From b5fd5ff49f8501dbf61669f9bd113a9f09b65238 Mon Sep 17 00:00:00 2001 From: Ibrahim-Ola Date: Wed, 21 Aug 2024 08:20:49 -0700 Subject: [PATCH 01/10] uploading neural nets tutorial --- book/_toc.yml | 8 + .../NN_with_Pytorch/00_ML_refresher.ipynb | 168 ++ .../01_Neural_Networks_Basics.ipynb | 160 ++ .../NN_with_Pytorch/02_Pytorch_Basics.ipynb | 379 +++ .../03_Data_Download_And_Cleaning.ipynb | 520 ++++ .../04_Building_And_Training_FFN.ipynb | 659 +++++ .../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 + 12 files changed, 4382 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 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..37e9d6c --- /dev/null +++ b/book/tutorials/NN_with_Pytorch/01_Neural_Networks_Basics.ipynb @@ -0,0 +1,160 @@ +{ + "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": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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": "3.11.9" + } + }, + "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..9e7b8cf --- /dev/null +++ b/book/tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb @@ -0,0 +1,379 @@ +{ + "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": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([1., 2., 3.])\n", + "tensor([[0.2943, 0.0991, 0.1719],\n", + " [0.4528, 0.2703, 0.6615]])\n", + "tensor([[0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.]])\n" + ] + } + ], + "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": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[0.2943, 0.0991],\n", + " [0.1719, 0.4528],\n", + " [0.2703, 0.6615]])\n", + "tensor([2., 4., 6.])\n", + "tensor(2.)\n" + ] + } + ], + "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": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available device: mps\n", + "tensor([1., 2., 3.], device='mps:0')\n" + ] + } + ], + "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": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(28.)\n" + ] + } + ], + "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": 17, + "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": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Features:\n", + "tensor([[0.4963, 0.7682],\n", + " [0.0885, 0.1320],\n", + " [0.3074, 0.6341],\n", + " [0.4901, 0.8964],\n", + " [0.4556, 0.6323],\n", + " [0.3489, 0.4017]])\n", + "\n", + "Target:\n", + "tensor([[0.0223],\n", + " [0.1689],\n", + " [0.2939],\n", + " [0.5185],\n", + " [0.6977],\n", + " [0.8000]])\n" + ] + } + ], + "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": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Batch 1:\n", + "========\n", + "Data:\n", + "tensor([[0.0885, 0.1320],\n", + " [0.3489, 0.4017]])\n", + "Targets:\n", + "tensor([[0.1689],\n", + " [0.8000]])\n", + "\n", + "Batch 2:\n", + "========\n", + "Data:\n", + "tensor([[0.3074, 0.6341],\n", + " [0.4556, 0.6323]])\n", + "Targets:\n", + "tensor([[0.2939],\n", + " [0.6977]])\n", + "\n", + "Batch 3:\n", + "========\n", + "Data:\n", + "tensor([[0.4963, 0.7682],\n", + " [0.4901, 0.8964]])\n", + "Targets:\n", + "tensor([[0.0223],\n", + " [0.5185]])\n", + "\n" + ] + } + ], + "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": "3.11.9" + } + }, + "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..43510d8 --- /dev/null +++ b/book/tutorials/NN_with_Pytorch/03_Data_Download_And_Cleaning.ipynb @@ -0,0 +1,520 @@ +{ + "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 metloom" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "MultiIndex: 4749 entries, (Timestamp('2010-01-01 08:00:00+0000', tz='UTC'), '502:WA:SNTL') to (Timestamp('2023-01-01 08:00:00+0000', tz='UTC'), '502:WA:SNTL')\n", + "Data columns (total 10 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 