Wayne H Nixalo - 09 Aug 2017
FADL2 L9: Generative Models
neural-style-GPU.ipynb
In [33]:
%matplotlib inline
import importlib
import os, sys
sys.path.insert(1, os.path.join('../utils'))
from utils2 import *
from scipy.optimize import fmin_l_bfgs_b
from scipy.misc import imsave
from keras import metrics
from vgg16_avg import VGG16_Avg
In [2]:
limit_mem()
In [4]:
path = '../data/nst/'
In [14]:
# names = os.listdir(path)
# pkl_out = open('fnames.pkl','wb')
# pickle.dump(names, pkl_out)
# pkl_out.close()
fnames = pickle.load(open(path + 'fnames.pkl', 'rb'))
In [16]:
fnames = glob.glob(path+'**/*.JPG', recursive=True)
fn = fnames[0]
In [17]:
fn
Out[17]:
'../data/nst/IMG_2934.JPG'
In [18]:
img = Image.open(fn); img
Out[18]:
In [19]:
# Subtracting mean and reversing color-channel order:
rn_mean = np.array([123.68,116.779,103.939], dtype=np.float32)
preproc = lambda x: (x - rn_mean)[:,:,:,::-1]
# later undoing preprocessing for image generation
deproc = lambda x,s: np.clip(x.reshape(s)[:,:,:,::-1] + rn_mean, 0, 255)
img_arr = preproc(np.expand_dims(np.array(img), 0))
shp = img_arr.shape
In [30]:
# had to fix some compatibility issues w/ Keras 1 -> Keras 2
import vgg16_avg
importlib.reload(vgg16_avg)
from vgg16_avg import VGG16_Avg
In [31]:
model = VGG16_Avg(include_top=False)
# grabbing activations from near the end of the CNN model
layer = model.get_layer('block5_conv1').output
# calculating layer's target activations
layer_model = Model(model.input, layer)
targ = K.variable(layer_model.predict(img_arr))
../utils/vgg16_avg.py:56: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation="relu", name="block1_conv1", padding="same")`
x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv1')(img_input)
../utils/vgg16_avg.py:57: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation="relu", name="block1_conv2", padding="same")`
x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv2')(x)
../utils/vgg16_avg.py:61: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), activation="relu", name="block2_conv1", padding="same")`
x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv1')(x)
../utils/vgg16_avg.py:62: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), activation="relu", name="block2_conv2", padding="same")`
x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv2')(x)
../utils/vgg16_avg.py:66: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), activation="relu", name="block3_conv1", padding="same")`
x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv1')(x)
../utils/vgg16_avg.py:67: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), activation="relu", name="block3_conv2", padding="same")`
x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv2')(x)
../utils/vgg16_avg.py:68: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), activation="relu", name="block3_conv3", padding="same")`
x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv3')(x)
../utils/vgg16_avg.py:72: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), activation="relu", name="block4_conv1", padding="same")`
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv1')(x)
../utils/vgg16_avg.py:73: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), activation="relu", name="block4_conv2", padding="same")`
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv2')(x)
../utils/vgg16_avg.py:74: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), activation="relu", name="block4_conv3", padding="same")`
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv3')(x)
../utils/vgg16_avg.py:78: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), activation="relu", name="block5_conv1", padding="same")`
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv1')(x)
../utils/vgg16_avg.py:79: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), activation="relu", name="block5_conv2", padding="same")`
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv2')(x)
../utils/vgg16_avg.py:80: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), activation="relu", name="block5_conv3", padding="same")`
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv3')(x)
Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5
58728448/58889256 [============================>.] - ETA: 0s
In this implementation, need to define an object that'll allow us to separately access the loss function and gradients of a function,
In [34]:
class Evaluator(object):
def __init__(self, f, shp): self.f, self.shp = f, shp
def loss(self, x):
loss_, self.grad_values = self.f([x.reshape(self.shp)])
return loss_.astype(np.float64)
def grads(self, x): return self.grad_values.flatten().astype(np.float64)
# Define loss function to calc MSE betwn the 2 outputs at specfd Conv layer
loss = metrics.mse(layer, targ)
grads = K.gradients(loss, model.input)
fn = K.function([model.input], [loss]+grads)
evaluator = Evaluator(fn, shp)
# optimize loss fn w/ deterministic approach using Line Search
def solve_image(eval_obj, niter, x):
for i in range(niter):
x, min_val, info = fmin_l_bfgs_b(eval_obj.loss, x.flatten(),
fprime=eval_obj.grads, maxfun=20)
x = np.clip(x, -127,127)
print('Current loss value:', min_val)
imsave(f'{path}/results/res_at_iteration_{i}.png', deproc(x.copy(), shp)[0])
return x
In [35]:
# generating a random image:
rand_img = lambda shape: np.random.uniform(-2.5,2.5,shape)/100
