Adam optimizer
Adam optimizer as described in Adam - A Method for Stochastic Optimization.
optimizer_adam(
lr = 0.001,
beta_1 = 0.9,
beta_2 = 0.999,
epsilon = NULL,
decay = 0,
amsgrad = FALSE,
clipnorm = NULL,
clipvalue = NULL
)
Arguments
lr | float >= 0. Learning rate. |
beta_1 | The exponential decay rate for the 1st moment estimates. float, 0 < beta < 1. Generally close to 1. |
beta_2 | The exponential decay rate for the 2nd moment estimates. float, 0 < beta < 1. Generally close to 1. |
epsilon | float >= 0. Fuzz factor. If |
decay | float >= 0. Learning rate decay over each update. |
amsgrad | Whether to apply the AMSGrad variant of this algorithm from the paper "On the Convergence of Adam and Beyond". |
clipnorm | Gradients will be clipped when their L2 norm exceeds this value. |
clipvalue | Gradients will be clipped when their absolute value exceeds this value. |
Note
Default parameters follow those provided in the original paper.
References
See also
Other optimizers:
optimizer_adadelta()
,
optimizer_adagrad()
,
optimizer_adamax()
,
optimizer_nadam()
,
optimizer_rmsprop()
,
optimizer_sgd()