Module monk.pytorch.finetune.level_11_optimizers_main

Classes

class prototype_optimizers (verbose=1)

Main class for all optimizers in expert mode

Args

verbose : int

Set verbosity levels 0 - Print Nothing 1 - Print desired details

Class that serves as a decorator to trace entry and exit from functions. Used by appending @TraceFunction on top of the definition of the function to trace.

Expand source code

class prototype_optimizers(prototype_schedulers):
    '''
    Main class for all optimizers in expert mode

    Args:
        verbose (int): Set verbosity levels
                        0 - Print Nothing
                        1 - Print desired details
    '''
    def __init__(self, verbose=1):
        super().__init__(verbose=verbose);


    ###############################################################################################################################################
    def optimizer_sgd(self, learning_rate, momentum=0, weight_decay=0, momentum_dampening_rate=0, clipnorm=0.0, clipvalue=0.0):
        '''
        Select stochastic gradient descent optimizer

        Args:
            learning_rate (float): Initial base learning rate
            momentum (float): Momentum value for driving the weights towards minima
            weight_decay (float): Value for regularizing weights post every update
            momentum_dampening_rate (float): Reduction rate for momentum
            clipnorm (float): Gradient clipping factor
            clipvalue (float): Value for clipping

        Returns:
            None
        '''
        self.system_dict = sgd(self.system_dict, learning_rate, 
                momentum=momentum, weight_decay=weight_decay, momentum_dampening_rate=momentum_dampening_rate, clipnorm=clipnorm, clipvalue=clipvalue);
        self.custom_print("Optimizer");
        self.custom_print("    Name:          {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
        self.custom_print("    Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
        self.custom_print("    Params:        {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
        self.custom_print("");
        ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
        self.custom_print("");
        ConstraintWarning("ArgumentWarning: momentum_dampening_rate is active only for pytorch in current version of Monk");
        self.custom_print("");
    ###############################################################################################################################################


    ###############################################################################################################################################
    def optimizer_nesterov_sgd(self, learning_rate, momentum=0, weight_decay=0, momentum_dampening_rate=0, clipnorm=0.0, clipvalue=0.0):
        '''
        Select stochastic gradient descent optimizer with nesterov acceleration

        Args:
            learning_rate (float): Initial base learning rate
            momentum (float): Momentum value for driving the weights towards minima
            weight_decay (float): Value for regularizing weights post every update
            momentum_dampening_rate (float): Reduction rate for momentum
            clipnorm (float): Gradient clipping factor
            clipvalue (float): Value for clipping

        Returns:
            None
        '''
        self.system_dict = nesterov_sgd(self.system_dict, learning_rate, 
                momentum=momentum, weight_decay=weight_decay, momentum_dampening_rate=momentum_dampening_rate, clipnorm=clipnorm, clipvalue=clipvalue);
        self.custom_print("Optimizer");
        self.custom_print("    Name:          {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
        self.custom_print("    Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
        self.custom_print("    Params:        {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
        self.custom_print("");
        ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
        self.custom_print("");
        ConstraintWarning("ArgumentWarning: momentum_dampening_rate is active only for pytorch in current version of Monk");
        self.custom_print("");
    ###############################################################################################################################################


    ###############################################################################################################################################
    def optimizer_rmsprop(self, learning_rate, decay_rate=0.99, epsilon=1e-08, weight_decay=0, clipnorm=0.0, clipvalue=0.0):
        '''
        Select root mean score prop optimizer

        Args:
            learning_rate (float): Initial base learning rate
            decay_rate (float): A decay factor of moving average over past squared gradient.
            weight_decay (float): Value for regularizing weights post every update
            epsilon (float): A value to avoid division by zero
            clipnorm (float): Gradient clipping factor
            clipvalue (float): Value for clipping

        Returns:
            None
        '''
        self.system_dict = rmsprop(self.system_dict , learning_rate, 
            decay_rate=decay_rate, epsilon=epsilon, weight_decay=weight_decay, clipnorm=clipnorm, clipvalue=clipvalue);
        self.custom_print("Optimizer");
        self.custom_print("    Name:          {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
        self.custom_print("    Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
        self.custom_print("    Params:        {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
        self.custom_print("");
        ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
        self.custom_print("");
    ###############################################################################################################################################


    ###############################################################################################################################################
    def optimizer_momentum_rmsprop(self, learning_rate, decay_rate=0.99, epsilon=1e-08, weight_decay=0, momentum=0.9):
        '''
        Select root mean score prop optimizer with momentum

