optimizer_lamb | R Documentation |
Layer-wise Adaptive Moments
optimizer_lamb( learning_rate = 0.001, beta_1 = 0.9, beta_2 = 0.999, epsilon = 1e-06, weight_decay_rate = 0, exclude_from_weight_decay = NULL, exclude_from_layer_adaptation = NULL, name = "LAMB", 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_rate |
weight decay rate. |
exclude_from_weight_decay |
List of regex patterns of variables excluded from weight decay. Variables whose name contain a substring matching the pattern will be excluded. |
exclude_from_layer_adaptation |
List of regex patterns of variables excluded from layer adaptation. Variables whose name contain a substring matching the pattern will be excluded. |
name |
Optional name for the operations created when applying gradients. Defaults to "LAMB". |
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_lamb(), loss='binary_crossentropy', metrics='accuracy' ) ## End(Not run)
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