DL4J supports all available Keras losses (except for logcosh), namely:

• mean_squared_error

• mean_absolute_error

• mean_absolute_percentage_error

• mean_squared_logarithmic_error

• squared_hinge

• hinge

• categorical_hinge

• logcosh

• categorical_crossentropy

• sparse_categorical_crossentropy

• binary_crossentropy

• kullback_leibler_divergence

• poisson

• cosine_proximity

The mapping of Keras loss functions can be found in KerasLossUtils.

All standard Keras optimizers are supported, but importing custom TensorFlow optimizers won't work:

• SGD

• RMSprop

• Adagrad

• Adadelta

• Adam

• Adamax

• Nadam

• TFOptimizer

Keras Model Import: Supported Features

While not every concept in DL4J has an equivalent in Keras and vice versa, many of the key concepts can be matched. Importing keras models into DL4J is done in our module. Below is a comprehensive list of currently supported features.

Mapping keras to DL4J layers is done in the sub-module of model import. The structure of this project loosely reflects the structure of Keras.

• ✅

• ✅

• ✅

• ✅

• ✅

• ✅

• ✅

• ✅

• ✅

• ❌ ActivityRegularization

• ✅

• ✅

• ✅

• ✅

• ❌ SeparableConv1D

• ❌ Conv3DTranspose

• ❌ GRU

• ❌ ConvLSTM2D

• ✅ Add / add

• ✅ Multiply / multiply

• ✅ Subtract / subtract

• ✅ Average / average

• ✅ Maximum / maximum

• ✅ Concatenate / concatenate

• ❌ Dot / dot

• ✅ ELU

Noise Layers

Layer Wrappers

• ❌ TimeDistributed

• ✅ mean_squared_error

• ✅ mean_absolute_error

• ✅ mean_absolute_percentage_error

• ✅ mean_squared_logarithmic_error

• ✅ squared_hinge

• ✅ hinge

• ✅ categorical_hinge

• ❌ logcosh

• ✅ categorical_crossentropy

• ✅ sparse_categorical_crossentropy

• ✅ binary_crossentropy

• ✅ kullback_leibler_divergence

• ✅ poisson

• ✅ cosine_proximity

• ✅ softmax

• ✅ elu

• ✅ selu

• ✅ softplus

• ✅ softsign

• ✅ relu

• ✅ tanh

• ✅ sigmoid

• ✅ hard_sigmoid

• ✅ linear

• ✅ Zeros

• ✅ Ones

• ✅ Constant

• ✅ RandomNormal

• ✅ RandomUniform

• ✅ TruncatedNormal

• ✅ VarianceScaling

• ✅ Orthogonal

• ✅ Identity

• ✅ lecun_uniform

• ✅ lecun_normal

• ✅ glorot_normal

• ✅ glorot_uniform

• ✅ he_normal

• ✅ he_uniform

• ✅ l1

• ✅ l2

• ✅ l1_l2

• ✅ max_norm

• ✅ non_neg

• ✅ unit_norm

• ✅ min_max_norm

• ✅ SGD

• ✅ RMSprop

• ✅ Adagrad

• ✅ Adadelta

• ✅ Adam

• ✅ Adamax

• ✅ Nadam

• ❌ TFOptimizer

DL4J supports all available Keras initializers, namely:

• Zeros

• Ones

• Constant

• RandomNormal

• RandomUniform

• TruncatedNormal

• VarianceScaling

• Orthogonal

• Identity

• lecun_uniform

• lecun_normal

• glorot_normal

• glorot_uniform

• he_normal

• he_uniform

The mapping of Keras to DL4J initializers can be found in KerasInitilizationUtils.

We support all Keras activation functions, namely:

• softmax

• elu

• selu

• softplus

• softsign

• relu

• tanh

• sigmoid

• hard_sigmoid

• linear

The mapping of Keras to DL4J activation functions is defined in KerasActivationUtils

All [Keras regularizers] are supported by DL4J model import:

• l1

• l2

• l1_l2

Mapping of regularizers can be found in KerasRegularizerUtils.

All Keras constraints are supported:

• max_norm

• non_neg

• unit_norm

• min_max_norm

Mapping Keras to DL4J constraints happens in KerasConstraintUtils.