ai4med.components.optimizers package
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class
Adam(**args) Bases:
ai4med.components.optimizers.optimizer.OptimizerOptimizer that implements the Adam algorithm. This uses tf’s AdamOptimizer with details and args information at: https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/AdamOptimizer
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get_optimizer(lr) This is the required method that an optimizer component must implement.
- Parameters
lr – the learning rate
- Returns
optimizer function
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class
Momentum(**args) Bases:
ai4med.components.optimizers.optimizer.OptimizerOptimizer that implements the Momentum algorithm (SGD + momentum). This uses the tf MomentumOptimizer with details and args information at: https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/MomentumOptimizer
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get_optimizer(lr) This is the required method that an optimizer component must implement.
- Parameters
lr – the learning rate
- Returns
optimizer function
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class
NovoGrad(**args) Bases:
ai4med.components.optimizers.optimizer.OptimizerOptimizer that implements the NovoGrad algorithm. Details and args information at:
ai4med.libs.optimizers.novograd-
get_optimizer(lr) Returns optimizer function
- Parameters
lr – learning rate
Returns: NovoGrad Optimizer function
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class
Optimizer(**args) Bases:
ai4med.common.graph_component.GraphComponentBase class of optimizer component.
An optimizer component is also a GraphComponent that builds part of the computation graph.
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build(build_ctx: ai4med.common.build_ctx.BuildContext) A graph building component must implement this method to build parts of the computation graph. While building, this method can use any objects in the build_ctx and put objects into the build_ctx. Therefore, different graph building components can use the build_ctx to share objects.
- Parameters
build_ctx (BuildContext) – the build context.
Returns: tensor(s) built
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abstract
get_optimizer(lr) This is the required method that an optimizer component must implement.
- Parameters
lr – the learning rate
- Returns
optimizer function
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is_valid_regex(x: str) → bool