garage.tf.optimizers package¶
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class
ConjugateGradientOptimizer
(cg_iters=10, reg_coeff=1e-05, subsample_factor=1.0, backtrack_ratio=0.8, max_backtracks=15, accept_violation=False, hvp_approach=None, num_slices=1)[source]¶ Bases:
object
Performs constrained optimization via line search.
The search direction is computed using a conjugate gradient algorithm, which gives x = A^{-1}g, where A is a second order approximation of the constraint and g is the gradient of the loss function.
Parameters: - cg_iters (int) – The number of CG iterations used to calculate A^-1 g
- reg_coeff (float) – A small value so that A -> A + reg*I
- subsample_factor (float) – Subsampling factor to reduce samples when using “conjugate gradient. Since the computation time for the descent direction dominates, this can greatly reduce the overall computation time.
- backtrack_ratio (float) – backtrack ratio for backtracking line search.
- max_backtracks (int) – Max number of iterations for backtrack linesearch.
- accept_violation (bool) – whether to accept the descent step if it violates the line search condition after exhausting all backtracking budgets.
- hvp_approach (HessianVectorProduct) – A class that computes Hessian-vector products.
- num_slices (int) – Hessian-vector product function’s inputs will be divided into num_slices and then averaged together to improve performance.
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constraint_val
(inputs, extra_inputs=None)[source]¶ Constraint value.
Parameters: Returns: Constraint value.
Return type:
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loss
(inputs, extra_inputs=None)[source]¶ Compute the loss value.
Parameters: Returns: Loss value.
Return type:
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optimize
(inputs, extra_inputs=None, subsample_grouped_inputs=None, name='optimize')[source]¶ Optimize the function.
Parameters: - inputs (list[numpy.ndarray]) – A list inputs, which could be subsampled if needed. It is assumed that the first dimension of these inputs should correspond to the number of data points
- extra_inputs (list[numpy.ndarray]) – A list of extra inputs which should not be subsampled.
- subsample_grouped_inputs (list[numpy.ndarray]) – Subsampled inputs to be used when subsample_factor is less than one.
- name (str) – The name argument for tf.name_scope.
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update_opt
(loss, target, leq_constraint, inputs, extra_inputs=None, name='ConjugateGradientOptimizer', constraint_name='constraint')[source]¶ Update the optimizer.
Build the functions for computing loss, gradient, and the constraint value.
Parameters: - loss (tf.Tensor) – Symbolic expression for the loss function.
- target (garage.tf.policies.Policy) – A parameterized object to optimize over.
- leq_constraint (tuple[tf.Tensor, float]) – A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
- inputs (list(tf.Tenosr)) – A list of symbolic variables as inputs, which could be subsampled if needed. It is assumed that the first dimension of these inputs should correspond to the number of data points.
- extra_inputs (list[tf.Tenosr]) – A list of symbolic variables as extra inputs which should not be subsampled.
- name (str) – Name to be passed to tf.name_scope.
- constraint_name (str) – A constraint name for prupose of logging and variable names.
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class
FiniteDifferenceHvp
(base_eps=1e-08, symmetric=True, num_slices=1)[source]¶ Bases:
garage.tf.optimizers.conjugate_gradient_optimizer.HessianVectorProduct
Computes Hessian-vector product using finite difference method.
Parameters: -
update_hvp
(f, target, inputs, reg_coeff, name='FiniteDifferenceHvp')[source]¶ Build the symbolic graph to compute the Hessian-vector product.
Parameters: - f (tf.Tensor) – The function whose Hessian needs to be computed.
- target (garage.tf.policies.Policy) – A parameterized object to optimize over.
- inputs (tuple[tf.Tensor]) – The inputs for function f.
- reg_coeff (float) – A small value so that A -> A + reg*I.
- name (str) – Name to be used in tf.name_scope.
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class
FirstOrderOptimizer
(optimizer=None, learning_rate=None, max_epochs=1000, tolerance=1e-06, batch_size=32, callback=None, verbose=False, name='FirstOrderOptimizer')[source]¶ Bases:
object
First order optimier.
Performs (stochastic) gradient descent, possibly using fancier methods like ADAM etc.
Parameters: - optimizer (tf.Optimizer) – Optimizer to be used.
- learning_rate (dict) – learning rate arguments. learning rates are our main interest parameters to tune optimizers.
- max_epochs (int) – Maximum number of epochs for update.
- tolerance (float) – Tolerance for difference in loss during update.
- batch_size (int) – Batch size for optimization.
- callback (callable) – Function to call during each epoch. Default is None.
- verbose (bool) – If true, intermediate log message will be printed.
- name (str) – Name scope of the optimizer.
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loss
(inputs, extra_inputs=None)[source]¶ The loss.
