fixed new train method layout in examples

Author: ThummeTo

examples/src/growing_horizon_ME.ipynb

@@ -641,10 +641,8 @@
    "outputs": [],
    "source": [
     "# train\n",
-    "paramsNet = FMIFlux.params(neuralFMU)\n",
-    "\n",
     "optim = Adam()\n",
-    "FMIFlux.train!(lossSum, paramsNet, Iterators.repeated((), 1000), optim; cb=()->callb(paramsNet)) "
+    "FMIFlux.train!(lossSum, neuralFMU, Iterators.repeated((), 1000), optim; cb=()->callb(paramsNet)) "
    ]
   },
   {

examples/src/juliacon_2023.ipynb

@@ -740,7 +740,7 @@
     "   \n",
     "    # the actual training\n",
     "    FMIFlux.train!(loss,                            # the loss function for training\n",
-    "                   params,                          # the parameters to train\n",
+    "                   neuralFMU,                          # the parameters to train\n",
     "                   Iterators.repeated((), steps),   # an iterator repeating `steps` times\n",
     "                   optim;                           # the optimizer to train\n",
     "                   gradient=:ForwardDiff,           # currently, only ForwarDiff leads to good results for multi-event systems\n",

examples/src/mdpi_2022.ipynb

@@ -611,14 +611,14 @@
         "\n",
         "# we use ForwardDiff for gradinet determination, because the FMU throws multiple events per time instant (this is not supported by reverse mode AD)\n",
         "# the chunk_size controls the nuber of forward evaluations of the model (the bigger, the less evaluations)\n",
-        "FMIFlux.train!(loss, params, Iterators.repeated((), batchLen), optim; gradient=:ForwardDiff, chunk_size=32, cb=updateScheduler) \n",
+        "FMIFlux.train!(loss, neuralFMU, Iterators.repeated((), batchLen), optim; gradient=:ForwardDiff, chunk_size=32, cb=updateScheduler) \n",
         "loss_after = batch_loss(params[1])"
       ]
     },
     {
       "attachments": {},
       "cell_type": "markdown",
-	  "metadata": {},
+      "metadata": {},
       "source": [
         "The batch loss (\"AVG\" and \"MAX\") is only updated every 5 steps, as defined in the scheduler. Every 25 steps, we plot the current batch element losses. Please note, that we only did around 100 training steps, so training has not converged for now. But we are curious and want to have a look on the intermediate results. \n",
         "\n",

examples/src/modelica_conference_2021.ipynb

@@ -891,7 +891,7 @@
    "outputs": [],
    "source": [
     "optim = Adam()\n",
-    "FMIFlux.train!(lossSum, paramsNet, Iterators.repeated((), 1), optim; cb=()->callb(paramsNet)) "
+    "FMIFlux.train!(lossSum, neuralFMU, Iterators.repeated((), 1), optim; cb=()->callb(paramsNet)) "
    ]
   },
   {
@@ -950,7 +950,7 @@
     "for run in 1:numRuns\n",
     "    @time for epoch in 1:numEpochs\n",
     "        @info \"Run: $(run)/$(numRuns)  Epoch: $(epoch)/$(numEpochs)\"\n",
-    "        FMIFlux.train!(lossSum, paramsNet, Iterators.repeated((), numIterations), optim; cb=()->callb(paramsNet))\n",
+    "        FMIFlux.train!(lossSum, neuralFMU, Iterators.repeated((), numIterations), optim; cb=()->callb(paramsNet))\n",
     "    end\n",
     "    flush(stderr)\n",
     "    flush(stdout)\n",

examples/src/simple_hybrid_CS.ipynb

@@ -530,7 +530,7 @@
     "paramsNet = FMIFlux.params(csNeuralFMU)\n",
     "\n",
     "optim = Adam()\n",
-    "FMIFlux.train!(lossSum, paramsNet, Iterators.repeated((), 250), optim; cb=()->callb(paramsNet))"
+    "FMIFlux.train!(lossSum, csNeuralFMU, Iterators.repeated((), 250), optim; cb=()->callb(paramsNet))"
    ]
   },
   {

examples/src/simple_hybrid_ME.ipynb

@@ -500,7 +500,7 @@
     "paramsNet = FMIFlux.params(neuralFMU)\n",
     "\n",
     "optim = Adam()\n",
-    "FMIFlux.train!(lossSum, paramsNet, Iterators.repeated((), 300), optim; cb=()->callb(paramsNet)) "
+    "FMIFlux.train!(lossSum, neuralFMU, Iterators.repeated((), 300), optim; cb=()->callb(paramsNet)) "
    ]
   },
   {
@@ -563,7 +563,7 @@
    },
    "outputs": [],
    "source": [
-    "FMIFlux.train!(lossSum, paramsNet, Iterators.repeated((), 1200), optim; cb=()->callb(paramsNet)) \n",
+    "FMIFlux.train!(lossSum, neuralFMU, Iterators.repeated((), 1200), optim; cb=()->callb(paramsNet)) \n",
     "# plot results mass.s\n",
     "solutionAfter = neuralFMU(x₀)\n",
     "Plots.plot!(fig, solutionAfter; stateIndices=1:1, values=false, label=\"NeuralFMU (1500 epochs)\", linewidth=2)\n",