tf.compat.v1.tpu.experimental.StochasticGradientDescentParameters

Optimization parameters for stochastic gradient descent for TPU embeddings.

tf.compat.v1.tpu.experimental.StochasticGradientDescentParameters(
    learning_rate: float,
    use_gradient_accumulation: bool = True,
    clip_weight_min: Optional[float] = None,
    clip_weight_max: Optional[float] = None,
    weight_decay_factor: Optional[float] = None,
    multiply_weight_decay_factor_by_learning_rate: Optional[bool] = None,
    clip_gradient_min: Optional[float] = None,
    clip_gradient_max: Optional[float] = 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.
`use_gradient_accumulation`	setting this to `False` makes embedding gradients calculation less accurate but faster. Please see `optimization_parameters.proto` 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.
`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.
`clip_gradient_min`	the minimum value to clip by; None means -infinity.
`clip_gradient_max`	the maximum value to clip by; None means +infinity.

tf.compat.v1.tpu.experimental.StochasticGradientDescentParameters

Args