diff --git a/Lesson1_FNN.ipynb b/Lesson1_FNN.ipynb
index 22ab743..7e1e233 100644
--- a/Lesson1_FNN.ipynb
+++ b/Lesson1_FNN.ipynb
@@ -3,11 +3,11 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
-        "<a href=\"https://colab.research.google.com/github/cc-ats/mlp_tutorial/blob/main/Lesson1_FNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+        "<a href=\"https://colab.research.google.com/github/cc-ats/mlp_class/blob/main/Lesson1_FNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
       ]
     },
     {
@@ -18,254 +18,110 @@
       "source": [
         "# Fitting Neural Network Models\n",
         "\n",
-        "In this tutorial, we will learn how to use a neural network (NN) model to predict the energy of points on the Müller-Brown potential energy surface.\n",
+        "In this tutorial, we will learn how to use a neural network model to predict the energy of points on the Müller-Brown potential energy surface. \n",
         "\n"
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 16,
+      "cell_type": "markdown",
       "metadata": {
-        "id": "1y8otrleFRXC"
+        "id": "G5gEWLszUpQV"
       },
-      "outputs": [],
-      "source": [
-        "from math import exp, pow, tanh\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "import plotly.graph_objects as go"
-      ]
-    },
-    {
-      "cell_type": "markdown",
       "source": [
         "## Defining the Müller-Brown Potential Energy Function\n",
         "\n",
-        "For the definition of Müller-Brown potential, see [here](https://www.wolframcloud.com/objects/demonstrations/TrajectoriesOnTheMullerBrownPotentialEnergySurface-source.nb.).\n",
+        "For the definition of Müller-Brown potential, see [here](https://www.wolframcloud.com/objects/demonstrations/TrajectoriesOnTheMullerBrownPotentialEnergySurface-source.nb).\n",
         "\n",
         "$v(x,y) = \\sum_{k=0}^3 A_k \\mathrm{exp}\\left[ a_k (x - x_k^0)^2 + b_k (x - x_k^0) (y - y_k^0) + c_k (y - y_k^0)^2 \\right] $"
-      ],
-      "metadata": {
-        "id": "G5gEWLszUpQV"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "def mueller_brown_potential(x, y):\n",
-        "  A = [-200, -100, -170, 15]\n",
-        "  a = [-1, -1, -6.5, 0.7]\n",
-        "  b = [0, 0, 11, 0.6]\n",
-        "  c = [-10, -10, -6.5, 0.7]\n",
-        "  x0 = [1, 0, -0.5, -1.0]\n",
-        "  y0 = [0, 0.5, 1.5, 1]\n",
-        "  value = 0\n",
-        "  for k in range(0, 4):\n",
-        "    value += 0.1 * A[k] * exp( a[k] * pow(x-x0[k], 2.0) + b[k] * (x-x0[k]) * (y-y0[k]) + c[k] * pow(y-y0[k], 2.0))\n",
-        "    # Scale the function by 0.1 to make plotting easier.\n",
-        "  return value"
-      ],
+      "execution_count": null,
       "metadata": {
         "id": "N0JzzAZ1UZdx"
       },
-      "execution_count": 17,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
+      "outputs": [],
       "source": [
-        "### Generate Training Data\n",
+        "from math import exp, pow\n",
         "\n",
-        "First, we need to generate data to train the neural network. The training data will be generated using the Müller-Brown Potential and a range of x and y values."
-      ],
-      "metadata": {
-        "id": "4GfIwnIKU9E-"
-      }
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 18,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "WIEw90Hx9XMr",
-        "outputId": "73aa645c-5ed6-47ec-8ea1-b526d2539e03"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Zmin: -14.599803525171698 Zmax: 1194.4772333054245\n",
-            "Size of Testing set: 896\n",
-            "Size of (future) training set: 696\n"
-          ]
-        }
-      ],
-      "source": [
-        "xx = np.arange(-1.8, 1.4, 0.1)\n",
-        "yy = np.arange(-0.4, 2.4, 0.1)\n",
-        "X, Y = np.meshgrid(xx, yy)\n",
-        "xy, xy_truncated = [],[]\n",
-        "z, z_truncated = [], []\n",
-        "for y in yy:\n",
-        "  for x in xx:\n",
-        "    v = mueller_brown_potential(x,y) # Now using x and y values from the xx and yy arrays.\n",
-        "    z.append(v) # Storing potential energy values in the z array.\n",
-        "    xy.append([x,y])\n",
-        "    if v < 10:  # Keep only low-energy points for training.\n",
-        "      xy_truncated.append([x, y])\n",
-        "      z_truncated.append(v)\n",
-        "Z = np.reshape(z,(len(yy),-1)) # Reshape z array so that we can plot our data on a 2-D surface that is len(xx) by len(yy).\n",
-        "print(\"Zmin:\", np.amin(Z), \"Zmax:\", np.amax(Z))\n",
-        "print(\"Size of Testing set:\", len(z))\n",
-        "print(\"Size of (future) training set:\", len(z_truncated))"
+        "def mueller_brown_potential(x, y):\n",
+        "    A = [-200, -100, -170, 15]\n",
+        "    a = [-1, -1, -6.5, 0.7]\n",
+        "    b = [0, 0, 11, 0.6]\n",
+        "    c = [-10, -10, -6.5, 0.7]\n",
+        "    x0 = [1, 0, -0.5, -1.0]\n",
+        "    y0 = [0, 0.5, 1.5, 1]\n",
+        "    z = 0\n",
+        "    for k in range(4):\n",
+        "        # Scale the function by 0.1 to make plotting easier\n",
+        "        z += 0.1 * A[k] * exp(a[k] * pow(x-x0[k], 2) + b[k] * (x-x0[k]) * (y-y0[k]) + c[k] * pow(y-y0[k], 2))\n",
+        "    return z"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "4GfIwnIKU9E-"
+      },
       "source": [
-        "### Visualizing Training Data: 3-D Projection Surface\n",
+        "### Generate Training Data\n",
         "\n",
-        "We will now create a 3-D plot of our training data. To make the plot more readable, we will replace the points that have extremely high energy with nan (not a number). This will keep our $Z$ array the same shape and help us ignore the high energy region that we are not interested in."