        Args:
            learning_rate (float): Initial base learning rate
            decay_rate (float): A decay factor of moving average over past squared gradient.
            epsilon (float): A value to avoid division by zero
            momentum (float): Momentum value for driving the weights towards minima
            weight_decay (float): Value for regularizing weights post every update
            clipnorm (float): Gradient clipping factor
            clipvalue (float): Value for clipping

        Returns:
            None
        '''
        self.system_dict = momentum_rmsprop(self.system_dict , learning_rate, 
            decay_rate=decay_rate, epsilon=epsilon, weight_decay=weight_decay, momentum=momentum);
        self.custom_print("Optimizer");
        self.custom_print("    Name:          {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
        self.custom_print("    Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
        self.custom_print("    Params:        {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
        self.custom_print("");
    ###############################################################################################################################################


    ###############################################################################################################################################
    def optimizer_adam(self, learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, weight_decay=0, amsgrad=False, 
        clipnorm=0.0, clipvalue=0.0):
        '''
        Select ADAM optimizer

        Args:
            learning_rate (float): Initial base learning rate
            beta1 (float): Exponential decay rate for first momentum estimates
            beta2 (float): Exponential decay rate for first second estimates
            weight_decay (float): Value for regularizing weights post every update
            amsgrad (bool): If True, AMSGrad variant of this algorithm is used
            epsilon (float): A value to avoid division by zero
            clipnorm (float): Gradient clipping factor
            clipvalue (float): Value for clipping

        Returns:
            None
        '''
        self.system_dict = adam(self.system_dict, learning_rate,
            beta1=beta1, beta2=beta2, epsilon=epsilon, weight_decay=weight_decay, amsgrad=amsgrad, clipnorm=clipnorm, clipvalue=clipvalue);
        
        self.custom_print("Optimizer");
        self.custom_print("    Name:          {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
        self.custom_print("    Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
        self.custom_print("    Params:        {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
        self.custom_print("");
        ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
        self.custom_print("");
        ConstraintWarning("ArgumentWarning: amsgrad is active only for keras and pytorch in current version of Monk");
        self.custom_print("");
    ###############################################################################################################################################


    ###############################################################################################################################################
    def optimizer_adamax(self, learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, weight_decay=0,
        clipnorm=0.0, clipvalue=0.0):
        '''
        Select Adamax optimizer

        Args:
            learning_rate (float): Initial base learning rate
            beta1 (float): Exponential decay rate for first momentum estimates
            beta2 (float): Exponential decay rate for first second estimates
            weight_decay (float): Value for regularizing weights post every update
            epsilon (float): A value to avoid division by zero
            clipnorm (float): Gradient clipping factor
            clipvalue (float): Value for clipping

        Returns:
            None
        '''

        self.system_dict = adamax(self.system_dict, learning_rate,
            beta1=beta1, beta2=beta2, epsilon=epsilon, weight_decay=weight_decay, clipnorm=clipnorm, clipvalue=clipvalue);  
        
        self.custom_print("Optimizer");
        self.custom_print("    Name:          {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
        self.custom_print("    Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
        self.custom_print("    Params:        {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
        self.custom_print("");
        ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
        self.custom_print("");
    ###############################################################################################################################################


    ###############################################################################################################################################
    def optimizer_adamw(self, learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, weight_decay=0, amsgrad=False):
        '''
        Select ADAMW variant of ADAM optimizer

        Args:
            learning_rate (float): Initial base learning rate
            beta1 (float): Exponential decay rate for first momentum estimates
            beta2 (float): Exponential decay rate for first second estimates
            weight_decay (float): Value for regularizing weights post every update
            amsgrad (bool): If True, AMSGrad variant of this algorithm is used
            epsilon (float): A value to avoid division by zero
            clipnorm (float): Gradient clipping factor
            clipvalue (float): Value for clipping

        Returns:
            None
        '''
        self.system_dict = adamw(self.system_dict, learning_rate,
            beta1=beta1, beta2=beta2, epsilon=epsilon, weight_decay=weight_decay, amsgrad=amsgrad);
        
        self.custom_print("Optimizer");
        self.custom_print("    Name:          {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
        self.custom_print("    Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
        self.custom_print("    Params:        {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
        self.custom_print("");
        ConstraintWarning("OptimizerWarning: adamw is active only for pytorch in current version of Monk");
        self.custom_print("");
    ###############################################################################################################################################


