이 노트북에서 "매우 깊은" 합성곱 신경망 VGGNet과 비슷한 모델을 훈련하여 옥스포드 꽃 데이터셋을 17개 카테고리로 분류합니다.
import numpy as np
np.random.seed(42)
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.callbacks import TensorBoard
이미 oxflower17.npz
파일을 다운받았다고 가정합니다. 이 파일이 없다면 10-2.alexnet_in_keras.ipynb
노트북을 다시 실행하세요.
# 코랩을 사용할 경우 다음 셀을 실행하세요.
!rm oxflower17*
!wget https://bit.ly/36QytdH -O oxflower17.npz
rm: cannot remove 'oxflower17*': No such file or directory --2022-12-05 14:17:30-- https://bit.ly/36QytdH Resolving bit.ly (bit.ly)... 67.199.248.10, 67.199.248.11 Connecting to bit.ly (bit.ly)|67.199.248.10|:443... connected. HTTP request sent, awaiting response... 301 Moved Permanently Location: https://onedrive.live.com/download?cid=822579D69D2DC3B5&resid=822579D69D2DC3B5!597859&authkey=AGd0CpvKFkK8GtE [following] --2022-12-05 14:17:31-- https://onedrive.live.com/download?cid=822579D69D2DC3B5&resid=822579D69D2DC3B5!597859&authkey=AGd0CpvKFkK8GtE Resolving onedrive.live.com (onedrive.live.com)... 13.107.43.13 Connecting to onedrive.live.com (onedrive.live.com)|13.107.43.13|:443... connected. HTTP request sent, awaiting response... 302 Found Location: https://57ucia.bl.files.1drv.com/y4mwsCeYKx-CkygdaC_AgNBSRavTkON1bJosV1qsqIgK4FmXOSxDzwUZP_QKqFEyAwN-9Acv2_g1Zm_Xb0q2y1HHqWZFlp9TTuP6tNE2_ppV8POZDV0NrqrkzbZmj0N5Dv0VXZ7mjeI5md5zohP6UYjakliqfzMK9drrL3oU8wI6gRkgd-Ut7pP1f32l7DSAengF_MEScPyAhXMV_Wg56vFJw/oxflower17.npz?download&psid=1 [following] --2022-12-05 14:17:31-- https://57ucia.bl.files.1drv.com/y4mwsCeYKx-CkygdaC_AgNBSRavTkON1bJosV1qsqIgK4FmXOSxDzwUZP_QKqFEyAwN-9Acv2_g1Zm_Xb0q2y1HHqWZFlp9TTuP6tNE2_ppV8POZDV0NrqrkzbZmj0N5Dv0VXZ7mjeI5md5zohP6UYjakliqfzMK9drrL3oU8wI6gRkgd-Ut7pP1f32l7DSAengF_MEScPyAhXMV_Wg56vFJw/oxflower17.npz?download&psid=1 Resolving 57ucia.bl.files.1drv.com (57ucia.bl.files.1drv.com)... 13.107.43.12 Connecting to 57ucia.bl.files.1drv.com (57ucia.bl.files.1drv.com)|13.107.43.12|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 252415092 (241M) [application/zip] Saving to: ‘oxflower17.npz’ oxflower17.npz 100%[===================>] 240.72M 28.3MB/s in 12s 2022-12-05 14:17:44 (19.7 MB/s) - ‘oxflower17.npz’ saved [252415092/252415092]
ls -al
total 246516 drwxr-xr-x 1 root root 4096 Dec 5 14:17 ./ drwxr-xr-x 1 root root 4096 Dec 5 14:17 ../ drwxr-xr-x 4 root root 4096 Dec 1 20:07 .config/ -rw-r--r-- 1 root root 252415092 Feb 7 2021 oxflower17.npz drwxr-xr-x 1 root root 4096 Dec 1 20:08 sample_data/
import numpy as np
data = np.load('oxflower17.npz', allow_pickle=True)
X = data['X']
Y = data['Y']
model = Sequential()
model.add(Conv2D(64, 3, activation='relu', input_shape=(224, 224, 3)))
model.add(Conv2D(64, 3, activation='relu'))
model.add(MaxPooling2D(2, 2))
model.add(BatchNormalization())
model.add(Conv2D(128, 3, activation='relu'))
model.add(Conv2D(128, 3, activation='relu'))
model.add(MaxPooling2D(2, 2))
model.add(BatchNormalization())
model.add(Conv2D(256, 3, activation='relu'))
model.add(Conv2D(256, 3, activation='relu'))
model.add(Conv2D(256, 3, activation='relu'))
model.add(MaxPooling2D(2, 2))
model.add(BatchNormalization())
model.add(Conv2D(512, 3, activation='relu'))
model.add(Conv2D(512, 3, activation='relu'))
model.add(Conv2D(512, 3, activation='relu'))
model.add(MaxPooling2D(2, 2))
model.add(BatchNormalization())
model.add(Conv2D(512, 3, activation='relu'))
model.add(Conv2D(512, 3, activation='relu'))
model.add(Conv2D(512, 3, activation='relu'))
model.add(MaxPooling2D(2, 2))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(17, activation='softmax'))
model.summary()
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d (Conv2D) (None, 222, 222, 64) 1792 conv2d_1 (Conv2D) (None, 220, 220, 64) 36928 max_pooling2d (MaxPooling2D (None, 110, 110, 64) 0 ) batch_normalization (BatchN (None, 110, 110, 64) 256 ormalization) conv2d_2 (Conv2D) (None, 108, 108, 128) 73856 conv2d_3 (Conv2D) (None, 106, 106, 128) 147584 max_pooling2d_1 (MaxPooling (None, 53, 53, 128) 0 2D) batch_normalization_1 (Batc (None, 53, 53, 128) 512 hNormalization) conv2d_4 (Conv2D) (None, 51, 51, 256) 295168 conv2d_5 (Conv2D) (None, 49, 49, 256) 590080 conv2d_6 (Conv2D) (None, 47, 47, 256) 590080 max_pooling2d_2 (MaxPooling (None, 23, 23, 256) 0 2D) batch_normalization_2 (Batc (None, 23, 23, 256) 1024 hNormalization) conv2d_7 (Conv2D) (None, 21, 21, 512) 1180160 conv2d_8 (Conv2D) (None, 19, 19, 512) 2359808 conv2d_9 (Conv2D) (None, 17, 17, 512) 2359808 max_pooling2d_3 (MaxPooling (None, 8, 8, 512) 0 2D) batch_normalization_3 (Batc (None, 8, 8, 512) 2048 hNormalization) conv2d_10 (Conv2D) (None, 6, 6, 512) 2359808 conv2d_11 (Conv2D) (None, 4, 4, 512) 2359808 conv2d_12 (Conv2D) (None, 2, 2, 512) 2359808 max_pooling2d_4 (MaxPooling (None, 1, 1, 512) 0 2D) batch_normalization_4 (Batc (None, 1, 1, 512) 2048 hNormalization) flatten (Flatten) (None, 512) 0 dense (Dense) (None, 4096) 2101248 dropout (Dropout) (None, 4096) 0 dense_1 (Dense) (None, 4096) 16781312 dropout_1 (Dropout) (None, 4096) 0 dense_2 (Dense) (None, 17) 69649 ================================================================= Total params: 33,672,785 Trainable params: 33,669,841 Non-trainable params: 2,944 _________________________________________________________________
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
