) loss: 19.410828
(Iteration 5751 / 50000) loss: 21.803060
(Iteration 5761 / 50000) loss: 21.324499
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(Iteration 5781 / 50000) loss: 20.613418
(Iteration 5791 / 50000) loss: 20.666250
(Iteration 5801 / 50000) loss: 21.124283
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(Iteration 5821 / 50000) loss: 18.405150
(Iteration 5831 / 50000) loss: 20.393583
(Iteration 5841 / 50000) loss: 21.792274
(Iteration 5851 / 50000) loss: 21.431253
(Iteration 5861 / 50000) loss: 21.189426
(Iteration 5871 / 50000) loss: 22.840624
(Iteration 5881 / 50000) loss: 21.984174
(Iteration 5891 / 50000) loss: 22.366338
(Iteration 5901 / 50000) loss: 18.913741
(Iteration 5911 / 50000) loss: 22.056459
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(Iteration 5931 / 50000) loss: 18.917200
(Iteration 5941 / 50000) loss: 19.105390
(Iteration 5951 / 50000) loss: 20.565251
(Iteration 5961 / 50000) loss: 20.369270
(Iteration 5971 / 50000) loss: 18.911205
(Iteration 5981 / 50000) loss: 20.799106
(Iteration 5991 / 50000) loss: 20.070229
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(Iteration 6011 / 50000) loss: 20.218269
(Iteration 6021 / 50000) loss: 20.200367
(Iteration 6031 / 50000) loss: 20.333937
(Iteration 6041 / 50000) loss: 20.970247
(Iteration 6051 / 50000) loss: 21.110310
(Iteration 6061 / 50000) loss: 23.306282
(Iteration 6071 / 50000) loss: 20.827773
(Iteration 6081 / 50000) loss: 20.329168
(Iteration 6091 / 50000) loss: 20.148261
(Iteration 6101 / 50000) loss: 20.354062
(Iteration 6111 / 50000) loss: 20.523364
(Iteration 6121 / 50000) loss: 21.570652
(Iteration 6131 / 50000) loss: 17.314943
(Iteration 6141 / 50000) loss: 17.422210
(Iteration 6151 / 50000) loss: 20.898131
(Iteration 6161 / 50000) loss: 20.846413
(Iteration 6171 / 50000) loss: 19.690817
(Iteration 6181 / 50000) loss: 19.164835
(Iteration 6191 / 50000) loss: 20.303410
(Iteration 6201 / 50000) loss: 19.081390
(Iteration 6211 / 50000) loss: 19.751259
(Iteration 6221 / 50000) loss: 20.038525
(Iteration 6231 / 50000) loss: 18.270613
(Iteration 6241 / 50000) loss: 18.278064
(Iteration 6251 / 50000) loss: 18.499522
(Iteration 6261 / 50000) loss: 19.733064
(Iteration 6271 / 50000) loss: 18.414712
(Iteration 6281 / 50000) loss: 19.180443
(Iteration 6291 / 50000) loss: 19.458727
(Iteration 6301 / 50000) loss: 19.637927
(Iteration 6311 / 50000) loss: 19.623687
(Iteration 6321 / 50000) loss: 19.233474
(Iteration 6331 / 50000) loss: 18.398342
(Iteration 6341 / 50000) loss: 20.706862
(Iteration 6351 / 50000) loss: 21.328848
(Iteration 6361 / 50000) loss: 17.883774
(Iteration 6371 / 50000) loss: 18.835936
(Iteration 6381 / 50000) loss: 19.785980
(Iteration 6391 / 50000) loss: 21.251250
(Iteration 6401 / 50000) loss: 20.523335
(Iteration 6411 / 50000) loss: 20.116770
(Iteration 6421 / 50000) loss: 20.002601
(Iteration 6431 / 50000) loss: 20.165428
(Iteration 6441 / 50000) loss: 20.511777
(Iteration 6451 / 50000) loss: 18.339521
(Iteration 6461 / 50000) loss: 20.356371
(Iteration 6471 / 50000) loss: 19.870485
(Iteration 6481 / 50000) loss: 20.009747
(Iteration 6491 / 50000) loss: 18.328134
(Iteration 6501 / 50000) loss: 19.456700
(Iteration 6511 / 50000) loss: 21.337666
(Iteration 6521 / 50000) loss: 19.458789
(Iteration 6531 / 50000) loss: 18.385450
(Iteration 6541 / 50000) loss: 20.027317
(Iteration 6551 / 50000) loss: 18.606620
(Iteration 6561 / 50000) loss: 17.697981
(Iteration 6571 / 50000) loss: 18.570554
(Iteration 6581 / 50000) loss: 19.005362
(Iteration 6591 / 50000) loss: 19.332094
(Iteration 6601 / 50000) loss: 21.319113
(Iteration 6611 / 50000) loss: 20.539046
(Iteration 6621 / 50000) loss: 19.211269
(Iteration 6631 / 50000) loss: 19.568886
(Iteration 6641 / 50000) loss: 19.442566
(Iteration 6651 / 50000) loss: 21.482987
(Iteration 6661 / 50000) loss: 19.846344
(Iteration 6671 / 50000) loss: 18.707803
(Iteration 6681 / 50000) loss: 20.110428
(Iteration 6691 / 50000) loss: 19.791817
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(Iteration 6711 / 50000) loss: 18.500482
(Iteration 6721 / 50000) loss: 18.915803
(Iteration 6731 / 50000) loss: 17.382782
(Iteration 6741 / 50000) loss: 19.619677
(Iteration 6751 / 50000) loss: 18.308075
(Iteration 6761 / 50000) loss: 19.669702
(Iteration 6771 / 50000) loss: 19.782305
(Iteration 6781 / 50000) loss: 19.449764
(Iteration 6791 / 50000) loss: 18.028271
(Iteration 6801 / 50000) loss: 18.950048
(Iteration 6811 / 50000) loss: 20.656126
(Iteration 6821 / 50000) loss: 18.984901
(Iteration 6831 / 50000) loss: 18.945427
(Iteration 6841 / 50000) loss: 18.366352
(Iteration 6851 / 50000) loss: 19.100345
(Iteration 6861 / 50000) loss: 19.338871
(Iteration 6871 / 50000) loss: 19.592446
(Iteration 6881 / 50000) loss: 19.448646
(Iteration 6891 / 50000) loss: 19.620853
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(Iteration 6911 / 50000) loss: 20.952203
(Iteration 6921 / 50000) loss: 22.775919
(Iteration 6931 / 50000) loss: 18.405753
(Iteration 6941 / 50000) loss: 18.079386
(Iteration 6951 / 50000) loss: 19.684543
(Iteration 6961 / 50000) loss: 19.829524
(Iteration 6971 / 50000) loss: 21.881164
(Iteration 6981 / 50000) loss: 17.532832
(Iteration 6991 / 50000) loss: 17.459927
(Iteration 7001 / 50000) loss: 17.958956
(Iteration 7011 / 50000) loss: 18.919425
(Iteration 7021 / 50000) loss: 18.679336
(Iteration 7031 / 50000) loss: 17.643844
(Iteration 7041 / 50000) loss: 20.107942
(Iteration 7051 / 50000) loss: 17.663355
(Iteration 7061 / 50000) loss: 20.203215
(Iteration 7071 / 50000) loss: 19.315467
(Iteration 7081 / 50000) loss: 17.935547
(Iteration 7091 / 50000) loss: 19.505259
(Iteration 7101 / 50000) loss: 19.591113
(Iteration 7111 / 50000) loss: 18.313607
(Iteration 7121 / 50000) loss: 19.502872
(Iteration 7131 / 50000) loss: 21.825251
(Iteration 7141 / 50000) loss: 21.121488
(Iteration 7151 / 50000) loss: 19.273408
(Iteration 7161 / 50000) loss: 18.573612
(Iteration 7171 / 50000) loss: 19.776891
(Iteration 7181 / 50000) loss: 20.941724
(Iteration 7191 / 50000) loss: 20.451837
(Iteration 7201 / 50000) loss: 19.499344
(Iteration 7211 / 50000) loss: 18.207035
(Iteration 7221 / 50000) loss: 17.530173
(Iteration 7231 / 50000) loss: 18.280858
(Iteration 7241 / 50000) loss: 18.946820
(Iteration 7251 / 50000) loss: 18.655773
(Iteration 7261 / 50000) loss: 20.152376
(Iteration 7271 / 50000) loss: 20.156445
(Iteration 7281 / 50000) loss: 19.400799
(Iteration 7291 / 50000) loss: 19.720933
(Iteration 7301 / 50000) loss: 19.642111
(Iteration 7311 / 50000) loss: 17.764900
(Iteration 7321 / 50000) loss: 17.939227
(Iteration 7331 / 50000) loss: 19.567266
(Iteration 7341 / 50000) loss: 21.254086
(Iteration 7351 / 50000) loss: 17.944472
(Iteration 7361 / 50000) loss: 17.912508
(Iteration 7371 / 50000) loss: 19.432748
(Iteration 7381 / 50000) loss: 19.820085
(Iteration 7391 / 50000) loss: 18.794662
(Iteration 7401 / 50000) loss: 19.292708
(Iteration 7411 / 50000) loss: 17.743046
(Iteration 7421 / 50000) loss: 18.779725
(Iteration 7431 / 50000) loss: 17.285560
(Iteration 7441 / 50000) loss: 19.763444
(Iteration 7451 / 50000) loss: 18.147894
(Iteration 7461 / 50000) loss: 20.235662
(Iteration 7471 / 50000) loss: 18.181342
(Iteration 7481 / 50000) loss: 18.766445
(Iteration 7491 / 50000) loss: 17.565340
(Iteration 7501 / 50000) loss: 16.960034
(Iteration 7511 / 50000) loss: 16.101397
(Iteration 7521 / 50000) loss: 16.729498
(Iteration 7531 / 50000) loss: 18.678913
(Iteration 7541 / 50000) loss: 17.928990
(Iteration 7551 / 50000) loss: 20.151434
(Iteration 7561 / 50000) loss: 17.705622
(Iteration 7571 / 50000) loss: 16.902790
(Iteration 7581 / 50000) loss: 18.067885
(Iteration 7591 / 50000) loss: 16.952416
(Iteration 7601 / 50000) loss: 18.492926
(Iteration 7611 / 50000) loss: 18.398710
(Iteration 7621 / 50000) loss: 19.033628
(Iteration 7631 / 50000) loss: 17.947896
(Iteration 7641 / 50000) loss: 19.537541
(Iteration 7651 / 50000) loss: 17.358078
(Iteration 7661 / 50000) loss: 18.947505
(Iteration 7671 / 50000) loss: 19.214473
(Iteration 7681 / 50000) loss: 18.417779
(Iteration 7691 / 50000) loss: 18.607622
(Iteration 7701 / 50000) loss: 19.507673
(Iteration 7711 / 50000) loss: 20.172791
(Iteration 7721 / 50000) loss: 18.599648
(Iteration 7731 / 50000) loss: 17.628087
(Iteration 7741 / 50000) loss: 18.244452
(Iteration 7751 / 50000) loss: 16.956513
(Iteration 7761 / 50000) loss: 17.161630
(Iteration 7771 / 50000) loss: 20.129473
(Iteration 7781 / 50000) loss: 18.816792
(Iteration 7791 / 50000) loss: 17.741674
(Iteration 7801 / 50000) loss: 17.977319
(Iteration 7811 / 50000) loss: 17.240084
(Iteration 7821 / 50000) loss: 17.525019
(Iteration 7831 / 50000) loss: 18.446755
(Iteration 7841 / 50000) loss: 19.122728
(Iteration 7851 / 50000) loss: 17.108321
(Iteration 7861 / 50000) loss: 18.727433
(Iteration 7871 / 50000) loss: 18.703713
(Iteration 7881 / 50000) loss: 18.070126
(Iteration 7891 / 50000) loss: 16.807122
(Iteration 7901 / 50000) loss: 17.695822
(Iteration 7911 / 50000) loss: 17.462358
(Iteration 7921 / 50000) loss: 18.063869
(Iteration 7931 / 50000) loss: 20.164772
(Iteration 7941 / 50000) loss: 17.588070
(Iteration 7951 / 50000) loss: 17.855903
(Iteration 7961 / 50000) loss: 19.258295
(Iteration 7971 / 50000) loss: 19.280157
(Iteration 7981 / 50000) loss: 17.134296
(Iteration 7991 / 50000) loss: 17.752265
(Iteration 8001 / 50000) loss: 17.073337
(Iteration 8011 / 50000) loss: 18.257042
(Iteration 8021 / 50000) loss: 16.541223
(Iteration 8031 / 50000) loss: 17.487458
(Iteration 8041 / 50000) loss: 17.221950
(Iteration 8051 / 50000) loss: 17.582704
(Iteration 8061 / 50000) loss: 17.643004
(Iteration 8071 / 50000) loss: 18.519407
(Iteration 8081 / 50000) loss: 18.897081
(Iteration 8091 / 50000) loss: 18.807134
(Iteration 8101 / 50000) loss: 18.431072
(Iteration 8111 / 50000) loss: 16.821774
(Iteration 8121 / 50000) loss: 18.024948
(Iteration 8131 / 50000) loss: 20.367276
(Iteration 8141 / 50000) loss: 16.537830
(Iteration 8151 / 50000) loss: 19.572197
(Iteration 8161 / 50000) loss: 18.282196
(Iteration 8171 / 50000) loss: 17.764949
(Iteration 8181 / 50000) loss: 18.778030
(Iteration 8191 / 50000) loss: 17.846059
(Iteration 8201 / 50000) loss: 19.745659
(Iteration 8211 / 50000) loss: 18.265191
(Iteration 8221 / 50000) loss: 16.120530
(Iteration 8231 / 50000) loss: 18.011215
(Iteration 8241 / 50000) loss: 16.415331
(Iteration 8251 / 50000) loss: 18.234777
(Iteration 8261 / 50000) loss: 17.668741
(Iteration 8271 / 50000) loss: 18.099680
(Iteration 8281 / 50000) loss: 17.807742
(Iteration 8291 / 50000) loss: 18.656129
(Iteration 8301 / 50000) loss: 17.626587
(Iteration 8311 / 50000) loss: 18.404431