    ###############################################################################################################################################
    def optimizer_adadelta(self, learning_rate, rho=0.9, epsilon=1e-06, weight_decay=0, 
        clipnorm=0.0, clipvalue=0.0):
        '''
        Select Adadelta optimizer

        Args:
            learning_rate (float): Initial base learning rate
            rho (float): Exponential decay rate for momentum estimates
            weight_decay (float): Value for regularizing weights post every update
            epsilon (float): A value to avoid division by zero
            clipnorm (float): Gradient clipping factor
            clipvalue (float): Value for clipping

        Returns:
            None
        '''
        self.system_dict = adadelta(self.system_dict, learning_rate,
            rho=rho, epsilon=epsilon, weight_decay=weight_decay, clipnorm=clipnorm, clipvalue=clipvalue);
        
        self.custom_print("Optimizer");
        self.custom_print("    Name:          {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
        self.custom_print("    Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
        self.custom_print("    Params:        {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
        self.custom_print("");
        ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
        self.custom_print("");
    ###############################################################################################################################################



    ###############################################################################################################################################
    def optimizer_adagrad(self, learning_rate, learning_rate_decay=0, weight_decay=0, epsilon=1e-08,
        clipnorm=0.0, clipvalue=0.0):
        '''
        Select Adagrad optimizer

        Args:
            learning_rate (float): Initial base learning rate
            learning_rate_decay (float): Learning rate decay factor
            weight_decay (float): Value for regularizing weights post every update
            epsilon (float): A value to avoid division by zero
            clipnorm (float): Gradient clipping factor
            clipvalue (float): Value for clipping

        Returns:
            None
        '''
        self.system_dict = adagrad(self.system_dict, learning_rate,
            learning_rate_decay=learning_rate_decay, weight_decay=weight_decay, epsilon=epsilon, 
            clipnorm=clipnorm, clipvalue=clipvalue);
        
        self.custom_print("Optimizer");
        self.custom_print("    Name:          {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
        self.custom_print("    Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
        self.custom_print("    Params:        {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
        self.custom_print("");
        ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
        self.custom_print("");
        ConstraintWarning("ArgumentWarning: learning_rate_decay is active only for pytorch in current version of Monk");
        self.custom_print("");

Ancestors

• pytorch.finetune.level_10_schedulers_main.prototype_schedulers
• pytorch.finetune.level_9_transforms_main.prototype_transforms
• pytorch.finetune.level_8_layers_main.prototype_layers
• pytorch.finetune.level_7_aux_main.prototype_aux
• pytorch.finetune.level_6_params_main.prototype_params
• pytorch.finetune.level_5_state_base.finetune_state
• pytorch.finetune.level_4_evaluation_base.finetune_evaluation
• pytorch.finetune.level_3_training_base.finetune_training
• pytorch.finetune.level_2_model_base.finetune_model
• pytorch.finetune.level_1_dataset_base.finetune_dataset
• system.base_class.system

Methods

def optimizer_adadelta(self, learning_rate, rho=0.9, epsilon=1e-06, weight_decay=0, clipnorm=0.0, clipvalue=0.0)

Select Adadelta optimizer

Args

learning_rate : float

Initial base learning rate

rho : float

Exponential decay rate for momentum estimates

weight_decay : float

Value for regularizing weights post every update

epsilon : float

A value to avoid division by zero

clipnorm : float

Gradient clipping factor

clipvalue : float

Value for clipping

Returns

None

 

Expand source code

def optimizer_adadelta(self, learning_rate, rho=0.9, epsilon=1e-06, weight_decay=0, 
    clipnorm=0.0, clipvalue=0.0):
    '''
    Select Adadelta optimizer

    Args:
        learning_rate (float): Initial base learning rate
        rho (float): Exponential decay rate for momentum estimates
        weight_decay (float): Value for regularizing weights post every update
        epsilon (float): A value to avoid division by zero
        clipnorm (float): Gradient clipping factor
        clipvalue (float): Value for clipping

    Returns:
        None
    '''
    self.system_dict = adadelta(self.system_dict, learning_rate,
        rho=rho, epsilon=epsilon, weight_decay=weight_decay, clipnorm=clipnorm, clipvalue=clipvalue);
    
    self.custom_print("Optimizer");
    self.custom_print("    Name:          {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
    self.custom_print("    Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
    self.custom_print("    Params:        {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
    self.custom_print("");
    ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
    self.custom_print("");

def optimizer_adagrad(self, learning_rate, learning_rate_decay=0, weight_decay=0, epsilon=1e-08, clipnorm=0.0, clipvalue=0.0)