Parameters: Returns: Loss.
Return type: Raises: Exception
– If loss function is None, i.e. not defined.
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optimize
(inputs, extra_inputs=None, callback=None)[source]¶ Perform optimization.
Parameters: Raises: NotImplementedError
– If inputs are invalid.Exception
– If loss function is None, i.e. not defined.
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update_opt
(loss, target, inputs, extra_inputs=None, **kwargs)[source]¶ Construct operation graph for the optimizer.
Parameters: - loss (tf.Tensor) – Loss objective to minimize.
- target (object) – Target object to optimize. The object should implemenet get_params() and get_param_values.
- inputs (list[tf.Tensor]) – List of input placeholders.
- extra_inputs (list[tf.Tensor]) – List of extra input placeholders.
- kwargs (dict) – Extra unused keyword arguments. Some optimizers have extra input, e.g. KL constraint.
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class
LbfgsOptimizer
(max_opt_itr=20, callback=None)[source]¶ Bases:
object
Limited-memory BFGS (L-BFGS) optimizer.
Performs unconstrained optimization via L-BFGS.
Parameters: - max_opt_itr (int) – Maximum iteration for update.
- callback (callable) – Function to call during optimization.
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loss
(inputs, extra_inputs=None)[source]¶ The loss.
Parameters: Returns: Loss.
Return type: Raises: Exception
– If loss function is None, i.e. not defined.
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optimize
(inputs, extra_inputs=None, name='optimize')[source]¶ Perform optimization.
Parameters: Raises: Exception
– If loss function is None, i.e. not defined.
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update_opt
(loss, target, inputs, extra_inputs=None, name='LbfgsOptimizer', **kwargs)[source]¶ Construct operation graph for the optimizer.
Parameters: - loss (tf.Tensor) – Loss objective to minimize.
- target (object) – Target object to optimize. The object should implemenet get_params() and get_param_values.
- inputs (list[tf.Tensor]) – List of input placeholders.
- extra_inputs (list[tf.Tensor]) – List of extra input placeholders.
- name (str) – Name scope.
- kwargs (dict) – Extra unused keyword arguments. Some optimizers have extra input, e.g. KL constraint.
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class
PenaltyLbfgsOptimizer
(max_opt_itr=20, initial_penalty=1.0, min_penalty=0.01, max_penalty=1000000.0, increase_penalty_factor=2, decrease_penalty_factor=0.5, max_penalty_itr=10, adapt_penalty=True)[source]¶ Bases:
object
Penalized Limited-memory BFGS (L-BFGS) optimizer.
Performs constrained optimization via penalized L-BFGS. The penalty term is adaptively adjusted to make sure that the constraint is satisfied.
Parameters: - max_opt_itr (int) – Maximum iteration for update.
- initial_penalty (float) – Initial penalty.
- min_penalty (float) – Minimum penalty allowed. Penalty will be clipped if lower than this value.
- max_penalty (float) – Maximum penalty allowed. Penalty will be clipped if higher than this value.
- increase_penalty_factor (float) – Factor to increase penalty in each penalty iteration.
- decrease_penalty_factor (float) – Factor to decrease penalty in each penalty iteration.
- max_penalty_itr (int) – Maximum penalty iterations to perform.
- adapt_penalty (bool) – Whether the penalty is adaptive or not. If false, penalty will not change.
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constraint_val
(inputs)[source]¶ The constraint value.
Parameters: inputs (list[numpy.ndarray]) – List of input values. Returns: Constraint value. Return type: float Raises: Exception
– If loss function is None, i.e. not defined.
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loss
(inputs)[source]¶ The loss.
Parameters: inputs (list[numpy.ndarray]) – List of input values. Returns: Loss. Return type: float Raises: Exception
– If loss function is None, i.e. not defined.
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optimize
(inputs, name='optimize')[source]¶ Perform optimization.
Parameters: Raises: Exception
– If loss function is None, i.e. not defined.
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update_opt
(loss, target, leq_constraint, inputs, constraint_name='constraint', name='PenaltyLbfgsOptimizer', **kwargs)[source]¶ Construct operation graph for the optimizer.
Parameters: - loss (tf.Tensor) – Loss objective to minimize.
- target (object) – Target object to optimize. The object should implemenet get_params() and get_param_values.
- leq_constraint (tuple) – It contains a tf.Tensor and a float value. The tf.Tensor represents the constraint term, and the float value is the constraint value.
- inputs (list[tf.Tensor]) – List of input placeholders.
- constraint_name (str) – Constraint name for logging.
- name (str) – Name scope.
- kwargs (dict) – Extra unused keyword arguments. Some optimizers have extra input, e.g. KL constraint.