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syntax = "proto2";
package object_detection.protos;
// Messages for configuring the optimizing strategy for training object
// detection models.
// Top level optimizer message.
message Optimizer {
oneof optimizer {
RMSPropOptimizer rms_prop_optimizer = 1;
MomentumOptimizer momentum_optimizer = 2;
AdamOptimizer adam_optimizer = 3;
}
optional bool use_moving_average = 4 [default = true];
optional float moving_average_decay = 5 [default = 0.9999];
}
// Configuration message for the RMSPropOptimizer
// See: https://www.tensorflow.org/api_docs/python/tf/train/RMSPropOptimizer
message RMSPropOptimizer {
optional LearningRate learning_rate = 1;
optional float momentum_optimizer_value = 2 [default = 0.9];
optional float decay = 3 [default = 0.9];
optional float epsilon = 4 [default = 1.0];
}
// Configuration message for the MomentumOptimizer
// See: https://www.tensorflow.org/api_docs/python/tf/train/MomentumOptimizer
message MomentumOptimizer {
optional LearningRate learning_rate = 1;
optional float momentum_optimizer_value = 2 [default = 0.9];
}
// Configuration message for the AdamOptimizer
// See: https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer
message AdamOptimizer {
optional LearningRate learning_rate = 1;
// Default value for epsilon (1e-8) matches default value in
// tf.train.AdamOptimizer. This differs from tf2 default of 1e-7
// in tf.keras.optimizers.Adam .
optional float epsilon = 2 [default = 1e-8];
}
// Configuration message for optimizer learning rate.
message LearningRate {
oneof learning_rate {
ConstantLearningRate constant_learning_rate = 1;
ExponentialDecayLearningRate exponential_decay_learning_rate = 2;
ManualStepLearningRate manual_step_learning_rate = 3;
CosineDecayLearningRate cosine_decay_learning_rate = 4;
}
}
// Configuration message for a constant learning rate.
message ConstantLearningRate {
optional float learning_rate = 1 [default = 0.002];
}
// Configuration message for an exponentially decaying learning rate.
// See https://www.tensorflow.org/versions/master/api_docs/python/train/ \
// decaying_the_learning_rate#exponential_decay
message ExponentialDecayLearningRate {
optional float initial_learning_rate = 1 [default = 0.002];
optional uint32 decay_steps = 2 [default = 4000000];
optional float decay_factor = 3 [default = 0.95];
optional bool staircase = 4 [default = true];
optional float burnin_learning_rate = 5 [default = 0.0];
optional uint32 burnin_steps = 6 [default = 0];
optional float min_learning_rate = 7 [default = 0.0];
}
// Configuration message for a manually defined learning rate schedule.
message ManualStepLearningRate {
optional float initial_learning_rate = 1 [default = 0.002];
message LearningRateSchedule {
optional uint32 step = 1;
optional float learning_rate = 2 [default = 0.002];
}
repeated LearningRateSchedule schedule = 2;
// Whether to linearly interpolate learning rates for steps in
// [0, schedule[0].step].
optional bool warmup = 3 [default = false];
}
// Configuration message for a cosine decaying learning rate as defined in
// object_detection/utils/learning_schedules.py
message CosineDecayLearningRate {
optional float learning_rate_base = 1 [default = 0.002];
optional uint32 total_steps = 2 [default = 4000000];
optional float warmup_learning_rate = 3 [default = 0.0002];
optional uint32 warmup_steps = 4 [default = 10000];
optional uint32 hold_base_rate_steps = 5 [default = 0];
}
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