geometry 4749 non-null geometry\n", + " 1 SWE 4748 non-null float64 \n", + " 2 SWE_units 4748 non-null object \n", + " 3 AVG AIR TEMP 4738 non-null float64 \n", + " 4 AVG AIR TEMP_units 4738 non-null object \n", + " 5 SNOWDEPTH 4748 non-null float64 \n", + " 6 SNOWDEPTH_units 4748 non-null object \n", + " 7 PRECIPITATION 4745 non-null float64 \n", + " 8 PRECIPITATION_units 4745 non-null object \n", + " 9 datasource 4749 non-null object \n", + "dtypes: float64(4), geometry(1), object(5)\n", + "memory usage: 551.3+ KB\n" + ] + }, + { + "data": { + "text/html": [ + "
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"2010-01-02 08:00:00+00:00 502:WA:SNTL degF 37.0 \n", + "2010-01-03 08:00:00+00:00 502:WA:SNTL degF 38.0 \n", + "2010-01-04 08:00:00+00:00 502:WA:SNTL degF 38.0 \n", + "2010-01-05 08:00:00+00:00 502:WA:SNTL degF 38.0 \n", + "\n", + " SNOWDEPTH_units PRECIPITATION \\\n", + "datetime site \n", + "2010-01-01 08:00:00+00:00 502:WA:SNTL in 0.5 \n", + "2010-01-02 08:00:00+00:00 502:WA:SNTL in 0.3 \n", + "2010-01-03 08:00:00+00:00 502:WA:SNTL in 0.1 \n", + "2010-01-04 08:00:00+00:00 502:WA:SNTL in 0.7 \n", + "2010-01-05 08:00:00+00:00 502:WA:SNTL in 0.1 \n", + "\n", + " PRECIPITATION_units datasource \n", + "datetime site \n", + "2010-01-01 08:00:00+00:00 502:WA:SNTL in NRCS \n", + "2010-01-02 08:00:00+00:00 502:WA:SNTL in NRCS \n", + "2010-01-03 08:00:00+00:00 502:WA:SNTL in NRCS \n", + "2010-01-04 08:00:00+00:00 502:WA:SNTL in NRCS \n", + "2010-01-05 08:00:00+00:00 502:WA:SNTL in NRCS " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "datetime 0\n", + "site 0\n", + "geometry 0\n", + "SWE 1\n", + "SWE_units 1\n", + "AVG AIR TEMP 11\n", + "AVG AIR TEMP_units 11\n", + "SNOWDEPTH 1\n", + "SNOWDEPTH_units 1\n", + "PRECIPITATION 4\n", + "PRECIPITATION_units 4\n", + "datasource 0\n", + "dtype: int64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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", + "\\begin{align*}\n", + "\\text{SWE} & = h_s \\times \\frac{\\rho_b}{1}\\\\\n", + "& = h_s \\times \\rho_b \\\\\n", + "\\rho_b & = \\frac{\\text{SWE}}{h_s}\n", + "\\end{align*}" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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swesnowdepthtempavg_7_days_avgprecip_7_days_avgsnowdensity
datetime
2010-01-08 08:00:00+00:0027.17891.44-1.4142860.6531430.297222
2010-01-09 08:00:00+00:0027.68691.44-1.5285710.5442860.302778
2010-01-10 08:00:00+00:0027.68691.44-0.9714290.4354290.302778
2010-01-11 08:00:00+00:0027.68688.90-0.5571430.3991430.311429
2010-01-12 08:00:00+00:0027.68688.90-0.2714290.1814290.311429
\n", + "
" + ], + "text/plain": [ + " swe snowdepth tempavg_7_days_avg \\\n", + "datetime \n", + "2010-01-08 08:00:00+00:00 27.178 91.44 -1.414286 \n", + "2010-01-09 08:00:00+00:00 27.686 91.44 -1.528571 \n", + "2010-01-10 08:00:00+00:00 27.686 91.44 -0.971429 \n", + "2010-01-11 08:00:00+00:00 27.686 88.90 -0.557143 \n", + "2010-01-12 08:00:00+00:00 27.686 88.90 -0.271429 \n", + "\n", + " precip_7_days_avg snowdensity \n", + "datetime \n", + "2010-01-08 08:00:00+00:00 0.653143 0.297222 \n", + "2010-01-09 08:00:00+00:00 0.544286 0.302778 \n", + "2010-01-10 08:00:00+00:00 0.435429 0.302778 \n", + "2010-01-11 08:00:00+00:00 0.399143 0.311429 \n", + "2010-01-12 08:00:00+00:00 0.181429 0.311429 " + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": 47, + "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": "3.11.9" + } + }, + "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..48e541f --- /dev/null +++ b/book/tutorials/NN_with_Pytorch/04_Building_And_Training_FFN.ipynb @@ -0,0 +1,659 @@ +{ + "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": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available