x = rand_img(shp)
plt.imshow(x[0])
Out[35]:
<matplotlib.image.AxesImage at 0x7fc688851898>
In [36]:
iterations = 10
x = solve_image(evaluator, iterations, x)
Current loss value: [[[ 36.84698486 25.31944656 19.05295372 26.72929001 42.29379272
42.4466095 36.8651123 25.77234459 31.87740707 43.05461121
47.14723969 48.08355713 85.19512177 74.53987122 90.32463837
52.39159012 59.66118622 64.09477234 35.54707336 19.38749695
26.45584297 29.91149139 31.43980217 29.77198219 24.52454376
25.90310478 28.49525833 26.08754349 22.75306129 19.04619026]
[ 22.21642303 20.12755775 14.17131519 17.12739944 22.81347847
16.16996193 11.96669006 14.62566471 21.61488914 29.53083038
34.73329544 46.74581909 58.82113266 44.07838821 41.14895248
40.42720032 86.28344727 69.63314056 23.93707848 14.28123856
15.62097168 15.23616982 14.34072685 18.44782257 14.81083012
15.40630436 17.61517525 17.93347931 15.7487011 9.74167347]
[ 19.57742691 24.88094711 21.20959854 17.02429199 16.04206276
11.64803505 10.16344929 12.62602997 16.65310097 25.14056969
29.60168839 37.40457916 39.83079147 33.47225952 40.24455261
61.45488358 66.42613983 37.97806549 19.88283157 28.19946861
17.68590546 13.24964905 11.38617039 16.90208817 11.6660862
8.5124836 8.11018372 6.87087727 5.87622786 2.94226074]
[ 24.40624619 15.42626762 18.24381447 6.81579018 5.53282499
5.63473511 8.45338249 8.4103384 8.87650013 17.13197708
23.67867279 32.10264587 44.59202576 32.94459915 36.64169312
39.07665253 24.5747242 18.97734261 22.58559418 34.4894104
12.30844021 7.7691474 9.11991692 14.07856178 10.26850605
5.20890141 4.51819086 3.01186919 3.99798989 2.35131383]
[ 26.87335777 15.99230289 19.77634048 3.67461252 3.19781733
6.18075562 8.52385235 6.16398668 8.44408417 24.09635544
28.84062576 36.3432312 44.44422913 53.32261658 68.31532288
49.60505676 23.63946724 17.21792221 18.50744629 19.89505196
7.37253666 7.82778835 10.797122 12.28849411 5.79283428
5.47257423 6.13826084 3.15261173 3.26059103 1.87902093]
[ 26.45553017 17.74158478 16.85414886 3.78730512 3.2499392
6.68403816 10.29344177 8.42188072 7.20945787 17.12720108
25.45149994 27.18572235 32.21635437 61.92987061 95.58205414
68.22597504 26.26948357 12.86272621 14.63485718 13.04049397
8.16737556 8.45832062 7.9544158 9.22803879 7.23541212
6.62557316 5.65645504 3.79809713 3.68650341 2.06147599]
[ 25.8517971 17.26992607 12.12336731 5.14541531 7.16089058
11.14670753 12.56089401 10.79888725 9.35702038 15.82734203
20.5882206 34.31065369 54.4598465 73.07779694 73.4697876
50.30710602 25.76651001 17.26823044 18.06002808 15.44981956
10.02947426 8.83589935 8.91701508 9.05738354 7.94933319
6.5726099 4.43359947 3.49646997 3.06789565 1.83218074]
[ 25.37178421 13.54181576 10.49773502 7.04174232 13.28252792
13.06725311 9.067523 8.00564098 9.4465065 17.76525879
22.64673233 33.83179474 64.70851898 79.98925781 70.03173065
44.53315735 29.77414322 23.86408806 14.68952751 13.13087177
11.11766434 11.78374577 12.24998283 11.04081535 6.25272083
6.22565126 6.83769417 5.50280142 5.01274967 2.64517593]
[ 24.53227234 18.75757217 13.19798946 6.11932945 11.43343353
9.31463909 4.99764633 6.56747627 9.12177849 14.71486187
23.46367073 34.51398849 66.44445038 95.44490814 91.27098083
53.69154358 31.95396042 16.58475876 11.88929558 13.57168579
20.56903458 24.88650894 14.85542774 9.25181198 5.18660927
6.56328392 6.55052662 3.88092685 4.33286667 3.75772047]
[ 30.24384308 28.41597939 30.39845276 8.5997715 8.89387512
6.86919117 5.81216621 7.89536762 9.59887505 18.69369698
24.69238281 36.85797119 58.10764313 82.20747375 87.02401733
50.94883728 32.12088013 19.54047394 15.52814102 20.06283569
32.51378632 30.65533066 13.7529583 7.76801252 7.33967113
7.19772625 4.89906979 3.26389647 4.22839546 3.49560356]
[ 28.34803391 21.94883537 28.91997147 10.76468468 7.13177299
8.67141533 12.28576565 15.47183514 18.61813164 27.48226357
24.83708954 31.96836472 43.7817955 54.18900299 65.69287872
42.41088104 27.03790665 18.83018875 17.08436584 22.90012741
31.19622421 31.11083412 16.33236122 12.39691544 8.6929493
3.90506887 1.99953854 2.80636644 4.92864609 3.88765454]
[ 22.45912552 17.92282867 20.95874023 9.88591957 6.28069115
9.02432346 18.15258598 23.93291473 19.27668571 15.97152328
15.61698723 17.87669373 25.29166985 34.23241425 52.69477844
46.93331146 29.00650024 22.22669792 27.63790321 22.48548317
21.26208878 21.70257187 21.99739647 24.43920898 17.40029526
7.02110052 2.49624777 2.39991283 5.014925 3.82599163]
[ 18.43977356 16.39016533 14.42775345 5.85423756 5.9852705
6.19780207 11.73160362 19.43947983 19.90852737 12.40694714
13.27849865 17.49628448 22.2340641 26.68162155 49.06315231
38.82772064 23.60436249 22.04231262 30.39455986 20.49438858
20.21575165 24.99136353 37.62200928 39.53920364 25.60078239
10.50090027 3.13724709 3.3449297 5.14494038 4.5373311 ]
[ 17.40605354 12.18313026 9.03986168 5.21709347 7.51325178
9.10673523 9.75004768 8.64521694 12.10320187 10.93111706
10.19101048 12.98019409 17.95272446 33.69387054 65.46780396
29.09403801 18.15204239 20.86292267 29.81663513 24.57368469
20.67206001 20.21802139 29.35870743 26.54339218 12.58208466
7.10161877 4.06659985 4.23154736 5.71033764 4.80606461]
[ 17.83746338 12.84800339 8.1638298 6.63124752 10.76703644
11.45411873 7.76015615 7.95398855 10.78273106 8.27191067
6.82434797 9.52967834 16.6133461 50.29860687 79.9420929
42.0272522 26.55464554 27.79265594 30.02518082 24.32280731