tensorbrd = TensorBoard('logs/vggnet')
model.fit(X, Y, batch_size=64, epochs=250, verbose=1, validation_split=0.1, shuffle=True, callbacks=[tensorbrd])
Epoch 1/250 20/20 [==============================] - 60s 2s/step - loss: 3.2662 - accuracy: 0.1283 - val_loss: 10.0563 - val_accuracy: 0.0441 Epoch 2/250 20/20 [==============================] - 14s 713ms/step - loss: 2.8892 - accuracy: 0.1961 - val_loss: 8.2696 - val_accuracy: 0.0368 Epoch 3/250 20/20 [==============================] - 15s 730ms/step - loss: 2.7490 - accuracy: 0.2435 - val_loss: 5.8112 - val_accuracy: 0.0809 Epoch 4/250 20/20 [==============================] - 15s 737ms/step - loss: 2.9122 - accuracy: 0.1520 - val_loss: 3.4217 - val_accuracy: 0.1765 Epoch 5/250 20/20 [==============================] - 15s 750ms/step - loss: 2.2502 - accuracy: 0.2394 - val_loss: 3.7264 - val_accuracy: 0.1176 Epoch 6/250 20/20 [==============================] - 15s 743ms/step - loss: 2.1105 - accuracy: 0.2606 - val_loss: 3.6896 - val_accuracy: 0.1324 Epoch 7/250 20/20 [==============================] - 15s 735ms/step - loss: 1.9533 - accuracy: 0.3039 - val_loss: 2.3240 - val_accuracy: 0.2132 Epoch 8/250 20/20 [==============================] - 15s 729ms/step - loss: 1.9497 - accuracy: 0.3154 - val_loss: 4.7434 - val_accuracy: 0.1029 Epoch 9/250 20/20 [==============================] - 15s 730ms/step - loss: 1.8397 - accuracy: 0.3317 - val_loss: 3.1084 - val_accuracy: 0.2132 Epoch 10/250 20/20 [==============================] - 15s 731ms/step - loss: 1.8157 - accuracy: 0.3268 - val_loss: 2.4423 - val_accuracy: 0.2059 Epoch 11/250 20/20 [==============================] - 15s 730ms/step - loss: 1.7919 - accuracy: 0.3464 - val_loss: 2.0414 - val_accuracy: 0.3015 Epoch 12/250 20/20 [==============================] - 15s 728ms/step - loss: 1.6605 - accuracy: 0.4093 - val_loss: 2.9098 - val_accuracy: 0.2206 Epoch 13/250 20/20 [==============================] - 15s 727ms/step - loss: 1.7663 - accuracy: 0.3668 - val_loss: 2.9539 - val_accuracy: 0.2279 Epoch 14/250 20/20 [==============================] - 15s 728ms/step - loss: 1.7100 - accuracy: 0.3750 - val_loss: 2.3156 - val_accuracy: 0.2574 Epoch 15/250 20/20 [==============================] - 15s 729ms/step - loss: 1.7604 - accuracy: 0.3685 - val_loss: 2.5862 - val_accuracy: 0.2721 Epoch 16/250 20/20 [==============================] - 15s 729ms/step - loss: 1.6886 - accuracy: 0.3807 - val_loss: 2.8375 - val_accuracy: 0.1985 Epoch 17/250 20/20 [==============================] - 15s 738ms/step - loss: 1.6031 - accuracy: 0.4126 - val_loss: 2.4606 - val_accuracy: 0.3309 Epoch 18/250 20/20 [==============================] - 15s 731ms/step - loss: 1.5542 - accuracy: 0.4216 - val_loss: 2.3299 - val_accuracy: 0.3162 Epoch 19/250 20/20 [==============================] - 15s 739ms/step - loss: 1.5380 - accuracy: 0.4338 - val_loss: 6.1641 - val_accuracy: 0.0956 Epoch 20/250 20/20 [==============================] - 15s 729ms/step - loss: 1.6000 - accuracy: 0.4289 - val_loss: 1.9594 - val_accuracy: 0.4044 Epoch 21/250 20/20 [==============================] - 15s 729ms/step - loss: 1.5976 - accuracy: 0.4150 - val_loss: 2.1397 - val_accuracy: 0.3897 Epoch 22/250 20/20 [==============================] - 15s 738ms/step - loss: 1.5957 - accuracy: 0.4150 - val_loss: 2.5208 - val_accuracy: 0.3750 Epoch 23/250 20/20 [==============================] - 15s 738ms/step - loss: 1.4529 - accuracy: 0.4592 - val_loss: 2.0267 - val_accuracy: 0.3897 Epoch 24/250 20/20 [==============================] - 15s 728ms/step - loss: 1.3847 - accuracy: 0.4943 - val_loss: 2.7812 - val_accuracy: 0.3529 Epoch 25/250 20/20 [==============================] - 15s 735ms/step - loss: 1.3626 - accuracy: 0.5139 - val_loss: 2.0514 - val_accuracy: 0.4265 Epoch 26/250 20/20 [==============================] - 15s 738ms/step - loss: 1.3875 - accuracy: 0.4894 - val_loss: 5.7763 - val_accuracy: 0.1765 Epoch 27/250 20/20 [==============================] - 15s 737ms/step - loss: 1.4368 - accuracy: 0.4820 - val_loss: 2.3196 - val_accuracy: 0.3529 Epoch 28/250 20/20 [==============================] - 15s 737ms/step - loss: 1.5101 - accuracy: 0.4698 - val_loss: 7.4431 - val_accuracy: 0.1471 Epoch 29/250 20/20 [==============================] - 15s 729ms/step - loss: 1.3423 - accuracy: 0.5172 - val_loss: 6.5513 - val_accuracy: 0.2794 Epoch 30/250 20/20 [==============================] - 15s 737ms/step - loss: 1.2838 - accuracy: 0.5523 - val_loss: 2.8749 - val_accuracy: 0.3162 Epoch 31/250 20/20 [==============================] - 15s 736ms/step - loss: 1.4849 - accuracy: 0.4502 - val_loss: 3.3545 - val_accuracy: 0.2500 Epoch 32/250 20/20 [==============================] - 15s 737ms/step - loss: 1.4781 - accuracy: 0.4730 - val_loss: 5.2195 - val_accuracy: 0.2132 Epoch 33/250 20/20 [==============================] - 15s 730ms/step - loss: 1.2084 - accuracy: 0.5629 - val_loss: 2.1107 - val_accuracy: 0.3971 Epoch 34/250 20/20 [==============================] - 15s 729ms/step - loss: 1.2114 - accuracy: 0.5711 - val_loss: 5.1488 - val_accuracy: 0.1765 Epoch 35/250 20/20 [==============================] - 15s 729ms/step - loss: 1.3754 - accuracy: 0.5253 - val_loss: 4.6591 - val_accuracy: 0.3015 Epoch 