How vohongquann/neurokit works
Neurokit is an educational, NumPy-based deep-learning framework. A typical user generates or supplies data, builds a model from modules and parameters, computes a loss, calls backward to accumulate gradients, and uses an optimizer to update parameters. Its internals center on Tensor/Function autograd, differentiable operations, and saved forward context. The README also documents synthetic data generation and regression/classification examples. Source excerpts support the core runtime links; some documented capabilities, such as particular optimizer and generator variants, are represented at their shared module paths rather than individually traced.
Main entry points: function.py Function dispatch · tensor.py Tensor and backward · parameter.py Trainable parameters · linear.py Linear layer
Components 12
Autograd core
- Tensor and backward
neurokit/tensor.py - Function dispatch
neurokit/function.py - Tensor operations
neurokit/ops.py - Saved context
neurokit/context.py
Model and loss
- Module management
neurokit/nn/module.py - Trainable parameters
neurokit/nn/parameter.py - Linear layer
neurokit/nn/linear.py - Activations
neurokit/nn/activations.py - Loss functions
neurokit/nn/loss.py
Optimization
- Optimizers
neurokit/optim.py
Data generation
- Synthetic generators
neurokit/data_generator.py
Other components
- Framework user
Connections 16
- Framework user → Synthetic generators: requests data
- Synthetic generators → Framework user: returns samples
- Framework user → Tensor and backward: creates tensors
- Framework user → Module management: builds model
- Framework user → Loss functions: computes loss
- Framework user → Optimizers: steps optimizer
- Tensor and backward → Function dispatch: dispatches operations
- Function dispatch → Saved context: saves intermediates
- Function dispatch → Tensor operations: runs operation
- Tensor and backward → Function dispatch: backpropagates gradients
- Module management → Trainable parameters: registers parameters
- Linear layer → Tensor and backward: multiplies and adds
- Linear layer → Trainable parameters: uses weights
- Activations → Function dispatch: applies activation
- Loss functions → Function dispatch: applies loss operation
- Optimizers → Trainable parameters: updates parameters