(Iteration 8321 / 50000) loss: 17.964798
(Iteration 8331 / 50000) loss: 18.289032
(Iteration 8341 / 50000) loss: 16.652279
(Iteration 8351 / 50000) loss: 18.262767
(Iteration 8361 / 50000) loss: 16.647480
(Iteration 8371 / 50000) loss: 18.246995
(Iteration 8381 / 50000) loss: 17.697705
(Iteration 8391 / 50000) loss: 17.177185
(Iteration 8401 / 50000) loss: 19.622441
(Iteration 8411 / 50000) loss: 18.281019
(Iteration 8421 / 50000) loss: 18.643746
(Iteration 8431 / 50000) loss: 16.699289
(Iteration 8441 / 50000) loss: 18.197147
(Iteration 8451 / 50000) loss: 17.164112
(Iteration 8461 / 50000) loss: 17.428307
(Iteration 8471 / 50000) loss: 19.780781
(Iteration 8481 / 50000) loss: 18.901711
(Iteration 8491 / 50000) loss: 15.845885
(Iteration 8501 / 50000) loss: 16.470592
(Iteration 8511 / 50000) loss: 16.991765
(Iteration 8521 / 50000) loss: 16.707910
(Iteration 8531 / 50000) loss: 15.673759
(Iteration 8541 / 50000) loss: 17.710295
(Iteration 8551 / 50000) loss: 16.368827
(Iteration 8561 / 50000) loss: 18.000798
(Iteration 8571 / 50000) loss: 17.910926
(Iteration 8581 / 50000) loss: 17.567604
(Iteration 8591 / 50000) loss: 18.788112
(Iteration 8601 / 50000) loss: 17.168847
(Iteration 8611 / 50000) loss: 15.332856
(Iteration 8621 / 50000) loss: 16.727200
(Iteration 8631 / 50000) loss: 18.131948
(Iteration 8641 / 50000) loss: 18.054578
(Iteration 8651 / 50000) loss: 17.522945
(Iteration 8661 / 50000) loss: 16.682996
(Iteration 8671 / 50000) loss: 16.815948
(Iteration 8681 / 50000) loss: 19.062542
(Iteration 8691 / 50000) loss: 16.807473
(Iteration 8701 / 50000) loss: 18.524285
(Iteration 8711 / 50000) loss: 17.118205
(Iteration 8721 / 50000) loss: 18.898090
(Iteration 8731 / 50000) loss: 16.681473
(Iteration 8741 / 50000) loss: 19.239302
(Iteration 8751 / 50000) loss: 16.805546
(Iteration 8761 / 50000) loss: 16.782028
(Iteration 8771 / 50000) loss: 17.302671
(Iteration 8781 / 50000) loss: 18.520628
(Iteration 8791 / 50000) loss: 16.597888
(Iteration 8801 / 50000) loss: 15.966570
(Iteration 8811 / 50000) loss: 18.676601
(Iteration 8821 / 50000) loss: 18.165537
(Iteration 8831 / 50000) loss: 19.003924
(Iteration 8841 / 50000) loss: 17.915025
(Iteration 8851 / 50000) loss: 18.186735
(Iteration 8861 / 50000) loss: 17.317318
(Iteration 8871 / 50000) loss: 16.307261
(Iteration 8881 / 50000) loss: 16.256647
(Iteration 8891 / 50000) loss: 16.212746
(Iteration 8901 / 50000) loss: 17.557186
(Iteration 8911 / 50000) loss: 18.376219
(Iteration 8921 / 50000) loss: 18.213310
(Iteration 8931 / 50000) loss: 17.545227
(Iteration 8941 / 50000) loss: 19.426384
(Iteration 8951 / 50000) loss: 18.714962
(Iteration 8961 / 50000) loss: 17.114706
(Iteration 8971 / 50000) loss: 17.509216
(Iteration 8981 / 50000) loss: 16.215570
(Iteration 8991 / 50000) loss: 17.375526
(Iteration 9001 / 50000) loss: 17.193062
(Iteration 9011 / 50000) loss: 17.680504
(Iteration 9021 / 50000) loss: 16.667845
(Iteration 9031 / 50000) loss: 17.521360
(Iteration 9041 / 50000) loss: 16.159589
(Iteration 9051 / 50000) loss: 16.249272
(Iteration 9061 / 50000) loss: 16.347158
(Iteration 9071 / 50000) loss: 16.803838
(Iteration 9081 / 50000) loss: 17.742362
(Iteration 9091 / 50000) loss: 16.693335
(Iteration 9101 / 50000) loss: 16.581633
(Iteration 9111 / 50000) loss: 16.434157
(Iteration 9121 / 50000) loss: 15.957248
(Iteration 9131 / 50000) loss: 19.755144
(Iteration 9141 / 50000) loss: 16.581782
(Iteration 9151 / 50000) loss: 17.155394
(Iteration 9161 / 50000) loss: 16.861450
(Iteration 9171 / 50000) loss: 17.448032
(Iteration 9181 / 50000) loss: 18.669689
(Iteration 9191 / 50000) loss: 15.555337
(Iteration 9201 / 50000) loss: 17.814621
(Iteration 9211 / 50000) loss: 15.551170
(Iteration 9221 / 50000) loss: 17.196796
(Iteration 9231 / 50000) loss: 16.880089
(Iteration 9241 / 50000) loss: 17.402382
(Iteration 9251 / 50000) loss: 16.506217
(Iteration 9261 / 50000) loss: 17.058196
(Iteration 9271 / 50000) loss: 16.353933
(Iteration 9281 / 50000) loss: 16.676791
(Iteration 9291 / 50000) loss: 15.673351
(Iteration 9301 / 50000) loss: 16.455683
(Iteration 9311 / 50000) loss: 15.978762
(Iteration 9321 / 50000) loss: 15.406118
(Iteration 9331 / 50000) loss: 19.031682
(Iteration 9341 / 50000) loss: 15.470888
(Iteration 9351 / 50000) loss: 15.188175
(Iteration 9361 / 50000) loss: 15.666013
(Iteration 9371 / 50000) loss: 15.762611
(Iteration 9381 / 50000) loss: 15.480892
(Iteration 9391 / 50000) loss: 18.041210
(Iteration 9401 / 50000) loss: 16.653803
(Iteration 9411 / 50000) loss: 17.230485
(Iteration 9421 / 50000) loss: 16.706672
(Iteration 9431 / 50000) loss: 16.488942
(Iteration 9441 / 50000) loss: 17.110530
(Iteration 9451 / 50000) loss: 14.856356
(Iteration 9461 / 50000) loss: 17.685101
(Iteration 9471 / 50000) loss: 17.540546
(Iteration 9481 / 50000) loss: 15.068992
(Iteration 9491 / 50000) loss: 15.609837
(Iteration 9501 / 50000) loss: 17.711725
(Iteration 9511 / 50000) loss: 16.795534
(Iteration 9521 / 50000) loss: 15.188682
(Iteration 9531 / 50000) loss: 17.370098
(Iteration 9541 / 50000) loss: 17.118295
(Iteration 9551 / 50000) loss: 17.052448
(Iteration 9561 / 50000) loss: 16.586125
(Iteration 9571 / 50000) loss: 18.598129
(Iteration 9581 / 50000) loss: 17.077304
(Iteration 9591 / 50000) loss: 17.526231
(Iteration 9601 / 50000) loss: 16.165526
(Iteration 9611 / 50000) loss: 17.217744
(Iteration 9621 / 50000) loss: 16.699322
(Iteration 9631 / 50000) loss: 16.937043
(Iteration 9641 / 50000) loss: 15.696933
(Iteration 9651 / 50000) loss: 15.594095
(Iteration 9661 / 50000) loss: 17.344318
(Iteration 9671 / 50000) loss: 16.198189
(Iteration 9681 / 50000) loss: 17.908091
(Iteration 9691 / 50000) loss: 17.400884
(Iteration 9701 / 50000) loss: 15.060644
(Iteration 9711 / 50000) loss: 15.586394
(Iteration 9721 / 50000) loss: 16.516205
(Iteration 9731 / 50000) loss: 17.056010
(Iteration 9741 / 50000) loss: 16.078699
(Iteration 9751 / 50000) loss: 18.157994
(Iteration 9761 / 50000) loss: 16.467600
(Iteration 9771 / 50000) loss: 17.049044
(Iteration 9781 / 50000) loss: 16.618214
(Iteration 9791 / 50000) loss: 18.521259
(Iteration 9801 / 50000) loss: 16.404452
(Iteration 9811 / 50000) loss: 15.168632
(Iteration 9821 / 50000) loss: 14.881513
(Iteration 9831 / 50000) loss: 18.009426
(Iteration 9841 / 50000) loss: 17.957841
(Iteration 9851 / 50000) loss: 16.313223
(Iteration 9861 / 50000) loss: 15.852134
(Iteration 9871 / 50000) loss: 16.870043
(Iteration 9881 / 50000) loss: 15.732664
(Iteration 9891 / 50000) loss: 17.831941
(Iteration 9901 / 50000) loss: 15.538980
(Iteration 9911 / 50000) loss: 16.379067
(Iteration 9921 / 50000) loss: 17.689081
(Iteration 9931 / 50000) loss: 17.150266
(Iteration 9941 / 50000) loss: 16.538504
(Iteration 9951 / 50000) loss: 14.868312
(Iteration 9961 / 50000) loss: 16.902443
(Iteration 9971 / 50000) loss: 15.325823
(Iteration 9981 / 50000) loss: 16.625908
(Iteration 9991 / 50000) loss: 15.614556
(Iteration 10001 / 50000) loss: 17.236104
(Iteration 10011 / 50000) loss: 17.549364
(Iteration 10021 / 50000) loss: 14.630394
(Iteration 10031 / 50000) loss: 16.469599
(Iteration 10041 / 50000) loss: 14.485995
(Iteration 10051 / 50000) loss: 16.944418
(Iteration 10061 / 50000) loss: 14.875517
(Iteration 10071 / 50000) loss: 16.319228
(Iteration 10081 / 50000) loss: 15.411546
(Iteration 10091 / 50000) loss: 17.135337
(Iteration 10101 / 50000) loss: 15.598701
(Iteration 10111 / 50000) loss: 17.581875
(Iteration 10121 / 50000) loss: 15.563860
(Iteration 10131 / 50000) loss: 15.966700
(Iteration 10141 / 50000) loss: 14.733457
(Iteration 10151 / 50000) loss: 14.110269
(Iteration 10161 / 50000) loss: 15.934618
(Iteration 10171 / 50000) loss: 15.086526
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(Iteration 10191 / 50000) loss: 17.008396
(Iteration 10201 / 50000) loss: 16.767724
(Iteration 10211 / 50000) loss: 18.026935
(Iteration 10221 / 50000) loss: 17.186840
(Iteration 10231 / 50000) loss: 16.662008
(Iteration 10241 / 50000) loss: 15.460845
(Iteration 10251 / 50000) loss: 14.272533
(Iteration 10261 / 50000) loss: 15.478552
(Iteration 10271 / 50000) loss: 15.593291
(Iteration 10281 / 50000) loss: 14.137836
(Iteration 10291 / 50000) loss: 16.641457
(Iteration 10301 / 50000) loss: 13.862440
(Iteration 10311 / 50000) loss: 16.420695
(Iteration 10321 / 50000) loss: 16.151679
(Iteration 10331 / 50000) loss: 15.369138
(Iteration 10341 / 50000) loss: 15.024248
(Iteration 10351 / 50000) loss: 13.723435
(Iteration 10361 / 50000) loss: 16.858463
(Iteration 10371 / 50000) loss: 14.083611
(Iteration 10381 / 50000) loss: 16.737886
(Iteration 10391 / 50000) loss: 16.345412
(Iteration 10401 / 50000) loss: 15.978486
(Iteration 10411 / 50000) loss: 15.786510
(Iteration 10421 / 50000) loss: 16.295959
(Iteration 10431 / 50000) loss: 18.019885
(Iteration 10441 / 50000) loss: 14.678747
(Iteration 10451 / 50000) loss: 15.033670
(Iteration 10461 / 50000) loss: 16.961459
(Iteration 10471 / 50000) loss: 16.227661
(Iteration 10481 / 50000) loss: 15.606098
(Iteration 10491 / 50000) loss: 16.282136
(Iteration 10501 / 50000) loss: 14.492209
(Iteration 10511 / 50000) loss: 13.798812
(Iteration 10521 / 50000) loss: 14.586155
(Iteration 10531 / 50000) loss: 15.055263
(Iteration 10541 / 50000) loss: 14.595962
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(Iteration 10561 / 50000) loss: 15.485250
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(Iteration 10621 / 50000) loss: 15.646996
(Iteration 10631 / 50000) loss: 16.005507
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(Iteration 10651 / 50000) loss: 15.761939
(Iteration 10661 / 50000) loss: 15.616799
(Iteration 10671 / 50000) loss: 15.367503
(Iteration 10681 / 50000) loss: 14.678549
(Iteration 10691 / 50000) loss: 15.845859
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(Iteration 10711 / 50000) loss: 16.420200
(Iteration 10721 / 50000) loss: 15.234358
(Iteration 10731 / 50000) loss: 14.572684
(Iteration 10741 / 50000) loss: 16.480985
(Iteration 10751 / 50000) loss: 15.535439
(Iteration 10761 / 50000) loss: 15.016944
(Iteration 10771 / 50000) loss: 15.872384
(Iteration 10781 / 50000) loss: 15.698025
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(Iteration 10821 / 50000) loss: 14.844195
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(Iteration 10841 / 50000) loss: 16.153551
(Iteration 10851 / 50000) loss: 15.347772
(Iteration 10861 / 50000) loss: 16.355364
(Iteration 10871 / 50000) loss: 15.664333
(Iteration 10881 / 50000) loss: 14.422206
(Iteration 10891 / 50000) loss: 15.844061
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(Iteration 10911 / 50000) loss: 14.173170
(Iteration 10921 / 50000) loss: 14.674755
