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 )
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. |
Default parameters follow those provided in the original paper.
Other optimizers:
optimizer_adadelta(),
optimizer_adagrad(),
optimizer_adamax(),
optimizer_nadam(),
optimizer_rmsprop(),
optimizer_sgd()
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