AdamParams

class mint.types.AdamParams ( StrictBase )¶

Fields:

learning_rate ( float ) – Learning rate for the optimizer
beta1 ( float ) – Coefficient used for computing running averages of gradient
beta2 ( float ) – Coefficient used for computing running averages of gradient square
eps ( float ) – Term added to the denominator to improve numerical stability
weight_decay ( float ) – Weight decay for the optimizer. Uses decoupled weight decay.
grad_clip_norm ( float ) – Maximum global gradient norm. If the global gradient norm is greater than this value, it will be clipped to this value. 0.0 means no clipping.