(Iteration 10931 / 50000) loss: 16.237118
(Iteration 10941 / 50000) loss: 13.446815
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(Iteration 10961 / 50000) loss: 16.052395
(Iteration 10971 / 50000) loss: 13.315091
(Iteration 10981 / 50000) loss: 15.641616
(Iteration 10991 / 50000) loss: 14.798618
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(Iteration 11011 / 50000) loss: 15.610517
(Iteration 11021 / 50000) loss: 14.978175
(Iteration 11031 / 50000) loss: 15.507785
(Iteration 11041 / 50000) loss: 14.801220
(Iteration 11051 / 50000) loss: 15.603042
(Iteration 11061 / 50000) loss: 14.437139
(Iteration 11071 / 50000) loss: 14.813405
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(Iteration 11091 / 50000) loss: 15.573694
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(Iteration 11121 / 50000) loss: 13.684040
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(Iteration 11141 / 50000) loss: 14.629847
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(Iteration 11161 / 50000) loss: 14.374112
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(Iteration 11241 / 50000) loss: 15.484590
(Iteration 11251 / 50000) loss: 14.141835
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(Iteration 11321 / 50000) loss: 14.967848
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(Iteration 11341 / 50000) loss: 14.685092
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(Iteration 11441 / 50000) loss: 13.781921
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(Iteration 11461 / 50000) loss: 14.249955
(Iteration 11471 / 50000) loss: 15.388795
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(Iteration 11521 / 50000) loss: 15.303973
(Iteration 11531 / 50000) loss: 14.728652
(Iteration 11541 / 50000) loss: 14.612109
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(Iteration 11561 / 50000) loss: 15.084764
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(Iteration 11621 / 50000) loss: 15.267354
(Iteration 11631 / 50000) loss: 15.036526
(Iteration 11641 / 50000) loss: 14.736187
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(Iteration 11661 / 50000) loss: 14.740794
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(Iteration 11721 / 50000) loss: 14.077916
(Iteration 11731 / 50000) loss: 15.801541
(Iteration 11741 / 50000) loss: 15.543674
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(Iteration 11821 / 50000) loss: 14.555124
(Iteration 11831 / 50000) loss: 14.557012
(Iteration 11841 / 50000) loss: 14.094957
(Iteration 11851 / 50000) loss: 15.190572
(Iteration 11861 / 50000) loss: 15.393720
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(Iteration 11911 / 50000) loss: 15.796865
(Iteration 11921 / 50000) loss: 14.626319
(Iteration 11931 / 50000) loss: 14.665143
(Iteration 11941 / 50000) loss: 14.413752
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(Iteration 12041 / 50000) loss: 14.798096
(Iteration 12051 / 50000) loss: 15.279035
(Iteration 12061 / 50000) loss: 14.968731
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(Iteration 12151 / 50000) loss: 12.464075
(Iteration 12161 / 50000) loss: 14.696920
(Iteration 12171 / 50000) loss: 15.696884
(Iteration 12181 / 50000) loss: 13.647912
(Iteration 12191 / 50000) loss: 16.797501
(Iteration 12201 / 50000) loss: 14.585297
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(Iteration 12221 / 50000) loss: 14.740819
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(Iteration 12261 / 50000) loss: 13.092440
(Iteration 12271 / 50000) loss: 14.122700
(Iteration 12281 / 50000) loss: 13.710731
(Iteration 12291 / 50000) loss: 14.120879
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(Iteration 12311 / 50000) loss: 13.713633
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(Iteration 12341 / 50000) loss: 13.199930
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(Iteration 12371 / 50000) loss: 14.540184
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(Iteration 12411 / 50000) loss: 14.595059
(Iteration 12421 / 50000) loss: 15.054561
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(Iteration 12461 / 50000) loss: 14.961584
(Iteration 12471 / 50000) loss: 13.536850
(Iteration 12481 / 50000) loss: 12.875490
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(Iteration 12511 / 50000) loss: 15.434793
(Iteration 12521 / 50000) loss: 14.486527
(Iteration 12531 / 50000) loss: 13.440075
(Iteration 12541 / 50000) loss: 13.258948
(Iteration 12551 / 50000) loss: 14.683145
(Iteration 12561 / 50000) loss: 14.528728
(Iteration 12571 / 50000) loss: 13.944834
(Iteration 12581 / 50000) loss: 13.998560
(Iteration 12591 / 50000) loss: 14.712468
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(Iteration 12621 / 50000) loss: 13.692284
(Iteration 12631 / 50000) loss: 14.856894
(Iteration 12641 / 50000) loss: 13.603528
(Iteration 12651 / 50000) loss: 14.889061
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(Iteration 12671 / 50000) loss: 13.964045
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(Iteration 12691 / 50000) loss: 15.764029
(Iteration 12701 / 50000) loss: 14.894100
(Iteration 12711 / 50000) loss: 14.313630
(Iteration 12721 / 50000) loss: 11.998873
(Iteration 12731 / 50000) loss: 13.293908
(Iteration 12741 / 50000) loss: 14.314851
(Iteration 12751 / 50000) loss: 12.307314
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(Iteration 12781 / 50000) loss: 13.724506
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(Iteration 12821 / 50000) loss: 12.720390
(Iteration 12831 / 50000) loss: 14.117117
(Iteration 12841 / 50000) loss: 13.446039
(Iteration 12851 / 50000) loss: 14.224435
(Iteration 12861 / 50000) loss: 13.754643
(Iteration 12871 / 50000) loss: 14.277512
(Iteration 12881 / 50000) loss: 13.782169
(Iteration 12891 / 50000) loss: 13.128468
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(Iteration 12931 / 50000) loss: 12.105065
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(Iteration 12971 / 50000) loss: 13.762842
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(Iteration 13031 / 50000) loss: 14.413839
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(Iteration 13161 / 50000) loss: 11.778832
(Iteration 13171 / 50000) loss: 12.965943
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(Iteration 13231 / 50000) loss: 11.866953
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(Iteration 13251 / 50000) loss: 14.290387
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(Iteration 13631 / 50000) loss: 13.263308
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(Iteration 14011 / 50000) loss: 13.852586
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(Iteration 14161 / 50000) loss: 13.316555
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(Iteration 14841 / 50000) loss: 13.409545
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(Iteration 14871 / 50000) loss: 11.707780
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(Iteration 15201 / 50000) loss: 11.651755
(Iteration 15211 / 50000) loss: 13.374680
(Iteration 15221 / 50000) loss: 11.293789
(Iteration 15231 / 50000) loss: 12.240737
(Iteration 15241 / 50000) loss: 11.724727
(Iteration 15251 / 50000) loss: 11.687255
(Iteration 15261 / 50000) loss: 12.555184
(Iteration 15271 / 50000) loss: 11.954406
(Iteration 15281 / 50000) loss: 11.278813
(Iteration 15291 / 50000) loss: 12.563500
(Iteration 15301 / 50000) loss: 11.653630
(Iteration 15311 / 50000) loss: 12.146879
(Iteration 15321 / 50000) loss: 12.798332
(Iteration 15331 / 50000) loss: 12.056937
(Iteration 15341 / 50000) loss: 12.759643
(Iteration 15351 / 50000) loss: 10.857654
(Iteration 15361 / 50000) loss: 11.936877
(Iteration 15371 / 50000) loss: 11.605721
(Iteration 15381 / 50000) loss: 10.577835
(Iteration 15391 / 50000) loss: 13.866457
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(Iteration 15411 / 50000) loss: 11.341328
(Iteration 15421 / 50000) loss: 12.195272
(Iteration 15431 / 50000) loss: 11.740646
(Iteration 15441 / 50000) loss: 11.837903
(Iteration 15451 / 50000) loss: 11.403308
(Iteration 15461 / 50000) loss: 11.611504
(Iteration 15471 / 50000) loss: 11.599004
(Iteration 15481 / 50000) loss: 13.251967
(Iteration 15491 / 50000) loss: 12.087936
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(Iteration 15511 / 50000) loss: 12.897188
(Iteration 15521 / 50000) loss: 11.616325
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(Iteration 15541 / 50000) loss: 13.110265
(Iteration 15551 / 50000) loss: 10.032874
(Iteration 15561 / 50000) loss: 12.048217
(Iteration 15571 / 50000) loss: 13.661204
(Iteration 15581 / 50000) loss: 12.378044
(Iteration 15591 / 50000) loss: 13.463606
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(Iteration 15621 / 50000) loss: 11.886029
(Iteration 15631 / 50000) loss: 12.203477
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(Iteration 15651 / 50000) loss: 11.473643
(Iteration 15661 / 50000) loss: 11.899258
(Iteration 15671 / 50000) loss: 11.358312
(Iteration 15681 / 50000) loss: 11.700593
(Iteration 15691 / 50000) loss: 12.089305
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(Iteration 15711 / 50000) loss: 11.769994
(Iteration 15721 / 50000) loss: 11.822524
(Iteration 15731 / 50000) loss: 11.825413
(Iteration 15741 / 50000) loss: 12.134475
(Iteration 15751 / 50000) loss: 11.153241
(Iteration 15761 / 50000) loss: 11.764351
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(Iteration 15781 / 50000) loss: 11.776622
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(Iteration 15831 / 50000) loss: 11.527045
(Iteration 15841 / 50000) loss: 11.369645
(Iteration 15851 / 50000) loss: 10.890027
(Iteration 15861 / 50000) loss: 11.307582
(Iteration 15871 / 50000) loss: 11.548539
(Iteration 15881 / 50000) loss: 12.477740
(Iteration 15891 / 50000) loss: 12.732750
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(Iteration 15921 / 50000) loss: 12.782837
(Iteration 15931 / 50000) loss: 10.484980
(Iteration 15941 / 50000) loss: 12.283507
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(Iteration 15971 / 50000) loss: 11.688822
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(Iteration 16011 / 50000) loss: 11.681976
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(Iteration 16031 / 50000) loss: 12.847421
(Iteration 16041 / 50000) loss: 10.802940
(Iteration 16051 / 50000) loss: 11.186993
(Iteration 16061 / 50000) loss: 12.435582
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(Iteration 16121 / 50000) loss: 12.023104
(Iteration 16131 / 50000) loss: 11.396548
(Iteration 16141 / 50000) loss: 11.315796
(Iteration 16151 / 50000) loss: 11.551324
