tf.compat.v1.tpu.experimental.StochasticGradientDescentParameters

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Optimization parameters for stochastic gradient descent for TPU embeddings.

tf.compat.v1.tpu.experimental.StochasticGradientDescentParameters(
    learning_rate, clip_weight_min=None, clip_weight_max=None,
    weight_decay_factor=None, multiply_weight_decay_factor_by_learning_rate=None
)

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_config_spec=tf.estimator.tpu.experimental.EmbeddingConfigSpec(
        ...
        optimization_parameters=(
            tf.tpu.experimental.StochasticGradientDescentParameters(0.1))))

Args:

  • learning_rate: a floating point value. The learning rate.
  • clip_weight_min: the minimum value to clip by; None means -infinity.
  • clip_weight_max: the maximum value to clip by; None means +infinity.
  • weight_decay_factor: amount of weight decay to apply; None means that the weights are not decayed.
  • multiply_weight_decay_factor_by_learning_rate: if true, weight_decay_factor is multiplied by the current learning rate.