tf.tpu.experimental.embedding.Adagrad Optimization parameters for Adagrad with TPU embeddings.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.tpu.experimental.embedding.Adagrad  
tf.tpu.experimental.embedding.Adagrad(
    learning_rate: float = 0.001,
    initial_accumulator_value: float = 0.1,
    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.Adagrad(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.Adagrad(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.Adagrad(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.
 


 Args
  learning_rate   The learning rate. It should be a floating point value or a callable taking no arguments for a dynamic learning rate.  
  initial_accumulator_value   initial accumulator for Adagrad.  
  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.