tf.compat.v1.tpu.experimental.StochasticGradientDescentParameters

Optimization parameters for stochastic gradient descent for TPU embeddings.

tf.compat.v1.tpu.experimental.StochasticGradientDescentParameters(
    learning_rate: float,
    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.
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.