Select Adagrad optimizer

Args

learning_rate : float

Initial base learning rate

learning_rate_decay : float

Learning rate decay factor

weight_decay : float

Value for regularizing weights post every update

epsilon : float

A value to avoid division by zero

clipnorm : float

Gradient clipping factor

clipvalue : float

Value for clipping

Returns

None

 

Expand source code

def optimizer_adagrad(self, learning_rate, learning_rate_decay=0, weight_decay=0, epsilon=1e-08,
    clipnorm=0.0, clipvalue=0.0):
    '''
    Select Adagrad optimizer

    Args:
        learning_rate (float): Initial base learning rate
        learning_rate_decay (float): Learning rate decay factor
        weight_decay (float): Value for regularizing weights post every update
        epsilon (float): A value to avoid division by zero
        clipnorm (float): Gradient clipping factor
        clipvalue (float): Value for clipping

    Returns:
        None
    '''
    self.system_dict = adagrad(self.system_dict, learning_rate,
        learning_rate_decay=learning_rate_decay, weight_decay=weight_decay, epsilon=epsilon, 
        clipnorm=clipnorm, clipvalue=clipvalue);
    
    self.custom_print("Optimizer");
    self.custom_print("    Name:          {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
    self.custom_print("    Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
    self.custom_print("    Params:        {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
    self.custom_print("");
    ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
    self.custom_print("");
    ConstraintWarning("ArgumentWarning: learning_rate_decay is active only for pytorch in current version of Monk");
    self.custom_print("");

def optimizer_adam(self, learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, weight_decay=0, amsgrad=False, clipnorm=0.0, clipvalue=0.0)

Select ADAM optimizer

Args

learning_rate : float

Initial base learning rate

beta1 : float

Exponential decay rate for first momentum estimates

beta2 : float

Exponential decay rate for first second estimates

weight_decay : float

Value for regularizing weights post every update

amsgrad : bool

If True, AMSGrad variant of this algorithm is used

epsilon : float

A value to avoid division by zero

clipnorm : float

Gradient clipping factor

clipvalue : float

Value for clipping

Returns

None

 

Expand source code

def optimizer_adam(self, learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, weight_decay=0, amsgrad=False, 
    clipnorm=0.0, clipvalue=0.0):
    '''
    Select ADAM optimizer

    Args:
        learning_rate (float): Initial base learning rate
        beta1 (float): Exponential decay rate for first momentum estimates
        beta2 (float): Exponential decay rate for first second estimates
        weight_decay (float): Value for regularizing weights post every update
        amsgrad (bool): If True, AMSGrad variant of this algorithm is used
        epsilon (float): A value to avoid division by zero
        clipnorm (float): Gradient clipping factor
        clipvalue (float): Value for clipping

    Returns:
        None
    '''
    self.system_dict = adam(self.system_dict, learning_rate,
        beta1=beta1, beta2=beta2, epsilon=epsilon, weight_decay=weight_decay, amsgrad=amsgrad, clipnorm=clipnorm, clipvalue=clipvalue);
    
    self.custom_print("Optimizer");
    self.custom_print("    Name:          {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
    self.custom_print("    Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
    self.custom_print("    Params:        {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
    self.custom_print("");
    ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
    self.custom_print("");
    ConstraintWarning("ArgumentWarning: amsgrad is active only for keras and pytorch in current version of Monk");
    self.custom_print("");

def optimizer_adamax(self, learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, weight_decay=0, clipnorm=0.0, clipvalue=0.0)

Select Adamax optimizer

Args

learning_rate : float

Initial base learning rate

beta1 : float

Exponential decay rate for first momentum estimates

beta2 : float

Exponential decay rate for first second estimates

weight_decay : float

Value for regularizing weights post every update

epsilon : float

A value to avoid division by zero

clipnorm : float

Gradient clipping factor

clipvalue : float

Value for clipping

Returns

None

 

Expand source code

def optimizer_adamax(self, learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, weight_decay=0,
    clipnorm=0.0, clipvalue=0.0):
    '''
    Select Adamax optimizer

    Args:
        learning_rate (float): Initial base learning rate
        beta1 (float): Exponential decay rate for first momentum estimates
        beta2 (float): Exponential decay rate for first second estimates
        weight_decay (float): Value for regularizing weights post every update
        epsilon (float): A value to avoid division by zero
        clipnorm (float): Gradient clipping factor
        clipvalue (float): Value for clipping

