tf.compat.v1.tpu.experimental.FtrlParameters

Optimization parameters for Ftrl with TPU embeddings.

tf.compat.v1.tpu.experimental.FtrlParameters(
    learning_rate, learning_rate_power=-0.5, initial_accumulator_value=0.1,
    l1_regularization_strength=0.0, l2_regularization_strength=0.0,
    use_gradient_accumulation=True, 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.FtrlParameters(0.1),
        ...))

Args
learning_rate	a floating point value. The learning rate.
learning_rate_power	A float value, must be less or equal to zero. Controls how the learning rate decreases during training. Use zero for a fixed learning rate. See section 3.1 in the paper.
initial_accumulator_value	The starting value for accumulators. Only zero or positive values are allowed.
l1_regularization_strength	A float value, must be greater than or equal to zero.
l2_regularization_strength	A float value, must be greater than or equal to zero.
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.