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Keras API reference /
Optimizers /
Ftrl

`Ftrl`

class```
tf.keras.optimizers.Ftrl(
learning_rate=0.001,
learning_rate_power=-0.5,
initial_accumulator_value=0.1,
l1_regularization_strength=0.0,
l2_regularization_strength=0.0,
name="Ftrl",
l2_shrinkage_regularization_strength=0.0,
**kwargs
)
```

Optimizer that implements the FTRL algorithm.

See Algorithm 1 of this paper. This version has support for both online L2 (the L2 penalty given in the paper above) and shrinkage-type L2 (which is the addition of an L2 penalty to the loss function).

**Arguments**

**learning_rate**: A`Tensor`

, floating point value, or a schedule that is a`tf.keras.optimizers.schedules.LearningRateSchedule`

. The learning rate.**learning_rate_power**: A float value, must be less or equal to zero. Controls how the learning rate decreases during training. Use zero for a fixed learning rate.**initial_accumulator_value**: The starting value for accumulators. Only zero or positive values are allowed.**l1_regularization_strength**: A float value, must be greater than or equal to zero.**l2_regularization_strength**: A float value, must be greater than or equal to zero.**name**: Optional name prefix for the operations created when applying gradients. Defaults to`"Ftrl"`

.**l2_shrinkage_regularization_strength**: A float value, must be greater than or equal to zero. This differs from L2 above in that the L2 above is a stabilization penalty, whereas this L2 shrinkage is a magnitude penalty. When input is sparse shrinkage will only happen on the active weights.****kwargs**: Keyword arguments. Allowed to be one of`"clipnorm"`

or`"clipvalue"`

.`"clipnorm"`

(float) clips gradients by norm;`"clipvalue"`

(float) clips gradients by value.

**Reference**