PiecewiseDecay¶

class paddle.fluid.dygraph.learning_rate_scheduler. PiecewiseDecay ( boundaries, values, begin, step=1, dtype='float32' ) [source]

Api_attr

imperative

Piecewise decay scheduler.

The algorithm can be described as the code below.

boundaries = [10000, 20000]
values = [1.0, 0.5, 0.1]
if global_step < 10000:
    learning_rate = 1.0
elif 10000 <= global_step < 20000:
    learning_rate = 0.5
else:
    learning_rate = 0.1

Parameters

• boundaries (list) – A list of steps numbers. The type of element in the list is python int.

• values (list) – A list of learning rate values that will be picked during different step boundaries. The type of element in the list is python float.

• begin (int) – The begin step to initialize the global_step in the description above.

• step (int, optional) – The step size used to calculate the new global_step in the description above. The default value is 1.

• dtype (str, optional) – The data type used to create the learning rate variable. The data type can be set as ‘float32’, ‘float64’. The default value is ‘float32’.

Returns

None.

Examples

import paddle.fluid as fluid
boundaries = [10000, 20000]
values = [1.0, 0.5, 0.1]
with fluid.dygraph.guard():
    emb = fluid.dygraph.Embedding( [10, 10] )
    optimizer = fluid.optimizer.SGD(
       learning_rate=fluid.dygraph.PiecewiseDecay(boundaries, values, 0),
       parameter_list = emb.parameters() )

create_lr_var ( lr )

create_lr_var¶

convert lr from float to variable

Parameters

lr – learning rate

Returns

learning rate variable

set_dict ( state_dict )

set_dict¶

Loads the schedulers state.

set_state_dict ( state_dict )

set_state_dict¶

Loads the schedulers state.

state_dict ( )

state_dict¶

Returns the state of the scheduler as a dict.

It is a subset of self.__dict__ .