device: mps\n" + ] + } + ], + "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": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 2469 entries, 0 to 2468\n", + "Data columns (total 5 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 swe 2469 non-null float64\n", + " 1 snowdepth 2469 non-null float64\n", + " 2 tempavg_7_days_avg 2469 non-null float64\n", + " 3 precip_7_days_avg 2469 non-null float64\n", + " 4 snowdensity 2469 non-null float64\n", + "dtypes: float64(5)\n", + "memory usage: 96.6 KB\n" + ] + }, + { + "data": { + "text/html": [ + "
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swesnowdepthtempavg_7_days_avgprecip_7_days_avgsnowdensity
027.17891.44-1.4142860.6531430.297222
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227.68691.44-0.9714290.4354290.302778
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\n", + "
" + ], + "text/plain": [ + " swe snowdepth tempavg_7_days_avg precip_7_days_avg snowdensity\n", + "0 27.178 91.44 -1.414286 0.653143 0.297222\n", + "1 27.686 91.44 -1.528571 0.544286 0.302778\n", + "2 27.686 91.44 -0.971429 0.435429 0.302778\n", + "3 27.686 88.90 -0.557143 0.399143 0.311429\n", + "4 27.686 88.90 -0.271429 0.181429 0.311429" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": 6, + "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": 7, + "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": 8, + "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": 9, + "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": 18, + "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": 20, + "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": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/5], Training Loss: 0.0035, Validation Loss: 0.0039\n", + "Epoch [2/5], Training Loss: 0.0032, Validation Loss: 0.0035\n", + "Epoch [3/5], Training Loss: 0.0029, Validation Loss: 0.0032\n", + "Epoch [4/5], Training Loss: 0.0027, Validation Loss: 0.0028\n", + "Epoch [5/5], Training Loss: 0.0024, Validation Loss: 0.0026\n" + ] + } + ], + "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": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Loss: 0.0027\n", + "R2 Score: 0.47\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SNOTELNN(\n", + " (fc1): Linear(in_features=4, out_features=128, bias=True)\n", + " (relu): ReLU()\n", + " (fc2): Linear(in_features=128, out_features=1, bias=True)\n", + ")" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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]()\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": "3.11.9" + } + }, + "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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zzQN#QL1R7_Pp!xiX;w1>XM5MOy=JQ`cB>i@U@`H=^^*#7L!bS(D*w71KY0#W{*kN($m9G1njf2+p|;H?(UN~ctpbnfWPCXmrvx@pBPm58Gt zkQmsoaplwLFMgf;`+icJeSYc+Qc1q`fqD(RqbzeGxUUAh$?#=%#P1#3gR*pR75g|B ze?6anZ;BCqUQ%B3;m9Kof%%~EmSWN!7*^?ph>OOBzp6@O>0HSzu15Y5(nQ9XM^nvu zM9OyXTEP^Ig^wSt>z9?UtBL6J|6*jeCpyX2$=+8iMlh$buZ~x=Es9&s{Ned$17#Ny z*Zm#Bj_ehC_eC;_Y7e{+u`io Date: Wed, 21 Aug 2024 09:25:00 -0700 Subject: [PATCH 02/10] uploading utils --- book/tutorials/NN_with_Pytorch/utils.py | 88 +++++++++++++++++++++++++ 1 file changed, 88 insertions(+) create mode 100644 book/tutorials/NN_with_Pytorch/utils.py diff --git a/book/tutorials/NN_with_Pytorch/utils.py b/book/tutorials/NN_with_Pytorch/utils.py new file mode 100644 index 0000000..10d15d1 --- /dev/null +++ b/book/tutorials/NN_with_Pytorch/utils.py @@ -0,0 +1,88 @@ +import numpy as np +import matplotlib.pyplot as plt + +def Sigmoid(z): + """ + A function that performs the sigmoid transformation + + Arguments: + --------- + -* z: array/list of numbers to activate + + Returns: + -------- + -* logistic: the transformed/activated version of the array + """ + logistic = 1 / (1 + np.exp(-z)) + return logistic + + +def Tanh(z): + """ + A function that performs the hyperbolic tangent transformation + + Arguments: + --------- + -* z: array/list of numbers to activate + + Returns: + -------- + -* hyp: the transformed/activated version of the array + """ + hyp = np.tanh(z) + return hyp + + +def ReLu(z): + """ + A function that performs the rectified linear unit transformation + + Arguments: + --------- + -* z: array/list of numbers to activate + + Returns: + -------- + -* points: the transformed/activated version of the array + """ + points = np.where(z < 0, 0, z) + return points + +def plot_activations(): + """ + A function to plot the Sigmoid, Tanh, and ReLU activation functions. + """ + z = np.linspace(-10, 10, 100) + fa = plt.figure(figsize=(16, 5)) + + # Plot Sigmoid + plt.subplot(1, 3, 1) + plt.plot(z, Sigmoid(z), color="red", label=r'$\frac{1}{1 + e^{-z}}$') + plt.grid(True, which='both') + plt.xlabel('z') + plt.ylabel('g(z)', fontsize=15) + plt.title("Sigmoid Activation Function") + plt.legend(loc='best', fontsize=22) + + # Plot Tanh + plt.subplot(1, 3, 2) + plt.plot(z, Tanh(z), color="red", label=r'$\tanh (z)$') + plt.grid(True, which='both') + plt.xlabel('z') + plt.ylabel('g(z)', fontsize=15) + plt.title("Hyperbolic Tangent Activation Function") + plt.legend(loc='best', fontsize=18) + + # Plot ReLU + plt.subplot(1, 3, 3) + plt.plot(z, ReLu(z), color="red", label=r'$\max(0,z)$') + plt.grid(True, which='both') + plt.xlabel('z') + plt.ylabel('g(z)', fontsize=15) + plt.title("Rectified Linear Unit Activation Function") + plt.legend(loc='best', fontsize=18) + + plt.tight_layout() + + plt.show() + From 06df5cbca4cf7fb06f2ec0ba53cc0879f79b61f4 Mon Sep 17 00:00:00 2001 From: Ibrahim-Ola Date: Wed, 21 Aug 2024 09:26:07 -0700 Subject: [PATCH 03/10] made the install quiet --- .../NN_with_Pytorch/03_Data_Download_And_Cleaning.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) 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 index 43510d8..32d374d 100644 --- a/book/tutorials/NN_with_Pytorch/03_Data_Download_And_Cleaning.ipynb +++ b/book/tutorials/NN_with_Pytorch/03_Data_Download_And_Cleaning.ipynb @@ -18,11 +18,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "%pip install metloom" + "%pip install -q metloom " ] }, { @@ -512,7 +512,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.5" } }, "nbformat": 4, From e729a44502a622d417cca9a68e2572d5744f986b Mon Sep 17 00:00:00 2001 From: Ibrahim-Ola Date: Wed, 21 Aug 2024 09:47:46 -0700 Subject: [PATCH 04/10] cleared output --- .../01_Neural_Networks_Basics.ipynb | 17 +- .../NN_with_Pytorch/02_Pytorch_Basics.ipynb | 124 +------ .../03_Data_Download_And_Cleaning.ipynb | 351 +----------------- .../04_Building_And_Training_FFN.ipynb | 206 +--------- 4 files changed, 44 insertions(+), 654 deletions(-) diff --git a/book/tutorials/NN_with_Pytorch/01_Neural_Networks_Basics.ipynb b/book/tutorials/NN_with_Pytorch/01_Neural_Networks_Basics.ipynb index 37e9d6c..d38f9d1 100644 --- a/book/tutorials/NN_with_Pytorch/01_Neural_Networks_Basics.ipynb +++ b/book/tutorials/NN_with_Pytorch/01_Neural_Networks_Basics.ipynb @@ -64,20 +64,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from utils import plot_activations # Import the function to plot the activations\n", "\n", @@ -152,7 +141,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/book/tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb b/book/tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb index 9e7b8cf..8418a8d 100644 --- a/book/tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb +++ b/book/tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb @@ -29,22 +29,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([1., 2., 3.])