Constraints

Mapping of Keras weight constraints to DL4J LayerConstraint implementations for model import in Eclipse Deeplearning4j 1.0.0-rewrite.

Keras allows weight constraints to be applied to layer parameters during training. These constraints clip or rescale the weights after each gradient update to keep them within a specified range. DL4J supports all standard Keras constraints via corresponding LayerConstraint implementations.

The mapping is implemented in KerasConstraintUtils.

Supported Constraints

Keras constraint	DL4J LayerConstraint	Description
max_norm	MaxNormConstraint	Clips incoming weights so
non_neg	NonNegativeConstraint	Forces all weights to be non-negative
unit_norm	UnitNormConstraint	Scales incoming weights to have unit L2 norm
min_max_norm	MinMaxNormConstraint	Clips weights so min_value ≤

Constraint Descriptions

max_norm

Clips the incoming weights for each neuron so that their L2 norm does not exceed max_value. Applied along the axis dimension (default: 0, i.e., incoming connections per unit).

# Keras
from keras.constraints import max_norm
layer = Dense(64, kernel_constraint=max_norm(2.0))

// DL4J equivalent
new MaxNormConstraint(2.0, 1)

Keras parameters:

Parameter	Default	Description
max_value	2	Maximum L2 norm value
axis	0	Axis along which to constrain

non_neg

Forces all weights to be greater than or equal to zero after each update. Any negative weights are clipped to zero. Useful for non-negative matrix factorisation tasks.

# Keras
from keras.constraints import non_neg
layer = Dense(64, kernel_constraint=non_neg())

// DL4J equivalent
new NonNegativeConstraint()

unit_norm

Scales incoming weights so that their L2 norm equals 1. Effectively normalises the weight vector for each unit.

# Keras
from keras.constraints import unit_norm
layer = Dense(64, kernel_constraint=unit_norm())

// DL4J equivalent
new UnitNormConstraint(1)

min_max_norm

Clips incoming weights so that their L2 norm is between min_value and max_value. The rate parameter controls the speed of clipping (rate=1.0 applies the constraint exactly; values less than 1.0 apply it more slowly).

# Keras
from keras.constraints import min_max_norm
layer = Dense(64, kernel_constraint=min_max_norm(min_value=0.5, max_value=2.0))

// DL4J equivalent
new MinMaxNormConstraint(0.5, 2.0, 1.0, 1)

Keras parameters:

Parameter	Default	Description
min_value	0.0	Minimum L2 norm value
max_value	1.0	Maximum L2 norm value
rate	1.0	Rate at which the constraint is applied
axis	0	Axis along which to constrain

Usage Example

import org.deeplearning4j.nn.modelimport.keras.KerasModelImport;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;

// Constraints are automatically read and applied to the imported network configuration
MultiLayerNetwork model = KerasModelImport.importKerasSequentialModelAndWeights("model.h5");

Notes

• Constraints are only relevant when the imported model is used for continued training in DL4J. For inference, constraints have no effect.

• The constraint axis parameter from Keras is mapped to the equivalent DL4J axis convention. Verify constraint behaviour when porting models with non-default axis values.

• Custom Python constraint classes cannot be imported.