diff --git a/pytorch_ipynb/autoencoder/ae-basic-with-rf.ipynb b/pytorch_ipynb/autoencoder/ae-basic-with-rf.ipynb
index 031311c..fec5ab0 100644
--- a/pytorch_ipynb/autoencoder/ae-basic-with-rf.ipynb
+++ b/pytorch_ipynb/autoencoder/ae-basic-with-rf.ipynb
@@ -151,58 +151,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "##########################\n",
-    "### MODEL\n",
-    "##########################\n",
-    "\n",
-    "class Autoencoder(torch.nn.Module):\n",
-    "\n",
-    "    def __init__(self, num_features):\n",
-    "        super(Autoencoder, self).__init__()\n",
-    "        \n",
-    "        ### ENCODER\n",
-    "        \n",
-    "        self.linear_1 = torch.nn.Linear(num_features, num_hidden_1)\n",
-    "        # The following to lones are not necessary, \n",
-    "        # but used here to demonstrate how to access the weights\n",
-    "        # and use a different weight initialization.\n",
-    "        # By default, PyTorch uses Xavier/Glorot initialization, which\n",
-    "        # should usually be preferred.\n",
-    "        self.linear_1.weight.detach().normal_(0.0, 0.1)\n",
-    "        self.linear_1.bias.detach().zero_()\n",
-    "        \n",
-    "        ### DECODER\n",
-    "        self.linear_2 = torch.nn.Linear(num_hidden_1, num_features)\n",
-    "        self.linear_1.weight.detach().normal_(0.0, 0.1)\n",
-    "        self.linear_1.bias.detach().zero_()\n",
-    "        \n",
-    "    def encoder(self, x):\n",
-    "        encoded = self.linear_1(x)\n",
-    "        encoded = F.leaky_relu(encoded)\n",
-    "        return encoded\n",
-    "    \n",
-    "    def decoder(self, encoded_x):\n",
-    "        logits = self.linear_2(encoded_x)\n",
-    "        decoded = torch.sigmoid(logits)\n",
-    "        return decoded\n",
-    "        \n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        \n",
-    "        ### ENCODER\n",
-    "        encoded = self.encoder(x)\n",
-    "        \n",
-    "        ### DECODER\n",
-    "        decoded = self.decoder(encoded)\n",
-    "        \n",
-    "        return decoded\n",
-    "\n",
-    "    \n",
-    "torch.manual_seed(random_seed)\n",
-    "model = Autoencoder(num_features=num_features)\n",
-    "model = model.to(device)\n",
-    "\n",
-    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  "
+    "##########################\n### MODEL\n##########################\n\nclass Autoencoder(torch.nn.Module):\n\n    def __init__(self, num_features):\n        super(Autoencoder, self).__init__()\n        \n        ### ENCODER\n        \n        self.linear_1 = torch.nn.Linear(num_features, num_hidden_1)\n        # The following to lones are not necessary, \n        # but used here to demonstrate how to access the weights\n        # and use a different weight initialization.\n        # By default, PyTorch uses Xavier/Glorot initialization, which\n        # should usually be preferred.\n        self.linear_1.weight.detach().normal_(0.0, 0.1)\n        self.linear_1.bias.detach().zero_()\n        \n        ### DECODER\n        self.linear_2 = torch.nn.Linear(num_hidden_1, num_features)\n        self.linear_2.weight.detach().normal_(0.0, 0.1)\n        self.linear_2.bias.detach().zero_()\n        \n    def encoder(self, x):\n        encoded = self.linear_1(x)\n        encoded = F.leaky_relu(encoded)\n        return encoded\n    \n    def decoder(self, encoded_x):\n        logits = self.linear_2(encoded_x)\n        decoded = torch.sigmoid(logits)\n        return decoded\n        \n\n    def forward(self, x):\n        \n        ### ENCODER\n        encoded = self.encoder(x)\n        \n        ### DECODER\n        decoded = self.decoder(encoded)\n        \n        return decoded\n\n    \ntorch.manual_seed(random_seed)\nmodel = Autoencoder(num_features=num_features)\nmodel = model.to(device)\n\noptimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  "