    Returns:
        None
    '''

    self.system_dict = adamax(self.system_dict, learning_rate,
        beta1=beta1, beta2=beta2, epsilon=epsilon, weight_decay=weight_decay, clipnorm=clipnorm, clipvalue=clipvalue);  
    
    self.custom_print("Optimizer");
    self.custom_print("    Name:          {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
    self.custom_print("    Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
    self.custom_print("    Params:        {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
    self.custom_print("");
    ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
    self.custom_print("");

def optimizer_adamw(self, learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, weight_decay=0, amsgrad=False)

Select ADAMW variant of ADAM optimizer

Args

learning_rate : float

Initial base learning rate

beta1 : float

Exponential decay rate for first momentum estimates

beta2 : float

Exponential decay rate for first second estimates

weight_decay : float

Value for regularizing weights post every update

amsgrad : bool

If True, AMSGrad variant of this algorithm is used

epsilon : float

A value to avoid division by zero

clipnorm : float

Gradient clipping factor

clipvalue : float

Value for clipping

Returns

None

 

Expand source code

def optimizer_adamw(self, learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, weight_decay=0, amsgrad=False):
    '''
    Select ADAMW variant of ADAM optimizer

    Args:
        learning_rate (float): Initial base learning rate
        beta1 (float): Exponential decay rate for first momentum estimates
        beta2 (float): Exponential decay rate for first second estimates
        weight_decay (float): Value for regularizing weights post every update
        amsgrad (bool): If True, AMSGrad variant of this algorithm is used
        epsilon (float): A value to avoid division by zero
        clipnorm (float): Gradient clipping factor
        clipvalue (float): Value for clipping

    Returns:
        None
    '''
    self.system_dict = adamw(self.system_dict, learning_rate,
        beta1=beta1, beta2=beta2, epsilon=epsilon, weight_decay=weight_decay, amsgrad=amsgrad);
    
    self.custom_print("Optimizer");
    self.custom_print("    Name:          {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
    self.custom_print("    Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
    self.custom_print("    Params:        {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
    self.custom_print("");
    ConstraintWarning("OptimizerWarning: adamw is active only for pytorch in current version of Monk");
    self.custom_print("");

def optimizer_momentum_rmsprop(self, learning_rate, decay_rate=0.99, epsilon=1e-08, weight_decay=0, momentum=0.9)

Select root mean score prop optimizer with momentum

Args

learning_rate : float

Initial base learning rate

decay_rate : float

A decay factor of moving average over past squared gradient.

epsilon : float

A value to avoid division by zero

momentum : float

Momentum value for driving the weights towards minima

weight_decay : float

Value for regularizing weights post every update

clipnorm : float

Gradient clipping factor

clipvalue : float

Value for clipping

Returns

None

 

Expand source code

def optimizer_momentum_rmsprop(self, learning_rate, decay_rate=0.99, epsilon=1e-08, weight_decay=0, momentum=0.9):
    '''
    Select root mean score prop optimizer with momentum

    Args:
        learning_rate (float): Initial base learning rate
        decay_rate (float): A decay factor of moving average over past squared gradient.
        epsilon (float): A value to avoid division by zero
        momentum (float): Momentum value for driving the weights towards minima
        weight_decay (float): Value for regularizing weights post every update
        clipnorm (float): Gradient clipping factor
        clipvalue (float): Value for clipping

    Returns:
        None
    '''
    self.system_dict = momentum_rmsprop(self.system_dict , learning_rate, 
        decay_rate=decay_rate, epsilon=epsilon, weight_decay=weight_decay, momentum=momentum);
    self.custom_print("Optimizer");
    self.custom_print("    Name:          {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
    self.custom_print("    Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
    self.custom_print("    Params:        {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
    self.custom_print("");

def optimizer_nesterov_sgd(self, learning_rate, momentum=0, weight_decay=0, momentum_dampening_rate=0, clipnorm=0.0, clipvalue=0.0)

Select stochastic gradient descent optimizer with nesterov acceleration

Args

learning_rate : float

Initial base learning rate

momentum : float

Momentum value for driving the weights towards minima

weight_decay : float

Value for regularizing weights post every update

momentum_dampening_rate : float

Reduction rate for momentum

clipnorm : float

Gradient clipping factor

clipvalue : float

Value for clipping

Returns

None

 

