LostTech.TensorFlow : API Documentation

Type Adam

Namespace tensorflow.keras.optimizers

Parent Optimizer

Interfaces IAdam

Optimizer that implements the Adam algorithm.

Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second-order moments. According to the paper [Adam: A Method for Stochastic Optimization. Kingma et al., 2014](http://arxiv.org/abs/1412.6980), the method is "*computationally efficient, has little memory requirement, invariant to diagonal rescaling of gradients, and is well suited for problems that are large in terms of data/parameters*".

For AMSGrad see [On The Convergence Of Adam And Beyond. Reddi et al., 5-8](https://openreview.net/pdf?id=ryQu7f-RZ).



Public static methods

Adam NewDyn(ImplicitContainer<T> learning_rate, ImplicitContainer<T> beta_1, ImplicitContainer<T> beta_2, ImplicitContainer<T> epsilon, ImplicitContainer<T> amsgrad, ImplicitContainer<T> name, IDictionary<string, object> kwargs)

Construct a new RMSprop optimizer.

Note that in the dense implementation of this algorithm, variables and their corresponding accumulators (momentum, gradient moving average, square gradient moving average) will be updated even if the gradient is zero (i.e. accumulators will decay, momentum will be applied). The sparse implementation (used when the gradient is an `IndexedSlices` object, typically because of tf.gather or an embedding lookup in the forward pass) will not update variable slices or their accumulators unless those slices were used in the forward pass (nor is there an "eventual" correction to account for these omitted updates). This leads to more efficient updates for large embedding lookup tables (where most of the slices are not accessed in a particular graph execution), but differs from the published algorithm.
ImplicitContainer<T> learning_rate
A Tensor or a floating point value. The learning rate.
ImplicitContainer<T> beta_1
ImplicitContainer<T> beta_2
ImplicitContainer<T> epsilon
Small value to avoid zero denominator.
ImplicitContainer<T> amsgrad
ImplicitContainer<T> name
Optional name prefix for the operations created when applying gradients. Defaults to "RMSprop". @compatibility(eager) When eager execution is enabled, `learning_rate`, `decay`, `momentum`, and `epsilon` can each be a callable that takes no arguments and returns the actual value to use. This can be useful for changing these values across different invocations of optimizer functions. @end_compatibility
IDictionary<string, object> kwargs
keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is clip gradients by value, `decay` is included for backward compatibility to allow time inverse decay of learning rate. `lr` is included for backward compatibility, recommended to use `learning_rate` instead.

Public properties

bool amsgrad get; set;

object clipnorm get; set;

object clipvalue get; set;

double epsilon get; set;

object iterations get; set;

object iterations_dyn get; set;

object PythonObject get;

IList<object> weights get;

object weights_dyn get;