diff --git a/part3/part3.ipynb b/part3/part3.ipynb
index d4ac8ae0ae914d825eb97e6f63ca53abb47602f4..aa225c3c2ec4e4a3f46b091707b58247a5a957ff 100644
--- a/part3/part3.ipynb
+++ b/part3/part3.ipynb
@@ -200,6 +200,73 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "cc0de85f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "y_pred_hls    = np.load(d+'/part2/y_pred_hls.npy')\n",
+    "y_pred_qkeras = np.load(d+'/part2/y_pred_qkeras.npy')\n",
+    "y_pred_float  = np.load(d+'/part2/y_pred_float.npy')\n",
+    "y_test        = np.load(d+'/part2/y_test.npy')\n",
+    "\n",
+    "y_pred_cmssw = np.concatenate((y_sig_cmssw, y_bkg_cmssw))\n",
+    "\n",
+    "ones_array = np.ones_like(y_sig_cmssw)\n",
+    "zeros_array = np.zeros_like(y_bkg_cmssw)\n",
+    "y_test_cmssw = np.concatenate((ones_array, zeros_array))\n",
+    "\n",
+    "# Lets plot it!\n",
+    "\n",
+    "from sklearn.metrics import roc_curve, roc_auc_score\n",
+    "\n",
+    "def totalMinBiasRate():\n",
+    "\n",
+    "    LHCfreq = 11245.6\n",
+    "    nCollBunch = 2544\n",
+    "\n",
+    "    return LHCfreq * nCollBunch / 1e3 # in kHz\n",
+    "\n",
+    "\n",
+    "fpr, tpr, thr = roc_curve(y_test, y_pred_float, pos_label=None, sample_weight=None, drop_intermediate=True)\n",
+    "roc_auc = roc_auc_score(y_test, y_pred_float)\n",
+    "\n",
+    "hlsfpr, hlstpr, hlsthr = roc_curve(y_test, y_pred_hls, pos_label=1, sample_weight=None, drop_intermediate=True)\n",
+    "hlsroc_auc = roc_auc_score(y_test, y_pred_hls)\n",
+    "\n",
+    "qfpr, qtpr, qthr = roc_curve(y_test, y_pred_qkeras, pos_label=None, sample_weight=None, drop_intermediate=True)\n",
+    "qroc_auc = roc_auc_score(y_test, y_pred_qkeras)\n",
+    "\n",
+    "cmsswfpr, cmsswtpr, cmsswthr = roc_curve(y_test_cmssw, y_pred_cmssw, pos_label=None, sample_weight=None, drop_intermediate=True)\n",
+    "cmsswroc_auc = roc_auc_score(y_test_cmssw, y_pred_cmssw)\n",
+    "\n",
+    "\n",
+    "fpr *= totalMinBiasRate()\n",
+    "qfpr *= totalMinBiasRate()\n",
+    "hlsfpr *= totalMinBiasRate()\n",
+    "cmsswfpr *= totalMinBiasRate()\n",
+    "\n",
+    "f, ax  = plt.subplots(figsize=(8,6))\n",
+    "# plt.plot([0, 1], [0, 1], color='navy', lw=1, linestyle='--')\n",
+    "ax.tick_params(axis='both', which='major', labelsize=14)\n",
+    "ax.tick_params(axis='both', which='minor', labelsize=14) \n",
+    "ax.set_xlim(0,100)\n",
+    "\n",
+    "ax.plot(fpr, tpr, color='#7b3294', lw=2, ls='dashed', label=f'Baseline (AUC = {roc_auc:.5f})')\n",
+    "ax.plot(qfpr, qtpr, color='#008837', lw=2, label=f'Quantized+Pruned (AUC = {qroc_auc:.5f})')\n",
+    "ax.plot(hlsfpr, hlstpr, color='#a6dba0', lw=2, ls='dotted', label=f'HLS Quantized+Pruned (AUC = {hlsroc_auc:.5f})')\n",
+    "ax.plot(cmsswfpr, cmsswtpr, color='red', lw=2, ls='dashed', label=f'CMSSW Quantized+Pruned (AUC = {cmsswroc_auc:.5f})')\n",
+    "ax.set_xlabel('L1 Rate (kHz)')\n",
+    "ax.set_ylabel('Signal efficiency')\n",
+    "ax.legend(loc=\"lower right\")\n",
+    "ax.grid(True)\n",
+    "plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c594b5e",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/solutions/part3.ipynb b/solutions/part3.ipynb
index 23b9f976243a7ecc676df1bd2e2a04599d39baf6..ceebaafbb1ccca2bad8d6e94a9cd64e0c3eef4a8 100644
--- a/solutions/part3.ipynb
+++ b/solutions/part3.ipynb
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "8d652e36",
    "metadata": {},
    "outputs": [],
@@ -136,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "e8fc68f0",
    "metadata": {},
    "outputs": [],
@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "d55f97b8",
    "metadata": {},
    "outputs": [],
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "eda30259",
    "metadata": {},