20.0477066 13.52071571 15.44428253 13.95771122 9.93310738
7.89379883 4.19698668 4.78543758 6.63001633 5.06693554]
[ 15.87668419 12.40820312 9.14640331 7.68857956 10.15717316
11.66932297 6.36829615 7.34377575 7.74601841 6.33887291
7.36724186 16.17697906 29.36457253 52.00777817 60.42685699
33.48775482 29.77257729 26.91317368 23.27618408 22.38869476
24.21271706 19.39748764 14.68226147 11.61074066 7.50149012
5.68187237 4.44064522 4.98696613 6.45942307 5.17210197]
[ 12.92306232 7.95920944 6.06601667 10.36039352 12.97352886
16.72977829 8.81846237 10.36949921 11.23659515 9.59153461
12.94584274 27.84108162 57.81919098 84.53384399 158.97982788
96.08143616 39.97628784 36.37443542 17.84990311 18.42169571
20.56800461 18.79102516 12.70943356 8.20521832 9.50011921
8.5895071 6.60711384 5.65675545 7.18488026 6.09510231]
[ 13.41681671 8.2195797 6.74190235 9.44470978 14.1685257
19.90387344 10.35804081 10.98664856 16.0337944 10.04139328
13.97473335 40.07172394 75.31188202 157.07867432 221.30093384
141.07743835 63.88210297 41.61660767 17.59001923 9.89108372
12.46729946 12.50958061 8.70855522 6.99825382 9.87698174
8.36843109 7.00560474 7.17883968 8.74594307 6.30434465]
[ 14.82688713 8.23512077 7.46528912 6.24091816 10.63988495
16.17170715 9.6863308 7.05316448 9.52746391 6.90135098
12.93098068 49.90251923 59.80786896 103.92541504 112.2303009
123.23220062 67.03277588 42.96932983 16.46970749 4.6576643
6.2024827 6.01095867 5.11962032 6.86762714 9.63426685
8.87859917 7.8537941 8.56534004 9.5058136 5.40066576]
[ 13.32890129 6.97966862 8.82277107 7.36613703 9.64198303
8.00553703 6.77865744 6.98492098 14.47236919 17.65263748
17.45125198 46.01140213 69.63754272 76.97796631 115.1215744
124.47766113 60.82207489 60.35151291 34.93093109 11.67560577
7.90226078 10.85835361 16.95126343 18.67480087 12.83389568
8.03400803 5.77185917 6.93891668 7.3060894 5.10761261]
[ 12.36916161 8.90360451 11.29246426 10.47753143 11.32680321
12.50506592 18.91886902 24.37705612 35.46416473 36.72691727
27.67515182 44.17044067 59.84904861 91.03405762 191.73422241
151.20932007 70.83256531 74.16079712 72.44602966 32.93637085
13.30335331 12.28443527 17.43498993 13.1918335 10.36067772
9.12405014 5.47388077 9.45927048 11.95039082 8.59208679]
[ 15.34762383 12.47671509 11.64379501 7.6903429 10.93289661
17.0461731 33.66313934 43.13021088 47.31269073 52.15312958
45.11149216 66.37770844 77.33177185 108.38127899 277.49377441
200.55682373 79.69937134 53.88995743 69.45628357 63.19589996
36.29901123 23.2282753 20.3712883 20.34020424 17.75492287
13.89548492 9.25919724 14.74749851 14.49842072 8.75744247]
[ 22.3662796 16.6730423 13.62574673 11.75206947 16.37050629
20.52509308 35.53752899 45.85739517 62.18890762 71.23145294
58.08383942 83.28904724 110.62566376 95.90843964 144.96954346
157.08380127 97.85627747 80.44046021 72.07536316 60.6656723
56.9094162 49.90436172 33.9822464 26.72576141 20.5460453
11.2945776 8.23498726 12.28023911 12.34329128 7.12724972]
[ 25.51141739 24.42103386 18.26966476 16.24827194 34.27363205
57.97428513 81.32649231 81.38375854 68.28410339 59.75276184
57.32948303 74.76094055 69.51121521 57.37756348 97.80252075
99.40531921 112.53370667 130.19979858 148.98825073 107.54998779
96.45822144 53.4659996 37.10967255 32.7314682 28.58465767
14.98313427 8.48437881 6.36657619 5.71120358 3.36257386]
[ 23.8848877 28.83255196 31.44088936 39.28152847 53.20536423
62.94708252 82.9467392 78.57965088 57.5449295 53.00551605
53.05452728 58.59263611 42.36648941 50.00153732 98.83109283
108.10786438 120.09384155 106.36047363 97.85395813 98.98187256
127.3739624 80.01171875 45.42490005 40.44134903 39.63466644
27.26070404 13.84435844 9.49979973 5.87198687 4.35484314]
[ 32.40687561 43.72882843 42.99148941 58.60381317 73.90912628
70.76618958 81.97972107 67.19429016 40.3654747 36.19136047
42.86167145 48.11315536 61.81523132 89.37221527 131.33099365
130.08872986 161.70274353 123.66903687 105.64864349 90.46429443
75.36128235 56.42850494 45.50717926 54.66439819 47.49055862
41.03205872 33.25825882 25.42322922 13.61657143 8.71987724]
[ 43.88284302 56.1951828 56.03739166 71.01376343 82.01865387
58.73814392 45.57513428 34.0021553 29.03989792 34.26357269
51.95457458 85.48791504 113.65590668 121.20000458 152.26501465
122.58648682 147.99058533 95.32862854 68.63296509 48.84579468
52.23950577 35.83456802 19.57123566 30.46207809 56.70370483
61.32459259 45.59409332 40.01547241 31.05666924 17.55149841]
[ 64.57681274 53.73843384 61.21253967 53.78304291 45.05437088
40.00626755 24.25611115 18.12685394 36.25395966 48.68135071
61.62170029 126.84129333 122.94155884 73.26490021 107.58103943
103.00952148 107.86577606 83.10978699 71.80714417 30.18749619
45.49108887 24.78409958 22.72003174 23.46670914 28.32693291
38.0867691 48.63722992 54.59058762 41.83346176 25.50140762]
[ 58.92792511 38.82582855 27.78372574 19.36591911 24.84260559
27.1991806 21.28651428 22.97083664 49.04473114 82.04023743
99.39442444 147.70066833 148.47434998 77.59602356 131.71847534
134.8193512 76.17941284 89.22505188 55.28446579 32.30503845
45.40515137 18.71821594 15.88380909 16.34761238 12.24054718
15.26989937 20.28105927 27.02042198 26.60529327 24.70442581]
[ 20.64253807 11.56664467 15.41681671 14.35499001 16.37995529
15.45108986 18.79897118 17.1397934 39.81903458 103.99052429
169.33856201 240.32829285 193.15710449 171.08969116 174.88760376
