Post-Training Quantization with TensorFlow Classification Model¶
This Jupyter notebook can be launched after a local installation only.
This example demonstrates how to quantize the OpenVINO model that was created in 301-tensorflow-training-openvino notebook, to improve inference speed. Quantization is performed with Post-training Quantization with NNCF. A custom dataloader and metric will be defined, and accuracy and performance will be computed for the original IR model and the quantized model.
Table of contents:¶
Preparation¶
The notebook requires that the training notebook has been run and that the Intermediate Representation (IR) models are created. If the IR models do not exist, running the next cell will run the training notebook. This will take a while.
%pip install -q tensorflow Pillow matplotlib numpy tqdm nncf
DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
Note: you may need to restart the kernel to use updated packages.
from pathlib import Path
import tensorflow as tf
model_xml = Path("model/flower/flower_ir.xml")
dataset_url = (
"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
)
data_dir = Path(tf.keras.utils.get_file("flower_photos", origin=dataset_url, untar=True))
if not model_xml.exists():
print("Executing training notebook. This will take a while...")
%run 301-tensorflow-training-openvino.ipynb
2024-02-10 01:09:00.730910: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0. 2024-02-10 01:09:00.766002: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-02-10 01:09:01.406366: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Executing training notebook. This will take a while...
DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
Note: you may need to restart the kernel to use updated packages.
3670
Found 3670 files belonging to 5 classes.
Using 2936 files for training.
2024-02-10 01:09:08.525687: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
2024-02-10 01:09:08.525725: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
2024-02-10 01:09:08.525729: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
2024-02-10 01:09:08.525856: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
2024-02-10 01:09:08.525872: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
2024-02-10 01:09:08.525876: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
Found 3670 files belonging to 5 classes.
Using 734 files for validation.
['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']
2024-02-10 01:09:08.855253: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
[[{{node Placeholder/_0}}]]
2024-02-10 01:09:08.855534: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
[[{{node Placeholder/_4}}]]
2024-02-10 01:09:09.711519: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
[[{{node Placeholder/_4}}]]
2024-02-10 01:09:09.711766: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
[[{{node Placeholder/_0}}]]
(32, 180, 180, 3)
(32,)
2024-02-10 01:09:10.063734: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
[[{{node Placeholder/_4}}]]
2024-02-10 01:09:10.064340: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
[[{{node Placeholder/_4}}]]
0.0 0.9970461
2024-02-10 01:09:10.875056: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
[[{{node Placeholder/_0}}]]
2024-02-10 01:09:10.875365: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
[[{{node Placeholder/_0}}]]
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
sequential_1 (Sequential) (None, 180, 180, 3) 0
rescaling_2 (Rescaling) (None, 180, 180, 3) 0
conv2d_3 (Conv2D) (None, 180, 180, 16) 448
max_pooling2d_3 (MaxPooling (None, 90, 90, 16) 0
2D)
conv2d_4 (Conv2D) (None, 90, 90, 32) 4640
max_pooling2d_4 (MaxPooling (None, 45, 45, 32) 0
2D)
conv2d_5 (Conv2D) (None, 45, 45, 64) 18496
max_pooling2d_5 (MaxPooling (None, 22, 22, 64) 0
2D)
dropout (Dropout) (None, 22, 22, 64) 0
flatten_1 (Flatten) (None, 30976) 0
dense_2 (Dense) (None, 128) 3965056
outputs (Dense) (None, 5) 645
=================================================================
Total params: 3,989,285
Trainable params: 3,989,285
Non-trainable params: 0
_________________________________________________________________
Epoch 1/15
2024-02-10 01:09:11.882327: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [2936]
[[{{node Placeholder/_0}}]]
2024-02-10 01:09:11.882802: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [2936]
[[{{node Placeholder/_4}}]]
1/92 [..............................] - ETA: 1:32 - loss: 1.6315 - accuracy: 0.1562
2/92 [..............................] - ETA: 6s - loss: 1.7632 - accuracy: 0.2812
3/92 [..............................] - ETA: 5s - loss: 1.7516 - accuracy: 0.2708
4/92 [>.............................] - ETA: 5s - loss: 1.7249 - accuracy: 0.2578
5/92 [>.............................] - ETA: 5s - loss: 1.6968 - accuracy: 0.2750
6/92 [>.............................] - ETA: 5s - loss: 1.6729 - accuracy: 0.2917
7/92 [=>............................] - ETA: 5s - loss: 1.6459 - accuracy: 0.3080
8/92 [=>............................] - ETA: 5s - loss: 1.6410 - accuracy: 0.3008
9/92 [=>............................] - ETA: 4s - loss: 1.6246 - accuracy: 0.3125
10/92 [==>………………………] - ETA: 4s - loss: 1.6151 - accuracy: 0.3000
11/92 [==>………………………] - ETA: 4s - loss: 1.6065 - accuracy: 0.3011
12/92 [==>………………………] - ETA: 4s - loss: 1.5947 - accuracy: 0.3047
13/92 [===>……………………..] - ETA: 4s - loss: 1.5839 - accuracy: 0.3077
14/92 [===>……………………..] - ETA: 4s - loss: 1.5719 - accuracy: 0.3125
15/92 [===>……………………..] - ETA: 4s - loss: 1.5604 - accuracy: 0.3187
16/92 [====>…………………….] - ETA: 4s - loss: 1.5477 - accuracy: 0.3203
17/92 [====>…………………….] - ETA: 4s - loss: 1.5317 - accuracy: 0.3272
18/92 [====>…………………….] - ETA: 4s - loss: 1.5153 - accuracy: 0.3368
19/92 [=====>……………………] - ETA: 4s - loss: 1.5118 - accuracy: 0.3355
20/92 [=====>……………………] - ETA: 4s - loss: 1.4901 - accuracy: 0.3484
21/92 [=====>……………………] - ETA: 4s - loss: 1.4818 - accuracy: 0.3569
22/92 [======>…………………..] - ETA: 4s - loss: 1.4839 - accuracy: 0.3563
23/92 [======>…………………..] - ETA: 4s - loss: 1.4731 - accuracy: 0.3599
24/92 [======>…………………..] - ETA: 3s - loss: 1.4556 - accuracy: 0.3724
25/92 [=======>………………….] - ETA: 3s - loss: 1.4413 - accuracy: 0.3788
26/92 [=======>………………….] - ETA: 3s - loss: 1.4353 - accuracy: 0.3774
27/92 [=======>………………….] - ETA: 3s - loss: 1.4367 - accuracy: 0.3762
28/92 [========>…………………] - ETA: 3s - loss: 1.4293 - accuracy: 0.3750
29/92 [========>…………………] - ETA: 3s - loss: 1.4196 - accuracy: 0.3793
30/92 [========>…………………] - ETA: 3s - loss: 1.4177 - accuracy: 0.3813
31/92 [=========>………………..] - ETA: 3s - loss: 1.4057 - accuracy: 0.3872
32/92 [=========>………………..] - ETA: 3s - loss: 1.4028 - accuracy: 0.3868
33/92 [=========>………………..] - ETA: 3s - loss: 1.3896 - accuracy: 0.3950
34/92 [==========>……………….] - ETA: 3s - loss: 1.3879 - accuracy: 0.3963
35/92 [==========>……………….] - ETA: 3s - loss: 1.3886 - accuracy: 0.3966
36/92 [==========>……………….] - ETA: 3s - loss: 1.3839 - accuracy: 0.3969
37/92 [===========>………………] - ETA: 3s - loss: 1.3853 - accuracy: 0.4022
38/92 [===========>………………] - ETA: 3s - loss: 1.3812 - accuracy: 0.4023
39/92 [===========>………………] - ETA: 3s - loss: 1.3746 - accuracy: 0.4065
40/92 [============>……………..] - ETA: 3s - loss: 1.3733 - accuracy: 0.4049
41/92 [============>……………..] - ETA: 2s - loss: 1.3684 - accuracy: 0.4064
42/92 [============>……………..] - ETA: 2s - loss: 1.3665 - accuracy: 0.4064
43/92 [=============>…………….] - ETA: 2s - loss: 1.3624 - accuracy: 0.4108
44/92 [=============>…………….] - ETA: 2s - loss: 1.3590 - accuracy: 0.4121
45/92 [=============>…………….] - ETA: 2s - loss: 1.3533 - accuracy: 0.4148
46/92 [==============>……………] - ETA: 2s - loss: 1.3472 - accuracy: 0.4167
47/92 [==============>……………] - ETA: 2s - loss: 1.3448 - accuracy: 0.4164
48/92 [==============>……………] - ETA: 2s - loss: 1.3409 - accuracy: 0.4162
49/92 [==============>……………] - ETA: 2s - loss: 1.3383 - accuracy: 0.4186
50/92 [===============>…………..] - ETA: 2s - loss: 1.3381 - accuracy: 0.4190
51/92 [===============>…………..] - ETA: 2s - loss: 1.3341 - accuracy: 0.4212
52/92 [===============>…………..] - ETA: 2s - loss: 1.3292 - accuracy: 0.4245
53/92 [================>………….] - ETA: 2s - loss: 1.3286 - accuracy: 0.4277
54/92 [================>………….] - ETA: 2s - loss: 1.3246 - accuracy: 0.4302
55/92 [================>………….] - ETA: 2s - loss: 1.3228 - accuracy: 0.4309
56/92 [=================>…………] - ETA: 2s - loss: 1.3231 - accuracy: 0.4355
57/92 [=================>…………] - ETA: 2s - loss: 1.3221 - accuracy: 0.4350
58/92 [=================>…………] - ETA: 1s - loss: 1.3200 - accuracy: 0.4378
59/92 [==================>………..] - ETA: 1s - loss: 1.3177 - accuracy: 0.4394
60/92 [==================>………..] - ETA: 1s - loss: 1.3148 - accuracy: 0.4409
61/92 [==================>………..] - ETA: 1s - loss: 1.3140 - accuracy: 0.4408
62/92 [===================>……….] - ETA: 1s - loss: 1.3080 - accuracy: 0.4443
63/92 [===================>……….] - ETA: 1s - loss: 1.3096 - accuracy: 0.4447
64/92 [===================>……….] - ETA: 1s - loss: 1.3068 - accuracy: 0.4451
65/92 [====================>………] - ETA: 1s - loss: 1.3014 - accuracy: 0.4469
66/92 [====================>………] - ETA: 1s - loss: 1.3013 - accuracy: 0.4468
67/92 [====================>………] - ETA: 1s - loss: 1.2977 - accuracy: 0.4480
68/92 [=====================>……..] - ETA: 1s - loss: 1.2948 - accuracy: 0.4493
69/92 [=====================>……..] - ETA: 1s - loss: 1.2914 - accuracy: 0.4500
70/92 [=====================>……..] - ETA: 1s - loss: 1.2929 - accuracy: 0.4476
71/92 [======================>…….] - ETA: 1s - loss: 1.2929 - accuracy: 0.4479
72/92 [======================>…….] - ETA: 1s - loss: 1.2902 - accuracy: 0.4495
73/92 [======================>…….] - ETA: 1s - loss: 1.2864 - accuracy: 0.4506
74/92 [=======================>……] - ETA: 1s - loss: 1.2854 - accuracy: 0.4504
75/92 [=======================>……] - ETA: 0s - loss: 1.2853 - accuracy: 0.4494
76/92 [=======================>……] - ETA: 0s - loss: 1.2809 - accuracy: 0.4513
77/92 [========================>…..] - ETA: 0s - loss: 1.2779 - accuracy: 0.4532
78/92 [========================>…..] - ETA: 0s - loss: 1.2774 - accuracy: 0.4538
79/92 [========================>…..] - ETA: 0s - loss: 1.2739 - accuracy: 0.4540
80/92 [=========================>….] - ETA: 0s - loss: 1.2722 - accuracy: 0.4542
81/92 [=========================>….] - ETA: 0s - loss: 1.2669 - accuracy: 0.4582
82/92 [=========================>….] - ETA: 0s - loss: 1.2654 - accuracy: 0.4599
83/92 [==========================>…] - ETA: 0s - loss: 1.2625 - accuracy: 0.4622
84/92 [==========================>…] - ETA: 0s - loss: 1.2580 - accuracy: 0.4642
85/92 [==========================>…] - ETA: 0s - loss: 1.2573 - accuracy: 0.4653
86/92 [===========================>..] - ETA: 0s - loss: 1.2566 - accuracy: 0.4665
87/92 [===========================>..] - ETA: 0s - loss: 1.2564 - accuracy: 0.4665
88/92 [===========================>..] - ETA: 0s - loss: 1.2530 - accuracy: 0.4679
89/92 [============================>.] - ETA: 0s - loss: 1.2492 - accuracy: 0.4690
90/92 [============================>.] - ETA: 0s - loss: 1.2445 - accuracy: 0.4714
91/92 [============================>.] - ETA: 0s - loss: 1.2402 - accuracy: 0.4735
92/92 [==============================] - ETA: 0s - loss: 1.2400 - accuracy: 0.4741
2024-02-10 01:09:18.229567: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [734]
[[{{node Placeholder/_0}}]]
2024-02-10 01:09:18.229847: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [734]
[[{{node Placeholder/_4}}]]
92/92 [==============================] - 7s 66ms/step - loss: 1.2400 - accuracy: 0.4741 - val_loss: 1.3762 - val_accuracy: 0.5014
Epoch 2/15
1/92 [..............................] - ETA: 7s - loss: 1.2018 - accuracy: 0.5625
2/92 [..............................] - ETA: 5s - loss: 1.0597 - accuracy: 0.5469
3/92 [..............................] - ETA: 5s - loss: 1.0781 - accuracy: 0.5521
4/92 [>.............................] - ETA: 5s - loss: 0.9952 - accuracy: 0.5938
5/92 [>.............................] - ETA: 5s - loss: 0.9872 - accuracy: 0.6187
6/92 [>.............................] - ETA: 5s - loss: 0.9615 - accuracy: 0.6094
7/92 [=>............................] - ETA: 4s - loss: 0.9715 - accuracy: 0.6205
8/92 [=>............................] - ETA: 4s - loss: 0.9608 - accuracy: 0.6211
9/92 [=>............................] - ETA: 4s - loss: 0.9538 - accuracy: 0.6250
10/92 [==>………………………] - ETA: 4s - loss: 0.9487 - accuracy: 0.6250
11/92 [==>………………………] - ETA: 4s - loss: 0.9616 - accuracy: 0.6307
12/92 [==>………………………] - ETA: 4s - loss: 0.9475 - accuracy: 0.6302
13/92 [===>……………………..] - ETA: 4s - loss: 0.9476 - accuracy: 0.6346
14/92 [===>……………………..] - ETA: 4s - loss: 0.9426 - accuracy: 0.6295
15/92 [===>……………………..] - ETA: 4s - loss: 0.9547 - accuracy: 0.6250
16/92 [====>…………………….] - ETA: 4s - loss: 0.9611 - accuracy: 0.6211
17/92 [====>…………………….] - ETA: 4s - loss: 0.9566 - accuracy: 0.6176
18/92 [====>…………………….] - ETA: 4s - loss: 0.9654 - accuracy: 0.6111
19/92 [=====>……………………] - ETA: 4s - loss: 0.9686 - accuracy: 0.6118
20/92 [=====>……………………] - ETA: 4s - loss: 0.9718 - accuracy: 0.6078
21/92 [=====>……………………] - ETA: 4s - loss: 0.9820 - accuracy: 0.6012
22/92 [======>…………………..] - ETA: 4s - loss: 0.9866 - accuracy: 0.5994
23/92 [======>…………………..] - ETA: 4s - loss: 0.9839 - accuracy: 0.6005
24/92 [======>…………………..] - ETA: 3s - loss: 0.9903 - accuracy: 0.5964
25/92 [=======>………………….] - ETA: 3s - loss: 0.9938 - accuracy: 0.5950
26/92 [=======>………………….] - ETA: 3s - loss: 1.0051 - accuracy: 0.5901
27/92 [=======>………………….] - ETA: 3s - loss: 0.9990 - accuracy: 0.5938
28/92 [========>…………………] - ETA: 3s - loss: 0.9965 - accuracy: 0.5949
29/92 [========>…………………] - ETA: 3s - loss: 0.9986 - accuracy: 0.5948
30/92 [========>…………………] - ETA: 3s - loss: 0.9992 - accuracy: 0.5917
31/92 [=========>………………..] - ETA: 3s - loss: 0.9976 - accuracy: 0.5927
33/92 [=========>………………..] - ETA: 3s - loss: 0.9922 - accuracy: 0.5964
34/92 [==========>……………….] - ETA: 3s - loss: 0.9964 - accuracy: 0.5954
35/92 [==========>……………….] - ETA: 3s - loss: 1.0031 - accuracy: 0.5935
36/92 [==========>……………….] - ETA: 3s - loss: 1.0041 - accuracy: 0.5909
37/92 [===========>………………] - ETA: 3s - loss: 1.0075 - accuracy: 0.5884
38/92 [===========>………………] - ETA: 3s - loss: 1.0049 - accuracy: 0.5886
39/92 [===========>………………] - ETA: 3s - loss: 1.0093 - accuracy: 0.5879
40/92 [============>……………..] - ETA: 3s - loss: 1.0040 - accuracy: 0.5912
41/92 [============>……………..] - ETA: 2s - loss: 1.0038 - accuracy: 0.5920
42/92 [============>……………..] - ETA: 2s - loss: 1.0080 - accuracy: 0.5898
43/92 [=============>…………….] - ETA: 2s - loss: 1.0082 - accuracy: 0.5914
44/92 [=============>…………….] - ETA: 2s - loss: 1.0021 - accuracy: 0.5936
45/92 [=============>…………….] - ETA: 2s - loss: 1.0045 - accuracy: 0.5936
46/92 [==============>……………] - ETA: 2s - loss: 1.0064 - accuracy: 0.5922
47/92 [==============>……………] - ETA: 2s - loss: 1.0063 - accuracy: 0.5936
48/92 [==============>……………] - ETA: 2s - loss: 1.0006 - accuracy: 0.5975
49/92 [==============>……………] - ETA: 2s - loss: 0.9997 - accuracy: 0.5981
50/92 [===============>…………..] - ETA: 2s - loss: 0.9995 - accuracy: 0.5980
51/92 [===============>…………..] - ETA: 2s - loss: 0.9969 - accuracy: 0.5998
52/92 [===============>…………..] - ETA: 2s - loss: 0.9952 - accuracy: 0.5996
53/92 [================>………….] - ETA: 2s - loss: 0.9974 - accuracy: 0.5983
54/92 [================>………….] - ETA: 2s - loss: 1.0079 - accuracy: 0.5913
55/92 [================>………….] - ETA: 2s - loss: 1.0106 - accuracy: 0.5908
56/92 [=================>…………] - ETA: 2s - loss: 1.0121 - accuracy: 0.5880
