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.