Optimizer that implements the Adam algorithm
optimizer_adam(
learning_rate = 0.001,
beta_1 = 0.9,
beta_2 = 0.999,
epsilon = 1e-07,
amsgrad = FALSE,
weight_decay = NULL,
clipnorm = NULL,
clipvalue = NULL,
global_clipnorm = NULL,
use_ema = FALSE,
ema_momentum = 0.99,
ema_overwrite_frequency = NULL,
jit_compile = TRUE,
name = "Adam",
...
)Optimizer for use with compile.keras.engine.training.Model.
A tf.Tensor, floating point value, a schedule that is a
tf.keras.optimizers.schedules.LearningRateSchedule, or a callable
that takes no arguments and returns the actual value to use. The
learning rate. Defaults to 0.001.
A float value or a constant float tensor, or a callable that takes no arguments and returns the actual value to use. The exponential decay rate for the 1st moment estimates. Defaults to 0.9.
A float value or a constant float tensor, or a callable that takes no arguments and returns the actual value to use. The exponential decay rate for the 2nd moment estimates. Defaults to 0.999.
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. Defaults to 1e-7.
Boolean. Whether to apply AMSGrad variant of this algorithm from
the paper "On the Convergence of Adam and beyond". Defaults to FALSE.
Float, defaults to NULL. If set, weight decay is applied.
Float. If set, the gradient of each weight is individually clipped so that its norm is no higher than this value.
Float. If set, the gradient of each weight is clipped to be no higher than this value.
Float. If set, the gradient of all weights is clipped so that their global norm is no higher than this value.
Boolean, defaults to FALSE. If TRUE, exponential moving average (EMA) is applied. EMA consists of computing an exponential moving average of the weights of the model (as the weight values change after each training batch), and periodically overwriting the weights with their moving average.
Float, defaults to 0.99. Only used if use_ema=TRUE. This is # noqa: E501
the momentum to use when computing the EMA of the model's weights:
new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value.
Int or NULL, defaults to NULL. Only used if
use_ema=TRUE. Every ema_overwrite_frequency steps of iterations, we
overwrite the model variable by its moving average. If NULL, the optimizer # noqa: E501
does not overwrite model variables in the middle of training, and you
need to explicitly overwrite the variables at the end of training
by calling optimizer.finalize_variable_values() (which updates the model # noqa: E501
variables in-place). When using the built-in fit() training loop, this
happens automatically after the last epoch, and you don't need to do
anything.
Boolean, defaults to TRUE. If TRUE, the optimizer will use XLA # noqa: E501 compilation. If no GPU device is found, this flag will be ignored.
String. The name to use for momentum accumulator weights created by the optimizer.
Used for backward and forward compatibility
Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second-order moments.
According to Kingma et al., 2014, the method is "computationally efficient, has little memory requirement, invariant to diagonal rescaling of gradients, and is well suited for problems that are large in terms of data/parameters".
Other optimizers:
optimizer_adadelta(),
optimizer_adagrad(),
optimizer_adamax(),
optimizer_ftrl(),
optimizer_nadam(),
optimizer_rmsprop(),
optimizer_sgd()