57/92 [=================>…………] - ETA: 2s - loss: 1.0146 - accuracy: 0.5887
58/92 [=================>…………] - ETA: 1s - loss: 1.0174 - accuracy: 0.5882
59/92 [==================>………..] - ETA: 1s - loss: 1.0150 - accuracy: 0.5883
60/92 [==================>………..] - ETA: 1s - loss: 1.0120 - accuracy: 0.5884
61/92 [==================>………..] - ETA: 1s - loss: 1.0103 - accuracy: 0.5890
62/92 [===================>……….] - ETA: 1s - loss: 1.0095 - accuracy: 0.5901
63/92 [===================>……….] - ETA: 1s - loss: 1.0114 - accuracy: 0.5886
64/92 [===================>……….] - ETA: 1s - loss: 1.0114 - accuracy: 0.5897
65/92 [====================>………] - ETA: 1s - loss: 1.0108 - accuracy: 0.5907
66/92 [====================>………] - ETA: 1s - loss: 1.0115 - accuracy: 0.5903
67/92 [====================>………] - ETA: 1s - loss: 1.0117 - accuracy: 0.5894
68/92 [=====================>……..] - ETA: 1s - loss: 1.0125 - accuracy: 0.5881
69/92 [=====================>……..] - ETA: 1s - loss: 1.0151 - accuracy: 0.5850
70/92 [=====================>……..] - ETA: 1s - loss: 1.0146 - accuracy: 0.5865
71/92 [======================>…….] - ETA: 1s - loss: 1.0158 - accuracy: 0.5866
72/92 [======================>…….] - ETA: 1s - loss: 1.0140 - accuracy: 0.5871
73/92 [======================>…….] - ETA: 1s - loss: 1.0130 - accuracy: 0.5872
74/92 [=======================>……] - ETA: 1s - loss: 1.0127 - accuracy: 0.5873
75/92 [=======================>……] - ETA: 0s - loss: 1.0109 - accuracy: 0.5886
76/92 [=======================>……] - ETA: 0s - loss: 1.0074 - accuracy: 0.5895
77/92 [========================>…..] - ETA: 0s - loss: 1.0038 - accuracy: 0.5916
78/92 [========================>…..] - ETA: 0s - loss: 1.0003 - accuracy: 0.5936
79/92 [========================>…..] - ETA: 0s - loss: 0.9982 - accuracy: 0.5944
80/92 [=========================>….] - ETA: 0s - loss: 0.9978 - accuracy: 0.5929
81/92 [=========================>….] - ETA: 0s - loss: 1.0002 - accuracy: 0.5940
82/92 [=========================>….] - ETA: 0s - loss: 0.9975 - accuracy: 0.5944
83/92 [==========================>…] - ETA: 0s - loss: 0.9982 - accuracy: 0.5948
84/92 [==========================>…] - ETA: 0s - loss: 0.9979 - accuracy: 0.5963
85/92 [==========================>…] - ETA: 0s - loss: 0.9960 - accuracy: 0.5970
86/92 [===========================>..] - ETA: 0s - loss: 0.9941 - accuracy: 0.5977
87/92 [===========================>..] - ETA: 0s - loss: 0.9931 - accuracy: 0.5973
88/92 [===========================>..] - ETA: 0s - loss: 0.9948 - accuracy: 0.5954
89/92 [============================>.] - ETA: 0s - loss: 0.9953 - accuracy: 0.5951
90/92 [============================>.] - ETA: 0s - loss: 0.9947 - accuracy: 0.5961
91/92 [============================>.] - ETA: 0s - loss: 0.9932 - accuracy: 0.5964
92/92 [==============================] - ETA: 0s - loss: 0.9956 - accuracy: 0.5974
92/92 [==============================] - 6s 64ms/step - loss: 0.9956 - accuracy: 0.5974 - val_loss: 0.9920 - val_accuracy: 0.6090
Epoch 3/15
1/92 [..............................] - ETA: 7s - loss: 1.2602 - accuracy: 0.4688
2/92 [..............................] - ETA: 5s - loss: 1.1814 - accuracy: 0.5781
3/92 [..............................] - ETA: 5s - loss: 1.1491 - accuracy: 0.5625
4/92 [>.............................] - ETA: 5s - loss: 1.0875 - accuracy: 0.5781
5/92 [>.............................] - ETA: 5s - loss: 1.0316 - accuracy: 0.5875
6/92 [>.............................] - ETA: 4s - loss: 1.0206 - accuracy: 0.5833
7/92 [=>............................] - ETA: 4s - loss: 0.9818 - accuracy: 0.5938
8/92 [=>............................] - ETA: 4s - loss: 1.0018 - accuracy: 0.5859
9/92 [=>............................] - ETA: 4s - loss: 0.9855 - accuracy: 0.5938
10/92 [==>………………………] - ETA: 4s - loss: 0.9760 - accuracy: 0.5969
11/92 [==>………………………] - ETA: 4s - loss: 0.9811 - accuracy: 0.5881
12/92 [==>………………………] - ETA: 4s - loss: 0.9836 - accuracy: 0.5859
13/92 [===>……………………..] - ETA: 4s - loss: 0.9757 - accuracy: 0.5889
14/92 [===>……………………..] - ETA: 4s - loss: 0.9660 - accuracy: 0.5893
15/92 [===>……………………..] - ETA: 4s - loss: 0.9619 - accuracy: 0.5938
16/92 [====>…………………….] - ETA: 4s - loss: 0.9688 - accuracy: 0.5898
17/92 [====>…………………….] - ETA: 4s - loss: 0.9691 - accuracy: 0.5919
18/92 [====>…………………….] - ETA: 4s - loss: 0.9729 - accuracy: 0.5938
20/92 [=====>……………………] - ETA: 4s - loss: 0.9704 - accuracy: 0.5934
21/92 [=====>……………………] - ETA: 4s - loss: 0.9652 - accuracy: 0.5934
22/92 [======>…………………..] - ETA: 4s - loss: 0.9528 - accuracy: 0.6006
23/92 [======>…………………..] - ETA: 4s - loss: 0.9511 - accuracy: 0.6044
24/92 [======>…………………..] - ETA: 3s - loss: 0.9597 - accuracy: 0.6026
25/92 [=======>………………….] - ETA: 3s - loss: 0.9707 - accuracy: 0.5972
26/92 [=======>………………….] - ETA: 3s - loss: 0.9649 - accuracy: 0.5971
27/92 [=======>………………….] - ETA: 3s - loss: 0.9528 - accuracy: 0.6016
28/92 [========>…………………] - ETA: 3s - loss: 0.9453 - accuracy: 0.6059
29/92 [========>…………………] - ETA: 3s - loss: 0.9458 - accuracy: 0.6065
30/92 [========>…………………] - ETA: 3s - loss: 0.9467 - accuracy: 0.6061
31/92 [=========>………………..] - ETA: 3s - loss: 0.9432 - accuracy: 0.6098
32/92 [=========>………………..] - ETA: 3s - loss: 0.9430 - accuracy: 0.6093
33/92 [=========>………………..] - ETA: 3s - loss: 0.9351 - accuracy: 0.6164
34/92 [==========>……………….] - ETA: 3s - loss: 0.9405 - accuracy: 0.6139
35/92 [==========>……………….] - ETA: 3s - loss: 0.9356 - accuracy: 0.6169
36/92 [==========>……………….] - ETA: 3s - loss: 0.9322 - accuracy: 0.6215
37/92 [===========>………………] - ETA: 3s - loss: 0.9363 - accuracy: 0.6199
38/92 [===========>………………] - ETA: 3s - loss: 0.9325 - accuracy: 0.6217
39/92 [===========>………………] - ETA: 3s - loss: 0.9322 - accuracy: 0.6234
40/92 [============>……………..] - ETA: 3s - loss: 0.9291 - accuracy: 0.6250
41/92 [============>……………..] - ETA: 2s - loss: 0.9252 - accuracy: 0.6242
42/92 [============>……………..] - ETA: 2s - loss: 0.9226 - accuracy: 0.6257
43/92 [=============>…………….] - ETA: 2s - loss: 0.9213 - accuracy: 0.6250
44/92 [=============>…………….] - ETA: 2s - loss: 0.9232 - accuracy: 0.6243
45/92 [=============>…………….] - ETA: 2s - loss: 0.9261 - accuracy: 0.6222
46/92 [==============>……………] - ETA: 2s - loss: 0.9277 - accuracy: 0.6209
47/92 [==============>……………] - ETA: 2s - loss: 0.9308 - accuracy: 0.6197
48/92 [==============>……………] - ETA: 2s - loss: 0.9392 - accuracy: 0.6145
49/92 [==============>……………] - ETA: 2s - loss: 0.9396 - accuracy: 0.6122
50/92 [===============>…………..] - ETA: 2s - loss: 0.9369 - accuracy: 0.6137
51/92 [===============>…………..] - ETA: 2s - loss: 0.9361 - accuracy: 0.6139
52/92 [===============>…………..] - ETA: 2s - loss: 0.9326 - accuracy: 0.6147
53/92 [================>………….] - ETA: 2s - loss: 0.9335 - accuracy: 0.6149
54/92 [================>………….] - ETA: 2s - loss: 0.9319 - accuracy: 0.6169
55/92 [================>………….] - ETA: 2s - loss: 0.9303 - accuracy: 0.6170
56/92 [=================>…………] - ETA: 2s - loss: 0.9289 - accuracy: 0.6183
57/92 [=================>…………] - ETA: 2s - loss: 0.9316 - accuracy: 0.6167
58/92 [=================>…………] - ETA: 1s - loss: 0.9314 - accuracy: 0.6163
59/92 [==================>………..] - ETA: 1s - loss: 0.9313 - accuracy: 0.6165
60/92 [==================>………..] - ETA: 1s - loss: 0.9282 - accuracy: 0.6182
61/92 [==================>………..] - ETA: 1s - loss: 0.9264 - accuracy: 0.6188
62/92 [===================>……….] - ETA: 1s - loss: 0.9249 - accuracy: 0.6194
63/92 [===================>……….] - ETA: 1s - loss: 0.9295 - accuracy: 0.6165
64/92 [===================>……….] - ETA: 1s - loss: 0.9288 - accuracy: 0.6157
65/92 [====================>………] - ETA: 1s - loss: 0.9234 - accuracy: 0.6178
66/92 [====================>………] - ETA: 1s - loss: 0.9228 - accuracy: 0.6179
67/92 [====================>………] - ETA: 1s - loss: 0.9232 - accuracy: 0.6170
68/92 [=====================>……..] - ETA: 1s - loss: 0.9240 - accuracy: 0.6172
69/92 [=====================>……..] - ETA: 1s - loss: 0.9200 - accuracy: 0.6205
70/92 [=====================>……..] - ETA: 1s - loss: 0.9244 - accuracy: 0.6196
71/92 [======================>…….] - ETA: 1s - loss: 0.9237 - accuracy: 0.6197
72/92 [======================>…….] - ETA: 1s - loss: 0.9253 - accuracy: 0.6193
73/92 [======================>…….] - ETA: 1s - loss: 0.9243 - accuracy: 0.6203
74/92 [=======================>……] - ETA: 1s - loss: 0.9259 - accuracy: 0.6195
75/92 [=======================>……] - ETA: 0s - loss: 0.9242 - accuracy: 0.6204
76/92 [=======================>……] - ETA: 0s - loss: 0.9216 - accuracy: 0.6213
77/92 [========================>…..] - ETA: 0s - loss: 0.9203 - accuracy: 0.6209
78/92 [========================>…..] - ETA: 0s - loss: 0.9200 - accuracy: 0.6214
79/92 [========================>…..] - ETA: 0s - loss: 0.9181 - accuracy: 0.6226
80/92 [=========================>….] - ETA: 0s - loss: 0.9185 - accuracy: 0.6226
81/92 [=========================>….] - ETA: 0s - loss: 0.9160 - accuracy: 0.6250
82/92 [=========================>….] - ETA: 0s - loss: 0.9186 - accuracy: 0.6250
83/92 [==========================>…] - ETA: 0s - loss: 0.9164 - accuracy: 0.6258
84/92 [==========================>…] - ETA: 0s - loss: 0.9167 - accuracy: 0.6269
85/92 [==========================>…] - ETA: 0s - loss: 0.9177 - accuracy: 0.6265
86/92 [===========================>..] - ETA: 0s - loss: 0.9183 - accuracy: 0.6276
87/92 [===========================>..] - ETA: 0s - loss: 0.9182 - accuracy: 0.6275
88/92 [===========================>..] - ETA: 0s - loss: 0.9156 - accuracy: 0.6278
89/92 [============================>.] - ETA: 0s - loss: 0.9135 - accuracy: 0.6292
90/92 [============================>.] - ETA: 0s - loss: 0.9121 - accuracy: 0.6302
91/92 [============================>.] - ETA: 0s - loss: 0.9129 - accuracy: 0.6298
92/92 [==============================] - ETA: 0s - loss: 0.9155 - accuracy: 0.6298
92/92 [==============================] - 6s 64ms/step - loss: 0.9155 - accuracy: 0.6298 - val_loss: 0.8959 - val_accuracy: 0.6621
Epoch 4/15
1/92 [..............................] - ETA: 7s - loss: 0.7704 - accuracy: 0.7812
2/92 [..............................] - ETA: 5s - loss: 0.8739 - accuracy: 0.6562
3/92 [..............................] - ETA: 5s - loss: 0.9644 - accuracy: 0.6146
4/92 [>.............................] - ETA: 5s - loss: 0.9070 - accuracy: 0.6484
5/92 [>.............................] - ETA: 4s - loss: 0.8696 - accuracy: 0.6625
6/92 [>.............................] - ETA: 4s - loss: 0.8536 - accuracy: 0.6562
7/92 [=>............................] - ETA: 4s - loss: 0.8587 - accuracy: 0.6473
8/92 [=>............................] - ETA: 4s - loss: 0.8727 - accuracy: 0.6523
9/92 [=>............................] - ETA: 4s - loss: 0.8413 - accuracy: 0.6701
10/92 [==>………………………] - ETA: 4s - loss: 0.8577 - accuracy: 0.6594
11/92 [==>………………………] - ETA: 4s - loss: 0.8386 - accuracy: 0.6733
12/92 [==>………………………] - ETA: 4s - loss: 0.8637 - accuracy: 0.6589
13/92 [===>……………………..] - ETA: 4s - loss: 0.8819 - accuracy: 0.6659
14/92 [===>……………………..] - ETA: 4s - loss: 0.8783 - accuracy: 0.6674
15/92 [===>……………………..] - ETA: 4s - loss: 0.8797 - accuracy: 0.6667
16/92 [====>…………………….] - ETA: 4s - loss: 0.8644 - accuracy: 0.6777
17/92 [====>…………………….] - ETA: 4s - loss: 0.8715 - accuracy: 0.6746
18/92 [====>…………………….] - ETA: 4s - loss: 0.8544 - accuracy: 0.6788
19/92 [=====>……………………] - ETA: 4s - loss: 0.8484 - accuracy: 0.6809
20/92 [=====>……………………] - ETA: 4s - loss: 0.8429 - accuracy: 0.6828
21/92 [=====>……………………] - ETA: 4s - loss: 0.8352 - accuracy: 0.6860
22/92 [======>…………………..] - ETA: 4s - loss: 0.8293 - accuracy: 0.6875
23/92 [======>…………………..] - ETA: 3s - loss: 0.8324 - accuracy: 0.6861
24/92 [======>…………………..] - ETA: 3s - loss: 0.8321 - accuracy: 0.6875
25/92 [=======>………………….] - ETA: 3s - loss: 0.8377 - accuracy: 0.6913
26/92 [=======>………………….] - ETA: 3s - loss: 0.8366 - accuracy: 0.6923
27/92 [=======>………………….] - ETA: 3s - loss: 0.8278 - accuracy: 0.6933
28/92 [========>…………………] - ETA: 3s - loss: 0.8303 - accuracy: 0.6942
29/92 [========>…………………] - ETA: 3s - loss: 0.8305 - accuracy: 0.6950
30/92 [========>…………………] - ETA: 3s - loss: 0.8342 - accuracy: 0.6958
31/92 [=========>………………..] - ETA: 3s - loss: 0.8350 - accuracy: 0.6956
32/92 [=========>………………..] - ETA: 3s - loss: 0.8386 - accuracy: 0.6914
33/92 [=========>………………..] - ETA: 3s - loss: 0.8354 - accuracy: 0.6922
34/92 [==========>……………….] - ETA: 3s - loss: 0.8424 - accuracy: 0.6921
35/92 [==========>……………….] - ETA: 3s - loss: 0.8367 - accuracy: 0.6920
36/92 [==========>……………….] - ETA: 3s - loss: 0.8349 - accuracy: 0.6936
37/92 [===========>………………] - ETA: 3s - loss: 0.8365 - accuracy: 0.6926
38/92 [===========>………………] - ETA: 3s - loss: 0.8451 - accuracy: 0.6891
39/92 [===========>………………] - ETA: 3s - loss: 0.8401 - accuracy: 0.6899
40/92 [============>……………..] - ETA: 3s - loss: 0.8397 - accuracy: 0.6891
41/92 [============>……………..] - ETA: 2s - loss: 0.8379 - accuracy: 0.6913
42/92 [============>……………..] - ETA: 2s - loss: 0.8459 - accuracy: 0.6868
43/92 [=============>…………….] - ETA: 2s - loss: 0.8410 - accuracy: 0.6860
44/92 [=============>…………….] - ETA: 2s - loss: 0.8352 - accuracy: 0.6889
46/92 [==============>……………] - ETA: 2s - loss: 0.8372 - accuracy: 0.6872
47/92 [==============>……………] - ETA: 2s - loss: 0.8341 - accuracy: 0.6885
48/92 [==============>……………] - ETA: 2s - loss: 0.8279 - accuracy: 0.6918
49/92 [==============>……………] - ETA: 2s - loss: 0.8294 - accuracy: 0.6917
50/92 [===============>…………..] - ETA: 2s - loss: 0.8300 - accuracy: 0.6928
51/92 [===============>…………..] - ETA: 2s - loss: 0.8275 - accuracy: 0.6940
52/92 [===============>…………..] - ETA: 2s - loss: 0.8267 - accuracy: 0.6944
53/92 [================>………….] - ETA: 2s - loss: 0.8255 - accuracy: 0.6961
54/92 [================>………….] - ETA: 2s - loss: 0.8220 - accuracy: 0.6977
55/92 [================>………….] - ETA: 2s - loss: 0.8198 - accuracy: 0.6975
56/92 [=================>…………] - ETA: 2s - loss: 0.8172 - accuracy: 0.6979
57/92 [=================>…………] - ETA: 2s - loss: 0.8155 - accuracy: 0.6982
58/92 [=================>…………] - ETA: 1s - loss: 0.8128 - accuracy: 0.6997
59/92 [==================>………..] - ETA: 1s - loss: 0.8129 - accuracy: 0.7000
60/92 [==================>………..] - ETA: 1s - loss: 0.8170 - accuracy: 0.6987
61/92 [==================>………..] - ETA: 1s - loss: 0.8182 - accuracy: 0.6980
62/92 [===================>……….] - ETA: 1s - loss: 0.8189 - accuracy: 0.6964
63/92 [===================>……….] - ETA: 1s - loss: 0.8193 - accuracy: 0.6952
64/92 [===================>……….] - ETA: 1s - loss: 0.8194 - accuracy: 0.6961
65/92 [====================>………] - ETA: 1s - loss: 0.8215 - accuracy: 0.6955
66/92 [====================>………] - ETA: 1s - loss: 0.8210 - accuracy: 0.6953
67/92 [====================>………] - ETA: 1s - loss: 0.8223 - accuracy: 0.6952
68/92 [=====================>……..] - ETA: 1s - loss: 0.8207 - accuracy: 0.6960
69/92 [=====================>……..] - ETA: 1s - loss: 0.8182 - accuracy: 0.6977
70/92 [=====================>……..] - ETA: 1s - loss: 0.8195 - accuracy: 0.6962
71/92 [======================>…….] - ETA: 1s - loss: 0.8194 - accuracy: 0.6961
72/92 [======================>…….] - ETA: 1s - loss: 0.8208 - accuracy: 0.6956
73/92 [======================>…….] - ETA: 1s - loss: 0.8190 - accuracy: 0.6963
74/92 [=======================>……] - ETA: 1s - loss: 0.8170 - accuracy: 0.6970
75/92 [=======================>……] - ETA: 0s - loss: 0.8158 - accuracy: 0.6977
76/92 [=======================>……] - ETA: 0s - loss: 0.8179 - accuracy: 0.6955
77/92 [========================>…..] - ETA: 0s - loss: 0.8176 - accuracy: 0.6958
78/92 [========================>…..] - ETA: 0s - loss: 0.8175 - accuracy: 0.6957
