output.txt

[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 551970982242999557
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 2403991552
locality {
  bus_id: 1
  links {
    link {
      device_id: 1
      type: "StreamExecutor"
      strength: 1
    }
  }
}
incarnation: 10096734097526406606
physical_device_desc: "device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:02:00.0, compute capability: 5.2"
, name: "/device:GPU:1"
device_type: "GPU"
memory_limit: 2421358592
locality {
  bus_id: 1
  links {
    link {
      type: "StreamExecutor"
      strength: 1
    }
  }
}
incarnation: 3303530385180522172
physical_device_desc: "device: 1, name: GeForce GTX TITAN X, pci bus id: 0000:03:00.0, compute capability: 5.2"
, name: "/device:GPU:2"
device_type: "GPU"
memory_limit: 2421358592
locality {
  bus_id: 2
  numa_node: 1
  links {
    link {
      device_id: 3
      type: "StreamExecutor"
      strength: 1
    }
  }
}
incarnation: 6077438548209564868
physical_device_desc: "device: 2, name: GeForce GTX TITAN X, pci bus id: 0000:82:00.0, compute capability: 5.2"
, name: "/device:GPU:3"
device_type: "GPU"
memory_limit: 2421358592
locality {
  bus_id: 2
  numa_node: 1
  links {
    link {
      device_id: 2
      type: "StreamExecutor"
      strength: 1
    }
  }
}
incarnation: 7810493206943392654
physical_device_desc: "device: 3, name: GeForce GTX TITAN X, pci bus id: 0000:83:00.0, compute capability: 5.2"
]
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 32, 32, 128)       3584      
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 30, 30, 128)       147584    
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 15, 15, 128)       0         
_________________________________________________________________
dropout (Dropout)            (None, 15, 15, 128)       0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 15, 15, 256)       295168    
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 13, 13, 256)       590080    
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 6, 6, 256)         0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 6, 6, 256)         0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 6, 6, 512)         1180160   
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 4, 4, 512)         2359808   
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 2, 2, 512)         0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 2, 2, 512)         0         
_________________________________________________________________
flatten (Flatten)            (None, 2048)              0         
_________________________________________________________________
dense (Dense)                (None, 1000)              2049000   
_________________________________________________________________
dropout_3 (Dropout)          (None, 1000)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 1000)              1001000   
_________________________________________________________________
dropout_4 (Dropout)          (None, 1000)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 100)               100100    
=================================================================
Total params: 7,726,484
Trainable params: 7,726,484
Non-trainable params: 0
_________________________________________________________________
Epoch 1/50
313/313 - 96s - loss: 4.5627 - accuracy: 0.0126 - val_loss: 4.4776 - val_accuracy: 0.0242
Epoch 2/50
313/313 - 46s - loss: 4.3533 - accuracy: 0.0287 - val_loss: 4.2234 - val_accuracy: 0.0470
Epoch 3/50
313/313 - 46s - loss: 4.1918 - accuracy: 0.0434 - val_loss: 4.0726 - val_accuracy: 0.0636
Epoch 4/50
313/313 - 45s - loss: 4.0665 - accuracy: 0.0612 - val_loss: 3.8846 - val_accuracy: 0.1007
Epoch 5/50
313/313 - 46s - loss: 3.9012 - accuracy: 0.0851 - val_loss: 3.7244 - val_accuracy: 0.1179
Epoch 6/50
313/313 - 45s - loss: 3.7531 - accuracy: 0.1077 - val_loss: 3.5380 - val_accuracy: 0.1524
Epoch 7/50
313/313 - 46s - loss: 3.6198 - accuracy: 0.1289 - val_loss: 3.4467 - val_accuracy: 0.1662
Epoch 8/50
313/313 - 46s - loss: 3.4991 - accuracy: 0.1494 - val_loss: 3.2980 - val_accuracy: 0.1921
Epoch 9/50
313/313 - 45s - loss: 3.3890 - accuracy: 0.1690 - val_loss: 3.1419 - val_accuracy: 0.2233
Epoch 10/50
313/313 - 46s - loss: 3.2652 - accuracy: 0.1891 - val_loss: 3.0157 - val_accuracy: 0.2478
Epoch 11/50
313/313 - 46s - loss: 3.1812 - accuracy: 0.2075 - val_loss: 2.8746 - val_accuracy: 0.2739
Epoch 12/50
313/313 - 46s - loss: 3.0862 - accuracy: 0.2230 - val_loss: 2.8107 - val_accuracy: 0.2834
Epoch 13/50
313/313 - 46s - loss: 3.0082 - accuracy: 0.2382 - val_loss: 2.7658 - val_accuracy: 0.2955
Epoch 14/50
313/313 - 45s - loss: 2.9284 - accuracy: 0.2527 - val_loss: 2.6229 - val_accuracy: 0.3169
Epoch 15/50
313/313 - 46s - loss: 2.8563 - accuracy: 0.2674 - val_loss: 2.6023 - val_accuracy: 0.3186
Epoch 16/50