-      ],
-      "metadata": {
-        "id": "GHmBKzxwVRGf"
-      }
+        "First, we need to generate data to train the neural network.\n"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
       "source": [
-        "fig = go.Figure(data=[go.Surface(z=Z, x=X, y=Y, colorscale='rainbow', cmin=-15, cmax=9)])\n",
-        "fig.update_traces(contours_z=dict(show=True, project_z=True))\n",
-        "fig.update_layout(title='Mueller-Brown Potential', width=500, height=500,\n",
-        "                  scene = dict(\n",
-        "                      zaxis = dict(dtick=3, range=[-15, 15]),\n",
-        "                      camera_eye = dict(x=-1.2, y=-1.2, z=1.2)))\n",
-        "fig.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 517
-        },
-        "id": "fj9-VeCjWRPR",
-        "outputId": "36501e9d-7c18-41ad-8787-345800f90ad5"
-      },
-      "execution_count": 19,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
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-              "<head><meta charset=\"utf-8\" /></head>\n",
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-        }
+        "import numpy as np\n",
+        "\n",
+        "# generate x and y  on a grid\n",
+        "x_range = np.arange(-1.8, 1.4, 0.1, dtype=np.float32)\n",
+        "y_range = np.arange(-0.4, 2.4, 0.1, dtype=np.float32)\n",
+        "X, Y = np.meshgrid(x_range, y_range)\n",
+        "\n",
+        "# compute the potential energy at each point on the grid\n",
+        "mueller_brown_potential_vectorized = np.vectorize(mueller_brown_potential, otypes=[np.float32])\n",
+        "Z = mueller_brown_potential_vectorized(X, Y)\n",
+        "\n",
+        "# keep only low-energy points for training.\n",
+        "train_mask = Z < 10\n",
+        "X_train, Y_train, Z_train = X[train_mask], Y[train_mask], Z[train_mask]\n",
+        "\n",
+        "print(f\"Z_min: {np.min(Z)}, Z_max: {np.max(Z)}\")\n",
+        "print(f\"Size of (future) training set: {len(Z_train)}\")"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "GHmBKzxwVRGf"
+      },
       "source": [
         "### Visualizing Training Data: 3-D Projection Surface\n",
         "\n",
-        "Since we are only keeping the low-energy points for training (v$<$10, see [1.1.1]), we will make a 3-D surface plot that more accurately reflects our training data."
-      ],
-      "metadata": {
-        "id": "hSjQ0UvfWrmG"
-      }
+        "We will now create a 3-D plot of our training data. We are going to use the `plotly` library to create an interactive plot. We only plot the part of the potential energy surface that is below 15 (arbitrary unit). For the actual training, only the part that is below 10 (arbitrary unit) will be used."
+      ]
     },
     {
       "cell_type": "code",
-      "execution_count": 20,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 517
         },
-        "outputId": "ab6e85d5-31c0-4c82-cacd-cee8cd5f6cdb",
-        "id": "fa6OpqW8pzr6"
+        "id": "fj9-VeCjWRPR",
+        "outputId": "86069b2e-543d-4d4e-c8a4-3fefd89275ea"
       },
-      "outputs": [
-        {
-          "output_type": "display_data",
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Potential\"},\"width\":500,\"height\":500},                        {\"responsive\": true}                    ).then(function(){\n",
-              "                            \n",
-              "var gd = document.getElementById('bf043885-9c0d-4851-a769-4fc8c720759f');\n",
-              "var x = new MutationObserver(function (mutations, observer) {{\n",
-              "        var display = window.getComputedStyle(gd).display;\n",
-              "        if (!display || display === 'none') {{\n",
-              "            console.log([gd, 'removed!']);\n",
-              "            Plotly.purge(gd);\n",
-              "            observer.disconnect();\n",
-              "        }}\n",
-              "}});\n",
-              "\n",
-              "// Listen for the removal of the full notebook cells\n",
-              "var notebookContainer = gd.closest('#notebook-container');\n",
-              "if (notebookContainer) {{\n",
-              "    x.observe(notebookContainer, {childList: true});\n",
-              "}}\n",
-              "\n",
-              "// Listen for the clearing of the current output cell\n",
-              "var outputEl = gd.closest('.output');\n",
-              "if (outputEl) {{\n",
-              "    x.observe(outputEl, {childList: true});\n",
-              "}}\n",
-              "\n",
-              "                        })                };                            </script>        </div>\n",
-              "</body>\n",
-              "</html>"
-            ]
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "import copy\n",
-        "\n",
-        "clean_Z = copy.copy(Z)\n",
-        "clean_Z[clean_Z>10] = np.nan\n",
-        "\n",
-        "fig = go.Figure(data=[go.Surface(z=clean_Z, x=X, y=Y, colorscale='rainbow', cmin=-15, cmax=9)])\n",
+      "outputs": [],
+      "source": [
+        "import plotly.graph_objects as go\n",
+        "\n",
+        "fig = go.Figure(data=[go.Surface(z=Z, x=X, y=Y, colorscale='rainbow', cmin=-15, cmax=9)])\n",
         "fig.update_traces(contours_z=dict(show=True, project_z=True))\n",
         "fig.update_layout(title='Mueller-Brown Potential', width=500, height=500,\n",
         "                  scene = dict(\n",
@@ -276,106 +132,56 @@
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Visualizing Training Data: Contour Surface\n",
-        "\n",
-        "To allow for an easier visualization of the potential energy surface, we can generate a 2-D contour surface."