(Iteration 16161 / 50000) loss: 11.917056
(Iteration 16171 / 50000) loss: 12.343707
(Iteration 16181 / 50000) loss: 11.319594
(Iteration 16191 / 50000) loss: 12.700071
(Iteration 16201 / 50000) loss: 11.871254
(Iteration 16211 / 50000) loss: 11.686509
(Iteration 16221 / 50000) loss: 10.915664
(Iteration 16231 / 50000) loss: 10.433999
(Iteration 16241 / 50000) loss: 11.929084
(Iteration 16251 / 50000) loss: 11.677173
(Iteration 16261 / 50000) loss: 12.464382
(Iteration 16271 / 50000) loss: 13.125372
(Iteration 16281 / 50000) loss: 11.932524
(Iteration 16291 / 50000) loss: 13.353013
(Iteration 16301 / 50000) loss: 11.513099
(Iteration 16311 / 50000) loss: 12.218660
(Iteration 16321 / 50000) loss: 12.441169
(Iteration 16331 / 50000) loss: 10.516090
(Iteration 16341 / 50000) loss: 11.981027
(Iteration 16351 / 50000) loss: 11.492979
(Iteration 16361 / 50000) loss: 12.092228
(Iteration 16371 / 50000) loss: 10.927425
(Iteration 16381 / 50000) loss: 12.121218
(Iteration 16391 / 50000) loss: 11.012550
(Iteration 16401 / 50000) loss: 10.793390
(Iteration 16411 / 50000) loss: 11.771472
(Iteration 16421 / 50000) loss: 10.854056
(Iteration 16431 / 50000) loss: 11.578832
(Iteration 16441 / 50000) loss: 10.967962
(Iteration 16451 / 50000) loss: 12.149971
(Iteration 16461 / 50000) loss: 11.593949
(Iteration 16471 / 50000) loss: 11.297883
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(Iteration 16491 / 50000) loss: 11.850568
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(Iteration 16521 / 50000) loss: 10.612351
(Iteration 16531 / 50000) loss: 11.352786
(Iteration 16541 / 50000) loss: 12.435870
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(Iteration 16571 / 50000) loss: 11.587207
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(Iteration 16591 / 50000) loss: 10.828225
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(Iteration 16621 / 50000) loss: 12.170773
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(Iteration 16651 / 50000) loss: 11.584072
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(Iteration 16671 / 50000) loss: 10.833974
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(Iteration 16741 / 50000) loss: 11.562343
(Iteration 16751 / 50000) loss: 12.115902
(Iteration 16761 / 50000) loss: 11.619846
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(Iteration 16781 / 50000) loss: 10.918501
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(Iteration 16811 / 50000) loss: 10.938954
(Iteration 16821 / 50000) loss: 10.678232
(Iteration 16831 / 50000) loss: 10.431115
(Iteration 16841 / 50000) loss: 10.760226
(Iteration 16851 / 50000) loss: 11.543540
(Iteration 16861 / 50000) loss: 10.608817
(Iteration 16871 / 50000) loss: 11.512707
(Iteration 16881 / 50000) loss: 12.005934
(Iteration 16891 / 50000) loss: 11.629787
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(Iteration 16921 / 50000) loss: 10.906245
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(Iteration 16971 / 50000) loss: 10.269312
(Iteration 16981 / 50000) loss: 11.849755
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(Iteration 17011 / 50000) loss: 12.082921
(Iteration 17021 / 50000) loss: 10.585973
(Iteration 17031 / 50000) loss: 11.696827
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(Iteration 17051 / 50000) loss: 11.230368
(Iteration 17061 / 50000) loss: 11.752436
(Iteration 17071 / 50000) loss: 11.603684
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(Iteration 17101 / 50000) loss: 9.507976
(Iteration 17111 / 50000) loss: 10.676257
(Iteration 17121 / 50000) loss: 10.120954
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(Iteration 17141 / 50000) loss: 10.648922
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(Iteration 17161 / 50000) loss: 11.643248
(Iteration 17171 / 50000) loss: 11.120327
(Iteration 17181 / 50000) loss: 11.835344
(Iteration 17191 / 50000) loss: 10.316847
(Iteration 17201 / 50000) loss: 9.736040
(Iteration 17211 / 50000) loss: 10.664222
(Iteration 17221 / 50000) loss: 11.476979
(Iteration 17231 / 50000) loss: 10.947952
(Iteration 17241 / 50000) loss: 10.867407
(Iteration 17251 / 50000) loss: 11.185871
(Iteration 17261 / 50000) loss: 12.049559
(Iteration 17271 / 50000) loss: 11.798857
(Iteration 17281 / 50000) loss: 10.427165
(Iteration 17291 / 50000) loss: 10.785813
(Iteration 17301 / 50000) loss: 10.760485
(Iteration 17311 / 50000) loss: 11.213517
(Iteration 17321 / 50000) loss: 11.020853
(Iteration 17331 / 50000) loss: 10.823077
(Iteration 17341 / 50000) loss: 12.008430
(Iteration 17351 / 50000) loss: 10.311567
(Iteration 17361 / 50000) loss: 11.777992
(Iteration 17371 / 50000) loss: 10.473614
(Iteration 17381 / 50000) loss: 10.306853
(Iteration 17391 / 50000) loss: 11.249818
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(Iteration 17421 / 50000) loss: 11.641846
(Iteration 17431 / 50000) loss: 12.312911
(Iteration 17441 / 50000) loss: 11.472314
(Iteration 17451 / 50000) loss: 11.130931
(Iteration 17461 / 50000) loss: 10.737630
(Iteration 17471 / 50000) loss: 11.932442
(Iteration 17481 / 50000) loss: 12.409054
(Iteration 17491 / 50000) loss: 11.304795
(Iteration 17501 / 50000) loss: 11.216029
(Iteration 17511 / 50000) loss: 10.326899
(Iteration 17521 / 50000) loss: 10.617266
(Iteration 17531 / 50000) loss: 10.728381
(Iteration 17541 / 50000) loss: 11.106885
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(Iteration 17571 / 50000) loss: 10.873164
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(Iteration 17591 / 50000) loss: 9.917652
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(Iteration 17611 / 50000) loss: 9.848011
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(Iteration 17651 / 50000) loss: 11.010025
(Iteration 17661 / 50000) loss: 12.643598
(Iteration 17671 / 50000) loss: 9.629636
(Iteration 17681 / 50000) loss: 10.267055
(Iteration 17691 / 50000) loss: 10.583009
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(Iteration 17741 / 50000) loss: 11.500731
(Iteration 17751 / 50000) loss: 11.172897
(Iteration 17761 / 50000) loss: 11.567817
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(Iteration 17781 / 50000) loss: 10.110706
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(Iteration 17831 / 50000) loss: 10.852395
(Iteration 17841 / 50000) loss: 10.972991
(Iteration 17851 / 50000) loss: 11.338946
(Iteration 17861 / 50000) loss: 9.860258
(Iteration 17871 / 50000) loss: 11.278821
(Iteration 17881 / 50000) loss: 9.505521
(Iteration 17891 / 50000) loss: 10.680456
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(Iteration 17921 / 50000) loss: 11.106735
(Iteration 17931 / 50000) loss: 11.018422
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(Iteration 17971 / 50000) loss: 10.587583
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(Iteration 18011 / 50000) loss: 11.459960
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(Iteration 18061 / 50000) loss: 11.386265
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(Iteration 18091 / 50000) loss: 10.734305
(Iteration 18101 / 50000) loss: 9.896938
(Iteration 18111 / 50000) loss: 10.662252
(Iteration 18121 / 50000) loss: 10.517017
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(Iteration 18141 / 50000) loss: 10.277673
(Iteration 18151 / 50000) loss: 10.729264
(Iteration 18161 / 50000) loss: 10.731922
(Iteration 18171 / 50000) loss: 10.735942
(Iteration 18181 / 50000) loss: 10.696041
(Iteration 18191 / 50000) loss: 9.986263
(Iteration 18201 / 50000) loss: 10.222195
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(Iteration 18221 / 50000) loss: 10.665185
(Iteration 18231 / 50000) loss: 11.205534
(Iteration 18241 / 50000) loss: 10.476006
(Iteration 18251 / 50000) loss: 9.865654
(Iteration 18261 / 50000) loss: 10.725330
(Iteration 18271 / 50000) loss: 10.061578
(Iteration 18281 / 50000) loss: 10.301649
(Iteration 18291 / 50000) loss: 11.359639
(Iteration 18301 / 50000) loss: 10.817521
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(Iteration 18331 / 50000) loss: 11.201472
(Iteration 18341 / 50000) loss: 10.159015
(Iteration 18351 / 50000) loss: 10.291038
(Iteration 18361 / 50000) loss: 9.938997
(Iteration 18371 / 50000) loss: 10.181904
(Iteration 18381 / 50000) loss: 10.849457
(Iteration 18391 / 50000) loss: 10.672127
(Iteration 18401 / 50000) loss: 9.263496
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(Iteration 18421 / 50000) loss: 10.510911
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(Iteration 18451 / 50000) loss: 10.104586
(Iteration 18461 / 50000) loss: 10.301331
(Iteration 18471 / 50000) loss: 10.139678
(Iteration 18481 / 50000) loss: 10.231183
(Iteration 18491 / 50000) loss: 10.070831
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(Iteration 18521 / 50000) loss: 9.898771
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(Iteration 18551 / 50000) loss: 9.938678
(Iteration 18561 / 50000) loss: 10.572805
(Iteration 18571 / 50000) loss: 9.577142
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(Iteration 18591 / 50000) loss: 11.200777
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(Iteration 18621 / 50000) loss: 10.176299
(Iteration 18631 / 50000) loss: 9.983980
(Iteration 18641 / 50000) loss: 9.509213
(Iteration 18651 / 50000) loss: 11.280973
(Iteration 18661 / 50000) loss: 9.929589
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(Iteration 18681 / 50000) loss: 10.918643
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(Iteration 18721 / 50000) loss: 10.719754
(Iteration 18731 / 50000) loss: 10.373231
(Iteration 18741 / 50000) loss: 10.692183
(Iteration 18751 / 50000) loss: 9.868389
(Iteration 18761 / 50000) loss: 10.468767
(Iteration 18771 / 50000) loss: 10.386947
(Iteration 18781 / 50000) loss: 10.767704
(Iteration 18791 / 50000) loss: 10.599739
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(Iteration 18811 / 50000) loss: 10.541379
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(Iteration 18841 / 50000) loss: 10.353258
(Iteration 18851 / 50000) loss: 10.646919
(Iteration 18861 / 50000) loss: 9.628460
(Iteration 18871 / 50000) loss: 10.072590
(Iteration 18881 / 50000) loss: 10.163578
(Iteration 18891 / 50000) loss: 10.620025
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(Iteration 19251 / 50000) loss: 8.938630
(Iteration 19261 / 50000) loss: 9.785966
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(Iteration 19921 / 50000) loss: 9.477805
(Iteration 19931 / 50000) loss: 10.821126
(Iteration 19941 / 50000) loss: 9.760634
(Iteration 19951 / 50000) loss: 9.071098
(Iteration 19961 / 50000) loss: 9.553391
(Iteration 19971 / 50000) loss: 10.082787
