diff --git a/notebooks/end2end_example/cybersecurity/1-train-mlp-with-brevitas.ipynb b/notebooks/end2end_example/cybersecurity/1-train-mlp-with-brevitas.ipynb
index 2c9f4a99ed3edd05a8e8d32db2fe6bcdad204716..1d34f1bdbac1de53397a4969a05f3bd0c350e503 100644
--- a/notebooks/end2end_example/cybersecurity/1-train-mlp-with-brevitas.ipynb
+++ b/notebooks/end2end_example/cybersecurity/1-train-mlp-with-brevitas.ipynb
@@ -107,20 +107,106 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "--2021-10-12 15:49:17--  https://zenodo.org/record/4519767/files/unsw_nb15_binarized.npz?download=1\n",
+      "--2022-01-13 11:50:50--  https://zenodo.org/record/4519767/files/unsw_nb15_binarized.npz?download=1\n",
       "Resolving zenodo.org (zenodo.org)... 137.138.76.77\n",
       "Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n",
       "HTTP request sent, awaiting response... 200 OK\n",
       "Length: 13391907 (13M) [application/octet-stream]\n",
-      "Saving to: ‘unsw_nb15_binarized.npz’\n",
-      "\n",
-      "unsw_nb15_binarized 100%[===================>]  12.77M  3.56MB/s    in 3.7s    \n",
+      "Saving to: 'unsw_nb15_binarized.npz'\n",
       "\n",
-      "2021-10-12 15:49:22 (3.44 MB/s) - ‘unsw_nb15_binarized.npz’ saved [13391907/13391907]\n",
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+      " 10600K .......... .......... .......... .......... .......... 81% 1.98M 1s\n",
+      " 10650K .......... .......... .......... .......... .......... 81% 2.53M 1s\n",
+      " 10700K .......... .......... .......... .......... .......... 82% 2.03M 1s\n",
+      " 10750K .......... .......... .......... .......... .......... 82% 1.26M 1s\n",
+      " 10800K .......... .......... .......... .......... .......... 82% 1.73M 1s\n",
+      " 10850K .......... .......... .......... .......... .......... 83% 2.74M 1s\n",
+      " 10900K .......... .......... .......... .......... .......... 83% 1.64M 1s\n",
+      " 10950K .......... .......... .......... .......... .......... 84% 1.21M 1s\n",
+      " 11000K .......... .......... .......... .......... .......... 84% 1.53M 1s\n",
+      " 11050K .......... .......... .......... .......... .......... 84% 2.89M 1s\n",
+      " 11100K .......... .......... .......... .......... .......... 85%  789K 1s\n",
+      " 11150K .......... .......... .......... .......... .......... 85% 1.85M 1s\n",
+      " 11200K .......... .......... .......... .......... .......... 86% 1.90M 1s\n",
+      " 11250K .......... .......... .......... .......... .......... 86%  936K 1s\n",
+      " 11300K .......... .......... .......... .......... .......... 86% 2.06M 1s\n",
+      " 11350K .......... .......... .......... .......... .......... 87% 1.95M 1s\n",
+      " 11400K .......... .......... .......... .......... .......... 87% 1.97M 1s\n",
+      " 11450K .......... .......... .......... .......... .......... 87% 2.29M 1s\n",
+      " 11500K .......... .......... .......... .......... .......... 88% 2.09M 1s\n",
+      " 11550K .......... .......... .......... .......... .......... 88%  957K 1s\n",
+      " 11600K .......... .......... .......... .......... .......... 89% 2.78M 1s\n",
+      " 11650K .......... .......... .......... .......... .......... 89% 1.89M 1s\n",
+      " 11700K .......... .......... .......... .......... .......... 89% 1.96M 1s\n",
+      " 11750K .......... .......... .......... .......... .......... 90% 1.71M 1s\n",
+      " 11800K .......... .......... .......... .......... .......... 90% 1.93M 1s\n",
+      " 11850K .......... .......... .......... .......... .......... 90%  523K 1s\n",
+      " 11900K .......... .......... .......... .......... .......... 91% 7.70M 1s\n",
+      " 11950K .......... .......... .......... .......... .......... 91% 42.7M 1s\n",
+      " 12000K .......... .......... .......... .......... .......... 92% 4.57M 1s\n",
+      " 12050K .......... .......... .......... .......... .......... 92% 1.63M 1s\n",
+      " 12100K .......... .......... .......... .......... .......... 92% 2.15M 1s\n",
+      " 12150K .......... .......... .......... .......... .......... 93% 1.64M 0s\n",
+      " 12200K .......... .......... .......... .......... .......... 93% 1.49M 0s\n",
+      " 12250K .......... .......... .......... .......... .......... 94% 2.46M 0s\n",
+      " 12300K .......... .......... .......... .......... .......... 94% 1.54M 0s\n",
+      " 12350K .......... .......... .......... .......... .......... 94% 1.24M 0s\n",
+      " 12400K .......... .......... .......... .......... .......... 95% 2.09M 0s\n",
+      " 12450K .......... .......... .......... .......... .......... 95% 1.63M 0s\n",
+      " 12500K .......... .......... .......... .......... .......... 95% 2.97M 0s\n",
+      " 12550K .......... .......... .......... .......... .......... 96% 2.20M 0s\n",
+      " 12600K .......... .......... .......... .......... .......... 96% 1.58M 0s\n",
+      " 12650K .......... .......... .......... .......... .......... 97% 2.16M 0s\n",
+      " 12700K .......... .......... .......... .......... .......... 97% 2.64M 0s\n",
+      " 12750K .......... .......... .......... .......... .......... 97% 1.12M 0s\n",
+      " 12800K .......... .......... .......... .......... .......... 98% 2.23M 0s\n",
+      " 12850K .......... .......... .......... .......... .......... 98% 2.04M 0s\n",
+      " 12900K .......... .......... .......... .......... .......... 99% 1.94M 0s\n",
+      " 12950K .......... .......... .......... .......... .......... 99% 2.25M 0s\n",
+      " 13000K .......... .......... .......... .......... .......... 99% 2.04M 0s\n",
+      " 13050K .......... .......... ........                        100% 1.63M=7.2s\n",
+      "\n",
+      "2022-01-13 11:50:58 (1.77 MB/s) - 'unsw_nb15_binarized.npz' saved [13391907/13391907]\n",
+      "\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -220,6 +487,33 @@
     "        break"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Define a PyTorch Device <a id='define_pytorch_device'></a> \n",
+    "\n",
+    "GPUs can significantly speed-up training of deep neural networks. We check for availability of a GPU and if so define it as target device."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Target device: cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(\"Target device: \" + str(device))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -236,7 +530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,9 +552,340 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sequential(\n",
+       "  (0): QuantLinear(\n",
+       "    in_features=593, out_features=64, bias=True\n",
+       "    (input_quant): ActQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "    )\n",
+       "    (output_quant): ActQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "    )\n",
+       "    (weight_quant): WeightQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      (tensor_quant): RescalingIntQuant(\n",
+       "        (int_quant): IntQuant(\n",
+       "          (float_to_int_impl): RoundSte()\n",
+       "          (tensor_clamp_impl): TensorClampSte()\n",
+       "          (delay_wrapper): DelayWrapper(\n",
+       "            (delay_impl): _NoDelay()\n",
+       "          )\n",
+       "        )\n",
+       "        (scaling_impl): StatsFromParameterScaling(\n",
+       "          (parameter_list_stats): _ParameterListStats(\n",
+       "            (first_tracked_param): _ViewParameterWrapper(\n",
+       "              (view_shape_impl): OverTensorView()\n",
+       "            )\n",
+       "            (stats): _Stats(\n",
+       "              (stats_impl): AbsMax()\n",
+       "            )\n",
+       "          )\n",
+       "          (stats_scaling_impl): _StatsScaling(\n",
+       "            (affine_rescaling): Identity()\n",
+       "            (restrict_clamp_scaling): _RestrictClampValue(\n",
+       "              (clamp_min_ste): ScalarClampMinSte()\n",
+       "              (restrict_value_impl): FloatRestrictValue()\n",
