LostTech.TensorFlow : API Documentation

Type Adamax

Namespace tensorflow.keras.optimizers

Parent Optimizer

Interfaces IAdamax

Optimizer that implements the Adamax algorithm.

It is a variant of Adam based on the infinity norm. Default parameters follow those provided in the paper. Adamax is sometimes superior to adam, specially in models with embeddings.

References see Section 7 of [Kingma et al., 2014](http://arxiv.org/abs/1412.6980) ([pdf](http://arxiv.org/pdf/1412.6980.pdf)).



Public static methods

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

Construct a new Adamax optimizer.


``` m_0 <- 0 (Initialize initial 1st moment vector) v_0 <- 0 (Initialize the exponentially weighted infinity norm) t <- 0 (Initialize timestep) ```

The update rule for `variable` with gradient `g` uses an optimization described at the end of section 7.1 of the paper:

``` t <- t + 1

m_t <- beta1 * m_{t-1} + (1 - beta1) * g v_t <- max(beta2 * v_{t-1}, abs(g)) variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon) ```

Similar to AdamOptimizer, the epsilon is added for numerical stability (especially to get rid of division by zero when v_t = 0).

Contrast to AdamOptimizer, the sparse implementation of this algorithm (used when the gradient is an IndexedSlices object, typically because of tf.gather or an embedding lookup in the forward pass) only updates variable slices and corresponding `m_t`, `v_t` terms when that part of the variable was used in the forward pass. This means that the sparse behavior is contrast to the dense behavior (similar to some momentum implementations which ignore momentum unless a variable slice was actually used).
ImplicitContainer<T> learning_rate
A Tensor or a floating point value. The learning rate.
ImplicitContainer<T> beta_1
A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates.
ImplicitContainer<T> beta_2
A float value or a constant float tensor. The exponential decay rate for the exponentially weighted infinity norm.
ImplicitContainer<T> epsilon
A small constant for numerical stability.
ImplicitContainer<T> name
Optional name for the operations created when applying gradients. Defaults to "Adamax".
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

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;