| batch_size=64 | |
| no_of_epochs=1 | |
| # do it | |
| vgg = Vgg16() | |
| # Grab a few images at a time for training and validation. | |
| # NB: They must be in subdirectories named based on their category | |
| train_batches = vgg.get_batches(train_path, batch_size=batch_size) | |
| val_batches = vgg.get_batches(valid_path, batch_size=batch_size*2) | |
| vgg.finetune(train_batches) | |
| vgg.model.optimizer.lr = 0.01 | |
| vgg.fit(train_batches, val_batches, nb_epoch=no_of_epochs) | |
| Found 23000 images belonging to 2 classes. | |
| Found 2000 images belonging to 2 classes. | |
| Epoch 1/1 | |
| 23000/23000 [==============================] - 654s - loss: 0.3473 - acc: 0.9701 - val_loss: 0.1633 - val_acc: 0.9865 |
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