class tf.train.GradientDescentOptimizer

tf.train.GradientDescentOptimizer.__init__(learning_rate, use_locking=False, name='GradientDescent')
Args:

  • learning_rate: A Tensor or a floating point value. The learning rate to use.
  • use_locking: If True use locks for update operation.s
  • name: Optional name prefix for the operations created when applying gradients. Defaults to "GradientDescent".

class tf.train.AdagradOptimizer

tf.train.AdagradOptimizer.__init__(learning_rate, initial_accumulator_value=0.1, use_locking=False, name='Adagrad')
Args:

  • learning_rate: A Tensor or a floating point value. The learning rate.
  • initial_accumulator_value: A floating point value. Starting value for the accumulators, must be positive.
  • use_locking: If True use locks for update operations.
  • name: Optional name prefix for the operations created when applying gradients. Defaults to "Adagrad".
    Raises:
  • ValueError: If the initial_accumulator_value is invalid.

class tf.train.MomentumOptimizer

tf.train.MomentumOptimizer.__init__(learning_rate, momentum, use_locking=False, name='Momentum')
Args:

  • learning_rate: A Tensor or a floating point value. The learning rate.
  • momentum: A Tensor or a floating point value. The momentum.
  • use_locking: If True use locks for update operations.
  • name: Optional name prefix for the operations created when applying gradients. Defaults to "Momentum".

class tf.train.AdamOptimizer

tf.train.AdamOptimizer.__init__(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, use_locking=False, name='Adam')
Args:

  • learning_rate: A Tensor or a floating point value. The learning rate.
  • beta1: A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates.
  • beta2: A float value or a constant float tensor. The exponential decay rate for the 2st moment estimates.
  • epsilon: A small constant for numerical stability.
  • use_locking: If True use locks for update operation.s
  • name: Optional name for the operations created when applying gradients. Defaults to "Adam".

class tf.train.FtrlOptimizer

tf.train.FtrlOptimizer.__init__(learning_rate, learning_rate_power=-0.5, initial_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.0, use_locking=False, name='Ftrl')
Args:

  • learning_rate: A float value or a constant float Tensor.
  • learning_rate_power: A float value, must be less or equal to zero.
  • initial_accumulator_value: The starting value for accumulators. Only 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_locking: If True use locks for update operations.
  • name: Optional name prefix for the operations created when applying gradients. Defaults to "Ftrl".
    Raises:
  • ValueError: if one of the arguments is invalid.

class tf.train.RMSPropOptimizer

tf.train.RMSPropOptimizer.__init__(learning_rate, decay, momentum=0.0, epsilon=1e-10, use_locking=False, name='RMSProp')
Args:

  • learning_rate: A Tensor or a floating point value. The learning rate.
  • decay: discounting factor for the history/coming gradient
  • momentum: a scalar tensor.
  • epsilon: small value to avoid zero denominator.
  • use_locking: If True use locks for update operation.
  • name: Optional name prefic for the operations created when applying gradients. Defaults to "RMSProp".