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Keras API reference /
Optimizers /
Adagrad

`Adagrad`

class```
tf.keras.optimizers.Adagrad(
learning_rate=0.001,
initial_accumulator_value=0.1,
epsilon=1e-07,
name="Adagrad",
**kwargs
)
```

Optimizer that implements the Adagrad algorithm.

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.

**Arguments**

**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 arguments are`clipvalue`

,`clipnorm`

,`global_clipnorm`

. If`clipvalue`

(float) is set, the gradient of each weight is clipped to be no higher than this value. If`clipnorm`

(float) is set, the gradient of each weight is individually clipped so that its norm is no higher than this value. If`global_clipnorm`

(float) is set the gradient of all weights is clipped so that their global norm is no higher than this value..

**Reference**