diff --git a/project-cancer-classification-pca-nn.ipynb b/project-cancer-classification-pca-nn.ipynb
index c32a729..4e9a861 100644
--- a/project-cancer-classification-pca-nn.ipynb
+++ b/project-cancer-classification-pca-nn.ipynb
@@ -541,8 +541,12 @@
     "        self.results = results\n",
     "\n",
     "        # Speichern der Daten in einer lokalen Datei\n",
-    "        with open('Experiments/results.pickle', 'wb') as f:\n",
-    "            pickle.dump(self.results, f)\n",
+    "        if len(n_pca_components) > 1:\n",
+    "            with open('Experiments/results.pickle', 'wb') as f:\n",
+    "                pickle.dump(self.results, f)\n",
+    "        else:\n",
+    "            with open(f'Experiments/{str(n_pca_components[0])}/results_{str(n_pca_components[0])}.pickle', 'wb') as f:\n",
+    "                pickle.dump(self.results, f)\n",
     "\n",
     "        plt.ion()\n",
     "\n",
@@ -610,14 +614,88 @@
     "        "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Starten der einzelnen Experimente, da in einer Schleife RAM Probleme auftreten"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "e = ExperimentationalExperiments()\n",
-    "results = e.run([1024, 512, 256, 128, 64, 32, 16], 10, n_epochs=500)"
+    "e1 = ExperimentationalExperiments()\n",
+    "results = e1.run([1024], 10, n_epochs=500)\n",
+    "del e1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "e2 = ExperimentationalExperiments()\n",
+    "results = e2.run([512], 10, n_epochs=500)\n",
+    "del e2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "e3 = ExperimentationalExperiments()\n",
+    "results = e3.run([256], 10, n_epochs=500)\n",
+    "del e3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "e4 = ExperimentationalExperiments()\n",
+    "results = e4.run([128], 10, n_epochs=500)\n",
+    "del e4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "e5 = ExperimentationalExperiments()\n",
+    "results = e5.run([64], 10, n_epochs=500)\n",
+    "del e5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "e6 = ExperimentationalExperiments()\n",
+    "results = e6.run([32], 10, n_epochs=500)\n",
+    "del e6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "e7 = ExperimentationalExperiments()\n",
+    "results = e7.run([16], 2, n_epochs=10)\n",
+    "del e7"
    ]
   },
   {