+       "            )\n",
+       "            (restrict_scaling_pre): Identity()\n",
+       "          )\n",
+       "        )\n",
+       "        (int_scaling_impl): IntScaling()\n",
+       "        (zero_point_impl): ZeroZeroPoint(\n",
+       "          (zero_point): StatelessBuffer()\n",
+       "        )\n",
+       "        (msb_clamp_bit_width_impl): BitWidthConst(\n",
+       "          (bit_width): StatelessBuffer()\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "    (bias_quant): BiasQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "    )\n",
+       "  )\n",
+       "  (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "  (2): Dropout(p=0.5, inplace=False)\n",
+       "  (3): QuantReLU(\n",
+       "    (input_quant): ActQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "    )\n",
+       "    (act_quant): ActQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      (fused_activation_quant_proxy): FusedActivationQuantProxy(\n",
+       "        (activation_impl): ReLU()\n",
+       "        (tensor_quant): RescalingIntQuant(\n",
+       "          (int_quant): IntQuant(\n",
+       "            (float_to_int_impl): RoundSte()\n",
+       "            (tensor_clamp_impl): TensorClamp()\n",
+       "            (delay_wrapper): DelayWrapper(\n",
+       "              (delay_impl): _NoDelay()\n",
+       "            )\n",
+       "          )\n",
+       "          (scaling_impl): ParameterFromRuntimeStatsScaling(\n",
+       "            (stats_input_view_shape_impl): OverTensorView()\n",
+       "            (stats): _Stats(\n",
+       "              (stats_impl): AbsPercentile()\n",
+       "            )\n",
+       "            (restrict_clamp_scaling): _RestrictClampValue(\n",
+       "              (clamp_min_ste): ScalarClampMinSte()\n",
+       "              (restrict_value_impl): FloatRestrictValue()\n",
+       "            )\n",
+       "            (restrict_inplace_preprocess): Identity()\n",
+       "            (restrict_preprocess): Identity()\n",
+       "          )\n",
+       "          (int_scaling_impl): IntScaling()\n",
+       "          (zero_point_impl): ZeroZeroPoint(\n",
+       "            (zero_point): StatelessBuffer()\n",
+       "          )\n",
+       "          (msb_clamp_bit_width_impl): BitWidthConst(\n",
+       "            (bit_width): StatelessBuffer()\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (4): QuantLinear(\n",
+       "    in_features=64, out_features=64, bias=True\n",
+       "    (input_quant): ActQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "    )\n",
+       "    (output_quant): ActQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "    )\n",
+       "    (weight_quant): WeightQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      (tensor_quant): RescalingIntQuant(\n",
+       "        (int_quant): IntQuant(\n",
+       "          (float_to_int_impl): RoundSte()\n",
+       "          (tensor_clamp_impl): TensorClampSte()\n",
+       "          (delay_wrapper): DelayWrapper(\n",
+       "            (delay_impl): _NoDelay()\n",
+       "          )\n",
+       "        )\n",
+       "        (scaling_impl): StatsFromParameterScaling(\n",
+       "          (parameter_list_stats): _ParameterListStats(\n",
+       "            (first_tracked_param): _ViewParameterWrapper(\n",
+       "              (view_shape_impl): OverTensorView()\n",
+       "            )\n",
+       "            (stats): _Stats(\n",
+       "              (stats_impl): AbsMax()\n",
+       "            )\n",
+       "          )\n",
+       "          (stats_scaling_impl): _StatsScaling(\n",
+       "            (affine_rescaling): Identity()\n",
+       "            (restrict_clamp_scaling): _RestrictClampValue(\n",
+       "              (clamp_min_ste): ScalarClampMinSte()\n",
+       "              (restrict_value_impl): FloatRestrictValue()\n",
+       "            )\n",
+       "            (restrict_scaling_pre): Identity()\n",
+       "          )\n",
+       "        )\n",
+       "        (int_scaling_impl): IntScaling()\n",
+       "        (zero_point_impl): ZeroZeroPoint(\n",
+       "          (zero_point): StatelessBuffer()\n",
+       "        )\n",
+       "        (msb_clamp_bit_width_impl): BitWidthConst(\n",
+       "          (bit_width): StatelessBuffer()\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "    (bias_quant): BiasQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "    )\n",
+       "  )\n",
+       "  (5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "  (6): Dropout(p=0.5, inplace=False)\n",
+       "  (7): QuantReLU(\n",
+       "    (input_quant): ActQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "    )\n",
+       "    (act_quant): ActQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      (fused_activation_quant_proxy): FusedActivationQuantProxy(\n",
+       "        (activation_impl): ReLU()\n",
+       "        (tensor_quant): RescalingIntQuant(\n",
+       "          (int_quant): IntQuant(\n",
+       "            (float_to_int_impl): RoundSte()\n",
+       "            (tensor_clamp_impl): TensorClamp()\n",
+       "            (delay_wrapper): DelayWrapper(\n",
+       "              (delay_impl): _NoDelay()\n",
+       "            )\n",
+       "          )\n",
+       "          (scaling_impl): ParameterFromRuntimeStatsScaling(\n",
+       "            (stats_input_view_shape_impl): OverTensorView()\n",
+       "            (stats): _Stats(\n",
+       "              (stats_impl): AbsPercentile()\n",
+       "            )\n",
+       "            (restrict_clamp_scaling): _RestrictClampValue(\n",
+       "              (clamp_min_ste): ScalarClampMinSte()\n",
+       "              (restrict_value_impl): FloatRestrictValue()\n",
+       "            )\n",
+       "            (restrict_inplace_preprocess): Identity()\n",
+       "            (restrict_preprocess): Identity()\n",
+       "          )\n",
+       "          (int_scaling_impl): IntScaling()\n",
+       "          (zero_point_impl): ZeroZeroPoint(\n",
+       "            (zero_point): StatelessBuffer()\n",
+       "          )\n",
+       "          (msb_clamp_bit_width_impl): BitWidthConst(\n",
+       "            (bit_width): StatelessBuffer()\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (8): QuantLinear(\n",
+       "    in_features=64, out_features=64, bias=True\n",
+       "    (input_quant): ActQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "    )\n",
+       "    (output_quant): ActQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "    )\n",
+       "    (weight_quant): WeightQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      (tensor_quant): RescalingIntQuant(\n",
+       "        (int_quant): IntQuant(\n",
+       "          (float_to_int_impl): RoundSte()\n",
+       "          (tensor_clamp_impl): TensorClampSte()\n",
+       "          (delay_wrapper): DelayWrapper(\n",
+       "            (delay_impl): _NoDelay()\n",
+       "          )\n",
+       "        )\n",
+       "        (scaling_impl): StatsFromParameterScaling(\n",
+       "          (parameter_list_stats): _ParameterListStats(\n",
+       "            (first_tracked_param): _ViewParameterWrapper(\n",
+       "              (view_shape_impl): OverTensorView()\n",
+       "            )\n",
+       "            (stats): _Stats(\n",
+       "              (stats_impl): AbsMax()\n",
+       "            )\n",
+       "          )\n",
+       "          (stats_scaling_impl): _StatsScaling(\n",
+       "            (affine_rescaling): Identity()\n",
+       "            (restrict_clamp_scaling): _RestrictClampValue(\n",
+       "              (clamp_min_ste): ScalarClampMinSte()\n",
+       "              (restrict_value_impl): FloatRestrictValue()\n",
+       "            )\n",
+       "            (restrict_scaling_pre): Identity()\n",
+       "          )\n",
+       "        )\n",
+       "        (int_scaling_impl): IntScaling()\n",