36/250 20/20 [==============================] - 15s 736ms/step - loss: 1.2198 - accuracy: 0.5605 - val_loss: 2.7805 - val_accuracy: 0.3824 Epoch 37/250 20/20 [==============================] - 15s 737ms/step - loss: 1.2980 - accuracy: 0.5580 - val_loss: 3.3633 - val_accuracy: 0.3603 Epoch 38/250 20/20 [==============================] - 15s 736ms/step - loss: 1.1182 - accuracy: 0.6070 - val_loss: 4.4305 - val_accuracy: 0.3309 Epoch 39/250 20/20 [==============================] - 15s 738ms/step - loss: 1.2001 - accuracy: 0.5850 - val_loss: 3.3975 - val_accuracy: 0.3529 Epoch 40/250 20/20 [==============================] - 15s 735ms/step - loss: 1.1188 - accuracy: 0.6062 - val_loss: 1.7927 - val_accuracy: 0.5368 Epoch 41/250 20/20 [==============================] - 15s 727ms/step - loss: 1.0248 - accuracy: 0.6389 - val_loss: 3.1558 - val_accuracy: 0.4118 Epoch 42/250 20/20 [==============================] - 15s 728ms/step - loss: 1.0201 - accuracy: 0.6626 - val_loss: 1.8570 - val_accuracy: 0.5515 Epoch 43/250 20/20 [==============================] - 15s 730ms/step - loss: 1.0910 - accuracy: 0.6340 - val_loss: 3.2766 - val_accuracy: 0.4044 Epoch 44/250 20/20 [==============================] - 15s 738ms/step - loss: 0.8742 - accuracy: 0.6895 - val_loss: 2.0596 - val_accuracy: 0.4706 Epoch 45/250 20/20 [==============================] - 15s 726ms/step - loss: 0.9692 - accuracy: 0.6601 - val_loss: 3.2208 - val_accuracy: 0.4265 Epoch 46/250 20/20 [==============================] - 15s 728ms/step - loss: 0.8393 - accuracy: 0.7018 - val_loss: 2.1105 - val_accuracy: 0.4853 Epoch 47/250 20/20 [==============================] - 15s 730ms/step - loss: 0.8139 - accuracy: 0.7206 - val_loss: 4.6318 - val_accuracy: 0.3456 Epoch 48/250 20/20 [==============================] - 15s 735ms/step - loss: 0.7749 - accuracy: 0.7206 - val_loss: 3.4388 - val_accuracy: 0.4118 Epoch 49/250 20/20 [==============================] - 15s 738ms/step - loss: 0.7540 - accuracy: 0.7467 - val_loss: 2.5389 - val_accuracy: 0.4118 Epoch 50/250 20/20 [==============================] - 15s 731ms/step - loss: 0.7877 - accuracy: 0.7377 - val_loss: 3.2288 - val_accuracy: 0.4412 Epoch 51/250 20/20 [==============================] - 15s 738ms/step - loss: 0.7410 - accuracy: 0.7377 - val_loss: 2.1626 - val_accuracy: 0.5294 Epoch 52/250 20/20 [==============================] - 15s 739ms/step - loss: 0.6719 - accuracy: 0.7688 - val_loss: 1.9679 - val_accuracy: 0.5368 Epoch 53/250 20/20 [==============================] - 15s 738ms/step - loss: 0.7928 - accuracy: 0.7198 - val_loss: 2.4499 - val_accuracy: 0.5588 Epoch 54/250 20/20 [==============================] - 15s 738ms/step - loss: 0.8884 - accuracy: 0.6977 - val_loss: 1.6234 - val_accuracy: 0.5882 Epoch 55/250 20/20 [==============================] - 15s 730ms/step - loss: 0.6612 - accuracy: 0.7802 - val_loss: 2.7018 - val_accuracy: 0.5147 Epoch 56/250 20/20 [==============================] - 15s 729ms/step - loss: 0.5996 - accuracy: 0.7998 - val_loss: 2.3317 - val_accuracy: 0.5294 Epoch 57/250 20/20 [==============================] - 15s 728ms/step - loss: 0.8003 - accuracy: 0.7337 - val_loss: 3.2246 - val_accuracy: 0.4485 Epoch 58/250 20/20 [==============================] - 15s 728ms/step - loss: 0.7348 - accuracy: 0.7402 - val_loss: 2.1631 - val_accuracy: 0.5735 Epoch 59/250 20/20 [==============================] - 15s 728ms/step - loss: 0.6789 - accuracy: 0.7688 - val_loss: 2.4024 - val_accuracy: 0.5368 Epoch 60/250 20/20 [==============================] - 15s 731ms/step - loss: 0.5542 - accuracy: 0.8178 - val_loss: 3.2382 - val_accuracy: 0.5441 Epoch 61/250 20/20 [==============================] - 15s 730ms/step - loss: 0.6183 - accuracy: 0.8031 - val_loss: 3.0212 - val_accuracy: 0.4779 Epoch 62/250 20/20 [==============================] - 15s 729ms/step - loss: 0.5349 - accuracy: 0.8284 - val_loss: 2.9785 - val_accuracy: 0.5662 Epoch 63/250 20/20 [==============================] - 15s 736ms/step - loss: 0.8073 - accuracy: 0.7426 - val_loss: 4.6083 - val_accuracy: 0.3235 Epoch 64/250 20/20 [==============================] - 15s 737ms/step - loss: 0.6623 - accuracy: 0.7876 - val_loss: 3.1860 - val_accuracy: 0.4706 Epoch 65/250 20/20 [==============================] - 15s 736ms/step - loss: 0.9305 - accuracy: 0.7312 - val_loss: 27.2112 - val_accuracy: 0.0662 Epoch 66/250 20/20 [==============================] - 15s 737ms/step - loss: 1.5177 - accuracy: 0.5057 - val_loss: 39.9631 - val_accuracy: 0.1618 Epoch 67/250 20/20 [==============================] - 15s 734ms/step - loss: 1.0039 - accuracy: 0.6797 - val_loss: 7.9617 - val_accuracy: 0.2574 Epoch 68/250 20/20 [==============================] - 15s 736ms/step - loss: 0.7978 - accuracy: 0.7328 - val_loss: 5.4666 - val_accuracy: 0.2941 Epoch 69/250 20/20 [==============================] - 15s 736ms/step - loss: 0.7404 - accuracy: 0.7459 - val_loss: 5.2302 - val_accuracy: 0.3603 Epoch 70/250 20/20 [==============================] - 15s 729ms/step - loss: 0.6279 - accuracy: 0.7851 - val_loss: 2.3682 - val_accuracy: 0.5368 Epoch 71/250 20/20 [==============================] - 15s 727ms/step - loss: 0.5185 - accuracy: 0.8317 - val_loss: 2.6149 - val_accuracy: 0.5147 Epoch 72/250 