-      ],
       "metadata": {
         "id": "S_BieFeHXuD7"
-      }
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 21,
-      "metadata": {
-        "id": "me07-Xz19jGO",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 377
-        },
-        "outputId": "70268012-626e-478e-b545-29ac2ec3f37c"
       },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 450x375 with 2 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "fig = plt.figure(figsize=(3,2.5), dpi=150)\n",
-        "levels = [-12, -8, -4, 0, 4, 8, 10]\n",
-        "ct = plt.contour(X, Y, Z, levels, colors='k')\n",
-        "plt.clabel(ct, inline=True, fmt='%3.0f', fontsize=8)\n",
-        "ct = plt.contourf(X, Y, Z, levels, cmap=plt.cm.rainbow, extend='both', vmin=-15, vmax=0)\n",
-        "plt.xlabel(\"x\", labelpad=-0.75)\n",
-        "plt.ylabel(\"y\", labelpad=2.5)\n",
-        "plt.tick_params(axis='both', pad=2,labelsize=8)\n",
-        "cbar = plt.colorbar()\n",
-        "cbar.ax.tick_params(labelsize=8)\n",
-        "plt.title('Müeller Brown Contour Surface', fontsize=8)\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
+      "source": [
+        "### Visualizing Training Data: Contour Surface\n",
+        "\n",
+        "Now we will create a contour surface plot of our training data. Since we will plot similar contour surface plots later, we will define a function to do this."
       ]
     },
     {
-      "cell_type": "markdown",
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
       "source": [
-        "## Loading PyTorch and Training Data\n",
+        "import matplotlib.pyplot as plt\n",
         "\n",
-        "After installing and importing pytorch, we will save our training data as a tensor data set.\n"
-      ],
-      "metadata": {
-        "id": "o5BkUIk5YK4i"
-      }
+        "def plot_contour_map(X, Y, Z, title, colorscale='rainbow', levels=[-12, -8, -4, 0, 4, 8, 10], vmin=-15, vmax=10):\n",
+        "    fig = plt.figure(figsize=(3, 2.5), dpi=150)\n",
+        "    ct = plt.contour(X, Y, Z, levels, colors='k')\n",
+        "    plt.clabel(ct, inline=True, fmt='%3.0f', fontsize=8)\n",
+        "    ct = plt.contourf(X, Y, Z, levels, cmap=colorscale, extend='both', vmin=vmin, vmax=vmax)\n",
+        "    plt.xlabel(\"x\", labelpad=-0.75)\n",
+        "    plt.ylabel(\"y\", labelpad=2.5)\n",
+        "    plt.tick_params(axis='both', pad=2, labelsize=8)\n",
+        "    cbar = plt.colorbar()\n",
+        "    cbar.ax.tick_params(labelsize=8)\n",
+        "    plt.title(title, fontsize=8)\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "plot_contour_map(X, Y, Z, 'Mueller-Brown Potential')"
+      ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 22,
+      "cell_type": "markdown",
       "metadata": {
-        "id": "LxoMnpPeDlYg",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "0141498d-991e-49b1-b19e-a9ed53b7d2e2"
+        "id": "LQUmBoB2cT4s"
       },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Size of training set: 696\n"
-          ]
-        }
-      ],
-      "source": [
-        "import math\n",
-        "import torch\n",
-        "import torch.nn as nn\n",
-        "import torch.nn.functional as F\n",
-        "from torch import Tensor\n",
-        "from torch.utils.data import TensorDataset, DataLoader, random_split\n",
-        "\n",
-        "dataset = TensorDataset(Tensor(xy_truncated), Tensor(z_truncated))\n",
-        "train_loader = DataLoader(dataset, batch_size=32, shuffle=True)\n",
-        "print(\"Size of training set:\", len(train_loader.dataset))"
+      "source": [
+        "## Defining the Neural Network Class"
       ]
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "eLXNRX8GHPeK"
-      },
+      "metadata": {},
       "source": [
-        "### A Basic Neural Network\n",
+        "### A Basic Neural Network \n",
         "\n",
-        "Below is a schematic of a neural network. Inputs ($x$, $y$) are given weights ($w$). For each neuron in the hidden layer a bias ($b$) is added to the weighted inputs to generate a value ($v$) for the activation function (tanh). The activation function decides the activity ($v'$) of each neuron in the hidden layer. Weights ($w'$), biases ($b'$), and the activation function on the neuron of the output layer produces the prediction ($V^{pred}$)."
+        "Below is a schematic of a neural network. Inputs ($x$, $y$) are given weights ($w$). For each neuron in the hidden layer a bias ($b$) is added to the weighted inputs to generate a value ($v$) for the activation function (tanh). The activation function decides the activity ($v'$) of each neuron in the hidden layer. Weights ($w'$), biases ($b'$), and the activation function on the neuron of the output layer produces the prediction ($V^{pred}$). "
       ]
     },
     {
@@ -389,483 +195,502 @@
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "## Defining the Neural Network Class\n",
-        "\n",
         "Here we define our neural network as a Python class. A function ($\\it{train\\_loop}$) is used to loop through our training data."
