tf.tpu.experimental.embedding.Adam

Optimization parameters for Adam with TPU embeddings.

tf.tpu.experimental.embedding.Adam(
    learning_rate: Union[float, Callable[[], float]] = 0.001,
    beta_1: float = 0.9,
    beta_2: float = 0.999,
    epsilon: float = 1e-07,
    lazy_adam: bool = True,
    sum_inside_sqrt: bool = True,
    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: bool = None,
    slot_variable_creation_fn: Optional[SlotVarCreationFnType] = None,
    clipvalue: Optional[ClipValueType] = None
)

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.Adam(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.Adam(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.Adam(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.
beta_1 A float value. The exponential decay rate for the 1st moment estimates.
beta_2 A float value. The exponential decay rate for the 2nd moment estimates.
epsilon A small constant for numerical stability.
lazy_adam Use lazy Adam instead of Adam. Lazy Adam trains faster.
sum_inside_sqrt When this is true, the Adam update formula is changed from m / (sqrt(v) + epsilon) to m / sqrt(v + epsilon**2). This option improves the performance of TPU training and is not expected to harm model quality.
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 tiple 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.