20/20 [==============================] - 15s 736ms/step - loss: 0.5704 - accuracy: 0.8137 - val_loss: 2.2288 - val_accuracy: 0.5294 Epoch 73/250 20/20 [==============================] - 15s 728ms/step - loss: 0.5701 - accuracy: 0.8219 - val_loss: 3.3443 - val_accuracy: 0.4853 Epoch 74/250 20/20 [==============================] - 15s 736ms/step - loss: 0.4282 - accuracy: 0.8595 - val_loss: 2.7132 - val_accuracy: 0.5074 Epoch 75/250 20/20 [==============================] - 15s 738ms/step - loss: 0.5848 - accuracy: 0.8301 - val_loss: 2.4077 - val_accuracy: 0.5441 Epoch 76/250 20/20 [==============================] - 15s 738ms/step - loss: 0.6240 - accuracy: 0.7925 - val_loss: 3.4086 - val_accuracy: 0.4853 Epoch 77/250 20/20 [==============================] - 15s 739ms/step - loss: 0.5727 - accuracy: 0.8227 - val_loss: 3.5711 - val_accuracy: 0.4779 Epoch 78/250 20/20 [==============================] - 15s 728ms/step - loss: 0.5671 - accuracy: 0.8219 - val_loss: 2.9115 - val_accuracy: 0.4485 Epoch 79/250 20/20 [==============================] - 15s 728ms/step - loss: 0.4861 - accuracy: 0.8513 - val_loss: 2.4437 - val_accuracy: 0.5662 Epoch 80/250 20/20 [==============================] - 15s 736ms/step - loss: 0.4361 - accuracy: 0.8627 - val_loss: 2.0699 - val_accuracy: 0.6176 Epoch 81/250 20/20 [==============================] - 15s 727ms/step - loss: 0.4324 - accuracy: 0.8546 - val_loss: 3.3301 - val_accuracy: 0.5074 Epoch 82/250 20/20 [==============================] - 15s 736ms/step - loss: 0.4392 - accuracy: 0.8636 - val_loss: 3.3029 - val_accuracy: 0.5147 Epoch 83/250 20/20 [==============================] - 15s 738ms/step - loss: 0.3867 - accuracy: 0.8709 - val_loss: 2.2068 - val_accuracy: 0.6397 Epoch 84/250 20/20 [==============================] - 15s 730ms/step - loss: 0.5964 - accuracy: 0.8186 - val_loss: 2.6369 - val_accuracy: 0.3897 Epoch 85/250 20/20 [==============================] - 15s 738ms/step - loss: 0.5212 - accuracy: 0.8309 - val_loss: 3.0138 - val_accuracy: 0.5294 Epoch 86/250 20/20 [==============================] - 15s 736ms/step - loss: 0.3761 - accuracy: 0.8742 - val_loss: 2.4456 - val_accuracy: 0.6029 Epoch 87/250 20/20 [==============================] - 15s 738ms/step - loss: 0.4721 - accuracy: 0.8538 - val_loss: 2.7435 - val_accuracy: 0.5074 Epoch 88/250 20/20 [==============================] - 15s 730ms/step - loss: 0.3834 - accuracy: 0.8897 - val_loss: 2.5227 - val_accuracy: 0.6029 Epoch 89/250 20/20 [==============================] - 15s 736ms/step - loss: 0.3329 - accuracy: 0.8946 - val_loss: 1.9727 - val_accuracy: 0.6397 Epoch 90/250 20/20 [==============================] - 15s 738ms/step - loss: 0.3188 - accuracy: 0.9011 - val_loss: 2.2982 - val_accuracy: 0.6029 Epoch 91/250 20/20 [==============================] - 15s 737ms/step - loss: 0.3258 - accuracy: 0.8979 - val_loss: 2.8195 - val_accuracy: 0.5368 Epoch 92/250 20/20 [==============================] - 15s 737ms/step - loss: 0.2151 - accuracy: 0.9306 - val_loss: 2.5224 - val_accuracy: 0.5588 Epoch 93/250 20/20 [==============================] - 15s 728ms/step - loss: 0.4403 - accuracy: 0.8668 - val_loss: 1.6825 - val_accuracy: 0.5588 Epoch 94/250 20/20 [==============================] - 15s 737ms/step - loss: 0.3292 - accuracy: 0.8962 - val_loss: 2.1945 - val_accuracy: 0.6029 Epoch 95/250 20/20 [==============================] - 15s 730ms/step - loss: 0.2402 - accuracy: 0.9322 - val_loss: 2.7774 - val_accuracy: 0.5809 Epoch 96/250 20/20 [==============================] - 15s 729ms/step - loss: 0.3277 - accuracy: 0.8971 - val_loss: 2.0833 - val_accuracy: 0.5956 Epoch 97/250 20/20 [==============================] - 15s 730ms/step - loss: 0.3645 - accuracy: 0.8938 - val_loss: 3.4449 - val_accuracy: 0.5956 Epoch 98/250 20/20 [==============================] - 15s 737ms/step - loss: 0.2481 - accuracy: 0.9158 - val_loss: 4.1259 - val_accuracy: 0.5368 Epoch 99/250 20/20 [==============================] - 15s 729ms/step - loss: 0.3086 - accuracy: 0.9183 - val_loss: 3.4797 - val_accuracy: 0.5956 Epoch 100/250 20/20 [==============================] - 15s 737ms/step - loss: 0.3048 - accuracy: 0.9150 - val_loss: 2.7096 - val_accuracy: 0.6103 Epoch 101/250 20/20 [==============================] - 15s 740ms/step - loss: 0.2251 - accuracy: 0.9248 - val_loss: 2.2701 - val_accuracy: 0.6103 Epoch 102/250 20/20 [==============================] - 15s 738ms/step - loss: 0.2167 - accuracy: 0.9346 - val_loss: 2.5304 - val_accuracy: 0.6176 Epoch 103/250 20/20 [==============================] - 15s 739ms/step - loss: 0.1994 - accuracy: 0.9461 - val_loss: 2.4895 - val_accuracy: 0.6250 Epoch 104/250 20/20 [==============================] - 15s 730ms/step - loss: 0.2938 - accuracy: 0.9134 - val_loss: 3.7247 - val_accuracy: 0.4559 Epoch 105/250 20/20 [==============================] - 15s 730ms/step - loss: 0.2316 - accuracy: 0.9322 - val_loss: 3.4561 - val_accuracy: 0.5662 Epoch 106/250 20/20 [==============================] - 15s 731ms/step - loss: 0.3199 - accuracy: 0.9093 - val_loss: 2.5353 - val_accuracy: 0.6250 Epoch 107/250 20/20 [==============================] - 15s 738ms/step - loss: 0.6820 - accuracy: 0.8007 - val_loss: 2.6267 - val_accuracy: 0.5368 Epoch 108/250 