135.57946777 60.07051086 100.17845154 59.42309952 41.82769775
42.23598099 21.28861046 15.24238014 12.95484829 12.74480915
16.58581543 22.25796127 22.15008926 13.50793076 10.06123829]
[ 12.25784016 18.22787476 18.90585136 13.06529522 12.32592773
12.14874744 15.29203796 10.27048492 30.85258102 95.57675934
78.07905579 133.1736145 233.03341675 265.17132568 254.25457764
143.09393311 72.55413818 96.28755188 85.16819763 55.58297729
39.92942047 22.08545303 15.01264 10.49587822 10.49056435
11.30630589 8.20960236 11.45411396 15.90427589 10.2773037 ]
[ 15.46028709 22.8337822 14.23488998 8.08880901 9.63381958
10.85446739 12.08478737 10.89756012 24.11711311 59.775383
40.07528687 54.23450851 126.96957397 177.54508972 181.17320251
131.85987854 113.82424927 94.39466095 101.8812561 68.80290222
47.09579086 26.21854782 14.34534359 10.64760208 10.432024
7.93575144 6.12837315 4.89411116 7.11410427 7.29241514]
[ 18.36441803 17.83355713 13.44521713 10.08459091 11.03539181
9.91426468 10.00385952 10.65867329 14.48249149 26.4181118
32.2106781 79.1705246 180.64855957 168.05621338 138.61766052
119.2815094 117.95308685 93.19009399 79.06348419 70.38059235
48.69833755 19.19200134 12.10754395 11.32983589 11.88951683
9.86532784 9.08862495 5.35394239 5.78888607 4.88436556]
[ 23.32094765 17.23487473 19.33973694 12.71365833 13.96926498
14.15538788 13.8437624 15.97000885 18.43057442 19.27611542
33.15295792 91.51343536 194.79109192 127.67789459 89.21136475
93.28656769 98.07833099 83.68389893 39.66999435 52.35884476
61.16949463 21.56866455 14.70232964 11.9698391 10.52364063
10.12268734 9.04450989 6.45737171 6.11844826 3.315485 ]
[ 21.21501923 21.02568054 23.01258469 18.50439644 19.30807877
18.42882729 20.52475929 20.34321594 24.98872375 24.61051941
32.12915039 76.94655609 168.42163086 120.6122818 85.80828094
82.14523315 77.80343628 93.95788574 44.42737579 41.62348175
58.21009827 28.92624855 19.01431847 14.96493435 13.26146126
8.46161842 8.47590065 13.7780838 13.8588829 9.17165756]
[ 20.36188126 23.21292496 20.44061279 18.9383049 14.40534019
10.28686619 14.75067997 17.37759399 20.64077759 19.19107819
22.46326828 50.94835663 137.35231018 137.5605011 110.46740723
116.71665955 125.47678375 127.11965179 59.7733078 38.12215424
45.57209015 27.78456116 21.71836853 17.16754913 16.80397224
10.68183899 11.18337631 20.78020096 21.17416954 12.80300045]
[ 24.28636551 16.67528152 13.44419384 11.51244164 8.58687496
8.52274132 12.03186226 15.30235386 14.16750336 15.19782639
17.84117699 34.35378265 83.87304688 92.9213028 88.23625183
110.84188843 81.92664337 93.53409576 55.79325104 26.9896431
16.72430801 13.69436169 14.19398212 11.08281517 11.22804642
9.05599117 7.7480526 12.15108204 15.49998856 10.67177582]]]
Current loss value: [[[ 36.84698486 25.31944656 19.05295372 26.72929001 42.29379272
42.4466095 36.8651123 25.77234459 31.87740707 43.05461121
47.14723969 48.08355713 85.19512177 74.53987122 90.32463837
52.39159012 59.66118622 64.09477234 35.54707336 19.38749695
26.45584297 29.91149139 31.43980217 29.77198219 24.52454376
25.90310478 28.49525833 26.08754349 22.75306129 19.04619026]
[ 22.21642303 20.12755775 14.17131519 17.12739944 22.81347847
16.16996193 11.96669006 14.62566471 21.61488914 29.53083038
34.73329544 46.74581909 58.82113266 44.07838821 41.14895248
40.42720032 86.28344727 69.63314056 23.93707848 14.28123856
15.62097168 15.23616982 14.34072685 18.44782257 14.81083012
15.40630436 17.61517525 17.93347931 15.7487011 9.74167347]
[ 19.57742691 24.88094711 21.20959854 17.02429199 16.04206276
11.64803505 10.16344833 12.62602901 16.65310097 25.14056969
29.60168839 37.40457916 39.83079147 33.47225952 40.24455261
61.45488358 66.42613983 37.97806549 19.88283157 28.19946861
17.68590546 13.24964905 11.38617039 16.90208817 11.6660862
8.5124836 8.11018372 6.87087727 5.87622786 2.94226074]
[ 24.40624619 15.42626762 18.24381447 6.81579018 5.53282499
5.63473511 8.45338249 8.4103384 8.87650013 17.13197708
23.67867279 32.10264587 44.59202576 32.94459915 36.64169312
39.07665253 24.5747242 18.97734261 22.58559418 34.4894104
12.30844021 7.7691474 9.11991692 14.07856178 10.26850605
5.20890141 4.51819086 3.01186919 3.99798989 2.35131383]
[ 26.87335777 15.99230289 19.77634048 3.67461252 3.19781733
6.18075562 8.52385235 6.16398668 8.44408417 24.09635544
28.84062576 36.3432312 44.44422913 53.32261658 68.31532288
49.60505676 23.63946724 17.21792221 18.50744629 19.89505196
7.37253666 7.82778835 10.797122 12.28849411 5.79283428
5.47257423 6.13826084 3.15261173 3.26059103 1.87902093]
[ 26.45552826 17.74158478 16.85414886 3.78730512 3.2499392
6.68403816 10.29344177 8.42188072 7.20945787 17.12720108
25.45149994 27.18572235 32.21635437 61.92987061 95.58205414
68.22597504 26.26948357 12.86272621 14.63485718 13.04049397
8.16737556 8.45832062 7.9544158 9.22803879 7.23541212
6.62557316 5.65645504 3.79809713 3.68650341 2.06147599]
[ 25.8517971 17.26992798 12.12336731 5.14541531 7.16089058
11.14670753 12.56089401 10.79888725 9.35702038 15.82734203
20.5882206 34.31065369 54.4598465 73.07779694 73.4697876
50.30710602 25.76651001 17.26823044 18.06002808 15.44981956
10.02947426 8.83589935 8.91701508 9.05738354 7.94933319
6.5726099 4.43359947 3.49646997 3.06789565 1.83218074]
[ 25.37178421 13.54181576 10.49773502 7.04174232 13.28252792
13.06725311 9.067523 8.00564098 9.4465065 17.76525879
22.64673233 33.83179474 64.70851898 79.98925781 70.03173065