313/313 - 46s - loss: 2.7810 - accuracy: 0.2837 - val_loss: 2.5003 - val_accuracy: 0.3452
Epoch 17/50
313/313 - 46s - loss: 2.7136 - accuracy: 0.2972 - val_loss: 2.4621 - val_accuracy: 0.3552
Epoch 18/50
313/313 - 46s - loss: 2.6537 - accuracy: 0.3099 - val_loss: 2.4113 - val_accuracy: 0.3647
Epoch 19/50
313/313 - 46s - loss: 2.5879 - accuracy: 0.3239 - val_loss: 2.3573 - val_accuracy: 0.3791
Epoch 20/50
313/313 - 45s - loss: 2.5320 - accuracy: 0.3356 - val_loss: 2.2350 - val_accuracy: 0.4064
Epoch 21/50
313/313 - 45s - loss: 2.4732 - accuracy: 0.3495 - val_loss: 2.2538 - val_accuracy: 0.3983
Epoch 22/50
313/313 - 46s - loss: 2.4372 - accuracy: 0.3578 - val_loss: 2.1552 - val_accuracy: 0.4205
Epoch 23/50
313/313 - 46s - loss: 2.3875 - accuracy: 0.3668 - val_loss: 2.2039 - val_accuracy: 0.4107
Epoch 24/50
313/313 - 46s - loss: 2.3463 - accuracy: 0.3759 - val_loss: 2.1000 - val_accuracy: 0.4335
Epoch 25/50
313/313 - 45s - loss: 2.3019 - accuracy: 0.3867 - val_loss: 2.1185 - val_accuracy: 0.4270
Epoch 26/50
313/313 - 46s - loss: 2.2693 - accuracy: 0.3960 - val_loss: 2.0701 - val_accuracy: 0.4421
Epoch 27/50
313/313 - 45s - loss: 2.2145 - accuracy: 0.4058 - val_loss: 2.0762 - val_accuracy: 0.4418
Epoch 28/50
313/313 - 46s - loss: 2.1881 - accuracy: 0.4101 - val_loss: 2.0070 - val_accuracy: 0.4548
Epoch 29/50
313/313 - 46s - loss: 2.1517 - accuracy: 0.4195 - val_loss: 1.9347 - val_accuracy: 0.4758
Epoch 30/50
313/313 - 46s - loss: 2.1113 - accuracy: 0.4312 - val_loss: 1.9287 - val_accuracy: 0.4716
Epoch 31/50
313/313 - 46s - loss: 2.0943 - accuracy: 0.4334 - val_loss: 1.9585 - val_accuracy: 0.4705
Epoch 32/50
313/313 - 46s - loss: 2.0462 - accuracy: 0.4440 - val_loss: 1.8643 - val_accuracy: 0.4879
Epoch 33/50
313/313 - 46s - loss: 2.0195 - accuracy: 0.4480 - val_loss: 1.9210 - val_accuracy: 0.4754
Epoch 34/50
313/313 - 46s - loss: 1.9908 - accuracy: 0.4555 - val_loss: 1.9023 - val_accuracy: 0.4871
Epoch 35/50
313/313 - 46s - loss: 1.9564 - accuracy: 0.4649 - val_loss: 1.8884 - val_accuracy: 0.4862
Epoch 36/50
313/313 - 46s - loss: 1.9434 - accuracy: 0.4695 - val_loss: 1.8998 - val_accuracy: 0.4893
Epoch 37/50
313/313 - 46s - loss: 1.9033 - accuracy: 0.4749 - val_loss: 1.8693 - val_accuracy: 0.4901
Epoch 38/50
313/313 - 45s - loss: 1.8821 - accuracy: 0.4847 - val_loss: 1.7958 - val_accuracy: 0.5109
Epoch 39/50
313/313 - 45s - loss: 1.8671 - accuracy: 0.4882 - val_loss: 1.8209 - val_accuracy: 0.5053
Epoch 40/50
313/313 - 46s - loss: 1.8225 - accuracy: 0.4968 - val_loss: 1.7467 - val_accuracy: 0.5191
Epoch 41/50
313/313 - 46s - loss: 1.8061 - accuracy: 0.5010 - val_loss: 1.8137 - val_accuracy: 0.5071
Epoch 42/50
313/313 - 46s - loss: 1.7910 - accuracy: 0.5038 - val_loss: 1.7452 - val_accuracy: 0.5245
Epoch 43/50
313/313 - 46s - loss: 1.7693 - accuracy: 0.5097 - val_loss: 1.7398 - val_accuracy: 0.5270
Epoch 44/50
313/313 - 46s - loss: 1.7477 - accuracy: 0.5134 - val_loss: 1.7259 - val_accuracy: 0.5319
Epoch 45/50
313/313 - 46s - loss: 1.7176 - accuracy: 0.5219 - val_loss: 1.7031 - val_accuracy: 0.5341
Epoch 46/50
313/313 - 46s - loss: 1.6958 - accuracy: 0.5271 - val_loss: 1.6630 - val_accuracy: 0.5456
Epoch 47/50
313/313 - 46s - loss: 1.6713 - accuracy: 0.5337 - val_loss: 1.6906 - val_accuracy: 0.5412
Epoch 48/50
313/313 - 46s - loss: 1.6568 - accuracy: 0.5319 - val_loss: 1.6614 - val_accuracy: 0.5447
Epoch 49/50
313/313 - 46s - loss: 1.6329 - accuracy: 0.5405 - val_loss: 1.6378 - val_accuracy: 0.5501
Epoch 50/50
313/313 - 46s - loss: 1.6241 - accuracy: 0.5420 - val_loss: 1.6548 - val_accuracy: 0.5485
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_6 (Conv2D)            (None, 32, 32, 128)       3584      
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 30, 30, 128)       147584    
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 15, 15, 128)       0         
_________________________________________________________________
dropout_5 (Dropout)          (None, 15, 15, 128)       0         
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 15, 15, 256)       295168    
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 13, 13, 256)       590080    
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 6, 6, 256)         0         
_________________________________________________________________
dropout_6 (Dropout)          (None, 6, 6, 256)         0         
_________________________________________________________________
conv2d_10 (Conv2D)           (None, 6, 6, 512)         1180160   
_________________________________________________________________
conv2d_11 (Conv2D)           (None, 4, 4, 512)         2359808   
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 2, 2, 512)         0         
_________________________________________________________________
dropout_7 (Dropout)          (None, 2, 2, 512)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 2048)              0         
_________________________________________________________________
dense_3 (Dense)              (None, 1000)              2049000   
_________________________________________________________________
dropout_8 (Dropout)          (None, 1000)              0         
_________________________________________________________________
dense_4 (Dense)              (None, 1000)              1001000   
_________________________________________________________________
dropout_9 (Dropout)          (None, 1000)              0         
_________________________________________________________________
dense_5 (Dense)              (None, 100)               100100    
=================================================================
Total params: 7,726,484
Trainable params: 7,726,484
Non-trainable params: 0
_________________________________________________________________
Epoch 1/50
313/313 - 48s - loss: 4.5584 - accuracy: 0.0126 - val_loss: 4.4796 - val_accuracy: 0.0134
Epoch 2/50
313/313 - 47s - loss: 4.4743 - accuracy: 0.0186 - val_loss: 4.4494 - val_accuracy: 0.0215
Epoch 3/50
313/313 - 47s - loss: 4.3786 - accuracy: 0.0221 - val_loss: 4.2716 - val_accuracy: 0.0344
Epoch 4/50
313/313 - 47s - loss: 4.2566 - accuracy: 0.0356 - val_loss: 4.0839 - val_accuracy: 0.0631
Epoch 5/50
313/313 - 47s - loss: 4.0394 - accuracy: 0.0633 - val_loss: 3.8183 - val_accuracy: 0.0950
Epoch 6/50
313/313 - 47s - loss: 3.8410 - accuracy: 0.0867 - val_loss: 3.6076 - val_accuracy: 0.1366
Epoch 7/50
313/313 - 47s - loss: 3.6798 - accuracy: 0.1128 - val_loss: 3.4160 - val_accuracy: 0.1677
Epoch 8/50
313/313 - 47s - loss: 3.5429 - accuracy: 0.1362 - val_loss: 3.2690 - val_accuracy: 0.1973
Epoch 9/50
313/313 - 47s - loss: 3.4179 - accuracy: 0.1617 - val_loss: 3.1576 - val_accuracy: 0.2117
Epoch 10/50
313/313 - 47s - loss: 3.3070 - accuracy: 0.1777 - val_loss: 3.0498 - val_accuracy: 0.2314
Epoch 11/50
313/313 - 47s - loss: 3.1974 - accuracy: 0.1986 - val_loss: 3.0040 - val_accuracy: 0.2406
Epoch 12/50
313/313 - 47s - loss: 3.1082 - accuracy: 0.2145 - val_loss: 2.8307 - val_accuracy: 0.2669
Epoch 13/50
313/313 - 47s - loss: 3.0067 - accuracy: 0.2332 - val_loss: 2.8213 - val_accuracy: 0.2792
Epoch 14/50
313/313 - 47s - loss: 2.9266 - accuracy: 0.2502 - val_loss: 2.6327 - val_accuracy: 0.3095
Epoch 15/50
313/313 - 47s - loss: 2.8429 - accuracy: 0.2682 - val_loss: 2.5886 - val_accuracy: 0.3191
Epoch 16/50
313/313 - 47s - loss: 2.7766 - accuracy: 0.2823 - val_loss: 2.5471 - val_accuracy: 0.3342
Epoch 17/50
313/313 - 47s - loss: 2.7050 - accuracy: 0.2955 - val_loss: 2.5059 - val_accuracy: 0.3442
Epoch 18/50
313/313 - 47s - loss: 2.6392 - accuracy: 0.3104 - val_loss: 2.3964 - val_accuracy: 0.3651
Epoch 19/50
313/313 - 47s - loss: 2.5827 - accuracy: 0.3195 - val_loss: 2.3141 - val_accuracy: 0.3833
Epoch 20/50
313/313 - 47s - loss: 2.5325 - accuracy: 0.3331 - val_loss: 2.3015 - val_accuracy: 0.3860
Epoch 21/50
313/313 - 47s - loss: 2.4803 - accuracy: 0.3439 - val_loss: 2.2351 - val_accuracy: 0.4010
Epoch 22/50
313/313 - 47s - loss: 2.4344 - accuracy: 0.3546 - val_loss: 2.2397 - val_accuracy: 0.4020
Epoch 23/50
313/313 - 47s - loss: 2.3818 - accuracy: 0.3666 - val_loss: 2.1886 - val_accuracy: 0.4073
Epoch 24/50
313/313 - 47s - loss: 2.3402 - accuracy: 0.3723 - val_loss: 2.1051 - val_accuracy: 0.4313
Epoch 25/50
313/313 - 47s - loss: 2.3104 - accuracy: 0.3822 - val_loss: 2.1155 - val_accuracy: 0.4293
Epoch 26/50
313/313 - 46s - loss: 2.2599 - accuracy: 0.3930 - val_loss: 2.1235 - val_accuracy: 0.4264
Epoch 27/50
313/313 - 42s - loss: 2.2235 - accuracy: 0.4006 - val_loss: 2.0431 - val_accuracy: 0.4461
Epoch 28/50
313/313 - 42s - loss: 2.1803 - accuracy: 0.4094 - val_loss: 1.9612 - val_accuracy: 0.4619
Epoch 29/50
313/313 - 42s - loss: 2.1470 - accuracy: 0.4174 - val_loss: 1.9434 - val_accuracy: 0.4653
Epoch 30/50
313/313 - 42s - loss: 2.1140 - accuracy: 0.4246 - val_loss: 2.0052 - val_accuracy: 0.4469
Epoch 31/50
313/313 - 42s - loss: 2.0884 - accuracy: 0.4307 - val_loss: 1.9213 - val_accuracy: 0.4689
Epoch 32/50
313/313 - 42s - loss: 2.0461 - accuracy: 0.4407 - val_loss: 1.8896 - val_accuracy: 0.4783
Epoch 33/50
313/313 - 42s - loss: 2.0140 - accuracy: 0.4507 - val_loss: 1.8598 - val_accuracy: 0.4894
Epoch 34/50
313/313 - 42s - loss: 1.9997 - accuracy: 0.4519 - val_loss: 1.8367 - val_accuracy: 0.4933
Epoch 35/50
313/313 - 41s - loss: 1.9621 - accuracy: 0.4594 - val_loss: 1.8556 - val_accuracy: 0.4889
Epoch 36/50
313/313 - 47s - loss: 1.9385 - accuracy: 0.4669 - val_loss: 1.8460 - val_accuracy: 0.4942
Epoch 37/50
313/313 - 47s - loss: 1.9129 - accuracy: 0.4707 - val_loss: 1.7726 - val_accuracy: 0.5137
Epoch 38/50
313/313 - 47s - loss: 1.8866 - accuracy: 0.4796 - val_loss: 1.7878 - val_accuracy: 0.5081
Epoch 39/50
313/313 - 47s - loss: 1.8640 - accuracy: 0.4854 - val_loss: 1.7799 - val_accuracy: 0.5097
Epoch 40/50
313/313 - 47s - loss: 1.8305 - accuracy: 0.4922 - val_loss: 1.7394 - val_accuracy: 0.5148
Epoch 41/50
313/313 - 47s - loss: 1.8080 - accuracy: 0.4964 - val_loss: 1.7427 - val_accuracy: 0.5149
Epoch 42/50
313/313 - 47s - loss: 1.7862 - accuracy: 0.5024 - val_loss: 1.6957 - val_accuracy: 0.5267
Epoch 43/50
313/313 - 47s - loss: 1.7802 - accuracy: 0.5052 - val_loss: 1.7259 - val_accuracy: 0.5193
Epoch 44/50
313/313 - 47s - loss: 1.7491 - accuracy: 0.5086 - val_loss: 1.6566 - val_accuracy: 0.5408
Epoch 45/50
313/313 - 47s - loss: 1.7234 - accuracy: 0.5179 - val_loss: 1.7564 - val_accuracy: 0.5240
Epoch 46/50
313/313 - 47s - loss: 1.6975 - accuracy: 0.5231 - val_loss: 1.6727 - val_accuracy: 0.5369
Epoch 47/50
313/313 - 47s - loss: 1.6830 - accuracy: 0.5263 - val_loss: 1.6689 - val_accuracy: 0.5413
Epoch 48/50
313/313 - 47s - loss: 1.6689 - accuracy: 0.5304 - val_loss: 1.6735 - val_accuracy: 0.5355
Epoch 49/50
313/313 - 47s - loss: 1.6423 - accuracy: 0.5379 - val_loss: 1.6160 - val_accuracy: 0.5530
Epoch 50/50
313/313 - 47s - loss: 1.6231 - accuracy: 0.5444 - val_loss: 1.6521 - val_accuracy: 0.5419
Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_12 (Conv2D)           (None, 32, 32, 128)       3584      
_________________________________________________________________
conv2d_13 (Conv2D)           (None, 30, 30, 128)       147584    
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 15, 15, 128)       0         
_________________________________________________________________
dropout_10 (Dropout)         (None, 15, 15, 128)       0         
_________________________________________________________________
conv2d_14 (Conv2D)           (None, 15, 15, 256)       295168    
_________________________________________________________________
conv2d_15 (Conv2D)           (None, 13, 13, 256)       590080    
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 6, 6, 256)         0         
_________________________________________________________________
dropout_11 (Dropout)         (None, 6, 6, 256)         0         
_________________________________________________________________
conv2d_16 (Conv2D)           (None, 6, 6, 512)         1180160   
_________________________________________________________________
conv2d_17 (Conv2D)           (None, 4, 4, 512)         2359808   
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 2, 2, 512)         0         
_________________________________________________________________
dropout_12 (Dropout)         (None, 2, 2, 512)         0         
_________________________________________________________________
flatten_2 (Flatten)          (None, 2048)              0         
_________________________________________________________________
dense_6 (Dense)              (None, 1000)              2049000   
_________________________________________________________________
dropout_13 (Dropout)         (None, 1000)              0         
_________________________________________________________________
dense_7 (Dense)              (None, 1000)              1001000   
_________________________________________________________________
dropout_14 (Dropout)         (None, 1000)              0         
_________________________________________________________________
dense_8 (Dense)              (None, 100)               100100    
=================================================================
Total params: 7,726,484
Trainable params: 7,726,484
Non-trainable params: 0
_________________________________________________________________
Epoch 1/50
313/313 - 49s - loss: 4.5638 - accuracy: 0.0127 - val_loss: 4.4975 - val_accuracy: 0.0256
Epoch 2/50
313/313 - 48s - loss: 4.3571 - accuracy: 0.0284 - val_loss: 4.2125 - val_accuracy: 0.0429
Epoch 3/50
313/313 - 48s - loss: 4.1796 - accuracy: 0.0448 - val_loss: 4.0339 - val_accuracy: 0.0641
Epoch 4/50
313/313 - 48s - loss: 4.0166 - accuracy: 0.0677 - val_loss: 3.7705 - val_accuracy: 0.1132
Epoch 5/50
313/313 - 48s - loss: 3.8618 - accuracy: 0.0870 - val_loss: 3.6712 - val_accuracy: 0.1269
Epoch 6/50
313/313 - 48s - loss: 3.7235 - accuracy: 0.1065 - val_loss: 3.4551 - val_accuracy: 0.1635
Epoch 7/50
313/313 - 48s - loss: 3.5704 - accuracy: 0.1359 - val_loss: 3.4059 - val_accuracy: 0.1652
Epoch 8/50
313/313 - 48s - loss: 3.4461 - accuracy: 0.1581 - val_loss: 3.1828 - val_accuracy: 0.2130
Epoch 9/50
313/313 - 48s - loss: 3.3260 - accuracy: 0.1786 - val_loss: 3.0753 - val_accuracy: 0.2325
Epoch 10/50
313/313 - 48s - loss: 3.2184 - accuracy: 0.1975 - val_loss: 3.0270 - val_accuracy: 0.2428
Epoch 11/50
313/313 - 48s - loss: 3.1130 - accuracy: 0.2181 - val_loss: 2.8363 - val_accuracy: 0.2770
Epoch 12/50
313/313 - 48s - loss: 3.0314 - accuracy: 0.2336 - val_loss: 2.7716 - val_accuracy: 0.2897
Epoch 13/50