79/92 [========================>…..] - ETA: 0s - loss: 0.8188 - accuracy: 0.6952
80/92 [=========================>….] - ETA: 0s - loss: 0.8172 - accuracy: 0.6959
81/92 [=========================>….] - ETA: 0s - loss: 0.8217 - accuracy: 0.6939
82/92 [=========================>….] - ETA: 0s - loss: 0.8197 - accuracy: 0.6950
83/92 [==========================>…] - ETA: 0s - loss: 0.8180 - accuracy: 0.6952
84/92 [==========================>…] - ETA: 0s - loss: 0.8183 - accuracy: 0.6951
85/92 [==========================>…] - ETA: 0s - loss: 0.8190 - accuracy: 0.6947
86/92 [===========================>..] - ETA: 0s - loss: 0.8208 - accuracy: 0.6928
87/92 [===========================>..] - ETA: 0s - loss: 0.8207 - accuracy: 0.6931
88/92 [===========================>..] - ETA: 0s - loss: 0.8205 - accuracy: 0.6930
89/92 [============================>.] - ETA: 0s - loss: 0.8188 - accuracy: 0.6937
90/92 [============================>.] - ETA: 0s - loss: 0.8180 - accuracy: 0.6939
91/92 [============================>.] - ETA: 0s - loss: 0.8167 - accuracy: 0.6946
92/92 [==============================] - ETA: 0s - loss: 0.8158 - accuracy: 0.6945
92/92 [==============================] - 6s 64ms/step - loss: 0.8158 - accuracy: 0.6945 - val_loss: 0.8530 - val_accuracy: 0.6757
Epoch 5/15
1/92 [..............................] - ETA: 7s - loss: 0.8907 - accuracy: 0.7188
2/92 [..............................] - ETA: 5s - loss: 0.8773 - accuracy: 0.6875
3/92 [..............................] - ETA: 5s - loss: 0.8330 - accuracy: 0.6771
4/92 [>.............................] - ETA: 5s - loss: 0.7960 - accuracy: 0.6953
5/92 [>.............................] - ETA: 5s - loss: 0.8390 - accuracy: 0.6812
6/92 [>.............................] - ETA: 5s - loss: 0.8144 - accuracy: 0.6771
7/92 [=>............................] - ETA: 5s - loss: 0.8024 - accuracy: 0.6920
8/92 [=>............................] - ETA: 4s - loss: 0.8119 - accuracy: 0.6914
9/92 [=>............................] - ETA: 4s - loss: 0.8164 - accuracy: 0.6875
10/92 [==>………………………] - ETA: 4s - loss: 0.7930 - accuracy: 0.7000
11/92 [==>………………………] - ETA: 4s - loss: 0.7694 - accuracy: 0.7102
12/92 [==>………………………] - ETA: 4s - loss: 0.7519 - accuracy: 0.7161
13/92 [===>……………………..] - ETA: 4s - loss: 0.7302 - accuracy: 0.7260
14/92 [===>……………………..] - ETA: 4s - loss: 0.7293 - accuracy: 0.7210
15/92 [===>……………………..] - ETA: 4s - loss: 0.7256 - accuracy: 0.7208
16/92 [====>…………………….] - ETA: 4s - loss: 0.7320 - accuracy: 0.7207
17/92 [====>…………………….] - ETA: 4s - loss: 0.7327 - accuracy: 0.7243
18/92 [====>…………………….] - ETA: 4s - loss: 0.7351 - accuracy: 0.7205
19/92 [=====>……………………] - ETA: 4s - loss: 0.7352 - accuracy: 0.7204
20/92 [=====>……………………] - ETA: 4s - loss: 0.7393 - accuracy: 0.7203
21/92 [=====>……………………] - ETA: 4s - loss: 0.7438 - accuracy: 0.7217
22/92 [======>…………………..] - ETA: 4s - loss: 0.7462 - accuracy: 0.7216
23/92 [======>…………………..] - ETA: 3s - loss: 0.7597 - accuracy: 0.7147
24/92 [======>…………………..] - ETA: 3s - loss: 0.7511 - accuracy: 0.7188
25/92 [=======>………………….] - ETA: 3s - loss: 0.7569 - accuracy: 0.7150
26/92 [=======>………………….] - ETA: 3s - loss: 0.7474 - accuracy: 0.7175
27/92 [=======>………………….] - ETA: 3s - loss: 0.7583 - accuracy: 0.7141
28/92 [========>…………………] - ETA: 3s - loss: 0.7548 - accuracy: 0.7154
29/92 [========>…………………] - ETA: 3s - loss: 0.7512 - accuracy: 0.7155
30/92 [========>…………………] - ETA: 3s - loss: 0.7478 - accuracy: 0.7156
31/92 [=========>………………..] - ETA: 3s - loss: 0.7464 - accuracy: 0.7157
32/92 [=========>………………..] - ETA: 3s - loss: 0.7567 - accuracy: 0.7129
33/92 [=========>………………..] - ETA: 3s - loss: 0.7518 - accuracy: 0.7159
34/92 [==========>……………….] - ETA: 3s - loss: 0.7569 - accuracy: 0.7160
35/92 [==========>……………….] - ETA: 3s - loss: 0.7563 - accuracy: 0.7152
36/92 [==========>……………….] - ETA: 3s - loss: 0.7563 - accuracy: 0.7153
37/92 [===========>………………] - ETA: 3s - loss: 0.7607 - accuracy: 0.7111
38/92 [===========>………………] - ETA: 3s - loss: 0.7650 - accuracy: 0.7072
39/92 [===========>………………] - ETA: 3s - loss: 0.7642 - accuracy: 0.7083
40/92 [============>……………..] - ETA: 3s - loss: 0.7700 - accuracy: 0.7078
41/92 [============>……………..] - ETA: 2s - loss: 0.7711 - accuracy: 0.7096
42/92 [============>……………..] - ETA: 2s - loss: 0.7661 - accuracy: 0.7128
43/92 [=============>…………….] - ETA: 2s - loss: 0.7633 - accuracy: 0.7129
44/92 [=============>…………….] - ETA: 2s - loss: 0.7632 - accuracy: 0.7124
45/92 [=============>…………….] - ETA: 2s - loss: 0.7625 - accuracy: 0.7125
46/92 [==============>……………] - ETA: 2s - loss: 0.7612 - accuracy: 0.7120
47/92 [==============>……………] - ETA: 2s - loss: 0.7590 - accuracy: 0.7108
48/92 [==============>……………] - ETA: 2s - loss: 0.7586 - accuracy: 0.7103
49/92 [==============>……………] - ETA: 2s - loss: 0.7561 - accuracy: 0.7111
50/92 [===============>…………..] - ETA: 2s - loss: 0.7645 - accuracy: 0.7050
52/92 [===============>…………..] - ETA: 2s - loss: 0.7645 - accuracy: 0.7047
53/92 [================>………….] - ETA: 2s - loss: 0.7692 - accuracy: 0.7038
54/92 [================>………….] - ETA: 2s - loss: 0.7705 - accuracy: 0.7035
55/92 [================>………….] - ETA: 2s - loss: 0.7802 - accuracy: 0.6986
56/92 [=================>…………] - ETA: 2s - loss: 0.7780 - accuracy: 0.6990
57/92 [=================>…………] - ETA: 2s - loss: 0.7770 - accuracy: 0.6982
58/92 [=================>…………] - ETA: 1s - loss: 0.7749 - accuracy: 0.6997
59/92 [==================>………..] - ETA: 1s - loss: 0.7784 - accuracy: 0.6984
60/92 [==================>………..] - ETA: 1s - loss: 0.7787 - accuracy: 0.6967
61/92 [==================>………..] - ETA: 1s - loss: 0.7795 - accuracy: 0.6955
62/92 [===================>……….] - ETA: 1s - loss: 0.7780 - accuracy: 0.6964
63/92 [===================>……….] - ETA: 1s - loss: 0.7771 - accuracy: 0.6977
64/92 [===================>……….] - ETA: 1s - loss: 0.7814 - accuracy: 0.6961
65/92 [====================>………] - ETA: 1s - loss: 0.7836 - accuracy: 0.6950
66/92 [====================>………] - ETA: 1s - loss: 0.7805 - accuracy: 0.6968
67/92 [====================>………] - ETA: 1s - loss: 0.7795 - accuracy: 0.6966
68/92 [=====================>……..] - ETA: 1s - loss: 0.7841 - accuracy: 0.6933
69/92 [=====================>……..] - ETA: 1s - loss: 0.7849 - accuracy: 0.6932
70/92 [=====================>……..] - ETA: 1s - loss: 0.7907 - accuracy: 0.6927
71/92 [======================>…….] - ETA: 1s - loss: 0.7915 - accuracy: 0.6917
72/92 [======================>…….] - ETA: 1s - loss: 0.7909 - accuracy: 0.6916
73/92 [======================>…….] - ETA: 1s - loss: 0.7939 - accuracy: 0.6903
74/92 [=======================>……] - ETA: 1s - loss: 0.7967 - accuracy: 0.6890
75/92 [=======================>……] - ETA: 0s - loss: 0.7993 - accuracy: 0.6873
76/92 [=======================>……] - ETA: 0s - loss: 0.8003 - accuracy: 0.6861
77/92 [========================>…..] - ETA: 0s - loss: 0.8013 - accuracy: 0.6853
78/92 [========================>…..] - ETA: 0s - loss: 0.8004 - accuracy: 0.6853
79/92 [========================>…..] - ETA: 0s - loss: 0.7983 - accuracy: 0.6861
80/92 [=========================>….] - ETA: 0s - loss: 0.8004 - accuracy: 0.6846
81/92 [=========================>….] - ETA: 0s - loss: 0.8001 - accuracy: 0.6854
82/92 [=========================>….] - ETA: 0s - loss: 0.7997 - accuracy: 0.6858
83/92 [==========================>…] - ETA: 0s - loss: 0.7988 - accuracy: 0.6869
84/92 [==========================>…] - ETA: 0s - loss: 0.7983 - accuracy: 0.6869
85/92 [==========================>…] - ETA: 0s - loss: 0.7975 - accuracy: 0.6881
86/92 [===========================>..] - ETA: 0s - loss: 0.7958 - accuracy: 0.6891
87/92 [===========================>..] - ETA: 0s - loss: 0.7945 - accuracy: 0.6902
88/92 [===========================>..] - ETA: 0s - loss: 0.7928 - accuracy: 0.6909
89/92 [============================>.] - ETA: 0s - loss: 0.7910 - accuracy: 0.6919
90/92 [============================>.] - ETA: 0s - loss: 0.7917 - accuracy: 0.6915
91/92 [============================>.] - ETA: 0s - loss: 0.7883 - accuracy: 0.6932
92/92 [==============================] - ETA: 0s - loss: 0.7896 - accuracy: 0.6931
92/92 [==============================] - 6s 63ms/step - loss: 0.7896 - accuracy: 0.6931 - val_loss: 0.8867 - val_accuracy: 0.6798
Epoch 6/15
1/92 [..............................] - ETA: 6s - loss: 0.5518 - accuracy: 0.7812
2/92 [..............................] - ETA: 5s - loss: 0.7630 - accuracy: 0.7500
3/92 [..............................] - ETA: 5s - loss: 0.7584 - accuracy: 0.7604
4/92 [>.............................] - ETA: 5s - loss: 0.7502 - accuracy: 0.7266
5/92 [>.............................] - ETA: 5s - loss: 0.7661 - accuracy: 0.7125
6/92 [>.............................] - ETA: 4s - loss: 0.7633 - accuracy: 0.7083
7/92 [=>............................] - ETA: 4s - loss: 0.7836 - accuracy: 0.7054
8/92 [=>............................] - ETA: 4s - loss: 0.7776 - accuracy: 0.7070
9/92 [=>............................] - ETA: 4s - loss: 0.7611 - accuracy: 0.7153
10/92 [==>………………………] - ETA: 4s - loss: 0.7587 - accuracy: 0.7125
11/92 [==>………………………] - ETA: 4s - loss: 0.7458 - accuracy: 0.7244
12/92 [==>………………………] - ETA: 4s - loss: 0.7555 - accuracy: 0.7266
13/92 [===>……………………..] - ETA: 4s - loss: 0.7522 - accuracy: 0.7308
14/92 [===>……………………..] - ETA: 4s - loss: 0.7398 - accuracy: 0.7277
15/92 [===>……………………..] - ETA: 4s - loss: 0.7376 - accuracy: 0.7312
16/92 [====>…………………….] - ETA: 4s - loss: 0.7344 - accuracy: 0.7285
17/92 [====>…………………….] - ETA: 4s - loss: 0.7325 - accuracy: 0.7298
18/92 [====>…………………….] - ETA: 4s - loss: 0.7301 - accuracy: 0.7292
19/92 [=====>……………………] - ETA: 4s - loss: 0.7525 - accuracy: 0.7237
20/92 [=====>……………………] - ETA: 4s - loss: 0.7644 - accuracy: 0.7188
21/92 [=====>……………………] - ETA: 4s - loss: 0.7723 - accuracy: 0.7158
22/92 [======>…………………..] - ETA: 4s - loss: 0.7636 - accuracy: 0.7202
23/92 [======>…………………..] - ETA: 3s - loss: 0.7545 - accuracy: 0.7228
24/92 [======>…………………..] - ETA: 3s - loss: 0.7522 - accuracy: 0.7227
25/92 [=======>………………….] - ETA: 3s - loss: 0.7536 - accuracy: 0.7200
26/92 [=======>………………….] - ETA: 3s - loss: 0.7700 - accuracy: 0.7103
27/92 [=======>………………….] - ETA: 3s - loss: 0.7697 - accuracy: 0.7106
28/92 [========>…………………] - ETA: 3s - loss: 0.7738 - accuracy: 0.7121
29/92 [========>…………………] - ETA: 3s - loss: 0.7679 - accuracy: 0.7155
30/92 [========>…………………] - ETA: 3s - loss: 0.7772 - accuracy: 0.7104
31/92 [=========>………………..] - ETA: 3s - loss: 0.7790 - accuracy: 0.7107
32/92 [=========>………………..] - ETA: 3s - loss: 0.7822 - accuracy: 0.7100
33/92 [=========>………………..] - ETA: 3s - loss: 0.7819 - accuracy: 0.7112
34/92 [==========>……………….] - ETA: 3s - loss: 0.7831 - accuracy: 0.7086
35/92 [==========>……………….] - ETA: 3s - loss: 0.7782 - accuracy: 0.7089
36/92 [==========>……………….] - ETA: 3s - loss: 0.7777 - accuracy: 0.7083
37/92 [===========>………………] - ETA: 3s - loss: 0.7794 - accuracy: 0.7078
38/92 [===========>………………] - ETA: 3s - loss: 0.7856 - accuracy: 0.7064
39/92 [===========>………………] - ETA: 3s - loss: 0.7847 - accuracy: 0.7067
40/92 [============>……………..] - ETA: 3s - loss: 0.7862 - accuracy: 0.7047
41/92 [============>……………..] - ETA: 2s - loss: 0.7830 - accuracy: 0.7058
42/92 [============>……………..] - ETA: 2s - loss: 0.7793 - accuracy: 0.7068
43/92 [=============>…………….] - ETA: 2s - loss: 0.7756 - accuracy: 0.7086
44/92 [=============>…………….] - ETA: 2s - loss: 0.7727 - accuracy: 0.7095
45/92 [=============>…………….] - ETA: 2s - loss: 0.7714 - accuracy: 0.7076
46/92 [==============>……………] - ETA: 2s - loss: 0.7723 - accuracy: 0.7079
47/92 [==============>……………] - ETA: 2s - loss: 0.7696 - accuracy: 0.7094
48/92 [==============>……………] - ETA: 2s - loss: 0.7686 - accuracy: 0.7109
49/92 [==============>……………] - ETA: 2s - loss: 0.7657 - accuracy: 0.7136
50/92 [===============>…………..] - ETA: 2s - loss: 0.7702 - accuracy: 0.7119
51/92 [===============>…………..] - ETA: 2s - loss: 0.7732 - accuracy: 0.7126
52/92 [===============>…………..] - ETA: 2s - loss: 0.7772 - accuracy: 0.7109
53/92 [================>………….] - ETA: 2s - loss: 0.7780 - accuracy: 0.7099
54/92 [================>………….] - ETA: 2s - loss: 0.7742 - accuracy: 0.7124
55/92 [================>………….] - ETA: 2s - loss: 0.7721 - accuracy: 0.7136
56/92 [=================>…………] - ETA: 2s - loss: 0.7717 - accuracy: 0.7132
57/92 [=================>…………] - ETA: 2s - loss: 0.7688 - accuracy: 0.7138
58/92 [=================>…………] - ETA: 1s - loss: 0.7733 - accuracy: 0.7101
59/92 [==================>………..] - ETA: 1s - loss: 0.7728 - accuracy: 0.7119
60/92 [==================>………..] - ETA: 1s - loss: 0.7746 - accuracy: 0.7109
61/92 [==================>………..] - ETA: 1s - loss: 0.7697 - accuracy: 0.7126
62/92 [===================>……….] - ETA: 1s - loss: 0.7746 - accuracy: 0.7117
63/92 [===================>……….] - ETA: 1s - loss: 0.7725 - accuracy: 0.7123
64/92 [===================>……….] - ETA: 1s - loss: 0.7711 - accuracy: 0.7134
65/92 [====================>………] - ETA: 1s - loss: 0.7713 - accuracy: 0.7130
66/92 [====================>………] - ETA: 1s - loss: 0.7690 - accuracy: 0.7135
67/92 [====================>………] - ETA: 1s - loss: 0.7682 - accuracy: 0.7141
68/92 [=====================>……..] - ETA: 1s - loss: 0.7670 - accuracy: 0.7146
69/92 [=====================>……..] - ETA: 1s - loss: 0.7655 - accuracy: 0.7156
70/92 [=====================>……..] - ETA: 1s - loss: 0.7639 - accuracy: 0.7161
71/92 [======================>…….] - ETA: 1s - loss: 0.7664 - accuracy: 0.7139
72/92 [======================>…….] - ETA: 1s - loss: 0.7650 - accuracy: 0.7153
73/92 [======================>…….] - ETA: 1s - loss: 0.7669 - accuracy: 0.7132
74/92 [=======================>……] - ETA: 1s - loss: 0.7677 - accuracy: 0.7124
76/92 [=======================>……] - ETA: 0s - loss: 0.7683 - accuracy: 0.7120
77/92 [========================>…..] - ETA: 0s - loss: 0.7676 - accuracy: 0.7113
78/92 [========================>…..] - ETA: 0s - loss: 0.7679 - accuracy: 0.7122
79/92 [========================>…..] - ETA: 0s - loss: 0.7655 - accuracy: 0.7127
80/92 [=========================>….] - ETA: 0s - loss: 0.7639 - accuracy: 0.7132
81/92 [=========================>….] - ETA: 0s - loss: 0.7643 - accuracy: 0.7136
82/92 [=========================>….] - ETA: 0s - loss: 0.7672 - accuracy: 0.7122
83/92 [==========================>…] - ETA: 0s - loss: 0.7681 - accuracy: 0.7115
84/92 [==========================>…] - ETA: 0s - loss: 0.7652 - accuracy: 0.7131
85/92 [==========================>…] - ETA: 0s - loss: 0.7664 - accuracy: 0.7124
86/92 [===========================>..] - ETA: 0s - loss: 0.7678 - accuracy: 0.7114
87/92 [===========================>..] - ETA: 0s - loss: 0.7660 - accuracy: 0.7118
88/92 [===========================>..] - ETA: 0s - loss: 0.7654 - accuracy: 0.7115
89/92 [============================>.] - ETA: 0s - loss: 0.7658 - accuracy: 0.7109
90/92 [============================>.] - ETA: 0s - loss: 0.7648 - accuracy: 0.7107
91/92 [============================>.] - ETA: 0s - loss: 0.7648 - accuracy: 0.7107
92/92 [==============================] - ETA: 0s - loss: 0.7647 - accuracy: 0.7115
92/92 [==============================] - 6s 64ms/step - loss: 0.7647 - accuracy: 0.7115 - val_loss: 0.7599 - val_accuracy: 0.7016
Epoch 7/15
1/92 [..............................] - ETA: 7s - loss: 0.4912 - accuracy: 0.8438
2/92 [..............................] - ETA: 5s - loss: 0.5197 - accuracy: 0.7812
3/92 [..............................] - ETA: 5s - loss: 0.6350 - accuracy: 0.7396
4/92 [>.............................] - ETA: 5s - loss: 0.6448 - accuracy: 0.7500
5/92 [>.............................] - ETA: 5s - loss: 0.6741 - accuracy: 0.7375