+       "        (zero_point_impl): ZeroZeroPoint(\n",
+       "          (zero_point): StatelessBuffer()\n",
+       "        )\n",
+       "        (msb_clamp_bit_width_impl): BitWidthConst(\n",
+       "          (bit_width): StatelessBuffer()\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "    (bias_quant): BiasQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "    )\n",
+       "  )\n",
+       "  (9): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "  (10): Dropout(p=0.5, inplace=False)\n",
+       "  (11): QuantReLU(\n",
+       "    (input_quant): ActQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "    )\n",
+       "    (act_quant): ActQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      (fused_activation_quant_proxy): FusedActivationQuantProxy(\n",
+       "        (activation_impl): ReLU()\n",
+       "        (tensor_quant): RescalingIntQuant(\n",
+       "          (int_quant): IntQuant(\n",
+       "            (float_to_int_impl): RoundSte()\n",
+       "            (tensor_clamp_impl): TensorClamp()\n",
+       "            (delay_wrapper): DelayWrapper(\n",
+       "              (delay_impl): _NoDelay()\n",
+       "            )\n",
+       "          )\n",
+       "          (scaling_impl): ParameterFromRuntimeStatsScaling(\n",
+       "            (stats_input_view_shape_impl): OverTensorView()\n",
+       "            (stats): _Stats(\n",
+       "              (stats_impl): AbsPercentile()\n",
+       "            )\n",
+       "            (restrict_clamp_scaling): _RestrictClampValue(\n",
+       "              (clamp_min_ste): ScalarClampMinSte()\n",
+       "              (restrict_value_impl): FloatRestrictValue()\n",
+       "            )\n",
+       "            (restrict_inplace_preprocess): Identity()\n",
+       "            (restrict_preprocess): Identity()\n",
+       "          )\n",
+       "          (int_scaling_impl): IntScaling()\n",
+       "          (zero_point_impl): ZeroZeroPoint(\n",
+       "            (zero_point): StatelessBuffer()\n",
+       "          )\n",
+       "          (msb_clamp_bit_width_impl): BitWidthConst(\n",
+       "            (bit_width): StatelessBuffer()\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (12): QuantLinear(\n",
+       "    in_features=64, out_features=1, bias=True\n",
+       "    (input_quant): ActQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "    )\n",
+       "    (output_quant): ActQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "    )\n",
+       "    (weight_quant): WeightQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      (tensor_quant): RescalingIntQuant(\n",
+       "        (int_quant): IntQuant(\n",
+       "          (float_to_int_impl): RoundSte()\n",
+       "          (tensor_clamp_impl): TensorClampSte()\n",
+       "          (delay_wrapper): DelayWrapper(\n",
+       "            (delay_impl): _NoDelay()\n",
+       "          )\n",
+       "        )\n",
+       "        (scaling_impl): StatsFromParameterScaling(\n",
+       "          (parameter_list_stats): _ParameterListStats(\n",
+       "            (first_tracked_param): _ViewParameterWrapper(\n",
+       "              (view_shape_impl): OverTensorView()\n",
+       "            )\n",
+       "            (stats): _Stats(\n",
+       "              (stats_impl): AbsMax()\n",
+       "            )\n",
+       "          )\n",
+       "          (stats_scaling_impl): _StatsScaling(\n",
+       "            (affine_rescaling): Identity()\n",
+       "            (restrict_clamp_scaling): _RestrictClampValue(\n",
+       "              (clamp_min_ste): ScalarClampMinSte()\n",
+       "              (restrict_value_impl): FloatRestrictValue()\n",
+       "            )\n",
+       "            (restrict_scaling_pre): Identity()\n",
+       "          )\n",
+       "        )\n",
+       "        (int_scaling_impl): IntScaling()\n",
+       "        (zero_point_impl): ZeroZeroPoint(\n",
+       "          (zero_point): StatelessBuffer()\n",
+       "        )\n",
+       "        (msb_clamp_bit_width_impl): BitWidthConst(\n",
+       "          (bit_width): StatelessBuffer()\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "    (bias_quant): BiasQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "    )\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from brevitas.nn import QuantLinear, QuantReLU\n",
     "import torch.nn as nn\n",
@@ -282,7 +907,9 @@
     "      nn.Dropout(0.5),\n",
     "      QuantReLU(bit_width=act_bit_width),\n",
     "      QuantLinear(hidden3, num_classes, bias=True, weight_bit_width=weight_bit_width)\n",
-    ")\n"
+    ")\n",
+    "\n",
+    "model.to(device)"
    ]
   },
   {
@@ -302,7 +929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -313,6 +940,7 @@
     "    \n",
     "    for i, data in enumerate(train_loader, 0):        \n",
     "        inputs, target = data\n",
+    "        inputs, target = inputs.to(device), target.to(device)\n",
     "        optimizer.zero_grad()   \n",
     "                \n",
     "        # forward pass\n",
@@ -324,14 +952,14 @@
     "        optimizer.step()\n",
     "        \n",
     "        # keep track of loss value\n",
-    "        losses.append(loss.data.numpy()) \n",
+    "        losses.append(loss.data.cpu().numpy()) \n",
     "           \n",
     "    return losses"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -347,12 +975,13 @@
     "    with torch.no_grad():\n",
     "        for data in test_loader:\n",
     "            inputs, target = data\n",
+    "            inputs, target = inputs.to(device), target.to(device)\n",
     "            output_orig = model(inputs.float())\n",
     "            # run the output through sigmoid\n",
     "            output = torch.sigmoid(output_orig)  \n",
     "            # compare against a threshold of 0.5 to generate 0/1\n",
-    "            pred = (output.detach().numpy() > 0.5) * 1\n",
-    "            target = target.float()\n",
+    "            pred = (output.detach().cpu().numpy() > 0.5) * 1\n",
+    "            target = target.cpu().float()\n",
     "            y_true.extend(target.tolist()) \n",
     "            y_pred.extend(pred.reshape(-1).tolist())\n",
     "        \n",
@@ -384,7 +1013,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -402,18 +1031,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
     "# loss criterion and optimizer\n",
-    "criterion = nn.BCEWithLogitsLoss()\n",
+    "criterion = nn.BCEWithLogitsLoss().to(device)\n",
     "optimizer = torch.optim.Adam(model.parameters(), lr=lr, betas=(0.9, 0.999))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "scrolled": true
    },
@@ -422,7 +1051,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Training loss = 0.132918 test accuracy = 0.798341: 100%|██████████| 10/10 [00:44<00:00,  4.45s/it]\n"
+      "Training loss = 0.132592 test accuracy = 0.808884: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [02:06<00:00, 12.67s/it]\n"
      ]
     }
    ],
