history = model.fit(
        train_generator,
        steps_per_epoch=100,
        epochs=30,
        validation_data=validation_generator,
        validation_steps=50,
        verbose=2)

Train for 100 steps, validate for 50 steps
Epoch 1/30
100/100 - 92s - loss: 0.9063 - accuracy: 0.5125 - val_loss: 0.7271 - val_accuracy: 0.5000
Epoch 2/30
100/100 - 56s - loss: 0.7020 - accuracy: 0.5625 - val_loss: 0.6551 - val_accuracy: 0.5480
Epoch 3/30
100/100 - 56s - loss: 0.6815 - accuracy: 0.5950 - val_loss: 0.6253 - val_accuracy: 0.6600
Epoch 4/30
100/100 - 57s - loss: 0.6594 - accuracy: 0.6220 - val_loss: 0.6262 - val_accuracy: 0.6350
Epoch 5/30
100/100 - 56s - loss: 0.6352 - accuracy: 0.6485 - val_loss: 0.5916 - val_accuracy: 0.6890
Epoch 6/30
100/100 - 56s - loss: 0.6336 - accuracy: 0.6675 - val_loss: 0.5774 - val_accuracy: 0.6790
Epoch 7/30
100/100 - 57s - loss: 0.6383 - accuracy: 0.6570 - val_loss: 0.5830 - val_accuracy: 0.6980
Epoch 8/30
100/100 - 57s - loss: 0.6048 - accuracy: 0.6855 - val_loss: 0.5484 - val_accuracy: 0.7220
Epoch 9/30
100/100 - 56s - loss: 0.6060 - accuracy: 0.6705 - val_loss: 0.5582 - val_accuracy: 0.7050
Epoch 10/30
100/100 - 56s - loss: 0.5921 - accuracy: 0.6750 - val_loss: 0.5395 - val_accuracy: 0.7270
Epoch 11/30
100/100 - 56s - loss: 0.5647 - accuracy: 0.7145 - val_loss: 0.5613 - val_accuracy: 0.6960
Epoch 12/30
100/100 - 56s - loss: 0.5921 - accuracy: 0.6880 - val_loss: 0.5432 - val_accuracy: 0.7210
Epoch 13/30
100/100 - 57s - loss: 0.5974 - accuracy: 0.7030 - val_loss: 0.5542 - val_accuracy: 0.7160
Epoch 14/30
100/100 - 56s - loss: 0.5828 - accuracy: 0.7145 - val_loss: 0.5376 - val_accuracy: 0.7350
Epoch 15/30
100/100 - 57s - loss: 0.5804 - accuracy: 0.7020 - val_loss: 0.5892 - val_accuracy: 0.6700
Epoch 16/30
100/100 - 56s - loss: 0.5738 - accuracy: 0.7050 - val_loss: 0.5828 - val_accuracy: 0.7110
Epoch 17/30
100/100 - 56s - loss: 0.5686 - accuracy: 0.7190 - val_loss: 0.5407 - val_accuracy: 0.7140
Epoch 18/30
100/100 - 57s - loss: 0.5610 - accuracy: 0.7165 - val_loss: 0.5241 - val_accuracy: 0.7470
Epoch 19/30
100/100 - 58s - loss: 0.5611 - accuracy: 0.7205 - val_loss: 0.5007 - val_accuracy: 0.7590
Epoch 20/30
100/100 - 57s - loss: 0.5569 - accuracy: 0.7245 - val_loss: 0.5168 - val_accuracy: 0.7460
Epoch 21/30
100/100 - 57s - loss: 0.5568 - accuracy: 0.7150 - val_loss: 0.6595 - val_accuracy: 0.6740
Epoch 22/30
100/100 - 56s - loss: 0.5414 - accuracy: 0.7225 - val_loss: 0.5355 - val_accuracy: 0.7510
Epoch 23/30
100/100 - 57s - loss: 0.5583 - accuracy: 0.7190 - val_loss: 0.5111 - val_accuracy: 0.7380
Epoch 24/30
100/100 - 58s - loss: 0.5524 - accuracy: 0.7185 - val_loss: 0.5660 - val_accuracy: 0.7130
Epoch 25/30
100/100 - 57s - loss: 0.5468 - accuracy: 0.7285 - val_loss: 0.5290 - val_accuracy: 0.7170
Epoch 26/30
100/100 - 57s - loss: 0.5374 - accuracy: 0.7320 - val_loss: 0.4902 - val_accuracy: 0.7520
Epoch 27/30
100/100 - 57s - loss: 0.5514 - accuracy: 0.7230 - val_loss: 0.5371 - val_accuracy: 0.7220
Epoch 28/30
100/100 - 56s - loss: 0.5455 - accuracy: 0.7355 - val_loss: 0.4915 - val_accuracy: 0.7820
Epoch 29/30
100/100 - 57s - loss: 0.5366 - accuracy: 0.7415 - val_loss: 0.5398 - val_accuracy: 0.7320
Epoch 30/30
100/100 - 57s - loss: 0.5477 - accuracy: 0.7295 - val_loss: 0.4697 - val_accuracy: 0.7880