omnibias.keras.activations.classical
Keras 3 operator layers and activation paths implemented with keras.ops for TensorFlow, JAX, or Torch.
import omnibias.keras.activations.classicalModule contract & mathematical conventions
Classical activations: exp, relu, silu, gelu.
Mirrors omnibias.torch.activations.classical on keras.ops.
exp / silu / gelu have exact all-orders fast paths (silu /
gelu via Leibniz on the analytic z*f(z) product, reusing the sigmoid /
Gaussian-Hermite towers); relu is closed form to all orders on the
almost-everywhere / regular-part convention (n >= 2 -> 0; singular delta at
the kink dropped).
Shared formulas do not imply universal floating-point identity. Verify dtype, shape, derivative-order support, and the stated numerical assumptions for the path you use.
EXP
omnibias.keras.activations.classical.EXPSource ↗EXP = register_activation(ActivationSpec(name='exp', forward=_exp_forward, derivative=_exp_forward, fastpath=_exp_fastpath, integral=_exp_forward, riccati_polynomial=(0.0, 1.0), noise_model='poisson', operator_role='K=2 collapse -> exp(z) (eigenfunction of d/dz); log-link Newton step for Poisson regression.'))This exported constant is defined by the expression above. Its exact value and annotation come from the pinned source; use it with the module contract.
Read the implementation
EXP = register_activation(
ActivationSpec(
name="exp",
forward=_exp_forward,
derivative=_exp_forward,
fastpath=_exp_fastpath,
integral=_exp_forward,
riccati_polynomial=(0.0, 1.0),
noise_model="poisson",
operator_role=(
"K=2 collapse -> exp(z) (eigenfunction of d/dz); "
"log-link Newton step for Poisson regression."
),
)
)GELU
omnibias.keras.activations.classical.GELUSource ↗GELU = register_activation(ActivationSpec(name='gelu', forward=_gelu_forward, derivative=_gelu_derivative, fastpath=_gelu_fastpath, integral=_gelu_integral, riccati_polynomial=None, noise_model='none', operator_role='K=2 collapse -> Phi(z) + z * phi(z); smoothed half-space gate, transformer FFN compatibility.'))This exported constant is defined by the expression above. Its exact value and annotation come from the pinned source; use it with the module contract.
Read the implementation
GELU = register_activation(
ActivationSpec(
name="gelu",
forward=_gelu_forward,
derivative=_gelu_derivative,
fastpath=_gelu_fastpath,
integral=_gelu_integral,
riccati_polynomial=None,
noise_model="none",
operator_role=(
"K=2 collapse -> Phi(z) + z * phi(z); "
"smoothed half-space gate, transformer FFN compatibility."
),
)
)RELU
omnibias.keras.activations.classical.RELUSource ↗RELU = register_activation(ActivationSpec(name='relu', forward=_relu_forward, derivative=_relu_derivative, fastpath=_relu_fastpath, integral=_relu_integral, riccati_polynomial=None, noise_model='none', operator_role='K=2 collapse -> Heaviside step; equality-constraint indicator, Moore-Penrose pseudoinverse limit.'))This exported constant is defined by the expression above. Its exact value and annotation come from the pinned source; use it with the module contract.
Read the implementation
RELU = register_activation(
ActivationSpec(
name="relu",
forward=_relu_forward,
derivative=_relu_derivative,
fastpath=_relu_fastpath,
integral=_relu_integral,
riccati_polynomial=None,
noise_model="none",
operator_role=(
"K=2 collapse -> Heaviside step; equality-constraint indicator, "
"Moore-Penrose pseudoinverse limit."
),
)
)SILU
omnibias.keras.activations.classical.SILUSource ↗SILU = register_activation(ActivationSpec(name='silu', forward=_silu_forward, derivative=_silu_derivative, fastpath=_silu_fastpath, riccati_polynomial=None, noise_model='none', operator_role='K=2 collapse -> sigmoid + z * sigmoid * (1 - sigmoid); smoothed gate for transformer FFN compatibility.', aliases=('swish',)))This exported constant is defined by the expression above. Its exact value and annotation come from the pinned source; use it with the module contract.
Read the implementation
SILU = register_activation(
ActivationSpec(
name="silu",
forward=_silu_forward,
derivative=_silu_derivative,
fastpath=_silu_fastpath,
riccati_polynomial=None,
noise_model="none",
operator_role=(
"K=2 collapse -> sigmoid + z * sigmoid * (1 - sigmoid); "
"smoothed gate for transformer FFN compatibility."
),
aliases=("swish",),
)
)