brainpy.optim.ExponentialLR#

class brainpy.optim.ExponentialLR(lr, gamma, last_epoch=-1)[source]#

Decays the learning rate of each parameter group by gamma every epoch. When last_epoch=-1, sets initial lr as lr.

Parameters:

  • lr (float) – Initial learning rate.

  • gamma (float) – Multiplicative factor of learning rate decay.

  • last_epoch (int) – The index of last epoch. Default: -1.

__init__(lr, gamma, last_epoch=-1)[source]#

Methods

__init__(lr, gamma[, last_epoch])

cpu()

Move all variable into the CPU device.

cuda()

Move all variables into the GPU device.

load_state_dict(state_dict[, warn, compatible])

Copy parameters and buffers from state_dict into this module and its descendants.

load_states(filename[, verbose])

Load the model states.

nodes([method, level, include_self])

Collect all children nodes.

register_implicit_nodes(*nodes[, node_cls])

register_implicit_vars(*variables[, var_cls])

save_states(filename[, variables])

Save the model states.

state_dict()

Returns a dictionary containing a whole state of the module.

step_call()

step_epoch()

to(device)

Moves all variables into the given device.

tpu()

Move all variables into the TPU device.

train_vars([method, level, include_self])

The shortcut for retrieving all trainable variables.

tree_flatten()

Flattens the object as a PyTree.

tree_unflatten(aux, dynamic_values)

Unflatten the data to construct an object of this class.

unique_name([name, type_])

Get the unique name for this object.

vars([method, level, include_self, ...])

Collect all variables in this node and the children nodes.

Attributes

name

Name of the model.