tf.tpu.experimental.StochasticGradientDescentParameters

View source on GitHub

Optimization parameters for stochastic gradient descent for TPU embeddings.

tf.tpu.experimental.StochasticGradientDescentParameters(
    learning_rate, clip_weight_min=None, clip_weight_max=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))))

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.