20/20 [==============================] - 15s 731ms/step - loss: 0.4088 - accuracy: 0.8775 - val_loss: 3.6561 - val_accuracy: 0.5882 Epoch 109/250 20/20 [==============================] - 15s 729ms/step - loss: 0.3424 - accuracy: 0.8971 - val_loss: 3.3292 - val_accuracy: 0.6029 Epoch 110/250 20/20 [==============================] - 15s 728ms/step - loss: 0.2149 - accuracy: 0.9281 - val_loss: 2.7010 - val_accuracy: 0.5882 Epoch 111/250 20/20 [==============================] - 15s 737ms/step - loss: 0.3383 - accuracy: 0.9199 - val_loss: 2.6733 - val_accuracy: 0.6397 Epoch 112/250 20/20 [==============================] - 15s 738ms/step - loss: 0.2748 - accuracy: 0.9134 - val_loss: 2.6641 - val_accuracy: 0.6250 Epoch 113/250 20/20 [==============================] - 15s 728ms/step - loss: 0.3857 - accuracy: 0.8930 - val_loss: 5.0618 - val_accuracy: 0.4412 Epoch 114/250 20/20 [==============================] - 15s 729ms/step - loss: 0.1929 - accuracy: 0.9355 - val_loss: 2.6654 - val_accuracy: 0.6838 Epoch 115/250 20/20 [==============================] - 15s 739ms/step - loss: 0.1124 - accuracy: 0.9641 - val_loss: 2.5195 - val_accuracy: 0.6250 Epoch 116/250 20/20 [==============================] - 15s 730ms/step - loss: 0.2161 - accuracy: 0.9412 - val_loss: 3.1727 - val_accuracy: 0.5515 Epoch 117/250 20/20 [==============================] - 15s 729ms/step - loss: 0.5466 - accuracy: 0.8423 - val_loss: 12.7212 - val_accuracy: 0.3824 Epoch 118/250 20/20 [==============================] - 15s 738ms/step - loss: 0.4371 - accuracy: 0.8717 - val_loss: 4.8675 - val_accuracy: 0.4706 Epoch 119/250 20/20 [==============================] - 15s 738ms/step - loss: 0.2553 - accuracy: 0.9297 - val_loss: 5.2941 - val_accuracy: 0.5074 Epoch 120/250 20/20 [==============================] - 15s 729ms/step - loss: 0.5379 - accuracy: 0.8652 - val_loss: 3.9211 - val_accuracy: 0.5000 Epoch 121/250 20/20 [==============================] - 15s 736ms/step - loss: 0.6321 - accuracy: 0.8219 - val_loss: 3.7585 - val_accuracy: 0.4338 Epoch 122/250 20/20 [==============================] - 15s 729ms/step - loss: 0.5189 - accuracy: 0.8489 - val_loss: 3.3254 - val_accuracy: 0.5294 Epoch 123/250 20/20 [==============================] - 15s 728ms/step - loss: 0.3651 - accuracy: 0.8856 - val_loss: 3.4844 - val_accuracy: 0.5515 Epoch 124/250 20/20 [==============================] - 15s 729ms/step - loss: 0.2544 - accuracy: 0.9158 - val_loss: 2.5919 - val_accuracy: 0.6176 Epoch 125/250 20/20 [==============================] - 15s 730ms/step - loss: 0.2967 - accuracy: 0.9183 - val_loss: 2.8812 - val_accuracy: 0.5735 Epoch 126/250 20/20 [==============================] - 15s 737ms/step - loss: 0.1552 - accuracy: 0.9493 - val_loss: 2.5907 - val_accuracy: 0.5809 Epoch 127/250 20/20 [==============================] - 15s 729ms/step - loss: 0.2166 - accuracy: 0.9363 - val_loss: 2.6084 - val_accuracy: 0.6618 Epoch 128/250 20/20 [==============================] - 15s 728ms/step - loss: 0.2143 - accuracy: 0.9371 - val_loss: 2.8957 - val_accuracy: 0.6103 Epoch 129/250 20/20 [==============================] - 15s 737ms/step - loss: 0.1722 - accuracy: 0.9477 - val_loss: 2.5440 - val_accuracy: 0.6691 Epoch 130/250 20/20 [==============================] - 15s 736ms/step - loss: 0.2072 - accuracy: 0.9453 - val_loss: 2.4369 - val_accuracy: 0.5588 Epoch 131/250 20/20 [==============================] - 15s 738ms/step - loss: 0.2161 - accuracy: 0.9322 - val_loss: 2.3118 - val_accuracy: 0.6029 Epoch 132/250 20/20 [==============================] - 15s 735ms/step - loss: 0.3386 - accuracy: 0.9028 - val_loss: 2.2970 - val_accuracy: 0.6176 Epoch 133/250 20/20 [==============================] - 15s 727ms/step - loss: 0.2233 - accuracy: 0.9314 - val_loss: 4.4512 - val_accuracy: 0.5147 Epoch 134/250 20/20 [==============================] - 15s 727ms/step - loss: 0.2226 - accuracy: 0.9322 - val_loss: 3.4755 - val_accuracy: 0.6103 Epoch 135/250 20/20 [==============================] - 15s 728ms/step - loss: 0.2066 - accuracy: 0.9404 - val_loss: 4.4259 - val_accuracy: 0.5735 Epoch 136/250 20/20 [==============================] - 15s 736ms/step - loss: 0.2263 - accuracy: 0.9248 - val_loss: 2.9096 - val_accuracy: 0.6471 Epoch 137/250 20/20 [==============================] - 15s 727ms/step - loss: 0.1498 - accuracy: 0.9542 - val_loss: 6.1692 - val_accuracy: 0.5809 Epoch 138/250 20/20 [==============================] - 15s 727ms/step - loss: 0.1396 - accuracy: 0.9714 - val_loss: 2.9781 - val_accuracy: 0.6691 Epoch 139/250 20/20 [==============================] - 15s 735ms/step - loss: 0.1784 - accuracy: 0.9534 - val_loss: 2.5399 - val_accuracy: 0.6691 Epoch 140/250 20/20 [==============================] - 15s 729ms/step - loss: 0.2872 - accuracy: 0.9134 - val_loss: 2.7547 - val_accuracy: 0.6250 Epoch 141/250 20/20 [==============================] - 15s 737ms/step - loss: 0.2601 - accuracy: 0.9322 - val_loss: 2.6683 - val_accuracy: 0.6618 Epoch 142/250 20/20 [==============================] - 15s 728ms/step - loss: 0.2106 - accuracy: 0.9371 - val_loss: 2.9890 - val_accuracy: 0.5882 Epoch 143/250 20/20 [==============================] - 15s 729ms/step - loss: 0.1632 - accuracy: 0.9526 - val_loss: 2.4284 - val_accuracy: 