tf.contrib.keras.optimizers.Adagrad

class tf.contrib.keras.optimizers.Adagrad

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

Adagrad optimizer.

It is recommended to leave the parameters of this optimizer at their default values.

Arguments:

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

References: - Adaptive Subgradient Methods for Online Learning and Stochastic Optimization

Methods

__init__

__init__(
    lr=0.01,
    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.