44.53315735 29.77414322 23.86408806 14.68952751 13.13087177
11.11766434 11.78374577 12.24998283 11.04081535 6.25272083
6.22565126 6.83769417 5.50280142 5.01274967 2.64517593]
[ 24.53227234 18.75757217 13.19798946 6.11932945 11.43343353
9.31463909 4.99764633 6.56747627 9.12177849 14.71486187
23.46367073 34.51398849 66.44445038 95.44490814 91.27098083
53.69154358 31.95396042 16.58475876 11.88929558 13.57168579
20.56903458 24.88650894 14.85542774 9.25181198 5.18660927
6.56328392 6.55052662 3.88092685 4.33286667 3.75772047]
[ 30.24384308 28.41597939 30.39845276 8.5997715 8.89387417
6.86919117 5.81216621 7.89536762 9.59887505 18.69369698
24.69238281 36.85797119 58.10764313 82.20747375 87.02401733
50.94883728 32.12088013 19.54047394 15.52814102 20.06283569
32.51378632 30.65533066 13.7529583 7.76801252 7.33967113
7.19772625 4.89906979 3.26389647 4.22839546 3.49560356]
[ 28.34803391 21.94883537 28.91997147 10.76468468 7.13177299
8.67141533 12.28576565 15.47183514 18.61813164 27.48226357
24.83708954 31.96836472 43.7817955 54.18900299 65.69287872
42.41088104 27.03790665 18.83018875 17.08436584 22.90012741
31.19622421 31.11083412 16.33236122 12.39691544 8.6929493
3.90506887 1.99953854 2.80636644 4.92864609 3.88765454]
[ 22.45912552 17.92282867 20.95874023 9.88591957 6.28069115
9.02432346 18.15258598 23.93291473 19.27668571 15.97152328
15.61698723 17.87669373 25.29166985 34.23241425 52.69477844
46.93331146 29.00650024 22.22669792 27.63790321 22.48548317
21.26208878 21.70257187 21.99739647 24.43920898 17.40029526
7.02110052 2.49624777 2.39991283 5.014925 3.82599163]
[ 18.43977356 16.39016533 14.42775345 5.85423756 5.9852705
6.19780207 11.73160362 19.43947983 19.90852737 12.40694714
13.27849865 17.49628448 22.2340641 26.68162155 49.06315231
38.82772064 23.60436249 22.04231262 30.39455986 20.49438858
20.21575165 24.99136353 37.62200928 39.53920364 25.60078239
10.50090027 3.13724709 3.3449297 5.14494038 4.5373311 ]
[ 17.40605354 12.18313026 9.03986168 5.21709347 7.51325178
9.10673523 9.75004768 8.64521694 12.10320187 10.93111706
10.19101048 12.98019409 17.95272446 33.69387054 65.46780396
29.09403801 18.15204239 20.86292267 29.81663513 24.57368469
20.67206001 20.21802139 29.35870743 26.54339218 12.58208466
7.10161877 4.06659985 4.23154736 5.71033764 4.80606461]
[ 17.83746338 12.84800339 8.1638298 6.63124752 10.76703644
11.45411873 7.76015615 7.95398855 10.78273106 8.27191067
6.82434797 9.52967834 16.6133461 50.29860687 79.9420929
42.0272522 26.55464554 27.79265594 30.02518082 24.32280731
20.0477066 13.52071571 15.44428253 13.95771122 9.93310738
7.89379883 4.19698668 4.78543758 6.63001633 5.06693554]
[ 15.87668419 12.40820312 9.14640331 7.68857956 10.15717316
11.66932297 6.36829615 7.34377575 7.74601841 6.33887291
7.36724186 16.17697906 29.36457253 52.00777817 60.42685699
33.48775482 29.77257729 26.91317368 23.27618408 22.38869476
24.21271706 19.39748764 14.68226147 11.61074066 7.50149012
5.68187237 4.44064522 4.98696613 6.45942307 5.17210197]
[ 12.92306232 7.95920944 6.06601667 10.36039352 12.97352886
16.72977829 8.81846237 10.36949921 11.23659515 9.59153461
12.94584274 27.84108162 57.81919098 84.53384399 158.97982788
96.08143616 39.97628784 36.37443542 17.84990311 18.42169571
20.56800461 18.79102516 12.70943356 8.20521832 9.50011921
8.5895071 6.60711384 5.65675545 7.18488026 6.09510231]
[ 13.41681671 8.2195797 6.74190235 9.44470978 14.1685257
19.90387344 10.35804081 10.98664856 16.0337944 10.04139328
13.97473335 40.07172394 75.31188202 157.07867432 221.30093384
141.07743835 63.88210297 41.61660767 17.59001923 9.89108372
12.46729946 12.50958061 8.70855522 6.99825382 9.87698174
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16.17170715 9.6863308 7.05316448 9.52746391 6.90135098
12.93098068 49.90251923 59.80786896 103.92541504 112.2303009
123.23220062 67.03277588 42.96932983 16.46970749 4.6576643
6.2024827 6.01095867 5.11962032 6.86762714 9.63426685
8.87859917 7.8537941 8.56534004 9.5058136 5.40066576]
[ 13.32890129 6.97966862 8.82277107 7.36613703 9.64198303
8.00553703 6.77865744 6.98492098 14.47236919 17.65263748
17.45125198 46.01140213 69.63754272 76.97796631 115.1215744
124.47766113 60.82207489 60.35151291 34.93093109 11.67560577
7.90226078 10.85835361 16.95126343 18.67480087 12.83389568
8.03400803 5.77185917 6.93891668 7.3060894 5.10761261]
[ 12.36916161 8.90360451 11.29246426 10.47753143 11.32680321
12.50506592 18.91886902 24.37705612 35.46416473 36.72691727
27.67515182 44.17044067 59.84904861 91.03405762 191.73422241
151.20932007 70.83256531 74.16079712 72.44602966 32.93637085
13.30335331 12.28443527 17.43498993 13.1918335 10.36067772
9.12405014 5.47388077 9.45927048 11.95039082 8.59208679]
[ 15.34762383 12.47671509 11.64379501 7.6903429 10.93289661
17.0461731 33.66313934 43.13021088 47.31269073 52.15312958
45.11149216 66.37770844 77.33177185 108.38127899 277.49377441
200.55682373 79.69937134 53.88995743 69.45628357 63.19589996
36.29901123 23.2282753 20.3712883 20.34020424 17.75492287
13.89548492 9.25919724 14.74749851 14.49842072 8.75744247]
[ 22.3662796 16.6730423 13.62574673 11.75206947 16.37050629
20.52509308 35.53752899 45.85739517 62.18890762 71.23145294
58.08383942 83.28904724 110.62566376 95.90843964 144.96954346
157.08380127 97.85627747 80.44046021 72.07536316 60.6656723
56.9094162 49.90436172 33.9822464 26.72576141 20.5460453
