From 039c4b600c21d4afe435ffd55429a2d96c144cde Mon Sep 17 00:00:00 2001 From: Ibrahim O Alabi <72909059+Ibrahim-Ola@users.noreply.github.com> Date: Wed, 21 Aug 2024 10:26:45 -0700 Subject: [PATCH] uploading neural nets tutorial (#29) * uploading neural nets tutorial * uploading utils * made the install quiet --- book/_toc.yml | 8 + .../NN_with_Pytorch/00_ML_refresher.ipynb | 168 ++ .../01_Neural_Networks_Basics.ipynb | 149 + .../NN_with_Pytorch/02_Pytorch_Basics.ipynb | 290 ++ .../03_Data_Download_And_Cleaning.ipynb | 191 ++ .../04_Building_And_Training_FFN.ipynb | 498 ++++ .../NN_with_Pytorch/data/clean_data.csv | 2470 +++++++++++++++++ .../NN_with_Pytorch/images/ML_flowchart.jpeg | Bin 0 -> 70321 bytes .../NN_with_Pytorch/images/feedforward.png | Bin 0 -> 38663 bytes .../NN_with_Pytorch/images/perceptron.png | Bin 0 -> 64947 bytes .../traditional_programming_flowchart.jpeg | Bin 0 -> 54836 bytes book/tutorials/NN_with_Pytorch/intro.md | 18 + book/tutorials/NN_with_Pytorch/utils.py | 88 + 13 files changed, 3880 insertions(+) create mode 100644 book/tutorials/NN_with_Pytorch/00_ML_refresher.ipynb create mode 100644 book/tutorials/NN_with_Pytorch/01_Neural_Networks_Basics.ipynb create mode 100644 book/tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb create mode 100644 book/tutorials/NN_with_Pytorch/03_Data_Download_And_Cleaning.ipynb create mode 100644 book/tutorials/NN_with_Pytorch/04_Building_And_Training_FFN.ipynb create mode 100644 book/tutorials/NN_with_Pytorch/data/clean_data.csv create mode 100644 book/tutorials/NN_with_Pytorch/images/ML_flowchart.jpeg create mode 100644 book/tutorials/NN_with_Pytorch/images/feedforward.png create mode 100644 book/tutorials/NN_with_Pytorch/images/perceptron.png create mode 100644 book/tutorials/NN_with_Pytorch/images/traditional_programming_flowchart.jpeg create mode 100644 book/tutorials/NN_with_Pytorch/intro.md create mode 100644 book/tutorials/NN_with_Pytorch/utils.py diff --git a/book/_toc.yml b/book/_toc.yml index bd5d821..4ded3b8 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -54,6 +54,14 @@ parts: title: Albedo sections: - file: tutorials/albedo/aviris-ng-data + - file: tutorials/NN_with_Pytorch/intro + title: Neural Networks with Pytorch + sections: + - file: tutorials/NN_with_Pytorch/00_ML_refresher + - file: tutorials/NN_with_Pytorch/01_Neural_Networks_Basics + - file: tutorials/NN_with_Pytorch/02_Pytorch_Basics + - file: tutorials/NN_with_Pytorch/03_Data_Download_And_Cleaning + - file: tutorials/NN_with_Pytorch/04_Building_And_Training_FFN - caption: Projects chapters: - file: projects/index diff --git a/book/tutorials/NN_with_Pytorch/00_ML_refresher.ipynb b/book/tutorials/NN_with_Pytorch/00_ML_refresher.ipynb new file mode 100644 index 0000000..87b2b63 --- /dev/null +++ b/book/tutorials/NN_with_Pytorch/00_ML_refresher.ipynb @@ -0,0 +1,168 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning Refresher (5 mins)\n", + "\n", + "Before we dive into neural networks, let's take a moment to understand the broader context in which they operate: Machine Learning (ML).\n", + "\n", + "## What is Machine Learning?\n", + "\n", + "Machine Learning (ML) is a field of artificial intelligence (AI) that focuses on developing algorithms or computer models using data. The goal is to use these “trained” computer models to make decisions. Unlike traditional programming, where we write explicit rules for every situation, ML models learn patterns from data to perform tasks. Here is a more general definition:\n", + "\n", + "```{epigraph}\n", + "Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed. \n", + "**— Arthur Samuel, 1959**\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Machine Learning Vs. Traditional Programming\n", + "\n", + "Machine Learning (ML) and traditional programming are two different paradigms used to solve problems and create intelligent systems. The primary difference lies in how they are instructed to solve a problem.\n", + "\n", + "## A Simple Task\n", + "\n", + "Suppose we want to build an intelligent system that identifies whether a given number is even or odd. This intelligent system can be represented mathematically as:\n", + "\n", + "$$\n", + "y=f(x)\n", + "$$\n", + "\n", + "Where:\n", + "\n", + "- $x \\to$ the number entered also called a feature.\n", + "- $y \\to$ the outcome we want to predict.\n", + "- $f \\to$ the model that gets the job done.\n", + "\n", + "\n", + "### Traditional Programming Approach\n", + "\n", + "In traditional programming, the programmer writes explicit rules (code) for the program to follow. The system follows these instructions exactly to produce a solution.\n", + "\n", + "```\n", + "def check_even_odd(number):\n", + " if number % 2 == 0:\n", + " return \"Even\"\n", + " else:\n", + " return \"Odd\"\n", + "\n", + "# Usage\n", + "result = check_even_odd(4) # Output: Even\n", + "```\n", + "\n", + "```{figure} ./images/traditional_programming_flowchart.jpeg\n", + "---\n", + "alt: A flowchart showing the traditional programming approach\n", + "width: 400px\n", + "---\n", + "Tradition Programming Flowchart\n", + "```\n", + "\n", + "### Machine Learning Approach\n", + "\n", + "In Machine Learning, instead of writing explicit instructions, we provide a model with data and let it learn the patterns. The model, after training, can then make predictions or decisions based on what it has learned. \n", + "\n", + "```{figure} ./images/ML_flowchart.jpeg\n", + "---\n", + "alt: A flowchart showing the machine learning process\n", + "width: 400px\n", + "name: ml-flow\n", + "---\n", + "Machine Learning Flowchart\n", + "```\n", + "\n", + "\n", + "```{important}\n", + "Machine Learning is useful when the function ($f$) cannot be explicitly programmed or when the relationship between the feature(s) and outcome is unknown.