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 deeplearning4j-modelimport module. Below is a comprehensive list of currently supported features.

Layers

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

Core Layers

Convolutional Layers

Pooling Layers

Locally-connected Layers

DL4J currently does not support Locally-connected layers.

  • LocallyConnected1D
  • LocallyConnected2D

Recurrent Layers

  • SimpleRNN
  • GRU
  • LSTM

Embedding Layers

Merge Layers

  • Add / add
  • Multiply / multiply
  • Subtract / subtract
  • Average / average
  • Maximum / maximum
  • Concatenate / concatenate
  • Dot / dot

Advanced Activation Layers

Normalization Layers

Noise Layers

Currently, DL4J does not support noise layers.

  • GaussianNoise
  • GaussianDropout
  • AlphaDropout

Layer Wrappers

DL4j does not have the concept of layer wrappers, but there is an implementation of bi-directional LSTMs available here.

  • TimeDistributed
  • Bidirectional

Losses

  • 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

Activations

  • softmax
  • elu
  • selu
  • softplus
  • softsign
  • relu
  • tanh
  • sigmoid
  • hard_sigmoid
  • linear

Initializers

  • Zeros
  • Ones
  • Constant
  • RandomNormal
  • RandomUniform
  • TruncatedNormal
  • VarianceScaling
  • Orthogonal
  • Identity
  • lecun_uniform
  • lecun_normal
  • glorot_normal
  • glorot_uniform
  • he_normal
  • he_uniform

Regularizers

  • l1
  • l2
  • l1_l2

Constraints

  • max_norm
  • non_neg
  • unit_norm
  • min_max_norm

Metrics

  • binary_accuracy
  • categorical_accuracy
  • sparse_categorical_accuracy
  • top_k_categorical_accuracy
  • sparse_top_k_categorical_accuracy

Optimizers

  • SGD
  • RMSprop
  • Adagrad
  • Adadelta
  • Adam
  • Adamax
  • Nadam
  • TFOptimizer
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