brainpy.optimizers.Adam
brainpy.optimizers.Adam#
- class brainpy.optimizers.Adam(lr, train_vars=None, beta1=0.9, beta2=0.999, eps=1e-08, name=None)[source]#
Optimizer that implements the Adam algorithm.
Adam 6 - a stochastic gradient descent method (SGD) that computes individual adaptive learning rates for different parameters from estimates of first- and second-order moments of the gradients.
- Parameters
beta1 (optional, float) – A positive scalar value for beta_1, the exponential decay rate for the first moment estimates (default 0.9).
beta2 (optional, float) – A positive scalar value for beta_2, the exponential decay rate for the second moment estimates (default 0.999).
eps (optional, float) – A positive scalar value for epsilon, a small constant for numerical stability (default 1e-8).
name (optional, str) – The optimizer name.
References
- 6
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Methods
__init__
(lr[, train_vars, beta1, beta2, ...])check_grads
(grads)load_states
(filename[, verbose])Load the model states.
nodes
([method, level, include_self])Collect all children nodes.
register_implicit_nodes
(nodes)register_implicit_vars
(variables)register_vars
([train_vars])save_states
(filename[, variables])Save the model states.
train_vars
([method, level, include_self])The shortcut for retrieving all trainable variables.
unique_name
([name, type_])Get the unique name for this object.
update
(grads)vars
([method, level, include_self])Collect all variables in this node and the children nodes.
Attributes
name