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How Do We Estimate $f$?\n", + "\n", + "We must think about ML **only** if it is suitable for our work flow! \n", + "\n", + "### Machine Learning Algorithms\n", + "\n", + "Machine learning algorithms can be categorized based on different criteria, each suited to different kinds of problems:\n", + "\n", + "- **Supervised Learning**: The most common type of ML algorithm, where the model is trained on labeled data. For example, given past snow data, we can train a model to predict future snow water equivalent. Supervised learning is typically divided into two main types:\n", + " * **Classification**: In classification tasks, the model predicts a discrete label or category. For example, a model might be trained to classify different types of snow conditions (e.g., powder, packed, ice) based on weather data.\n", + " * **Regression**: In regression tasks, the model predicts a continuous value. For instance, predicting snow density using weather data.\n", + "- **Unsupervised Learning**: Here, the model works with unlabeled data, finding patterns or groupings in the data without explicit instructions. Examples include clustering or anomaly detection For example, grouping SNOTEL sites based on similar snow accumulation trends or temperature profiles.\n", + "- **Reinforcement Learning**: A more advanced type of ML where the model learns by making decisions and receiving feedback (rewards or penalties). It's commonly used in robotics, game playing, and optimization problems.\n", + "\n", + "```{note}\n", + "Most Machine Learning techniques can be characterised as either *parametric* or *non-parametric*. \n", + "\n", + "- **Parametric:** The parametric approach simplifies the problem of estimating $f$ to a parameter estimation problem. The disadvantage is that we assume a particular shape of $f$ that may not match the true shape of $f$. A common parametric method is the Linear Regression.\n", + "\n", + "- **Non-parametric:** Non-parametric approaches do not assume any shape for $f$. Instead, they try to estimate $f$ that gets as close to the data points as possible. The disadvantage of non-parametric approaches is that they may require extensive training observations to estimate $f$ accurately. Common examples of non-parametric methods are the tree models, neural networks, etc.\n", + "```\n", + "\n", + "::::{dropdown} 🏋️ Exercise: Where Do Neural Networks Fit In in this Tutorial?\n", + "::::{tip}\n", + "We're predicting snow density - a continuous value.\n", + "::::\n", + "::::" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Machine Learning Workflow\n", + "\n", + "To wrap up our introduction to Machine Learning, here’s a typical workflow that you would follow when developing a machine learning model:\n", + "\n", + "1. **Problem Definition**\n", + " - Clearly define the problem you’re trying to solve. For example, predicting snow density from weather data.\n", + " \n", + "2. **Data Collection**\n", + " - Gather relevant data. In our case, we would collect data from SNOTEL sites, including snow depth, temperature, and precipitation measurements.\n", + "\n", + "3. **Data Preprocessing**\n", + " - Clean and preprocess the data to make it suitable for modeling. This might include handling missing values, normalizing data, or encoding categorical variables.\n", + " \n", + "4. **Feature Engineering**\n", + " - Select or create features (input variables) that will be used by the model to make predictions. For instance, we might use features like average temperature and total precipitation over a certain period.\n", + "\n", + "5. **Model Selection**\n", + " - Choose the type of model to use. For example, a neural network.\n", + "\n", + "6. **Model Training**\n", + " - Train the model using the training data. This involves feeding the data into the model and adjusting the model’s parameters to minimize prediction errors.\n", + "\n", + "7. **Model Evaluation**\n", + " - Evaluate the model’s performance using validation data. This helps ensure that the model generalizes well to unseen data.\n", + "\n", + "8. **Model Tuning**\n", + " - Fine-tune the model by adjusting hyperparameters or using techniques like cross-validation to improve performance.\n", + "\n", + "9. **Model Deployment**\n", + " - Deploy the model to a production environment where it can make real-time predictions.\n", + "\n", + "10. **Monitoring and Maintenance**\n", + " - Continuously monitor the model’s performance and update it as necessary to ensure it remains accurate and relevant.\n", + "\n", + "This workflow (same as {numref}`ml-flow`) provides a high-level overview of the steps involved in a typical machine learning project. In the rest of this tutorial, we'll be applying parts of this workflow to predict snow density using neural networks and PyTorch.\n" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/book/tutorials/NN_with_Pytorch/01_Neural_Networks_Basics.ipynb b/book/tutorials/NN_with_Pytorch/01_Neural_Networks_Basics.ipynb new file mode 100644 index 0000000..d38f9d1 --- /dev/null +++ b/book/tutorials/NN_with_Pytorch/01_Neural_Networks_Basics.ipynb @@ -0,0 +1,149 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction to Neural Networks\n", + "\n", + "## What is a Neural Network?\n", + "\n", + "A neural network is a computational model that was inspired by the functionality of biological neurons. It consists of layers of interconnected units called **neurons**, which process input data to produce an output. Neural networks (NN) are a type of non-parametric method. Non-parametric approaches do not assume any shape for $f$. Instead, the try to estimate $f$ (using $\\hat{f}$) that gets as close to the data points as possible. \n", + "\n", + "```{important}\n", + "Neural networks are not models of the brain as there is no proven evidence that the brain operates the same way neural networks learn representations. However, some of the concepts were inspired by understanding the brain.