contrib.keras.optimizers.Adam

tf.contrib.keras.optimizers.Adam

class tf.contrib.keras.optimizers.Adam

Defined in tensorflow/contrib/keras/python/keras/optimizers.py.

Adam 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.
decay: float >= 0. Learning rate decay over each update.

References: - Adam - A Method for Stochastic Optimization

Methods

__init__

__init__(
    lr=0.001,
    beta_1=0.9,
    beta_2=0.999,
    epsilon=1e-08,
    decay=0.0,
    **kwargs
)

from_config

from_config(
    cls,
    config
)

get_config

get_config()

get_gradients

get_gradients(
    loss,
    params
)

get_updates

get_updates(
    params,
    constraints,
    loss
)

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.