tf.keras.optimizers.Adagrad
| View source on GitHub |
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 | A Tensor, floating point value, or a schedule that is a tf.keras.optimizers.schedules.LearningRateSchedule. The learning rate. |
initial_accumulator_value | A floating point value. Starting value for the accumulators, must be non-negative. |
epsilon | A small floating point value to avoid zero denominator. |
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; "clipvalue" (float) clips gradients by value. |
Reference:
| Raises | |
|---|---|
ValueError | in case of any invalid argument. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/optimizers/Adagrad
