# Rolling Memory

Rolling memory is a type of experience replay. Each training step, a memory is replaced with the top scoring batch item.

Each `types` pairing becomes a discriminator that is added to the loss.

## examples

```javascript
{
    "class": "function:hypergan.train_hooks.experimental.rolling_memory_2_train_hook.RollingMemoryTrainHook",
    "types": ["mx-/g(mz-)"]
}
```

mx- is a memory of x that gets updated each training step. g(mz-) is a memory of z that gets run through a generator and updated each trainng step.

A discriminator `d(mx-, g(mz-))` is created and added to the gan loss.

## options

| attribute |                             description                            |       type       |
| :-------: | :----------------------------------------------------------------: | :--------------: |
|   types   | What memories and how they are paired.  See **memory types** below | array of strings |
|   top\_k  |    How many memory items to replace per frame.  Defaults to `1`    |      integer     |
|    only   |  Overrides all other losses when this is set. Defaults to `false`  |      boolean     |

### memory types

| memory | description                                        |
| ------ | -------------------------------------------------- |
| mx-    | x reverse sorted by d\_real                        |
| mx+    | x sorted by d\_real                                |
| mg-    | memory of g reverse sorted by d\_fake              |
| mg+    | memory of g sorted by d\_fake                      |
| g(mz-) | generator of memory of z reverse sorted by d\_fake |
| g(mz+) | generator of memory of z sorted by d\_fake         |
| x      | gan.inputs.x                                       |
| g      | gan.generator.sample                               |