    ]
   },
   {
@@ -684,4 +633,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/pytorch_ipynb/autoencoder/ae-basic.ipynb b/pytorch_ipynb/autoencoder/ae-basic.ipynb
index 4f7f681..6564422 100644
--- a/pytorch_ipynb/autoencoder/ae-basic.ipynb
+++ b/pytorch_ipynb/autoencoder/ae-basic.ipynb
@@ -151,49 +151,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "##########################\n",
-    "### MODEL\n",
-    "##########################\n",
-    "\n",
-    "class Autoencoder(torch.nn.Module):\n",
-    "\n",
-    "    def __init__(self, num_features):\n",
-    "        super(Autoencoder, self).__init__()\n",
-    "        \n",
-    "        ### ENCODER\n",
-    "        self.linear_1 = torch.nn.Linear(num_features, num_hidden_1)\n",
-    "        # The following to lones are not necessary, \n",
-    "        # but used here to demonstrate how to access the weights\n",
-    "        # and use a different weight initialization.\n",
-    "        # By default, PyTorch uses Xavier/Glorot initialization, which\n",
-    "        # should usually be preferred.\n",
-    "        self.linear_1.weight.detach().normal_(0.0, 0.1)\n",
-    "        self.linear_1.bias.detach().zero_()\n",
-    "        \n",
-    "        ### DECODER\n",
-    "        self.linear_2 = torch.nn.Linear(num_hidden_1, num_features)\n",
-    "        self.linear_1.weight.detach().normal_(0.0, 0.1)\n",
-    "        self.linear_1.bias.detach().zero_()\n",
-    "        \n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        \n",
-    "        ### ENCODER\n",
-    "        encoded = self.linear_1(x)\n",
-    "        encoded = F.leaky_relu(encoded)\n",
-    "        \n",
-    "        ### DECODER\n",
-    "        logits = self.linear_2(encoded)\n",
-    "        decoded = torch.sigmoid(logits)\n",
-    "        \n",
-    "        return decoded\n",
-    "\n",
-    "    \n",
-    "torch.manual_seed(random_seed)\n",
-    "model = Autoencoder(num_features=num_features)\n",
-    "model = model.to(device)\n",
-    "\n",
-    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  "
+    "##########################\n### MODEL\n##########################\n\nclass Autoencoder(torch.nn.Module):\n\n    def __init__(self, num_features):\n        super(Autoencoder, self).__init__()\n        \n        ### ENCODER\n        self.linear_1 = torch.nn.Linear(num_features, num_hidden_1)\n        # The following to lones are not necessary, \n        # but used here to demonstrate how to access the weights\n        # and use a different weight initialization.\n        # By default, PyTorch uses Xavier/Glorot initialization, which\n        # should usually be preferred.\n        self.linear_1.weight.detach().normal_(0.0, 0.1)\n        self.linear_1.bias.detach().zero_()\n        \n        ### DECODER\n        self.linear_2 = torch.nn.Linear(num_hidden_1, num_features)\n        self.linear_2.weight.detach().normal_(0.0, 0.1)\n        self.linear_2.bias.detach().zero_()\n        \n\n    def forward(self, x):\n        \n        ### ENCODER\n        encoded = self.linear_1(x)\n        encoded = F.leaky_relu(encoded)\n        \n        ### DECODER\n        logits = self.linear_2(encoded)\n        decoded = torch.sigmoid(logits)\n        \n        return decoded\n\n    \ntorch.manual_seed(random_seed)\nmodel = Autoencoder(num_features=num_features)\nmodel = model.to(device)\n\noptimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  "
    ]
   },
   {
@@ -377,4 +335,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file