@@ -450,14 +1079,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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K210u6RlJWyTdNM7yGyVtlrRe0vcknZG0t0n6iaRNybJ35WzzeUkvSFqX/LRN+WiLqC2Tpqd/iGdfOlrsUszMTspUzzgeAaolNQPfBt4DfH6yDSSlgNuAK4BVwLWSVo1Z7QlgdURcANwH3JK0dwPvjYhzgcuBz0hK52z34YhoS37WTfEYiqrNr5I1s1liqsGhiOgGfhP4bES8Azj3ONtcDGyJiPaI6APuAa7KXSEiHkr2C/Ao0JK0PxsRzyXTu4G9QNMUay1JSxfWsKCmwh3kZjbjTTk4JL0e+B3g35O21HG2aQZ25MzvTNom8n7ggXE++GKgEng+p/kTySWsT0uqmqDg6yWtlbS2o6PjOKUWniRaM2mPWWVmM95Ug+NPgI8CX4+ITZJWAA9NVxGS3g2sBm4d03468H+B342I4V7ljwLnABcBC4GPjLfPiLgjIlZHxOqmptI4WWltSfPsS0fo6h0odilmZidsSsEREQ9HxJUR8bdJJ/m+iPjj42y2C8jkzLckbaNIugz4GHBlRPTmtNeTPbv5WEQ8mlPLnsjqBe4ie0lsRmjLpBkK2LDLL3Yys5lrqndVfVlSvaRaYCOwWdKHj7PZY8BKScslVQLXAGvG7PdC4HayobE3p70S+DrwxYi4b8w2pye/BVyd1DMjDL9K1v0cZjaTTfVS1aqI6CT7h/oBYDnZO6smFBEDwA3Ag8BTwL3JZa6bJV2ZrHYrMB/4SnJr7XCwvBO4BHjfOLfd3i1pA7ABaAT+ZorHUHQLaytZurDGd1aZ2YxWPsX1KpLnNq4G/jEi+iUdd6jXiLgfuH9M28dzpi+bYLsvAV+aYNmlU6y5JLVl0qzdeqDYZZiZnbCpnnHcDmwFaoFHkgf1OgtV1GzWmkmz+3APezt7il2KmdkJmWrn+D9ERHNEvC3pmN4GvKXAtc1KbZkGwA8CmtnMNdXO8QZJnxp+LkLS/yR79mF5OndJA+Vl8vMcZjZjTfVS1eeAI2Q7rd9J9jLVXYUqajarrkhxzul1PuMwsxlrqp3jZ0bEb+XM/3dJ6wpQz5zQ2pJmzbrdDA0FZWUqdjlmZnmZ6hnHMUlvGp6R9EbA70E9QW2ZNEd6B2jf11XsUszM8jbVM44PAF+U1JDMHwSuK0xJs1/uSLlnLZ5f3GLMzPI01buqnoyIVuAC4IKIuBCY0c9TFNOKpvnMryr3E+RmNiPl9QbAiOhMniAHuLEA9cwJqTJxQUuD76wysxnpZF4d617dk9CaSfPUnk56+geLXYqZWV5OJjiOO+SITay1JU3/YLB5jx/AN7OZZdLOcUlHGD8gBMwrSEVzxIVL00B2pNxfWLqguMWYmeVh0uCIiLpTVchcc1p9Na+qr/aDgGY245zMpSo7SW2ZtO+sMrMZx8FRRK2ZNFv3d3Oou6/YpZiZTZmDo4haPVKumc1ADo4iuqAljQRP7vA7yM1s5nBwFNH8qnJWLp7vBwHNbEZxcBRZa0uadTsOEeHHYsxsZihocEi6XNIzkrZIummc5TdK2ixpvaTvJa+kHV52naTnkp/rctp/UdKGZJ//IGlGP8HetjTNga4+dh70YMNmNjMULDgkpYDbgCuAVcC1klaNWe0JYHVEXADcB9ySbLsQ+CvgtcDFwF9JGn5K7p+APwBWJj+XF+oYToXWljQAT7iD3MxmiEKecVwMbImI9ojoA+4BrspdISIeiojuZPZRoCWZ/jXgOxFxICIOAt8BLpd0OlAfEY9G9trOF4GrC3gMBffqV9VRVV7m5znMbMYoZHA0Azty5ncmbRN5P/DAcbZtTqaPu09J1w+/I72joyPP0k+dilQZ5zc3ODjMbMYoic5xSe8GVgO3Ttc+I+KOiFgdEaubmpqma7cF0ZpJs2HXYfoHh4pdipnZcRUyOHYBmZz5lqRtFEmXAR8DroyI3uNsu4uXL2dNuM+Zpi2TpndgiGdePFLsUszMjquQwfEYsFLSckmVwDXAmtwVJF0I3E42NPbmLHoQ+FVJC5JO8V8FHoyIPUCnpNcld1O9F/hGAY/hlBh+layf5zCzmaBgwRERA8ANZEPgKeDeiNgk6WZJVyar3QrMB74iaZ2kNcm2B4D/QTZ8HgNuTtoA/ivwz8AW4Hle7heZsVoWzGNhbSXrth8qdilmZsc16bDqJysi7gfuH9P28ZzpyybZ9nPA58ZpXwucN41lFp2k7Ei5PuMwsxmgJDrHLfs8x3N7j3K0d6DYpZiZTcrBUSJaMw1EwHqfdZhZiXNwlIiRDnKPlGtmJc7BUSLSNZUsW1TjBwHNrOQ5OEpIaybtlzqZWclzcJSQtkyaFzt7ePFwT7FLMTObkIOjhLQm/Rw+6zCzUubgKCGrTq+nIiU/z2FmJc3BUUKqK1K85vR6d5CbWUlzcJSY1pY063ceZnDIr5I1s9Lk4CgxbZk0R3sHaO84WuxSzMzG5eAoMe4gN7NS5+AoMSsaa6mrLndwmFnJcnCUmLIy0drikXLNrHQ5OEpQa6aBp/ccoad/sNilmJm9goOjBLW2pBkYCjbt9oCHZlZ6HBwlqG2kg9zBYWalx8FRghbXV7OkodoPAppZSXJwlCiPlGtmpaqgwSHpcknPSNoi6aZxll8i6eeSBiS9Paf9LZLW5fz0SLo6WfZ5SS/kLGsr5DEUS1smzfYD3Rzo6it2KWZmoxQsOCSlgNuAK4BVwLWSVo1ZbTvwPuDLuY0R8VBEtEVEG3Ap0A18O2eVDw8vj4h1hTmC4modeSPgoaLWYWY2ViHPOC4GtkREe0T0AfcAV+WuEBFbI2I9MDTJft4OPBAR3YUrtfSc39xAmfwEuZmVnkIGRzOwI2d+Z9KWr2uAfxnT9glJ6yV9WlLViRZYymqryjn7tDo/CGhmJaekO8clnQ6cDzyY0/xR4BzgImAh8JEJtr1e0lpJazs6OgpeayG0tqR5cschIjxSrpmVjkIGxy4gkzPfkrTl453A1yOif7ghIvZEVi9wF9lLYq8QEXdExOqIWN3U1JTnx5aGtqVpDnb3s/3AnLpKZ2YlrpDB8RiwUtJySZVkLzmtyXMf1zLmMlVyFoIkAVcDG0++1NLU2pIG3M9hZqWlYMEREQPADWQvMz0F3BsRmyTdLOlKAEkXSdoJvAO4XdKm4e0lLSN7xvLwmF3fLWkDsAFoBP6mUMdQbGefNp95FSkHh5mVlPJC7jwi7gfuH9P28Zzpx8hewhpv262M05keEZdOb5WlqzxVxvnNDb4l18xKSkl3jlt2pNyNuzvpH5zsjmUzs1PHwVHiWjNp+gaGeHrPkWKXYmYGODhK3shIuX6ew8xKhIOjxDWn59Gcnsct33qaf/jecxzp6T/+RmZmBeTgKHGS+MLvXcwbzlzEp77zLJfc8hC3P/w8x/r8dkAzKw7NhaeSV69eHWvXri12GSdt/c5DfOo7z/KDZzponF/FDW85k2tfu5Sq8lSxSzOzWUjS4xGx+hXtDo6ZZ+3WA/zdt5/h0fYDLGmo5oZLV/KO1S1UpHwCaWbTx8Exi4Jj2I+37OPWbz/DE9sPsXRhDX9y2UquamsmVaZil2Zms8BEweF/os5gbzirka/94Ru4630XUVddzo33Psmvfvphvrl+N0NDs/8fBGZWHA6OGU4SbzlnMd/84Jv43+/+BVJl4oYvP8Hb/uGHfGfzSx5Z18ymnYNjlpDE5eedzgMfuoS/v6aNnv5B/uCLa7n6tv/gkWc7HCBmNm0cHLNMqkxc1dbMd2/8ZW75rQvYd7SP937uZ7zr9kf5afv+YpdnZrOAO8dnud6BQe59bAf/6/tb2Hukl19a2ciNbz2bC5cuKHZpZlbifFfVHA2OYT39g3zp0W189gfPc6Crj8tes5g/fevZnLukodilmVmJcnDM8eAY1tU7wOd/vJXbH36ezp4Bfv380/nTt67krMV1xS7NzEqMg8PBMcrhY/3c+cN27vzRCxzrH+TqtmY+dNlKzlhUW+zSzKxEODgcHOM60NXH7Q8/zxd+spX+weAdv9jCB39lJc3pecUuzcyKzMHh4JjU3s4ePvuD5/nyT7cDcO3FGf7oLWexuL66yJWZWbE4OBwcU7Lr0DH+8fvP8ZW1O0mVievesIwP/PKZLKytLHZpZnaKOTgcHHnZtr+Lv//uc3x93S5qKlL83puW8/u/tIKGeRXFLs3MTpGiBIeky4G/B1LAP0fEJ8csvwT4DHABcE1E3JezbBDYkMxuj4grk/blwD3AIuBx4D0R0TdZHQ6OE/fcS0f4zHef49837KGuupxLVjZxbnM95zc3cN6SBhb4TMRs1jrlwSEpBTwLvBXYCTwGXBsRm3PWWQbUA38OrBkTHEcjYv44+70X+FpE3CPpfwNPRsQ/TVaLg+Pkbdp9mP/zSDuPbz/IjgPHRtqb0/M4LwmSc5MwaaqrKmKlZjZdJgqO8gJ+5sXAlohoTwq4B7gKGAmOiNiaLBuayg4lCbgU+O2k6QvAXwOTBoedvHOXNPCZay4E4FB3H5t2d7Jh12E27jrMpt2dPLjppZF1X1VfzXnN9Zy7pCF7ZtLcwGn1VWT/5zOzma6QwdEM7MiZ3wm8No/tqyWtBQaAT0bEv5G9PHUoIgZy9tk83saSrgeuB1i6dGl+lduk0jWVvPGsRt54VuNIW2dPP5t3d7IxCZONuzv53tN7GT6hbZxfxXnN9Zy3JBsk5zXX05ye5zAxm4EKGRwn64yI2CVpBfB9SRuAw1PdOCLuAO6A7KWqAtVoifrqCl63YhGvW7FopK2rd4Cn9mTDZMOuTjbtPswPn9vHYPKukAU1FZzX3JBzZlLP0oU1DhOzElfI4NgFZHLmW5K2KYmIXcnvdkk/AC4EvgqkJZUnZx157dNOrdqqclYvW8jqZQtH2nr6B7NhsruTjTsPs3H3Ye78UTv9g9kwqasuT85K6pMzkwaWL6qlzG81NCsZhQyOx4CVyV1Qu4BreLlvYlKSFgDdEdErqRF4I3BLRISkh4C3k72z6jrgGwWp3gqiuiLFhUsXjBqdt3dgkGdfPMrG3YfZsOswm3Yd5gs/2UbfQLbrq7YyxblLGkbu5jp3SQPLGmuoKk8V6zDM5rRC3477NrK326aAz0XEJyTdDKyNiDWSLgK+DiwAeoAXI+JcSW8AbgeGyL4z5DMRcWeyzxVkQ2Mh8ATw7ojonawO31U18/QPDvHcS9kwGe432bynk57+bJiUCTILa1jeWMuKxvmsaKplRWMtK5rmuyPebJr4AUAHx4w3MDhE+74uNu0+zAsdXTy/r4v2ji5e2Hd0JFAAaipT2UBpms/yxlrObMqGy/KmWuZXlXK3nllpKcbtuGbTqjxVxtmn1XH2aaOHgB8aCl7s7BkJkec7umjf18W6HQf55vrd5P7baHFdFSuaalneOD8bKMl0ZsE8ylN+IabZVDg4bMYrKxNL0vNYkp7Hm1Y2jlrW0z/I9gPdtHdkA+WFfV20dxzlgY17ONTdP7JeRUosXVgzEijDZywrmmpZVFvpS19mORwcNqtVV6TGPUsBONjVR/u+o7QnZyjtHUd5YV8XjzzbQd/gy5e+6qrLWdE0nzMba0ddAlveWMu8SnfQ29zj4LA5a0FtJb9Yu5BfPGPhqPbBoWDXwWMjofLCvi7a9x3lJ+37+doTo+/+flV9NWcsqmHZolrOaEx+L6rhjEXuT7HZy/9lm42RKhNLF9WwdFENb3716GXdfQPJ5a5soGzb3822/V187+m97Ds6+ua+xvlVLEtCZFmyv2WLalm2qJaGGo8ybDOXg8MsDzWV5dlnSpY0vGLZ0d4Btu3PhsnW/V1s29fNtgNd/Pj5fXz15z2j1k3XVIwEytjfC92nYiXOwWE2TeZXTRwqw530W/flBMv+bh7fdpD/9+RuhnLu/KqrKueMxrGBkr0EtrjOz6hY8Tk4zE6ByTrpewcG2XnwGNv2d7F1X3c2YPZ3sXl3Jw9ufJGBnFSZV5FK+lCG+1NqWTS/kqGhYGAoGBz5PUT/4Oj5gaFgcPDl9fqHhkbNj1ovmR8YHMpZFgyM7DPb3p8zX1NZzuK6KhbXV3NafRWnJb8X11WzuL6KRbVVpDx0zKzg4DArsqryFGc2zefMple8foaBwSF2H+pJzlC62Jr0qTzf0cVDT4+++ysfqTJRnvykykR5qmykLfd3xSvas/NVFeU5+8i2He0dYPfhHtbtOMT+rle+Wy1VJprmV2XDpL6axXU54VJfzWlJwCysqfTYZCXOwWFWwspTZSMd9dA0atlg8uDjwa4+ylPDf9zLcsLg5T/qYwOh0Je7+gaG6Djay0udPezt7GXvkR5e6uzhpc5s244D3azdeoCDOc/SjBxzmUaduSyuywmXnLYFNRW+bFckDg6zGSpVJprT82hOzyt2Ka9QWV42pdp6+gfpOJINlr1JqLx05OXAeWFfF4+2H+DwsVcGTGWqjKa6qpHLYsNhU1ddTlV5GVXlqezvipenqytSLy+rKBuZriwv82W0PDg4zKxoqitSZBbWkFlYM+l6Pf2D2WAZFTAvTz+39yg/2rKPIz0Dk+5nMhUpvRw25WVUDYdMRU7bmMAZG0zD4VRZXkZFaswZYHJJcOzZ38hZYWr89orUmPXKVPRLeQ4OMyt51RWpnEt2EzvWN0hX3wC9A0P09g9mf4+dHhiktz873TPSnvzuz5ketd0gR3sH2H+0b2R5T866w68AOFUkXhk8EwTUnddddNzvLV8ODjObNeZVpooyDMzQUNA3ODqYcu9G6x97d9pQzvzgOO2D49/l9vKdba9sf+VnZNerqpj+wTsdHGZmJ6msTFSXpaiuSAGzf1QAjyNtZmZ5cXCYmVleHBxmZpYXB4eZmeWloMEh6XJJz0jaIummcZZfIunnkgYkvT2nvU3STyRtkrRe0rtyln1e0guS1iU/bYU8BjMzG61gd1VJSgG3AW8FdgKPSVoTEZtzVtsOvA/48zGbdwPvjYjnJC0BHpf0YEQcSpZ/OCLuK1TtZmY2sULejnsxsCUi2gEk3QNcBYwER0RsTZaNenomIp7Nmd4taS/ZgXoOFbBeMzObgkJeqmoGduTM70za8iLpYqASeD6n+RPJJaxPS6qaYLvrJa2VtLajoyPfjzUzswmU9AOAkk4H/i9wXUQMn5V8FHiRbJjcAXwEuHnsthFxR7IcSR2Stp1gGY3AvhPcdjby9/Eyfxej+fsYbTZ8H2eM11jI4NgFZHLmW5K2KZFUD/w78LGIeHS4PSL2JJO9ku7ilf0jrxARTcdbZ5I61kbE6hPdfrbx9/Eyfxej+fsYbTZ/H4W8VPUYsFLSckmVwDXAmqlsmKz/deCLYzvBk7MQlB2I/2pg43QWbWZmkytYcETEAHAD8CDwFHBvRGySdLOkKwEkXSRpJ/AO4HZJm5LN3wlcArxvnNtu75a0AdhA9lTwbwp1DGZm9kqKiOOvNYdJuj7pLzH8feTydzGav4/RZvP34eAwM7O8eMgRMzPLi4PDzMzy4uCYxPHG2porJGUkPSRpczJ+2IeKXVMpkJSS9ISkbxa7lmKTlJZ0n6SnJT0l6fXFrqlYJP1p8v+TjZL+RVJ1sWuabg6OCeSMtXUFsAq4VtKq4lZVNAPAn0XEKuB1wB/N4e8i14fI3jFo8PfAtyLiHKCVOfq9SGoG/hhYHRHnASmyjyLMKg6OiY2MtRURfcDwWFtzTkTsiYifJ9NHyP5RyHv4mNlEUgvw68A/F7uWYpPUQPb2+TsBIqIvZ0DSuagcmCepHKgBdhe5nmnn4JjYtIy1NdtIWgZcCPy0yKUU22eAvwCGjrPeXLAc6ADuSi7d/bOk2mIXVQwRsQv4O7Ijf+8BDkfEt4tb1fRzcNiUSZoPfBX4k4joLHY9xSLpN4C9EfF4sWspEeXALwD/FBEXAl3AnOwTlLSA7JWJ5cASoFbSu4tb1fRzcEzspMbamm0kVZANjbsj4mvFrqfI3ghcKWkr2UuYl0r6UnFLKqqdwM6IGD4LvY9skMxFlwEvRERHRPQDXwPeUOSapp2DY2InPNbWbJOMC3Yn8FREfKrY9RRbRHw0IloiYhnZ/y6+HxGz7l+VUxURLwI7JL06afoVct67M8dsB14nqSb5/82vMAtvFCjpYdWLKSIGJA2PtZUCPhcRm46z2Wz1RuA9wAZJ65K2v4yI+4tXkpWYD5IdR64SaAd+t8j1FEVE/FTSfcDPyd6N+ATJ6x1mEw85YmZmefGlKjMzy4uDw8zM8uLgMDOzvDg4zMwsLw4OMzPLi4PD7DgkHU1+L5P029O8778cM//j6dy/WSE4OMymbhmQV3AkA91NZlRwRMSse8rYZh8Hh9nUfRL4JUnrkncupCTdKukxSesl/RcASW+W9ENJa0ieoJb0b5IeT97TcH3S9kmyo6iuk3R30jZ8dqNk3xslbZD0rpx9/yDn3Rd3J08oI+mTyTtT1kv6u1P+7dic4SfHzabuJuDPI+I3AJIAOBwRF0mqAv5D0vBIqL8AnBcRLyTzvxcRByTNAx6T9NWIuEnSDRHRNs5n/SbQRvbdFo3JNo8kyy4EziU7XPd/AG+U9BTwn4FzIiIkpaf30M1e5jMOsxP3q8B7k2FYfgosAlYmy36WExoAfyzpSeBRsoNnrmRybwL+JSIGI+Il4GHgopx974yIIWAd2Utoh4Ee4E5Jvwl0n+SxmU3IwWF24gR8MCLakp/lOe9e6BpZSXoz2VFTXx8RrWTHLzqZ14n25kwPAuURMUD25WP3Ab8BfOsk9m82KQeH2dQdAepy5h8E/jAZch5JZ0/wAqMG4GBEdEs6h+zrd4f1D28/xg+BdyX9KE1k37D3s4kKS96V0pAMPPmnZC9xmRWE+zjMpm49MJhccvo82fdsLwN+nnRQdwBXj7Pdt4APJP0Qz5C9XDXsDmC9pJ9HxO/ktH8deD3wJBDAX0TEi0nwjKcO+IakarJnQjee0BGaTYFHxzUzs7z4UpWZmeXFwWFmZnlxcJiZWV4cHGZmlhcHh5mZ5cXBYWZmeXFwmJlZXv4/QAgzW/yBXxUAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -478,12 +1107,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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AjogYTRZbgbsjIvIO3wL8raQRkiT2ofzeTzZ92ju6uWjlfGpqPMSGmZ2spP0aI+Je4N5xZe8bt/7+Asd9H7islLEZRASZbI5fuvzccodiZhWoUhqprQyy3X0cPT7IFndxNbMCnCCqWKZjdJIgN1Cb2amcIKrY6BhMF7kGYWYFOEFUsUxHjlWLmlnQVF/uUMysAjlBVLFMtpstfkDOzCbgBFGl+oeGeazTQ2yY2cScIKrUnmeOMTwSHmLDzCbkBFGlxnowuQZhZhNwgqhSmWw3jXU1rF86r9yhmFmFcoKoUplsjk3nzKeu1v8EzKwwXx2qVHuHJwkys8k5QVShzlw/B4/1+wlqM5uUE0QV2p1NGqg9BpOZTcYJogplPMSGmRXBCaIKtXfkWDG/kaWtjeUOxcwqmBNEFcpku93+YGZTcoKoMkPDIzz69DG3P5jZlJwgqszjB3sYGB7xEBtmNiUniCrTnvUQG2ZWnJImCEnXStotaY+kWwts/0tJO9PXI5KO5G17k6RH09ebShlnNcl0dFNXIy5Y3lruUMyswtWV6sSSaoE7gGuA/cB2SdsiYtfoPhHxrrz93wFcmS4vAf4QaAMCuD899nCp4q0WmWyOC1e00lDnyqOZTa6UV4mrgD0RsTciBoC7gesn2f8G4PPp8suBr0dEV5oUvg5cW8JYq0amo9tDbJhZUUqZIFYB+/LW96dlp5C0DtgAfON0jpV0s6QdknZ0dnZOS9Bz2dHeQX56tM9dXM2sKJVyn2ErcE9EDJ/OQRFxZ0S0RUTb8uXLSxTa3DH6BLVrEGZWjFImiAPAmrz11WlZIVs5cXvpdI+1ImVGx2ByDcLMilDKBLEd2Chpg6QGkiSwbfxOkjYDi4H/zCu+D3iZpMWSFgMvS8vsLGSy3SyeV8+K+R5iw8ymVrJeTBExJOkWkgt7LXBXRDws6XZgR0SMJoutwN0REXnHdkn6AEmSAbg9IrpKFWu1yGRzbF65AEnlDsXMZoGSJQiAiLgXuHdc2fvGrb9/gmPvAu4qWXBVZmQk2J3N8as/s2bqnc3MqJxGaiuxfYd76R0YdgO1mRXNCaJKtHd4iA0zOz1OEFUik+1Ggk3nuAZhZsVxgqgSmY4cG5a20NxQW+5QzGyWcIKoEskkQa49mFnxJuzFJOk1RRzfl/ZUsgrW0z/Ek129vObZq8sdipnNIpN1c/048E/AZJ3mX8S4bqxWeR55OkeEh9gws9MzWYL4l4j4jckOlvSZaY7HSsBDbJjZmZiwDSIi3jDVwcXsY+WX6eimtbGOVYuayx2Kmc0iRTdSS7pQ0mckfUnS80sZlE2v9myOi1bOp6bGQ2yYWfEma6Ruioi+vKIPAO9Nl78KXFHCuGyaRASZjm5e9azzyh2Kmc0yk9UgvirpjXnrg8B6YB1wWvM2WPl0HO2ju2/IkwSZ2WmbLEFcCyyQ9DVJLwJ+j2Qq0F8Gfm0mgrOzNzpJ0Bb3YDKz0zThLaZ0drePSfp74H8BbwP+ICIem6ng7OyNjsG0yQnCzE7TZG0QzwXeAwwAHwSOA38s6QDwgYg4MiMR2lnJZHOsXtzMgqb6codiZrPMZM9B/C3wCqAV+GREXA1slfRi4Askt5uswmU6uj2Cq5mdkcnaIIY40Sg9MFoYEd+OCCeHWaBvcJi9B3vY4jGYzOwMTFaDuBF4C0lyeOMk+1mF2vPMMYZHwjUIMzsjkzVSPwL87gzGYtNsdIgNj+JqZmdiwltMkv55qoOn2kfStZJ2S9oj6dYJ9nm9pF2SHpb0ubzyYUk709e2qWKxU2U6ummsq2H90pZyh2Jms9Bkt5h+dooLs4CLJ9wo1QJ3ANcA+4HtkrZFxK68fTYCtwFXR8RhSSvyTnE8Iq4o4m+wCWTSITZqPcSGmZ2ByRLE9UUcPzDJtquAPRGxF0DS3ek5d+Xt81vAHRFxGCAininiM61ImWw3P795xdQ7mpkVMFkbxLfP8tyrgH156/uB547bZxOApO8BtcD7I+Jr6bYmSTtIelN9KCK+Mv4DJN0M3Aywdu3aswx3bunM9XPw2IAbqM3sjE1Wg5ipz98IvARYDXxH0mXpQ3jrIuKApPOBb0h6aPxT3BFxJ3AnQFtbW8xo5BVudIgNN1Cb2Zkq5ZzUB4A1eeur07J8+4FtETEYEY8Dj5AkDCLiQPq+F/gWcGUJY51zMukQG65BmNmZmjJBSHqVpDNJJNuBjZI2SGoAtgLjG72/QlJ7QNIykltOeyUtltSYV341J7dd2BTas92cs6CRJS0N5Q7FzGapYi78vwo8KulPJW0u9sQRMQTcAtwHtANfjIiHJd0u6bp0t/uAQ5J2Ad8E3hMRh4AtwA5JP07LP5Tf+8mmlunIufZgZmdlyjaIiHiDpAXADcCnJAXwSeDzEZGb4th7gXvHlb0vbzmAd6ev/H2+D1xW7B9hJxscHmHPM8d44aZl5Q7FzGaxom4dRUQ3cA9wN3AuyZwQP5L0jhLGZmfo8YM9DAyPsMU1CDM7C8W0QVwn6R9JGorrgasi4heBZ+GhOCpSe4d7MJnZ2Summ+trgb+MiO/kF0ZEr6TfLE1YdjYy2Rz1teL8Za3lDsXMZrFiEsT7gY7RFUnNwDkR8URE/HupArMzl+no5oLlrTTUlbIXs5nNdcVcQf4BGMlbH07LrEJlsjm2nOv2BzM7O8UkiLqIyJ8waABw5/oKdaR3gI6jfWz2HNRmdpaKSRCdec8tIOl64GDpQrKzcWIOCNcgzOzsFNMG8Vbgs5I+RjLE9z48w1zFyqQ9mLa4BmFmZ6mYB+UeA54nqTVdP1byqOyMZbI5lrQ0sHx+Y7lDMbNZrqjRXCX9EnAJyRDcAETE7SWMy85QezbH5pXzGf3vZGZ2pop5UO5vSMZjegfJLaZfAdaVOC47AyMjwSNZj8FkZtOjmEbqF0TEG4HDEfFHwPNJJ/qxyvJUVy/HB4f9BLWZTYtiEkRf+t4r6TxgkGQ8JqswY5MEuYHazKZBMW0QX5W0CPgz4EdAAB8vZVB2Zto7ctQINq5wgjCzszdpgkgnCvr3dArQL0n6Z6ApIo7ORHB2ejLZbtYva6G5obbcoZjZHDDpLaaIGAHuyFvvd3KoXJlszkN8m9m0KaYN4t8lvVbuN1nRevqHePJQr9sfzGzaFJMg3kIyOF+/pG5JOUndJY7LTtPupz3EhplNr2KepPZP0lkg05EmCNcgzGyaFPOg3IsKvYo5uaRrJe2WtEfSrRPs83pJuyQ9LOlzeeVvkvRo+npT8X9Sdcpku2ltrGP14uZyh2Jmc0Qx3Vzfk7fcBFwF3A/8/GQHSaolaeC+BtgPbJe0LSJ25e2zEbgNuDoiDktakZYvAf4QaCPpVnt/euzhov+yKpPp8BAbZja9pqxBRMSr8l7XAJcCxVyorwL2RMTedA6Ju4Hrx+3zW8Adoxf+iHgmLX858PWI6Eq3fR24trg/qfpEBO3Zbj9BbWbT6kzmpNwPbCliv1UkQ4PnH7dq3D6bgE2SvifpB5KuPY1jkXSzpB2SdnR2dhb9B8w1Pz3aR65vyGMwmdm0mvIWk6S/IrnNA0lCuYLkierp+vyNwEuA1cB3JF1W7MERcSdwJ0BbW1tMsfucNTYHhGsQZjaNimmD2JG3PAR8PiK+V8RxB4A1eeur07J8+4EfRsQg8LikR0gSxgGSpJF/7LeK+MyqNDqL3KZznCDMbPoUkyDuAfoiYhiSxmdJ8yKid4rjtgMbJW0gueBvBW4ct89XgBuAT0paRnLLaS/wGPBBSYvT/V5G0phtBbR3dLNmSTPzm+rLHYqZzSFFPUkN5PedbAb+baqDImIIuAW4D2gHvhgRD0u6PW+O6/uAQ5J2Ad8E3hMRhyKiC/gASZLZDtyellkBGc8BYWYlUEwNoil/mtGIOCZpXjEnj4h7gXvHlb0vbzmAd6ev8cfeBdxVzOdUs77BYfZ2HuMVl64sdyhmNscUU4PokfTs0RVJzwGOly4kOx17njnGSHiIDTObfsXUIH4H+AdJPyWZcnQlyRSkVgHaOzxJkJmVRjFjMW2XtBm4KC3anfY6sgqQyeZoqq9h3dKWcodiZnNMMWMxvR1oiYifRMRPgFZJ/730oVkxMtluLjpnPrU1HmLDzKZXMW0Qv5XOKAdAOvTFb5UsIitaRNDe4R5MZlYaxSSI2vzJgtJB+BpKF5IVq/NYP109Ax6DycxKophG6q8BX5D0t+n6W9IyK7MTc0C4BmFm06+YBPE/gJuBt6XrXwc+XrKIrGiZrHswmVnpFDPc90hE/E1EvC4iXgfsAv6q9KHZVDIdOVYuaGJxi+/4mdn0K6YGgaQrScZMej3wOPDlUgZlxWnP5tz+YGYlM2GCkLSJJCncABwEvgAoIn5uhmKzSQwOj7DnmRwv3rS83KGY2Rw1WQ0iA3wXeGVE7AGQ9K4ZicqmtLezh8Hh8BwQZlYyk7VBvAboAL4p6eOSXkoy1IZVgBMN1O7BZGalMWGCiIivRMRWYDPJUNy/A6yQ9NeSXjZD8dkE2jty1NeK85d7iA0zK41iejH1RMTnIuJVJDO7PUDS9dXKKJPt5sIV86mvPZNpxc3MpnZaV5eIOBwRd0bES0sVkBUn05Fji59/MLMS8s/PWehwzwDZ7j53cTWzknKCmIUyWQ+xYWal5wQxC431YHINwsxKqKQJQtK1knZL2iPp1gLbb5LUKWln+npz3rbhvPJtpYxztsl05Fja0sDy1sZyh2Jmc1hRQ22ciXRY8DuAa4D9wHZJ2yJi17hdvxARtxQ4xfGIuKJU8c1mmWw3m8+dT94o7GZm066UNYirgD0RsTciBoC7getL+HlVYXgk2P20Jwkys9IrZYJYBezLW9+flo33WkkPSrpH0pq88iZJOyT9QNKrC32ApJvTfXZ0dnZOX+QV7MlDPfQNjniIbzMruXI3Un8VWB8Rl5PMM/HpvG3rIqINuBH4iKQLxh+cPpPRFhFty5dXx6B1u9MeTFvOdQ3CzEqrlAniAJBfI1idlo2JiEMR0Z+ufgJ4Tt62A+n7XuBbwJUljHXWaM/mqBFcuKK13KGY2RxXygSxHdgoaYOkBmArcFJvJEnn5q1eB7Sn5YslNabLy4CrSSYqqnqZjm42LGuhqb623KGY2RxXsl5METEk6RbgPqAWuCsiHpZ0O7AjIrYBvy3pOmAI6AJuSg/fAvytpBGSJPahAr2fqlImm+Oy1QvLHYaZVYGSJQiAiLgXuHdc2fvylm8Dbitw3PeBy0oZ22x0rH+Ip7p6eX3b6nKHYmZVoNyN1HYadnuIDTObQU4Qs4iH2DCzmeQEMYtkOnLMb6xj1aLmcodiZlXACWIW8RAbZjaTnCBmiYgg0+EhNsxs5jhBzBIHjhwn1z/k9gczmzFOELNEpsM9mMxsZjlBzBKjPZgu8iB9ZjZDnCBmifZsjrVL5tHaWNJnG83MxjhBzBKZjm4P8W1mM8oJYhboGxzm8YM9bPYQ32Y2g5wgZoFHnz7GSMAW1yDMbAY5QcwC7WNDbLgGYWYzxwliFsh05Giur2XtknnlDsXMqogTxCyQyXazaeV8ams8xIaZzRwniAoXEbR3dLv9wcxmnBNEhevM9XO4d9BdXM1sxjlBVLj20UmC3EBtZjPMCaLCZTrSHkyuQZjZDCtpgpB0raTdkvZIurXA9pskdUramb7enLftTZIeTV9vKmWclSyTzXHuwiYWzWsodyhmVmVKNrCPpFrgDuAaYD+wXdK2iNg1btcvRMQt445dAvwh0AYEcH967OFSxVup2j3EhpmVSSlrEFcBeyJib0QMAHcD1xd57MuBr0dEV5oUvg5cW6I4K9bA0AiPdR5z+4OZlUUpE8QqYF/e+v60bLzXSnpQ0j2S1pzOsZJulrRD0o7Ozs7pirti7D14jMHhcA3CzMqi3I3UXwXWR8TlJLWET5/OwRFxZ0S0RUTb8uXLSxJgOY1OErTFNQgzK4NSJogDwJq89dVp2ZiIOBQR/enqJ4DnFHtsNWjPdtNQW8OGZS3lDsXMqlApE8R2YKOkDZIagK3AtvwdJJ2bt3od0J4u3we8TNJiSYuBl6VlVSXTkePCFa3U15a7omdm1ahkvZgiYkjSLSQX9lrgroh4WNLtwI6I2Ab8tqTrgCGgC7gpPbZL0gdIkgzA7RHRVapYK1Um283VFy4rdxhmVqVKOn9lRNwL3Duu7H15y7cBt01w7F3AXaWMr5J19QzwdHc/W1a6/cHMysP3LipUZmwOCPdgMrPycIKoUKM9mDa7BmFmZeIEUaEy2W6WtTawfH5juUMxsyrlBFGhMtmcaw9mVlZOEBVoeCTYnc35CWozKysniAr0xKEe+odGPAaTmZWVE0QFOtFA7RqEmZWPE0QFymS7qa0RF65oLXcoZlbFnCAqUCab4/xlLTTV15Y7FDOrYk4QFSiT7eYi314yszJzgqgwub5B9nUd9xDfZlZ2ThAV5pGn3UBtZpXBCaLCtI/2YHINwszKzAmiwmSy3cxvquO8hU3lDsXMqpwTRIXJdOTYsnIBksodiplVOSeIChIRyRhMHuLbzCqAE0QF2X/4OMf6hzxIn5lVBCeICpLJjjZQuwZhZuXnBFFBMh3JLHIXneMEYWblV9IEIelaSbsl7ZF06yT7vVZSSGpL19dLOi5pZ/r6m1LGWSky2Rzrls6jpbGkU4WbmRWlZFciSbXAHcA1wH5gu6RtEbFr3H7zgXcCPxx3isci4opSxVeJ2rPdfkDOzCpGKX+qXgXsiYi9AJLuBq4Hdo3b7wPAnwDvKWEsExoeCQ4e60eCGil9gdL30bIT2xlbn86uqMcHhnniYA+vuvy8aTunmdnZKGWCWAXsy1vfDzw3fwdJzwbWRMT/kzQ+QWyQ9ADQDfxBRHy3FEEe6R3guR/89zM6Nj9pTJ1Q8ren+9ec2H9oZISRgC1uoDazClG2m92SaoAPAzcV2NwBrI2IQ5KeA3xF0iUR0T3uHDcDNwOsXbv2jOJoaazjg798GSMRRAQjASPpe7J+oiwCRkby14vYP68sIhgZOXX/4XS/521YytUXLjujv8PMbLqVMkEcANbkra9Oy0bNBy4FvpXeqlkJbJN0XUTsAPoBIuJ+SY8Bm4Ad+R8QEXcCdwK0tbXFmQTZVF/Ljc89s+RiZjaXlbIX03Zgo6QNkhqArcC20Y0RcTQilkXE+ohYD/wAuC4idkhanjZyI+l8YCOwt4SxmpnZOCWrQUTEkKRbgPuAWuCuiHhY0u3AjojYNsnhLwJulzQIjABvjYiuUsVqZmanUsQZ3ZmpOG1tbbFjx46pdzQzszGS7o+ItkLb/CS1mZkV5ARhZmYFOUGYmVlBThBmZlaQE4SZmRU0Z3oxSeoEnjyLUywDDk5TOLOdv4uT+fs4mb+PE+bCd7EuIpYX2jBnEsTZkrRjoq5e1cbfxcn8fZzM38cJc/278C0mMzMryAnCzMwKcoI44c5yB1BB/F2czN/Hyfx9nDCnvwu3QZiZWUGuQZiZWUFOEGZmVlDVJwhJ10raLWmPpFvLHU85SVoj6ZuSdkl6WNI7yx1TuUmqlfSApH8udyzlJmmRpHskZSS1S3p+uWMqJ0nvSv8/+Ymkz0tqKndM062qE0Q6KdEdwC8CFwM3SLq4vFGV1RDwuxFxMfA84O1V/n0AvBNoL3cQFeKjwNciYjPwLKr4e5G0CvhtoC0iLiWZ82ZreaOaflWdIICrgD0RsTciBoC7gevLHFPZRERHRPwoXc6RXABWlTeq8pG0Gvgl4BPljqXcJC0kmcjr7wAiYiAijpQ1qPKrA5ol1QHzgJ+WOZ5pV+0JYhWwL299P1V8QcwnaT1wJfDDModSTh8B3ksyq2G12wB0Ap9Mb7l9QlJLuYMql4g4APw58BTQARyNiH8tb1TTr9oThBUgqRX4EvA7EdFd7njKQdIrgWci4v5yx1Ih6oBnA38dEVcCPUDVttlJWkxyt2EDcB7QIukN5Y1q+lV7gjgArMlbX52WVS1J9STJ4bMR8eVyx1NGVwPXSXqC5Nbjz0v6THlDKqv9wP6IGK1R3kOSMKrVLwCPR0RnRAwCXwZeUOaYpl21J4jtwEZJGyQ1kDQybStzTGUjSST3mNsj4sPljqecIuK2iFgdEetJ/l18IyLm3C/EYkVEFtgn6aK06KXArjKGVG5PAc+TNC/9/+alzMFG+7pyB1BOETEk6RbgPpJeCHdFxMNlDqucrgZ+HXhI0s607Pcj4t7yhWQV5B3AZ9MfU3uB/1bmeMomIn4o6R7gRyS9/x5gDg674aE2zMysoGq/xWRmZhNwgjAzs4KcIMzMrCAnCDMzK8gJwszMCnKCMEtJOpa+r5d04zSf+/fHrX9/Os9vVgpOEGanWg+cVoJIB2ybzEkJIiLm3FO3Nvc4QZid6kPACyXtTMf8r5X0Z5K2S3pQ0lsAJL1E0nclbSN9qljSVyTdn84TcHNa9iGSUT93SvpsWjZaW1F67p9IekjSr+ad+1t58y98Nn1iF0kfSufseFDSn8/4t2NVo6qfpDabwK3A70XEKwHSC/3RiPgZSY3A9ySNjtz5bODSiHg8Xf+NiOiS1Axsl/SliLhV0i0RcUWBz3oNcAXJ/ArL0mO+k267EriEZBjp7wFXS2oHfhnYHBEhadH0/ulmJ7gGYTa1lwFvTIcf+SGwFNiYbvuvvOQA8NuSfgz8gGQgyI1M7meBz0fEcEQ8DXwb+Jm8c++PiBFgJ8mtr6NAH/B3kl4D9J7l32Y2IScIs6kJeEdEXJG+NuSN/d8ztpP0EpJRPp8fEc8iGZ/nbKah7M9bHgbqImKIZKKre4BXAl87i/ObTcoJwuxUOWB+3vp9wNvSodCRtGmCyXIWAocjolfSZpJpW0cNjh4/zneBX03bOZaTzNr2XxMFls7VsTAdQPFdJLemzErCbRBmp3oQGE5vFX2KZC7m9cCP0obiTuDVBY77GvDWtJ1gN8ltplF3Ag9K+lFE/Fpe+T8Czwd+DATw3ojIpgmmkPnAP0lqIqnZvPuM/kKzIng0VzMzK8i3mMzMrCAnCDMzK8gJwszMCnKCMDOzgpwgzMysICcIMzMryAnCzMwK+v9dS7Ovcb84WwAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -501,16 +1130,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.798340863819657"
+       "0.8088835446727882"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -521,7 +1150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -540,7 +1169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -549,7 +1178,7 @@
        "<All keys matched successfully>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -557,6 +1186,10 @@
    "source": [
     "import torch\n",
     "\n",
+    "# Make sure the model is on CPU before loading a pretrained state_dict\n",
+    "model = model.cpu()\n",
+    "\n",
+    "# Load pretrained weights\n",
     "trained_state_dict = torch.load(\"state_dict.pth\")[\"models_state_dict\"][0]\n",
     "\n",
     "model.load_state_dict(trained_state_dict, strict=False)"
@@ -564,7 +1197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "scrolled": true
    },
@@ -575,12 +1208,16 @@
        "0.9188772287810328"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# Move the model back to it's target device\n",
+    "model.to(device)\n",
+    "\n",
+    "# Test for accuracy\n",
     "test(model, test_quantized_loader)"
    ]
   },
@@ -600,6 +1237,16 @@
     "Sometimes, it's desirable to make some changes to our trained network prior to export (this is known in general as \"network surgery\"). This depends on the model and is not generally necessary, but in this case we want to make a couple of changes to get better results with FINN."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Move the model to CPU before surgery\n",
+    "model = model.cpu()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -609,7 +1256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -618,7 +1265,7 @@
        "(64, 593)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -634,7 +1281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -643,7 +1290,7 @@
        "(64, 600)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -658,7 +1305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -667,7 +1314,7 @@
        "torch.Size([64, 600])"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -690,9 +1337,375 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CybSecMLPForExport(\n",
+       "  (pretrained): Sequential(\n",
+       "    (0): QuantLinear(\n",
+       "      in_features=593, out_features=64, bias=True\n",
+       "      (input_quant): ActQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      )\n",
+       "      (output_quant): ActQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      )\n",
+       "      (weight_quant): WeightQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "        (tensor_quant): RescalingIntQuant(\n",
+       "          (int_quant): IntQuant(\n",
+       "            (float_to_int_impl): RoundSte()\n",
+       "            (tensor_clamp_impl): TensorClampSte()\n",
+       "            (delay_wrapper): DelayWrapper(\n",
+       "              (delay_impl): _NoDelay()\n",
+       "            )\n",
+       "          )\n",
+       "          (scaling_impl): StatsFromParameterScaling(\n",
+       "            (parameter_list_stats): _ParameterListStats(\n",
+       "              (first_tracked_param): _ViewParameterWrapper(\n",
+       "                (view_shape_impl): OverTensorView()\n",
+       "              )\n",
+       "              (stats): _Stats(\n",
+       "                (stats_impl): AbsMax()\n",
+       "              )\n",
+       "            )\n",
+       "            (stats_scaling_impl): _StatsScaling(\n",
+       "              (affine_rescaling): Identity()\n",
+       "              (restrict_clamp_scaling): _RestrictClampValue(\n",
+       "                (clamp_min_ste): ScalarClampMinSte()\n",
+       "                (restrict_value_impl): FloatRestrictValue()\n",
+       "              )\n",
+       "              (restrict_scaling_pre): Identity()\n",
+       "            )\n",
+       "          )\n",
+       "          (int_scaling_impl): IntScaling()\n",
+       "          (zero_point_impl): ZeroZeroPoint(\n",
+       "            (zero_point): StatelessBuffer()\n",
+       "          )\n",
+       "          (msb_clamp_bit_width_impl): BitWidthConst(\n",
+       "            (bit_width): StatelessBuffer()\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "      (bias_quant): BiasQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      )\n",
+       "    )\n",
+       "    (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "    (2): Dropout(p=0.5, inplace=False)\n",
+       "    (3): QuantReLU(\n",
+       "      (input_quant): ActQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      )\n",
+       "      (act_quant): ActQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "        (fused_activation_quant_proxy): FusedActivationQuantProxy(\n",
+       "          (activation_impl): ReLU()\n",
+       "          (tensor_quant): RescalingIntQuant(\n",
+       "            (int_quant): IntQuant(\n",
+       "              (float_to_int_impl): RoundSte()\n",
+       "              (tensor_clamp_impl): TensorClamp()\n",
+       "              (delay_wrapper): DelayWrapper(\n",
+       "                (delay_impl): _NoDelay()\n",
+       "              )\n",
+       "            )\n",
+       "            (scaling_impl): ParameterFromRuntimeStatsScaling(\n",
+       "              (stats_input_view_shape_impl): OverTensorView()\n",
+       "              (stats): _Stats(\n",
+       "                (stats_impl): AbsPercentile()\n",
+       "              )\n",
+       "              (restrict_clamp_scaling): _RestrictClampValue(\n",
+       "                (clamp_min_ste): ScalarClampMinSte()\n",
+       "                (restrict_value_impl): FloatRestrictValue()\n",
+       "              )\n",
+       "              (restrict_inplace_preprocess): Identity()\n",
+       "              (restrict_preprocess): Identity()\n",
+       "            )\n",
+       "            (int_scaling_impl): IntScaling()\n",
+       "            (zero_point_impl): ZeroZeroPoint(\n",
+       "              (zero_point): StatelessBuffer()\n",
+       "            )\n",
+       "            (msb_clamp_bit_width_impl): BitWidthConst(\n",
+       "              (bit_width): StatelessBuffer()\n",
+       "            )\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "    (4): QuantLinear(\n",
+       "      in_features=64, out_features=64, bias=True\n",
+       "      (input_quant): ActQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      )\n",
+       "      (output_quant): ActQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      )\n",
