Train on 26048 samples, validate on 6513 samples
Epoch 1/50
26048/26048 [==============================] - 1s 28us/step - loss: 2.3308 - val_loss: 0.4450
Epoch 2/50
26048/26048 [==============================] - 0s 8us/step - loss: 1.5018 - val_loss: 0.5353
Epoch 3/50
26048/26048 [==============================] - 0s 9us/step - loss: 1.3662 - val_loss: 0.5634
Epoch 4/50
26048/26048 [==============================] - 0s 8us/step - loss: 1.3522 - val_loss: 0.6502
Epoch 5/50
26048/26048 [==============================] - 0s 8us/step - loss: 1.3053 - val_loss: 0.5451
Epoch 6/50
26048/26048 [==============================] - 0s 8us/step - loss: 1.2348 - val_loss: 0.5146
Epoch 7/50
26048/26048 [==============================] - 0s 8us/step - loss: 1.2083 - val_loss: 0.4880
Epoch 8/50
26048/26048 [==============================] - 0s 9us/step - loss: 1.2280 - val_loss: 0.7679
Epoch 9/50
26048/26048 [==============================] - 0s 8us/step - loss: 1.1979 - val_loss: 0.4658
Epoch 10/50
26048/26048 [==============================] - 0s 7us/step - loss: 1.1313 - val_loss: 0.5112
Epoch 11/50
26048/26048 [==============================] - 0s 8us/step - loss: 1.1138 - val_loss: 0.5580
Epoch 12/50
26048/26048 [==============================] - 0s 9us/step - loss: 1.2020 - val_loss: 0.4981
Epoch 13/50
26048/26048 [==============================] - 0s 7us/step - loss: 1.0844 - val_loss: 0.4940
Epoch 14/50
26048/26048 [==============================] - 0s 10us/step - loss: 1.0802 - val_loss: 0.5090
Epoch 15/50
26048/26048 [==============================] - 0s 7us/step - loss: 1.0761 - val_loss: 0.5058
Epoch 16/50
26048/26048 [==============================] - 0s 7us/step - loss: 1.0470 - val_loss: 0.5143
Epoch 17/50
26048/26048 [==============================] - 0s 7us/step - loss: 1.0285 - val_loss: 0.5553
Epoch 18/50
26048/26048 [==============================] - 0s 7us/step - loss: 1.0215 - val_loss: 0.5479
Epoch 19/50
26048/26048 [==============================] - 0s 8us/step - loss: 1.0137 - val_loss: 0.5628
Epoch 20/50
26048/26048 [==============================] - 0s 8us/step - loss: 1.0022 - val_loss: 0.5426
Epoch 21/50
26048/26048 [==============================] - 0s 8us/step - loss: 0.9641 - val_loss: 0.5291
Epoch 22/50
26048/26048 [==============================] - 0s 8us/step - loss: 0.9765 - val_loss: 0.7090
Epoch 23/50
26048/26048 [==============================] - 0s 8us/step - loss: 1.0097 - val_loss: 0.4819
Epoch 24/50
26048/26048 [==============================] - 0s 7us/step - loss: 0.9100 - val_loss: 0.4874
Epoch 25/50
26048/26048 [==============================] - 0s 9us/step - loss: 0.8821 - val_loss: 0.4724
Epoch 26/50
26048/26048 [==============================] - 0s 9us/step - loss: 0.8653 - val_loss: 0.5671
Epoch 27/50
26048/26048 [==============================] - 0s 8us/step - loss: 1.0496 - val_loss: 0.6884
Epoch 28/50
26048/26048 [==============================] - 0s 8us/step - loss: 0.9529 - val_loss: 0.5993
Epoch 29/50
26048/26048 [==============================] - 0s 7us/step - loss: 0.9255 - val_loss: 0.5297
Epoch 30/50
26048/26048 [==============================] - 0s 7us/step - loss: 0.8726 - val_loss: 0.4880
Epoch 31/50
26048/26048 [==============================] - 0s 8us/step - loss: 0.8523 - val_loss: 0.4730
Epoch 32/50
26048/26048 [==============================] - 0s 7us/step - loss: 0.8526 - val_loss: 0.4683
Epoch 33/50
26048/26048 [==============================] - 0s 8us/step - loss: 0.7988 - val_loss: 0.4655
Epoch 34/50
26048/26048 [==============================] - 0s 7us/step - loss: 0.7920 - val_loss: 0.4560
Epoch 35/50
26048/26048 [==============================] - 0s 7us/step - loss: 0.7629 - val_loss: 0.4449
Epoch 36/50
26048/26048 [==============================] - 0s 8us/step - loss: 0.7506 - val_loss: 0.4388
Epoch 37/50
26048/26048 [==============================] - 0s 8us/step - loss: 0.7266 - val_loss: 0.4366
Epoch 38/50
26048/26048 [==============================] - 0s 7us/step - loss: 0.7460 - val_loss: 0.4239
Epoch 39/50
26048/26048 [==============================] - 0s 7us/step - loss: 0.7268 - val_loss: 0.4159
Epoch 40/50
26048/26048 [==============================] - 0s 10us/step - loss: 0.7199 - val_loss: 0.4025
Epoch 41/50
26048/26048 [==============================] - 0s 9us/step - loss: 0.6725 - val_loss: 0.4090
Epoch 42/50
26048/26048 [==============================] - 0s 7us/step - loss: 0.7740 - val_loss: 0.4576
Epoch 43/50
26048/26048 [==============================] - 0s 7us/step - loss: 0.7491 - val_loss: 0.4111
Epoch 44/50
26048/26048 [==============================] - 0s 7us/step - loss: 0.6639 - val_loss: 0.4068
Epoch 45/50
26048/26048 [==============================] - 0s 7us/step - loss: 0.6734 - val_loss: 0.4218
Epoch 46/50
26048/26048 [==============================] - 0s 7us/step - loss: 0.6580 - val_loss: 0.3993
Epoch 47/50
26048/26048 [==============================] - 0s 7us/step - loss: 0.6516 - val_loss: 0.4000
Epoch 48/50
26048/26048 [==============================] - 0s 7us/step - loss: 0.6464 - val_loss: 0.3989
Epoch 49/50
26048/26048 [==============================] - 0s 7us/step - loss: 0.6258 - val_loss: 0.4004
Epoch 50/50
26048/26048 [==============================] - 0s 7us/step - loss: 0.6157 - val_loss: 0.4005