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(Iteration 20001 / 50000) loss: 9.777065
(Iteration 20011 / 50000) loss: 10.200590
(Iteration 20021 / 50000) loss: 9.396489
(Iteration 20031 / 50000) loss: 9.583538
(Iteration 20041 / 50000) loss: 8.413725
(Iteration 20051 / 50000) loss: 9.448106
(Iteration 20061 / 50000) loss: 9.316168
(Iteration 20071 / 50000) loss: 9.510789
(Iteration 20081 / 50000) loss: 9.023071
(Iteration 20091 / 50000) loss: 9.622688
(Iteration 20101 / 50000) loss: 9.261488
(Iteration 20111 / 50000) loss: 9.793390
(Iteration 20121 / 50000) loss: 9.341188
(Iteration 20131 / 50000) loss: 8.952203
(Iteration 20141 / 50000) loss: 9.070444
(Iteration 20151 / 50000) loss: 9.737986
(Iteration 20161 / 50000) loss: 10.170590
(Iteration 20171 / 50000) loss: 10.037182
(Iteration 20181 / 50000) loss: 9.335745
(Iteration 20191 / 50000) loss: 10.751371
(Iteration 20201 / 50000) loss: 10.224881
(Iteration 20211 / 50000) loss: 9.202674
(Iteration 20221 / 50000) loss: 9.904796
(Iteration 20231 / 50000) loss: 10.076535
(Iteration 20241 / 50000) loss: 8.995797
(Iteration 20251 / 50000) loss: 9.548426
(Iteration 20261 / 50000) loss: 9.621029
(Iteration 20271 / 50000) loss: 9.837357
(Iteration 20281 / 50000) loss: 8.902033
(Iteration 20291 / 50000) loss: 9.588188
(Iteration 20301 / 50000) loss: 10.330018
(Iteration 20311 / 50000) loss: 10.168080
(Iteration 20321 / 50000) loss: 9.471613
(Iteration 20331 / 50000) loss: 10.226764
(Iteration 20341 / 50000) loss: 10.020547
(Iteration 20351 / 50000) loss: 9.528696
(Iteration 20361 / 50000) loss: 10.326015
(Iteration 20371 / 50000) loss: 9.383863
(Iteration 20381 / 50000) loss: 8.677970
(Iteration 20391 / 50000) loss: 8.657609
(Iteration 20401 / 50000) loss: 9.958138
(Iteration 20411 / 50000) loss: 8.617082
(Iteration 20421 / 50000) loss: 10.035840
(Iteration 20431 / 50000) loss: 9.860879
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(Iteration 20451 / 50000) loss: 9.989788
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(Iteration 20511 / 50000) loss: 9.694217
(Iteration 20521 / 50000) loss: 10.155543
(Iteration 20531 / 50000) loss: 10.752902
(Iteration 20541 / 50000) loss: 9.399323
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(Iteration 20611 / 50000) loss: 8.841669
(Iteration 20621 / 50000) loss: 9.509892
(Iteration 20631 / 50000) loss: 10.198972
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(Iteration 20691 / 50000) loss: 9.157465
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(Iteration 20711 / 50000) loss: 9.925468
(Iteration 20721 / 50000) loss: 10.204095
(Iteration 20731 / 50000) loss: 9.842916
(Iteration 20741 / 50000) loss: 9.267500
(Iteration 20751 / 50000) loss: 9.040181
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(Iteration 20781 / 50000) loss: 10.202384
(Iteration 20791 / 50000) loss: 9.370142
(Iteration 20801 / 50000) loss: 9.515065
(Iteration 20811 / 50000) loss: 9.025579
(Iteration 20821 / 50000) loss: 8.877830
(Iteration 20831 / 50000) loss: 9.769890
(Iteration 20841 / 50000) loss: 9.744019
(Iteration 20851 / 50000) loss: 8.884290
(Iteration 20861 / 50000) loss: 8.885941
(Iteration 20871 / 50000) loss: 8.867761
(Iteration 20881 / 50000) loss: 8.693495
(Iteration 20891 / 50000) loss: 9.639863
(Iteration 20901 / 50000) loss: 8.946441
(Iteration 20911 / 50000) loss: 8.844769
(Iteration 20921 / 50000) loss: 9.994848
(Iteration 20931 / 50000) loss: 10.598706
(Iteration 20941 / 50000) loss: 8.976510
(Iteration 20951 / 50000) loss: 10.003313
(Iteration 20961 / 50000) loss: 9.055531
(Iteration 20971 / 50000) loss: 10.032047
(Iteration 20981 / 50000) loss: 9.168206
(Iteration 20991 / 50000) loss: 9.432503
(Iteration 21001 / 50000) loss: 9.185262
(Iteration 21011 / 50000) loss: 8.727891
(Iteration 21021 / 50000) loss: 9.688075
(Iteration 21031 / 50000) loss: 8.799508
(Iteration 21041 / 50000) loss: 8.574701
(Iteration 21051 / 50000) loss: 8.981119
(Iteration 21061 / 50000) loss: 9.300615
(Iteration 21071 / 50000) loss: 9.969541
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(Iteration 21091 / 50000) loss: 9.362092
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(Iteration 21131 / 50000) loss: 9.154927
(Iteration 21141 / 50000) loss: 9.045409
(Iteration 21151 / 50000) loss: 9.718047
(Iteration 21161 / 50000) loss: 9.917458
(Iteration 21171 / 50000) loss: 9.318614
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(Iteration 21211 / 50000) loss: 9.283163
(Iteration 21221 / 50000) loss: 8.717003
(Iteration 21231 / 50000) loss: 9.008152
(Iteration 21241 / 50000) loss: 10.693272
(Iteration 21251 / 50000) loss: 10.002656
(Iteration 21261 / 50000) loss: 8.647387
(Iteration 21271 / 50000) loss: 9.753452
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(Iteration 21311 / 50000) loss: 8.996730
(Iteration 21321 / 50000) loss: 9.845915
(Iteration 21331 / 50000) loss: 9.271611
(Iteration 21341 / 50000) loss: 9.116343
(Iteration 21351 / 50000) loss: 9.011598
(Iteration 21361 / 50000) loss: 9.384234
(Iteration 21371 / 50000) loss: 10.536725
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(Iteration 21411 / 50000) loss: 7.527517
(Iteration 21421 / 50000) loss: 8.748132
(Iteration 21431 / 50000) loss: 8.999865
(Iteration 21441 / 50000) loss: 8.804122
(Iteration 21451 / 50000) loss: 9.144144
(Iteration 21461 / 50000) loss: 8.223530
(Iteration 21471 / 50000) loss: 9.212712
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(Iteration 21511 / 50000) loss: 8.838616
(Iteration 21521 / 50000) loss: 9.390242
(Iteration 21531 / 50000) loss: 8.581557
(Iteration 21541 / 50000) loss: 10.073692
(Iteration 21551 / 50000) loss: 9.883387
(Iteration 21561 / 50000) loss: 8.301261
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(Iteration 21631 / 50000) loss: 8.476268
(Iteration 21641 / 50000) loss: 10.124056
(Iteration 21651 / 50000) loss: 8.884519
(Iteration 21661 / 50000) loss: 9.600210
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(Iteration 21711 / 50000) loss: 8.802332
(Iteration 21721 / 50000) loss: 8.843689
(Iteration 21731 / 50000) loss: 8.633809
(Iteration 21741 / 50000) loss: 8.557644
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(Iteration 21761 / 50000) loss: 8.968644
(Iteration 21771 / 50000) loss: 8.731195
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(Iteration 21791 / 50000) loss: 9.121968
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(Iteration 21811 / 50000) loss: 8.864856
(Iteration 21821 / 50000) loss: 8.169686
(Iteration 21831 / 50000) loss: 9.910982
(Iteration 21841 / 50000) loss: 9.550921
(Iteration 21851 / 50000) loss: 8.846084
(Iteration 21861 / 50000) loss: 8.145724
(Iteration 21871 / 50000) loss: 8.793788
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(Iteration 21931 / 50000) loss: 8.747023
(Iteration 21941 / 50000) loss: 9.139087
(Iteration 21951 / 50000) loss: 9.168682
(Iteration 21961 / 50000) loss: 8.836778
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(Iteration 22011 / 50000) loss: 9.281360
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(Iteration 22031 / 50000) loss: 8.616042
(Iteration 22041 / 50000) loss: 8.397938
(Iteration 22051 / 50000) loss: 8.591973
(Iteration 22061 / 50000) loss: 9.027519
(Iteration 22071 / 50000) loss: 8.862233
(Iteration 22081 / 50000) loss: 8.952689
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(Iteration 22161 / 50000) loss: 9.070115
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(Iteration 22211 / 50000) loss: 9.082031
(Iteration 22221 / 50000) loss: 7.490505
(Iteration 22231 / 50000) loss: 9.172272
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(Iteration 22251 / 50000) loss: 8.047768
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(Iteration 22311 / 50000) loss: 8.266673
(Iteration 22321 / 50000) loss: 8.783620
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(Iteration 22341 / 50000) loss: 9.829550
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(Iteration 22421 / 50000) loss: 8.376865
(Iteration 22431 / 50000) loss: 8.494549
(Iteration 22441 / 50000) loss: 8.323891
(Iteration 22451 / 50000) loss: 9.479876
(Iteration 22461 / 50000) loss: 8.667582
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(Iteration 22541 / 50000) loss: 9.260224
(Iteration 22551 / 50000) loss: 8.779277
(Iteration 22561 / 50000) loss: 7.209487
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(Iteration 22621 / 50000) loss: 9.042249
(Iteration 22631 / 50000) loss: 8.448622
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(Iteration 22661 / 50000) loss: 9.279467
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(Iteration 22721 / 50000) loss: 9.018317
(Iteration 22731 / 50000) loss: 9.069303
(Iteration 22741 / 50000) loss: 8.406179
(Iteration 22751 / 50000) loss: 9.420952
(Iteration 22761 / 50000) loss: 8.667871
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(Iteration 22811 / 50000) loss: 8.858448
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(Iteration 22831 / 50000) loss: 9.007707
(Iteration 22841 / 50000) loss: 9.006035
(Iteration 22851 / 50000) loss: 8.809841
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(Iteration 22931 / 50000) loss: 8.033333
(Iteration 22941 / 50000) loss: 8.553583
(Iteration 22951 / 50000) loss: 8.059711
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(Iteration 23031 / 50000) loss: 9.122428
(Iteration 23041 / 50000) loss: 8.448828
(Iteration 23051 / 50000) loss: 8.058612
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(Iteration 23191 / 50000) loss: 8.249695
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(Iteration 23231 / 50000) loss: 7.781013
(Iteration 23241 / 50000) loss: 8.920095
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(Iteration 23331 / 50000) loss: 9.300387
(Iteration 23341 / 50000) loss: 7.841499
(Iteration 23351 / 50000) loss: 8.853027
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(Iteration 23431 / 50000) loss: 8.751678
(Iteration 23441 / 50000) loss: 8.397959
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(Iteration 23461 / 50000) loss: 8.305688
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(Iteration 23621 / 50000) loss: 7.682929
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(Iteration 24041 / 50000) loss: 7.597805
(Iteration 24051 / 50000) loss: 7.944381
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(Iteration 24431 / 50000) loss: 7.408210
(Iteration 24441 / 50000) loss: 8.435351
(Iteration 24451 / 50000) loss: 8.432334
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(Iteration 24471 / 50000) loss: 7.811767
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(Iteration 24491 / 50000) loss: 7.145633
