Missed TensorFlow Dev Summit? Check out the video playlist. Watch recordings

tf.compat.v1.tpu.experimental.AdagradParameters

View source on GitHub

Class AdagradParameters

Optimization parameters for Adagrad with TPU embeddings.

Pass this to tf.estimator.tpu.experimental.EmbeddingConfigSpec via the optimization_parameters argument to set the optimizer and its parameters. See the documentation for tf.estimator.tpu.experimental.EmbeddingConfigSpec for more details.

estimator = tf.estimator.tpu.TPUEstimator(
    ...
    embedding_spec=tf.estimator.tpu.experimental.EmbeddingConfigSpec(
        ...
        optimization_parameters=tf.tpu.experimental.AdagradParameters(0.1),
        ...))

__init__

View source

__init__(
    learning_rate,
    initial_accumulator=0.1,
    use_gradient_accumulation=True,
    clip_weight_min=None,
    clip_weight_max=None
)

Optimization parameters for Adagrad.

Args:

  • learning_rate: used for updating embedding table.
  • initial_accumulator: initial accumulator for Adagrad.
  • use_gradient_accumulation: setting this to False makes embedding gradients calculation less accurate but faster. Please see optimization_parameters.proto for details. for details.
  • clip_weight_min: the minimum value to clip by; None means -infinity.
  • clip_weight_max: the maximum value to clip by; None means +infinity.