Keras 3 guide#

mononet.keras uses keras.ops, so the same code runs whether Keras is configured to use JAX, TensorFlow, or PyTorch under the hood. The GPU devcontainer ships with KERAS_BACKEND=jax.

Install#

pip install "mononet[keras]"

Public API#

mononet.keras.layers.MonoDense — monotonic analogue of keras.layers.Dense (non-decreasing in all inputs).
mononet.keras.layers.MonoResidual — dual-gated monotone residual block, warm-started near identity.
mononet.keras.layers.MonoInput — sign-flip layer encoding per-feature monotonicity directions.

mononet ships layers only — stack them yourself; there is no composed MonoMLP model.

Example#

import keras
from mononet.keras import MonoDense, MonoInput

# A monotonic MLP: non-decreasing in every input feature.
# MonoDense infers the input width at build time (Keras style) — no in_features.
net = keras.Sequential([
    MonoInput(1),                     # +1 => non-decreasing; -1 => non-increasing
    MonoDense(32, mode="switch"),
    MonoDense(1, mode="switch"),
])
y = net(keras.ops.zeros((8, 4)))      # (8, 1), guaranteed monotone in all inputs

For per-feature monotonicity directions, pass a MonotonicityMask (a 1-D array of {-1, +1}) to MonoInput. MonoDense and MonoInput implement get_config/from_config, so models serialize with the standard Keras saving APIs.

See also#