+       "      (weight_quant): WeightQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "        (tensor_quant): RescalingIntQuant(\n",
+       "          (int_quant): IntQuant(\n",
+       "            (float_to_int_impl): RoundSte()\n",
+       "            (tensor_clamp_impl): TensorClampSte()\n",
+       "            (delay_wrapper): DelayWrapper(\n",
+       "              (delay_impl): _NoDelay()\n",
+       "            )\n",
+       "          )\n",
+       "          (scaling_impl): StatsFromParameterScaling(\n",
+       "            (parameter_list_stats): _ParameterListStats(\n",
+       "              (first_tracked_param): _ViewParameterWrapper(\n",
+       "                (view_shape_impl): OverTensorView()\n",
+       "              )\n",
+       "              (stats): _Stats(\n",
+       "                (stats_impl): AbsMax()\n",
+       "              )\n",
+       "            )\n",
+       "            (stats_scaling_impl): _StatsScaling(\n",
+       "              (affine_rescaling): Identity()\n",
+       "              (restrict_clamp_scaling): _RestrictClampValue(\n",
+       "                (clamp_min_ste): ScalarClampMinSte()\n",
+       "                (restrict_value_impl): FloatRestrictValue()\n",
+       "              )\n",
+       "              (restrict_scaling_pre): Identity()\n",
+       "            )\n",
+       "          )\n",
+       "          (int_scaling_impl): IntScaling()\n",
+       "          (zero_point_impl): ZeroZeroPoint(\n",
+       "            (zero_point): StatelessBuffer()\n",
+       "          )\n",
+       "          (msb_clamp_bit_width_impl): BitWidthConst(\n",
+       "            (bit_width): StatelessBuffer()\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "      (bias_quant): BiasQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      )\n",
+       "    )\n",
+       "    (5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "    (6): Dropout(p=0.5, inplace=False)\n",
+       "    (7): QuantReLU(\n",
+       "      (input_quant): ActQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      )\n",
+       "      (act_quant): ActQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "        (fused_activation_quant_proxy): FusedActivationQuantProxy(\n",
+       "          (activation_impl): ReLU()\n",
+       "          (tensor_quant): RescalingIntQuant(\n",
+       "            (int_quant): IntQuant(\n",
+       "              (float_to_int_impl): RoundSte()\n",
+       "              (tensor_clamp_impl): TensorClamp()\n",
+       "              (delay_wrapper): DelayWrapper(\n",
+       "                (delay_impl): _NoDelay()\n",
+       "              )\n",
+       "            )\n",
+       "            (scaling_impl): ParameterFromRuntimeStatsScaling(\n",
+       "              (stats_input_view_shape_impl): OverTensorView()\n",
+       "              (stats): _Stats(\n",
+       "                (stats_impl): AbsPercentile()\n",
+       "              )\n",
+       "              (restrict_clamp_scaling): _RestrictClampValue(\n",
+       "                (clamp_min_ste): ScalarClampMinSte()\n",
+       "                (restrict_value_impl): FloatRestrictValue()\n",
+       "              )\n",
+       "              (restrict_inplace_preprocess): Identity()\n",
+       "              (restrict_preprocess): Identity()\n",
+       "            )\n",
+       "            (int_scaling_impl): IntScaling()\n",
+       "            (zero_point_impl): ZeroZeroPoint(\n",
+       "              (zero_point): StatelessBuffer()\n",
+       "            )\n",
+       "            (msb_clamp_bit_width_impl): BitWidthConst(\n",
+       "              (bit_width): StatelessBuffer()\n",
+       "            )\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "    (8): QuantLinear(\n",
+       "      in_features=64, out_features=64, bias=True\n",
+       "      (input_quant): ActQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      )\n",
+       "      (output_quant): ActQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      )\n",
+       "      (weight_quant): WeightQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "        (tensor_quant): RescalingIntQuant(\n",
+       "          (int_quant): IntQuant(\n",
+       "            (float_to_int_impl): RoundSte()\n",
+       "            (tensor_clamp_impl): TensorClampSte()\n",
+       "            (delay_wrapper): DelayWrapper(\n",
+       "              (delay_impl): _NoDelay()\n",
+       "            )\n",
+       "          )\n",
+       "          (scaling_impl): StatsFromParameterScaling(\n",
+       "            (parameter_list_stats): _ParameterListStats(\n",
+       "              (first_tracked_param): _ViewParameterWrapper(\n",
+       "                (view_shape_impl): OverTensorView()\n",
+       "              )\n",
+       "              (stats): _Stats(\n",
+       "                (stats_impl): AbsMax()\n",
+       "              )\n",
+       "            )\n",
+       "            (stats_scaling_impl): _StatsScaling(\n",
+       "              (affine_rescaling): Identity()\n",
+       "              (restrict_clamp_scaling): _RestrictClampValue(\n",
+       "                (clamp_min_ste): ScalarClampMinSte()\n",
+       "                (restrict_value_impl): FloatRestrictValue()\n",
+       "              )\n",
+       "              (restrict_scaling_pre): Identity()\n",
+       "            )\n",
+       "          )\n",
+       "          (int_scaling_impl): IntScaling()\n",
+       "          (zero_point_impl): ZeroZeroPoint(\n",
+       "            (zero_point): StatelessBuffer()\n",
+       "          )\n",
+       "          (msb_clamp_bit_width_impl): BitWidthConst(\n",
+       "            (bit_width): StatelessBuffer()\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "      (bias_quant): BiasQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      )\n",
+       "    )\n",
+       "    (9): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "    (10): Dropout(p=0.5, inplace=False)\n",
+       "    (11): QuantReLU(\n",
+       "      (input_quant): ActQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      )\n",
+       "      (act_quant): ActQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "        (fused_activation_quant_proxy): FusedActivationQuantProxy(\n",
+       "          (activation_impl): ReLU()\n",
+       "          (tensor_quant): RescalingIntQuant(\n",
+       "            (int_quant): IntQuant(\n",
+       "              (float_to_int_impl): RoundSte()\n",
+       "              (tensor_clamp_impl): TensorClamp()\n",
+       "              (delay_wrapper): DelayWrapper(\n",
+       "                (delay_impl): _NoDelay()\n",
+       "              )\n",
+       "            )\n",
+       "            (scaling_impl): ParameterFromRuntimeStatsScaling(\n",
+       "              (stats_input_view_shape_impl): OverTensorView()\n",
+       "              (stats): _Stats(\n",
+       "                (stats_impl): AbsPercentile()\n",
+       "              )\n",
+       "              (restrict_clamp_scaling): _RestrictClampValue(\n",
+       "                (clamp_min_ste): ScalarClampMinSte()\n",
+       "                (restrict_value_impl): FloatRestrictValue()\n",
+       "              )\n",
+       "              (restrict_inplace_preprocess): Identity()\n",
+       "              (restrict_preprocess): Identity()\n",
+       "            )\n",
+       "            (int_scaling_impl): IntScaling()\n",
+       "            (zero_point_impl): ZeroZeroPoint(\n",
+       "              (zero_point): StatelessBuffer()\n",
+       "            )\n",
+       "            (msb_clamp_bit_width_impl): BitWidthConst(\n",
+       "              (bit_width): StatelessBuffer()\n",
+       "            )\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "    (12): QuantLinear(\n",
+       "      in_features=64, out_features=1, bias=True\n",
