class ResNetModelConfig(base_configs.ModelConfig):
"""Configuration for the ResNet model."""
name: str = 'ResNet'
num_classes: int = 1000
model_params: base_config.Config = dataclasses.field(
# pylint: disable=g-long-lambda
default_factory=lambda: {
'num_classes': 1000,
'batch_size': None,
'use_l2_regularizer': True,
'rescale_inputs': False,
})
# pylint: enable=g-long-lambda
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 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_epochs = 1
train_steps = 1
lr = optimizer_factory.build_learning_rate(params=params,
batch_size=batch_size,
train_epochs=train_epochs,
train_steps=train_steps)
self.assertTrue(
issubclass(type(lr),
tf.keras.optimizers.schedules.LearningRateSchedule))
