I'm trying to make a layer in Keras (R) which (matrix) exponential a layer of shape (d,d).
ie.: Input to layer is a dxd matrix and the output is a dxd matrix which is the (matrix) exponential of the input matrix.
What I've Implemented to Date:
Here's what I've done (its a degree 4 approximation because I'm also not sure how to get the tensorflow matrix exponential command working in Keras):
# Matrix Exponential
Matrix_Exp<- R6::R6Class("KerasLayer",
inherit = KerasLayer,
public = list(
call = function(x, mask = NULL) {
# Initialize Tenor-like Object -> Tensor Objects
ord0 = k_eye((k_shape(x)[1]))
ord1 = x
ord2 = (1/2)*k_dot(x,x) # note x is square so this works
ord3 = (1/6)*k_dot(x,ord2)
ord4 = (1/24)*k_dot(x,ord3)
ord0+ord1+ord2 +ord3+ord4
},
compute_output_shape = function(input_shape) {
c(d,d)
}
)
)
# Create layer wrapper function
layer_Matrix_Exp <- function(object) {
create_layer(Matrix_Exp, object)
}
I'm plugging a model with this summary into the custom layer:
Model: "sequential_32"
_________________________________________________________________________________________________________________________________________________________________
Layer (type) Output Shape Param #
=================================================================================================================================================================
dense_63 (Dense) (None, 100) 400
_________________________________________________________________________________________________________________________________________________________________
dense_64 (Dense) (None, 4) 404
_________________________________________________________________________________________________________________________________________________________________
reshape_10 (Reshape) (None, 2, 2) 0
=================================================================================================================================================================
Total params: 804
Trainable params: 804
Non-trainable params: 0
_________________________________________________________________________________________________________________________________________________________________
Problem/Error:
But I run into this error when passing layers_NE %>% layer_Matrix_Exp
WARNING:tensorflow:Entity <function wrap_fn.<locals>.fn at 0x7fbdd0cf2b90> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Evaluation error: object 'size' not found.
Error in py_call_impl(callable, dots$args, dots$keywords) :
RuntimeError: in converted code:
/scratch/users/BIM/R/x86_64-redhat-linux-gnu-library/3.6/keras/python/kerastools/layer.py:30 call *
return self.r_call(inputs, mask)
<string>:4 fn
/scratch/users/BIM/R/x86_64-redhat-linux-gnu-library/3.6/reticulate/python/rpytools/call.py:21 python_function
raise RuntimeError(res[kErrorKey])
RuntimeError: Evaluation error: object 'size' not found.
Note:
- The problem is coming from the identity part but I don't know how to fix this.
Question:
- How to fix error.
- How to replace the order 4 (manual) approximation to the matrix exponential with the keras equivalent to the tensorflow matrix exponential command.
Thanks in advance.