-      ],
-      "metadata": {
-        "id": "LQUmBoB2cT4s"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "execution_count": 23,
+      "execution_count": null,
       "metadata": {
         "id": "z3Si6tnBFj93"
       },
       "outputs": [],
       "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "\n",
+        "# Set default data type to float32\n",
+        "torch.set_default_dtype(torch.float32)\n",
+        "\n",
+        "\n",
         "class NeuralNetwork(nn.Module):\n",
-        "    def __init__(self, n1=20):  #n1 is the number of neurons for the first layer\n",
+        "    def __init__(self, n_hidden=20):\n",
+        "        \"\"\"\n",
+        "        Args:\n",
+        "            n_hidden (int): Number of neurons in the hidden layer.\n",
+        "        \"\"\"\n",
         "        super().__init__()\n",
-        "        self.model = nn.Sequential(\n",
-        "            nn.Linear(2,n1), # Linear function taking 2 inputs and outputs data for 20 neurons.\n",
+        "        self.linear = nn.Sequential(\n",
+        "            nn.Linear(2, n_hidden), # Linear function taking 2 inputs and outputs data for n_hidden neurons.\n",
         "            nn.Tanh(),\n",
-        "            nn.Linear(n1, 1) # Linear function taking data from 20 neurons and producing one output value.\n",
+        "            nn.Linear(n_hidden, 1) # Linear function taking data from n_hidden neurons and producing one output value. \n",
         "        )\n",
         "\n",
-        "    def forward(self, x):\n",
-        "        return self.model(x)\n",
+        "    def forward(self, x): \n",
+        "        return self.linear(x)\n",
         "\n",
-        "def train_loop(dataloader, model, optimizer, i_epoch):\n",
         "\n",
-        "    size = len(dataloader.dataset)\n",
-        "    for batch, (X, y) in enumerate(dataloader):\n",
+        "def train_loop(dataloader, model, loss_fn, optimizer):\n",
+        "    model.train() # Set model to training mode.\n",
+        "    num_batches = len(dataloader)\n",
+        "    train_loss = 0\n",
+        "    for X, y in dataloader:\n",
         "        # Compute prediction and loss\n",
         "        pred = model(X)\n",
-        "        loss = F.mse_loss(pred.squeeze(), y)\n",
+        "        loss = loss_fn(pred.squeeze(), y)\n",
         "\n",
         "        # Backpropagation - using the loss function gradients to update the weights and biases.\n",
-        "        optimizer.zero_grad() # Zero out the gradients from the previous iteration to replace them.\n",
+        "        optimizer.zero_grad() # Zero out the gradients from the previous iteration to replace them. \n",
         "        loss.backward() # Update the gradients of the loss function.\n",
-        "        optimizer.step() # Uses step() to optimize the weights and biases with the updated gradients.\n",
+        "        optimizer.step() # Uses step() to optimize the weights and biases with the updated gradients. \n",
+        "\n",
+        "        with torch.no_grad():\n",
+        "            train_loss += loss.item()\n",
         "\n",
-        "        if batch % 15 == 0 and i_epoch % 100 == 0:\n",
-        "            loss, current = loss.item(), batch * len(X)\n",
-        "            print(f\"epoch: {i_epoch:>3d} loss: {loss:>7.3f}  [{current:>5d}/{size:>5d}]\")"
+        "    train_loss /= num_batches\n",
+        "    return train_loss"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "## Training the Model\n",
-        "\n",
-        "Now we can train the neural network model. We will finish our training when the desired number of epochs has been reached. We will also define some terms used for training below. \\\n",
+        "## Loading PyTorch and Training Data\n",
         "\n",
-        "Epochs - Number of forward/backward passes through the entire neural network.\n",
+        "After installing and importing pytorch, we will save our training data as a tensor data set."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from torch.utils.data import TensorDataset, DataLoader\n",
         "\n",
-        "Learning Rate - Determines the step size as we try to minimize the loss function. A faster learning rate would have a larger step size. \\\n",
+        "# turn NumPy arrays into PyTorch tensors\n",
+        "X_tensor = torch.from_numpy(np.column_stack((X_train, Y_train)))\n",
+        "Y_tensor = torch.from_numpy(Z_train)\n",
         "\n",
-        "Stochastic Gradient Descent (SGD) - The algorithm used for minimizing the loss function."
-      ],
+        "dataset = TensorDataset(X_tensor, Y_tensor)\n",
+        "print(f\"Size of training set: {len(dataset)}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
       "metadata": {
         "id": "TXpLmuWBcbpg"
-      }
+      },
+      "source": [
+        "## Training the Model\n",
+        "\n",
+        "Now we can train the neural network model. We will finish our training when the desired number of epochs has been reached. We will also define some terms used for training below.\n",
+        "\n",
+        "Epochs - Number of forward/backward passes through the entire neural network. \n",
+        "\n",
+        "Learning Rate - Determines the step size as we try to minimize the loss function. A faster learning rate would have a larger step size.\n",
+        "\n",
+        "Stochastic Gradient Descent (SGD) - The algorithm used for minimizing the loss function.\n",
+        "\n",
+        "Note: In this example, there are 696 training points broken into 87 batches of batch size 8."