6/92 [>.............................] - ETA: 5s - loss: 0.7069 - accuracy: 0.7344
7/92 [=>............................] - ETA: 5s - loss: 0.7105 - accuracy: 0.7321
8/92 [=>............................] - ETA: 5s - loss: 0.7082 - accuracy: 0.7344
9/92 [=>............................] - ETA: 4s - loss: 0.7131 - accuracy: 0.7326
10/92 [==>………………………] - ETA: 4s - loss: 0.7040 - accuracy: 0.7312
11/92 [==>………………………] - ETA: 4s - loss: 0.7117 - accuracy: 0.7244
12/92 [==>………………………] - ETA: 4s - loss: 0.7376 - accuracy: 0.7161
13/92 [===>……………………..] - ETA: 4s - loss: 0.7223 - accuracy: 0.7236
14/92 [===>……………………..] - ETA: 4s - loss: 0.7167 - accuracy: 0.7210
15/92 [===>……………………..] - ETA: 4s - loss: 0.7110 - accuracy: 0.7250
16/92 [====>…………………….] - ETA: 4s - loss: 0.6943 - accuracy: 0.7324
17/92 [====>…………………….] - ETA: 4s - loss: 0.6881 - accuracy: 0.7335
18/92 [====>…………………….] - ETA: 4s - loss: 0.6882 - accuracy: 0.7326
19/92 [=====>……………………] - ETA: 4s - loss: 0.6898 - accuracy: 0.7319
20/92 [=====>……………………] - ETA: 4s - loss: 0.6850 - accuracy: 0.7328
21/92 [=====>……………………] - ETA: 4s - loss: 0.6983 - accuracy: 0.7292
22/92 [======>…………………..] - ETA: 4s - loss: 0.6962 - accuracy: 0.7301
23/92 [======>…………………..] - ETA: 4s - loss: 0.6905 - accuracy: 0.7323
24/92 [======>…………………..] - ETA: 4s - loss: 0.6827 - accuracy: 0.7370
25/92 [=======>………………….] - ETA: 3s - loss: 0.6814 - accuracy: 0.7350
26/92 [=======>………………….] - ETA: 3s - loss: 0.6826 - accuracy: 0.7332
27/92 [=======>………………….] - ETA: 3s - loss: 0.6718 - accuracy: 0.7396
28/92 [========>…………………] - ETA: 3s - loss: 0.6691 - accuracy: 0.7388
29/92 [========>…………………] - ETA: 3s - loss: 0.6769 - accuracy: 0.7349
30/92 [========>…………………] - ETA: 3s - loss: 0.6747 - accuracy: 0.7365
31/92 [=========>………………..] - ETA: 3s - loss: 0.6848 - accuracy: 0.7339
32/92 [=========>………………..] - ETA: 3s - loss: 0.6793 - accuracy: 0.7383
33/92 [=========>………………..] - ETA: 3s - loss: 0.6826 - accuracy: 0.7377
34/92 [==========>……………….] - ETA: 3s - loss: 0.6777 - accuracy: 0.7408
35/92 [==========>……………….] - ETA: 3s - loss: 0.6865 - accuracy: 0.7357
36/92 [==========>……………….] - ETA: 3s - loss: 0.6899 - accuracy: 0.7352
37/92 [===========>………………] - ETA: 3s - loss: 0.6925 - accuracy: 0.7340
38/92 [===========>………………] - ETA: 3s - loss: 0.6955 - accuracy: 0.7327
39/92 [===========>………………] - ETA: 3s - loss: 0.6942 - accuracy: 0.7324
40/92 [============>……………..] - ETA: 3s - loss: 0.7007 - accuracy: 0.7297
41/92 [============>……………..] - ETA: 2s - loss: 0.7064 - accuracy: 0.7264
42/92 [============>……………..] - ETA: 2s - loss: 0.7118 - accuracy: 0.7262
43/92 [=============>…………….] - ETA: 2s - loss: 0.7098 - accuracy: 0.7267
44/92 [=============>…………….] - ETA: 2s - loss: 0.7088 - accuracy: 0.7294
45/92 [=============>…………….] - ETA: 2s - loss: 0.7112 - accuracy: 0.7292
46/92 [==============>……………] - ETA: 2s - loss: 0.7105 - accuracy: 0.7283
47/92 [==============>……………] - ETA: 2s - loss: 0.7076 - accuracy: 0.7294
48/92 [==============>……………] - ETA: 2s - loss: 0.7085 - accuracy: 0.7272
49/92 [==============>……………] - ETA: 2s - loss: 0.7099 - accuracy: 0.7277
50/92 [===============>…………..] - ETA: 2s - loss: 0.7086 - accuracy: 0.7287
51/92 [===============>…………..] - ETA: 2s - loss: 0.7087 - accuracy: 0.7298
52/92 [===============>…………..] - ETA: 2s - loss: 0.7098 - accuracy: 0.7302
53/92 [================>………….] - ETA: 2s - loss: 0.7106 - accuracy: 0.7300
54/92 [================>………….] - ETA: 2s - loss: 0.7093 - accuracy: 0.7315
55/92 [================>………….] - ETA: 2s - loss: 0.7085 - accuracy: 0.7318
56/92 [=================>…………] - ETA: 2s - loss: 0.7107 - accuracy: 0.7299
57/92 [=================>…………] - ETA: 2s - loss: 0.7082 - accuracy: 0.7308
58/92 [=================>…………] - ETA: 1s - loss: 0.7098 - accuracy: 0.7295
59/92 [==================>………..] - ETA: 1s - loss: 0.7162 - accuracy: 0.7251
60/92 [==================>………..] - ETA: 1s - loss: 0.7135 - accuracy: 0.7260
61/92 [==================>………..] - ETA: 1s - loss: 0.7132 - accuracy: 0.7259
62/92 [===================>……….] - ETA: 1s - loss: 0.7137 - accuracy: 0.7263
63/92 [===================>……….] - ETA: 1s - loss: 0.7139 - accuracy: 0.7257
64/92 [===================>……….] - ETA: 1s - loss: 0.7139 - accuracy: 0.7251
65/92 [====================>………] - ETA: 1s - loss: 0.7107 - accuracy: 0.7264
66/92 [====================>………] - ETA: 1s - loss: 0.7091 - accuracy: 0.7268
67/92 [====================>………] - ETA: 1s - loss: 0.7080 - accuracy: 0.7276
68/92 [=====================>……..] - ETA: 1s - loss: 0.7085 - accuracy: 0.7275
69/92 [=====================>……..] - ETA: 1s - loss: 0.7085 - accuracy: 0.7274
70/92 [=====================>……..] - ETA: 1s - loss: 0.7086 - accuracy: 0.7277
71/92 [======================>…….] - ETA: 1s - loss: 0.7068 - accuracy: 0.7293
72/92 [======================>…….] - ETA: 1s - loss: 0.7058 - accuracy: 0.7300
74/92 [=======================>……] - ETA: 1s - loss: 0.7033 - accuracy: 0.7292
75/92 [=======================>……] - ETA: 0s - loss: 0.7020 - accuracy: 0.7304
76/92 [=======================>……] - ETA: 0s - loss: 0.7010 - accuracy: 0.7306
77/92 [========================>…..] - ETA: 0s - loss: 0.6969 - accuracy: 0.7325
78/92 [========================>…..] - ETA: 0s - loss: 0.6963 - accuracy: 0.7327
79/92 [========================>…..] - ETA: 0s - loss: 0.6964 - accuracy: 0.7329
80/92 [=========================>….] - ETA: 0s - loss: 0.6943 - accuracy: 0.7343
81/92 [=========================>….] - ETA: 0s - loss: 0.6968 - accuracy: 0.7345
82/92 [=========================>….] - ETA: 0s - loss: 0.6975 - accuracy: 0.7343
83/92 [==========================>…] - ETA: 0s - loss: 0.7000 - accuracy: 0.7330
84/92 [==========================>…] - ETA: 0s - loss: 0.7012 - accuracy: 0.7328
85/92 [==========================>…] - ETA: 0s - loss: 0.6997 - accuracy: 0.7334
86/92 [===========================>..] - ETA: 0s - loss: 0.6986 - accuracy: 0.7336
87/92 [===========================>..] - ETA: 0s - loss: 0.6983 - accuracy: 0.7338
88/92 [===========================>..] - ETA: 0s - loss: 0.6983 - accuracy: 0.7340
89/92 [============================>.] - ETA: 0s - loss: 0.6967 - accuracy: 0.7338
90/92 [============================>.] - ETA: 0s - loss: 0.6940 - accuracy: 0.7357
91/92 [============================>.] - ETA: 0s - loss: 0.6932 - accuracy: 0.7362
92/92 [==============================] - ETA: 0s - loss: 0.6932 - accuracy: 0.7360
92/92 [==============================] - 6s 64ms/step - loss: 0.6932 - accuracy: 0.7360 - val_loss: 0.7731 - val_accuracy: 0.6853
Epoch 8/15
1/92 [..............................] - ETA: 7s - loss: 0.5886 - accuracy: 0.7500
2/92 [..............................] - ETA: 5s - loss: 0.6209 - accuracy: 0.7500
3/92 [..............................] - ETA: 5s - loss: 0.6834 - accuracy: 0.7396
4/92 [>.............................] - ETA: 5s - loss: 0.6812 - accuracy: 0.7266
5/92 [>.............................] - ETA: 5s - loss: 0.6540 - accuracy: 0.7312
6/92 [>.............................] - ETA: 5s - loss: 0.6633 - accuracy: 0.7240
7/92 [=>............................] - ETA: 4s - loss: 0.6415 - accuracy: 0.7411
8/92 [=>............................] - ETA: 4s - loss: 0.6256 - accuracy: 0.7539
9/92 [=>............................] - ETA: 4s - loss: 0.5989 - accuracy: 0.7604
10/92 [==>………………………] - ETA: 4s - loss: 0.6047 - accuracy: 0.7500
11/92 [==>………………………] - ETA: 4s - loss: 0.6068 - accuracy: 0.7500
12/92 [==>………………………] - ETA: 4s - loss: 0.6024 - accuracy: 0.7500
13/92 [===>……………………..] - ETA: 4s - loss: 0.6013 - accuracy: 0.7548
14/92 [===>……………………..] - ETA: 4s - loss: 0.5978 - accuracy: 0.7545
15/92 [===>……………………..] - ETA: 4s - loss: 0.6020 - accuracy: 0.7479
16/92 [====>…………………….] - ETA: 4s - loss: 0.5969 - accuracy: 0.7500
17/92 [====>…………………….] - ETA: 4s - loss: 0.6147 - accuracy: 0.7408
18/92 [====>…………………….] - ETA: 4s - loss: 0.6109 - accuracy: 0.7448
19/92 [=====>……………………] - ETA: 4s - loss: 0.6156 - accuracy: 0.7467
20/92 [=====>……………………] - ETA: 4s - loss: 0.6125 - accuracy: 0.7516
21/92 [=====>……………………] - ETA: 4s - loss: 0.6098 - accuracy: 0.7530
22/92 [======>…………………..] - ETA: 4s - loss: 0.6131 - accuracy: 0.7528
23/92 [======>…………………..] - ETA: 4s - loss: 0.6149 - accuracy: 0.7527
24/92 [======>…………………..] - ETA: 3s - loss: 0.6234 - accuracy: 0.7474
25/92 [=======>………………….] - ETA: 3s - loss: 0.6214 - accuracy: 0.7487
26/92 [=======>………………….] - ETA: 3s - loss: 0.6183 - accuracy: 0.7512
27/92 [=======>………………….] - ETA: 3s - loss: 0.6153 - accuracy: 0.7535
28/92 [========>…………………] - ETA: 3s - loss: 0.6128 - accuracy: 0.7556
29/92 [========>…………………] - ETA: 3s - loss: 0.6245 - accuracy: 0.7522
30/92 [========>…………………] - ETA: 3s - loss: 0.6246 - accuracy: 0.7510
31/92 [=========>………………..] - ETA: 3s - loss: 0.6228 - accuracy: 0.7530
32/92 [=========>………………..] - ETA: 3s - loss: 0.6247 - accuracy: 0.7539
33/92 [=========>………………..] - ETA: 3s - loss: 0.6282 - accuracy: 0.7509
34/92 [==========>……………….] - ETA: 3s - loss: 0.6380 - accuracy: 0.7491
35/92 [==========>……………….] - ETA: 3s - loss: 0.6360 - accuracy: 0.7500
36/92 [==========>……………….] - ETA: 3s - loss: 0.6347 - accuracy: 0.7526
37/92 [===========>………………] - ETA: 3s - loss: 0.6365 - accuracy: 0.7525
38/92 [===========>………………] - ETA: 3s - loss: 0.6336 - accuracy: 0.7549
39/92 [===========>………………] - ETA: 3s - loss: 0.6360 - accuracy: 0.7532
40/92 [============>……………..] - ETA: 3s - loss: 0.6311 - accuracy: 0.7547
41/92 [============>……………..] - ETA: 2s - loss: 0.6326 - accuracy: 0.7553
42/92 [============>……………..] - ETA: 2s - loss: 0.6372 - accuracy: 0.7545
43/92 [=============>…………….] - ETA: 2s - loss: 0.6439 - accuracy: 0.7536
44/92 [=============>…………….] - ETA: 2s - loss: 0.6455 - accuracy: 0.7521
46/92 [==============>……………] - ETA: 2s - loss: 0.6455 - accuracy: 0.7514
47/92 [==============>……………] - ETA: 2s - loss: 0.6464 - accuracy: 0.7520
48/92 [==============>……………] - ETA: 2s - loss: 0.6517 - accuracy: 0.7513
49/92 [==============>……………] - ETA: 2s - loss: 0.6550 - accuracy: 0.7513
50/92 [===============>…………..] - ETA: 2s - loss: 0.6535 - accuracy: 0.7519
51/92 [===============>…………..] - ETA: 2s - loss: 0.6543 - accuracy: 0.7525
52/92 [===============>…………..] - ETA: 2s - loss: 0.6592 - accuracy: 0.7482
53/92 [================>………….] - ETA: 2s - loss: 0.6656 - accuracy: 0.7447
54/92 [================>………….] - ETA: 2s - loss: 0.6636 - accuracy: 0.7459
55/92 [================>………….] - ETA: 2s - loss: 0.6646 - accuracy: 0.7454
56/92 [=================>…………] - ETA: 2s - loss: 0.6710 - accuracy: 0.7444
57/92 [=================>…………] - ETA: 2s - loss: 0.6695 - accuracy: 0.7450
58/92 [=================>…………] - ETA: 1s - loss: 0.6672 - accuracy: 0.7462
59/92 [==================>………..] - ETA: 1s - loss: 0.6702 - accuracy: 0.7452
60/92 [==================>………..] - ETA: 1s - loss: 0.6684 - accuracy: 0.7453
61/92 [==================>………..] - ETA: 1s - loss: 0.6693 - accuracy: 0.7454
62/92 [===================>……….] - ETA: 1s - loss: 0.6713 - accuracy: 0.7449
63/92 [===================>……….] - ETA: 1s - loss: 0.6722 - accuracy: 0.7440
64/92 [===================>……….] - ETA: 1s - loss: 0.6705 - accuracy: 0.7451
65/92 [====================>………] - ETA: 1s - loss: 0.6720 - accuracy: 0.7452
66/92 [====================>………] - ETA: 1s - loss: 0.6696 - accuracy: 0.7462
67/92 [====================>………] - ETA: 1s - loss: 0.6726 - accuracy: 0.7458
68/92 [=====================>……..] - ETA: 1s - loss: 0.6737 - accuracy: 0.7454
69/92 [=====================>……..] - ETA: 1s - loss: 0.6717 - accuracy: 0.7459
70/92 [=====================>……..] - ETA: 1s - loss: 0.6693 - accuracy: 0.7460
71/92 [======================>…….] - ETA: 1s - loss: 0.6691 - accuracy: 0.7460
72/92 [======================>…….] - ETA: 1s - loss: 0.6727 - accuracy: 0.7443
73/92 [======================>…….] - ETA: 1s - loss: 0.6732 - accuracy: 0.7440
74/92 [=======================>……] - ETA: 1s - loss: 0.6720 - accuracy: 0.7445
75/92 [=======================>……] - ETA: 0s - loss: 0.6715 - accuracy: 0.7458
76/92 [=======================>……] - ETA: 0s - loss: 0.6703 - accuracy: 0.7459
77/92 [========================>…..] - ETA: 0s - loss: 0.6712 - accuracy: 0.7455
78/92 [========================>…..] - ETA: 0s - loss: 0.6727 - accuracy: 0.7456
79/92 [========================>…..] - ETA: 0s - loss: 0.6713 - accuracy: 0.7460
80/92 [=========================>….] - ETA: 0s - loss: 0.6690 - accuracy: 0.7473
81/92 [=========================>….] - ETA: 0s - loss: 0.6698 - accuracy: 0.7457
82/92 [=========================>….] - ETA: 0s - loss: 0.6702 - accuracy: 0.7450
83/92 [==========================>…] - ETA: 0s - loss: 0.6702 - accuracy: 0.7455
84/92 [==========================>…] - ETA: 0s - loss: 0.6699 - accuracy: 0.7451
85/92 [==========================>…] - ETA: 0s - loss: 0.6690 - accuracy: 0.7463
86/92 [===========================>..] - ETA: 0s - loss: 0.6715 - accuracy: 0.7449
87/92 [===========================>..] - ETA: 0s - loss: 0.6732 - accuracy: 0.7439
88/92 [===========================>..] - ETA: 0s - loss: 0.6717 - accuracy: 0.7439
89/92 [============================>.] - ETA: 0s - loss: 0.6743 - accuracy: 0.7433
90/92 [============================>.] - ETA: 0s - loss: 0.6769 - accuracy: 0.7427
91/92 [============================>.] - ETA: 0s - loss: 0.6763 - accuracy: 0.7428
92/92 [==============================] - ETA: 0s - loss: 0.6821 - accuracy: 0.7398
92/92 [==============================] - 6s 64ms/step - loss: 0.6821 - accuracy: 0.7398 - val_loss: 0.7942 - val_accuracy: 0.6812
Epoch 9/15
1/92 [..............................] - ETA: 6s - loss: 0.5542 - accuracy: 0.7812
2/92 [..............................] - ETA: 5s - loss: 0.6572 - accuracy: 0.7500
3/92 [..............................] - ETA: 5s - loss: 0.6516 - accuracy: 0.7604
4/92 [>.............................] - ETA: 5s - loss: 0.6615 - accuracy: 0.7344
5/92 [>.............................] - ETA: 5s - loss: 0.6472 - accuracy: 0.7625
6/92 [>.............................] - ETA: 5s - loss: 0.6448 - accuracy: 0.7604
7/92 [=>............................] - ETA: 4s - loss: 0.6265 - accuracy: 0.7545
8/92 [=>............................] - ETA: 4s - loss: 0.6567 - accuracy: 0.7422
9/92 [=>............................] - ETA: 4s - loss: 0.6387 - accuracy: 0.7465
10/92 [==>………………………] - ETA: 4s - loss: 0.6596 - accuracy: 0.7406
11/92 [==>………………………] - ETA: 4s - loss: 0.6548 - accuracy: 0.7415
12/92 [==>………………………] - ETA: 4s - loss: 0.6587 - accuracy: 0.7370
13/92 [===>……………………..] - ETA: 4s - loss: 0.6604 - accuracy: 0.7356
14/92 [===>……………………..] - ETA: 4s - loss: 0.6458 - accuracy: 0.7433
15/92 [===>……………………..] - ETA: 4s - loss: 0.6432 - accuracy: 0.7479
16/92 [====>…………………….] - ETA: 4s - loss: 0.6368 - accuracy: 0.7500
17/92 [====>…………………….] - ETA: 4s - loss: 0.6318 - accuracy: 0.7574
18/92 [====>…………………….] - ETA: 4s - loss: 0.6263 - accuracy: 0.7604
19/92 [=====>……………………] - ETA: 4s - loss: 0.6300 - accuracy: 0.7599
20/92 [=====>……………………] - ETA: 4s - loss: 0.6349 - accuracy: 0.7578
21/92 [=====>……………………] - ETA: 4s - loss: 0.6289 - accuracy: 0.7589
22/92 [======>…………………..] - ETA: 4s - loss: 0.6234 - accuracy: 0.7614
23/92 [======>…………………..] - ETA: 4s - loss: 0.6200 - accuracy: 0.7609
24/92 [======>…………………..] - ETA: 3s - loss: 0.6159 - accuracy: 0.7630
25/92 [=======>………………….] - ETA: 3s - loss: 0.6139 - accuracy: 0.7625
26/92 [=======>………………….] - ETA: 3s - loss: 0.6118 - accuracy: 0.7620
27/92 [=======>………………….] - ETA: 3s - loss: 0.6134 - accuracy: 0.7581
28/92 [========>…………………] - ETA: 3s - loss: 0.6103 - accuracy: 0.7567
29/92 [========>…………………] - ETA: 3s - loss: 0.6040 - accuracy: 0.7586
30/92 [========>…………………] - ETA: 3s - loss: 0.6029 - accuracy: 0.7604
31/92 [=========>………………..] - ETA: 3s - loss: 0.6022 - accuracy: 0.7621
32/92 [=========>………………..] - ETA: 3s - loss: 0.6037 - accuracy: 0.7607
33/92 [=========>………………..] - ETA: 3s - loss: 0.6135 - accuracy: 0.7566
34/92 [==========>……………….] - ETA: 3s - loss: 0.6141 - accuracy: 0.7574
35/92 [==========>……………….] - ETA: 3s - loss: 0.6172 - accuracy: 0.7554
36/92 [==========>……………….] - ETA: 3s - loss: 0.6220 - accuracy: 0.7552
37/92 [===========>………………] - ETA: 3s - loss: 0.6279 - accuracy: 0.7525
38/92 [===========>………………] - ETA: 3s - loss: 0.6295 - accuracy: 0.7500
39/92 [===========>………………] - ETA: 3s - loss: 0.6328 - accuracy: 0.7492
40/92 [============>……………..] - ETA: 3s - loss: 0.6366 - accuracy: 0.7477
41/92 [============>……………..] - ETA: 2s - loss: 0.6435 - accuracy: 0.7470
42/92 [============>……………..] - ETA: 2s - loss: 0.6465 - accuracy: 0.7448
43/92 [=============>…………….] - ETA: 2s - loss: 0.6493 - accuracy: 0.7442
44/92 [=============>…………….] - ETA: 2s - loss: 0.6462 - accuracy: 0.7450
45/92 [=============>…………….] - ETA: 2s - loss: 0.6506 - accuracy: 0.7437
46/92 [==============>……………] - ETA: 2s - loss: 0.6490 - accuracy: 0.7432
47/92 [==============>……………] - ETA: 2s - loss: 0.6511 - accuracy: 0.7414
48/92 [==============>……………] - ETA: 2s - loss: 0.6528 - accuracy: 0.7415
49/92 [==============>……………] - ETA: 2s - loss: 0.6529 - accuracy: 0.7411
50/92 [===============>…………..] - ETA: 2s - loss: 0.6542 - accuracy: 0.7419
51/92 [===============>…………..] - ETA: 2s - loss: 0.6546 - accuracy: 0.7408
52/92 [===============>…………..] - ETA: 2s - loss: 0.6496 - accuracy: 0.7434
53/92 [================>………….] - ETA: 2s - loss: 0.6550 - accuracy: 0.7417
54/92 [================>………….] - ETA: 2s - loss: 0.6544 - accuracy: 0.7413
55/92 [================>………….] - ETA: 2s - loss: 0.6563 - accuracy: 0.7420
56/92 [=================>…………] - ETA: 2s - loss: 0.6577 - accuracy: 0.7416
57/92 [=================>…………] - ETA: 2s - loss: 0.6562 - accuracy: 0.7429
58/92 [=================>…………] - ETA: 1s - loss: 0.6543 - accuracy: 0.7441
59/92 [==================>………..] - ETA: 1s - loss: 0.6554 - accuracy: 0.7431
60/92 [==================>………..] - ETA: 1s - loss: 0.6605 - accuracy: 0.7401
61/92 [==================>………..] - ETA: 1s - loss: 0.6579 - accuracy: 0.7423
62/92 [===================>……….] - ETA: 1s - loss: 0.6568 - accuracy: 0.7414
64/92 [===================>……….] - ETA: 1s - loss: 0.6570 - accuracy: 0.7412
65/92 [====================>………] - ETA: 1s - loss: 0.6617 - accuracy: 0.7384
66/92 [====================>………] - ETA: 1s - loss: 0.6606 - accuracy: 0.7395
67/92 [====================>………] - ETA: 1s - loss: 0.6609 - accuracy: 0.7392
68/92 [=====================>……..] - ETA: 1s - loss: 0.6580 - accuracy: 0.7408
69/92 [=====================>……..] - ETA: 1s - loss: 0.6592 - accuracy: 0.7409
70/92 [=====================>……..] - ETA: 1s - loss: 0.6564 - accuracy: 0.7424
71/92 [======================>…….] - ETA: 1s - loss: 0.6546 - accuracy: 0.7434
72/92 [======================>…….] - ETA: 1s - loss: 0.6555 - accuracy: 0.7435
73/92 [======================>…….] - ETA: 1s - loss: 0.6536 - accuracy: 0.7444
74/92 [=======================>……] - ETA: 1s - loss: 0.6532 - accuracy: 0.7449
75/92 [=======================>……] - ETA: 0s - loss: 0.6585 - accuracy: 0.7425
76/92 [=======================>……] - ETA: 0s - loss: 0.6595 - accuracy: 0.7426
77/92 [========================>…..] - ETA: 0s - loss: 0.6558 - accuracy: 0.7447
78/92 [========================>…..] - ETA: 0s - loss: 0.6535 - accuracy: 0.7456
79/92 [========================>…..] - ETA: 0s - loss: 0.6509 - accuracy: 0.7472
80/92 [=========================>….] - ETA: 0s - loss: 0.6487 - accuracy: 0.7480
81/92 [=========================>….] - ETA: 0s - loss: 0.6491 - accuracy: 0.7473
82/92 [=========================>….] - ETA: 0s - loss: 0.6480 - accuracy: 0.7477
83/92 [==========================>…] - ETA: 0s - loss: 0.6493 - accuracy: 0.7481
84/92 [==========================>…] - ETA: 0s - loss: 0.6504 - accuracy: 0.7466
85/92 [==========================>…] - ETA: 0s - loss: 0.6491 - accuracy: 0.7474
86/92 [===========================>..] - ETA: 0s - loss: 0.6478 - accuracy: 0.7482
87/92 [===========================>..] - ETA: 0s - loss: 0.6478 - accuracy: 0.7486
88/92 [===========================>..] - ETA: 0s - loss: 0.6470 - accuracy: 0.7504
89/92 [============================>.] - ETA: 0s - loss: 0.6463 - accuracy: 0.7511
90/92 [============================>.] - ETA: 0s - loss: 0.6476 - accuracy: 0.7510
91/92 [============================>.] - ETA: 0s - loss: 0.6475 - accuracy: 0.7507
92/92 [==============================] - ETA: 0s - loss: 0.6469 - accuracy: 0.7510
92/92 [==============================] - 6s 63ms/step - loss: 0.6469 - accuracy: 0.7510 - val_loss: 0.7705 - val_accuracy: 0.6921
Epoch 10/15
1/92 [..............................] - ETA: 7s - loss: 0.5019 - accuracy: 0.8438
2/92 [..............................] - ETA: 5s - loss: 0.5805 - accuracy: 0.7969
3/92 [..............................] - ETA: 5s - loss: 0.6209 - accuracy: 0.7604
4/92 [>.............................] - ETA: 5s - loss: 0.6745 - accuracy: 0.7109
5/92 [>.............................] - ETA: 5s - loss: 0.6841 - accuracy: 0.7125
6/92 [>.............................] - ETA: 4s - loss: 0.6510 - accuracy: 0.7188
7/92 [=>............................] - ETA: 4s - loss: 0.6254 - accuracy: 0.7411
8/92 [=>............................] - ETA: 4s - loss: 0.6364 - accuracy: 0.7383
9/92 [=>............................] - ETA: 4s - loss: 0.6488 - accuracy: 0.7292
10/92 [==>………………………] - ETA: 4s - loss: 0.6263 - accuracy: 0.7406
11/92 [==>………………………] - ETA: 4s - loss: 0.6172 - accuracy: 0.7443
12/92 [==>………………………] - ETA: 4s - loss: 0.6176 - accuracy: 0.7422
13/92 [===>……………………..] - ETA: 4s - loss: 0.6043 - accuracy: 0.7452
14/92 [===>……………………..] - ETA: 4s - loss: 0.6265 - accuracy: 0.7433
15/92 [===>……………………..] - ETA: 4s - loss: 0.6181 - accuracy: 0.7479
16/92 [====>…………………….] - ETA: 4s - loss: 0.6257 - accuracy: 0.7520
17/92 [====>…………………….] - ETA: 4s - loss: 0.6240 - accuracy: 0.7574
18/92 [====>…………………….] - ETA: 4s - loss: 0.6256 - accuracy: 0.7535
19/92 [=====>……………………] - ETA: 4s - loss: 0.6189 - accuracy: 0.7566
20/92 [=====>……………………] - ETA: 4s - loss: 0.6213 - accuracy: 0.7578
21/92 [=====>……………………] - ETA: 4s - loss: 0.6196 - accuracy: 0.7589
22/92 [======>…………………..] - ETA: 4s - loss: 0.6144 - accuracy: 0.7642
23/92 [======>…………………..] - ETA: 3s - loss: 0.6133 - accuracy: 0.7649
24/92 [======>…………………..] - ETA: 3s - loss: 0.6115 - accuracy: 0.7669
25/92 [=======>………………….] - ETA: 3s - loss: 0.6141 - accuracy: 0.7638
26/92 [=======>………………….] - ETA: 3s - loss: 0.6078 - accuracy: 0.7656
27/92 [=======>………………….] - ETA: 3s - loss: 0.6107 - accuracy: 0.7639
28/92 [========>…………………] - ETA: 3s - loss: 0.6194 - accuracy: 0.7578
29/92 [========>…………………] - ETA: 3s - loss: 0.6195 - accuracy: 0.7575
30/92 [========>…………………] - ETA: 3s - loss: 0.6170 - accuracy: 0.7604
31/92 [=========>………………..] - ETA: 3s - loss: 0.6153 - accuracy: 0.7601
32/92 [=========>………………..] - ETA: 3s - loss: 0.6169 - accuracy: 0.7588
33/92 [=========>………………..] - ETA: 3s - loss: 0.6183 - accuracy: 0.7576
34/92 [==========>……………….] - ETA: 3s - loss: 0.6117 - accuracy: 0.7610
35/92 [==========>……………….] - ETA: 3s - loss: 0.6171 - accuracy: 0.7607
36/92 [==========>……………….] - ETA: 3s - loss: 0.6148 - accuracy: 0.7613
37/92 [===========>………………] - ETA: 3s - loss: 0.6160 - accuracy: 0.7601
38/92 [===========>………………] - ETA: 3s - loss: 0.6141 - accuracy: 0.7615
39/92 [===========>………………] - ETA: 3s - loss: 0.6149 - accuracy: 0.7612
40/92 [============>……………..] - ETA: 3s - loss: 0.6139 - accuracy: 0.7625
41/92 [============>……………..] - ETA: 2s - loss: 0.6141 - accuracy: 0.7630
42/92 [============>……………..] - ETA: 2s - loss: 0.6113 - accuracy: 0.7634
43/92 [=============>…………….] - ETA: 2s - loss: 0.6049 - accuracy: 0.7667
44/92 [=============>…………….] - ETA: 2s - loss: 0.6052 - accuracy: 0.7670
46/92 [==============>……………] - ETA: 2s - loss: 0.6061 - accuracy: 0.7678
47/92 [==============>……………] - ETA: 2s - loss: 0.6016 - accuracy: 0.7701
48/92 [==============>……………] - ETA: 2s - loss: 0.6008 - accuracy: 0.7703
49/92 [==============>……………] - ETA: 2s - loss: 0.6031 - accuracy: 0.7705
50/92 [===============>…………..] - ETA: 2s - loss: 0.6029 - accuracy: 0.7714
51/92 [===============>…………..] - ETA: 2s - loss: 0.6044 - accuracy: 0.7691
52/92 [===============>…………..] - ETA: 2s - loss: 0.6106 - accuracy: 0.7645
53/92 [================>………….] - ETA: 2s - loss: 0.6055 - accuracy: 0.7660
54/92 [================>………….] - ETA: 2s - loss: 0.6085 - accuracy: 0.7645
55/92 [================>………….] - ETA: 2s - loss: 0.6119 - accuracy: 0.7637
56/92 [=================>…………] - ETA: 2s - loss: 0.6117 - accuracy: 0.7640
57/92 [=================>…………] - ETA: 2s - loss: 0.6108 - accuracy: 0.7649
58/92 [=================>…………] - ETA: 1s - loss: 0.6119 - accuracy: 0.7641
59/92 [==================>………..] - ETA: 1s - loss: 0.6111 - accuracy: 0.7660
60/92 [==================>………..] - ETA: 1s - loss: 0.6074 - accuracy: 0.7667
61/92 [==================>………..] - ETA: 1s - loss: 0.6087 - accuracy: 0.7665
62/92 [===================>……….] - ETA: 1s - loss: 0.6106 - accuracy: 0.7672
63/92 [===================>……….] - ETA: 1s - loss: 0.6161 - accuracy: 0.7659
64/92 [===================>……….] - ETA: 1s - loss: 0.6146 - accuracy: 0.7672
65/92 [====================>………] - ETA: 1s - loss: 0.6118 - accuracy: 0.7688
66/92 [====================>………] - ETA: 1s - loss: 0.6131 - accuracy: 0.7681
67/92 [====================>………] - ETA: 1s - loss: 0.6130 - accuracy: 0.7678
68/92 [=====================>……..] - ETA: 1s - loss: 0.6126 - accuracy: 0.7680
69/92 [=====================>……..] - ETA: 1s - loss: 0.6167 - accuracy: 0.7655
70/92 [=====================>……..] - ETA: 1s - loss: 0.6165 - accuracy: 0.7652
71/92 [======================>…….] - ETA: 1s - loss: 0.6148 - accuracy: 0.7655
72/92 [======================>…….] - ETA: 1s - loss: 0.6191 - accuracy: 0.7635
73/92 [======================>…….] - ETA: 1s - loss: 0.6240 - accuracy: 0.7625
74/92 [=======================>……] - ETA: 1s - loss: 0.6252 - accuracy: 0.7614
75/92 [=======================>……] - ETA: 0s - loss: 0.6270 - accuracy: 0.7596
76/92 [=======================>……] - ETA: 0s - loss: 0.6247 - accuracy: 0.7603
77/92 [========================>…..] - ETA: 0s - loss: 0.6228 - accuracy: 0.7610
78/92 [========================>…..] - ETA: 0s - loss: 0.6224 - accuracy: 0.7609
79/92 [========================>…..] - ETA: 0s - loss: 0.6223 - accuracy: 0.7615
80/92 [=========================>….] - ETA: 0s - loss: 0.6221 - accuracy: 0.7614
81/92 [=========================>….] - ETA: 0s - loss: 0.6233 - accuracy: 0.7608
82/92 [=========================>….] - ETA: 0s - loss: 0.6214 - accuracy: 0.7615
83/92 [==========================>…] - ETA: 0s - loss: 0.6221 - accuracy: 0.7610
84/92 [==========================>…] - ETA: 0s - loss: 0.6238 - accuracy: 0.7616
85/92 [==========================>…] - ETA: 0s - loss: 0.6236 - accuracy: 0.7622
86/92 [===========================>..] - ETA: 0s - loss: 0.6234 - accuracy: 0.7624
87/92 [===========================>..] - ETA: 0s - loss: 0.6231 - accuracy: 0.7630
88/92 [===========================>..] - ETA: 0s - loss: 0.6217 - accuracy: 0.7632
89/92 [============================>.] - ETA: 0s - loss: 0.6214 - accuracy: 0.7641
90/92 [============================>.] - ETA: 0s - loss: 0.6248 - accuracy: 0.7639
91/92 [============================>.] - ETA: 0s - loss: 0.6238 - accuracy: 0.7645
92/92 [==============================] - ETA: 0s - loss: 0.6230 - accuracy: 0.7646
92/92 [==============================] - 6s 63ms/step - loss: 0.6230 - accuracy: 0.7646 - val_loss: 0.7725 - val_accuracy: 0.7153
Epoch 11/15
1/92 [..............................] - ETA: 7s - loss: 0.6668 - accuracy: 0.6875
2/92 [..............................] - ETA: 5s - loss: 0.5528 - accuracy: 0.7656
3/92 [..............................] - ETA: 5s - loss: 0.5535 - accuracy: 0.7917
4/92 [>.............................] - ETA: 5s - loss: 0.5296 - accuracy: 0.7969
5/92 [>.............................] - ETA: 5s - loss: 0.5133 - accuracy: 0.8062
6/92 [>.............................] - ETA: 4s - loss: 0.5098 - accuracy: 0.8073
7/92 [=>............................] - ETA: 4s - loss: 0.5332 - accuracy: 0.8036
8/92 [=>............................] - ETA: 4s - loss: 0.5426 - accuracy: 0.7969
9/92 [=>............................] - ETA: 4s - loss: 0.5770 - accuracy: 0.7778
10/92 [==>………………………] - ETA: 4s - loss: 0.6016 - accuracy: 0.7719
11/92 [==>………………………] - ETA: 4s - loss: 0.5975 - accuracy: 0.7699
12/92 [==>………………………] - ETA: 4s - loss: 0.5897 - accuracy: 0.7734
13/92 [===>……………………..] - ETA: 4s - loss: 0.5994 - accuracy: 0.7716
14/92 [===>……………………..] - ETA: 4s - loss: 0.5986 - accuracy: 0.7746
15/92 [===>……………………..] - ETA: 4s - loss: 0.5995 - accuracy: 0.7708
16/92 [====>…………………….] - ETA: 4s - loss: 0.6063 - accuracy: 0.7656
17/92 [====>…………………….] - ETA: 4s - loss: 0.6023 - accuracy: 0.7665
18/92 [====>…………………….] - ETA: 4s - loss: 0.6027 - accuracy: 0.7656
19/92 [=====>……………………] - ETA: 4s - loss: 0.5956 - accuracy: 0.7664
21/92 [=====>……………………] - ETA: 4s - loss: 0.5947 - accuracy: 0.7666
22/92 [======>…………………..] - ETA: 4s - loss: 0.5901 - accuracy: 0.7672
23/92 [======>…………………..] - ETA: 3s - loss: 0.5811 - accuracy: 0.7720
24/92 [======>…………………..] - ETA: 3s - loss: 0.5810 - accuracy: 0.7737
25/92 [=======>………………….] - ETA: 3s - loss: 0.5865 - accuracy: 0.7715
26/92 [=======>………………….] - ETA: 3s - loss: 0.5784 - accuracy: 0.7767
27/92 [=======>………………….] - ETA: 3s - loss: 0.5786 - accuracy: 0.7745
28/92 [========>…………………] - ETA: 3s - loss: 0.5726 - accuracy: 0.7770
29/92 [========>…………………] - ETA: 3s - loss: 0.5660 - accuracy: 0.7793
30/92 [========>…………………] - ETA: 3s - loss: 0.5637 - accuracy: 0.7805
31/92 [=========>………………..] - ETA: 3s - loss: 0.5661 - accuracy: 0.7785
32/92 [=========>………………..] - ETA: 3s - loss: 0.5745 - accuracy: 0.7766
33/92 [=========>………………..] - ETA: 3s - loss: 0.5816 - accuracy: 0.7748
34/92 [==========>……………….] - ETA: 3s - loss: 0.5781 - accuracy: 0.7750
35/92 [==========>……………….] - ETA: 3s - loss: 0.5772 - accuracy: 0.7734
36/92 [==========>……………….] - ETA: 3s - loss: 0.5729 - accuracy: 0.7753
37/92 [===========>………………] - ETA: 3s - loss: 0.5776 - accuracy: 0.7747
38/92 [===========>………………] - ETA: 3s - loss: 0.5781 - accuracy: 0.7781
39/92 [===========>………………] - ETA: 3s - loss: 0.5773 - accuracy: 0.7790
40/92 [============>……………..] - ETA: 3s - loss: 0.5783 - accuracy: 0.7799
41/92 [============>……………..] - ETA: 2s - loss: 0.5778 - accuracy: 0.7799
42/92 [============>……………..] - ETA: 2s - loss: 0.5755 - accuracy: 0.7792
43/92 [=============>…………….] - ETA: 2s - loss: 0.5769 - accuracy: 0.7792
44/92 [=============>…………….] - ETA: 2s - loss: 0.5776 - accuracy: 0.7786
45/92 [=============>…………….] - ETA: 2s - loss: 0.5752 - accuracy: 0.7793
46/92 [==============>……………] - ETA: 2s - loss: 0.5734 - accuracy: 0.7801
47/92 [==============>……………] - ETA: 2s - loss: 0.5765 - accuracy: 0.7787
48/92 [==============>……………] - ETA: 2s - loss: 0.5780 - accuracy: 0.7788
49/92 [==============>……………] - ETA: 2s - loss: 0.5799 - accuracy: 0.7776
50/92 [===============>…………..] - ETA: 2s - loss: 0.5836 - accuracy: 0.7770
51/92 [===============>…………..] - ETA: 2s - loss: 0.5823 - accuracy: 0.7777
52/92 [===============>…………..] - ETA: 2s - loss: 0.5808 - accuracy: 0.7790
53/92 [================>………….] - ETA: 2s - loss: 0.5838 - accuracy: 0.7778
