update readme

README.md

@@ -40,7 +40,7 @@ n_in = nvars + naugs # with augmentation
 n = 1024
 
 # Model
-using ContinuousNormalizingFlows, Lux #, CUDA, ComputationalResources
+using ContinuousNormalizingFlows, Lux #, CUDA, ComputationalResources, Zygote
 nn = Chain(Dense(n_in => 3 * n_in, tanh), Dense(3 * n_in => n_in, tanh))
 # icnf = construct(RNODE, nn, nvars) # use defaults
 icnf = construct(
@@ -50,6 +50,8 @@ icnf = construct(
     naugs; # number of augmented dimensions
     tspan = (0.0f0, 13.0f0), # have bigger time span
     steer_rate = 1.0f-1, # add random noise to end of the time span
+    # compute_mode = DIJacVecVectorMode, # process data one by one
+    # autodiff_backend = AutoZygote(), # use Zygote
     # resource = CUDALibs(), # process data by GPU
 )
 
@@ -60,15 +62,15 @@ r = rand(data_dist, nvars, n)
 r = convert.(Float32, r)
 
 # Fit It
-using DataFrames, MLJBase #, ForwardDiff, ADTypes, OptimizationOptimisers
+using DataFrames, MLJBase #, Zygote, ADTypes, OptimizationOptimisers
 df = DataFrame(transpose(r), :auto)
 # model = ICNFModel(icnf) # use defaults
 model = ICNFModel(
     icnf;
     batch_size = 256, # have bigger batchs
     # n_epochs = 100, # have less epochs
     # optimizers = (Adam(),), # use a different optimizer
-    # adtype = AutoForwardDiff(), # use ForwardDiff
+    # adtype = AutoZygote(), # use Zygote
 )
 mach = machine(model, df)
 fit!(mach)