+      ]
     },
     {
       "cell_type": "code",
-      "execution_count": 24,
+      "execution_count": null,
       "metadata": {
-        "id": "PGry3oNqGjof",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "45bb5715-7412-4746-b956-2159d34429c5"
+        "id": "PGry3oNqGjof",
+        "outputId": "6d220c99-5d29-4278-c11f-3be60e6e1ca7"
       },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "epoch:   0 loss:  24.184  [    0/  696]\n",
-            "epoch:   0 loss:  20.096  [  480/  696]\n",
-            "epoch: 100 loss:   3.399  [    0/  696]\n",
-            "epoch: 100 loss:   3.420  [  480/  696]\n",
-            "epoch: 200 loss:   3.263  [    0/  696]\n",
-            "epoch: 200 loss:   1.841  [  480/  696]\n",
-            "epoch: 300 loss:   1.967  [    0/  696]\n",
-            "epoch: 300 loss:   1.181  [  480/  696]\n",
-            "epoch: 400 loss:   1.310  [    0/  696]\n",
-            "epoch: 400 loss:   1.013  [  480/  696]\n",
-            "epoch: 500 loss:   1.100  [    0/  696]\n",
-            "epoch: 500 loss:   1.045  [  480/  696]\n",
-            "epoch: 600 loss:   1.210  [    0/  696]\n",
-            "epoch: 600 loss:   0.887  [  480/  696]\n",
-            "epoch: 700 loss:   0.553  [    0/  696]\n",
-            "epoch: 700 loss:   0.611  [  480/  696]\n",
-            "epoch: 800 loss:   0.626  [    0/  696]\n",
-            "epoch: 800 loss:   0.786  [  480/  696]\n",
-            "epoch: 900 loss:   0.822  [    0/  696]\n",
-            "epoch: 900 loss:   0.877  [  480/  696]\n",
-            "Done with Training!\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
-        "learning_rate = 1e-2\n",
+        "# Set random seed for reproducibility\n",
+        "torch.manual_seed(314)\n",
+        "\n",
+        "# Hyperparameters\n",
+        "batch_size = 8\n",
         "epochs = 1000\n",
-        "n1 = 20\n",
-        "model = NeuralNetwork(n1)\n",
-        "loss_fn = F.mse_loss\n",
+        "learning_rate = 1e-2\n",
+        "n_hidden = 20\n",
+        "\n",
+        "train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n",
+        "model = NeuralNetwork(n_hidden)\n",
+        "loss_fn = nn.MSELoss()\n",
         "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
         "\n",
-        "for t in range(epochs):\n",
-        "    train_loop(train_loader, model, optimizer, t)\n",
-        "    # Note: In this example, there are 696 training points broken into 21 batches of batch size 32 (672 training points).\n",
-        "    # That means there is an extra batch of 24 training points. When added together, this gives the full 696 training points.\n",
+        "print(\"Training Errors:\")\n",
+        "for epoch in range(epochs):\n",
+        "    train_loss = train_loop(train_loader, model, loss_fn, optimizer)\n",
+        "    if (epoch + 1) % 100 == 0:\n",
+        "        print(f\"epoch: {epoch:>3d}   loss: {train_loss:>6.3f}\")\n",
         "print(\"Done with Training!\")"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "aNfktUgEc17B"
+      },
       "source": [
         "## Plotting Reference, Predicted, and Difference Surfaces\n",
         "\n",
         "Finally, we will plot the Müller-Brown potential energy surface using the analytical function (reference), using the neural network (predicted), and we will show the difference between the predicted and reference surfaces."
-      ],
-      "metadata": {
-        "id": "aNfktUgEc17B"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "execution_count": 25,
-      "metadata": {
-        "id": "fudclASTHAaO",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "outputId": "bfbc79e4-b81d-468a-f436-9d063790c17a"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "diff, min, max: -1183.1701730637253 2.8438615778985206\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 450x1125 with 6 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "def show_surface(model):\n",
-        "\n",
-        "  z_pred = model(Tensor(xy))\n",
-        "  Z_pred = np.reshape(z_pred.detach().numpy(),(len(yy),-1))\n",
-        "  Zdiff = np.subtract(Z_pred, Z)\n",
-        "\n",
-        "  fig = plt.figure(figsize=(3,7.5), dpi=150)\n",
-        "\n",
-        "\n",
-        "  plt.subplot(3, 1, 1)\n",
-        "  levels = [-12, -8, -4, 0, 4, 8]\n",
-        "  ct = plt.contour(X, Y, Z, levels, colors='k')\n",
-        "  plt.clabel(ct, inline=True, fmt='%3.0f', fontsize=8)\n",
-        "  ct = plt.contourf(X, Y, Z, levels, cmap=plt.cm.rainbow, extend='both', vmin=-15, vmax=0)\n",
-        "  plt.title(\"(a) Reference\", fontsize=8)\n",
-        "  plt.xlabel(\"x\", labelpad=-0.75)\n",
-        "  plt.ylabel(\"y\", labelpad=2.5)\n",
-        "  plt.tick_params(axis='both', pad=2,labelsize=8)\n",
-        "  cbar= plt.colorbar()\n",
-        "  cbar.ax.tick_params(labelsize=8)\n",
-        "\n",
-        "  plt.subplot(3, 1, 2)\n",
-        "  ct = plt.contour(X, Y, Z_pred, levels, colors='k')\n",
-        "  plt.clabel(ct, inline=True, fmt='%3.0f', fontsize=8)\n",
-        "  ct = plt.contourf(X, Y, Z_pred, levels, cmap=plt.cm.rainbow, extend='both', vmin=-15, vmax=0)\n",
-        "  plt.title(\"(b) Predicted\", fontsize=8)\n",
-        "  plt.xlabel(\"x\", labelpad=-0.75)\n",
-        "  plt.ylabel(\"y\", labelpad=2.5)\n",
-        "  plt.tick_params(axis='both', pad=2,labelsize=8)\n",
-        "  cbar= plt.colorbar()\n",
-        "  cbar.ax.tick_params(labelsize=8)\n",
-        "\n",
-        "\n",
-        "  plt.subplot(3, 1, 3)\n",
-        "  levels = [-4, -2, 0, 2, 4]\n",
-        "  ct = plt.contour(X, Y, Zdiff, levels, colors='k')\n",
-        "  plt.clabel(ct, inline=True, fmt='%3.0f', fontsize=8)\n",
-        "  ct = plt.contourf(X, Y, Zdiff, levels, cmap=plt.cm.rainbow, extend='both', vmin=-4, vmax=4)\n",
-        "  plt.title(\"(c) Difference\", fontsize=8)\n",
-        "  plt.xlabel(\"x\", labelpad=-0.75)\n",
-        "  plt.ylabel(\"y\", labelpad=2.5)\n",
-        "  print(\"diff, min, max:\", np.amin(Zdiff), np.amax(Zdiff))\n",
-        "  plt.tick_params(axis='both', pad=2,labelsize=8)\n",
-        "  cbar= plt.colorbar()\n",
-        "  cbar.ax.tick_params(labelsize=8)\n",
-        "\n",
-        "  plt.tight_layout()\n",
-        "\n",
-        "  plt.show()\n",
-        "\n",
-        "show_surface(model)"
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model.eval() # Set model to evaluation mode\n",
+        "Z_pred = model(torch.from_numpy(np.column_stack((X.flatten(), Y.flatten())))).detach().numpy().reshape(Z.shape)\n",
+        "Z_diff = Z_pred - Z\n",
+        "\n",
+        "plot_contour_map(X, Y, Z, title='(a) Reference')\n",
+        "plot_contour_map(X, Y, Z_pred, title='(b) Predicted')\n",
+        "plot_contour_map(X, Y, Z_diff, title='(c) Difference', levels=[-4, -2, 0, 2, 4])"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "Jb4-6Rp_dDjn"
+      },
       "source": [
-        "## Taking a Look at the Neural Network parameters\n",
+        "## Taking a Look at the NN parameters\n",
         "\n",
         "In order to take a closer look at the neural network parameters, we will plug them into the linear functions."
