Optimizer that implements the Adagrad algorithm.
Inherits From: Optimizer
tf.keras.optimizers.Adagrad(
learning_rate=0.001,
initial_accumulator_value=0.1,
epsilon=1e-07,
name='Adagrad',
**kwargs
)
Adagrad is an optimizer with parameter-specific learning rates,
which are adapted relative to how frequently a parameter gets
updated during training. The more updates a parameter receives,
the smaller the updates.
Args |
learning_rate
|
Initial value for the learning rate:
either a floating point value,
or a tf.keras.optimizers.schedules.LearningRateSchedule instance.
Defaults to 0.001.
Note that Adagrad tends to benefit from higher initial learning rate
values compared to other optimizers.
To match the exact form in the original paper, use 1.0.
|
initial_accumulator_value
|
Floating point value.
Starting value for the accumulators (per-parameter momentum values).
Must be non-negative.
|
epsilon
|
Small floating point value used to maintain numerical stability.
|
name
|
Optional name prefix for the operations created when applying
gradients. Defaults to "Adagrad" .
|
**kwargs
|
Keyword arguments. Allowed to be one of
"clipnorm" or "clipvalue" .
"clipnorm" (float) clips gradients by norm and represents
the maximum L2 norm of each weight variable;
"clipvalue" (float) clips gradient by value and represents the
maximum absolute value of each weight variable.
|
Raises |
ValueError
|
in case of any invalid argument.
|