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".
