fixed backward compatibility problem with invertible networks

Author: PetersBas

examples/GeneralizedMinkowski/ConstrainedNeuralNetworkSegmentation_cameraman.ipynb

@@ -362,7 +362,7 @@
     "(nx, ny)  = size(image)[1:2]\n",
     "α         = 0.3^2\n",
     "batchsize = 1\n",
-    "HN = H = NetworkHyperbolic(nx, ny,n_chan_in, batchsize, architecture; α)\n",
+    "HN = H = NetworkHyperbolic(n_chan_in, architecture; α)\n",
     "\n",
     "#Test reversibility: neural network forward prop -> inverse prop\n",
     "X_prev, X_curr, lgdet = HN.forward(deepcopy(image),deepcopy(image))\n",

examples/GeneralizedMinkowski/ConstrainedNeuralNetworkSegmentation_cameraman.jl

@@ -286,7 +286,7 @@ n_chan_in = size(image,3)
 (nx, ny)  = size(image)[1:2]
 α         = 0.3^2
 batchsize = 1
-HN = H = NetworkHyperbolic(nx, ny,n_chan_in, batchsize, architecture; α)
+HN = H = NetworkHyperbolic(n_chan_in, architecture; α)
 
 #squared distance function to a constraint set
 function Dist2ToSet(input,P,active_channels,example_ind)

examples/GeneralizedMinkowski/ConstrainedNeuralNetworkSegmentation_motorbike.jl

@@ -299,7 +299,7 @@ n_chan_in = size(image,3)
 (nx, ny)  = size(image)[1:2]
 α         = 0.3^2
 batchsize = 1
-HN = H = NetworkHyperbolic(nx, ny,n_chan_in, batchsize, architecture; α)
+HN = H = NetworkHyperbolic(n_chan_in, architecture; α)
 
 #squared distance function to a constraint set
 function Dist2ToSet(input,P,active_channels,example_ind)

examples/GeneralizedMinkowski/ConstrainedNeuralNetworkSegmentation_zebra.ipynb

@@ -329,7 +329,7 @@
     "(nx, ny)  = size(image)[1:2]\n",
     "α         = 0.3^2\n",
     "batchsize = 1\n",
-    "HN = H = NetworkHyperbolic(nx, ny,n_chan_in, batchsize, architecture; α)\n",
+    "HN = H = NetworkHyperbolic(n_chan_in, architecture; α)\n",
     "\n",
     "#Test reversibility: neural network forward prop -> inverse prop\n",
     "X_prev, X_curr, lgdet = HN.forward(deepcopy(image),deepcopy(image))\n",

examples/GeneralizedMinkowski/ConstrainedNeuralNetworkSegmentation_zebra.jl

@@ -269,7 +269,7 @@ n_chan_in = size(image,3)
 (nx, ny)  = size(image)[1:2]
 α         = 0.3^2
 batchsize = 1
-HN = H = NetworkHyperbolic(nx, ny,n_chan_in, batchsize, architecture; α)
+HN = H = NetworkHyperbolic(n_chan_in, architecture; α)
 
 #squared distance function to a constraint set
 function Dist2ToSet(input,P,active_channels,example_ind)