-      ],
-      "metadata": {
-        "id": "Jb4-6Rp_dDjn"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "execution_count": 26,
+      "execution_count": null,
       "metadata": {
-        "id": "fZiTIfxDN9el",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "4abce9e6-aec6-47ca-f3b5-3046ea2bce55"
+        "id": "fZiTIfxDN9el",
+        "outputId": "66e2d9d5-7620-4a23-9679-9e78944f3af5"
       },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "model: NeuralNetwork(\n",
-            "  (model): Sequential(\n",
-            "    (0): Linear(in_features=2, out_features=20, bias=True)\n",
-            "    (1): Tanh()\n",
-            "    (2): Linear(in_features=20, out_features=1, bias=True)\n",
-            "  )\n",
-            ")\n",
-            "z0: -13.178241729736328 z_pred: -13.178243043170763\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "print(\"model:\", model)\n",
         "\n",
         "for name, param in model.named_parameters():\n",
-        "    if name == 'model.0.weight': weights0 = param.data.detach().numpy()\n",
-        "    elif name == 'model.0.bias': bias0 = param.data.detach().numpy()\n",
-        "    elif name == 'model.2.weight': weights2 = param.data.detach().numpy()\n",
-        "    elif name == 'model.2.bias': bias2 = param.data.detach().numpy()\n",
+        "    if name == 'linear.0.weight': weights0 = param.data.detach().numpy()\n",
+        "    elif name == 'linear.0.bias': bias0 = param.data.detach().numpy()\n",
+        "    elif name == 'linear.2.weight': weights2 = param.data.detach().numpy()\n",
+        "    elif name == 'linear.2.bias': bias2 = param.data.detach().numpy()\n",
         "\n",
-        "xy0 = np.array([-0.5, 1.5])\n",
-        "z0 = model(Tensor(xy0))\n",
+        "xy0 = torch.tensor([-0.5, 1.5], dtype=torch.float32)\n",
+        "z0 = model(xy0)\n",
         "\n",
-        "# First linear function\n",
-        "v1 = np.zeros(n1)\n",
-        "for i in range(0, n1):\n",
-        "  v1[i] += weights0[i,0] * xy0[0] + weights0[i,1] * xy0[1] + bias0[i]\n",
+        "#first linear function\n",
+        "v1 = np.zeros(n_hidden)\n",
+        "for i in range(n_hidden):\n",
+        "    v1[i] += weights0[i, 0] * xy0[0] + weights0[i, 1] * xy0[1] + bias0[i]\n",
         "\n",
-        "# Activation function\n",
-        "v2 = np.zeros(n1)\n",
-        "for i in range(0, n1):\n",
-        "  v2[i] = tanh(v1[i])\n",
+        "#activation function\n",
+        "v2 = np.zeros(n_hidden)\n",
+        "for i in range(n_hidden):\n",
+        "    v2[i] = np.tanh(v1[i])\n",
         "\n",
-        "# Second linear function\n",
+        "#second linear function\n",
         "z_pred = 0.0\n",
-        "for i in range(0, n1):\n",
-        "  z_pred += weights2[0,i] * v2[i]\n",
+        "for i in range(n_hidden):\n",
+        "    z_pred += weights2[0,i] * v2[i]\n",
         "z_pred += bias2[0]\n",
         "\n",
-        "print(\"z0:\", z0.item(), \"z_pred:\", z_pred)"
+        "print(\"z0:\", z0, \"z_pred:\", z_pred)"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "bM_h4sSEccJV"
+      },
       "source": [
         "## A More Automated/Refined Implementation\n",
         "\n",
-        "### Implementing a Neural Network with PyTorch-Lightning\n",
+        "### A PyTorch-Lightning Implementation\n",
         "\n",
         "Below is a more professional implementation of the neural network that saves epoch infromation in the logs_csv/ directory."
