HenryAI/KerasBERTv1-Data · Datasets at Hugging Face

text stringlengths 0 4.99k
Hinton, 2012
Adamax
Adamax class
tf.keras.optimizers.Adamax(
learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, name="Adamax", **kwargs
)
Optimizer that implements the Adamax algorithm.

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).

Arguments

learning_rate: A Tensor, 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 exponentially weighted infinity norm.
epsilon: A small constant for numerical stability.
name: Optional name for the operations created when applying gradients. Defaults to "Adamax".
**kwargs: Keyword arguments. Allowed to be one of "clipnorm" or "clipvalue". "clipnorm" (float) clips gradients by norm; "clipvalue" (float) clips gradients by value.
Reference

Kingma et al., 2014
Ftrl
Ftrl class
tf.keras.optimizers.Ftrl(
learning_rate=0.001,
learning_rate_power=-0.5,
initial_accumulator_value=0.1,
l1_regularization_strength=0.0,
l2_regularization_strength=0.0,
name="Ftrl",
l2_shrinkage_regularization_strength=0.0,
beta=0.0,
**kwargs
)
Optimizer that implements the FTRL algorithm.

"Follow The Regularized Leader" (FTRL) is an optimization algorithm developed at Google for click-through rate prediction in the early 2010s. It is most suitable for shallow models with large and sparse feature spaces. The algorithm is described in this paper. The Keras version has support for both online L2 regularization (the L2 regularization described in the paper above) and shrinkage-type L2 regularization (which is the addition of an L2 penalty to the loss function).

Initialization:

n = 0
sigma = 0
z = 0
Update rule for one variable w:

prev_n = n
n = n + g ** 2
sigma = (sqrt(n) - sqrt(prev_n)) / lr
z = z + g - sigma * w
if abs(z) < lambda_1:
w = 0
else:
w = (sgn(z) * lambda_1 - z) / ((beta + sqrt(n)) / alpha + lambda_2)
Notation:

lr is the learning rate
g is the gradient for the variable
lambda_1 is the L1 regularization strength
lambda_2 is the L2 regularization strength
Check the documentation for the l2_shrinkage_regularization_strength parameter for more details when shrinkage is enabled, in which case gradient is replaced with a gradient with shrinkage.

Arguments

learning_rate: A Tensor, floating point value, or a schedule that is a tf.keras.optimizers.schedules.LearningRateSchedule. The learning rate.
learning_rate_power: A float value, must be less or equal to zero. Controls how the learning rate decreases during training. Use zero for a fixed learning rate.
initial_accumulator_value: The starting value for accumulators. Only zero or positive values are allowed.
l1_regularization_strength: A float value, must be greater than or equal to zero. Defaults to 0.0.
l2_regularization_strength: A float value, must be greater than or equal to zero. Defaults to 0.0.
name: Optional name prefix for the operations created when applying gradients. Defaults to "Ftrl".
l2_shrinkage_regularization_strength: A float value, must be greater than or equal to zero. This differs from L2 above in that the L2 above is a stabilization penalty, whereas this L2 shrinkage is a magnitude penalty. When input is sparse shrinkage will only happen on the active weights.
beta: A float value, representing the beta value from the paper. Defaults to 0.0.
**kwargs: Keyword arguments. Allowed to be one of "clipnorm" or "clipvalue". "clipnorm" (float) clips gradients by norm; "clipvalue" (float) clips gradients by value.
Reference

Original paper
Nadam
Nadam class
tf.keras.optimizers.Nadam(
learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, name="Nadam", **kwargs
)
Optimizer that implements the NAdam algorithm. Much like Adam is essentially RMSprop with momentum, Nadam is Adam with Nesterov momentum.

Arguments

Hinton, 2012

Adamax

Adamax class

tf.keras.optimizers.Adamax(

learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, name="Adamax", **kwargs

)

Optimizer that implements the Adamax algorithm.

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).

Arguments

learning_rate: A Tensor, 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 exponentially weighted infinity norm.

epsilon: A small constant for numerical stability.

name: Optional name for the operations created when applying gradients. Defaults to "Adamax".

**kwargs: Keyword arguments. Allowed to be one of "clipnorm" or "clipvalue". "clipnorm" (float) clips gradients by norm; "clipvalue" (float) clips gradients by value.

Reference

Kingma et al., 2014

Ftrl

Ftrl class

tf.keras.optimizers.Ftrl(

learning_rate=0.001,

learning_rate_power=-0.5,

initial_accumulator_value=0.1,

l1_regularization_strength=0.0,

l2_regularization_strength=0.0,

name="Ftrl",

l2_shrinkage_regularization_strength=0.0,

beta=0.0,

**kwargs

)

Optimizer that implements the FTRL algorithm.

"Follow The Regularized Leader" (FTRL) is an optimization algorithm developed at Google for click-through rate prediction in the early 2010s. It is most suitable for shallow models with large and sparse feature spaces. The algorithm is described in this paper. The Keras version has support for both online L2 regularization (the L2 regularization described in the paper above) and shrinkage-type L2 regularization (which is the addition of an L2 penalty to the loss function).

Initialization:

n = 0

sigma = 0

z = 0

Update rule for one variable w:

prev_n = n

n = n + g ** 2

sigma = (sqrt(n) - sqrt(prev_n)) / lr

z = z + g - sigma * w

if abs(z) < lambda_1:

w = 0

else:

w = (sgn(z) * lambda_1 - z) / ((beta + sqrt(n)) / alpha + lambda_2)

Notation:

lr is the learning rate

g is the gradient for the variable

lambda_1 is the L1 regularization strength

lambda_2 is the L2 regularization strength

Check the documentation for the l2_shrinkage_regularization_strength parameter for more details when shrinkage is enabled, in which case gradient is replaced with a gradient with shrinkage.

Arguments

learning_rate: A Tensor, floating point value, or a schedule that is a tf.keras.optimizers.schedules.LearningRateSchedule. The learning rate.

learning_rate_power: A float value, must be less or equal to zero. Controls how the learning rate decreases during training. Use zero for a fixed learning rate.

initial_accumulator_value: The starting value for accumulators. Only zero or positive values are allowed.

l1_regularization_strength: A float value, must be greater than or equal to zero. Defaults to 0.0.

l2_regularization_strength: A float value, must be greater than or equal to zero. Defaults to 0.0.

name: Optional name prefix for the operations created when applying gradients. Defaults to "Ftrl".

l2_shrinkage_regularization_strength: A float value, must be greater than or equal to zero. This differs from L2 above in that the L2 above is a stabilization penalty, whereas this L2 shrinkage is a magnitude penalty. When input is sparse shrinkage will only happen on the active weights.

beta: A float value, representing the beta value from the paper. Defaults to 0.0.

**kwargs: Keyword arguments. Allowed to be one of "clipnorm" or "clipvalue". "clipnorm" (float) clips gradients by norm; "clipvalue" (float) clips gradients by value.

Reference

Original paper

Nadam

Nadam class

tf.keras.optimizers.Nadam(

learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, name="Nadam", **kwargs

)

Optimizer that implements the NAdam algorithm. Much like Adam is essentially RMSprop with momentum, Nadam is Adam with Nesterov momentum.

Arguments