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(Iteration 24511 / 50000) loss: 8.464608
(Iteration 24521 / 50000) loss: 7.616299
(Iteration 24531 / 50000) loss: 7.902122
(Iteration 24541 / 50000) loss: 7.992880
(Iteration 24551 / 50000) loss: 7.681488
(Iteration 24561 / 50000) loss: 8.211501
(Iteration 24571 / 50000) loss: 7.505250
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(Iteration 24661 / 50000) loss: 8.520651
(Iteration 24671 / 50000) loss: 8.440612
(Iteration 24681 / 50000) loss: 7.848160
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(Iteration 24721 / 50000) loss: 8.249089
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(Iteration 24781 / 50000) loss: 7.430469
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(Iteration 24841 / 50000) loss: 7.264117
(Iteration 24851 / 50000) loss: 7.802521
(Iteration 24861 / 50000) loss: 7.479374
(Iteration 24871 / 50000) loss: 8.474685
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(Iteration 24921 / 50000) loss: 7.856967
(Iteration 24931 / 50000) loss: 7.748634
(Iteration 24941 / 50000) loss: 8.301973
(Iteration 24951 / 50000) loss: 8.079628
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(Iteration 24971 / 50000) loss: 8.267796
(Iteration 24981 / 50000) loss: 8.312485
(Iteration 24991 / 50000) loss: 7.555561
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(Iteration 25041 / 50000) loss: 7.867768
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(Iteration 25061 / 50000) loss: 7.927852
(Iteration 25071 / 50000) loss: 8.203074
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(Iteration 25111 / 50000) loss: 7.586772
(Iteration 25121 / 50000) loss: 7.877896
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(Iteration 25151 / 50000) loss: 7.743054
(Iteration 25161 / 50000) loss: 7.802484
(Iteration 25171 / 50000) loss: 8.541362
(Iteration 25181 / 50000) loss: 8.437155
(Iteration 25191 / 50000) loss: 8.459423
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(Iteration 25211 / 50000) loss: 7.311219
(Iteration 25221 / 50000) loss: 7.971913
(Iteration 25231 / 50000) loss: 8.413351
(Iteration 25241 / 50000) loss: 7.441964
(Iteration 25251 / 50000) loss: 8.375287
(Iteration 25261 / 50000) loss: 7.825249
(Iteration 25271 / 50000) loss: 8.515885
(Iteration 25281 / 50000) loss: 8.656430
(Iteration 25291 / 50000) loss: 7.531369
(Iteration 25301 / 50000) loss: 8.111380
(Iteration 25311 / 50000) loss: 7.571697
(Iteration 25321 / 50000) loss: 7.518395
(Iteration 25331 / 50000) loss: 7.293624
(Iteration 25341 / 50000) loss: 8.269860
(Iteration 25351 / 50000) loss: 7.759951
(Iteration 25361 / 50000) loss: 7.336838
(Iteration 25371 / 50000) loss: 7.722897
(Iteration 25381 / 50000) loss: 8.387203
(Iteration 25391 / 50000) loss: 7.867406
(Iteration 25401 / 50000) loss: 8.209749
(Iteration 25411 / 50000) loss: 8.485877
(Iteration 25421 / 50000) loss: 8.254173
(Iteration 25431 / 50000) loss: 7.808305
(Iteration 25441 / 50000) loss: 8.051764
(Iteration 25451 / 50000) loss: 7.581742
(Iteration 25461 / 50000) loss: 7.510599
(Iteration 25471 / 50000) loss: 8.668167
(Iteration 25481 / 50000) loss: 7.781871
(Iteration 25491 / 50000) loss: 7.889341
(Iteration 25501 / 50000) loss: 6.697689
(Iteration 25511 / 50000) loss: 8.401791
(Iteration 25521 / 50000) loss: 7.569456
(Iteration 25531 / 50000) loss: 7.893210
(Iteration 25541 / 50000) loss: 7.516563
(Iteration 25551 / 50000) loss: 7.389350
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(Iteration 25611 / 50000) loss: 8.277686
(Iteration 25621 / 50000) loss: 8.270017
(Iteration 25631 / 50000) loss: 7.537334
(Iteration 25641 / 50000) loss: 6.518239
(Iteration 25651 / 50000) loss: 8.112953
(Iteration 25661 / 50000) loss: 7.895146
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(Iteration 25681 / 50000) loss: 8.310107
(Iteration 25691 / 50000) loss: 7.508219
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(Iteration 25711 / 50000) loss: 7.554858
(Iteration 25721 / 50000) loss: 7.688189
(Iteration 25731 / 50000) loss: 7.181843
(Iteration 25741 / 50000) loss: 7.363787
(Iteration 25751 / 50000) loss: 7.756234
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(Iteration 25841 / 50000) loss: 8.458892
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(Iteration 25871 / 50000) loss: 7.540890
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(Iteration 25911 / 50000) loss: 7.807913
(Iteration 25921 / 50000) loss: 7.781466
(Iteration 25931 / 50000) loss: 8.119266
(Iteration 25941 / 50000) loss: 7.911963
(Iteration 25951 / 50000) loss: 7.865986
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(Iteration 25971 / 50000) loss: 7.524239
(Iteration 25981 / 50000) loss: 8.441215
(Iteration 25991 / 50000) loss: 7.928577
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(Iteration 26041 / 50000) loss: 7.627136
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(Iteration 26171 / 50000) loss: 8.316936
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(Iteration 26251 / 50000) loss: 7.800477
(Iteration 26261 / 50000) loss: 8.179725
(Iteration 26271 / 50000) loss: 7.441531
(Iteration 26281 / 50000) loss: 7.327582
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(Iteration 26311 / 50000) loss: 8.223009
(Iteration 26321 / 50000) loss: 8.616586
(Iteration 26331 / 50000) loss: 8.025665
(Iteration 26341 / 50000) loss: 7.376438
(Iteration 26351 / 50000) loss: 8.549404
(Iteration 26361 / 50000) loss: 7.704421
(Iteration 26371 / 50000) loss: 7.690894
(Iteration 26381 / 50000) loss: 7.186907
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(Iteration 26421 / 50000) loss: 6.856884
(Iteration 26431 / 50000) loss: 8.013830
(Iteration 26441 / 50000) loss: 6.917641
(Iteration 26451 / 50000) loss: 7.342820
(Iteration 26461 / 50000) loss: 7.958167
(Iteration 26471 / 50000) loss: 7.575996
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(Iteration 26521 / 50000) loss: 7.858870
(Iteration 26531 / 50000) loss: 7.991191
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(Iteration 26561 / 50000) loss: 7.633142
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(Iteration 26641 / 50000) loss: 8.138605
(Iteration 26651 / 50000) loss: 6.790598
(Iteration 26661 / 50000) loss: 7.226410
(Iteration 26671 / 50000) loss: 8.210968
(Iteration 26681 / 50000) loss: 7.818577
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(Iteration 26711 / 50000) loss: 8.765531
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(Iteration 26731 / 50000) loss: 7.621042
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(Iteration 26751 / 50000) loss: 8.011016
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(Iteration 26851 / 50000) loss: 7.187354
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(Iteration 27221 / 50000) loss: 7.947496
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(Iteration 27241 / 50000) loss: 6.816972
(Iteration 27251 / 50000) loss: 7.381038
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(Iteration 27291 / 50000) loss: 8.787505
(Iteration 27301 / 50000) loss: 8.010296
(Iteration 27311 / 50000) loss: 7.515618
(Iteration 27321 / 50000) loss: 6.978251
(Iteration 27331 / 50000) loss: 7.176751
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(Iteration 27351 / 50000) loss: 8.022135
(Iteration 27361 / 50000) loss: 7.534135
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(Iteration 27381 / 50000) loss: 7.046296
(Iteration 27391 / 50000) loss: 7.580141
(Iteration 27401 / 50000) loss: 7.130490
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(Iteration 27421 / 50000) loss: 7.817950
(Iteration 27431 / 50000) loss: 8.364658
(Iteration 27441 / 50000) loss: 6.100786
(Iteration 27451 / 50000) loss: 6.867558
(Iteration 27461 / 50000) loss: 6.241940
(Iteration 27471 / 50000) loss: 7.924798
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(Iteration 27491 / 50000) loss: 7.380916
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(Iteration 27651 / 50000) loss: 6.979448
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(Iteration 27671 / 50000) loss: 7.402050
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(Iteration 27841 / 50000) loss: 6.895704
(Iteration 27851 / 50000) loss: 6.838322
(Iteration 27861 / 50000) loss: 6.938224
(Iteration 27871 / 50000) loss: 7.248393
(Iteration 27881 / 50000) loss: 8.215472
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(Iteration 27961 / 50000) loss: 6.778589
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(Iteration 28011 / 50000) loss: 7.368647
(Iteration 28021 / 50000) loss: 6.531797
(Iteration 28031 / 50000) loss: 6.681814
(Iteration 28041 / 50000) loss: 7.156660
(Iteration 28051 / 50000) loss: 6.751592
(Iteration 28061 / 50000) loss: 6.833828
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(Iteration 28111 / 50000) loss: 7.864802
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(Iteration 28151 / 50000) loss: 6.836557
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(Iteration 29011 / 50000) loss: 7.251649
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(Iteration 29421 / 50000) loss: 6.376636
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(Iteration 29511 / 50000) loss: 6.600810
(Iteration 29521 / 50000) loss: 7.354079
(Iteration 29531 / 50000) loss: 7.072084
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(Iteration 29571 / 50000) loss: 7.462034
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(Iteration 29631 / 50000) loss: 6.584206
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(Iteration 30251 / 50000) loss: 7.118362
(Iteration 30261 / 50000) loss: 7.280914
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(Iteration 33811 / 50000) loss: 6.084623
(Iteration 33821 / 50000) loss: 6.179813
(Iteration 33831 / 50000) loss: 6.551014
(Iteration 33841 / 50000) loss: 5.901298
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(Iteration 33921 / 50000) loss: 6.615595
(Iteration 33931 / 50000) loss: 5.465714
(Iteration 33941 / 50000) loss: 5.814246
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(Iteration 34041 / 50000) loss: 6.703792
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(Iteration 34161 / 50000) loss: 5.892824
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(Iteration 34201 / 50000) loss: 5.745043
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(Iteration 34251 / 50000) loss: 6.210519
(Iteration 34261 / 50000) loss: 6.547621
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(Iteration 34281 / 50000) loss: 6.310601
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(Iteration 34361 / 50000) loss: 6.245280