\n", + "```\n", + "\n", + "### Basic Structure of a Neural Network\n", + "\n", + "Every NN consists of three distinct layers;\n", + "\n", + "- **Input Layer**: The layer that receives the input data (e.g., features like temperature and snow depth).\n", + "- **Hidden Layers**: Layers where the data is processed. Each layer extracts different features or patterns from the data.\n", + "- **Output Layer**: The final layer that produces the prediction or classification (e.g., predicted snow density).\n", + "\n", + "Neural networks can vary in complexity, from simple single-layer networks to deep networks with many hidden layers. Here is an image of a sample neural network:\n", + "\n", + "```{figure} ./images/feedforward.png\n", + "---\n", + "alt: A Feed-forward Neural Network\n", + "width: 400px\n", + "name: ffn\n", + "---\n", + "A Simple Feed-forward Neural Network\n", + "```\n", + "\n", + "\n", + "### A Perceptron or Single Neuron\n", + "\n", + "To understand the functioning of these layers, we begin by exploring the building blocks of neural networks; a single neuron or perceptron. Each layer in a NN consists of small individual units called neurons (usually represented with a circle). A neuron receives inputs from other neurons, performs some mathematical operations, and then produces an output. Each neuron in the input layer represents a feature. In essence, the number of neurons in the input layer equals the number of features. Each neuron in the input layer is connected to some or every neuron in the hidden layer. The number of neurons in the hidden layer is not fixed, it is problem dependent and it is often determined via hyperparameter optimization (more on this later). Every inter-neuron connection has an associated weight, these weights are what the neural network learns during the training process.\n", + "\n", + "```{figure} ./images/perceptron.png\n", + "---\n", + "alt: A Perceptron\n", + "width: 400px\n", + "name: perceptron\n", + "---\n", + "A Single Neuron\n", + "```\n", + "\n", + "#### How the perceptron works\n", + "\n", + "Consider a dataset $\\mathcal{D}_n = \\left\\lbrace (\\textbf{x}_1, y_1), (\\textbf{x}_2, y_2), \\cdots, (\\textbf{x}_n, y_n) \\right\\rbrace$ where $\\textbf{x}_i^\\top \\equiv ({x}_{i1}, {x}_{i2}, \\cdots, {x}_{ik})$ denotes the $k$-dimensional vector of features, and $y_i$ represents the corresponding outcome. Given a set of input fed into the network through the input layer, the output of a neuron in the hidden layer is\n", + "\n", + "$$\n", + " z = f(\\textbf{x}_i;\\textbf{w}) = g(w_0 + \\textbf{w}^\\top \\textbf{x}_i),\n", + "$$\n", + "\n", + "where $\\textbf{w} = (w_1, w_2, \\cdots, w_k)^\\top$ is a vector of weights and $w_0$ is the bias term associated with the neuron. The weights can be thought of as the slopes in a linear regression and the bias as the intercept. The function $g(\\cdot)$ is known as the activation function and it is used to introduce non-linearity into the network. There exists a number of activation functions in practice (see them [here](https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity)), the commonly used ones are:\n", + "\n", + "- **ReLU (Rectified Linear Unit)**: Outputs the input directly if positive, otherwise, it outputs zero. It’s simple and effective, making it the default choice for most hidden layers.\n", + "- **Sigmoid**: Squashes the input to a range between 0 and 1, often used in the output layer of a binary classification problem and in hidden layers.\n", + "- **Tanh**: Similar to Sigmoid but outputs between -1 and 1, often used in hidden layers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from utils import plot_activations # Import the function to plot the activations\n", + "\n", + "plot_activations()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Common Neural Network Architectures\n", + "\n", + "- **Feedforward Networks**: Often used for structural data, these networks consist of layers of neurons where the data moves forward from the input to the output without cycles.\n", + "\n", + "- **Convolutional Neural Networks (CNNs)**: The gold standard for image analysis, including, image classification, object detection, and image segmentation. 1D CNN can also be used for sequence data, such as text and time series.\n", + "\n", + "- **Recurrent Neural Networks (RNNs)**: Well-suited for sequence data such as texts, time series, and even tasks like drawing generation. Variants include LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units) which address the vanishing gradient problem.\n", + "\n", + "- **Encoder-Decoders**: Commonly used for tasks like machine translation, where the input data is encoded into a context vector and then decoded into the target language or format.\n", + "\n", + "- **Generative Adversarial Networks (GANs)**: Used for generating realistic data, including 3D modeling for video games, animation, and generating high-quality images. GANs consist of two networks (a generator and a discriminator) that compete against each other.\n", + "\n", + "- **Graph Neural Networks (GNNs)**: These networks are designed to work with graph data structures, making them suitable for tasks like social network analysis, molecular structure analysis, and recommendation systems.