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Training

class Training

Builds model and runs training.

The following pretrained CNNs from Keras can be used for transfer learning:

  • Xception

  • VGG16

  • VGG19

  • ResNet50, ResNet101, ResNet152

  • ResNet50V2, ResNet101V2, ResNet152V2

  • ResNeXt50, ResNeXt101

  • InceptionV3

  • InceptionResNetV2

  • MobileNet

  • MobileNetV2

  • DenseNet121, DenseNet169, DenseNet201

  • NASNetLarge, NASNetMobile

Training is split into two phases, at first only the last dense layer gets trained, and then all layers are trained. The maximum number of epochs for each phase is set by epochs_train_dense (default: 100) and epochs_train_all (default: 100), respectively. Similarly, learning_rate_dense (default: 0.001) and learning_rate_all (default: 0.0003) can be set.

For each phase the learning rate is reduced after a patience period if no improvement in validation accuracy has been observed. The patience period depends on the average number of samples per class:

  • if n_per_class < 200: patience = 5 epochs

  • if n_per_class >= 200 and < 500: patience = 4 epochs

  • if n_per_class >= 500: patience = 2 epochs

The training is stopped early after a patience period that is three times the learning rate patience to allow for two learning rate adjustments with no validation accuracy improvement before stopping training.

Attributes

  • image_dir: Directory with images used for training.

  • job_dir: Directory with train_samples.json, val_samples.json, and class_mapping.json.

  • epochs_train_dense: Maximum number of epochs to train dense layers (default 100).

  • epochs_train_all: Maximum number of epochs to train all layers (default 100).

  • learning_rate_dense: Learning rate for dense training phase (default 0.001).

  • learning_rate_all: Learning rate for all training phase (default 0.0003).

  • batch_size: Number of images per batch (default 64).

  • dropout_rate: Fraction of nodes before output layer set to random value (default 0.75).

  • base_model_name: Name of pretrained CNN (default MobileNet).

__init__

def __init__(image_dir, job_dir, epochs_train_dense, epochs_train_all, learning_rate_dense, learning_rate_all, batch_size, dropout_rate, base_model_name, loss, **kwargs)

Inits training component.

Checks whether multiprocessing is available and sets number of workers for training.

run

def run()

Builds the model and runs training.