313/313 - 48s - loss: 2.9501 - accuracy: 0.2478 - val_loss: 2.6754 - val_accuracy: 0.3068
Epoch 14/50
313/313 - 48s - loss: 2.8722 - accuracy: 0.2659 - val_loss: 2.6243 - val_accuracy: 0.3196
Epoch 15/50
313/313 - 48s - loss: 2.7969 - accuracy: 0.2824 - val_loss: 2.5756 - val_accuracy: 0.3257
Epoch 16/50
313/313 - 48s - loss: 2.7318 - accuracy: 0.2937 - val_loss: 2.5086 - val_accuracy: 0.3405
Epoch 17/50
313/313 - 48s - loss: 2.6645 - accuracy: 0.3051 - val_loss: 2.3874 - val_accuracy: 0.3676
Epoch 18/50
313/313 - 48s - loss: 2.5996 - accuracy: 0.3188 - val_loss: 2.3881 - val_accuracy: 0.3712
Epoch 19/50
313/313 - 48s - loss: 2.5470 - accuracy: 0.3309 - val_loss: 2.3073 - val_accuracy: 0.3840
Epoch 20/50
313/313 - 48s - loss: 2.4907 - accuracy: 0.3442 - val_loss: 2.3091 - val_accuracy: 0.3851
Epoch 21/50
313/313 - 48s - loss: 2.4504 - accuracy: 0.3539 - val_loss: 2.2197 - val_accuracy: 0.4020
Epoch 22/50
313/313 - 48s - loss: 2.4035 - accuracy: 0.3603 - val_loss: 2.1829 - val_accuracy: 0.4092
Epoch 23/50
313/313 - 48s - loss: 2.3421 - accuracy: 0.3776 - val_loss: 2.1024 - val_accuracy: 0.4275
Epoch 24/50
313/313 - 48s - loss: 2.3116 - accuracy: 0.3830 - val_loss: 2.1657 - val_accuracy: 0.4205
Epoch 25/50
313/313 - 48s - loss: 2.2660 - accuracy: 0.3946 - val_loss: 2.1332 - val_accuracy: 0.4270
Epoch 26/50
313/313 - 48s - loss: 2.2320 - accuracy: 0.4002 - val_loss: 2.0643 - val_accuracy: 0.4476
Epoch 27/50
313/313 - 48s - loss: 2.1934 - accuracy: 0.4103 - val_loss: 2.0547 - val_accuracy: 0.4440
Epoch 28/50
313/313 - 48s - loss: 2.1632 - accuracy: 0.4186 - val_loss: 2.0554 - val_accuracy: 0.4455
Epoch 29/50
313/313 - 48s - loss: 2.1199 - accuracy: 0.4275 - val_loss: 2.0494 - val_accuracy: 0.4468
Epoch 30/50
313/313 - 48s - loss: 2.0955 - accuracy: 0.4342 - val_loss: 1.9326 - val_accuracy: 0.4739
Epoch 31/50
313/313 - 48s - loss: 2.0543 - accuracy: 0.4407 - val_loss: 1.8611 - val_accuracy: 0.4889
Epoch 32/50
313/313 - 48s - loss: 2.0329 - accuracy: 0.4483 - val_loss: 1.8743 - val_accuracy: 0.4896
Epoch 33/50
313/313 - 48s - loss: 2.0032 - accuracy: 0.4523 - val_loss: 1.8867 - val_accuracy: 0.4824
Epoch 34/50
313/313 - 48s - loss: 1.9792 - accuracy: 0.4583 - val_loss: 1.8219 - val_accuracy: 0.4995
Epoch 35/50
313/313 - 48s - loss: 1.9442 - accuracy: 0.4661 - val_loss: 1.8730 - val_accuracy: 0.4928
Epoch 36/50
313/313 - 48s - loss: 1.9225 - accuracy: 0.4709 - val_loss: 1.8170 - val_accuracy: 0.5047
Epoch 37/50
313/313 - 48s - loss: 1.8915 - accuracy: 0.4817 - val_loss: 1.8489 - val_accuracy: 0.4952
Epoch 38/50
313/313 - 48s - loss: 1.8611 - accuracy: 0.4875 - val_loss: 1.7860 - val_accuracy: 0.5089
Epoch 39/50
313/313 - 48s - loss: 1.8438 - accuracy: 0.4920 - val_loss: 1.7283 - val_accuracy: 0.5260
Epoch 40/50
313/313 - 48s - loss: 1.8238 - accuracy: 0.4947 - val_loss: 1.7185 - val_accuracy: 0.5244
Epoch 41/50
313/313 - 48s - loss: 1.8003 - accuracy: 0.5028 - val_loss: 1.7957 - val_accuracy: 0.5122
Epoch 42/50
313/313 - 48s - loss: 1.7856 - accuracy: 0.5048 - val_loss: 1.7412 - val_accuracy: 0.5214
Epoch 43/50
313/313 - 48s - loss: 1.7515 - accuracy: 0.5134 - val_loss: 1.7712 - val_accuracy: 0.5212
Epoch 44/50
313/313 - 48s - loss: 1.7360 - accuracy: 0.5157 - val_loss: 1.6801 - val_accuracy: 0.5348
Epoch 45/50
313/313 - 48s - loss: 1.7040 - accuracy: 0.5239 - val_loss: 1.6963 - val_accuracy: 0.5369
Epoch 46/50
313/313 - 48s - loss: 1.6852 - accuracy: 0.5288 - val_loss: 1.7367 - val_accuracy: 0.5254
Epoch 47/50
313/313 - 48s - loss: 1.6784 - accuracy: 0.5310 - val_loss: 1.6457 - val_accuracy: 0.5480
Epoch 48/50
313/313 - 48s - loss: 1.6445 - accuracy: 0.5396 - val_loss: 1.6656 - val_accuracy: 0.5445
Epoch 49/50
313/313 - 48s - loss: 1.6261 - accuracy: 0.5426 - val_loss: 1.6421 - val_accuracy: 0.5468
Epoch 50/50
313/313 - 48s - loss: 1.6057 - accuracy: 0.5502 - val_loss: 1.6429 - val_accuracy: 0.5515
Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_18 (Conv2D)           (None, 32, 32, 128)       3584      
_________________________________________________________________
conv2d_19 (Conv2D)           (None, 30, 30, 128)       147584    
_________________________________________________________________
max_pooling2d_9 (MaxPooling2 (None, 15, 15, 128)       0         
_________________________________________________________________
dropout_15 (Dropout)         (None, 15, 15, 128)       0         
_________________________________________________________________
conv2d_20 (Conv2D)           (None, 15, 15, 256)       295168    
_________________________________________________________________
conv2d_21 (Conv2D)           (None, 13, 13, 256)       590080    
_________________________________________________________________
max_pooling2d_10 (MaxPooling (None, 6, 6, 256)         0         
_________________________________________________________________
dropout_16 (Dropout)         (None, 6, 6, 256)         0         