\n", + "\n", + "- **Transformers**: A revolutionary architecture primarily used in NLP but increasingly in other areas like vision (Vision Transformers). Transformers rely on self-attention mechanisms to process input data in parallel, making them highly efficient for tasks like language modeling, translation, and more. Transformers are the backbone of models like OPenAI's GPT, Google's BERT, Google's Gemini, and others." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training a Feedforward Neural Network\n", + "\n", + "Training a neural network involves adjusting the weights based on the errors made on the training data.\n", + "\n", + "### Forward Propagation\n", + "In forward propagation, the input data passes through the network, layer by layer, to produce an output. The output is then compared to the actual target using a **loss function**.\n", + "\n", + "### Loss Function\n", + "The loss function quantifies how far the network's predictions are from the actual targets. For regression tasks, a common loss function is Mean Squared Error (MSE). See others [here](https://pytorch.org/docs/stable/nn.html#loss-functions).\n", + "\n", + "### Backward Propagation and Gradient Descent\n", + "- **Backward Propagation**: Computes the gradient of the loss function with respect to each weight using the chain rule. This gradient indicates the direction to adjust the weights to minimize the loss.\n", + "- **Gradient Descent**: An optimization algorithm that adjusts the weights in the direction that minimizes the loss function. This process is repeated over many iterations to improve the model's accuracy. The algorithm is as follows:\n", + " 1. Initialize weights and biases with random numbers.\n", + " 2. Loop until convergence:\n", + " 1. Compute the gradient using backpropagation ($\\widehat{\\mathcal{L}}_n(\\textbf{w})$ is the loss function); $\\frac{\\partial \\widehat{\\mathcal{R}}_n(\\textbf{w})}{\\partial \\textbf{w}}$\n", + " 2. Updated weights; $\\textbf{w} \\leftarrow \\textbf{w} - \\eta \\frac{\\partial \\widehat{\\mathcal{R}}_n(\\textbf{w})}{\\partial \\textbf{w}}$\n", + " 3. Return the weights\n", + "\n", + "```{note}\n", + "In practice we do not go through the entire dataset before updating the weight as this might be computationally expensive. So, we update the weight in batches. This is called mini-batch gradient descent. When the batch size is 1, it is called stochastic gradient descent. Additionally, we often use optimizers to extend the ideas of gradient descent to improve the efficiency, stability, and convergence of the training process.\n", + "\n", + "You can think of an optimizer as gradient descent plus some additional optimization techniques that improve or modify the basic gradient descent process.\n", + "```\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/book/tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb b/book/tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb new file mode 100644 index 0000000..23e83c7 --- /dev/null +++ b/book/tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb @@ -0,0 +1,290 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PyTorch Basics\n", + "\n", + "## Introduction to PyTorch\n", + "\n", + "### What is PyTorch?\n", + "\n", + "[PyTorch](https://pytorch.org/get-started/locally/) is a popular open-source deep learning framework known for its flexibility and ease of use. It's widely adopted in both research and industry for tasks ranging from simple machine learning models to complex neural networks.\n", + "\n", + "### Why PyTorch?\n", + "\n", + "- Dynamic computation graph: PyTorch's ability to dynamically build the computation graph at runtime makes it intuitive and easy to debug.\n", + "- Strong community support and integration with Python: PyTorch is Pythonic and integrates well with the Python data science stack.\n", + "- GPU acceleration: PyTorch makes it easy to move tensors to and from GPUs (supports Apple's Metal and Nvidia GPUs), which is crucial for training large models efficiently.\n", + "\n", + "## Tensors in PyTorch\n", + "\n", + "### What is a Tensor?\n", + "\n", + "Tensors are the fundamental data structures in PyTorch, similar to NumPy arrays but with the added capability of being used on a GPU.\n", + "\n", + "### Creating Tensors" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -q torch torchvision torchaudio" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "# Creating a tensor from a list\n", + "tensor_a = torch.tensor([1.0, 2.0, 3.0])\n", + "print(tensor_a)\n", + "\n", + "# Creating a tensor with random values\n", + "tensor_b = torch.rand((2, 3)) # A 2x3 matrix of random numbers\n", + "print(tensor_b)\n", + "\n", + "# Creating a tensor with zeros\n", + "tensor_c = torch.zeros((3, 3)) # A 3x3 matrix of zeros\n", + "print(tensor_c)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Basic Tensor Operators" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Reshaping a tensor\n", + "tensor_reshaped = tensor_b.view(3, 2) # Reshape to 3x2\n", + "print(tensor_reshaped)\n", + "\n", + "# Tensor addition\n", + "tensor_sum = tensor_a + tensor_a\n", + "print(tensor_sum)\n", + "\n", + "# Indexing\n", + "print(tensor_a[1]) # Access the second element\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Moving a tensor to GPU\n", + "\n", + "# Check which device is available\n", + "available_device = (\n", + " \"cuda\"\n", + " if torch.cuda.is_available()\n", + " else \"mps\"\n", + " if torch.backends.mps.is_available()\n", + " else \"cpu\"\n", + ")\n", + "\n", + "print(f\"Available device: {available_device}\")\n", + "\n", + "tensor_a_gpu = tensor_a.to(available_device)\n", + "print(tensor_a_gpu)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Autograd: Automatic Differentiation\n", + "\n", + "- PyTorch’s autograd system automatically calculates gradients, which are essential for training neural networks.\n", + "- Every operation on tensors keeps track of the computation history, allowing PyTorch to backpropagate errors automatically." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a tensor with gradient tracking enabled\n", + "x = torch.tensor(2.0, requires_grad=True)\n", + "\n", + "# Perform a computation\n", + "y = x ** 2 + 2* x ** 3\n", + "\n", + "# Backpropagate to compute the gradient\n", + "y.backward()\n", + "\n", + "# Print the gradient\n", + "print(x.grad) # Should output 28.0, the derivative of x^2 + 2x^3 at x=2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{important}\n", + "This example shows how PyTorch automatically calculates the gradient of a tensor operation, which is essential for updating the weights during training.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset and DataLoaders\n", + "\n", + "### What is a Dataset in PyTorch?\n", + "\n", + "* Purpose: The `Dataset` class in PyTorch serves as an abstraction that allows you to manage, preprocess, and access your data in a consistent way.\n", + "\n", + "* Key Features:\n", + " - Handles how data is stored and accessed.\n", + " - Allows for custom data transformations and preprocessing.\n", + " - Integrates seamlessly with PyTorch’s `DataLoader` for efficient batching and shuffling.\n", + "\n", + "### What is a DataLoader in PyTorch?\n", + "\n", + "* Purpose: The `DataLoader` is an iterable that abstracts the complexity of handling data in batches, shuffling, and parallel loading.\n", + "\n", + "* Key Features:\n", + " - Efficiently loads data in mini-batches during training.\n", + " - Automatically shuffles data at the start of each epoch (if specified).\n", + " - Supports parallel data loading using multiple workers.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from torch.utils.data import Dataset\n", + "\n", + "class CustomDataset(Dataset):\n", + " def __init__(self, data, targets):\n", + " self.data = data\n", + " self.targets = targets\n", + "\n", + " def __len__(self):\n", + " # Return the total number of samples\n", + " return len(self.data)\n", + "\n", + " def __getitem__(self, idx):\n", + " # Retrieve the data sample and label at the specified index\n", + " sample = self.data[idx]\n", + " target = self.targets[idx]\n", + " return sample, target" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explaining `__len__` and `__getitem__`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* __len__: Returns the total number of samples in your dataset. PyTorch uses this method to know how many iterations to run during training.\n", + "* __getitem__: Retrieves a specific sample from the dataset using its index. This method returns the data and its corresponding label, which PyTorch uses during training to form mini-batches." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "# Generate random data: 6 samples, each with 2 features\n", + "torch.manual_seed(0) # For reproducibility\n", + "features = torch.rand(6, 2)\n", + "\n", + "# Generate random target values (e.g., for a regression problem)\n", + "targets = torch.rand(6, 1)\n", + "\n", + "print(f\"Features:\\n{features}\")\n", + "print(f\"\\nTarget:\\n{targets}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from torch.utils.data import DataLoader\n", + "\n", + "# Create an instance of the custom dataset\n", + "dataset = CustomDataset(data=features, targets=targets)\n", + "\n", + "# Create a DataLoader\n", + "data_loader = DataLoader(dataset, batch_size=2, shuffle=True)\n", + "\n", + "# Example of iterating through the DataLoader\n", + "for idx, (batch_data, batch_labels) in enumerate(data_loader):\n", + " print(f\"Batch {idx+1}:\\n========\")\n", + " print(f\"Data:\\n{batch_data}\")\n", + " print(f\"Targets:\\n{batch_labels}\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* `shuffle=True` ensures that the data is shuffled at the beginning of each epoch, which helps prevent the model from learning patterns based on the order of the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "::::{dropdown} 🏋️ Exercise: What if we set `shuffle` to `False` and `batch_size` to 3?\n", + "::::{tip}\n", + "Check the Sample Size.\n", + "::::\n", + "::::" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/book/tutorials/NN_with_Pytorch/03_Data_Download_And_Cleaning.ipynb b/book/tutorials/NN_with_Pytorch/03_Data_Download_And_Cleaning.ipynb new file mode 100644 index 0000000..4225f33 --- /dev/null +++ b/book/tutorials/NN_with_Pytorch/03_Data_Download_And_Cleaning.ipynb @@ -0,0 +1,191 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Download and Cleaning\n", + "\n", + "We will download SNOTEL data set using the [metloom](https://metloom.readthedocs.io/en/latest/installation.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -q metloom " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "from metloom.pointdata import SnotelPointData\n", + "\n", + "ALLOWED_VARIABLES = [\n", + " SnotelPointData.ALLOWED_VARIABLES.SWE,\n", + " SnotelPointData.ALLOWED_VARIABLES.TEMPAVG,\n", + " SnotelPointData.ALLOWED_VARIABLES.SNOWDEPTH,\n", + " SnotelPointData.ALLOWED_VARIABLES.PRECIPITATION,\n", + "]\n", + "\n", + "# You can get triplets from: https://wcc.sc.egov.usda.gov/nwcc/yearcount?network=sntl&state=&counttype=statelist\n", + "\n", + "snotel_point = SnotelPointData(station_id=\"502:WA:SNTL\", name=\"Green Lake\")\n", + "data = snotel_point.get_daily_data(\n", + " start_date=datetime(*(2010, 1, 1)),\n", + " end_date=datetime(*(2023, 1, 1)),\n", + " variables=ALLOWED_VARIABLES,\n", + " )\n", + "\n", + "data.info()\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "for_plotting=data.reset_index()\n", + "\n", + "units={\n", + " \"SWE\": \"in\",\n", + " \"SNOWDEPTH\": \"in\",\n", + " \"AVG AIR TEMP\": \"degF\",\n", + " \"PRECIPITATION\": \"in\"\n", + "}\n", + "\n", + "variables_to_plot = [\n", + " \"SWE\", \"SNOWDEPTH\", \"AVG AIR TEMP\", \"PRECIPITATION\"\n", + "]\n", + "\n", + "plt.figure(figsize=(12, 8))\n", + "\n", + "for variable in variables_to_plot:\n", + " plt.subplot(2, 2, variables_to_plot.index(variable) + 1)\n", + " plt.plot(for_plotting[\"datetime\"], for_plotting[variable], label=variable)\n", + " plt.ylabel(f\"{variable} ({units[variable]})\", fontsize=14)\n", + " plt.xlabel(\"Date\", fontsize=14)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for_plotting.isnull().sum() # Check for missing values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Some Background\n", + "\n", + "At a given point, snow depth ($h_s$) is related to Snow Water Equivalent (SWE) by the local bulk density ($\\rho_b$):\n", + "\n", + "$$\n", + "\\text{SWE} = h_s \\frac{\\rho_b}{\\rho_w}\n", + "$$\n", + "\n", + "where depth is measured in centimeters, density in grams per centimeters cubed, $\\rho_w$ is the density of water (1 g cm $^{-3}$), and SWE is measured in centimeters of water. As such,\n", + "\n", + "$$\n", + "\\text{SWE} = h_s \\times \\frac{\\rho_b}{1}\n", + "$$\n", + "\n", + "$$\n", + "\\rho_b = \\frac{\\text{SWE}}{h_s}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clean_df=(\n", + " for_plotting\n", + " .assign(\n", + " swe=lambda x: x.SWE.map(lambda y: y*2.54 if y is not None else None),\n", + " snowdepth=lambda x: x.SNOWDEPTH.map(lambda y: y*2.54 if y is not None else None),\n", + " precipitation=lambda x: x.PRECIPITATION.map(lambda y: y*2.54 if y is not None else None),\n", + " tempavg=lambda x: x['AVG AIR TEMP'].map(lambda y: (y-32)*5/9 if y is not None else None)\n", + " )\n", + " .set_index('datetime')\n", + " .assign(\n", + " precip_7_days_avg=lambda x: x.precipitation.shift().rolling(window=\"7D\", min_periods=7).mean(),\n", + " tempavg_7_days_avg=lambda x: x.tempavg.shift().rolling(window=\"7D\", min_periods=7).mean(),\n", + " )\n", + " .filter([\"datetime\", \"swe\", \"snowdepth\", \"tempavg_7_days_avg\", \"precip_7_days_avg\"])\n", + " .dropna()\n", + " .query(\n", + " \"snowdepth != 0 and swe != 0 and \"\n", + " \"snowdepth > 5 and swe > 3\"\n", + " )\n", + " .assign(snowdensity=lambda x: x.swe / x.snowdepth)\n", + ")\n", + "\n", + "clean_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# let's store data for later use\n", + "\n", + "import os\n", + "\n", + "os.makedirs(\"data\", exist_ok=True)\n", + "clean_df.to_csv(\"data/clean_data.csv\", index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/book/tutorials/NN_with_Pytorch/04_Building_And_Training_FFN.ipynb b/book/tutorials/NN_with_Pytorch/04_Building_And_Training_FFN.ipynb new file mode 100644 index 0000000..dae2a96 --- /dev/null +++ b/book/tutorials/NN_with_Pytorch/04_Building_And_Training_FFN.ipynb @@ -0,0 +1,498 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building and Training a Feed Forward Neural Network in PyTorch\n", + "\n", + "In this notebook, we’ll build a simple neural network using PyTorch, train it on the SNOTEL dataset, and evaluate its performance. This hands-on exercise will reinforce our understanding of the PyTorch framework and the steps involved in building and training neural networks on real-world data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Libraries\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -q torch torchvision torchaudio" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "from torch.utils.data import Dataset\n", + "from torch.utils.data import DataLoader\n", + "\n", + "available_device = (\n", + " \"cuda\"\n", + " if torch.cuda.is_available()\n", + " else \"mps\"\n", + " if torch.backends.mps.is_available()\n", + " else \"cpu\"\n", + ")\n", + "\n", + "print(f\"Available device: {available_device}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preparing the Dataset\n", + "\n", + "### Step 1: Load Dataset\n", + "\n", + "We'll start by loading the SNOTEL dataset from a CSV file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "snotel_data=pd.read_csv(\"data/clean_data.csv\")\n", + "snotel_data.info()\n", + "snotel_data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2: Data Split\n", + "\n", + "We’ll split the data into training, validation, and testing sets. Typically, a common split might be 70% training, 15% validation, and 15% testing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "features = snotel_data.drop('snowdensity', axis=1).values\n", + "targets = snotel_data['snowdensity'].values\n", + "\n", + "# Split the dataset into training and temp sets (85% train, 15% temp)\n", + "features_train, features_temp, targets_train, targets_temp = train_test_split(\n", + " features, targets, test_size=0.3, random_state=0\n", + ")\n", + "\n", + "# Further split the temp set into validation and test sets (15% each)\n", + "features_val, features_test, targets_val, targets_test = train_test_split(\n", + " features_temp, targets_temp, test_size=0.5, random_state=0\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3: Preprocess Data\n", + "\n", + "Now that we've split the data, we can apply scaling. The scaler should be fit on the training data and then used to transform the training, validation, and test sets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scaler = StandardScaler()\n", + "\n", + "scaler.fit(features_train)\n", + "\n", + "# Transform the training, validation, and test sets\n", + "features_train = scaler.transform(features_train)\n", + "features_val = scaler.transform(features_val)\n", + "features_test = scaler.transform(features_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4: Creating Custom Datasets\n", + "\n", + "Next, we define custom `Dataset` classes for each of the three sets: training, validation, and testing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class