\n", - "tensor([[0.2943, 0.0991, 0.1719],\n", - " [0.4528, 0.2703, 0.6615]])\n", - "tensor([[0., 0., 0.],\n", - " [0., 0., 0.],\n", - " [0., 0., 0.]])\n" - ] - } - ], + "outputs": [], "source": [ "import torch\n", "\n", @@ -70,21 +57,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[0.2943, 0.0991],\n", - " [0.1719, 0.4528],\n", - " [0.2703, 0.6615]])\n", - "tensor([2., 4., 6.])\n", - "tensor(2.)\n" - ] - } - ], + "outputs": [], "source": [ "# Reshaping a tensor\n", "tensor_reshaped = tensor_b.view(3, 2) # Reshape to 3x2\n", @@ -100,18 +75,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Available device: mps\n", - "tensor([1., 2., 3.], device='mps:0')\n" - ] - } - ], + "outputs": [], "source": [ "# Moving a tensor to GPU\n", "\n", @@ -142,17 +108,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(28.)\n" - ] - } - ], + "outputs": [], "source": [ "# Create a tensor with gradient tracking enabled\n", "x = torch.tensor(2.0, requires_grad=True)\n", @@ -203,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -242,31 +200,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Features:\n", - "tensor([[0.4963, 0.7682],\n", - " [0.0885, 0.1320],\n", - " [0.3074, 0.6341],\n", - " [0.4901, 0.8964],\n", - " [0.4556, 0.6323],\n", - " [0.3489, 0.4017]])\n", - "\n", - "Target:\n", - "tensor([[0.0223],\n", - " [0.1689],\n", - " [0.2939],\n", - " [0.5185],\n", - " [0.6977],\n", - " [0.8000]])\n" - ] - } - ], + "outputs": [], "source": [ "import torch\n", "\n", @@ -283,43 +219,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch 1:\n", - "========\n", - "Data:\n", - "tensor([[0.0885, 0.1320],\n", - " [0.3489, 0.4017]])\n", - "Targets:\n", - "tensor([[0.1689],\n", - " [0.8000]])\n", - "\n", - "Batch 2:\n", - "========\n", - "Data:\n", - "tensor([[0.3074, 0.6341],\n", - " [0.4556, 0.6323]])\n", - "Targets:\n", - "tensor([[0.2939],\n", - " [0.6977]])\n", - "\n", - "Batch 3:\n", - "========\n", - "Data:\n", - "tensor([[0.4963, 0.7682],\n", - " [0.4901, 0.8964]])\n", - "Targets:\n", - "tensor([[0.0223],\n", - " [0.5185]])\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "from torch.utils.data import DataLoader\n", "\n", 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 index 32d374d..85afcdb 100644 --- a/book/tutorials/NN_with_Pytorch/03_Data_Download_And_Cleaning.ipynb +++ b/book/tutorials/NN_with_Pytorch/03_Data_Download_And_Cleaning.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -27,202 +27,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "MultiIndex: 4749 entries, (Timestamp('2010-01-01 08:00:00+0000', tz='UTC'), '502:WA:SNTL') to (Timestamp('2023-01-01 08:00:00+0000', tz='UTC'), '502:WA:SNTL')\n", - "Data columns (total 10 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 geometry 4749 non-null geometry\n", - " 1 SWE 4748 non-null float64 \n", - " 2 SWE_units 4748 non-null object \n", - " 3 AVG AIR TEMP 4738 non-null float64 \n", - " 4 AVG AIR TEMP_units 4738 non-null object \n", - " 5 SNOWDEPTH 4748 non-null float64 \n", - " 6 SNOWDEPTH_units 4748 non-null object \n", - " 7 PRECIPITATION 4745 non-null float64 \n", - " 8 PRECIPITATION_units 4745 non-null object \n", - " 9 datasource 4749 non-null object \n", - 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2010-01-05 08:00:00+00:00502:WA:SNTLPOINT Z (-121.17093 46.54741 5920.00000)10.8in36.86degF38.0in0.1inNRCS
