From 24591a0e172cc153c5c19f39b9a72d698bff23de Mon Sep 17 00:00:00 2001 From: bismillah Date: Mon, 19 Aug 2024 19:10:15 +1000 Subject: [PATCH] updated read me --- README.md | 46 +++++++++++++++++++++++++++++++++++++++++++++- src/Node.mojo | 0 src/graph.mojo | 0 src/main.mojo | 4 ++++ 4 files changed, 49 insertions(+), 1 deletion(-) create mode 100644 src/Node.mojo create mode 100644 src/graph.mojo diff --git a/README.md b/README.md index a871ec6..ce818c6 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,46 @@ # mojomagic -A Mojo neural network library from scratchh (mostly) \ No newline at end of file + + +# How to Create a Neural Network Library Like TensorFlow in Mojo + +Creating a neural network library similar to TensorFlow in Mojo involves several key steps. Below is a high-level guide: + +## 1. Define the Core Architecture +- **Tensors**: Implement a class to handle multi-dimensional arrays (tensors) with support for operations like addition, multiplication, reshaping, etc. +- **Graph/Layer Abstraction**: Define layers (e.g., Dense, Convolutional) that can be combined to form neural network architectures. +- **Computation Graph**: Create a system to manage operations in a graph-based structure, allowing for differentiation and backpropagation. + +## 2. Implement Automatic Differentiation +- **Forward Pass**: Perform operations on tensors while recording operations in a computation graph. +- **Backward Pass**: Use the computation graph to calculate gradients during backpropagation. + +## 3. Develop Optimization Algorithms +- Implement optimizers like Stochastic Gradient Descent (SGD), Adam, etc., to adjust weights based on gradients during training. + +## 4. Build the Model Training Loop +- **Forward Propagation**: Pass data through the model. +- **Loss Calculation**: Compute loss using appropriate loss functions (e.g., Mean Squared Error, Cross Entropy). +- **Backward Propagation**: Use gradients to update weights via the chosen optimizer. + +## 5. Leverage Parallelization and Hardware Acceleration +- Use Mojo’s support for efficient tensor operations across different hardware (CPUs, GPUs, etc.). +- Implement parallelized computation, particularly for matrix multiplications and convolution operations. + +## 6. Implement Model Serialization and Deployment +- Allow models to be saved, loaded, and deployed. +- Implement methods for exporting trained models and utilizing them in production environments. + +## 7. Design a User-Friendly API +- Create an intuitive API that abstracts away complexity: + - High-level functions for defining models and training. + - Support for both functional (sequential models) and subclassing (custom models). + +## 8. Ensure Extensibility +- Build the library in a modular fashion, making it easy to extend with new layers, loss functions, and optimizers as needed. + +## 9. Perform Testing and Benchmarking +- Test your library on common neural network tasks (like MNIST, CIFAR-10) to ensure correctness and performance. +- Compare your results with existing libraries. + +## 10. Provide Documentation and Examples +- Offer thorough documentation and usage examples to make your library accessible to users, even those unfamiliar with Mojo. diff --git a/src/Node.mojo b/src/Node.mojo new file mode 100644 index 0000000..e69de29 diff --git a/src/graph.mojo b/src/graph.mojo new file mode 100644 index 0000000..e69de29 diff --git a/src/main.mojo b/src/main.mojo index e69de29..2b3f5a9 100644 --- a/src/main.mojo +++ b/src/main.mojo @@ -0,0 +1,4 @@ +from tensor import Tensor + +fn main(): + pass