SNOTELDataset(Dataset):\n", + " def __init__(self, data, targets):\n", + " self.data = torch.tensor(data, dtype=torch.float32)\n", + " self.targets = torch.tensor(targets, dtype=torch.float32).view(-1, 1)\n", + "\n", + " def __len__(self):\n", + " return len(self.data)\n", + "\n", + " def __getitem__(self, idx):\n", + " sample = self.data[idx]\n", + " target = self.targets[idx]\n", + " return sample, target\n", + "\n", + "# Create instances of the custom datasets for training, validation, and testing sets\n", + "train_dataset = SNOTELDataset(data=features_train, targets=targets_train)\n", + "val_dataset = SNOTELDataset(data=features_val, targets=targets_val)\n", + "test_dataset = SNOTELDataset(data=features_test, targets=targets_test)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5: Using DataLoader\n", + "\n", + "Now, we use `DataLoader` to manage our data in mini-batches during training, validation, and testing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create DataLoaders for training, validation, and testing sets\n", + "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n", + "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)\n", + "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining the Neural Network\n", + "\n", + "We define a simple feedforward neural network using `torch.nn.Module`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class SNOTELNN(nn.Module):\n", + " def __init__(self, input_size, hidden_size, output_size):\n", + " super(SNOTELNN, self).__init__() # super class to inherit from nn.Module\n", + " # Define the layers\n", + " self.fc1 = nn.Linear(input_size, hidden_size) # Fully connected layer 1\n", + " self.relu = nn.ReLU() # ReLU activation function\n", + " self.fc2 = nn.Linear(hidden_size, output_size) # Fully connected layer 2\n", + " \n", + " def forward(self, x): # x is the batch of input\n", + " # Define the forward pass\n", + " out = self.fc1(x) # Pass input through first layer\n", + " out = self.relu(out) # Apply ReLU activation\n", + " out = self.fc2(out) # Pass through second layer to get output\n", + " return out\n", + "\n", + "# Instantiate the model\n", + "# Instantiate the model and move it to the device (GPU or CPU)\n", + "model = SNOTELNN(input_size=features_train.shape[1], hidden_size=128, output_size=1).to(available_device)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `forward` method defines how the input data flows through the network layers. It specifies the sequence of operations that the data undergoes as it moves from the input layer to the output layer. This method is automatically called when you pass data through the model (e.g., `outputs = model(inputs)`)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting the Loss Function and Optimizer\n", + "\n", + "For this example, we’ll use Mean Squared Error Loss since we’re dealing with a regression problem. We’ll use the Adam optimizer, which is a good default choice due to its adaptive learning rates." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "criterion = nn.MSELoss()\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training the Network\n", + "\n", + "We now write the training loop, which includes zeroing the gradients, performing the forward pass, computing the loss, backpropagating, and updating the model parameters. We will also validate the model on the validation set after each epoch.\n", + "\n", + "```{note}\n", + "An **Epoch** refers to one complete pass through the entire training dataset. During each epoch, the model sees every example in the dataset once.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "num_epochs = 5\n", + "\n", + "# Lists to store the training and validation losses\n", + "train_losses = []\n", + "val_losses = []\n", + "\n", + "for epoch in range(num_epochs):\n", + " # Training phase\n", + " model.train()\n", + " train_loss = 0.0 # Initialize cumulative training loss\n", + " \n", + " for inputs, labels in train_loader:\n", + " # Move data to the appropriate device\n", + " inputs, labels = inputs.to(available_device), labels.to(available_device)\n", + " \n", + " # Zero the gradients from the previous iteration\n", + " optimizer.zero_grad()\n", + " \n", + " # Perform forward pass\n", + " outputs = model(inputs)\n", + " \n", + " # Compute the loss\n", + " loss = criterion(outputs, labels)\n", + " \n", + " # Perform backward pass (compute gradients)\n", + " loss.backward()\n", + " \n", + " # Update the model parameters\n", + " optimizer.step()\n", + " \n", + " # Accumulate training loss\n", + " train_loss += loss.item()\n", + " \n", + " # Average training loss\n", + " train_loss /= len(train_loader)\n", + " train_losses.append(train_loss) # Store the training loss for this epoch\n", + " \n", + " # Validation phase\n", + " model.eval() # Set model to evaluation mode\n", + " val_loss = 0.0\n", + " \n", + " with torch.no_grad():\n", + " for inputs, labels in val_loader:\n", + " inputs, labels = inputs.to(available_device), labels.to(available_device) # Move to device\n", + " outputs = model(inputs)\n", + " loss = criterion(outputs, labels)\n", + " val_loss += loss.item()\n", + " \n", + " # Average validation loss\n", + " val_loss /= len(val_loader)\n", + " val_losses.append(val_loss) # Store the validation loss for this epoch\n", + " \n", + " print(f'Epoch [{epoch+1}/{num_epochs}], Training Loss: {train_loss:.4f}, Validation Loss: {val_loss:.4f}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plotting the training and validation losses\n", + "plt.figure(figsize=(10, 5))\n", + "plt.plot(range(1, num_epochs + 1), train_losses, label='Training Loss')\n", + "plt.plot(range(1, num_epochs + 1), val_losses, label='Validation Loss')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss')\n", + "plt.title('Training