54/92 [================>………….] - ETA: 2s - loss: 0.5808 - accuracy: 0.7791
55/92 [================>………….] - ETA: 2s - loss: 0.5803 - accuracy: 0.7780
56/92 [=================>…………] - ETA: 2s - loss: 0.5826 - accuracy: 0.7775
57/92 [=================>…………] - ETA: 2s - loss: 0.5896 - accuracy: 0.7742
58/92 [=================>…………] - ETA: 1s - loss: 0.5896 - accuracy: 0.7738
59/92 [==================>………..] - ETA: 1s - loss: 0.5893 - accuracy: 0.7739
60/92 [==================>………..] - ETA: 1s - loss: 0.5915 - accuracy: 0.7735
61/92 [==================>………..] - ETA: 1s - loss: 0.5931 - accuracy: 0.7726
62/92 [===================>……….] - ETA: 1s - loss: 0.5908 - accuracy: 0.7733
63/92 [===================>……….] - ETA: 1s - loss: 0.5886 - accuracy: 0.7744
64/92 [===================>……….] - ETA: 1s - loss: 0.5892 - accuracy: 0.7745
65/92 [====================>………] - ETA: 1s - loss: 0.5897 - accuracy: 0.7736
66/92 [====================>………] - ETA: 1s - loss: 0.5921 - accuracy: 0.7728
67/92 [====================>………] - ETA: 1s - loss: 0.5900 - accuracy: 0.7748
68/92 [=====================>……..] - ETA: 1s - loss: 0.5871 - accuracy: 0.7763
69/92 [=====================>……..] - ETA: 1s - loss: 0.5849 - accuracy: 0.7759
70/92 [=====================>……..] - ETA: 1s - loss: 0.5883 - accuracy: 0.7742
71/92 [======================>…….] - ETA: 1s - loss: 0.5860 - accuracy: 0.7752
72/92 [======================>…….] - ETA: 1s - loss: 0.5845 - accuracy: 0.7757
73/92 [======================>…….] - ETA: 1s - loss: 0.5850 - accuracy: 0.7762
74/92 [=======================>……] - ETA: 1s - loss: 0.5829 - accuracy: 0.7771
75/92 [=======================>……] - ETA: 0s - loss: 0.5818 - accuracy: 0.7780
76/92 [=======================>……] - ETA: 0s - loss: 0.5827 - accuracy: 0.7772
77/92 [========================>…..] - ETA: 0s - loss: 0.5818 - accuracy: 0.7761
78/92 [========================>…..] - ETA: 0s - loss: 0.5856 - accuracy: 0.7761
79/92 [========================>…..] - ETA: 0s - loss: 0.5870 - accuracy: 0.7758
80/92 [=========================>….] - ETA: 0s - loss: 0.5876 - accuracy: 0.7743
81/92 [=========================>….] - ETA: 0s - loss: 0.5868 - accuracy: 0.7748
82/92 [=========================>….] - ETA: 0s - loss: 0.5895 - accuracy: 0.7737
83/92 [==========================>…] - ETA: 0s - loss: 0.5908 - accuracy: 0.7727
84/92 [==========================>…] - ETA: 0s - loss: 0.5928 - accuracy: 0.7720
85/92 [==========================>…] - ETA: 0s - loss: 0.5920 - accuracy: 0.7718
86/92 [===========================>..] - ETA: 0s - loss: 0.5914 - accuracy: 0.7726
87/92 [===========================>..] - ETA: 0s - loss: 0.5921 - accuracy: 0.7723
88/92 [===========================>..] - ETA: 0s - loss: 0.5911 - accuracy: 0.7724
89/92 [============================>.] - ETA: 0s - loss: 0.5913 - accuracy: 0.7725
90/92 [============================>.] - ETA: 0s - loss: 0.5910 - accuracy: 0.7726
91/92 [============================>.] - ETA: 0s - loss: 0.5901 - accuracy: 0.7724
92/92 [==============================] - ETA: 0s - loss: 0.5883 - accuracy: 0.7725
92/92 [==============================] - 6s 64ms/step - loss: 0.5883 - accuracy: 0.7725 - val_loss: 0.7175 - val_accuracy: 0.7234
Epoch 12/15
1/92 [..............................] - ETA: 7s - loss: 0.5461 - accuracy: 0.7812
2/92 [..............................] - ETA: 5s - loss: 0.6078 - accuracy: 0.7500
3/92 [..............................] - ETA: 5s - loss: 0.5296 - accuracy: 0.7917
4/92 [>.............................] - ETA: 5s - loss: 0.5025 - accuracy: 0.8047
5/92 [>.............................] - ETA: 5s - loss: 0.5195 - accuracy: 0.7812
6/92 [>.............................] - ETA: 4s - loss: 0.4948 - accuracy: 0.7917
7/92 [=>............................] - ETA: 4s - loss: 0.4886 - accuracy: 0.7857
8/92 [=>............................] - ETA: 4s - loss: 0.5058 - accuracy: 0.7773
9/92 [=>............................] - ETA: 4s - loss: 0.4985 - accuracy: 0.7882
10/92 [==>………………………] - ETA: 4s - loss: 0.4993 - accuracy: 0.7969
11/92 [==>………………………] - ETA: 4s - loss: 0.4915 - accuracy: 0.8011
12/92 [==>………………………] - ETA: 4s - loss: 0.5063 - accuracy: 0.8047
13/92 [===>……………………..] - ETA: 4s - loss: 0.5257 - accuracy: 0.7981
14/92 [===>……………………..] - ETA: 4s - loss: 0.5310 - accuracy: 0.7969
15/92 [===>……………………..] - ETA: 4s - loss: 0.5420 - accuracy: 0.7875
16/92 [====>…………………….] - ETA: 4s - loss: 0.5323 - accuracy: 0.7910
17/92 [====>…………………….] - ETA: 4s - loss: 0.5475 - accuracy: 0.7831
18/92 [====>…………………….] - ETA: 4s - loss: 0.5417 - accuracy: 0.7830
19/92 [=====>……………………] - ETA: 4s - loss: 0.5430 - accuracy: 0.7829
20/92 [=====>……………………] - ETA: 4s - loss: 0.5497 - accuracy: 0.7766
21/92 [=====>……………………] - ETA: 4s - loss: 0.5450 - accuracy: 0.7783
22/92 [======>…………………..] - ETA: 4s - loss: 0.5501 - accuracy: 0.7784
23/92 [======>…………………..] - ETA: 3s - loss: 0.5590 - accuracy: 0.7799
24/92 [======>…………………..] - ETA: 3s - loss: 0.5482 - accuracy: 0.7865
25/92 [=======>………………….] - ETA: 3s - loss: 0.5465 - accuracy: 0.7875
26/92 [=======>………………….] - ETA: 3s - loss: 0.5421 - accuracy: 0.7885
27/92 [=======>………………….] - ETA: 3s - loss: 0.5454 - accuracy: 0.7882
28/92 [========>…………………] - ETA: 3s - loss: 0.5488 - accuracy: 0.7868
29/92 [========>…………………] - ETA: 3s - loss: 0.5551 - accuracy: 0.7812
30/92 [========>…………………] - ETA: 3s - loss: 0.5555 - accuracy: 0.7802
31/92 [=========>………………..] - ETA: 3s - loss: 0.5554 - accuracy: 0.7782
32/92 [=========>………………..] - ETA: 3s - loss: 0.5517 - accuracy: 0.7773
33/92 [=========>………………..] - ETA: 3s - loss: 0.5521 - accuracy: 0.7794
34/92 [==========>……………….] - ETA: 3s - loss: 0.5585 - accuracy: 0.7776
35/92 [==========>……………….] - ETA: 3s - loss: 0.5562 - accuracy: 0.7777
36/92 [==========>……………….] - ETA: 3s - loss: 0.5507 - accuracy: 0.7812
37/92 [===========>………………] - ETA: 3s - loss: 0.5586 - accuracy: 0.7779
38/92 [===========>………………] - ETA: 3s - loss: 0.5564 - accuracy: 0.7796
39/92 [===========>………………] - ETA: 3s - loss: 0.5534 - accuracy: 0.7812
40/92 [============>……………..] - ETA: 3s - loss: 0.5536 - accuracy: 0.7820
41/92 [============>……………..] - ETA: 2s - loss: 0.5572 - accuracy: 0.7805
42/92 [============>……………..] - ETA: 2s - loss: 0.5569 - accuracy: 0.7805
43/92 [=============>…………….] - ETA: 2s - loss: 0.5613 - accuracy: 0.7776
44/92 [=============>…………….] - ETA: 2s - loss: 0.5597 - accuracy: 0.7784
45/92 [=============>…………….] - ETA: 2s - loss: 0.5597 - accuracy: 0.7799
46/92 [==============>……………] - ETA: 2s - loss: 0.5566 - accuracy: 0.7812
47/92 [==============>……………] - ETA: 2s - loss: 0.5596 - accuracy: 0.7793
48/92 [==============>……………] - ETA: 2s - loss: 0.5574 - accuracy: 0.7806
49/92 [==============>……………] - ETA: 2s - loss: 0.5618 - accuracy: 0.7793
50/92 [===============>…………..] - ETA: 2s - loss: 0.5593 - accuracy: 0.7806
51/92 [===============>…………..] - ETA: 2s - loss: 0.5628 - accuracy: 0.7794
52/92 [===============>…………..] - ETA: 2s - loss: 0.5675 - accuracy: 0.7776
53/92 [================>………….] - ETA: 2s - loss: 0.5661 - accuracy: 0.7783
54/92 [================>………….] - ETA: 2s - loss: 0.5638 - accuracy: 0.7795
55/92 [================>………….] - ETA: 2s - loss: 0.5641 - accuracy: 0.7790
56/92 [=================>…………] - ETA: 2s - loss: 0.5638 - accuracy: 0.7785
57/92 [=================>…………] - ETA: 2s - loss: 0.5621 - accuracy: 0.7785
58/92 [=================>…………] - ETA: 1s - loss: 0.5647 - accuracy: 0.7775
59/92 [==================>………..] - ETA: 1s - loss: 0.5642 - accuracy: 0.7770
60/92 [==================>………..] - ETA: 1s - loss: 0.5652 - accuracy: 0.7760
61/92 [==================>………..] - ETA: 1s - loss: 0.5647 - accuracy: 0.7772
62/92 [===================>……….] - ETA: 1s - loss: 0.5618 - accuracy: 0.7792
63/92 [===================>……….] - ETA: 1s - loss: 0.5621 - accuracy: 0.7783
64/92 [===================>……….] - ETA: 1s - loss: 0.5602 - accuracy: 0.7788
65/92 [====================>………] - ETA: 1s - loss: 0.5594 - accuracy: 0.7793
66/92 [====================>………] - ETA: 1s - loss: 0.5603 - accuracy: 0.7784
67/92 [====================>………] - ETA: 1s - loss: 0.5598 - accuracy: 0.7789
68/92 [=====================>……..] - ETA: 1s - loss: 0.5633 - accuracy: 0.7776
69/92 [=====================>……..] - ETA: 1s - loss: 0.5660 - accuracy: 0.7767
70/92 [=====================>……..] - ETA: 1s - loss: 0.5666 - accuracy: 0.7763
71/92 [======================>…….] - ETA: 1s - loss: 0.5642 - accuracy: 0.7777
72/92 [======================>…….] - ETA: 1s - loss: 0.5633 - accuracy: 0.7778
73/92 [======================>…….] - ETA: 1s - loss: 0.5618 - accuracy: 0.7787
74/92 [=======================>……] - ETA: 1s - loss: 0.5634 - accuracy: 0.7779
75/92 [=======================>……] - ETA: 0s - loss: 0.5613 - accuracy: 0.7783
77/92 [========================>…..] - ETA: 0s - loss: 0.5630 - accuracy: 0.7769
78/92 [========================>…..] - ETA: 0s - loss: 0.5655 - accuracy: 0.7761
79/92 [========================>…..] - ETA: 0s - loss: 0.5639 - accuracy: 0.7770
80/92 [=========================>….] - ETA: 0s - loss: 0.5662 - accuracy: 0.7766
81/92 [=========================>….] - ETA: 0s - loss: 0.5626 - accuracy: 0.7783
82/92 [=========================>….] - ETA: 0s - loss: 0.5621 - accuracy: 0.7794
83/92 [==========================>…] - ETA: 0s - loss: 0.5608 - accuracy: 0.7806
84/92 [==========================>…] - ETA: 0s - loss: 0.5608 - accuracy: 0.7806
85/92 [==========================>…] - ETA: 0s - loss: 0.5613 - accuracy: 0.7802
86/92 [===========================>..] - ETA: 0s - loss: 0.5608 - accuracy: 0.7806
87/92 [===========================>..] - ETA: 0s - loss: 0.5642 - accuracy: 0.7803
88/92 [===========================>..] - ETA: 0s - loss: 0.5625 - accuracy: 0.7810
89/92 [============================>.] - ETA: 0s - loss: 0.5648 - accuracy: 0.7806
90/92 [============================>.] - ETA: 0s - loss: 0.5615 - accuracy: 0.7824
91/92 [============================>.] - ETA: 0s - loss: 0.5611 - accuracy: 0.7820
92/92 [==============================] - ETA: 0s - loss: 0.5609 - accuracy: 0.7827
92/92 [==============================] - 6s 64ms/step - loss: 0.5609 - accuracy: 0.7827 - val_loss: 0.6652 - val_accuracy: 0.7357
Epoch 13/15
1/92 [..............................] - ETA: 7s - loss: 0.5252 - accuracy: 0.8438
2/92 [..............................] - ETA: 5s - loss: 0.5595 - accuracy: 0.7969
3/92 [..............................] - ETA: 5s - loss: 0.5306 - accuracy: 0.8125
4/92 [>.............................] - ETA: 5s - loss: 0.5318 - accuracy: 0.8125
5/92 [>.............................] - ETA: 5s - loss: 0.4936 - accuracy: 0.8313
6/92 [>.............................] - ETA: 5s - loss: 0.4675 - accuracy: 0.8438
7/92 [=>............................] - ETA: 4s - loss: 0.4796 - accuracy: 0.8348
8/92 [=>............................] - ETA: 4s - loss: 0.5024 - accuracy: 0.8164
9/92 [=>............................] - ETA: 4s - loss: 0.4919 - accuracy: 0.8264
10/92 [==>………………………] - ETA: 4s - loss: 0.5071 - accuracy: 0.8219
11/92 [==>………………………] - ETA: 4s - loss: 0.5112 - accuracy: 0.8182
12/92 [==>………………………] - ETA: 4s - loss: 0.5037 - accuracy: 0.8203
13/92 [===>……………………..] - ETA: 4s - loss: 0.4893 - accuracy: 0.8245
14/92 [===>……………………..] - ETA: 4s - loss: 0.4904 - accuracy: 0.8281
15/92 [===>……………………..] - ETA: 4s - loss: 0.4893 - accuracy: 0.8271
16/92 [====>…………………….] - ETA: 4s - loss: 0.4908 - accuracy: 0.8242
17/92 [====>…………………….] - ETA: 4s - loss: 0.4963 - accuracy: 0.8180
18/92 [====>…………………….] - ETA: 4s - loss: 0.4981 - accuracy: 0.8177
19/92 [=====>……………………] - ETA: 4s - loss: 0.5066 - accuracy: 0.8141
20/92 [=====>……………………] - ETA: 4s - loss: 0.5055 - accuracy: 0.8125
21/92 [=====>……………………] - ETA: 4s - loss: 0.5156 - accuracy: 0.8065
22/92 [======>…………………..] - ETA: 4s - loss: 0.5282 - accuracy: 0.8054
23/92 [======>…………………..] - ETA: 3s - loss: 0.5264 - accuracy: 0.8084
24/92 [======>…………………..] - ETA: 3s - loss: 0.5195 - accuracy: 0.8099
25/92 [=======>………………….] - ETA: 3s - loss: 0.5105 - accuracy: 0.8138
26/92 [=======>………………….] - ETA: 3s - loss: 0.5050 - accuracy: 0.8149
27/92 [=======>………………….] - ETA: 3s - loss: 0.5063 - accuracy: 0.8171
28/92 [========>…………………] - ETA: 3s - loss: 0.5090 - accuracy: 0.8147
29/92 [========>…………………] - ETA: 3s - loss: 0.5016 - accuracy: 0.8179
30/92 [========>…………………] - ETA: 3s - loss: 0.4980 - accuracy: 0.8188
31/92 [=========>………………..] - ETA: 3s - loss: 0.5032 - accuracy: 0.8155
32/92 [=========>………………..] - ETA: 3s - loss: 0.4998 - accuracy: 0.8164
33/92 [=========>………………..] - ETA: 3s - loss: 0.4974 - accuracy: 0.8163
34/92 [==========>……………….] - ETA: 3s - loss: 0.5017 - accuracy: 0.8134
35/92 [==========>……………….] - ETA: 3s - loss: 0.5067 - accuracy: 0.8116
36/92 [==========>……………….] - ETA: 3s - loss: 0.5030 - accuracy: 0.8134
37/92 [===========>………………] - ETA: 3s - loss: 0.4970 - accuracy: 0.8159
38/92 [===========>………………] - ETA: 3s - loss: 0.4961 - accuracy: 0.8158
39/92 [===========>………………] - ETA: 3s - loss: 0.4938 - accuracy: 0.8165
40/92 [============>……………..] - ETA: 3s - loss: 0.4929 - accuracy: 0.8164
41/92 [============>……………..] - ETA: 2s - loss: 0.4964 - accuracy: 0.8171
42/92 [============>……………..] - ETA: 2s - loss: 0.4964 - accuracy: 0.8155
43/92 [=============>…………….] - ETA: 2s - loss: 0.4995 - accuracy: 0.8147
44/92 [=============>…………….] - ETA: 2s - loss: 0.5069 - accuracy: 0.8118
45/92 [=============>…………….] - ETA: 2s - loss: 0.5136 - accuracy: 0.8083
46/92 [==============>……………] - ETA: 2s - loss: 0.5124 - accuracy: 0.8084
47/92 [==============>……………] - ETA: 2s - loss: 0.5139 - accuracy: 0.8072
48/92 [==============>……………] - ETA: 2s - loss: 0.5154 - accuracy: 0.8073
49/92 [==============>……………] - ETA: 2s - loss: 0.5157 - accuracy: 0.8068
50/92 [===============>…………..] - ETA: 2s - loss: 0.5195 - accuracy: 0.8062
51/92 [===============>…………..] - ETA: 2s - loss: 0.5191 - accuracy: 0.8064
52/92 [===============>…………..] - ETA: 2s - loss: 0.5174 - accuracy: 0.8077
53/92 [================>………….] - ETA: 2s - loss: 0.5186 - accuracy: 0.8078
54/92 [================>………….] - ETA: 2s - loss: 0.5211 - accuracy: 0.8079
55/92 [================>………….] - ETA: 2s - loss: 0.5175 - accuracy: 0.8091
56/92 [=================>…………] - ETA: 2s - loss: 0.5185 - accuracy: 0.8092
57/92 [=================>…………] - ETA: 2s - loss: 0.5193 - accuracy: 0.8087
58/92 [=================>…………] - ETA: 1s - loss: 0.5222 - accuracy: 0.8071
59/92 [==================>………..] - ETA: 1s - loss: 0.5193 - accuracy: 0.8088
60/92 [==================>………..] - ETA: 1s - loss: 0.5179 - accuracy: 0.8094
61/92 [==================>………..] - ETA: 1s - loss: 0.5190 - accuracy: 0.8094
62/92 [===================>……….] - ETA: 1s - loss: 0.5203 - accuracy: 0.8095
63/92 [===================>……….] - ETA: 1s - loss: 0.5183 - accuracy: 0.8100
64/92 [===================>……….] - ETA: 1s - loss: 0.5221 - accuracy: 0.8091
65/92 [====================>………] - ETA: 1s - loss: 0.5257 - accuracy: 0.8077
66/92 [====================>………] - ETA: 1s - loss: 0.5256 - accuracy: 0.8078
67/92 [====================>………] - ETA: 1s - loss: 0.5246 - accuracy: 0.8083
68/92 [=====================>……..] - ETA: 1s - loss: 0.5257 - accuracy: 0.8065
69/92 [=====================>……..] - ETA: 1s - loss: 0.5277 - accuracy: 0.8057
70/92 [=====================>……..] - ETA: 1s - loss: 0.5288 - accuracy: 0.8049
71/92 [======================>…….] - ETA: 1s - loss: 0.5315 - accuracy: 0.8041
72/92 [======================>…….] - ETA: 1s - loss: 0.5324 - accuracy: 0.8030
73/92 [======================>…….] - ETA: 1s - loss: 0.5341 - accuracy: 0.8031
74/92 [=======================>……] - ETA: 1s - loss: 0.5340 - accuracy: 0.8032
