TensorFlow

API

 tf.compat / v1 / v1.tpu.shutdown_system


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),
        ...))

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.
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.

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