and Validation Loss Over Epochs')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Evaluate the model on the test set and collect predictions\n", + "model.eval() # Set the model to evaluation mode\n", + "test_loss = 0.0 # Initialize cumulative test loss\n", + "all_preds = []\n", + "all_labels = []\n", + "\n", + "with torch.no_grad(): # Disable gradient computation for inference\n", + " for inputs, labels in test_loader:\n", + " # Move data to the appropriate device\n", + " inputs, labels = inputs.to(available_device), labels.to(available_device)\n", + " \n", + " # Perform forward pass\n", + " outputs = model(inputs)\n", + " \n", + " # Compute the loss\n", + " loss = criterion(outputs, labels)\n", + " \n", + " # Accumulate test loss\n", + " test_loss += loss.item()\n", + " \n", + " # Store the predictions and the corresponding labels\n", + " all_preds.extend(outputs.cpu().numpy())\n", + " all_labels.extend(labels.cpu().numpy())\n", + "\n", + "# Calculate the average test loss\n", + "test_loss /= len(test_loader)\n", + "print(f'Test Loss: {test_loss:.4f}')\n", + "\n", + "# Convert lists to numpy arrays for plotting\n", + "all_preds = np.array(all_preds)\n", + "all_labels = np.array(all_labels)\n", + "\n", + "# Plot observed vs predicted\n", + "plt.figure(figsize=(8, 8))\n", + "plt.scatter(all_labels, all_preds, alpha=0.7)\n", + "plt.xlabel('Observed (Actual) Values')\n", + "plt.ylabel('Predicted Values')\n", + "plt.title('Observed vs. Predicted Values')\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Saving the Model\n", + "\n", + "Saving your trained model is an essential part of any machine learning project. It allows you to reuse the model for predictions, further training, or sharing with others without having to retrain it from scratch. In PyTorch, saving and loading models is straightforward and can be done using the `torch.save` and `torch.load` functions. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Save the model's state dictionary\n", + "torch.save(model.state_dict(), 'snotel_nn_model.pth')\n", + "\n", + "\n", + "# Initialize the model architecture\n", + "model = SNOTELNN(input_size=features_train.shape[1], hidden_size=128, output_size=1)\n", + "\n", + "# Load the model's state dictionary\n", + "model.load_state_dict(torch.load('snotel_nn_model.pth', weights_only=True))\n", + "\n", + "# Set the model to evaluation mode before inference\n", + "model.eval()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hyperparameter Tuning\n", + "\n", + "Hyperparameter tuning is a critical step in building machine learning models. Unlike model parameters (like weights and biases), which are learned from the data during training, hyperparameters are the settings you choose before the training process begins. These include:\n", + "\n", + "- **Learning Rate**: Controls how much to adjust the model’s weights with respect to the loss gradient.\n", + "- **Batch Size**: Determines the number of training examples utilized in one iteration.\n", + "- **Number of Hidden Layers and Neurons**: Specifies the architecture of the neural network.\n", + "- **Optimizer**: The algorithm used to update model weights based on the computed gradients (e.g., Adam, SGD).\n", + "\n", + "\n", + "Tuning these hyperparameters can significantly affect the performance of your model. However, finding the optimal set of hyperparameters can be a challenging and time-consuming process, often requiring experimentation.\n", + "\n", + "### Manual vs. Automated Tuning\n", + "\n", + "- **Manual Tuning**: Involves adjusting hyperparameters based on intuition, experience, or trial and error. While straightforward, this approach can be inefficient and might not always yield the best results.\n", + "- **Automated Tuning**: Tools like **Optuna** can help automate the search for the best hyperparameters. These tools explore the hyperparameter space more systematically and can save a lot of time compared to manual tuning. Sample PyTorch hyperparameter tuning for Optuna can be found [here](https://github.com/optuna/optuna-examples/tree/main/pytorch).\n", + "\n", + "### Further Reading and Tools\n", + "\n", + "Since hyperparameter tuning is a vast topic and we have limited time, I recommend exploring the following resources and tools for a deeper dive\n", + "\n", + "* Optuna: [documentation](https://optuna.org/)\n", + "* Ray Tune: A scalable hyperparameter tuning library, particularly useful if you need to distribute tuning across multiple machines. See [documentation](https://docs.ray.io/en/latest/tune/index.html) for more." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Acknowledgements\n", + "\n", + "* Many thanks to HP Marshall (my advisor) for his mentorship and support. \n", + "* Many thanks to e-Science institute and all organizing members for allowing me deploy/present this tutorial. A huge thanks to eveyone for listening." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reference\n", + "\n", + "1. [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python)\n", + "2. [Machine Learning Bookcamp](https://www.manning.com/books/machine-learning-bookcamp?query=machine)\n", + "3. [An Introduction to Statistical Learning with Applications in R](https://link.springer.com/book/10.1007%2F978-1-4614-7138-7) (available online for free)\n", + "4. [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646)\n", + "[Ensemble Methods for Machine Learning](https://www.manning.com/books/ensemble-methods-for-machine-learning)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/book/tutorials/NN_with_Pytorch/data/clean_data.csv b/book/tutorials/NN_with_Pytorch/data/clean_data.csv new file mode 100644 index 0000000..8e08a69 --- /dev/null +++ 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