optimizer_novograd | R Documentation |
NovoGrad
optimizer_novograd( learning_rate = 0.001, beta_1 = 0.9, beta_2 = 0.999, epsilon = 1e-07, weight_decay = 0, grad_averaging = FALSE, amsgrad = FALSE, name = "NovoGrad", clipnorm = NULL, clipvalue = NULL, decay = NULL, lr = NULL )
learning_rate |
A 'Tensor' or a floating point value. or a schedule that is a 'tf$keras$optimizers$schedules$LearningRateSchedule' The learning rate. |
beta_1 |
A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates. |
beta_2 |
A float value or a constant float tensor. The exponential decay rate for the 2nd moment estimates. |
epsilon |
A small constant for numerical stability. |
weight_decay |
A floating point value. Weight decay for each param. |
grad_averaging |
determines whether to use Adam style exponential moving averaging for the first order moments. |
amsgrad |
boolean. Whether to apply AMSGrad variant of this algorithm from the paper "On the Convergence of Adam and beyond" |
name |
Optional name for the operations created when applying gradients. Defaults to "NovoGrad". |
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. |
Optimizer for use with 'keras::compile()'
## Not run: keras_model_sequential() %>% layer_dense(32, input_shape = c(784)) %>% compile( optimizer = optimizer_novograd(), loss='binary_crossentropy', metrics='accuracy' ) ## End(Not run)
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