-      ],
-      "metadata": {
-        "id": "bM_h4sSEccJV"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "%pip install pytorch-lightning > /dev/null\n",
-        "import pytorch_lightning as pl\n",
-        "from pytorch_lightning import loggers as pl_loggers"
-      ],
+      "execution_count": 74,
       "metadata": {
         "id": "Ks_KCDRQ3tDk"
       },
-      "execution_count": 27,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "%pip install lightning --quiet\n",
+        "import lightning.pytorch as pl\n",
+        "from lightning.pytorch.loggers import CSVLogger"
+      ]
     },
     {
       "cell_type": "code",
-      "execution_count": 28,
+      "execution_count": null,
       "metadata": {
-        "id": "Ki02WaRgHFar",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 255
+          "height": 272
         },
-        "outputId": "674b4778-2d2e-4be0-c84d-ab397c25fc41"
+        "id": "Ki02WaRgHFar",
+        "outputId": "74783b71-fe19-4282-f2ea-1a2db3ff942d"
       },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False\n",
-            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
-            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
-            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
-            "INFO:pytorch_lightning.callbacks.model_summary:\n",
-            "  | Name  | Type       | Params\n",
-            "-------------------------------------\n",
-            "0 | model | Sequential | 81    \n",
-            "-------------------------------------\n",
-            "81        Trainable params\n",
-            "0         Non-trainable params\n",
-            "81        Total params\n",
-            "0.000     Total estimated model params size (MB)\n",
-            "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
-        "%%capture\n",
-        "class pl_model(pl.LightningModule):\n",
-        "    def __init__(self, n1=20):\n",
+        "class NeuralNetworkLightning(pl.LightningModule):\n",
+        "    def __init__(self, model, learning_rate):\n",
         "        super().__init__()\n",
-        "        self.model = nn.Sequential(\n",
-        "            nn.Linear(2,n1),\n",
-        "            nn.Tanh(),\n",
-        "            nn.Linear(n1, 1)\n",
-        "        )\n",
-        "        self.learning_rate = 1e-2\n",
+        "        self.model = model\n",
+        "        self.learning_rate = learning_rate\n",
         "\n",
         "    def forward(self, x):\n",
         "        return self.model(x)\n",
         "\n",
         "    def training_step(self, batch, batch_idx):\n",
-        "        xy, z = batch\n",
-        "        z_pred = self(xy)\n",
-        "        loss = F.mse_loss(z_pred.squeeze(), z)\n",
+        "        x, y = batch\n",
+        "        y_pred = self.model(x)\n",
+        "        loss = F.mse_loss(y_pred.squeeze(), y)\n",
         "        self.log('train_loss', loss)\n",
         "        return loss\n",
         "\n",
         "    def configure_optimizers(self):\n",
-        "        # Using the Adam optimzation algorithm instead of SGD.\n",
+        "        # Using the Adam optimzation algorithm instead of SGD. \n",
         "        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n",
-        "        # We can use the scheduler to adjust the learning rate.\n",
-        "        scheduler = {'scheduler': torch.optim.lr_scheduler.ExponentialLR(optimizer, 0.95),\n",
-        "                     'interval': 'epoch',\n",
-        "                     'frequency': 1,\n",
-        "                    }\n",
-        "        return [optimizer]#, [scheduler]\n",
+        "        return [optimizer]\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "# Set random seed for reproducibility\n",
+        "pl.seed_everything(314, workers=True);\n",
+        "\n",
+        "# Hyperparameters\n",
+        "batch_size = 32\n",
+        "epochs = 1000\n",
+        "learning_rate = 1e-2\n",
+        "n_hidden = 20\n",
         "\n",
-        "csv_logger = pl_loggers.CSVLogger('logs_csv/')\n",
-        "trainer = pl.Trainer(max_epochs=1000, logger=csv_logger)\n",
-        "n1 = 20\n",
-        "model = pl_model(n1)\n",
-        "trainer.fit(model, train_loader)\n"
+        "train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n",
+        "csv_logger = CSVLogger('logs_csv', version=0)\n",
+        "trainer = pl.Trainer(max_epochs=epochs, logger=csv_logger, deterministic=True)\n",
+        "model = NeuralNetworkLightning(NeuralNetwork(n_hidden), learning_rate)\n",
+        "trainer.fit(model, train_loader)"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "-lIsI61egS0Q"
+      },
       "source": [
         "### Plotting Training Error\n",
         "\n",
-        "Now we can plot the training error as the number of epochs increases.\n",
-        "With more training iterations, we should see a decrease in the error of the model.\n"
-      ],
-      "metadata": {
-        "id": "-lIsI61egS0Q"
-      }
+        "Now we can plot the training error as the number of epochs increases.\n"
+      ]
     },
     {
       "cell_type": "code",
-      "execution_count": 29,
+      "execution_count": null,
       "metadata": {
-        "id": "phUovRNxYrHE",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 487
+          "height": 466
         },
-        "outputId": "dd3fa839-322c-400f-d68c-56d13f155c12"
+        "id": "phUovRNxYrHE",
+        "outputId": "2c2c4688-bc77-49c9-c824-06952372ab35"
       },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ],
+      "outputs": [],
       "source": [
         "import pandas as pd\n",
-        "loss = pd.read_csv(f'logs_csv/lightning_logs/version_{csv_logger.version}/metrics.csv')\n",
+        "loss = pd.read_csv(\"logs_csv/lightning_logs/version_0/metrics.csv\")\n",
         "\n",
         "fig, ax = plt.subplots()\n",
-        "ax.semilogy(loss[\"epoch\"], loss[\"train_loss\"], color='dodgerblue')\n",
-        "ax.set_xlabel(\"Epoch\", fontsize=14)\n",
-        "ax.set_ylabel(\"Training Errors\", fontsize=14)\n",
-        "plt.xticks(fontsize=12)\n",
-        "plt.yticks(fontsize=12)\n",
-        "plt.tight_layout()"
+        "ax.semilogy(loss[\"epoch\"], loss[\"train_loss\"])\n",
+        "ax.set_xlabel(\"Epoch\")\n",
+        "ax.set_ylabel(\"Training Errors\")\n"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "MexXVUU3gfOB"
+      },
       "source": [
         "### Plotting Reference, Predicted, and Difference Surfaces\n",
         "\n",
         "Again, we plot the reference, predicted, and difference surfaces using the more refined neural network implementation.\n"