75/92 [=======================>……] - ETA: 0s - loss: 0.5340 - accuracy: 0.8029
76/92 [=======================>……] - ETA: 0s - loss: 0.5359 - accuracy: 0.8014
77/92 [========================>…..] - ETA: 0s - loss: 0.5358 - accuracy: 0.8019
78/92 [========================>…..] - ETA: 0s - loss: 0.5377 - accuracy: 0.8021
79/92 [========================>…..] - ETA: 0s - loss: 0.5358 - accuracy: 0.8030
80/92 [=========================>….] - ETA: 0s - loss: 0.5352 - accuracy: 0.8035
81/92 [=========================>….] - ETA: 0s - loss: 0.5352 - accuracy: 0.8025
82/92 [=========================>….] - ETA: 0s - loss: 0.5368 - accuracy: 0.8018
83/92 [==========================>…] - ETA: 0s - loss: 0.5345 - accuracy: 0.8027
84/92 [==========================>…] - ETA: 0s - loss: 0.5322 - accuracy: 0.8036
86/92 [===========================>..] - ETA: 0s - loss: 0.5315 - accuracy: 0.8043
87/92 [===========================>..] - ETA: 0s - loss: 0.5294 - accuracy: 0.8044
88/92 [===========================>..] - ETA: 0s - loss: 0.5280 - accuracy: 0.8052
89/92 [============================>.] - ETA: 0s - loss: 0.5288 - accuracy: 0.8042
90/92 [============================>.] - ETA: 0s - loss: 0.5304 - accuracy: 0.8043
91/92 [============================>.] - ETA: 0s - loss: 0.5279 - accuracy: 0.8054
92/92 [==============================] - ETA: 0s - loss: 0.5255 - accuracy: 0.8072
92/92 [==============================] - 6s 64ms/step - loss: 0.5255 - accuracy: 0.8072 - val_loss: 0.7346 - val_accuracy: 0.7384
Epoch 14/15
1/92 [..............................] - ETA: 7s - loss: 0.3946 - accuracy: 0.8125
2/92 [..............................] - ETA: 5s - loss: 0.4186 - accuracy: 0.8438
3/92 [..............................] - ETA: 5s - loss: 0.5825 - accuracy: 0.7708
4/92 [>.............................] - ETA: 5s - loss: 0.5210 - accuracy: 0.8047
5/92 [>.............................] - ETA: 5s - loss: 0.5927 - accuracy: 0.7750
6/92 [>.............................] - ETA: 4s - loss: 0.5649 - accuracy: 0.7865
7/92 [=>............................] - ETA: 4s - loss: 0.5574 - accuracy: 0.7902
8/92 [=>............................] - ETA: 4s - loss: 0.5343 - accuracy: 0.7969
9/92 [=>............................] - ETA: 4s - loss: 0.5348 - accuracy: 0.7951
10/92 [==>………………………] - ETA: 4s - loss: 0.5328 - accuracy: 0.7937
11/92 [==>………………………] - ETA: 4s - loss: 0.5417 - accuracy: 0.7898
12/92 [==>………………………] - ETA: 4s - loss: 0.5423 - accuracy: 0.7839
13/92 [===>……………………..] - ETA: 4s - loss: 0.5293 - accuracy: 0.7957
14/92 [===>……………………..] - ETA: 4s - loss: 0.5237 - accuracy: 0.8013
15/92 [===>……………………..] - ETA: 4s - loss: 0.5192 - accuracy: 0.7958
16/92 [====>…………………….] - ETA: 4s - loss: 0.5227 - accuracy: 0.7969
17/92 [====>…………………….] - ETA: 4s - loss: 0.5219 - accuracy: 0.7996
18/92 [====>…………………….] - ETA: 4s - loss: 0.5389 - accuracy: 0.7951
20/92 [=====>……………………] - ETA: 4s - loss: 0.5362 - accuracy: 0.7927
21/92 [=====>……………………] - ETA: 4s - loss: 0.5310 - accuracy: 0.7937
22/92 [======>…………………..] - ETA: 4s - loss: 0.5365 - accuracy: 0.7945
23/92 [======>…………………..] - ETA: 3s - loss: 0.5421 - accuracy: 0.7940
24/92 [======>…………………..] - ETA: 3s - loss: 0.5364 - accuracy: 0.7947
25/92 [=======>………………….] - ETA: 3s - loss: 0.5400 - accuracy: 0.7942
26/92 [=======>………………….] - ETA: 3s - loss: 0.5380 - accuracy: 0.7949
27/92 [=======>………………….] - ETA: 3s - loss: 0.5330 - accuracy: 0.7967
28/92 [========>…………………] - ETA: 3s - loss: 0.5423 - accuracy: 0.7928
29/92 [========>…………………] - ETA: 3s - loss: 0.5431 - accuracy: 0.7935
30/92 [========>…………………] - ETA: 3s - loss: 0.5458 - accuracy: 0.7920
31/92 [=========>………………..] - ETA: 3s - loss: 0.5496 - accuracy: 0.7907
32/92 [=========>………………..] - ETA: 3s - loss: 0.5505 - accuracy: 0.7884
33/92 [=========>………………..] - ETA: 3s - loss: 0.5535 - accuracy: 0.7872
34/92 [==========>……………….] - ETA: 3s - loss: 0.5620 - accuracy: 0.7870
35/92 [==========>……………….] - ETA: 3s - loss: 0.5640 - accuracy: 0.7878
36/92 [==========>……………….] - ETA: 3s - loss: 0.5689 - accuracy: 0.7858
37/92 [===========>………………] - ETA: 3s - loss: 0.5683 - accuracy: 0.7849
38/92 [===========>………………] - ETA: 3s - loss: 0.5653 - accuracy: 0.7848
39/92 [===========>………………] - ETA: 3s - loss: 0.5606 - accuracy: 0.7863
40/92 [============>……………..] - ETA: 2s - loss: 0.5612 - accuracy: 0.7862
41/92 [============>……………..] - ETA: 2s - loss: 0.5642 - accuracy: 0.7860
42/92 [============>……………..] - ETA: 2s - loss: 0.5674 - accuracy: 0.7844
43/92 [=============>…………….] - ETA: 2s - loss: 0.5612 - accuracy: 0.7880
44/92 [=============>…………….] - ETA: 2s - loss: 0.5614 - accuracy: 0.7879
45/92 [=============>…………….] - ETA: 2s - loss: 0.5615 - accuracy: 0.7863
46/92 [==============>……………] - ETA: 2s - loss: 0.5615 - accuracy: 0.7855
47/92 [==============>……………] - ETA: 2s - loss: 0.5600 - accuracy: 0.7861
48/92 [==============>……………] - ETA: 2s - loss: 0.5585 - accuracy: 0.7880
49/92 [==============>……………] - ETA: 2s - loss: 0.5641 - accuracy: 0.7846
50/92 [===============>…………..] - ETA: 2s - loss: 0.5635 - accuracy: 0.7864
51/92 [===============>…………..] - ETA: 2s - loss: 0.5630 - accuracy: 0.7857
52/92 [===============>…………..] - ETA: 2s - loss: 0.5604 - accuracy: 0.7874
53/92 [================>………….] - ETA: 2s - loss: 0.5621 - accuracy: 0.7855
54/92 [================>………….] - ETA: 2s - loss: 0.5613 - accuracy: 0.7855
55/92 [================>………….] - ETA: 2s - loss: 0.5630 - accuracy: 0.7842
56/92 [=================>…………] - ETA: 2s - loss: 0.5613 - accuracy: 0.7853
57/92 [=================>…………] - ETA: 2s - loss: 0.5607 - accuracy: 0.7858
58/92 [=================>…………] - ETA: 1s - loss: 0.5580 - accuracy: 0.7873
59/92 [==================>………..] - ETA: 1s - loss: 0.5560 - accuracy: 0.7872
60/92 [==================>………..] - ETA: 1s - loss: 0.5529 - accuracy: 0.7887
61/92 [==================>………..] - ETA: 1s - loss: 0.5507 - accuracy: 0.7896
62/92 [===================>……….] - ETA: 1s - loss: 0.5497 - accuracy: 0.7900
63/92 [===================>……….] - ETA: 1s - loss: 0.5490 - accuracy: 0.7903
64/92 [===================>……….] - ETA: 1s - loss: 0.5505 - accuracy: 0.7887
65/92 [====================>………] - ETA: 1s - loss: 0.5467 - accuracy: 0.7901
66/92 [====================>………] - ETA: 1s - loss: 0.5484 - accuracy: 0.7899
67/92 [====================>………] - ETA: 1s - loss: 0.5478 - accuracy: 0.7903
68/92 [=====================>……..] - ETA: 1s - loss: 0.5458 - accuracy: 0.7911
69/92 [=====================>……..] - ETA: 1s - loss: 0.5454 - accuracy: 0.7914
70/92 [=====================>……..] - ETA: 1s - loss: 0.5482 - accuracy: 0.7899
71/92 [======================>…….] - ETA: 1s - loss: 0.5472 - accuracy: 0.7902
72/92 [======================>…….] - ETA: 1s - loss: 0.5475 - accuracy: 0.7888
73/92 [======================>…….] - ETA: 1s - loss: 0.5450 - accuracy: 0.7904
74/92 [=======================>……] - ETA: 1s - loss: 0.5426 - accuracy: 0.7911
75/92 [=======================>……] - ETA: 0s - loss: 0.5456 - accuracy: 0.7901
76/92 [=======================>……] - ETA: 0s - loss: 0.5460 - accuracy: 0.7892
77/92 [========================>…..] - ETA: 0s - loss: 0.5432 - accuracy: 0.7911
78/92 [========================>…..] - ETA: 0s - loss: 0.5412 - accuracy: 0.7914
79/92 [========================>…..] - ETA: 0s - loss: 0.5420 - accuracy: 0.7909
80/92 [=========================>….] - ETA: 0s - loss: 0.5455 - accuracy: 0.7888
81/92 [=========================>….] - ETA: 0s - loss: 0.5470 - accuracy: 0.7891
82/92 [=========================>….] - ETA: 0s - loss: 0.5454 - accuracy: 0.7898
83/92 [==========================>…] - ETA: 0s - loss: 0.5431 - accuracy: 0.7908
84/92 [==========================>…] - ETA: 0s - loss: 0.5453 - accuracy: 0.7907
85/92 [==========================>…] - ETA: 0s - loss: 0.5463 - accuracy: 0.7906
86/92 [===========================>..] - ETA: 0s - loss: 0.5460 - accuracy: 0.7905
87/92 [===========================>..] - ETA: 0s - loss: 0.5441 - accuracy: 0.7903
88/92 [===========================>..] - ETA: 0s - loss: 0.5444 - accuracy: 0.7899
89/92 [============================>.] - ETA: 0s - loss: 0.5429 - accuracy: 0.7901
90/92 [============================>.] - ETA: 0s - loss: 0.5423 - accuracy: 0.7904
91/92 [============================>.] - ETA: 0s - loss: 0.5448 - accuracy: 0.7893
92/92 [==============================] - ETA: 0s - loss: 0.5438 - accuracy: 0.7895
92/92 [==============================] - 6s 64ms/step - loss: 0.5438 - accuracy: 0.7895 - val_loss: 0.7761 - val_accuracy: 0.7275
Epoch 15/15
1/92 [..............................] - ETA: 7s - loss: 0.5374 - accuracy: 0.8750
2/92 [..............................] - ETA: 5s - loss: 0.4638 - accuracy: 0.8906
3/92 [..............................] - ETA: 5s - loss: 0.4361 - accuracy: 0.8750
4/92 [>.............................] - ETA: 5s - loss: 0.4714 - accuracy: 0.8281
5/92 [>.............................] - ETA: 4s - loss: 0.4472 - accuracy: 0.8375
6/92 [>.............................] - ETA: 4s - loss: 0.4562 - accuracy: 0.8281
7/92 [=>............................] - ETA: 4s - loss: 0.4228 - accuracy: 0.8438
8/92 [=>............................] - ETA: 4s - loss: 0.4377 - accuracy: 0.8359
9/92 [=>............................] - ETA: 4s - loss: 0.4744 - accuracy: 0.8264
10/92 [==>………………………] - ETA: 4s - loss: 0.4706 - accuracy: 0.8313
11/92 [==>………………………] - ETA: 4s - loss: 0.4714 - accuracy: 0.8324
12/92 [==>………………………] - ETA: 4s - loss: 0.4935 - accuracy: 0.8255
13/92 [===>……………………..] - ETA: 4s - loss: 0.4925 - accuracy: 0.8245
14/92 [===>……………………..] - ETA: 4s - loss: 0.4784 - accuracy: 0.8281
15/92 [===>……………………..] - ETA: 4s - loss: 0.4763 - accuracy: 0.8271
16/92 [====>…………………….] - ETA: 4s - loss: 0.4744 - accuracy: 0.8301
17/92 [====>…………………….] - ETA: 4s - loss: 0.4797 - accuracy: 0.8254
18/92 [====>…………………….] - ETA: 4s - loss: 0.4824 - accuracy: 0.8264
19/92 [=====>……………………] - ETA: 4s - loss: 0.4777 - accuracy: 0.8273
20/92 [=====>……………………] - ETA: 4s - loss: 0.4756 - accuracy: 0.8297
21/92 [=====>……………………] - ETA: 4s - loss: 0.4717 - accuracy: 0.8304
22/92 [======>…………………..] - ETA: 4s - loss: 0.4740 - accuracy: 0.8281
23/92 [======>…………………..] - ETA: 3s - loss: 0.4742 - accuracy: 0.8261
24/92 [======>…………………..] - ETA: 3s - loss: 0.4757 - accuracy: 0.8229
25/92 [=======>………………….] - ETA: 3s - loss: 0.4786 - accuracy: 0.8213
26/92 [=======>………………….] - ETA: 3s - loss: 0.4786 - accuracy: 0.8209
27/92 [=======>………………….] - ETA: 3s - loss: 0.4836 - accuracy: 0.8183
28/92 [========>…………………] - ETA: 3s - loss: 0.4816 - accuracy: 0.8181
29/92 [========>…………………] - ETA: 3s - loss: 0.4780 - accuracy: 0.8190
30/92 [========>…………………] - ETA: 3s - loss: 0.4789 - accuracy: 0.8177
31/92 [=========>………………..] - ETA: 3s - loss: 0.4751 - accuracy: 0.8175
32/92 [=========>………………..] - ETA: 3s - loss: 0.4703 - accuracy: 0.8184
33/92 [=========>………………..] - ETA: 3s - loss: 0.4701 - accuracy: 0.8191
34/92 [==========>……………….] - ETA: 3s - loss: 0.4664 - accuracy: 0.8199
35/92 [==========>……………….] - ETA: 3s - loss: 0.4676 - accuracy: 0.8196
36/92 [==========>……………….] - ETA: 3s - loss: 0.4686 - accuracy: 0.8203
37/92 [===========>………………] - ETA: 3s - loss: 0.4684 - accuracy: 0.8209
38/92 [===========>………………] - ETA: 3s - loss: 0.4648 - accuracy: 0.8199
39/92 [===========>………………] - ETA: 3s - loss: 0.4673 - accuracy: 0.8181
40/92 [============>……………..] - ETA: 2s - loss: 0.4649 - accuracy: 0.8188
41/92 [============>……………..] - ETA: 2s - loss: 0.4656 - accuracy: 0.8186
42/92 [============>……………..] - ETA: 2s - loss: 0.4694 - accuracy: 0.8177
43/92 [=============>…………….] - ETA: 2s - loss: 0.4750 - accuracy: 0.8154
44/92 [=============>…………….] - ETA: 2s - loss: 0.4788 - accuracy: 0.8118
45/92 [=============>…………….] - ETA: 2s - loss: 0.4772 - accuracy: 0.8132
46/92 [==============>……………] - ETA: 2s - loss: 0.4774 - accuracy: 0.8132
47/92 [==============>……………] - ETA: 2s - loss: 0.4781 - accuracy: 0.8145
48/92 [==============>……………] - ETA: 2s - loss: 0.4757 - accuracy: 0.8158
49/92 [==============>……………] - ETA: 2s - loss: 0.4758 - accuracy: 0.8157
50/92 [===============>…………..] - ETA: 2s - loss: 0.4741 - accuracy: 0.8169
51/92 [===============>…………..] - ETA: 2s - loss: 0.4775 - accuracy: 0.8168
52/92 [===============>…………..] - ETA: 2s - loss: 0.4794 - accuracy: 0.8149
53/92 [================>………….] - ETA: 2s - loss: 0.4896 - accuracy: 0.8125
54/92 [================>………….] - ETA: 2s - loss: 0.4885 - accuracy: 0.8137
55/92 [================>………….] - ETA: 2s - loss: 0.4864 - accuracy: 0.8136
56/92 [=================>…………] - ETA: 2s - loss: 0.4861 - accuracy: 0.8136
57/92 [=================>…………] - ETA: 2s - loss: 0.4869 - accuracy: 0.8130
58/92 [=================>…………] - ETA: 1s - loss: 0.4843 - accuracy: 0.8141
59/92 [==================>………..] - ETA: 1s - loss: 0.4882 - accuracy: 0.8130
60/92 [==================>………..] - ETA: 1s - loss: 0.4926 - accuracy: 0.8109
61/92 [==================>………..] - ETA: 1s - loss: 0.4920 - accuracy: 0.8115
62/92 [===================>……….] - ETA: 1s - loss: 0.4945 - accuracy: 0.8095
63/92 [===================>……….] - ETA: 1s - loss: 0.4951 - accuracy: 0.8095
64/92 [===================>……….] - ETA: 1s - loss: 0.4949 - accuracy: 0.8101
65/92 [====================>………] - ETA: 1s - loss: 0.4933 - accuracy: 0.8096
66/92 [====================>………] - ETA: 1s - loss: 0.4981 - accuracy: 0.8073
67/92 [====================>………] - ETA: 1s - loss: 0.4964 - accuracy: 0.8083
68/92 [=====================>……..] - ETA: 1s - loss: 0.4934 - accuracy: 0.8093
69/92 [=====================>……..] - ETA: 1s - loss: 0.4972 - accuracy: 0.8093
70/92 [=====================>……..] - ETA: 1s - loss: 0.4996 - accuracy: 0.8080
71/92 [======================>…….] - ETA: 1s - loss: 0.5009 - accuracy: 0.8072
72/92 [======================>…….] - ETA: 1s - loss: 0.5067 - accuracy: 0.8064
73/92 [======================>…….] - ETA: 1s - loss: 0.5055 - accuracy: 0.8061
74/92 [=======================>……] - ETA: 1s - loss: 0.5039 - accuracy: 0.8066
75/92 [=======================>……] - ETA: 0s - loss: 0.5070 - accuracy: 0.8046
76/92 [=======================>……] - ETA: 0s - loss: 0.5080 - accuracy: 0.8047
77/92 [========================>…..] - ETA: 0s - loss: 0.5113 - accuracy: 0.8040
78/92 [========================>…..] - ETA: 0s - loss: 0.5107 - accuracy: 0.8045
79/92 [========================>…..] - ETA: 0s - loss: 0.5141 - accuracy: 0.8026
80/92 [=========================>….] - ETA: 0s - loss: 0.5121 - accuracy: 0.8035
81/92 [=========================>….] - ETA: 0s - loss: 0.5107 - accuracy: 0.8044
82/92 [=========================>….] - ETA: 0s - loss: 0.5102 - accuracy: 0.8041
83/92 [==========================>…] - ETA: 0s - loss: 0.5116 - accuracy: 0.8035
84/92 [==========================>…] - ETA: 0s - loss: 0.5117 - accuracy: 0.8032
85/92 [==========================>…] - ETA: 0s - loss: 0.5099 - accuracy: 0.8037
86/92 [===========================>..] - ETA: 0s - loss: 0.5129 - accuracy: 0.8016
87/92 [===========================>..] - ETA: 0s - loss: 0.5155 - accuracy: 0.8006
89/92 [============================>.] - ETA: 0s - loss: 0.5151 - accuracy: 0.8011
90/92 [============================>.] - ETA: 0s - loss: 0.5154 - accuracy: 0.8012
91/92 [============================>.] - ETA: 0s - loss: 0.5143 - accuracy: 0.8017
92/92 [==============================] - ETA: 0s - loss: 0.5165 - accuracy: 0.8004
92/92 [==============================] - 6s 64ms/step - loss: 0.5165 - accuracy: 0.8004 - val_loss: 0.7822 - val_accuracy: 0.7289
1/1 [==============================] - ETA: 0s
1/1 [==============================] - 0s 74ms/step
This image most likely belongs to sunflowers with a 99.24 percent confidence.
