Formulas

Supported formulas, architectures, and activations

Trainable Formulas

These formulas compile directly to PyTorch models and can be trained on your CSV data.

Linear Regression

Simple linear model

\(y = mx + b\)
Multiple Linear Regression

Multi-feature linear

\(y = \beta_0 + \beta_1 x_1 + \beta_2 x_2\)
Logistic Regression

Binary classification

\(y = \sigma(w^T x + b)\)
Polynomial Regression

Curved relationships

\(y = \beta_0 + \beta_1 x + \beta_2 x^2 + \beta_3 x^3\)
Single Layer (Sigmoid)

Neural layer with sigmoid

\(y = \sigma(Wx + b)\)
Single Layer (ReLU)

Neural layer with ReLU

\(y = \text{ReLU}(Wx + b)\)
Single Layer (Tanh)

Neural layer with tanh

\(y = \tanh(Wx + b)\)
Softmax Classifier

Multi-class classification

\(y = \text{softmax}(Wx + b)\)
2-Layer MLP

Two hidden layers

\(y = \sigma(W_2 \cdot \text{ReLU}(W_1 x + b_1) + b_2)\)
3-Layer MLP

Three hidden layers

\(y = \sigma(W_3 \cdot \text{ReLU}(W_2 \cdot \text{ReLU}(W_1 x + b_1) + b_2) + b_3)\)

Computable Formulas

These are evaluated symbolically via SymPy (no data needed). Supports:

  • Arithmetic: 2^{10} + 3 \times 7
  • Algebra: solve equations, simplify, expand, factor
  • Calculus: derivatives, integrals, limits, series
  • Linear algebra: determinants, eigenvalues, matrix operations
  • Anything SymPy can handle, with LLM fallback for complex expressions

Architecture Support

Advanced architectures available via the pipeline (whiteboard) mode.

Attention
\(\text{softmax}(\frac{QK^T}{\sqrt{d_k}})V\)
Supported
Conv1D / Conv2D
\(\text{Conv}(x)\)
Supported
LSTM
\(\text{LSTM}(x)\)
Supported
GRU
\(\text{GRU}(x)\)
Supported
Transformer Encoder
\(\text{Transformer}(x)\)
Supported
Residual Block
\(y = x + F(x)\)
Supported

Supported Activations

sigmoidrelutanhsoftmaxleaky_reluelugeluselusilu

Tips for Successful Compilation

  • Use standard notation: W for weights, b for bias, x for input
  • Subscripts indicate layers: W_1, W_2, b_1, b_2
  • Wrap activations clearly: \sigma(...), \text{ReLU}(...)
  • For multi-layer networks, nest from inside out
  • If a formula doesn't compile, the system falls back to logistic regression. Check the warning banner
  • Use the whiteboard mode for complex architectures (attention, residual, etc.)
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