Require implementation of switching between two operations
test_random_binomial
Require implementation of random binomial
test_ctc
Require implementation of ctc_batch_cost
test_map
Require implementation of mappinf function over elements
test_foldl
Require implementation of reducing elements using function
test_foldr
Require implementation of reducing elements using function
test_Eigenvalue_reg
Not supported
Function incompatible with mxnet:
Test cases
Reason
test_nn_operations
MXNet Categorical_crossentropy doesn't support from_logits
test_arange
Keras requires that when start >= stop and step > 0, this function should return an empty sequence. Currently mxnet returns error
test_batchnorm_mode_0_or_2
Currently keras mxnet doesn't support batchnorm mode 2
test_shared_batchnorm
Currently keras mxnet doesn't support batchnorm mode 2
variational_autoencoder_deconv
Too big target shape for mx.sym.deconvolution operator
mnist_acgan
For mxnet backend, parameters are not shared between concatenated model and other separate models. So user needs to directly train generator instead of combine generator and discriminator to one model.
test_sequential_model_saving
Optimizer states not preserved when saving/loading model
mnist_net2net
mxnet doesn't support set weights to larger shape
Test case image ordering issue. Passing after modifying test cases. These are actually not failing tests:
Require sparse support:
Require rnn symbolic loop implementation:
Utility function issue, not major blocker:
Function incompatible with mxnet:
Test case image ordering issue. Passing after modifying test cases. These are actually not failing tests: