Adam optimizer
Adam optimizer as described in Adam - A Method for StochasticOptimization.
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