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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Configuration definitions for ResNet losses, learning rates, and optimizers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import dataclasses
from official.legacy.image_classification.configs import base_configs
from official.modeling.hyperparams import base_config
@dataclasses.dataclass
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 = dataclasses.field(
default_factory=lambda: base_configs.LossConfig( # pylint: disable=g-long-lambda
name='sparse_categorical_crossentropy'
)
)
optimizer: base_configs.OptimizerConfig = dataclasses.field(
default_factory=lambda: base_configs.OptimizerConfig( # pylint: disable=g-long-lambda
name='momentum',
decay=0.9,
epsilon=0.001,
momentum=0.9,
moving_average_decay=None,
)
)
learning_rate: base_configs.LearningRateConfig = dataclasses.field(
default_factory=lambda: base_configs.LearningRateConfig( # pylint: disable=g-long-lambda
name='stepwise',
initial_lr=0.1,
examples_per_epoch=1281167,
boundaries=[30, 60, 80],
warmup_epochs=5,
scale_by_batch_size=1.0 / 256.0,
multipliers=[0.1 / 256, 0.01 / 256, 0.001 / 256, 0.0001 / 256],
)
)
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