tf.tpu.experimental.embedding.AdagradMomentum

Optimization parameters for Adagrad + Momentum with TPU embeddings.

tf.tpu.experimental.embedding.AdagradMomentum( learning_rate: Union[float, Callable[[], float]] = 0.001, momentum: float = 0.0, use_nesterov: bool = False, exponent: float = 2, beta2: float = 1, epsilon: float = 1e-10, 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, slot_variable_creation_fn: Optional[SlotVarCreationFnType] = None, clipvalue: Optional[ClipValueType] = None, low_dimensional_packing_status: bool = False )

Pass this to tf.tpu.experimental.embedding.TPUEmbedding via the optimizer argument to set the global optimizer and its parameters:

embedding = tf.tpu.experimental.embedding.TPUEmbedding( ... optimizer=tf.tpu.experimental.embedding.AdagradMomentum(0.1))

This can also be used in a tf.tpu.experimental.embedding.TableConfig as the optimizer parameter to set a table specific optimizer. This will override the optimizer and parameters for global embedding optimizer defined above:

table_one = tf.tpu.experimental.embedding.TableConfig( vocabulary_size=..., dim=..., optimizer=tf.tpu.experimental.embedding.AdagradMomentum(0.2)) table_two = tf.tpu.experimental.embedding.TableConfig( vocabulary_size=..., dim=...) feature_config = ( tf.tpu.experimental.embedding.FeatureConfig( table=table_one), tf.tpu.experimental.embedding.FeatureConfig( table=table_two)) embedding = tf.tpu.experimental.embedding.TPUEmbedding( feature_config=feature_config, batch_size=... optimizer=tf.tpu.experimental.embedding.AdagradMomentum(0.1))

In the above example, the first feature will be looked up in a table that has a learning rate of 0.2 while the second feature will be looked up in a table that has a learning rate of 0.1.

See 'tensorflow/core/protobuf/tpu/optimization_parameters.proto' for a complete description of these parameters and their impacts on the optimizer algorithm.

learning_rate The learning rate. It should be a floating point value or a callable taking no arguments for a dynamic learning rate.
momentum Moving average parameter for the momentum accumulator.
use_nesterov Whether to use the Nesterov variant of momentum. See Sutskever et al., 2013.
exponent Exponent for the Adagrad accumulator.
beta2 Moving average parameter for the Adagrad accumulator.
epsilon initial accumulator for Adagrad accumulator.
use_gradient_accumulation setting this to False makes embedding gradients calculation less accurate but faster.
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.
slot_variable_creation_fn If you wish do directly control the creation of the slot variables, set this to a callable taking three parameters: a table variable, a list of slot names to create for it, and a list of initializers. This function should return a dict with the slot names as keys and the created variables as values with types matching the table variable. When set to None (the default), uses the built-in variable creation.
clipvalue Controls clipping of the gradient. Set to either a single positive scalar value to get clipping or a tuple of scalar values (min, max) to set a separate maximum or minimum. If one of the two entries is None, then there will be no clipping that direction.
low_dimensional_packing_status Status of the low-dimensional embedding packing optimization controls whether to optimize the packing of 1-dimensional, 2-dimensional, and 4-dimensional embedding tables in memory.

Methods

__eq__

View source

__eq__( other: Any ) -> Union[Any, bool]

Return self==value.