class ResNetModelConfig(base_configs.ModelConfig): """Configuration for the ResNet model.""" name: str = 'ResNet' num_classes: int = 1000 model_params: base_config.Config = dataclasses.field( default_factory=lambda: { 'num_classes': 1000, 'batch_size': None, 'use_l2_regularizer': True, 'rescale_inputs': False, }) 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='stepwise', initial_lr=0.1, examples_per_epoch=1281167, boundaries=[30, 60, 80], warmup_epochs=5, scale_by_batch_size=1. / 256., multipliers=[0.1 / 256, 0.01 / 256, 0.001 / 256, 0.0001 / 256]))
class ResNetModelConfig(base_configs.ModelConfig): """Configuration for the ResNet model.""" name: str = 'ResNet' num_classes: int = 1000 model_params: base_config.Config = dataclasses.field( default_factory=lambda: { 'num_classes': 1000, 'batch_size': None, 'use_l2_regularizer': True, 'rescale_inputs': False, }) 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=_RESNET_LR_WARMUP_EPOCHS, boundaries=_RESNET_LR_BOUNDARIES, multipliers=_RESNET_LR_MULTIPLIERS))
class EfficientNetModelConfig(base_configs.ModelConfig): """Configuration for the EfficientNet model. This configuration will default to settings used for training efficientnet-b0 on a v3-8 TPU on ImageNet. Attributes: name: The name of the model. Defaults to 'EfficientNet'. num_classes: The number of classes in the model. model_params: A dictionary that represents the parameters of the EfficientNet model. These will be passed in to the "from_name" function. loss: The configuration for loss. Defaults to a categorical cross entropy implementation. optimizer: The configuration for optimizations. Defaults to an RMSProp configuration. learning_rate: The configuration for learning rate. Defaults to an exponential configuration. """ name: str = 'EfficientNet' num_classes: int = 1000 model_params: base_config.Config = dataclasses.field( default_factory=lambda: { 'model_name': 'efficientnet-b0', 'model_weights_path': '', 'weights_format': 'saved_model', 'overrides': { 'batch_norm': 'default', 'rescale_input': True, 'num_classes': 1000, 'activation': 'swish', 'dtype': 'float32', } }) loss: base_configs.LossConfig = base_configs.LossConfig( name='categorical_crossentropy', label_smoothing=0.1) optimizer: base_configs.OptimizerConfig = base_configs.OptimizerConfig( name='rmsprop', decay=0.9, epsilon=0.001, momentum=0.9, moving_average_decay=None) learning_rate: base_configs.LearningRateConfig = base_configs.LearningRateConfig( # pylint: disable=line-too-long name='exponential', initial_lr=0.008, decay_epochs=2.4, decay_rate=0.97, warmup_epochs=5, scale_by_batch_size=1. / 128., staircase=True)
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))