0.6765 Epoch 144/250 20/20 [==============================] - 15s 728ms/step - loss: 0.1242 - accuracy: 0.9706 - val_loss: 2.7937 - val_accuracy: 0.6176 Epoch 145/250 20/20 [==============================] - 15s 729ms/step - loss: 0.1971 - accuracy: 0.9444 - val_loss: 4.1914 - val_accuracy: 0.5882 Epoch 146/250 20/20 [==============================] - 15s 729ms/step - loss: 0.1343 - accuracy: 0.9665 - val_loss: 3.4227 - val_accuracy: 0.6471 Epoch 147/250 20/20 [==============================] - 15s 727ms/step - loss: 0.2261 - accuracy: 0.9485 - val_loss: 3.5983 - val_accuracy: 0.5809 Epoch 148/250 20/20 [==============================] - 15s 737ms/step - loss: 0.4567 - accuracy: 0.8824 - val_loss: 3.1307 - val_accuracy: 0.5956 Epoch 149/250 20/20 [==============================] - 15s 730ms/step - loss: 0.2179 - accuracy: 0.9363 - val_loss: 2.6638 - val_accuracy: 0.6544 Epoch 150/250 20/20 [==============================] - 15s 729ms/step - loss: 0.4942 - accuracy: 0.8725 - val_loss: 3.1587 - val_accuracy: 0.4779 Epoch 151/250 20/20 [==============================] - 15s 728ms/step - loss: 0.4567 - accuracy: 0.8627 - val_loss: 2.6603 - val_accuracy: 0.5662 Epoch 152/250 20/20 [==============================] - 15s 736ms/step - loss: 1.1382 - accuracy: 0.6863 - val_loss: 4.3152 - val_accuracy: 0.4044 Epoch 153/250 20/20 [==============================] - 15s 731ms/step - loss: 0.4697 - accuracy: 0.8489 - val_loss: 4.2249 - val_accuracy: 0.4559 Epoch 154/250 20/20 [==============================] - 15s 729ms/step - loss: 0.2749 - accuracy: 0.9126 - val_loss: 5.0660 - val_accuracy: 0.4412 Epoch 155/250 20/20 [==============================] - 15s 727ms/step - loss: 0.2565 - accuracy: 0.9232 - val_loss: 3.9567 - val_accuracy: 0.4559 Epoch 156/250 20/20 [==============================] - 15s 735ms/step - loss: 0.4221 - accuracy: 0.8832 - val_loss: 3.9464 - val_accuracy: 0.4632 Epoch 157/250 20/20 [==============================] - 15s 737ms/step - loss: 0.2936 - accuracy: 0.9167 - val_loss: 2.9204 - val_accuracy: 0.5956 Epoch 158/250 20/20 [==============================] - 15s 730ms/step - loss: 0.1349 - accuracy: 0.9583 - val_loss: 3.0951 - val_accuracy: 0.5735 Epoch 159/250 20/20 [==============================] - 15s 729ms/step - loss: 0.2387 - accuracy: 0.9338 - val_loss: 2.6730 - val_accuracy: 0.5588 Epoch 160/250 20/20 [==============================] - 15s 735ms/step - loss: 0.1126 - accuracy: 0.9632 - val_loss: 2.5732 - val_accuracy: 0.6471 Epoch 161/250 20/20 [==============================] - 15s 737ms/step - loss: 0.0850 - accuracy: 0.9714 - val_loss: 2.9416 - val_accuracy: 0.6176 Epoch 162/250 20/20 [==============================] - 15s 729ms/step - loss: 0.0938 - accuracy: 0.9690 - val_loss: 2.7792 - val_accuracy: 0.6324 Epoch 163/250 20/20 [==============================] - 15s 728ms/step - loss: 0.0877 - accuracy: 0.9722 - val_loss: 2.3340 - val_accuracy: 0.6324 Epoch 164/250 20/20 [==============================] - 15s 728ms/step - loss: 0.5006 - accuracy: 0.8611 - val_loss: 6.7806 - val_accuracy: 0.3603 Epoch 165/250 20/20 [==============================] - 15s 727ms/step - loss: 1.2129 - accuracy: 0.6846 - val_loss: 44.4650 - val_accuracy: 0.1544 Epoch 166/250 20/20 [==============================] - 15s 727ms/step - loss: 1.2097 - accuracy: 0.6340 - val_loss: 22.3854 - val_accuracy: 0.2132 Epoch 167/250 20/20 [==============================] - 15s 737ms/step - loss: 0.8357 - accuracy: 0.7337 - val_loss: 9.3122 - val_accuracy: 0.3162 Epoch 168/250 20/20 [==============================] - 15s 728ms/step - loss: 0.9829 - accuracy: 0.6944 - val_loss: 4.6894 - val_accuracy: 0.3382 Epoch 169/250 20/20 [==============================] - 15s 735ms/step - loss: 0.5872 - accuracy: 0.8007 - val_loss: 2.5263 - val_accuracy: 0.5074 Epoch 170/250 20/20 [==============================] - 15s 729ms/step - loss: 0.4276 - accuracy: 0.8521 - val_loss: 2.9490 - val_accuracy: 0.4559 Epoch 171/250 20/20 [==============================] - 15s 737ms/step - loss: 0.3349 - accuracy: 0.8807 - val_loss: 2.2318 - val_accuracy: 0.6029 Epoch 172/250 20/20 [==============================] - 15s 728ms/step - loss: 0.3334 - accuracy: 0.8938 - val_loss: 2.3285 - val_accuracy: 0.5441 Epoch 173/250 20/20 [==============================] - 15s 728ms/step - loss: 0.3521 - accuracy: 0.8938 - val_loss: 2.4853 - val_accuracy: 0.5368 Epoch 174/250 20/20 [==============================] - 15s 737ms/step - loss: 0.3810 - accuracy: 0.8791 - val_loss: 2.6499 - val_accuracy: 0.5441 Epoch 175/250 20/20 [==============================] - 15s 739ms/step - loss: 0.4385 - accuracy: 0.8807 - val_loss: 2.8500 - val_accuracy: 0.5074 Epoch 176/250 20/20 [==============================] - 15s 729ms/step - loss: 0.2017 - accuracy: 0.9395 - val_loss: 2.7507 - val_accuracy: 0.5735 Epoch 177/250 20/20 [==============================] - 15s 728ms/step - loss: 0.2694 - accuracy: 0.9306 - val_loss: 3.3662 - val_accuracy: 0.5221 Epoch 178/250 20/20 [==============================] - 15s 737ms/step - loss: 0.2765 - accuracy: 0.9199 - val_loss: 2.5776 - val_accuracy: 0.5588 Epoch 179/250 20/20 [==============================] - 15s 727ms/step - loss: 0.1781 - accuracy: 0.9485 - val_loss: 2.0949 - val_accuracy: 0.6103 Epoch 180/250 20/20 [==============================] - 15s 728ms/step - loss: 0.1503 - accuracy: 0.9526 - val_loss: 2.5389 - val_accuracy: 0.5809 Epoch 181/250 20/20 [==============================] - 15s 729ms/step - loss: 0.1195 - accuracy: 0.9624 - val_loss: 2.4658 - val_accuracy: 0.5809 Epoch 182/250 20/20 [==============================] - 15s 728ms/step - loss: 0.2646 - accuracy: 0.9199 - val_loss: 2.5406 - val_accuracy: 0.5662 Epoch 183/250 20/20 [==============================] - 15s 739ms/step - loss: 0.3142 - accuracy: 0.9101 - val_loss: 2.2599 - val_accuracy: 0.6176 Epoch 184/250 20/20 [==============================] - 15s 736ms/step - loss: 0.3990 - accuracy: 0.8913 - val_loss: 3.7274 - val_accuracy: 0.4706 Epoch 185/250 20/20 [==============================] - 15s 736ms/step - loss: 0.2269 - accuracy: 0.9379 - val_loss: 3.0041 - val_accuracy: 0.5956 Epoch 186/250 20/20 [==============================] - 15s 727ms/step - loss: 0.1512 - accuracy: 0.9542 - val_loss: 2.4126 - val_accuracy: 0.6250 Epoch 187/250 20/20 [==============================] - 15s 738ms/step - loss: 0.1259 - accuracy: 0.9616 - val_loss: 3.3927 - val_accuracy: 0.5441 Epoch 188/250 20/20 [==============================] - 15s 738ms/step - loss: 1.0651 - accuracy: 0.7402 - val_loss: 7.3412 - val_accuracy: 0.2132 Epoch 189/250 20/20 [==============================] - 15s 736ms/step - loss: 0.6672 - accuracy: 0.7982 - val_loss: 2.1885 - val_accuracy: 0.5074 Epoch 190/250 20/20 [==============================] - 15s 736ms/step - loss: 0.3971 - accuracy: 0.8913 - val_loss: 2.3195 - val_accuracy: 0.5735 Epoch 191/250 20/20 [==============================] - 15s 729ms/step - loss: 0.3038 - accuracy: 0.9109 - val_loss: 2.2835 - val_accuracy: 0.5882 Epoch 192/250 20/20 [==============================] - 15s 729ms/step - loss: 0.2168 - accuracy: 0.9314 - val_loss: 2.2191 - val_accuracy: 0.6176 Epoch 193/250 20/20 [==============================] - 15s 738ms/step - loss: 0.1414 - accuracy: 0.9542 - val_loss: 2.0841 - val_accuracy: 0.6103 Epoch 194/250 20/20 [==============================] - 15s 736ms/step - loss: 0.1847 - accuracy: 0.9526 - val_loss: 2.3761 - val_accuracy: 0.5735 Epoch 195/250 20/20 [==============================] - 15s 737ms/step - loss: 0.1652 - accuracy: 0.9592 - val_loss: 2.2241 - val_accuracy: 0.5956 Epoch 196/250 20/20 [==============================] - 15s 729ms/step - loss: 0.1288 - accuracy: 0.9632 - val_loss: 3.1524 - val_accuracy: 0.5882 Epoch 197/250 20/20 [==============================] - 15s 738ms/step - loss: 0.1374 - accuracy: 0.9681 - val_loss: 2.9827 - val_accuracy: 0.6250 Epoch 198/250 20/20 [==============================] - 15s 737ms/step - loss: 0.1301 - accuracy: 0.9592 - val_loss: 3.0995 - val_accuracy: 0.6250 Epoch 199/250 20/20 [==============================] - 15s 738ms/step - loss: 0.0832 - accuracy: 0.9722 - val_loss: 3.0490 - val_accuracy: 0.5956 Epoch 200/250 20/20 [==============================] - 15s 730ms/step - loss: 0.1782 - accuracy: 0.9534 - val_loss: 3.2922 - val_accuracy: 0.5809 Epoch 201/250 20/20 [==============================] - 15s 728ms/step - loss: 0.0896 - accuracy: 0.9747 - val_loss: 2.7171 - val_accuracy: 0.6324 Epoch 202/250 20/20 [==============================] - 15s 736ms/step - loss: 0.1080 - accuracy: 0.9673 - val_loss: 2.9443 - val_accuracy: 0.6029 Epoch 203/250 20/20 [==============================] - 15s 728ms/step - loss: 0.2232 - accuracy: 0.9477 - val_loss: 3.0214 - val_accuracy: 0.6103 Epoch 204/250 20/20 [==============================] - 15s 728ms/step - loss: 0.1760 - accuracy: 0.9583 - val_loss: 2.6577 - val_accuracy: 0.6397 Epoch 205/250 20/20 [==============================] - 15s 737ms/step - loss: 0.0930 - accuracy: 0.9730 - val_loss: 2.8268 - val_accuracy: 0.6176 Epoch 206/250 20/20 [==============================] - 15s 728ms/step - loss: 0.1137 - accuracy: 0.9632 - val_loss: 3.4081 - val_accuracy: 0.6029 Epoch 207/250 20/20 [==============================] - 15s 727ms/step - loss: 0.1062 - accuracy: 0.9714 - val_loss: 3.8998 - val_accuracy: 0.6397 Epoch 208/250 20/20 [==============================] - 15s 736ms/step - loss: 0.0630 - accuracy: 0.9796 - val_loss: 2.6259 - val_accuracy: 0.6397 Epoch 209/250 20/20 [==============================] - 15s 737ms/step - loss: 0.0512 - accuracy: 0.9902 - val_loss: 2.7408 - val_accuracy: 0.6103 Epoch 210/250 20/20 [==============================] - 15s 736ms/step - loss: 0.2230 - accuracy: 0.9379 - val_loss: 5.9677 - val_accuracy: 0.3897 Epoch 211/250 20/20 [==============================] - 15s 736ms/step - loss: 0.6728 - accuracy: 0.8178 - val_loss: 6.7094 - val_accuracy: 0.5074 Epoch 212/250 20/20 [==============================] - 15s 736ms/step - loss: 0.4207 - accuracy: 0.8685 - val_loss: 4.2323 - val_accuracy: 0.5809 Epoch 213/250 20/20 [==============================] - 15s 736ms/step - loss: 0.3048 - accuracy: 0.9036 - val_loss: 2.9643 - val_accuracy: 0.5515 Epoch 214/250 20/20 [==============================] - 15s 737ms/step - loss: 0.2672 - accuracy: 0.9297 - val_loss: 2.3335 - val_accuracy: 0.6397 Epoch 215/250 20/20 [==============================] - 15s 727ms/step - loss: 