Expand source code

def optimizer_nesterov_sgd(self, learning_rate, momentum=0, weight_decay=0, momentum_dampening_rate=0, clipnorm=0.0, clipvalue=0.0):
    '''
    Select stochastic gradient descent optimizer with nesterov acceleration

    Args:
        learning_rate (float): Initial base learning rate
        momentum (float): Momentum value for driving the weights towards minima
        weight_decay (float): Value for regularizing weights post every update
        momentum_dampening_rate (float): Reduction rate for momentum
        clipnorm (float): Gradient clipping factor
        clipvalue (float): Value for clipping

    Returns:
        None
    '''
    self.system_dict = nesterov_sgd(self.system_dict, learning_rate, 
            momentum=momentum, weight_decay=weight_decay, momentum_dampening_rate=momentum_dampening_rate, clipnorm=clipnorm, clipvalue=clipvalue);
    self.custom_print("Optimizer");
    self.custom_print("    Name:          {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
    self.custom_print("    Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
    self.custom_print("    Params:        {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
    self.custom_print("");
    ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
    self.custom_print("");
    ConstraintWarning("ArgumentWarning: momentum_dampening_rate is active only for pytorch in current version of Monk");
    self.custom_print("");

def optimizer_rmsprop(self, learning_rate, decay_rate=0.99, epsilon=1e-08, weight_decay=0, clipnorm=0.0, clipvalue=0.0)

Select root mean score prop optimizer

Args

learning_rate : float

Initial base learning rate

decay_rate : float

A decay factor of moving average over past squared gradient.

weight_decay : float

Value for regularizing weights post every update

epsilon : float

A value to avoid division by zero

clipnorm : float

Gradient clipping factor

clipvalue : float

Value for clipping

Returns

None

 

Expand source code

def optimizer_rmsprop(self, learning_rate, decay_rate=0.99, epsilon=1e-08, weight_decay=0, clipnorm=0.0, clipvalue=0.0):
    '''
    Select root mean score prop optimizer

    Args:
        learning_rate (float): Initial base learning rate
        decay_rate (float): A decay factor of moving average over past squared gradient.
        weight_decay (float): Value for regularizing weights post every update
        epsilon (float): A value to avoid division by zero
        clipnorm (float): Gradient clipping factor
        clipvalue (float): Value for clipping

    Returns:
        None
    '''
    self.system_dict = rmsprop(self.system_dict , learning_rate, 
        decay_rate=decay_rate, epsilon=epsilon, weight_decay=weight_decay, clipnorm=clipnorm, clipvalue=clipvalue);
    self.custom_print("Optimizer");
    self.custom_print("    Name:          {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
    self.custom_print("    Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
    self.custom_print("    Params:        {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
    self.custom_print("");
    ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
    self.custom_print("");

def optimizer_sgd(self, learning_rate, momentum=0, weight_decay=0, momentum_dampening_rate=0, clipnorm=0.0, clipvalue=0.0)

Select stochastic gradient descent optimizer

Args

learning_rate : float

Initial base learning rate

momentum : float

Momentum value for driving the weights towards minima

weight_decay : float

Value for regularizing weights post every update

momentum_dampening_rate : float

Reduction rate for momentum

clipnorm : float

Gradient clipping factor

clipvalue : float

Value for clipping

Returns

None

 

Expand source code

def optimizer_sgd(self, learning_rate, momentum=0, weight_decay=0, momentum_dampening_rate=0, clipnorm=0.0, clipvalue=0.0):
    '''
    Select stochastic gradient descent optimizer

    Args:
        learning_rate (float): Initial base learning rate
        momentum (float): Momentum value for driving the weights towards minima
        weight_decay (float): Value for regularizing weights post every update
        momentum_dampening_rate (float): Reduction rate for momentum
        clipnorm (float): Gradient clipping factor
        clipvalue (float): Value for clipping

    Returns:
        None
    '''
    self.system_dict = sgd(self.system_dict, learning_rate, 
            momentum=momentum, weight_decay=weight_decay, momentum_dampening_rate=momentum_dampening_rate, clipnorm=clipnorm, clipvalue=clipvalue);
    self.custom_print("Optimizer");
    self.custom_print("    Name:          {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
    self.custom_print("    Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
    self.custom_print("    Params:        {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
    self.custom_print("");
    ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
    self.custom_print("");
    ConstraintWarning("ArgumentWarning: momentum_dampening_rate is active only for pytorch in current version of Monk");
    self.custom_print("");