    "outputs": [
@@ -202,7 +202,7 @@
        "Text(0, 0.5, 'Frequency')"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -229,8 +229,106 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "cc0de85f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/afs/cern.ch/user/s/ssummers/.local/lib/python3.9/site-packages/numpy/core/getlimits.py:500: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
+      "  setattr(self, word, getattr(machar, word).flat[0])\n",
+      "/afs/cern.ch/user/s/ssummers/.local/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
+      "  return self._float_to_str(self.smallest_subnormal)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<module 'matplotlib.pyplot' from '/cvmfs/cms.cern.ch/slc7_amd64_gcc11/external/py3-matplotlib/3.7.1-437a2eea83d29aac3bc5f3984f238002/lib/python3.9/site-packages/matplotlib/pyplot.py'>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_pred_hls    = np.load(d+'/part2/y_pred_hls.npy')\n",
+    "y_pred_qkeras = np.load(d+'/part2/y_pred_qkeras.npy')\n",
+    "y_pred_float  = np.load(d+'/part2/y_pred_float.npy')\n",
+    "y_test        = np.load(d+'/part2/y_test.npy')\n",
+    "\n",
+    "y_pred_cmssw = np.concatenate((y_sig_cmssw, y_bkg_cmssw))\n",
+    "\n",
+    "ones_array = np.ones_like(y_sig_cmssw)\n",
+    "zeros_array = np.zeros_like(y_bkg_cmssw)\n",
+    "y_test_cmssw = np.concatenate((ones_array, zeros_array))\n",
+    "\n",
+    "# Lets plot it!\n",
+    "\n",
+    "from sklearn.metrics import roc_curve, roc_auc_score\n",
+    "\n",
+    "def totalMinBiasRate():\n",
+    "\n",
+    "    LHCfreq = 11245.6\n",
+    "    nCollBunch = 2544\n",
+    "\n",
+    "    return LHCfreq * nCollBunch / 1e3 # in kHz\n",
+    "\n",
+    "\n",
+    "fpr, tpr, thr = roc_curve(y_test, y_pred_float, pos_label=None, sample_weight=None, drop_intermediate=True)\n",
+    "roc_auc = roc_auc_score(y_test, y_pred_float)\n",
+    "\n",
+    "hlsfpr, hlstpr, hlsthr = roc_curve(y_test, y_pred_hls, pos_label=1, sample_weight=None, drop_intermediate=True)\n",
+    "hlsroc_auc = roc_auc_score(y_test, y_pred_hls)\n",
+    "\n",
+    "qfpr, qtpr, qthr = roc_curve(y_test, y_pred_qkeras, pos_label=None, sample_weight=None, drop_intermediate=True)\n",
+    "qroc_auc = roc_auc_score(y_test, y_pred_qkeras)\n",
+    "\n",
+    "cmsswfpr, cmsswtpr, cmsswthr = roc_curve(y_test_cmssw, y_pred_cmssw, pos_label=None, sample_weight=None, drop_intermediate=True)\n",
+    "cmsswroc_auc = roc_auc_score(y_test_cmssw, y_pred_cmssw)\n",
+    "\n",
+    "\n",
+    "fpr *= totalMinBiasRate()\n",
+    "qfpr *= totalMinBiasRate()\n",
+    "hlsfpr *= totalMinBiasRate()\n",
+    "cmsswfpr *= totalMinBiasRate()\n",
+    "\n",
+    "f, ax  = plt.subplots(figsize=(8,6))\n",
+    "# plt.plot([0, 1], [0, 1], color='navy', lw=1, linestyle='--')\n",
+    "ax.tick_params(axis='both', which='major', labelsize=14)\n",
+    "ax.tick_params(axis='both', which='minor', labelsize=14) \n",
+    "ax.set_xlim(0,100)\n",
+    "\n",
+    "ax.plot(fpr, tpr, color='#7b3294', lw=2, ls='dashed', label=f'Baseline (AUC = {roc_auc:.5f})')\n",
+    "ax.plot(qfpr, qtpr, color='#008837', lw=2, label=f'Quantized+Pruned (AUC = {qroc_auc:.5f})')\n",
+    "ax.plot(hlsfpr, hlstpr, color='#a6dba0', lw=2, ls='dotted', label=f'HLS Quantized+Pruned (AUC = {hlsroc_auc:.5f})')\n",
+    "ax.plot(cmsswfpr, cmsswtpr, color='red', lw=2, ls='dashed', label=f'CMSSW Quantized+Pruned (AUC = {cmsswroc_auc:.5f})')\n",
+    "ax.set_xlabel('L1 Rate (kHz)')\n",
+    "ax.set_ylabel('Signal efficiency')\n",
+    "ax.legend(loc=\"lower right\")\n",
+    "ax.grid(True)\n",
+    "plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "188e5815",
    "metadata": {},
    "outputs": [],
    "source": []