{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "view-in-github" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "FMj6evQUH4rQ" }, "source": [ "# Feedforward Neural Network Models\n", "\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": "markdown", "metadata": { "id": "G5gEWLszUpQV" }, "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", "\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] $" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "N0JzzAZ1UZdx" }, "outputs": [], "source": [ "from math import exp, pow\n", "\n", "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": [ "### Generate Training Data\n", "\n", "First, we need to generate data to train the neural network.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "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", "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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 517 }, "id": "fj9-VeCjWRPR", "outputId": "86069b2e-543d-4d4e-c8a4-3fefd89275ea" }, "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", " zaxis = dict(dtick=3, range=[-15, 15]),\n", " camera_eye = dict(x=-1.2, y=-1.2, z=1.2)))" ] }, { "cell_type": "markdown", "metadata": { "id": "S_BieFeHXuD7" }, "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": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", "def plot_contour_map(X, Y, Z, ax, title, colorscale='rainbow', levels=None):\n", " if levels is None:\n", " levels = [-12, -8, -4, 0, 4, 8, 10]\n", " ct = ax.contour(X, Y, Z, levels, colors='k')\n", " ax.clabel(ct, inline=True, fmt='%3.0f', fontsize=8)\n", " ct = ax.contourf(X, Y, Z, levels, cmap=colorscale, extend='both', vmin=levels[0], vmax=levels[-1])\n", " ax.set_xlabel(\"x\", labelpad=-0.75)\n", " ax.set_ylabel(\"y\", labelpad=2.5)\n", " ax.tick_params(axis='both', pad=2, labelsize=8)\n", " cbar = plt.colorbar(ct)\n", " cbar.ax.tick_params(labelsize=8)\n", " ax.set_title(title, fontsize=8)\n", "\n", "\n", "fig, ax = plt.subplots(figsize=(3, 2.5), dpi=150)\n", "plot_contour_map(X, Y, Z, ax=ax, title='Mueller-Brown Potential')\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": { "id": "LQUmBoB2cT4s" }, "source": [ "## Defining the Neural Network Class" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 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 ($V^{pred}$). " ] }, { "cell_type": "markdown", "metadata": { "id": "VXw5fMG99XMv" }, "source": [ "![NN_diagram.png](data:image/png;base64,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)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we define our neural network as a Python class. A function ($\\it{train\\_loop}$) is used to loop through our training data." ] }, { "cell_type": "code", "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, n_hidden=20):\n", " \"\"\"\n", " Args:\n", " n_hidden (int): Number of neurons in the hidden layer.\n", " \"\"\"\n", " super().__init__()\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(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.linear(x)\n", "\n", "\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 = 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", " 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", "\n", " train_loss /= num_batches\n", " return train_loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading PyTorch and Training Data\n", "\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", "# 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", "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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PGry3oNqGjof", "outputId": "6d220c99-5d29-4278-c11f-3be60e6e1ca7" }, "outputs": [], "source": [ "# Set random seed for reproducibility\n", "torch.manual_seed(314)\n", "\n", "# Hyperparameters\n", "batch_size = 32\n", "epochs = 1000\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", "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." ] }, { "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", "fig, ax = plt.subplots(3, figsize=(3, 7.5), dpi=150)\n", "plot_contour_map(X, Y, Z, ax=ax[0], title='(a) Reference')\n", "plot_contour_map(X, Y, Z_pred, ax=ax[1], title='(b) Predicted')\n", "plot_contour_map(X, Y, Z_diff, ax=ax[2], title='(c) Difference', levels=[-4, -2, 0, 2, 4])\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": { "id": "Jb4-6Rp_dDjn" }, "source": [ "## 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." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fZiTIfxDN9el", "outputId": "66e2d9d5-7620-4a23-9679-9e78944f3af5" }, "outputs": [], "source": [ "print(\"model:\", model)\n", "\n", "for name, param in model.named_parameters():\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 = torch.tensor([-0.5, 1.5], dtype=torch.float32)\n", "z0 = model(xy0)\n", "\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(n_hidden)\n", "for i in range(n_hidden):\n", " v2[i] = np.tanh(v1[i])\n", "\n", "#second linear function\n", "z_pred = 0.0\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, \"z_pred:\", z_pred)" ] }, { "cell_type": "markdown", "metadata": { "id": "bM_h4sSEccJV" }, "source": [ "## A More Automated/Refined Implementation\n", "\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." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Ks_KCDRQ3tDk" }, "outputs": [], "source": [ "%pip install lightning --quiet\n", "import lightning.pytorch as pl\n", "from lightning.pytorch.loggers import CSVLogger" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 272 }, "id": "Ki02WaRgHFar", "outputId": "74783b71-fe19-4282-f2ea-1a2db3ff942d" }, "outputs": [], "source": [ "class NeuralNetworkLightning(pl.LightningModule):\n", " def __init__(self, model, learning_rate):\n", " super().__init__()\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", " 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", " optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\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", "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" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 466 }, "id": "phUovRNxYrHE", "outputId": "2c2c4688-bc77-49c9-c824-06952372ab35" }, "outputs": [], "source": [ "import pandas as pd\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\"])\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" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "OZFqz5UVGIrt", "outputId": "753d0404-7cf1-4588-d435-17e5fd4d7070" }, "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", "fig, ax = plt.subplots(3, figsize=(3, 7.5), dpi=150)\n", "plot_contour_map(X, Y, Z, ax=ax[0], title='(a) Reference')\n", "plot_contour_map(X, Y, Z_pred, ax=ax[1], title='(b) Predicted')\n", "plot_contour_map(X, Y, Z_diff, ax=ax[2], title='(c) Difference', levels=[-4, -2, 0, 2, 4])\n", "fig.tight_layout()" ] }, { "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 = 32\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", "fig, ax = plt.subplots(3, figsize=(3, 7.5), dpi=150)\n", "plot_contour_map(X, Y, Z, ax=ax[0], title='(a) Reference')\n", "plot_contour_map(X, Y, Z_pred, ax=ax[1], title='(b) Predicted')\n", "plot_contour_map(X, Y, Z_diff, ax=ax[2], title='(c) Difference', levels=[-4, -2, 0, 2, 4])\n", "fig.tight_layout()" ] } ], "metadata": { "colab": { "include_colab_link": true, "provenance": [] }, "kernelspec": { "display_name": "Python 3.8.8 ('base')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.13" }, "vscode": { "interpreter": { "hash": "cd78fef2128015050713e82ca51c6520b11aee7c9ee8df750520bbbc7384cbaa" } } }, "nbformat": 4, "nbformat_minor": 0 }