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Optimizer that implements the NAdam algorithm.

Inherits From: `Optimizer`

`learning_rate` A Tensor or a floating point value. The learning rate.
`beta_1` A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates.
`beta_2` A float value or a constant float tensor. The exponential decay rate for the exponentially weighted infinity norm.
`epsilon` A small constant for numerical stability.
`name` Optional name for the operations created when applying gradients. Defaults to `"Nadam"`.
`**kwargs` Keyword arguments. Allowed to be one of `"clipnorm"` or `"clipvalue"`. `"clipnorm"` (float) clips gradients by norm; `"clipvalue"` (float) clips gradients by value.

#### Usage Example:

````opt = tf.keras.optimizers.Nadam(learning_rate=0.2)`
`var1 = tf.Variable(10.0)`
`loss = lambda: (var1 ** 2) / 2.0`
`step_count = opt.minimize(loss, [var1]).numpy()`
`"{:.1f}".format(var1.numpy())`
`9.8`
```

#### Reference:

`ValueError` in case of any invalid argument.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]