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TensorFlow 1 version
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View source on GitHub
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Optimizer that implements the Adamax algorithm.
Inherits From: Optimizer
tf.keras.optimizers.Adamax(
learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07,
name='Adamax', **kwargs
)
It is a variant of Adam based on the infinity norm. Default parameters follow those provided in the paper. Adamax is sometimes superior to adam, specially in models with embeddings.
Initialization:
m = 0 # Initialize initial 1st moment vector
v = 0 # Initialize the exponentially weighted infinity norm
t = 0 # Initialize timestep
The update rule for parameter w with gradient g is
described at the end of section 7.1 of the paper:
t += 1
m = beta1 * m + (1 - beta) * g
v = max(beta2 * v, abs(g))
current_lr = learning_rate / (1 - beta1 ** t)
w = w - current_lr * m / (v + epsilon)
Similarly to Adam, the epsilon is added for numerical stability
(especially to get rid of division by zero when v_t == 0).
In contrast to Adam, the sparse implementation of this algorithm
(used when the gradient is an IndexedSlices object, typically because of
tf.gather or an embedding lookup in the forward pass) only updates
variable slices and corresponding m_t, v_t terms when that part of
the variable was used in the forward pass. This means that the sparse
behavior is contrast to the dense behavior (similar to some momentum
implementations which ignore momentum unless a variable slice was actually
used).
Args | |
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learning_rate
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A Tensor, floating point value, or a schedule that is a
tf.keras.optimizers.schedules.LearningRateSchedule. The learning rate.
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beta_1
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A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates. |
beta_2
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A float value or a constant float tensor. The exponential decay rate for the exponentially weighted infinity norm. |
epsilon
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A small constant for numerical stability. |
name
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Optional name for the operations created when applying gradients.
Defaults to "Adamax".
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**kwargs
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Keyword arguments. Allowed to be one of
"clipnorm" or "clipvalue".
"clipnorm" (float) clips gradients by norm; "clipvalue" (float) clips
gradients by value.
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Reference:
Raises | |
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ValueError
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in case of any invalid argument. |