API - Optimizers¶
TensorLayer provides rich layer implementations trailed for
various benchmarks and domain-specific problems. In addition, we also
support transparent access to native TensorFlow parameters.
For example, we provide not only layers for local response normalization, but also
layers that allow user to apply
More functions can be found in TensorFlow API.
TensorLayer provides simple API and tools to ease research, development and reduce the time to production. Therefore, we provide the latest state of the art optimizers that work with Tensorflow.
||Implementation of the AMSGrad optimization algorithm.|
AMSGrad(learning_rate=0.01, beta1=0.9, beta2=0.99, epsilon=1e-08, use_locking=False, name='AMSGrad')¶
Implementation of the AMSGrad optimization algorithm.
- learning_rate (float) – A Tensor or a floating point value. The learning rate.
- beta1 (float) – A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates.
- beta2 (float) – A float value or a constant float tensor. The exponential decay rate for the 2nd moment estimates.
- epsilon (float) – A small constant for numerical stability. This epsilon is “epsilon hat” in the Kingma and Ba paper (in the formula just before Section 2.1), not the epsilon in Algorithm 1 of the paper.
- use_locking (bool) – If True use locks for update operations.
- name (str) – Optional name for the operations created when applying gradients. Defaults to “AMSGrad”.