0.1939 - accuracy: 0.9436 - val_loss: 2.4418 - val_accuracy: 0.5662 Epoch 216/250 20/20 [==============================] - 15s 728ms/step - loss: 0.2710 - accuracy: 0.9314 - val_loss: 2.7091 - val_accuracy: 0.5441 Epoch 217/250 20/20 [==============================] - 15s 736ms/step - loss: 0.1613 - accuracy: 0.9551 - val_loss: 2.6761 - val_accuracy: 0.6397 Epoch 218/250 20/20 [==============================] - 15s 738ms/step - loss: 0.2350 - accuracy: 0.9257 - val_loss: 2.9476 - val_accuracy: 0.6176 Epoch 219/250 20/20 [==============================] - 15s 736ms/step - loss: 0.1618 - accuracy: 0.9534 - val_loss: 4.7399 - val_accuracy: 0.5956 Epoch 220/250 20/20 [==============================] - 15s 728ms/step - loss: 0.2992 - accuracy: 0.9167 - val_loss: 2.5617 - val_accuracy: 0.5735 Epoch 221/250 20/20 [==============================] - 15s 728ms/step - loss: 0.2729 - accuracy: 0.9338 - val_loss: 2.9277 - val_accuracy: 0.5956 Epoch 222/250 20/20 [==============================] - 15s 736ms/step - loss: 0.1159 - accuracy: 0.9616 - val_loss: 2.6784 - val_accuracy: 0.6397 Epoch 223/250 20/20 [==============================] - 15s 737ms/step - loss: 0.1831 - accuracy: 0.9518 - val_loss: 4.0484 - val_accuracy: 0.5882 Epoch 224/250 20/20 [==============================] - 15s 729ms/step - loss: 0.1747 - accuracy: 0.9632 - val_loss: 2.7723 - val_accuracy: 0.6250 Epoch 225/250 20/20 [==============================] - 15s 727ms/step - loss: 0.1140 - accuracy: 0.9673 - val_loss: 3.3044 - val_accuracy: 0.6029 Epoch 226/250 20/20 [==============================] - 15s 738ms/step - loss: 0.1380 - accuracy: 0.9600 - val_loss: 2.8139 - val_accuracy: 0.6324 Epoch 227/250 20/20 [==============================] - 15s 729ms/step - loss: 0.1065 - accuracy: 0.9665 - val_loss: 3.0242 - val_accuracy: 0.6250 Epoch 228/250 20/20 [==============================] - 15s 728ms/step - loss: 0.0922 - accuracy: 0.9755 - val_loss: 3.0599 - val_accuracy: 0.5956 Epoch 229/250 20/20 [==============================] - 15s 727ms/step - loss: 0.0526 - accuracy: 0.9877 - val_loss: 2.8209 - val_accuracy: 0.6544 Epoch 230/250 20/20 [==============================] - 15s 737ms/step - loss: 0.0211 - accuracy: 0.9959 - val_loss: 2.8154 - val_accuracy: 0.6912 Epoch 231/250 20/20 [==============================] - 15s 728ms/step - loss: 0.0679 - accuracy: 0.9812 - val_loss: 2.9188 - val_accuracy: 0.6618 Epoch 232/250 20/20 [==============================] - 15s 728ms/step - loss: 0.0305 - accuracy: 0.9918 - val_loss: 3.1998 - val_accuracy: 0.6544 Epoch 233/250 20/20 [==============================] - 15s 728ms/step - loss: 0.1122 - accuracy: 0.9673 - val_loss: 3.4848 - val_accuracy: 0.6250 Epoch 234/250 20/20 [==============================] - 15s 730ms/step - loss: 0.2208 - accuracy: 0.9420 - val_loss: 3.6313 - val_accuracy: 0.5882 Epoch 235/250 20/20 [==============================] - 15s 737ms/step - loss: 0.1803 - accuracy: 0.9469 - val_loss: 3.2286 - val_accuracy: 0.6103 Epoch 236/250 20/20 [==============================] - 15s 737ms/step - loss: 0.0681 - accuracy: 0.9722 - val_loss: 2.5510 - val_accuracy: 0.6765 Epoch 237/250 20/20 [==============================] - 15s 728ms/step - loss: 0.1305 - accuracy: 0.9698 - val_loss: 2.9013 - val_accuracy: 0.6250 Epoch 238/250 20/20 [==============================] - 15s 736ms/step - loss: 0.0852 - accuracy: 0.9714 - val_loss: 3.1888 - val_accuracy: 0.6471 Epoch 239/250 20/20 [==============================] - 15s 738ms/step - loss: 0.1595 - accuracy: 0.9567 - val_loss: 3.1303 - val_accuracy: 0.6618 Epoch 240/250 20/20 [==============================] - 15s 737ms/step - loss: 0.1517 - accuracy: 0.9624 - val_loss: 3.6736 - val_accuracy: 0.6029 Epoch 241/250 20/20 [==============================] - 15s 737ms/step - loss: 0.1679 - accuracy: 0.9567 - val_loss: 3.0746 - val_accuracy: 0.6103 Epoch 242/250 20/20 [==============================] - 15s 737ms/step - loss: 0.1704 - accuracy: 0.9592 - val_loss: 3.5271 - val_accuracy: 0.5882 Epoch 243/250 20/20 [==============================] - 15s 728ms/step - loss: 0.1806 - accuracy: 0.9567 - val_loss: 3.2975 - val_accuracy: 0.5809 Epoch 244/250 20/20 [==============================] - 15s 737ms/step - loss: 0.1679 - accuracy: 0.9600 - val_loss: 2.9738 - val_accuracy: 0.6103 Epoch 245/250 20/20 [==============================] - 15s 729ms/step - loss: 0.0777 - accuracy: 0.9796 - val_loss: 3.6021 - val_accuracy: 0.6103 Epoch 246/250 20/20 [==============================] - 15s 729ms/step - loss: 0.2468 - accuracy: 0.9551 - val_loss: 3.0834 - val_accuracy: 0.5956 Epoch 247/250 20/20 [==============================] - 15s 729ms/step - loss: 0.1754 - accuracy: 0.9485 - val_loss: 3.2508 - val_accuracy: 0.6176 Epoch 248/250 20/20 [==============================] - 15s 735ms/step - loss: 0.1908 - accuracy: 0.9502 - val_loss: 3.4634 - val_accuracy: 0.6029 Epoch 249/250 20/20 [==============================] - 15s 736ms/step - loss: 0.1347 - accuracy: 0.9714 - val_loss: 2.7130 - val_accuracy: 0.6324 Epoch 250/250 20/20 [==============================] - 15s 736ms/step - loss: 0.1137 - accuracy: 0.9649 - val_loss: 2.3323 - val_accuracy: 0.6397
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