# W3cubDocs

/TensorFlow Python

Inherits From: Optimizer

Default parameters follow those provided in the original paper.

#### Arguments:

• lr: float >= 0. Learning rate.
• beta_1: float, 0 < beta < 1. Generally close to 1.
• beta_2: float, 0 < beta < 1. Generally close to 1.
• epsilon: float >= 0. Fuzz factor. If None, defaults to K.epsilon().
• decay: float >= 0. Learning rate decay over each update.
• amsgrad: boolean. Whether to apply the AMSGrad variant of this algorithm from the paper "On the Convergence of Adam and Beyond".

## Methods

### __init__

__init__(
lr=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=None,
decay=0.0,
**kwargs
)

Initialize self. See help(type(self)) for accurate signature.

from_config(
cls,
config
)

### get_config

get_config()

loss,
params
)

Returns gradients of loss with respect to params.

#### Arguments:

• loss: Loss tensor.
• params: List of variables.

#### Raises:

• ValueError: In case any gradient cannot be computed (e.g. if gradient function not implemented).

loss,
params
)

### get_weights

get_weights()

Returns the current value of the weights of the optimizer.

#### Returns:

A list of numpy arrays.

### set_weights

set_weights(weights)

Sets the weights of the optimizer, from Numpy arrays.

Should only be called after computing the gradients (otherwise the optimizer has no weights).

#### Arguments:

• weights: a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the optimizer (i.e. it should match the output of get_weights).

#### Raises:

• ValueError: in case of incompatible weight shapes.