_________________________________________________________________
conv2d_22 (Conv2D)           (None, 6, 6, 512)         1180160   
_________________________________________________________________
conv2d_23 (Conv2D)           (None, 4, 4, 512)         2359808   
_________________________________________________________________
max_pooling2d_11 (MaxPooling (None, 2, 2, 512)         0         
_________________________________________________________________
dropout_17 (Dropout)         (None, 2, 2, 512)         0         
_________________________________________________________________
flatten_3 (Flatten)          (None, 2048)              0         
_________________________________________________________________
dense_9 (Dense)              (None, 1000)              2049000   
_________________________________________________________________
dropout_18 (Dropout)         (None, 1000)              0         
_________________________________________________________________
dense_10 (Dense)             (None, 1000)              1001000   
_________________________________________________________________
dropout_19 (Dropout)         (None, 1000)              0         
_________________________________________________________________
dense_11 (Dense)             (None, 100)               100100    
=================================================================
Total params: 7,726,484
Trainable params: 7,726,484
Non-trainable params: 0
_________________________________________________________________
Epoch 1/50
313/313 - 49s - loss: 4.5748 - accuracy: 0.0111 - val_loss: 4.5022 - val_accuracy: 0.0182
Epoch 2/50
313/313 - 48s - loss: 4.4805 - accuracy: 0.0181 - val_loss: 4.4517 - val_accuracy: 0.0213
Epoch 3/50
313/313 - 48s - loss: 4.3593 - accuracy: 0.0231 - val_loss: 4.2750 - val_accuracy: 0.0380
Epoch 4/50
313/313 - 48s - loss: 4.2229 - accuracy: 0.0402 - val_loss: 4.0272 - val_accuracy: 0.0678
Epoch 5/50
313/313 - 48s - loss: 4.0063 - accuracy: 0.0674 - val_loss: 3.7564 - val_accuracy: 0.1087
Epoch 6/50
313/313 - 48s - loss: 3.8181 - accuracy: 0.0922 - val_loss: 3.5876 - val_accuracy: 0.1345
Epoch 7/50
313/313 - 48s - loss: 3.6761 - accuracy: 0.1143 - val_loss: 3.4217 - val_accuracy: 0.1666
Epoch 8/50
313/313 - 48s - loss: 3.5350 - accuracy: 0.1387 - val_loss: 3.2677 - val_accuracy: 0.1916
Epoch 9/50
313/313 - 48s - loss: 3.4115 - accuracy: 0.1595 - val_loss: 3.2097 - val_accuracy: 0.2049
Epoch 10/50
313/313 - 48s - loss: 3.2909 - accuracy: 0.1817 - val_loss: 3.0126 - val_accuracy: 0.2406
Epoch 11/50
313/313 - 48s - loss: 3.1869 - accuracy: 0.2016 - val_loss: 3.0104 - val_accuracy: 0.2368
Epoch 12/50
313/313 - 48s - loss: 3.1018 - accuracy: 0.2170 - val_loss: 2.8295 - val_accuracy: 0.2739
Epoch 13/50
313/313 - 48s - loss: 3.0131 - accuracy: 0.2353 - val_loss: 2.7254 - val_accuracy: 0.3013
Epoch 14/50
313/313 - 48s - loss: 2.9238 - accuracy: 0.2515 - val_loss: 2.6819 - val_accuracy: 0.3047
Epoch 15/50
313/313 - 48s - loss: 2.8422 - accuracy: 0.2709 - val_loss: 2.5971 - val_accuracy: 0.3207
Epoch 16/50
313/313 - 48s - loss: 2.7657 - accuracy: 0.2836 - val_loss: 2.5050 - val_accuracy: 0.3426
Epoch 17/50
313/313 - 48s - loss: 2.7088 - accuracy: 0.2936 - val_loss: 2.4267 - val_accuracy: 0.3623
Epoch 18/50
313/313 - 48s - loss: 2.6589 - accuracy: 0.3064 - val_loss: 2.3963 - val_accuracy: 0.3672
Epoch 19/50
313/313 - 48s - loss: 2.5847 - accuracy: 0.3198 - val_loss: 2.3523 - val_accuracy: 0.3801
Epoch 20/50
313/313 - 48s - loss: 2.5376 - accuracy: 0.3306 - val_loss: 2.3038 - val_accuracy: 0.3930
Epoch 21/50
313/313 - 48s - loss: 2.4776 - accuracy: 0.3432 - val_loss: 2.2354 - val_accuracy: 0.4038
Epoch 22/50
313/313 - 48s - loss: 2.4353 - accuracy: 0.3534 - val_loss: 2.2137 - val_accuracy: 0.4112
Epoch 23/50
313/313 - 48s - loss: 2.3813 - accuracy: 0.3634 - val_loss: 2.1533 - val_accuracy: 0.4257
Epoch 24/50
313/313 - 48s - loss: 2.3510 - accuracy: 0.3706 - val_loss: 2.1253 - val_accuracy: 0.4247
Epoch 25/50
313/313 - 48s - loss: 2.2968 - accuracy: 0.3808 - val_loss: 2.0546 - val_accuracy: 0.4412
Epoch 26/50
313/313 - 48s - loss: 2.2666 - accuracy: 0.3901 - val_loss: 2.0750 - val_accuracy: 0.4366
Epoch 27/50
313/313 - 48s - loss: 2.2319 - accuracy: 0.3998 - val_loss: 2.0425 - val_accuracy: 0.4458
Epoch 28/50
313/313 - 48s - loss: 2.1839 - accuracy: 0.4117 - val_loss: 1.9782 - val_accuracy: 0.4594
Epoch 29/50
313/313 - 48s - loss: 2.1536 - accuracy: 0.4146 - val_loss: 1.9398 - val_accuracy: 0.4679
Epoch 30/50
313/313 - 48s - loss: 2.1264 - accuracy: 0.4238 - val_loss: 1.9833 - val_accuracy: 0.4595
Epoch 31/50
313/313 - 48s - loss: 2.0869 - accuracy: 0.4315 - val_loss: 1.9122 - val_accuracy: 0.4804
Epoch 32/50
313/313 - 48s - loss: 2.0607 - accuracy: 0.4387 - val_loss: 1.9129 - val_accuracy: 0.4788
Epoch 33/50