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(Iteration 34671 / 50000) loss: 6.456310
(Iteration 34681 / 50000) loss: 6.438612
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(Iteration 34741 / 50000) loss: 6.698931
(Iteration 34751 / 50000) loss: 6.491751
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(Iteration 34771 / 50000) loss: 5.842072
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(Iteration 35261 / 50000) loss: 5.356376
(Iteration 35271 / 50000) loss: 5.806860
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(Iteration 35381 / 50000) loss: 5.965900
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(Iteration 37251 / 50000) loss: 5.849229
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(Iteration 38361 / 50000) loss: 6.148258
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(Iteration 38411 / 50000) loss: 5.806882
(Iteration 38421 / 50000) loss: 4.645729
(Iteration 38431 / 50000) loss: 5.334190
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(Iteration 38451 / 50000) loss: 6.112029
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(Iteration 38571 / 50000) loss: 5.316460
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(Iteration 38731 / 50000) loss: 5.489800
(Iteration 38741 / 50000) loss: 5.669317
(Iteration 38751 / 50000) loss: 5.528761
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(Iteration 38821 / 50000) loss: 5.260470
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(Iteration 39291 / 50000) loss: 4.744284
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(Iteration 39311 / 50000) loss: 5.188102
(Iteration 39321 / 50000) loss: 5.625708
(Iteration 39331 / 50000) loss: 5.119836
(Iteration 39341 / 50000) loss: 5.973935
(Iteration 39351 / 50000) loss: 5.762032
(Iteration 39361 / 50000) loss: 5.558610
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(Iteration 39421 / 50000) loss: 5.506855
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(Iteration 42921 / 50000) loss: 5.026462
(Iteration 42931 / 50000) loss: 5.738497
(Iteration 42941 / 50000) loss: 5.354862
(Iteration 42951 / 50000) loss: 4.939656
(Iteration 42961 / 50000) loss: 5.010988
(Iteration 42971 / 50000) loss: 5.457916
(Iteration 42981 / 50000) loss: 5.491435
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(Iteration 43031 / 50000) loss: 5.390814
(Iteration 43041 / 50000) loss: 4.811515
(Iteration 43051 / 50000) loss: 4.543898
(Iteration 43061 / 50000) loss: 4.750349
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(Iteration 43161 / 50000) loss: 4.976221
(Iteration 43171 / 50000) loss: 5.169958
(Iteration 43181 / 50000) loss: 5.335230
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(Iteration 43211 / 50000) loss: 5.372652
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(Iteration 43231 / 50000) loss: 4.982352
(Iteration 43241 / 50000) loss: 5.456767
(Iteration 43251 / 50000) loss: 5.481292
(Iteration 43261 / 50000) loss: 4.794006
(Iteration 43271 / 50000) loss: 5.247368
(Iteration 43281 / 50000) loss: 5.442085
(Iteration 43291 / 50000) loss: 5.141669
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(Iteration 43311 / 50000) loss: 5.523241
(Iteration 43321 / 50000) loss: 5.006747
(Iteration 43331 / 50000) loss: 5.515036
(Iteration 43341 / 50000) loss: 5.596003
(Iteration 43351 / 50000) loss: 5.155901
(Iteration 43361 / 50000) loss: 4.882652
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(Iteration 43411 / 50000) loss: 4.842057
(Iteration 43421 / 50000) loss: 5.789438
(Iteration 43431 / 50000) loss: 4.935156
(Iteration 43441 / 50000) loss: 4.902918
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(Iteration 43511 / 50000) loss: 4.748884
(Iteration 43521 / 50000) loss: 5.338470
(Iteration 43531 / 50000) loss: 5.498346
(Iteration 43541 / 50000) loss: 5.450516
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(Iteration 43621 / 50000) loss: 4.735703
(Iteration 43631 / 50000) loss: 5.233298
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(Iteration 43661 / 50000) loss: 5.331033
(Iteration 43671 / 50000) loss: 4.994873
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(Iteration 43721 / 50000) loss: 4.648491
(Iteration 43731 / 50000) loss: 5.694596
(Iteration 43741 / 50000) loss: 4.956635
(Iteration 43751 / 50000) loss: 5.453378
(Iteration 43761 / 50000) loss: 4.681377
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(Iteration 43811 / 50000) loss: 5.090594
(Iteration 43821 / 50000) loss: 4.527797
(Iteration 43831 / 50000) loss: 5.231988
(Iteration 43841 / 50000) loss: 5.311808
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(Iteration 43861 / 50000) loss: 5.191983
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(Iteration 43901 / 50000) loss: 4.828996
(Iteration 43911 / 50000) loss: 4.487235
(Iteration 43921 / 50000) loss: 4.613873
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(Iteration 43941 / 50000) loss: 5.201627
(Iteration 43951 / 50000) loss: 5.193782
(Iteration 43961 / 50000) loss: 4.230243
(Iteration 43971 / 50000) loss: 5.253839
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(Iteration 44011 / 50000) loss: 5.512025
(Iteration 44021 / 50000) loss: 5.463587
(Iteration 44031 / 50000) loss: 4.924236
(Iteration 44041 / 50000) loss: 5.111295
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(Iteration 44061 / 50000) loss: 5.705987
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(Iteration 44101 / 50000) loss: 5.905477
(Iteration 44111 / 50000) loss: 5.294932
(Iteration 44121 / 50000) loss: 4.766038
(Iteration 44131 / 50000) loss: 5.371398
(Iteration 44141 / 50000) loss: 4.978839
(Iteration 44151 / 50000) loss: 5.120658
(Iteration 44161 / 50000) loss: 5.636966
(Iteration 44171 / 50000) loss: 6.027669
(Iteration 44181 / 50000) loss: 5.119032
(Iteration 44191 / 50000) loss: 5.548153
(Iteration 44201 / 50000) loss: 5.181743
(Iteration 44211 / 50000) loss: 4.682301
(Iteration 44221 / 50000) loss: 4.806877
(Iteration 44231 / 50000) loss: 5.224691
(Iteration 44241 / 50000) loss: 4.918099
(Iteration 44251 / 50000) loss: 5.199224
(Iteration 44261 / 50000) loss: 4.394568
(Iteration 44271 / 50000) loss: 4.993088
(Iteration 44281 / 50000) loss: 5.385722
(Iteration 44291 / 50000) loss: 5.562110
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(Iteration 44311 / 50000) loss: 5.045357
(Iteration 44321 / 50000) loss: 5.174937
(Iteration 44331 / 50000) loss: 4.578071
(Iteration 44341 / 50000) loss: 4.266064
(Iteration 44351 / 50000) loss: 4.413052
(Iteration 44361 / 50000) loss: 5.149018
(Iteration 44371 / 50000) loss: 5.931766
(Iteration 44381 / 50000) loss: 5.460862
(Iteration 44391 / 50000) loss: 5.566345
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(Iteration 44411 / 50000) loss: 4.705345
(Iteration 44421 / 50000) loss: 5.176616
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(Iteration 44441 / 50000) loss: 4.821331
(Iteration 44451 / 50000) loss: 5.513298
(Iteration 44461 / 50000) loss: 5.112529
(Iteration 44471 / 50000) loss: 5.689818
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(Iteration 44511 / 50000) loss: 4.942736
(Iteration 44521 / 50000) loss: 5.065837
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(Iteration 44561 / 50000) loss: 5.401501
(Iteration 44571 / 50000) loss: 5.619292
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(Iteration 44611 / 50000) loss: 5.384250
(Iteration 44621 / 50000) loss: 4.977408
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(Iteration 44711 / 50000) loss: 5.426543
(Iteration 44721 / 50000) loss: 4.744429
(Iteration 44731 / 50000) loss: 5.307176
(Iteration 44741 / 50000) loss: 5.617985
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(Iteration 44881 / 50000) loss: 4.756216
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(Iteration 44921 / 50000) loss: 5.736442
(Iteration 44931 / 50000) loss: 5.367849
(Iteration 44941 / 50000) loss: 4.963418
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(Iteration 45041 / 50000) loss: 4.773791
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(Iteration 45121 / 50000) loss: 5.315617
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(Iteration 45161 / 50000) loss: 5.348040
(Iteration 45171 / 50000) loss: 5.267598
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(Iteration 45191 / 50000) loss: 4.964868
(Iteration 45201 / 50000) loss: 5.160988
(Iteration 45211 / 50000) loss: 4.733652
(Iteration 45221 / 50000) loss: 5.165364
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(Iteration 45241 / 50000) loss: 5.025283
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(Iteration 45261 / 50000) loss: 4.798735
(Iteration 45271 / 50000) loss: 5.211899
(Iteration 45281 / 50000) loss: 5.138305
(Iteration 45291 / 50000) loss: 5.078218
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(Iteration 45351 / 50000) loss: 4.868462
(Iteration 45361 / 50000) loss: 4.736218
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(Iteration 45881 / 50000) loss: 4.702260
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(Iteration 45921 / 50000) loss: 5.686111
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(Iteration 45961 / 50000) loss: 5.873787
(Iteration 45971 / 50000) loss: 4.990044
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(Iteration 46281 / 50000) loss: 5.326949
(Iteration 46291 / 50000) loss: 5.530299
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(Iteration 46351 / 50000) loss: 4.691865
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(Iteration 47191 / 50000) loss: 5.489570
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(Iteration 47521 / 50000) loss: 5.317827
(Iteration 47531 / 50000) loss: 5.036849
(Iteration 47541 / 50000) loss: 4.934437
(Iteration 47551 / 50000) loss: 4.745998
(Iteration 47561 / 50000) loss: 5.637244
(Iteration 47571 / 50000) loss: 5.016647
(Iteration 47581 / 50000) loss: 4.857729
(Iteration 47591 / 50000) loss: 5.156177
(Iteration 47601 / 50000) loss: 4.976842
(Iteration 47611 / 50000) loss: 4.302502
(Iteration 47621 / 50000) loss: 5.024882
(Iteration 47631 / 50000) loss: 4.956566
(Iteration 47641 / 50000) loss: 4.656335
(Iteration 47651 / 50000) loss: 5.045079
(Iteration 47661 / 50000) loss: 4.869484
(Iteration 47671 / 50000) loss: 4.858334
(Iteration 47681 / 50000) loss: 5.172522
(Iteration 47691 / 50000) loss: 4.730553
(Iteration 47701 / 50000) loss: 5.344105
(Iteration 47711 / 50000) loss: 4.761704
(Iteration 47721 / 50000) loss: 4.274773
(Iteration 47731 / 50000) loss: 5.347785
(Iteration 47741 / 50000) loss: 5.154635
(Iteration 47751 / 50000) loss: 4.821087
(Iteration 47761 / 50000) loss: 5.046308
(Iteration 47771 / 50000) loss: 5.055055