-      ],
-      "metadata": {
-        "id": "MexXVUU3gfOB"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "execution_count": 30,
+      "execution_count": null,
       "metadata": {
-        "id": "OZFqz5UVGIrt",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000
         },
-        "outputId": "6aab7ef8-5bf1-4976-aa54-4a7a50d932a5"
+        "id": "OZFqz5UVGIrt",
+        "outputId": "753d0404-7cf1-4588-d435-17e5fd4d7070"
       },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "diff, min, max: -1162.8676160721482 7.673532201115929\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 450x1125 with 6 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "show_surface(model)"
+      "outputs": [],
+      "source": [
+        "model.eval() # Set model to evaluation mode\n",
+        "Z_pred = model(torch.from_numpy(np.column_stack((X.flatten(), Y.flatten())))).detach().numpy().reshape(Z.shape)\n",
+        "Z_diff = Z_pred - Z\n",
+        "\n",
+        "plot_contour_map(X, Y, Z, title='(a) Reference')\n",
+        "plot_contour_map(X, Y, Z_pred, title='(b) Predicted')\n",
+        "plot_contour_map(X, Y, Z_diff, title='(c) Difference', levels=[-4, -2, 0, 2, 4])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train with Energy and Gradient"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def mueller_brown_potential_gradient(x, y):\n",
+        "    A = [-200, -100, -170, 15]\n",
+        "    a = [-1, -1, -6.5, 0.7]\n",
+        "    b = [0, 0, 11, 0.6]\n",
+        "    c = [-10, -10, -6.5, 0.7]\n",
+        "    x0 = [1, 0, -0.5, -1.0]\n",
+        "    y0 = [0, 0.5, 1.5, 1]\n",
+        "    dx = 0\n",
+        "    dy = 0\n",
+        "    for k in range(4):\n",
+        "        dx += 0.1 * A[k] * exp(a[k] * pow(x-x0[k], 2) + b[k] * (x-x0[k]) * (y-y0[k]) + c[k] * pow(y-y0[k], 2)) * (a[k] * 2 *(x-x0[k]) + b[k] * (y-y0[k]))\n",
+        "        dy += 0.1 * A[k] * exp(a[k] * pow(x-x0[k], 2) + b[k] * (x-x0[k]) * (y-y0[k]) + c[k] * pow(y-y0[k], 2)) * (b[k] * (x-x0[k])+ c[k] * 2 * (y-y0[k]))\n",
+        "    return dx, dy"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# compute the potential energy gradient at each point on the grid\n",
+        "mueller_brown_potential_gradient_vectorized = np.vectorize(mueller_brown_potential_gradient, otypes=[np.float32, np.float32])\n",
+        "dX, dY = mueller_brown_potential_gradient_vectorized(X, Y)\n",
+        "\n",
+        "# keep only low-energy points for training\n",
+        "dX_train, dY_train = dX[train_mask], dY[train_mask]\n",
+        "\n",
+        "dX_tensor = torch.from_numpy(np.column_stack((dX_train, dY_train)))\n",
+        "dataset = TensorDataset(X_tensor, Y_tensor, dX_tensor)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def train_loop_with_gradient(dataloader, model, loss_fn, optimizer):\n",
+        "    model.train() # Set model to training mode.\n",
+        "    num_batches = len(dataloader)\n",
+        "    train_loss = 0\n",
+        "    train_loss_energy = 0\n",
+        "    train_loss_gradient = 0\n",
+        "    for X, y, dX in dataloader:\n",
+        "        # Compute prediction and loss\n",
+        "        y_pred = model(X)\n",
+        "        dX_pred = torch.autograd.grad(y_pred, X, grad_outputs=torch.ones_like(y_pred), create_graph=True)[0]\n",
+        "        loss_energy = loss_fn(y_pred.squeeze(), y)\n",
+        "        loss_gradient = loss_fn(dX_pred.squeeze(), dX)\n",
+        "        loss = loss_energy + loss_gradient\n",
+        "\n",
+        "        # Backpropagation - using the loss function gradients to update the weights and biases.\n",
+        "        optimizer.zero_grad() # Zero out the gradients from the previous iteration to replace them. \n",
+        "        loss.backward() # Update the gradients of the loss function.\n",
+        "        optimizer.step() # Uses step() to optimize the weights and biases with the updated gradients. \n",
+        "\n",
+        "        with torch.no_grad():\n",
+        "            train_loss += loss.item()\n",
+        "            train_loss_energy += loss_energy.item()\n",
+        "            train_loss_gradient += loss_gradient.item()\n",
+        "\n",
+        "    train_loss /= num_batches\n",
+        "    train_loss_energy /= num_batches\n",
+        "    train_loss_gradient /= num_batches\n",
+        "    return train_loss, train_loss_energy, train_loss_gradient"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Set random seed for reproducibility\n",
+        "torch.manual_seed(314)\n",
+        "\n",
+        "# Hyperparameters\n",
+        "batch_size = 8\n",
+        "epochs = 1000\n",
+        "learning_rate = 1e-3\n",
+        "n_hidden = 20\n",
+        "\n",
+        "train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n",
+        "model = NeuralNetwork(n_hidden)\n",
+        "loss_fn = nn.MSELoss()\n",
+        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
+        "\n",
+        "print(\"Training errors:\")\n",
+        "for epoch in range(epochs):\n",
+        "    X_tensor.requires_grad_(True)\n",
+        "    train_loss, train_loss_energy, train_loss_gradient = train_loop_with_gradient(train_loader, model, loss_fn, optimizer)\n",
+        "    if (epoch + 1) % 100 == 0:\n",
+        "        print(f\"epoch: {epoch:>3d}   loss: {train_loss:>6.3f}   energy loss: {train_loss_energy:>6.3f}   gradient loss: {train_loss_gradient:>6.3f}\")\n",
+        "print(\"Done with Training!\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model.eval() # Set model to evaluation mode\n",
+        "Z_pred = model(torch.from_numpy(np.column_stack((X.flatten(), Y.flatten())))).detach().numpy().reshape(Z.shape)\n",
+        "Z_diff = Z_pred - Z\n",
+        "\n",
+        "plot_contour_map(X, Y, Z, title='(a) Reference')\n",
+        "plot_contour_map(X, Y, Z_pred, title='(b) Predicted')\n",
+        "plot_contour_map(X, Y, Z_diff, title='(c) Difference', levels=[-4, -2, 0, 2, 4])"
       ]
     }
   ],
   "metadata": {
     "colab": {
-      "provenance": [],
-      "include_colab_link": true
+      "include_colab_link": true,
+      "provenance": []
     },
     "kernelspec": {
       "display_name": "Python 3.8.8 ('base')",
@@ -882,7 +707,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.8"
+      "version": "3.10.12"
     },
     "vscode": {
       "interpreter": {
@@ -892,4 +717,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/requirements.txt b/requirements.txt
index 5c34e66..77c7c1c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,4 +4,5 @@ numpy
 pandas
 plotly
 torch
+lightning
 kora