313/313 - 48s - loss: 2.0327 - accuracy: 0.4418 - val_loss: 1.8317 - val_accuracy: 0.4957
Epoch 34/50
313/313 - 48s - loss: 1.9979 - accuracy: 0.4521 - val_loss: 1.8930 - val_accuracy: 0.4834
Epoch 35/50
313/313 - 48s - loss: 1.9801 - accuracy: 0.4576 - val_loss: 1.8614 - val_accuracy: 0.4896
Epoch 36/50
313/313 - 48s - loss: 1.9419 - accuracy: 0.4660 - val_loss: 1.7870 - val_accuracy: 0.5066
Epoch 37/50
313/313 - 48s - loss: 1.9230 - accuracy: 0.4689 - val_loss: 1.8757 - val_accuracy: 0.4885
Epoch 38/50
313/313 - 48s - loss: 1.9041 - accuracy: 0.4724 - val_loss: 1.7859 - val_accuracy: 0.5106
Epoch 39/50
313/313 - 48s - loss: 1.8709 - accuracy: 0.4828 - val_loss: 1.7635 - val_accuracy: 0.5124
Epoch 40/50
313/313 - 48s - loss: 1.8486 - accuracy: 0.4865 - val_loss: 1.7504 - val_accuracy: 0.5168
Epoch 41/50
313/313 - 48s - loss: 1.8158 - accuracy: 0.4947 - val_loss: 1.7286 - val_accuracy: 0.5197
Epoch 42/50
313/313 - 48s - loss: 1.7996 - accuracy: 0.4976 - val_loss: 1.7381 - val_accuracy: 0.5231
Epoch 43/50
313/313 - 48s - loss: 1.7788 - accuracy: 0.5044 - val_loss: 1.7133 - val_accuracy: 0.5262
Epoch 44/50
313/313 - 48s - loss: 1.7632 - accuracy: 0.5072 - val_loss: 1.7157 - val_accuracy: 0.5290
Epoch 45/50
313/313 - 48s - loss: 1.7286 - accuracy: 0.5171 - val_loss: 1.7328 - val_accuracy: 0.5215
Epoch 46/50
313/313 - 48s - loss: 1.7139 - accuracy: 0.5203 - val_loss: 1.6498 - val_accuracy: 0.5410
Epoch 47/50
313/313 - 48s - loss: 1.6961 - accuracy: 0.5232 - val_loss: 1.6781 - val_accuracy: 0.5381
Epoch 48/50
313/313 - 48s - loss: 1.6662 - accuracy: 0.5337 - val_loss: 1.6601 - val_accuracy: 0.5441
Epoch 49/50
313/313 - 48s - loss: 1.6557 - accuracy: 0.5358 - val_loss: 1.6954 - val_accuracy: 0.5343
Epoch 50/50
313/313 - 48s - loss: 1.6378 - accuracy: 0.5370 - val_loss: 1.5824 - val_accuracy: 0.5563
Model: "sequential_4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_24 (Conv2D)           (None, 32, 32, 128)       3584      
_________________________________________________________________
conv2d_25 (Conv2D)           (None, 30, 30, 128)       147584    
_________________________________________________________________
max_pooling2d_12 (MaxPooling (None, 15, 15, 128)       0         
_________________________________________________________________
dropout_20 (Dropout)         (None, 15, 15, 128)       0         
_________________________________________________________________
conv2d_26 (Conv2D)           (None, 15, 15, 256)       295168    
_________________________________________________________________
conv2d_27 (Conv2D)           (None, 13, 13, 256)       590080    
_________________________________________________________________
max_pooling2d_13 (MaxPooling (None, 6, 6, 256)         0         
_________________________________________________________________
dropout_21 (Dropout)         (None, 6, 6, 256)         0         
_________________________________________________________________
conv2d_28 (Conv2D)           (None, 6, 6, 512)         1180160   
_________________________________________________________________
conv2d_29 (Conv2D)           (None, 4, 4, 512)         2359808   
_________________________________________________________________
max_pooling2d_14 (MaxPooling (None, 2, 2, 512)         0         
_________________________________________________________________
dropout_22 (Dropout)         (None, 2, 2, 512)         0         
_________________________________________________________________
flatten_4 (Flatten)          (None, 2048)              0         
_________________________________________________________________
dense_12 (Dense)             (None, 1000)              2049000   
_________________________________________________________________
dropout_23 (Dropout)         (None, 1000)              0         
_________________________________________________________________
dense_13 (Dense)             (None, 1000)              1001000   
_________________________________________________________________
dropout_24 (Dropout)         (None, 1000)              0         
_________________________________________________________________
dense_14 (Dense)             (None, 100)               100100    
=================================================================
Total params: 7,726,484
Trainable params: 7,726,484
Non-trainable params: 0
_________________________________________________________________
Epoch 1/50
313/313 - 49s - loss: 4.5523 - accuracy: 0.0134 - val_loss: 4.4756 - val_accuracy: 0.0193
Epoch 2/50
313/313 - 48s - loss: 4.4611 - accuracy: 0.0191 - val_loss: 4.3372 - val_accuracy: 0.0198
Epoch 3/50
313/313 - 48s - loss: 4.3132 - accuracy: 0.0273 - val_loss: 4.2258 - val_accuracy: 0.0401
Epoch 4/50
313/313 - 48s - loss: 4.1835 - accuracy: 0.0452 - val_loss: 3.9609 - val_accuracy: 0.0801
Epoch 5/50
