| @@ -32,6 +32,7 @@ def piecewise_constant_lr(milestone, learning_rates): | |||
| Args: | |||
| milestone (Union[list[int], tuple[int]]): A list of milestone. This list is a monotone increasing list. | |||
| Every element is a milestone step, and must be greater than 0. | |||
| learning_rates (Union[list[float], tuple[float]]): A list of learning rates. | |||
| Returns: | |||
| @@ -40,7 +41,7 @@ def piecewise_constant_lr(milestone, learning_rates): | |||
| Examples: | |||
| >>> milestone = [2, 5, 10] | |||
| >>> learning_rates = [0.1, 0.05, 0.01] | |||
| >>> lr = piecewise_constant_lr(milestone, learning_rates) | |||
| >>> piecewise_constant_lr(milestone, learning_rates) | |||
| [0.1, 0.1, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01] | |||
| """ | |||
| validator.check_value_type('milestone', milestone, (tuple, list), None) | |||
| @@ -100,7 +101,7 @@ def exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, | |||
| >>> total_step = 6 | |||
| >>> step_per_epoch = 2 | |||
| >>> decay_epoch = 1 | |||
| >>> lr = exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch) | |||
| >>> exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch) | |||
| [0.1, 0.1, 0.09000000000000001, 0.09000000000000001, 0.08100000000000002, 0.08100000000000002] | |||
| """ | |||
| _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair) | |||
| @@ -142,7 +143,7 @@ def natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, | |||
| >>> total_step = 6 | |||
| >>> step_per_epoch = 2 | |||
| >>> decay_epoch = 2 | |||
| >>> lr = natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True) | |||
| >>> natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True) | |||
| [0.1, 0.1, 0.1, 0.1, 0.016529888822158657, 0.016529888822158657] | |||
| """ | |||
| _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair) | |||
| @@ -185,7 +186,7 @@ def inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, deca | |||
| >>> total_step = 6 | |||
| >>> step_per_epoch = 1 | |||
| >>> decay_epoch = 1 | |||
| >>> lr = inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True) | |||
| >>> inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True) | |||
| [0.1, 0.06666666666666667, 0.05, 0.04, 0.03333333333333333, 0.028571428571428574] | |||
| """ | |||
| _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair) | |||
| @@ -227,7 +228,7 @@ def cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch): | |||
| >>> total_step = 6 | |||
| >>> step_per_epoch = 2 | |||
| >>> decay_epoch = 2 | |||
| >>> lr = cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch) | |||
| >>> cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch) | |||
| [0.1, 0.1, 0.05500000000000001, 0.05500000000000001, 0.01, 0.01] | |||
| """ | |||
| validator.check_float_positive('min_lr', min_lr, None) | |||
| @@ -282,7 +283,7 @@ def polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_e | |||
| >>> step_per_epoch = 2 | |||
| >>> decay_epoch = 2 | |||
| >>> power = 0.5 | |||
| >>> lr = polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power) | |||
| >>> polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power) | |||
| [0.1, 0.1, 0.07363961030678928, 0.07363961030678928, 0.01, 0.01] | |||
| """ | |||
| validator.check_float_positive('learning_rate', learning_rate, None) | |||