(Iteration 47781 / 50000) loss: 4.725170
(Iteration 47791 / 50000) loss: 4.527396
(Iteration 47801 / 50000) loss: 4.487073
(Iteration 47811 / 50000) loss: 4.639754
(Iteration 47821 / 50000) loss: 5.402688
(Iteration 47831 / 50000) loss: 5.358788
(Iteration 47841 / 50000) loss: 5.388831
(Iteration 47851 / 50000) loss: 5.539782
(Iteration 47861 / 50000) loss: 4.977380
(Iteration 47871 / 50000) loss: 4.743683
(Iteration 47881 / 50000) loss: 5.744875
(Iteration 47891 / 50000) loss: 4.760842
(Iteration 47901 / 50000) loss: 4.671541
(Iteration 47911 / 50000) loss: 5.298233
(Iteration 47921 / 50000) loss: 5.325019
(Iteration 47931 / 50000) loss: 5.079854
(Iteration 47941 / 50000) loss: 4.849681
(Iteration 47951 / 50000) loss: 5.136692
(Iteration 47961 / 50000) loss: 4.513616
(Iteration 47971 / 50000) loss: 5.502441
(Iteration 47981 / 50000) loss: 4.783224
(Iteration 47991 / 50000) loss: 4.970872
(Iteration 48001 / 50000) loss: 5.214979
(Iteration 48011 / 50000) loss: 5.029698
(Iteration 48021 / 50000) loss: 5.374228
(Iteration 48031 / 50000) loss: 4.867151
(Iteration 48041 / 50000) loss: 4.729684
(Iteration 48051 / 50000) loss: 5.083028
(Iteration 48061 / 50000) loss: 5.130319
(Iteration 48071 / 50000) loss: 5.140748
(Iteration 48081 / 50000) loss: 4.854952
(Iteration 48091 / 50000) loss: 4.345643
(Iteration 48101 / 50000) loss: 5.858382
(Iteration 48111 / 50000) loss: 4.692747
(Iteration 48121 / 50000) loss: 4.796843
(Iteration 48131 / 50000) loss: 5.054175
(Iteration 48141 / 50000) loss: 4.408984
(Iteration 48151 / 50000) loss: 5.586374
(Iteration 48161 / 50000) loss: 4.729210
(Iteration 48171 / 50000) loss: 4.956761
(Iteration 48181 / 50000) loss: 4.486281
(Iteration 48191 / 50000) loss: 5.410888
(Iteration 48201 / 50000) loss: 4.483176
(Iteration 48211 / 50000) loss: 5.062662
(Iteration 48221 / 50000) loss: 4.866526
(Iteration 48231 / 50000) loss: 5.485916
(Iteration 48241 / 50000) loss: 4.921504
(Iteration 48251 / 50000) loss: 4.959250
(Iteration 48261 / 50000) loss: 4.725866
(Iteration 48271 / 50000) loss: 5.965253
(Iteration 48281 / 50000) loss: 4.680338
(Iteration 48291 / 50000) loss: 5.103705
(Iteration 48301 / 50000) loss: 4.808215
(Iteration 48311 / 50000) loss: 4.575053
(Iteration 48321 / 50000) loss: 4.947476
(Iteration 48331 / 50000) loss: 4.887461
(Iteration 48341 / 50000) loss: 4.466528
(Iteration 48351 / 50000) loss: 5.151607
(Iteration 48361 / 50000) loss: 4.767912
(Iteration 48371 / 50000) loss: 5.488635
(Iteration 48381 / 50000) loss: 5.102046
(Iteration 48391 / 50000) loss: 4.570899
(Iteration 48401 / 50000) loss: 4.521377
(Iteration 48411 / 50000) loss: 5.012564
(Iteration 48421 / 50000) loss: 4.801443
(Iteration 48431 / 50000) loss: 5.175388
(Iteration 48441 / 50000) loss: 4.981082
(Iteration 48451 / 50000) loss: 4.862914
(Iteration 48461 / 50000) loss: 4.733837
(Iteration 48471 / 50000) loss: 4.818900
(Iteration 48481 / 50000) loss: 5.991330
(Iteration 48491 / 50000) loss: 5.208624
(Iteration 48501 / 50000) loss: 4.944359
(Iteration 48511 / 50000) loss: 4.814369
(Iteration 48521 / 50000) loss: 4.469385
(Iteration 48531 / 50000) loss: 4.430485
(Iteration 48541 / 50000) loss: 5.097204
(Iteration 48551 / 50000) loss: 4.905378
(Iteration 48561 / 50000) loss: 5.210978
(Iteration 48571 / 50000) loss: 4.874699
(Iteration 48581 / 50000) loss: 4.572843
(Iteration 48591 / 50000) loss: 4.395446
(Iteration 48601 / 50000) loss: 5.059456
(Iteration 48611 / 50000) loss: 4.589462
(Iteration 48621 / 50000) loss: 4.658811
(Iteration 48631 / 50000) loss: 4.764175
(Iteration 48641 / 50000) loss: 5.207649
(Iteration 48651 / 50000) loss: 4.526176
(Iteration 48661 / 50000) loss: 5.004376
(Iteration 48671 / 50000) loss: 4.787407
(Iteration 48681 / 50000) loss: 5.527824
(Iteration 48691 / 50000) loss: 4.157642
(Iteration 48701 / 50000) loss: 4.825883
(Iteration 48711 / 50000) loss: 4.675910
(Iteration 48721 / 50000) loss: 4.928158
(Iteration 48731 / 50000) loss: 4.308931
(Iteration 48741 / 50000) loss: 5.254661
(Iteration 48751 / 50000) loss: 5.354953
(Iteration 48761 / 50000) loss: 5.408144
(Iteration 48771 / 50000) loss: 4.914572
(Iteration 48781 / 50000) loss: 4.659526
(Iteration 48791 / 50000) loss: 5.600999
(Iteration 48801 / 50000) loss: 4.432065
(Iteration 48811 / 50000) loss: 4.790988
(Iteration 48821 / 50000) loss: 5.317098
(Iteration 48831 / 50000) loss: 5.207159
(Iteration 48841 / 50000) loss: 5.578248
(Iteration 48851 / 50000) loss: 4.997896
(Iteration 48861 / 50000) loss: 5.171075
(Iteration 48871 / 50000) loss: 5.497323
(Iteration 48881 / 50000) loss: 5.232023
(Iteration 48891 / 50000) loss: 5.425415
(Iteration 48901 / 50000) loss: 4.654369
(Iteration 48911 / 50000) loss: 4.663005
(Iteration 48921 / 50000) loss: 5.192826
(Iteration 48931 / 50000) loss: 5.197409
(Iteration 48941 / 50000) loss: 5.317922
(Iteration 48951 / 50000) loss: 4.355244
(Iteration 48961 / 50000) loss: 4.449171
(Iteration 48971 / 50000) loss: 5.042314
(Iteration 48981 / 50000) loss: 4.645004
(Iteration 48991 / 50000) loss: 4.430694
(Iteration 49001 / 50000) loss: 5.003181
(Iteration 49011 / 50000) loss: 5.253469
(Iteration 49021 / 50000) loss: 4.921456
(Iteration 49031 / 50000) loss: 4.725998
(Iteration 49041 / 50000) loss: 4.830995
(Iteration 49051 / 50000) loss: 5.074775
(Iteration 49061 / 50000) loss: 5.043628
(Iteration 49071 / 50000) loss: 5.892237
(Iteration 49081 / 50000) loss: 4.516867
(Iteration 49091 / 50000) loss: 4.594828
(Iteration 49101 / 50000) loss: 5.265089
(Iteration 49111 / 50000) loss: 5.262130
(Iteration 49121 / 50000) loss: 4.883457
(Iteration 49131 / 50000) loss: 4.819940
(Iteration 49141 / 50000) loss: 5.205019
(Iteration 49151 / 50000) loss: 4.514503
(Iteration 49161 / 50000) loss: 4.811991
(Iteration 49171 / 50000) loss: 4.889469
(Iteration 49181 / 50000) loss: 5.090885
(Iteration 49191 / 50000) loss: 5.302499
(Iteration 49201 / 50000) loss: 5.148423
(Iteration 49211 / 50000) loss: 4.763197
(Iteration 49221 / 50000) loss: 5.171199
(Iteration 49231 / 50000) loss: 4.374295
(Iteration 49241 / 50000) loss: 4.803613
(Iteration 49251 / 50000) loss: 5.126912
(Iteration 49261 / 50000) loss: 4.804142
(Iteration 49271 / 50000) loss: 5.043730
(Iteration 49281 / 50000) loss: 5.190020
(Iteration 49291 / 50000) loss: 4.927842
(Iteration 49301 / 50000) loss: 4.728107
(Iteration 49311 / 50000) loss: 5.266110
(Iteration 49321 / 50000) loss: 4.995474
(Iteration 49331 / 50000) loss: 4.857098
(Iteration 49341 / 50000) loss: 4.656197
(Iteration 49351 / 50000) loss: 4.731958
(Iteration 49361 / 50000) loss: 5.098492
(Iteration 49371 / 50000) loss: 4.245257
(Iteration 49381 / 50000) loss: 5.184144
(Iteration 49391 / 50000) loss: 5.147379
(Iteration 49401 / 50000) loss: 4.893561
(Iteration 49411 / 50000) loss: 5.101745
(Iteration 49421 / 50000) loss: 4.933324
(Iteration 49431 / 50000) loss: 4.726953
(Iteration 49441 / 50000) loss: 5.169777
(Iteration 49451 / 50000) loss: 4.991100
(Iteration 49461 / 50000) loss: 5.293823
(Iteration 49471 / 50000) loss: 4.813910
(Iteration 49481 / 50000) loss: 4.551907
(Iteration 49491 / 50000) loss: 5.255053
(Iteration 49501 / 50000) loss: 4.211944
(Iteration 49511 / 50000) loss: 4.735895
(Iteration 49521 / 50000) loss: 4.344431
(Iteration 49531 / 50000) loss: 5.567289
(Iteration 49541 / 50000) loss: 5.114772
(Iteration 49551 / 50000) loss: 4.918598
(Iteration 49561 / 50000) loss: 5.111366
(Iteration 49571 / 50000) loss: 4.990022
(Iteration 49581 / 50000) loss: 4.785156
(Iteration 49591 / 50000) loss: 4.968936
(Iteration 49601 / 50000) loss: 4.839501
(Iteration 49611 / 50000) loss: 4.541588
(Iteration 49621 / 50000) loss: 5.169776
(Iteration 49631 / 50000) loss: 5.478606
(Iteration 49641 / 50000) loss: 4.937177
(Iteration 49651 / 50000) loss: 4.201233
(Iteration 49661 / 50000) loss: 4.551475
(Iteration 49671 / 50000) loss: 4.787985
(Iteration 49681 / 50000) loss: 4.883517
(Iteration 49691 / 50000) loss: 4.352181
(Iteration 49701 / 50000) loss: 5.384501
(Iteration 49711 / 50000) loss: 5.415583
(Iteration 49721 / 50000) loss: 5.563091
(Iteration 49731 / 50000) loss: 4.801522
(Iteration 49741 / 50000) loss: 4.629277
(Iteration 49751 / 50000) loss: 4.907603
(Iteration 49761 / 50000) loss: 4.563795
(Iteration 49771 / 50000) loss: 5.147919
(Iteration 49781 / 50000) loss: 4.927869
(Iteration 49791 / 50000) loss: 4.856292
(Iteration 49801 / 50000) loss: 4.921810
(Iteration 49811 / 50000) loss: 4.640391
(Iteration 49821 / 50000) loss: 5.008207
(Iteration 49831 / 50000) loss: 4.999980
(Iteration 49841 / 50000) loss: 4.700072
(Iteration 49851 / 50000) loss: 4.832501
(Iteration 49861 / 50000) loss: 5.002167
(Iteration 49871 / 50000) loss: 5.382934
(Iteration 49881 / 50000) loss: 4.727874
(Iteration 49891 / 50000) loss: 5.036825
(Iteration 49901 / 50000) loss: 4.670415
(Iteration 49911 / 50000) loss: 5.619590
(Iteration 49921 / 50000) loss: 5.009828
(Iteration 49931 / 50000) loss: 4.854103
(Iteration 49941 / 50000) loss: 4.611403
(Iteration 49951 / 50000) loss: 4.977466
(Iteration 49961 / 50000) loss: 4.946524
(Iteration 49971 / 50000) loss: 4.131866
(Iteration 49981 / 50000) loss: 4.165108
(Iteration 49991 / 50000) loss: 4.977619
In [48]:
# Plot the training losses
plt.plot(small_lstm_solver.loss_history)
plt.xlabel('Iteration')
plt.ylabel('Loss')
plt.title('Training loss history')
plt.show()
for split in ['train', 'val']:
minibatch = sample_coco_minibatch(small_data, split=split, batch_size=3)
gt_captions, features, urls = minibatch
gt_captions = decode_captions(gt_captions, data['idx_to_word'])
sample_captions = small_lstm_model.sample(features)
sample_captions = decode_captions(sample_captions, data['idx_to_word'])
for gt_caption, sample_caption, url in zip(gt_captions, sample_captions, urls):
plt.imshow(image_from_url(url))
plt.title('%s\n%s\nGT:%s' % (split, sample_caption, gt_caption))
plt.axis('off')
plt.show()
In [60]:
#import pickle
#f = open("/tmp/model.pck", "wb")
#pickle.dump(small_lstm_model.params, f)
#f.close()
#f = open("/tmp/trainer.pck", "wb")
#pickle.dump(small_lstm_solver.optim_configs, f)
#f.close()
In [61]:
for split in ['train', 'val']:
minibatch = sample_coco_minibatch(small_data, split=split, batch_size=3)
gt_captions, features, urls = minibatch
gt_captions = decode_captions(gt_captions, data['idx_to_word'])
sample_captions = small_lstm_model.sample(features)
sample_captions = decode_captions(sample_captions, data['idx_to_word'])
for gt_caption, sample_caption, url in zip(gt_captions, sample_captions, urls):
plt.imshow(image_from_url(url))
plt.title('%s\n%s\nGT:%s' % (split, sample_caption, gt_caption))
plt.axis('off')
plt.show()
Content source: kadircet/CENG
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