# optimizer_adam

##### Adam optimizer

Adam optimizer as described in Adam - A Method for Stochastic Optimization.

##### Usage

```
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

`NULL`

, defaults to`k_epsilon()`

.- 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`

*Documentation reproduced from package keras, version 2.2.4, License: MIT + file LICENSE*