class_docstr: Optimizer that implements the Adagrad algorithm.
  
  Adagrad is an optimizer with parameter-specific learning rates,
  which are adapted relative to how frequently a parameter gets
  updated during training. The more updates a parameter receives,
  the smaller the updates.
  
  Args:
    learning_rate: Initial value for the learning rate:
      either a floating point value,
      or a `tf.keras.optimizers.schedules.LearningRateSchedule` instance.
      Defaults to 0.001.
      Note that `Adagrad` tends to benefit from higher initial learning rate
      values compared to other optimizers.
      To match the exact form in the original paper, use 1.0.
    initial_accumulator_value: Floating point value.
      Starting value for the accumulators (per-parameter momentum values).
      Must be non-negative.
    epsilon: Small floating point value used to maintain numerical stability.
    name: Optional name prefix for the operations created when applying
      gradients.  Defaults to `"Adagrad"`.
    **kwargs: Keyword arguments. Allowed to be one of
      `"clipnorm"` or `"clipvalue"`.
      `"clipnorm"` (float) clips gradients by norm and represents
      the maximum L2 norm of each weight variable;
      `"clipvalue"` (float) clips gradient by value and represents the
      maximum absolute value of each weight variable.
  
  Reference:
    - [Duchi et al., 2011](
      http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf).
class_docstr_tok: ["Optimizer","that","implements","the","Adagrad","algorithm",".","Adagrad","is","an","optimizer","with","parameter-specific","learning","rates",",","which","are","adapted","relative","to","how","frequently","a","parameter","gets","updated","during","training",".","The","more","updates","a","parameter","receives",",","the","smaller","the","updates",".","Args",":","learning_rate",":","Initial","value","for","the","learning","rate",":","either","a","floating","point","value",",","or","a","`","tf.keras.optimizers.schedules.LearningRateSchedule","`","instance",".","Defaults","to","0.001",".","Note","that","`","Adagrad","`","tends","to","benefit","from","higher","initial","learning","rate","values","compared","to","other","optimizers",".","To","match","the","exact","form","in","the","original","paper",",","use","1.0.","initial_accumulator_value",":","Floating","point","value",".","Starting","value","for","the","accumulators","(","per-parameter","momentum","values",")",".","Must","be","non-negative",".","epsilon",":","Small","floating","point","value","used","to","maintain","numerical","stability",".","name",":","Optional","name","prefix","for","the","operations","created","when","applying","gradients",".","Defaults","to","`","``","Adagrad","''","`",".","*","*","kwargs",":","Keyword","arguments",".","Allowed","to","be","one","of","`","``","clipnorm","''","`","or","`","``","clipvalue","''","`",".","`","``","clipnorm","''","`","(","float",")","clips","gradients","by","norm","and","represents","the","maximum","L2","norm","of","each","weight","variable",";","`","``","clipvalue","''","`","(","float",")","clips","gradient","by","value","and","represents","the","maximum","absolute","value","of","each","weight","variable",".","Reference",":","-","[","Duchi","et","al.",",","2011","]","(","http",":","\/\/www.jmlr.org\/papers\/volume12\/duchi11a\/duchi11a.pdf",")","."]
code: def _create_slots(self, var_list):
      for var in var_list:
          dtype = var.dtype.base_dtype
          init = init_ops.constant_initializer(
              self._initial_accumulator_value, dtype=dtype
          )
          self.add_slot(var, "accumulator", init)
  
file_path: tensorflow/tensorflow/python/keras/optimizer_v2/adagrad.py
imports: import numpy
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import tensor_conversion
  from tensorflow.python.keras import backend_config
  from tensorflow.python.keras.optimizer_v2 import optimizer_v2
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_training_ops
  from tensorflow.python.ops import init_ops
label_desc: Use image node_id 2 for calling the Adagrad obj's underlying member method code with example usage: obj._create_slots(var_list) without return types