+       "      (input_quant): ActQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      )\n",
+       "      (output_quant): ActQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      )\n",
+       "      (weight_quant): WeightQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "        (tensor_quant): RescalingIntQuant(\n",
+       "          (int_quant): IntQuant(\n",
+       "            (float_to_int_impl): RoundSte()\n",
+       "            (tensor_clamp_impl): TensorClampSte()\n",
+       "            (delay_wrapper): DelayWrapper(\n",
+       "              (delay_impl): _NoDelay()\n",
+       "            )\n",
+       "          )\n",
+       "          (scaling_impl): StatsFromParameterScaling(\n",
+       "            (parameter_list_stats): _ParameterListStats(\n",
+       "              (first_tracked_param): _ViewParameterWrapper(\n",
+       "                (view_shape_impl): OverTensorView()\n",
+       "              )\n",
+       "              (stats): _Stats(\n",
+       "                (stats_impl): AbsMax()\n",
+       "              )\n",
+       "            )\n",
+       "            (stats_scaling_impl): _StatsScaling(\n",
+       "              (affine_rescaling): Identity()\n",
+       "              (restrict_clamp_scaling): _RestrictClampValue(\n",
+       "                (clamp_min_ste): ScalarClampMinSte()\n",
+       "                (restrict_value_impl): FloatRestrictValue()\n",
+       "              )\n",
+       "              (restrict_scaling_pre): Identity()\n",
+       "            )\n",
+       "          )\n",
+       "          (int_scaling_impl): IntScaling()\n",
+       "          (zero_point_impl): ZeroZeroPoint(\n",
+       "            (zero_point): StatelessBuffer()\n",
+       "          )\n",
+       "          (msb_clamp_bit_width_impl): BitWidthConst(\n",
+       "            (bit_width): StatelessBuffer()\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "      (bias_quant): BiasQuantProxyFromInjector(\n",
+       "        (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (qnt_output): QuantIdentity(\n",
+       "    (input_quant): ActQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "    )\n",
+       "    (act_quant): ActQuantProxyFromInjector(\n",
+       "      (_zero_hw_sentinel): StatelessBuffer()\n",
+       "      (fused_activation_quant_proxy): FusedActivationQuantProxy(\n",
+       "        (activation_impl): Identity()\n",
+       "        (tensor_quant): ClampedBinaryQuant(\n",
+       "          (scaling_impl): ParameterFromRuntimeStatsScaling(\n",
+       "            (stats_input_view_shape_impl): OverTensorView()\n",
+       "            (stats): _Stats(\n",
+       "              (stats_impl): AbsPercentile()\n",
+       "            )\n",
+       "            (restrict_clamp_scaling): _RestrictClampValue(\n",
+       "              (clamp_min_ste): ScalarClampMinSte()\n",
+       "              (restrict_value_impl): FloatRestrictValue()\n",
+       "            )\n",
+       "            (restrict_inplace_preprocess): Identity()\n",
+       "            (restrict_preprocess): Identity()\n",
+       "          )\n",
+       "          (bit_width): BitWidthConst(\n",
+       "            (bit_width): StatelessBuffer()\n",
+       "          )\n",
+       "          (zero_point): StatelessBuffer()\n",
+       "          (delay_wrapper): DelayWrapper(\n",
+       "            (delay_impl): _NoDelay()\n",
+       "          )\n",
+       "          (tensor_clamp_impl): TensorClamp()\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from brevitas.core.quant import QuantType\n",
     "from brevitas.nn import QuantIdentity\n",
@@ -708,17 +1721,18 @@
     "        # assume x contains bipolar {-1,1} elems\n",
     "        # shift from {-1,1} -> {0,1} since that is the\n",
     "        # input range for the trained network\n",
-    "        x = (x + torch.tensor([1.0])) / 2.0  \n",
+    "        x = (x + torch.tensor([1.0]).to(x.device)) / 2.0  \n",
     "        out_original = self.pretrained(x)\n",
     "        out_final = self.qnt_output(out_original)   # output as {-1,1}     \n",
     "        return out_final\n",
     "\n",
-    "model_for_export = CybSecMLPForExport(modified_model)"
+    "model_for_export = CybSecMLPForExport(modified_model)\n",
+    "model_for_export.to(device)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -731,16 +1745,17 @@
     "    with torch.no_grad():\n",
     "        for data in test_loader:\n",
     "            inputs, target = data\n",
+    "            inputs, target = inputs.to(device), target.to(device)\n",
     "            # pad inputs to 600 elements\n",
-    "            input_padded = np.pad(inputs, [(0,0), (0,7)])\n",
+    "            input_padded = torch.nn.functional.pad(inputs, (0,7,0,0))\n",
     "            # convert inputs to {-1,+1}\n",
-    "            input_scaled = 2*input_padded - 1\n",
+    "            input_scaled = 2 * input_padded - 1\n",
     "            # run the model\n",
-    "            output = model(torch.from_numpy(input_scaled).float())\n",
-    "            y_pred.extend(list(output.flatten()))\n",
+    "            output = model(input_scaled.float())\n",
+    "            y_pred.extend(list(output.flatten().cpu().numpy()))\n",
     "            # make targets bipolar {-1,+1}\n",
-    "            expected = 2*target.float() - 1\n",
-    "            expected = expected.detach().numpy()\n",
+    "            expected = 2 * target.float() - 1\n",
+    "            expected = expected.cpu().numpy()\n",
     "            y_true.extend(list(expected.flatten()))\n",
     "        \n",
     "    return accuracy_score(y_true, y_pred)"
@@ -748,7 +1763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -757,7 +1772,7 @@
        "0.9188772287810328"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -780,7 +1795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 27,
    "metadata": {
     "scrolled": true
    },
@@ -791,16 +1806,6 @@
      "text": [
       "Model saved to cybsec-mlp-ready.onnx\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-22-78c27bb59095>:15: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
-      "  x = (x + torch.tensor([1.0])) / 2.0\n",
-      "/workspace/brevitas/src/brevitas/quant_tensor/__init__.py:74: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
-      "  training = torch.tensor(training, dtype=torch.bool)\n"
-     ]
     }
    ],
    "source": [
@@ -809,6 +1814,7 @@
     "\n",
     "ready_model_filename = \"cybsec-mlp-ready.onnx\"\n",
     "input_shape = (1, 600)\n",
+    "\n",
     "# create a QuantTensor instance to mark input as bipolar during export\n",
     "input_a = np.random.randint(0, 1, size=input_shape).astype(np.float32)\n",
     "input_a = 2 * input_a - 1\n",
@@ -818,6 +1824,10 @@
     "    input_t, scale=torch.tensor(scale), bit_width=torch.tensor(1.0), signed=True\n",
     ")\n",
     "\n",
+    "#Move to CPU before export\n",
+    "model_for_export.cpu()\n",
+    "\n",
+    "# Export to ONNX\n",
     "bo.export_finn_onnx(\n",
     "    model_for_export, export_path=ready_model_filename, input_t=input_qt\n",
     ")\n",
@@ -843,38 +1853,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Serving 'cybsec-mlp-ready.onnx' at http://0.0.0.0:8081\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"100%\"\n",
-       "            height=\"400\"\n",
-       "            src=\"http://localhost:8081/\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7fb36398c3a0>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from finn.util.visualization import showInNetron\n",
     "\n",
@@ -888,18 +1869,11 @@
     "## That's it! <a id=\"thats_it\" ></a>\n",
     "You created, trained and tested a quantized MLP that is ready to be loaded into FINN, congratulations! You can now proceed to the next notebook."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -913,7 +1887,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.0"
   }
  },
  "nbformat": 4,