Supported Features
Supported Keras 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.
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.
- ❌ ActivityRegularization
- ❌ SeparableConv1D
- ❌ Conv3DTranspose
- ✅ Add / add
- ✅ Multiply / multiply
- ✅ Subtract / subtract
- ✅ Average / average
- ✅ Maximum / maximum
- ✅ Concatenate / concatenate
- ❌ Dot / dot
- ✅ ELU
- ❌ 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
Last modified 2yr ago