2024-02-10 01:10:41.607321: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'random_flip_input' with dtype float and shape [?,180,180,3]
[[{{node random_flip_input}}]]
2024-02-10 01:10:41.692936: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
[[{{node inputs}}]]
2024-02-10 01:10:41.703478: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'random_flip_input' with dtype float and shape [?,180,180,3]
[[{{node random_flip_input}}]]
2024-02-10 01:10:41.714441: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
[[{{node inputs}}]]
2024-02-10 01:10:41.722136: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
[[{{node inputs}}]]
2024-02-10 01:10:41.728943: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
[[{{node inputs}}]]
2024-02-10 01:10:41.739850: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
[[{{node inputs}}]]
2024-02-10 01:10:41.778944: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'sequential_1_input' with dtype float and shape [?,180,180,3]
[[{{node sequential_1_input}}]]
2024-02-10 01:10:41.847949: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
[[{{node inputs}}]]
2024-02-10 01:10:41.868730: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'sequential_1_input' with dtype float and shape [?,180,180,3]
[[{{node sequential_1_input}}]]
2024-02-10 01:10:41.907533: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,22,22,64]
[[{{node inputs}}]]
2024-02-10 01:10:41.933427: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
[[{{node inputs}}]]
2024-02-10 01:10:42.007195: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
[[{{node inputs}}]]
2024-02-10 01:10:42.149634: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
[[{{node inputs}}]]
2024-02-10 01:10:42.286901: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,22,22,64]
[[{{node inputs}}]]
2024-02-10 01:10:42.489392: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
[[{{node inputs}}]]
2024-02-10 01:10:42.517820: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
[[{{node inputs}}]]
2024-02-10 01:10:42.563861: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,180,180,3]
[[{{node inputs}}]]
WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla while saving (showing 4 of 4). These functions will not be directly callable after loading.
INFO:tensorflow:Assets written to: model/flower/saved_model/assets
INFO:tensorflow:Assets written to: model/flower/saved_model/assets
output/A_Close_Up_Photo_of_a_Dandelion.jpg: 0%| | 0.00/21.7k [00:00<?, ?B/s]
(1, 180, 180, 3)
[1,180,180,3]
This image most likely belongs to dandelion with a 97.96 percent confidence.
Imports¶
The Post Training Quantization API is implemented in the nncf
library.
import sys
import matplotlib.pyplot as plt
import numpy as np
import nncf
from openvino.runtime import Core
from openvino.runtime import serialize
from PIL import Image
from sklearn.metrics import accuracy_score
sys.path.append("../utils")
from notebook_utils import download_file
INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
Post-training Quantization with NNCF¶
NNCF provides a suite of advanced algorithms for Neural Networks inference optimization in OpenVINO with minimal accuracy drop.
Create a quantized model from the pre-trained FP32 model and the calibration dataset. The optimization process contains the following steps:
Create a Dataset for quantization.
Run nncf.quantize for getting an optimized model.
The validation dataset already defined in the training notebook.
img_height = 180
img_width = 180
val_dataset = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation",
seed=123,
image_size=(img_height, img_width),
batch_size=1
)
for a, b in val_dataset:
print(type(a), type(b))
break
Found 3670 files belonging to 5 classes.
Using 734 files for validation.
<class 'tensorflow.python.framework.ops.EagerTensor'> <class 'tensorflow.python.framework.ops.EagerTensor'>
2024-02-10 01:10:45.668839: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [734]
[[{{node Placeholder/_0}}]]
2024-02-10 01:10:45.669302: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [734]
[[{{node Placeholder/_0}}]]
The validation dataset can be reused in quantization process. But it returns a tuple (images, labels), whereas calibration_dataset should only return images. The transformation function helps to transform a user validation dataset to the calibration dataset.
def transform_fn(data_item):
"""
The transformation function transforms a data item into model input data.
This function should be passed when the data item cannot be used as model's input.
"""
images, _ = data_item
return images.numpy()
calibration_dataset = nncf.Dataset(val_dataset, transform_fn)
Download Intermediate Representation (IR) model.
core = Core()
ir_model = core.read_model(model_xml)
Use Basic Quantization Flow. To use the most advanced quantization flow that allows to apply 8-bit quantization to the model with accuracy control see Quantizing with accuracy control.
quantized_model = nncf.quantize(
ir_model,
calibration_dataset,
subset_size=1000
)
Output()
Exception in thread Thread-88:
Traceback (most recent call last):
File "/usr/lib/python3.8/threading.py", line 932, in _bootstrap_inner
self.run()
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/live.py", line 32, in run
self.live.refresh()
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/live.py", line 223, in refresh
self._live_render.set_renderable(self.renderable)
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/live.py", line 203, in renderable
renderable = self.get_renderable()
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/live.py", line 98, in get_renderable
self._get_renderable()
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/progress.py", line 1537, in get_renderable
renderable = Group(*self.get_renderables())
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/progress.py", line 1542, in get_renderables
table = self.make_tasks_table(self.tasks)
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/progress.py", line 1566, in make_tasks_table
table.add_row(
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/progress.py", line 1571, in <genexpr>
else column(task)
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/progress.py", line 528, in __call__
renderable = self.render(task)
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/nncf/common/logging/track_progress.py", line 58, in render
text = super().render(task)
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/progress.py", line 787, in render
task_time = task.time_remaining
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/progress.py", line 1039, in time_remaining
estimate = ceil(remaining / speed)
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/tensorflow/python/ops/math_ops.py", line 1569, in _truediv_python3
raise TypeError(f"`x` and `y` must have the same dtype, "
TypeError: `x` and `y` must have the same dtype, got tf.int64 != tf.float32.
Output()
Save quantized model to benchmark.
compressed_model_dir = Path("model/optimized")
compressed_model_dir.mkdir(parents=True, exist_ok=True)
compressed_model_xml = compressed_model_dir / "flower_ir.xml"
serialize(quantized_model, str(compressed_model_xml))
Select inference device¶
select device from dropdown list for running inference using OpenVINO
import ipywidgets as widgets
device = widgets.Dropdown(
options=core.available_devices + ["AUTO"],
value='AUTO',
description='Device:',
disabled=False,
)
device
Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
Compare Metrics¶
Define a metric to determine the performance of the model.
For this demo we define validate function to compute accuracy metrics.
def validate(model, validation_loader):
"""
Evaluate model and compute accuracy metrics.
:param model: Model to validate
:param validation_loader: Validation dataset
:returns: Accuracy scores
"""
predictions = []
references = []
output = model.outputs[0]
for images, target in validation_loader:
pred = model(images.numpy())[output]
predictions.append(np.argmax(pred, axis=1))
references.append(target)
predictions = np.concatenate(predictions, axis=0)
references = np.concatenate(references, axis=0)
scores = accuracy_score(references, predictions)
return scores
Calculate accuracy for the original model and the quantized model.
original_compiled_model = core.compile_model(model=ir_model, device_name=device.value)
quantized_compiled_model = core.compile_model(model=quantized_model, device_name=device.value)
original_accuracy = validate(original_compiled_model, val_dataset)
quantized_accuracy = validate(quantized_compiled_model, val_dataset)
print(f"Accuracy of the original model: {original_accuracy:.3f}")
print(f"Accuracy of the quantized model: {quantized_accuracy:.3f}")
Accuracy of the original model: 0.729
Accuracy of the quantized model: 0.729
Compare file size of the models.
original_model_size = model_xml.with_suffix(".bin").stat().st_size / 1024
quantized_model_size = compressed_model_xml.with_suffix(".bin").stat().st_size / 1024
print(f"Original model size: {original_model_size:.2f} KB")
print(f"Quantized model size: {quantized_model_size:.2f} KB")
Original model size: 7791.65 KB
Quantized model size: 3897.08 KB
So, we can see that the original and quantized models have similar accuracy with a much smaller size of the quantized model.
Run Inference on Quantized Model¶
Copy the preprocess function from the training notebook and run inference on the quantized model with OpenVINO. See the OpenVINO API tutorial for more information about running inference with OpenVINO Python API.
def pre_process_image(imagePath, img_height=180):
# Model input format
n, c, h, w = [1, 3, img_height, img_height]
image = Image.open(imagePath)
image = image.resize((h, w), resample=Image.BILINEAR)
# Convert to array and change data layout from HWC to CHW
image = np.array(image)
input_image = image.reshape((n, h, w, c))
return input_image
# Get the names of the input and output layer
# model_pot = ie.read_model(model="model/optimized/flower_ir.xml")
input_layer = quantized_compiled_model.input(0)
output_layer = quantized_compiled_model.output(0)
# Get the class names: a list of directory names in alphabetical order
class_names = sorted([item.name for item in Path(data_dir).iterdir() if item.is_dir()])
# Run inference on an input image...
inp_img_url = (
"https://upload.wikimedia.org/wikipedia/commons/4/48/A_Close_Up_Photo_of_a_Dandelion.jpg"
)
directory = "output"
inp_file_name = "A_Close_Up_Photo_of_a_Dandelion.jpg"
file_path = Path(directory)/Path(inp_file_name)
# Download the image if it does not exist yet
if not Path(inp_file_name).exists():
download_file(inp_img_url, inp_file_name, directory=directory)
# Pre-process the image and get it ready for inference.
input_image = pre_process_image(imagePath=file_path)
print(f'input image shape: {input_image.shape}')
print(f'input layer shape: {input_layer.shape}')
res = quantized_compiled_model([input_image])[output_layer]
score = tf.nn.softmax(res[0])
# Show the results
image = Image.open(file_path)
plt.imshow(image)
print(
"This image most likely belongs to {} with a {:.2f} percent confidence.".format(
class_names[np.argmax(score)], 100 * np.max(score)
)
)
'output/A_Close_Up_Photo_of_a_Dandelion.jpg' already exists.
input image shape: (1, 180, 180, 3)
input layer shape: [1,180,180,3]
This image most likely belongs to dandelion with a 98.03 percent confidence.
Compare Inference Speed¶
Measure inference speed with the OpenVINO Benchmark App.
Benchmark App is a command line tool that measures raw inference
performance for a specified OpenVINO IR model. Run
benchmark_app --help to see a list of available parameters. By
default, Benchmark App tests the performance of the model specified with
the -m parameter with asynchronous inference on CPU, for one minute.
Use the -d parameter to test performance on a different device, for
example an Intel integrated Graphics (iGPU), and -t to set the
number of seconds to run inference. See the
documentation
for more information.
This tutorial uses a wrapper function from Notebook
Utils.
It prints the benchmark_app command with the chosen parameters.
In the next cells, inference speed will be measured for the original and quantized model on CPU. If an iGPU is available, inference speed will be measured for CPU+GPU as well. The number of seconds is set to 15.
NOTE: For the most accurate performance estimation, it is recommended to run
benchmark_appin a terminal/command prompt after closing other applications.
# print the available devices on this system
print("Device information:")
print(core.get_property("CPU", "FULL_DEVICE_NAME"))
if "GPU" in core.available_devices:
print(core.get_property("GPU", "FULL_DEVICE_NAME"))
Device information:
Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz
# Original model - CPU
! benchmark_app -m $model_xml -d CPU -t 15 -api async
[Step 1/11] Parsing and validating input arguments
[ INFO ] Parsing input parameters
[Step 2/11] Loading OpenVINO Runtime
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2023.3.0-13775-ceeafaf64f3-releases/2023/3
[ INFO ]
[ INFO ] Device info:
[ INFO ] CPU
[ INFO ] Build ................................. 2023.3.0-13775-ceeafaf64f3-releases/2023/3
[ INFO ]
[ INFO ]
[Step 3/11] Setting device configuration
[ WARNING ] Performance hint was not explicitly specified in command line. Device(CPU) performance hint will be set to PerformanceMode.THROUGHPUT.
[Step 4/11] Reading model files
[ INFO ] Loading model files
[ INFO ] Read model took 12.98 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ] sequential_1_input (node: sequential_1_input) : f32 / [...] / [1,180,180,3]
[ INFO ] Model outputs:
[ INFO ] outputs (node: sequential_2/outputs/BiasAdd) : f32 / [...] / [1,5]
[Step 5/11] Resizing model to match image sizes and given batch
[ INFO ] Model batch size: 1
[Step 6/11] Configuring input of the model
[ INFO ] Model inputs:
[ INFO ] sequential_1_input (node: sequential_1_input) : u8 / [N,H,W,C] / [1,180,180,3]
[ INFO ] Model outputs:
[ INFO ] outputs (node: sequential_2/outputs/BiasAdd) : f32 / [...] / [1,5]
[Step 7/11] Loading the model to the device
[ INFO ] Compile model took 71.95 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ] NETWORK_NAME: TensorFlow_Frontend_IR
[ INFO ] OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
[ INFO ] NUM_STREAMS: 12
[ INFO ] AFFINITY: Affinity.CORE
[ INFO ] INFERENCE_NUM_THREADS: 24
[ INFO ] PERF_COUNT: NO
[ INFO ] INFERENCE_PRECISION_HINT: <Type: 'float32'>
[ INFO ] PERFORMANCE_HINT: THROUGHPUT
[ INFO ] EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
[ INFO ] PERFORMANCE_HINT_NUM_REQUESTS: 0
[ INFO ] ENABLE_CPU_PINNING: True
[ INFO ] SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
[ INFO ] ENABLE_HYPER_THREADING: True
[ INFO ] EXECUTION_DEVICES: ['CPU']
[ INFO ] CPU_DENORMALS_OPTIMIZATION: False
[ INFO ] CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
[Step 9/11] Creating infer requests and preparing input tensors
[ WARNING ] No input files were given for input 'sequential_1_input'!. This input will be filled with random values!
[ INFO ] Fill input 'sequential_1_input' with random values
[Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
[ INFO ] First inference took 7.48 ms
[Step 11/11] Dumping statistics report
[ INFO ] Execution Devices:['CPU']
[ INFO ] Count: 57660 iterations
[ INFO ] Duration: 15004.59 ms
[ INFO ] Latency:
[ INFO ] Median: 2.95 ms
[ INFO ] Average: 2.95 ms
[ INFO ] Min: 1.69 ms
[ INFO ] Max: 12.80 ms
[ INFO ] Throughput: 3842.82 FPS
# Quantized model - CPU
! benchmark_app -m $compressed_model_xml -d CPU -t 15 -api async
[Step 1/11] Parsing and validating input arguments
[ INFO ] Parsing input parameters
[Step 2/11] Loading OpenVINO Runtime
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2023.3.0-13775-ceeafaf64f3-releases/2023/3
[ INFO ]
[ INFO ] Device info:
[ INFO ] CPU
[ INFO ] Build ................................. 2023.3.0-13775-ceeafaf64f3-releases/2023/3
[ INFO ]
[ INFO ]
[Step 3/11] Setting device configuration
[ WARNING ] Performance hint was not explicitly specified in command line. Device(CPU) performance hint will be set to PerformanceMode.THROUGHPUT.
[Step 4/11] Reading model files
[ INFO ] Loading model files
[ INFO ] Read model took 15.15 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ] sequential_1_input (node: sequential_1_input) : f32 / [...] / [1,180,180,3]
[ INFO ] Model outputs:
[ INFO ] outputs (node: sequential_2/outputs/BiasAdd) : f32 / [...] / [1,5]
[Step 5/11] Resizing model to match image sizes and given batch
[ INFO ] Model batch size: 1
[Step 6/11] Configuring input of the model
[ INFO ] Model inputs:
[ INFO ] sequential_1_input (node: sequential_1_input) : u8 / [N,H,W,C] / [1,180,180,3]
[ INFO ] Model outputs:
[ INFO ] outputs (node: sequential_2/outputs/BiasAdd) : f32 / [...] / [1,5]
[Step 7/11] Loading the model to the device
[ INFO ] Compile model took 67.57 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ] NETWORK_NAME: TensorFlow_Frontend_IR
[ INFO ] OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
[ INFO ] NUM_STREAMS: 12
[ INFO ] AFFINITY: Affinity.CORE
[ INFO ] INFERENCE_NUM_THREADS: 24
[ INFO ] PERF_COUNT: NO
[ INFO ] INFERENCE_PRECISION_HINT: <Type: 'float32'>
[ INFO ] PERFORMANCE_HINT: THROUGHPUT
[ INFO ] EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
[ INFO ] PERFORMANCE_HINT_NUM_REQUESTS: 0
[ INFO ] ENABLE_CPU_PINNING: True
[ INFO ] SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
[ INFO ] ENABLE_HYPER_THREADING: True
[ INFO ] EXECUTION_DEVICES: ['CPU']
[ INFO ] CPU_DENORMALS_OPTIMIZATION: False
[ INFO ] CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
[Step 9/11] Creating infer requests and preparing input tensors
[ WARNING ] No input files were given for input 'sequential_1_input'!. This input will be filled with random values!
[ INFO ] Fill input 'sequential_1_input' with random values
[Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
[ INFO ] First inference took 1.99 ms
[Step 11/11] Dumping statistics report
[ INFO ] Execution Devices:['CPU']
[ INFO ] Count: 178152 iterations
[ INFO ] Duration: 15001.85 ms
[ INFO ] Latency:
[ INFO ] Median: 0.94 ms
[ INFO ] Average: 0.98 ms
[ INFO ] Min: 0.55 ms
[ INFO ] Max: 11.77 ms
[ INFO ] Throughput: 11875.34 FPS
Benchmark on MULTI:CPU,GPU
With a recent Intel CPU, the best performance can often be achieved by
doing inference on both the CPU and the iGPU, with OpenVINO’s Multi
Device
Plugin.
It takes a bit longer to load a model on GPU than on CPU, so this
benchmark will take a bit longer to complete than the CPU benchmark,
when run for the first time. Benchmark App supports caching, by
specifying the --cdir parameter. In the cells below, the model will
cached to the model_cache directory.
# Original model - MULTI:CPU,GPU
if "GPU" in core.available_devices:
! benchmark_app -m $model_xml -d MULTI:CPU,GPU -t 15 -api async
else:
print("A supported integrated GPU is not available on this system.")
A supported integrated GPU is not available on this system.
# Quantized model - MULTI:CPU,GPU
if "GPU" in core.available_devices:
! benchmark_app -m $compressed_model_xml -d MULTI:CPU,GPU -t 15 -api async
else:
print("A supported integrated GPU is not available on this system.")
A supported integrated GPU is not available on this system.
# print the available devices on this system
print("Device information:")
print(core.get_property("CPU", "FULL_DEVICE_NAME"))
if "GPU" in core.available_devices:
print(core.get_property("GPU", "FULL_DEVICE_NAME"))
Device information:
Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz
Original IR model - CPU
benchmark_output = %sx benchmark_app -m $model_xml -t 15 -api async
# Remove logging info from benchmark_app output and show only the results
benchmark_result = benchmark_output[-8:]
print("\n".join(benchmark_result))
[ INFO ] Count: 57840 iterations
[ INFO ] Duration: 15004.24 ms
[ INFO ] Latency:
[ INFO ] Median: 2.94 ms
[ INFO ] Average: 2.94 ms
[ INFO ] Min: 1.98 ms
[ INFO ] Max: 12.12 ms
[ INFO ] Throughput: 3854.91 FPS
Quantized IR model - CPU
benchmark_output = %sx benchmark_app -m $compressed_model_xml -t 15 -api async
# Remove logging info from benchmark_app output and show only the results
benchmark_result = benchmark_output[-8:]
print("\n".join(benchmark_result))
[ INFO ] Count: 178836 iterations
[ INFO ] Duration: 15001.19 ms
[ INFO ] Latency:
[ INFO ] Median: 0.94 ms
[ INFO ] Average: 0.97 ms
[ INFO ] Min: 0.58 ms
[ INFO ] Max: 6.85 ms
[ INFO ] Throughput: 11921.45 FPS
Original IR model - MULTI:CPU,GPU
With a recent Intel CPU, the best performance can often be achieved by doing inference on both the CPU and the iGPU, with OpenVINO’s Multi Device Plugin. It takes a bit longer to load a model on GPU than on CPU, so this benchmark will take a bit longer to complete than the CPU benchmark.
if "GPU" in core.available_devices:
benchmark_output = %sx benchmark_app -m $model_xml -d MULTI:CPU,GPU -t 15 -api async
# Remove logging info from benchmark_app output and show only the results
benchmark_result = benchmark_output[-8:]
print("\n".join(benchmark_result))
else:
print("An GPU is not available on this system.")
An GPU is not available on this system.
Quantized IR model - MULTI:CPU,GPU
if "GPU" in core.available_devices:
benchmark_output = %sx benchmark_app -m $compressed_model_xml -d MULTI:CPU,GPU -t 15 -api async
# Remove logging info from benchmark_app output and show only the results
benchmark_result = benchmark_output[-8:]
print("\n".join(benchmark_result))
else:
print("An GPU is not available on this system.")
An GPU is not available on this system.