11.2945776 8.23498726 12.28023911 12.34329128 7.12724972]
[ 25.51141739 24.42103386 18.26966476 16.24827194 34.27363205
57.97428513 81.32649231 81.38375854 68.28410339 59.75276184
57.32948303 74.76094055 69.51121521 57.37756348 97.80252075
99.40531921 112.53370667 130.19979858 148.98825073 107.54998779
96.45822144 53.4659996 37.10967255 32.7314682 28.58465767
14.98313427 8.48437881 6.36657619 5.71120358 3.36257386]
[ 23.8848877 28.83255196 31.44088936 39.28152847 53.20536423
62.94708252 82.9467392 78.57965088 57.5449295 53.00551605
53.05452728 58.59263611 42.36648941 50.00153732 98.83109283
108.10786438 120.09384155 106.36047363 97.85395813 98.98187256
127.3739624 80.01171875 45.42490005 40.44134903 39.63466644
27.26070404 13.84435844 9.49979973 5.87198687 4.35484314]
[ 32.40687561 43.72882843 42.99148941 58.60381317 73.90912628
70.76618958 81.97972107 67.19429016 40.3654747 36.19136047
42.86167145 48.11315536 61.81523132 89.37221527 131.33099365
130.08872986 161.70274353 123.66903687 105.64864349 90.46429443
75.36128235 56.42850494 45.50717926 54.66439819 47.49055862
41.03205872 33.25825882 25.42322922 13.61657143 8.71987724]
[ 43.88284302 56.1951828 56.03739166 71.01376343 82.01865387
58.73814392 45.57513428 34.0021553 29.03989792 34.26357269
51.95457458 85.48791504 113.65590668 121.20000458 152.26501465
122.58648682 147.99058533 95.32862854 68.63296509 48.84579468
52.23950577 35.83456802 19.57123566 30.46207809 56.70370483
61.32459259 45.59409332 40.01547241 31.05666924 17.55149841]
[ 64.57681274 53.73843384 61.21253967 53.78304291 45.05437088
40.00626755 24.25611115 18.12685394 36.25395966 48.68135071
61.62170029 126.84129333 122.94155884 73.26490021 107.58103943
103.00952148 107.86577606 83.10978699 71.80714417 30.18749619
45.49108887 24.78409958 22.72003174 23.46670914 28.32693291
38.0867691 48.63722992 54.59058762 41.83346176 25.50140762]
[ 58.92792511 38.82582855 27.78372574 19.36591911 24.84260559
27.1991806 21.28651428 22.97083664 49.04473114 82.04023743
99.39442444 147.70066833 148.47434998 77.59602356 131.71847534
134.8193512 76.17941284 89.22505188 55.28446579 32.30503845
45.40515137 18.71821594 15.88380909 16.34761238 12.24054718
15.26989937 20.28105927 27.02042198 26.60529327 24.70442581]
[ 20.64253807 11.56664467 15.41681671 14.35499001 16.37995529
15.45108986 18.79897118 17.1397934 39.81903458 103.99052429
169.33856201 240.32829285 193.15710449 171.08969116 174.88760376
135.57946777 60.07051086 100.17845154 59.42309952 41.82769775
42.23598099 21.28861046 15.24238014 12.95484829 12.74480915
16.58581543 22.25796127 22.15008926 13.50793076 10.06123829]
[ 12.25784016 18.22787476 18.90585136 13.06529522 12.32592773
12.14874744 15.29203796 10.27048492 30.85258102 95.57675934
78.07905579 133.1736145 233.03341675 265.17132568 254.25457764
143.09393311 72.55413818 96.28755188 85.16819763 55.58297729
39.92942047 22.08545303 15.01264 10.49587822 10.49056435
11.30630589 8.20960236 11.45411396 15.90427589 10.2773037 ]
[ 15.46028709 22.8337822 14.23488998 8.08880901 9.63381958
10.85446739 12.08478737 10.89756012 24.11711311 59.775383
40.07528687 54.23450851 126.96957397 177.54508972 181.17320251
131.85987854 113.82424927 94.39466095 101.8812561 68.80290222
47.09579086 26.21854782 14.34534359 10.64760208 10.432024
7.93575144 6.12837315 4.89411116 7.11410427 7.29241514]
[ 18.36441803 17.83355713 13.44521713 10.08459091 11.03539181
9.91426468 10.00385952 10.65867329 14.48249149 26.4181118
32.2106781 79.1705246 180.64855957 168.05621338 138.61766052
119.2815094 117.95308685 93.19009399 79.06348419 70.38059235
48.69833755 19.19200134 12.10754395 11.32983589 11.88951683
9.86532784 9.08862495 5.35394239 5.78888607 4.88436556]
[ 23.32094765 17.23487473 19.33973694 12.71365833 13.96926498
14.15538788 13.8437624 15.97000885 18.43057442 19.27611542
33.15295792 91.51343536 194.79109192 127.67789459 89.21136475
93.28656769 98.07833099 83.68389893 39.66999435 52.35884476
61.16949463 21.56866455 14.70232964 11.9698391 10.52364063
10.12268734 9.04450989 6.45737171 6.11844826 3.315485 ]
[ 21.21501923 21.02568054 23.01258469 18.50439644 19.30807877
18.42882729 20.52475929 20.34321594 24.98872375 24.61051941
32.12915039 76.94655609 168.42163086 120.6122818 85.80828094
82.14523315 77.80343628 93.95788574 44.42737579 41.62348175
58.21009827 28.92624855 19.01431847 14.96493435 13.26146126
8.46161842 8.47590065 13.7780838 13.8588829 9.17165756]
[ 20.36188126 23.21292496 20.44061279 18.9383049 14.40534019
10.28686619 14.75067997 17.37759399 20.64077759 19.19107819
22.46326828 50.94835663 137.35231018 137.5605011 110.46740723
116.71665955 125.47678375 127.11965179 59.7733078 38.12215424
45.57209015 27.78456116 21.71836853 17.16754913 16.80397224
10.68183899 11.18337631 20.78020096 21.17416954 12.80300045]
[ 24.28636551 16.67528152 13.44419384 11.51244164 8.58687496
8.52274132 12.03186226 15.30235386 14.16750336 15.19782639
17.84117699 34.35378265 83.87304688 92.9213028 88.23625183
110.84188843 81.92664337 93.53409576 55.79325104 26.9896431
16.72430801 13.69436169 14.19398212 11.08281517 11.22804642
9.05599117 7.7480526 12.15108204 15.49998856 10.67177582]]]
Current loss value: [[[ 36.84698486 25.31944656 19.05295372 26.72929001 42.29379272
42.4466095 36.8651123 25.77234459 31.87740707 43.05461121