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" - ], - "text/plain": [ - " geometry \\\n", - "datetime site \n", - "2010-01-01 08:00:00+00:00 502:WA:SNTL POINT Z (-121.17093 46.54741 5920.00000) \n", - "2010-01-02 08:00:00+00:00 502:WA:SNTL POINT Z (-121.17093 46.54741 5920.00000) \n", - "2010-01-03 08:00:00+00:00 502:WA:SNTL POINT Z (-121.17093 46.54741 5920.00000) \n", - "2010-01-04 08:00:00+00:00 502:WA:SNTL POINT Z (-121.17093 46.54741 5920.00000) \n", - "2010-01-05 08:00:00+00:00 502:WA:SNTL POINT Z (-121.17093 46.54741 5920.00000) \n", - "\n", - " SWE SWE_units AVG AIR TEMP \\\n", - "datetime site \n", - "2010-01-01 08:00:00+00:00 502:WA:SNTL 9.2 in 32.18 \n", - "2010-01-02 08:00:00+00:00 502:WA:SNTL 9.7 in 29.30 \n", - "2010-01-03 08:00:00+00:00 502:WA:SNTL 10.0 in 28.94 \n", - "2010-01-04 08:00:00+00:00 502:WA:SNTL 10.1 in 33.80 \n", - "2010-01-05 08:00:00+00:00 502:WA:SNTL 10.8 in 36.86 \n", - "\n", - " AVG AIR TEMP_units SNOWDEPTH \\\n", - "datetime site \n", - "2010-01-01 08:00:00+00:00 502:WA:SNTL degF 34.0 \n", - "2010-01-02 08:00:00+00:00 502:WA:SNTL degF 37.0 \n", - "2010-01-03 08:00:00+00:00 502:WA:SNTL degF 38.0 \n", - "2010-01-04 08:00:00+00:00 502:WA:SNTL degF 38.0 \n", - "2010-01-05 08:00:00+00:00 502:WA:SNTL degF 38.0 \n", - "\n", - " SNOWDEPTH_units PRECIPITATION \\\n", - "datetime site \n", - "2010-01-01 08:00:00+00:00 502:WA:SNTL in 0.5 \n", - "2010-01-02 08:00:00+00:00 502:WA:SNTL in 0.3 \n", - "2010-01-03 08:00:00+00:00 502:WA:SNTL in 0.1 \n", - "2010-01-04 08:00:00+00:00 502:WA:SNTL in 0.7 \n", - "2010-01-05 08:00:00+00:00 502:WA:SNTL in 0.1 \n", - "\n", - " PRECIPITATION_units datasource \n", - "datetime site \n", - "2010-01-01 08:00:00+00:00 502:WA:SNTL in NRCS \n", - "2010-01-02 08:00:00+00:00 502:WA:SNTL in NRCS \n", - "2010-01-03 08:00:00+00:00 502:WA:SNTL in NRCS \n", - "2010-01-04 08:00:00+00:00 502:WA:SNTL in NRCS \n", - "2010-01-05 08:00:00+00:00 502:WA:SNTL in NRCS " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from datetime import datetime\n", "from metloom.pointdata import SnotelPointData\n", @@ -249,20 +56,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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"code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -300,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -342,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -365,21 +252,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch [1/5], Training Loss: 0.0035, Validation Loss: 0.0039\n", - "Epoch [2/5], Training Loss: 0.0032, Validation Loss: 0.0035\n", - "Epoch [3/5], Training Loss: 0.0029, Validation Loss: 0.0032\n", - "Epoch [4/5], Training Loss: 0.0027, Validation Loss: 0.0028\n", - "Epoch [5/5], Training Loss: 0.0024, Validation Loss: 0.0026\n" - ] - } - ], + "outputs": [], "source": [ "num_epochs = 5\n", "\n", @@ -438,20 +313,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plotting the training and validation losses\n", "plt.figure(figsize=(10, 5))\n", @@ -473,28 +337,9 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test Loss: 0.0027\n", - "R2 Score: 0.47\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Evaluate the model on the test set and collect predictions\n", "model.eval() # Set the model to evaluation mode\n", @@ -549,24 +394,9 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "SNOTELNN(\n", - " (fc1): Linear(in_features=4, out_features=128, bias=True)\n", - " (relu): ReLU()\n", - " (fc2): Linear(in_features=128, out_features=1, bias=True)\n", - ")" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Save the model's state dictionary\n", "torch.save(model.state_dict(), 'snotel_nn_model.pth')\n", From 64960ca9e235ddd307708f9f7f3811158121faec Mon Sep 17 00:00:00 2001 From: Ibrahim-Ola Date: Wed, 21 Aug 2024 09:54:00 -0700 Subject: [PATCH 05/10] cleared output --- .../tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/book/tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb b/book/tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb index 8418a8d..9ce7fc8 100644 --- a/book/tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb +++ b/book/tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb @@ -27,6 +27,15 @@ "### Creating Tensors" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -q torch torchvision torchaudio" + ] + }, { "cell_type": "code", "execution_count": null, @@ -273,7 +282,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.5" } }, "nbformat": 4, From 5ba609ace6fa733f8f680008891a6af95d6a6408 Mon Sep 17 00:00:00 2001 From: Ibrahim-Ola Date: Wed, 21 Aug 2024 09:55:32 -0700 Subject: [PATCH 06/10] cleared output --- .../NN_with_Pytorch/04_Building_And_Training_FFN.ipynb | 9 +++++++++ 1 file changed, 9 insertions(+) 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 index 45d8cc2..7be5662 100644 --- a/book/tutorials/NN_with_Pytorch/04_Building_And_Training_FFN.ipynb +++ b/book/tutorials/NN_with_Pytorch/04_Building_And_Training_FFN.ipynb @@ -16,6 +16,15 @@ "## Load Libraries\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -q torch torchvision torchaudio" + ] + }, { "cell_type": "code", "execution_count": null, From 8cd0ff5f940b542e78d8e750aab6412ffde62fb4 Mon Sep 17 00:00:00 2001 From: Ibrahim-Ola Date: Wed, 21 Aug 2024 09:58:21 -0700 Subject: [PATCH 07/10] cleared output --- .../NN_with_Pytorch/04_Building_And_Training_FFN.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 index 7be5662..ef33fe1 100644 --- a/book/tutorials/NN_with_Pytorch/04_Building_And_Training_FFN.ipynb +++ b/book/tutorials/NN_with_Pytorch/04_Building_And_Training_FFN.ipynb @@ -490,7 +490,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.5" } }, "nbformat": 4, From 6a5fc7d050e17729cc443f157259af02d00ce5a0 Mon Sep 17 00:00:00 2001 From: Ibrahim-Ola Date: Wed, 21 Aug 2024 10:02:54 -0700 Subject: [PATCH 08/10] cleared output --- book/tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/book/tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb b/book/tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb index 9ce7fc8..23e83c7 100644 --- a/book/tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb +++ b/book/tutorials/NN_with_Pytorch/02_Pytorch_Basics.ipynb @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ From 282ff430a87bfff0f8bc1f5ded8e0e3e112d5420 Mon Sep 17 00:00:00 2001 From: Ibrahim-Ola Date: Wed, 21 Aug 2024 10:10:09 -0700 Subject: [PATCH 09/10] cleared output --- .../NN_with_Pytorch/04_Building_And_Training_FFN.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 index ef33fe1..dae2a96 100644 --- a/book/tutorials/NN_with_Pytorch/04_Building_And_Training_FFN.ipynb +++ b/book/tutorials/NN_with_Pytorch/04_Building_And_Training_FFN.ipynb @@ -446,7 +446,7 @@ "\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]()\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." ] }, From 078d3c2aecae156af96eb28b553dad51a269a9dc Mon Sep 17 00:00:00 2001 From: Ibrahim-Ola Date: Wed, 21 Aug 2024 10:20:17 -0700 Subject: [PATCH 10/10] cleared output --- .../03_Data_Download_And_Cleaning.ipynb | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) 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 index 85afcdb..4225f33 100644 --- a/book/tutorials/NN_with_Pytorch/03_Data_Download_And_Cleaning.ipynb +++ b/book/tutorials/NN_with_Pytorch/03_Data_Download_And_Cleaning.ipynb @@ -112,11 +112,13 @@ "\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", - "\\begin{align*}\n", - "\\text{SWE} & = h_s \\times \\frac{\\rho_b}{1}\\\\\n", - "& = h_s \\times \\rho_b \\\\\n", - "\\rho_b & = \\frac{\\text{SWE}}{h_s}\n", - "\\end{align*}" + "$$\n", + "\\text{SWE} = h_s \\times \\frac{\\rho_b}{1}\n", + "$$\n", + "\n", + "$$\n", + "\\rho_b = \\frac{\\text{SWE}}{h_s}\n", + "$$" ] }, {