def test_learning_rate_without_decay_or_warmups(self): params = base_configs.LearningRateConfig(name='exponential', initial_lr=0.01, decay_rate=0.01, decay_epochs=None, warmup_epochs=None, scale_by_batch_size=0.01, examples_per_epoch=1, boundaries=[0], multipliers=[0, 1]) batch_size = 1 train_steps = 1 lr = optimizer_factory.build_learning_rate(params=params, batch_size=batch_size, train_steps=train_steps) self.assertTrue( issubclass(type(lr), tf.keras.optimizers.schedules.LearningRateSchedule))
def test_learning_rate_with_decay_and_warmup(self, lr_decay_type): """Basic smoke test for syntax.""" params = base_configs.LearningRateConfig(name=lr_decay_type, initial_lr=0.01, decay_rate=0.01, decay_epochs=1, warmup_epochs=1, scale_by_batch_size=0.01, examples_per_epoch=1, boundaries=[0], multipliers=[0, 1]) batch_size = 1 train_steps = 1 lr = optimizer_factory.build_learning_rate(params=params, batch_size=batch_size, train_steps=train_steps) self.assertTrue( issubclass(type(lr), tf.keras.optimizers.schedules.LearningRateSchedule))
class SqueezeNetModelConfig(base_configs.ModelConfig): """Configuration for the SqueezeNet model.""" name: str = 'SqueezeNet' num_classes: int = 1000 model_params: Mapping[str, Any] = dataclasses.field(default_factory=lambda: { 'num_classes': 1000, 'batch_size': None, }) loss: base_configs.LossConfig = base_configs.LossConfig( name='sparse_categorical_crossentropy') optimizer: base_configs.OptimizerConfig = base_configs.OptimizerConfig( name='momentum', decay=0.9, epsilon=0.001, momentum=0.9, moving_average_decay=None) learning_rate: base_configs.LearningRateConfig = ( base_configs.LearningRateConfig(name='piecewise_constant_with_warmup', examples_per_epoch=1281167, warmup_epochs=_LR_WARMUP_EPOCHS, boundaries=_LR_BOUNDARIES, multipliers=_LR_MULTIPLIERS))