47.14723969 48.08355713 85.19512177 74.53987122 90.32463837
52.39159012 59.66118622 64.09477234 35.54707336 19.38749695
26.45584297 29.91149139 31.43980217 29.77198219 24.52454376
25.90310478 28.49525833 26.08754349 22.75306129 19.04619026]
[ 22.21642303 20.12755775 14.17131519 17.12739944 22.81347847
16.16996193 11.96669006 14.62566471 21.61488914 29.53083038
34.73329544 46.74581909 58.82113266 44.07838821 41.14895248
40.42720032 86.28344727 69.63314056 23.93707848 14.28123856
15.62097168 15.23616982 14.34072685 18.44782257 14.81083012
15.40630436 17.61517525 17.93347931 15.7487011 9.74167347]
[ 19.57742691 24.88094711 21.20959854 17.02429199 16.04206276
11.64803505 10.16344833 12.62602901 16.65310097 25.14056969
29.60168839 37.40457916 39.83079147 33.47225952 40.24455261
61.45488358 66.42613983 37.97806549 19.88283157 28.19946861
17.68590546 13.24964905 11.38617039 16.90208817 11.6660862
8.5124836 8.11018372 6.87087727 5.87622786 2.94226074]
[ 24.40624619 15.42626762 18.24381447 6.81579018 5.53282499
5.63473511 8.45338249 8.4103384 8.87650013 17.13197708
23.67867279 32.10264587 44.59202576 32.94459915 36.64169312
39.07665253 24.5747242 18.97734261 22.58559418 34.4894104
12.30844021 7.7691474 9.11991692 14.07856178 10.26850605
5.20890141 4.51819086 3.01186919 3.99798989 2.35131383]
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169.33856201 240.32829285 193.15710449 171.08969116 174.88760376
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78.07905579 133.1736145 233.03341675 265.17132568 254.25457764
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131.85987854 113.82424927 94.39466095 101.8812561 68.80290222
47.09579086 26.21854782 14.34534359 10.64760208 10.432024
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33.15295792 91.51343536 194.79109192 127.67789459 89.21136475
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32.12915039 76.94655609 168.42163086 120.6122818 85.80828094
82.14523315 77.80343628 93.95788574 44.42737579 41.62348175
58.21009827 28.92624855 19.01431847 14.96493435 13.26146126
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22.46326828 50.94835663 137.35231018 137.5605011 110.46740723
116.71665955 125.47678375 127.11965179 59.7733078 38.12215424
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16.72430801 13.69436169 14.19398212 11.08281517 11.22804642
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Current loss value: [[[ 36.84698486 25.31944656 19.05295372 26.72929001 42.29379272
42.4466095 36.8651123 25.77234459 31.87740707 43.05461121
47.14723969 48.08355713 85.19512177 74.53987122 90.32463837
52.39159012 59.66118622 64.09477234 35.54707336 19.38749695
26.45584297 29.91149139 31.43980217 29.77198219 24.52454376
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34.73329544 46.74581909 58.82113266 44.07838821 41.14895248
40.42720032 86.28344727 69.63314056 23.93707848 14.28123856
15.62097168 15.23616982 14.34072685 18.44782257 14.81083012
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24.83708954 31.96836472 43.7817955 54.18900299 65.69287872
42.41088104 27.03790665 18.83018875 17.08436584 22.90012741
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20.21575165 24.99136353 37.62200928 39.53920364 25.60078239
10.50090027 3.13724709 3.3449297 5.14494038 4.5373311 ]
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10.19101048 12.98019409 17.95272446 33.69387054 65.46780396
29.09403801 18.15204239 20.86292267 29.81663513 24.57368469
20.67206001 20.21802139 29.35870743 26.54339218 12.58208466
7.10161877 4.06659985 4.23154736 5.71033764 4.80606461]
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20.0477066 13.52071571 15.44428253 13.95771122 9.93310738
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33.48775482 29.77257729 26.91317368 23.27618408 22.38869476
24.21271706 19.39748764 14.68226147 11.61074066 7.50149012
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13.97473335 40.07172394 75.31188202 157.07867432 221.30093384
141.07743835 63.88210297 41.61660767 17.59001923 9.89108372
12.46729946 12.50958061 8.70855522 6.99825382 9.87698174
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123.23220062 67.03277588 42.96932983 16.46970749 4.6576643
6.2024827 6.01095867 5.11962032 6.86762714 9.63426685
8.87859917 7.8537941 8.56534004 9.5058136 5.40066576]
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124.47766113 60.82207489 60.35151291 34.93093109 11.67560577
7.90226078 10.85835361 16.95126343 18.67480087 12.83389568
8.03400803 5.77185917 6.93891668 7.3060894 5.10761261]
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27.67515182 44.17044067 59.84904861 91.03405762 191.73422241
151.20932007 70.83256531 74.16079712 72.44602966 32.93637085
13.30335331 12.28443527 17.43498993 13.1918335 10.36067772
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45.11149216 66.37770844 77.33177185 108.38127899 277.49377441
200.55682373 79.69937134 53.88995743 69.45628357 63.19589996
36.29901123 23.2282753 20.3712883 20.34020424 17.75492287
13.89548492 9.25919724 14.74749851 14.49842072 8.75744247]
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58.08383942 83.28904724 110.62566376 95.90843964 144.96954346
157.08380127 97.85627747 80.44046021 72.07536316 60.6656723
56.9094162 49.90436172 33.9822464 26.72576141 20.5460453
11.2945776 8.23498726 12.28023911 12.34329128 7.12724972]