313/313 - 48s - loss: 3.9705 - accuracy: 0.0706 - val_loss: 3.7852 - val_accuracy: 0.1019
Epoch 6/50
313/313 - 48s - loss: 3.8004 - accuracy: 0.0944 - val_loss: 3.6226 - val_accuracy: 0.1339
Epoch 7/50
313/313 - 48s - loss: 3.6419 - accuracy: 0.1163 - val_loss: 3.4493 - val_accuracy: 0.1596
Epoch 8/50
313/313 - 48s - loss: 3.5093 - accuracy: 0.1408 - val_loss: 3.2853 - val_accuracy: 0.1879
Epoch 9/50
313/313 - 48s - loss: 3.3849 - accuracy: 0.1596 - val_loss: 3.1519 - val_accuracy: 0.2183
Epoch 10/50
313/313 - 48s - loss: 3.2836 - accuracy: 0.1840 - val_loss: 3.0731 - val_accuracy: 0.2175
Epoch 11/50
313/313 - 48s - loss: 3.1782 - accuracy: 0.2033 - val_loss: 2.8912 - val_accuracy: 0.2621
Epoch 12/50
313/313 - 48s - loss: 3.0878 - accuracy: 0.2190 - val_loss: 2.8348 - val_accuracy: 0.2738
Epoch 13/50
313/313 - 48s - loss: 3.0043 - accuracy: 0.2397 - val_loss: 2.7050 - val_accuracy: 0.2988
Epoch 14/50
313/313 - 48s - loss: 2.9175 - accuracy: 0.2544 - val_loss: 2.6330 - val_accuracy: 0.3175
Epoch 15/50
313/313 - 46s - loss: 2.8399 - accuracy: 0.2704 - val_loss: 2.5640 - val_accuracy: 0.3288
Epoch 16/50
313/313 - 43s - loss: 2.7687 - accuracy: 0.2830 - val_loss: 2.5094 - val_accuracy: 0.3400
Epoch 17/50
313/313 - 43s - loss: 2.6931 - accuracy: 0.2989 - val_loss: 2.4587 - val_accuracy: 0.3537
Epoch 18/50
313/313 - 43s - loss: 2.6288 - accuracy: 0.3154 - val_loss: 2.4005 - val_accuracy: 0.3614
Epoch 19/50
313/313 - 43s - loss: 2.5762 - accuracy: 0.3241 - val_loss: 2.2977 - val_accuracy: 0.3881
Epoch 20/50
313/313 - 43s - loss: 2.5268 - accuracy: 0.3335 - val_loss: 2.3077 - val_accuracy: 0.3833
Epoch 21/50
313/313 - 43s - loss: 2.4748 - accuracy: 0.3451 - val_loss: 2.2484 - val_accuracy: 0.4000
Epoch 22/50
313/313 - 43s - loss: 2.4202 - accuracy: 0.3550 - val_loss: 2.1712 - val_accuracy: 0.4135
Epoch 23/50
313/313 - 43s - loss: 2.3776 - accuracy: 0.3683 - val_loss: 2.1494 - val_accuracy: 0.4159
Epoch 24/50
313/313 - 43s - loss: 2.3328 - accuracy: 0.3765 - val_loss: 2.1832 - val_accuracy: 0.4108
Epoch 25/50
313/313 - 48s - loss: 2.2932 - accuracy: 0.3810 - val_loss: 2.0881 - val_accuracy: 0.4307
Epoch 26/50
313/313 - 48s - loss: 2.2571 - accuracy: 0.3913 - val_loss: 2.0531 - val_accuracy: 0.4421
Epoch 27/50
313/313 - 48s - loss: 2.2218 - accuracy: 0.4007 - val_loss: 2.1374 - val_accuracy: 0.4289
Epoch 28/50
313/313 - 48s - loss: 2.1894 - accuracy: 0.4090 - val_loss: 1.9739 - val_accuracy: 0.4608
Epoch 29/50
313/313 - 48s - loss: 2.1478 - accuracy: 0.4177 - val_loss: 1.9253 - val_accuracy: 0.4724
Epoch 30/50
313/313 - 48s - loss: 2.1158 - accuracy: 0.4273 - val_loss: 1.9649 - val_accuracy: 0.4573
Epoch 31/50
313/313 - 48s - loss: 2.0896 - accuracy: 0.4346 - val_loss: 2.0402 - val_accuracy: 0.4503
Epoch 32/50
313/313 - 48s - loss: 2.0512 - accuracy: 0.4408 - val_loss: 1.8843 - val_accuracy: 0.4811
Epoch 33/50
313/313 - 48s - loss: 2.0230 - accuracy: 0.4489 - val_loss: 1.8830 - val_accuracy: 0.4834
Epoch 34/50
313/313 - 48s - loss: 1.9919 - accuracy: 0.4564 - val_loss: 1.9074 - val_accuracy: 0.4827
Epoch 35/50
313/313 - 48s - loss: 1.9671 - accuracy: 0.4623 - val_loss: 1.8758 - val_accuracy: 0.4861
Epoch 36/50
313/313 - 48s - loss: 1.9369 - accuracy: 0.4662 - val_loss: 1.8304 - val_accuracy: 0.4959
Epoch 37/50
313/313 - 48s - loss: 1.9162 - accuracy: 0.4746 - val_loss: 1.8138 - val_accuracy: 0.5008
Epoch 38/50
313/313 - 48s - loss: 1.8926 - accuracy: 0.4800 - val_loss: 1.8278 - val_accuracy: 0.4965
Epoch 39/50
313/313 - 48s - loss: 1.8696 - accuracy: 0.4846 - val_loss: 1.8296 - val_accuracy: 0.4979
Epoch 40/50
313/313 - 48s - loss: 1.8420 - accuracy: 0.4929 - val_loss: 1.7450 - val_accuracy: 0.5178
Epoch 41/50
313/313 - 48s - loss: 1.8072 - accuracy: 0.4994 - val_loss: 1.7600 - val_accuracy: 0.5147
Epoch 42/50
313/313 - 48s - loss: 1.7857 - accuracy: 0.5053 - val_loss: 1.6995 - val_accuracy: 0.5282
Epoch 43/50
313/313 - 48s - loss: 1.7681 - accuracy: 0.5083 - val_loss: 1.7125 - val_accuracy: 0.5256
Epoch 44/50
313/313 - 48s - loss: 1.7487 - accuracy: 0.5133 - val_loss: 1.7166 - val_accuracy: 0.5258
Epoch 45/50
313/313 - 48s - loss: 1.7219 - accuracy: 0.5233 - val_loss: 1.6826 - val_accuracy: 0.5339
Epoch 46/50
313/313 - 48s - loss: 1.6979 - accuracy: 0.5246 - val_loss: 1.6346 - val_accuracy: 0.5425
Epoch 47/50
313/313 - 48s - loss: 1.6914 - accuracy: 0.5260 - val_loss: 1.6819 - val_accuracy: 0.5380
Epoch 48/50
313/313 - 48s - loss: 1.6582 - accuracy: 0.5345 - val_loss: 1.6224 - val_accuracy: 0.5478
Epoch 49/50
313/313 - 48s - loss: 1.6473 - accuracy: 0.5357 - val_loss: 1.6802 - val_accuracy: 0.5372
Epoch 50/50
313/313 - 48s - loss: 1.6243 - accuracy: 0.5412 - val_loss: 1.6264 - val_accuracy: 0.5497
출력형식 : [Data augmentation-Dropout-l2 reg] (교차검증 시도/평균)
[010] ( [0.5447999835014343, 0.5440999865531921, 0.5526999831199646, 0.557200014591217, 0.5454999804496765] / 0.5488599896430969 )