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57.97428513 81.32649231 81.38375854 68.28410339 59.75276184
57.32948303 74.76094055 69.51121521 57.37756348 97.80252075
99.40531921 112.53370667 130.19979858 148.98825073 107.54998779
96.45822144 53.4659996 37.10967255 32.7314682 28.58465767
14.98313427 8.48437881 6.36657619 5.71120358 3.36257386]
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62.94708252 82.9467392 78.57965088 57.5449295 53.00551605
53.05452728 58.59263611 42.36648941 50.00153732 98.83109283
108.10786438 120.09384155 106.36047363 97.85395813 98.98187256
127.3739624 80.01171875 45.42490005 40.44134903 39.63466644
27.26070404 13.84435844 9.49979973 5.87198687 4.35484314]
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42.86167145 48.11315536 61.81523132 89.37221527 131.33099365
130.08872986 161.70274353 123.66903687 105.64864349 90.46429443
75.36128235 56.42850494 45.50717926 54.66439819 47.49055862
41.03205872 33.25825882 25.42322922 13.61657143 8.71987724]
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58.73814392 45.57513428 34.0021553 29.03989792 34.26357269
51.95457458 85.48791504 113.65590668 121.20000458 152.26501465
122.58648682 147.99058533 95.32862854 68.63296509 48.84579468
52.23950577 35.83456802 19.57123566 30.46207809 56.70370483
61.32459259 45.59409332 40.01547241 31.05666924 17.55149841]
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61.62170029 126.84129333 122.94155884 73.26490021 107.58103943
103.00952148 107.86577606 83.10978699 71.80714417 30.18749619
45.49108887 24.78409958 22.72003174 23.46670914 28.32693291
38.0867691 48.63722992 54.59058762 41.83346176 25.50140762]
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99.39442444 147.70066833 148.47434998 77.59602356 131.71847534
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45.40515137 18.71821594 15.88380909 16.34761238 12.24054718
15.26989937 20.28105927 27.02042198 26.60529327 24.70442581]
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93.28656769 98.07833099 83.68389893 39.66999435 52.35884476
61.16949463 21.56866455 14.70232964 11.9698391 10.52364063
10.12268734 9.04450989 6.45737171 6.11844826 3.315485 ]
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58.21009827 28.92624855 19.01431847 14.96493435 13.26146126
8.46161842 8.47590065 13.7780838 13.8588829 9.17165756]
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10.28686619 14.75067997 17.37759399 20.64077759 19.19107819
22.46326828 50.94835663 137.35231018 137.5605011 110.46740723
116.71665955 125.47678375 127.11965179 59.7733078 38.12215424
45.57209015 27.78456116 21.71836853 17.16754913 16.80397224
10.68183899 11.18337631 20.78020096 21.17416954 12.80300045]
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8.52274132 12.03186226 15.30235386 14.16750336 15.19782639
17.84117699 34.35378265 83.87304688 92.9213028 88.23625183
110.84188843 81.92664337 93.53409576 55.79325104 26.9896431
16.72430801 13.69436169 14.19398212 11.08281517 11.22804642
9.05599117 7.7480526 12.15108204 15.49998856 10.67177582]]]
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-36-57e5dd99086c> in <module>()
1 iterations = 10
----> 2 x = solve_image(evaluator, iterations, x)
<ipython-input-34-050382ea2c1b> in solve_image(eval_obj, niter, x)
18 for i in range(niter):
19 x, min_val, info = fmin_l_bfgs_b(eval_obj.loss, x.flatten(),
---> 20 fprime=eval_obj.grads, maxfun=20)
21 x = np.clip(x, -127,127)
22 print('Current loss value:', min_val)
~/miniconda3/envs/FAI3/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py in fmin_l_bfgs_b(func, x0, fprime, args, approx_grad, bounds, m, factr, pgtol, epsilon, iprint, maxfun, maxiter, disp, callback, maxls)
191
192 res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,
--> 193 **opts)
194 d = {'grad': res['jac'],
195 'task': res['message'],
~/miniconda3/envs/FAI3/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py in _minimize_lbfgsb(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, **unknown_options)
260 raise ValueError('length of x0 != length of bounds')
261 # unbounded variables must use None, not +-inf, for optimizer to work properly
--> 262 bounds = [(None if l == -np.inf else l, None if u == np.inf else u) for l, u in bounds]
263
264 if disp is not None:
~/miniconda3/envs/FAI3/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py in <listcomp>(.0)
260 raise ValueError('length of x0 != length of bounds')
261 # unbounded variables must use None, not +-inf, for optimizer to work properly
--> 262 bounds = [(None if l == -np.inf else l, None if u == np.inf else u) for l, u in bounds]
263
264 if disp is not None:
KeyboardInterrupt:
In [41]:
Image.open(path + 'results/res_at_iteration_1.png')
Out[41]:
In [ ]:
# Looking at result for earlier Conv block (4):
layer = model.get_layer('block4_conv1').output
layer_model = Model(model.input, layer)
targ = K.variable(layer_model.predict(img_arr))
loss = metrics.mse(layer, targ)
grads = K.gradients(loss, model.input)
fn = K.function([model.input], [loss]+grads)
evaluator = Evaluator(fn, shp)
x = solve_image(evaluator, iterations, x)
In [ ]:
Image.open(path + 'results/res_at_iteration_9.png')
In [ ]:
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Content source: WNoxchi/Kaukasos
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